diff --git a/Dockerfile b/Dockerfile
index b1b975c..f8b5998 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,13 +1,12 @@
# needed only for numpy/scipy/matplotlib/etc
-FROM tensorflow/tensorflow:1.10.1-gpu-py3
+FROM tensorflow/tensorflow:1.10.1-py3
-RUN useradd -u 1053 maksym
RUN alias lst="ls -lrth"; alias nv="watch -n 1 nvidia-smi"; alias python="python3"
RUN apt-get update -y
-RUN apt-get install -y htop curl vim python3-tk git
+RUN apt-get install -y htop wget curl vim python3-tk git memory_profiler
RUN pip install --upgrade pip
-RUN pip install numba==0.43.1 numexpr seaborn
+RUN pip install numba==0.43.1 numexpr seaborn billiard robustml
RUN pip install ipdb
RUN cd /
diff --git a/README.md b/README.md
index e9ad9cc..32d49f3 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,7 @@
# Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
+
+
**NeurIPS 2019**
**Maksym Andriushchenko, Matthias Hein**
@@ -8,26 +10,30 @@
**Paper:** [http://arxiv.org/abs/1906.03526](http://arxiv.org/abs/1906.03526)
-This repository contains transparent `python` code for training provably robust boosted decision
-stumps and trees. To foster reproducible research, we also provide code for **all** experiments
+
+This repository contains `python` code for training provably robust boosted decision
+stumps and trees. To foster reproducible research, we also provide code for all main experiments
reported in the paper (see `exps.sh`).
-Moreover, we also provide code for **all** figures shown in the paper, each as a separate
-Jupyter notebook
-(`notebooks/toy2d.ipynb`, `notebooks/model_analysis.ipynb`, `notebooks/exact_adv.ipynb`).
+Moreover, we also provide code for all figures shown in the paper, each as a separate Jupyter notebook
+(see folder `notebooks`).
All dependencies are collected in `Dockerfile`.
-**Update**: now all our provably robust tree models are publicly available in folder `models`.
-Since we perform sound, but incomplete robustness verification, there is still some room for improvement
-(e.g. on MNIST 2-6), especially for larger Linf radii. We encourage researchers that work on verification
-of tree ensembles to benchmark the speed of their methods on our robust ensembles since
-naturally trained ensembles are *extremely non-robust* (see tables and adversarial examples).
+**Models:** All our provably robust models (stumps and trees) are publicly available by
+[this link](https://drive.google.com/open?id=15p2ihucMVfNXEmqZBJYYvPHDeQjBixV6)
+including our MNIST, FMNIST, and CIFAR-10 models.
+
+**Version 2.0 of the repository (corresponds to the NeurIPS'19 camera-ready version):**
+- multi-class extension
+- significant speed-up via a parallel tree construction and parallel fitting of stumps to different coordinates
+- fixed memory leak issues due to `numba`
+- improved efficiency of individual tree predictions and certification using `numba`
## Main idea of the paper
We propose provable defenses against adversarial attack for boosted decision stumps and trees.
Here is the effect of our method on a 2D dataset.
-
+
## Provably robust training
@@ -51,7 +57,14 @@ For more details, see the paper.
## Experiments
Experimental results show the efficiency of the robust training methods for boosted stumps and
boosted trees:
-
+
+
+
+Moreover, although decision trees as weak learners are obviously not suitable for computer vision tasks, our robust
+boosted trees nonetheless show provable robustness **competitive to provably robust CNNs** outperforming almost all
+proposed in the literature approaches:
+
+
## Effect of robust training
@@ -61,9 +74,9 @@ that robust models select
## Exact adversarial examples
-Using our exact certification routine, we can also efficiently find provably minimal (exact) adversarial examples
-wrt Linf-norm for boosted stumps:
-
+Using our exact certification routine, we can also *efficiently* (without any combinatorial solvers) find provably
+minimal (exact) adversarial examples wrt Linf-norm for boosted stumps:
+
## Interpretability
@@ -84,41 +97,45 @@ import numpy as np
import data
from tree_ensemble import TreeEnsemble
-n_trees = 20 # total number of trees in the ensemble
+n_trees = 50 # total number of trees in the ensemble
model = 'robust_bound' # robust tree ensemble
-X_train, y_train, X_test, y_test, eps = data.all_datasets_dict['diabetes']()
+X_train, y_train, X_test, y_test, eps = data.all_datasets_dict['breast_cancer']()
+X_train, X_test = data.convert_to_float32(X_train), data.convert_to_float32(X_test)
# initialize a tree ensemble with some hyperparameters
ensemble = TreeEnsemble(weak_learner='tree', n_trials_coord=X_train.shape[1],
- lr=1.0, min_samples_split=5, min_samples_leaf=10, max_depth=2)
+ lr=0.01, min_samples_split=10, min_samples_leaf=5, max_depth=4,
+ max_weight=1.0, idx_clsf=0)
# initialize gammas, per-example weights which are recalculated each iteration
gamma = np.ones(X_train.shape[0])
for i in range(1, n_trees + 1):
# fit a new tree in order to minimize the robust loss of the whole ensemble
weak_learner = ensemble.fit_tree(X_train, y_train, gamma, model, eps, depth=1)
+ margin_prev = ensemble.certify_treewise(X_train, y_train, eps) # needed for pruning
ensemble.add_weak_learner(weak_learner)
- ensemble.prune_last_tree(X_train, y_train, eps, model)
+ ensemble.prune_last_tree(X_train, y_train, margin_prev, eps, model)
# calculate per-example weights for the next iteration
- gamma = np.exp(-ensemble.certify_treewise_bound(X_train, y_train, eps))
+ gamma = np.exp(-ensemble.certify_treewise(X_train, y_train, eps))
# track generalization and robustness
yf_test = y_test * ensemble.predict(X_test)
- min_yf_test = ensemble.certify_treewise_bound(X_test, y_test, eps)
- print('Iteration: {}, test error: {:.2%}, upper bound on robust test error: {:.2%}'.format(
- i, np.mean(yf_test < 0.0), np.mean(min_yf_test < 0.0)))
+ min_yf_test = ensemble.certify_treewise(X_test, y_test, eps)
+ if i == 1 or i % 5 == 0:
+ print('Iteration: {}, test error: {:.2%}, upper bound on robust test error: {:.2%}'.format(
+ i, np.mean(yf_test < 0.0), np.mean(min_yf_test < 0.0)))
```
```
-Iteration: 1, test error: 24.68%, upper bound on robust test error: 32.47%
-Iteration: 2, test error: 23.38%, upper bound on robust test error: 32.47%
-Iteration: 3, test error: 23.38%, upper bound on robust test error: 32.47%
-Iteration: 4, test error: 23.38%, upper bound on robust test error: 32.47%
-Iteration: 5, test error: 24.03%, upper bound on robust test error: 33.12%
-Iteration: 6, test error: 24.03%, upper bound on robust test error: 33.12%
-Iteration: 7, test error: 24.03%, upper bound on robust test error: 33.12%
-Iteration: 8, test error: 24.03%, upper bound on robust test error: 33.12%
-Iteration: 9, test error: 24.03%, upper bound on robust test error: 33.12%
-Iteration: 10, test error: 24.03%, upper bound on robust test error: 33.12%
-
+Iteration: 1, test error: 2.92%, upper bound on robust test error: 10.95%
+Iteration: 5, test error: 2.92%, upper bound on robust test error: 10.95%
+Iteration: 10, test error: 2.19%, upper bound on robust test error: 10.22%
+Iteration: 15, test error: 2.19%, upper bound on robust test error: 10.22%
+Iteration: 20, test error: 2.19%, upper bound on robust test error: 10.22%
+Iteration: 25, test error: 2.19%, upper bound on robust test error: 10.22%
+Iteration: 30, test error: 1.46%, upper bound on robust test error: 8.03%
+Iteration: 35, test error: 1.46%, upper bound on robust test error: 8.03%
+Iteration: 40, test error: 1.46%, upper bound on robust test error: 7.30%
+Iteration: 45, test error: 1.46%, upper bound on robust test error: 7.30%
+Iteration: 50, test error: 0.73%, upper bound on robust test error: 6.57%
```
@@ -127,33 +144,35 @@ One can train robust stumps or trees using `train.py`. The full list of possibl
available by `python train.py --help`.
Boosted stumps models:
-- `python train.py --dataset=mnist_2_6 --weak_learner=stump --model=plain `
+- `python train.py --dataset=mnist_2_6 --weak_learner=stump --model=plain`
+- `python train.py --dataset=mnist_2_6 --weak_learner=stump --model=at_cube --lr=0.01`
- `python train.py --dataset=mnist_2_6 --weak_learner=stump --model=robust_bound`
- `python train.py --dataset=mnist_2_6 --weak_learner=stump --model=robust_exact`
Boosted trees models:
-- `python train.py --dataset=mnist_2_6 --weak_learner=tree --model=plain `
-- `python train.py --dataset=mnist_2_6 --weak_learner=tree --model=robust_bound`
+- `python train.py --dataset=mnist_2_6 --weak_learner=tree --model=plain --lr=0.2`
+- `python train.py --dataset=mnist_2_6 --weak_learner=tree --model=at_cube --lr=0.2`
+- `python train.py --dataset=mnist_2_6 --weak_learner=tree --model=robust_bound --lr=0.2`
Note that Linf epsilons for adversarial attacks are specified for each dataset separately in `data.py`.
-In case you experience excessive memory usage, just set `parallel=False` in the decorators of
-functions `fit_plain_stumps()` or `fit_robust_bound_stumps()`.
-This might happen to a bug in `numba` that does not free the used memory.
-This memory issue will be fixed in the next version of this repository.
-
### Evaluation
-`eval.py` and `exact_adv.ipynb` show how one can restore a trained model in order to evaluate it (e.g., to
-show the exact adversarial examples).
+`eval.py` and `notebooks/adv_examples.ipynb` show how one can restore a trained model in order to evaluate it (e.g.,
+calculate the robustness bounds or to show the adversarial examples).
+
+In order to evaluate the boosted tree models using MILP, we refer to [this repository](https://github.com/chenhongge/RobustTrees).
### Jupyter notebooks to reproduce the figures
- `notebooks/toy2d.ipynb` - Figure 1: toy dataset which shows that the usual training is non-robust, while our robust models
can robustly classify all training points.
-- `notebooks/model_analysis.ipynb` - Figure 2: histograms of splitting thresholds, where we can observe a clear effect of
-robust training on the choice of the splitting thresholds.
-- `notebooks/exact_adv.ipynb` - Figure 3: exact adversarial examples for boosted stumps,
+- `notebooks/minmax_objective.ipynb` - Figure 2: visualization of the min-max objective which is convex wrt its parameters.
+- `notebooks/model_analysis.ipynb` - Figures 3, 8, 9, 10: histograms of splitting thresholds, where we can observe a clear effect of
+robust training on the choice of the splitting thresholds. Also: Figures 5, 6, 7 show the feature importance plots based
+on the number of times some feature was used for a split.
+- `notebooks/robustness_generalization.ipynb` - Figure 4: the robustness vs accuracy trade-off.
+- `notebooks/adv_examples.ipynb` - Figures 11, 12, 13: exact adversarial examples for boosted stumps,
which are much larger in Linf-norm for robust models.
@@ -176,4 +195,4 @@ Please contact [Maksym Andriushchenko](https://github.com/max-andr) regarding th
conference={Advances in Neural Information Processing Systems},
year={2019}
}
-```
+```
\ No newline at end of file
diff --git a/attacks.py b/attacks.py
new file mode 100644
index 0000000..fe655bc
--- /dev/null
+++ b/attacks.py
@@ -0,0 +1,230 @@
+import numpy as np
+
+
+def sampling_attack(f, X, y, eps, n_trials):
+ """ A simple attack just by sampling in the Linf-box around the points. More of a sanity check.
+ `f` is any function that has f.predict() method that returns class scores.
+ """
+ num, dim = X.shape
+ f_x_vals = np.zeros((num, n_trials))
+ # Note: for efficiency, we sample the same random direction for all points
+ deltas = np.random.uniform(-eps, eps, size=(dim, n_trials))
+ for i in range(n_trials - 1):
+ # let's keep them as real images, although not strictly needed
+ perturbed_pts = np.clip(X + deltas[:, i], 0.0, 1.0)
+ f_x_vals[:, i] = f.fmargin(perturbed_pts)
+ # maybe in some corner cases, the predictions at the original point is more worst-case than the sampled points
+ f_x_vals[:, n_trials - 1] = f.fmargin(X, np.ones(X.shape[0]))
+
+ idx_min = np.argmin(y[:, None] * f_x_vals, axis=1)
+ f_x_min = (y[:, None] * f_x_vals)[idx_min]
+ deltas = deltas[:, idx_min]
+ return f_x_min, deltas
+
+
+def cube_attack(f, X, y, eps, n_trials, p=0.5, deltas_init=None, independent_delta=False, min_val=0.0, max_val=1.0):
+ """ A simple, but efficient black-box attack that just adds random steps of values in {-2eps, 0, 2eps}
+ (i.e., the considered points are always corners). The random change is added if the loss decreases for a
+ particular point. The only disadvantage of this method is that it will never find decision regions inside the
+ Linf-ball which do not intersect any corner. But tight LRTE (compared to RTE/URTE) suggest that this doesn't happen.
+ `f` is any function that has f.fmargin() method that returns class scores.
+ `eps` can be a scalar or a vector of size X.shape[0].
+ `min_val`, `max_val` are min/max allowed values for values in X (e.g. 0 and 1 for images). This can be adjusted
+ depending on the feature range of the data. It's also possible to specify the as numpy vectors.
+ """
+ assert type(eps) is float or type(eps) is np.ndarray
+
+ p_neg_eps = p/2 # probability of sampling -2eps
+ p_pos_eps = p/2 # probability of sampling +2eps
+ p_zero = 1 - p # probability of not doing an update
+ num, dim = X.shape
+ # independent deltas work better for adv. training but slow down attacks
+ size_delta = (num, dim) if independent_delta else (1, dim)
+
+ if deltas_init is None:
+ deltas_init = np.zeros(size_delta)
+ # this init is important, s.t. there is no violation of bounds
+ f_x_vals_min = f.fmargin(X, y)
+
+ if deltas_init is not None: # evaluate the provided deltas and take them if they are better
+ X_adv = np.clip(X + deltas_init, np.maximum(min_val, X - eps), np.minimum(max_val, X + eps))
+ deltas = X_adv - X # because of the projection above, the new delta vector is not just +-eps
+ f_x_vals = f.fmargin(X_adv, y)
+ idx_improved = f_x_vals < f_x_vals_min
+ f_x_vals_min = idx_improved * f_x_vals + ~idx_improved * f_x_vals_min
+ deltas = idx_improved[:, None] * deltas_init + ~idx_improved[:, None] * deltas
+ else:
+ deltas = deltas_init
+
+ i_trial = 0
+ while i_trial < n_trials:
+ # +-2*eps is *very* important to escape local minima; +-eps has very unstable performance
+ new_deltas = np.random.choice([-1, 0, 1], p=[p_neg_eps, p_zero, p_pos_eps], size=size_delta)
+ new_deltas = 2 * eps * new_deltas # if eps is a vector, then it's an outer product num x 1 times 1 x dim
+ X_adv = np.clip(X + deltas + new_deltas, np.maximum(min_val, X - eps), np.minimum(max_val, X + eps))
+ new_deltas = X_adv - X # because of the projection above, the new delta vector is not just +-eps
+ f_x_vals = f.fmargin(X_adv, y)
+ idx_improved = f_x_vals < f_x_vals_min
+ f_x_vals_min = idx_improved * f_x_vals + ~idx_improved * f_x_vals_min
+ deltas = idx_improved[:, None] * new_deltas + ~idx_improved[:, None] * deltas
+ i_trial += 1
+
+ return f_x_vals_min, deltas
+
+
+def binary_search_attack(attack, f, X, y, n_trials_attack, cleanup=True):
+ """
+ Binary search to find the minimal perturbation that changes the class using `attack`.
+ Supports a single eps only.
+ """
+ n_iter_bs = 10 # precision up to the 4th digit
+ num, dim = X.shape
+ deltas = np.zeros([num, dim])
+ eps = np.ones((num, 1))
+ eps_step = 1.0
+ for i_iter_bs in range(n_iter_bs):
+ f_x_vals, new_deltas = attack(f, X, y, eps, n_trials_attack, p=0.5, deltas_init=deltas)
+ print('iter_bs {}: yf={}, eps={}'.format(i_iter_bs, f_x_vals, eps.flatten()))
+ idx_adv = f_x_vals[:, None] < 0.0 # if adversarial, reduce the eps
+ eps = idx_adv * (eps - eps_step/2) + ~idx_adv * (eps + eps_step/2)
+ deltas = idx_adv * new_deltas + ~idx_adv * deltas
+ eps_step /= 2
+
+ yf = f.fmargin(X + deltas, y)
+ print('yf after binary search: yf={}, Linf={}'.format(yf, np.abs(deltas).max(1)))
+ if np.any(yf >= 0.0):
+ print('The class was not changed (before cleanup)! Some bug apparently!')
+
+ if cleanup:
+ # If some eps/-eps do not change the prediction for a particular example, use delta_i = 0 instead.
+ # Better for interpretability. Caution: needs num * dim function evaluations, thus advisable to use only
+ # for visualizations, but not for LRTE.
+ for i in range(dim):
+ deltas_i_zeroed = np.copy(deltas)
+ deltas_i_zeroed[:, i] = 0.0
+ f_x_vals = f.fmargin(X + deltas_i_zeroed, y)
+ idx_adv = f_x_vals < 0.0
+ deltas = idx_adv[:, None] * deltas_i_zeroed + ~idx_adv[:, None] * deltas
+
+ yf = f.fmargin(X + deltas, y)
+ print('yf after cleanup: yf={}, Linf={}'.format(yf, np.abs(deltas).max(1)))
+ if np.any(yf >= 0.0):
+ print('The class was not changed (after cleanup)! Some bug apparently!')
+
+ return deltas
+
+
+def coord_descent_attack_trees(f, X, y, eps, n_trials, deltas=None):
+ """ A simple, but relatively efficient (if multiple passes through the coordinates are allowed) white-box attack
+ just by iterating over coordinates (in the importance order) and checking whether -eps, 0 or eps is better.
+ Needs 2 function evaluations per coordinate.
+ `f` is a TreeEnsemble object.
+ """
+ num, dim = X.shape
+ if deltas is None:
+ deltas = np.zeros((num, dim))
+ # this init is important, s.t. there is no violation of bounds
+ f_x_vals_min = y * f.fmargin(np.clip(X + deltas, np.maximum(0.0, X - eps), np.minimum(1.0, X + eps)))
+
+ coords_per_tree = np.zeros(dim)
+ for tree in f.trees:
+ coords_curr_tree = np.array(tree.to_list(), dtype=int)[:, 6]
+ for coord in coords_curr_tree: # 6 is coord, 7 is min_loss
+ coords_per_tree[coord] += 1
+ idx_coords_sorted = np.argsort(-coords_per_tree) # sort in the reverse order
+ coords_nnz_usage = np.where(coords_per_tree[idx_coords_sorted] != 0)[0]
+ coords_to_consider = idx_coords_sorted[coords_nnz_usage]
+ # print('The most important coords:', coords_to_consider[:20])
+
+ i_trial, id_coord = 0, 0
+ X_adv = X
+ while i_trial < n_trials:
+ # if len(coords_to_consider) < n_trials, then we do more than 1 cycle of the coordinate descent scheme
+ coord = coords_to_consider[id_coord % len(coords_to_consider)]
+ for new_delta in [-eps, eps]:
+ X_adv_new = X + deltas
+ # because of multiple cycles of coordinate descent, we also need to consider +-eps constraints
+ X_adv_new[:, coord] = np.clip(X_adv_new[:, coord] + new_delta, np.maximum(0.0, X[:, coord] - eps),
+ np.minimum(1.0, X[:, coord] + eps))
+ # because of constraint projections, the new delta vector is not just +-eps
+ new_delta_vector = X_adv_new[:, coord] - X_adv[:, coord]
+ f_x_vals = y * f.fmargin(X_adv_new)
+ improved = (f_x_vals < f_x_vals_min)
+ f_x_vals_min = improved * f_x_vals + ~improved * f_x_vals_min
+ deltas[:, coord] = improved * new_delta_vector + ~improved * deltas[:, coord]
+ i_trial += 1
+ id_coord += 1
+
+ return f_x_vals_min, deltas
+
+
+def exact_attack_stumps(f, X, y):
+ """ Fast exact adv. examples for boosted stumps.
+ `f` is a StumpEnsemble object.
+ """
+ min_val = 1e-7
+ num, dim = X.shape
+ deltas = np.zeros([num, dim])
+ db_dists = np.full(num, np.inf)
+
+ for i in range(num):
+ # 0.0 means we just check whether the point is originally misclassified; if yes => db_dist=0
+ eps_all_i = np.array([0.0] + [np.abs(tree.b - X[i, tree.coord] + min_val*np.sign(tree.b - X[i, tree.coord]))
+ for tree in f.trees])
+ eps_sorted = np.sort(eps_all_i)
+ for eps in eps_sorted:
+ # Vectorized but obscure version that doesn't return deltas; just a sanity check for eps
+ # f_x_min = self.certify_exact(X[None, i], y[None, i], eps)
+
+ # Clear unvectorized version
+ yf_min = 0.0
+ delta = np.zeros(dim)
+ for coord in f.coords_trees.keys():
+ trees_current_coord = f.coords_trees[coord]
+
+ yf_min_coord_base, yf_orig_pt = 0.0, 0.0
+ for tree in trees_current_coord:
+ yf_min_coord_base += y[i] * tree.predict(X[None, i] - eps)
+ yf_orig_pt += y[i] * tree.predict(X[None, i])
+
+ unstable_thresholds, unstable_wr_values = [X[i, coord] - eps], [0.0]
+ for tree in trees_current_coord:
+ # excluding the left equality since we have already evaluated it
+ if X[i, coord] - eps < tree.b <= X[i, coord] + eps:
+ unstable_thresholds.append(tree.b)
+ unstable_wr_values.append(tree.w_r)
+ unstable_thresholds = np.array(unstable_thresholds)
+ unstable_wr_values = np.array(unstable_wr_values)
+ idx = np.argsort(unstable_thresholds)
+ unstable_thresholds = unstable_thresholds[idx]
+
+ sorted_y_wr = (y[i] * np.array(unstable_wr_values))[idx]
+ yf_coord_interval_vals = np.cumsum(sorted_y_wr)
+ yf_min_coord = yf_min_coord_base + yf_coord_interval_vals.min()
+ yf_min += yf_min_coord
+
+ i_opt_threshold = yf_coord_interval_vals.argmin()
+ # if the min value is attained at the point itself, take it instead; so that we do not take
+ # unnecessary -eps deltas (which would not anyway influence Linf size, but would bias the picture)
+ if yf_min_coord == yf_orig_pt:
+ opt_threshold = X[i, coord] # i.e. the final delta is 0.0
+ else:
+ opt_threshold = unstable_thresholds[i_opt_threshold]
+ delta[coord] = opt_threshold - X[i, coord]
+
+ x_adv_clipped = np.clip(X[i] + delta, 0, 1) # make sure that the images are valid
+ delta = x_adv_clipped - X[i]
+
+ yf = float(y[i] * f.predict(X[None, i] + delta[None]))
+ print('eps_max={:.3f}, eps_delta={:.3f}, yf={:.3f}, nnz={}'.format(
+ eps, np.abs(delta).max(), yf, (delta != 0.0).sum()))
+ if yf_min < 0:
+ db_dists[i] = eps
+ deltas[i] = delta
+ break
+ print()
+ yf = y[i] * f.predict(X[None, i] + deltas[None, i])
+ if yf >= 0.0:
+ print('The class was not changed! Some bug apparently!')
+ return deltas
+
diff --git a/classifiers.py b/classifiers.py
new file mode 100644
index 0000000..9b31383
--- /dev/null
+++ b/classifiers.py
@@ -0,0 +1,99 @@
+import numpy as np
+from tree_ensemble import Tree
+
+
+class OneVsAllClassifier:
+ def __init__(self, models):
+ self.models = models
+ self.n_clsf = len(models)
+
+ def predict(self, X):
+ preds = np.zeros([self.n_clsf, X.shape[0]])
+ for i_cls in range(self.n_clsf):
+ preds[i_cls] = self.models[i_cls].predict(X)
+ return preds
+
+ def predict_class(self, X):
+ preds = self.predict(X)
+ if self.n_clsf == 1:
+ return (0.5*((preds > 0)+1)).astype(int).flatten()
+ else:
+ return np.argmax(preds, 0)
+
+ def certify_treewise(self, X, y, eps):
+ preds = np.zeros([self.n_clsf, X.shape[0]])
+ for i_cls in range(self.n_clsf):
+ preds[i_cls] = self.models[i_cls].certify_treewise(X, y[i_cls], eps)
+ return preds
+
+ def certify_exact(self, X, y, eps):
+ preds = np.zeros([self.n_clsf, X.shape[0]])
+ for i_cls in range(self.n_clsf):
+ preds[i_cls] = self.models[i_cls].certify_exact(X, y[i_cls], eps)
+ return preds
+
+ def fmargin(self, X, y, fx_vals=None):
+ if fx_vals is None: # if fx_vals have not been provided
+ fx_vals = self.predict(X)
+ if self.n_clsf > 1:
+ preds_correct_class = (fx_vals * (y == 1)).sum(0, keepdims=True)
+ diff = preds_correct_class - fx_vals # difference between the correct class and all other classes
+ diff[y == 1] = np.inf # to exclude zeros coming from f_correct - f_correct
+ fx_vals = diff.min(0, keepdims=True)
+ else:
+ fx_vals = y * fx_vals
+ return fx_vals[0]
+
+ def fmargin_treewise(self, X, y, eps, fx_vals=None):
+ if fx_vals is None: # if fx_vals have not been provided
+ fx_vals = self.certify_treewise(X, y, eps)
+ if self.n_clsf > 1:
+ cert_correct_class = (fx_vals * (y == 1)).sum(0, keepdims=True)
+ diff = cert_correct_class + fx_vals # plus because of [min -f] in cert for all classes
+ fx_vals = np.min(diff, 0, keepdims=True)
+ return fx_vals[0]
+
+ def fmargin_exact(self, X, y, eps):
+ fx_vals = self.certify_exact(X, y, eps)
+ if self.n_clsf > 1:
+ cert_correct_class = (fx_vals * (y == 1)).sum(0, keepdims=True)
+ diff = cert_correct_class + fx_vals # plus because of [min -f] in cert for all classes
+ fx_vals = np.min(diff, 0, keepdims=True)
+ return fx_vals[0]
+
+ def save(self, model_path):
+ if model_path != '':
+ model_lst = []
+ for model in self.models:
+ model_lst.append(model.export_model())
+ model_arr = np.array(model_lst)
+ np.save(model_path, model_arr)
+
+ def load(self, model_path, iteration=-1):
+ model_data = np.load(model_path, allow_pickle=True)
+ for i_clsf in range(self.n_clsf):
+ self.models[i_clsf].load(model_data[i_clsf], iteration)
+ if type(model_data[0]) is dict:
+ n_trees = max(model_data[0].keys()) + 1
+ else:
+ n_trees = model_data.shape[1]
+ true_iteration = iteration + 1 if iteration != -1 else n_trees
+ print('Ensemble of {}/{} trees restored: {}'.format(true_iteration, n_trees, model_path))
+
+ def dump_model(self):
+ """ Returns the model in JSON format compatible with XGBoost. """
+ # Works for trees
+ n_cls = len(self.models)
+ n_trees = max([len(model.trees) for model in self.models])
+
+ list_of_tree_dicts = []
+ for i_tree in range(n_trees):
+ for i_cls in range(n_cls):
+ if i_tree < len(self.models[i_cls].trees):
+ tree = self.models[i_cls].trees[i_tree]
+ else:
+ tree = Tree()
+ tree_dict, _ = tree.get_json_dict(counter_terminal_nodes=-10)
+ list_of_tree_dicts.append(tree_dict)
+
+ return list_of_tree_dicts
diff --git a/data.py b/data.py
index 22991c4..e74aa10 100644
--- a/data.py
+++ b/data.py
@@ -1,8 +1,10 @@
import numpy as np
import csv
import scipy.io
-import ipdb as pdb
-from tensorflow.keras.datasets import mnist, fashion_mnist
+from tensorflow.keras.datasets import mnist as mnist_keras, fashion_mnist as fashion_mnist_keras, \
+ cifar10 as cifar10_keras
+
+data_dir = '/home/maksym/boost/data/'
def split_train_test(X_all, y_all, frac_train):
@@ -31,13 +33,25 @@ def normalize_per_feature_0_1(X_train, X_test):
return X_train, X_test
-def split_train_validation(X_train_orig, y_train_orig, shuffle=True):
+def split_train_validation(X_train_orig, y_train_orig, frac_valid, shuffle=True):
num_total = X_train_orig.shape[0]
- frac_train = 0.8
- n_train = int(frac_train*num_total)
+ n_valid = int(frac_valid*num_total)
idx = np.random.permutation(num_total) if shuffle else np.arange(num_total)
- X_train, y_train = X_train_orig[idx][:n_train], y_train_orig[idx][:n_train]
- X_valid, y_valid = X_train_orig[idx][n_train:], y_train_orig[idx][n_train:]
+ if shuffle:
+ X_valid, y_valid = X_train_orig[idx][:n_valid], y_train_orig[idx][:n_valid]
+ X_train, y_train = X_train_orig[idx][n_valid:], y_train_orig[idx][n_valid:]
+ else:
+ # If no shuffle, then one has to ensure that the classes are balanced
+ idx_valid, idx_train = [], []
+ for cls in np.unique(y_train_orig):
+ indices_cls = np.where(y_train_orig == cls)[0]
+ proportion_cls = len(indices_cls) / num_total
+ n_class_balanced_valid = int(proportion_cls * n_valid)
+ idx_valid.extend(list(indices_cls[:n_class_balanced_valid]))
+ idx_train.extend(list(indices_cls[n_class_balanced_valid:]))
+ idx_valid, idx_train = np.array(idx_valid), np.array(idx_train)
+ X_valid, y_valid = X_train_orig[idx_valid], y_train_orig[idx_valid]
+ X_train, y_train = X_train_orig[idx_train], y_train_orig[idx_train]
return X_train, y_train, X_valid, y_valid
@@ -55,6 +69,23 @@ def binary_from_multiclass(X_train, y_train, X_test, y_test, classes):
return X_train, y_train, X_test, y_test
+def transform_labels_one_vs_all(y_train_orig, y_valid_orig, y_test_orig):
+ n_cls = int(y_train_orig.max()) + 1
+ if n_cls == 2:
+ return y_train_orig[None, :], y_valid_orig[None, :], y_test_orig[None, :]
+
+ labels = np.unique(y_train_orig)
+ n_cls = len(labels)
+ n_train, n_valid, n_test = y_train_orig.shape[0], y_valid_orig.shape[0], y_test_orig.shape[0]
+ y_train, y_valid, y_test = np.zeros([n_cls, n_train]), np.zeros([n_cls, n_valid]), np.zeros([n_cls, n_test])
+ for i_cls in range(n_cls):
+ # convert from False/True to -1/1 compatible with One-vs-All formulation
+ y_train[i_cls] = 2 * (y_train_orig == i_cls) - 1
+ y_valid[i_cls] = 2 * (y_valid_orig == i_cls) - 1
+ y_test[i_cls] = 2 * (y_test_orig == i_cls) - 1
+ return y_train, y_valid, y_test
+
+
def toy_2d_stumps():
X = np.array([[0.38, 0.75], [0.50, 0.93], [0.05, 0.70], [0.30, 0.90], [0.15, 0.80],
# [0.15, 1.0], [0.125, 0.75], [0.1, 0.85], [0.045, 0.22], [0.725, 0.955], # small margin
@@ -114,7 +145,7 @@ def breast_cancer():
train: 546x10, test: 137x10
"""
eps_dataset = 0.3 # same as in Chen et al, 2019, worked well for them
- path = 'data/breast_cancer/breast-cancer-wisconsin.data'
+ path = data_dir + 'breast_cancer/breast-cancer-wisconsin.data'
lst = []
for line in csv.reader(open(path, 'r').readlines()):
@@ -139,7 +170,7 @@ def diabetes():
train: 614x8, test: 154x8
"""
eps_dataset = 0.05 # Chen et al, 2019 used 0.2, but it was too high
- path = 'data/diabetes/diabetes.csv'
+ path = data_dir + 'diabetes/diabetes.csv'
data_arr = np.loadtxt(path, delimiter=',', skiprows=1) # loaded as float64
X_all, y_all = data_arr[:, :8], data_arr[:, 8]
@@ -159,8 +190,8 @@ def ijcnn1():
train: 49990x22, test: 91701x22
note: imbalanced classes (-1: 90.3% vs 1: 9.7%)
"""
- eps_dataset = 0.05 # Chen et al, 2019 used 0.1, but it was too high
- folder = 'data/ijcnn1/'
+ eps_dataset = 0.01 # Chen et al, 2019 used 0.1, but it was too high
+ folder = data_dir + 'ijcnn1/'
path_train, path_val, path_test = folder + 'ijcnn1.tr', folder + 'ijcnn1.val', folder + 'ijcnn1.t'
num_train, num_test, dim = 49990, 91701, 22
@@ -212,7 +243,7 @@ def cod_rna():
train: 59535x8, test: 271617x8
"""
eps_dataset = 0.025 # Chen et al, 2019 used 0.2, but it was too high
- folder = 'data/cod_rna/'
+ folder = data_dir + 'cod_rna/'
path_train, path_test = folder + 'cod-rna.tr', folder + 'cod-rna.t'
num_train, num_test, dim = 59535, 271617, 8
@@ -244,6 +275,26 @@ def cod_rna():
X_train, X_test = normalize_per_feature_0_1(X_train, X_test)
+ # n_test_final = 10000 # take 10k test examples instead of all 270k
+ n_test_final = num_test
+ idx = np.random.permutation(num_test)[:n_test_final]
+ X_test, y_test = X_test[idx], y_test[idx]
+ return X_train, y_train, X_test, y_test, eps_dataset
+
+
+def mnist_1_5():
+ """
+ train: (12163, 784), test: (2027, 784)
+ """
+ eps_dataset = 0.3
+ classes = [1, 5] # 2 is 1, 6 is -1 in the binary classification scheme
+
+ (X_train, y_train), (X_test, y_test) = mnist_keras.load_data()
+ X_train, X_test = X_train.astype(np.float64) / 255.0, X_test.astype(np.float64) / 255.0
+ X_train = np.reshape(X_train, [X_train.shape[0], -1])
+ X_test = np.reshape(X_test, [X_test.shape[0], -1])
+
+ X_train, y_train, X_test, y_test = binary_from_multiclass(X_train, y_train, X_test, y_test, classes)
return X_train, y_train, X_test, y_test, eps_dataset
@@ -254,7 +305,7 @@ def mnist_2_6():
eps_dataset = 0.3
classes = [2, 6] # 2 is 1, 6 is -1 in the binary classification scheme
- (X_train, y_train), (X_test, y_test) = mnist.load_data()
+ (X_train, y_train), (X_test, y_test) = mnist_keras.load_data()
X_train, X_test = X_train.astype(np.float64) / 255.0, X_test.astype(np.float64) / 255.0
X_train = np.reshape(X_train, [X_train.shape[0], -1])
X_test = np.reshape(X_test, [X_test.shape[0], -1])
@@ -263,19 +314,33 @@ def mnist_2_6():
return X_train, y_train, X_test, y_test, eps_dataset
-def mnist_1_5():
+def mnist():
"""
- train: (11876, 784), test: (1990, 784)
+ train: (60000, 784), test: (10000, 784)
"""
eps_dataset = 0.3
- classes = [1, 5] # 2 is 1, 6 is -1 in the binary classification scheme
- (X_train, y_train), (X_test, y_test) = mnist.load_data()
+ (X_train, y_train), (X_test, y_test) = mnist_keras.load_data()
X_train, X_test = X_train.astype(np.float64) / 255.0, X_test.astype(np.float64) / 255.0
X_train = np.reshape(X_train, [X_train.shape[0], -1])
X_test = np.reshape(X_test, [X_test.shape[0], -1])
- X_train, y_train, X_test, y_test = binary_from_multiclass(X_train, y_train, X_test, y_test, classes)
+ return X_train, y_train, X_test, y_test, eps_dataset
+
+
+def cifar10():
+ """
+ train: (60000, 3072), test: (10000, 3072)
+ """
+ eps_dataset = 8/255
+
+ (X_train, y_train), (X_test, y_test) = cifar10_keras.load_data()
+ X_train, X_test = X_train.astype(np.float64) / 255.0, X_test.astype(np.float64) / 255.0
+ X_train = np.reshape(X_train, [X_train.shape[0], -1])
+ X_test = np.reshape(X_test, [X_test.shape[0], -1])
+
+ y_train, y_test = y_train.flatten(), y_test.flatten()
+
return X_train, y_train, X_test, y_test, eps_dataset
@@ -298,7 +363,7 @@ def fmnist_sandal_sneaker():
eps_dataset = 0.1
classes = [5, 7] # 5 is 1, 7 is -1 in the binary classification scheme
- (X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()
+ (X_train, y_train), (X_test, y_test) = fashion_mnist_keras.load_data()
X_train, X_test = X_train.astype(np.float64) / 255.0, X_test.astype(np.float64) / 255.0
X_train = np.reshape(X_train, [X_train.shape[0], -1])
X_test = np.reshape(X_test, [X_test.shape[0], -1])
@@ -307,19 +372,33 @@ def fmnist_sandal_sneaker():
return X_train, y_train, X_test, y_test, eps_dataset
-def gts_30_70():
+def fmnist():
+ """
+ train: (60000, 784), test: (10000, 784)
+ """
+ eps_dataset = 0.1
+
+ (X_train, y_train), (X_test, y_test) = fashion_mnist_keras.load_data()
+ X_train, X_test = X_train.astype(np.float64) / 255.0, X_test.astype(np.float64) / 255.0
+ X_train = np.reshape(X_train, [X_train.shape[0], -1])
+ X_test = np.reshape(X_test, [X_test.shape[0], -1])
+
+ return X_train, y_train, X_test, y_test, eps_dataset
+
+
+def gts_100_roadworks():
"""
the class ids can be checked in the original data folders, for example:
1: speed 30, 4: speed 70, 7: speed 100, 8: speed 120, 18: warning, 25: roadworks
- train: 4200x3072, test: 1380x3072
+ train: (2940, 3072), test: (930, 3072)
"""
eps_dataset = 8 / 255 # following Madry et al, 2017 for cifar10
- classes = [1, 4]
+ classes = [7, 25]
# Originally, all pixels values are uint8 values in [0, 255]
- train = scipy.io.loadmat('data/gts/gts_int_train.mat')
- test = scipy.io.loadmat('data/gts/gts_int_test.mat')
+ train = scipy.io.loadmat(data_dir + 'gts/gts_int_train.mat')
+ test = scipy.io.loadmat(data_dir + 'gts/gts_int_test.mat')
X_train, y_train, X_test, y_test = train['images'], train['labels'], test['images'], test['labels']
X_train, X_test = X_train.reshape(X_train.shape[0], -1), X_test.reshape(X_test.shape[0], -1)
X_train, X_test = X_train / 255.0, X_test / 255.0
@@ -329,19 +408,19 @@ def gts_30_70():
return X_train, y_train, X_test, y_test, eps_dataset
-def gts_100_roadworks():
+def gts_30_70():
"""
the class ids can be checked in the original data folders, for example:
1: speed 30, 4: speed 70, 7: speed 100, 8: speed 120, 18: warning, 25: roadworks
- train: (2940, 3072), test: (930, 3072)
+ train: 4200x3072, test: 1380x3072
"""
eps_dataset = 8 / 255 # following Madry et al, 2017 for cifar10
- classes = [7, 25]
+ classes = [1, 4]
# Originally, all pixels values are uint8 values in [0, 255]
- train = scipy.io.loadmat('data/gts/gts_int_train.mat')
- test = scipy.io.loadmat('data/gts/gts_int_test.mat')
+ train = scipy.io.loadmat(data_dir + 'gts/gts_int_train.mat')
+ test = scipy.io.loadmat(data_dir + 'gts/gts_int_test.mat')
X_train, y_train, X_test, y_test = train['images'], train['labels'], test['images'], test['labels']
X_train, X_test = X_train.reshape(X_train.shape[0], -1), X_test.reshape(X_test.shape[0], -1)
X_train, X_test = X_train / 255.0, X_test / 255.0
@@ -352,14 +431,95 @@ def gts_100_roadworks():
def har():
- eps_dataset = 0.05
- path = 'data/har/'
- X_train, X_test = np.loadtxt(path + 'X_train.txt'), np.loadtxt(path + 'X_test.txt') # (7352, 561), (2947, 561)
- y_train, y_test = np.loadtxt(path + 'y_train.txt'), np.loadtxt(path + 'y_test.txt') # 6 classes
+ """
+ Human activity recognition dataset from https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones
+ Note: Wong and Kolter, ICML 2018 used eps=0.05, but the data points were from -1 to 1.
+ We use equivalently eps=0.025, but data points from 0 to 1.
+
+ The labels are in {0, 1, 2, 3, 4, 5}.
+
+ train: (7352, 561), test: (2947, 561), n classes: 6.
+ """
+ eps_dataset = 0.025
+ path_train, path_test = data_dir + 'har/train/', data_dir + 'har/test/'
+ X_train, X_test = np.loadtxt(path_train + 'X_train.txt'), np.loadtxt(path_test + 'X_test.txt')
+ y_train, y_test = np.loadtxt(path_train + 'y_train.txt'), np.loadtxt(path_test + 'y_test.txt')
y_train, y_test = y_train - 1, y_test - 1 # make the class numeration start from 0
+ X_train, X_test = (X_train + 1) / 2, (X_test + 1) / 2 # from [-1, 1] to [0, 1]
return X_train, y_train, X_test, y_test, eps_dataset
+def convert_to_float32(X):
+ return X.astype(np.float32)
+
+
+def random_crop(image, n_crop):
+ h, w, _ = image.shape
+ top = np.random.randint(0, n_crop)
+ left = np.random.randint(0, n_crop)
+ bottom = h - (n_crop - top)
+ right = w - (n_crop - left)
+ image = image[top:bottom, left:right, :]
+ return image
+
+
+def horizontal_flip(images, prob=0.5):
+ if np.random.rand() < prob:
+ images = images[:, :, ::-1, :]
+ return images
+
+
+def data_augment(X, dataset):
+ num, dim = X.shape
+ img_shape = datasets_img_shapes[dataset]
+ X_img = np.reshape(np.copy(X), [num, *img_shape])
+ if len(img_shape) == 2: # introduce a fake last dimension for grayscale datasets
+ X_img = X_img[:, :, :, None]
+
+ n_crop = 2
+ X_img_pad = np.pad(X_img, [(0, 0), (n_crop//2, n_crop//2), (n_crop//2, n_crop//2), (0, 0)], 'constant', constant_values=0) # zero padding
+ for i in range(num):
+ X_img[i] = random_crop(X_img_pad[i], n_crop=n_crop) # up to `n_crop` pixels are cropped
+
+ if dataset in ['cifar10']:
+ X_img = horizontal_flip(X_img)
+
+ return np.reshape(X_img, [num, dim])
+
+
+def crop_batch(X_img, n_h, n_w, n_crop):
+ _, h, w, _ = X_img.shape
+ bottom, right = h - (n_crop - n_h), w - (n_crop - n_w)
+ return X_img[:, n_h:bottom, n_w:right, :]
+
+
+def extend_dataset(X, dataset):
+ num, dim = X.shape
+ img_shape = datasets_img_shapes[dataset]
+ X_img = np.reshape(np.copy(X), [num, *img_shape])
+ if len(img_shape) == 2: # introduce a fake last dimension for grayscale datasets
+ X_img = X_img[:, :, :, None]
+
+ n_crop = 2
+ X_img_pad = np.pad(X_img, [(0, 0), (n_crop // 2, n_crop // 2), (n_crop // 2, n_crop // 2), (0, 0)], 'constant',
+ constant_values=0)
+
+ # Note: (1, 1) is the original image
+ X_img_l = crop_batch(X_img_pad, 1, 0, n_crop)
+ X_img_r = crop_batch(X_img_pad, 1, 2, n_crop)
+ X_img_t = crop_batch(X_img_pad, 0, 1, n_crop)
+ X_img_b = crop_batch(X_img_pad, 2, 1, n_crop)
+
+ X_img_extended = np.vstack([X_img, X_img_l, X_img_r, X_img_t, X_img_b])
+
+ # if dataset in ['cifar10']: # would lead to 10x expansion of the training data - might be too comp. expensive
+ # X_img_horiz_flip = X_img_extended[:, :, ::-1, :]
+ # X_img_extended = np.vstack([X_img_extended, X_img_horiz_flip])
+
+ X_final = np.reshape(X_img_extended, [-1, dim])
+ return X_final
+
+
all_datasets_dict = {
'toy_2d_stumps': toy_2d_stumps,
'toy_2d_trees': toy_2d_trees,
@@ -378,4 +538,45 @@ def har():
'gts_30_70': gts_30_70,
'har': har,
+ 'mnist': mnist,
+ 'fmnist': fmnist,
+ 'cifar10': cifar10,
+}
+dataset_names_dict = {
+ 'toy_2d_stumps': 'toy_2d_stumps',
+ 'toy_2d_trees': 'toy_2d_trees',
+ 'toy_2d_xor': 'toy_2d_xor',
+ 'toy_2d_wong': 'toy_2d_wong',
+
+ 'breast_cancer': 'breast-cancer',
+ 'diabetes': 'diabetes',
+ 'ijcnn1': 'IJCNN1',
+ 'cod_rna': 'cod-rna',
+
+ 'mnist_1_5': 'MNIST 1-5',
+ 'mnist_2_6': 'MNIST 2-6',
+ 'fmnist_sandal_sneaker': 'FMNIST shoes',
+ 'gts_100_roadworks': 'GTS 100-rw',
+ 'gts_30_70': 'GTS 30-70',
+
+ 'har': 'har',
+ 'mnist': 'mnist',
+ 'fmnist': 'fmnist',
+ 'cifar10': 'cifar10',
+}
+datasets_img_shapes = {
+ 'mnist_1_5': (28, 28),
+ 'mnist_2_6': (28, 28),
+ 'mnist': (28, 28),
+ 'fmnist': (28, 28),
+ 'fmnist_sandal_sneaker': (28, 28),
+ 'gts_100_roadworks': (32, 32, 3),
+ 'gts_30_70': (32, 32, 3),
+ 'cifar10': (32, 32, 3),
}
+datasets_feature_names = {
+ 'breast_cancer': ['radius', 'texture', 'perimeter', 'area', 'smoothness', 'compactness', 'concavity', 'concave points', 'symmetry', 'fractal dimension'],
+ 'diabetes': ['# pregnancies', 'glucose', 'blood pressure', 'skin thickness', 'insulin', 'body mass index', 'diabetes pedigree', 'age'],
+ 'cod_rna': ['Dynalign score', 'shorter seq. length', 'A freq. of seq. 1', 'U freq. of seq. 1', 'C freq. of seq. 1', 'A freq. of seq. 2', 'U freq. of seq. 2', 'C freq. of seq. 2'],
+}
+
diff --git a/eval.py b/eval.py
index 8b0bebf..b9cea50 100644
--- a/eval.py
+++ b/eval.py
@@ -1,39 +1,94 @@
import argparse
import numpy as np
+import time
import data
-from tree_ensemble import TreeEnsemble
+import attacks
from stump_ensemble import StumpEnsemble
+from tree_ensemble import TreeEnsemble
+from utils import extract_hyperparam
+from classifiers import OneVsAllClassifier
+
np.random.seed(1)
np.set_printoptions(precision=10)
parser = argparse.ArgumentParser(description='Define hyperparameters.')
-parser.add_argument('--dataset', type=str, default='mnist_2_6', help='Dataset: toy2d, mnist_2_6, har.')
-parser.add_argument('--weak_learner', type=str, default='tree', help='Weak learner: stump or tree.')
-parser.add_argument('--n_eval', type=int, default='-1', help='On how many points to evaluate.')
-parser.add_argument('--model_path', type=str, default='2019-07-07 10:05:48 dataset=mnist_2_6 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0',
+parser.add_argument('--n_eval', type=int, default='-1', help='On how many points to eval.')
+parser.add_argument('--iter', type=int, default=-1, help='Which iteration (i.e. number of trees) to take.')
+parser.add_argument('--n_iter_attack', type=int, default=1, help='Which iteration (i.e. number of trees) to take.')
+parser.add_argument('--exp_folder', type=str, default='models/models_trees_multiclass', help='Experiment name')
+parser.add_argument('--model_path', type=str, default='2019-08-06 14:59:51 dataset=fmnist weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=784 eps=0.100 max_depth=30 lr=0.05',
help='Model name.')
args = parser.parse_args()
+exp_folder = args.exp_folder
-X_train, y_train, X_test, y_test, eps = data.all_datasets_dict[args.dataset]()
+# the info about dataset, weak_learner is already encoded in the model path
+dataset = extract_hyperparam(args.model_path, 'dataset=')
+weak_learner = extract_hyperparam(args.model_path, 'weak_learner=')
+max_depth = extract_hyperparam(args.model_path, 'max_depth=')
+model = extract_hyperparam(args.model_path, 'model=')
-# the hyperparameters of recreated models do not matter (they matter only for training)
-if args.weak_learner == 'stump':
- ensemble = StumpEnsemble('stump', 0, 0)
-elif args.weak_learner == 'tree':
- ensemble = TreeEnsemble('tree', 0, 0, 0, 0, 0)
+X_train, y_train, X_test, y_test, eps = data.all_datasets_dict[dataset]()
+X_train, X_test = data.convert_to_float32(X_train), data.convert_to_float32(X_test)
+n_cls = int(y_train.max()) + 1
+y_train, _, y_test = data.transform_labels_one_vs_all(y_train, y_train, y_test)
+
+metrics = np.loadtxt(exp_folder + '/' + args.model_path + '.metrics')
+if args.iter == -1:
+ valid_errs, valid_adv_errs_lb, valid_adv_errs = metrics[:, 8], metrics[:, 9], metrics[:, 10]
+ # Model selection
+ if model == 'plain':
+ iter_to_take = np.argmin(valid_errs)
+ elif model in ['at_cube', 'robust_bound', 'robust_exact']:
+ iter_to_take = np.argmin(valid_adv_errs)
+ else:
+ raise ValueError('wrong model name')
else:
- raise ValueError('wrong weak learner')
+ iter_to_take = args.iter
+
+ensembles = []
+n_classifiers = n_cls if n_cls > 2 else 1
+for i_clsf in range(n_classifiers):
+ if weak_learner == 'stump':
+ # the hyperparameters of recreated models do not matter (they matter only for training)
+ ensemble = StumpEnsemble(weak_learner, 0, 0, 0, 0, 0)
+ elif weak_learner == 'tree':
+ ensemble = TreeEnsemble(weak_learner, 0, 0, 0, 0, 0, 0, 0, 0, 0)
+ else:
+ raise ValueError('wrong weak learner')
+ ensembles.append(ensemble)
-ensemble.load('models/{}.model.npy'.format(args.model_path))
+model_ova = OneVsAllClassifier(ensembles)
+model_ova.load('{}/{}.model.npy'.format(exp_folder, args.model_path), iteration=iter_to_take)
-test_err = np.mean(y_test * ensemble.predict(X_test) < 0.0)
-print('test err: {:.2%}'.format(test_err))
+if args.n_eval != -1:
+ X_test, y_test = X_test[:args.n_eval], y_test[:, :args.n_eval]
+
+time_te_start = time.time()
+fmargin = model_ova.fmargin(X_test, y_test)
+test_err = 1 - np.mean(fmargin > 0.0)
+time_te = time.time() - time_te_start
+print('te={:.2%} ({:.4f}s)'.format(test_err, time_te))
+
+time_lrte_start = time.time()
+fmargin_attack = attacks.cube_attack(model_ova, X_test, y_test, eps, args.n_iter_attack, p=0.15)[0]
+lrte = 1 - (fmargin_attack > 0.0).mean()
+time_lrte = time.time() - time_lrte_start
+print('lrte={:.2%} ({:.4f}s)'.format(lrte, time_lrte))
+
+if weak_learner == 'stump':
+ cert_f = model_ova.fmargin_exact
+elif weak_learner == 'tree':
+ cert_f = model_ova.fmargin_treewise
+else:
+ raise ValueError('wrong weak learner')
+# the first time numba takes some time to compile, thus we need this line to properly measure the certification speed
+_ = 1 - (cert_f(X_train[:1000], y_train[:, :1000], eps) > 0.0).mean()
-# if args.n_eval != -1:
-# X_test, y_test = X_test[:args.n_eval], y_test[:args.n_eval]
-#
-# deltas = ensemble.exact_adv_example(X_test, y_test)
-# avg_db_dist = np.abs(deltas).max(1).mean(0)
-# print('avg dist to db: {:.3f}'.format(avg_db_dist))
+time_urte_start = time.time()
+fmargin_cert = cert_f(X_test, y_test, eps)
+urte = 1 - (fmargin_cert > 0.0).mean()
+time_urte = time.time() - time_urte_start
+print('urte={:.2%} ({:.5f}s)'.format(urte, time_urte))
+print('TE: {:.2%} ({:.4f}s) LRTE: {:.2%} ({:.4f}s) URTE: {:.2%} ({:.5f}s)'.format(test_err, time_te, lrte, time_lrte, urte, time_urte))
diff --git a/exps.sh b/exps.sh
index ecb31ee..7518de9 100755
--- a/exps.sh
+++ b/exps.sh
@@ -1,16 +1,52 @@
#!/usr/bin/env bash
-# All datasets: breast_cancer diabetes cod_rna mnist_2_6 fmnist_sandal_sneaker gts_120_warning gts_30_70
+### Note: the best way to compare to our results is just to directly use the provided models.
+### Some models retrained from scratch may give slightly different numbers.
-weak_learner=stump
-for dataset in breast_cancer diabetes cod_rna mnist_2_6 fmnist_sandal_sneaker gts_100_roadworks gts_30_70; do
- nohup python train.py --dataset=$dataset --weak_learner=$weak_learner --model=plain >> run_logs/${dataset}-${weak_learner}-plain.out &
- nohup python train.py --dataset=$dataset --weak_learner=$weak_learner --model=robust_bound >> run_logs/${dataset}-${weak_learner}-robust_bound.out &
- nohup python train.py --dataset=$dataset --weak_learner=$weak_learner --model=robust_exact >> run_logs/${dataset}-${weak_learner}-robust_exact.out &
+# All datasets: breast_cancer diabetes cod_rna mnist_1_5 mnist_2_6 fmnist_sandal_sneaker gts_120_warning gts_30_70
+
+# Train stumps on binary classification datasets
+for model in plain robust_bound robust_exact; do
+ nohup python train.py --dataset=breast_cancer --weak_learner=stump --model=${model} --lr=1.0 >> run_logs/breast_cancer-stump-${model}.out &
+ nohup python train.py --dataset=diabetes --weak_learner=stump --model=${model} --lr=1.0 >> run_logs/diabetes-stump-${model}.out &
+ nohup python train.py --dataset=cod_rna --weak_learner=stump --model=${model} --lr=1.0 >> run_logs/cod_rna-stump-${model}.out &
+ nohup python train.py --dataset=mnist_1_5 --weak_learner=stump --model=${model} --lr=1.0 >> run_logs/mnist_1_5-stump-${model}.out &
+ nohup python train.py --dataset=mnist_2_6 --weak_learner=stump --model=${model} --lr=1.0 >> run_logs/mnist_2_6-stump-${model}.out &
+ nohup python train.py --dataset=fmnist_sandal_sneaker --weak_learner=stump --model=${model} --lr=1.0 >> run_logs/fmnist_sandal_sneaker-stump-${model}.out &
+ nohup python train.py --dataset=gts_100_roadworks --weak_learner=stump --model=${model} --lr=1.0 >> run_logs/gts_100_roadworks-stump-${model}.out &
+ nohup python train.py --dataset=gts_30_70 --weak_learner=stump --model=${model} --lr=1.0 >> run_logs/gts_30_70-stump-${model}.out &
+done
+
+# Train stumps with adversarial training (note: requires smaller learning rate)
+for model in at_cube; do
+ nohup python train.py --dataset=breast_cancer --weak_learner=stump --model=${model} --lr=0.1 >> run_logs/breast_cancer-stump-${model}.out &
+ nohup python train.py --dataset=diabetes --weak_learner=stump --model=${model} --lr=0.1 >> run_logs/diabetes-stump-${model}.out &
+ nohup python train.py --dataset=cod_rna --weak_learner=stump --model=${model} --lr=0.1 >> run_logs/cod_rna-stump-${model}.out &
+ nohup python train.py --dataset=mnist_1_5 --weak_learner=stump --model=${model} --lr=0.1 >> run_logs/mnist_1_5-stump-${model}.out &
+ nohup python train.py --dataset=mnist_2_6 --weak_learner=stump --model=${model} --lr=0.1 >> run_logs/mnist_2_6-stump-${model}.out &
+ nohup python train.py --dataset=fmnist_sandal_sneaker --weak_learner=stump --model=${model} --lr=0.1 >> run_logs/fmnist_sandal_sneaker-stump-${model}.out &
+ nohup python train.py --dataset=gts_100_roadworks --weak_learner=stump --model=${model} --lr=0.1 >> run_logs/gts_100_roadworks-stump-${model}.out &
+ nohup python train.py --dataset=gts_30_70 --weak_learner=stump --model=${model} --lr=0.1 >> run_logs/gts_30_70-stump-${model}.out &
done
-weak_learner=tree
-for dataset in breast_cancer diabetes cod_rna mnist_2_6 fmnist_sandal_sneaker gts_100_roadworks gts_30_70; do
- nohup python train.py --dataset=$dataset --weak_learner=$weak_learner --model=plain >> run_logs/${dataset}-${weak_learner}-plain.out &
- nohup python train.py --dataset=$dataset --weak_learner=$weak_learner --model=robust_bound >> run_logs/${dataset}-${weak_learner}-robust_bound.out &
+# Train trees on binary classification datasets
+max_depth=4
+for model in plain at_cube robust_bound; do
+ nohup python train.py --dataset=breast_cancer --weak_learner=tree --max_depth=$max_depth --model=${model} --lr=0.01 >> run_logs/breast_cancer-tree-${model}.out &
+ nohup python train.py --dataset=diabetes --weak_learner=tree --max_depth=$max_depth --model=${model} --lr=0.2 >> run_logs/diabetes-tree-${model}.out &
+ nohup python train.py --dataset=cod_rna --weak_learner=tree --max_depth=$max_depth --model=${model} --lr=0.2 >> run_logs/cod_rna-tree-${model}.out &
+ nohup python train.py --dataset=mnist_1_5 --weak_learner=tree --max_depth=$max_depth --model=${model} --lr=0.2 >> run_logs/mnist_1_5-tree-${model}.out &
+ nohup python train.py --dataset=mnist_2_6 --weak_learner=tree --max_depth=$max_depth --model=${model} --lr=0.2 >> run_logs/mnist_2_6-tree-${model}.out &
+ nohup python train.py --dataset=fmnist_sandal_sneaker --weak_learner=tree --max_depth=$max_depth --model=${model} --lr=0.2 >> run_logs/fmnist_sandal_sneaker-tree-${model}.out &
+ nohup python train.py --dataset=gts_100_roadworks --weak_learner=tree --max_depth=$max_depth --model=${model} --lr=0.01 >> run_logs/gts_100_roadworks-tree-${model}.out &
+ nohup python train.py --dataset=gts_30_70 --weak_learner=tree --max_depth=$max_depth --model=${model} --lr=0.01 >> run_logs/gts_30_70-tree-${model}.out &
done
+
+
+### Multi-class experiments: robust models on MNIST, FMNIST, CIFAR-10
+# Advice: multiclass models require quite some time to train. In case you want to get results faster you can try to
+# subsample the thresholds by setting e.g. n_bins=10. However, this might slightly negatively affect the results.
+nohup python train.py --dataset=mnist --weak_learner=tree --max_depth=30 --model=robust_bound --lr=0.05 >> run_logs/mnist-tree-robust_bound.out &
+nohup python train.py --dataset=fmnist --weak_learner=tree --max_depth=30 --model=robust_bound --lr=0.05 >> run_logs/fmnist-tree-robust_bound.out &
+nohup python train.py --dataset=cifar10 --weak_learner=tree --max_depth=4 --model=robust_bound --lr=0.1 >> run_logs/cifar10-tree-robust_bound.out &
+
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diff --git a/models/2019-07-06 19:46:23 dataset=diabetes weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.050 max_depth=4 lr=1.0.log b/models/2019-07-06 19:46:23 dataset=diabetes weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.050 max_depth=4 lr=1.0.log
deleted file mode 100644
index 31829ab..0000000
--- a/models/2019-07-06 19:46:23 dataset=diabetes weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.050 max_depth=4 lr=1.0.log
+++ /dev/null
@@ -1,54 +0,0 @@
-Boosting started: 2019-07-06 19:46:23 dataset=diabetes weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.050 min_samples_split=10 min_samples_leaf=5 max_depth=4 lr=1.0
-iter: 1 [test] err 27.92% adv_err_lb 31.82% adv_err_ub 31.82% | [valid] err 28.46% adv_err 32.52% | [train] err 24.44% adv_err 29.94% loss 0.89214 | [tree] depth 4->4 nodes 8->8 (12.46s)
-iter: 2 [test] err 27.92% adv_err_lb 31.82% adv_err_ub 31.82% | [valid] err 26.83% adv_err 31.71% | [train] err 23.42% adv_err 29.53% loss 0.86976 | [tree] depth 4->4 nodes 7->7 (12.91s)
-iter: 3 [test] err 28.57% adv_err_lb 33.12% adv_err_ub 33.12% | [valid] err 22.76% adv_err 30.89% | [train] err 22.61% adv_err 28.92% loss 0.84073 | [tree] depth 4->4 nodes 7->7 (13.36s)
-iter: 4 [test] err 29.22% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 22.76% adv_err 30.89% | [train] err 22.61% adv_err 28.92% loss 0.83801 | [tree] depth 4->4 nodes 4->4 (13.79s)
-iter: 5 [test] err 29.22% adv_err_lb 34.42% adv_err_ub 34.42% | [valid] err 21.95% adv_err 31.71% | [train] err 21.59% adv_err 28.31% loss 0.83074 | [tree] depth 4->4 nodes 8->8 (14.53s)
-iter: 6 [test] err 29.22% adv_err_lb 34.42% adv_err_ub 34.42% | [valid] err 21.95% adv_err 31.71% | [train] err 21.18% adv_err 27.90% loss 0.82863 | [tree] depth 4->4 nodes 5->5 (15.28s)
-iter: 7 [test] err 29.22% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 22.76% adv_err 31.71% | [train] err 20.98% adv_err 27.70% loss 0.82635 | [tree] depth 4->4 nodes 5->5 (15.94s)
-iter: 8 [test] err 28.57% adv_err_lb 34.42% adv_err_ub 34.42% | [valid] err 23.58% adv_err 32.52% | [train] err 21.18% adv_err 27.70% loss 0.81942 | [tree] depth 4->4 nodes 5->5 (16.76s)
-iter: 9 [test] err 28.57% adv_err_lb 34.42% adv_err_ub 34.42% | [valid] err 23.58% adv_err 32.52% | [train] err 21.18% adv_err 27.70% loss 0.81872 | [tree] depth 4->4 nodes 7->7 (17.60s)
-iter: 10 [test] err 28.57% adv_err_lb 34.42% adv_err_ub 34.42% | [valid] err 23.58% adv_err 32.52% | [train] err 21.18% adv_err 27.70% loss 0.81853 | [tree] depth 4->3 nodes 4->3 (18.37s)
-iter: 11 [test] err 28.57% adv_err_lb 34.42% adv_err_ub 34.42% | [valid] err 23.58% adv_err 32.52% | [train] err 21.18% adv_err 27.70% loss 0.81837 | [tree] depth 4->4 nodes 4->4 (19.09s)
-iter: 12 [test] err 28.57% adv_err_lb 34.42% adv_err_ub 34.42% | [valid] err 23.58% adv_err 32.52% | [train] err 21.18% adv_err 27.70% loss 0.81825 | [tree] depth 4->3 nodes 5->3 (20.10s)
-iter: 13 [test] err 28.57% adv_err_lb 34.42% adv_err_ub 34.42% | [valid] err 23.58% adv_err 32.52% | [train] err 21.18% adv_err 27.70% loss 0.81814 | [tree] depth 4->3 nodes 5->3 (21.00s)
-iter: 14 [test] err 28.57% adv_err_lb 34.42% adv_err_ub 34.42% | [valid] err 23.58% adv_err 32.52% | [train] err 21.18% adv_err 27.70% loss 0.81805 | [tree] depth 4->3 nodes 5->3 (21.92s)
-iter: 15 [test] err 27.92% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 24.39% adv_err 32.52% | [train] err 19.96% adv_err 26.88% loss 0.81034 | [tree] depth 4->4 nodes 5->5 (23.03s)
-iter: 16 [test] err 27.92% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 24.39% adv_err 32.52% | [train] err 19.76% adv_err 27.09% loss 0.80559 | [tree] depth 4->4 nodes 5->5 (23.99s)
-iter: 17 [test] err 27.92% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 24.39% adv_err 32.52% | [train] err 19.76% adv_err 27.09% loss 0.80435 | [tree] depth 4->3 nodes 5->3 (25.03s)
-iter: 18 [test] err 27.27% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 24.39% adv_err 32.52% | [train] err 19.76% adv_err 27.29% loss 0.80000 | [tree] depth 4->4 nodes 6->6 (25.97s)
-iter: 19 [test] err 28.57% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 26.88% loss 0.79668 | [tree] depth 4->4 nodes 7->6 (27.04s)
-iter: 20 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79661 | [tree] depth 4->2 nodes 4->2 (28.11s)
-iter: 21 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79655 | [tree] depth 4->4 nodes 4->4 (29.21s)
-iter: 22 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79655 | [tree] depth 4->1 nodes 4->1 (30.15s)
-iter: 23 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79650 | [tree] depth 4->2 nodes 7->2 (31.20s)
-iter: 24 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79646 | [tree] depth 4->2 nodes 6->2 (33.19s)
-iter: 25 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79642 | [tree] depth 4->2 nodes 6->2 (34.34s)
-iter: 26 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79639 | [tree] depth 4->2 nodes 6->2 (35.59s)
-iter: 27 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79635 | [tree] depth 4->2 nodes 6->2 (36.71s)
-iter: 28 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79633 | [tree] depth 4->2 nodes 8->2 (38.20s)
-iter: 29 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79630 | [tree] depth 4->2 nodes 8->2 (39.44s)
-iter: 30 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79627 | [tree] depth 4->2 nodes 8->2 (40.70s)
-iter: 31 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79625 | [tree] depth 4->2 nodes 6->2 (41.92s)
-iter: 32 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79623 | [tree] depth 4->2 nodes 8->2 (43.18s)
-iter: 33 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79621 | [tree] depth 4->2 nodes 6->2 (44.37s)
-iter: 34 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79619 | [tree] depth 4->2 nodes 6->2 (45.74s)
-iter: 35 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79617 | [tree] depth 4->2 nodes 6->2 (47.18s)
-iter: 36 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79616 | [tree] depth 4->2 nodes 6->2 (48.81s)
-iter: 37 [test] err 27.92% adv_err_lb 35.06% adv_err_ub 35.06% | [valid] err 24.39% adv_err 32.52% | [train] err 19.55% adv_err 27.09% loss 0.79614 | [tree] depth 4->2 nodes 8->2 (50.62s)
-iter: 38 [test] err 26.62% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 22.76% adv_err 34.15% | [train] err 18.74% adv_err 25.87% loss 0.78039 | [tree] depth 4->4 nodes 7->7 (52.30s)
-iter: 39 [test] err 26.62% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 22.76% adv_err 34.15% | [train] err 18.74% adv_err 25.87% loss 0.78029 | [tree] depth 4->1 nodes 4->1 (53.69s)
-iter: 40 [test] err 26.62% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 22.76% adv_err 34.15% | [train] err 18.74% adv_err 25.87% loss 0.78028 | [tree] depth 4->4 nodes 4->4 (54.95s)
-iter: 41 [test] err 25.97% adv_err_lb 34.42% adv_err_ub 34.42% | [valid] err 24.39% adv_err 40.65% | [train] err 17.52% adv_err 24.85% loss 0.77640 | [tree] depth 4->4 nodes 7->5 (56.32s)
-iter: 42 [test] err 25.32% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 25.20% adv_err 41.46% | [train] err 17.31% adv_err 23.83% loss 0.76472 | [tree] depth 4->4 nodes 8->8 (57.80s)
-iter: 43 [test] err 25.32% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 25.20% adv_err 41.46% | [train] err 17.31% adv_err 23.83% loss 0.76395 | [tree] depth 4->4 nodes 6->6 (59.33s)
-iter: 44 [test] err 25.32% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 25.20% adv_err 41.46% | [train] err 17.31% adv_err 23.83% loss 0.76394 | [tree] depth 4->4 nodes 4->4 (60.73s)
-iter: 45 [test] err 25.32% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 25.20% adv_err 41.46% | [train] err 17.31% adv_err 23.83% loss 0.76392 | [tree] depth 4->4 nodes 4->4 (62.13s)
-iter: 46 [test] err 25.32% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 25.20% adv_err 41.46% | [train] err 17.31% adv_err 23.83% loss 0.76391 | [tree] depth 4->3 nodes 4->3 (63.60s)
-iter: 47 [test] err 25.32% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 25.20% adv_err 41.46% | [train] err 17.31% adv_err 23.83% loss 0.76390 | [tree] depth 4->2 nodes 8->2 (65.21s)
-iter: 48 [test] err 25.32% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 25.20% adv_err 41.46% | [train] err 17.31% adv_err 23.83% loss 0.76389 | [tree] depth 4->2 nodes 6->2 (66.72s)
-iter: 49 [test] err 25.32% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 25.20% adv_err 41.46% | [train] err 17.31% adv_err 23.83% loss 0.76388 | [tree] depth 4->2 nodes 6->2 (68.29s)
-iter: 50 [test] err 25.32% adv_err_lb 33.77% adv_err_ub 33.77% | [valid] err 25.20% adv_err 41.46% | [train] err 17.31% adv_err 23.83% loss 0.76388 | [tree] depth 4->2 nodes 8->2 (70.04s)
-(done in 1.17 min)
-Model path: exps/2019-07-06 19:46:23 dataset=diabetes weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.050 max_depth=4 lr=1.0.model.npy
-Metrics path: exps/2019-07-06 19:46:23 dataset=diabetes weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.050 max_depth=4 lr=1.0.metrics
diff --git a/models/2019-07-06 19:46:23 dataset=diabetes weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.050 max_depth=4 lr=1.0.metrics b/models/2019-07-06 19:46:23 dataset=diabetes weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.050 max_depth=4 lr=1.0.metrics
deleted file mode 100644
index 4b734be..0000000
--- a/models/2019-07-06 19:46:23 dataset=diabetes weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.050 max_depth=4 lr=1.0.metrics
+++ /dev/null
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diff --git a/models/2019-07-06 19:46:23 dataset=diabetes weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.050 max_depth=4 lr=1.0.model.npy b/models/2019-07-06 19:46:23 dataset=diabetes weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.050 max_depth=4 lr=1.0.model.npy
deleted file mode 100644
index 1997fd0..0000000
Binary files a/models/2019-07-06 19:46:23 dataset=diabetes weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.050 max_depth=4 lr=1.0.model.npy and /dev/null differ
diff --git a/models/2019-07-06 19:46:27 dataset=mnist_1_5 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.log b/models/2019-07-06 19:46:27 dataset=mnist_1_5 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.log
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index 555f13f..0000000
--- a/models/2019-07-06 19:46:27 dataset=mnist_1_5 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.log
+++ /dev/null
@@ -1,54 +0,0 @@
-Boosting started: 2019-07-06 19:46:27 dataset=mnist_1_5 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 min_samples_split=10 min_samples_leaf=5 max_depth=4 lr=1.0
-iter: 1 [test] err 3.16% adv_err_lb 6.66% adv_err_ub 6.81% | [valid] err 4.11% adv_err 7.97% | [train] err 3.37% adv_err 6.86% loss 0.39317 | [tree] depth 4->4 nodes 11->11 (59.87s)
-iter: 2 [test] err 1.92% adv_err_lb 4.79% adv_err_ub 5.08% | [valid] err 2.84% adv_err 5.84% | [train] err 2.07% adv_err 5.16% loss 0.30079 | [tree] depth 4->4 nodes 14->14 (121.08s)
-iter: 3 [test] err 1.83% adv_err_lb 4.24% adv_err_ub 4.59% | [valid] err 2.34% adv_err 5.22% | [train] err 1.95% adv_err 4.66% loss 0.25457 | [tree] depth 4->4 nodes 13->13 (187.57s)
-iter: 4 [test] err 1.23% adv_err_lb 3.85% adv_err_ub 4.00% | [valid] err 1.73% adv_err 4.27% | [train] err 1.19% adv_err 3.95% loss 0.20877 | [tree] depth 4->4 nodes 12->12 (257.24s)
-iter: 5 [test] err 0.89% adv_err_lb 3.26% adv_err_ub 3.70% | [valid] err 0.90% adv_err 3.99% | [train] err 0.85% adv_err 3.35% loss 0.16139 | [tree] depth 4->4 nodes 12->12 (337.34s)
-iter: 6 [test] err 0.79% adv_err_lb 2.76% adv_err_ub 3.45% | [valid] err 1.11% adv_err 4.11% | [train] err 0.72% adv_err 3.11% loss 0.14666 | [tree] depth 4->4 nodes 8->8 (419.65s)
-iter: 7 [test] err 0.64% adv_err_lb 2.52% adv_err_ub 3.11% | [valid] err 0.78% adv_err 3.58% | [train] err 0.64% adv_err 2.72% loss 0.12779 | [tree] depth 4->4 nodes 12->12 (514.81s)
-iter: 8 [test] err 0.64% adv_err_lb 2.17% adv_err_ub 2.81% | [valid] err 0.78% adv_err 3.12% | [train] err 0.54% adv_err 2.44% loss 0.11865 | [tree] depth 4->4 nodes 8->8 (607.53s)
-iter: 9 [test] err 0.49% adv_err_lb 1.97% adv_err_ub 2.61% | [valid] err 0.66% adv_err 3.25% | [train] err 0.37% adv_err 2.18% loss 0.10192 | [tree] depth 4->4 nodes 14->14 (713.40s)
-iter: 10 [test] err 0.54% adv_err_lb 1.92% adv_err_ub 2.66% | [valid] err 0.58% adv_err 2.92% | [train] err 0.32% adv_err 1.96% loss 0.09111 | [tree] depth 4->4 nodes 10->10 (820.26s)
-iter: 11 [test] err 0.64% adv_err_lb 2.07% adv_err_ub 2.71% | [valid] err 0.62% adv_err 2.59% | [train] err 0.27% adv_err 1.85% loss 0.08672 | [tree] depth 4->4 nodes 9->9 (937.68s)
-iter: 12 [test] err 0.54% adv_err_lb 1.97% adv_err_ub 2.61% | [valid] err 0.49% adv_err 2.55% | [train] err 0.26% adv_err 1.78% loss 0.08273 | [tree] depth 4->4 nodes 6->6 (1063.35s)
-iter: 13 [test] err 0.54% adv_err_lb 1.97% adv_err_ub 2.66% | [valid] err 0.45% adv_err 2.59% | [train] err 0.25% adv_err 1.70% loss 0.07804 | [tree] depth 4->4 nodes 14->14 (1200.22s)
-iter: 14 [test] err 0.49% adv_err_lb 1.87% adv_err_ub 2.42% | [valid] err 0.53% adv_err 2.30% | [train] err 0.24% adv_err 1.48% loss 0.07360 | [tree] depth 4->4 nodes 8->8 (1344.24s)
-iter: 15 [test] err 0.30% adv_err_lb 1.78% adv_err_ub 2.52% | [valid] err 0.58% adv_err 2.42% | [train] err 0.16% adv_err 1.43% loss 0.06654 | [tree] depth 4->4 nodes 11->11 (1491.84s)
-iter: 16 [test] err 0.35% adv_err_lb 1.87% adv_err_ub 2.71% | [valid] err 0.53% adv_err 2.51% | [train] err 0.13% adv_err 1.34% loss 0.05491 | [tree] depth 4->4 nodes 14->14 (1655.45s)
-iter: 17 [test] err 0.35% adv_err_lb 1.58% adv_err_ub 2.27% | [valid] err 0.49% adv_err 2.55% | [train] err 0.08% adv_err 1.09% loss 0.04449 | [tree] depth 4->4 nodes 15->15 (1827.36s)
-iter: 18 [test] err 0.35% adv_err_lb 1.38% adv_err_ub 2.07% | [valid] err 0.45% adv_err 2.34% | [train] err 0.10% adv_err 0.90% loss 0.03937 | [tree] depth 4->4 nodes 14->14 (2003.60s)
-iter: 19 [test] err 0.30% adv_err_lb 1.48% adv_err_ub 2.02% | [valid] err 0.45% adv_err 2.26% | [train] err 0.08% adv_err 0.81% loss 0.03736 | [tree] depth 4->4 nodes 10->10 (2182.02s)
-iter: 20 [test] err 0.39% adv_err_lb 1.58% adv_err_ub 2.12% | [valid] err 0.41% adv_err 2.30% | [train] err 0.09% adv_err 0.83% loss 0.03543 | [tree] depth 4->4 nodes 7->7 (2360.30s)
-iter: 21 [test] err 0.35% adv_err_lb 1.33% adv_err_ub 1.92% | [valid] err 0.37% adv_err 2.42% | [train] err 0.08% adv_err 0.70% loss 0.03167 | [tree] depth 4->4 nodes 15->15 (2538.80s)
-iter: 22 [test] err 0.35% adv_err_lb 1.58% adv_err_ub 2.12% | [valid] err 0.33% adv_err 2.38% | [train] err 0.06% adv_err 0.66% loss 0.02824 | [tree] depth 4->4 nodes 14->14 (2726.58s)
-iter: 23 [test] err 0.39% adv_err_lb 1.58% adv_err_ub 2.12% | [valid] err 0.33% adv_err 2.38% | [train] err 0.06% adv_err 0.63% loss 0.02723 | [tree] depth 4->4 nodes 8->8 (2922.15s)
-iter: 24 [test] err 0.35% adv_err_lb 1.63% adv_err_ub 2.12% | [valid] err 0.33% adv_err 2.42% | [train] err 0.05% adv_err 0.62% loss 0.02504 | [tree] depth 4->4 nodes 15->14 (3126.00s)
-iter: 25 [test] err 0.30% adv_err_lb 1.38% adv_err_ub 2.12% | [valid] err 0.29% adv_err 2.42% | [train] err 0.03% adv_err 0.49% loss 0.02174 | [tree] depth 4->4 nodes 15->15 (3342.18s)
-iter: 26 [test] err 0.30% adv_err_lb 1.38% adv_err_ub 2.12% | [valid] err 0.33% adv_err 2.42% | [train] err 0.03% adv_err 0.41% loss 0.01997 | [tree] depth 4->4 nodes 14->14 (3564.59s)
-iter: 27 [test] err 0.30% adv_err_lb 1.38% adv_err_ub 2.07% | [valid] err 0.33% adv_err 2.55% | [train] err 0.01% adv_err 0.44% loss 0.01739 | [tree] depth 4->4 nodes 14->14 (3790.84s)
-iter: 28 [test] err 0.25% adv_err_lb 1.18% adv_err_ub 2.02% | [valid] err 0.33% adv_err 2.55% | [train] err 0.00% adv_err 0.34% loss 0.01431 | [tree] depth 4->4 nodes 15->15 (4024.84s)
-iter: 29 [test] err 0.25% adv_err_lb 1.23% adv_err_ub 1.92% | [valid] err 0.25% adv_err 2.34% | [train] err 0.00% adv_err 0.28% loss 0.01219 | [tree] depth 4->4 nodes 14->14 (4265.67s)
-iter: 30 [test] err 0.25% adv_err_lb 1.13% adv_err_ub 1.87% | [valid] err 0.21% adv_err 2.42% | [train] err 0.00% adv_err 0.21% loss 0.01111 | [tree] depth 4->4 nodes 12->12 (4517.69s)
-iter: 31 [test] err 0.25% adv_err_lb 1.04% adv_err_ub 1.92% | [valid] err 0.25% adv_err 2.38% | [train] err 0.00% adv_err 0.22% loss 0.00983 | [tree] depth 4->4 nodes 14->14 (4779.29s)
-iter: 32 [test] err 0.20% adv_err_lb 1.09% adv_err_ub 1.87% | [valid] err 0.29% adv_err 2.55% | [train] err 0.00% adv_err 0.17% loss 0.00898 | [tree] depth 4->4 nodes 8->8 (5045.83s)
-iter: 33 [test] err 0.15% adv_err_lb 1.04% adv_err_ub 1.97% | [valid] err 0.25% adv_err 2.51% | [train] err 0.00% adv_err 0.15% loss 0.00828 | [tree] depth 4->4 nodes 13->13 (5319.98s)
-iter: 34 [test] err 0.15% adv_err_lb 1.04% adv_err_ub 1.97% | [valid] err 0.25% adv_err 2.63% | [train] err 0.00% adv_err 0.14% loss 0.00736 | [tree] depth 4->4 nodes 15->15 (5606.55s)
-iter: 35 [test] err 0.15% adv_err_lb 0.99% adv_err_ub 1.97% | [valid] err 0.29% adv_err 2.55% | [train] err 0.00% adv_err 0.11% loss 0.00690 | [tree] depth 4->4 nodes 12->12 (5900.18s)
-iter: 36 [test] err 0.15% adv_err_lb 1.04% adv_err_ub 1.92% | [valid] err 0.29% adv_err 2.55% | [train] err 0.00% adv_err 0.07% loss 0.00617 | [tree] depth 4->4 nodes 13->13 (6199.20s)
-iter: 37 [test] err 0.15% adv_err_lb 0.99% adv_err_ub 1.97% | [valid] err 0.29% adv_err 2.55% | [train] err 0.00% adv_err 0.06% loss 0.00553 | [tree] depth 4->4 nodes 15->15 (6510.64s)
-iter: 38 [test] err 0.15% adv_err_lb 1.13% adv_err_ub 1.97% | [valid] err 0.29% adv_err 2.67% | [train] err 0.00% adv_err 0.05% loss 0.00503 | [tree] depth 4->4 nodes 7->7 (6825.95s)
-iter: 39 [test] err 0.10% adv_err_lb 1.09% adv_err_ub 2.02% | [valid] err 0.29% adv_err 2.59% | [train] err 0.00% adv_err 0.03% loss 0.00426 | [tree] depth 4->4 nodes 15->15 (7149.99s)
-iter: 40 [test] err 0.10% adv_err_lb 1.09% adv_err_ub 1.87% | [valid] err 0.29% adv_err 2.55% | [train] err 0.00% adv_err 0.01% loss 0.00378 | [tree] depth 4->4 nodes 15->15 (7479.04s)
-iter: 41 [test] err 0.10% adv_err_lb 1.09% adv_err_ub 1.87% | [valid] err 0.25% adv_err 2.67% | [train] err 0.00% adv_err 0.01% loss 0.00327 | [tree] depth 4->4 nodes 12->12 (7818.38s)
-iter: 42 [test] err 0.10% adv_err_lb 0.99% adv_err_ub 1.87% | [valid] err 0.29% adv_err 2.67% | [train] err 0.00% adv_err 0.01% loss 0.00300 | [tree] depth 4->4 nodes 12->12 (8166.67s)
-iter: 43 [test] err 0.10% adv_err_lb 0.99% adv_err_ub 1.87% | [valid] err 0.29% adv_err 2.79% | [train] err 0.00% adv_err 0.01% loss 0.00285 | [tree] depth 4->4 nodes 10->10 (8515.43s)
-iter: 44 [test] err 0.15% adv_err_lb 0.89% adv_err_ub 1.83% | [valid] err 0.29% adv_err 2.79% | [train] err 0.00% adv_err 0.01% loss 0.00259 | [tree] depth 4->4 nodes 15->15 (8872.72s)
-iter: 45 [test] err 0.15% adv_err_lb 0.74% adv_err_ub 1.63% | [valid] err 0.33% adv_err 2.67% | [train] err 0.00% adv_err 0.00% loss 0.00209 | [tree] depth 4->4 nodes 15->15 (9237.92s)
-iter: 46 [test] err 0.15% adv_err_lb 0.84% adv_err_ub 1.73% | [valid] err 0.33% adv_err 2.75% | [train] err 0.00% adv_err 0.00% loss 0.00189 | [tree] depth 4->4 nodes 12->12 (9606.08s)
-iter: 47 [test] err 0.15% adv_err_lb 0.84% adv_err_ub 1.73% | [valid] err 0.33% adv_err 2.71% | [train] err 0.00% adv_err 0.00% loss 0.00184 | [tree] depth 4->4 nodes 10->10 (9980.27s)
-iter: 48 [test] err 0.15% adv_err_lb 0.84% adv_err_ub 1.78% | [valid] err 0.33% adv_err 2.59% | [train] err 0.00% adv_err 0.00% loss 0.00175 | [tree] depth 4->4 nodes 11->11 (10359.88s)
-iter: 49 [test] err 0.15% adv_err_lb 0.94% adv_err_ub 1.92% | [valid] err 0.33% adv_err 2.47% | [train] err 0.00% adv_err 0.00% loss 0.00158 | [tree] depth 4->4 nodes 15->15 (10751.21s)
-iter: 50 [test] err 0.15% adv_err_lb 0.74% adv_err_ub 1.92% | [valid] err 0.33% adv_err 2.38% | [train] err 0.00% adv_err 0.00% loss 0.00142 | [tree] depth 4->4 nodes 14->14 (11142.78s)
-(done in 185.71 min)
-Model path: exps/2019-07-06 19:46:27 dataset=mnist_1_5 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.model.npy
-Metrics path: exps/2019-07-06 19:46:27 dataset=mnist_1_5 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.metrics
diff --git a/models/2019-07-06 19:46:27 dataset=mnist_1_5 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.metrics b/models/2019-07-06 19:46:27 dataset=mnist_1_5 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.metrics
deleted file mode 100644
index 00c5de4..0000000
--- a/models/2019-07-06 19:46:27 dataset=mnist_1_5 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.metrics
+++ /dev/null
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diff --git a/models/2019-07-06 19:46:27 dataset=mnist_1_5 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.model.npy b/models/2019-07-06 19:46:27 dataset=mnist_1_5 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.model.npy
deleted file mode 100644
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Binary files a/models/2019-07-06 19:46:27 dataset=mnist_1_5 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.model.npy and /dev/null differ
diff --git a/models/2019-07-06 19:46:28 dataset=breast_cancer weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.log b/models/2019-07-06 19:46:28 dataset=breast_cancer weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.log
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--- a/models/2019-07-06 19:46:28 dataset=breast_cancer weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.log
+++ /dev/null
@@ -1,54 +0,0 @@
-Boosting started: 2019-07-06 19:46:28 dataset=breast_cancer weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 min_samples_split=10 min_samples_leaf=5 max_depth=4 lr=1.0
-iter: 1 [test] err 2.92% adv_err_lb 10.22% adv_err_ub 10.22% | [valid] err 8.18% adv_err 16.36% | [train] err 6.19% adv_err 13.53% loss 0.61962 | [tree] depth 4->4 nodes 13->13 (6.98s)
-iter: 2 [test] err 2.92% adv_err_lb 10.22% adv_err_ub 16.79% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->2 nodes 7->2 (7.34s)
-iter: 3 [test] err 2.92% adv_err_lb 10.22% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->2 nodes 7->2 (7.80s)
-iter: 4 [test] err 2.92% adv_err_lb 18.25% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (8.41s)
-iter: 5 [test] err 2.92% adv_err_lb 10.22% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 12->1 (9.34s)
-iter: 6 [test] err 2.92% adv_err_lb 18.25% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (10.41s)
-iter: 7 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (11.30s)
-iter: 8 [test] err 2.92% adv_err_lb 10.22% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (12.13s)
-iter: 9 [test] err 2.92% adv_err_lb 17.52% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (13.05s)
-iter: 10 [test] err 2.92% adv_err_lb 17.52% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (13.85s)
-iter: 11 [test] err 2.92% adv_err_lb 10.22% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (14.78s)
-iter: 12 [test] err 2.92% adv_err_lb 11.68% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (15.81s)
-iter: 13 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (16.71s)
-iter: 14 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (17.70s)
-iter: 15 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->2 nodes 14->2 (19.01s)
-iter: 16 [test] err 2.92% adv_err_lb 14.60% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (19.98s)
-iter: 17 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (20.77s)
-iter: 18 [test] err 2.92% adv_err_lb 17.52% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (21.51s)
-iter: 19 [test] err 2.92% adv_err_lb 18.25% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (22.30s)
-iter: 20 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (22.97s)
-iter: 21 [test] err 2.92% adv_err_lb 18.25% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 13->1 (23.88s)
-iter: 22 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (24.62s)
-iter: 23 [test] err 2.92% adv_err_lb 11.68% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 14->1 (25.40s)
-iter: 24 [test] err 2.92% adv_err_lb 10.22% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 12->1 (26.14s)
-iter: 25 [test] err 2.92% adv_err_lb 18.25% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (26.89s)
-iter: 26 [test] err 2.92% adv_err_lb 10.22% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 12->1 (27.81s)
-iter: 27 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (28.76s)
-iter: 28 [test] err 2.92% adv_err_lb 11.68% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 12->1 (29.64s)
-iter: 29 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->2 nodes 13->2 (30.57s)
-iter: 30 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 12->1 (31.30s)
-iter: 31 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (31.93s)
-iter: 32 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (32.58s)
-iter: 33 [test] err 2.92% adv_err_lb 10.22% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (33.27s)
-iter: 34 [test] err 2.92% adv_err_lb 10.22% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 12->1 (34.00s)
-iter: 35 [test] err 2.92% adv_err_lb 10.22% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 12->1 (34.71s)
-iter: 36 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (35.47s)
-iter: 37 [test] err 2.92% adv_err_lb 18.25% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (36.21s)
-iter: 38 [test] err 2.92% adv_err_lb 17.52% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 13->1 (37.11s)
-iter: 39 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 12->1 (37.87s)
-iter: 40 [test] err 2.92% adv_err_lb 10.95% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (38.72s)
-iter: 41 [test] err 2.92% adv_err_lb 14.60% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (39.54s)
-iter: 42 [test] err 2.92% adv_err_lb 11.68% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 13->1 (40.41s)
-iter: 43 [test] err 2.92% adv_err_lb 10.22% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (41.35s)
-iter: 44 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 13->1 (42.44s)
-iter: 45 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (43.38s)
-iter: 46 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (44.41s)
-iter: 47 [test] err 2.92% adv_err_lb 17.52% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 12->1 (45.37s)
-iter: 48 [test] err 2.92% adv_err_lb 16.79% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (46.47s)
-iter: 49 [test] err 2.92% adv_err_lb 17.52% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (47.66s)
-iter: 50 [test] err 2.92% adv_err_lb 14.60% adv_err_ub 18.25% | [valid] err 8.18% adv_err 17.27% | [train] err 6.19% adv_err 13.53% loss 0.61762 | [tree] depth 4->1 nodes 11->1 (48.60s)
-(done in 0.81 min)
-Model path: exps/2019-07-06 19:46:28 dataset=breast_cancer weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.model.npy
-Metrics path: exps/2019-07-06 19:46:28 dataset=breast_cancer weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.metrics
diff --git a/models/2019-07-06 19:46:28 dataset=breast_cancer weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.metrics b/models/2019-07-06 19:46:28 dataset=breast_cancer weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.metrics
deleted file mode 100644
index 693d49f..0000000
--- a/models/2019-07-06 19:46:28 dataset=breast_cancer weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.metrics
+++ /dev/null
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diff --git a/models/2019-07-06 19:46:28 dataset=breast_cancer weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.model.npy b/models/2019-07-06 19:46:28 dataset=breast_cancer weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.model.npy
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Binary files a/models/2019-07-06 19:46:28 dataset=breast_cancer weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.model.npy and /dev/null differ
diff --git a/models/2019-07-06 19:46:29 dataset=cod_rna weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.025 max_depth=4 lr=1.0.log b/models/2019-07-06 19:46:29 dataset=cod_rna weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.025 max_depth=4 lr=1.0.log
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--- a/models/2019-07-06 19:46:29 dataset=cod_rna weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.025 max_depth=4 lr=1.0.log
+++ /dev/null
@@ -1,54 +0,0 @@
-Boosting started: 2019-07-06 19:46:29 dataset=cod_rna weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.025 min_samples_split=10 min_samples_leaf=5 max_depth=4 lr=1.0
-iter: 1 [test] err 12.11% adv_err_lb 23.87% adv_err_ub 24.06% | [valid] err 12.68% adv_err 24.21% | [train] err 12.94% adv_err 23.96% loss 0.79003 | [tree] depth 4->4 nodes 15->15 (38.89s)
-iter: 2 [test] err 10.78% adv_err_lb 23.89% adv_err_ub 24.03% | [valid] err 11.53% adv_err 24.29% | [train] err 11.80% adv_err 23.96% loss 0.77150 | [tree] depth 4->4 nodes 15->13 (81.48s)
-iter: 3 [test] err 9.77% adv_err_lb 23.21% adv_err_ub 23.23% | [valid] err 10.72% adv_err 23.57% | [train] err 10.88% adv_err 23.09% loss 0.76580 | [tree] depth 4->4 nodes 15->6 (134.44s)
-iter: 4 [test] err 9.77% adv_err_lb 23.22% adv_err_ub 23.23% | [valid] err 10.72% adv_err 23.57% | [train] err 10.88% adv_err 23.09% loss 0.76430 | [tree] depth 4->1 nodes 15->1 (189.71s)
-iter: 5 [test] err 9.39% adv_err_lb 22.98% adv_err_ub 23.18% | [valid] err 10.44% adv_err 23.54% | [train] err 10.57% adv_err 23.07% loss 0.75788 | [tree] depth 4->4 nodes 14->14 (266.35s)
-iter: 6 [test] err 9.34% adv_err_lb 23.20% adv_err_ub 23.24% | [valid] err 10.33% adv_err 23.49% | [train] err 10.50% adv_err 22.99% loss 0.75682 | [tree] depth 4->1 nodes 15->1 (345.65s)
-iter: 7 [test] err 8.62% adv_err_lb 23.24% adv_err_ub 23.36% | [valid] err 9.58% adv_err 23.50% | [train] err 9.76% adv_err 23.28% loss 0.75267 | [tree] depth 4->4 nodes 15->13 (439.04s)
-iter: 8 [test] err 8.32% adv_err_lb 23.12% adv_err_ub 23.25% | [valid] err 9.38% adv_err 23.40% | [train] err 9.50% adv_err 23.15% loss 0.75124 | [tree] depth 4->4 nodes 15->6 (539.08s)
-iter: 9 [test] err 8.46% adv_err_lb 22.95% adv_err_ub 23.23% | [valid] err 9.48% adv_err 23.41% | [train] err 9.62% adv_err 23.13% loss 0.75070 | [tree] depth 4->1 nodes 15->1 (639.16s)
-iter: 10 [test] err 8.39% adv_err_lb 23.13% adv_err_ub 23.21% | [valid] err 9.46% adv_err 23.46% | [train] err 9.58% adv_err 23.11% loss 0.74954 | [tree] depth 4->4 nodes 10->10 (757.33s)
-iter: 11 [test] err 8.36% adv_err_lb 23.01% adv_err_ub 23.24% | [valid] err 9.38% adv_err 23.44% | [train] err 9.53% adv_err 23.11% loss 0.74944 | [tree] depth 4->1 nodes 15->1 (884.08s)
-iter: 12 [test] err 8.37% adv_err_lb 23.14% adv_err_ub 23.24% | [valid] err 9.38% adv_err 23.46% | [train] err 9.53% adv_err 23.12% loss 0.74937 | [tree] depth 4->1 nodes 15->1 (1009.24s)
-iter: 13 [test] err 8.34% adv_err_lb 22.76% adv_err_ub 23.21% | [valid] err 9.33% adv_err 23.39% | [train] err 9.48% adv_err 22.99% loss 0.74431 | [tree] depth 4->4 nodes 15->9 (1146.39s)
-iter: 14 [test] err 8.28% adv_err_lb 22.83% adv_err_ub 23.15% | [valid] err 9.31% adv_err 23.38% | [train] err 9.46% adv_err 22.97% loss 0.74426 | [tree] depth 4->1 nodes 15->1 (1288.08s)
-iter: 15 [test] err 8.13% adv_err_lb 23.13% adv_err_ub 23.23% | [valid] err 9.24% adv_err 23.45% | [train] err 9.31% adv_err 22.92% loss 0.74301 | [tree] depth 4->4 nodes 12->12 (1446.36s)
-iter: 16 [test] err 8.12% adv_err_lb 22.98% adv_err_ub 23.49% | [valid] err 9.20% adv_err 23.43% | [train] err 9.28% adv_err 23.10% loss 0.74124 | [tree] depth 4->4 nodes 15->9 (1614.01s)
-iter: 17 [test] err 8.02% adv_err_lb 23.07% adv_err_ub 23.54% | [valid] err 9.09% adv_err 23.57% | [train] err 9.18% adv_err 23.15% loss 0.74004 | [tree] depth 4->4 nodes 15->9 (1790.52s)
-iter: 18 [test] err 8.01% adv_err_lb 23.41% adv_err_ub 23.51% | [valid] err 9.10% adv_err 23.62% | [train] err 9.17% adv_err 23.14% loss 0.73955 | [tree] depth 4->4 nodes 12->6 (1976.31s)
-iter: 19 [test] err 7.98% adv_err_lb 23.36% adv_err_ub 23.52% | [valid] err 9.12% adv_err 23.61% | [train] err 9.16% adv_err 23.08% loss 0.73938 | [tree] depth 4->4 nodes 11->5 (2162.43s)
-iter: 20 [test] err 7.96% adv_err_lb 23.21% adv_err_ub 23.46% | [valid] err 9.07% adv_err 23.59% | [train] err 9.12% adv_err 23.03% loss 0.73910 | [tree] depth 4->2 nodes 15->2 (2341.29s)
-iter: 21 [test] err 7.92% adv_err_lb 23.43% adv_err_ub 23.53% | [valid] err 9.05% adv_err 23.69% | [train] err 9.06% adv_err 23.03% loss 0.73856 | [tree] depth 4->4 nodes 11->11 (2528.80s)
-iter: 22 [test] err 7.91% adv_err_lb 23.18% adv_err_ub 23.50% | [valid] err 9.06% adv_err 23.64% | [train] err 9.03% adv_err 23.00% loss 0.73851 | [tree] depth 4->1 nodes 15->1 (2724.92s)
-iter: 23 [test] err 7.90% adv_err_lb 23.41% adv_err_ub 23.56% | [valid] err 9.03% adv_err 23.65% | [train] err 9.00% adv_err 23.03% loss 0.73847 | [tree] depth 4->3 nodes 11->3 (2920.63s)
-iter: 24 [test] err 7.76% adv_err_lb 22.88% adv_err_ub 23.40% | [valid] err 8.94% adv_err 23.44% | [train] err 8.89% adv_err 22.92% loss 0.73749 | [tree] depth 4->4 nodes 14->14 (3129.74s)
-iter: 25 [test] err 7.75% adv_err_lb 23.21% adv_err_ub 23.46% | [valid] err 8.94% adv_err 23.52% | [train] err 8.87% adv_err 22.91% loss 0.73706 | [tree] depth 4->4 nodes 8->6 (3354.74s)
-iter: 26 [test] err 7.76% adv_err_lb 23.06% adv_err_ub 23.43% | [valid] err 8.96% adv_err 23.52% | [train] err 8.89% adv_err 22.92% loss 0.73704 | [tree] depth 4->1 nodes 15->1 (3582.76s)
-iter: 27 [test] err 7.73% adv_err_lb 23.26% adv_err_ub 23.44% | [valid] err 8.89% adv_err 23.43% | [train] err 8.85% adv_err 22.86% loss 0.73666 | [tree] depth 4->4 nodes 12->7 (3814.73s)
-iter: 28 [test] err 7.73% adv_err_lb 23.13% adv_err_ub 23.46% | [valid] err 8.91% adv_err 23.46% | [train] err 8.84% adv_err 22.86% loss 0.73618 | [tree] depth 4->4 nodes 12->10 (4054.74s)
-iter: 29 [test] err 7.70% adv_err_lb 23.23% adv_err_ub 23.49% | [valid] err 8.86% adv_err 23.47% | [train] err 8.76% adv_err 22.85% loss 0.73547 | [tree] depth 4->4 nodes 15->10 (4310.27s)
-iter: 30 [test] err 7.63% adv_err_lb 23.30% adv_err_ub 23.52% | [valid] err 8.85% adv_err 23.54% | [train] err 8.72% adv_err 22.86% loss 0.73518 | [tree] depth 4->4 nodes 14->9 (4576.53s)
-iter: 31 [test] err 7.61% adv_err_lb 23.24% adv_err_ub 23.51% | [valid] err 8.84% adv_err 23.52% | [train] err 8.70% adv_err 22.85% loss 0.73492 | [tree] depth 4->4 nodes 12->12 (4861.94s)
-iter: 32 [test] err 7.61% adv_err_lb 23.11% adv_err_ub 23.50% | [valid] err 8.84% adv_err 23.52% | [train] err 8.70% adv_err 22.85% loss 0.73492 | [tree] depth 4->1 nodes 14->1 (5147.80s)
-iter: 33 [test] err 7.61% adv_err_lb 23.30% adv_err_ub 23.50% | [valid] err 8.84% adv_err 23.51% | [train] err 8.71% adv_err 22.85% loss 0.73479 | [tree] depth 4->4 nodes 14->5 (5436.12s)
-iter: 34 [test] err 7.61% adv_err_lb 23.35% adv_err_ub 23.50% | [valid] err 8.84% adv_err 23.52% | [train] err 8.69% adv_err 22.86% loss 0.73478 | [tree] depth 4->2 nodes 15->2 (5731.21s)
-iter: 35 [test] err 7.54% adv_err_lb 23.02% adv_err_ub 23.51% | [valid] err 8.73% adv_err 23.48% | [train] err 8.63% adv_err 22.82% loss 0.73439 | [tree] depth 4->4 nodes 15->9 (6036.63s)
-iter: 36 [test] err 7.55% adv_err_lb 23.33% adv_err_ub 23.52% | [valid] err 8.75% adv_err 23.52% | [train] err 8.64% adv_err 22.85% loss 0.73439 | [tree] depth 4->1 nodes 14->1 (6346.65s)
-iter: 37 [test] err 7.53% adv_err_lb 23.28% adv_err_ub 23.50% | [valid] err 8.72% adv_err 23.48% | [train] err 8.62% adv_err 22.84% loss 0.73402 | [tree] depth 4->4 nodes 12->12 (6670.87s)
-iter: 38 [test] err 7.50% adv_err_lb 23.15% adv_err_ub 23.51% | [valid] err 8.69% adv_err 23.47% | [train] err 8.57% adv_err 22.89% loss 0.73373 | [tree] depth 4->4 nodes 15->15 (7010.38s)
-iter: 39 [test] err 7.56% adv_err_lb 23.17% adv_err_ub 23.59% | [valid] err 8.74% adv_err 23.73% | [train] err 8.60% adv_err 22.92% loss 0.73315 | [tree] depth 4->4 nodes 15->13 (7362.39s)
-iter: 40 [test] err 7.56% adv_err_lb 23.42% adv_err_ub 23.60% | [valid] err 8.76% adv_err 23.70% | [train] err 8.60% adv_err 22.91% loss 0.73303 | [tree] depth 4->4 nodes 12->12 (7735.19s)
-iter: 41 [test] err 7.56% adv_err_lb 23.47% adv_err_ub 23.60% | [valid] err 8.76% adv_err 23.70% | [train] err 8.60% adv_err 22.91% loss 0.73302 | [tree] depth 4->2 nodes 15->2 (8115.30s)
-iter: 42 [test] err 7.55% adv_err_lb 22.94% adv_err_ub 23.61% | [valid] err 8.79% adv_err 23.68% | [train] err 8.60% adv_err 22.92% loss 0.73298 | [tree] depth 4->4 nodes 10->8 (8501.50s)
-iter: 43 [test] err 7.55% adv_err_lb 23.39% adv_err_ub 23.61% | [valid] err 8.79% adv_err 23.68% | [train] err 8.61% adv_err 22.92% loss 0.73297 | [tree] depth 4->1 nodes 15->1 (8891.54s)
-iter: 44 [test] err 7.54% adv_err_lb 23.32% adv_err_ub 23.62% | [valid] err 8.79% adv_err 23.68% | [train] err 8.60% adv_err 22.92% loss 0.73232 | [tree] depth 4->4 nodes 14->8 (9287.26s)
-iter: 45 [test] err 7.54% adv_err_lb 23.46% adv_err_ub 23.62% | [valid] err 8.78% adv_err 23.70% | [train] err 8.60% adv_err 22.92% loss 0.73229 | [tree] depth 4->4 nodes 13->7 (9701.93s)
-iter: 46 [test] err 7.54% adv_err_lb 23.35% adv_err_ub 23.62% | [valid] err 8.78% adv_err 23.69% | [train] err 8.60% adv_err 22.93% loss 0.73222 | [tree] depth 4->4 nodes 7->7 (10125.30s)
-iter: 47 [test] err 7.54% adv_err_lb 23.34% adv_err_ub 23.62% | [valid] err 8.79% adv_err 23.73% | [train] err 8.59% adv_err 22.92% loss 0.73218 | [tree] depth 4->4 nodes 11->9 (10567.36s)
-iter: 48 [test] err 7.55% adv_err_lb 23.36% adv_err_ub 23.62% | [valid] err 8.80% adv_err 23.73% | [train] err 8.59% adv_err 22.92% loss 0.73203 | [tree] depth 4->4 nodes 13->11 (11018.75s)
-iter: 49 [test] err 7.56% adv_err_lb 23.21% adv_err_ub 23.63% | [valid] err 8.79% adv_err 23.74% | [train] err 8.60% adv_err 22.93% loss 0.73185 | [tree] depth 4->4 nodes 14->14 (11378.62s)
-iter: 50 [test] err 7.55% adv_err_lb 23.39% adv_err_ub 23.63% | [valid] err 8.79% adv_err 23.73% | [train] err 8.60% adv_err 22.92% loss 0.73175 | [tree] depth 4->4 nodes 13->11 (11719.75s)
-(done in 195.33 min)
-Model path: exps/2019-07-06 19:46:29 dataset=cod_rna weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.025 max_depth=4 lr=1.0.model.npy
-Metrics path: exps/2019-07-06 19:46:29 dataset=cod_rna weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.025 max_depth=4 lr=1.0.metrics
diff --git a/models/2019-07-06 19:46:29 dataset=cod_rna weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.025 max_depth=4 lr=1.0.metrics b/models/2019-07-06 19:46:29 dataset=cod_rna weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.025 max_depth=4 lr=1.0.metrics
deleted file mode 100644
index 5663f69..0000000
--- a/models/2019-07-06 19:46:29 dataset=cod_rna weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.025 max_depth=4 lr=1.0.metrics
+++ /dev/null
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diff --git a/models/2019-07-06 19:46:29 dataset=cod_rna weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.025 max_depth=4 lr=1.0.model.npy b/models/2019-07-06 19:46:29 dataset=cod_rna weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.025 max_depth=4 lr=1.0.model.npy
deleted file mode 100644
index 03a768d..0000000
Binary files a/models/2019-07-06 19:46:29 dataset=cod_rna weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.025 max_depth=4 lr=1.0.model.npy and /dev/null differ
diff --git a/models/2019-07-06 19:46:29 dataset=fmnist_sandal_sneaker weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.100 max_depth=4 lr=1.0.log b/models/2019-07-06 19:46:29 dataset=fmnist_sandal_sneaker weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.100 max_depth=4 lr=1.0.log
deleted file mode 100644
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--- a/models/2019-07-06 19:46:29 dataset=fmnist_sandal_sneaker weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.100 max_depth=4 lr=1.0.log
+++ /dev/null
@@ -1,54 +0,0 @@
-Boosting started: 2019-07-06 19:46:29 dataset=fmnist_sandal_sneaker weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.100 min_samples_split=10 min_samples_leaf=5 max_depth=4 lr=1.0
-iter: 1 [test] err 11.60% adv_err_lb 15.15% adv_err_ub 15.20% | [valid] err 12.50% adv_err 17.00% | [train] err 12.09% adv_err 16.51% loss 0.64087 | [tree] depth 4->4 nodes 15->15 (63.84s)
-iter: 2 [test] err 8.20% adv_err_lb 12.25% adv_err_ub 12.30% | [valid] err 9.08% adv_err 13.21% | [train] err 9.20% adv_err 13.32% loss 0.57309 | [tree] depth 4->4 nodes 11->11 (115.17s)
-iter: 3 [test] err 7.00% adv_err_lb 11.55% adv_err_ub 11.70% | [valid] err 7.71% adv_err 12.54% | [train] err 7.28% adv_err 12.26% loss 0.54540 | [tree] depth 4->4 nodes 15->15 (169.65s)
-iter: 4 [test] err 5.95% adv_err_lb 10.35% adv_err_ub 10.55% | [valid] err 7.37% adv_err 12.42% | [train] err 6.77% adv_err 11.60% loss 0.52446 | [tree] depth 4->4 nodes 10->10 (218.42s)
-iter: 5 [test] err 5.95% adv_err_lb 10.40% adv_err_ub 10.55% | [valid] err 7.25% adv_err 12.33% | [train] err 6.39% adv_err 11.05% loss 0.50837 | [tree] depth 4->4 nodes 12->12 (272.55s)
-iter: 6 [test] err 5.80% adv_err_lb 9.85% adv_err_ub 10.15% | [valid] err 6.96% adv_err 12.17% | [train] err 6.12% adv_err 10.86% loss 0.50076 | [tree] depth 4->4 nodes 10->10 (340.05s)
-iter: 7 [test] err 5.40% adv_err_lb 10.45% adv_err_ub 10.85% | [valid] err 6.63% adv_err 11.75% | [train] err 5.42% adv_err 10.58% loss 0.48899 | [tree] depth 4->4 nodes 15->14 (417.86s)
-iter: 8 [test] err 4.75% adv_err_lb 9.40% adv_err_ub 10.50% | [valid] err 5.92% adv_err 11.62% | [train] err 4.85% adv_err 10.32% loss 0.47846 | [tree] depth 4->4 nodes 14->14 (497.71s)
-iter: 9 [test] err 4.70% adv_err_lb 10.10% adv_err_ub 10.65% | [valid] err 5.54% adv_err 11.71% | [train] err 4.76% adv_err 10.30% loss 0.47247 | [tree] depth 4->4 nodes 14->14 (574.94s)
-iter: 10 [test] err 4.65% adv_err_lb 9.60% adv_err_ub 10.40% | [valid] err 5.54% adv_err 11.71% | [train] err 4.75% adv_err 10.25% loss 0.46873 | [tree] depth 4->4 nodes 8->8 (655.87s)
-iter: 11 [test] err 3.80% adv_err_lb 9.10% adv_err_ub 9.80% | [valid] err 5.33% adv_err 11.62% | [train] err 4.28% adv_err 9.94% loss 0.46022 | [tree] depth 4->4 nodes 15->13 (743.53s)
-iter: 12 [test] err 3.85% adv_err_lb 8.75% adv_err_ub 9.65% | [valid] err 5.50% adv_err 11.88% | [train] err 4.21% adv_err 9.83% loss 0.45707 | [tree] depth 4->4 nodes 11->11 (836.60s)
-iter: 13 [test] err 3.95% adv_err_lb 9.05% adv_err_ub 9.70% | [valid] err 5.42% adv_err 11.71% | [train] err 4.11% adv_err 9.70% loss 0.45382 | [tree] depth 4->4 nodes 13->13 (934.12s)
-iter: 14 [test] err 3.95% adv_err_lb 8.90% adv_err_ub 9.80% | [valid] err 5.33% adv_err 11.67% | [train] err 4.08% adv_err 9.62% loss 0.45126 | [tree] depth 4->4 nodes 7->7 (1036.61s)
-iter: 15 [test] err 3.90% adv_err_lb 8.60% adv_err_ub 9.80% | [valid] err 5.17% adv_err 11.79% | [train] err 4.06% adv_err 9.53% loss 0.44844 | [tree] depth 4->4 nodes 8->8 (1141.90s)
-iter: 16 [test] err 3.85% adv_err_lb 9.00% adv_err_ub 10.05% | [valid] err 5.29% adv_err 12.00% | [train] err 4.03% adv_err 9.45% loss 0.44441 | [tree] depth 4->4 nodes 15->15 (1258.24s)
-iter: 17 [test] err 3.85% adv_err_lb 9.05% adv_err_ub 10.05% | [valid] err 5.37% adv_err 11.83% | [train] err 3.98% adv_err 9.40% loss 0.44198 | [tree] depth 4->4 nodes 15->15 (1375.53s)
-iter: 18 [test] err 3.90% adv_err_lb 8.80% adv_err_ub 10.10% | [valid] err 5.42% adv_err 12.00% | [train] err 4.00% adv_err 9.34% loss 0.44143 | [tree] depth 4->4 nodes 12->6 (1498.96s)
-iter: 19 [test] err 4.00% adv_err_lb 9.30% adv_err_ub 10.25% | [valid] err 5.50% adv_err 12.12% | [train] err 3.91% adv_err 9.35% loss 0.44043 | [tree] depth 4->4 nodes 14->8 (1624.49s)
-iter: 20 [test] err 4.05% adv_err_lb 8.95% adv_err_ub 9.90% | [valid] err 5.42% adv_err 12.00% | [train] err 3.74% adv_err 9.21% loss 0.43367 | [tree] depth 4->4 nodes 14->14 (1752.07s)
-iter: 21 [test] err 3.95% adv_err_lb 9.00% adv_err_ub 9.95% | [valid] err 5.37% adv_err 11.92% | [train] err 3.74% adv_err 9.15% loss 0.43213 | [tree] depth 4->4 nodes 15->15 (1888.21s)
-iter: 22 [test] err 3.95% adv_err_lb 9.15% adv_err_ub 10.25% | [valid] err 5.42% adv_err 12.04% | [train] err 3.75% adv_err 9.20% loss 0.43002 | [tree] depth 4->4 nodes 13->13 (2029.14s)
-iter: 23 [test] err 3.95% adv_err_lb 9.05% adv_err_ub 10.20% | [valid] err 5.25% adv_err 12.00% | [train] err 3.73% adv_err 9.16% loss 0.42849 | [tree] depth 4->4 nodes 15->15 (2179.78s)
-iter: 24 [test] err 4.00% adv_err_lb 8.95% adv_err_ub 10.40% | [valid] err 5.33% adv_err 12.25% | [train] err 3.68% adv_err 9.08% loss 0.42006 | [tree] depth 4->4 nodes 15->15 (2348.87s)
-iter: 25 [test] err 3.90% adv_err_lb 8.90% adv_err_ub 10.40% | [valid] err 5.25% adv_err 12.12% | [train] err 3.70% adv_err 8.99% loss 0.41884 | [tree] depth 4->4 nodes 14->8 (2521.89s)
-iter: 26 [test] err 3.85% adv_err_lb 9.10% adv_err_ub 10.50% | [valid] err 5.33% adv_err 12.21% | [train] err 3.66% adv_err 8.91% loss 0.41614 | [tree] depth 4->4 nodes 15->15 (2703.04s)
-iter: 27 [test] err 3.80% adv_err_lb 8.90% adv_err_ub 10.15% | [valid] err 5.33% adv_err 12.29% | [train] err 3.65% adv_err 8.94% loss 0.41594 | [tree] depth 4->1 nodes 15->1 (2888.53s)
-iter: 28 [test] err 3.75% adv_err_lb 8.85% adv_err_ub 10.20% | [valid] err 5.46% adv_err 12.29% | [train] err 3.64% adv_err 8.90% loss 0.41370 | [tree] depth 4->4 nodes 9->9 (3073.06s)
-iter: 29 [test] err 3.75% adv_err_lb 8.95% adv_err_ub 10.25% | [valid] err 5.50% adv_err 12.33% | [train] err 3.61% adv_err 8.88% loss 0.41279 | [tree] depth 4->4 nodes 8->8 (3266.14s)
-iter: 30 [test] err 3.70% adv_err_lb 9.35% adv_err_ub 10.65% | [valid] err 5.42% adv_err 12.21% | [train] err 3.48% adv_err 8.85% loss 0.41107 | [tree] depth 4->4 nodes 11->11 (3459.21s)
-iter: 31 [test] err 3.95% adv_err_lb 9.60% adv_err_ub 10.85% | [valid] err 5.50% adv_err 12.54% | [train] err 3.44% adv_err 8.83% loss 0.40891 | [tree] depth 4->4 nodes 14->12 (3662.15s)
-iter: 32 [test] err 3.95% adv_err_lb 9.15% adv_err_ub 10.85% | [valid] err 5.54% adv_err 12.54% | [train] err 3.43% adv_err 8.78% loss 0.40739 | [tree] depth 4->4 nodes 12->12 (3871.25s)
-iter: 33 [test] err 3.90% adv_err_lb 9.55% adv_err_ub 10.85% | [valid] err 5.46% adv_err 12.50% | [train] err 3.44% adv_err 8.73% loss 0.40671 | [tree] depth 4->4 nodes 12->6 (4081.88s)
-iter: 34 [test] err 4.00% adv_err_lb 9.30% adv_err_ub 10.90% | [valid] err 5.50% adv_err 12.67% | [train] err 3.43% adv_err 8.68% loss 0.40396 | [tree] depth 4->4 nodes 15->13 (4297.38s)
-iter: 35 [test] err 4.05% adv_err_lb 9.50% adv_err_ub 10.90% | [valid] err 5.50% adv_err 12.62% | [train] err 3.43% adv_err 8.67% loss 0.40317 | [tree] depth 4->4 nodes 15->15 (4522.86s)
-iter: 36 [test] err 4.15% adv_err_lb 9.55% adv_err_ub 10.80% | [valid] err 5.46% adv_err 12.62% | [train] err 3.42% adv_err 8.66% loss 0.40234 | [tree] depth 4->4 nodes 14->14 (4751.44s)
-iter: 37 [test] err 4.20% adv_err_lb 9.75% adv_err_ub 11.10% | [valid] err 5.54% adv_err 12.71% | [train] err 3.41% adv_err 8.64% loss 0.40034 | [tree] depth 4->4 nodes 15->15 (4982.27s)
-iter: 38 [test] err 3.90% adv_err_lb 9.40% adv_err_ub 11.20% | [valid] err 5.25% adv_err 12.33% | [train] err 3.33% adv_err 8.47% loss 0.39656 | [tree] depth 4->4 nodes 15->15 (5223.48s)
-iter: 39 [test] err 3.40% adv_err_lb 8.75% adv_err_ub 10.45% | [valid] err 5.00% adv_err 12.29% | [train] err 3.10% adv_err 8.24% loss 0.38947 | [tree] depth 4->4 nodes 15->13 (5471.58s)
-iter: 40 [test] err 3.40% adv_err_lb 8.70% adv_err_ub 10.40% | [valid] err 5.00% adv_err 12.29% | [train] err 3.09% adv_err 8.21% loss 0.38941 | [tree] depth 4->1 nodes 15->1 (5715.75s)
-iter: 41 [test] err 3.35% adv_err_lb 8.90% adv_err_ub 10.30% | [valid] err 5.00% adv_err 12.38% | [train] err 3.11% adv_err 8.18% loss 0.38903 | [tree] depth 4->4 nodes 12->10 (5972.76s)
-iter: 42 [test] err 3.30% adv_err_lb 8.65% adv_err_ub 10.40% | [valid] err 5.04% adv_err 12.54% | [train] err 3.06% adv_err 8.23% loss 0.38873 | [tree] depth 4->4 nodes 14->12 (6233.96s)
-iter: 43 [test] err 3.55% adv_err_lb 9.30% adv_err_ub 10.70% | [valid] err 4.92% adv_err 12.58% | [train] err 2.98% adv_err 8.28% loss 0.38647 | [tree] depth 4->4 nodes 14->5 (6493.84s)
-iter: 44 [test] err 3.55% adv_err_lb 8.80% adv_err_ub 10.60% | [valid] err 4.96% adv_err 12.46% | [train] err 2.98% adv_err 8.28% loss 0.38440 | [tree] depth 4->4 nodes 8->8 (6759.54s)
-iter: 45 [test] err 3.50% adv_err_lb 9.15% adv_err_ub 10.80% | [valid] err 5.00% adv_err 12.46% | [train] err 2.97% adv_err 8.26% loss 0.38266 | [tree] depth 4->4 nodes 14->10 (7019.50s)
-iter: 46 [test] err 3.55% adv_err_lb 9.60% adv_err_ub 10.95% | [valid] err 4.83% adv_err 12.71% | [train] err 2.85% adv_err 8.12% loss 0.37917 | [tree] depth 4->4 nodes 14->14 (7290.44s)
-iter: 47 [test] err 3.55% adv_err_lb 9.10% adv_err_ub 11.10% | [valid] err 4.92% adv_err 12.75% | [train] err 2.85% adv_err 8.14% loss 0.37851 | [tree] depth 4->4 nodes 9->9 (7564.09s)
-iter: 48 [test] err 3.45% adv_err_lb 9.30% adv_err_ub 11.00% | [valid] err 4.96% adv_err 12.75% | [train] err 2.83% adv_err 8.15% loss 0.37810 | [tree] depth 4->4 nodes 15->15 (7845.57s)
-iter: 49 [test] err 3.30% adv_err_lb 9.15% adv_err_ub 10.70% | [valid] err 4.87% adv_err 12.62% | [train] err 2.79% adv_err 8.04% loss 0.37488 | [tree] depth 4->4 nodes 14->12 (8133.75s)
-iter: 50 [test] err 3.35% adv_err_lb 9.40% adv_err_ub 10.85% | [valid] err 4.92% adv_err 12.71% | [train] err 2.79% adv_err 8.04% loss 0.37440 | [tree] depth 4->4 nodes 13->13 (8445.55s)
-(done in 140.76 min)
-Model path: exps/2019-07-06 19:46:29 dataset=fmnist_sandal_sneaker weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.100 max_depth=4 lr=1.0.model.npy
-Metrics path: exps/2019-07-06 19:46:29 dataset=fmnist_sandal_sneaker weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.100 max_depth=4 lr=1.0.metrics
diff --git a/models/2019-07-06 19:46:29 dataset=fmnist_sandal_sneaker weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.100 max_depth=4 lr=1.0.metrics b/models/2019-07-06 19:46:29 dataset=fmnist_sandal_sneaker weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.100 max_depth=4 lr=1.0.metrics
deleted file mode 100644
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--- a/models/2019-07-06 19:46:29 dataset=fmnist_sandal_sneaker weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.100 max_depth=4 lr=1.0.metrics
+++ /dev/null
@@ -1,50 +0,0 @@
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diff --git a/models/2019-07-06 19:46:29 dataset=fmnist_sandal_sneaker weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.100 max_depth=4 lr=1.0.model.npy b/models/2019-07-06 19:46:29 dataset=fmnist_sandal_sneaker weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.100 max_depth=4 lr=1.0.model.npy
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Binary files a/models/2019-07-06 19:46:29 dataset=fmnist_sandal_sneaker weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.100 max_depth=4 lr=1.0.model.npy and /dev/null differ
diff --git a/models/2019-07-06 19:46:29 dataset=gts_100_roadworks weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.log b/models/2019-07-06 19:46:29 dataset=gts_100_roadworks weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.log
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--- a/models/2019-07-06 19:46:29 dataset=gts_100_roadworks weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.log
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@@ -1,54 +0,0 @@
-Boosting started: 2019-07-06 19:46:29 dataset=gts_100_roadworks weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 min_samples_split=10 min_samples_leaf=5 max_depth=4 lr=1.0
-iter: 1 [test] err 9.78% adv_err_lb 14.52% adv_err_ub 14.52% | [valid] err 11.73% adv_err 17.69% | [train] err 13.35% adv_err 18.28% loss 0.58085 | [tree] depth 4->4 nodes 13->13 (45.12s)
-iter: 2 [test] err 5.27% adv_err_lb 10.86% adv_err_ub 11.08% | [valid] err 5.10% adv_err 9.52% | [train] err 6.04% adv_err 10.42% loss 0.34054 | [tree] depth 4->4 nodes 14->14 (85.25s)
-iter: 3 [test] err 5.48% adv_err_lb 10.86% adv_err_ub 10.97% | [valid] err 4.08% adv_err 9.01% | [train] err 4.59% adv_err 8.16% loss 0.27642 | [tree] depth 4->4 nodes 15->15 (136.45s)
-iter: 4 [test] err 8.06% adv_err_lb 12.58% adv_err_ub 12.90% | [valid] err 4.76% adv_err 9.35% | [train] err 4.72% adv_err 8.12% loss 0.22708 | [tree] depth 4->4 nodes 11->11 (197.99s)
-iter: 5 [test] err 5.05% adv_err_lb 10.65% adv_err_ub 10.97% | [valid] err 3.74% adv_err 8.67% | [train] err 3.44% adv_err 6.08% loss 0.18017 | [tree] depth 4->4 nodes 12->12 (268.90s)
-iter: 6 [test] err 4.73% adv_err_lb 10.22% adv_err_ub 10.43% | [valid] err 4.25% adv_err 7.65% | [train] err 3.36% adv_err 5.27% loss 0.13904 | [tree] depth 4->4 nodes 15->15 (353.94s)
-iter: 7 [test] err 4.09% adv_err_lb 9.14% adv_err_ub 9.46% | [valid] err 2.55% adv_err 7.48% | [train] err 2.47% adv_err 4.42% loss 0.12160 | [tree] depth 4->4 nodes 15->15 (447.68s)
-iter: 8 [test] err 4.52% adv_err_lb 9.25% adv_err_ub 9.68% | [valid] err 2.55% adv_err 7.31% | [train] err 2.30% adv_err 3.83% loss 0.11189 | [tree] depth 4->4 nodes 15->15 (547.70s)
-iter: 9 [test] err 4.09% adv_err_lb 9.57% adv_err_ub 10.11% | [valid] err 2.89% adv_err 7.65% | [train] err 2.30% adv_err 3.61% loss 0.10249 | [tree] depth 4->4 nodes 14->14 (656.51s)
-iter: 10 [test] err 4.62% adv_err_lb 9.57% adv_err_ub 10.32% | [valid] err 2.89% adv_err 7.14% | [train] err 2.34% adv_err 3.02% loss 0.08498 | [tree] depth 4->4 nodes 15->15 (776.80s)
-iter: 11 [test] err 4.73% adv_err_lb 9.25% adv_err_ub 10.11% | [valid] err 2.72% adv_err 6.97% | [train] err 2.21% adv_err 2.85% loss 0.07879 | [tree] depth 4->4 nodes 14->14 (905.69s)
-iter: 12 [test] err 4.84% adv_err_lb 10.86% adv_err_ub 11.40% | [valid] err 2.38% adv_err 8.33% | [train] err 2.17% adv_err 2.64% loss 0.07210 | [tree] depth 4->4 nodes 15->15 (1046.53s)
-iter: 13 [test] err 4.52% adv_err_lb 10.75% adv_err_ub 12.37% | [valid] err 2.04% adv_err 8.67% | [train] err 1.57% adv_err 1.83% loss 0.06015 | [tree] depth 4->4 nodes 14->14 (1199.61s)
-iter: 14 [test] err 4.73% adv_err_lb 11.29% adv_err_ub 12.69% | [valid] err 1.87% adv_err 8.84% | [train] err 1.57% adv_err 1.79% loss 0.05777 | [tree] depth 4->4 nodes 14->14 (1360.76s)
-iter: 15 [test] err 4.41% adv_err_lb 10.65% adv_err_ub 11.94% | [valid] err 1.53% adv_err 8.50% | [train] err 1.57% adv_err 1.74% loss 0.05341 | [tree] depth 4->4 nodes 15->15 (1525.55s)
-iter: 16 [test] err 4.41% adv_err_lb 11.51% adv_err_ub 13.33% | [valid] err 1.70% adv_err 7.65% | [train] err 1.36% adv_err 1.66% loss 0.04608 | [tree] depth 4->4 nodes 15->15 (1704.34s)
-iter: 17 [test] err 4.30% adv_err_lb 11.18% adv_err_ub 13.55% | [valid] err 1.53% adv_err 8.50% | [train] err 1.02% adv_err 1.66% loss 0.04168 | [tree] depth 4->4 nodes 15->15 (1896.41s)
-iter: 18 [test] err 4.41% adv_err_lb 11.83% adv_err_ub 14.52% | [valid] err 1.02% adv_err 7.48% | [train] err 0.85% adv_err 1.45% loss 0.03997 | [tree] depth 4->4 nodes 15->13 (2094.94s)
-iter: 19 [test] err 4.62% adv_err_lb 11.40% adv_err_ub 14.84% | [valid] err 1.19% adv_err 7.99% | [train] err 0.85% adv_err 1.36% loss 0.03754 | [tree] depth 4->4 nodes 15->15 (2290.09s)
-iter: 20 [test] err 4.84% adv_err_lb 11.18% adv_err_ub 15.16% | [valid] err 0.51% adv_err 7.99% | [train] err 0.13% adv_err 1.11% loss 0.03616 | [tree] depth 4->4 nodes 15->13 (2483.68s)
-iter: 21 [test] err 4.52% adv_err_lb 11.40% adv_err_ub 15.70% | [valid] err 0.51% adv_err 8.50% | [train] err 0.04% adv_err 0.98% loss 0.03331 | [tree] depth 4->4 nodes 15->15 (2686.90s)
-iter: 22 [test] err 4.09% adv_err_lb 11.08% adv_err_ub 15.91% | [valid] err 0.34% adv_err 8.67% | [train] err 0.04% adv_err 0.72% loss 0.03061 | [tree] depth 4->4 nodes 15->15 (2897.48s)
-iter: 23 [test] err 3.98% adv_err_lb 11.08% adv_err_ub 16.34% | [valid] err 0.51% adv_err 8.84% | [train] err 0.00% adv_err 0.94% loss 0.02926 | [tree] depth 4->4 nodes 14->12 (3121.59s)
-iter: 24 [test] err 3.87% adv_err_lb 11.61% adv_err_ub 16.88% | [valid] err 0.34% adv_err 8.50% | [train] err 0.04% adv_err 0.68% loss 0.02677 | [tree] depth 4->4 nodes 15->15 (3357.82s)
-iter: 25 [test] err 3.76% adv_err_lb 11.51% adv_err_ub 15.91% | [valid] err 0.34% adv_err 7.99% | [train] err 0.17% adv_err 0.60% loss 0.02471 | [tree] depth 4->4 nodes 15->15 (3602.72s)
-iter: 26 [test] err 4.19% adv_err_lb 11.18% adv_err_ub 16.45% | [valid] err 0.34% adv_err 8.50% | [train] err 0.00% adv_err 0.51% loss 0.02285 | [tree] depth 4->4 nodes 15->15 (3857.82s)
-iter: 27 [test] err 3.87% adv_err_lb 10.65% adv_err_ub 15.81% | [valid] err 0.34% adv_err 8.84% | [train] err 0.00% adv_err 0.55% loss 0.02139 | [tree] depth 4->4 nodes 15->15 (4113.67s)
-iter: 28 [test] err 4.41% adv_err_lb 10.86% adv_err_ub 16.34% | [valid] err 0.34% adv_err 8.84% | [train] err 0.00% adv_err 0.55% loss 0.02053 | [tree] depth 4->4 nodes 15->15 (4386.74s)
-iter: 29 [test] err 4.09% adv_err_lb 11.18% adv_err_ub 16.45% | [valid] err 0.34% adv_err 8.33% | [train] err 0.00% adv_err 0.38% loss 0.02003 | [tree] depth 4->4 nodes 12->8 (4668.58s)
-iter: 30 [test] err 3.87% adv_err_lb 10.54% adv_err_ub 15.91% | [valid] err 0.17% adv_err 9.18% | [train] err 0.00% adv_err 0.43% loss 0.01921 | [tree] depth 4->4 nodes 15->15 (4957.43s)
-iter: 31 [test] err 3.55% adv_err_lb 10.43% adv_err_ub 15.27% | [valid] err 0.17% adv_err 10.20% | [train] err 0.00% adv_err 0.43% loss 0.01882 | [tree] depth 4->4 nodes 15->15 (5255.15s)
-iter: 32 [test] err 3.23% adv_err_lb 10.22% adv_err_ub 16.77% | [valid] err 0.17% adv_err 10.20% | [train] err 0.00% adv_err 0.43% loss 0.01818 | [tree] depth 4->4 nodes 15->13 (5564.08s)
-iter: 33 [test] err 3.01% adv_err_lb 10.43% adv_err_ub 17.10% | [valid] err 0.17% adv_err 10.20% | [train] err 0.00% adv_err 0.34% loss 0.01797 | [tree] depth 4->4 nodes 15->15 (5883.33s)
-iter: 34 [test] err 2.90% adv_err_lb 10.22% adv_err_ub 16.88% | [valid] err 0.17% adv_err 10.54% | [train] err 0.00% adv_err 0.34% loss 0.01715 | [tree] depth 4->4 nodes 15->14 (6207.09s)
-iter: 35 [test] err 2.80% adv_err_lb 10.11% adv_err_ub 16.77% | [valid] err 0.17% adv_err 10.54% | [train] err 0.00% adv_err 0.38% loss 0.01700 | [tree] depth 4->4 nodes 14->8 (6542.74s)
-iter: 36 [test] err 2.58% adv_err_lb 9.25% adv_err_ub 17.20% | [valid] err 0.17% adv_err 10.71% | [train] err 0.00% adv_err 0.38% loss 0.01660 | [tree] depth 4->4 nodes 15->15 (6878.66s)
-iter: 37 [test] err 2.58% adv_err_lb 9.35% adv_err_ub 17.74% | [valid] err 0.17% adv_err 10.71% | [train] err 0.00% adv_err 0.38% loss 0.01511 | [tree] depth 4->4 nodes 15->15 (7229.06s)
-iter: 38 [test] err 2.58% adv_err_lb 9.14% adv_err_ub 18.17% | [valid] err 0.17% adv_err 10.37% | [train] err 0.00% adv_err 0.38% loss 0.01466 | [tree] depth 4->4 nodes 15->15 (7588.25s)
-iter: 39 [test] err 2.58% adv_err_lb 9.03% adv_err_ub 18.17% | [valid] err 0.17% adv_err 11.22% | [train] err 0.00% adv_err 0.38% loss 0.01340 | [tree] depth 4->4 nodes 15->15 (7961.17s)
-iter: 40 [test] err 2.90% adv_err_lb 9.89% adv_err_ub 18.60% | [valid] err 0.17% adv_err 11.39% | [train] err 0.00% adv_err 0.38% loss 0.01328 | [tree] depth 4->4 nodes 15->7 (8338.77s)
-iter: 41 [test] err 2.90% adv_err_lb 9.89% adv_err_ub 18.28% | [valid] err 0.17% adv_err 11.90% | [train] err 0.00% adv_err 0.30% loss 0.01287 | [tree] depth 4->4 nodes 15->14 (8723.65s)
-iter: 42 [test] err 3.01% adv_err_lb 9.78% adv_err_ub 18.92% | [valid] err 0.34% adv_err 12.24% | [train] err 0.00% adv_err 0.30% loss 0.01160 | [tree] depth 4->4 nodes 15->15 (9129.57s)
-iter: 43 [test] err 2.90% adv_err_lb 9.25% adv_err_ub 19.14% | [valid] err 0.34% adv_err 12.41% | [train] err 0.00% adv_err 0.26% loss 0.01105 | [tree] depth 4->4 nodes 15->8 (9545.86s)
-iter: 44 [test] err 2.90% adv_err_lb 10.00% adv_err_ub 19.25% | [valid] err 0.34% adv_err 13.10% | [train] err 0.00% adv_err 0.17% loss 0.01056 | [tree] depth 4->4 nodes 13->11 (9965.90s)
-iter: 45 [test] err 2.90% adv_err_lb 10.00% adv_err_ub 19.68% | [valid] err 0.34% adv_err 12.41% | [train] err 0.00% adv_err 0.17% loss 0.01021 | [tree] depth 4->4 nodes 15->15 (10384.73s)
-iter: 46 [test] err 2.90% adv_err_lb 10.11% adv_err_ub 19.46% | [valid] err 0.34% adv_err 12.76% | [train] err 0.00% adv_err 0.13% loss 0.00988 | [tree] depth 4->4 nodes 15->13 (10804.40s)
-iter: 47 [test] err 2.90% adv_err_lb 9.03% adv_err_ub 18.39% | [valid] err 0.34% adv_err 12.41% | [train] err 0.00% adv_err 0.13% loss 0.00961 | [tree] depth 4->4 nodes 15->15 (11205.35s)
-iter: 48 [test] err 2.47% adv_err_lb 8.60% adv_err_ub 18.39% | [valid] err 0.34% adv_err 12.41% | [train] err 0.00% adv_err 0.17% loss 0.00845 | [tree] depth 4->4 nodes 15->15 (11542.89s)
-iter: 49 [test] err 2.90% adv_err_lb 8.28% adv_err_ub 17.96% | [valid] err 0.34% adv_err 12.24% | [train] err 0.00% adv_err 0.17% loss 0.00817 | [tree] depth 4->4 nodes 15->15 (11856.16s)
-iter: 50 [test] err 3.12% adv_err_lb 7.85% adv_err_ub 18.06% | [valid] err 0.34% adv_err 12.07% | [train] err 0.00% adv_err 0.09% loss 0.00759 | [tree] depth 4->4 nodes 15->15 (12164.20s)
-(done in 202.74 min)
-Model path: exps/2019-07-06 19:46:29 dataset=gts_100_roadworks weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.model.npy
-Metrics path: exps/2019-07-06 19:46:29 dataset=gts_100_roadworks weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.metrics
diff --git a/models/2019-07-06 19:46:29 dataset=gts_100_roadworks weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.metrics b/models/2019-07-06 19:46:29 dataset=gts_100_roadworks weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.metrics
deleted file mode 100644
index f44b07c..0000000
--- a/models/2019-07-06 19:46:29 dataset=gts_100_roadworks weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.metrics
+++ /dev/null
@@ -1,50 +0,0 @@
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diff --git a/models/2019-07-06 19:46:29 dataset=gts_100_roadworks weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.model.npy b/models/2019-07-06 19:46:29 dataset=gts_100_roadworks weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.model.npy
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diff --git a/models/2019-07-06 19:46:31 dataset=gts_30_70 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.log b/models/2019-07-06 19:46:31 dataset=gts_30_70 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.log
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--- a/models/2019-07-06 19:46:31 dataset=gts_30_70 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.log
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@@ -1,54 +0,0 @@
-Boosting started: 2019-07-06 19:46:31 dataset=gts_30_70 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 min_samples_split=10 min_samples_leaf=5 max_depth=4 lr=1.0
-iter: 1 [test] err 25.14% adv_err_lb 31.16% adv_err_ub 31.30% | [valid] err 20.00% adv_err 26.43% | [train] err 20.89% adv_err 27.32% loss 0.80020 | [tree] depth 4->4 nodes 13->13 (57.46s)
-iter: 2 [test] err 22.17% adv_err_lb 28.77% adv_err_ub 28.91% | [valid] err 15.36% adv_err 22.98% | [train] err 14.70% adv_err 21.07% loss 0.67703 | [tree] depth 4->4 nodes 15->15 (111.67s)
-iter: 3 [test] err 22.32% adv_err_lb 29.20% adv_err_ub 29.35% | [valid] err 13.45% adv_err 21.19% | [train] err 13.45% adv_err 19.35% loss 0.61925 | [tree] depth 4->4 nodes 13->13 (184.22s)
-iter: 4 [test] err 22.39% adv_err_lb 29.35% adv_err_ub 29.42% | [valid] err 11.43% adv_err 19.52% | [train] err 11.49% adv_err 17.08% loss 0.55002 | [tree] depth 4->4 nodes 15->15 (280.40s)
-iter: 5 [test] err 19.35% adv_err_lb 28.84% adv_err_ub 29.20% | [valid] err 8.45% adv_err 17.38% | [train] err 9.35% adv_err 15.36% loss 0.50094 | [tree] depth 4->4 nodes 14->14 (380.13s)
-iter: 6 [test] err 16.38% adv_err_lb 26.01% adv_err_ub 26.38% | [valid] err 6.19% adv_err 15.00% | [train] err 6.96% adv_err 12.95% loss 0.44035 | [tree] depth 4->4 nodes 13->13 (484.84s)
-iter: 7 [test] err 16.59% adv_err_lb 26.09% adv_err_ub 26.45% | [valid] err 6.31% adv_err 14.88% | [train] err 6.22% adv_err 12.20% loss 0.40883 | [tree] depth 4->4 nodes 14->14 (610.22s)
-iter: 8 [test] err 16.23% adv_err_lb 25.36% adv_err_ub 25.80% | [valid] err 6.55% adv_err 15.12% | [train] err 5.65% adv_err 11.73% loss 0.38858 | [tree] depth 4->4 nodes 15->15 (755.68s)
-iter: 9 [test] err 15.43% adv_err_lb 24.86% adv_err_ub 25.14% | [valid] err 6.31% adv_err 15.36% | [train] err 4.32% adv_err 9.82% loss 0.34986 | [tree] depth 4->4 nodes 14->14 (911.51s)
-iter: 10 [test] err 14.57% adv_err_lb 24.20% adv_err_ub 24.71% | [valid] err 5.60% adv_err 15.48% | [train] err 3.96% adv_err 9.11% loss 0.33244 | [tree] depth 4->4 nodes 15->15 (1070.46s)
-iter: 11 [test] err 14.57% adv_err_lb 24.13% adv_err_ub 24.86% | [valid] err 5.95% adv_err 15.24% | [train] err 3.54% adv_err 8.69% loss 0.31157 | [tree] depth 4->4 nodes 14->14 (1252.50s)
-iter: 12 [test] err 15.07% adv_err_lb 25.58% adv_err_ub 26.23% | [valid] err 5.36% adv_err 14.76% | [train] err 3.01% adv_err 7.92% loss 0.28842 | [tree] depth 4->4 nodes 15->15 (1445.29s)
-iter: 13 [test] err 15.07% adv_err_lb 26.30% adv_err_ub 27.32% | [valid] err 5.24% adv_err 14.64% | [train] err 2.56% adv_err 6.73% loss 0.26457 | [tree] depth 4->4 nodes 15->15 (1645.10s)
-iter: 14 [test] err 14.86% adv_err_lb 25.58% adv_err_ub 27.17% | [valid] err 5.12% adv_err 14.17% | [train] err 2.35% adv_err 6.16% loss 0.24793 | [tree] depth 4->4 nodes 15->15 (1856.57s)
-iter: 15 [test] err 14.71% adv_err_lb 25.80% adv_err_ub 27.17% | [valid] err 4.64% adv_err 14.40% | [train] err 1.96% adv_err 5.98% loss 0.22879 | [tree] depth 4->4 nodes 15->14 (2079.55s)
-iter: 16 [test] err 14.64% adv_err_lb 26.59% adv_err_ub 27.97% | [valid] err 4.88% adv_err 15.95% | [train] err 1.93% adv_err 5.71% loss 0.21920 | [tree] depth 4->4 nodes 15->15 (2325.22s)
-iter: 17 [test] err 14.93% adv_err_lb 27.32% adv_err_ub 29.06% | [valid] err 4.64% adv_err 15.71% | [train] err 1.76% adv_err 5.33% loss 0.21459 | [tree] depth 4->4 nodes 14->14 (2605.19s)
-iter: 18 [test] err 14.93% adv_err_lb 28.33% adv_err_ub 29.35% | [valid] err 4.40% adv_err 15.12% | [train] err 1.58% adv_err 5.15% loss 0.20681 | [tree] depth 4->4 nodes 15->15 (2906.54s)
-iter: 19 [test] err 14.42% adv_err_lb 28.62% adv_err_ub 30.94% | [valid] err 4.17% adv_err 15.12% | [train] err 1.31% adv_err 4.97% loss 0.19614 | [tree] depth 4->4 nodes 15->15 (3236.04s)
-iter: 20 [test] err 14.49% adv_err_lb 29.49% adv_err_ub 32.32% | [valid] err 4.17% adv_err 15.12% | [train] err 1.22% adv_err 4.67% loss 0.18481 | [tree] depth 4->4 nodes 15->15 (3571.60s)
-iter: 21 [test] err 14.49% adv_err_lb 28.77% adv_err_ub 31.59% | [valid] err 3.81% adv_err 14.76% | [train] err 1.10% adv_err 4.29% loss 0.17854 | [tree] depth 4->4 nodes 13->13 (3931.92s)
-iter: 22 [test] err 14.78% adv_err_lb 28.70% adv_err_ub 32.10% | [valid] err 3.93% adv_err 14.64% | [train] err 1.04% adv_err 4.11% loss 0.16800 | [tree] depth 4->4 nodes 15->15 (4301.97s)
-iter: 23 [test] err 14.28% adv_err_lb 29.06% adv_err_ub 32.17% | [valid] err 3.33% adv_err 15.71% | [train] err 0.98% adv_err 3.93% loss 0.15952 | [tree] depth 4->4 nodes 15->15 (4683.30s)
-iter: 24 [test] err 14.28% adv_err_lb 29.13% adv_err_ub 32.17% | [valid] err 3.33% adv_err 16.07% | [train] err 0.95% adv_err 3.60% loss 0.15172 | [tree] depth 4->4 nodes 13->13 (5079.24s)
-iter: 25 [test] err 13.99% adv_err_lb 28.33% adv_err_ub 31.45% | [valid] err 3.33% adv_err 15.95% | [train] err 0.86% adv_err 3.30% loss 0.14320 | [tree] depth 4->4 nodes 15->15 (5490.35s)
-iter: 26 [test] err 14.42% adv_err_lb 29.13% adv_err_ub 32.46% | [valid] err 3.69% adv_err 15.95% | [train] err 0.89% adv_err 3.04% loss 0.13658 | [tree] depth 4->4 nodes 15->15 (5922.34s)
-iter: 27 [test] err 14.20% adv_err_lb 29.35% adv_err_ub 33.04% | [valid] err 3.69% adv_err 16.19% | [train] err 0.83% adv_err 2.65% loss 0.12703 | [tree] depth 4->4 nodes 15->15 (6359.25s)
-iter: 28 [test] err 14.42% adv_err_lb 29.64% adv_err_ub 33.62% | [valid] err 3.33% adv_err 16.19% | [train] err 0.74% adv_err 2.71% loss 0.12432 | [tree] depth 4->4 nodes 13->13 (6811.11s)
-iter: 29 [test] err 13.48% adv_err_lb 28.77% adv_err_ub 32.75% | [valid] err 3.33% adv_err 16.43% | [train] err 0.74% adv_err 2.62% loss 0.11889 | [tree] depth 4->4 nodes 15->15 (7293.50s)
-iter: 30 [test] err 13.70% adv_err_lb 28.33% adv_err_ub 33.12% | [valid] err 3.10% adv_err 16.67% | [train] err 0.71% adv_err 2.77% loss 0.11218 | [tree] depth 4->4 nodes 14->14 (7791.50s)
-iter: 31 [test] err 13.41% adv_err_lb 28.62% adv_err_ub 33.41% | [valid] err 3.10% adv_err 16.55% | [train] err 0.68% adv_err 2.56% loss 0.10755 | [tree] depth 4->4 nodes 14->14 (8292.71s)
-iter: 32 [test] err 13.41% adv_err_lb 28.70% adv_err_ub 33.84% | [valid] err 2.98% adv_err 16.55% | [train] err 0.71% adv_err 2.50% loss 0.10346 | [tree] depth 4->4 nodes 15->15 (8777.73s)
-iter: 33 [test] err 13.48% adv_err_lb 28.77% adv_err_ub 33.77% | [valid] err 2.86% adv_err 16.07% | [train] err 0.62% adv_err 2.26% loss 0.09656 | [tree] depth 4->4 nodes 15->15 (9237.90s)
-iter: 34 [test] err 13.33% adv_err_lb 28.12% adv_err_ub 33.62% | [valid] err 2.98% adv_err 16.55% | [train] err 0.60% adv_err 2.11% loss 0.09265 | [tree] depth 4->4 nodes 15->15 (9705.23s)
-iter: 35 [test] err 13.55% adv_err_lb 28.04% adv_err_ub 34.28% | [valid] err 3.10% adv_err 16.43% | [train] err 0.62% adv_err 1.99% loss 0.08919 | [tree] depth 4->4 nodes 15->15 (10162.69s)
-iter: 36 [test] err 13.77% adv_err_lb 28.99% adv_err_ub 36.09% | [valid] err 2.50% adv_err 16.55% | [train] err 0.24% adv_err 2.14% loss 0.08805 | [tree] depth 4->4 nodes 13->13 (10624.63s)
-iter: 37 [test] err 13.62% adv_err_lb 28.26% adv_err_ub 35.14% | [valid] err 2.50% adv_err 15.83% | [train] err 0.21% adv_err 1.99% loss 0.08065 | [tree] depth 4->4 nodes 15->15 (11067.50s)
-iter: 38 [test] err 13.33% adv_err_lb 28.04% adv_err_ub 35.43% | [valid] err 2.14% adv_err 15.83% | [train] err 0.18% adv_err 1.73% loss 0.07513 | [tree] depth 4->4 nodes 15->15 (11511.31s)
-iter: 39 [test] err 13.77% adv_err_lb 27.39% adv_err_ub 35.72% | [valid] err 2.50% adv_err 15.83% | [train] err 0.18% adv_err 1.55% loss 0.07088 | [tree] depth 4->4 nodes 15->15 (11958.21s)
-iter: 40 [test] err 13.77% adv_err_lb 27.17% adv_err_ub 35.14% | [valid] err 2.02% adv_err 15.36% | [train] err 0.18% adv_err 1.52% loss 0.06890 | [tree] depth 4->4 nodes 15->15 (12418.77s)
-iter: 41 [test] err 13.48% adv_err_lb 27.17% adv_err_ub 34.57% | [valid] err 2.26% adv_err 15.48% | [train] err 0.18% adv_err 1.43% loss 0.06583 | [tree] depth 4->4 nodes 15->15 (12886.25s)
-iter: 42 [test] err 13.33% adv_err_lb 26.88% adv_err_ub 34.78% | [valid] err 1.90% adv_err 15.36% | [train] err 0.18% adv_err 1.46% loss 0.06417 | [tree] depth 4->4 nodes 15->15 (13364.16s)
-iter: 43 [test] err 13.33% adv_err_lb 26.23% adv_err_ub 34.86% | [valid] err 2.02% adv_err 14.76% | [train] err 0.21% adv_err 1.37% loss 0.06100 | [tree] depth 4->4 nodes 15->15 (13848.68s)
-iter: 44 [test] err 12.97% adv_err_lb 26.09% adv_err_ub 35.07% | [valid] err 2.02% adv_err 15.00% | [train] err 0.18% adv_err 1.28% loss 0.05813 | [tree] depth 4->4 nodes 14->14 (14337.65s)
-iter: 45 [test] err 13.26% adv_err_lb 25.58% adv_err_ub 34.86% | [valid] err 1.90% adv_err 15.12% | [train] err 0.18% adv_err 1.07% loss 0.05536 | [tree] depth 4->4 nodes 15->15 (14833.93s)
-iter: 46 [test] err 13.33% adv_err_lb 25.58% adv_err_ub 34.20% | [valid] err 1.67% adv_err 15.00% | [train] err 0.18% adv_err 1.07% loss 0.05343 | [tree] depth 4->4 nodes 14->14 (15338.62s)
-iter: 47 [test] err 13.33% adv_err_lb 25.65% adv_err_ub 34.49% | [valid] err 1.55% adv_err 15.24% | [train] err 0.18% adv_err 0.95% loss 0.05075 | [tree] depth 4->4 nodes 14->14 (15863.73s)
-iter: 48 [test] err 13.33% adv_err_lb 25.65% adv_err_ub 34.57% | [valid] err 1.43% adv_err 15.95% | [train] err 0.18% adv_err 0.95% loss 0.04877 | [tree] depth 4->4 nodes 15->15 (16386.79s)
-iter: 49 [test] err 13.12% adv_err_lb 24.78% adv_err_ub 34.42% | [valid] err 1.31% adv_err 15.83% | [train] err 0.18% adv_err 0.92% loss 0.04797 | [tree] depth 4->4 nodes 15->15 (16918.01s)
-iter: 50 [test] err 13.26% adv_err_lb 25.94% adv_err_ub 34.78% | [valid] err 1.31% adv_err 15.36% | [train] err 0.18% adv_err 0.92% loss 0.04624 | [tree] depth 4->4 nodes 15->15 (17434.79s)
-(done in 290.58 min)
-Model path: exps/2019-07-06 19:46:31 dataset=gts_30_70 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.model.npy
-Metrics path: exps/2019-07-06 19:46:31 dataset=gts_30_70 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.metrics
diff --git a/models/2019-07-06 19:46:31 dataset=gts_30_70 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.metrics b/models/2019-07-06 19:46:31 dataset=gts_30_70 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.metrics
deleted file mode 100644
index 8d5a6e8..0000000
--- a/models/2019-07-06 19:46:31 dataset=gts_30_70 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.metrics
+++ /dev/null
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diff --git a/models/2019-07-06 19:46:31 dataset=gts_30_70 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.model.npy b/models/2019-07-06 19:46:31 dataset=gts_30_70 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.model.npy
deleted file mode 100644
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Binary files a/models/2019-07-06 19:46:31 dataset=gts_30_70 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.031 max_depth=4 lr=1.0.model.npy and /dev/null differ
diff --git a/models/2019-07-07 10:05:48 dataset=mnist_2_6 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.log b/models/2019-07-07 10:05:48 dataset=mnist_2_6 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.log
deleted file mode 100644
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--- a/models/2019-07-07 10:05:48 dataset=mnist_2_6 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.log
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@@ -1,54 +0,0 @@
-Boosting started: 2019-07-07 10:05:48 dataset=mnist_2_6 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 min_samples_split=10 min_samples_leaf=5 max_depth=4 lr=1.0
-iter: 1 [test] err 8.44% adv_err_lb 15.18% adv_err_ub 15.53% | [valid] err 11.32% adv_err 20.12% | [train] err 11.24% adv_err 19.13% loss 0.63228 | [tree] depth 4->4 nodes 15->15 (41.56s)
-iter: 2 [test] err 4.37% adv_err_lb 10.05% adv_err_ub 10.50% | [valid] err 3.96% adv_err 10.19% | [train] err 3.88% adv_err 8.72% loss 0.50499 | [tree] depth 4->4 nodes 15->15 (76.96s)
-iter: 3 [test] err 3.47% adv_err_lb 9.25% adv_err_ub 9.40% | [valid] err 3.16% adv_err 9.64% | [train] err 3.03% adv_err 8.16% loss 0.45889 | [tree] depth 4->4 nodes 13->13 (115.34s)
-iter: 4 [test] err 3.37% adv_err_lb 8.64% adv_err_ub 8.79% | [valid] err 2.99% adv_err 9.18% | [train] err 2.91% adv_err 7.79% loss 0.42680 | [tree] depth 4->4 nodes 12->12 (155.17s)
-iter: 5 [test] err 3.07% adv_err_lb 8.79% adv_err_ub 9.05% | [valid] err 2.57% adv_err 8.92% | [train] err 2.57% adv_err 7.57% loss 0.39380 | [tree] depth 4->4 nodes 15->15 (199.98s)
-iter: 6 [test] err 2.41% adv_err_lb 6.88% adv_err_ub 7.24% | [valid] err 1.85% adv_err 8.21% | [train] err 1.94% adv_err 6.48% loss 0.34887 | [tree] depth 4->4 nodes 15->15 (250.72s)
-iter: 7 [test] err 2.31% adv_err_lb 6.58% adv_err_ub 7.04% | [valid] err 1.81% adv_err 8.08% | [train] err 1.74% adv_err 6.26% loss 0.33549 | [tree] depth 4->4 nodes 9->9 (298.76s)
-iter: 8 [test] err 2.21% adv_err_lb 6.63% adv_err_ub 7.09% | [valid] err 1.52% adv_err 7.41% | [train] err 1.52% adv_err 5.78% loss 0.31121 | [tree] depth 4->4 nodes 13->13 (354.67s)
-iter: 9 [test] err 2.16% adv_err_lb 6.48% adv_err_ub 6.83% | [valid] err 1.30% adv_err 6.78% | [train] err 1.42% adv_err 5.52% loss 0.29193 | [tree] depth 4->4 nodes 14->14 (413.88s)
-iter: 10 [test] err 2.06% adv_err_lb 6.23% adv_err_ub 7.09% | [valid] err 1.35% adv_err 7.15% | [train] err 1.39% adv_err 5.41% loss 0.28534 | [tree] depth 4->4 nodes 7->7 (473.21s)
-iter: 11 [test] err 1.96% adv_err_lb 6.33% adv_err_ub 6.93% | [valid] err 1.43% adv_err 7.03% | [train] err 1.32% adv_err 5.21% loss 0.27977 | [tree] depth 4->4 nodes 5->5 (534.78s)
-iter: 12 [test] err 1.66% adv_err_lb 5.73% adv_err_ub 6.58% | [valid] err 1.22% adv_err 6.61% | [train] err 1.01% adv_err 4.92% loss 0.25703 | [tree] depth 4->4 nodes 15->15 (605.74s)
-iter: 13 [test] err 1.41% adv_err_lb 5.38% adv_err_ub 6.43% | [valid] err 1.18% adv_err 6.69% | [train] err 0.99% adv_err 4.66% loss 0.24942 | [tree] depth 4->4 nodes 10->10 (677.93s)
-iter: 14 [test] err 1.41% adv_err_lb 5.63% adv_err_ub 6.48% | [valid] err 1.22% adv_err 6.73% | [train] err 0.95% adv_err 4.65% loss 0.24613 | [tree] depth 4->4 nodes 6->6 (750.67s)
-iter: 15 [test] err 1.31% adv_err_lb 5.53% adv_err_ub 6.53% | [valid] err 1.35% adv_err 6.78% | [train] err 0.83% adv_err 4.71% loss 0.23312 | [tree] depth 4->4 nodes 14->14 (833.60s)
-iter: 16 [test] err 1.56% adv_err_lb 5.38% adv_err_ub 6.43% | [valid] err 1.14% adv_err 6.48% | [train] err 0.81% adv_err 4.72% loss 0.22514 | [tree] depth 4->4 nodes 12->12 (922.00s)
-iter: 17 [test] err 1.41% adv_err_lb 5.08% adv_err_ub 6.03% | [valid] err 1.09% adv_err 6.36% | [train] err 0.68% adv_err 4.51% loss 0.21646 | [tree] depth 4->4 nodes 7->7 (1008.13s)
-iter: 18 [test] err 1.16% adv_err_lb 4.92% adv_err_ub 5.83% | [valid] err 1.18% adv_err 5.93% | [train] err 0.55% adv_err 4.01% loss 0.20011 | [tree] depth 4->4 nodes 14->14 (1101.18s)
-iter: 19 [test] err 1.06% adv_err_lb 4.82% adv_err_ub 5.88% | [valid] err 1.30% adv_err 6.06% | [train] err 0.64% adv_err 3.83% loss 0.19362 | [tree] depth 4->4 nodes 11->10 (1192.88s)
-iter: 20 [test] err 1.06% adv_err_lb 4.92% adv_err_ub 5.93% | [valid] err 1.30% adv_err 6.19% | [train] err 0.61% adv_err 3.71% loss 0.18584 | [tree] depth 4->4 nodes 12->12 (1293.15s)
-iter: 21 [test] err 1.06% adv_err_lb 4.92% adv_err_ub 5.68% | [valid] err 1.09% adv_err 5.93% | [train] err 0.48% adv_err 3.53% loss 0.18133 | [tree] depth 4->4 nodes 8->8 (1391.17s)
-iter: 22 [test] err 1.11% adv_err_lb 4.87% adv_err_ub 5.83% | [valid] err 1.18% adv_err 6.27% | [train] err 0.44% adv_err 3.48% loss 0.17519 | [tree] depth 4->4 nodes 13->13 (1495.88s)
-iter: 23 [test] err 0.95% adv_err_lb 4.92% adv_err_ub 5.78% | [valid] err 1.22% adv_err 6.23% | [train] err 0.40% adv_err 3.40% loss 0.16342 | [tree] depth 4->4 nodes 14->14 (1606.89s)
-iter: 24 [test] err 0.95% adv_err_lb 4.52% adv_err_ub 5.83% | [valid] err 1.09% adv_err 6.27% | [train] err 0.37% adv_err 3.29% loss 0.15476 | [tree] depth 4->4 nodes 15->15 (1720.51s)
-iter: 25 [test] err 0.90% adv_err_lb 4.67% adv_err_ub 5.73% | [valid] err 1.01% adv_err 6.23% | [train] err 0.35% adv_err 3.23% loss 0.15270 | [tree] depth 4->4 nodes 8->8 (1836.03s)
-iter: 26 [test] err 0.80% adv_err_lb 4.52% adv_err_ub 5.68% | [valid] err 1.05% adv_err 6.23% | [train] err 0.35% adv_err 3.16% loss 0.15074 | [tree] depth 4->4 nodes 6->6 (1953.84s)
-iter: 27 [test] err 0.70% adv_err_lb 4.62% adv_err_ub 5.68% | [valid] err 1.09% adv_err 6.19% | [train] err 0.33% adv_err 3.04% loss 0.14663 | [tree] depth 4->4 nodes 8->8 (2079.26s)
-iter: 28 [test] err 0.90% adv_err_lb 4.37% adv_err_ub 5.83% | [valid] err 1.05% adv_err 6.40% | [train] err 0.26% adv_err 3.11% loss 0.14172 | [tree] depth 4->4 nodes 14->12 (2213.83s)
-iter: 29 [test] err 0.80% adv_err_lb 4.77% adv_err_ub 5.63% | [valid] err 1.09% adv_err 6.52% | [train] err 0.32% adv_err 2.96% loss 0.13612 | [tree] depth 4->4 nodes 12->12 (2346.00s)
-iter: 30 [test] err 0.95% adv_err_lb 4.62% adv_err_ub 5.43% | [valid] err 0.93% adv_err 6.44% | [train] err 0.31% adv_err 2.91% loss 0.13011 | [tree] depth 4->4 nodes 11->11 (2485.75s)
-iter: 31 [test] err 0.85% adv_err_lb 4.32% adv_err_ub 5.38% | [valid] err 0.97% adv_err 6.27% | [train] err 0.28% adv_err 2.72% loss 0.12834 | [tree] depth 4->4 nodes 11->11 (2628.93s)
-iter: 32 [test] err 0.85% adv_err_lb 4.27% adv_err_ub 5.43% | [valid] err 0.97% adv_err 6.23% | [train] err 0.28% adv_err 2.67% loss 0.12665 | [tree] depth 4->4 nodes 10->10 (2771.51s)
-iter: 33 [test] err 0.80% adv_err_lb 4.52% adv_err_ub 5.53% | [valid] err 1.05% adv_err 6.14% | [train] err 0.28% adv_err 2.64% loss 0.12523 | [tree] depth 4->4 nodes 11->11 (2921.09s)
-iter: 34 [test] err 0.80% adv_err_lb 4.52% adv_err_ub 5.53% | [valid] err 1.05% adv_err 6.19% | [train] err 0.27% adv_err 2.57% loss 0.12342 | [tree] depth 4->4 nodes 8->8 (3074.39s)
-iter: 35 [test] err 0.80% adv_err_lb 4.32% adv_err_ub 5.48% | [valid] err 1.05% adv_err 6.27% | [train] err 0.25% adv_err 2.53% loss 0.12039 | [tree] depth 4->4 nodes 14->14 (3231.76s)
-iter: 36 [test] err 0.70% adv_err_lb 4.47% adv_err_ub 5.38% | [valid] err 0.93% adv_err 6.10% | [train] err 0.23% adv_err 2.43% loss 0.11302 | [tree] depth 4->4 nodes 15->15 (3394.76s)
-iter: 37 [test] err 0.70% adv_err_lb 4.12% adv_err_ub 5.33% | [valid] err 0.80% adv_err 5.93% | [train] err 0.21% adv_err 2.23% loss 0.10088 | [tree] depth 4->4 nodes 15->15 (3561.62s)
-iter: 38 [test] err 0.75% adv_err_lb 4.07% adv_err_ub 5.28% | [valid] err 0.80% adv_err 5.85% | [train] err 0.21% adv_err 2.21% loss 0.09692 | [tree] depth 4->4 nodes 12->12 (3726.14s)
-iter: 39 [test] err 0.75% adv_err_lb 3.92% adv_err_ub 4.97% | [valid] err 0.80% adv_err 5.89% | [train] err 0.17% adv_err 2.03% loss 0.08879 | [tree] depth 4->4 nodes 15->15 (3901.25s)
-iter: 40 [test] err 0.65% adv_err_lb 3.87% adv_err_ub 5.03% | [valid] err 0.72% adv_err 5.60% | [train] err 0.14% adv_err 1.87% loss 0.08166 | [tree] depth 4->4 nodes 15->15 (4080.66s)
-iter: 41 [test] err 0.65% adv_err_lb 3.72% adv_err_ub 5.03% | [valid] err 0.72% adv_err 5.56% | [train] err 0.14% adv_err 1.85% loss 0.08078 | [tree] depth 4->4 nodes 9->9 (4259.55s)
-iter: 42 [test] err 0.60% adv_err_lb 3.57% adv_err_ub 4.87% | [valid] err 0.72% adv_err 5.51% | [train] err 0.14% adv_err 1.76% loss 0.07980 | [tree] depth 4->4 nodes 9->9 (4440.34s)
-iter: 43 [test] err 0.60% adv_err_lb 3.37% adv_err_ub 4.92% | [valid] err 0.72% adv_err 5.56% | [train] err 0.14% adv_err 1.68% loss 0.07493 | [tree] depth 4->4 nodes 15->7 (4628.70s)
-iter: 44 [test] err 0.55% adv_err_lb 3.47% adv_err_ub 5.08% | [valid] err 0.67% adv_err 5.47% | [train] err 0.12% adv_err 1.67% loss 0.07158 | [tree] depth 4->4 nodes 13->13 (4820.14s)
-iter: 45 [test] err 0.60% adv_err_lb 3.32% adv_err_ub 5.03% | [valid] err 0.63% adv_err 5.43% | [train] err 0.11% adv_err 1.67% loss 0.07036 | [tree] depth 4->4 nodes 8->6 (5007.36s)
-iter: 46 [test] err 0.60% adv_err_lb 3.22% adv_err_ub 4.97% | [valid] err 0.59% adv_err 5.64% | [train] err 0.11% adv_err 1.67% loss 0.06790 | [tree] depth 4->4 nodes 15->15 (5218.13s)
-iter: 47 [test] err 0.70% adv_err_lb 3.47% adv_err_ub 4.97% | [valid] err 0.55% adv_err 5.26% | [train] err 0.09% adv_err 1.53% loss 0.06522 | [tree] depth 4->4 nodes 15->15 (5426.91s)
-iter: 48 [test] err 0.65% adv_err_lb 3.27% adv_err_ub 5.03% | [valid] err 0.67% adv_err 5.51% | [train] err 0.09% adv_err 1.56% loss 0.06309 | [tree] depth 4->4 nodes 12->12 (5629.09s)
-iter: 49 [test] err 0.70% adv_err_lb 3.17% adv_err_ub 5.18% | [valid] err 0.72% adv_err 5.47% | [train] err 0.08% adv_err 1.52% loss 0.05992 | [tree] depth 4->4 nodes 15->15 (5836.34s)
-iter: 50 [test] err 0.65% adv_err_lb 3.02% adv_err_ub 4.82% | [valid] err 0.72% adv_err 5.43% | [train] err 0.05% adv_err 1.52% loss 0.05711 | [tree] depth 4->4 nodes 9->9 (6046.04s)
-(done in 100.77 min)
-Model path: exps/2019-07-07 10:05:48 dataset=mnist_2_6 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.model.npy
-Metrics path: exps/2019-07-07 10:05:48 dataset=mnist_2_6 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.metrics
diff --git a/models/2019-07-07 10:05:48 dataset=mnist_2_6 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.metrics b/models/2019-07-07 10:05:48 dataset=mnist_2_6 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.metrics
deleted file mode 100644
index 2653ac9..0000000
--- a/models/2019-07-07 10:05:48 dataset=mnist_2_6 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.metrics
+++ /dev/null
@@ -1,50 +0,0 @@
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diff --git a/models/2019-07-07 10:05:48 dataset=mnist_2_6 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.model.npy b/models/2019-07-07 10:05:48 dataset=mnist_2_6 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.model.npy
deleted file mode 100644
index 5cc424f..0000000
Binary files a/models/2019-07-07 10:05:48 dataset=mnist_2_6 weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=100 eps=0.300 max_depth=4 lr=1.0.model.npy and /dev/null differ
diff --git a/notebooks/adv_examples.ipynb b/notebooks/adv_examples.ipynb
new file mode 100644
index 0000000..f5f20d0
--- /dev/null
+++ b/notebooks/adv_examples.ipynb
@@ -0,0 +1,247 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "pycharm": {}
+ },
+ "source": "# Adversarial examples for boosted stumps and trees"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true,
+ "pycharm": {
+ "is_executing": false
+ }
+ },
+ "outputs": [],
+ "source": "%load_ext autoreload\n%autoreload 2\n\nimport os\nos.chdir(\"../\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport data\nimport utils\nfrom classifiers import OneVsAllClassifier\nfrom stump_ensemble import StumpEnsemble\nfrom tree_ensemble import TreeEnsemble\nfrom attacks import exact_attack_stumps, cube_attack, binary_search_attack\n\n%matplotlib inline\nsns.set(font_scale\u003d1)\n# sns.set_style(\"white\")\nnp.set_printoptions(precision\u003d6, suppress\u003dTrue)\n\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "outputs": [
+ {
+ "name": "stdout",
+ "text": [
+ "Model name: 2019-08-11 14:28:05 dataset\u003dmnist_1_5 weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\nBest iter to take the model: 71\nEnsemble of 72/150 trees restored: exps_diff_depth/2019-08-11 14:28:05 dataset\u003dmnist_1_5 weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2.model.npy\n",
+ "iter_bs 0: yf\u003d[ -6.756728 -9.250336 -6.845145 -6.649005 -10.368723 -8.868709\n -10.368723 -9.416708], eps\u003d[1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ "iter_bs 1: yf\u003d[ -3.797247 -10.490554 -1.563872 0.176218 -11.853583 -12.051593\n -10.512027 -9.921702], eps\u003d[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]\n",
+ "iter_bs 2: yf\u003d[ 1.30749 -9.728162 2.389437 -4.009847 -11.201712 -11.488387\n -11.59265 -10.490178], eps\u003d[0.25 0.25 0.25 0.75 0.25 0.25 0.25 0.25]\n",
+ "iter_bs 3: yf\u003d[ -2.590427 -9.026817 -0.784776 -1.687919 -11.569015 -10.718706\n -10.987304 -9.85365 ], eps\u003d[0.375 0.125 0.375 0.625 0.125 0.125 0.125 0.125]\n",
+ "iter_bs 4: yf\u003d[ -2.076866 -6.654438 -0.384776 -0.193868 -10.614713 -10.696894\n -9.909837 -9.553053], eps\u003d[0.3125 0.0625 0.3125 0.5625 0.0625 0.0625 0.0625 0.0625]\n",
+ "iter_bs 5: yf\u003d[-1.676866 -4.48611 -0.302954 -0.000797 -9.428071 -9.102359 -8.290993\n -5.18897 ], eps\u003d[0.28125 0.03125 0.28125 0.53125 0.03125 0.03125 0.03125 0.03125]\n",
+ "iter_bs 6: yf\u003d[-1.276866 -3.116097 0.892351 -0.000797 -7.457273 -7.89032 -6.389053\n -3.562914], eps\u003d[0.265625 0.015625 0.265625 0.515625 0.015625 0.015625 0.015625 0.015625]\n",
+ "iter_bs 7: yf\u003d[-0.876866 -1.288539 0.288102 -0.000797 -6.981601 -7.041667 -5.401839\n -2.257633], eps\u003d[0.257812 0.007812 0.273438 0.507812 0.007812 0.007812 0.007812 0.007812]\n",
+ "iter_bs 8: yf\u003d[-0.876866 3.232026 0.193002 -0.160597 -3.034747 -3.126815 -0.925891\n 1.323151], eps\u003d[0.253906 0.003906 0.277344 0.503906 0.003906 0.003906 0.003906 0.003906]\n",
+ "iter_bs 9: yf\u003d[-0.876866 2.736004 0.038953 -0.160597 8.797682 8.162356 9.940892\n 0.21919 ], eps\u003d[0.251953 0.005859 0.279297 0.501953 0.001953 0.001953 0.001953 0.005859]\nyf after binary search: yf\u003d[-2.590427 -1.288539 -0.784776 -0.160597 -3.034747 -3.126815 -0.925891\n -2.257633], Linf\u003d[0.375 0.007812 0.375 0.503906 0.003906 0.003906 0.003906 0.007812]\n",
+ "yf after cleanup: yf\u003d[-0.331423 -0.01431 -0.406828 -0.123705 -0.271768 -0.225611 -0.031278\n -0.080387], Linf\u003d[0.375 0.007812 0.375 0.503906 0.003906 0.003906 0.003906 0.007812]\n\n\n\n\n\n\n\n\nModel name: 2019-08-11 14:28:05 dataset\u003dmnist_1_5 weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\nBest iter to take the model: 4\nEnsemble of 5/150 trees restored: exps_diff_depth/2019-08-11 14:28:05 dataset\u003dmnist_1_5 weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2.model.npy\n",
+ "iter_bs 0: yf\u003d[-0.8 -0.8 -0.8 -0.8 -0.8 -0.8 -0.8 -0.8], eps\u003d[1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ "iter_bs 1: yf\u003d[-0.8 -0.4 -0.8 -0.6 -0.8 -0.8 -0.4 -0.4], eps\u003d[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]\n",
+ "iter_bs 2: yf\u003d[0.232192 0.4 0.4 0.4 0.4 0. 0.4 0.4 ], eps\u003d[0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25]\n",
+ "iter_bs 3: yf\u003d[-0.8 -0.4 -0.6 -0.6 -0.8 -0.8 -0.4 -0.4], eps\u003d[0.375 0.375 0.375 0.375 0.375 0.375 0.375 0.375]\n",
+ "iter_bs 4: yf\u003d[-0.58257 -0.4 -0.2 -0.6 -0.4 -0.8 -0.4 -0.4 ], eps\u003d[0.3125 0.3125 0.3125 0.3125 0.3125 0.3125 0.3125 0.3125]\n",
+ "iter_bs 5: yf\u003d[0.232192 0.4 0.4 0.4 0.4 0. 0.4 0.4 ], eps\u003d[0.28125 0.28125 0.28125 0.28125 0.28125 0.28125 0.28125 0.28125]\n",
+ "iter_bs 6: yf\u003d[0.232192 0.4 0.4 0.32344 0.4 0. 0.4 0.4 ], eps\u003d[0.296875 0.296875 0.296875 0.296875 0.296875 0.296875 0.296875 0.296875]\n",
+ "iter_bs 7: yf\u003d[-0.253105 -0.4 0.2 -0.2 -0.4 -0.8 -0.4\n -0.4 ], eps\u003d[0.304688 0.304688 0.304688 0.304688 0.304688 0.304688 0.304688 0.304688]\n",
+ "iter_bs 8: yf\u003d[ 0.232192 0.4 -0.144342 0.12344 0.4 0. 0.4\n 0.4 ], eps\u003d[0.300781 0.300781 0.308594 0.300781 0.300781 0.300781 0.300781 0.300781]\n",
+ "iter_bs 9: yf\u003d[-0.18257 -0.4 -0.144342 -0.2 -0.4 -0.8 -0.4\n -0.4 ], eps\u003d[0.302734 0.302734 0.306641 0.302734 0.302734 0.302734 0.302734 0.302734]\nyf after binary search: yf\u003d[-0.253105 -0.4 -0.144342 -0.6 -0.4 -0.8 -0.4\n -0.4 ], Linf\u003d[0.304688 0.304688 0.308594 0.5 0.304688 0.304688 0.375 0.304688]\n",
+ "yf after cleanup: yf\u003d[-0.053105 -0.01743 -0.144342 -0. -0.01743 -0.327231 -0.01743\n -0.4 ], Linf\u003d[0.304688 0.304688 0.308594 0.5 0.304688 0.304688 0.375 0.304688]\n\n\n\n\n\n\n\n\nModel name: 2019-08-11 14:28:05 dataset\u003dmnist_1_5 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\nBest iter to take the model: 125\n",
+ "Ensemble of 126/150 trees restored: exps_diff_depth/2019-08-11 14:28:05 dataset\u003dmnist_1_5 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2.model.npy\n",
+ "iter_bs 0: yf\u003d[ -9.228521 -18.495863 -6.909791 -10.030146 -18.261786 -19.383817\n -18.495863 -18.495863], eps\u003d[1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ "iter_bs 1: yf\u003d[ -0.738211 -20.87364 -0.274252 1.879567 -21.201428 -21.560648\n -20.335036 -19.904256], eps\u003d[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]\n",
+ "iter_bs 2: yf\u003d[ 13.976287 5.11486 4.276043 -10.030146 3.417294 4.081276\n 5.403501 5.786388], eps\u003d[0.25 0.25 0.25 0.75 0.25 0.25 0.25 0.25]\n",
+ "iter_bs 3: yf\u003d[ 4.684621 -18.472658 0.82659 -3.443999 -18.090127 -19.571086\n -18.986411 -17.99899 ], eps\u003d[0.375 0.375 0.375 0.625 0.375 0.375 0.375 0.375]\n",
+ "iter_bs 4: yf\u003d[ 4.095618 -15.634044 0.395941 -3.171483 -13.906105 -16.066201\n -14.576594 -15.891076], eps\u003d[0.4375 0.3125 0.4375 0.5625 0.3125 0.3125 0.3125 0.3125]\n",
+ "iter_bs 5: yf\u003d[0.872295 5.11486 0.986345 0.624926 3.417294 3.741148 4.875768 5.4194 ], eps\u003d[0.46875 0.28125 0.46875 0.53125 0.28125 0.28125 0.28125 0.28125]\n",
+ "iter_bs 6: yf\u003d[ 2.49655 5.11486 1.128174 -1.376928 3.417294 3.62477 4.764046\n 4.793318], eps\u003d[0.484375 0.296875 0.484375 0.546875 0.296875 0.296875 0.296875 0.296875]\n",
+ "iter_bs 7: yf\u003d[-0.824365 -9.480572 0.458532 -3.858394 -8.304213 -9.972372 -9.073027\n -9.580455], eps\u003d[0.492188 0.304688 0.492188 0.539062 0.304688 0.304688 0.304688 0.304688]\n",
+ "iter_bs 8: yf\u003d[ 1.600145 5.11486 -0.34627 -3.858394 3.417294 3.62477 4.65756\n 4.793318], eps\u003d[0.488281 0.300781 0.496094 0.535156 0.300781 0.300781 0.300781 0.300781]\n",
+ "iter_bs 9: yf\u003d[-0.741988 -5.439384 -0.34627 -3.858394 -5.936414 -6.556464 -5.797666\n -5.399015], eps\u003d[0.490234 0.302734 0.494141 0.533203 0.302734 0.302734 0.302734 0.302734]\nyf after binary search: yf\u003d[-0.741988 -5.439384 -0.34627 -3.858394 -5.936414 -6.556464 -5.797666\n -5.399015], Linf\u003d[0.490234 0.302734 0.496094 0.539062 0.302734 0.302734 0.302734 0.302734]\n",
+ "yf after cleanup: yf\u003d[-0.673569 -0.046826 -0.330826 -0.693164 -0.124706 -0.029311 -0.384939\n -0.16481 ], Linf\u003d[0.490234 0.302734 0.496094 0.539062 0.302734 0.302734 0.302734 0.302734]\n\n\n\n\n\n\n\n\n",
+ "Model name: 2019-08-11 14:28:05 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\nBest iter to take the model: 78\nEnsemble of 79/150 trees restored: exps_diff_depth/2019-08-11 14:28:05 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2.model.npy\n",
+ "iter_bs 0: yf\u003d[-7.844066 -7.282227 -7.448543 -7.641134 -8.024554 -7.543217 -7.844066\n -9.639075], eps\u003d[1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ "iter_bs 1: yf\u003d[-7.837802 -8.267483 -3.746438 -4.740143 -5.054736 -4.459847 -8.848463\n -8.959832], eps\u003d[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]\n",
+ "iter_bs 2: yf\u003d[-6.387821 -5.987602 -3.249004 -4.941302 -3.811914 -3.762601 -8.827098\n -5.218635], eps\u003d[0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25]\n",
+ "iter_bs 3: yf\u003d[-5.890972 -5.015262 -1.6861 -3.10618 -2.947452 -2.059826 -7.031459\n -2.831088], eps\u003d[0.125 0.125 0.125 0.125 0.125 0.125 0.125 0.125]\n",
+ "iter_bs 4: yf\u003d[-4.295149 -2.79503 -0.18848 -2.258667 -2.171427 -0.895093 -4.565945\n -1.606078], eps\u003d[0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625]\n",
+ "iter_bs 5: yf\u003d[-0.584743 -0.861106 1.198174 -2.374077 -1.593618 0.370066 -1.464655\n 1.689443], eps\u003d[0.03125 0.03125 0.03125 0.03125 0.03125 0.03125 0.03125 0.03125]\n",
+ "iter_bs 6: yf\u003d[ 1.261106 0.707761 1.378715 0.492327 2.826617 -0.405612 -0.851077\n 1.175111], eps\u003d[0.015625 0.015625 0.046875 0.015625 0.015625 0.046875 0.015625 0.046875]\n",
+ "iter_bs 7: yf\u003d[-0.335354 -0.881901 0.18901 -1.57301 -0.589442 -0.384581 1.677316\n 0.912525], eps\u003d[0.023438 0.023438 0.054688 0.023438 0.023438 0.039062 0.007812 0.054688]\n",
+ "iter_bs 8: yf\u003d[-0.080957 -1.248664 0.677803 -0.905424 -0.080998 0.244274 -0.036278\n -0.6488 ], eps\u003d[0.019531 0.019531 0.058594 0.019531 0.019531 0.035156 0.011719 0.058594]\n",
+ "iter_bs 9: yf\u003d[ 0.48938 0.866586 -0.050437 0.040944 2.401817 -0.211053 1.677316\n 0.82499 ], eps\u003d[0.017578 0.017578 0.060547 0.017578 0.017578 0.037109 0.009766 0.056641]\nyf after binary search: yf\u003d[-0.080957 -1.248664 -0.050437 -0.905424 -0.080998 -0.211053 -0.036278\n -0.6488 ], Linf\u003d[0.019531 0.019531 0.060547 0.019531 0.019531 0.037109 0.011719 0.058594]\n",
+ "yf after cleanup: yf\u003d[-0.047848 -0.2295 -0.040784 -0.094357 -0.205566 -0.257131 -0.098589\n -0.063225], Linf\u003d[0.019531 0.019531 0.060547 0.019531 0.019531 0.037109 0.011719 0.058594]\n\n\n\n\n\n\n\n\nModel name: 2019-08-11 14:28:05 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\nBest iter to take the model: 5\nEnsemble of 6/150 trees restored: exps_diff_depth/2019-08-11 14:28:05 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2.model.npy\n",
+ "iter_bs 0: yf\u003d[-1. -1. -1. -1. -1. -1. -1. -1.], eps\u003d[1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ "iter_bs 1: yf\u003d[-1. -1. -0.123488 -0.467346 -0.143858 -0.467346 -1.\n -0.881704], eps\u003d[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]\n",
+ "iter_bs 2: yf\u003d[ 0.350472 -0.412413 0.6 -0.067346 0.42753 0.104043 0.467346\n 0.467346], eps\u003d[0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25]\n",
+ "iter_bs 3: yf\u003d[-0.2 -0.012413 0.2 0.276512 -0.143858 -0.295957 -1.\n -0.2 ], eps\u003d[0.375 0.125 0.375 0.125 0.375 0.375 0.375 0.375]\n",
+ "iter_bs 4: yf\u003d[-0.2 0.387587 0.2 -0.067346 0.02753 -0.295957 -0.6\n -0.2 ], eps\u003d[0.3125 0.0625 0.4375 0.1875 0.3125 0.3125 0.3125 0.3125]\n",
+ "iter_bs 5: yf\u003d[ 0.350472 -0.012413 -0.123488 0.276512 -0.143858 0.104043 0.27396\n 0.467346], eps\u003d[0.28125 0.09375 0.46875 0.15625 0.34375 0.28125 0.28125 0.28125]\n",
+ "iter_bs 6: yf\u003d[-0.049528 -0.012413 0.2 0.276512 0.02753 0.104043 0.27396\n 0.467346], eps\u003d[0.296875 0.078125 0.453125 0.171875 0.328125 0.296875 0.296875 0.296875]\n",
+ "iter_bs 7: yf\u003d[ 0.350472 -0.012413 0.2 0.276512 0.02753 0.104043 -0.049528\n -0.049528], eps\u003d[0.289062 0.070312 0.460938 0.179688 0.335938 0.304688 0.304688 0.304688]\n",
+ "iter_bs 8: yf\u003d[ 0.350472 -0.012413 -0.123488 -0.067346 -0.143858 -0.295957 0.27396\n 0.27396 ], eps\u003d[0.292969 0.066406 0.464844 0.183594 0.339844 0.308594 0.300781 0.300781]\n",
+ "iter_bs 9: yf\u003d[ 0.350472 -0.012413 0.2 -0.067346 0.02753 0.104043 -0.049528\n -0.049528], eps\u003d[0.294922 0.064453 0.462891 0.181641 0.337891 0.306641 0.302734 0.302734]\nyf after binary search: yf\u003d[-0.049528 -1. -0.123488 -0.467346 -0.143858 -0.295957 -0.6\n -0.049528], Linf\u003d[0.296875 0.5 0.5 0.5 0.5 0.375 0.3125 0.304688]\n",
+ "yf after cleanup: yf\u003d[-0.049528 -0.012413 -0.123488 -0.067346 -0.143858 -0.295957 -0.049528\n -0.049528], Linf\u003d[0.296875 0.5 0.5 0.5 0.5 0.375 0.3125 0.304688]\n\n\n\n\n\n\n\n\nModel name: 2019-08-11 14:28:05 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\nBest iter to take the model: 87\nEnsemble of 88/150 trees restored: exps_diff_depth/2019-08-11 14:28:05 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2.model.npy\n",
+ "iter_bs 0: yf\u003d[-12.175129 -11.35119 -6.233969 -7.583337 -6.233969 -6.89401\n -9.94054 -11.35119 ], eps\u003d[1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ "iter_bs 1: yf\u003d[-11.441047 -10.556189 -3.79928 -5.41545 -5.31318 -7.690835\n -10.01455 -8.903292], eps\u003d[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]\n",
+ "iter_bs 2: yf\u003d[2.990158 0.963095 4.787043 2.041204 2.371622 2.072107 2.855569 2.647856], eps\u003d[0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25]\n",
+ "iter_bs 3: yf\u003d[-9.166817 -9.413019 -2.314729 -2.912834 -2.754601 -4.430827 -8.638881\n -8.303679], eps\u003d[0.375 0.375 0.375 0.375 0.375 0.375 0.375 0.375]\n",
+ "iter_bs 4: yf\u003d[-3.458796 -4.168835 0.142033 -2.814856 -2.115852 -3.003098 -3.329448\n -2.934425], eps\u003d[0.3125 0.3125 0.3125 0.3125 0.3125 0.3125 0.3125 0.3125]\n",
+ "iter_bs 5: yf\u003d[ 2.942936 0.946548 -1.088586 2.041204 2.371622 1.680652 2.80256\n 2.06776 ], eps\u003d[0.28125 0.28125 0.34375 0.28125 0.28125 0.28125 0.28125 0.28125]\n",
+ "iter_bs 6: yf\u003d[ 2.942936 0.946548 -0.389472 2.041204 2.371622 1.47561 2.599018\n 2.06776 ], eps\u003d[0.296875 0.296875 0.328125 0.296875 0.296875 0.296875 0.296875 0.296875]\n",
+ "iter_bs 7: yf\u003d[-1.326328 -2.845838 -0.031983 -0.083699 -0.195407 -1.032941 -1.834031\n -1.595662], eps\u003d[0.304688 0.304688 0.320312 0.304688 0.304688 0.304688 0.304688 0.304688]\n",
+ "iter_bs 8: yf\u003d[ 2.342553 0.346165 -0.031983 0.848563 1.370843 0.474831 1.998635\n 1.172287], eps\u003d[0.300781 0.300781 0.316406 0.300781 0.300781 0.300781 0.300781 0.300781]\n",
+ "iter_bs 9: yf\u003d[-1.406794 -2.713554 -0.285518 0.371869 -0.098922 -0.740714 -1.646923\n -1.624509], eps\u003d[0.302734 0.302734 0.314453 0.302734 0.302734 0.302734 0.302734 0.302734]\nyf after binary search: yf\u003d[-1.406794 -2.713554 -0.285518 -0.083699 -0.098922 -0.740714 -1.646923\n -1.595662], Linf\u003d[0.302734 0.302734 0.314453 0.304688 0.302734 0.302734 0.302734 0.304688]\n",
+ "yf after cleanup: yf\u003d[-0.068797 -0.073501 -0.182372 -0.148111 -0.122173 -0.014003 -0.084512\n -0.033435], Linf\u003d[0.302734 0.302734 0.314453 0.304688 0.302734 0.302734 0.302734 0.304688]\n\n\n\n\n\n\n\n\n",
+ "Model name: 2019-08-11 14:28:07 dataset\u003dgts_100_roadworks weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\nBest iter to take the model: 147\nEnsemble of 148/150 trees restored: exps_diff_depth/2019-08-11 14:28:07 dataset\u003dgts_100_roadworks weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01.model.npy\n",
+ "iter_bs 0: yf\u003d[-1.400289 -1.394608 -1.394608 -1.305612 -1.19249 -1.30304 -1.305612\n -1.405245], eps\u003d[1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ "iter_bs 1: yf\u003d[ 0.05 -1.469565 -1.469565 -1.319263 -1.299683 -1.397758 -1.337969\n -1.266274], eps\u003d[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]\n",
+ "iter_bs 2: yf\u003d[-1.31399 -0.28925 -1.47 -1.132715 -1.450024 -1.301613 -1.248421\n -1.47 ], eps\u003d[0.75 0.25 0.25 0.25 0.25 0.25 0.25 0.25]\n",
+ "iter_bs 3: yf\u003d[-1.304801 1.074683 -1.47 -0.597319 -1.41 -0.232022 -0.339925\n -1.377062], eps\u003d[0.625 0.125 0.125 0.125 0.125 0.125 0.125 0.125]\n",
+ "iter_bs 4: yf\u003d[-1.159569 0.458247 0.198629 0.383218 -0.91 0.428012 0.288012\n 0.173057], eps\u003d[0.5625 0.1875 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625]\n",
+ "iter_bs 5: yf\u003d[ 0.05 0.308635 -1.45 0.166399 0.408272 -0.153601 -0.039925\n -0.749138], eps\u003d[0.53125 0.21875 0.09375 0.09375 0.03125 0.09375 0.09375 0.09375]\n",
+ "iter_bs 6: yf\u003d[-0.703889 -0.060937 -1.27914 -0.458898 -0.11 0.246399 0.166399\n -0.569138], eps\u003d[0.546875 0.234375 0.078125 0.109375 0.046875 0.078125 0.078125 0.078125]\n",
+ "iter_bs 7: yf\u003d[-0.130435 0.308635 -0.192499 -0.273601 -0.078491 0.166399 -0.039925\n 0.250862], eps\u003d[0.539062 0.226562 0.070312 0.101562 0.039062 0.085938 0.085938 0.070312]\n",
+ "iter_bs 8: yf\u003d[-0.090435 -0.001729 -0.11701 -0.033601 0.341509 0.166399 0.146399\n -0.329138], eps\u003d[0.535156 0.230469 0.066406 0.097656 0.035156 0.089844 0.082031 0.074219]\n",
+ "iter_bs 9: yf\u003d[-0.03 0.308635 -0.05288 0.106399 0.341509 0.166399 0.146399\n 0.250862], eps\u003d[0.533203 0.228516 0.064453 0.095703 0.037109 0.091797 0.083984 0.072266]\nyf after binary search: yf\u003d[-0.03 -0.28925 -1.27914 -0.273601 -1.41 -0.153601 -0.339925\n -1.377062], Linf\u003d[0.533203 0.25 0.078125 0.101563 0.125 0.09375 0.125 0.125 ]\n",
+ "yf after cleanup: yf\u003d[-0.01 -0.013598 -0.0196 -0.003692 -0.115846 -0.022335 -0.027666\n -0.00438 ], Linf\u003d[0.533203 0.25 0.078125 0.101562 0.125 0.09375 0.125 0.125 ]\n\n\n\n\n\n\n\n\nModel name: 2019-08-11 14:28:07 dataset\u003dgts_100_roadworks weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\nBest iter to take the model: 39\nEnsemble of 40/150 trees restored: exps_diff_depth/2019-08-11 14:28:07 dataset\u003dgts_100_roadworks weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01.model.npy\n",
+ "iter_bs 0: yf\u003d[-0.244734 -0.257445 -0.29043 -0.248842 -0.234626 -0.242333 -0.248842\n -0.288416], eps\u003d[1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ "iter_bs 1: yf\u003d[-0.02124 -0.210553 -0.28708 -0.251716 -0.257769 -0.248609 -0.225094\n -0.297944], eps\u003d[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]\n",
+ "iter_bs 2: yf\u003d[ 0.38 -0.181853 -0.272896 -0.148996 -0.262573 -0.183357 -0.200476\n -0.277807], eps\u003d[0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25]\n",
+ "iter_bs 3: yf\u003d[ 0.23016 0.266924 -0.193011 -0.104764 -0.183089 -0.05157 -0.074042\n -0.163772], eps\u003d[0.375 0.125 0.125 0.125 0.125 0.125 0.125 0.125]\n",
+ "iter_bs 4: yf\u003d[ 0.17 0.036134 -0.029637 0.070713 -0.069335 0.01551 0.012131\n -0.055192], eps\u003d[0.4375 0.1875 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625]\n",
+ "iter_bs 5: yf\u003d[ 0.012484 -0.097489 0.233082 -0.032597 0.042051 -0.042161 -0.021627\n 0.076492], eps\u003d[0.46875 0.21875 0.03125 0.09375 0.03125 0.09375 0.09375 0.03125]\n",
+ "iter_bs 6: yf\u003d[ 0.01876 -0.041158 0.139151 0.021574 -0.009141 0.019868 0.011997\n -0.019603], eps\u003d[0.484375 0.203125 0.046875 0.078125 0.046875 0.078125 0.078125 0.046875]\n",
+ "iter_bs 7: yf\u003d[-0.00124 -0.011722 0.061013 -0.012597 0.012463 -0.006628 -0.003945\n 0.025696], eps\u003d[0.492188 0.195312 0.054688 0.085938 0.039062 0.085938 0.085938 0.039062]\n",
+ "iter_bs 8: yf\u003d[ 0.012484 0.028278 0.002297 -0.012597 -0.011807 0.003457 -0.008003\n 0.009455], eps\u003d[0.488281 0.191406 0.058594 0.082031 0.042969 0.082031 0.082031 0.042969]\n",
+ "iter_bs 9: yf\u003d[-0.00124 0.027947 0.002297 0.005243 0.000152 -0.002374 0.011997\n 0.004996], eps\u003d[0.490234 0.193359 0.060547 0.080078 0.041016 0.083984 0.080078 0.044922]\nyf after binary search: yf\u003d[-0.02124 -0.097489 -0.029637 -0.032597 -0.011807 -0.002374 -0.008003\n -0.019603], Linf\u003d[0.5 0.21875 0.0625 0.09375 0.042969 0.083984 0.082031 0.046875]\n",
+ "yf after cleanup: yf\u003d[-0.003724 -0.005055 -0.003913 -0.002922 -0.006208 -0.002374 -0.00195\n -0.006442], Linf\u003d[0.5 0.21875 0.0625 0.09375 0.042969 0.083984 0.082031 0.046875]\n\n\n\n\n\n\n\n\nModel name: 2019-08-11 14:28:08 dataset\u003dgts_100_roadworks weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\nBest iter to take the model: 104\nEnsemble of 105/150 trees restored: exps_diff_depth/2019-08-11 14:28:08 dataset\u003dgts_100_roadworks weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01.model.npy\n",
+ "iter_bs 0: yf\u003d[-1.553538 -1.645257 -1.645257 -1.553538 -1.553538 -1.553538 -1.553538\n -1.525797], eps\u003d[1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ "iter_bs 1: yf\u003d[-0.244355 -1.650692 -1.775872 -1.52413 -1.520803 -1.540563 -1.540563\n -1.541346], eps\u003d[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]\n",
+ "iter_bs 2: yf\u003d[ 1.675337 -0.478226 -1.157049 -0.50483 -1.139845 -0.732229 -0.687056\n -1.139845], eps\u003d[0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25]\n",
+ "iter_bs 3: yf\u003d[ 1.470453 1.545905 -0.575921 -0.099966 -0.793298 -0.2412 -0.144998\n -0.32183 ], eps\u003d[0.375 0.125 0.125 0.125 0.125 0.125 0.125 0.125]\n",
+ "iter_bs 4: yf\u003d[ 1.081085 1.411821 0.146734 0.170571 -0.051864 -0.053878 0.058787\n 0.047882], eps\u003d[0.4375 0.1875 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625]\n",
+ "iter_bs 5: yf\u003d[ 0.461867 0.112475 -0.339293 -0.099966 -0.040485 0.292875 -0.030083\n -0.040485], eps\u003d[0.46875 0.21875 0.09375 0.09375 0.03125 0.03125 0.09375 0.09375]\n",
+ "iter_bs 6: yf\u003d[-0.234387 -0.138287 0.035742 0.080605 -0.026111 0.292875 0.043763\n -0.026111], eps\u003d[0.484375 0.234375 0.078125 0.078125 0.015625 0.046875 0.078125 0.078125]\n",
+ "iter_bs 7: yf\u003d[ 0.461867 -0.014089 -0.179295 0.010528 0.084881 0.181777 -0.030083\n -0.026111], eps\u003d[0.476562 0.226562 0.085938 0.085938 0.007812 0.054688 0.085938 0.070312]\n",
+ "iter_bs 8: yf\u003d[ 0.461867 -0.003548 0.035742 -0.099966 -0.026111 -0.053878 0.041767\n 0.047882], eps\u003d[0.480469 0.222656 0.082031 0.089844 0.011719 0.058594 0.082031 0.066406]\n",
+ "iter_bs 9: yf\u003d[ 0.438582 0.074564 -0.023676 0.010528 0.084881 0.181777 0.041767\n 0.047882], eps\u003d[0.482422 0.220703 0.083984 0.087891 0.009766 0.056641 0.083984 0.068359]\nyf after binary search: yf\u003d[-0.234387 -0.014089 -0.339293 -0.099966 -1.139845 -0.053878 -0.030083\n -0.32183 ], Linf\u003d[0.484375 0.226563 0.09375 0.125 0.25 0.0625 0.09375 0.125 ]\n",
+ "yf after cleanup: yf\u003d[-0.023029 -0.015465 -0.084032 -0.030083 -0.348253 -0.013519 -0.030083\n -0.029641], Linf\u003d[0.484375 0.226563 0.09375 0.125 0.25 0.0625 0.09375 0.125 ]\n\n\n\n\n\n\n\n\n",
+ "Model name: 2019-08-11 14:28:07 dataset\u003dgts_30_70 weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\nBest iter to take the model: 147\n",
+ "Ensemble of 148/150 trees restored: exps_diff_depth/2019-08-11 14:28:07 dataset\u003dgts_30_70 weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01.model.npy\n",
+ "iter_bs 0: yf\u003d[-1.355085 -1.168262 -1.355085 -1.168262 -1.30931 -1.168262 -1.355085\n -1.355085], eps\u003d[1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ "iter_bs 1: yf\u003d[-1.352458 -0.835542 -1.389235 -1.322883 -0.956109 -1.098753 -1.234604\n -1.352458], eps\u003d[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]\n",
+ "iter_bs 2: yf\u003d[-1.400227 -0.032829 -1.449297 -1.022505 -0.136695 0.227325 -1.164886\n -1.326263], eps\u003d[0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25]\n",
+ "iter_bs 3: yf\u003d[-1.320362 0.555631 -0.509395 -0.960757 0.40337 -0.447914 -1.037601\n -1.228524], eps\u003d[0.125 0.125 0.125 0.125 0.125 0.375 0.125 0.125]\n",
+ "iter_bs 4: yf\u003d[-0.615236 0.351378 0.752629 -0.171009 0.062417 0.073122 -0.226755\n 0.923745], eps\u003d[0.0625 0.1875 0.0625 0.0625 0.1875 0.3125 0.0625 0.0625]\n",
+ "iter_bs 5: yf\u003d[ 0.314242 0.282445 -0.245965 0.044115 0.02893 -0.25657 0.053003\n -1.228524], eps\u003d[0.03125 0.21875 0.09375 0.03125 0.21875 0.34375 0.03125 0.09375]\n",
+ "iter_bs 6: yf\u003d[-0.413776 -0.003499 0.155862 -0.171009 -0.140165 -0.264822 -0.091868\n -0.617634], eps\u003d[0.046875 0.234375 0.078125 0.046875 0.234375 0.328125 0.046875 0.078125]\n",
+ "iter_bs 7: yf\u003d[-0.174826 -0.003499 -0.296813 -0.171009 -0.155289 0.042452 0.053003\n -0.290894], eps\u003d[0.039062 0.226562 0.085938 0.039062 0.226562 0.320312 0.039062 0.070312]\n",
+ "iter_bs 8: yf\u003d[ 0.008583 -0.003499 0.009499 -0.155885 0.0391 -0.268963 0.104362\n 0.449106], eps\u003d[0.035156 0.222656 0.082031 0.035156 0.222656 0.324219 0.042969 0.066406]\n",
+ "iter_bs 9: yf\u003d[ 0.008583 0.282445 -0.010501 0.044115 0.0391 0.037881 0.104362\n 0.449106], eps\u003d[0.037109 0.220703 0.083984 0.033203 0.224609 0.322266 0.044922 0.068359]\nyf after binary search: yf\u003d[-0.413776 -0.032829 -0.010501 -0.960757 -0.155289 -0.268963 -1.164886\n -0.290894], Linf\u003d[0.046875 0.25 0.083984 0.125 0.226563 0.324219 0.25 0.070313]\n",
+ "yf after cleanup: yf\u003d[-0.013443 -0.000411 -0.001349 -0.273359 -0.007694 -0.025392 -0.006276\n -0.026324], Linf\u003d[0.046875 0.25 0.083984 0.125 0.226562 0.324219 0.25 0.070312]\n\n\n\n\n\n\n\n\nModel name: 2019-08-11 14:28:08 dataset\u003dgts_30_70 weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\nBest iter to take the model: 25\nEnsemble of 26/150 trees restored: exps_diff_depth/2019-08-11 14:28:08 dataset\u003dgts_30_70 weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01.model.npy\n",
+ "iter_bs 0: yf\u003d[-0.157748 -0.164589 -0.180425 -0.177234 -0.177234 -0.178096 -0.18197\n -0.181696], eps\u003d[1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ "iter_bs 1: yf\u003d[-0.169678 -0.195845 -0.185373 -0.177234 -0.151485 -0.155154 -0.147317\n -0.192296], eps\u003d[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]\n",
+ "iter_bs 2: yf\u003d[-0.18393 0.001867 -0.187601 -0.145906 -0.105987 0.05895 -0.192414\n -0.177991], eps\u003d[0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25]\n",
+ "iter_bs 3: yf\u003d[-0.148673 -0.167842 -0.128848 -0.053576 0.00202 -0.084314 -0.054765\n -0.092785], eps\u003d[0.125 0.375 0.125 0.125 0.125 0.375 0.125 0.125]\n",
+ "iter_bs 4: yf\u003d[ 0.064043 -0.11486 -0.047577 0.072351 -0.038309 -0.018101 0.064849\n 0.116226], eps\u003d[0.0625 0.3125 0.0625 0.0625 0.1875 0.3125 0.0625 0.0625]\n",
+ "iter_bs 5: yf\u003d[-0.062225 -0.060958 0.043215 -0.017504 -0.012444 -0.003218 0.019196\n 0.005848], eps\u003d[0.09375 0.28125 0.03125 0.09375 0.15625 0.28125 0.09375 0.09375]\n",
+ "iter_bs 6: yf\u003d[-0.015331 -0.046101 -0.023393 0.045819 -0.002306 0.031215 -0.0446\n 0.003887], eps\u003d[0.078125 0.265625 0.046875 0.078125 0.140625 0.265625 0.109375 0.109375]\n",
+ "iter_bs 7: yf\u003d[ 0.022171 -0.046101 0.008956 0.00378 0.003802 0.01876 -0.0246\n -0.084074], eps\u003d[0.070312 0.257812 0.039062 0.085938 0.132812 0.273438 0.101562 0.117188]\n",
+ "iter_bs 8: yf\u003d[ 0.003487 -0.046101 0.008956 0.000141 0.004191 0.00943 0.001823\n -0.043284], eps\u003d[0.074219 0.253906 0.042969 0.089844 0.136719 0.277344 0.097656 0.113281]\n",
+ "iter_bs 9: yf\u003d[-0.021635 -0.034398 0.008956 0.00204 0.004191 0.017017 0.003701\n 0.012134], eps\u003d[0.076172 0.251953 0.044922 0.091797 0.138672 0.279297 0.099609 0.111328]\nyf after binary search: yf\u003d[-0.021635 -0.034398 -0.023393 -0.017504 -0.002306 -0.003218 -0.0446\n -0.084074], Linf\u003d[0.076172 0.251953 0.046875 0.09375 0.140625 0.28125 0.109375 0.117188]\n",
+ "yf after cleanup: yf\u003d[-0.000229 -0.01091 -0.0007 -0.000761 -0.004781 -0.003218 -0.002138\n -0.002074], Linf\u003d[0.076172 0.251953 0.046875 0.09375 0.140625 0.28125 0.109375 0.117188]\n\n\n\n\n\n\n\n\nModel name: 2019-08-11 14:28:08 dataset\u003dgts_30_70 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\nBest iter to take the model: 147\nEnsemble of 148/150 trees restored: exps_diff_depth/2019-08-11 14:28:08 dataset\u003dgts_30_70 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01.model.npy\n",
+ "iter_bs 0: yf\u003d[-1.592915 -1.459522 -1.691325 -1.256494 -1.393642 -1.343824 -1.64447\n -1.592915], eps\u003d[1. 1. 1. 1. 1. 1. 1. 1.]\n",
+ "iter_bs 1: yf\u003d[-1.477568 -0.898177 -1.499182 -1.238629 -0.907778 -1.058635 -1.69994\n -1.488657], eps\u003d[0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5]\n",
+ "iter_bs 2: yf\u003d[-1.200824 0.08738 -1.374769 -0.795493 -0.039547 0.257392 -1.434445\n -1.07207 ], eps\u003d[0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25]\n",
+ "iter_bs 3: yf\u003d[-0.808928 -0.860407 -1.02489 -0.50192 0.364495 -0.173397 -0.83591\n -0.599776], eps\u003d[0.125 0.375 0.125 0.125 0.125 0.375 0.125 0.125]\n",
+ "iter_bs 4: yf\u003d[-0.147365 -0.655825 0.476217 -0.078033 0.05865 0.043373 0.021858\n 0.316058], eps\u003d[0.0625 0.3125 0.0625 0.0625 0.1875 0.3125 0.0625 0.0625]\n",
+ "iter_bs 5: yf\u003d[ 0.39907 -0.502352 -0.436837 0.057676 -0.039062 -0.092156 -0.41657\n -0.394542], eps\u003d[0.03125 0.28125 0.09375 0.03125 0.21875 0.34375 0.09375 0.09375]\n",
+ "iter_bs 6: yf\u003d[ 0.181775 0.049829 -0.11553 -0.052044 -0.048408 -0.048354 -0.026731\n 0.144246], eps\u003d[0.046875 0.265625 0.078125 0.046875 0.203125 0.328125 0.078125 0.078125]\n",
+ "iter_bs 7: yf\u003d[ 0.089739 -0.340057 0.368591 0.043382 -0.048408 0.06879 -0.007105\n -0.394542], eps\u003d[0.054688 0.273438 0.070312 0.039062 0.195312 0.320312 0.070312 0.085938]\n",
+ "iter_bs 8: yf\u003d[-0.147365 -0.007341 0.365219 0.03272 -0.058347 0.016824 0.021858\n -0.103058], eps\u003d[0.058594 0.269531 0.074219 0.042969 0.191406 0.324219 0.066406 0.082031]\n",
+ "iter_bs 9: yf\u003d[ 0.089739 0.01533 0.365219 0.03272 0.025535 -0.002732 0.021858\n 0.144246], eps\u003d[0.056641 0.267578 0.076172 0.044922 0.189453 0.326172 0.068359 0.080078]\nyf after binary search: yf\u003d[-0.808928 -0.007341 -0.11553 -0.078033 -0.058347 -0.002732 -1.434445\n -0.394542], Linf\u003d[0.125 0.269531 0.078125 0.0625 0.191406 0.326172 0.25 0.09375 ]\n",
+ "yf after cleanup: yf\u003d[-0.219814 -0.018141 -0.025993 -0.019206 -0.003582 -0.001954 -0.069936\n -0.000969], Linf\u003d[0.125 0.269531 0.078125 0.0625 0.191406 0.326172 0.25 0.09375 ]\n\n\n\n\n\n\n\n\n"
+ ],
+ "output_type": "stream"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 1684.8x4492.8 with 24 Axes\u003e",
+ "image/png": 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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 1684.8x4492.8 with 24 Axes\u003e",
+ "image/png": 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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 1684.8x4492.8 with 24 Axes\u003e",
+ "image/png": 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\u003d\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 1684.8x4492.8 with 24 Axes\u003e",
+ "image/png": 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\u003d\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": "# datasets \u003d [\u0027breast_cancer\u0027, \u0027diabetes\u0027, \u0027cod_rna\u0027, \u0027mnist_1_5\u0027, \u0027mnist_2_6\u0027, \u0027fmnist_sandal_sneaker\u0027, \u0027gts_100_roadworks\u0027, \u0027gts_30_70\u0027]\ndatasets \u003d [\u0027mnist_1_5\u0027, \u0027mnist_2_6\u0027, \u0027gts_100_roadworks\u0027, \u0027gts_30_70\u0027]\n# datasets \u003d [\u0027mnist_1_5\u0027]\nfor dataset in datasets:\n _, _, X_test, y_test, eps \u003d data.all_datasets_dict[dataset]()\n if \u0027gts\u0027 in dataset:\n img_shape \u003d (32, 32, 3)\n elif \u0027mnist\u0027 in dataset:\n img_shape \u003d (28, 28)\n else:\n raise ValueError(\u0027wrong dataset for getting img_shape\u0027)\n \n np.random.seed(1)\n datasets \u003d [dataset]\n models \u003d [\u0027plain\u0027, \u0027at_cube\u0027, \u0027robust_bound\u0027]\n # models \u003d [\u0027robust_bound\u0027]\n exp_folder \u003d \u0027exps_diff_depth\u0027\n weak_learner \u003d \u0027tree\u0027\n tree_depth \u003d 4\n model_names \u003d utils.get_model_names(datasets, models, exp_folder, weak_learner, tree_depth)\n \n n_trials_attack \u003d 1000 # 1000 is quite slow\n idx_examples \u003d np.arange(2, 10) if dataset !\u003d \u0027gts_100_roadworks\u0027 else np.array([0, 1, 2, 4, 6, 7, 8, 9])\n \n plot_height \u003d 6\n sns.set(font_scale\u003d2)\n fig_width \u003d 1.3*len(model_names)*plot_height\n fig_height \u003d 1.3*len(idx_examples)*plot_height\n \n fig, axs \u003d plt.subplots(len(idx_examples), len(model_names), figsize\u003d(fig_width, fig_height)) \n \n \n for i, model_name in enumerate(model_names):\n print(\u0027Model name: {}\u0027.format(model_name))\n model \u003d model_name.split(\u0027model\u003d\u0027)[1].split(\u0027 \u0027)[0]\n eps \u003d model_name.split(\u0027eps\u003d\u0027)[1].split(\u0027 \u0027)[0]\n \n model_path \u003d model_name + \u0027.model.npy\u0027\n metrics_path \u003d model_name + \u0027.metrics\u0027\n metrics \u003d np.loadtxt(exp_folder + \u0027/\u0027 + metrics_path)\n valid_errs, valid_adv_errs \u003d metrics[:, 8], metrics[:, 10]\n \n # Model selection\n # best_iter \u003d len(valid_errs) - 1 # otherwise, the counts are not comparable between different model types\n if model \u003d\u003d \u0027plain\u0027:\n best_iter \u003d np.argmin(valid_errs)\n elif model in [\u0027at_cube\u0027, \u0027robust_bound\u0027, \u0027robust_exact\u0027]:\n best_iter \u003d np.argmin(valid_adv_errs)\n else:\n raise ValueError(\u0027wrong model name\u0027)\n print(\u0027Best iter to take the model: {}\u0027.format(best_iter))\n \n if weak_learner \u003d\u003d \u0027stump\u0027:\n # the hyperparameters of recreated models do not matter (they matter only for training)\n ensemble \u003d StumpEnsemble(weak_learner, 0, 0, 0, 0, 0)\n elif weak_learner \u003d\u003d \u0027tree\u0027:\n ensemble \u003d TreeEnsemble(weak_learner, 0, 0, 0, 0, 0, 0, 0, 0, 0)\n else:\n raise ValueError(\u0027wrong weak learner\u0027)\n model_ova \u003d OneVsAllClassifier([ensemble])\n model_ova.load(\u0027{}/{}\u0027.format(exp_folder, model_path), iteration\u003dbest_iter)\n \n # adversarial examples generation\n if weak_learner \u003d\u003d \u0027stump\u0027:\n deltas \u003d exact_attack_stumps(model_ova, X_test[idx_examples], y_test[idx_examples])\n elif weak_learner \u003d\u003d \u0027tree\u0027:\n deltas \u003d binary_search_attack(cube_attack, model_ova, X_test[idx_examples], y_test[idx_examples], n_trials_attack)\n else:\n raise ValueError(\u0027wrong weak learner\u0027)\n \n for i_idx, idx in enumerate(idx_examples):\n plot_name_short \u003d \u0027$||\\delta||_\\infty$\u003d{:.3f}\u0027.format(np.abs(deltas[i_idx]).max())\n ax \u003d axs[i_idx][i]\n ax.imshow((X_test[idx] + deltas[i_idx]).reshape(img_shape))\n ax.axis(\u0027off\u0027)\n ax.set_title(plot_name_short, fontsize\u003d30, pad\u003d15)\n print(\u0027\u0027)\n \n plot_name_save \u003d \u0027adv_ex-exp\u003d{}-dataset\u003d{}-weak_learner\u003d{}\u0027.format(\n exp_folder, dataset, weak_learner)\n # fig.tight_layout()\n fig.subplots_adjust(wspace\u003d-0.2)\n plt.savefig(\u0027plots/{}.pdf\u0027.format(plot_name_save), bbox_inches\u003d\u0027tight\u0027,\n transparent\u003dTrue)\n\n",
+ "metadata": {
+ "pycharm": {
+ "metadata": false,
+ "name": "#%%\n",
+ "is_executing": false
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": "\n",
+ "metadata": {
+ "pycharm": {
+ "metadata": false,
+ "name": "#%%\n"
+ }
+ }
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "name": "python3",
+ "language": "python",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/notebooks/exact_adv.ipynb b/notebooks/exact_adv.ipynb
deleted file mode 100644
index 3a97d82..0000000
--- a/notebooks/exact_adv.ipynb
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "pycharm": {}
- },
- "source": "# Exact adversarial examples"
- },
- {
- "cell_type": "code",
- "execution_count": 77,
- "metadata": {
- "collapsed": true,
- "pycharm": {
- "is_executing": false
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "text": [
- "The autoreload extension is already loaded. To reload it, use:\n %reload_ext autoreload\n"
- ],
- "output_type": "stream"
- }
- ],
- "source": "%load_ext autoreload\n%autoreload 2\n\nimport os\nos.chdir(\"../\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn\nimport data\nfrom stump_ensemble import StumpEnsemble\n\n%matplotlib inline\nseaborn.set(font_scale\u003d2)\nseaborn.set_style(\"white\")\nnp.random.seed(1)\nnp.set_printoptions(precision\u003d6, suppress\u003dTrue)\n\ndataset \u003d \u0027mnist_1_5\u0027\n# dataset \u003d \u0027mnist_2_6\u0027\n# dataset \u003d \u0027fmnist_sandal_sneaker\u0027\n# dataset \u003d \u0027gts_100_roadworks\u0027\n# dataset \u003d \u0027gts_30_70\u0027\n_, _, X_test, y_test, eps \u003d data.all_datasets_dict[dataset]()\nif \u0027gts\u0027 in dataset:\n img_shape \u003d (32, 32, 3)\nelif \u0027mnist\u0027 in dataset:\n img_shape \u003d (28, 28)\nelse:\n raise ValueError(\u0027wrong dataset for getting img_shape\u0027)"
- },
- {
- "cell_type": "code",
- "execution_count": 78,
- "outputs": [
- {
- "name": "stdout",
- "text": [
- "Model name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 415 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d19.647, nnz\u003d0\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d19.109, nnz\u003d1\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d19.109, nnz\u003d1\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d19.109, nnz\u003d1\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d-26.154, nnz\u003d21\n\nModel name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 364 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d4.478, nnz\u003d0\neps_max\u003d0.016, eps_delta\u003d0.000, yf\u003d4.478, nnz\u003d0\neps_max\u003d0.020, eps_delta\u003d0.020, yf\u003d4.276, nnz\u003d1\n",
- "eps_max\u003d0.031, eps_delta\u003d0.031, yf\u003d4.276, nnz\u003d1\neps_max\u003d0.047, eps_delta\u003d0.047, yf\u003d4.276, nnz\u003d1\neps_max\u003d0.071, eps_delta\u003d0.071, yf\u003d4.174, nnz\u003d2\neps_max\u003d0.176, eps_delta\u003d0.176, yf\u003d3.970, nnz\u003d3\neps_max\u003d0.263, eps_delta\u003d0.263, yf\u003d3.890, nnz\u003d4\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d3.890, nnz\u003d4\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d3.890, nnz\u003d4\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d-59.770, nnz\u003d23\n\nModel name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 197 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d4.256, nnz\u003d0\neps_max\u003d0.016, eps_delta\u003d0.000, yf\u003d4.256, nnz\u003d0\neps_max\u003d0.020, eps_delta\u003d0.020, yf\u003d3.946, nnz\u003d1\neps_max\u003d0.020, eps_delta\u003d0.020, yf\u003d3.946, nnz\u003d1\n",
- "eps_max\u003d0.031, eps_delta\u003d0.031, yf\u003d3.946, nnz\u003d1\neps_max\u003d0.047, eps_delta\u003d0.047, yf\u003d3.946, nnz\u003d1\neps_max\u003d0.071, eps_delta\u003d0.071, yf\u003d3.862, nnz\u003d2\neps_max\u003d0.125, eps_delta\u003d0.125, yf\u003d3.760, nnz\u003d3\neps_max\u003d0.176, eps_delta\u003d0.176, yf\u003d3.556, nnz\u003d4\neps_max\u003d0.216, eps_delta\u003d0.216, yf\u003d3.505, nnz\u003d5\neps_max\u003d0.263, eps_delta\u003d0.263, yf\u003d3.394, nnz\u003d6\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d3.394, nnz\u003d6\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d-49.981, nnz\u003d21\n\nModel name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 415 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d21.539, nnz\u003d0\neps_max\u003d0.002, eps_delta\u003d0.000, yf\u003d21.539, nnz\u003d0\n",
- "eps_max\u003d0.002, eps_delta\u003d0.000, yf\u003d21.539, nnz\u003d0\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d-24.790, nnz\u003d21\n\nModel name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 364 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d5.304, nnz\u003d0\neps_max\u003d0.008, eps_delta\u003d0.000, yf\u003d5.304, nnz\u003d0\neps_max\u003d0.008, eps_delta\u003d0.000, yf\u003d5.304, nnz\u003d0\neps_max\u003d0.012, eps_delta\u003d0.000, yf\u003d5.304, nnz\u003d0\neps_max\u003d0.027, eps_delta\u003d0.000, yf\u003d5.304, nnz\u003d0\neps_max\u003d0.071, eps_delta\u003d0.000, yf\u003d5.304, nnz\u003d0\n",
- "eps_max\u003d0.071, eps_delta\u003d0.000, yf\u003d5.304, nnz\u003d0\neps_max\u003d0.137, eps_delta\u003d0.137, yf\u003d5.108, nnz\u003d1\neps_max\u003d0.188, eps_delta\u003d0.188, yf\u003d4.906, nnz\u003d1\neps_max\u003d0.208, eps_delta\u003d0.208, yf\u003d4.799, nnz\u003d2\neps_max\u003d0.220, eps_delta\u003d0.220, yf\u003d4.799, nnz\u003d2\neps_max\u003d0.282, eps_delta\u003d0.282, yf\u003d4.718, nnz\u003d3\neps_max\u003d0.290, eps_delta\u003d0.290, yf\u003d4.657, nnz\u003d4\neps_max\u003d0.298, eps_delta\u003d0.298, yf\u003d4.439, nnz\u003d5\neps_max\u003d0.298, eps_delta\u003d0.298, yf\u003d4.439, nnz\u003d5\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d4.439, nnz\u003d5\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d4.439, nnz\u003d5\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d-58.942, nnz\u003d22\n\n",
- "Model name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 197 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d5.054, nnz\u003d0\neps_max\u003d0.008, eps_delta\u003d0.000, yf\u003d5.054, nnz\u003d0\neps_max\u003d0.012, eps_delta\u003d0.000, yf\u003d5.054, nnz\u003d0\neps_max\u003d0.027, eps_delta\u003d0.000, yf\u003d5.054, nnz\u003d0\neps_max\u003d0.137, eps_delta\u003d0.137, yf\u003d4.858, nnz\u003d1\neps_max\u003d0.188, eps_delta\u003d0.188, yf\u003d4.548, nnz\u003d1\neps_max\u003d0.188, eps_delta\u003d0.188, yf\u003d4.548, nnz\u003d1\neps_max\u003d0.212, eps_delta\u003d0.212, yf\u003d4.404, nnz\u003d2\neps_max\u003d0.282, eps_delta\u003d0.282, yf\u003d4.293, nnz\u003d3\neps_max\u003d0.290, eps_delta\u003d0.290, yf\u003d4.195, nnz\u003d4\neps_max\u003d0.298, eps_delta\u003d0.298, yf\u003d3.897, nnz\u003d5",
- "\neps_max\u003d0.298, eps_delta\u003d0.298, yf\u003d3.897, nnz\u003d5\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d3.897, nnz\u003d5\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d-49.082, nnz\u003d18\n\nModel name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 415 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d42.812, nnz\u003d0\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d41.592, nnz\u003d1\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d41.592, nnz\u003d1\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d41.592, nnz\u003d1",
- "\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d41.592, nnz\u003d1\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d41.592, nnz\u003d1\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d41.592, nnz\u003d1\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d41.592, nnz\u003d1\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\n",
- "eps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\n",
- "eps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d37.962, nnz\u003d5\neps_max\u003d0.004, eps_delta\u003d0.004, yf\u003d37.131, nnz\u003d6\neps_max\u003d0.004, eps_delta\u003d0.004, yf\u003d37.131, nnz\u003d6\neps_max\u003d0.004, eps_delta\u003d0.004, yf\u003d37.131, nnz\u003d6\neps_max\u003d0.004, eps_delta\u003d0.004, yf\u003d37.131, nnz\u003d6\neps_max\u003d0.004, eps_delta\u003d0.004, yf\u003d37.131, nnz\u003d6\neps_max\u003d0.004, eps_delta\u003d0.004, yf\u003d37.131, nnz\u003d6\n",
- "eps_max\u003d0.004, eps_delta\u003d0.004, yf\u003d37.131, nnz\u003d6\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d34.437, nnz\u003d8\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d34.437, nnz\u003d8\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d34.437, nnz\u003d8\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d34.437, nnz\u003d8\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d34.437, nnz\u003d8\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d34.437, nnz\u003d8\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d34.437, nnz\u003d8\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d34.437, nnz\u003d8\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d34.437, nnz\u003d8\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d34.437, nnz\u003d8\n",
- "eps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d34.437, nnz\u003d8\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d32.102, nnz\u003d9\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d32.102, nnz\u003d9\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d31.726, nnz\u003d9\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d31.726, nnz\u003d9\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d31.726, nnz\u003d9\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d31.726, nnz\u003d9\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d31.726, nnz\u003d9\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d31.726, nnz\u003d9\neps_max\u003d0.012, eps_delta\u003d0.012, yf\u003d26.617, nnz\u003d10\neps_max\u003d0.012, eps_delta\u003d0.012, yf\u003d26.617, nnz\u003d10\n",
- "eps_max\u003d0.012, eps_delta\u003d0.012, yf\u003d26.617, nnz\u003d10\neps_max\u003d0.014, eps_delta\u003d0.014, yf\u003d26.617, nnz\u003d10\neps_max\u003d0.014, eps_delta\u003d0.014, yf\u003d26.617, nnz\u003d10\neps_max\u003d0.014, eps_delta\u003d0.014, yf\u003d26.617, nnz\u003d10\neps_max\u003d0.018, eps_delta\u003d0.018, yf\u003d26.617, nnz\u003d10\neps_max\u003d0.022, eps_delta\u003d0.022, yf\u003d26.617, nnz\u003d10\neps_max\u003d0.022, eps_delta\u003d0.022, yf\u003d26.617, nnz\u003d10\neps_max\u003d0.022, eps_delta\u003d0.022, yf\u003d26.617, nnz\u003d10\neps_max\u003d0.025, eps_delta\u003d0.025, yf\u003d26.181, nnz\u003d11\neps_max\u003d0.029, eps_delta\u003d0.029, yf\u003d25.022, nnz\u003d12\neps_max\u003d0.029, eps_delta\u003d0.029, yf\u003d25.022, nnz\u003d12\neps_max\u003d0.029, eps_delta\u003d0.029, yf\u003d25.022, nnz\u003d12\n",
- "eps_max\u003d0.029, eps_delta\u003d0.029, yf\u003d25.022, nnz\u003d12\neps_max\u003d0.029, eps_delta\u003d0.029, yf\u003d25.022, nnz\u003d12\neps_max\u003d0.029, eps_delta\u003d0.029, yf\u003d25.022, nnz\u003d12\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d24.745, nnz\u003d13\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d24.745, nnz\u003d13\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d24.745, nnz\u003d13\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d24.745, nnz\u003d13\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d24.745, nnz\u003d13\neps_max\u003d0.035, eps_delta\u003d0.035, yf\u003d24.745, nnz\u003d13\neps_max\u003d0.037, eps_delta\u003d0.037, yf\u003d23.249, nnz\u003d14\neps_max\u003d0.037, eps_delta\u003d0.037, yf\u003d21.790, nnz\u003d16\neps_max\u003d0.037, eps_delta\u003d0.037, yf\u003d21.790, nnz\u003d16\neps_max\u003d0.037, eps_delta\u003d0.037, yf\u003d21.790, nnz\u003d16\n",
- "eps_max\u003d0.037, eps_delta\u003d0.037, yf\u003d21.790, nnz\u003d16\neps_max\u003d0.039, eps_delta\u003d0.039, yf\u003d21.790, nnz\u003d16\neps_max\u003d0.041, eps_delta\u003d0.041, yf\u003d21.790, nnz\u003d16\neps_max\u003d0.041, eps_delta\u003d0.041, yf\u003d21.790, nnz\u003d16\neps_max\u003d0.045, eps_delta\u003d0.045, yf\u003d21.790, nnz\u003d16\neps_max\u003d0.045, eps_delta\u003d0.045, yf\u003d21.790, nnz\u003d16\neps_max\u003d0.045, eps_delta\u003d0.045, yf\u003d21.790, nnz\u003d16\neps_max\u003d0.047, eps_delta\u003d0.047, yf\u003d20.930, nnz\u003d17\neps_max\u003d0.049, eps_delta\u003d0.049, yf\u003d19.710, nnz\u003d19\neps_max\u003d0.049, eps_delta\u003d0.049, yf\u003d19.710, nnz\u003d19\neps_max\u003d0.049, eps_delta\u003d0.049, yf\u003d19.710, nnz\u003d19\neps_max\u003d0.053, eps_delta\u003d0.053, yf\u003d19.069, nnz\u003d20\n",
- "eps_max\u003d0.057, eps_delta\u003d0.057, yf\u003d19.069, nnz\u003d20\neps_max\u003d0.061, eps_delta\u003d0.061, yf\u003d18.766, nnz\u003d21\neps_max\u003d0.061, eps_delta\u003d0.061, yf\u003d18.766, nnz\u003d21\neps_max\u003d0.061, eps_delta\u003d0.061, yf\u003d18.766, nnz\u003d21\neps_max\u003d0.069, eps_delta\u003d0.069, yf\u003d18.333, nnz\u003d22\neps_max\u003d0.069, eps_delta\u003d0.069, yf\u003d18.333, nnz\u003d22\neps_max\u003d0.071, eps_delta\u003d0.071, yf\u003d18.333, nnz\u003d22\neps_max\u003d0.073, eps_delta\u003d0.073, yf\u003d18.333, nnz\u003d22\neps_max\u003d0.080, eps_delta\u003d0.080, yf\u003d18.333, nnz\u003d22\neps_max\u003d0.080, eps_delta\u003d0.080, yf\u003d18.333, nnz\u003d22\neps_max\u003d0.084, eps_delta\u003d0.084, yf\u003d17.610, nnz\u003d23\neps_max\u003d0.084, eps_delta\u003d0.084, yf\u003d16.210, nnz\u003d23\n",
- "eps_max\u003d0.088, eps_delta\u003d0.088, yf\u003d15.486, nnz\u003d23\neps_max\u003d0.088, eps_delta\u003d0.088, yf\u003d14.603, nnz\u003d24\neps_max\u003d0.092, eps_delta\u003d0.092, yf\u003d14.069, nnz\u003d25\neps_max\u003d0.094, eps_delta\u003d0.094, yf\u003d14.069, nnz\u003d25\neps_max\u003d0.094, eps_delta\u003d0.094, yf\u003d14.069, nnz\u003d25\neps_max\u003d0.096, eps_delta\u003d0.096, yf\u003d14.069, nnz\u003d25\neps_max\u003d0.100, eps_delta\u003d0.100, yf\u003d14.069, nnz\u003d25\neps_max\u003d0.100, eps_delta\u003d0.100, yf\u003d14.069, nnz\u003d25\neps_max\u003d0.106, eps_delta\u003d0.106, yf\u003d4.069, nnz\u003d26\neps_max\u003d0.108, eps_delta\u003d0.108, yf\u003d3.708, nnz\u003d27\n",
- "eps_max\u003d0.116, eps_delta\u003d0.116, yf\u003d3.044, nnz\u003d27\neps_max\u003d0.116, eps_delta\u003d0.116, yf\u003d3.044, nnz\u003d27\neps_max\u003d0.120, eps_delta\u003d0.120, yf\u003d3.044, nnz\u003d27\neps_max\u003d0.122, eps_delta\u003d0.122, yf\u003d3.044, nnz\u003d27\neps_max\u003d0.131, eps_delta\u003d0.131, yf\u003d2.601, nnz\u003d28\neps_max\u003d0.131, eps_delta\u003d0.131, yf\u003d2.601, nnz\u003d28\neps_max\u003d0.131, eps_delta\u003d0.131, yf\u003d2.601, nnz\u003d28\neps_max\u003d0.131, eps_delta\u003d0.131, yf\u003d2.601, nnz\u003d28\n",
- "eps_max\u003d0.131, eps_delta\u003d0.131, yf\u003d2.601, nnz\u003d28\neps_max\u003d0.133, eps_delta\u003d0.133, yf\u003d2.601, nnz\u003d28\neps_max\u003d0.135, eps_delta\u003d0.135, yf\u003d2.601, nnz\u003d28\neps_max\u003d0.135, eps_delta\u003d0.135, yf\u003d2.601, nnz\u003d28\neps_max\u003d0.139, eps_delta\u003d0.139, yf\u003d2.135, nnz\u003d29\neps_max\u003d0.147, eps_delta\u003d0.147, yf\u003d1.226, nnz\u003d30\neps_max\u003d0.151, eps_delta\u003d0.151, yf\u003d0.688, nnz\u003d31\neps_max\u003d0.151, eps_delta\u003d0.151, yf\u003d0.688, nnz\u003d31\neps_max\u003d0.155, eps_delta\u003d0.155, yf\u003d0.688, nnz\u003d31\neps_max\u003d0.159, eps_delta\u003d0.159, yf\u003d0.154, nnz\u003d32\n",
- "eps_max\u003d0.163, eps_delta\u003d0.163, yf\u003d0.154, nnz\u003d32\neps_max\u003d0.163, eps_delta\u003d0.163, yf\u003d0.154, nnz\u003d32\neps_max\u003d0.163, eps_delta\u003d0.163, yf\u003d0.154, nnz\u003d32\neps_max\u003d0.167, eps_delta\u003d0.167, yf\u003d0.154, nnz\u003d32\neps_max\u003d0.169, eps_delta\u003d0.169, yf\u003d-1.191, nnz\u003d33\n\nModel name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 364 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d60.081, nnz\u003d0\neps_max\u003d0.020, eps_delta\u003d0.020, yf\u003d50.081, nnz\u003d1\n",
- "eps_max\u003d0.031, eps_delta\u003d0.031, yf\u003d49.736, nnz\u003d2\neps_max\u003d0.035, eps_delta\u003d0.035, yf\u003d39.736, nnz\u003d2\neps_max\u003d0.067, eps_delta\u003d0.067, yf\u003d38.894, nnz\u003d4\neps_max\u003d0.067, eps_delta\u003d0.067, yf\u003d38.894, nnz\u003d4\neps_max\u003d0.067, eps_delta\u003d0.067, yf\u003d38.894, nnz\u003d4\neps_max\u003d0.094, eps_delta\u003d0.094, yf\u003d38.778, nnz\u003d5\neps_max\u003d0.102, eps_delta\u003d0.102, yf\u003d38.301, nnz\u003d5\neps_max\u003d0.122, eps_delta\u003d0.122, yf\u003d38.301, nnz\u003d5\neps_max\u003d0.141, eps_delta\u003d0.141, yf\u003d37.842, nnz\u003d6\neps_max\u003d0.157, eps_delta\u003d0.157, yf\u003d37.842, nnz\u003d6\neps_max\u003d0.161, eps_delta\u003d0.161, yf\u003d37.315, nnz\u003d7\neps_max\u003d0.161, eps_delta\u003d0.161, yf\u003d37.315, nnz\u003d7\n",
- "eps_max\u003d0.165, eps_delta\u003d0.165, yf\u003d37.315, nnz\u003d7\neps_max\u003d0.180, eps_delta\u003d0.180, yf\u003d37.315, nnz\u003d7\neps_max\u003d0.200, eps_delta\u003d0.200, yf\u003d37.198, nnz\u003d8\neps_max\u003d0.200, eps_delta\u003d0.200, yf\u003d37.198, nnz\u003d8\neps_max\u003d0.212, eps_delta\u003d0.212, yf\u003d37.022, nnz\u003d9\neps_max\u003d0.212, eps_delta\u003d0.212, yf\u003d37.022, nnz\u003d9\neps_max\u003d0.212, eps_delta\u003d0.212, yf\u003d37.022, nnz\u003d9\neps_max\u003d0.239, eps_delta\u003d0.239, yf\u003d37.022, nnz\u003d9\neps_max\u003d0.263, eps_delta\u003d0.263, yf\u003d37.022, nnz\u003d9\neps_max\u003d0.263, eps_delta\u003d0.263, yf\u003d37.022, nnz\u003d9\neps_max\u003d0.263, eps_delta\u003d0.263, yf\u003d37.022, nnz\u003d9\neps_max\u003d0.282, eps_delta\u003d0.282, yf\u003d36.717, nnz\u003d9\n",
- "eps_max\u003d0.282, eps_delta\u003d0.282, yf\u003d36.717, nnz\u003d9\neps_max\u003d0.290, eps_delta\u003d0.290, yf\u003d36.717, nnz\u003d9\neps_max\u003d0.298, eps_delta\u003d0.298, yf\u003d36.235, nnz\u003d10\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\n",
- "eps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.304, eps_delta\u003d0.304, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.304, eps_delta\u003d0.304, yf\u003d26.235, nnz\u003d11\n",
- "eps_max\u003d0.304, eps_delta\u003d0.304, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.304, eps_delta\u003d0.304, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.304, eps_delta\u003d0.304, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d26.235, nnz\u003d11\n",
- "eps_max\u003d0.308, eps_delta\u003d0.308, yf\u003d26.235, nnz\u003d11\neps_max\u003d0.310, eps_delta\u003d0.310, yf\u003d16.235, nnz\u003d12\neps_max\u003d0.310, eps_delta\u003d0.310, yf\u003d16.235, nnz\u003d12\neps_max\u003d0.310, eps_delta\u003d0.310, yf\u003d16.235, nnz\u003d12\neps_max\u003d0.310, eps_delta\u003d0.310, yf\u003d16.235, nnz\u003d12\neps_max\u003d0.312, eps_delta\u003d0.312, yf\u003d16.235, nnz\u003d12\neps_max\u003d0.314, eps_delta\u003d0.314, yf\u003d16.161, nnz\u003d13\neps_max\u003d0.314, eps_delta\u003d0.314, yf\u003d16.161, nnz\u003d13\neps_max\u003d0.316, eps_delta\u003d0.316, yf\u003d16.161, nnz\u003d13\neps_max\u003d0.318, eps_delta\u003d0.318, yf\u003d15.826, nnz\u003d14\neps_max\u003d0.322, eps_delta\u003d0.322, yf\u003d15.826, nnz\u003d14\neps_max\u003d0.324, eps_delta\u003d0.324, yf\u003d15.826, nnz\u003d14\neps_max\u003d0.325, eps_delta\u003d0.325, yf\u003d15.826, nnz\u003d14\n",
- "eps_max\u003d0.325, eps_delta\u003d0.325, yf\u003d15.826, nnz\u003d14\neps_max\u003d0.325, eps_delta\u003d0.325, yf\u003d15.826, nnz\u003d14\neps_max\u003d0.325, eps_delta\u003d0.325, yf\u003d15.826, nnz\u003d14\neps_max\u003d0.329, eps_delta\u003d0.329, yf\u003d15.716, nnz\u003d15\neps_max\u003d0.329, eps_delta\u003d0.329, yf\u003d15.716, nnz\u003d15\neps_max\u003d0.329, eps_delta\u003d0.329, yf\u003d15.716, nnz\u003d15\neps_max\u003d0.329, eps_delta\u003d0.329, yf\u003d15.716, nnz\u003d15\neps_max\u003d0.329, eps_delta\u003d0.329, yf\u003d15.716, nnz\u003d15\neps_max\u003d0.329, eps_delta\u003d0.329, yf\u003d15.716, nnz\u003d15\neps_max\u003d0.331, eps_delta\u003d0.331, yf\u003d5.716, nnz\u003d16\neps_max\u003d0.331, eps_delta\u003d0.331, yf\u003d5.716, nnz\u003d16\neps_max\u003d0.333, eps_delta\u003d0.333, yf\u003d5.716, nnz\u003d16\neps_max\u003d0.333, eps_delta\u003d0.333, yf\u003d5.716, nnz\u003d16\neps_max\u003d0.333, eps_delta\u003d0.333, yf\u003d5.716, nnz\u003d16\n",
- "eps_max\u003d0.333, eps_delta\u003d0.333, yf\u003d5.716, nnz\u003d16\neps_max\u003d0.333, eps_delta\u003d0.333, yf\u003d5.716, nnz\u003d16\neps_max\u003d0.337, eps_delta\u003d0.337, yf\u003d4.854, nnz\u003d18\neps_max\u003d0.337, eps_delta\u003d0.337, yf\u003d4.854, nnz\u003d18\neps_max\u003d0.337, eps_delta\u003d0.337, yf\u003d4.854, nnz\u003d18\neps_max\u003d0.337, eps_delta\u003d0.337, yf\u003d4.854, nnz\u003d18\neps_max\u003d0.337, eps_delta\u003d0.337, yf\u003d4.854, nnz\u003d18\neps_max\u003d0.339, eps_delta\u003d0.339, yf\u003d4.854, nnz\u003d18\neps_max\u003d0.341, eps_delta\u003d0.341, yf\u003d-1.137, nnz\u003d19\n\nModel name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 197 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d37.980, nnz\u003d0\n",
- "eps_max\u003d0.035, eps_delta\u003d0.035, yf\u003d27.980, nnz\u003d1\neps_max\u003d0.067, eps_delta\u003d0.067, yf\u003d27.255, nnz\u003d3\neps_max\u003d0.067, eps_delta\u003d0.067, yf\u003d27.255, nnz\u003d3\neps_max\u003d0.102, eps_delta\u003d0.102, yf\u003d26.778, nnz\u003d4\neps_max\u003d0.122, eps_delta\u003d0.122, yf\u003d26.778, nnz\u003d4\neps_max\u003d0.141, eps_delta\u003d0.141, yf\u003d26.319, nnz\u003d5\neps_max\u003d0.155, eps_delta\u003d0.155, yf\u003d26.319, nnz\u003d5\neps_max\u003d0.161, eps_delta\u003d0.161, yf\u003d26.050, nnz\u003d6\neps_max\u003d0.180, eps_delta\u003d0.180, yf\u003d26.050, nnz\u003d6\neps_max\u003d0.212, eps_delta\u003d0.212, yf\u003d26.050, nnz\u003d6\neps_max\u003d0.212, eps_delta\u003d0.212, yf\u003d26.050, nnz\u003d6\neps_max\u003d0.263, eps_delta\u003d0.263, yf\u003d26.050, nnz\u003d6\neps_max\u003d0.265, eps_delta\u003d0.265, yf\u003d26.050, nnz\u003d6\neps_max\u003d0.282, eps_delta\u003d0.282, yf\u003d25.747, nnz\u003d7\neps_max\u003d0.290, eps_delta\u003d0.290, yf\u003d25.747, nnz\u003d7\neps_max\u003d0.298, eps_delta\u003d0.298, yf\u003d25.265, nnz\u003d8\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d15.265, nnz\u003d9\n",
- "eps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.304, eps_delta\u003d0.304, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.304, eps_delta\u003d0.304, yf\u003d15.265, nnz\u003d9\n",
- "eps_max\u003d0.304, eps_delta\u003d0.304, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.304, eps_delta\u003d0.304, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.304, eps_delta\u003d0.304, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.306, eps_delta\u003d0.306, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.308, eps_delta\u003d0.308, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.310, eps_delta\u003d0.310, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.314, eps_delta\u003d0.314, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.316, eps_delta\u003d0.316, yf\u003d15.265, nnz\u003d9\neps_max\u003d0.318, eps_delta\u003d0.318, yf\u003d14.930, nnz\u003d10\neps_max\u003d0.324, eps_delta\u003d0.324, yf\u003d14.930, nnz\u003d10\neps_max\u003d0.325, eps_delta\u003d0.325, yf\u003d14.930, nnz\u003d10\neps_max\u003d0.329, eps_delta\u003d0.329, yf\u003d14.930, nnz\u003d10\neps_max\u003d0.329, eps_delta\u003d0.329, yf\u003d14.930, nnz\u003d10\neps_max\u003d0.329, eps_delta\u003d0.329, yf\u003d14.930, nnz\u003d10",
- "\neps_max\u003d0.329, eps_delta\u003d0.329, yf\u003d14.930, nnz\u003d10\neps_max\u003d0.329, eps_delta\u003d0.329, yf\u003d14.930, nnz\u003d10\neps_max\u003d0.331, eps_delta\u003d0.331, yf\u003d14.930, nnz\u003d10\neps_max\u003d0.333, eps_delta\u003d0.333, yf\u003d14.725, nnz\u003d12\neps_max\u003d0.333, eps_delta\u003d0.333, yf\u003d14.725, nnz\u003d12\neps_max\u003d0.333, eps_delta\u003d0.333, yf\u003d14.725, nnz\u003d12\neps_max\u003d0.333, eps_delta\u003d0.333, yf\u003d14.725, nnz\u003d12\neps_max\u003d0.333, eps_delta\u003d0.333, yf\u003d14.725, nnz\u003d12\neps_max\u003d0.333, eps_delta\u003d0.333, yf\u003d14.725, nnz\u003d12\neps_max\u003d0.337, eps_delta\u003d0.337, yf\u003d14.725, nnz\u003d12\neps_max\u003d0.341, eps_delta\u003d0.341, yf\u003d7.570, nnz\u003d13\neps_max\u003d0.341, eps_delta\u003d0.341, yf\u003d7.570, nnz\u003d13\neps_max\u003d0.343, eps_delta\u003d0.343, yf\u003d7.079, nnz\u003d14\neps_max\u003d0.349, eps_delta\u003d0.349, yf\u003d6.905, nnz\u003d14\n",
- "eps_max\u003d0.349, eps_delta\u003d0.349, yf\u003d6.905, nnz\u003d14\neps_max\u003d0.349, eps_delta\u003d0.349, yf\u003d6.905, nnz\u003d14\neps_max\u003d0.349, eps_delta\u003d0.349, yf\u003d6.905, nnz\u003d14\neps_max\u003d0.353, eps_delta\u003d0.353, yf\u003d3.971, nnz\u003d16\neps_max\u003d0.353, eps_delta\u003d0.353, yf\u003d3.971, nnz\u003d16\neps_max\u003d0.353, eps_delta\u003d0.353, yf\u003d3.971, nnz\u003d16\neps_max\u003d0.353, eps_delta\u003d0.353, yf\u003d3.971, nnz\u003d16\neps_max\u003d0.357, eps_delta\u003d0.357, yf\u003d3.470, nnz\u003d18\neps_max\u003d0.357, eps_delta\u003d0.357, yf\u003d3.470, nnz\u003d18\neps_max\u003d0.357, eps_delta\u003d0.357, yf\u003d3.470, nnz\u003d18\neps_max\u003d0.363, eps_delta\u003d0.363, yf\u003d3.470, nnz\u003d18\neps_max\u003d0.365, eps_delta\u003d0.365, yf\u003d2.138, nnz\u003d20\neps_max\u003d0.365, eps_delta\u003d0.365, yf\u003d2.138, nnz\u003d20\neps_max\u003d0.367, eps_delta\u003d0.367, yf\u003d2.138, nnz\u003d20\neps_max\u003d0.369, eps_delta\u003d0.369, yf\u003d2.138, nnz\u003d20\n",
- "eps_max\u003d0.371, eps_delta\u003d0.371, yf\u003d2.138, nnz\u003d20\neps_max\u003d0.373, eps_delta\u003d0.373, yf\u003d1.839, nnz\u003d21\neps_max\u003d0.373, eps_delta\u003d0.373, yf\u003d1.839, nnz\u003d21\neps_max\u003d0.376, eps_delta\u003d0.376, yf\u003d1.374, nnz\u003d22\neps_max\u003d0.380, eps_delta\u003d0.380, yf\u003d1.211, nnz\u003d24\neps_max\u003d0.380, eps_delta\u003d0.380, yf\u003d1.211, nnz\u003d24\neps_max\u003d0.380, eps_delta\u003d0.380, yf\u003d1.211, nnz\u003d24\neps_max\u003d0.382, eps_delta\u003d0.382, yf\u003d-8.789, nnz\u003d25\n\nModel name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 415 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d27.719, nnz\u003d0\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d25.865, nnz\u003d1\n",
- "eps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d25.865, nnz\u003d1\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d-19.397, nnz\u003d21\n\nModel name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 364 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d5.238, nnz\u003d0\neps_max\u003d0.024, eps_delta\u003d0.000, yf\u003d5.238, nnz\u003d0\neps_max\u003d0.024, eps_delta\u003d0.000, yf\u003d5.238, nnz\u003d0\neps_max\u003d0.027, eps_delta\u003d0.000, yf\u003d5.238, nnz\u003d0\neps_max\u003d0.035, eps_delta\u003d0.035, yf\u003d5.050, nnz\u003d1\n",
- "eps_max\u003d0.035, eps_delta\u003d0.035, yf\u003d5.050, nnz\u003d1\neps_max\u003d0.035, eps_delta\u003d0.035, yf\u003d5.050, nnz\u003d1\neps_max\u003d0.051, eps_delta\u003d0.051, yf\u003d4.675, nnz\u003d2\neps_max\u003d0.086, eps_delta\u003d0.086, yf\u003d4.675, nnz\u003d2\neps_max\u003d0.106, eps_delta\u003d0.106, yf\u003d4.675, nnz\u003d2\neps_max\u003d0.106, eps_delta\u003d0.106, yf\u003d4.675, nnz\u003d2\neps_max\u003d0.153, eps_delta\u003d0.153, yf\u003d4.483, nnz\u003d3\neps_max\u003d0.184, eps_delta\u003d0.184, yf\u003d4.149, nnz\u003d3\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d4.149, nnz\u003d3\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d4.149, nnz\u003d3\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d-59.592, nnz\u003d23\n\nModel name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 197 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\n",
- "eps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d4.828, nnz\u003d0\neps_max\u003d0.024, eps_delta\u003d0.000, yf\u003d4.828, nnz\u003d0\neps_max\u003d0.027, eps_delta\u003d0.000, yf\u003d4.828, nnz\u003d0\neps_max\u003d0.051, eps_delta\u003d0.051, yf\u003d4.454, nnz\u003d1\neps_max\u003d0.086, eps_delta\u003d0.051, yf\u003d4.454, nnz\u003d1\neps_max\u003d0.153, eps_delta\u003d0.153, yf\u003d4.280, nnz\u003d2\neps_max\u003d0.184, eps_delta\u003d0.184, yf\u003d3.945, nnz\u003d2\neps_max\u003d0.249, eps_delta\u003d0.249, yf\u003d3.945, nnz\u003d2\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d3.945, nnz\u003d2\neps_max\u003d0.302, eps_delta\u003d0.302, yf\u003d-49.541, nnz\u003d18\n\nModel name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 415 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d29.426, nnz\u003d0\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.975, nnz\u003d1",
- "\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.975, nnz\u003d1\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\n",
- "eps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.002, eps_delta\u003d0.002, yf\u003d28.109, nnz\u003d3\n",
- "eps_max\u003d0.004, eps_delta\u003d0.004, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.004, eps_delta\u003d0.004, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.004, eps_delta\u003d0.004, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.004, eps_delta\u003d0.004, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.004, eps_delta\u003d0.004, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.004, eps_delta\u003d0.004, yf\u003d28.109, nnz\u003d3\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d26.638, nnz\u003d5\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d26.638, nnz\u003d5\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d26.638, nnz\u003d5\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d26.638, nnz\u003d5\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d26.638, nnz\u003d5\n",
- "eps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d26.638, nnz\u003d5\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d26.638, nnz\u003d5\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d26.638, nnz\u003d5\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d26.638, nnz\u003d5\neps_max\u003d0.006, eps_delta\u003d0.006, yf\u003d26.638, nnz\u003d5\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d25.751, nnz\u003d6\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d24.410, nnz\u003d8\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d24.410, nnz\u003d8\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d24.410, nnz\u003d8\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d24.410, nnz\u003d8\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d24.410, nnz\u003d8\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d24.410, nnz\u003d8\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d24.410, nnz\u003d8\n",
- "eps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d24.410, nnz\u003d8\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d24.410, nnz\u003d8\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d24.410, nnz\u003d8\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d24.410, nnz\u003d8\neps_max\u003d0.010, eps_delta\u003d0.010, yf\u003d24.410, nnz\u003d8\neps_max\u003d0.012, eps_delta\u003d0.012, yf\u003d16.892, nnz\u003d10\neps_max\u003d0.012, eps_delta\u003d0.012, yf\u003d16.892, nnz\u003d10\neps_max\u003d0.012, eps_delta\u003d0.012, yf\u003d16.892, nnz\u003d10\neps_max\u003d0.014, eps_delta\u003d0.014, yf\u003d16.892, nnz\u003d10\neps_max\u003d0.014, eps_delta\u003d0.014, yf\u003d16.892, nnz\u003d10\neps_max\u003d0.018, eps_delta\u003d0.018, yf\u003d16.892, nnz\u003d10\neps_max\u003d0.022, eps_delta\u003d0.022, yf\u003d15.391, nnz\u003d11\neps_max\u003d0.022, eps_delta\u003d0.022, yf\u003d15.391, nnz\u003d11\n",
- "eps_max\u003d0.022, eps_delta\u003d0.022, yf\u003d15.391, nnz\u003d11\neps_max\u003d0.022, eps_delta\u003d0.022, yf\u003d15.391, nnz\u003d11\neps_max\u003d0.022, eps_delta\u003d0.022, yf\u003d15.391, nnz\u003d11\neps_max\u003d0.025, eps_delta\u003d0.025, yf\u003d15.391, nnz\u003d11\neps_max\u003d0.025, eps_delta\u003d0.025, yf\u003d14.964, nnz\u003d12\neps_max\u003d0.025, eps_delta\u003d0.025, yf\u003d14.964, nnz\u003d12\neps_max\u003d0.029, eps_delta\u003d0.029, yf\u003d13.805, nnz\u003d13\neps_max\u003d0.029, eps_delta\u003d0.029, yf\u003d13.805, nnz\u003d13\neps_max\u003d0.029, eps_delta\u003d0.029, yf\u003d13.805, nnz\u003d13\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d11.294, nnz\u003d14\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d9.019, nnz\u003d16\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d9.019, nnz\u003d16\n",
- "eps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d9.019, nnz\u003d16\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d9.019, nnz\u003d16\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d9.019, nnz\u003d16\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d9.019, nnz\u003d16\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d9.019, nnz\u003d16\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d9.019, nnz\u003d16\neps_max\u003d0.033, eps_delta\u003d0.033, yf\u003d9.019, nnz\u003d16\neps_max\u003d0.035, eps_delta\u003d0.035, yf\u003d9.019, nnz\u003d16\neps_max\u003d0.037, eps_delta\u003d0.037, yf\u003d8.285, nnz\u003d17\neps_max\u003d0.037, eps_delta\u003d0.037, yf\u003d8.285, nnz\u003d17\neps_max\u003d0.041, eps_delta\u003d0.041, yf\u003d8.285, nnz\u003d17\neps_max\u003d0.045, eps_delta\u003d0.045, yf\u003d7.811, nnz\u003d18\neps_max\u003d0.045, eps_delta\u003d0.045, yf\u003d7.811, nnz\u003d18\n",
- "eps_max\u003d0.045, eps_delta\u003d0.045, yf\u003d7.811, nnz\u003d18\neps_max\u003d0.045, eps_delta\u003d0.045, yf\u003d7.811, nnz\u003d18\neps_max\u003d0.045, eps_delta\u003d0.045, yf\u003d7.811, nnz\u003d18\neps_max\u003d0.049, eps_delta\u003d0.049, yf\u003d7.431, nnz\u003d18\neps_max\u003d0.049, eps_delta\u003d0.049, yf\u003d7.431, nnz\u003d18\neps_max\u003d0.049, eps_delta\u003d0.049, yf\u003d7.431, nnz\u003d18\neps_max\u003d0.053, eps_delta\u003d0.053, yf\u003d7.431, nnz\u003d18\neps_max\u003d0.053, eps_delta\u003d0.053, yf\u003d7.431, nnz\u003d18\neps_max\u003d0.053, eps_delta\u003d0.053, yf\u003d7.431, nnz\u003d18\neps_max\u003d0.057, eps_delta\u003d0.057, yf\u003d7.431, nnz\u003d18\neps_max\u003d0.061, eps_delta\u003d0.061, yf\u003d7.128, nnz\u003d19\neps_max\u003d0.061, eps_delta\u003d0.061, yf\u003d7.128, nnz\u003d19\n",
- "eps_max\u003d0.061, eps_delta\u003d0.061, yf\u003d7.128, nnz\u003d19\neps_max\u003d0.061, eps_delta\u003d0.061, yf\u003d7.128, nnz\u003d19\neps_max\u003d0.063, eps_delta\u003d0.063, yf\u003d-2.872, nnz\u003d20\n\nModel name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\nEnsemble of 364 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d11.469, nnz\u003d0\neps_max\u003d0.018, eps_delta\u003d0.018, yf\u003d10.223, nnz\u003d1\neps_max\u003d0.020, eps_delta\u003d0.020, yf\u003d10.144, nnz\u003d2\neps_max\u003d0.043, eps_delta\u003d0.043, yf\u003d10.144, nnz\u003d2\n",
- "eps_max\u003d0.055, eps_delta\u003d0.055, yf\u003d10.144, nnz\u003d2\neps_max\u003d0.086, eps_delta\u003d0.086, yf\u003d9.964, nnz\u003d3\neps_max\u003d0.086, eps_delta\u003d0.086, yf\u003d9.964, nnz\u003d3\neps_max\u003d0.090, eps_delta\u003d0.090, yf\u003d9.813, nnz\u003d3\neps_max\u003d0.118, eps_delta\u003d0.118, yf\u003d8.666, nnz\u003d4\neps_max\u003d0.133, eps_delta\u003d0.133, yf\u003d3.188, nnz\u003d4\neps_max\u003d0.133, eps_delta\u003d0.133, yf\u003d3.188, nnz\u003d4\neps_max\u003d0.133, eps_delta\u003d0.133, yf\u003d3.188, nnz\u003d4\neps_max\u003d0.161, eps_delta\u003d0.161, yf\u003d3.188, nnz\u003d4\neps_max\u003d0.165, eps_delta\u003d0.165, yf\u003d3.188, nnz\u003d4\neps_max\u003d0.169, eps_delta\u003d0.169, yf\u003d2.619, nnz\u003d5\neps_max\u003d0.224, eps_delta\u003d0.224, yf\u003d2.619, nnz\u003d5\neps_max\u003d0.224, eps_delta\u003d0.224, yf\u003d2.619, nnz\u003d5\n",
- "eps_max\u003d0.224, eps_delta\u003d0.224, yf\u003d2.619, nnz\u003d5\neps_max\u003d0.227, eps_delta\u003d0.227, yf\u003d2.552, nnz\u003d6\neps_max\u003d0.259, eps_delta\u003d0.259, yf\u003d2.417, nnz\u003d7\neps_max\u003d0.259, eps_delta\u003d0.259, yf\u003d2.417, nnz\u003d7\neps_max\u003d0.259, eps_delta\u003d0.259, yf\u003d2.417, nnz\u003d7\neps_max\u003d0.259, eps_delta\u003d0.259, yf\u003d2.417, nnz\u003d7\neps_max\u003d0.267, eps_delta\u003d0.267, yf\u003d2.110, nnz\u003d9\n",
- "eps_max\u003d0.267, eps_delta\u003d0.267, yf\u003d2.110, nnz\u003d9\neps_max\u003d0.271, eps_delta\u003d0.271, yf\u003d2.065, nnz\u003d10\neps_max\u003d0.278, eps_delta\u003d0.278, yf\u003d2.065, nnz\u003d10\neps_max\u003d0.278, eps_delta\u003d0.278, yf\u003d2.065, nnz\u003d10\neps_max\u003d0.282, eps_delta\u003d0.282, yf\u003d2.015, nnz\u003d11\neps_max\u003d0.294, eps_delta\u003d0.294, yf\u003d2.015, nnz\u003d11\neps_max\u003d0.298, eps_delta\u003d0.298, yf\u003d1.760, nnz\u003d11\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d-8.240, nnz\u003d12\n\nModel name: 2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\n",
- "Ensemble of 197 learners restored: exps/2019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0.model\neps_max\u003d0.000, eps_delta\u003d0.000, yf\u003d7.565, nnz\u003d0\neps_max\u003d0.018, eps_delta\u003d0.018, yf\u003d6.319, nnz\u003d1\neps_max\u003d0.086, eps_delta\u003d0.086, yf\u003d6.203, nnz\u003d2\neps_max\u003d0.090, eps_delta\u003d0.090, yf\u003d5.993, nnz\u003d2\neps_max\u003d0.133, eps_delta\u003d0.133, yf\u003d3.275, nnz\u003d3\neps_max\u003d0.161, eps_delta\u003d0.161, yf\u003d3.275, nnz\u003d3\neps_max\u003d0.169, eps_delta\u003d0.169, yf\u003d2.787, nnz\u003d4\neps_max\u003d0.243, eps_delta\u003d0.243, yf\u003d2.684, nnz\u003d5\neps_max\u003d0.259, eps_delta\u003d0.259, yf\u003d2.602, nnz\u003d6\neps_max\u003d0.259, eps_delta\u003d0.259, yf\u003d2.602, nnz\u003d6\neps_max\u003d0.267, eps_delta\u003d0.267, yf\u003d2.398, nnz\u003d7\neps_max\u003d0.278, eps_delta\u003d0.278, yf\u003d2.398, nnz\u003d7\neps_max\u003d0.294, eps_delta\u003d0.294, yf\u003d2.398, nnz\u003d7\n",
- "eps_max\u003d0.298, eps_delta\u003d0.298, yf\u003d2.144, nnz\u003d8\neps_max\u003d0.300, eps_delta\u003d0.300, yf\u003d-7.856, nnz\u003d9\n\n"
- ],
- "output_type": "stream"
- },
- {
- "data": {
- "text/plain": "\u003cFigure size 1684.8x2808 with 15 Axes\u003e",
- "image/png": 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69GVur6+vz9ChQ9O/f/8ccsghWbBgQeNtzf0ut7hJkyblrLPOyt57753tttsugwYNygEHHJARI0Y0/r60Zs4svt6dNm1afvSjH+Xzn/98BgwYkIMOOmiZ+7cku5uzXm+P7K5Gblf6uB01t5defqWvsdzunDpqbrdkbq2d3Q7zb4Hu3btn7bXXzptvvpnZs2cXjbn22mtz/fXXJ0lqamrSq1evzJo1K5MmTcqkSZPyP//zP7nhhhtW+CIvz2uvvZbTTz89U6ZMyeqrr54uXbrk9ddfz6233ppHHnkkd955Z0VbT95///0MHTq0ceXfpUuX9O7dOzNnzsybb76ZiRMnZubMmRk4cGB69OiRddddN2+//XYWLFiQXr16LXHag7XXXrvin+fDTJ48OSeffHKmT5+eXr165f33389LL72UkSNH5tVXX80PfvCDjBo1Ktdee226deuWHj16ZM6cORk7dmyefPLJ3Hrrrdlkk02aXPYrr7yS733ve5kxY0Z69uyZLl26ZMqUKbn++uvzwAMP5Oabb17mxMSVPF8Nxo8fn8MOOyxJ8stf/jI777xzqz9PH2bDDTfMueeem1NPPTXdu3fPjTfemJEjR6ZPnz7Zd999c/zxx6/Uh6fNnj278cPh8g5R2H777dO7d+/MmjUrjz76aDbddNM2X3Zbziv54D25+AcraI6GQ2YWt3Dhwpx++um56667kiTdunXLGmuskWnTpuXuu+/OPffckx/84Af5+te/vtzlNmc93FyVrr9bK/PkrJyVs4u0JM8+TMMX9Q033LBd5yVnO64TTjghf/nLX/Lcc8/ljDPOyLXXXrvE7WPGjMlDDz2UHj165KKLLlrinH0t/S530003ZeTIkY3Z2bD+azgH48SJE3PTTTe1yTr/X//6V7773e/mrbfeyuqrr77MuQhbmt3tmdtJdTK0Wp8XSrVlbieVv8bNye2k+tkttztmbrfH3CrJbmVqC8ybNy8zZsxIkuILQ33sYx/LSSedlM9+9rPZbLPNUlNTk7q6uvzzn//MpZdemoceeijDhg3Lb3/723Tp0qWi+YwYMSIbbLBBLr744gwcODDvv/9+xo4dm9NOOy1TpkzJ1Vdfne9///vFy7v77rvz2GOPZfXVV8/ZZ5+dffbZJ6uttlrq6uoybdq0/PGPf2wskffbb7/st99++eY3v5nHHnssZ555Zg455JCK5l+pkSNH5pOf/GSuu+66bLHFFnn33Xdzww035Kc//WnGjBmTvn375sYbb8wZZ5yRr3zlK+nZs2eef/75nHjiifm///u/XHrppfnpT3/a5LIvuOCCrLPOOrn88sszaNCgLFy4MA8++GCGDx+el19+OaeddlrjB6nmPF8dyVNPPZUrrrgiP/jBD3LooYcmWbQV6LLLLsuvfvWr3H///bn55ptbdQXZkbz00kuNWxk/+clPNnmfrl275hOf+ET+8Y9/rHAvvNZcdlvO6/33388TTzyRJI2HRUAlnnjiicydOzfJoq33S7vuuuty1113pUuXLjnxxBNz2GGHpVevXpk2bVrOO++83Hvvvfnxj3+czTffPDvuuGOTj9Gc9XBzVbr+bq3Mk7NyVs4u0tw8W545c+bk1Vdfza9//evcc889SZJvfOMb7TYvOdux1dbWZtSoUTnkkEPy5z//OWPGjGn8/XjppZdy0UUXJUmGDRuWzTbbbImxLfku9/vf/z4jRoxIkuy999757ne/27j8mTNn5s9//nOee+65JG2zzj///POz4YYb5sorr8wOO+yQJHn55Zcbb29pdrdnbifVydBqfV4o1Za5nVT+Gsvtzqmj5nZbz63S7FamtsBtt93W+EI27A78YRq2sCyuW7duGTBgQK666qocfPDBeeGFF/L4449np512qmg+3bt3zw033NDYptfU1OTzn/98vv3tb+eCCy7IfffdV1GZ+ve//z1JctBBBy1xCEi3bt3Sr1+/Jn+521PXrl1zzTXXZM0110yy6Fy2xx13XMaPH59x48bl0ksvzQknnJDDDz+8ccynPvWpjBgxIt/4xjfy4IMPZqET1PYAACAASURBVP78+enevfsyy54/f36uvfbafPzjH298rL322iu9evXK4Ycfnocffjh//etfM2jQoMYxHf35aspTTz2VI444IkcddVRjUCTJeuutlxEjRuTNN9/Mgw8+mHPPPTejR4+u4kyXdPnll+dnP/tZs8Z+5zvfyQknnND478UPH1jR1vKG25o6HGx5WrLstpzXmDFjMn369HTt2jUHH3xw8ThYsGBBxo0blx/+8IdJFn0h3W+//Za4z5w5c3L11VcnSY4++ugcd9xxjbetv/76ueSSSzJ9+vT87W9/y09+8pOMGTOmycdqznq4uaq1/u7ouSFnW07Otm2eLe61117L4MGDl/n/1VZbLccee+wyvx9yduXx5JNPZvfdd1/hfe6777706tWr8d+bbbZZhg0blhEjRuSiiy7Krrvumo033jinnHJK5s2blz322GOJ92yD5n6XW7BgQUaOHJkk2X///XPxxRcvsYw+ffrkwAMPbDxXYFuoqanJDTfcsMT5HxvWwa2R3e2Z20l1MqGj51Bb5nZS+Wvc0Z+vpsjtjpvbbT23SrPbOVMr1HD1ttGjRzdutdxggw3yuc99rsXL7t69e3bbbbckaWzEK/Gf//mfTR4qsNdeeyVZtNt/w55EJRo+cDT3zdHWvvrVrzYGxeIansPa2toceeSRy9y+ww47ZLXVVsv8+fPzyiuvNLnsfffdtzEkFrfLLrs0Hoaw9EVXmvN87bzzzpk4cWImTpzY7ocvvPPOOznxxBOz1lpr5eijj27yPl/+8peTJOPGjcu8efPac3or1LNnz6y77rrN+tNwnscG7777buPfFz/sZmkNt1XyHmrJsttqXhMmTGg8Gfc3vvGN5W7Rg+SDL6i77757dtttt2y77bY56qijMmXKlHTt2jVnn312PvrRjy4x5pFHHsns2bNTW1ubo446apllduvWrfFL2l//+tflrjObsx5urmrlnZyVs3I2y9xWSc4urmvXro1zaDh8uaamJsccc0yTX8jk7MpjwYIFeeONN1b4p6lDNg899NDsscceeffdd3PKKafksssuyzPPPJM+ffrkvPPOq/gowRV9l3v00Uczbdq0dOvWraKdW1rTQQcdtESRurjWyO72zO2kOhm6Kud2Uvlr3Nznq1rZLbcX6ai53ZZza0522zO1wGOPPdbkiZ6TpG/fvrniiiuWu/WmKZMmTcqYMWPy+OOPZ8qUKZk7d+4yJzNuzgWjGk7Uu7TFT8o7a9asZd4sy7Pnnnvm2muvzQMPPJBjjz02hxxySHbcccc2OS9bc3zqU59q8v/XWWedJItK7sWvDtega9euWXvttfPaa6/l7bffbnIZK9oreKeddsqTTz7ZeBhOg47+fC1t9OjRee211/Ltb397ub+/Dc/l+++/n3feeWeFK6z2NHTo0MYTS1Pu9ddfz/HHH5958+Zl6623zimnnFLtKdHBNXxBXVqfPn1y3XXXNZk7Decx2mKLLZZ7nu4dd9wx3bp1S11dXZ577rkmt0o3Zz3cXNVaf3f03JCzLSNn29d6662Xhx9+OMmic529/PLLufbaa3P55Zfn9ttvzzXXXNPmh9zL2erYaaedctNNN1U8rkuXLhk5cmQOOOCAPPPMM40XHPnhD3/Y5EVNGjTnu9xTTz2VZFE2rmjZbWnp81IurjWyuz1zO6lOJnT0HGrL3E4qf407+vO1NLndvjpCbifNz257phaora1tbMz79u2bjTfeOLvvvntOOeWU/O53v8uWW25ZvKzf/e53OeiggzJmzJg8//zzeffdd9O7d+9ltgos3riXamrFmCzaTbrB4lej/DA77bRTTjzxxNTU1OSPf/xjTjjhhOyyyy7Zd999c8EFF+Rf//pXxXNsTcs76XO3bt2SrHi374b7vP/++03evqIPOQ23NZwvt0FHf76WdscddyRJdt111+Xe56233kqy6MNmJRcv60xWX331xr+vaOtiw22lGyNauuzWntfMmTMzdOjQTJ48OZtsskmuueaaJdYN0JSddtqpcc+Ap59+Or/5zW+y9957Z+bMmTnzzDOb/MDdsG5c0Xp0tdVWa/wgvfS6tEFz1sPNVa31d0fPDTnbMnJ2kbbM2eVpOF/aeeedlyOPPDJTp07N97///SX2TpSzJIvWYyeddFLjv/fZZ59lTl+zuOZ+l2vYMPmxj32sDX6KMg0lUFNaI7vbM7eT6mRCR8+htsztpPLXuKM/X0uT24t01Nxui7m1JLuVqQUGDhyYhx9+OA8//HAeeuih/O///m+uv/76HHXUURW9gWbMmJHhw4dnwYIF2W+//XL77bfnH//4Rx5//PHG5Tecv2TprZvVcvzxx+fee+/NySefnD322CO9evXKSy+9lOuvvz5f+MIXGq/2yCKd5fl69dVXG7eYL28LZvLBlfQ22GCDlfYLweIfKla0R3jDbZVcubEly27Nec2aNStDhw7N888/n379+i1zviwo0b1792yxxRb56U9/mj322CMTJ07MWWedtdz7v/fee+04u5ar1vq7s+RGR9FZni85+4G2zNkSDee8e+6555bYY0rOkiR1dXW58847G/89YcKE5R4W2hm/yy2ua9cP/+ovuzvmY3ZmneX5ktsf6Ki53dpza2l2O8y/Hf35z3/O3Llz88lPfjIXX3xxk4H25ptvVmFmK7bRRhvlmGOOyTHHHJO6urr89a9/zeWXX57HH388Z599dj7zmc/kIx/5SEXLbDhEZHmBXe2r+pW8MZe3dbctnq/WNnny5Ma/r2iDwAMPPJDkg/PsdBSjR49u9tVAv/Wtby1xCMSmm26aLl26pL6+Pi+++GKTV2VcuHBh/u///i9Jlrmq64q0ZNmtNa+5c+fm6KOPzjPPPJO+ffvmhhtuSL9+/Yp/Blhaly5dMnz48HzhC1/Ivffem8cee2yJw74a1o3//ve/l7uM9957LzNnzlzi/ktrznq4pdlSrfW3nG36NjlbPZ0lZ0ssvifVK6+8kgEDBrTqvORs53bNNdfkySefTO/evdOrV6/861//yvnnn59zzjlnmfu25Ltcwxf0qVOntu4P0EpaI7ubu17vjNm9KuZ20vzXWG63vVUht1tzbq2R3fZMbUevvfZakqR///5Nhm99fX3GjRvX3tOqSLdu3bLzzjvn5z//eWprazN37tzG8wslaTxR+4dtje3du3eSZNq0aU3e/vTTT7fSjJvn8ccfX+5tjz32WJJkq622+tDlfNjzVS11dXWNf2/4ULS0Bx98MJMmTUqSfOlLX2qzuTT1weDDTkcxd+7cD73QwPL+LL23Qa9evRpX0A3nbFnaU089lVmzZiVZ8WEfS2vJsltjXvPmzcuxxx6bJ598Mn369MkNN9yQTTbZpHj+sDyf+MQnsu+++yZJLr300iVu23rrrZMkL7/88nLX8Y8//njjYWTLW5c2Zz3cmtlSsv4uzbxKyFk529pW5pwtsfgX5MVPiSVnefbZZ3PFFVckSYYPH57zzz8/Xbp0ya9//euMHTt2mfu35LvcdtttlySZOHHictfLTWmLnGlKa2R3c9fr7Znd1cjt0sft6LmdtE52y+0ylWb3qpDbrTW31spuZWo7alhBvvDCC02uSG+55ZYVXj2vvc2fP3+5t3Xv3r3xQ8Ti92u4Yl/DL+/yNOw637BlZ+nH/cUvflHxfFvTPffck1dffXWZ/3/88ccbr865zz77LHFbc56vatlwww0b/97UazBjxoz86Ec/SpJ87nOfy/bbb9/qc3jqqacyZMiQfPrTn86hhx6aGTNm5JVXXslBBx2UbbbZJgcffHBeeumlJseecMIJjedxrPTPCSecsMzy9t9//yTJb3/72ya3uDZs5dt6662b3Pq1Ii1ZdkvGzp8/P9/5zncyfvz4rLnmmrn++uvb5QTerDoatmA/8cQTGT9+fOP/77777unVq1cWLFiQ6667bplxdXV1ufLKK5MkgwYNWu7hN81ZDzc3W5q7/i7NvOWRs3I2kbNJ83O2rq7uQ0uR0aNHJ1l0heCln2c5u+qaN29eTjnllCxYsCB77713vvjFL2aXXXbJEUcckSQ588wzlzm/Z0u+y+26665Zf/31U1dXlwsvvLB4ni3NmVKtkd3NWa8n7Zvd1cjt0sft6LmdVP4ay+3KNTe7V5XcbuncWjO7lantaNddd02XLl3y/PPPZ8SIEXnnnXeSLNrqcN111+Wcc85Jnz59qjzLD5x66qk5/fTT85e//GWJLSOTJ0/Oqaeemvfeey89evTIpz/96cbbGn4R77///hWGRcMeTbfccktuv/32xhXoCy+8kKOPPnqFhxC0h9ra2hx11FGNobBw4cI8+OCDOfHEE5Ms+sCx+M+dNO/5Gj9+fPr375/+/fsvUUa0tU022SRbbLFFkmTUqFF56KGHkizaWvrII4/ka1/7WqZNm5YNNtigycOcWqq+vj5nnnlmzj777IwfPz59+/bNsGHDctJJJ2W33XbLww8/nG9961s544wzWv2xm/LVr341G2ywQebMmZNjjz02L774YpJF780LL7ww999/f5IscYGCBnfccUfja7j4VrTWWHZzx9bV1WXYsGH5y1/+kjXWWCPXXntt4x4H0Fq22mqrxkOcrrrqqsb/79mzZ/7f//t/SZKbbropV111VebMmZNk0d4WJ510Uv72t7+la9eu+d73vrfc5TdnPdzcbGnO+jspz7zlkbNyVs62LGf//e9/50tf+lJuu+22xr0Gk0W/T//85z9z8skn59Zbb02y6BxsSx+6KWdXXaNGjcqkSZPSt2/fJd6DJ510UjbffPNMnz49P/zhD5cY05LvcrW1tTnttNOSJHfffXe++93vNu7hlizaE+6WW27JiBEjlhjX0pwp1RrZ3Zz1etK+2V2N3C593I6e20nlr3Fzn69qZHe1c7vhsTpKdrckt5PlZ3dLc7slc2vt7HbO1Ha06aab5vDDD8+NN96Ym2++OTfffHPWXHPNzJ49OwsXLswee+yRAQMG5Oc//3m1p5pk0Xlx7rnnntxxxx3p0qVLevfunQULFjRenbJbt245++yzlzgvyoEHHpjRo0fnb3/7W3bZZZess846qa2tzfrrr59f/epXjff7yle+krvuuitPPfVUzjjjjJx11lnp0aNHZs+enT59+uS8887L8ccf3+4/c4NTTz01l1xySb72ta+lZ8+eWbhwYeMV4T7+8Y/n/PPPX2ZMc56vavrxj3+cww47rPEKdkv/nFtuuWUuv/zyFV71sbmmTJmSfv365TOf+UyS5IILLsj++++fjTbaKKeeemqS5IADDshNN92UOXPmLLN7f2vr0aNHrrzyyhx++OF59tln84UvfCG9evXK3Llzs3DhwnTp0iUnnXRS9thjj3ZddnPHPvHEE7nvvvuSLLoi54reSx/96Edz++23V/xzQZIcddRReeSRR/Loo4/m73//e+PW46FDh2bSpEm566678pOf/CSXX355evXqlXfeeSf19fXp2rVrhg8fnh133HG5y27Oeri52dLc9Xdp5i2PnJWzcrblOfvss8/mzDPPTLLoauM9e/bMnDlzltjT6ZBDDskpp5zSavOSsx3Lk08+md13332F99l3330zfPjwJMlDDz2Um2++OUly3nnnLVGAdu/ePRdddFG+8pWv5P77788dd9yRQw45JEnLv8vtt99+mTZtWi688MLce++9uffee9OzZ8/U1NQ0FrOLn4M8aXnOVKKl2d2c9XrSvtldjdwufdyOnttJ5a+x3K5MR8rujprbLZlba2e3MrWdnX766dlss83yq1/9Ki+++GLq6uqy5ZZb5qCDDsqhhx7aePhER3DyySdnhx12yLhx4/Lyyy9n+vTpqaury8Ybb5xBgwbl8MMPb9x602CzzTbLDTfckKuvvjpPP/103njjjSxcuHCZZdfW1ub666/PlVdemXvvvTevv/56Vl999fzHf/xH1UMiSTbeeOPcfvvt+dnPfpaHHnooM2bMyAYbbJC99947xx13XONhPotrzvNVTdtuu23uuOOOXHnllRk3blxmzpyZNdZYIwMHDsz++++fL37xi6mpaZtVRH19feO5g5JFH1w/8pGP5O23387ChQuXOA9Ve10NdYsttsjdd9+dq6++On/6058ybdq09OnTJ9tuu22OOOKIFp0LpiXLbs7Yxd9z77333gqvzLqyXoWS9rH77rtnq622ynPPPZcrr7wy11xzTZJFH44vuOCCDBkyJLfcckueeeaZzJkzJ3379s1OO+2UI488comTyTelOevh5mZLc9ffpZnXmo8rZ+VsiVUlZ9dbb71ceumlGTduXP7xj39k+vTpmTlzZrp3756NN94422+/fQ455JAm94ZrybzkbMeyYMGCvPHGGyu8T8NecW+//XZOP/301NfX5+tf/3r23HPPZe675ZZb5oQTTsgll1ySc889NzvttFPjIcAt/S535JFHZtddd80vfvGLjB8/PtOnT09tbW369++fnXfeOQcffPAS929pzlSipdndnPV60r7ZXY3cLn3cjp7bSeWvsdyuTEfL7o6a282dW2tnd5f69voEBZ3AkCFDMmXKlPzyl7/MzjvvXO3pFOnfv3+SRSe0b87t1XjshQsX5oADDsipp56a7bbbLldffXWeeeaZzJ49O9tss02GDRuWRx55JKNHj84tt9zS6vMGoDrkbPs8tpwFoLV0tuzujLmdyO7Oxp6pQLvr2rVrRo4cmWHDhuXll1/OgAEDcuWVVzaeEHrQoEHZZJNNctlll1V7qgDQ6chZAOhcZHfnokwFqmLbbbfN/fffn3nz5qVHjx6N//+b3/wms2fPbrzqJQBQOTkLAJ2L7O48un74XQDazuIh0UBIAEDrkLMA0LnI7o5PmQoAAAAAUKBTXoCqW22/ak8BOoxPbrroqokvvjSrWbd31semc6lbMLXaU1hlyUxoGTlLe5OZ1SMzofOT26uWamWmMhWANueLYfXITIDORWZWj8wE6FyqlZkO8wcAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKKBMBQAAAAAooEwFAAAAACigTAUAAAAAKFBT7QkAAAC0p2H9Blc8ZtTUsW0wEwCgs7FnKgAAAABAAWUqAAAAAEABZSoAAAAAQAFlKgAAAABAAWUqAAAAAEABZSoAAAAAQAFlKgAAAABAAWUqAAAAAEABZSoAAAAAQAFlKgAAAABAAWUqAAAAAEABZSoAAAAAQIGaak8AaH/D+g2ueMyIv46oeMyd2/6g4jGHzvhLxWPqFtZVPAYASnTkzPxb9/qKx4yaOrbiMSsjzwNA65OZrCrsmQoAAAAAUECZCgAAAABQQJkKAAAAAFBAmQoAAAAAUECZCgAAAABQQJkKAAAAAFBAmQoAAAAAUECZCgAAAABQQJkKAAAAAFBAmQoAAAAAUECZCgAAAABQQJkKAAAAAFCgS319fX21J1GpbrX9qj0F6DD69f5IxWOe/80pFY+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- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": "exp_folder \u003d \u0027exps\u0027\nmodel_names \u003d {\n \u0027mnist_1_5\u0027: \n [\u00272019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\u0027,\n \u00272019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\u0027,\n \u00272019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\u0027\n ],\n \u0027mnist_2_6\u0027: \n [\u00272019-05-25 11:21:22 dataset\u003dmnist_2_6 weak_learner\u003dstump model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 max_depth\u003d4 lr\u003d1.0\u0027,\n \u00272019-05-25 11:21:22 dataset\u003dmnist_2_6 weak_learner\u003dstump model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 max_depth\u003d4 lr\u003d1.0\u0027,\n \u00272019-05-25 11:21:22 dataset\u003dmnist_2_6 weak_learner\u003dstump model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 max_depth\u003d4 lr\u003d1.0\u0027\n ],\n \u0027fmnist_sandal_sneaker\u0027: \n [\u00272019-05-21 15:41:28 dataset\u003dfmnist_sandal_sneaker model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.100 lr\u003d1.0\u0027,\n \u00272019-05-21 15:41:29 dataset\u003dfmnist_sandal_sneaker model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.100 lr\u003d1.0\u0027,\n \u00272019-05-21 15:41:28 dataset\u003dfmnist_sandal_sneaker model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.100 lr\u003d1.0\u0027,\n ],\n \u0027gts_100_roadworks\u0027:\n [\u00272019-05-21 18:47:22 dataset\u003dgts_120_warning model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.031 lr\u003d1.0\u0027,\n \u00272019-05-21 18:47:22 dataset\u003dgts_120_warning model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.031 lr\u003d1.0\u0027,\n \u00272019-05-21 18:47:22 dataset\u003dgts_120_warning model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.031 lr\u003d1.0\u0027\n ],\n \u0027gts_30_70\u0027: \n [\u00272019-05-21 18:47:22 dataset\u003dgts_30_70 model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.031 lr\u003d1.0\u0027,\n \u00272019-05-21 18:47:22 dataset\u003dgts_30_70 model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.031 lr\u003d1.0\u0027,\n \u00272019-05-21 18:47:22 dataset\u003dgts_30_70 model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.031 lr\u003d1.0\u0027]\n}[dataset]\n\nidx_examples \u003d np.arange(5)\n\nplot_height \u003d 6\nfig_width \u003d 1.3*len(model_names)*plot_height\nfig_height \u003d 1.3*len(idx_examples)*plot_height\nfig, axs \u003d plt.subplots(len(idx_examples), len(model_names), figsize\u003d(fig_width, fig_height))\n\nfor i_ex in idx_examples:\n for i, model_name in enumerate(model_names):\n print(\u0027Model name: {}\u0027.format(model_name))\n model \u003d model_name.split(\u0027model\u003d\u0027)[1].split(\u0027 \u0027)[0]\n # weak_learner \u003d model_name.split(\u0027weak_learner\u003d\u0027)[1].split(\u0027 \u0027)[0] # doesn\u0027t exist for stumps\n weak_learner \u003d \u0027stump\u0027\n eps \u003d model_name.split(\u0027eps\u003d\u0027)[1].split(\u0027 \u0027)[0]\n \n model_path \u003d model_name + \u0027.model\u0027\n metrics_path \u003d model_name + \u0027.metrics\u0027\n metrics \u003d np.loadtxt(exp_folder + \u0027/\u0027 + metrics_path)\n valid_errs, valid_adv_errs \u003d metrics[:, 8], metrics[:, 10]\n # Model selection\n best_iter \u003d np.argmin(valid_errs) if model \u003d\u003d \u0027plain\u0027 else np.argmin(valid_adv_errs)\n \n if model \u003d\u003d \u0027robust_bound\u0027:\n model \u003d \u0027robust\u0027\n elif model \u003d\u003d \u0027robust_exact\u0027:\n model \u003d \u0027exact robust\u0027\n \n ensemble \u003d StumpEnsemble(\u0027stump\u0027, 10, 1.0) # the hps here do not matter (they matter only for training)\n ensemble.load(\u0027exps/{}\u0027.format(model_path), iteration\u003dbest_iter)\n \n delta \u003d ensemble.exact_adv_example(X_test[None, i_ex], y_test[None, i_ex])\n \n dataset_ \u003d dataset.upper().replace(\u0027_\u0027, \u0027 \u0027).replace(\u0027120 WARNING\u0027, \u0027120-warn\u0027).replace(\u00272 6\u0027, \u00272-6\u0027)\n plot_name_short \u003d \u0027{} stumps: $||\\delta||_\\infty$\u003d{:.3f}\u0027.format(model.capitalize(), np.abs(delta).max())\n ax \u003d axs[i_ex - min(idx_examples)][i]\n ax.imshow((X_test[i_ex] + delta).reshape(img_shape))\n ax.axis(\u0027off\u0027)\n ax.set_title(plot_name_short)\n \nplot_name_save \u003d \u0027exact_adv-dataset\u003d{}-weak_learner\u003dstump\u0027.format(dataset)\n# fig.tight_layout()\n# fig.subplots_adjust(wspace\u003d0.25)\nplt.savefig(\u0027plots/{}.pdf\u0027.format(plot_name_save), bbox_inches\u003d\u0027tight\u0027)\n\n",
- "metadata": {
- "pycharm": {
- "metadata": false,
- "name": "#%%\n",
- "is_executing": false
- }
- }
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": "\n",
- "metadata": {
- "pycharm": {
- "metadata": false,
- "name": "#%%\n"
- }
- }
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "kernelspec": {
- "name": "python3",
- "language": "python",
- "display_name": "Python 3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.5.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
\ No newline at end of file
diff --git a/notebooks/main_metrics.ipynb b/notebooks/main_metrics.ipynb
new file mode 100644
index 0000000..247e879
--- /dev/null
+++ b/notebooks/main_metrics.ipynb
@@ -0,0 +1,78 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "pycharm": {}
+ },
+ "source": "# Plots for experiments"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true,
+ "pycharm": {
+ "is_executing": false
+ }
+ },
+ "outputs": [],
+ "source": "%load_ext autoreload\n%autoreload 2\n\nimport os\nos.chdir(\"../\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport data\nimport utils\n\n%matplotlib inline\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "outputs": [
+ {
+ "name": "stdout",
+ "text": [
+ "Model (depth\u003d4): 2019-08-11 14:28:04 dataset\u003dbreast_cancer weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 max_depth\u003d4 lr\u003d0.01\niter: 46/150 [test] err 0.73% adv_err_lb 6.57% adv_err 6.57% adv_err_ub 6.57% | [valid] err 1.83% adv_err_lb 8.26% adv_err 8.26% | [train] err: 7.32% adv_err: 13.73% loss: 0.77129 (cert 2.356s, total 26.63 min)\nModel (depth\u003d4): 2019-08-11 14:28:04 dataset\u003ddiabetes weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d8 eps\u003d0.050 max_depth\u003d4 lr\u003d0.2\niter: 9/150 [test] err 27.27% adv_err_lb 35.71% adv_err 35.71% adv_err_ub 35.71% | [valid] err 22.13% adv_err_lb 30.33% adv_err 30.33% | [train] err: 21.75% adv_err: 28.46% loss: 0.87517 (cert 3.165s, total 26.82 min)\nModel (depth\u003d4): 2019-08-11 14:28:08 dataset\u003dcod_rna weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d8 eps\u003d0.025 max_depth\u003d4 lr\u003d0.2\niter: 36/150 [test] err 6.91% adv_err_lb 21.26% adv_err 21.37% adv_err_ub 21.37% | [valid] err 7.95% adv_err_lb 21.56% adv_err 21.59% | [train] err: 7.74% adv_err: 21.06% loss: 0.70865 (cert 5.511s, total 258.96 min)\n\nModel (depth\u003d4): 2019-08-11 14:28:05 dataset\u003dmnist_1_5 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\niter: 126/150 [test] err 0.25% adv_err_lb 1.33% adv_err 1.43% adv_err_ub 1.43% | [valid] err 0.37% adv_err_lb 1.56% adv_err 1.60% | [train] err: 0.10% adv_err: 0.70% loss: 0.03662 (cert 0.443s, total 277.92 min)\nModel (depth\u003d4): 2019-08-11 14:28:05 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\niter: 88/150 [test] err 0.70% adv_err_lb 3.77% adv_err 4.07% adv_err_ub 4.07% | [valid] err 0.76% adv_err_lb 3.62% adv_err 4.13% | [train] err: 0.54% adv_err: 2.36% loss: 0.15701 (cert 0.284s, total 263.83 min)\nModel (depth\u003d4): 2019-08-11 14:28:05 dataset\u003dfmnist_sandal_sneaker weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.100 max_depth\u003d4 lr\u003d0.2\niter: 128/150 [test] err 3.65% adv_err_lb 7.70% adv_err 8.10% adv_err_ub 8.10% | [valid] err 4.00% adv_err_lb 8.63% adv_err 9.00% | [train] err: 2.46% adv_err: 6.32% loss: 0.32549 (cert 0.303s, total 350.61 min)\n\nModel (depth\u003d4): 2019-08-11 14:28:08 dataset\u003dgts_100_roadworks weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\niter: 105/150 [test] err 2.58% adv_err_lb 4.73% adv_err 4.73% adv_err_ub 4.73% | [valid] err 3.74% adv_err_lb 7.31% adv_err 7.31% | [train] err: 2.93% adv_err: 5.65% loss: 0.41661 (cert 0.253s, total 415.60 min)\nModel (depth\u003d4): 2019-08-11 14:28:08 dataset\u003dgts_30_70 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\niter: 148/150 [test] err 13.84% adv_err_lb 20.94% adv_err 21.38% adv_err_ub 21.38% | [valid] err 7.26% adv_err_lb 14.64% adv_err 14.88% | [train] err: 5.42% adv_err: 9.88% loss: 0.56638 (cert 0.261s, total 528.54 min)\n\nLatex table:\n0.7 \u0026 6.6 \\\\\n27.3 \u0026 35.7 \\\\\n6.9 \u0026 21.3 \\\\\n0.2 \u0026 1.3 \\\\\n0.7 \u0026 3.8 \\\\\n3.6 \u0026 7.7 \\\\\n2.6 \u0026 4.7 \\\\\n13.8 \u0026 20.9 \\\\\n\n"
+ ],
+ "output_type": "stream"
+ }
+ ],
+ "source": "sns.set(font_scale\u003d2.0)\nnp.random.seed(1)\nnp.set_printoptions(precision\u003d6, suppress\u003dTrue)\nplot_height, legend_size \u003d 10, 18\nmarker_size, line_width \u003d 4.0, 1.5\neps_format_dict \u003d {\u00270.300\u0027: \u00270.3\u0027,\n \u00270.050\u0027: \u00270.05\u0027,\n \u00270.025\u0027: \u00270.025\u0027,\n \u00270.100\u0027: \u00270.1\u0027,\n \u00270.031\u0027: \u00278/255\u0027}\n\ndatasets \u003d [\u0027breast_cancer\u0027, \u0027diabetes\u0027, \u0027cod_rna\u0027, \u0027mnist_1_5\u0027, \u0027mnist_2_6\u0027, \u0027fmnist_sandal_sneaker\u0027, \u0027gts_100_roadworks\u0027, \u0027gts_30_70\u0027, \u0027har\u0027, \u0027ijcnn1\u0027]\nmodels \u003d [\u0027plain\u0027, \u0027at_cube\u0027, \u0027robust_bound\u0027, \u0027robust_exact\u0027]\nexp_folder \u003d \u0027exps_diff_depth\u0027 # exps_diff_depth\nweak_learner \u003d \u0027tree\u0027\ntree_depth \u003d \u00274\u0027\nmodel_names \u003d utils.get_model_names(datasets, models, exp_folder, weak_learner, tree_depth)\n\nflag_plot \u003d False\nflag_latex \u003d True\nflag_n_trees_latex \u003d True if weak_learner \u003d\u003d \u0027tree\u0027 else False\nflag_pruning_stats \u003d False \n \nlatex_table, latex_str \u003d \u0027\u0027, \u0027\u0027\nfor i, model_name in enumerate(model_names):\n dataset \u003d model_name.split(\u0027dataset\u003d\u0027)[1].split(\u0027 \u0027)[0]\n model \u003d model_name.split(\u0027model\u003d\u0027)[1].split(\u0027 \u0027)[0]\n eps \u003d model_name.split(\u0027eps\u003d\u0027)[1].split(\u0027 \u0027)[0]\n max_depth \u003d model_name.split(\u0027max_depth\u003d\u0027)[1].split(\u0027 \u0027)[0]\n print(\u0027Model (depth\u003d{}): {}\u0027.format(max_depth, model_name))\n \n metrics_path \u003d model_name + \u0027.metrics\u0027\n metrics \u003d np.loadtxt(exp_folder + \u0027/\u0027 + metrics_path)\n \n if metrics.shape[1] \u003c 10:\n print(\u0027An old model encountered! Just skipping.\u0027)\n continue\n \n # needed for plots\n iters \u003d metrics[:, 0]\n test_errs, test_adv_errs \u003d metrics[:, 1], metrics[:, 3]\n train_errs, train_adv_errs \u003d metrics[:, 5], metrics[:, 6]\n train_losses \u003d metrics[:, 7]\n valid_errs, valid_adv_errs_lb, valid_adv_errs \u003d metrics[:, 8], metrics[:, 9], metrics[:, 10]\n \n # Model selection is done\n if model \u003d\u003d \u0027plain\u0027: \n iter_to_print \u003d np.argmin(valid_errs)\n elif model in [\u0027at_cube\u0027, \u0027robust_bound\u0027, \u0027robust_exact\u0027]:\n # note that `da_uniform` models are mostly taken from first iterations (unless one takes them by TE)\n # iter_to_print \u003d np.argmin((valid_errs + valid_adv_errs)/2)\n iter_to_print \u003d np.argmin(valid_adv_errs)\n else:\n raise ValueError(\u0027wrong model name\u0027)\n \n # TODO: the last entries have to be revisited; I added a time_cert_test and removed depths/n_nodes before pruning\n # needed to print it directly or for latex table\n last_iter, n_iter_done, time_total \u003d int(metrics[iter_to_print, 0]), len(metrics[:, 0]), metrics[-1, 12]\n test_err, test_adv_err_lb, test_adv_err, test_adv_err_ub \u003d metrics[iter_to_print, 1:5]\n train_err, train_adv_err, train_loss \u003d metrics[iter_to_print, 5:8]\n valid_err, valid_adv_err_lb, valid_adv_err, valid_adv_err_ub \u003d metrics[iter_to_print, 8:12]\n time_cert \u003d metrics[iter_to_print, 13]\n\n test_str \u003d \u0027iter: {}/{} [test] err {:.2%} adv_err_lb {:.2%} adv_err {:.2%} adv_err_ub {:.2%}\u0027.format(\n last_iter, n_iter_done, test_err, test_adv_err_lb, test_adv_err, test_adv_err_ub)\n valid_str \u003d \u0027[valid] err {:.2%} adv_err_lb {:.2%} adv_err {:.2%}\u0027.format(\n valid_err, valid_adv_err_lb, valid_adv_err)\n train_str \u003d \u0027[train] err: {:.2%} adv_err: {:.2%} loss: {:.5f}\u0027.format(\n train_err, train_adv_err, train_loss)\n pruning_str \u003d \u0027\u0027\n if flag_pruning_stats:\n d_before, d_after, nodes_before, nodes_after \u003d metrics[:iter_to_print+1, 13:17].mean(0)\n pruning_str \u003d \u0027 | depth {:.2f}-\u003e{:.2f} nodes {:.2f}-\u003e{:.2f}\u0027.format(d_before, d_after, nodes_before, nodes_after)\n print(\u0027{} | {} | {} {} (cert {:.3f}s, total {:.2f} min)\u0027.format(test_str, valid_str, train_str, pruning_str, \n time_cert, time_total/60))\n # form the latex table\n if flag_latex:\n if model \u003d\u003d \u0027plain\u0027:\n latex_str +\u003d \u0027{} \u0026 {} \u0026 \u0027.format(data.dataset_names_dict[dataset], eps)\n if weak_learner \u003d\u003d \u0027stump\u0027:\n latex_str +\u003d \u0027{:.1f} \u0026 {:.1f} \u0026 {:.1f}\u0027.format(\n test_err*100, test_adv_err*100, test_adv_err_ub*100)\n else:\n latex_str +\u003d \u0027{:.1f} \u0026 {:.1f} \u0026 {:.1f}\u0027.format(\n test_err*100, test_adv_err_lb*100, test_adv_err_ub*100)\n \n if flag_n_trees_latex: # add the number of trees\n latex_str +\u003d \u0027 \u0026 {}\u0027.format(last_iter)\n \n # if the last column of a block\n if weak_learner \u003d\u003d \u0027stump\u0027 and model \u003d\u003d \u0027robust_exact\u0027 or \\\n weak_learner \u003d\u003d \u0027tree\u0027 and model \u003d\u003d \u0027robust_bound\u0027:\n curr_row_final \u003d utils.finalize_curr_row(latex_str, weak_learner, flag_n_trees_latex)\n latex_table +\u003d curr_row_final\n latex_str \u003d \u0027\u0027 # re-initialize to an empty string\n else:\n latex_str +\u003d \u0027 \u0026 \u0027 \n \n if flag_plot:\n plot_name_short \u003d \u0027{}-{}\u0027.format(dataset, model)\n plot_name_long \u003d \u0027dataset\u003d{}-model\u003d{}-iter\u003d{}\u0027.format(dataset, model, last_iter)\n fig, axs \u003d plt.subplots(1, 3, figsize\u003d(3*plot_height, plot_height)) # sharex\u003dTrue, sharey\u003dTrue\n \n axs[0].plot(iters, test_errs, label\u003d\u0027test error\u0027, linestyle\u003d\u0027solid\u0027, linewidth\u003dline_width, marker\u003d\u0027o\u0027, markersize\u003dmarker_size)\n axs[0].plot(iters, test_adv_errs, label\u003d\u0027test adv error\u0027, linestyle\u003d\u0027solid\u0027, linewidth\u003dline_width, marker\u003d\u0027o\u0027, markersize\u003dmarker_size)\n axs[0].plot(iters, valid_errs, label\u003d\u0027valid error\u0027, linestyle\u003d\u0027solid\u0027, linewidth\u003dline_width, marker\u003d\u0027o\u0027, markersize\u003dmarker_size)\n axs[0].plot(iters, valid_adv_errs, label\u003d\u0027valid adv error\u0027, linestyle\u003d\u0027solid\u0027, linewidth\u003dline_width, marker\u003d\u0027o\u0027, markersize\u003dmarker_size)\n axs[0].set_yticklabels([\u0027{:.0%}\u0027.format(x) for x in axs[0].get_yticks()])\n axs[0].set_xlabel(\u0027iteration\u0027)\n axs[0].set_ylabel(\u0027test error\u0027)\n # prec \u003d 1 if np.round(test_adv_errs.max() - test_errs.min(), 1) !\u003d 0.0 else 3\n # y_min, y_max \u003d test_errs.min().round(prec), test_adv_errs.max().round(prec)\n # axs[0].set_yticks(np.arange(y_min, y_max, (y_max - y_min) / 10))\n axs[0].grid(which\u003d\u0027both\u0027, alpha\u003d0.5, linestyle\u003d\u0027--\u0027)\n axs[0].legend(loc\u003d\u0027best\u0027, prop\u003d{\u0027size\u0027: legend_size})\n axs[0].set_title(plot_name_short)\n \n axs[1].plot(iters, train_adv_errs, label\u003d\u0027train error\u0027, linestyle\u003d\u0027solid\u0027, linewidth\u003dline_width, marker\u003d\u0027o\u0027, markersize\u003dmarker_size)\n axs[1].set_yticklabels([\u0027{:.0%}\u0027.format(x) for x in axs[1].get_yticks()])\n axs[1].set_xlabel(\u0027iteration\u0027)\n axs[1].set_ylabel(\u0027training error\u0027)\n # prec \u003d 1 if np.round(test_adv_errs.max() - train_adv_errs.min(), 1) !\u003d 0.0 else 3\n # y_min, y_max \u003d train_adv_errs.min().round(prec), train_adv_errs.max().round(prec)\n # axs[1].set_yticks(np.arange(y_min, y_max, (y_max - y_min) / 10))\n axs[1].grid(which\u003d\u0027both\u0027, alpha\u003d0.5, linestyle\u003d\u0027--\u0027)\n axs[1].legend(loc\u003d\u0027best\u0027, prop\u003d{\u0027size\u0027: legend_size})\n axs[1].set_title(plot_name_short)\n \n axs[2].plot(iters, train_losses, label\u003d\u0027train loss\u0027, linestyle\u003d\u0027solid\u0027, linewidth\u003dline_width, marker\u003d\u0027o\u0027, markersize\u003dmarker_size)\n # axs[2] \u003d sns.lineplot(iters, train_losses, linewidth\u003dline_width, \n # marker\u003d\u0027o\u0027, markersize\u003dmarker_size, color\u003d\"black\")\n axs[2].set_title(plot_name_short)\n axs[2].set_xlabel(\u0027iteration\u0027)\n axs[2].set_ylabel(\u0027training loss\u0027)\n # prec \u003d 1 if np.round(train_losses.max() - train_losses.min(), 1) !\u003d 0.0 else 3\n # y_min, y_max \u003d train_losses.min().round(prec), train_losses.max().round(prec)\n # axs[2].set_yticks(np.arange(y_min, y_max, (y_max - y_min) / 10))\n axs[2].grid(which\u003d\u0027both\u0027, alpha\u003d0.5, linestyle\u003d\u0027--\u0027)\n axs[2].legend(loc\u003d\u0027best\u0027, prop\u003d{\u0027size\u0027: legend_size})\n axs[2].set_title(plot_name_short)\n \n plt.savefig(\u0027plots/{}.pdf\u0027.format(plot_name_long), bbox_inches\u003d\u0027tight\u0027)\n if weak_learner \u003d\u003d \u0027stump\u0027 and i % 3 \u003d\u003d 2:\n print()\n if weak_learner \u003d\u003d \u0027tree\u0027 and i % 3 \u003d\u003d 2:\n print()\n\nif flag_latex:\n # Global post-processing of the latex table\n latex_table \u003d latex_table.replace(\u0027100.0\u0027, \u0027100\u0027) # to save some width in the table \n for eps_orig in eps_format_dict:\n latex_table \u003d latex_table.replace(eps_orig, eps_format_dict[eps_orig])\n \n print()\n print(\u0027Latex table:\u0027)\n print(latex_table)\n",
+ "metadata": {
+ "pycharm": {
+ "metadata": false,
+ "name": "#%%\n",
+ "is_executing": false
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": " \n",
+ "metadata": {
+ "pycharm": {
+ "metadata": false,
+ "name": "#%%\n"
+ }
+ }
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "name": "python3",
+ "language": "python",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/notebooks/minmax_objective.ipynb b/notebooks/minmax_objective.ipynb
index 835951d..cf38c08 100644
--- a/notebooks/minmax_objective.ipynb
+++ b/notebooks/minmax_objective.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 139,
+ "execution_count": 1,
"metadata": {
"collapsed": true,
"pycharm": {
@@ -17,59 +17,31 @@
}
},
"outputs": [],
- "source": "import os\nos.chdir(\"../\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn\n\n%matplotlib inline\nseaborn.set(font_scale\u003d1.65)\nseaborn.set_style(\"white\")\n"
+ "source": "import numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn\n\n%matplotlib inline\nseaborn.set(font_scale\u003d1.65)\nseaborn.set_style(\"white\")\n"
},
{
"cell_type": "code",
- "execution_count": 168,
+ "execution_count": 223,
"outputs": [
{
"name": "stdout",
"text": [
- "4.006387148158443 7.12971950976526 13.500045533658492\n"
+ "54.88954404578718 210.94100026063305 1810.0989095555713\n[ 0. 27.42514634 54.85029267 82.27543901 109.70058534\n 137.12573168 164.55087802 191.97602435 219.40117069 246.82631702\n 274.25146336 301.6766097 329.10175603 356.52690237 383.95204871\n 411.37719504 438.80234138 466.22748771 493.65263405 521.07778039\n 548.50292672 575.92807306 603.35321939 630.77836573 658.20351207\n 685.6286584 713.05380474 740.47895107 767.90409741 795.32924375\n 822.75439008 850.17953642 877.60468275 905.02982909 932.45497543\n 959.88012176 987.3052681 1014.73041444 1042.15556077 1069.58070711\n 1097.00585344 1124.43099978 1151.85614612 1179.28129245 1206.70643879\n 1234.13158512 1261.55673146 1288.9818778 1316.40702413 1343.83217047\n 1371.2573168 1398.68246314 1426.10760948 1453.53275581 1480.95790215\n 1508.38304848 1535.80819482 1563.23334116 1590.65848749 1618.08363383\n 1645.50878017 1672.9339265 1700.35907284 1727.78421917 1755.20936551]\n"
],
"output_type": "stream"
},
{
"data": {
"text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
- "image/png": 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\u003d\n"
+ "image/png": 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\u003d\n"
},
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": "def f(w_l, w_r):\n \"\"\"\n Note: the values that we select for y, G_k_sum, h_r, h_l are arbitrary. \n This plot aims to show the qualitative behaviour of the objective that we optimize.\n \"\"\"\n y \u003d np.array([-1, 1, 1, -1, 1])\n # This is the joint contribution of the previous weak learners\n G_k_sum_y_wl \u003d np.array([4.5, -1.0, 2.0, -0.5, 3.0])\n h_r \u003d np.array([2.0, -2.0, 5.0, -3.0, -1.0])\n h_l \u003d np.array([5.0, -1.0, -3.5, -2.0, -4.0]) \n # If some coordinate didn\u0027t have a split yet, then h_l\u003dh_r\u003d0.\n # Then the problem simplifies to just 2 case distinctions. \n # h_r \u003d np.zeros(5)\n # h_l \u003d np.zeros(5)\n\n margin \u003d G_k_sum_y_wl + y*w_l + np.minimum(h_l, h_r + y*w_r)\n losses \u003d np.exp(-margin)\n obj \u003d np.sum(losses)\n # for better visualization, we rather plot log objective\n log_obj \u003d np.log(obj)\n return log_obj\n\nnp.random.seed(1)\nplot_name \u003d \u0027minmax_objective_stumps\u0027\n\ngrid_size \u003d 200\nmin_val, max_val \u003d -5, 5\nXX, YY \u003d np.meshgrid(np.linspace(min_val, max_val, grid_size), \n np.linspace(min_val, max_val, grid_size))\nX0 \u003d np.stack([np.ravel(XX), np.ravel(YY)]).T\nf_vals \u003d np.zeros(X0.shape[0])\nfor i in range(len(f_vals)):\n f_vals[i] \u003d f(X0[i, 0], X0[i, 1])\nZZ \u003d f_vals.reshape(grid_size, grid_size)\n\nprint(f_vals.min(), f_vals.mean(), f_vals.max())\n\nax \u003d plt.gca()\nplt.contourf(XX,YY,ZZ, cmap\u003d\"coolwarm\", \n levels\u003dnp.linspace(f_vals.min(), f_vals.max(), 40))\naxis_margin \u003d 0.0\nax.set_xlim([min_val-axis_margin, max_val+axis_margin])\nax.set_ylim([min_val-axis_margin, max_val+axis_margin])\n\nticks \u003d [-5, -3, -1, 1, 3, 5]\nax.set_xticks(ticks)\nax.set_yticks(ticks)\nax.set_xlabel(\u0027$w_l$\u0027)\nax.set_ylabel(\u0027$w_r$\u0027)\nax.set_title(\u0027Exact robust loss wrt $w_l$ and $w_r$\u0027)#, fontsize\u003d15)\n \nplt.savefig(\u0027plots/{}.pdf\u0027.format(plot_name), bbox_inches\u003d\u0027tight\u0027)\n",
- "metadata": {
- "pycharm": {
- "metadata": false,
- "name": "#%%\n",
- "is_executing": false
- }
- }
- },
- {
- "cell_type": "code",
- "execution_count": 164,
- "outputs": [
- {
- "data": {
- "text/plain": "[\u003cmatplotlib.lines.Line2D at 0x7f83f71ed7f0\u003e]"
- },
- "metadata": {},
- "output_type": "execute_result",
- "execution_count": 164
- },
- {
- "data": {
- "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
- "image/png": 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\u003d\u003d\n"
+ "metadata": {
+ "needs_background": "light"
},
- "metadata": {},
"output_type": "display_data"
}
],
- "source": "# Plot just wrt w_r. Not so evident.\nw_r_vals \u003d np.linspace(-1, 3, 500)\nf_vals \u003d np.zeros(w_r_vals.shape[0])\nfor i in range(len(w_r_vals)):\n f_vals[i] \u003d f(-1.0, w_r_vals[i])\nplt.plot(w_r_vals, f_vals)\n",
+ "source": "def f(w_l, w_r):\n \"\"\"\n Note: the values that we select for y, G_k_sum, h_r, h_l are arbitrary. \n This plot aims to show the qualitative behaviour of the objective that we optimize.\n \"\"\"\n y \u003d np.array([-1, 1, 1, -1, 1])\n # This is the joint contribution of the previous weak learners\n G_k_sum_y_wl \u003d np.array([4.5, -1.0, 2.0, -0.5, 3.0])\n h_r \u003d np.array([2.0, -2.0, 5.0, -3.0, -1.0])\n h_l \u003d np.array([5.0, -1.0, -3.5, -2.0, -4.0]) \n\n margin \u003d G_k_sum_y_wl + y*w_l + np.minimum(h_l, h_r + y*w_r)\n losses \u003d np.exp(-margin)\n obj \u003d np.sum(losses)\n log_obj \u003d obj\n return log_obj\n\nnp.random.seed(1)\nplot_name \u003d \u0027minmax_objective_stumps\u0027\n\ngrid_size \u003d 200\nmin_val, max_val \u003d -2.2, 2.0\nXX, YY \u003d np.meshgrid(np.linspace(min_val, max_val, grid_size), \n np.linspace(min_val, max_val, grid_size))\nX0 \u003d np.stack([np.ravel(XX), np.ravel(YY)]).T\nf_vals \u003d np.zeros(X0.shape[0])\nfor i in range(len(f_vals)):\n f_vals[i] \u003d f(X0[i, 0], X0[i, 1])\n# For color adjustment\n# f_vals \u003d f_vals / 1000\n# f_vals \u003d np.clip(f_vals, f_vals.min(), f_vals.max()*0.75)\nZZ \u003d f_vals.reshape(grid_size, grid_size)\n\n# f_vals \u003d np.clip(f_vals, f_vals.min(), f_vals.max()/3)\n# f_vals \u003d f_vals / 5\nprint(f_vals.min(), f_vals.mean(), f_vals.max())\n\nax \u003d plt.gca()\n# countour_vals \u003d np.linspace(f_vals.min(), f_vals.max(), 60)\ncountour_vals \u003d np.linspace(0, f_vals.max() - f_vals.min(), 65)\n# countour_vals2 \u003d np.linspace(f_vals.mean()+1, f_vals.max(), 5)\n# countour_vals \u003d np.hstack([countour_vals1, countour_vals2])\nprint(countour_vals)\nplt.contourf(XX, YY, ZZ, cmap\u003d\"coolwarm\", vmin\u003d0, vmax\u003df_vals.max(), \n levels\u003d90)\naxis_margin \u003d 0.0\nax.set_xlim([min_val-axis_margin, max_val+axis_margin])\nax.set_ylim([min_val-axis_margin, max_val+axis_margin])\n\n# ticks \u003d [-5, -3, -1, 1, 3, 5]\nticks \u003d [-2, -1, 0, 1, 2]\nax.set_xticks(ticks)\nax.set_yticks(ticks)\nax.set_xlabel(\u0027$w_l$\u0027)\nax.set_ylabel(\u0027$w_r$\u0027)\nax.set_title(\u0027Exact robust loss wrt $w_l$ and $w_r$\u0027)#, fontsize\u003d15)\n \nplt.savefig(\u0027../plots/{}.pdf\u0027.format(plot_name), bbox_inches\u003d\u0027tight\u0027)\n\n\n",
"metadata": {
"pycharm": {
"metadata": false,
@@ -77,18 +49,6 @@
"is_executing": false
}
}
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": "\n",
- "metadata": {
- "pycharm": {
- "metadata": false,
- "name": "#%%\n"
- }
- }
}
],
"metadata": {
diff --git a/notebooks/model_analysis.ipynb b/notebooks/model_analysis.ipynb
index febdd85..e19e880 100644
--- a/notebooks/model_analysis.ipynb
+++ b/notebooks/model_analysis.ipynb
@@ -1,53 +1,343 @@
{
"cells": [
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": 2,
"metadata": {
- "pycharm": {}
+ "collapsed": true,
+ "pycharm": {
+ "is_executing": false
+ }
},
- "source": "# Analysis of the splitting thresholds. "
+ "outputs": [],
+ "source": "%load_ext autoreload\n%autoreload 2\n\n\nimport os\nos.chdir(\"../\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\nimport data\nimport utils\nfrom classifiers import OneVsAllClassifier\nfrom stump_ensemble import StumpEnsemble\nfrom tree_ensemble import TreeEnsemble\n\n%matplotlib inline\nsns.set(font_scale\u003d1)\nsns.set_style(\"white\")\nnp.set_printoptions(precision\u003d6, suppress\u003dTrue)\n"
+ },
+ {
+ "cell_type": "markdown",
+ "source": "# Feature importance visualization (barplots + heatmaps)",
+ "metadata": {
+ "pycharm": {
+ "metadata": false
+ }
+ }
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": 5,
+ "outputs": [
+ {
+ "name": "stdout",
+ "text": [
+ "Model name: 2019-08-11 14:28:04 dataset\u003dbreast_cancer weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 max_depth\u003d4 lr\u003d0.01\nBest iter to take the model: 77\nEnsemble of 78/150 trees restored: exps_diff_depth/2019-08-11 14:28:04 dataset\u003dbreast_cancer weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 max_depth\u003d4 lr\u003d0.01.model.npy\n",
+ "Model name: 2019-08-11 14:28:04 dataset\u003dbreast_cancer weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 max_depth\u003d4 lr\u003d0.01\nBest iter to take the model: 2\nEnsemble of 3/150 trees restored: exps_diff_depth/2019-08-11 14:28:04 dataset\u003dbreast_cancer weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 max_depth\u003d4 lr\u003d0.01.model.npy\n",
+ "Model name: 2019-08-11 14:28:04 dataset\u003dbreast_cancer weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 max_depth\u003d4 lr\u003d0.01\nBest iter to take the model: 45\nEnsemble of 46/150 trees restored: exps_diff_depth/2019-08-11 14:28:04 dataset\u003dbreast_cancer weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 max_depth\u003d4 lr\u003d0.01.model.npy\n"
+ ],
+ "output_type": "stream"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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XZ8yYMco+Bw4cYN68eQwbNow9e/bQrVs3xo0bx4ULF15bZkhICF999RWbNm3i3r17BAcHAzBo0CDc3d3p0KEDUVFRREVF4ejoyPr164mMjGTx4sXs37+fuXPnUr169YL86oQQQpSQd6pZPj09nVWrVhEQEEC3bt0AqFOnjrJ+48aNWFpaMn36dFQqFTY2NiQkJLBmzRp8fHyU7Tp06EDv3r0BGDduHBEREfz++++0bduW77//HhMTExYsWICuri4ANjY2yr7u7u75YgoICMDNzY2kpCRq1KiBh4cHffr04e7du1SvXp2MjAwOHjzI3/72NwD27duHjo4OAQEBShnz5s3jww8/5F//+hdNmjT5y/P/8ssvadmyJQDBwcG4urpy8uRJWrduzd///nd69epFnz59ABgxYgSnT59m7dq1fPvtt39Z5vjx43FycgLAy8tLeb5eoUIFDAwMyMnJydecf/v2baysrGjWrBkqlQoLC4u/LFsIIUTpeKfu3OPj40lPT8fZ2fmV669evYqjoyMqlUpZ5uTkxN27d3ny5ImyTK1WK58NDAyoXLkyDx48APKa5Js1a6Yk9lfFMH78eFxdXXF0dMTDwwOAxMREAJo0aYKlpSX79u0D4Pjx42RmZuLm5gbApUuXuHbtGo6OjspP69atyc7O5saNG689/xcTf/Xq1alVqxZXr15Vzv15kn7x3K9du/baMhs2bKh8NjMz4/79+6/dvnv37ly8eJFPPvmEwMBAoqOjX7u9EEKIkvdO3bkXFT29l0/7TU3iz40YMQJLS0vmzp2LmZkZT58+pXfv3mRmZirbdO3alR9//BEfHx/27t2Lm5sbhoaGAKSmptKkSROl+ftFpqamhTyjwnuxLlQqFW+aAdje3p4jR45w/PhxTpw4wahRo+jSpQtz5swp7lCFEEIU0Dt15163bl0MDAyIjY195XobGxvOnj2bL0GdOXMGc3NzKlYs2Fy4arWa06dPK8/YX5SSkkJcXByjRo2iRYsW2NjY8PDhw5e28/Dw4Pz581y6dInIyEjl7h6gUaNGxMfHY2pqipWVVb6fN8V4/vx55XNycjKJiYnKIwMbGxvOnDmTb/szZ87ke6SgKX19/VfWQ+XKlenatStBQUEEBgayc+fOAl8cCSGEKH7vVHIvX748Q4cOZd68eezZs4ebN29y+vRpdu/eDUC/fv24efMmgYGBXLt2jf379xMWFsagQYMKfIz+/fvz8OFDJk+ezIULF4iLi2Pbtm0kJiZibGyMiYkJmzdv5saNG5w4ceKVz7Otra1p3Lgxfn5+GBkZ4eLioqzz8PCgUqVKjB49mlOnTnHz5k2io6P56quvePTo0WtjCwkJITY2lkuXLuHv74+VlRWtWrUC4IsvvmDbtm1s3ryZ+Ph4wsLCiIqK4osvvijwuf8vCwsLLl26RFxcHA8ePCAzM5Pw8HD27dvHtWvXuHr1KocPH8bKygodnXfqn5IQQmi1d65ZfuTIkahUKhYuXEhycjLm5uZKZ7kaNWqwcuVK5s+fz6ZNm6hSpQre3t4MHDiwwOVXqVKFiIgIgoOD6devH3p6etjb29O2bVt0dHRYtGgRc+bMoUuXLtSvXx8/P79XXjx069aNoKAgpYznjIyM2LBhAwsWLMDX15fU1FRq1qyJi4sL5cuXf21sEyZMICAggOvXr2Nvb09ISIiSVDt16kRycjJhYWHMnj2bOnXq8N1339G4ceMCn/v/+uyzz/j111/x9PQkNTWVdevWYWhoSFhYGDdu3EBXVxcHBwdCQkIKfQwhhBBFT5X7poesotTFxsYycOBAzpw5Q4UKFUo7nLfWuXMXmTimgLRv4hijYj+OmVklkpMfF/txtIXUl+ZKus50dFSYmhbs0bKyTzHFIoQQQohSIsldCCGE0DLSLC9KXHJyMtnZ2tHUXNx0dXW0pq50dfWA4p/PXZqZNSP1pbl3oVn+netQJ959OjqGgFxTFoT84RVCFIY0ywshhBBaRpK7EEIIoWWkWV6UuJycNK15jlzckpOfaU1dldQzdyGEJHdRCry9fWScewFp3zh3Se5ClARplhdCCCG0jCR3IYQQQstIchdCCCG0jCT391hsbCxqtZqnT5+WdihCCCGKkCR38UYZGRmlHYIQQggNSHIvpP3799O1a1fs7e1xdnZm8ODBnD59Gjs7Ox48eJBv2+nTpzNixAggb052T09PNm3aRNu2bXF0dGTevHlkZ2fz3Xff4ezsTJs2bdiyZYuyf0JCAmq1mv3799O3b1+aNGlCnz59SExMJCYmBg8PDxwdHRk3bhypqanKfjk5OYSGhuLq6oqDgwOffvopR48eVcp8PhWuk5MTarUaf39/ALy8vJgzZw5z5szB2dmZ0aNHM2PGDEaNGpXvvNLS0mjWrBkHDhwo8voVQghReDIUrhDu3r3LxIkTmTx5Mh07duTJkyfExMTg6OhI7dq1+eGHH5Q55tPS0ti/fz9BQUHK/nFxccTGxrJ27VquXr3K+PHj+fe//429vT2bN2/m4MGDzJo1CxcXFywsLJT9lixZwvTp0zEzM8PPz48JEyZgYGDA3LlzyczMxNfXl4iICEaOHAlAWFgYe/fuVeZ3P3nyJKNHj2bLli2o1WpCQkIYM2YMhw8fxsDAAAMDA+VY27dvx8vLi02bNgHw559/MmDAAB48eEDVqlUBOHjwIHp6enTo0KG4q1wIIYQG5M69EJKTk8nKyuLjjz+mdu3a2Nra4uPjg46ODr169WLnzp3KtgcPHkRfX/+lBBgYGEj9+vXp1KkTDg4O3Lt3j3HjxlG3bl2GDBmCgYEBp06dyrfP0KFDcXFxoWHDhvTv35+zZ88yZcoU7O3tcXJywt3dndjYWCCvKT0sLIygoCBcXFywtLSkT58+fPzxx2zZsgVdXV2MjY0BqFq1KmZmZlSqVEk5Vr169ZgwYQLW1tZYW1vTtGlTrKys+OGHH5Rtdu7ciYeHB+XKydhlIYQoSyS5F4KtrS0fffQRHh4ejBs3jm3btvHkyRMAevTowX/+8x8uXrwI/DcB6uvrK/tbWlpiZGSkfK9WrRr169dXvuvo6FClSpWXmvfVarXy2dTUFCDffqampty/fx+A69evk5aWhre3N46OjsrPoUOHuHnz5hvP0c7O7qVlPXv2ZMeOHQDcvn2b2NhYevbs+cayhBBClCxpli8EXV1dIiIiOHPmDL/88gtr1qxhyZIl7Nixg2rVqtGuXTt27NhBlSpViI2NVZ5lP6enl7/aVSpVvuT/fFlOTv43k724n0qlAsi3n0ql4vkMvs+fva9atQozM7N85bzY/P5XXrz4eK579+4sXLiQCxcucOzYMdRqNR988MEbyxJCCFGyJLkXko6ODs2bN6d58+b4+vrSqlUroqKi6NGjB7169WLq1KlUqlQJW1tbbG1tSzw+Gxsb9PX1SUpKonnz5q/c5vmFwf9eRPwVU1NTOnTowI4dO/jll18YMGBAkcUrhBCi6EhyL4Rz584RHR2Ni4sLVatW5bfffiM1NRVra2sA2rVrh76+PqtWrWLKlCmlEmPFihXx8fEhMDCQrKwsnJycePToEadOncLc3Bx3d3csLCxQqVRERkbSunVrypcvT4UKFV5bbq9evfD19QXAw8OjJE5FCCGEhiS5F0LFihX57bffCA8PJzU1ldq1azN79mwcHByAvGb77t27ExERQdeuXUstzokTJ2JqasqKFSu4desWlStXxt7eXknO5ubmjBkzhvnz5+Pn50ePHj2YN2/ea8ts3bo1JiYmODk5UaVKlZI4DSGEEBpS5T5/SCuKlJ+fH+np6SxevLi0QylST548oU2bNnz33Xe0a9euUGV07txFZoUrIO2bFe7lvhxFzcysEsnJj4v9ONpC6ktzJV1nOjoqTE0rarRPoe7cc3NzSUpK4vbt29ja2r6y89X76vHjx1y6dImffvqJv//976UdTpHJyckhJSWF1atXY2ZmRps2bUo7JCGEEH9B4+T+/fffs2LFCu7du4dKpWLbtm00btyY0aNH07x5c+XlLe+rUaNG8fvvv+Pt7f2XHdneRYmJibi5uVGrVi2++eYbdHRkFKUQQpRVGiX31atXs3jxYoYOHYqzszPe3t7Kuo8++ogff/zxvU/u69evL+0QikXt2rW5fPlykZQVERFOdrZ2NDUXN11dHa2pK11d6eIjREnR6H/bxo0b+fLLLxk6dCjZ2dn51llbWxMfH1+UsQktpaNjCEhXj4KQ56FCiMLQqG01OTn5lW8ug7xx3+np6UUSlBBCCCEKT6PkbmVlxa+//vrKdb/99hs2NjZFEpQQQgghCk+jZnlvb29mzZqFvr4+nTp1AuD+/fts3bqV8PBwZs+eXSxBCu2Sk5OmNc+Ri1ty8jOpKw2V1TrL63MgkyyJkqHxOPfVq1ezbNkynj17przH3NDQEF9fX4YMGVIsQQrtIuPcC06bxrmXlLJaZyU1zl9T0q9Dc1o5zn3IkCH07duXf/7zn6SkpGBsbIyjo2O+6UKFEEIIUXoKnNzT09MZOXIkw4cPx9nZmdatWxdnXEIIIYQopAJ3qCtfvjy///57gWcQE4Xn5eVFcHBwaYchhBDiHaVRb3lXV1cOHz5cXLGI/y8kJESZ3KU4qdVqIiMji/04QgghSpZGz9xbt27N/PnzSU5Opm3btlSrVg2VSpVvm8JOJiIgIyODcuXKYWJiUtqhaCQzM1OZG14IIUTp06i3vK2t7esLU6n4448/3jqod4WXlxdqtZqsrCz27NlDuXLl8PHxYfjw4QD8+eefBAcHc+TIEbKysmjatCnTpk1T3gcQEhJCZGQkn332GStXriQlJYWzZ8/i5eWFnZ2dMhe8q6srffr04cqVKxw5cgRTU1MCAgKwtrZm+vTpnD17lgYNGvDNN99Qt25dJb7Dhw8TEhLCtWvXqFGjBp999hmDBw9GR0cHV1dXbt26pWxrYWHBzz///Mb9IO+OPyAggJ9//pmYmBjGjBmj0UgJ6S1fcGW153dZVlbrTHrLaw+t6y1/5MgRjQp/H2zfvp3PP/+crVu3cvbsWb7++mvq1KmDu7s748aNw8jIiDVr1lChQgXWrVvHoEGD+Omnn5SZ9OLi4oiMjGT58uUvtYK8aO3atUyaNIkxY8YQGhrK5MmT+eCDD/D29mbGjBnMmDGDWbNmKTPRnTp1Cn9/f2bMmEGzZs24du0aM2fOpFy5cnh7e7Nt2zZatmzJ/PnzadWqFbq6ugXa77klS5YwefJkZsyYgZ6evDNcCCHKEo3+KltYWBRXHO8sS0tL/Pz8AKhXrx4XLlwgIiICMzMzLly4QFRUFOXK5b24YubMmRw6dIijR4/SuXNnALKysggODn5jU7yrqyu9e/cGYOTIkezcuZM2bdrQvn17IO8FQ5MmTSInJwcdHR2WLl3KiBEj6NGjhxLnyJEjWb9+Pd7e3lStWhWAypUrY2ZmphznTfs9161bN2UbIYQQZYtGyf0///nPG7epX79+oYN5Fzk4OOT73rRpU/bs2cPly5d5/Pgxzs7O+dY/e/aMmzdvKt9r165doGfsarVa+WxqagpAgwYNlGXVqlUjMzOTR48eYWJiwqVLlzhz5gzLli1TtsnOzn7jaIeC7mdvb//GmIUQQpQOjZJ7165dX9t0DLxXz9xf5+nTp9SoUYPw8PCX1hkbGyufDQ0NC1Tei03fz38Hr2oOf56EU1NTGTduHG5ubpqEXeD9Chq3EEKIkqdRcl+3bt1Lyx49esQvv/xCVFQUM2bMKLLA3hXnz5/P9/3cuXPY2NjQqFEj7t69S7ly5ahZs2aJx9WoUSPi4+OxsrL6y2309fVfmrq3IPsJIYQo2zRK7h999NErl3fs2JFFixbx008/0aFDhyIJ7F1x48YNFixYQM+ePfnnP//J1q1bmTdvHq1atcLe3p5Ro0YxadIk6tSpw507dzhy5Ai9evUq9hn0Ro4cyahRozA3N1cm+bl48SK3bt1i5MiRQF4fiujoaBwcHChXrhzGxsYF2k8IIUTZVmTdnFu0aMHo0aOLqrh3Rs+ePfnzzz/p2bMn5cqVY+TIkUpnudWrV/Ptt98yZcoUHj58SPXq1fnoo49KZBx7u3bXSpFDAAAgAElEQVTtWL58OcuXLycsLIxy5cpRv359+vXrp2wzZcoU5s2bx6ZNmzA3N+fnn38u0H5CCCHKNo1nhfsrQUFBHDx48L1649n/jkcXBSPj3AuurI7ZLsvKap3JOHftoXXj3MeOHfvSsszMTK5du8b169cZP368RgcXQgghRNHTKLk/ePDgpWXly5enefPmTJ06VV49KwokIiKc7Oyyd2dVFunq6khdaais1pmurrzsSZScImuWF6Kg7t9/Qk6O/LMrCGky1ZzUmWakvjT3LjTLazQr3NSpU/O9gOVFt27dYurUqRodXAghhBBFT6PkvnPnTlJSUl65LiUlhV27dhVJUEIIIYQoPI2S++v8+9//Vt5XLoQQQojS88YeHhEREcqb6VQqFb6+vspEKM+lp6dz//59Pv300+KJUmiVnJy0MtnhqSTkdaoq98bthBDibbwxudevX5//+7//A+Dvf/87zs7O+WYRAyhXrhzW1tbKy1uEeB1vb5/3dpx73lhnSe5CiOL1xuTu4uKCi4sLABUqVKB3796Ym5sXe2BCCCGEKByNBl6+j6+XFUIIId41Gr9V4ezZs2zbto34+HjS09NfWr9t27YiCUwIIYQQhaNRb/kTJ04wYMAAkpKSOH36NFWrVsXIyIhLly7x8OFDGjRoUFxximKSm5tLVlZWaYchhBCiCGmU3JcsWcLAgQNZuXIlkPeu+XXr1nHgwAH09PRwdnYuliCFZo4ePUrfvn1p3rw5zs7O+Pr6cvt2Xge22NhY1Go1x48fp0ePHtjZ2XHx4kUADh8+TPfu3bG3t+fjjz9m1apV5OT8t1f76tWr6dq1Kw4ODrRv356goKBXtt4IIYQoXRol9//85z+0bdsWHR0dVCoVaWlpQN684GPGjGHFihXFEqTQTFpaGoMHD2b79u2sXbuWtLS0lyb1WbhwIVOmTGHfvn3UrVuXU6dO4e/vzxdffMG+ffuYMWMG69evZ/369co+enp6zJw5kx9//JE5c+Zw+PBhli1bVtKnJ4QQ4g00euZevnx5cnJyUKlUmJmZcePGDZo3bw5AxYoVuXPnTrEEKTTj7u6e73tAQABubm4kJSUpy8aNG0fLli2V70uXLmXEiBH06NEDAEtLS0aOHMn69evx9vYGwMfHR9m+du3a+Pr6smLFCiZMmFCMZyOEEEJTGiV3W1tb4uLicHFxoWXLloSFhWFubo6+vj6LFy+mYcOGxRWn0EB8fDyLFy/m3Llz+V4XnJiYqHy2s7PLt8+lS5c4c+ZMvjvx7OzsfM3yJ0+eJDQ0lGvXrvH06dOX1gshhCgbNEru3t7eJCQkADBhwgRGjBjB4MGDAahRowZLly4t+giFxkaMGIGlpSVz587FzMyMp0+f0rt3bzIzM5VtDA0N8+2TmprKuHHjcHNze2WZCQkJDB8+nAEDBjBx4kQqV67MiRMnmD17drGeixBCCM1plNxfnK/d3NycHTt2cP36dZ49e0a9evVeei2tKHkpKSnExcURFBSEo6MjAMePH3/jfo0aNSI+Ph4rK6tXrr9w4QIAU6ZMUZZt3769CCIWQghR1DQe5/5cbm4ud+/epXbt2ujpFboYUcSMjY0xMTFh8+bNmJqacvPmTb799ts37jdy5EhGjRqFubk5nTp1AuDixYvcunWLkSNHYmVlRUZGBhs2bKBdu3bExsayc+fO4j4dIYQQhaDxrHDHjh2jd+/e2Nvb0759ey5fvgzAjBkz2L17d5EHKDSjo6PDokWLOH/+PF26dGHBggX4+fm9cb927dqxfPlyjh8/jqenJ3379mXjxo1YWFgAef0tpk6dSmhoKF27duXQoUOMGzeuuE9HCCFEIahyc3NzC7rxrl27mDZtGh4eHrRo0YKpU6eyfft2GjduzOrVqzl27Fi+oVNCvErnzl3e84ljjAq8vZlZJZKTHxdjRNpH6kwzUl+aK+k609FRYWpaUbN9NNl4xYoVDB48mODgYLp165ZvXYMGDbh69apGBxdCCCFE0dMouScmJtKqVatXritXrhxPnjwpkqCEEEIIUXga9YSrWbMmf/zxR76Xnzz3r3/96y97WgvxooiIcLKz38/x8bq60vlUCFH8NPpL06tXL5YuXYqpqSkdO3YE8nrNR0dHs3r1anx9fYslSKFddHQMgQJ39RBCCKEhjZL70KFDuX37Nv7+/ujq6gLQt29fcnJy6NOnDwMHDiyWIIUQQghRcBr1ln/uxo0bREdHk5KSgrGxMS1atMDa2ro44hNa6P79J+TkyJ17QUhPZs1JnWlG6ktz70Jv+TfeuQ8aNIgZM2ZQr149ZdmtW7fw8PDAyKjgQ3qEeC4nJ+29feauqeTkZ1pTV3n9DeQtlkKUhDcm95MnT+brBZ+dnc2gQYPYtm0bjRs3LtbghHby9vZ5b8e5a0pPT4esLO1I7nlj/CW5C1ESNH5DHeR1ohNCCCFE2VSo5C6EEEKIsqvQyV2lUhVlHKIA1Go1kZGRpR2GEEKIMq5AQ+GGDBmiDH17zsfH56VlANHR0UUT2XssJCSEyMhIduzYUdqhCCGEeAe9MbmPHj26JOIQQgghRBGR5A7s37+fpUuXcv36dYyMjLCzs2PVqlVMmzaN1NRUGjZsyIYNG8jJyWH48OF4eXkxZ84c9u7di4mJCTNnzqRDhw5KeTExMcyfP58rV65QpUoV+vTpw6hRo9DRyXsK8vDhQ2bPns2xY8fIysqiRYsW/O1vf6NWrVrs2LGDpUuXAnnN8ABBQUF4enoCcO/ePYYPH05MTAyWlpZ8/fXXNG/eHIAdO3YQHBxMcHAwQUFBJCcn07p1awIDA6lUqRIAOTk5rFy5ki1btnD//n3q1avH2LFjad++PQB//vknAQEBREVFkZaWRq1atRg7dizu7u5kZGQQFBTEwYMHefToEdWrV8fHxwcvL68S+T0JIYQomPf+Rdd3795l4sSJTJ48mY4dO/LkyRNiYmKU9VFRUVSvXp2NGzcSHR1NQEAA0dHRtG/fnh07drBhwwamTJlCZGQkFSpUICkpiWHDhvHZZ5/xzTffcOXKFWbOnEnFihXx8fEBwN/fn1u3brFy5UoMDAwICgrC19eXHTt20LlzZ/79739z8uRJVq9eDaAkZoDly5czZcoUpk6dSkhICJMmTeLQoUPo6+sD8PTpUzZs2MB3331HWloaY8eOZeXKlUycOBGAsLAw9u7dy+zZs6lTpw4nT55k9OjRbNmyhUaNGrF48WKuXr3K6tWrMTExIS4uTrkoWb9+PZGRkSxevJiaNWuSkJDAw4cPS+LXJIQQQgPvfXJPTk4mKyuLjz/+GAsLCwBsbW2V9VWrVmXatGno6OhQr149wsLCKFeuHAMGDABg1KhRrF+/nj/++IPmzZuzceNGLC0tmT59OiqVChsbGxISElizZg0+Pj7ExcURGRnJ1q1badKkCQALFizA1dWVkydP4uLigpGREbq6upiZmb0Ub8+ePfnkk0+AvFaVTz75hBs3bmBjYwNAZmYmAQEB1KpVC4BPP/1UuVjJyMggLCyMdevWKcfu06cPMTExbNmyha+//prExEQ++OAD7O3tAbC0tFSOffv2baysrGjWrBkqlUqpLyGEEGXLez8UztbWlo8++ggPDw/GjRvHtm3b8r20p0GDBsqdK0C1atVo0KCB8r1q1aro6ury4MEDAK5evYqjo2O+0QROTk7cvXuXJ0+ecPXqVfT19ZXkCWBubo6FhQVXr159Y7zPm+oBJfk/PzZAxYoVlcT+fJv79+8DcP36ddLS0vD29sbR0VH5OXToEDdv3gTykv2PP/5Ijx49WLBgAefPn1fK6t69OxcvXuSTTz4hMDBQOk8KIUQZ9d7fuevq6hIREcGZM2f45ZdfWLNmDUuWLFF6qj9v7n6Rnt7L1ZaTUzJvEXvx2M8vIF489v/GplKplJcOpaamArBq1aqXWgUMDAwA6NChAz///DNHjx7lxIkT9OvXD19fX0aOHIm9vT1Hjhzh+PHjnDhxglGjRtGlSxfmzJlT9CcqhBCi0N77O3cAHR0dmjdvzvjx49m9ezepqalERUUVqiwbGxvOnj2b7y1+Z86cwdzcnIoVK2JjY0NmZia///67sv7OnTvcunWL+vXrA3kXFMVxsWBjY4O+vj5JSUlYWVnl+zE3N1e2q1atGr169WLRokV8+eWXbNu2TVlXuXJlunbtSlBQEIGBgezcubPELmyEEEIUzHt/537u3Dmio6NxcXGhatWq/Pbbb6SmpmJtbZ2vY11B9evXj/DwcAIDA+nXrx9XrlwhLCxMGXVgbW1Nhw4dmD59OrNmzVI61DVs2JCWLVsCYGFhwY0bN7h06RLVq1enYsWKlCv39u/kft6pLzAwkKysLJycnHj06BGnTp3C3Nwcd3d3lixZQuPGjWnQoIFykfN80qDw8HCqV6+Ora0tubm5HD58GCsrq3yPLYQQQpS+9z65V6xYkd9++43w8HBSU1OpXbs2s2fPxsHBgX/84x8al1ejRg1WrlzJ/Pnz2bRpE1WqVMHb2zvfXPdBQUHMmTOHoUOHkp2dTYsWLQgODlaa2Tt16sShQ4fw8vLi0aNH+YbCva2JEydiamrKihUruHXrFpUrV8be3h5fX18gr1l/wYIF3Lp1C0NDQ5ydnZk+fToAhoaGhIWFcePGDXR1dXFwcCAkJKRI4hJCCFF0CjWfuxBvo3PnLjIrXAFp36xwxT9NtMxPrhmpL829C/O5S3uqEEIIoWUkuQshhBBa5r1/5i5KXkREONnZ2tHUXNx0dXW0pq50deXPjRAlRf63iRKno2MISFePgpDnoUKIwpBmeSGEEELLSHIXQgghtIw0y4sSl5OTpjXPkYtbcvIzramrvGfub/8yJiHEm0lyFyXO29tHxrkXkPaNc5fkLkRJkGZ5IYQQQstIchdCCCG0jCT390hwcDBeXl7Kdy8vL4KDg0sxIiGEEMVBnrm/x0JCQl45N70QQoh3m/xlfwdlZGQUyRSwJiYmRRCNEEKIskaS+zvAy8sLtVoNwJ49e3BwcOCjjz5i165d3Lx5kypVqtCpUycmTJhA+fLllf1WrFjBunXryMjIwMPD46W7dC8vL+zs7JgyZQoAarWa0NBQOnToAMDTp09xcnJi3bp1ODs78+effxIQEEBUVBRpaWnUqlWLsWPH4u7uXkI1IYQQoiAkub8jtm/fjpeXF5s2bQLg2LFjzJw5EwsLC+Lj4/nqq68oX748EyZMAGDv3r2sWLGCr7/+mqZNm7J161Y2b95M48aNCx3D4sWLuXr1KqtXr8bExIS4uDh0dKTbhhBClDWS3N8R9erVUxI3gLW1tfK5du3a+Pr6smLFCmWb9evX89lnn+Hp6QnAlClTiIqKeqsYEhMT+eCDD7C3twfA0tLyrcoTQghRPCS5vyPs7OzyfT958iShoaFcu3aNp0+fkp2dTU7Of192cu3aNQYMGJBvHwcHB65fv17oGPr06cPYsWP5448/aN26Nf/3f/9HkyZNCl2eEEKI4iFtqu8IIyMj5XNCQgLDhw+ncePGLFu2jB07duDn50dmZuZbHUOlUpGb+9/Z2rKysvKt79ChAz///DMDBgzg1q1b9OvXjxUrVrzVMYUQQhQ9Se7voAsXLgB5Te0ODg5YW1uTlJSUb5t69epx7ty5fMv+9/v/qlq1Kvfu3VO+X7p06aVtqlWrRq9evVi0aBFffvkl27ZtK+xpCCGEKCbSLP8OsrKyIiMjgw0bNtCuXTtiY2PZuXNnvm369+/PzJkzsbOzw8HBga1bt3Lr1q3XDn/76KOP2LBhA02aNCE1NZVFixblW79kyRIaN25MgwYNSE1NJSoqinr16hXLOQohhCg8uXN/B9na2jJ16lRCQ0Pp2rUrhw4dYty4cfm26datG8OGDSMoKIiePXvy+PFjpXPdX/H396dq1ar07duXr776ijFjxuRbr6enx4IFC+jatSve3t6YmJgwZ86cIj8/IYQQb0eV++JDViFKQOfOXWRWuALSvlnhjN684VsyM6tEcvLjYj+OtpD60lxJ15mOjgpT04qa7VNMsQghhBCilEhyF0IIIbSMdKgTJS4iIpzsbO1oai5uuro6WlNXurry50aIkiL/20SJ09ExBKSrR0HI81AhRGFIs7wQQgihZSS5CyGEEFpGmuVFicvJSdOa58jFLTn5mdbUVd4z93KlHYYQ7wVJ7qLEeXv7yDj3AtK+ce6S3IUoCdIsL4QQQmgZSe5CCCGElpHkLoQQQmgZSe7vKC8vL4KDg0s7DCGEEGWQJHchhBBCy7wXyT0nJ4fQ0FDc3Nyws7PD1dWViIgIAGJiYvD09MTOzo42bdqwdOlScnL+2zvZ1dWVsLAwJk6cSNOmTXFzc+PEiRMkJibyxRdf0LRpU3r37k18fLyyT0hICJ6ennz//fe0adOGpk2bMnnyZNLS0pRtjh49St++fWnevDnOzs74+vpy+3b+HuSXL19myJAhODo64uTkhJeXF3fu3MHf359ff/2VtWvXolarUavVJCQkEBsbi1qtJjo6mu7du9O0aVN8fHy4c+dOvnK3bNlCp06dsLe3p3Pnzmzfvl1Zl5GRwaxZs3BxccHe3h43NzfWr18PQG5uLkuWLKFdu3bY2dnRtm1bFi5cWGS/JyGEEEXjvUjuy5YtIzw8nLFjx7Jv3z7mzZuHsbExSUlJDBs2DCcnJ3bv3s20adMIDw9n3bp1+fZfu3YtLVq0YNeuXXz44YdMnjyZmTNn4u3tzfbt29HT02PWrFn59omLi+Pnn39m1apVrFixglOnTjF//nxlfVpaGoMHD2b79u2sXbuWtLQ0xo8fr6xPSUlh4MCBVKpUiQ0bNrB161Y8PDzIzs5m+vTpODo68vnnnxMVFUVUVBQ1a9ZU9g0JCeGrr75i06ZN3Lt3L1/z/Q8//MDSpUuZPHky+/btY/To0QQGBnL48GEA1q9fT2RkJIsXL2b//v3MnTuX6tWrA3DgwAEiIiIICAjg4MGDLF68GGtr66L7RQkhhCgSWj/OPT09nVWrVhEQEEC3bt0AqFOnDgALFy7E0tKS6dOno1KpsLGxISEhgTVr1uDj46OU4erqSu/evQEYOXIkO3fupE2bNrRv3x4Ab29vJk2aRE5ODjo6eddLmZmZBAcHU61aNQCmTJnCpEmTmDRpEhUqVMDd3T1fnAEBAbi5uZGUlESNGjX4/vvvMTExYcGCBejq6gJgY2OjbK+vr4+hoSFmZmYvnfP48eNxcnIC8p7NL1myRFkXEhLC1KlT6dixIwCWlpZcvHiRzZs307FjR27fvo2VlRXNmjVDpVJhYWGh7Hv79m2qVatGq1at0NfXp1atWjg6Omr4GxFCCFHctP7OPT4+nvT0dJydnV9ad/XqVRwdHVGpVMoyJycn7t69y5MnT5RlarVa+WxqagpAgwYNlGXVqlUjMzOTR48eKcssLCyUxA7g6OhIZmYmN2/eVOIaP348rq6uODo64uHhAUBiYiKQ1yTfrFkzJbFromHDhspnMzMz7t+/D0Bqaio3btzA398fR0dH5Sc8PJwbN24A0L17dy5evMgnn3xCYGAg0dHRSlmdOnUiLS2Njz/+mL/97W8cOXIk3yMMIYQQZYPW37kXBT29/1bT8wuBF5c9p0miGzFiBJaWlsydOxczMzOePn1K7969yczMLPJ4c3PzZmBLTU0FICgoiMaNG79yH3t7e44cOcLx48c5ceIEo0aNokuXLsyZM4datWpx4MABTpw4wYkTJ5g+fTqNGzdm9erV+S6QhBBClC6tv3OvW7cuBgYGxMbGvrTOxsaGs2fPKskP4MyZM5ibm1OxYsW3Ou6tW7eUO2aAf/7zn+jr62NpaUlKSgpxcXGMGjWKFi1aYGNjw8OHD/Ptr1arOX36NNnZ2a8sX19f/y/X/ZVq1aphZmbGzZs3sbKyyvfzYvN75cqV6dq1K0FBQQQGBrJz507lwsXQ0JCOHTvy1VdfERYWRlRU1EsdAYUQQpQurb9zL1++PEOHDmXevHno6urStGlT7t69S0JCAv369SM8PJzAwED69evHlStXCAsLY/To0W99XH19ffz9/Zk4cSIPHz4kODiYnj17UqFCBQwNDTExMWHz5s2Ymppy8+ZNvv3223z79+/fn/Xr1zN58mQGDx6MkZERp0+fplWrVtSqVQsLCwvOnTvHrVu3lPIKwtfXl/nz51OhQgVcXFxIT0/n3LlzZGdnK/VRvXp1bG1tyc3N5fDhw1hZWaGjo8OOHTvIycnBwcGB8uXL8+OPP1K5cuV8jx+EEEKUPq1P7pDXCU6lUrFw4UKSk5MxNzfHx8eHGjVqsHLlSubPn8+mTZuoUqUK3t7eDBw48K2PaW1tTbt27RgyZAhPnjzBzc0NPz8/AHR0dFi0aBFz5syhS5cu1K9fHz8/PwYNGqTsX6VKFSIiIggODqZfv37o6elhb29P27ZtARg0aBD+/v507tyZZ8+eceTIkQLF9fnnn2NoaMjatWuZN28eFSpUwNbWliFDhgB5d+ZhYWHcuHEDXV1dHBwcCAkJAfLu6MPCwpg7dy65ubl88MEHhIWFUa6cTAYihBBliSr3xTZpUSRCQkKIjIxkx44dpR1KmdS5cxeZFa6AtG9WOKNiP46ZWSWSkx8X+3G0hdSX5kq6znR0VJiaavaoWOufuQshhBDvm/eiWV6ULRER4WRna8fdaHHT1dXRmrrS1ZU/N0KUFGmWFyXu/v0n5OTIP7uCkCZTzUmdaUbqS3PSLC+EEEKIEifJXQghhNAyktyFEEIILSM9XESJy8lJ05pOYprK61Qm7wUQQhQvSe6ixHl7+7y349zzxnpLchdCFC9plhdCCCG0TIkm96ysLKZMmcKHH36IWq3mjz/+KMnDF4harSYyMlKjfTw9PZVXtBa2jJIWEhKCp6dnaYchhBCiGJRos/yBAwc4fPgwERERmJubU6VKlWI7llqtJjQ0lA4dOhTbMf5KVFQUxsbGJX5cTQwaNIgBAwaUdhhCCCGKQYkm9+vXr2NlZYWdnd1fbpORkfHOT0RiZmZW2iG8UYUKFahQoUJphyGEEKIYlFizvL+/P4sXL+bChQuo1WpcXV0B8PLyYs6cOcyZMwdnZ2dlutXVq1fTtWtXHBwcaN++PUFBQaSnp+cr8/Dhw3h6emJvb0/Lli2VWdeelz1ixIh8x4qPj2fEiBG0atUKR0dH+vTpw6lTpzQ6j6dPnzJp0iSaNm1KmzZt+P7771/a5sVm+YSEBNRqNfv376dv3740adKEPn36kJiYSExMDB4eHjg6OjJu3DhSU1OVMnJycggNDcXV1RUHBwc+/fRTjh49qqyPjY1FrVYTHR1N9+7dadq0KT4+Pty5c0fZJiYmhp49e+Lg4MCHH35I//79efDgAfBys3xOTg5LliyhTZs22NnZ4enpya+//qrR8YQQQpQNJZbcp0+fzqBBg7C1tSUqKopt27Yp67Zv346RkRGbNm1i6tSpAOjp6TFz5kx+/PFH5syZw+HDh1m2bJmyT2xsLGPHjqVjx47s2rWLNWvWoFarAZSy58+fn+9YT58+pUOHDoSHh7Njxw4cHR0ZPny4kvAKIjg4mDNnzrBixQpWrVrFkSNHiIuLe+N+S5YsYcyYMWzbto309HQmTJhAaGgoc+fOZc2aNcTGxhIREaFsHxYWxp49e5g9ezZ79+6lb9++jB49mosXL+YrNyQkhK+++opNmzZx7949goODgbz+DaNHj8bZ2Zm9e/eyceNGevTo8ZfxhYeHs27dOqZNm8bu3btxcnJi2LBhLyXvvzqeEEKIsqPEmuUrVaqEkZERurq6LzVb16tXjwkTJuRb5uPjo3yuXbs2vr6+rFixQtlu6dKldOvWjVGjRinbNWrUCICqVasCefOPv3isxo0b07hxY+W7v78/Bw8e5JdffqF79+5vPIcnT56wY8cOFi1aRMuWLQGYN2+e0jLwOkOHDsXFxQWA/v37M2PGDHbt2sUHH3wAgLu7O7GxsYwcOZKMjAzCwsJYt24dTZo0AaBPnz7ExMSwZcsWvv76a6Xc8ePH4+TkBOS1gixZskSJ9fHjx7Rv3x5LS0sAGjRo8JfxrV27luHDh+Pu7g7kXYxFR0ezceNGxo8f/8bjCSGEKDvKxDj3Vz2DP3nyJKGhoVy7do2nT5+SnZ1NTs5/X3xy5coVevbsqdFxnj59SkhICEePHuXevXtkZ2fz7Nkzbt8u2JjrhIQEMjMzlYQLUL16dWrVqvXGfZ+3KgCYmpoCUL9+/XzLfvvtNyCvb0JaWhre3t75ysjMzMTZ2TnfsoYNGyqfzczMuH//PgAmJiZ069aNIUOG0KpVK1q3bo27u7ty7Bc9fvyY5ORkJWkDqFQqHB0duXr1aoGOJ4QQouwoE8ndyMgo3/eEhASGDx/OgAEDmDhxIpUrV+bEiRPMnj37rY4THBxMTEwMfn5+WFpaYmBgwLBhw8jMzHyrcgtCT++/Va1SqQDQ19fPt+z5BH3Pn72vWrXqpVYOAwOD15b74iR/33zzDd7e3hw/fpydO3fy3Xff8Y9//OO1d/CanodMKiiEEGVPmXyJzYULFwCYMmUKDg4OWFtbk5SUlG+bhg0bEhsb+5dl6Ovrk52dnW/Z2bNn6dmzJx07dkStVmNiYvJSua9Tu3Zt9PX1OX/+vLIsOTmZxMTEApdREDY2Nujr65OUlISVlVW+H3Nzc43KsrOzY9SoUWzbto3q1atz4MCBl7apVKkSZmZmnDlzRlmWm5vL2bNnsbGxeevzEUIIUbLKxJ37/7KysiIjI4MNGzbQrl07YmNj2blzZ75tRo8ezaBBg6hTpw6dOnUiIyODmJgY5Vm9hYUF0dHRODg4UK5cOYyNjbGysuLAgQO0a9eOnJwcvv32W3R0Cn59U7FiRXr06EFwcDCVK1fG2NiYb775JvKFIKAAABJ5SURBVN8deFGoWLEiPj4+BAYGkpWVhZOTE48ePeLUqVOYm5srz8Vf5+bNm2zZsgVXV1fMzc25fPkyt2/fpl69eq/cftCgQSxfvhxLS0saNmzIxo0buXXrFv369SvScxNCCFH8ymRyt7W1ZerUqYSGhvLNN9/QokULxo0bx4wZM5RtnJ2dWbhwIcuWLWPZsmVUrlyZdu3aKeunTJnCvHnz2LTp/7V3/8FUZ/8fwJ/cUj51RVhUdnaTe5NfoaIirNjNxzYVsUWNRL/oh0gZ7SosKf1kmCR9JTJWmXYbqd1haxrUbjslth/bT6S265ZS8vOe7x99vKfrqujHlev1mLkze8857/M+75f39rrv837fe3Kgo6ODoqIibNiwAeHh4fDy8oKmpiaWL1+Ox48f92hsGzZsQGRkJJYsWQI+n49ly5b1uI/uCAkJgaamJlJSUnDv3j2oqanB1NQUgYGB3dpeVVUVN2/eRH5+Purr66Grq4vly5fD1dW1y/a+vr549uwZYmJiUF9fD4FAgNTU1B7PFBBCCOl9SoxumhI5c3X9bz9fOOY/b2/4P9rafIhEDR9xRIqHYtYzFK+ek3fMlJWVoKk5tGfbfKSxEEIIIaSXUHInhBBCFMwnec+dKLaMjP9De7vk7Q0VEI9H/8sRQj4++peGyN2AAf+BsjI96tFdyspKvT2EPodi1jMUr56TZ8zeZV/0QB0hhBCiYOieOyGEEKJgKLkTQgghCoaSOyGEEKJgKLkTQgghCoaSOyGEEKJgKLkTQgghCoaSOyGEEKJgKLkTQgghCoaSOyGEEKJgKLkTQgghCoaSO/mgsrKy8NVXX8HU1BSenp4oLy8HADDGEBMTgwkTJmD69Ok4c+aM1HaHDh3C2rVre2PIvSIxMRFCoVDq9c033wCgWHX4448/sGzZMtja2kIoFKK4uFiqvrm5GZs3b4a1tTUsLCywcuVKiMVirv7Ro0fw9/eHhYUFvvvuO1RXV0ttv2rVKuTk5MjlWOTlbTFbsGCBzHn3ww8/cPX9LWZ79+6Fu7s7LCwsMHnyZAQFBeHOnTtSbfrqeUbJnXwwBQUFiIuLQ2BgIPLz8yEUCuHv749Hjx6hqKgIhYWFyMjIgI+PD8LCwiCRvFwZ7t9//0V6ejoiIiJ6+Qjka+zYsTh79iz3ys7OBgCK1f80NjZCKBQiMjKyy/rY2FgUFxdj165dyMzMxMOHD7Fq1Squfu/evWhpacHRo0cxcuRIxMfHc3VFRUWoq6uDl5fXRz8OeXpbzABg3rx5UuddWFgYV9ffYnb+/Hl4e3sjNzcXBw4cQEtLC/z8/NDU1MS16bPnGSPkA/Hw8GBRUVHc+/b2dmZra8vS0tJYamoqCw4OZowx1tTUxAQCAROLxYwxxgIDA1lubm6vjLm37Nmzh82ePbvLOoqVLIFAwIqKirj3T58+ZcbGxqywsJAru3HjBhMIBKy8vJwxxpi/vz87fPgwY4yx06dPM1dXV8YYY8+ePWPOzs7sxo0bcjwC+escM8YY8/HxYVu2bHntNv09ZmKxmAkEAnbhwgXGWN8+z+jKnXwQLS0tqKysxNSpU7kyZWVlTJkyBRcvXoRAIEBlZSUaGhpQVlYGbW1taGho4LfffsOTJ0/g4eHRi6PvHbdu3YKtrS2cnJywbt06PHjwAAAoVt1QUVGB1tZWqfPNwMAAI0aMwMWLFwEAhoaGOH/+PNrb23Hu3DkIBAIAwO7du+Hm5gYDA4NeGXtvy8/Ph7W1Ndzc3LBz506pq9T+HrOGhgYAwLBhwwD07fOMkjv5IB4/foz29nZoaWlJlWtqakIkEsHe3h4uLi6YNWsWdu3ahR07dqCxsRFbt27F5s2bkZSUBBcXF3h7e+PmzZu9dBTyY2Zmhri4OKSlpWHTpk2orq6Gt7c3GhsbKVbdUFdXh8GDB2Po0KFS5ZqamqirqwMALF26FG1tbXBycsK1a9cQFhaGiooKlJSUwMfHByEhIXByckJISAiePXvWG4chd25ubti2bRsOHjyIgIAA5OfnY/369Vx9f44ZYwxxcXGYNGkSl5D79HnWK/MFROE8ePCACQQCdunSJany+Ph45uXl1eU2MTExLCkpiRUVFbEZM2awhoYGlpOTw+bMmSOPIX9Snjx5wiwtLdnRo0e7rO/vseo8xfzzzz8zMzMzmXbu7u5sx44dXfbR1tbG5syZw86fP8+2bNnCwsLCWGtrKwsJCXnjVHVf1dW0fGclJSVMIBCwmpqaLuv7U8w2bdrEHB0d2YMHD7iyvnye0ZU7+SA0NDTA4/G4T7MdxGIxtLW1ZdpfvnwZZWVlCAgIQFlZGezt7TF06FB8++23qKioULirgrdRU1PDF198gbt378rUUaxkaWlpoampSebYxWKxzOxRh8zMTBgZGWHixIkoKyvDjBkzMGDAALi5ueHcuXPyGPYnx9zcHABQVVXVZX1/iVl0dDSKioqQkZEBHR0drrwvn2eU3MkHoaKiAmNjY5SUlHBlEokEpaWlGD9+vFTb9vZ2REZGYtOmTVBRUYFEIkFbWxsAoLW1ldu2P3n+/Dmqq6tlPghRrLpmYmKCgQMHSp1vt27dQm1trcz5BgC1tbXIysringzvHMf29nb5DPwTc+XKFQDo8gN4f4gZYwxRUVE4deoUMjIyoK+vL1Xfl8+zAXLbE1F4ixYtwvr162FsbAwzMzNkZGSgqakJs2fPlmqXkZEBU1NTWFlZAQCsrKwQFxeHOXPm4MSJEzA0NISamlpvHILcxMfHw9HRESNGjMDDhw+RmJgIHo8HV1dXqXb9OVbPnz+XuqKsqanBlStXoKWlBW1tbbi7uyMuLg5qamoYOnQo99sApqamMn1FRUUhODiYi5WVlRUOHz6M0aNH4/Dhw1x8+7o3xezFixf45ZdfYG9vD3V1dVy7dg1xcXGwsbHBmDFjZPrqDzHbvHkzjh8/juTkZAwZMgQikQgAwOfzMXjwYPD5/L57nsntBgDpFzIzM5mDgwMzNjZmHh4eMvfga2pqmIuLC3v69ClX1t7ezqKjo9mECROYm5sbu3z5sryHLXdr1qxhU6dOZcbGxszOzo4FBwezqqoqqTb9PVZlZWVMIBDIvPbs2cMYe/k1wU2bNrGJEycyc3NzFhQUxEQikUw/J06cYAEBAVJlYrGYLV68mI0fP54tXryY+6phX/emmNXW1jJvb282adIkZmJiwpydndm2bdtYQ0ODTD/9JWZdxUogELAjR45wbfrqeabEGGPy+yhBCCGEkI+N7rkTQgghCoaSOyGEEKJgKLkTQgghCoaSOyGEEKJgKLkTQgghCoaSOyGEEKJgKLkTQmQkJiZCKBTKvHx9feWy/5s3byIxMVHmZz9/+uknCIVCNDc3y2Uc3fXnn39i1qxZMDU1xbhx4z7qvkJDQ+Hp6cm97xwTkUiExMRE1NbWftRxkE8b/UIdIaRLfD4faWlpMmXycPv2bSQlJWHu3LlSK3I5OTnB0NAQKioqchlHd23cuBF6enpIT0+X+9g6x6Surg5JSUmYPHkyRowYIdexkE8HJXdCSJd4PF6Xv5/9Ok1NTRg8ePBHHBEwfPhwDB8+/KPuo6ckEgnu3r2LBQsWYOLEiXLf/6cYE9L7aFqeENJjbW1tEAqFyMjIQExMDGxsbDBr1iwAQFFREXx9fWFjYwNLS0t4eXlJLbzR4erVq1iyZAmsrKxgYWEBT09PlJaWoqSkBIGBgQAAe3t7CIVCODs7A+h6Wl4sFmPdunWYNGkSzM3NsXDhQlRWVkrta9q0aUhISEB6ejrs7OwwadKkbq+vXVJSAg8PD5iammLq1KmIiorCixcvuDojIyNIJBJERUVBKBQiIiKiy34YY0hJScH06dNhamqKKVOmwN/fH2KxmOtLKBSipKQEAQEBGD9+PBwdHZGbm/vG8b0ak7t373J/B29vbwiFQu42QUtLC+Li4mBvbw8TExPY2toiKCiIW9iEKBa6cieEvFbnf/h5PB6UlJS49/v27YO1tTW2bt2Kjl+yrqmpgZOTE/z9/aGkpITff/8dixcvRk5ODrfE6D///IN58+bBwMAAUVFRGDZsGCoqKnD//n24uLggNDQUCQkJSElJwfDhwzFo0KDXjnH58uWora1FeHg41NTUkJaWhgULFuDYsWNSq3wdP34cRkZGiImJwf3797FlyxZoaGhg48aNr+376tWrCAgIgJ2dHVauXIl79+4hISEB9+7dw969e2FmZobs7GzMnz8f/v7+cHZ2hqamZpd9HTlyBPv27UNoaCjGjBmD+vp6lJaWoqmpSapdeHg4Zs+ejYULF+LkyZP4/vvvoauri2nTpr12nB309PQQHx+P9evXcx82Ov5eKSkpKCgowNq1azFq1CiIRCKcPn26X64q2B9QcieEdKm+vh7GxsZSZQcOHMCUKVO49zo6Oti+fbtUm4ULF3L/LZFIYG1tjevXryMvL49L7omJiVBXV0dWVhaXuG1tbbntvvzySwDAuHHjoKur+9oxFhcX49KlS8jOzuZW3LKxsYGjoyPS09MRGRnJtR00aBCSkpLA4/EAANevX8epU6femNyTk5Ohr6+P5ORkKCu/nOjk8/kIDQ1FeXk5zMzMuGMaNWrUG29jlJeXY9q0aZg/fz5X5uLiItPO0dERa9asAQDY2dmhqqoKKSkp3UruKioqEAqFAAADAwOp8ZSXl2PmzJlSqzR2XoWQKA6alieEdInP5yMvL0/qZWZmJtXGwcFBZrv79+9j3bp1sLOzw7hx42BsbIyysjLcuXOHa3Pu3Dm4urq+8Yq8O8rLy6GtrS21lOaQIUNgb2+PCxcuSLW1trbmEjvwMvmJRKI3rrFdXl4OFxcXLrEDwNdffw1lZWX89ddfPRqrkZERiouLkZiYiPLy8tdeMXfcgnj1fUVFBd53jS8jIyPk5eVh//79uHbt2nv1RT59dOVOCOkSj8frcs3qV3Wegm5vb8fSpUvR3NyMNWvWQF9fH6qqqti5cyd3f5sxhvr6emhra7/3GEUiEbS0tGTKtbS0cP78eamyzuveq6ioQCKRoK2tTSrpd+6/8zGqqKhATU0N9fX1PRqrp6cnXrx4gdzcXCQlJWH48OGYN28egoKCpD48dN6fpqYmWlpaUF9fDw0NjR7t81VBQUHg8Xg4dOgQtm7dCl1dXQQEBMDHx+ed+ySfLkruhJB39ur9d+DlV9iuXbsmM33/6n1lJSUlqKurQyQSvff+tbW1uQfSXlVXV4dhw4Z9kP4fPXokVdba2oqnT59CXV29R33xeDz4+fnBz88PtbW1OHbsGHbv3g09PT3MnTuXa9f5eMRiMVRUVHq8v84GDx6M4OBgBAcH4/bt28jOzkZ0dDRGjx4t9bciioGm5QkhH0xHEn/1u97V1dW4dOmSVDsbGxsUFBSgpaWly34GDhwIAG/9sRpzc3M8fPhQaoq8sbERZ86ckZqqf1dmZmY4deqU1BT6yZMnIZFIYGlp+c79jhgxAsuXL8eoUaNw48YNqbpff/1V5r2JiYnMB6nX6Yjd62ILvHymITw8HAMGDJDZP1EMdOVOCPlgDA0N8dlnnyE2NharV69GQ0MD9uzZAx0dHal2q1atgoeHB3x8fODr6wt1dXVUVlZCS0sLs2fPxujRowEAhw8fxowZM6CqqgqBQCCzPwcHB5ibm2P16tUICQmBmpoa9u/fj9bWVvj5+b338axYsQLu7u4ICgqCl5cXamtrsX37djg4OMg8f/A2ERER0NTUhJmZGfh8PkpLS1FTUwMbGxupdsXFxVBXV4eVlRVOnjyJc+fOITU1tdv7GTlyJFRUVHD06FGoqqpi4MCBMDExwbJly2Bubg4jIyMMGjQIJ06cAIBe+W4++fgouRNCPpiOJ9KjoqKwcuVK6OnpYcWKFTh79iyqqqq4dgYGBsjOzkZCQgIiIiKgpKQEQ0NDBAcHAwD09fURGhqKrKwsHDx4ECNHjpS5ou2QkpKCuLg4/Pjjj2huboa5uTkyMzOlvgb3rsaOHYvU1FTs2LEDgYGB4PP5mDlzJkJDQ3vcl4WFBfLy8pCTk4OWlhZ8/vnniI2NhaOjo1S72NhYpKen48CBA1BXV8fmzZthb2/f7f2oqqoiOjoaycnJKCwshEQiwd9//w1LS0sUFhYiLS0NEokEhoaGSEpKgpGRUY+PhXz6lNj7PoJJCCHkvZWUlGDRokUoKCiAgYFBbw+H9HF0z50QQghRMJTcCSGEEAVD0/KEEEKIgqErd0IIIUTBUHInhBBCFAwld0IIIUTBUHInhBBCFAwld0IIIUTBUHInhBBCFMz/A8TsH4IwpUq0AAAAAElFTkSuQmCC\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": "np.random.seed(1)\nmodels \u003d [\u0027plain\u0027, \u0027at_cube\u0027, \u0027robust_bound\u0027]\n# models \u003d [\u0027robust_bound\u0027]\nexp_folder \u003d \u0027exps_diff_depth\u0027\nweak_learner \u003d \u0027tree\u0027\ntree_depth \u003d 4\n# datasets \u003d [\u0027breast_cancer\u0027, \u0027diabetes\u0027, \u0027cod_rna\u0027, \u0027mnist_1_5\u0027, \u0027mnist_2_6\u0027, \u0027fmnist_sandal_sneaker\u0027, \u0027gts_100_roadworks\u0027, \u0027gts_30_70\u0027]\n# datasets \u003d [\u0027mnist_1_5\u0027, \u0027mnist_2_6\u0027, \u0027gts_100_roadworks\u0027, \u0027gts_30_70\u0027]\ndatasets \u003d [\u0027breast_cancer\u0027]\nfor dataset in datasets:\n _, _, X_test, y_test, eps \u003d data.all_datasets_dict[dataset]()\n if dataset in data.datasets_img_shapes:\n flag_image_data \u003d True\n final_shape \u003d data.datasets_img_shapes[dataset]\n feature_names \u003d np.arange(X_test.shape[1])\n sns.set(font_scale\u003d0.4)\n else:\n flag_image_data \u003d False\n final_shape \u003d (1, X_test.shape[1]) # still important\n if dataset in data.datasets_feature_names:\n feature_names \u003d data.datasets_feature_names[dataset]\n else:\n feature_names \u003d [\u0027f\u0027 + str(i) for i in np.arange(X_test.shape[1])]\n sns.set(font_scale\u003d1.25)\n\n model_names \u003d utils.get_model_names([dataset], models, exp_folder, weak_learner, tree_depth)\n for i, model_name in enumerate(model_names):\n print(\u0027Model name: {}\u0027.format(model_name))\n model \u003d model_name.split(\u0027model\u003d\u0027)[1].split(\u0027 \u0027)[0]\n eps \u003d model_name.split(\u0027eps\u003d\u0027)[1].split(\u0027 \u0027)[0]\n \n model_path \u003d model_name + \u0027.model.npy\u0027\n metrics_path \u003d model_name + \u0027.metrics\u0027\n metrics \u003d np.loadtxt(exp_folder + \u0027/\u0027 + metrics_path)\n valid_errs, valid_adv_errs \u003d metrics[:, 8], metrics[:, 10]\n # Model selection\n # best_iter \u003d len(valid_errs) - 1 # otherwise, the counts are not comparable between different model types\n if model \u003d\u003d \u0027plain\u0027:\n best_iter \u003d np.argmin(valid_errs)\n elif model in [\u0027at_cube\u0027, \u0027robust_bound\u0027, \u0027robust_exact\u0027]:\n best_iter \u003d np.argmin(valid_adv_errs)\n else:\n raise ValueError(\u0027wrong model name\u0027)\n print(\u0027Best iter to take the model: {}\u0027.format(best_iter))\n \n if weak_learner \u003d\u003d \u0027stump\u0027:\n # the hyperparameters of recreated models do not matter (they matter only for training)\n ensemble \u003d StumpEnsemble(weak_learner, 0, 0, 0, 0, 0)\n elif weak_learner \u003d\u003d \u0027tree\u0027:\n ensemble \u003d TreeEnsemble(weak_learner, 0, 0, 0, 0, 0, 0, 0, 0, 0)\n else:\n raise ValueError(\u0027wrong weak learner\u0027)\n model_ova \u003d OneVsAllClassifier([ensemble])\n model_ova.load(\u0027{}/{}\u0027.format(exp_folder, model_path), iteration\u003dbest_iter)\n \n # importance visualizations for trees\n coords_per_tree \u003d np.zeros(X_test.shape[1])\n for tree in ensemble.trees:\n if weak_learner \u003d\u003d \u0027stump\u0027:\n coords_per_tree[tree.coord] +\u003d 1\n else:\n coords_curr_tree \u003d np.array(tree.to_list(), dtype\u003dint)[:, 6]\n for coord in coords_curr_tree: # 6 is coord, 7 is min_loss\n coords_per_tree[coord] +\u003d 1\n if flag_image_data:\n coords_per_tree \u003d coords_per_tree.reshape(final_shape)\n coords_per_tree \u003d coords_per_tree.sum(2) if len(final_shape) \u003d\u003d 3 else coords_per_tree\n # cbar_kws \u003d {\u0027ticks\u0027: np.linspace(0, coords_per_tree.max(), 6)}\n # set annot\u003dTrue for plotting also the number of splits\n ax \u003d sns.heatmap(coords_per_tree, linewidths\u003d0.0, square\u003dTrue, cbar\u003dFalse,\n xticklabels\u003dFalse, yticklabels\u003dFalse)\n else:\n coords_per_tree \u003d coords_per_tree / coords_per_tree.sum()\n coords_per_tree \u003d pd.DataFrame({\u0027Feature\u0027: feature_names, \u0027Fraction of splits\u0027: coords_per_tree})\n \n # Assumed that the plain model goes first among the three\n if model \u003d\u003d \u0027plain\u0027:\n idx \u003d coords_per_tree.sort_values([\u0027Fraction of splits\u0027], ascending\u003dFalse).index\n coords_per_tree \u003d coords_per_tree.loc[idx]\n \n ax \u003d sns.barplot(x\u003d\u0027Fraction of splits\u0027, y\u003d\u0027Feature\u0027, data\u003dcoords_per_tree, color\u003d\u0027black\u0027, alpha\u003d0.8)\n # palette\u003d\"RdBu\", linewidth\u003d3.5, facecolor\u003d(1, 1, 1, 0), edgecolor\u003d\u0027.1\u0027)\n ax.set_xticklabels([\u0027{:.0%}\u0027.format(x) for x in ax.get_xticks()])\n plot_name_save \u003d \u0027count_coord_splits-exp\u003d{}-dataset\u003d{}-weak_learner\u003d{}-model\u003d{}\u0027.format(\n exp_folder, dataset, weak_learner, model)\n plt.savefig(\u0027plots/{}.pdf\u0027.format(plot_name_save), bbox_inches\u003d\u0027tight\u0027, pad_inches\u003d0.0,\n transparent\u003dTrue)\n plt.show()\n",
"metadata": {
- "collapsed": true,
"pycharm": {
+ "metadata": false,
+ "name": "#%%\n",
"is_executing": false
}
- },
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": "# Feature importance visualization (gif)",
+ "metadata": {
+ "pycharm": {
+ "metadata": false
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
"outputs": [
{
"name": "stdout",
"text": [
- "The autoreload extension is already loaded. To reload it, use:\n %reload_ext autoreload\n"
+ "(1990, 784)\nModel name: 2019-07-08 18:04:42 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d100 eps\u003d0.300 max_depth\u003d4 lr\u003d1.0\nBest iter to take the model: 150\nEnsemble of 150 learners restored: exps_diff_depth/2019-07-08 18:04:42 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d100 eps\u003d0.300 max_depth\u003d4 lr\u003d1.0.model.npy\n"
],
"output_type": "stream"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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OuvV1Kl4VbxP2zPT22WyebtuTU/GqfJvIwp5ZU293ui97u0PV/ZbRXcGeWTNvlbN5um1PTsWr8m0iC3tm4tOYuZpiz0yUwtvD5svDWt+wmiLEJHCYCTEJ7Jk1827obDf3wIB9F1xfP8mq2n7Xh86emd5KZ3PVBZt5v9kzN4Cuq7c7vTH7UjPvN3tmBTtNX/Vu7GyuumAz7zd7ZuLzmLkH9jbsmYlScFgbDlZThJgEVlOaeaucTVdvZzqrKXr7bDZdvWV0GVhNaertTqe3mrorWE1p5q1yNl29ZXQZWE0Roij8FUhCNIU9s0lw9eEMT7tefvDDN+CRmRCTwJ7ZJN6uTjl789cMbydbfZ3OtjGuF1/wdqazZ6a3V08568m2PfX25duEPXMD6Gb2docn2TzZtqfevnybsGc2Wa/YmF8z9DSbJ9v21NuXbxP2zEQavpttPuraM3OYCVEUfmiEEE3hMBNiEtgza+atcjZdvZ3p7Jnp7bPZdPWW0WVgz6yptzud3mrqrmDPrJm3ytl09ZbRZWA1RYii8FS7XsTdhydcwQ9WEG/DaooQk8BhJsQksGeup+8M34qqP13qjeuF3uyZfbpXdPe9XlV/utSdTm/1bhNZ2DN7+TvDnmzbl68XentHdwV7Zi99Z1jVny51p9NbvdtEFvbMdYDVFGlI2DN7EU8G0pMHAhn4YEFYTRFiEjjMhJgE9swN5H1zRw3Uvad2p/Hc1b7r7Uxnz+wD3oBnPTXPXW0ubxldBvbMCveGnuKr14uu3jK6K9gzN1Jv6ElPzXNXm8tbRpeBPXMDwWqK1BX2zB7g6cC5GigOG/E2rKYIMQmspurpK46AfD2kYg1Cb/VuE1ZT9eTtSXXkqbfK1wu9WU0pWwd4uzpSdb/d6fRWU3cFqykvVEeeeqt8vdCb1ZRP4M13swmpK6ymPIDDSHwZVlOEmAQOMyEmgT2zZt4qZ9PV25nOnpnePptNV28ZXQb2zJp6u9PprabuCvbMmnmrnE1XbxldBvbMhChKXXtmvptNiEngMBNiEjjMhJgE9syaeaucTVdvZzp7Znr7bDZdvWV0Gdgza+rtTqe3mror2DNr5q1yNl29ZXQZ2DMToijsmQnRFA4zISaBw0yISWDPrJm3ytl09Xams2emt89m09VbRpeBPbOm3u50equpu4I9s2beKmfT1VtGl4E9MyGKov15s12dyJ7nxSZmhtUUISbBdNWUq59llf3JVW/rKtYg9FbvNmE1dROufnaVNYh62XT1ltFl0KKa8ua2Vd5vX82mq7eM7gpTV1OufnaVNYh62XT1ltFlMF01xXeziVnQvpriwBJdYTVFiEngMBNiEkzXM9Pbd7Pp6u1MZ89Mb5/Npqu3jC6DFj0zveuu01tN3RWm7pnp7VvZdPWW0WUwXc9MiFngqXYJ0RQOMyEmgcNMiElgz6yZt8rZdPV2prNnprfPZtPVW0aXgT2zpt7udHqrqbuCPbNm3ipn09VbRpeBPTMhisKemRBN4TATYhI4zISYBPbMmnmrnE1Xb2c6e2Z6+2w2Xb1ldBnYM2vq7U6nt5q6K9gza+atcjZdvWV0GdgzE6Io7JkJ0RQOMyEmgdWUZt4qZ9PV25nOaorePptNV28ZXQZWU5p6u9PprabuClZTmnmrnE1XbxldBlZThCgKqylCNIXDTIhJ4DATYhLYM2vmrXI2Xb2d6eyZ6e2z2XT1ltFlYM+sqbc7nd5q6q5gz6yZt8rZdPWW0WVgz0yIorBnJkRTOMyEmAQOMyEmgT2zZt4qZ9PV25nOnpnePptNV28ZXQb2zJp6u9PprabuCvbMmnmrnE1XbxldBvbMhCgKe2ZCNIXDTIhJ4DATYhLYM2vmrXI2Xb2d6eyZ6e2z2XT1ltFlYM+sqbc7nd5q6q5gz6yZt8rZdPWW0WVgz0yIorBnJkRTOMyEmARWU5p5q5xNV29nOqspevtsNl29ZXQZWE1p6u1Op7eauitYTWnmrXI2Xb1ldBlYTRGiKKymCNEUDjMhJoHDTIhJYM+smbfK2XT1dqazZ6a3z2bT1VtGl4E9s6be7nR6q6m7gj2zZt4qZ9PVW0aXgT0zIYrCnpkQTbmtIzMhRD14ZCbEJHCYCTEJHGZCTAKHmRCTwGEmxCRwmAkxCRxmQkwCh5kQk8BhJsQkcJgJMQkcZkJMAoeZEJPw/3Jr5KspYvSgAAAAAElFTkSuQmCC\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": "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\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": "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\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": "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\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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uWyR9Xy67DpGmHJIaru/QDhm3d8PtPbuHbX/k2ZFISEhA+o3XW7Q0OzZq3KTEUlmJlH5DUFlyJuy2P326Cc0eG4iy40UR+abWTTc3p+8XO4g0dZmQp9nWyNPs0IQrTclm9sPkTc3tm+ObC3fsvVOtPwkB4Ivi/7D6phjTIPRX8pySb7Rta9pmFmlKMIpuIwvRRTazIMQI7M08eHB/TJn8BHreeVvYdk5b0767ZdyE1xaFzgNO2U32z/Xt5Lqkd++EX00YhC69u4XdnusbAPrNHInmN7YNu72T96pdRGc2oAOb7p/r28l1ObBjHxo1uxJlF8rCbs/13f6erji8JfRvdNGZ/4+aqvWazjTsZCZjN66L97wXS6e/jWZtrQ9umOSqDq3Q8ub2aNHVuiqJHURndqHWa1IHNt0/17eT65LR/w4M+q9MnAqhFXPmTdk3LvwY+Z98iSPbg4vuUe1FZ65hiDRlDWfs1NNskaZCE/c6M+fG4148nW+Thz4o3xTceXMOZlDr0idJXzZ3dcVxrV0Htea6edlpz0V0ZkGIU2QzC0KMEHPxzP6xueHGDHPjV3W+/eORgeDYWk48stPz5sQMU3Ha7RIDi8b3mzkSpSdPo+TEKQDAflWqbe9PuGuumxfAiyG3Y5d4Zj84uqRp37rY2po8b07MMNWW0omp9pw1Nx1DLjqzTTsHU77txNbWxHlzYoaptpROTLXnrPnliiEXndmAJmraty62tibPmxMzTLWldGKqPWfNTceQi85sgUhT4SPSlDU1TZqKuVS7Th7c0EFd+Beb99La9yWEzn0FAPvLT4W0URtqeIvbtXbqYIdubtS8qM1IzXt8hX6zr6qtaU9s1pqGSFOCECPIZhaEGCHmdGadncoPbVJnpvrundomoP/qeupPCRU+W3r3Tugx8C7UbVAXxw8crbJXnvfZw9V629ZO0/Z9wFsUsi01t+rzqj43Sie+MrGOdmztKy79Uuz4eH807nodLhQVw3uqqt99yaHXzX/NQvnX2URnjlLbaNid0pmptpSeSsUEc7ReTrwxwJsbNW5qbN6fSpBUJyXk2DjrJjqzTbubY0Sd6JvSU6mYYI7WG61440jmRo2bGtv+pXnY/T8fo/Vv7oiovc6/6MyE3ekYUad0ZqotpadSMcEcrZcTbwzw5kaNmxpby3634IbJD6DIwLqJzuwyKGnKpM5M9e2kNMWNOdbNjStNXZfcSGvve6G21q6TpnRrZgfRmQkozfOdIxu1dt2Gom7ad4gNx3kzoOY17Uie1k4dYNDd9EvSK7VtFx/RbwhqXXRjoxIAUIdGqA2XVRj5wQ9Ko16YpH+joe4H3dxMvBGINCUIMYLrpKmbUlv57FZSxNfFh7XtdfIQJcFw+gb0Eo3/vKz8U76pcDydhHNP6rkA3ym33Iy699yN2rffBu+WbdhenBpgry6L5ZUciHhs3BBIfyKRh3Rjy1CB4ZXVpa2vEvWymf/crO4H3dz8xx1q7DElTVFSAkceMtm3nfY6/9xwPFLC2bINZd8fwPk164JslCzmZAgkVx6i2uukLc68ojF2O7hamrIrk0TyON9k33ba2/EfaTienb5TOqajbE/wprWbjtaJEEiuPES110lb3HKw0ZK2dLhamqKkBI48ZLJvO+11/rnheNTckJwMVV5uaaJkMSdDILnyENVeJ21x5hWNsdvBddKUo0+zGX0DvKfZlG/O0+wZzUNs6v9j8RH9t5NpR/O0dpMhkBScMEbu02zO3Ow8zY75VLucMEY3xxRTcMdOvRlwoGKOdRo5V+vl1Fim3mCpsVEauO4N2s698n1heJ/SIk3FASY3MgV12EWIHrKZBSFGcJ3OTNk5KWXdnO42nLGHq7f6a62h2tuxhbLr0uH6h24C+jDESHxTWi9H+6fGRoVn6s4OUOG4ADBxyvigNdDhap3ZdHlRt6a7pewm9Vau1ssJ3+T6Nqn9c7V97v1iB1frzHbtkeL2dLehMKm3crVeTvhmtHRmiki0/2ho+zrf0cDVOrPp8qJuTXdL2U3qrVytlxO+yfVtUvvnavvc+8UOIk35EavSlOmn2TppihO6aQeRpi4Rc5uZE5NsOt5ZB7XhqJvWJLoax4A+zJF6E6I2BLd+s84/90CL2+KZRZoShBhBNrMgxAjszTx4cH9MmfwEet55W9h2TlugSpt7bdEfIhqbyb65vnvedycenToa3X52c0RjM23vN3Mkmt/YNqK2urmld++EX00YhC69uxkZN2ddqbbce5my2yGmdWaOb65myfHN1VNN2ikdmaPtc9P8cmOKTWr7kmrXpp2C45vTd6S+o6WnmsBuvHModHOLVprfSHxHq62k2jWk5XJ8czVLjm+unmrSTunIHG2fm+aXG1NsUtuXVLsWiDR1+RFpyhq3SVMxt5l1mNyMFCaTLgD6xAvtlD6VLpW7mhOvTPnmhkhyD3booK6JaURnFoQ4RTazIMQIcRXPzC3ZyrFz82ZT89blBG9crXBJ9bzYVFlVXbwyoC83W9139fZUqVrdmgH6UrYAHXOs65+6Jv5ISVcHY4adLifLyZtNzVvXN6UTm4xXptpTbbmlbDn9O32/2CEudGZO36bsnLzZFLq+KZ3YZLwy1Z5qyy1ly+0fcHf5YXbhuIv62MaNX4Vt57QFLumC3+zYjY3rvgzLzvXNtWf0vwOtO7fBvq17w25LzVvX98aFH6N1j04oKz0fZAOq9NTKikqk33g9vt0aLL1Q7XW+qfZUW8pOjZ3Tv9P3ix1EmrpMiDQVfls7iDR1CdeVdOWiE/qpzcw9NKLbsNThB+rQCHVA4QuNjSrJWrxUnzju8We3a+26DdWOeCOg6itTLEzS23Vjo9aU+wZ8uRFpShBihJiTpuyW0Yx2yVZAX472gDfwvHE0U+lSY/cft1X75wdnAAC2HzyBef/YhopKhbzdBVAKaHFlA3zyj0tpb8OVh6rLWkCgNNXYLxNv9ZKq1bGyU2VX/Qn3fuCWF9atuR27SFN+cFLpmkzbSvXPDbeLdOxdr22KDs2vxLHTpRh9943Y9UNh2PPiSFu6kqp27JxUvabLC4s0ZdMeimik0qXglqO14nKU/7TTPsHib1x5SCdN6Uqq2rFHI1Wv6fLCEgIZYZgiJ5WuybStVP/ccLtIx/7d8VPYduAEml1RD2+v+QadW6YFtaXmxUnFqyupasfOSdVrurywhEBaQD1x5pTRdPJpNjfcTjd2atwmn2ZTslZ7r9ZMQpVd1eH2p9kSNSUIcYrrPpkLs/R6a9qSyAPCuUnqqWLtuk+oVxH8VNefHif+rbVTOrSucPiq2rxE9NTBC93BEOpACpUgYFld68SBF1mVVF9r1yVO4J47oNCNnbregHwyC0Lc4upUu8ndfoaUPr9BSp9BUfcN6FOrUmlbqbSwVGrWuh2vxdWPD0KD2zqHPW6q746P90f6o/3QoI31p7Vu7FTf1LwBfSpeTrpb3ZrZ8a3r206qW879Qo095lPtorISiY3SoEqLo+4b0OuxnDBDgNY8G/XJQOVZ66+/3FS8lB5rsnQpN8RSt+66NbPjmxMSS7Wn7hdq7DGvMyekXoHzy3KQeKX+t4sTIZRcvTW5cUOczP0UqT1CJ8uLNBUvpceaLF3KDbHUQa0ZNw0wYC7c1s715vp3tc6Mykqk9BuCypIzUfcN6PVYSqvl6q0/fboJzR4biDKL9txUvJQea7J0KZWKl5PuVrdmdnzr+raj83LuF2rsMakzy9Nsa+RptjXyNPsSrguB5GxWQH8BqM3IRXvTl5dr21IHFDihgtSBFWpdqPa68EuAPhCjo3kr629lF1ldUKK16zbskrRe2rZZhXla+4vN9e17HNW3jzYiTQlG4WxkITxkMwtCjOBqndmkjkzppabtOt2RakvpyJSdsy5OllVNueVmNMgaitSxj0TUN3W/6NbNjg6s07ilpKtBHZmb1pVr1+mOVFtu3C9nXZwsq+rdsg1l3x/A+TXrIuqbul9060a1pTRuiWeOkt0KbtpVrl2nO1JtuXG/OtxcVhUAUjqmo2yP9abhxoHbWbdQbe1q3JJq10AqXm5aV65dpztSbVv2uwWNu16Hk5v3RGTnrAsnvTFAp8PVtk9OhtKoAlTf1P2iWzeqLZWCWFLtRoCT0pSOCYQ09edk/fsqR5qa6g2ttQJ8aUoH9TSb0pnXpwcnSPAnq0D/5VIXs2xamprGlKZivqQrpcfqDkBwb5yjBQ21dh37KvSHG7gHO0zWCuYentDB3eyzUvTHI78L/eiATIxAbWaKTU0zQtokBFIQhJDIZhaEGKFG68zcmGKOpknFp1J2nabJnZfJa8LVmXV2zvUCaH0d0GvBHJ2Zssd9PDM3l7FJTZOKT6XsOk2TOy+T14SrM3PyhVN9U/o6pQVzdGbKHvfxzJSdG1NModM0qfhUyq7TNKM1L5PXxATc60XpxJQWzNGZKXvcxzNzcxlzYmcpTZOKT6Xsuphj7rxMXhMqbpdjZ10v0HHcVLyzrj33Xo3LeGYKkaasEWnKmniSplwXz0xBHWAY0yD0xV1dV9939lEiCD8p8jrH19XmLTWVIGA9o29OYQGA90bCrZm9qgWjvnMKr5wslTDiwXPfR9w2EkSaEoQYQTazIMQIMVefuVtK04A+/GsB71eBNX+r1+u9MrGOz2ZVj7dxtV8l/n0DCOhf13eo/u3YQtk5tYL91xSIfu1onW+7tlB2qoaybt2o65lXckDru3X9Sz8/rNbldPmluti6NQ3VfuRvhwfNQUeN1pkp3ZGTw9l0fmhd/9xYaZPr6qTGzT13oLNz45G5GjnV3g41Wmem4ORwNp0fWtc/N1b6IibW1Q0ad6TnDnR2bjwyVyPntgdquM5M6Y6cHM6m80Pr+qd8m64VbLJ2tJPnDnR26npyc5lzaonbpcbpzJSMopWmiBzOnPzQVP9U31w4tYKdlKa4UOcOdFDXk4pHpuQlnUZuJ2tp3uHPyNf4U+N0ZgrdhqIOIIxP0RcG1x1AAPSJ6LNO5GnbUjclpa9ziq1z4fim3kioQyFTi/WJF3Rv7hTU2Kj7SZf0wUShdpGmBCFGqNEhkCbTujoZTmc63S1nXblhihzfnBTCF9FdM52N6p9bAjjuQyCNpnV1MJzOdLpbk9KUSd+cFMKA/ppR15Pqn1sCOO5DICk4j/udDKczne6W8q8jGhJKpL45KYQB/TWLRjlYHdGSE3XEtDTFkQucDKejJBZuGKJJacqkb+qacKRK6npS/XNLAMdlCCRHRjEZTgfoQ+qocDru02wd3CfKnDBF40+ziTTCnKfZunKwduA+zY75EEhu/KsOarNy8iBz9FCAJy9RG4bqm7MhW6U20ban+s5CntZO5a7uW1Ea0vYs9OVgKchrcpnztIs0JRjFtMYtXEI2syDECDUuBJITdlad6nZd+CRAh8Tpxu4fqgeEHwJpMsTxjPdsyLZU34A+LNW/b6r/SHz3Tm0T8Jrq16y9uiQF1e14LZoM/TmQmADvDyfxDwQ+5Kh+P/nfS5Gsmy480/96hmr/QvakoDXQUaN1Zq7mqbObLtHJCYE0qc+b1v5N3g/UNaPS3XLuJ+7ZgbjXmaOleVphukQnJwSS69sOJvum2kfqm7pmVLrbaNxP3LMDcaszczVPnZ0bEsfRkp0McTSt/Zu8H6hrRqW75dxP3LMDcakzc8LOKChN0klpymSII/eJs5MRWyalKepeouamu+ZxoTOb3KyURk1tVk7+aCrvtUm4BzdMwn2jId9gNZv9YEnoVLh2oMauO+hTmCWpdgVBCIFsZkGIEVwdz8yNneXEO5subarzbbJkqx07J27XZDwzd16APmaZMzfuuiR3+xlS+vwGKX0GhXwNhat1ZpM6MtW/6dKmOt8m09nasXPidk3GM5vWoTlz464LKiuR2CgNqrQ49GsIXK0zm9SR7fZvqrSpzvflSGdrx24SJzRw0zHLHBJSr8D5ZTlIvDLyh5Gu1plN6shU/6ZLm+p8m0xna8fOids1Gc9sWofmzI27LqisREq/IagsOWNtt4HrdGYnpSkqZSxHwrGTWlVHTS3ZypXFuDq1Tofmxitz2D6Avh+u+N/wUu26bjNTmNxQVG5rSujnlOk0+UbC3RAm50XBfaPifdJxAAAJI0lEQVTRzZ2al8mx27km4R4aEWlKEGIE2cyCECPUuHhmqqSrP+GW0aRKulIxxVSstc43VRaVM29uTHG05hWJb868q8+9ev/+86LG7kScd1zFM5vUernxp5RW7KTGbVL752jkTs6bGruTcd52cbXObNceKZySrtTYKK3YSY3bTv8m5xWp72jBOdfgZJw3hat1Zie1Xm78KaUVO6lxm9T+ORq5k/Omxu5knLddRJryQ6Qpa0SaiozLLU25Lp6Ze4hAd4GozcpJNA/oL77Jm9I03CB9HU7GUutK8AIAmurNTtaltkKkKUGIEVwnTVFSBJXeVCejUNLTAe+l38eR+NbNzXS622jZTVwTzrrYtUUytsH12gS07/h4fzTueh0uFBXDe6oUXyVeSjlkJclx5MS4k6ZMSg3csqluTnfr5mvi5vBLXclYbliqSFNM39GQniL1bddusm83XpNo9c8hlG9dydhohaVGOjY7uFqaMik1cMumujndrZuviZvDL3UlY7lhqXEpTbn5abaTlRidxOTYTT/N1o1tSVovbduFSce1do6cGBfSFAXn4nOlJyc3JMe36ZKuFCY1co4GTtXMpqB890kKLX1NK+b5tkKkKcEoTurI8YZsZkGIEVydatdkOluqvZtTyro5na3Ja2oyNTN3XpRvQJ/m106aYIoarTNzQhyp9m5OKevmdLYmr6nJ1MymywtzSwTbISZ05lCYTtVrB5M6s5O+ndSRTaZmNlVeOFolgnXUaJ2ZE+JItXdzSlk3p7M1eU1NpmY2XV6YWyLYDjVOZ6bQhTly0vTawaQ85KQ0xcF0tBgnNbPp0FCtNGWjPHC4OrPrNjNVb5dTdrV3qvW75kWosqvUm4HujYQ6YMDNF66rLb26Qn/4weRhGu68qBh0Tnim28NSJdWuIMQpspkFIUZwtc4M8LQ5na6Y3r0TfjVhELr07mZp55aT5ZSL5foG9Oum659aF67GzZkbd12c1MCjUY6WwtU6M1eb0+mKVDwzt5wsJ20r1ze1bk7GeXPmxl0XJzVwrt0OrtaZTWpzVDwzV7PkpG3l+qbWzck4b87conVuwAkNPFp2Ha7WmbnanE5XpOKZueVkOWlbub6pdXMyzpszN+66OKmBc+12EGnKD5GmrBFpKvy+o0HMxzNT6DYsd7NScNpzfe9LuBBx3wfBS6Wr2xROHtSJN0SaEoQYQTazIMQIMa0zc7RkbjwzR5Ok2lO+uVqxyVhrjp3Smd0cay06M1Nn5mjJ3HhmjiZJtad8c7Vik7HWHDs3d7XozDZxo85sWkvmYkrvjJZWrMOJvNfRyl0tOnMI3Kwzc7RkbjwzR5Ok2lO+uVqxyVhrjp2bu1p0ZgvcrDMPb3F7SJtpaUoHN16ZQifJcVMMmx67Dkpn5lRiFJ3ZMDkl3zg9hJCYvKnZN61mM1Pj1r0BAsA7jLza3A3jZN1qal2+YPYfbUSaEoQYwdXSFDekzaQ0ZTKdLVeCoeata89pa8fuVlmMu6bc+yXmpSluSJtJacpkOluuBEPNW9ee09aO3a2yGHdNTZe6tYOrpSmu78shTZlIZ8uVYOxKU1btOW3DsXNwou+aUALY1dIUN6TNpDRlMp0tV4Kh5q1rz2lrx+5WWYy7pqZL3drBddIUNy2sSWmK8zSbmzKWeqqrm/c7RzZG3NZOex2m5R9Hn2YbLAEMxECqXZNQG+ZVNNDaHzz3fcS+qVhqasOYrFvN0WoBs3m5uW9yOrhnGkwjqXaFIDg1jLlIvPHlQzazIMQISdOnT58ebqOXZsz1/Xvw4P4Y8OtfIDEhAYcOBX8t0Nk5bSOxt64feMqq5313ou8DfQAFHC04hl8gxWer2/FaNBn6cyAxAd4fqn5LL/P7zd0t4yZMfuFprFr5ueUaVbe3rZ0WYE/v3gk9Bt6Fug3q4viBo/i6+LB2XlfUrqf1fcZ7NuJ5Hyr9MWRbak2psfmPi+rfyuY/9urjBsAae+/UNgH2fjNHovTkaZScqLrOeSUHWOvCtb+QPSnodTpcrTObtuv03EZ9MlB5NnQqHm6on063NK2/c9IAmxybaf1dZ+eG20oIZBTacuw6PTe5cUOczP0UqT1CJ8rjYEe3NKW/c9IAmx6bzmYyBDJaaZ0lBNJQ2BhHz/3p001o9thAlBkIIwT0uqVp/Z2TBtjk2Ezr7zo7N9xWQiAvM/EqTZmUdwCevk4h0pR94mozc6FurPEVoevxfpcS0gSAH/ppMgyRghNLzc2LvT49TWu/dsserd3NiM4sCHGKbGZBiBHiSmfmtqU0zwx16Td3x8f7o3HX63ChqBjeU6UoSgrsv7qmudV7IsCu07HD1Zn9dWCq70h8+2vo1fXzA94ibftGter7bFZrerr8bMi2APBI2qW5pdxyM+reczdq334bvFu2AQD++2hhyHXxx8l7MZRddObL5JvSPL0/lSCpjvUPZUrTBHipdk3G/ZpM88st2erdsg1l3x/A+TXrLO1uvRft2O0Q1zozpy2lee5fmofd//MxWv/mjiCbXU2TixPpcDlxv9GIL0/pmI6yPfo3Srfei3bsOuJWZzapUQNAy3634IbJD6DIQrekNE2Al2rXZNyvyTS/3JKtSE6GKi+3tsG996Idux1EmgoDkaasEWnKDKIzuxST8ciA/vCEk/mhKd/UYRrTCQDcjOjMghCnyGYWhBhBdObL5JvSenU6McCL6+XqzNTYOPPWadQAAnTqcPV1amx2bU7ZRWeuAb4BZ/NqO1m6VNe3k7mpnb4fRGeu4b4pTMf1cjAxbzfkpnb6fhCduYb5BpzNq+1k6VJd307mpnb6fhCduQYh0pQ1Ik2FRnRmQYgRRGcWhDhFpKk48+3mscWr71B2kabEd40dW7z6tmO3g0hTceqbsotvd9p1iDQVZ77dPLZ49W3Hbgd5mi0ILkWeZgtCnCKbWRBiBNnMghAjiM4cZ77dPLZ49R3KLjqz+K6xY4tX33bsdhCdOU59U3bx7U67DtGZ48y3m8cWr77t2O0gOrMguBTRmQUhTonok1kQBPchn8yCECPIZhaEGEE2syDECLKZBSFGkM0sCDGCbGZBiBFkMwtCjCCbWRBiBNnMghAjyGYWhBhBNrMgxAiymQUhRvj/92l1c/rdjhwAAAAASUVORK5CYII\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
],
- "source": "%load_ext autoreload\n%autoreload 2\n\n\nimport os\nos.chdir(\"../\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn\n\n%matplotlib inline\nseaborn.set(font_scale\u003d2)\nseaborn.set_style(\"white\")\nnp.random.seed(1)\nnp.set_printoptions(precision\u003d6, suppress\u003dTrue)\nplot_height, legend_size, marker_size, line_width \u003d 8, 18, 0.4, 1.2\n"
+ "source": "# models \u003d [\u0027plain\u0027, \u0027da_uniform\u0027, \u0027robust_bound\u0027]\nmodels \u003d [\u0027robust_bound\u0027]\nexp_folder \u003d \u0027exps_diff_depth\u0027\nweak_learner \u003d \u0027tree\u0027\ntree_depth \u003d 4\n# datasets \u003d [\u0027breast_cancer\u0027, \u0027diabetes\u0027, \u0027cod_rna\u0027, \u0027mnist_1_5\u0027, \u0027mnist_2_6\u0027, \u0027fmnist_sandal_sneaker\u0027, \u0027gts_100_roadworks\u0027, \u0027gts_30_70\u0027]\n# datasets \u003d [\u0027mnist_1_5\u0027, \u0027mnist_2_6\u0027, \u0027gts_100_roadworks\u0027, \u0027gts_30_70\u0027]\ndatasets \u003d [\u0027mnist_2_6\u0027]\nfor dataset in datasets:\n _, _, X_test, y_test, eps \u003d data.all_datasets_dict[dataset]()\n print(X_test.shape)\n if dataset in data.datasets_img_shapes:\n flag_image_data \u003d True\n final_shape \u003d data.datasets_img_shapes[dataset]\n feature_names \u003d np.arange(X_test.shape[1])\n sns.set(font_scale\u003d0.4)\n else:\n flag_image_data \u003d False\n final_shape \u003d (1, X_test.shape[1]) # still important\n if dataset in data.datasets_feature_names:\n feature_names \u003d data.datasets_feature_names[dataset]\n else:\n feature_names \u003d [\u0027f\u0027 + str(i) for i in np.arange(X_test.shape[1])]\n sns.set(font_scale\u003d1.25)\n\n model_names \u003d utils.get_model_names([dataset], models, exp_folder, weak_learner, tree_depth)\n \n for i, model_name in enumerate(model_names):\n print(\u0027Model name: {}\u0027.format(model_name))\n model \u003d model_name.split(\u0027model\u003d\u0027)[1].split(\u0027 \u0027)[0]\n eps \u003d model_name.split(\u0027eps\u003d\u0027)[1].split(\u0027 \u0027)[0]\n \n model_path \u003d model_name + \u0027.model.npy\u0027\n metrics_path \u003d model_name + \u0027.metrics\u0027\n metrics \u003d np.loadtxt(exp_folder + \u0027/\u0027 + metrics_path)\n valid_errs, valid_adv_errs \u003d metrics[:, 8], metrics[:, 10]\n \n # Model selection\n # best_iter \u003d len(valid_errs) - 1 # otherwise, the counts are not comparable between different model types\n if model \u003d\u003d \u0027plain\u0027:\n best_iter \u003d np.argmin(valid_errs)\n elif model in [\u0027at_cube\u0027, \u0027robust_bound\u0027, \u0027robust_exact\u0027]:\n best_iter \u003d np.argmin(valid_adv_errs)\n else:\n raise ValueError(\u0027wrong model name\u0027)\n print(\u0027Best iter to take the model: {}\u0027.format(best_iter))\n \n if weak_learner \u003d\u003d \u0027stump\u0027:\n # the hyperparameters of recreated models do not matter (they matter only for training)\n ensemble \u003d StumpEnsemble(weak_learner, 0, 0, 0, 0, 0)\n elif weak_learner \u003d\u003d \u0027tree\u0027:\n ensemble \u003d TreeEnsemble(weak_learner, 0, 0, 0, 0, 0, 0, 0, 0, 0)\n else:\n raise ValueError(\u0027wrong weak learner\u0027)\n model_ova \u003d OneVsAllClassifier([ensemble])\n model_ova.load(\u0027{}/{}\u0027.format(exp_folder, model_path), iteration\u003dbest_iter)\n \n # importance visualizations for trees\n coords_per_tree \u003d np.zeros(X_test.shape[1])\n idx_trees_to_visualize \u003d [0, 9, 19, 29, 49, 69, 89, 109, 129, 149]\n # idx_trees_to_visualize \u003d [149]\n for i_tree, tree in enumerate(ensemble.trees):\n if weak_learner \u003d\u003d \u0027stump\u0027:\n coords_per_tree[tree.coord] +\u003d 1\n else:\n coords_curr_tree \u003d np.array(tree.to_list(), dtype\u003dint)[:, 6]\n for coord in coords_curr_tree: # 6 is coord, 7 is min_loss\n coords_per_tree[coord] +\u003d 1\n if i_tree in idx_trees_to_visualize:\n coords_per_tree_plt \u003d coords_per_tree.reshape(final_shape)\n coords_per_tree_plt \u003d coords_per_tree_plt.sum(2) if len(final_shape) \u003d\u003d 3 else coords_per_tree_plt\n ax \u003d sns.heatmap(coords_per_tree_plt, annot\u003dTrue, linewidths\u003d0.0, square\u003dTrue, cbar\u003dFalse,\n xticklabels\u003dFalse, yticklabels\u003dFalse, vmin\u003d0, vmax\u003d12)\n ax.set_title(\u0027MNIST 2 vs 6: # times a pixel was used (# trees: {})\u0027.format(i_tree+1),\n fontsize\u003d8)\n plot_name_save \u003d \u0027feature_importance_gif-exp\u003d{}-dataset\u003d{}-weak_learner\u003d{}-model\u003d{}-{}\u0027.format(\n exp_folder, dataset, weak_learner, model, i_tree+1)\n plt.savefig(\u0027plots/{}.png\u0027.format(plot_name_save), bbox_inches\u003d\u0027tight\u0027, pad_inches\u003d0.2,\n transparent\u003dFalse, dpi\u003d300)\n plt.show()\n \n",
+ "metadata": {
+ "pycharm": {
+ "metadata": false,
+ "name": "#%%np.random.seed(1)\n",
+ "is_executing": false
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": "# Analysis of the values of splitting thresholds",
+ "metadata": {
+ "pycharm": {
+ "metadata": false
+ }
+ }
},
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": 72,
"outputs": [
{
"name": "stdout",
"text": [
- "Model name: 2019-05-21 18:47:22 dataset\u003dgts_120_warning model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.031 lr\u003d1.0\nModel name: 2019-05-21 18:47:22 dataset\u003dgts_120_warning model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.031 lr\u003d1.0\nModel name: 2019-05-21 18:47:22 dataset\u003dgts_120_warning model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.031 lr\u003d1.0\n"
+ "--- Dataset: breast_cancer ---\nModel name: 2019-08-11 14:28:04 dataset\u003dbreast_cancer weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 max_depth\u003d4 lr\u003d0.01\n",
+ "Model name: 2019-08-11 14:28:04 dataset\u003dbreast_cancer weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 max_depth\u003d4 lr\u003d0.01\n",
+ "Model name: 2019-08-11 14:28:04 dataset\u003dbreast_cancer weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 max_depth\u003d4 lr\u003d0.01\n",
+ "--- Dataset: mnist_1_5 ---\n",
+ "Model name: 2019-08-11 14:28:05 dataset\u003dmnist_1_5 weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\n",
+ "Model name: 2019-08-11 14:28:05 dataset\u003dmnist_1_5 weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\n",
+ "Model name: 2019-08-11 14:28:05 dataset\u003dmnist_1_5 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\n",
+ "--- Dataset: mnist_2_6 ---\n",
+ "Model name: 2019-08-11 14:28:05 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\n",
+ "Model name: 2019-08-11 14:28:05 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\n",
+ "Model name: 2019-08-11 14:28:05 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.300 max_depth\u003d4 lr\u003d0.2\n",
+ "--- Dataset: gts_100_roadworks ---\n",
+ "Model name: 2019-08-11 14:28:07 dataset\u003dgts_100_roadworks weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\n",
+ "Model name: 2019-08-11 14:28:07 dataset\u003dgts_100_roadworks weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\n",
+ "Model name: 2019-08-11 14:28:08 dataset\u003dgts_100_roadworks weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\n",
+ "--- Dataset: gts_30_70 ---\n",
+ "Model name: 2019-08-11 14:28:07 dataset\u003dgts_30_70 weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\n",
+ "Model name: 2019-08-11 14:28:08 dataset\u003dgts_30_70 weak_learner\u003dtree model\u003dat_cube n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\n",
+ "Model name: 2019-08-11 14:28:08 dataset\u003dgts_30_70 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d3072 eps\u003d0.031 max_depth\u003d4 lr\u003d0.01\n"
],
"output_type": "stream"
},
{
"data": {
- "text/plain": "\u003cFigure size 2246.4x576 with 3 Axes\u003e",
- "image/png": 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\n"
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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q7t272L59Ow4cOKC32ImISD9e+Eev+vLw4UP861//QnR0NBo0aFCu3sHBARkZGVAqlTh79qz6QYqIiAh88MEHsLKyMnTIRERUTQY/M6rMkiVL8NZbb6Fr1664detWufrg4GAsWbIEgwcPhouLC6ZNm4Zjx47h8ePHcHd3x7Rp03Dt2jX0798fCxcuhJmZmeh9W1tbVN6oBlIoCmBqKu57hYmJFDJZ40rbiWlTVzzbF/ro75eFro6lNvRhTYzpZVajklFSUhJu3bqFVatWaW3TqlUrbNu2Tb2cl5eHlStXYvPmzVi6dCk6duyI9evXY8qUKdi3bx8mTpwoev85OXlQqYRqHYMxKJUqlJSoRLdVKB4/t41M1rjSNnVFRX2h6/5+WejyffGy9yE/I6WkUonOvsRr/WoyefJk9Qyuhw4d0nhqTV8yMzORnZ2NLl26oGPHjvD09AQAjBo1Sj0k0bPWrFmDUaNG4bXXXkNGRgbefPNNmJubw8vLCxkZGXqPmYiIqk/rmdG5c+fw+HFp5l+4cCH27t2LZs2a6TWYuXPnwt/fX7187949TJkyBbGxsXBycirX/sKFCzh79qz6oQWVSoWSkhIAQHFxMX+MS0T0ktCajFq0aIHU1FQ0bNgQgiDg1q1baNiwodYN2dvbi9phfn4+bty4oV6+desWsrOzYWNjA1tbW9ja2qrryvbXtm1bjXIAKCkpQWhoKEJDQ9X3hVxdXbFjxw4EBAQgMTERI0eOFBUTEREZl9ZkNH36dISFhSE+Ph4SiUQ9y+uzBEGARCIRPT5dVlYWJk+erF6OjIwEAAQGBlZp3qT4+Hh06dIFXbt2VZctWrQIH374IXx9fTFw4MAq3S8iIiLj0ZqMxo0bh0GDBuH69et45513sGTJEtFnP8/Ts2dPXLp0SVTb1q1ba207ZcqUcmVt2rRBQkJCteIjIiLDe+7TdDY2NrCxsUFgYCAGDx5c7lIZERGRLoh6tDswMBBA6Xhxv/zyCx49eoQmTZpALpfD3NxcrwESEVHtJ/p3Rlu3bsWWLVuQl5envk9kYWGB6dOnIyAgQJ8xEhFRLScqGX366aeIiYnBhAkT4O3tDWtra+Tk5CA5ORkxMTEwNzfXeCiBiIioKkQlo88++wzTpk3D3Llz1WXt2rWDm5sbLC0tsXPnTiYjIiJ6YaIGh7pz5w569uxZYV2PHj1w9+5dnQZFRER1i6hk9Morr+D06dMV1v3vf//DK6+8otOgiIiobhF1mW7SpEmIjIzEo0ePMHToUNjY2CAnJwepqalISkrCv//9b33HSUREtZioZPTOO+/A3Nwc69evx8GDByGRSCAIApo3b46wsDD4+vrqO04iIqrFRD/aPW7cOPj6+uLu3btQKBSQyWRo0aIFJBKJPuMjIqI6oErzGUkkErRs2RItW7bUVzxERFQH1bhpx4mIqO5hMiIiIqNjMiIiIqNjMiIiIqNjMiIiIqMT/TRdamoqvvzyS9y9exeFhYXl6g8cOKDTwIiIqO4QlYzWrVuHDRs24PXXX0f79u05hxEREemUqGR04MABTJs2DfPmzdN3PEREVAeJumeUn5+P3r176zsWIiKqo0QlI29vb5w6dUonOzx79ixmzJgBd3d3ODo64sSJExr1e/fuxaRJk9CtWzc4OjoiPz9foz43NxcBAQHo2rUrJkyYgJs3b2rUz549GwkJCTqJlYiIDENUMurduzeOHj2KhQsX4vDhw/jqq6/K/RPryZMncHR0REhISIX1T58+Rb9+/TBjxowK6zdv3oyioiIkJiaiVatWWL58ubouLS0N9+/fx/jx40XHQ0RExifqnlHZDK9JSUlISkoqVy+RSJCdnS1qhx4eHvDw8NBa/9577wEAMjMzK6y/du0avL29YWdnh5EjR6qTUX5+PqKjo/Hxxx9z8FYiopeMqGR0/PhxfcchmoODA86cOQNfX19kZmZCLpcDANauXYvhw4ejffv2Ro6QiIiqSlQyatWqlb7jEG369OlYvHgxBg8eDHt7e0RERCArKwvp6enYsWMHgoKCcP78ebi4uCAsLAwWFhait21tLb5tTaJQFMDUVNzvl01MpJDJGlfaTkybuuLZvtBHf78sdHUstaEPa2JMLzPRP3otKSnB0aNHce7cOTx8+BBNmzaFq6srPD09YWpapZkoqqVJkyaIjY1VLyuVSgQGBiIkJARbt26FqakpvvjiCyxYsAAbNmzA/PnzRW87JycPKpWgj7D1SqlUoaREJbqtQvH4uW1kssaVtqkrKuoLXff3y0KX74uXvQ/5GSkllUp09iVe1FeTnJwc+Pj4YN68eTh58iRu3bqFkydPYt68eRg7dixyc3N1EsyL2LlzJzp06AA3NzdkZGRg2LBhMDU1xfDhw7XedyIioppF1ClNVFQUHj58iH379sHZ2VldfuHCBcyePRtRUVFYuXKl3oLU5vfff8fu3btx8OBBAIBKpUJJSQkAoLi4GEql0uAxERFR1Yk6Mzp16hT++c9/aiQiAHB2dsa8efOq9Gh3fn4+srOz1U/f3bp1C9nZ2VAoFAAAhUKB7Oxs3LhxAwDwyy+/IDs7u9zvjQAgPDwcc+fOhaWlJQDA1dUVe/bswbVr17Bnzx64urqKjouIiIxHVDIqKipCo0aNKqxr1KgRiouLRe8wKysLo0aNwqhRowAAkZGRGDVqlPqHqgkJCRg1ahQWLVoEAJgwYQJGjRqFrKwsje2kpqZCpVLB29tbXRYYGAiJRAIfHx9IpVIEBgaKjouIiIxH1GW6Ll26YOvWrejVqxcaNmyoLn/y5Am2bt2KLl26iN5hz549cenSJa31s2bNwqxZsyrdjpeXF7y8vDTKrKysEBcXJzoWIiKqGUQlowULFmDy5MkYMGAA+vbtC2tra+Tm5uL06dMQBAE7d+7Ud5xERFSLibpM16FDB3zxxRcYN24ccnNzkZ6ejpycHEyYMAFffPEFXn/9dX3HSUREtZjoHwhZWVnhn//8pz5jISKiOorTjhMRkdFpPTMaO3YsoqOjYW9vDx8fn0oHH+W040RE9KK0JiMHBwfUq1dP/X+OhE1ERPqiNRlFRUWp/x8dHW2QYIiIqG4Sdc9o4cKF5WZULXP79m0sXLhQp0EREVHdIioZJSUl4cGDBxXWPXjwAIcOHdJpUEREVLdU+2m6y5cvw8rKShexEBFRHaX1nlF8fDx27NgBoHRa8Q8++ADm5uYabQoLC5GTk4PRo0frN0oiIqrVtCYje3t7eHp6AgC2b9+Onj17QiaTabQxNzeHnZ2dxmClREREVaU1GfXt2xd9+/YFUDoyt6+vL2xtbQ0WGBER1R2i7hn5+Pjg/v37Fdb99NNPuHPnjk6DIiKiukVUMgoNDcV///vfCuuOHDmCsLAwnQZFRER1i6hkdP78efTq1avCup49e+L8+fM6DYqIiOoWUaN2FxQUPHc4oKdPn+osICLDK4JSWVJhjUJRAKVS9UypoP+QiOoYUclILpfjyJEjGDBgQLm6I0eOwN7eXtdxERmMUlkCL69hFdaZmkpRUqKZjFJTkw0RFlGdIioZTZs2DbNmzUJRURHGjBkDmUwGhUKBpKQkHD16FOvWrdN3nEREVIuJSkZDhgxBdHQ0YmJicPToUUgkEgiCAFtbW6xcuRJvvPGGvuMkIqJaTPRMr6NGjcLIkSNx7do1PHz4EE2bNkW7du04tQQREVWb6GQElA4L1L59e33FAgAYNGgQbt++Xa787bffxpIlS7B06VIcOnQITZs2xZIlS9C/f391m127duG7775DTEyMXmMkIiLdEp2M8vLycPz4cVy/fh2FhYXl6oODg3US0IEDB6BUKtXLly9fhp+fH7y8vJCWlobU1FTEx8fj7NmzCA4ORnp6OqRSKf744w988skn2L9/v07iICIiwxGVjG7cuIEJEyagoKAAT58+hZWVFR49eoSSkhI0adIEFhYWOktGz44AvmXLFrRt2xY9evRAXFwcevTogU6dOsHe3h5RUVF4+PAhrKysEBERgZkzZ8La2loncRAZh/bHzJ9lYmIKwLzSdkQvA1HJaNmyZXBycsLatWvh4uKCLVu24PXXX0dycjJiYmKwevVqvQRXVFSE//73v/Dz84NEIoFcLseBAwfw+PFjfPfdd5DJZGjWrBmOHTuGR48eYezYsXqJg8hQnveY+bNSU1NgYsJkRLWDqGT0448/IjIyUj2FRHFxMUxMTDBixAg8ePAAy5YtQ0JCgs6DO3bsGB4/fqyeosLDwwPffvstRo0aBUtLS8TExODJkydYsWIFNm3ahPXr1+Pw4cOQyWQIDw+v8v0ta2sLnR+DISgUBTA1FTc1lYmJFDJZ40rbiWlTW1TWfxXV6bq/xcZSnW0/fPgQxcXFotubmZmhadOmGmXa9lfVbUsk+utDQ6mJMb3MRCWjwsJCWFhYQCqVokmTJrh37566zsHBAT///LNegjt48CD69++vMVp4UFAQgoKC1MtLly7FyJEj8dtvvyElJQWJiYn4/PPPERwcjIMHD1Zpfzk5eVCpXr5f1yuVqnI/zHxeW4Xi8XPbyGSNK21Tmzyv/yr60SsAnfa32Fiqv+1C0WddQOmZ19+3/7z3RdW3nay34zSEuvYZ0UYqlejsS7yoryavvfaa+gm3jh07IiEhAYWFhSguLsaBAwfQvHlznQTzd7dv30Z6evpzL739+OOPyMjIwNSpU5GRkQEPDw9YWFhgxIgRyMrKQl5ens7jIiIi3RN1ZvTmm2+qz37mzJmDKVOmwNXVFRKJBCqVClFRUToPLDExEdbW1hUOQQQASqUSISEhCA0Nhbm5OVQqFVSq0m9aZZcLypaJiKhmE5WM/Pz81P93cXHBkSNH8PXXX6OgoAC9evWCXC7XaVAqlQqJiYkYNWoUTE0rDjE+Ph5OTk5wdXUFALi6uiIqKgpjxoxBSkoKHBwcYGlpqdO4iIhIPypNRoWFhYiIiMDYsWPh4uICAGjZsiXGjRunt6DS09Px+++/w8fHp8L627dvY+/evThw4IC6zNPTE99++y0mT56MFi1aIDo6Wm/xERGRblWajOrVq4fPP/8cI0aMMEQ8AAB3d3dcunRJa32rVq3wxRdfaJRJpVIsWrQIixYt0nd4RESkY6IeYOjVqxcyMzP1HQsREdVRou4ZTZw4EYsWLcLTp0/Rv39/2NjYlBsglXMaERHRixKVjAICAgAA27dvx/bt2zUSkSAIkEgkyM7O1k+ERERU64lKRjt27NB3HEREVIdpTUYLFy7E+++/jzZt2kAikaBjx45o1KiRIWMjIqI6QusDDIcOHcKDBw8AAJMnT8bVq1cNFhQREdUtWs+MZDIZMjMz0b59ewiCgMLCQjx9+lTrhho0aKCXAImIqPbTmozGjRuHVatWISYmBhKJBJMnT37uhvgAAxERvSitySgwMBADBgzA1atXMX/+fMycORNt27Y1ZGxERFRHPPdpus6dO6Nz587IyMjAmDFj0KZNG0PFRUREdYioR7v1MSo3ERFRGXFTLRIREekRkxERERkdkxERERkdkxERERkdkxERERkdkxERERmdqEe7iYyvCEpliejWJiamAMz1Fw4R6RSTEb0UlMoSeHkNE90+NTUFJiZMRkQvC16mIyIio2MyIiIio6vxySgkJASOjo7YtWsXAKCwsBDz5s1Dt27dMGLECGRlZWm0X758OVatWmWMUImI6AXV6GR04sQJnD9/Hs2bN1eX7d27F7/88gv27dsHd3d3LF68WF138eJFnDhxAh988IExwiUiohdUY5PR/fv3ERoaihUrVsDMzExdfu3aNQwcOBD29vYYO3Ysfv31VwCASqXCkiVLEBISgvr16xsrbCIiegE1NhktXLgQkyZNgqOjo0a5g4MDvvvuOxQVFSEjIwNyuRwAsHPnTrRv3x69e/c2RrhERFQNNfLR7l27duHp06fw9/cvV+fr64vs7GwMHToULVq0QEREBO7evYsdO3Zgz549CA8Px6lTp9CuXTtERkZqXOKrjLW1hS4Pw2AUigKYmor7XmFiIoVM1rjSdmLaGFJVjhEQf5xitl1Rna77W2wshtq2tu1r219Vtw3orw8NpSbG9DKrccno6tWr2LhxI/ZJqcKaAAAZiklEQVTt2weptPyb1dzcHJGRkRpl77//PmbNmoWjR4/iypUrSE5OxoYNGxAZGYnY2FjR+87JyYNKJVT7GAxNqVShpEQluq1C8fi5bWSyxpW2MbSqHGNZe7HH8Lxtm5pKK6zTZX+LjcWQ265o+897X1R124D++tAQauJnxBikUonOvsTXuMt0P/zwA3Jzc+Hp6YmOHTuiY8eOuH37NpYuXQovL69y7b/88ksUFBRg1KhRyMjIwNChQ2Fubo7hw4cjMzPTCEdARERVVePOjN544w107txZo2zKlCkYM2YMRo8erVGel5eHjz76CHFxcQBKH2IoLi4GABQXF0OpVBomaCIiqpYal4wsLS1haWmpUWZmZgaZTIbXXntNo3z16tXw8fFBmzZtAACurq44dOgQ+vXrh/j4eLi6uhoqbCIiqoYal4zEunDhAr777jvs379fXTZx4kRcvHgRvr6+kMvlWLlypREjJCIisV6KZJSWllauzNnZGUlJSRpl9evX5+gLREQvoRr3AAMREdU9TEZERGR0TEZERGR0TEZERGR0TEZERGR0L8XTdERkeFKpFErlE/WyQlEApVLbED4v3zBaVLMwGRFRhVQqJby8vNXL2sbpA4DU1GRDhUW1FC/TERGR0TEZERGR0TEZERGR0TEZERGR0TEZERGR0fFpur9RqZ4+59FVTSYmpgDM9RsQEVEdwWT0N++++x5+//2OqLapqSkwMWEyqp4iKJUlItvydyxEtRmTERmNUlkCL69hotrydyxEtRvvGRERkdHxzIgqIf5SGu+jGdazw/VUjpc6qeZiMqLnqtqlNN5HM6Rnh+upDC91Uk3Gy3RERGR0TEZERGR0NTYZ7d69G4MGDYKTkxPGjRuHCxcuAAAEQUBkZCS6d++ON954A6dOndJYb9euXZg3b54xQiYiohdUI5NRcnIyoqKi8MEHHyApKQmOjo4ICAhAbm4u0tLSkJqaivj4eLzzzjsIDg6GSlX6Q9U//vgDn3zyCf79738b+QjI2Mpu7ov5xxv7RMZXIx9g2L59O8aPHw8fHx8AQFhYGE6ePImkpCSoVCr06NEDnTp1gr29PaKiovDw4UNYWVkhIiICM2fOhLW1tZGPgIytKjf3eWOfyPhqXDIqKirCTz/9hJkzZ6rLpFIp+vTpg/Pnz2Ps2LE4cOAAHj9+jO+++w4ymQzNmjXDsWPH8OjRI4wdO/aF9928eXPRbU1MpJBKJS+8L92S4pVXWopqKTbuv9roftt/20sVtm0ium1V2z+vrYmJtNwQUVXb9svZJxW1r6gvDBFLzfqs/aUmxmRouuwDiSAINeoaxR9//IH+/ftj//79cHZ2VpevWLEC3333HRISErBq1SokJyfD0tISCxcuRKdOnTB69Ghs2rQJn3/+OQ4fPgyZTIbw8HC0b9/eiEdDRERi1Mh7RpUJCgrC8ePHkZSUhB49emDNmjUYOXIkfvvtN6SkpCAxMRFvvfUWgoODjR0qERGJUOOSUbNmzWBiYoL79+9rlOfk5EAmk5Vr/+OPPyIjIwNTp05FRkYGPDw8YGFhgREjRiArKwt5eXmGCp2IiF5QjUtG5ubm6NSpE9LT09VlKpUK33zzDVxcXDTaKpVKhISEIDQ0FObm5lCpVCgpKR26pri4WL0uERHVbDUuGQGAn58f9u7di6SkJFy9ehWhoaEoKCjA6NGjNdrFx8fDyckJrq6uAABXV1ccPXoU2dnZ2LZtGxwcHGBpaWmMQyAioiqocU/TAYC3tzdyc3MRGxsLhUKBDh06IC4uDlZWVuo2t2/fxt69e3HgwAF1maenJ7799ltMnjwZLVq0QHR0tDHCJyKiKqpxT9MREVHdUyMv0xERUd3CZEREREbHZEREREbHZEREREZXZ5KRtikptElJSYGXlxecnJwwYsSIclNVvMyq0hf79u3D22+/DTc3N/To0QP+/v748ccfDRitflX1fVFmy5YtcHR0xPLly/UcoeFUtS8ePXqEkJAQ9OnTB05OThg2bBjOnDljoGj1qyp9UVJSgpiYGAwaNAjOzs7w9PTEtm3bDBit/pw9exYzZsyAu7s7HB0dceLEiUrXeeG/nUId8PnnnwudOnUSDhw4IFy+fFlYtGiR4ObmJuTk5FTY/ty5c0KHDh2ErVu3CleuXBFWr14tdOrUSbhy5YqBI9e9qvbFvHnzhF27dgkXL14Urly5IixYsEDo3r278Mcffxg4ct2ral+UycrKEgYOHCiMGDFCiI6ONlC0+lXVvigsLBRGjx4tTJs2TTh37pxw8+ZNIT09vU5+RjZs2CD06tVLOHnypHDz5k3hyJEjgrOzs5CUlGTgyHXv5MmTQkxMjHD06FFBLpcLaWlpz21fnb+ddSIZjR07VggPD1cvK5VKwd3dXYiLi6uw/Zw5c4Tp06drlPn6+gphYWF6jdMQqtoXzyopKRG6du0q/Pe//9VXiAbzIn3x5MkTYdiwYcKpU6eEd955p9Yko6r2xWeffSYMHjxYKCoqMlSIBlPVvpg2bZqwePFijTJ/f/9a8ffi78Qko+r87az1l+nKpqTo27evuuzvU1JU5Pz58xrtAcDd3V1r+5fFi/TFs54+fYqSkhI0adJEX2EaxIv2RXR0NHr27Il+/foZIkyDeJG+SEtLg4uLC0JDQ9GnTx+MGDECn376KYSX/GeLL9IXXbt2RXp6Oq5fvw6gdLzMrKysWvUeEas6fztr5AgMuvTgwQMolUrY2NholFtbW+O3336rcJ379++Xm6DP2toaCoVCb3Eawov0xbNWrVqFli1bolevXvoI0WBepC9OnDiBjIwMHDp0yBAhGsyL9MXNmzfxzTffYPTo0di6dSuuXLmC8PBwSCQSvPvuu4YIWy9epC+mTZuGP//8E0OHDoWpqSkEQcCCBQswcOBAQ4Rco1Tnb2etT0akO1u3bkVycjJ27twJc3NzY4djULm5uVi8eDE2btyIBg0aGDscoxMEATKZDKGhoTAxMUGnTp1w8+ZNJCQkvNTJ6EWkpKQgNTUVa9asQbt27fDjjz8iOjoaLVu2xJAhQ4wd3kuj1iejqk5JAQA2NjbIyckR3f5l8SJ9UWbbtm3YvHkztm/fDrlcrs8wDaKqfXH58mUoFApMmDBBXaZUKnH27Fns2rXrpX7C8EU/I2ZmZjAxMVGXtW/fHnfu3NFrrPr2In2xYsUKzJw5E8OGDQMAODo64vr169i6dWudS0bV+dtZ6+8ZVWVKijIuLi743//+p1GWnp6utf3L4kX6Aig9I9q4cSPi4uLg5ORkiFD1rqp94eTkhMOHD+PQoUPqf507d8bo0aORmJhoyNB17kXeF127dsWNGzc0pmi5fv06WrYUP/V4TfQifVFQUKCRlIHSadXr4vQ11fnbaRIaGhqqp7hqDAsLC6xZswYtW7aEubk51q5di59//hlLly5FgwYNEBwcjAsXLqBPnz4AgObNm2PNmjVo0KABLC0tsXv3bqSkpGDZsmUaI4e/jKraF1u2bEFsbCxWrFgBR0dHPHnyBE+ePAGAl/5SXVX6wszMDNbW1hr/jhw5grZt2+Ktt94y9qFUW1XfF6+++io++eQTPHjwAK1bt8a5c+ewatUqBAQEoEuXLkY+muqpal9cvXoV//nPf2BnZwcTExOcPn0asbGx8PHxgZubm5GPpnry8/Nx9epV3L9/HwkJCXBxcVF/7hs1aqTTv521/jIdUPmUFHfu3IFU+tdJYrdu3fDRRx9hzZo1iImJwWuvvYYNGzagffv2xjoEnalqXyQkJKC4uBizZ8/W2E5gYCBmzZpl0Nh1rap9UZtVtS9atWqFuLg4REVFYc+ePWjZsiVmzJiBiRMnGusQdKaqfbFo0SKsWbMGISEhyMnJga2tLfz8/DB16lRjHYLOZGVlYfLkyerlyMhIAH99/nX5t5NTSBARkdHVja99RERUozEZERGR0TEZERGR0TEZERGR0TEZERGR0TEZERGR0TEZUaXWrVuHnj17GjuMcvbt24dBgwahY8eOmDRpUoVtLly4gHXr1pUrr6nH9HeJiYlwdHREfn4+AODWrVvlJjjbunUrMjMzNdYrKirCunXrkJ2drVFe0fr6VlF8QOmQObt27TJYHBXRVQxi+3XXrl1wdHSs9v5qKyYjeikpFAqEhoZi8ODB2LlzJ0JCQipsd+HCBaxfv97A0elH8+bNsXfvXri6uqrL4uLiys2uWlxcjPXr15dLRhWtr28VxUdUkToxAgPVPr/99huUSiV8fHzw+uuvGzscgzA3N6/W+IjVXb8mKCwsRL169YwdBukBz4xqqcTERHTu3Bl//vmnRvnly5fh6OioHgjy5MmT8PPzQ+/evdGtWzeMGzcOp0+frnTbf798VGbQoEFYvny5RtmxY8cwZswYODk5oW/fvlixYgWKi4srjX/Xrl3w9PRE586dMWTIEHz66afqunXr1qmHnRk5ciQcHR0rHKw0MTERERERAEovyTg6Opa7nHfx4kWMGzcOXbp0wahRo/Dtt9+W287+/fvx5ptvonPnzhg4cCC2bt1aafzHjx/HmDFj4OLiAjc3N/j6+mqcITg6OmL79u2IjIxEjx490L17d0RERKCoqEjrNp+9HDRo0CA8fPgQ69evVx9fZmYmunXrBgBYuHChuvzWrVsVXk4qe80+/fRT9O/fH25ubpg7d265983PP/+MCRMmwMnJCW+++Sa++uorjBkzBgsWLNAar7b4yiiVSsTExKBXr17o3bs3wsLCNI6/7H124cIFTJo0Cc7OzoiLiwNQmpRWrFgBDw8PdO7cGW+99Ra++uqrKr0GYmIAgOzsbLz77rvo0qUL3NzcEBQUVG5U72cVFRUhPDwc3bt3R48ePbBs2TKUlJQ8d526jmdGtdQbb7yBJUuW4Msvv4SPj4+6PDk5GTY2Nur7Jbdu3cLAgQPh7+8PqVSKU6dOYerUqdi1a1e1L+ckJycjKCgI48ePx7x583Djxg3ExMRAEATMnz9f63r79u1DREQE/Pz84O7ujszMTERHR6OoqAjTpk2Dr68vrKysEB4ejo8++ght2rRB27Zty21nwIAB8Pf3xyeffIK9e/cCKB0Es0xBQQHmz5+P9957DzY2NtiwYQMCAwNx4sQJ9ZxFcXFxWL16NQICAtCjRw/89NNPWLt2LRo0aIB33nmnwvhv3LiBOXPmYNKkSfjwww9RVFSErKwsPHr0SKPdJ598AhcXF6xcuRJXrlzB6tWrYW5u/ty++bv169dj8uTJGDp0KHx9fQEA9vb2iI+Px7vvvouZM2diwIABAEov0d27d6/C7aSkpMDR0RERERG4e/cuoqOjERMTg7IxlJ8+fYqAgADY2NggJiYGhYWFWLZsGf7888/nTieiLb4y27dvR69evbBy5UpcunQJMTExeOWVV8qN6TZv3jy8/fbb+OCDD2BpaQkAmD17Ni5cuIBZs2ahbdu2SElJwcyZM3Hw4EF06NBB9GtQWQy5ubmYNGkS2rdvj1WrViE/Px+rVq2Cn58fDh48qHWw4I8++gj79+/H3Llz0b59e+zfvx+pqala+4oAvOB06PQSmDFjhuDv769R5unpqXU+eqVSKRQXFwv+/v7CggUL1OWxsbFCjx491MsHDx4U5HK5kJeXp7H+wIEDhejoaEEQBEGlUgkDBgzQ2I4gCML+/fsFJycnITc3V2sM7u7u5dYLCQkRunXrJhQUFAiCIAgZGRmCXC4XLl269LwuEHbu3CnI5fJy5bGxsYJcLhfS09PVZRcvXhTkcrnw1VdfCYIgCI8fPxZcXFyEdevWaay7Zs0aoU+fPkJJSUmF+0xJSdHor4rI5XJh6NChglKpVJdt3LhRcHZ2Fh48eCAIQvl+vnnzpiCXy4W0tDT1Oj169BBiY2M1tp2XlyfI5XLh4MGDGuUVrT9w4EBh8ODBQnFxsbosMjJS6NOnj3p5165dQqdOnYS7d++qy3744QdBLpcL8+fPf+5xVhRf2fG//fbbGmUzZ84UfH191ctlx//pp59qtEtPTxfkcrmQmZmpUf72228Ls2bNEgRB/GtQWQwrV64UXF1dhcePH6vLzp8/L8jlcuHw4cOCIJTv19zcXMHJyUnYvHmzeh2lUikMHTq0wvcileJlulrM29sbGRkZePDgAYDSyw3Xr1+Ht7e3us3du3cxf/589OvXDx07dkSnTp1w+vRpXL9+vVr7/vXXX/H777/Dy8sLJSUl6n+9evVCYWEhLl++XOF6d+/exb179+Dl5VXuWPLy8nDp0qVqxfV3ZmZmGk/UlY0s/McffwAAvv/+ezx58qTCY7h//z7u3r1b4XblcjkeP36M+fPn4/Tp0+opN541ePBgjRGPPT09UVBQoLVv9KVnz54wNf3rIom9vT1ycnLUl1N//PFHdOrUCba2tuo2zs7O5abmrqq+fftqLNvb21fYp2Vnd2XS09Mhk8nQrVs3jdeld+/eyMrKAiD+NagshgsXLqBv374aZ9RdunRBq1atcO7cuQq3+csvv6CwsBCDBw9Wl0mlUo1lKo+X6WqxQYMGwdTUFEePHsX48eORnJyMFi1aqC+/qVQqzJw5E/n5+Zg9ezZeffVVNGjQALGxseVma6yqsgQ4bdq0Cuu1zQiqUCgAANbW1hrlZcvPXmapjkaNGmkkg7JLLoWFhQD+OoY333yzwvXv3LmDVq1alStv164dNm7ciC1btmDatGkwNTXFkCFD8O9//1tjTpdnj7GsrqwPDKXs0lcZMzMzCIKAoqIimJmZQaFQoFmzZuXWq+7cXhXtt6zv/+7Zfnrw4AEUCgU6depUrm3ZJHdiX4PKYlAoFHBwcCi3HxsbG63vxbL7Sdrew1QxJqNarFGjRvDw8EBycjLGjx+PlJQUeHl5QSKRACh9Iu3ixYvYunUr+vfvr16voKDgudste5rp2QcR/v7hbNq0KQAgIiICHTp0KLeN1q1bV7jtsumJK5q6GACaNGny3Nh0qWxfmzdvrvAPiZ2dndZ1BwwYgAEDBuDx48c4efIkli1bhoiICKxevVrd5tljzM3NBYAaN729TCbDr7/+Wq68LF59K3u/lmnSpAlsbW2xYcOG564n5jWojEwmq/CL2f379ytMhgDUZ4w5OTnqz0HZMmnHy3S13JtvvomzZ88iLS0NN2/e1PiWX/YN8O83YW/fvo3vv//+udssu1xz9epVddkPP/yAvLw89bKdnR1sbW1x+/ZtODk5lftX0TdtAGjRogWaN29e7mZvSkoKLCwsqvyjQTMzMwCo8Bt3Zbp27Yr69evj3r17FR7D3y/daNO4cWOMGDECQ4YMwZUrVzTqjh8/rjE19dGjR1G/fv0Kv4lrU9HZRHWOuSJOTk746aef1JcvgdLLV5U9UaYtvurq3bs37t+/j4YNG1b4ujzrea9BZbp06YLTp09rvLcvXLiA27dva33ARy6Xo169ejh+/Li6TKVSaSxTeTwzquU8PDxQv359LFmyBK1bt4azs7O6rl27dmjRogWWL1+OOXPmID8/H7GxsWjevPlzt+ns7AxbW1ssXboUc+bMwcOHDxEXF6fxx1kqlWLBggUIDg5GXl4e+vfvDzMzM9y8eRPHjh1DbGys+om1v5NKpZg1axaWLFmCpk2bom/fvjh79iz27NmDefPmVfk3Ju3atQMAxMfHo1evXrCwsFCXVcbS0hKBgYFYunQpbt++DTc3N6hUKly/fh2ZmZlav5knJCTg/Pnz6NevH5o3b47r168jNTUVI0eO1GiXn5+POXPmwNfXF1euXMHGjRsxceJEjW/TYo7vq6++Qr9+/dCwYUPY2dnBwsICrVu3RkpKChwcHFCvXr1q/fJ/zJgx+PjjjzF9+nQEBgaioKAA69atg5WVVbmzFrHxVUffvn3h7u4Of39/TJ06Ffb29sjLy8PPP/+MwsJCBAUFiX4NKuPn54c9e/YgICAAAQEBePLkCVatWgW5XA5PT88K12nWrBnGjRuHdevWwdTUFPb29ti/f7/W+1ZUismolqtfvz4GDRqEw4cPl7t/Y25ujnXr1iE8PByzZ89GixYtMGPGDJw5cwa//PKL1m2am5tj/fr1CAsLw+zZs2FnZ4fQ0FB8+OGHGu28vb3RqFEjbN68GQcPHoRUKkWbNm0wYMAA9bf3iowbNw6FhYXYsWMHdu7cCVtbWyxYsADvvfdelY+/e/fumDJlCnbs2IGYmBi4ublh586dotefOnUqmjdvjvj4eGzfvh316tXDa6+9pvEQyLMcHR2RlpaGqKgoPHr0CDKZDL6+vpgzZ45GO39/f9y8eRNBQUFQqVQYO3Ys5s2bV6XjCw4ORnh4OKZPn46nT59ix44d6NmzJ8LCwrB8+XL4+fmhqKioWt/KGzRogLi4OISGhuIf//gHWrVqhQ8//BArV66sNLFoi686JBIJ1q9fj02bNiE+Ph537txBkyZN8Prrr6t/Ryb2NaiMlZUVduzYgejoaAQFBcHMzAweHh5YuHCh1se6y467pKQEGzZsgFQqxVtvvQU/Pz9ER0dX69hrM047TmQEjo6OWLx4sdbfKtV0N2/ehJeXF8LDwzV+x0b0onhmRESV2rx5M5o3b45XXnkFd+7cwebNm9GsWTMMHTrU2KFRLcFkRESVKrs0du/ePZibm6N79+4IDg6u9v0fojK8TEdEREbHR7uJiMjomIyIiMjomIyIiMjomIyIiMjomIyIiMjomIyIiMjo/h/ImcmBOqMEXwAAAABJRU5ErkJggg\u003d\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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JiUp0/crqmptLNeqIbVupVEGhyBEdhy5x69q2vshkzUzyvDUR++IJ9kUpqVSity/xFX4tPXPmDHJySjt7wYIFyMjI0MsTipWZmYnU1NTnDr1duHABaWlpmD59OtLS0uDp6QlLS0uMHDkS6enpyM3NNWLERERUVRWeGdnb2yM5ORlNmjSBIAj466+/0KRJkwobcnR01Gtg+/btg62tLQYOHKh1u1KpREhICEJDQ2FhYQGVSgWVqvRbdnFxMQCoHxMRUc1WYTJ67733sGTJEsTFxUEikWgMjT1NEARIJBK93p9OpVJh37598PHxgbm59hDj4uLg4uICNzc3AICbmxsiIiIwZswYJCUloUOHDrCystJbTEREZDgVJqMJEyZg8ODBuHnzJqZMmYLFixfr/eynIqmpqfj7778xduxYrdszMzOxa9cu7N27V13m5eWF06dPw9fXF/b29oiMjDRKrEREVH3PnU1nZ2cHOzs7BAYGYsiQIRoTCQypX79+uHLlSoXb27Zti2+++UajTCqVYuHChVi4cKGhwzMC8ReDlqp9ky6IiJ4mamp3YGAggNLrfn7//Xc8evQIzZs3h1wuh4UFL0TUN10uBgWA5OREA0ZDRGR4oq8z2rx5MzZt2oTc3Fz170SWlpZ47733MG3aNEPGSEREdZyoZPTFF18gKioKkyZNwogRI2Bra4usrCwkJiYiKioKFhYW8PX1NXSsRERUR4lKRl999RVmzJiBDz/8UF3Wvn17uLu7w8rKCtu3b2cyIiKiKhN1L5bbt2+jZ8+eWrd5eHjgzp07eg2KiIjqF1HJqE2bNjhx4oTWbf/973/Rpk0bvQZFRET1i6hhuqlTp2Lp0qV49OgRhg0bBjs7O2RlZSE5ORn79+/Hxx9/bOg4iYioDhOVjKZMmQILCwvExMQgISEBEokEgiCgZcuWWLJkCcaPH2/oOImIqA4TPbV7woQJGD9+PO7cuQOFQgGZTAZ7e3tIJBJDxkdERPWATusZSSQStG7dGq1btzZUPEREVA/V6JVeiYiofmAyIiIik2MyIiIik2MyIiIik2MyIiIikxM9my45ORnffvst7ty5g8LCwnLbn17ojoiISBeiktHatWuxbt06dOzYEQ4ODlzDiIiI9EpUMtq7dy9mzJiBuXPnGjoeIiKqh0T9ZpSXl4fevXsbOhYiIqqnRCWjESNG4Pjx44aOhYiI6ilRw3S9e/fGp59+iuzsbPTp0wdWVlbl6nh6euoloLVr1yImJkajrF27dkhOToYgCFi2bBkOHDiAFi1aYPHixRgwYIC6Xnx8PM6ePYuoqCi9xEJERMYhKhmVrfC6f/9+7N+/v9x2iUSCy5cv6y2ojh07IjY2Vv3YzMwMAJCSkoLk5GTExcXh1KlTCA4ORmpqKqRSKe7evYstW7Zgz549eouDiIiMQ1QyOnbsmKHj0GBmZgaZTFau/MaNG/Dw8ICzszMcHR0RERGB7Oxs2NjYIDw8HAEBAbC1tTVqrEREVH2iklHbtm0NHYeGGzduoF+/fmjYsCG6d++OoKAg2NvbQy6XY+/evcjJycHZs2chk8lgbW2No0eP4tGjRxg3bpxR4yQiIv0QfdFrSUkJjhw5gjNnziA7OxstWrSAm5sbvLy8YG6u00oUz+Xq6oqIiAi0a9cOCoUC69atw+TJk3Hw4EF4enri9OnT8PHxgZWVFaKiopCfn48VK1Zgw4YNiImJwcGDByGTyRAWFgYHBwedntvW1lJvx1EdCkUBzM11uzmGLvXF1H26jti2zcykAApExyGRGK7tBg0aoEWLFqLrP49M1kwv7dQF7Isn2Bf6JSqLZGVlwd/fH1euXEHbtm1hZ2eHc+fO4csvv0THjh2xZcsW2NjY6CWgpydCdOzYEV27dsWgQYPwzTffYPTo0QgKCkJQUJC6zrJlyzBq1CjcunULSUlJ2LdvHw4fPozg4GAkJCTo9NxZWblQqQS9HEd1KJUqlJSodNpHl/qV1TU3l2rUEdu2UqmEt/cI0XEkJycasO0kKBQ5outXRCZrppd26gL2xRPsi1JSqURvX+JFfS0t+21m9+7dOHbsGHbt2oVjx45h9+7dyM7ORkREhF6C0cbKygqvvPIKbt26VW7bhQsXkJaWhunTpyMtLQ2enp6wtLTEyJEjkZ6ejtzcXIPFRURE+iMqGR0/fhz/+te/4OrqqlHu6uqKuXPn4ocffjBIcEDpBbcZGRnlJjQolUqEhIQgNDQUFhYWUKlUKCkpAQAUFxcDAFQq3c4uqL4qglKZr/VPoVCUKwOKTB0wUZ0japiuqKgITZs21bqtadOm6g9/fVi+fDkGDRqENm3a4N69e1i7di3MzMwwYoTmEE1cXBxcXFzg5uYGAHBzc0NERATGjBmDpKQkdOjQQev1UETPUipL4O09XOu2Z4csgdIhQDMz3p+RSJ9EJaOuXbti8+bN6NWrF5o0aaIuz8/Px+bNm9G1a1e9BXTnzh3MnTtXPWW7R48e2L17N6ytrdV1MjMzsWvXLo07hXt5eeH06dPw9fWFvb09IiMj9RYTEREZlqhkNH/+fPj6+mLgwIHo27cvbG1t8eDBA5w4cQKCIGD79u16C2j16tWV1mnbti2++eYbjTKpVIqFCxdi4cKFeotFf4qgVJboUN/0kyiIiIxJVDLq1KkTvvnmG2zZsgUXLlzAlStXIJPJMGnSJLzzzjt6m0lXVz1vGEib5OREA0ZDRFTziL5AyMbGBv/6178MGQsREdVTXHaciIhMrsIzo3HjxiEyMhKOjo4YO3YsJBLJcxvisuNERFRVFSajDh06oGHDhup/V5aMiIiIqqrCZPT0XRU4TZqIiAxJ1G9GCxYsQEZGhtZtmZmZWLBggV6DIiKi+kVUMtq/fz8ePnyoddvDhw9x4MABvQZFRET1S7Vn0129epXXGRERUbVU+JtRXFwctm3bBqB0WfEPPvgAFhaa9+MqLCxEVlYWRo8ebdgoiYioTqswGTk6OsLLywsAsHXrVvTs2bPcnbMtLCzQrl27cjcxJSIi0kWFyahv377o27cvgNI7c48fPx6tWrUyWmBERFR/iPrNaOzYsbh//77WbRcvXsTt27f1GhQREdUvopJRaGgovv76a63bDh06hCVLlug1KCIiql9EJaNz586hV69eWrf17NkT586d02tQRERUv4hKRgUFBc+9HdDjx4/1FhAREdU/opKRXC7HoUOHtG47dOgQHB0d9RoUERHVL6LWM5oxYwZmzZqFoqIijBkzBjKZDAqFAvv378eRI0ewdu1aQ8dJRER1mKhk9NprryEyMhJRUVE4cuQIJBIJBEFAq1atsHLlSgwdOtTQcRIRUR0meqVXHx8fjBo1Cjdu3EB2djZatGiB9u3bc2kJIiKqNp3uTSeRSODg4AA3Nzc4ODgYJRGFhITAyckJ8fHxAEpvQTR37lx0794dI0eORHp6ukb95cuXY9WqVQaPi4iI9Ef0mVFubi6OHTuGmzdvorCwsNz24OBgvQYGAN999x3OnTuHli1bqst27dqF33//Hbt370ZCQgIWLVqE/fv3AwAuXbqE7777jncRJyKqZUQloz///BOTJk1CQUEBHj9+DBsbGzx69AglJSVo3rw5LC0t9Z6M7t+/j9DQUGzatAkBAQHq8hs3bmDQoEFwdHTEuHHjsGPHDgCASqXC4sWLERISgkaNGuk1FiIiMixRw3SffPIJXFxckJqaCkEQsGnTJvz6669YuXIlmjRpgtWrV+s9sAULFmDq1KlwcnLSKO/QoQPOnj2LoqIipKWlQS6XAwC2b98OBwcH9O7dW++xEBGRYYlKRhcuXMCkSZPUS0gUFxfDzMwMI0eOhJ+fHz755BO9BhUfH4/Hjx/D39+/3Lbx48ejXbt2GDZsGA4dOoTw8HDcuXMH27ZtQ1BQEMLCwjB06FDMmDED9+7d02tcRERkGKKG6QoLC2FpaQmpVIrmzZtrfMh36NABv/32m94Cun79OtavX4/du3dDKi2fKy0sLLB06VKNsvfffx+zZs3CkSNHcO3aNSQmJmLdunVYunQpoqOjRT+3ra1ltePXRqEogLm5busYGrK+mLpP19F321Wtr0tdMzMpgAJRdSWS57f97DYzMylksmaiY6lL6utxa8O+0C9RyeiVV15BZmYm3N3d0blzZ+zcuROenp6QSqXYu3evxgSD6vr111/x4MED9VpKAKBUKrFs2TLEx8cjOTlZo/63336LgoIC+Pj4IDAwEMOGDYOFhQXeeOMNTJkyRafnzsrKhUol6OU4nqZUqlBSotJpH0PWr6yuublUo44+265OfV3qKpVKeHuLW2crOTmxwraf7YvStlVQKHJEx1JXyGTN6uVxa8O+KCWVSvT2JV5UMnr99dfVZz9z5szBu+++Czc3N0gkEqhUKkREROglGAAYOnQounTpolH27rvvYsyYMeVWlM3NzcWnn36K2NhYAKWTGIqLiwGUDiUqlUq9xUVERIYjKhn5+fmp/92tWzccOnQIP/74IwoKCtCrVy/1JAJ9sLKygpWVlUZZgwYNIJPJ8Morr2iUr169GmPHjsWLL74IAHBzc8OBAwfQv39/xMXFwc3NTW9xERGR4VSajAoLCxEeHo5x48ahW7duAIDWrVtjwoQJBg/uec6fP4+zZ89iz5496rLJkyfj0qVLGD9+PORyOVauXGnCCImISKxKk1HDhg1x+PBhjBw50hjxaJWSklKuzNXVVX2xa5lGjRrx7gtERLWQqOlJvXr1wsmTJw0dSy1TBKUyX9QfoP9JEUREdYmo34wmT56MhQsX4vHjxxgwYADs7OzK3Zeuvq1ppFSWwNt7uKi6ycmJBo6GiKh2E5WMpk2bBgDYunUrtm7dqpGIBEGARCLB5cuXDRMhERHVeaKS0bZt2wwdB1GtIZVK/2/4tXJmZuYALAwbEFEdUGEyWrBgAd5//328+OKLkEgk6Ny5M5o2bWrM2IhqJJVKlwtqk2BmxmREVJkKJzAcOHAADx8+BAD4+vri+vXrRguKiIjqlwrPjGQyGU6ePAkHBwcIgoDCwkI8fvy4woYaN25skACJiKjuqzAZTZgwAatWrUJUVBQkEgl8fX2f2xAnMBARUVVVmIwCAwMxcOBAXL9+HfPmzUNAQABeeuklY8ZGRET1xHNn03Xp0gVdunRBWloaxowZo74HHBGRpiIolSWia3OWIT1L1NRufd6Vm4jqHl0uAgc4y5DK020lNCIiIgNgMiIiIpNjMiIiIpNjMiIiIpNjMiIiIpMTNZuOiEifeLNZehaTEREZHW82S8/iMB0REZkcz4yIqAKad1VQKAqgVKoqqCsYJySqs5iMiEirZ++qYG4uRUmJ9mSUnJxorLCojqpxw3QbN27E2LFj8eqrr6J3794IDAzEzZs31dsFQcDSpUvRo0cPDB06FMePH9fYPz4+HnPnzjVy1EREVB017szo559/xuTJk+Hi4gKlUomoqCj4+/sjMTERjRo1QkpKCpKTkxEXF4dTp04hODgYqampkEqluHv3LrZs2YI9e/aY+jCIAOg2awzgzLG6hTeP1UWNS0aff/65xuPIyEj07t0bly5dQvfu3XHjxg14eHjA2dkZjo6OiIiIQHZ2NmxsbBAeHo6AgADY2tqaKHoiTbrMGgM4c6wu4c1jdVPjhumelZOTAwBo3rw5AEAul+PixYvIyclBWloaZDIZrK2tcfToUTx69Ajjxo0zZbhERFQFNe7M6GmCICAiIgIeHh5wcHAAAHh6euL06dPw8fGBlZUVoqKikJ+fjxUrVmDDhg2IiYnBwYMHIZPJEBYWpt5PDFtbS9F1FYoCmJuLz+W61DV0fTF1n65TU47TVG1r22aouM3MpJDJmomub0ja3uO69tPziK1fk/okOzsbxcXFUCgKKq0rkdTe194UanQyCgsLw++//44dO3ZolAcFBSEoKEj9eNmyZRg1ahRu3bqFpKQk7Nu3D4cPH0ZwcDASEhJEP19WVi5UKnFTVJVKVYUzi7TRpa6h61dW99lZUzXlOE3RdkUzyAwVt1KpgkKRI7q+IT37Hn/ebDrAcK9PzeqTQnh7D6+0L4DSGYa19bUXSyqV6PQl/rlt6aUVAwgPD0dKSgri4uLdEKYIAAAY6UlEQVTQqlWrCutduHABaWlpmD59OtLS0uDp6QlLS0uMHDkS6enpyM3NNWLURERUFTXuzEgQBISHh+Pbb7/F9u3bn7vUuVKpREhICEJDQ2FhYQGVSgWVqvSbSHFxMQCoHxORbrO76seFrOyTmqLGJaMlS5bg0KFDWL9+PZo2bQqFQgEAaNasGRo1aqRRNy4uDi4uLnBzcwMAuLm5ISIiAmPGjEFSUhI6dOgAKysrox8DUU2k++yuun8hK/uk5qhxyajs96GpU6dqlJclmTKZmZnYtWsX9u7dqy7z8vLC6dOn4evrC3t7e0RGRhonaCIiqpYal4yuXLkiql7btm3xzTffaJRJpVIsXLgQCxcuNERoRAbHpRXK44XDpiB2+FIKQD8TGGpcMiKqz7i0Qnm8cNj4xA5ftmnTGomJh/XynDV2Nh0REdUfPDMiqqV0Hb6qLzPBdOuX+tEntQGTEVEtpfvwVf2YCabbUGf96JPagMN0RERkcjwzIiKq82r+xb1MRkREdVxtuLiXw3RERGRyTEZERGRyHKYjIqoB6vvdN5iMiIhqgPp+9w0O0xERkcnxzIiIqJapi3ffYDIiIqpl6uLdNzhMR0REJsdkREREJsdkREREJsdkREREJsdkREREJsdkREREJldjk9GXX36JwYMHw8XFBRMmTMD58+cBAIIgYOnSpejRoweGDh2K48ePa+wXHx+PuXPnmiJkIiKqohqZjBITExEREYEPPvgA+/fvh5OTE6ZNm4YHDx4gJSUFycnJiIuLw5QpUxAcHAyVSgUAuHv3LrZs2YKPP/64Ss+rUj2GUpkv6q82XERGRFRb1MiLXrdu3YqJEydi7NixAIAlS5bg+++/x/79+6FSqeDh4QFnZ2c4OjoiIiIC2dnZsLGxQXh4OAICAmBra1ul53377Xfw99+3RdWtDReRERHVFjUuGRUVFeHixYsICAhQl0mlUvTp0wfnzp3DuHHjsHfvXuTk5ODs2bOQyWSwtrbG0aNH8ejRI4wbN67Kz92yZUvRdc3MzNCmTWu91zV0fTF1zcykUCpVBmm7qvVN1fbTfaHvtqtb39jvK219YYxYamLbz+uL6rat77qGrK/LZ2ZlJIIg1Kjxprt372LAgAHYs2cPXF1d1eUrVqzA2bNnsXPnTqxatQqJiYmwsrLCggUL4OzsjNGjR2PDhg04fPgwDh48CJlMhrCwMDg4OJjwaIiISIwa+ZtRZYKCgnDs2DHs378fHh4eWLNmDUaNGoVbt24hKSkJ+/btw5tvvong4GBTh0pERCLUuGRkbW0NMzMz3L9/X6M8KysLMpmsXP0LFy4gLS0N06dPR1paGjw9PWFpaYmRI0ciPT0dubm5xgqdiIiqqMYlIwsLCzg7OyM1NVVdplKp8NNPP6Fbt24adZVKJUJCQhAaGgoLCwuoVCqUlJQAAIqLi9X7EhFRzVbjkhEA+Pn5YdeuXdi/fz+uX7+O0NBQFBQUYPTo0Rr14uLi4OLiAjc3NwCAm5sbjhw5gsuXL+Pzzz9Hhw4dYGVlZYpDICIiHdS42XQAMGLECDx48ADR0dFQKBTo1KkTYmNjYWNjo66TmZmJXbt2Ye/eveoyLy8vnD59Gr6+vrC3t0dkZKQpwiciIh3VuNl0RERU/9TIYToiIqpfmIyIiMjkmIyIiMjkmIyIiMjk6k0yqmhJiookJSXB29sbLi4uGDlyZLmlKmozXfpi9+7deOutt+Du7g4PDw/4+/vjwoULRozWsHR9X5TZtGkTnJycsHz5cgNHaDy69sWjR48QEhKCPn36wMXFBcOHD8fPP/9spGgNS5e+KCkpQVRUFAYPHgxXV1d4eXnh888/N2K0hnPq1CnMnDkT/fr1g5OTE7777rtK96nyZ6dQDxw+fFhwdnYW9u7dK1y9elVYuHCh4O7uLmRlZWmtf+bMGaFTp07C5s2bhWvXrgmrV68WnJ2dhWvXrhk5cv3TtS/mzp0rxMfHC5cuXRKuXbsmzJ8/X+jRo4dw9+5dI0euf7r2RZn09HRh0KBBwsiRI4XIyEgjRWtYuvZFYWGhMHr0aGHGjBnCmTNnhIyMDCE1NbVe/h9Zt26d0KtXL+H7778XMjIyhEOHDgmurq7C/v37jRy5/n3//fdCVFSUcOTIEUEulwspKSnPrV+dz856kYzGjRsnhIWFqR8rlUqhX79+QmxsrNb6c+bMEd577z2NsvHjxwtLliwxaJzGoGtfPKukpER49dVXha+//tpQIRpNVfoiPz9fGD58uHD8+HFhypQpdSYZ6doXX331lTBkyBChqKjIWCEaja59MWPGDGHRokUaZf7+/nXi8+JpYpJRdT476/wwXdmSFH379lWXPb0khTbnzp3TqA8A/fr1q7B+bVGVvnjW48ePUVJSgubNmxsqTKOoal9ERkaiZ8+e6N+/vzHCNIqq9EVKSgq6deuG0NBQ9OnTByNHjsQXX3wBoZZftliVvnj11VeRmpqKmzdvAii9X2Z6enqdeo+IVZ3Pzhp5BwZ9evjwIZRKJezs7DTKbW1tcevWLa373L9/v9wCfba2tlAoFAaL0xiq0hfPWrVqFVq3bo1evXoZIkSjqUpffPfdd0hLS8OBAweMEaLRVKUvMjIy8NNPP2H06NHYvHkzrl27hrCwMEgkErz99tvGCNsgqtIXM2bMwD///INhw4bB3NwcgiBg/vz5GDRokDFCrlGq89lZ55MR6c/mzZuRmJiI7du3w8LCwtThGNWDBw+waNEirF+/Ho0bNzZ1OCYnCAJkMhlCQ0NhZmYGZ2dnZGRkYOfOnbU6GVVFUlISkpOTsWbNGrRv3x4XLlxAZGQkWrdujddee83U4dUadT4Z6bokBQDY2dkhKytLdP3aoip9Uebzzz/Hxo0bsXXrVsjlckOGaRS69sXVq1ehUCgwadIkdZlSqcSpU6cQHx9fq2cYVvX/SIMGDWBmZqYuc3BwwO3btw0aq6FVpS9WrFiBgIAADB8+HADg5OSEmzdvYvPmzfUuGVXns7PO/2aky5IUZbp164b//ve/GmWpqakV1q8tqtIXQOkZ0fr16xEbGwsXFxdjhGpwuvaFi4sLDh48iAMHDqj/unTpgtGjR2Pfvn3GDF3vqvK+ePXVV/Hnn39qLNFy8+ZNtG4tfmnrmqgqfVFQUKCRlIHSZbvr4/I11fnsNAsNDQ01UFw1hqWlJdasWYPWrVvDwsICn332GX777TcsW7YMjRs3RnBwMM6fP48+ffoAKF3Xfc2aNWjcuDGsrKzw5ZdfIikpCZ988onGncNrI137YtOmTYiOjsaKFSvg5OSE/Px85OfnA0CtH6rTpS8aNGgAW1tbjb9Dhw7hpZdewptvvmnqQ6k2Xd8XL7/8MrZs2YKHDx/ihRdewJkzZ7Bq1SpMmzYNXbt2NfHRVI+ufXH9+nX85z//Qbt27WBmZoYTJ04gOjoaY8eOhbu7u4mPpnry8vJw/fp13L9/Hzt37kS3bt3U/++bNm2q18/OOj9MB1S+JMXt27chlT45SezevTs+/fRTrFmzBlFRUXjllVewbt06ODg4mOoQ9EbXvti5cyeKi4sxe/ZsjXYCAwMxa9Yso8aub7r2RV2ma1+0bdsWsbGxiIiIwI4dO9C6dWvMnDkTkydPNtUh6I2ufbFw4UKsWbMGISEhyMrKQqtWreDn54fp06eb6hD0Jj09Hb6+vurHS5cuBfDk/78+Pzu5hAQREZlc/fjaR0RENRqTERERmRyTERERmRyTERERmRyTERERmRyTERERmRyTEVVq7dq16Nmzp6nDKGf37t0YPHgwOnfujKlTp2qtc/78eaxdu7ZceU09pqft27cPTk5OyMvLAwD89ddf5RY427x5M06ePKmxX1FREdauXYvLly9rlGvb39C0xQeU3jInPj7eaHFoo68YxPZrfHw8nJycqv18dRWTEdVKCoUCoaGhGDJkCLZv346QkBCt9c6fP4+YmBgjR2cYLVu2xK5du+Dm5qYui42NLbe6anFxMWJiYsolI237G5q2+Ii0qRd3YKC659atW1AqlRg7diw6duxo6nCMwsLColr3R6zu/jVBYWEhGjZsaOowyAB4ZlRH7du3D126dME///yjUX716lU4OTmpbwT5/fffw8/PD71790b37t0xYcIEnDhxotK2nx4+KjN48GAsX75co+zo0aMYM2YMXFxc0LdvX6xYsQLFxcWVxh8fHw8vLy906dIFr732Gr744gv1trVr16pvOzNq1Cg4OTlpvVnpvn37EB4eDqB0SMbJyanccN6lS5cwYcIEdO3aFT4+Pjh9+nS5dvbs2YPXX38dXbp0waBBg7B58+ZK4z927BjGjBmDbt26wd3dHePHj9c4Q3BycsLWrVuxdOlSeHh4oEePHggPD0dRUVGFbT47HDR48GBkZ2cjJiZGfXwnT55E9+7dAQALFixQl//1119ah5PKXrMvvvgCAwYMgLu7Oz788MNy75vffvsNkyZNgouLC15//XX88MMPGDNmDObPn19hvBXFV0apVCIqKgq9evVC7969sWTJEo3jL3ufnT9/HlOnToWrqytiY2MBlCalFStWwNPTE126dMGbb76JH374QafXQEwMAHD58mW8/fbb6Nq1K9zd3REUFFTurt7PKioqQlhYGHr06AEPDw988sknKCkpee4+9R3PjOqooUOHYvHixfj2228xduxYdXliYiLs7OzUv5f89ddfGDRoEPz9/SGVSnH8+HFMnz4d8fHx1R7OSUxMRFBQECZOnIi5c+fizz//RFRUFARBwLx58yrcb/fu3QgPD4efnx/69euHkydPIjIyEkVFRZgxYwbGjx8PGxsbhIWF4dNPP8WLL76Il156qVw7AwcOhL+/P7Zs2YJdu3YBKL0JZpmCggLMmzcP77zzDuzs7LBu3ToEBgbiu+++U69ZFBsbi9WrV2PatGnw8PDAxYsX8dlnn6Fx48aYMmWK1vj//PNPzJkzB1OnTsVHH32EoqIipKen49GjRxr1tmzZgm7dumHlypW4du0aVq9eDQsLi+f2zdNiYmLg6+uLYcOGYfz48QAAR0dHxMXF4e2330ZAQAAGDhwIoHSI7t69e1rbSUpKgpOTE8LDw3Hnzh1ERkYiKioKZfdQfvz4MaZNmwY7OztERUWhsLAQn3zyCf7555/nLidSUXxltm7dil69emHlypW4cuUKoqKi0KZNm3L3dJs7dy7eeustfPDBB7CysgIAzJ49G+fPn8esWbPw0ksvISkpCQEBAUhISECnTp1EvwaVxfDgwQNMnToVDg4OWLVqFfLy8rBq1Sr4+fkhISGhwpsFf/rpp9izZw8+/PBDODg4YM+ePUhOTq6wrwhAFZdDp1pg5syZgr+/v0aZl5dXhevRK5VKobi4WPD39xfmz5+vLo+OjhY8PDzUjxMSEgS5XC7k5uZq7D9o0CAhMjJSEARBUKlUwsCBAzXaEQRB2LNnj+Di4iI8ePCgwhj69etXbr+QkBChe/fuQkFBgSAIgpCWlibI5XLhypUrz+sCYfv27YJcLi9XHh0dLcjlciE1NVVddunSJUEulws//PCDIAiCkJOTI3Tr1k1Yu3atxr5r1qwR+vTpI5SUlGh9zqSkJI3+0kYulwvDhg0TlEqlumz9+vWCq6ur8PDhQ0EQyvdzRkaGIJfLhZSUFPU+Hh4eQnR0tEbbubm5glwuFxISEjTKte0/aNAgYciQIUJxcbG6bOnSpUKfPn3Uj+Pj4wVnZ2fhzp076rJff/1VkMvlwrx58557nNriKzv+t956S6MsICBAGD9+vPpx2fF/8cUXGvVSU1MFuVwunDx5UqP8rbfeEmbNmiUIgvjXoLIYVq5cKbi5uQk5OTnqsnPnzglyuVw4ePCgIAjl+/XBgweCi4uLsHHjRvU+SqVSGDZsmNb3IpXiMF0dNmLECKSlpeHhw4cASocbbt68iREjRqjr3LlzB/PmzUP//v3RuXNnODs748SJE7h582a1nvuPP/7A33//DW9vb5SUlKj/evXqhcLCQly9elXrfnfu3MG9e/fg7e1d7lhyc3Nx5cqVasX1tAYNGmjMqCu7s/Ddu3cBAL/88gvy8/O1HsP9+/dx584dre3K5XLk5ORg3rx5OHHihHrJjWcNGTJE447HXl5eKCgoqLBvDKVnz54wN38ySOLo6IisrCz1cOqFCxfg7OyMVq1aqeu4urqWW5pbV3379tV47OjoqLVPy87uyqSmpkImk6F79+4ar0vv3r2Rnp4OQPxrUFkM58+fR9++fTXOqLt27Yq2bdvizJkzWtv8/fffUVhYiCFDhqjLpFKpxmMqj8N0ddjgwYNhbm6OI0eOYOLEiUhMTIS9vb16+E2lUiEgIAB5eXmYPXs2Xn75ZTRu3BjR0dHlVmvUVVkCnDFjhtbtFa0IqlAoAAC2trYa5WWPnx1mqY6mTZtqJIOyIZfCwkIAT47h9ddf17r/7du30bZt23Ll7du3x/r167Fp0ybMmDED5ubmeO211/Dxxx9rrOny7DGWbSvrA2MpG/oq06BBAwiCgKKiIjRo0AAKhQLW1tbl9qvu2l7anres75/2bD89fPgQCoUCzs7O5eqWLXIn9jWoLAaFQoEOHTqUex47O7sK34tlvydV9B4m7ZiM6rCmTZvC09MTiYmJmDhxIpKSkuDt7Q2JRAKgdEbapUuXsHnzZgwYMEC9X0FBwXPbLZvN9OxEhKf/c7Zo0QIAEB4ejk6dOpVr44UXXtDadtnyxNqWLgaA5s2bPzc2fSp7ro0bN2r9IGnXrl2F+w4cOBADBw5ETk4Ovv/+e3zyyScIDw/H6tWr1XWePcYHDx4AQI1b3l4mk+GPP/4oV14Wr6GVvV/LNG/eHK1atcK6deueu5+Y16AyMplM6xez+/fva02GANRnjFlZWer/B2WPqWIcpqvjXn/9dZw6dQopKSnIyMjQ+JZf9g3w6R9hMzMz8csvvzy3zbLhmuvXr6vLfv31V+Tm5qoft2vXDq1atUJmZiZcXFzK/Wn7pg0A9vb2aNmyZbkfe5OSkmBpaanzRYMNGjQAAK3fuCvz6quvolGjRrh3757WY3h66KYizZo1w8iRI/Haa6/h2rVrGtuOHTumsTT1kSNH0KhRI63fxCui7WyiOsesjYuLCy5evKgevgRKh68qm1FWUXzV1bt3b9y/fx9NmjTR+ro863mvQWW6du2KEydOaLy3z58/j8zMzAon+MjlcjRs2BDHjh1Tl6lUKo3HVB7PjOo4T09PNGrUCIsXL8YLL7wAV1dX9bb27dvD3t4ey5cvx5w5c5CXl4fo6Gi0bNnyuW26urqiVatWWLZsGebMmYPs7GzExsZqfDhLpVLMnz8fwcHByM3NxYABA9CgQQNkZGTg6NGjiI6OVs9Ye5pUKsWsWbOwePFitGjRAn379sWpU6ewY8cOzJ07V+drTNq3bw8AiIuLQ69evWBpaakuq4yVlRUCAwOxbNkyZGZmwt3dHSqVCjdv3sTJkycr/Ga+c+dOnDt3Dv3790fLli1x8+ZNJCcnY9SoURr18vLyMGfOHIwfPx7Xrl3D+vXrMXnyZI1v02KO74cffkD//v3RpEkTtGvXDpaWlnjhhReQlJSEDh06oGHDhtW68n/MmDH497//jffeew+BgYEoKCjA2rVrYWNjU+6sRWx81dG3b1/069cP/v7+mD59OhwdHZGbm4vffvsNhYWFCAoKEv0aVMbPzw87duzAtGnTMG3aNOTn52PVqlWQy+Xw8vLSuo+1tTUmTJiAtWvXwtzcHI6OjtizZ0+Fv1tRKSajOq5Ro0YYPHgwDh48WO73GwsLC6xduxZhYWGYPXs27O3tMXPmTPz888/4/fffK2zTwsICMTExWLJkCWbPno127dohNDQUH330kUa9ESNGoGnTpti4cSMSEhIglUrx4osvYuDAgepv79pMmDABhYWF2LZtG7Zv345WrVph/vz5eOedd3Q+/h49euDdd9/Ftm3bEBUVBXd3d2zfvl30/tOnT0fLli0RFxeHrVu3omHDhnjllVc0JoE8y8nJCSkpKYiIiMCjR48gk8kwfvx4zJkzR6Oev78/MjIyEBQUBJVKhXHjxmHu3Lk6HV9wcDDCwsLw3nvv4fHjx9i2bRt69uyJJUuWYPny5fDz80NRUVG1vpU3btwYsbGxCA0Nxf/+7/+ibdu2+Oijj7By5cpKE0tF8VWHRCJBTEwMNmzYgLi4ONy+fRvNmzdHx44d1deRiX0NKmNjY4Nt27YhMjISQUFBaNCgATw9PbFgwYIKp3WXHXdJSQnWrVsHqVSKN998E35+foiMjKzWsddlXHacyAScnJywaNGiCq9VqukyMjLg7e2NsLAwjevYiKqKZ0ZEVKmNGzeiZcuWaNOmDW7fvo2NGzfC2toaw4YNM3VoVEcwGRFRpcqGxu7duwcLCwv06NEDwcHB1f79h6gMh+mIiMjkOLWbiIhMjsmIiIhMjsmIiIhMjsmIiIhMjsmIiIhMjsmIiIhM7v8Dyb7VR2s+BPoAAAAASUVORK5CYII\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\u003d\n"
},
"metadata": {},
"output_type": "display_data"
}
],
- "source": "exp_folder \u003d \u0027exps\u0027\nmodel_names \u003d [\u00272019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\u0027,\n \u00272019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\u0027,\n \u00272019-05-21 15:41:28 dataset\u003dmnist_2_6 model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.300 lr\u003d1.0\u0027]\n# model_names \u003d [\u00272019-05-21 18:47:22 dataset\u003dgts_120_warning model\u003dplain n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.031 lr\u003d1.0\u0027,\n# \u00272019-05-21 18:47:22 dataset\u003dgts_120_warning model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.031 lr\u003d1.0\u0027,\n# \u00272019-05-21 18:47:22 dataset\u003dgts_120_warning model\u003drobust_exact n_train\u003d-1 n_trials_coord\u003d10 eps\u003d0.031 lr\u003d1.0\u0027]\n\nfig_width \u003d 1.3*len(model_names)*plot_height\nfig_height \u003d plot_height\nfig, axs \u003d plt.subplots(1, len(model_names), figsize\u003d(fig_width, fig_height))\nfor i, model_name in enumerate(model_names):\n print(\u0027Model name: {}\u0027.format(model_name))\n dataset \u003d model_name.split(\u0027dataset\u003d\u0027)[1].split(\u0027 \u0027)[0]\n model \u003d model_name.split(\u0027model\u003d\u0027)[1].split(\u0027 \u0027)[0]\n # weak_learner \u003d model_name.split(\u0027weak_learner\u003d\u0027)[1].split(\u0027 \u0027)[0] # doesn\u0027t exist for stumps\n weak_learner \u003d \u0027stump\u0027\n eps \u003d model_name.split(\u0027eps\u003d\u0027)[1].split(\u0027 \u0027)[0]\n \n model_path \u003d model_name + \u0027.model\u0027\n model_arr \u003d np.loadtxt(exp_folder + \u0027/\u0027 + model_path)\n bs \u003d model_arr[:, 2]\n \n if model \u003d\u003d \u0027robust_bound\u0027:\n model \u003d \u0027robust\u0027\n elif model \u003d\u003d \u0027robust_exact\u0027:\n model \u003d \u0027exact robust\u0027\n\n dataset_ \u003d dataset.upper().replace(\u0027_\u0027, \u0027 \u0027).replace(\u0027120 WARNING\u0027, \u0027120-warn\u0027).replace(\u00272 6\u0027, \u00272-6\u0027)\n plot_name_short \u003d \u0027{}: {} stumps\u0027.format(dataset_, model)\n ax \u003d axs[i]\n ax.hist(bs, bins\u003dnp.linspace(0.0, 1.0, 21))\n ax.set_xlim(0.0, 1.0)\n ax.set_xlabel(\u0027value of the splitting threshold\u0027)\n ax.set_ylabel(\u0027number of stumps\u0027)\n ax.set_xticks(np.linspace(0.0, 1.0, 6))\n ax.tick_params(which\u003d\u0027both\u0027, width\u003d2)\n ax.grid(which\u003d\u0027both\u0027, alpha\u003d0.5, linestyle\u003d\u0027--\u0027)\n ax.set_title(plot_name_short)\n \nplot_name_save \u003d \u0027dataset\u003d{}-weak_learner\u003dstump-histogram_of_thresholds\u0027.format(dataset)\n# fig.tight_layout()\nfig.subplots_adjust(wspace\u003d0.25)\nplt.savefig(\u0027plots/{}.pdf\u0027.format(plot_name_save), bbox_inches\u003d\u0027tight\u0027)\n\n# 415, 364, 197 weak learners in total",
+ "source": "np.random.seed(1)\nmodels \u003d [\u0027plain\u0027, \u0027at_cube\u0027, \u0027robust_bound\u0027]\n# models \u003d [\u0027plain\u0027]\nexp_folder \u003d \u0027exps_diff_depth\u0027\nweak_learner \u003d \u0027tree\u0027\ntree_depth \u003d 4\n# datasets \u003d [\u0027breast_cancer\u0027, \u0027diabetes\u0027, \u0027cod_rna\u0027, \u0027mnist_1_5\u0027, \u0027mnist_2_6\u0027, \u0027fmnist_sandal_sneaker\u0027, \u0027gts_100_roadworks\u0027, \u0027gts_30_70\u0027]\ndatasets \u003d [\u0027breast_cancer\u0027, \u0027mnist_1_5\u0027, \u0027mnist_2_6\u0027, \u0027gts_100_roadworks\u0027, \u0027gts_30_70\u0027] # tabular datasets are also possible\n# datasets \u003d [\u0027gts_30_70\u0027] \nfor dataset in datasets:\n print(\u0027--- Dataset: {} ---\u0027. format(dataset))\n _, _, X_test, y_test, eps \u003d data.all_datasets_dict[dataset]()\n model_names \u003d utils.get_model_names([dataset], models, exp_folder, weak_learner, tree_depth)\n \n sns.set(font_scale\u003d1.25)\n for i, model_name in enumerate(model_names):\n print(\u0027Model name: {}\u0027.format(model_name))\n dataset \u003d model_name.split(\u0027dataset\u003d\u0027)[1].split(\u0027 \u0027)[0]\n model \u003d model_name.split(\u0027model\u003d\u0027)[1].split(\u0027 \u0027)[0]\n weak_learner \u003d model_name.split(\u0027weak_learner\u003d\u0027)[1].split(\u0027 \u0027)[0]\n eps \u003d model_name.split(\u0027eps\u003d\u0027)[1].split(\u0027 \u0027)[0]\n \n model_path \u003d model_name + \u0027.model.npy\u0027\n metrics_path \u003d model_name + \u0027.metrics\u0027\n metrics \u003d np.loadtxt(exp_folder + \u0027/\u0027 + metrics_path)\n valid_errs, valid_adv_errs \u003d metrics[:, 8], metrics[:, 10]\n # Model selection\n # best_iter \u003d len(valid_errs) - 1 # otherwise, the counts are not comparable between different model types\n if model \u003d\u003d \u0027plain\u0027:\n best_iter \u003d np.argmin(valid_errs)\n elif model in [\u0027at_cube\u0027, \u0027robust_bound\u0027, \u0027robust_exact\u0027]:\n best_iter \u003d np.argmin(valid_adv_errs)\n else:\n raise ValueError(\u0027wrong model name\u0027)\n \n model_data \u003d np.load(exp_folder + \u0027/\u0027 + model_path)\n bs, n_bs \u003d [], 0\n for i_tree in model_data[0].keys():\n if i_tree \u003c\u003d best_iter:\n thresholds_tree \u003d model_data[0][i_tree][:, 5]\n bs.extend(thresholds_tree)\n n_bs +\u003d len(thresholds_tree)\n bs \u003d np.array(bs)\n \n # 30 bins is important so that the bin [1 - 8/255, 1] is empty.\n ax \u003d sns.distplot(bs, kde\u003dFalse, bins\u003dnp.linspace(0.0, 1.0, 30), \n hist_kws\u003d{\u0027color\u0027: \u0027black\u0027, \u0027alpha\u0027: 0.8})\n # hist_kws\u003d{\u0027color\u0027: \u0027blue\u0027, \u0027alpha\u0027: 0.75}\n ax.set_yticklabels([\u0027{:.0%}\u0027.format(x/n_bs) for x in ax.get_yticks()])\n ax.set_xlim(0.0, 1.0)\n ax.set_xlabel(\u0027value of the splitting threshold\u0027)\n ax.set_ylabel(\u0027fraction of splits\u0027)\n ax.set_xticks(np.linspace(0.0, 1.0, 6))\n ax.tick_params(which\u003d\u0027both\u0027, width\u003d2)\n # ax.grid(which\u003d\u0027both\u0027, alpha\u003d0.5, linestyle\u003d\u0027--\u0027)\n \n plot_name_save \u003d \u0027histogram_of_thresholds-dataset\u003d{}-weak_learner\u003d{}-model\u003d{}\u0027.format(dataset, weak_learner, model)\n plt.savefig(\u0027plots/{}.pdf\u0027.format(plot_name_save), bbox_inches\u003d\u0027tight\u0027)\n plt.show()",
"metadata": {
"pycharm": {
"metadata": false,
@@ -87,6 +377,15 @@
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.4"
+ },
+ "stem_cell": {
+ "cell_type": "raw",
+ "source": "",
+ "metadata": {
+ "pycharm": {
+ "metadata": false
+ }
+ }
}
},
"nbformat": 4,
diff --git a/notebooks/plots.ipynb b/notebooks/plots.ipynb
deleted file mode 100644
index f9f1e0f..0000000
--- a/notebooks/plots.ipynb
+++ /dev/null
@@ -1,100 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "pycharm": {}
- },
- "source": "# Plots for experiments"
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true,
- "pycharm": {
- "is_executing": false
- }
- },
- "outputs": [],
- "source": "%load_ext autoreload\n%autoreload 2\n\nimport os\nos.chdir(\"../\")\nimport glob\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn\n\n%matplotlib inline\nseaborn.set(font_scale\u003d2)\nseaborn.set_style(\"white\")\nnp.random.seed(1)\nnp.set_printoptions(precision\u003d6, suppress\u003dTrue)\nplot_height, legend_size, marker_size, line_width \u003d 10, 18, 0.4, 1.2\n"
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "outputs": [
- {
- "name": "stdout",
- "text": [
- "Model name: 2019-05-25 13:26:04 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003dplain n_train\u003d-1 n_trials_coord\u003d100 eps\u003d0.300 max_depth\u003d4 lr\u003d1.0\niter: 27 [test] err 1.16% adv_err_lb 98.14% adv_err 100.00% adv_err_ub 100.00% | [valid] err 0.38% adv_err 100.00% | [train] err: 0.00% adv_err: 0.00% loss: 0.00000 (29.83 min)\n",
- "Model name: 2019-05-25 13:26:04 dataset\u003dmnist_2_6 weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d100 eps\u003d0.300 max_depth\u003d4 lr\u003d1.0\niter: 47 [test] err 0.70% adv_err_lb 3.47% adv_err 4.97% adv_err_ub 4.97% | [valid] err 0.55% adv_err 5.26% | [train] err: 0.09% adv_err: 1.53% loss: 0.06522 (90.80 min)\n",
- "\n\nLatex table:\nmnist 2 6 \u0026 0.300 \u00261.2 \u0026 98.1 \u0026 100.0 \u0026 0.7 \u0026 3.5 \u0026 5.0 \\\\\n\n"
- ],
- "output_type": "stream"
- },
- {
- "data": {
- "text/plain": "\u003cFigure size 2160x720 with 3 Axes\u003e",
- "image/png": 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lbEdZ21DOEpaOFRvK2YZytqOsbShnO8pawtBxYkdZ21DOdpS1DeVsJ05Zq3NORERERERERERERERExIg650RERERERERERERERESMqHMuJmbPnt3sJkwIytmOsrahnO0oaxvKWcLSsWJDOdtQznaUtQ3lbEdZSxg6TuwoaxvK2Y6ytqGc7cQp63SzGyAiIiIiIiIykW3cuJHVq1eTzWab3ZRIFItFEolEs5sxIQwn60wmw9y5c5kxY0aDWyUiItJ83d3drFmzhu7ubnK5XLObU5dqJzths06n07S2tjJnzhxaW1sb0hZ1zsXE2rVrmT9/frObMe4pZzvK2oZytqOsbShnCUvHig3lbEM524lj1hs3bmTVqlVsu+22TJ48eVx8MdPb20tLS0uzmzEhhM26WCyyZcsWXnjhBQB10E0wcfzdN14paxvK2c5YzXrDhg2sWrWKOXPmMG/ePNLpdKxrLNVOdsJkXSwWyeVybNq0iWeffZb29nZmzpwZeVvUOSciIiIiIiLSJKtXr2bbbbdlypQpzW6KjGOJRIIpU6aw7bbbsmLFinHZOeecexdwBrAISAFPAFcBV3jvC8PYzynAUcDewDxgFrAJeAz4CfA97/2g21ydc1cDp9bZtffe7xa2HSIiMnJr165lwYIFqq9kRBKJBJlMhq222opJkyaxcuVKdc6JiIiIiIiIjCfZbJbJkyc3uxkyQUyePHncDJ/an3Pu28CHgW7gTiALHAZcDhzmnHvHMDrozgAOBB4HHgI2APNLy14LnOKcO9x731Vj+z8AT1VZ/mLI1xcRkVHq7e1VfSWRmDx5Mj09PQ3ZtzrnYmL69OnNbsKEoJztKGsbytmOsrahnCUsHSs2lLMN5WwnrlnHeZilkUilUs1uwoQx3KzH27EG4JxbQtAxtxJ4g/f+ydLyduAu4HjgI8ClIXf5CeAf3vv1Fa+zAPgtcADwaeDLNba/0nt/9TDfRkPF9XffeKSsbShnO2M567H0N0+1k5041U7Jhu1ZhmUs/6IbS5SzHWVtQznbUdY2lLOEpWPFhnK2oZztKGsb+oLJjrIG4LOlx8+UO+YAvPerCO6CAzjHORfqOzDv/YOVHXOl5c8DXyv9eMQo2mtOv/vsKGsbytmOsrahv+d24pS1OudiYtWqVc1uwoSgnO0oaxvK2Y6ytqGcJSwdKzaUsw3lbEdZ2xiPwybG1UTPunQ3275AL3Bj5fPe+3uAFwjmjjsggpfMlR4bM75Vg+h3nx1lbUM521HWNib633NLccpanXMxkc/nm92ECUE521HWNpSzHWVtQzlLWDpWbChnG8rZjrK2USwWm/ba55xzDs65pr2+tWZmHRP7lB4f895vqbHOQxXrjohzbjbwqdKPv6iz6iHOuW84577vnDvPOXdk2Lv2GkW/++woaxvK2Y6ytjEW/p43qsa6+eabcc7xwAMPRL7vauKUdWznnHPBJ30UsB+wGNgVSAAneO9vGsV+30UwrMEiIAU8AVwFXDGMyYFFREREREREJKRly5Zxxx13cPzxx7NgwYJmN0fGj51Kj8/UWefZinVDcc4dAywh+O5oG+C1QCtwNXB5nU3fU2XZ4865k7z3fxtOG0RERIaiGmvsim3nHEEH2sei3KFz7tsEkwR3A3cCWeAwgqLqMOfcO5rVQZfJZJrxshOOcrajrG0oZzvK2oZylrB0rNhQzjaUsx1lbSORSAxatmzZMi6//HJe85rXNPSLo/POO4+lS5c2bP9xUy3rCWZa6bGrzjqbSo/DnThpL+DUimWXAOd676uNifUX4BHgDoIOwRnAq4GvlvZ1h3Pu1d77F4bZDlavXk0yGdx8N3v2bADWrl3b9/z06dOZPn06q1at6rvLJZPJMGfOHNavX09XVxcrVqwAoL29nWw2S0dHR9/2M2fOZOrUqX3rALS2ttLW1kZHRwfd3d19y+fPn09XVxcbNmzoW9bW1kYmkxkw/N2UKVOYNWsWa9as6RtCLJVK0d7eTmdnJ52dnX3rjuQ9bd68uW/dOL2nctbj6T3F8XMq5zye3lNcP6euri66urrG3HvK5/P09vb2LS8Wi+Ryub7tU6kUqVSqbx2AZDJJOp0ml8tRKLzcRdDS0kI+nx9wF2E6nSaRSAwYIrG8fTab7bs7K5FIkMlkqm4P9LWpUCiQz+dJpVJVt69sU//39Le//Y3LL7+cxYsXs2DBgoa9py9+8Yt8/vOfJ5vNhnpP/XOu957K6xeLxQE/N+pzKrdjOJ9TPp/vO/6rHXv9fwcMR5w75/4OXAQ8TFDc/BA4eKQ7c84tIeiYWwm8oTxJsHOuHbgLOB74CHDp6Jo9MnPmzGnGy044ytmOsrahnO0oaxvKWcLSsWJDOdtQznaUtY0oOkHLX6pNnjzZ/LUbYaj3s2nTJqZNm1b1uXoq3+9Ic5PBvPfnA+c751qAHYATgXOA451zb/beP16x/iUVu+gCfu2c+y1wD8Gcd58FzhpuW+bOncv8+fMHLKv8GYIv1SvNmjWLWbNmDViWSqWqbl9tWVtb26BlU6dOZerUqaG2r/Z7t/zFZpjt9Z70nvrTe9J7qlTrPb344ou0tLT0LUskEgN+Lqu2rNwh01+5kyjM9tVqkdFuX61N5fdUfq58EcdQ7ymfz7NlyxYmT548rDZVtiuq91R+TCQSJJPJWH5O1c6J/sde/07C4YjtnHPe+yu995/23t/gvf9nBLv8bOnxM+WOudLrrCK4Sw/gnGaNA75+/fpmvOyEo5ztKGsbytmOsrahnCUsHSs2lLMN5WxHWdvof8UzwGWXXcZnPxv8L/l73vMenHM45zjnnHOAl+caue+++/j2t7/N4YcfzqJFi7jtttsAuPfeezn77LM57LDDWLRoEYsXL+Z973sfDz744KDXrjYfSnlZZ2cnX/7ylznwwAPZc889Oemkk3j00UdDv6/Ozk4uuugijjjiCBYuXMgBBxzAJz7xCZ577rkB6w31fg499FD+7d/+jccff5zTTz+dfffdl7e97W1923d0dLB06VIOPvhgFi5cyMEHH8zSpUtZt27dsF5ngijfFTf4W+CXlXs9O+usU5P3vtd7/6T3/qvAaQQdddc450Ldtui97wW+XvrxzSNpw2jpd58dZW1DOdtR1jYqa6ewxkuNVU3Yeqinp4fLLruMI488kr322ovFixdzzDHHcOGFFw5Y7+677+bd7343+++/P4sWLeKNb3wjZ511Fk8//fSo2jkacb5zLjLOuQXAvkAvcGPl8977e5xzLwDbElzJdJ9tC2Hz5s2DrlKQ6ClnO8rahnK2o6xtKOeXRT1PrnPuKOATBHP5tgL/Aq4HLvbe99TYZjLBMOPvABwwCVgL/BG41Ht/z3DbERUdKzaUsw3lbEdZ26i8eviII45gzZo1/PSnP+Xf//3f2XnnnQHYfvvtB6x34YUXksvlOPHEE5k6dSo77RRMEXbLLbewYcMGjjvuOObNm8eqVau48cYbOe2007jmmmtYvHhxqHadfvrptLW1ceaZZ7J+/XquuuoqPvjBD3LnnXcOeddaZ2cnJ510EitWrGDJkiXssssurFmzhuuuu44TTjiBn/3sZ2y77bah3g/AihUrOPXUUznqqKN405ve1DekWGdnJyeffDLPPPMMS5YsYY899mDZsmVcf/31/PGPf+TGG28c1NZ6rzMBLC897lBnne0q1h2Nm4GNBN8x7QiE/UbvidLjtnXXahD97rOjrG0oZzvK2sZI77waDzVWNcOph5YuXcrPfvYzjjvuOPbZZx/y+TzLly/ngQce6Nvfgw8+yBlnnMEuu+zC+9//fmbNmsXq1au5//77efbZZ5tWO02Izjlgn9LjY977LTXWeYigSNqHJnTOiYiIiJRFPU+uc+7TwIVAHrgbWEcwXPj5wFudc4d57zdXbDOTYAimvQiuNP8DwZdRuxMMB368c+5s731ThgQXEZGxY7fddmPvvffmpz/9KQcddBD7779/1fW6u7u59dZbBw3JeN555zFlypQBy0466STe8pa38L3vfS/0F0d77LEH5557bt/Pr3jFKzj77LP51a9+xUknnVR320svvZTnnnuOG264gd12261v+fHHH88xxxzDZZddxgUXXBDq/QA8//zznH/++ZxwwgkDll955ZUsX76cL33pS5xyyil9y3fffXe+8pWvcOWVV3L22WeHfp0J4M+lx1c55ybX+M5nv4p1R8x7X3TOvUQwn9xcwnfObV163FR3LRERkWEYDzVWNcOph+644w7e8IY3DLpTrr8777yTQqHAVVddxfTp0/uGuTzzzDOH3bYoTZTOuXLX5zN11nm2Yt0xJ7epi/WPP8akmTMpZLspZrvpeuafTJm/3YD1EpNamLVX9RN1JIr5PL3rN5Dr3Ej36jVMmr1133M9a1+iZauZFHt7gjb19rB5xbMD2rR5xXOD2li5LKp1Ov0y1rndTV4rbutYv35l1s1uT9zXGel2E/mYtv58dEzbrNPT3Q1VxpGfSKKeJ9c5txi4ANgMHOq9f6C0fBrwa+ANwFeBj1ds+hmCjrk/AUd47zv67fN04ErgIufcT0rDhDdNNlfg+dWd7DR/ZjObISISuU1bsvT0jmyYo/5Wr9vM3K2mDL3iECa1pJk2uXFzup188slVO5j6f2nU1dVFb28vyWSSvfbaa1hDJp122mkDfj7ggAMAeOaZel8VQLFY5Je//CX77bcfc+fOpaOj708ikydPZu+99+bee+8N/X4gmA/n7W9/+6Dlv/3tb2lra+Od73zngOXvfOc7ufzyy7njjjsGdc7Ve53xznv/nHPuT8CrgROAa/o/75w7GFhAUFfdP9rXc87tTHDHXIFgFIKwTiw9PjTaNkShuyfH6nWb2X7ejGY3RUSkKeJWYyUpVJ0XLSpxrbFqGU49NG3aNJ566in+8Y9/sOuuu1bdX3kOw9/85jccd9xxDc16OCZK51z53smuOuuUr14aPNvkEFavXt034eLs2bMBWLt2bd/z5UksV61aRT6fB4IJB+fMmcP69evZvHkzhUKBFStW0N7eTjabHVDsz5w5k6lTp7JixYq+Za2trbS1tdHR0UF3dzfZl15ixXnnU+gJ8UslCdt/5P1sd+jRrFmzhmw2CwQTG7a3t9PZ2Uln58tDsaf/9Tgv/uwGCoUCxVyBQq4A+SL57l4KPXkKvfnhRtZUa/lNs5swYShrG8rZjrJuvNTkJLPbt6G7rX3Iv09h/+b2/5s6RtScJ9c5dwbBnW/nOOcuC3n33DlAAriw3DFX2t8m59x7gSeBDzvnlnrv+08ocEjp8eL+HXOlbX9YuhtvV4Jhnf5neG9x9PpPvnzL3U9y811Pcckn3si8retNOSPDVW2CdYmecrYzlrLO5wucfv7/srl79F8cRWVKa5rrvnI0qVT9qdozmZF14NUaUujZZ5/lm9/8Jvfeey8bN24c8FwiEWraLwC2227gRUFbbbUVMPR8Oh0dHaxfv557772XAw88sOo65e8E+qs3RNJ2221HKpUatPz5559n4cKFpNMDv65Jp9PsuOOOPP7448N6nQni6wRTmFzonLvPe/8UgHNuLvCd0joX9K+bnHNnAWcBD3rv39Nv+R7A3sDN3vvu/i/inFsIXE1QV93svV/T77m9CToBb/Pe5/stTxMMEf7R0qJvRvKOh6nyd993fvYojz65hgvPer1qp4iNpb8zY5lytjMesx7LNdZIxbXGqmU49dDnPvc5Pv3pT3PMMcew3Xbbsf/++3PIIYdw6KGH9tVnp5xyCnfeeSdLly7l4osvZt999+X1r389b33rW2lraxtRG6MwUTrnGmru3LnMr7jKv/JnqP7LbNasWcyaNYvu7m5aW1uB4EvIattXW9bW1ka2s5NHv/AFMlOLtO3fTvvbTqdlznYkWybTtfyfTHML+9bPbVjHXz72UVZefQ1z99yHOXPmDdrn9OnTmdLSwpq77uKFm35K95qNJFIwbedtyGw9nWRLmnzPFjY+/gyzD9qdKdvOIz2llUQyx8a/PUDbQUeRmTWPQrabl+6+hblveR8tcxeQbJlCsqWVTU8tY9qur+p7vU3/eGzAz9WWRbVOx9//TNvCfequY9key3WsX78y62a3J+5CYyJjAAAgAElEQVTrjHS7iXxMW38+OqZt1ln3+F+Z7hZWvVKm3PFWaai/uSMdu70Zop4n1znXAhxd+vHaKvv7l3PufuC1wJuB6/o9XXUeuirWDr1K9LLZbN+XmlNaM8xtm6Ivlxqgf87SOMrZzljKOpVK8sMvvClWV3VPakmH+tKoWCwO6wudsvL/E/fX1dXFKaecwpYtWzj11FPZddddmTp1Kslkku9973v88Y9/DL3/Wp99sVisu135+YMOOogPfOADoV+v2vspi/JOt3qvMxF4729yzl1BMFfv35xzd/DykOAzgFsJhgbvbzbBfLorK5bPJaiZukp35L1AMOfujgSddgngQeBDFdvtCNwCdJS2W00wlOWewHyCO+0+7b1vytV+lb/7WjIpXrXz1qqdGmAs/Z0Zy5SznfGYdRxrrEw62bCOOYhvjRWFww8/nN/97nfcc889PPTQQ9x3333cdNNNLF68mKuuuoqWlha22morbrrpJh5++GH+8Ic/8PDDD/P1r3+dyy67jO9///vss88+Q79QA0yUzrnyXXH1qo7y3XWdddZpmI6OjqpfLpb1vNRB7/p1TH/FKwYsz/f08NiXvkyhZwO7f/b/0brtzmS2ernDbcYeew9Yv6VtDntfehmPfuJj/PWTH2fRf36TSbNfXr+3Yx0rfvVrXvzVr6GQZdqOM9jlY58m0TKV6f06+QA6/d8HLdvqwLcNeP0pO+054GeAGbvvVffnRq7TO2dbkql03XUs22O5jvXrV2bd7PbEfZ2RbjeRj2nrz0fHtM06PVsPvmhkgol6nlwHTAE6vPf/rLO/15b2179z7naCeek+6Zz7TcWwlu8juGvuz8DDQ7ShIfrXTpl0ckRfAsvQhqpRJRrK2c5Yy3ra5Ewkw0huPdN2yMNcLjdouKCR/p6+//77Wb16NV/72tdYsmTJgOcuueSSEbdxONra2pgxYwabNm3ioIMOauhrbbfddjz99NPkcrkBV4vncjmWL18+6Mp0CXjvP+ycuxc4k6B+SQFPAD8CrhjGXL2PAV8AXg/sRnDRVJrgYqTbgBuAH/e/O67kUYIhx18D7FHavgg8D1wFfNt7/8iI3+AoVf7uSyUTZNLj68v2uBhrf2fGKuVsZ7xmHbcaq7e3d8TbjuUaq5bh1kOzZs3i2GOP5dhjj6VYLHLxxRdz5ZVXcuedd3L00cH1yqlUiv3335999tmHlpYWnnjiCZYsWcIVV1zB97//fdP3VzZROueWlx53qLNO+RNdXmedpvn75z5F98r1zD74dexwyim0ts+lmM/jL7yInlXPs+M7D2f6wnD/k9DSNpu9vnEpj378o/ztkx9n0cXfJNu5meeuv5aOh/5KekqSRCLPTqcfz9wjTyKRrF6wVXbMAYM64ip/FhERkbqinie3vM6zddaptb9LgNcBbwGWO+f+AGwk+MJpD4KhLE8fxpddDZNOJcnmmt4MERGpozynyYYNG4a1XflK7Morr++9995hzYUyGslkkmOOOYZrr72W22+/naOOOmrQOi+99BJbb711la2H5/DDD+e73/0uN954IyeffHLf8htuuIGOjo5Bc6/Iy7z31zHwQqN6654LnFtl+RqCuXiH+9pPA2cPuWJMpNNJcluyzW6GiIhEYCzXWLWErYfy+TxdXV3MmPHyHKqJRII99tgDeDmTjo6OQcNX7rzzzkyaNGnYuUVponTO/bn0+Crn3OQaV6LvV7FurBS6N9MyCzY++kce+f19zH7da8lv2ULnssdoP3Aec95y+rD219I2m0XfuIS/fvzjPHLGmRR6YdJWSeYduiuz9t6bDX/+A20HHFazY05EREQaIup5cke8P+99t3PuOIIvqD4F9P8m8gXgLuClEG0YJIr5ejs7O/vm66WYp7unt29+3jDz9ZbNnz+frq6uAQV5W1sbmUyGVatW9S2bMmUKs2bNCjVf70jfU9lI5yBuxHsq5zye3lMcP6dyzuPpPcX1c+rs7KSrqytW7ymfz5PP50mlUmSz2b4vSBKJBJlMhlwuN2CI5kwmQ7FYJJd7eSimVCpFKpUacNV1MpkknU4P2r6lpaXvNcvS6TSJRKKv7f23r9amatsDfW0qFAqD3tNuu+1GMpnku9/9LuvWraO1tZVtt92WRYsW9e0/l8v1vYfye9pzzz2ZPXs2F1xwAc899xzz58/nscce45e//CW77LILTz755IAcy+0qFAp976m8rHz1df/3VDbUezrzzDN55JFHOPvssznyyCNZtGgRmUyGF198kXvvvZfdd9+d888/f8A2xWJxwGdSfk/FYpFCoUBvb++gz+k973kPt912G1/5yld47LHHcM6xbNkybrnlFnbaaSdOP/30vn32PwaG8znl8/m+43+czNcrw5RJJcnnGz/UmIiINN6ee+7ZV2Nt2LCBKVOmsGDBAvbaa/BoRf3tu+++zJkzhwsvvJAXXniBefPmsWzZMn7+85+z66678o9//MPoHQz2/ve/n9tvv52vfOUrPP744+y+++4sW7aMm266iZ122on3v//9QDA05+te9zoOPfRQ9thjD9ra2nj++ee5/vrrmTlzJocccggAX/ziF1m5ciWve93rmDt3Lrlcjttuu42uri6OPfbYpr3PCdE5571/rjTm96uBE4Br+j/vnDuYYOLelcD99i0M/qe3nmK+yOwDFzPjlXNZe8/tbHn2KbqeXUnbrknmn/wpkumWuttXM2nruex81hn84+JL2fHUtzH/uHf1dcZNX/j6cXnX21A5S3SUtQ3lbEdZ21DO8eGc24Zg7pTdgY8CPwfWAQuB84GLgCOdc0dVGdqprijm6y1/uQ4wuXUSRZKh9lltsuepU6f27Wuo7efMmTNo2UjnQywrv6f+hjsHcaWo3lP/nGttP9beU5jtrd9TmJzH2nsKs30z3lP/rOPynjZs2NB35XImM3h4pf5D+ZQlEolBw0YCVZdV277cSRRm+2ptGmr7csdc/+132GEHvva1r/GDH/yA8847j2w2y/HHH8/ixYv7hmNKp9OD2jB79mx++MMfctFFF3HdddeRy+VYuHAhP/jBD7jpppv6OufKbSq/bvkikJaWlr5l5SxG8p623nprfvKTn/CjH/2I22+/nbvuuotUKsW8efPYd999OeGEE/rWLb9Orc8pkUiQTCYHPFfepvw63/rWt/jd737HLbfcwtZbb81JJ53ERz7ykQHHUv/Pdjjvqdp5Plbn65VwKmvsdCpJLq/PuRH0/zM2lLMdZW1jNPP6zZ8/v6/GWrp0aV+NNVTn3IwZM7jyyiu56KKL+PGPfzyoxmpm59z06dO5/vrr++qhm2++eUA9NG1acB1ya2srp556Kvfffz/3338/XV1dzJ07l0MPPZQPfehDffXNsccey80338wtt9xCR0cH06ZN45WvfCXf+ta3OPLII5v2PhMWk/JFwTl3N8G44Sd472+qsc7XgeOBW7z3n6147h3AjQQdcK/33j9VWj6X4MrvPYCzvfeXDqNNOwJP33nnnSxYsGDY72k4Hjj5RLZ5y+Fs/+4PsvnJR1j9q8vpeWkj2yz5d2bsc8So9l1t7jgREZHx4vnnn+ewww4D2Ml7v7zJzanLOfdRgvlKbvXeH19jnUsJOsv+03v/ySH29zaCTrW/eO+rznDsnPs48A3gZ977d/Rb/r/AEcDJ3vufVGzTAjxC0FH3Pu/9VSHf3440oHZ66PGVfOunf+G/lw4eZkxEJO6WLVvG7rvv3uxmyAQy1DE3lmqn8a5RtdP1/+t54pkOln7gwMj2KSISJ6qvJEqNqp2So29aYzjnXu2c+2P5H8FdbwBfq1je3zaAKz0OUOrQuwKYB/zNOfdL59zNwJMEHXO3Apc36v0Mpf+QKtUU80VSk4Kr6qbssi/zTvwsk+fPo3WHPUf92hOpY26onCU6ytqGcrajrG0o58jnyS2vs/1w9uec25agY64XGHRRlPe+//LDQ7Qjcv2PlXQqSVZXfzeEzkkbytmOsrbRfyhHaSxlLWFU/u5LpxLkNF9vQ+jvjA3lbEdZ29Dfcztxyjq2nXPADGD/fv/KYzfsUrE8NO/9h4FTgD8R3IV3JPAUcBawZLhDMlkqFookJ7X2/dy67a5sc/IXaWkbf0NPioiITGAD5smtsc5w5sl9AtgCtDnnXlFjnddU2V+5M6/Le5+juvWlx8FjwBnLpJNk9QWTiIiISCiZdJJ8YWyMpCUiIjJexXbOOe/93UBimNucBpw2xDrXAdeNtF3NEnTOTRqwbDzOCSciIjKRRT1Prve+1zl3G/B2gguUvlKxv52BAwnukPt1v6fKl0du5Zzb1XtfbbD58jhITw/VjkZLpzVvioiIiEhY6VRSd86JiIg0WZzvnJtQWltb6z5fzENqUq0L6CWsoXKW6ChrG8rZjrK2oZwB+Hrp8ULn3CvLC0vz5H6n9OMF3vtCv+fOcs494Zwb0JlXXhcoAp9xzr2m3zbTgB8R1IPf8d6X74TDe/8M8HDpxx8559r779A5dypwYunHAfPRWel/rGRSSQqFoq4AbwCdkzaUsx1lbSOZ1FcNVpS1hFH5u09DgjeO/s7YUM52lLUN/T23E6esY3vn3ETT1lZ7RKhiPhhtM6lfhqNWL2eJlrK2oZztKGsbyjmYJ9c5dwVwBsE8uXcAWeAwgmG/q82TO5tg3t2VVfb3kHPuHOBC4D7n3O8IhqQ8GJgLPAB8vkpTTgfuAl4LPOmcexBYB7wKKM+E/B/e+/8bxdsdsf7HSjodFNe5fIFUMtWM5oxbOidtKGc7ytpGOq2vGqwoawmj8ndfOpXQqAMNor8zNpSzHWVtQ3/P7cQp6/h0E05wHR0dNZ/Ld3cDkGzVnXOjVS9niZaytqGc7ShrG8o5EPU8ud77/wCOJuhs2w84BlgLfAE42Hu/uco2fwUWAhcDzxAMY3kcsBXwC+Bo7/1nRvL+otD/WMmUOuc071z0dE7aUM52lLWNXK7WdKUSNWUtYVT+7kunkuTVOdcQ+jtjQznbUdY29PfcTpyyjk834QTXXeqAqya/pQuAlO6cG7V6OUu0lLUN5WxHWdtQzi8bzjy53vtzgXOHWOd24PZhtuFF4FOlf7HS/1jJpIK75TR3SvR0TtpQznbimnWxWCSRGNaU67FWKOj3sZXhZl0sagjoiajyd186nSSb17HQCHH9OzPeKGc7YznrsVRfqXayE6faSXfOjQF9nXOTpzS5JSIiIiLxk04H/8OlO+dEZCxKp9OxuoJXxrdcLher4ZykOdKppC5qEpFxLZVKkc1mm90MGQey2SypVGOmz1Dn3BhQ2LIFgGSrOudEREREKmXSpTvnNDyTiIxBra2tbNq0qdnNkAmis7OTVo3KM+GlU0nVTSIyrk2fPp2NGzc2uxkyDmzcuJHp06c3ZN/qnIuJ+fPn13wu3x10zunOudGrl7NES1nbUM52lLUN5Sxh9T9W0qnynXPDmopPQtA5aUM524lj1nPmzGHNmjVs3rx53Aw52NLS0uwmTBhhsy4Wi2zevJm1a9cyZ86cBrdK4qbyd186lVDnXIPE8e/MeKSc7YzVrNva2li3bh1r166lt7c39jWWaic7YbIuFov09vaydu1a1q1bR1tbW0PaorEMYqKrq4upU6dWfa7cOZdsnWzZpHGpXs4SLWVtQznbUdY2lLOE1f9YefnOuXj/D9dYpHPShnK2E8esW1tbaW9vZ+XKlfT09DS7OZEoFAokk7oW2MJwsp40aRLt7e26c24CqvzdF9w5p7qpEeL4d2Y8Us52xmrWkyZNYvvtt6ejo4Ply5eTz8f7Qk7VTnbCZp1KpZg+fTrbb789kyZNakhb1DkXExs2bKj5i67Q3Q1JSKYzxq0af+rlLNFS1jaUsx1lbUM5S1j9j5VUMkEyoTvnGkHnpA3lbCeuWc+cOZOZM2c2uxmRWbFixZi90n6sUdYSRuXvvnRaw1o2Slz/zow3ytnOWM560qRJbLPNNmyzzTbNbsqQ9PfcTpyyVnfsGFDo2UIiAYlkYyYeFBERERnr0umUrgAXERERCSGdSlIoFMkXVDuJiIg0izrnxoB8dzcJ9cuJiIiI1JRJJXTnnIiIiEgI6VTwdWBed8+JiIg0jTrnYqLepIKFnh4SyYRha8avRk3eKIMpaxvK2Y6ytqGcJazKYyWjO+caQuekDeVsR1nbUM52lLWEUXmcpFPBd0wa2jJ6OidtKGc7ytqGcrYTp6zVORcTmUzt+eSCzjl9VFGol7NES1nbUM52lLUN5SxhVR4rad051xA6J20oZzvK2oZytqOsJYzBdVPwHZMubIqezkkbytmOsrahnO3EKWv1+MTEqlWraj5X6Okhkdadc1Gol7NES1nbUM52lLUN5SxhVR4rmXSKXE5fMEVN56QN5WxHWdtQznaUtYQxuG4qd87pzrmo6Zy0oZztKGsbytlOnLJW59wYUOjtIZHSRyUiIiJSSzqdIJvXnXMiIiIiQ0mVRmfK5dQ5JyIi0izq8RkDCr29JNU5JyIiIlJTJpUiqzvnRERERIaULt85V1DnnIiISLOoxycmpkyZUvO5Qm8viVTKsDXjV72cJVrK2oZytqOsbShnCavyWEmnE+Q051zkdE7aUM52lLUN5WxHWUsYg+qmVDB1iu6ci57OSRvK2Y6ytqGc7cQpa3XOxcSsWbNqPlfIZklk1DkXhXo5S7SUtQ3lbEdZ21DOElblsZJJp8hq3pTI6Zy0oZztKGsbytmOspYwBtVNqfKccxp1IGo6J20oZzvK2oZythOnrNU5FxNr1qyp+VwwrKU656JQL2eJlrK2oZztKGsbylnCqjxW0qkEWV39HTmdkzaUsx1lbUM521HWEkblcZJMlu6c04VNkdM5aUM521HWNpSznThlrc65mMhmszWfK2RzJDJpw9aMX/VylmgpaxvK2Y6ytqGcJazKYyWTTmlopgbQOWlDOdtR1jaUsx1lLWFUHieJRIJ0KqnOuQbQOWlDOdtR1jaUs504Za3OuTGgkM2STKtzTkRERKSWdCqhYS1FREREQsqkE+qcExERaSJ1zsVEqs6wlcWc7pyLSr2cJVrK2oZytqOsbShnCavyWMmkUxrWsgF0TtpQznaUtQ3lbEdZSxjVjpN0KkkupznnoqZz0oZytqOsbShnO3HKWp1zMdHe3l7zuUI2RzKTMWzN+FUvZ4mWsrahnO0oaxvKWcKqPFbSKV393Qg6J20oZzvK2oZytqOsJYxqx4mGtWwMnZM2lLMdZW1DOduJU9bqnIuJzs7Oms8VszmSLeqci0K9nCVaytqGcrajrG0oZwmr8ljRnXONoXPShnK2o6xtKGc7ylrCqHacpFJJDQneADonbShnO8rahnK2E6es1TkXE/UOikI+r2EtIxKnk2+8U9Y2lLMdZW1DOUtYlceK7pxrDJ2TNpSzHWVtQznbUdYSRrXjJJNKklftFDmdkzaUsx1lbUM524lT1uqcGwOKuTzJTEuzmyEiIiISW7pzTkRERCS8dFoXNomIiDSTOufGgGKuQLJlUrObISIiIhJbunNOREREJLx0Kkk2V2x2M0RERCYsdc7FxOzZs2s+V8gXSE7SnXNRqJezREtZ21DOdpS1DeUsYVUeK7pzrjF0TtpQznaUtQ3lbEdZSxjVjpNUKqkLmxpA56QN5WxHWdtQznbilLU658aAYr5AqkWdcyIiIiK1ZNJJcuqcExEREQlFc86JiIg0lzrnYmLt2rU1nyvmCyQ0rGUk6uUs0VLWNpSzHWVtQzlLWJXHSjqVJKsvmCKnc9KGcrajrG0oZzvKWsKodpykdedcQ+ictKGc7ShrG8rZTpyyVufcGFDMF0lNam12M0RERERiS3fOiYiIiISXTiV0YZOIiEgTqXNuDCgWiqRadeeciIiISC3ptO6cExEREQkrlUqSyxWb3QwREZEJS51zMTF9+vSqy4v5PBQhqTvnIlErZ4mesrahnO0oaxvKWcKqPFYyKd051wg6J20oZzvK2oZytqOsJYxqx0kmnSRfUO0UNZ2TNpSzHWVtQznbiVPW6pyLiVoHRSGbBdQ5F5U4nXzjnbK2oZztKGsbylnCqjxW0ukkWXXORU7npA3lbEdZ21DOdpS1hFHtOEmnVDs1gs5JG8rZjrK2oZztxClrdc7FxKpVq6ouL/QGnXOp1smWzRm3auUs0VPWNpSzHWVtQzlLWJXHSiadJKdhLSOnc9KGcrajrG0oZzvKWsKodpykUwnVTg2gc9KGcrajrG0oZztxylqdczGRz+erL+/tAdQ5F5VaOUv0lLUN5WxHWdtQzhJW5bGS0dXfDaFz0oZytqOsbShnO8pawqh2nKRTSXJ5zTkXNZ2TNpSzHWVtQznbiVPW6pyLufyWzQAkJ09pcktERERE4iutO+dEREREQktrvl4REZGmUudcTGQymarLC1u2AJCarDvnolArZ4mesrahnO0oaxvKWcKqPFZ051xj6Jy0oZztKGsbytmOspYwqh0n6XSSXEG1U9R0TtpQznaUtQ3lbCdOWatzLibmzJlTdXm+O+icS2pYy0jUylmip6xtKGc7ytqGcpawKo8VzTnXGDonbShnO8rahnK2o6wljGrHie6cawydkzaUsx1lbUM524lT1uqci4n169dXXV7o3gwJSKZbjFs0PtXKWaKnrG0oZzvK2oZylrAqj5XyvCmFguZOiZLOSRvK2Y6ytqGc7ShrCaPacZJOJXRhUwPonLShnO0oaxvK2U6cslbnXExs3ry56vJ8dzeJJCTS8bndciyrlbNET1nbUM52lLUN5SxhVR4rmXRQ1upLpmjpnLShnO0oaxvK2Y6yljCqHSflC5skWjonbShnO8rahnK2E6es1TkXc4XuLeqcExERERlCWp1zIiIiIqEFnXOqm0RERJpFnXMxV+jpJpEAkulmN0VEREQktjKpoKzNau4UERERkSGpc05ERKS51DkXE+3t7VWX53t6SKQgkUgYt2h8qpWzRE9Z21DOdpS1DeUsYVUeK7pzrjF0TtpQznaUtQ3lbEdZSxjVjpN0OkFOFzVFTuekDeVsR1nbUM524pS1OudiIpvNVl1e6OkhkdLHFJVaOUv0lLUN5WxHWdtQzhJW5bFSnnNOd85FS+ekDeVsR1nbUM52lLWEUe04yejOuYbQOWlDOdtR1jaUs504Za1en5jo6OiourzQ000iqbvmolIrZ4mesrahnO0oaxvKWcKqPFbSGtayIXRO2lDOdpS1DeVsR1lLGNWOk1QqSS5fbEJrxjedkzaUsx1lbUM524lT1uqci7lCb6/unBMREREZQkbDWoqIiIiEpjnnREREmku9PjFX6OkhkdbHJCIiIlJPqjTSgO6cExERERmahrUUERFprnSzGyCBmTNnVl1e6O0lmUoZt2b8qpWzRE9Z21DOdpS1DeX8Mufcu4AzgEVACngCuAq4wns/7G9SnHNHAZ8AFgOtwL+A64GLvfc9VdYPO87Rqd77a4bbntGqPFYSiQSZdFKdcxHTOWlDOdtR1jaUsx1lHYiqbnLOnQIcBewNzANmAZuAx4CfAN/z3tecrCbq+i0q1Y6TdDpBTnVT5HRO2lDOdpS1DeVsJ05Z65asmJg6dWrV5YVslkRanXNRqZWzRE9Z21DOdpS1DeUccM59G7iWoCPt98BvgV2By4GbnHPDquGcc58GbgMOBf4E/BqYC5wP3O2cm1Jls/+q8++u0jpF4J7htCUq1Y4VDc8UPZ2TNpSzHWVtQznbUdaR101nAO8q/fdDwE3AX4HXAJcB/+ecqxp61PVblKodJ6lUklxBc85FTeekDeVsR1nbUM524pS1OudiYsWKFVWXF3p71TkXoVo5S/SUtQ3lbEdZ21DO4JxbAnwYWAks8t6/1Xt/PLALsAw4HvjIMPa3GLgA2Ay81nt/uPf+BGBn4P+AA4CvVm7nvT+t1j+Cq8AB7vDePzPiNzsK1Y4V3TkXPZ2TNpSzHWVtQznbmehZR103EYwysLX3fk/v/Zu99yd77w8mqJueIKibPm3QjkhVO07SqaTunGuAiX5OWlHOdpS1DeVsJ05Zq3Mu5grZHEl1zomIiEwkny09fsZ7/2R5ofd+FcHV3ADnDOPq63OABHCh9/6BfvvbBLwXKAAfds7NCrMz51wrcHLpxx+GbIOJTFp3zomIiEwwkdZN3vsHvffrqyx/Hvha6ccjGt0OC5pzTkREpLliUxRIdcVslkRaUwOKiIhMBM65BcC+QC9wY+Xz3vt7gBcI5kA5IMT+WoCjSz9eW2V//wLuB1qAN4ds5hKC+Vc6gFtDbmMindKdcyIiIhNF1HVTCLnS44C5epvQjkhoOHAREZHmUudcTLS2tlZdXshmSWbUOReVWjlL9JS1DeVsR1nbUM7sU3p8zHu/pcY6D1WsW48DpgAd3vt/RrA/gPeVHn/sve+pu2YDVTtWdOdc9HRO2lDOdpS1DeVsZ4JnHXXdVJNzbjbwqdKPv2hWO0aq2nGSSiXI5YsUi5p3LkoT/Jw0o5ztKGsbytlOnLJWr09MtLW1VV1eyOVIZiYZt2b8qpWzRE9Z21DOdpS1DeXMTqXHevO4PVuxbpj9PVtnndD7c87tCBxS+rGpQ1pWO1Z051z0dE7aUM52lLUN5Wxngmcddd3Uxzl3DMFoASlgG+C1QCtwNXC5VTuiUu04yaSD6/XzhSLpVMK6SePWBD8nzShnO8rahnK2E6esdedcTHR0dFRdXszmSWRajFszftXKWaKnrG0oZzvK2oZyZlrpsavOOptKj9ObsL/3Esxf97D3/q8h1m+YaseK7pyLns5JG8rZjrK2oZztTPCso65z+tsLOBV4N3AYQcfcJcDZ3vusYTsiUe04SaeCrwRzurApUhP8nDSjnO0oaxvK2U6cstadczHR3d1ddXkxlyPZkjFuzfhVK2eJnrK2oZztKGsbyjm+nHNJ4LTSj1eAyegAACAASURBVD8azb5Wr15NMhl8ITR79mwA1q5d2/f89OnTmT59OqtWrSKfzwOQyWSYM2cO69evZ/PmzXR2dtLd3U17ezvZbJaOjg4K+RxrX1pHV9dcpk6dyooVK/r22draSltbGx0dHQOOs/nz59PV1cWGDRv6lrW1tZHJZFi1alXfsilTpjBr1izWrFlDNht8J5dKpWhvb6ezs5POzs6+dUf6nsr6v6eymTNnNuU9lXMeT+8pjp9TOefx9J7i+jl1dnYyadKkcfWe4vg5dXZ2kslkxtV7iuvnVP7dEeV7itOXVs3ivT8fOL80f+8OwInAOcDxzrk3e+8ft2zPaGunNWvW9B1ffcfmS2sAeO6FFcxv31rnm2qnMfU5qXZS7TTePifVThOzdkpobOmRKw3t9PSdd97JggULRrWvFStWMH/+/EHLHzrtPbTt80pe8bEvjWr/EqiVs0RPWdtQznaUtY1G5Pz8889z2GGHAezkvV8e6c4j5pz7KHApcKv3/vga61wKfBT4T+/9J4fY39uAnwN/8d5XnePEOfdx4BvAz7z376izrzcBvwG2ANt47zfUWrfOPnakgbXT56/4A692c1ly6C6j2re8TL/7bChnO8rahnK2M5Frp6jrphCvtwS4CXgE2M97X2x0O6KqnaodJ2vWbeF95/8v15x7JFtNj8/8O2Odfv/ZUM52lLUN5WwnTrWThrWMuWIuT7JFw1qKiIhMEMtLjzvUWWe7inXD7G/7CPb3vtLjz0bSMWchnU6S1bCWIiIiE8Xy0mNUddNQbgY2AvsCOzaxHZEozzOXz+uifRERkWZQ51xM1OqtLebzJFsmGbdm/NIVCHaUtQ3lbEdZ21DO/Ln0+Crn3OQa6+xXsW49TxDc6dbmnHtFjXVeM9T+nHNtwHGlH38Y4nUbrtqxkkklNW9KxHRO2lDOdpS1DeVsZ4JnHXXdVFfpTrmXSj/ObVY7RqLacZJOl+ac04VNkZrg56QZ5WxHWdtQznbilLU652Kiq6v6vMGFfEFzzkWoVs4SPWVtQznbUdY2JnrO3vvngD8BLcAJlc875w4GFgArgftD7K8XuK304ylV9rczcCDQC/y6zq5OASYB/wTuGep1LVQ7VtLpJFl1zkVqop+TVpSzHWVtQznbmchZR103DaVUN+0IFIB/NasdI1G1bkoFXwmqdorWRD4nLSlnO8rahnK2E6es1TkXE/0nKuyvmC+SnKSxv6NSK2eJnrK2oZztKGsbyhmAr5ceL3TOvbK80Dk3F/hO6ccLvPeFfs+d5Zx7wjl3TZX9XQAUgc84517Tb5tpwI8I6sHveO/X12lTeUjLH5XnV2m2asdKJp3U1d8R0zlpQznbUdY2lLMdZR1d3eSc28M59y7n3KAvYZxzC4EbgARwi/d+zWjbYanacVLunFPtFC2dkzaUsx1lbUM524lT1ulmN0DqKxYKJCdpzjkREZGJwnt/k3PuCuAM4G/OuTuALHAYMAO4Fbi8YrPZgCO4Irtyfw85584BLgTuc879DlgPHEwwJNMDwOdrtcc5tw+wN5AHrh7Vm2uwTEp3zomIiEwkEddNc4FrgS7n3J+AFwhGDtiRoBZKAA8CH4qoHU2lOedERESaS3fOxVgxn4cCunNORERkgvHef5hgKMk/EXSiHQk8BZwFLPHe54e5v/8AjgbuIpjz5BhgLfAF4GDv/eY6m5fvmvuN937FcF7Xmoa1FBERmXgirJseI6iN7gW2B94GvAVoJxgm/DTgIO/9S9U2jrp+a7REIkEqmdCdcyIiIk2iO+dioq2tbdCyQi4HQEqdc5GplrM0hrK2oZztKGsbyvll3vvrgOtCrnsucO4Q69wO3D6CdnwE+Mhwt2u0asdKJpVkS3euCa0Zv3RO2lDOdpS1DeVsR1kHoqibSkNVftWqHZZqHSfpdJKsOucipXPShnK2o6xtKGc7ccpad87FRCaTGbSs0NsL6M65KFXLWRpDWdtQznaUtQ3lLGFVO1YyunMucjonbShnO8rahnK2o6wljFrHSTqVJKfaKVI6J20oZzvK2oZythOnrNU5FxOrVq0atKzQmwUgNVmdc1GplrM0hrK2oZztKGsbylnCqnaspFNJDc0UMZ2TNpSzHWVtQznbUdYSRq3jJKPaKXI6J20oZzvK2oZythOnrNU5F2N9d861TmlyS0RERETiT3fOiYiIiISXSiXI5YvNboaIiMiEpM65GCtmg865VOvkJrdEREREJP5055yIiIhIeKqdREREmkedczExZcrgu+PyPT2A5pyLUrWcpTGUtQ3lbEdZ21DOEla1Y0V3zkVP56QN5WxHWdtQznaUtYRR6zhR51z0dE7aUM52lLUN5WwnTlmrcy4mZs2aNWhZfstmSECyZVITWjQ+VctZGkNZ21DOdpS1DeUsYVU7VtLpJFl9wRQpnZM2lLMdZW1DOdtR1hJGreMkk06S04VNkdI5aUM521HWNpSznThlrc65mFizZs2gZYXubhJJSKQzTWjR+FQtZ2kMZW1DOdtR1jaUs4RV7VjJpPQFU9R0TtpQznaUtQ3lbEdZSxi1jpNUKkGuoDnn/j979x4fV13nf/w1tzZNGhpD22hauaofFQFRdPEGAj8X8QZVURdcdMX9KQhe+LErrq4LrhdEcBdBQEUBke4KyOL9gqyiLojsoruK8BW5iBBJ2w0tadI0k8vvjzMTJtPMzEl65jOnk/fz8ehjMjPnnPmed86XHOZzvt+TJPVJH8rZj7L2oZz9pClrFedSolgs7vDa5LbRqDiXW9KCFrWnuXKW5lDWPpSzH2XtQzlLXHMdK3lNa5k49UkfytmPsvahnP0oa4mj1nGS14VNiVOf9KGc/ShrH8rZT5qyVnEuxaa2byOTgUw+3+qmiIiIiKReQdNaioiIiMSme86JiIi0jopzKZHL5XZ4bXJsO2Qhk9fIuaTMlbM0h7L2oZz9KGsfylnimutY0bSWyVOf9KGc/ShrH8rZj7KWOGodJwUV5xKnPulDOftR1j6Us580Za3iXEr09fXt8NrU9tI953IaOZeUuXKW5lDWPpSzH2XtQzlLXHMdK5rWMnnqkz6Usx9l7UM5+1HWEket4ySXyzAxqXvOJUl90ody9qOsfShnP2nKWsW5lBgeHt7htZniXDY91dxd3Vw5S3Moax/K2Y+y9qGcJa65jpVCXld/J0190ody9qOsfShnP8pa4qh1nGhay+SpT/pQzn6UtQ/l7CdNWas4lxJzF+e2k8npV5SkNHW+dqesfShnP8rah3KWuOY6VvI5jZxLmvqkD+XsR1n7UM5+lLXEUbM4l9eU4ElTn/ShnP0oax/K2U+aslblJ8WmxreTyWVa3QwRERGRXUJ55Nz0tKZnEhEREWlE95wTERFpHRXnUmxqfFwj50RERERiypfOm3TvFBEREZHG8rksRRXnREREWkKVn5RYuXLlDq9NjY+Tzel+c0maK2dpDmXtQzn7UdY+lLPENdexUsiXi3P6kikp6pM+lLMfZe1DOftR1hJHreMkl8swqYuaEqU+6UM5+1HWPpSznzRlreJcik2Nj5PJqzgnIiIiEkd55JzuOyciIiLSmKa1FBERaZ3UF+fM7Hgz+6mZbTGzrWb2n2b2LjObd9vN7Alm9nEz+7WZjZjZdjP7g5ldZWbPbkb749q0adMOr02NF1WcS9hcOUtzKGsfytmPsvahnCWuuY4VjZxLnvqkD+XsR1n7UM5+lLXEUes4yeezTOiipkSpT/pQzn6UtQ/l7CdNWae6OGdmnwWuBg4GfgrcCDwNuAi4bj4FOjPbA/gV8AHgicCPgG8CReDNwO1m9rpEd2AnTRWLZFWcExEREYlFI+dERERE4tM950RERFontcW5UqHsFOAR4IAQwqtCCOuApwJ3AeuA0+axyXOAPYDvAHuWtvd6omLf2UAe+JyZFRLcjZ0yVSySyedb3QwRERGRXYJGzomIiIjEp3vOiYiItE5qi3NEI9wA3h9CuKf8YghhEDi59PTMeYyeO7z0+NEQwmjF9qaAfwS2AbsTFf/cdXd37/Da1HiRbEHFuSTNlbM0h7L2oZz9KGsfylnimutYKRfnNHIuOeqTPpSzH2XtQzn7UdYSR63jRPecS576pA/l7EdZ+1DOftKUdSqLc2a2FnguMA5cW/1+COFm4GGi6SkPibnZ7Q3eL18q1JJJR+c6KKYnJsioOJeoNHW+dqesfShnP8rah3KWuOY6Vh6f1nLSuzltS33Sh3L2o6x9KGc/ylriqHWc5FWcS5z6pA/l7EdZ+1DOftKUdSqLc8BBpcc7Qwjbaixze9WyjXyv9PghM+ssv2hmGeDvgU7gGyGEDfNtbBIGBwd3eG1qYlIj5xI2V87SHMrah3L2o6x9KGeJa65jJZPJkM9lmJjQ9ExJUZ/0oZz9KGsfytmPspY4ah0n+XxWMw4kTH3Sh3L2o6x9KGc/aco6rZWfvUuPf6izzINVyzbyIaJC3iuAP5jZz4lG0x0I7Al8hegedy0xObnjFd7TExNkC0ta0Jr2NVfO0hzK2ody9qOsfShniavWsVLIZynqOEqM+qQP5exHWftQzn6UtcRR6zjJ57JMTqk4lyT1SR/K2Y+y9qGc/aQp67SOnFteehyps8zW0mOscYghhE3AEcCVwErgVcDrgKcA9wE3hxCGF9TaJpmemCS7pNDqZoiIiIjsMvK5rEbOiYiIiMSgGQdERERaJ60j5xJnZk8HvkFUzPtL4IfANqJ7230K+IKZvTCE8Lb5bnvDhg1ks1Gdc+XKlQBs2vT4reu6u7vp7u5mcHBwpjJbKBRYtWoVmzdvZnR0lJGREQYGBujr66NYLDI0NMTk+ATjU1OMjIzQ1dXFwMDAzDY7Ojro7e1laGiIsbGxmdf7+/sZGRlhy5YtM6/19vZSKBRmDdns7Oykp6eHjRs3UiwWAcjlcvT19TE8PMzw8ON1yoXuU1nlPpWtWLGiJftUzrmd9imtv6eRkRHGxsbaap/S+HsaGRnRfyOc9qm8H+20T2n8PZX3I8l9qmyztI9CYe4LmAr5rO45l6BaOUuylLMfZe1DOftR1hJHreMkn8tS1D3nEqU+6UM5+1HWPpSznzRlnZmeTt8VMmb2buAC4IYQwroay1wAvBs4P4RwRoPt5YH/Bp4BvCiEcGvV+73Ab4E+4IgQwo9itnMv4P6bbrqJtWvXxlllXn7+xjfxpJc9lz3f/jeJb1tERGSxeOihhzjyyCMB9g4hPNDi5ixqzT53Ajjpoz/gba9+Fi86sL8p2xcREWl3OndKj2afO918x0Nc/f27+fwH/k/i2xYREVksFnrulNZpLR8oPe5ZZ5knVy1bz58BzwTury7MAYQQhoDvlp625Ixk8+bNO7w2PTlFdunSFrSmfc2VszSHsvahnP0oax/KWeKqdaxo5Fyy1Cd9KGc/ytqHcvajrCWOWsdJPp9lUiPnEqU+6UM5+1HWPpSznzRlndbi3C9Lj/uZ2bIayzyvatl69ig9bqmzTPm30htje4mrnDqsbHpyiuySJS1oTfuaK2dpDmXtQzn7UdY+lLPEVetYyeeyTOhLpsSoT/pQzn6UtQ/l7EdZSxw1z5uyGZ03JUx90ody9qOsfShnP2nKOpXFuRDCH4E7gCXAcdXvm9lhwFrgEWCHkXBzKN8M5+lm1lNjmUNKj/fPr7XNMT01xfTUtEbOiYiIiMxDNHJOXzKJiIiINJLPZylOpO92NyIiIotBKotzJZ8oPX7SzJ5SftHMVgMXl56eE0KYqnjvVDO728y+XLWtW4kKdMuAL5rZbhXrZM3sQ0TFuQnga8nvyvxNFYsA5FScExEREYktn8tS1BXgIiIiIg1pxgEREZHWybe6AbWEEK4zs0uAk4Ffm9kPgSJwJLAbcANwUdVqKwEjGlFXua1xM3sr8HXgtcBhZnY7sA14NrA3MAW8N4Rwb9N2qo6+vr5Zz6dLxbnM0o5WNKdtVecszaOsfShnP8rah3KWuGodK4V8jgmNnEuM+qQP5exHWftQzn6UtcRR6zhRcS556pM+lLMfZe1DOftJU9ZpHjlHCOEU4ASiKS4PA44Cfg+cCrwuhDA5j23dCBwIXAr8L/BS4JVEBcp/BV4UQvhsku2fj2KpGFc2NR49z3fUuuWeLER1ztI8ytqHcvajrH0oZ4mr1rFSyGvkXJLUJ30oZz/K2ody9qOsJY5ax0k+l2FS502JUp/0oZz9KGsfytlPmrJO7ci5shDCemB9zGXPAs6q8/49RCPxUmdoaIj+/v6Z51PFcQAyHZrWMknVOUvzKGsfytmPsvahnCWuWsdKPqd7ziVJfdKHcvajrH0oZz/KWuKod940NQ2TU9PkspkWtKz9qE/6UM5+lLUP5ewnTVmneuTcYja1PSrO5Zd2trglIiIiIruOQj6raS1FREREYsjno68FNbWliIiIPxXnUmqqNLwy26F7zomIiIjElc9pWksRERGROAq5UnFOFzaJiIi4U3EuJVasWDHr+dT4OGQgu0TTWiapOmdpHmXtQzn7UdY+lLPEVetY0ci5ZKlP+lDOfpS1D+XsR1lLHLWOk3xOI+eSpj7pQzn7UdY+lLOfNGWt4lxKdHV1zXo+NT5OJguZXKFFLWpP1TlL8yhrH8rZj7L2oZwlrlrHSj6ve84lSX3Sh3L2o6x9KGc/ylriqHWc5HLRfeZUnEuO+qQP5exHWftQzn7SlLWKcykxMDAw6/nk2DYyGcjkl7SoRe2pOmdpHmXtQzn7UdY+lLPEVetYKeSz+oIpQeqTPpSzH2XtQzn7UdYSR63j5PGRc9OezWlr6pM+lLMfZe1DOftJU9YqzqXU1Ni2aORcPt/qpoiIiIjsMvI5jZwTERERiaOQ17SWIiIiraLiXEpNjo1BViPnREREROZDI+dERERE4tE950RERFpHxbmU6OjomPV8avuY7jnXBNU5S/Moax/K2Y+y9qGcJa5ax4pGziVLfdKHcvajrH0oZz/KWuKodZzkysU5nTslRn3Sh3L2o6x9KGc/acpaxbmU6O3tnfV8cmysdM85FeeSVJ2zNI+y9qGc/ShrH8pZ4qp1rGjkXLLUJ30oZz/K2ody9qOsJY5ax0kumyGb0ci5JKlP+lDOfpS1D+XsJ01ZqziXEkNDQ7OeT20v33NOxbkkVecszaOsfShnP8rah3KWuGodKxo5lyz1SR/K2Y+y9qGc/ShriaPecZLPZZmYnHZsTXtTn/ShnP0oax/K2U+ass63ugESGRsbm/V8avs4mSyQ1a8oSdU5S/Moax/K2Y+y9qGcH2dmxwMnAwcAOeBu4HLgkhDCvKtPZvZy4HTgYKADuA/4F+C8EML2Buu+Cfgr4CBgBfC/wJ3A1SGEK+bbliTUOlYKeRXnkqQ+6UM5+1HWPpSzH2UtcdQ7TvL5rKa1TJD6pA/l7EdZ+1DOftKUtUbOpdTU+HYyuQyZTKbVTRERERFnZvZZ4GqiQtpPgRuBpwEXAdeZ2bzO4czsb4HvAkcAdwDfBlYDHwV+bGadNdbrMLNvERXxXgT8Erge+D3wfODN8965Jouu/tYXTCIiIiJx5LJZJqZ07iQiIuJNw7JSKirOqXYqIiKy2JjZ64BTgEeAQ0MI95Re7wN+BKwDTgMuiLm9g4FzgFHgiBDCbaXXlxMV6Q4FPga8b47VrwBeCXwVeGcIYXPFdpcC+81/D5tLI+dERERE4ivkMxo5JyIi0gKq/qREf3//rOdT28fJ5HItak37qs5ZmkdZ+1DOfpS1D+UMwAdKj+8vF+YAQgiDRNNcApw5j9FzZwIZ4JPlwlxpe1uJpqqcAk4xs57KlczsKOCNwH8DJ1QW5krrbw8h3BF/t5JV61gpaORcotQnfShnP8rah3L2o6wljnrHie45lyz1SR/K2Y+y9qGc/aQpaxXnUmJkZGTW86nxcTJ5/XqSVp2zNI+y9qGc/ShrH4s9ZzNbCzwXGAeurX4/hHAz8DDwROCQGNtbAhxdenr1HNu7D7gVWAK8ourtU0uPF4QQJmPugptax0peI+cStdj7pBfl7EdZ+1DOfpS1xFHvOMnnshR1YVNi1Cd9KGc/ytqHcvaTpqxV/UmJLVu2zHo+VSySzWvkXNKqc5bmUdY+lLMfZe1DOXNQ6fHOEMK2GsvcXrVsPQZ0AkMhhHvjbs/MckT3pwP4qZmtNbO/MbNLzew8M3udmbV0evRax0ohr5FzSVKf9KGc/ShrH8rZj7KWOOodJ/l8lkmdOyVGfdKHcvajrH0oZz9pylr3nEupaOScinMiIiKLzN6lxz/UWebBqmXjbO/BOsvMtb19iYp6AC8GPlvxvOwuM3tNCOH3MdrhppDTyDkRERGRuPJZXdgkIiLSCho5l1JTxQmyedVORUREFpnlpcd68yxsLT12N3F7vRU/fx64BXhOaZnnl54/A/i2mS2N0Q43+XyWCRXnRERERGLJ5zM6dxIREWkBVX9Sore3d9bz6WKRzDL9epJWnbM0j7L2oZz9KGsfyjk1Ki/g+iPwyhDCeOn57WZ2FHAP8DTgeODy+Wx8w4YNZLPRR6xcuRKATZs2zbzf3d1Nd3c3g4ODTE5Gt7srFAqsWrWKzZs3Mzo6ysTEBAMDA/T19VEsFhkaGgJg86NbKU5OMT09zZ/+9KeZbXZ0dNDb28vQ0BBjY2Mzr/f39zMyMjJraove3l4KhQKDg4Mzr3V2dtLT08PGjRspFosA5HI5+vr6GB4eZnh4eGbZhe5TWfU+AaxYsYKuri4GBgZc96mcczvtUxp/T+Wc22mf0vp7mpiYYGRkpK32KY2/p4mJCYaHh9tqn9L6eyoUCgCJ7lNlm6U91DvHju45N+3Ymvam/5/xoZz9KGsfytlPmrJW9SclyifUZdHIuSUtak37qs5ZmkdZ+1DOfpS1D+U8M4qtq84y5dFww3WW2dntVf58ZUVhDoAQwlYz+wpwBnA48yzOrV69mv7+/lmvVT+H6IvNaj09PfT09DA5OUkuF00BnsvlZtYfm97C9PRdTE1Nz7nNuU7Eu7q66OraMaK51l+1atUOr5W/2Iyzfr19qlS5T4222cx9qsy51vq72j7FWd97n+LkvKvtU5z1W7FPlVm3yz412mYr9qky53bZp0pp2qdyQS3JfZqa0iiqdlPvHDuf0z3nkqT/n/GhnP0oax/K2U+asta0lilReeUbwNTEBNkl6TlQ2kV1ztI8ytqHcvajrH0oZx4oPe5ZZ5knVy0bZ3t7zHN7lT/fX2O98utPjNGOxNU6Vgr56PRW951LhvqkD+XsR1n7UM5+lLXEUe84yed0z7kkqU/6UM5+lLUP5ewnTVlr5FxKTU9MksmrOCciIrLI/LL0uJ+ZLQshbJtjmedVLVvP3cA2oNfM9g0h3DvHMs+v3l4IYdjM7gGeCuxeY9srS49ba7zfEuXinL5kEhERWVzM7HjgZOAAIEd0HnQ5cEkIIdaJgZllgUOAVwBHEN1jdzkwBPwX8PkQwg011j0L+Ic6m98eQuiItTOOomktdd4kIiLiTSPnUmq6qJFzIiIii00I4Y/AHcAS4Ljq983sMGAt8Ahwa4ztjQPfLT09YY7t7QO8ABgHvl319vWlxyNrbL78+n82aoenfE4j50RERBYbM/sscDVwMPBT4Eaie+NeBFxXKrrFsQ/wH8AHAQN+AXwN+ANwNPBvZna5mWXqbOO/gSvn+HfVPHfLRT6fYULnTSIiIu40ci4lOjs7Zz2fmpwku0T3nEtadc7SPMrah3L2o6x9KGcAPgFcC3zSzG4JIfwewMxWAxeXljmn8gpwMzsVOBX4RQjhxKrtnQOsA95vZt8LIfyitM5y4EtEF2tdHELYXLXeBcC7gFeZ2V+FEGbuK2dm7wMOBUaY5/3mklLrWJmZ1lJXgCdCfdKHcvajrH0oZz/KGszsdcApRBcvHRpCuKf0eh/wI6LzoNOIzm0amQb+HfgUcGMIYbLicw4jupjprcBPqH0OdEMI4ayF7Euz1DtOomktpx1b097UJ30oZz/K2ody9pOmrDVyLiWqbxw9PTFFNkU3J2wX1TlL8yhrH8rZj7L2oZwhhHAdcAnRvdx+bWbfNLPrgXuAZwI3EF0FXmkl0dXdO9xbLoRwO3Am0AncYmY/MLNrgHuBw4DbiK4Or17vT8CJwATwJTP7lZlda2Z3Ap8GtgN/WVrOXa1jpTxyTleAJ0N90ody9qOsfShnP8oagA+UHt9fLswBhBAGiaa5BDgzzui5EMK9IYQjQwjfqyzMld67meiiJ4A3J9BuN/WOk3wuy6QuakqM+qQP5exHWftQzn7SlLWKcymxcePGWc+nJ6fILl3aota0r+qcpXmUtQ/l7EdZ+1DOkRDCKUTTUN5BVEA7Cvg90ei411V/WRRje+cSTcX0I6J71r0a2AR8CDgshDBaY71/I5oe6hqiYuExwBOA9cDzSu+3RK1jRSPnkqU+6UM5+1HWPpSzn8WetZmtBZ5LNEX3tdXvlwpqDxOdxxySwEeW79G7NoFtual3nOiec8la7H3Si3L2o6x9KGc/acpa01qmRLFYnPl5eno6Ks4tUXEuaZU5S3Mpax/K2Y+y9qGcHxdCWE9UBIuz7FnAWQ2W+R7wvQW047+BN853vWardazonnPJUp/0oZz9KGsfytmPsuag0uOdIYRtNZa5HVhTWvaWnfy8p5Ye680c8Bwz+yTRBU1DRLMUfLt0L+CWqHec5HMZthfndd2X1KE+6UM5+1HWPpSznzRlreJcCk2XDhDdc05ERERkfrLZDLlshgldAS4iIrIY7F16/EOdZR6sWnZBzKwTeHfp6dfqLPrq0r9KD5nZm0sj+VIln88ysm2i1c0QERFZdFScS4lcLjfz89R4qTjXoZFzSavMWZpLWftQzn6UtQ/lLHHVO1by+axGziVEfdKHcvajrH0oZz/KmuWlx5E6y2wtPXbv5GddTFTgMyKzkQAAIABJREFU+y3w+Tnev5fo/nffBe4HlgD7A/9ANE35d8zsBSGE/5nvB2/YsIFsNpodYOXKlQBs2rRp5v3u7m66u7sZHBxkcjIaBVcoFFi1ahWbN29m69atDAwMANDX10exWGRoaAiAsW2jbB+PzpvKywB0dHTQ29vL0NAQY2NjM6/39/czMjLCli1bZl7r7e2lUCgwODg481pnZyc9PT1s3LhxZpRCLpejr6+P4eFhhoeHZ5ZdyD6Njj4+K3v1PgGsWLGCrq4u930qZ91O+5TG31M553bap7T+nrZu3crIyEhb7VMaf09bt25leHi4rfYprb+n8fFoIHuS+1TZ5vnITE9PL2hFATPbC7j/pptuYu3a5KYbH3/0UW5/69uxd7+JlUcel9h2RUREFqOHHnqII488EmDvEMIDLW7Ootasc6dqf/Gh7/C3f3kwB9nqpn2GiIhIu9qVzp3M7O+AjwFXhxDeXGOZjwF/B3w+hPCOBX7O3wMfAbYALwoh3DnP9a8DXkc0veWr5rHeXjT53Omq797FH/70GB962581ZfsiIiLtbqHnTtmmtUjmpbIiOzNybqlGziWtMmdpLmXtQzn7UdY+lLPEVe9YyeezFDWtZSLUJ30oZz/K2ody9qOsZ0bFddVZpjy6bkFhmdnpRIW5rcDR8y3MlXyk9PgyMysspB07o+55Uy6r6cATpD7pQzn7UdY+lLOfNGWt4lxKzC7ORUMrcx3LWtWctpWmztfulLUP5exHWftQzhJXvWOlkM8yoWktE6E+6UM5+1HWPpSzH2XNA6XHPess8+SqZWMzs9OA84FtwKtCCLfOdxsld5celwArF7iNBatfnNO9epOkPulDOftR1j6Us580Za3iXApNFaPiXHZpR4tbIiIiIrLryed0zzkREZFF4pelx/3MrNYVzs+rWjYWM3sX8BlgDHhNCOHmhTURgN0rft5ac6kWKOSzTEzqljciIiLeVJxLoanxImQgu0TTWoqIiIjMV/Qlk4pzIiIi7S6E8EfgDqIRacdVv29mhwFrgUeA2KPezOydwEXAduDYEMIPd7Kpb3i8ySE9l+wDuazOm0RERFpBxbmUWLny8VkNpsbHIQOZvPs05G2vMmdpLmXtQzn7UdY+lLPEVe9Y0ci55KhP+lDOfpS1D+XsR1kD8InS4yfN7CnlF81sNXBx6ek5IYSpivdONbO7zezL1Rszs78urbcdWBdC+H6jBpjZHmZ2vJktrXo9Y2Z/WdHGf5rPjiWl7nmTLmpKlPqkD+XsR1n7UM5+0pR1vtUNkB3lOpeRy8P45mE6W90YERERkV1MIa/inIiIyGIRQrjOzC4BTgZ+bWY/BIrAkcBuwA1Eo+AqrQSMaETdDDN7NvA5IAPcD7zRzN44x8duCiGcUfG8F7gauNTM7gAGgG5gP2Dv0jIXhRA+t+AdbZJCLqN79YqIiLSARs6lxKZNm2Z+7n7KU+h9RoaOJz6xhS1qT5U5S3Mpax/K2Y+y9qGcJa56x0o+pyvAk6I+6UM5+1HWPpSzH2UdCSGcApxANMXlYcBRwO+BU4HXhRAmY26qh6gwB/B04C01/r2+ar0/Ap8C/gvYFzgWeBnR925fBY4MIZy2kH1LQuPzJt1zLinqkz6Usx9l7UM5+0lT1ho5l0LTU5Pkl06TyWlaSxEREZH50sg5ERGRxSeEsB5YH3PZs4Cz5nj9xzxenJvPZ/8v8LfzXS8NNK2liIhIa2jkXApNTxQB3XNOREREZCE0ck5EREQknlxW500iIiKtoOJcSnR3d8/8rOJc81TmLM2lrH0oZz/K2odylrjqHSsaOZcc9UkfytmPsvahnP0oa4mj0XmTinPJUZ/0oZz9KGsfytlPmrJWcS4lZhXnJkvFudySVjWnbaWp87U7Ze1DOftR1j6Us8RV90smjZxLjPqkD+XsR1n7UM5+lLXEUe84yecyTOiipsSoT/pQzn6UtQ/l7CdNWas4lxKDg4MzP09PjAOQyeuWgEmrzFmaS1n7UM5+lLUP5Sxx1TtW8ho5lxj1SR/K2Y+y9qGc/ShriaPueVMuy8TUtGNr2pv6pA/l7EdZ+1DOftKUtYpzKTE5OTnz8+PTWmrkXNIqc5bmUtY+lLMfZe1DOUtc9Y4VTc+UHPVJH8rZj7L2oZz9KGuJo95xks9lNXIuQeqTPpSzH2XtQzn7SVPWKs6l0OPTWmrknIiIiMh8FXIaOSciIiISRz6XZXJqmimNnhMREXGl4lxKFAqFmZ+nJ4qQzZHJ5lrYovZUmbM0l7L2oZz9KGsfylniqnes5PO6Ajwp6pM+lLMfZe1DOftR1hJHo/MmgMkpnTslQX3Sh3L2o6x9KGc/acpaxbmUWLVq1czP05NFyKkw1wyVOUtzKWsfytmPsvahnCWuesdKIZ+lqGktE6E+6UM5+1HWPpSzH2UtcdQ7TvK5DAATkxo5lwT1SR/K2Y+y9qGc/aQpaxXnUmLz5s2PP8lkyBY6KD76SOsa1KZm5SxNpax9KGc/ytqHcpa46h0rmtYyOeqTPpSzH2XtQzn7UdYSR73jJJ+LvhrU/XqToT7pQzn7UdY+lLOfNGWt4lxKjI6Ozvy8bI/9WPPWT1B4whNb2KL2VJmzNJey9qGc/ShrH8pZ4qp3rGhay+SoT/pQzn6UtQ/l7EdZSxx1z5vKxTmdOyVCfdKHcvajrH0oZz9pylrFuZRSYU5ERERkYTStpYiIiEg85eKczp1ERER8qTgnIiIiIm0ln9PIOREREZE48vnoq8FJ3XNORETElYpzKdHX19fqJiwKytmPsvahnP0oax/KWeKqd6xo5Fxy1Cd9KGc/ytqHcvajrCWOesdJPpcBdM+5pKhP+lDOfpS1D+XsJ01ZqziXEsVisdVNWBSUsx9l7UM5+1HWPpSzxFXvWMnnshQ1ci4R6pM+lLMfZe1DOftR1hJHo/MmUHEuKeqTPpSzH2XtQzn7SVPWKs6lxNDQUKubsCgoZz/K2ody9qOsfShniavesVLIZ/UFU0LUJ30oZz/K2ody9qOsJY56x0kuG42c04VNyVCf9KGc/ShrH8rZT5qyVnFORERERNqKRs6JiIiIxJPJZKL79erCJhEREVcqzomIiIhIW9HIOREREZH4CvkMk5PTrW6GiIjIoqLiXEqsWLGi1U1YFJSzH2XtQzn7UdY+lLPEVe9Y0ci55KhP+lDOfpS1D+XsR1lLHI2Ok1w2S1EXNiVCfdKHcvajrH0oZz9pylrFuZTo6upqdRMWBeXsR1n7UM5+lLUP5Sxx1TtWNHIuOeqTPpSzH2XtQzn7UdYSR6PjJK9zp8SoT/pQzn6UtQ/l7CdNWas4lxIDAwOtbsKioJz9KGsfytmPsvahnCWuesdKPq+Rc0lRn/ShnP0oax/K2Y+yljgaHSf5XJYJnTslQn3Sh3L2o6x9KGc/acpaxTkRERERaSuFXJapqWkmp3TvFBEREZFGCrms7jknIiLiTMU5EREREWkr+Xx0iqvpmUREREQay+czuueciIiIs3yrGyCRjo6OVjdhUVDOfpS1D+XsR1n7SGvOZvbp0o//HEJ4sKWNEaD+sVIoFeeKE1MsLeS8mtSW0ton241y9qOsfShnP8pa4mh0nOSyuudcUtQnfShnP8rah3L2k6asVZxLid7e3lY3YVFQzn6UtQ/l7EdZ+0hxzu8GJoAzWt0QidQ7VvK50sg53Ttlp6W4T7YV5exHWftQzn6UtcTR6DjJ51WcS4r6pA/l7EdZ+1DOftKUtaa1TImhoaFWN2FRUM5+lLUP5exHWftIcc4bgNEQgr61SIl6x0rlyDnZOSnuk21FOftR1j6Usx9lLXE0Ok4KORXnkqI+6UM5+1HWPpSznzRlreJcSoyNjbW6CYuCcvajrH0oZz/K2keKc74FWGFmT251QyRS71gp5HTPuaSkuE+2FeXsR1n7UM5+lLXE0eg4yeeyTExMO7WmvalP+lDOfpS1D+XsJ01ZqzgnIiIi0th5wGTpUVIuPzNybrLFLRERERFJv1wuo4uaREREnKk4JyIiItJACOHnwJuBo83sZjM7xsxWm1mm1W2THT0+ck5XgIuIiIg0kte0liIiIu7yrW6ARPr7+1vdhEVBOftR1j6Usx9l7SOtOZtZ5RCsF5f+ld+rtdp0CEHnWk1S71jJ5bJkMxo5l4S09sl2o5z9KGsfytmPspY4Gh0nhbyKc0lRn/ShnP0oax/K2U+astbIuZQYGRlpdRMWBeXsR1n7UM5+lLWPFOecWcA/nWc1UaNjJZ/PaeRcAlLcJ9uKcvajrH0oZz9pztrMlpjZDhcqmVnGzE42s381s38zs3eYmc6bmqjheVMuq/OmhKS5T7YT5exHWftQzn7SlLVOflJiy5YtrW7CoqCc/ShrH8rZj7L2keKc917gP2mSRsdKIZdhcCg9J927qhT3ybainP0oax/K2U9aszaz/wtsA66Y4+1vAhcBxwHHABcDN7g1bhFqdJzonnPJSWufbDfK2Y+y9qGc/aQpa021JCIiItJACOEPrW6DzE82m+Xam+7hmXvvzhN372p1c0RERBabo0uPX6580cxeDbwCmAa+SlTAOwF4pZkdH0JY79pKAaL79U5MqDgnIiLiSSPnRERERKTtLC1kefVL9lZhTkREpDX2Kz3+our1vyQqzH0ihHB8COEk4DSiKcFPdGyfVIimtVRxTkRExJNGzqVEb29vq5uwKChnP8rah3L2o6x97Co5m9lq4DnAqtJLG4E7QggbWteqxaXRsVLI51i+bIlTa9rXrtInd3XK2Y+y9qGc/aQ469XASAhhc9XrR5Qev1Dx2leAS4GDPBq2GDU6TvL5LGPjE06taW8p7pNtRTn7UdY+lLOfNGWt4lxKFAqFVjdhUVDOfpS1D+XsR1n7SHvOZvZi4KPAS2q8/xPgQyGE/0jgs44HTgYOAHLA3cDlwCUhhHlf2mxmLwdOBw4GOoD7gH8BzgshbJ9j+beWPq+eJ4UQHplvW5LQ6FjJ53UFeBLS3ifbhXL2o6x9KGc/Kc56GTBe+YKZGdAL3Fs5ZXgIYZuZbQZ6fJu4eDQ8b8plmZicdmpNe0txn2wrytmPsvahnP2kKWtNa5kSg4ODrW7CoqCc/ShrH8rZj7L2keaczeydwI+ICnMZYArYUPo3WXrtMODHZvaOnfyszwJXExXSfgrcCDwNuAi4zszmdQ5nZn8LfJfoavU7gG8TXdH+0VJ7O+usfi9wZY1/2+bTjiQ1OlYKuSxF3Ttlp6W5T7YT5exHWftQzn5SnPUGoNPM1lS8Vr4P3c/mWL4D2NL0Vi1SjY6TfC6ji5oSkuI+2VaUsx9l7UM5+0lT1ho5JyIiItKAmR1EVBjLEn2h9I/AT8ojzsxsKVFh7u+BFwEXmdkvQgi/XMBnvQ44BXgEODSEcE/p9T6i4uA6onuzXBBzewcD5wCjwBEhhNtKry8nKtIdCnwMeF+NTfwshPDW+e5HqxXyKs6JiIi00G1E5yz/ULpoaXfgVKL7zf2gckEz24NopN093o2USF4XNYmIiLjTyDkRERGRxv4f0XnTNcBLQwg3Vk4FGULYHkL4AVGB7jqiaShPX+BnfaD0+P5yYa70GYNE01wCnDmP0XNnEo3q+2S5MFfa3lbgr4hGAJ5iZm01lZSmtRQREWmpC4nOP04iGhH3R2Af4GHg+qpl/7z0eIdb62SWfC7LpM6bREREXKk4lxKdnfVmk5KkKGc/ytqHcvajrH2kOOfDiK70fl+9+72V3ntvadmXzvdDzGwt8Fyie7RcO8f2byb6UuuJwCExtreEx6eQunqO7d0H3AosAV4x3/a2UqNjRdNaJiPFfbKtKGc/ytqHcvaT1qxL5yzvBEaA5cBSopFx6+a41+3bSo8/9Gvh4tLoONE955KT1j7ZbpSzH2XtQzn7SVPWmtYyJXp62upi9dRSzn6UtQ/l7EdZ+0hxzquAzSGEPzVaMIQwYGabS+vM10GlxztDCLXu53Y7sKa07C0NtmdAJzAUQri3zvZeVNre+jnef4qZfZToHnWPEV3V/o3SyLuWaXSs5DWtZSJS3CfbinL2o6x9KGc/ac46hPB5M7sKeBbROcQ91Rc5mVkB+GTp6U3OTVw0Gp436Z5ziUlzn2wnytmPsvahnP2kKWuNnEuJjRs3troJi4Jy9qOsfShnP8raR4pzfgzoNrOuRguWltmttM587V16/EOdZR6sWjbO9h6ss0yj7b0I+CDw10TTe14NPGhmr4/x+U3T6FgpaFrLRKS4T7YV5exHWftQzn7SnnUIYVsI4fYQ2eEPcwihGEL4eulfSy/8aWeNjpN8PktR502JSHufbBfK2Y+y9qGc/aQpa42cS4lisdjqJiwKytmPsvahnP0oax8pzvkO4GXAu4FPNFj2PUT3nPuvBXzO8tLjSJ1lyl9cdTd5e38CPgp8A7gPmACeAfwtsA74qpm9IoTw/RjtmGXDhg1ks9E1YitXrgRg06ZNM+93d3fT3d3N4OAgk5OTABQKBVatWsXmzZsZHR1leHiYYrFIX18fxWKRoaGhmfVXrFhBIZfl0c2PMTAwAEBHRwe9vb0MDQ0xNjY2s2x/fz8jIyNs2bJl5rXe3l4KhQKDg4Mzr3V2dtLT08PGjRtnjtNcLkdfXx/Dw8MMDw/PLLvQfSqrtU9dXV0z++O1T+Wc22mf0vh7KufcTvuU1t/T8PAwnZ2dbbVPafw9DQ8P09HR0Vb7lNbf09jYGKtWrUp0nyrb3CxmlgOeSjTd5a/rTRsuO6/RObbuOZecFP//TFtRzn6UtQ/l7CdNWas4JyIiItLY54E/B/6xNDLuUyGELZULmNmTgL8hKuBNl9bZZZWKbtWFt58DrzWz84HTgfPnWKah1atX09/fP+u16ucQfbFZraenh56eHgYGBmbWyeVyO6yfz2dZurRzh9d7e3t32GZXVxddXTsOipyrTatW7ThbafmLzTjr19unSnPtU61tNnOfKnOutf6utk9x1vfepzg572r7FGf9VuzTwMDATPvaZZ8abbMV+zQwMDDzfrvsU6U07VO50JfkPk1N7XyRxsz2A04A7g0hfLHqvSOBK4EnlV4aMLO/DCH8eKc/WBYkuuecinMiIiKeNK1lSuRyuVY3YVFQzn6UtQ/l7EdZ+0hrziGE64GriM6dPgA8YmY/N7Ovmdm3zezXwP1Eo+aywJdDCP+2gI8qj2KrN31meTTccJ1lmrW9so8Ck8B+ZrbHPNZLTKNjRdNaJiOtfbLdKGc/ytqHcvaT4qzfArwfmFV1NLMnAjcA/UCm9G8N8E0z29O7kYtFw/OmXJaJiWmn1rS3FPfJtqKc/ShrH8rZT5qyTv3IOTM7HjgZOIBoiqi7gcuBSxYy7UFp6oS/Bo4H9iP6smoj8Cvg8yGEbybU9HmZ68o7SZ5y9qOsm+uxxx5jw4YNO0yVI82lrH3EzblQKLB69Wp22223JrdoxluBu4Azie4p9/w5lnkM+Dhw3gI/44HSY70vp55ctWyc7dUroM1newCEEB41sw1EV7yvof497Zqi0d+ZQi7L6PYJp9a0L/0996Gc/ShrH8rZT4qzPrz0eH3V6ycTfQ/zP8AbgDHgCuAw4H3Ae53at6g0Ok5yuYzuOZeQFPfJtqKc/ShrH8rZT5qynldxzsweBaaA54UQ7mtOk2Z93meBU4hO1m4CisCRwEXAkWb2+vkU6Mxsd+C7wPOAIeBWonuwPBn4P8Ag0JLi3PDw8JzTTUiylLMfZd08jz32GIODg6xZs4YlS5aQz6f+Oou2MDk5maqra9pV3Jynp6fZtm0bDz/8MIBLgS6EMA2cY2YXEt1/7jlAeQ6pjUT3pftBCGG0xibi+GXpcT8zWxZC2DbHMs+rWraeu4FtQK+Z7RtCuHeOZcpFxjjbA2YudlpRerq13rLN0ujvTD6fZWJEXzLtLP0996Gc/ShrH8rZT4qz7if6/uiBqtdfTTT999+FEH4HYGanAb8mOr+SJoh13qTiXCJS3CfbinL2o6x9KGc/acp6vt/oLgGKToW51xEV5h4BDg0h3FN6vQ/4EbAOOA24IOb2ssA3iL7QugA4M4QwVvF+N7BXgrswL2k6KNqZcvajrJtnw4YNrFmzhs7OTsbHx1vdnEVDxTkfcXPOZDJ0dnayZs0aBgYGPEfPEUIYIZqO6YYmbPuPZnYHUeHvOODLle+b2WHAWqLzo1tjbG/czL4LvJbovi8fqdrePsALgHHg2/No6quATqKpMO+ex3qJafR3ppDP6grwBOjvuQ/l7EdZ+1DOflKc9UpgSwhhsvyCmS0nmhVpG/CD8ushhDvNbIwWfifT7hoW53JZJnXelIgU98m2opz9KGsfytlPmrKe7z3nHiQq0Hn4QOnx/eXCHEAIYZBoGgSAM0tFtzj+Gngh8K0QwnsrC3Ol7Q6HEH69s40WEWm2YrHIsmXLWt0MkVRYtmwZxWKx6Z9jZo+a2f+WilnN9onS4yfN7CkVbVgNXFx6ek7l7AFmdqqZ3W1ms4p55WWJrlB/v5k9v2Kd5cCXiM4HLw4hbK54r9PMTi4tM4uZvRL4QunpZ0MIzf8FLEAhl6U4oS+ZREREWmQ7sKLqO5sXE5133BZCqJ57eq7ZAsRJIaeRcyIiIt7mW5z7BrDUzJo61YCZrQWeS3QV97XV74cQbgYeBp4IHBJzs6eWHj+dRBtFRFopk8m0ugkiqeDYF5YAOY/ZA0II1wGXEJ3n/NrMvmlm1wP3AM8kGrF3UdVqKwFjjnvLhRBuJ7pPXidwi5n9wMyuAe4lur/LbcAHq1ZbQlQI3Ghm/2Fm/2pm15vZXcC3iKbzvB74cBL73Az5fJYJFedERERa5XdE3zn9ecVrxxNdMPSTygXNrINouuxH3Fons+RyGYoT061uhoiIyKIy32ktPw68HviCmR0dQrirCW0COKj0eGeNe60A3A6sKS17S72NmdmTgGcBk8CtZvY04I1E00INATcD3y/dS6YlVq5c2aqPXlSUsx9l7UP3m/OjrH2kOOcHgT29PiyEcIqZ/Qx4F1EBLUc0feSXgEvmc8/d0vbONbP/Af4f0RTfHcB9wGeA80II26tWGQU+SnQ/OgMOJCrYbSS6WOvKEML1C9y9RDT6O6NpLZOhv+c+lLMfZe1DOftJcdZfJ5qm+wozOx94EtEU2wDXVC37PKJC3v1+zVtcGh0n+dLIuenpaV0IupNS3CfbinL2o6x9KGc/acp6vt+AHUN0JfeHgV+W7mFyK9EXNZO1VgohzDXFUj17lx7/UGeZB6uWrWf/0uP/Ek2JeS6z9/1MoivJ14UQNsynoSIiIrIofAM4w8xeFkK40eMDQwjrgfUxlz0LOKvBMt8Dvhdze+PA38dZNq2iaS1rnp6KiIhIc/0T8CbgGURTbANkgM/NcaH364lG1P3YrXUySyEfTaw1NTVNLqfinIiIiIf5Tmt5BfBJYDnR1dOvIbovymXA5XX+zVf5/iYjdZbZWnqMc/e+3orHTxNNlflMYDfgCOAuovvR7TCFppdNmza16qMXFeXsR1n7mJiovlWDNIuy9pHinD8OPEA0e8AzWtwWofHfmXw+x4SmZ9pp+nvuQzn7UdY+lLOftGYdQtgKvIDo4qHvEY2We0sI4eTK5cysADwb+B/gO87NXDQanjfloq8HNevAzktrn2w3ytmPsvahnP2kKev5jpz7CdHVTLuachEyD/wshHB8xXs/MrM/J5oP/VAzOzyE8KP5bHzDhg1ks9FHlIdFVv6Su7u76e7uZnBwkMnJ6AruQqHAqlWr2Lx5M6OjowwPDzMwMEBfXx/FYpGhoaGZ9VesWEFXVxcDAwMzr3V0dNDb28vQ0BBjY2Mzr/f39zMyMsKWLVtmXuvt7aVQKDA4ODjzWmdnJz09PWzcuJFisQhALpejr6+P4eFhhoeHZ5Zd6D6VpWmfyjm30z6l9fc0PDzM2NhYW+1TWn5P09PTjI+PAzA1NcXk5CS5XG7mNYBsNks+n2diYoKpqcf/B2vJkiVMTk7OfA5E0whmMpmZtleuXywWmZ6O/rOfyWQoFApzrg+ziyq5XI5cLjfn+tVtKhQKTE9P77D+7373O77//e9zzDHHsGbNmqbv0xVXXEF3dzfHHnvsnPtUXmdn9qkdf09J71O9nGvt0+Tk5Eyfnqs/Vf43YCd4zR4gCSnkMxQnNXJORESkVUIIjwEfabBMkWgKb2mh8mi5icld8Ss/ERGRXdO8inMhhJc2qR3VyqPiuuosUx5dN1xnmbLKZb5Q/WYI4SEz+zbRVAqHA/Mqzq1evZr+/v5Zr1U/h+hL9Wo9PT309PQwMDAws04ul5tz/ble6+3t3eG1rq4uurp2jG6u9VetWrXDa+UvNuOsX2+fKqVlnypzrrX+rrZPcdZvxT4NDAzQ0dFRc/1dcZ8are+1T5lMhiVLlgAwPj5OLpcDmHmt0lz37yoXVKrNtX6hUEh8/bnaVLlPZXfddReXXHIJL3jBC9h778dnMG7WPl199dWsWbOGN7zhDXOuXy5A7cw+1WrTrvx7qrXNhe5TvZxrrT9XP6/sT5VFwp1wBdEFSuV5fl5T+teIinMtUsjlKE7o6m8RERGRRsoj5yZ07iQiIuJmviPnvDxQetyzzjJPrlq2nvtr/DzXMk+Msb3EzfXlvSRPOftR1j7mKlRIczQj661bt7J8+fI53ysWi0xNTbF06dKmfk7apPiY3lVnD2hbjf7O5PMZfcGUAP0996Gc/ShrH8rZz66StZk9H3gOUL6icSNwRwjhFwlt/3jgZOAAIAfcTXSblUtCCLFOCMwsCxwCvILoFijPILowfAj4L+DzIYQbGmzj5cDpwMFAB3Af8C/AeSGE7fPfs2Q0Ok42PxY17eGNw/R07/z/fyxmu0qf3NUpZz/K2ody9pOmrNNanPtl6XE/M1sWQtg2xzLPq1q2nkB0/7ouYPcay6zufTDwAAAgAElEQVQsPW6t8X5TpemgaGfK2Y+y9pHiQsZOufDCC7nooosAOPHEE2deX7duHeecE91Pfnx8nC996Ut885vf5MEHH2Tp0qUcfPDBvPvd7+aZz3zmzDpTU1N8+ctf5mtf+xoPPfQQmUyGVatW8dznPpezzz6bQqGAmQHw8MMPz/wMcNNNN7F27Vqgdtbf+c53uOqqq7j77ruZmpriaU97GieddBIvf/nLZy1nZqxbt45jjjmGz3zmM9x9990861nP4qqrrprZ329961tcd911fPe732Xjxo1cccUV/Nmf/RkA1157LevXr+fee++lUChw4IEHcsopp3DwwQfH/pxdQVqPacfZAySmRn9nCvmc7puSAP0996Gc/ShrH8rZT9qzLhXN/hHYq8b79wMfCiH86058xmeBU4Ax4CagCBwJXAQcaWavj1mg2wf4j9LPQ8AvgEdLrx8NHG1mVwBvCyHscNGWmf0t8EmiKc9/XFr3MOCjwKvM7MgQwmj1eh4aHSdPfmL0/orlKsztrLT3yXahnP0oax/K2U+asl5wcc7M9iGaBnKHK5+Aa0MItUaoNRRC+KOZ3VHa9nFUTQllZocBa4FHiO730mh7RTP7FvBGohO0WVc6lW5AfGjp6X8utN07Y3BwcM4p7SRZytmPsvZRLBbnnP5vV/eyl72MjRs38tWvfpV3vvOd7LPPPgDsscceQLTfJ510Er/85S855phjOOGEE9i6dSvXXHMNf/EXf8FXvvIV9t9/fwAuueQSPvOZz3D44Yfzpje9iVwux0MPPcS///u/Mz4+TqFQ4Nxzz+UTn/gET3jCE3jnO985047KqU7nyvqf/umfuPTSS3nJS17Ce97zHrLZLDfeeCPvec97+PCHP8wJJ5wwa/nf/OY3fP/73+cNb3gD69at22G/zzjjDDo6Onjb294GPD5V6qc+9Skuu+wyDjjgAE4//fSZfX3LW97CxRdfzGGHHTavz0mztB7TZrZb6ceREIJuZJYCjf7OFHJZTWuZAP0996Gc/ShrH8rZT5qzNrOPAWfy+LTgDwMPlX5eC6whKnxdbWbPCiF8aAGf8TqiwtwjwKEhhHtKr/cR3bJkHXAacEGMzU0D/w58Crix8pyv9D3Ut4G3Es2ocHlVOw4GzgFGgSNCCLeVXl9eWu9Q4GPA++a7j0lodJwsKUQXyG0f12nuzkpzn2wnytmPsvahnP2kKet5F+fMbBnRSc3biE6wMlWLHAd83MwuA95XY9RbHJ8ArgU+aWa3hBB+X/r81cDFpWXOqbz6ycxOBU4FfhFCOHGO7R0H/F8z+1YI4fuldXJEVzbtS3Si+G8LbO9OmZzUCZAH5exHWfuYnm7PWfae/vSn8+xnP5uvfvWrvPCFL5wZPVZ29dVX84tf/ILLLruMl7zkJTOvH3/88bzqVa/i3HPPnRkp9sMf/pB9992XSy+9dNY2zjjjjJmfjznmGC644AJWrlzJMcccM2ebqrO+8847ufTSS3nHO97B6aefPvP6iSeeyCmnnML555/PMcccM2s6yXvuuYfLL7+cF77whXN+xm677cbll18+635t9913H1/84hd5znOew5VXXjlzf7bjjjuOV77ylZx99tnceOONs0acNfqcNEvxMb0ZmAL2Bv7Y4rYIjf/O5PNZTWuZAP0996Gc/ShrH8rZT1qzNrPDgQ+Unv4LcHYI4XdVyzwVOBt4E/ABM/thCOHH8/yo8me8v1yYAwghDJrZyUQj2M40swsbjZ4LIdxLdEH3XO/dbGbnEI0CfDNVxTkeL0J+slyYK6231cz+CrgHOMXMzg4hbJ7XHiag0XGSy2bI57JsL6bzeNqVpLVPthvl7EdZ+1DOftKU9byKc6X5t79OdLKSISpm/ZjZVz69lOjqp78G9jazl8813L+REMJ1ZnYJ0ZzhvzazH/L41AS7EY1+u6hqtZWAEV0xVb29/zaz9xIVFr9rZr8otfsgoiu1tgDH7UQxUUSk5bZuK7J9fGKnt7Ph0VFWP6Fzp7ezdEme5cuSHwX1jW98g3322Yf99tuPoaGhWe+98IUv5IYbbmBsbIyOjg6WL1/Ogw8+yH/+53/uMAXkzvjmN79JJpPh2GOP3aENRxxxBDfddBO/+tWvePGLXzzz+tOf/vS6BbO3vOUtswpzEE2tOT09zdvf/vaZwhxAX18fr33ta7nyyiv57W9/OzNSMM7nyIJsBSZCCCrM7SIK+SzFySmmp6fJZKqvJRMREZEmO41oJNqFIYT3zrVAqZh2vJltIrrQ+t1E3zHFYmZrgecC40QXd1dv/2Yze5joO6pDgFvmuQ/VyrdVWVvVjiVE014CXD1HO+4zs1uBFxHdz279TrajKZYuyTGu4pyIiIib+Y6c+yvg/xDN4/0e4LLqwpuZZYgKcxeUlv0r4EsLaVwI4RQz+xnwLqJ5uss39f0S87ipb8X2LjSzXwNnEJ2YPQf4E/B54BMhhAcW0s4kpHEKr3aknP0oax+VXzhPTk5x0kd/wOjYzhfnktLZkWf9R44ml8smut17772XsbExXvCCF9Rc5tFHH+VJT3oSp59+Ou9617s44YQTWL16Nc9//vN56UtfylFHHTWr2NVI9Zf79957L9PT0xx99NE11oBNmzbNer7XXnvV/Yy53n/ooej6l6c+9ak7vFd+7Y9//OOs4lyjz0mzFBdR7gfMzPIhhPR0skWs0d+ZQj7L9DRMTU2Ty6X2uEo9/T33oZz9KGsfytlPirN+AVFx7uwYy55FNDXlfK8uO6j0eGedC61vJyrOHcTOF+fKJ+R/qnrdgE5gqDT6rlY7XlRqh3txLs5xsrSQ07SWCUhxn2wrytmPsvahnP2kKev5FudOJDq5encI4bK5FigV6z5vZtPA54C3sMDiXGl764l54hJCOIvopK7eMj9mHldieSnfV0iaSzn7UdY+Kv+g5HJZvvihP0/dyLmkC3MQTX34tKc9jQ984AM1lynfL+6ggw7ixhtv5Gc/+xm33XYbt912G9/61re45JJLWL9+PT09PbE+s/qPd3k0zhe+8IVZU0pWespTnjLr+bJly+p+RkdHR6y2NNLoc9IsTSdJVa4BPgIcC1zX4rYIjf/O5Ev/7SlOTDXlv0OLhf6e+1DOfpS1D+XsJ8VZ9wJbQgiPNlowhDBkZluAeCfmj9u79PiHOss8WLXsgphZJ9HIPoCv1WjHg9SWSDsWKs5xsrSQ07SWCUhxn2wrytmPsvahnP2kKev5Fuf2J5pa8soYy15JNO3k/o0WFNi8eXPsL4hl4ZSzH2XtY2JiYtY0iMuXFRKZRnL3Fa0v7tQbQbXnnnvy6KOPcsghh5DNNv7Svauri6OOOoqjjjoKiO5Z95GPfITrrruOt7/97bHaU531XnvtxU9/+lP6+/vZd999Y21jIZ785CcD0X3k9thjj1nv/f73v5+1TDuozjlFPgW8BvicmT0aQrip1Q1a7Br9nSnkS8W5ySmSKXsvTvp77kM5+1HWPpSznxRnPQSsMrPeEMJQvQXNrBdYAWyc52eUb+48UmeZraXH7nluu9rFRIW13xLNvtSqdixInONk6RKNnEtCivtkW1HOfpS1D+XsJ01Zz/fbr2XAaAih2GjBEMK4mY2U1pEGRkdHU3NQtDPl7EdZ+5iamtfsvruUzs5o5N6WLVt2eO/YY4/l3HPP5fLLL+ekk07a4f1NmzaxcuVKAIaGhmZG0ZXtt99+O2y7q6uLzZtr35u9OuvXvOY1XHXVVXz605/mM5/5zA6j5yrbsDOOOOIIzjvvPL74xS9y6KGHzows27BhA9dffz1r1qzhmc985k5/Tlqk+Jg+E/h34BnAD8zsf4Bbib5EqvktRgjhIz7NW3wa/Z0pF+cmJlJ7TO0S9Pfch3L2o6x9KGc/Kc76VuAY4MPAnPecq3AWkC2tkzpm9vdEs0JtAd4QQtju3YYNGzbMXJRY/n+Myin0u7u76e7uZnBwkMnJ6NS0UCiwatUqNm/ezODgIKOjo0B07+pisTjrvtkrVqxgaSHHhk1DDAxE/7/R0dFBb28vQ0NDjI2NzSzb39/PyMjIrP+X6u3tpVAoMDg4OPNaZ2cnPT09bNy4kWIx+hoxl8vR19fH8PAww8PDM8suZJ/K+1Nvn7q6uhgYGJh5zWOfhoeHGR0dbat9SuPvqZxzO+1TWn9Pw8PDFAqFttqnNP6ehoeHyeVybbVPaf09jY2NJb5PlW2ej/kW5waAvczsKSGE39db0MyeRjQlwf0LapmIiCxq+++/P9lslksvvZQtW7bQ2dnJ2rVrOfDAAznxxBO55ZZbOPfcc/n5z3/OIYccwvLlyxkYGODnP/85S5Ys4aqrrgLgFa94Bc9+9rM54IADWL16NRs3buSaa66hUCjwyle+cubzDjzwQK677jr++Z//mX333ZdsNsvhhx8+UySsdsABB3Daaadx4YUXcuyxx3LUUUfR19fHhg0buPPOO/nJT37Cb37zm53OYZ999uGkk07isssu481vfjNHH300IyMjXHPNNYyOjnLeeefVnFZTEnUW0dTe5SGdBwIH1Fk+U1pexbkWqZzWUkRERNxdSDQd+GlmthL4WAjhrsoFzOxg4O+IinjTwGfm+Rnl0WhddZYpj2obrrNMTWZ2OtH53Fbg6BDCna1ox+rVq+nv75/1WvVziL7YrNbT08Po6Ois5XO53A7rLynkWNa5fIfXqy90hOjCxq6uHXd3rjbNNX1Y+YvNOOvX2qfqovRc+1Rrm83cp4GBgVnrtMM+xVnfe5/i5Lyr7VOc9VuxTwMDAzPta5d9arTNVuzTwMDAzPvtsk+V0rRP5UJfkvu00AvN51uc+yHw10RTOr0yhDA210Jm1gFcSnRydeOCWiYiIotaf38/H//4x/nCF77A2WefTbFYZN26dRx44IEUCgU+97nPsX79er7+9a9z4YUXAtH/tO6///6sW7duZjtve9vbuPnmm7nqqqsYHh5m991358ADD+Qd73gHT3/602eWe9/73seWLVtYv349jz32GNPT09x00001i3MAp556Ks961rO46qqr+PKXv8zo6Ci77747T33qU/ngBz+YWBZ/8zd/w5577sn69es5//zzKRQKHHjggZx//vkcfPDBiX2O1PVlovMa2UVUTmspIiIivkIIPzKzjxMV3/4C+Asz2wg8DHQAT+bxYlYG+GgI4cfz/JgHSo971lmmPP/7A3WWmZOZnQacD2wDXhVCqDWyr7ztPWq8v1Pt8KJpLUVERHxlpqfjf89kZvsAvwGWAvcCnwZ+zOMnV3sAhwPvAfqBMWD/EMJ9ibY6JcxsL+D+m266ibVr1+7UtiYnJzXywYFy9qOsm+euu+7iGc94BgDT09N1780myVHWPhaSc2WfmMtDDz3EkUceCbB3COGBnWqg7BTPc6fixCSvff+3uOiMw9nzSbvt1GctZvp77kM5+1HWPpSzn2ZkneS5k5m96f+zd+fxUVX3/8dfmckkQIiEyFIRFGQ5iAKiIhhEZKmAQiFYC4qkKlpRrBtapS61VmURqzZWBBEVJP2KC2ET/RKw/ooWlyK1RTh+jbILBmJYAiHb/P64kyGT9c5M5sw183k+HnlMuHPm3jNv7uVe7rnnHOBPQG2TNH8DPKS1XhrCujsAO4FiIEVrfbyGMruA9sAlWuuPglj3VOB5rPtao7XWOXWUTQAKsKZ16aK1zq2hzAZgAHCd1npJEPXoSANcO9nZT2a+9hk/O7UZ1486J+TtCPn3zxTJ2RzJ2gzJ2RwnXTu5gtmIr5HtV8AxoAvwV2AL1kXIPuBTYBZwuq/M+MbaMNfQKsY3FZElOZsjWZsRzAMWIjyStRmSs7CrvvOM2yXDWjYEOZ+bITmbI1mbITmb4/Sstdb/o7XuCpwP3ARM9/3cBJyvte4WSsOcb927gE1AAnB11feVUoOwGub2EcR8dkqpKVgNcyeAsXU1zPnqUQys8f1xYg3rOwu4GKsRcbXdejQkO/tJYoKbEyXScy5cTj8mGwvJ2RzJ2gzJ2RwnZR1U4xyA1noV1jwrrwCHsYYfqPxzCFgI9PaVFTaEOmmgCI7kbI5kbUZpaWm0qxAzJGszJGdhV33nGZcrjnh3nDTOhUnO52ZIzuZI1mZIzub8VLLWWm/WWi/UWs/y/SzUWm9ugFXP8L3OUkp1qViolGoDvOD740ytdXml925XSm1TSi2qujKl1M2+z50A0rXW79usx0ysIdDvV0pdVGl9zbHukbmAF7TWBfa/WsOxs58keGRYy4bwUzkmf+okZ3MkazMkZ3OclHWwc84B/h50k4HJvieAKmbPy5OeckIIIYRorJRSnYC7gZ9jzR3SRGsdX+n9FOAOrJszM7XWznkkKwZ54l2UypxzQgghRKOltX5LKTUXuBX4j1IqBygBhgKnANlYveAqawUorB51fkqp84B5WA+efweMV0qNr2GzB7TW91apx2dKqQewRpP6WCm1HmuUqUFAG+AToOEmpY6ARI+bH4vk0lUIIYQwJajGOaXUL3y/fqy1PgD+hjppkBNCCCFEo6aUSgcWAc2wbtqA1Qjnp7UuUEoNAQYCXwFvG62kCBDvdknPOSGEEKKR01rf5pvTbSpWY5gb2IbVY21u5V5z9Ujh5DVed99PTXYA91ZdqLWerZT6EpgG9AWaYN0v+wswR2t9wmY9oiIxQXrOCSGEECYF23MuGygFUiNQl5jWokWLaFchJkjO5kjWZshkseZI1mY4NWelVHdgCdZNlnm+398BTq2h+EvApcAopHEuYuycZzzxLkpK5SZTOOR8bobkbI5kbYbkbI4TslZKNdQD216tdedQPqi1zgKybJZ9FHi0huV/52TjXEi01u8B74Wzjkiws58ketwUy5xzYXPCMRkLJGdzJGszJGdznJR1sI1z+QBa66MRqEtMS0pKinYVYoLkbI5kbYZTGzIaI8naDAfnfB9Ww9wzWutpAEqp2u5e5PheL6rlfdEA7Jxn4t0uSsu89ZYTtZPzuRmSszmStRmSszkOybpjA61HTtoRYmc/SfC4OSGNc2FzyDHZ6EnO5kjWZkjO5jgpa1eQ5bcALZRSp0SiMrFs79690a5CTJCczZGszSguLo52FWKGZG2Gg3MeinXDaHZ9BbXW+4FCrDnpRITYOc9Iz7nwyfncDMnZHMnaDMnZHIdkPbiBfoaYrnissLOfJCZI41xDcMgx2ehJzuZI1mZIzuY4Ketge87Nx5pD5bfAEw1fHSGEEEIIR/oZcMTX8GbHCaB5BOsjbPDEuyktkznnhBBCiEjSWn8Y7TqI8MmwlkIIIYRZQfWc01ovATKBPyql/qSUkrnnhBBCCBELCoEkpVS9424qpZKBFHzDgYvoiXfHUVIqjXNCCCGEEPVJTHBzolga54QQQghTguo5p5Ra7/v1GPB74H6l1DdAHlDbGdyrtR4aehVjQ5MmTaJdhZggOZsjWZvhcgU7OrEIlWRthoNz3gIMAC4APq2n7HisB6D+FelKxTI75xlPvFsa58Ik53MzJGdzJGszJGdzJGthh539JFHmnGsQckyaITmbI1mbITmb46Ssgx3W8rIaPt/d91MbmdDXhtRU6YRoguRsjmRtRnx8sP+Mi1BJ1mY4OOelwCXAn5RSI7XWNbb4KKV6AjOxrn+WGKxfzLFznvHEu2RYyzDJ+dwMydkcydoMydkcyVrYYWc/SfRIz7mGIMekGZKzOZK1GZKzOU7KOtg7YDdEpBaC/Px8R+0YjZXkbI5kbUZpaamTGzMaFcnaDAfnPA+4CRgGrFNK/QXfdZSvQe5MYCRwPdAU2AC8EZWaxgg755l4t0t6zoVJzudmSM7mSNZmSM7mSNbCDjv7SWKCNeec1+slLi7OUM0aHzkmzZCczZGszZCczXFS1kHd/dJavxapisS6oqKiaFchJkjO5kjWZpSXy01nUyKVdWZmJs8//zzr1q2jffv2EdnGT4lT92mtdYlSagSwAhgEXFrp7c2Vfo8DNgLjtNYyekAE2TnPeOKlcS5ccj43Q3I2R7I2Q3I2R7IWdtjZTxI9bsq9UFpWjie+3mmWRS3kmDRDcjZHsjZDcjbHSVkHNbGLUuoO30+7SFVICCGEANi6dSuZmZns3r3byPZeffVV3nnnHSPbEj9NWut9QBrwG+BjoASrMS4OKMeai+5W4FKt9YFo1VOcFC/DWgohhBBC2JLgsRrkZGhLIYQQwoxgx416BigDXoxAXYQQQgi/rVu38vzzz3PRRRcZ6VG2aNEiTj/9dMaNGxfxbYmfLq11KbAAWKCUcgOpWA87HfS9JxzEI8NaCiGEEELYkpjga5wrKaN5lOsihBBCxIJgG+cOAPFa6+JIVCaWtWsnnRFNkJzNkazNSEhIiHYVYkasZX306FGaN6/5v+VFRUXEx8c3yNxwVbfzU8pZa10G5EW7HrHKznlGhrUMn5zPzZCczZGszZCczZGshR129pNEz8nGORE6OSbNkJzNkazNkJzNcVLWQQ1rCWwCWiilWkeiMrGssLAw2lWICZKzOZK1GWVljfM/TpmZmUyfPh2AjIwMlFIopXjggQf8ZYqLi3nxxRe58sor6dmzJxdeeCFTpkzhq6++ClhXeXk5r776KqNHj6ZPnz6cf/75DB8+nN///veUlJQAoJRiz549fPrpp/5tKaUChtSsKeslS5Zw4403MnDgQM4991wuueQS7r333hqH4iwvL2fevHkMGTKEnj17MmrUKFasWFGt3FNPPYVSim3btlV778iRI/Tq1Yvbbrut3gy9Xi9ZWVmMGzeO3r1706dPHyZNmsTGjRsDyu3evRulFJmZmbz77ruMGzeOXr168fjjjwPwwAMPoJQiPz+f6dOnk5aWxnnnnce+ffsAKC0tZf78+VxxxRX07NmTfv36MXXqVLTWQW2nQmPdp0XDs3OekWEtwyfnczMkZ3MkazMkZ3Mka2GHnf1EhrVsGHJMmiE5myNZmyE5m+OkrIN95P0vwHDgYeCOhq9O7Dp06BBJSUnRrkajJzmbI1mbUVZWhtvd+Cbr/vnPf05eXh5vvPEGU6ZM4ayzzgLgjDPOAKCkpITJkyfzxRdfMGbMGCZOnMjRo0dZunQp11xzDa+//jo9e/YEYO7cufzlL39h8ODBTJgwAbfbze7du1m/fj3FxcV4PB5mz57NjBkzaNmyJVOmTPHXIzU11f97TVkvXLiQ8847j0mTJpGSksLXX3/NW2+9xcaNG1m5ciUtW7b0l50xYwaLFi2ib9++XH/99Rw8eJDHHnuMDh06BKwzPT2dBQsWsHz5crp37x7w3po1azhx4gTp6en1ZnjfffexevVqhg8fzrhx4yguLmblypXceOONZGZmMnTo0IDyOTk5LF68mGuuuYYJEyZU6zV3ww030KpVK2677TaOHTtGs2bNALj33ntZs2YNAwYM4JprruHAgQMsWbKECRMmsGTJEnr06BHUdhrrPi0anp3zjMft4niRjDYaDjmfmyE5myNZmyE5myNZCzvs7Cfx7jhcrjjpORcmOSbNkJzNkazNkJzNcVLWQTXOaa3XKKXuBWYqpVoCc7TW/45M1YQQQsSq7t27c9555/HGG2+QlpZGv379At5fsmQJn376KQsWLGDgwIH+5ddeey2jRo1i9uzZLF68GLAagzp37syLLwZOl3rvvff6fx8zZgzPPfccrVq1YsyYMbbruXLlSn8jVYWhQ4dy/fXX89Zbb3HzzTcD8O2337J48WL69+/PwoUL/Y1Pl19+OVdddVXA57t06cK5557LypUruffeewMaqrKzs0lJSWHQoEF11mvt2rWsXLmSxx57jPHjx/uXZ2Rk8Ktf/YonnniCIUOGEBcX53/vm2++YcWKFXTu3LnGdXbt2pU5c+YELPvoo49Ys2YNI0eO5JlnnvGvb+TIkYwbN47HH3+crKysgM/Utx0hGpIMaymEEEJEh1JqfZAfOQEUAFuBtVrrfzZ8rURd4uLiSPS4peecEEIIYUhQjXNKqW99v5YC1wLXKqWOAweB2s7eXq213IETQggDyooK8ZacCHs9pYfyiG8R/gjGcZ5E3E0a/mmUFStWcNZZZ3HOOeeQn58f8F5aWhrZ2dkUFRXRpEkTmjdvzs6dO/n888+58MILG7QeFQ1z5eXlFBYWUlJSglKK5ORkvvzyS3+5devW4fV6ueGGGwIa28455xwGDBjAhg0bAtabnp7On/70Jz766CMuvfRSAHbt2sWmTZuYOHFivfOyrVixgqSkJIYNG1YtnyFDhpCZmcn27dvp1KmTf/mgQYPqbDCbPHlytWVr164FYMqUKQENfd27d2fw4MHk5OSQn58f0AOxvu0I0ZBkWEshhBAiai6r9LsXiKulXNX3vMAflFL/BCZprb+LTPVETRIT3BRLzzkhhBDCiGCHtexYw7Jmvp/aeIPcRkyqfONSRI7kbI5kbUZ8/Ml/xr3lZex8fgreE8eiWKNAcYnN6HjPq8S5GnaYwtzcXIqKirj44otrLfPjjz9y2mmncc899zB16lQmTpxImzZtuOiii7jssssYPnx4vY1clVXOusI///lPXnjhBf79739z4kRgo+ihQ4f8v+/atQvAPzxnZZ07d67WOHfllVcyc+ZMli9f7m+cW758OV6v11bPvtzcXAoLC0lLS6u1zMGDBwMa5zp27FjnOmt6f/fu3bhcrhob27p06UJOTg67d+8O+Pegvu3UlLMQNbFznvG4pedcuOR8bobkbI5kbYbkbI6Ds74BSMWaFqUF8AHwD2Cv7/3TgIHAYOBH4E9Y948uBMYBaUCOUqqP1vqw2ao3Pnb3kwSPW4a1DJODj8lGRXI2R7I2Q3I2x0lZB3sHbHBEaiHweDzRrkJMkJzNkazNqNxbKc7l5ozbX3Rcz7mGbpgD8Hq9dOvWjenTp9dapuJk26dPH9auXcuGDRv45JNP+OSTT1i1aoolJn4AACAASURBVBVz584lKyuLlJQUW9usnDXAl19+yeTJkznjjDOYNm0a7du3p0mTJsTFxXH33Xfj9Yb+bErLli0ZNGgQOTk5HD16lObNm7N8+XI6d+5Mr1696v281+slNTWVp59+utYyXbt2Dfhz06ZN61xnfe/bVd96quYsRG3snGc80nMubHI+N0NyNkeyNkNyNsfBWS8HPgOOAyO01p/WVEgp1RdYBvwG6Ke1fk4p9QdgHdYD4ncAjxupcSNmdz+RYS3D5+BjslGRnM2RrM2QnM1xUtbBzjn3YaQqEuv2799Pu3btol2NRk9yNkeyNqOkpCSg95e7SRI0wDCS8cnRf4qkrkaaM888kx9//JH+/fvjcrnqXVdSUhLDhw9n+PDhgDVn3WOPPcZbb73FTTfdZKs+VbNetWoVZWVlvPTSS3To0MG//NixYxw+HPhwb8X73377LWeccUbAe7m5uTVuLz09nZycHN577z06derEzp07mTZtmq26nnnmmWzfvp3evXtHdJLbDh06UF5eTm5uLt27dw94r+J7tW/fPqh1Vs1ZiNrYOc/Ey5xzYZPzuRmSszmStRmSszkOzvoR4Czgytoa5gC01p8ppW4C3sXqZfc7rfW3Sqm7sBrtxiCNc2Gzu58kJkjPuXA5+JhsVCRncyRrMyRnc5yUdf13NIUQQogoqJjPrfLwkBXGjh1LXl4er7zySo2fPXDggP/3qnOugTXXW9V1JyUlUVBQYLt+leeOq2zevHmUlwc2BgwZMoS4uDheeeUVyspO/md3y5YtfPzxxzWuZ9CgQbRs2ZLly5ezfPlyXC6XrSEtwcqnvLycP//5zzW+XzmfcAwbNgyA+fPnB/QU/Prrr1m/fj0XXHCBo4YLELHHGtZSbjAJIYQQUTAGOK61fq++gr4yx7GGs6ywBigFukSmeqImiR6Zc04IIYQwJeSJXZRS8cAFQAegmdZ6UYPVSgghRMzr2bMnLpeLF198kUOHDtGsWTPat29P7969ycjI4OOPP2b27Nls3LiR/v3707x5c/bu3cvGjRtJSEhg8eLFAFxxxRWcd9559OrVizZt2pCXl8fSpUvxeDxceeWV/u317t2bt956i2effZbOnTvjcrkYPHiwv5GwqmHDhvHqq69y8803M378eDweDx999BFaa1q2bBlQtnPnzkycOJHXX3+dX//611x++eUcPHiQJUuW0L17d7766qtq6/d4PIwaNYrXX3+d//73v6SlpdG2bVtb2Y0YMYJx48bx+uuvs2XLFgYPHkzLli3Zt28fmzdvZseOHaxbt87uX0WtBgwYwMiRI1m9ejWHDh1i8ODB5OXlkZWVRWJiIg899FDY2xAiHNawljL9sRBCCBEF7YDiIMqX+T4DgNa6WCl1GIjcMBCiGhnWUgghhDAnpMY5pdT9wH1A5buPiyq9nwJ8DCQAl2qt9yLqVNvNX9GwJGdzJGsz7Azp+FPVrl07nnzySV566SX++Mc/UlJSQnp6Or1798bj8TBv3jyysrJYvnw5mZmZALRp04aePXuSnp7uX8+NN97Ihx9+yOLFizly5AinnnoqvXv35pZbbgkYivHuu+/m0KFDZGVlcfjwYbxeL+vWrfPvy1WzvuCCC8jMzOSFF17gueeeIzExkbS0NF5//XWuu+66at/nwQcfpFWrVixdupTZs2fTsWNHHnnkEXbs2FFj4xxYPeAWL17MsWPHbPeaqzBjxgz69evH0qVLmTdvHiUlJbRu3ZoePXrYHh7Tjjlz5tCjRw+WLVvGzJkzadasGX379uXOO+9EKRX0+py6Tyul1gf5kRNAAbAVWKu1/mfD1yq22TnPxEvPubDJ+dwMydkcydoMydkcB2d9EDhNKdVfa72xroJKqf5Ac+D7SsvigZTKy0To7O4nMqxl+Bx8TDYqkrM5krUZkrM5Tso6rvIwVHYopZYAE3x//A6r51y81tpdpdxcrAl979Na1zyu1k+cUqoj8N26deuCnlNHCCFCtXXrVs4+++xoV0MIx6jvmNi9ezdDhw4F6KS13h7KNpRSlccq9QK1TYpY9b2KC61/ApO01t+Fsv3GwvS100f/3svClf/l5Ycuj/i2hBBCiMaiga6dXgZuADQwQmu9o5ZyZwDvA92AV7TWN/mW9wD+C3ystb4klDo0BqavnZ5e8i9OSUrg5rE9I74tIYQQorEI9dopqMfTlVITgGuAfUCa1roLUH0yH8sSrJtTw4LZRqzKy8uLdhViguRsjmRtRklJSbSrEDMkazMcnPMNwDSs3nAAHwCPAVN8P38EKnrX/QjcA9wNZAFFQBqQo5Q6xWCdGzU75xlrWMvyesuJ2sn53AzJ2RzJ2gzJ2RwHZ/0I1v2ibsBXSqnXlFI3K6VG+X5uVkq9BnwFKKxrrEcrfX6i7zXY0QtEDezuJ9JzLnwOPiYbFcnZHMnaDMnZHCdlHeywlpOxngC/S2v9ST1lPwfKgXNDqViscfDNyEZFcjZHsjYj2N7PInSStRkOznk58BlwHOvp709rKqSU6gsswxo9oJ/W+jml1B+AdUBH4A7gcSM1buTsnGesYS2lcS4ccj43Q3I2R7I2Q3I2x6lZa633KKWGAG8CXYHrfD9VxQHfAFdrrXdXWv4JcDOQE+m6xgK7+0mCx82RY8FMFSiqcuox2dhIzuZI1mZIzuY4KetgG+f6YDXOraivoNa6SCl1CGgdSsWEEEIIIRzkEeAs4MraGuYAtNafKaVuAt4FHgZ+p7X+Vil1F1aj3RhsNs4ppa4FbgV6AW5gG/AKMFdrHXSLk1JqBFaPvguBJsC3wN+AOVrrEzbXcTnW0FMAq7XWo4Kth0meeGmcE0IIIaJFa/2lUqoncC0wDuueUivf2weAL7Cuj7KqXotoreu97yQaXqLHzYFi6TknhBBCmBBs41xz4IjdGzhAAiBndRvcbnf9hUTYJGdzJGsz4uJqm/ZKNDTJ2gwH5zwGOK61fq++glrr95RSx7FuQv3Ot3gNUAp0sbMxpdRfgduwhsRcB5QAQ4HngaFKqV8G00CnlPodMAvruuzvWENvDsJqKByllBqqtT5WzzpaAAuoe849Y+ycZ2RYy/DJ+dwMydkcydoMydkcp2ettS4GXvX9iCixu58kJrgplmEtw+L0Y7KxkJzNkazNkJzNcVLWQc05B+QBpyilkusrqJTqCiQBu+srK6Bt27bRrkJMkJzNkazN8Hg80a5CzJCszXBwzu0I7oGjMt9nAP+NqcNYPdbqpJS6Cqthbh/QS2s9SmudjjUk1FYgHfit3YoopS4EZgLHgAFa62Fa66uxegL+P6A/8ISNVT0DnA7Ms7vtSLJznol3uygt81Je7tjhUh1PzudmSM7mSNZmSM7mSNbCDrv7SUK8zDkXLjkmzZCczZGszZCczXFS1sE2zn3ke73aRtn7sJ6s/iDIbcSkI0eORLsKMUFyNkeyNqOsTP7jZIpkbYaDcz4INFdK9a+voK9Mc6zeaRXL4oEUrCGc6jPd93q/1vr/KhZqrfdjDXMJ8IBSyu513ANYPd1mVZ4zWGt9FLgBa47g25RSKbWtQCk10lf2Waw5YKLOznnGE29FJL3nQifnczMkZ3MkazMkZ3Mka2GH3f0kMcHNCRnWMixyTJohOZsjWZshOZvjpKyDHdYyE/gV8LhS6lOt9X+rFlBKJWLNy3IT1s2e58OuZQw4cuQIycn1dkgUYZKczZGszSgrK3NUd+zGTLI2w8E5v4/VOPWKUmqE1npHTYWUUmdgzQvnxRrKskI3rIeiavxcpc+3By4AioE3q76vtf5QKbUHqwdbf+DjetaXAIz0/XFJDev7Vin1T2AAcAWQVcM6UoCXgG+Ah4DxdW3TFDvnmcqNcwkeR+5XjifnczMkZ3MkazMkZ3OcnrXvOmIUcC7QEqhrmASv1nqykYrFGLv7SaJHes6Fy+nHZGMhOZsjWZshOZvjpKyDapzTWn+klHoKq1fcJ0qpHCAZQCn1Z+AM4DKsCy6AR7TWWxquukIIIYQQUfEI1rxz3YCvlFJvARuA733vnwZcAlwFNMPqNfdopc9P9L2ur2c7fXyvW7TWx2sp8xlW41wf6mmcA5SvPvla69w61jfAt75qjXPAc1hDdA7WWh9XStWzSeeId1uNcyWl0nNOCCGEME0pdQcwg5PDetc3Z60XkMa5KJI554QQQghzgu05h9b6fqXUXuBPwOhKb93JyQutQmC61lp6zQkhhBDiJ09rvUcpNQSrN1tX4DrfT1VxWD3MrtZaV5539xPgZiCnnk118r3W1cNuZ5Wydta3s44yta5PKTUayABe1Fp/aGN7jlLRc04a54QQQgizlFITsIbDBsjDGoVgD1AUtUqJeiV6ZFhLIYQQwpSgG+cAtNbPKaVexXo6PA3raXEXsB/4J/Cm1jq/oSoZC1q1ahXtKsQEydkcydqM+PiQ/hkXIZCszXByzlrrL5VSPYFrgXFYPc0q/rE7AHwBLAOytNYnqnx2hc3NNPe9FtZR5qjv1c44DCGvTynVEpgH7AJ+Z2Nbtv3www+4XFbDWcX54sCBk9PxJScnk5yczP79+/3zEHo8Hlq3bk1BQQHHjh2jrKyMvXv30rZtW0pKSsjPP3np2aJFC5KSkjhw4AcA9uz9Hld5CqmpqeTn51NUdPK+YLt27SgsLOTQoUP+ZampqXg8Hvbv3+9f1qxZM1JSUsjLy6OkpAQAt9tN27ZtOXLkSMC49aF+pwp1fae9e/f6lzVp0iTi36ki58b0nZz491SRc2P6Tk79eyorK6OwsLBRfScn/j2VlZX5hwxqLN/JqX9PCQkJAA36nSrXOQx3+l7fBH6ttZZGuSiy+/9zGdYyfHIvxAzJ2RzJ2gzJ2RwnZR3yHTCt9SFgoe9HCCGEEKLR01oXA6/6fhq7TKwHsK7QWjfojMlt2rShXbt2Acuq/hmsG5tVpaSkkJKSQnFxsf+GpNvtrvHzHU5vB3xB6qmtSU212h5TU1OrlUtKSiIpKana8prW2bp162rLKm5s2vl8Xd+pstq+U03LIvmdKudc2+d/at/JzudNfyc7Of/UvpOdz0fjO1XOurF8p/rWGY3vVDnnxvKdKnPSdyouLgYa9juVlzdIj/NzsYapvF0a5n46EhOk55wQQghhiivaFRCWyk+uiciRnM2RrM0oLS2NdhVihmRthuTs78VW/U7cSRW94ew0mIW0PqXUGKx58hZprdfY2I5xds4zFcNalpbJsJahkvO5GZKzOZK1GZKzOQ7OuhQ4pLXOi3ZFhP39JDHBTVm5V66dwuDgY7JRkZzNkazNkJzNcVLW0jgnhBAipmRmZqKUYvfuk9OBvfPOOyil+OSTT2ytY8iQIUyaNClSVRSxbbvv9cw6ynSoUtbO+s4Icn3pvteeSqm/V/4BHvC9d3Gl5c1xILfbhStO5pwTQgghomAzkKyUOiXaFRH2JXjcABTL0JZCCCFExDl3YhchhBBCCIdRSqUAo7CGamoJeOoo7tVaTw5yE1/4Xs9RSjXVWh+voUzfKmXrsg04DqQqpTprrXNrKHNRHevrU8e6U4FBvt8de00Z73ZJ45wQQghh3p+xrhOmAjOiXBdhU6Kvce5EcRnNmtR1mSuEEEKIcDn2RkqsqWkceNHwJGdzJGsz3G53tKvQKIwZM4Yrr7wSj6f2/4BK1mY4OWel1B1YN5ea+BbF1fMRLxBU45zWepdSahNwPnA1sKhKHQYB7YF9wD9trK9YKbUGGIc1TOVjVdZ3FnAxUAysrvS564Hra1qnUup64BVgtdZ6lL1v1vDsnmc88S5KpXEuZHI+N0NyNkeyNkNyNsepWWutVyqlHgH+qJTyAs/V8tCRMMDufpKY4Guck55zIXPqMdnYSM7mSNZmSM7mOClraZxzCCftFI2Z5GyOZG2Gkxsyfkrcbne9WUYr66NHj9K8ec0jBhYVFREfH098fPin87q2Y5JT92ml1ATgWd8f84D3gT1AUQQ2NwN4E5illPpYa/2Nrw5tgBd8ZWZqrf0tTkqp24HbgU+11hlV1jcTa5jK+5VS72mtP/V9pjmwEGuY8xe01gUR+C4RY/c8Ex/vokTmTQmZnM/NkJzNkazNkJzNcWrWSqn1vl8LgSeAh5VSX1H3nLlerfXQiFcuBtlunPNI41y4nHpMNjaSszmStRmSszlOylrmnHOI/fv3R7sKMUFyNkeyNqOkpCTaVYiIDz/8EKUUixYtqvH98ePH079/f//3//LLL3nggQcYPnw4vXv3pk+fPkyYMIG1a9fa2l5tc859//333HnnnVxwwQWcf/75TJkyhZ07dwb1XbxeL1lZWYwbN85ft0mTJrFx48aAcrt370YpRWZmJu+++y7jxo2jV69ePP744wA88MADKKXIz89n+vTppKWlcd5557Fv3z4ASktLmT9/PldccQU9e/akX79+TJ06Fa11UNuJNgfv03f6Xt8EztRaZ2itp2ut/1jXTygb0lq/BcwFfgb8Rym1Uin1DvB/QA8gG3i+ysdaAYoa5pbTWn+GNU9cM+BjpdT/KqWWArlYw019AjwYSl2jye55xuN2UVIqN5hCJedzMyRncyRrMyRncxyc9WW+nxZYow00BS6otLy2HxEBtq+b4l3ExVnDWorQOPiYbFQkZ3MkazMkZ3OclLX0nHOIsjK58DFBcjZHsjbD6/VGuwoRcckll9C6dWuys7PJyAjsBLR9+3Y2b97MpEmT/MNQrl27lm+//ZYRI0Zw+umnU1BQwLJly7j99tuZM2cOo0ePDroOhw8fZuLEiezbt48JEybQsWNHNm3aREZGBkVF9jtL3XfffaxevZrhw4czbtw4iouLWblyJTfeeCOZmZkMHRr4cHBOTg6LFy/mmmuuYcKECdV6s91www20atWK2267jWPHjtGsWTMA7r33XtasWcOAAQO45pprOHDgAEuWLGHChAksWbKEHj16BLWdaHHwPn0u1jCVt2utI9FbLoDW+jal1AaseVoGAW6s+eMWAnMr95qzub7ZSqkvgWlYc9Y1Ab4F/gLM0VqfaMj6m2D3POOJd1Na6tj9yvHkfG6G5GyOZG2G5GyOg7O+IdoVECfZ3U/i4uJI8Lil51wYHHxMNiqSszmStRmSszlOyloa54QQQjiO2+1m9OjRLFy4kG+++YYuXbr438vOzgYgPT3dv+zWW29l2rRpAeuYNGkSY8eOZe7cuSE1zi1YsIA9e/bw5JNPctVVV1FcXExGRgZPPPFErT36qlq7di0rV67kscceY/z48f7lGRkZ/OpXv+KJJ55gyJAhxMWdnLrsm2++YcWKFXTu3LnGdXbt2pU5c+YELPvoo49Ys2YNI0eO5JlnnvGvb+TIkYwbN47HH3+crKysgM/Utx1RTSlwSGudZ2qDWussIKveglbZR4FH6ynzHvBeA9TrVeDVcNdjSnx8HCUOuvgWQgghYoHW+rVo10GEJtHjlp5zQgghhAFBDWuplFqolPpzEOVnK6VeDr5asaei94eILMnZHMnajMqNOgCFxcfIP14Q9o8+8G2DrKew+FjI362i8a2iMQ6sXlUrVqygW7dunHPOOf7lFb3HAI4fP86PP/7I8ePH6d+/P7m5uRw9ejTo7efk5NCqVSvGjh0LnMz65ptvtr2OFStWkJSUxLBhw8jPz/f/HD58mCFDhrBnzx62b98e8JlBgwbV2WA2efLkassqhu+cMmVKwD7RvXt3Bg8ezL/+9S/y8/OD2k60VN2nHWQzkKyUOiXaFREWu+cZj9tNSanMORcqOZ+bITmbI1mbITmbI1kLO4LZTxIT3BRLz7mQyTFphuRsjmRthuRsjpOyDrbn3PXAPuAem+Wvxpr7pPqdRBGgdevW0a5CTJCczZGszah8QikrL+O2VQ9yvCTiI+7Z1tTThIVj5+B2uYP+bEUD3MqVK7nnnntwuVx89tln7Nmzh/vuuy+g7MGDB3n22WdZt24dBw8erLauw4cPBz1s465du+jZsydut1X3iqzbtGnDKafYa5/Jzc2lsLCQtLS0WsscPHiQTp06+f/csWPHOtdZ0/u7d+/G5XLV2NjWpUsXcnJy2L17N6mpqba3Ey1Oukiq4s9Yw0tOBWZEuS4C++cZT7yLUmmcC5mcz82QnM2RrM2QnM2RrIUdwewnCfEyrGU45Jg0Q3I2R7I2Q3I2x0lZR3pYyzis+VlEPQoKCkhJSYl2NRo9ydkcydqM0tJS4uOtf8rdLjcvjHqCE2XFYa83rzCf1kmp9ResR6I7IaSGuQpjxozhySefZOPGjaSlpZGdnY3b7eYXv/iFv4zX6+XGG28kNzeXjIwMzj33XJKTk3G73bz99tusWrWK8vLwb8xXztour9dLamoqTz/9dK1lunbtGvDnpk2b1rnO+t63q6HW09BCydkErfVKpdQjwB+VUl7gOa318WjXK5bZPc/Ex7soKZPGuVDJ+dwMydkcydoMydkcJ2StlLrU9+sxrfXnVZYFRWv9/xqsYsIvmP0kMUGGtQyHE47JWCA5myNZmyE5m+OkrCN290sp5QLaAIWR2kZjcuzYMcfsFI2Z5GyOZG1G1UanpIRmJNGsltL2pTZ1xt/d6NGjeeqpp8jOzub888/n/fffJy0tjTZt2vjLaK3Ztm0bU6dO5Y477gj4/Jtvvhnytjt06MCOHTsoKyvD7Xb7s/7hhx84fPiwrXWceeaZbN++nd69e5OUlBRyXezUtby8nNzcXLp37x7wXm5uLgDt27eP2PYbUkM0pEaCUmq979dC4AngYaXUV8CROj7m1VoPjXjlYpTd84zH7ZJhLcMg53MzJGdzJGszJGdzHJL137EezNZAjyrLguEl8g+Rx6Rg9pNEj/ScC4dDjslGT3I2R7I2Q3I2x0lZ13nR45tXpWpN3UqpDli94moS5/tMBtAE+He4lRRCCBGbUlNTGThwIGvXrqVv374cPXrUPxddBZfLmj7V6w38v//XX3/tn4stFEOHDmX+/PlkZ2dz1VVX+Ze/9NJLttcxduxYPvjgA/785z/z8MMPV3v/wIEDtGrVKuQ6Vhg2bBhZWVnMnz+fp59+2j9v29dff8369eu54IILAoa0FCG5rMqfmwIX1PMZGT3AAeJlWEshhBAi0nZiXffsrWGZ+IlJ9Micc0IIIYQJ9T2RdDfwSJVlrYDtQWzD/l1MIYQQoor09HTWr1/PzJkzSU5OZtiwYQHvd+7cma5du7JgwQKKioro1KkT3333HW+88QbdunVjy5YtIW33pptuYtWqVTz88MNs2bKFTp068a9//YvNmzfTsmVLW+sYMWIE48aN4/XXX2fLli0MHjyYli1bsm/fPjZv3syOHTtYt25dSPWrbMCAAYwcOZLVq1dz6NAhBg8eTF5eHllZWSQmJvLQQw+FvQ3BDdGugAiNR4a1FEIIISJKa93RzjLx0yDDWgohhBBm1Nc4F0dgDzkvtfeYq1zmMLAFWKC1fjXk2sWQtm3bRrsKMUFyNkeyNsPj8US7ChF32WWXkZKSQkFBAVdffTWJiYkB77vdbubNm8esWbNYtmwZx48fp2vXrsyaNYtt27aF3DjXokULlixZwsyZM8nOzgbgoosuYtGiRVx//fW21zNjxgz69evH0qVLmTdvHiUlJbRu3ZoePXowbdq0kOpWkzlz5tCjRw+WLVvGzJkzadasGX379uXOO+9EKdVg24k0p+7TWuvXol0HEcjueUaGtQyPnM/NkJzNkazNkJzNkayFHcHsJzKsZXjkmDRDcjZHsjZDcjbHSVnHVR0GrC5KqXJgn9a6XeSq9NOhlOoIfLdu3bqw5/IpKiqiSZMmDVIvUTvJ2RzJOnK2bt3K2WefDVjzc1UM6ygiS7I2I5ScKx8TNdm9ezdDhw4F6KS13h5WBUVYonHt9MzfNtEsMZ5bxvUKa3uxSs7nZkjO5kjWZkjO5kQia7l2co6GunYKZj/5yxtfEB/v4rareoe8vVgm//6ZITmbI1mbITmb46Rrp2DvMi4Clgb5GWFDfn5+tKsQEyRncyRrM0pLS6NdhZghWZshOQu77J5nZFjL8Mj53AzJ2RzJ2gzJ2RzJWtgRzH6S4JFhLcMhx6QZkrM5krUZkrM5Tsq6vmEtA2itr49QPYQQQgghHEEpdanv12Na68+rLAuK1vr/NVjFREg8bhfHTkijrxBCCBENSqkRwC+Bc4GWQF1jmHu11p2NVEzUKtHjpuDoiWhXQwghhGj0gmqcq49SaiQwCEgE3tdav9eQ6xdCCCGEMODvWHPoaqBHlWXB8NLA11oiePHxLkoLpeecEEIIYZJSygO8AYzxLYqz8bFgr7VEBCQmSM85IYQQwoSgbhgppX4FPAus1lrfXOW9F4HKy+5QSs3TWt8WfjUbvxYtWkS7CjFBcjZHsjbD7XZHuwoxQ7I2wyE578S6ObS3hmXCIeyeZ2RYy/DI+dwMydkcydoMydkcB2d9PzAW6/ppNZAN7AGKolmpWBXMfpLocVNcIo1zoXLwMdmoSM7mSNZmSM7mOCnrYJ/mHgu0Bd6tvNA31NNvfH/cCBwHLgNuUUqt0loHlBfVJSUlRbsKMUFyNkeyNsMhDRkxQbI2wwk5a6072lkmosvuecbjdlFSKo1zoZLzuRmSszmStRmSszkOznoiVsPcdK317GhXJtYFs58keNyckMa5kDn4mGxUJGdzJGszJGdznJS1K8jy5/teq86fcqPvdb7WOk1rPRR4GGvYgpvCqF/M2Lt3b/2FRNgkZ3MkazOKi4ujXYWYIVmbITkLu+yeZ+LjXZRK41zI5HxuhuRsjmRthuRsjoOz7giUA5lRrocguP1EhrUMj4OPyUZFcjZHsjZDcjbHSVkH23OuNVCktT5YZfnlWE9EPVtp2V+Bx4GLQq+eEEIIIYQQoZNhLYUQQoioKAAStdbHTW1QKXUtcCvQC3AD24BXgLlaa9sXA0qpDsAo4EKgnF2yPAAAIABJREFUL9YcxG7gPq31nDo+9yjwhzpWfUJr3cRuPaIlUXrOCSGEEEYE2ziXDByrvEAp1RH4GbBHa72tYrnW+pBSqgCrQU8IIYQQQgjjPG7pOSeEEEJEwYfA1UqpDlrrXZHemFLqr8BtWHParQNKgKHA88BQpdQvg2iguwp4Jozq/BvYXMPykjDWaUxigsw5J4QQQpgQbONcPtBaKZWqtc73Lfu573VDDeU9wNFQKxdLmjRx/MNTjYLkbI5kbYbLFezoxCJUkrUZTs9ZKTUC+CVwLtAS61qnNl6tdWcjFYtBds8z8fEy51w45HxuhuRsjmRthuRsjoOzfhwYDcwCro3khpRSV2E1zO0DLtVa/59veVvgAyAd+C3wnM1Vfucr+y/gc2A6MCmIKmVrrR8NonzEBbOfJHhkWMtwOPiYbFQkZ3MkazMkZ3OclHWwjXObgOHA3cDDSqmmwFSsIS1zKhdUSv0MSAJ2NEA9G73U1NRoVyEmSM7mSNZmxMcH+8+4CJVkbYZTc1ZKeYA3gDG+RXE2PuaNXI2E3fOMNayl3GAKlZzPzZCczZGszZCczXFq1lrr/yqlxgJvKKXWYDXSfaa1LozA5qb7Xu+vaJjz1WG/UupW4O/AA0qpTDu957TWy4HlFX9WSv3kn/IJZj+RYS3D49RjsrGRnM2RrM2QnM1xUtbB3gGbB4wAfq+UGge0ANph9ahbWqXsYN/rl2HVMEbk5+c7asdorCRncyRrM0pLSx3bmNHYSNZmODjn+4GxWA1uq4FsYA/W0EkiCuyeZzxuN6Wl0k4aKjmfmyE5myNZmyE5m+PUrJVSlVt3Lvf9oJSq62NerXVQF4JKqfbABUAx8GbV97XWHyql9gCnA/2Bj4NZf2MRzH6SmOCmpLScsnIvbped59FEZU49JhsbydkcydoMydkcJ2Ud1EWP1nq5UmoG1g2qs32L84FJWusjVYr/2veag6hXUZHc2zNBcjZHsjajvPwn/xDnT4ZkbYaDc56I1TA3XWs9O9qVEfbPM/HxcZSUytPfoZLzuRmSszmStRmSszkOzjqUVp1QPtPH97pFa328ljKfYTXO9cFM49z5SqlZWEOg5wOfAKu11sUGtl2jYPaTRI8bgJKSMtyJjnxoztEcfEw2KpKzOZK1GZKzOU7KOuiJXbTWDwKdgfHASKCL1npN5TK+oZ/exRr+ckUD1FMIIYRoEJmZmSil2L17t3/ZO++8g1KKTz75xNY6hgwZwqRJwUw7EZ4HHnigvqeMReR1BMqBzCjXQwTJE++mtEx6zgkhhBCGdQrxJ5TtQN1TquysUjbSRgO/A27Gerj9HSBXKTXI0PbDUtE4J0NbCiGEEJEV0iMwWusd1HHho7UuAf4SaqWEEEIIIRymAEis44ls4VAet0t6zgkhhBCG+e4bmdDc91rXXHZHfa/JEa5LLtb8d2uA74AEoCfwB2AQ8K5S6mKtddDTv/zwww+4XNbz9a1atQLgwIED/veTk5NJTk5m//79lPnm2vV4PLRu3ZqCggKOHDnC3r17AWjbti0lJSXk5+f7P9+iRQuSkpLYu3cvR46VALA/7yAtmrcjPz8/oJdBu3btKCws5NChQ/5lqampeDwe9u/f71/WrFkzUlJSyMvLo6TEWqfb7aZt27YcOXKEI0dODsAVync6duyYv2x936lCkyZNSE1Njeh3qsi6MX0nJ/49VeTcmL6TU/+ejhw5QmFhYaP6Tk78e6p4vzF9J6f+PVV8piG/U+U6B0P6pztEu3btol2FmCA5myNZm5GQkBDtKjQKY8aM4corr8Tj8dRaRrI2w8E5fwhcrZTqoLXeFe3KCPvnmfh4FyWljh0u1fHkfG6G5GyOZG2G5GyOZO0cWuvFNSz+APhAKfUWcBXwJDAq2HW3adOm2t91TX/3bdu2rbYsJSWFlJSUgGVut7vGz7dr146iE6XAZpo2s9oya5qXJykpiaSkpBo/X1Xr1q2rLau4sWnn8w3xnaqS7yTfyc7n5TvJd6pMvpN8p6oqf6dQp2gJelhLAKVUJ6XUX5RSW5VSR5VSpVXeT1FKPaKUetg3xKWoR2FhXQ95iYYiOZsjWZtR8YSGCI/b7SYxMdH/NGpNJOtAXq+3zuP86NGjtb5Xl6o517cdgx4HioBZ0a6IsNjdLzzxLkrLpHEuVA45/ho9ydkcydoMydkcydrfK676nbiTKnrXHamjTKQ95nv9eTTukwWznyTIsJZhkWPSDMnZHMnaDMnZHCdlHXTPOaVUOrAIaMbJyXoDJvLQWhcopYYAA4GvgLfDrGejd+jQoRpbdUXDkpzNkazNKCsrw+12R7saDe7DDz/kN7/5DQ8++CAZGRnV3h8/fjw7duzgH//4Bx6Phy+//JKsrCy++OIL9u3bh8vlQinF5MmT+fnPf17v9t555x2mT5/OokWL6Nevn3/5999/z8yZM9mwYQNer5eLLrqI3//+97a/R3l5OfPmzWPDhg1s376dQ4cO0apVKwYNGsRdd91Fy5YtA8qfOHGCZ599lpUrV3L48GG6devGXXfdVW29d911Fzk5OfzjH/+oto5vv/2WkSNHkpGRwYMPPlhn/YqLi1m4cCErV65k586dJCYmcuGFF3LHHXfQo0cPf7lPPvmEjIwMZsyYwfHjx1myZAk7d+7klltu4be//S2TJk1iz549vPbaazz11FNs3LiRQ4cOobUG4NixY8ydO5c1a9awb98+WrRowYABA7jzzjs5/fTTbW8nmrTW/1VKjQXeUEqtwWqk+0xr7Zyruhhj9zzjiXdR7oWysnLc7pCeS4tpcj43Q3I2R7I2Q3I2xwlZK6Ue8f16QGv9QpVlQdFaP1Z/qQDbfa9n1lGmQ5Wy0bDN95oAtAK+N7nxYPYTlysOT7yLE8XSOBcKJxyTsUByNkeyNkNyNsdJWQfVOKeU6g4sAZoA83y/vwOcWkPxl4BLsbrrS+OcEEII2y655BJat25NdnZ2tca57du3s3nzZiZNmuQfhnLt2rV8++23jBgxgtNPP52CggKWLVvG7bffzpw5cxg9enTQdTh8+DATJ05k3759TJgwgY4dO7Jp0yYyMjICxrSuS0lJCS+//DKXX345Q4cOpWnTpvznP//h7bffZtOmTbz99tsBwzjec8895OTkMHjwYAYOHMjOnTv57W9/S/v27QPWm56ezpo1a1i9ejXXXXddwHvLly/3l6mvbpMnT+aLL75gzJgxTJw4kaNHj7J06VKuueYaXn/9dXr27Bnwmddee42CggKuvvpqWrduzc9+9jP/e4WFhVx33XWcf/753HXXXf7xtiu2s2nTJoYPH84NN9zAjh07+Nvf/sZHH33E22+/HbCe+rYTLUqpyncnLvf9oJSq62NerbUMIR5l8b4GuZJSaZwTQgghIuRRrIe2NfBClWV2xfnKB9s494Xv9RylVNNa5gfuW6VsNFS+bxbaEBMGJXrc0nNOCCGEiLBgbxjdh9Uw94zWehpUu1lVWY7v9aIQ6yaEECJGud1uRo8ezcKFC/nmm2/o0qWL/73s7GwgsPHp1ltvZdq0aQHrmDRpEmPHjmXu3LkhNc4tWLCAPXv28OSTT3LVVVdRXFxMRkYGTzzxBIsWLbK1joSEBDZs2ECTJk38y6655hr69OnDQw89RE5ODldccQUAGzZsICcnh/T0dGbOnOkv37dvX6ZOnRqw3sqNl5Ub57xeLytWrKBbt24BPd9qsmTJEj799FMWLFjAwIED/cuvvfZaRo0axezZs1m8OHDajO+//541a9Zw6qnVn8kpKChgypQp3H333QHLly1bxqZNm5g8eTK/+93v/MvT0tK45ZZbePrpp3nqqadsbyeK4uov0iCfEQ3ME281yMnQlkIIIUTELMJqWPu+hmURpbXepZTaBJwPXO3brp9SahDQHtgH/DPS9anDr3yvWmsdzeE1bUlMcEvPOSGEECLCgm2cG4p1cTW7voJa6/1KqUJODh8g6lDTBIai4UnO5kjWZsTHB/4zXnq0kLITJ8Je74kf8khsU31i1GC5ExOJbx5aV/H09HQWLlxIdnY29957LxDY+HTOOef4yzZr1sz/+/HjxykqKsLr9dK/f3/+53/+h6NHj9K8efNq26hLTk4OrVq1YuzYscDJrG+++WbbjXNxcXH+hrmysjIKCwspLS2lf//+AHz55Zf+xrmcHOuZlsmTJwesY9iwYXTq1InvvvvOv6xy42Vubi6dO3cGrGEh9+7dy/33319v3VasWMFZZ53FOeec4+/lViEtLY3s7GyKiooCGhbHjBlTZ4NZ1bqD1avR5XJxyy23BCy/7LLLOPvss1m3bh3l5eUB8/3Vt50o6RTtCohAds8zFY1zJaXSOBcKOZ+bITmbI1mbITmb44SstdbX21kWQTOAN4FZSqmPtdbfACil2nCyJ99MrbX/YkApdTtwO/Cp1rr6OPpBUkqdAVwCvK21PlFpeRxwna+OAM+Eu61QBLufJHjcFEvPuZA44ZiMBZKzOZK1GZKzOU7KOtjGuZ8BR7TW+22WP8HJiXdFHSqGZhORJTmbI1mbERd3smOOt6yMz2+eQtmxY1GsUSB3s2b0e/1V4kKYF6+iAW7lypXcc889uFwuPvvsM/bs2cN9990XUPbgwYM8++yzrFu3joMHD1Zb1+HDh4NunNu1axc9e/b0z+lXkXWbNm045ZRTbK/n3Xff5ZVXXmHr1q2UlJQEvHfo0KGA7blcLjp27FhtHZ07dw5onAMYO3YsCxcuZPny5dxzzz2ANaRlRcNdfXJzcykqKuLiiy+utcyPP/7Iaaed5v9zTXWrkJqaWmMuu3fvpk2bNrRo0aLae126dGHr1q38+OOPAY1xdW0nWrTWO6JdBxHI7nmm8rCWInhyPjdDcjZHsjZDcjZHsgat9VtKqbnArcB/lFI5QAnWA+anANnA81U+1gpQWD3qAiilTgOWVVrU2ff6W6XULystT9daV/QWTMWa+uVFX0++vUAycA4nH/J6Xms9L7RvGZ5g9xMZ1jJ0ckyaITmbI1mbITmb46Ssg22cKwROUUq5tdZ1nqWVUslACvBDqJWLJfv376ddu3bRrkajJzmbI1mbUVJS4p+zLM7t5sKXXnRcz7lQGuYqjBkzhieffJKNGzf6e3O53W5+8Ytf+Mt4vV5uvPFGcnNzycjI4NxzzyU5ORm3283bb7/NqlWrKC8P/6Z85azt+t///V/uvvtuevXqxe9//3tOO+00EhMTKSsr46abbsLrDX2kH6UUZ599NitXruTuu++mqKiI999/nwEDBtC6df1/d16vl27dujF9+vRay1R9mqhp06a1lq3rvWA15LpE42X3PCPDWoZHzudmSM7mSNZmSM7mSNYWrfVtSqkNwFRgEOAGtgELgbmVe83ZkAj0q2H5Gb6fyuUq7AKewprfrgvWFC8urMa/N4D5Wuv1QdShQQW7n8iwlqGTY9IMydkcydoMydkcJ2UdbOPcFmAAcAHwaT1lx2NdiPwrhHoJIYQIQXzzpJCHkaws8VRndPEePXo0Tz31FNnZ2Zx//vm8//77pKWl0aZNG38ZrTXbtm1j6tSp3HHHHQGff/PNN0PedocOHdixYwdlZWX+3nMAP/zwA4cPH7a1juXLl5OYmMiiRYsCGpxyc3Nr3F55eTnbt2+na9euAe/VVB6s3nMzZsxg48aN5OXlUVhYGDAXX13OPPNMfvzxR/r37x8wpGRD69ChA//4xz84fPhwtZ51ubm5NG/enJYtW0Zs+0JIzzkhhBAiNmits4Asm2UfBR6t5b3tBDl3sNb6IPC7egv+RCTKsJZCCCFExAXbOLcUawztPymlRtb25JFSqicwE2t+uiXhVVEIIUSsSk1NZeDAgaxdu5a+ffty9OjRao1PFQ1LVXuhff3116xduzbkbQ8dOpT58+eTnZ3NVVdd5V/+0ksv2V6H2+0mLi4uoOee1+tl7ty5NW7vb3/7Gy+//DIzZ870L8/Jyak2pGWFisbL5cuXk5eXR3JyMkOHDrVVt7FjxzJ79mxeeeWVGueKO3DgAK1atbK1rroMGzaMDz/8kPnz5/vnDgT48MMP+eqrr/jFL34R0cbBUCilHvH9ekBr/UKVZUHRWj/WYBUTIZHGOSGEECJ6lFJ9gSlYD3q3A+p6ktCrtQ72PpWIgAQZ1lIIIYSIuGAveuYBNwHDgHVKqb9UrMPXIHcmMBK4HmgKbMDqvh8ypdS1WOOG9+LksASvEPywBDWt+zdY3wngr1rr28NZXziaNWsWrU3HFMnZHMnaDKc1akRCeno669evZ+bMmSQnJzNs2LCA9zt37kzXrl1ZsGABRUVFdOrUie+++4433niDbt26sWXLlpC2e9NNN7Fq1SoefvhhtmzZwllnncXnn3/O5s2bbff0Gj58OO+//z6//vWvGTt2LKWlpeTk5HD8+PFqZQcOHMjgwYNZtmwZBQUFDBw4kF27dvm/x9dff13tM6eeeioDBw7k/fff58SJE/zyl78kMTGxWrmaZGRk8PHHHzN79mw2btxI//79ad68OXv37mXjxo0kJCSwePFiW+uqS3p6OsuWLeOll15iz549XHjhhezcuZOsrCxatWrlny/PYR7FesBIAy9UWWZXnK+8NM5FiN3zjMsVR7w7Toa1DJGcz82QnM2RrM2QnM1xctZKqfuBJ7BGVbIjqN5qwr5g9xMZ1jJ0Tj4mGxPJ2RzJ2gzJ2RwnZR1U45zWukQpNQJYgTWG96WV3t5c6fc4YCMwTmsd8oQ6Sqm/ArcBRcA6Tk7o+zwwVCn1y1Ab6JRSZwJzsG6cRf0CMCUlJdpViAmSszmStRnx8Y3/wdLLLruMlJQUCgoKuPrqq6s1PrndbubNm8esWbNYtmwZx48fp2vXrsyaNYtt27aF3DjXokULlixZwsyZM8nOzgbgoosuYtGiRVx//fW21nHllVdSWFjIq6++yqxZs2jRogWDBw9m2rRp9OtXfRqLZ599lmeffZaVK1fy8ccf061bNzIzM1m1alWNjXNgNX598MEHgDVHn10ej4d58+aRlZXF8uXLyczMBKBNmzb07NnT9vCYdrbz8ssvM3fuXN59913Wrl1LcnIyI0aM4K677uK0005rkO00sEVY1wff17BMOEQw55l4t0t6zoVIzudmSM7mSNZmSM7mODVrpdRgYAZQBjwCrAI2AXnAxUBbrAe/f+v7yGTgS/M1jQ3B7ieJ0nMuZE49JhsbydkcydoMydkcJ2UdV3UYMDuUUvFYveN+jTXZbYLvrTLgc+BV4GWtdWmoFVNKXQW8hTV57qVa6//zLW8LfACcDdyltX4uhHXHAWuB/r5t/JoQes4ppToC361bt4727dsHW40AeXl5tG7dOqx1iPpJzuZI1pGzdetWzj77bABKSkrweDxRrlFskKzNCCXnysdETXbv3l0x3Gcn3xwiIkqide107cPvcu91F3K+alN/YRFAzudmSM7mSNZmSM7mRCLrhrh2Ukq9A4wBHtZaP+lbVg7s01q3q1TuNODvQEugj9Z6T3i1b1wa6top2P3khbf/TWlpOXeM7xPyNmOV/PtnhuRsjmRthuRsjpOunUIaD01rXaq1XqC1Hog1Xnhb4DSgqdb6Yq31vHAa5nym+17vr2iY8217P9YwlwAP/H/27j88suq+8/y7VCpJrVJJ1dVSC9QNBgP5JnHChBj/ihPjmGRtvCTPEEKSsTOJSSaTQGCeiTNrMM4PZsYs4CU7jzMYZtkJHtsDyQ4kIdmwJrGxjZ1ACAlOwmBzwg9joAVqqdVSV0utVunH/nGr1OpCP26Vqr51u+rzep5+Ct0699Y5H53TXdSpc66Z1dOGXyFagfdR4MWdVLJRSqVSq6vQEZSzH2Xto54vWEh9lLUP5Sxx1fLvTKa7iyWtnKuL/j33oZz9KGsfytlPgrOubBNxV9Xxkz7DCSG8SrRj0jBwg0O9OlKt/aQ3o20t65XgMdlWlLMfZe1DOftJUtY73g8thLBMtC1Bw5jZfuDNwCJw3wav+YiZHQD2Ea1+e7SGa58NfILofni3A7/diDqLiIiISDJpW0sRERF3w8BcCGFq3bElYKMbvXwJOAZc4lEx2Z62tRQREWm+mladmdkLZvbXNZT/mpk9X3u1qKybfzqEcGyTMk9UlY1TnxRwN9Gk5C/u5H54jZZOp1tdhY6gnP0oax+pVMtvmdkxlLUP5Sxx1fLvTKa7i9KyJufqoX/PfShnP8rah3L2k+CsDxPd+qT6WNbMhtYfLH82s0K0I5M0Qa39pLdHK+fqleAx2VaUsx9l7UM5+0lS1rWunDsL6Kuh/H7gzBpfA+Ds8uO3tyjzUlXZOK4B3g1cH0L4pzrq1TSjo6OtrkJHUM5+lLUP3QPNj7L2kfSczewtRNtjvxMYI9reezOrIYQd71IgG6vl35lMd5qlJX3AVA/9e+5DOftR1j6Us58EZ/0KcIGZDYQQjpaPfQN4F9HnMn9SKWhm/4zoPdW0dyU7Ra39RCvn6pfgMdlWlLMfZe1DOftJUtbN/sAoQ/Ttp1oNlB/ntihTeXOXi3NBMzsHuAX4W+C2Ouq0qYMHD9LVFS1CHB4eBmBq6sTODblcjlwux8TEBMvL0ZubTCbDyMgIMzMzzM/Pc/z4cXp7exkdHaVUKjE9feI96dDQENlslvHx8bVjfX19FAoFpqenWVhYWDs+NjbG3Nwcs7Oza8cKhQKZTIaJiYm1Y/39/eTzeSYnJ9f2WU2n04yOjlIsFikWi2tl621TRZLaVMm5ndqU1N/T8ePHOf3009uqTUn5Pa2urrK4uAhE9+fq7u4mnU6vHQPo6uqiu7ubpaUlVlZO/DXc09PD8vLy2usAdHd3k0qlTtpzuXJ+qVRauwdYKpUik8lseD7A0tKJW42m02nS6fSG51fXKZPJsLq6uuH5SWpTpWw7tSmJv6dK2VratLy8vDamNxpP6/8O2Akzuw64ifg7D2gZYBMVi0VyuVhvA+lOp7StZZ1qyVnqp5z9KGsfytlPgrP+O6Kdjt4GPFw+9qfARcBtZjYO/D3wvUQ7HK0Cj7Sgnh2h1n7So8m5uiV4TLYV5exHWftQzn6SlHXTJufMbBDYS7RtQUut284yQ7SdZUPfYezdu5exsbGTjlX/DBvPyubzefL5POPj42vnpNPpDc/f6FihUHjdsWw2Szb7+i/yb3T+yMjI645VPtiMc/5WbVovKW1an/Nm559qbYpzfivaND4+Tl9f36bnn4pt2u58rzalUil6enoAWFxcXFuOXTm2XmXyYr3KhEq1jc7faBXTTs/fqE7r27TdNVvVpsoEVDu1aavzW9WmrXLe7PyNxvn68bR+krBeZvbDwM1E2zP9FvBnwJNE9919BzAK/AhwbfmUXwT+cccvLJuq5Q11pjutbS3rlKT/cWlnytmPsvahnP0kOOsHgF8CfoYTk3N3Eu1AcB6w/pYpKWAeuNGxfh2l1n6ibS3rl+Ax2VaUsx9l7UM5+0lS1ltOzpnZ+cD3VR3eZWY/t8VpKSAP/ASQ5sS94WpRWRW31VZRldV1xS3KVPwboq0T/kMIQR+UiYiISK2uJfpG92+HEP53ADMDWA4hvAC8ADxmZv8V+Arwe9RwX1xprkx3F0taOSciIuLpz4lWxa1tsRBCWDCzi4BPAj8O9BK9v3oM+LUQwlOtqKi8nra1FBERab7tVs5dRvTt8PUGgU/HuHaK6E3YzXXU68Xy4xu2KHNGVdmtXFZ+/NHyG8H1zqqUMbPvAY6GEC6NcU0RkZZZXV0lldKOeSKVrS8dvK38eFfV8ZO2uAwhvGpmVwNfAG4AftWhbrKN7nSXtrUUERFxFEJYAZ7e4PhrwE+bWQYYBorr7kknCdGTSbOoyTkREZGm2m5y7kXgq+t+vggoEX2raTMrwBGiN2GfCyGEOur19fLjm8xsVwjh2AZl3lJVNo53bPHcWPnP7BZlmqZyHyppLuXsR1k3TyaT4dixY/T392+4daA0h7L2UWvOx44d23ALzCYYBuZCCFPrji0B/RuU/RJwDLjEo2KdqpZ/ZzLdXdrWsk7699yHcvajrH0oZz9JzdrMfrz8n49WvX8CIIRQAl71rVXnqrWf9PZo5Vy9kjom241y9qOsfShnP0nKestPwEIInwE+U/nZzFaA6RDCDzezUiGEl83sSeD7gSuAz65/vrz6bT/wGltPFFau9+7NnjOzG4HfBj4VQrim/lqLiPjYu3cvBw4cYN++ffT29ra6OiItsbq6yrFjxzhw4MCG92tsgsNEWy9VHxs2s6EQwtqXe0IIq+X3TKfv5AXN7APAVcD5RFuFP0O0e8Gd5W+j13q99wEfBi4E+oi24vx94LYQwvENyr8fuJxoe84xoAAsAAH4Y+B3T5VvundrW0sRERFvDxB9ken1N/aWxOvNRPec044tIiIizVPrMoArib4J7uFm4D7gVjN7NITwHICZ7QXuKJe5Zf2HU2Z2DXAN8DchhK3ui5c4U1NTjI2NtboabU85+1HWzTM4OAjA+Pg4CwsLpNPpFteoMywvLytrB7XknMlkGB0dXRsTTfYKcIGZDaybkPoG0T1t3w38SaWgmf0zovvmTtf7Ymb2KeBqosmwh4l2LrgYuB242Mx+spYJOjP7CHArsEx0T7zDRDsifBy41MwuDiHMV532AeCDwD8B/wAcAvYS7URwIfAhM3tXeXsqd7X8O5PRtpZ107/nPpSzH2XtQzn7SXDW0wCnyhd52l2t/aS3J3o/vri0Qm9G/w9UiwSPybainP0oax/K2U+Ssq5pcq68ks5FCOF+M7uT6BvjT5nZFznxwdQg0bewbq86bRgwohV1IiJta3BwkMHBQcbHxxPzD0q7U9Y+Epzz3xGtIHsb0WQZwJ8STXDdZmbjwN8D3wvcDawCj9TzQmZ2OdHE3GvAu0IIz5aPjwJfJrqX7rXAJ2Ne70LgFmD4JqpaAAAgAElEQVQeeE8I4fHy8QHgQaIJxpuAX6s69Tbg10MIE1XXKxCtnHsX0YTfz9feSl/a1lJERMTd08APmNlgCOFIqysjtalMyC2WljU5JyIi0iRdra7AVkIIVxN9Y/tJog+/3gs8R7Q67vIQgjbAFhEREQ8PACngZ9YduxN4FjgH+GuiVW5PEG1DeQy4sc7X+mj58brKxBxAeZLsqvKP15tZ3Pdx15frfmtlYq58vaNEuyKsAFebWX79SSGEv6+emCsfnwZ+o/zjj8asQ0tpW0sRERF3dxFty31tqysitatMyB1f1MduIiIizVLrtpbuQgj3AvfGLHsjNX4QVs85zZDL5VpdhY6gnP0oax/K2Y+y9pHgnP+caFXcYuVACGGhfB/cTwI/TnRPulWi++H+WgjhqVpfxMz2A28uv8591c+HEB4xswPAPuDtwKPbXK8HuKT84z0bXO8FM3sMeCfwfmK+5yK6hwzA6+5V56WWvpJJd3FsYWn7gvI6CR6TbUU5+1HWPpSzn6RmHUK4x8zeCvx7M+sD/lP5Cz7SArX2k8q2lsdLmpyrVVLHZLtRzn6UtQ/l7CdJWSd+cq5TJKlTtDPl7EdZ+1DOfpS1j6TmXL6/29MbHH8N+GkzyxBtr13c4b1VLig/Ph1C2Ow+v08QTc5dwDaTc0TbffcD0yGE57e43jvL19t2cq68HeZvlX/80+3KN0tNk3Pa1rJuSR2T7UY5+1HWPpSzn6RmbWZfKv/nPHADcJ2ZPQdMEt0DdyOrIYSLPerXaWrtJz1aOVe3pI7JdqOc/ShrH8rZT5Ky1uRcQkxMTDA6OtrqarQ95exHWftQzn6UtY+k5mxmP17+z0dDCFPVz4cQSsCrDXips8uP396izEtVZeNc76Utymx5PTN7B/DLRNuh7yVasTcEfB74zRh1aIpa+kp3Wtta1iupY7LdKGc/ytqHcvaT4KzfXfVzN/Cd5T+bWW1abTpcrf2kO91FuivFolbO1SzBY7KtKGc/ytqHcvaTpKw1OZcQy8t6w+NBOftR1j6Usx9l7SPBOT9AtJVjocmvM1B+nNuiTGVlXpyvezXieucAP1917A+AfxtCOBKjDk1RS1/Ryrn6JXhMthXl7EdZ+1DOfhKc9ZWtroCcUE8/6e1Ja+VcHRI8JtuKcvajrH0oZz9JylqTcyIiIiLbmwbY4ZaVp6QQwn8H/ruZdQNnEN3D7kbgG2Z2WQjhq7Ve8+DBg3R1dQEwPDwMwNTUiQWJuVyOXC7HxMTE2hvnTCbDyMgIMzMzzM/PUywWGR8fZ3R0lFKpxPT0idvYDA0Nkc1mGR8fB2B+/ijHjkW3x5uenmZhYWGt7NjYGHNzc8zOzq4dKxQKZDIZJiYm1o719/eTz+eZnJykVCoBkE6nGR0dpVgsUiwW18rW26aKOG0C6Ovro1AoNLVNlZzbqU1J/D1Vcm6nNiX191QsFpmbm2urNiXx91R5vp3alNTfU+WcRrZpfZ3rFUL4zI4vIi3Vm0nrnnMiIiJNpMm5hMhkMq2uQkdQzn6UtQ/l7EdZ+0hwzk8DP2Bmg01eLVaZ/MtuUaayGq64RZmGXy+EsAR8C7jDzP4O+CvgHjOzEML8VudW27t3L2NjYycdq/4Z2HCriXw+v/bB6sjICBB9CLnR+ZVje3Yfg5eiKhYKr1/8mM1myWZfH9FG16y85nqVDzbjnL9Vm9bbrk3rNbNN63Pe7PxTrU1xzvduU5ycT7U2xTm/FW2anJxcq1+7tGm7a7aiTZOTk2vPt0ub1ktSmyYnJ4HGtmllRavN200977F7ezQ5V48E//9MW1HOfpS1D+XsJ0lZd7W6AhLZ6I20NJ5y9qOsfShnP8raR4JzvgtIA9c2+XVeLD++YYsyZ1SVjXO9Mxt0PQBCCI8D3wT2A2+Le14j1dJXtK1l/RI8JtuKcvajrH0oZz9JzdrMXjCzv66h/NfM7Plm1qmT1dNPejLa1rIeSR2T7UY5+1HWPpSznyRlrcm5hJiZmWl1FTqCcvajrH0oZz/K2kdScw4h3AP8Z+Dfm9l/NLNm3Xvu6+XHN5nZrk3KvKWq7FaeAY4BBTM7Z5Myb63heutNlh/31nheQ9TSVzLdXSwtaXKuHkkdk+1GOftR1j6Us58EZ30WW385qNr+8jnSBPX0E21rWZ8Ej8m2opz9KGsfytlPkrLW5FxCrN+bXppHOftR1j6Usx9l7SOpOZvZl4DvBeaBG4DXzOwbZvaImX1pkz8P1/o6IYSXgSeBHuCKDepxEdGHV68Bj8W43iLw+fKPH9zgem8E3gEsAg/GraeZDQJvLv/4bNzzGqmWvtKd7qK0pA+X6pHUMdlulLMfZe1DOftpo6wzgL5J0yT19JPeHq2cq0cbjclEU85+lLUP5ewnSVnrnnMiIiIi23t31c/dwHeW/2xmtc7Xuhm4D7jVzB4NITwHYGZ7gTvKZW4JIax9gGVm1wDXAH8TQvi5quvdAlwGXGdmD4UQ/qZ8zgBwN9GXte4IIcysu95e4HLgnup77JnZWcB/AQaBvw0hPFlnO91kutOUluv9dYiIiEgzlb/0sxc43Oq6yAm9mTSL+nKTiIhI02hyTkRERGR7V3q9UAjhfjO7E7gKeMrMvgiUgIuJJsQeAG6vOm0YMKIVddXXe8LMrgduBR4trwKcAS4i+iDsceBjVaf1E00E/icz+3vg20STeGcC30/0HvI54Kd33GAH0baW+nBJRESkWczsfOD7qg7vMrPqLw2tlwLywE8Q3dv3iSZVT+qge86JiIg0lybnEmJ0dLTVVegIytmPsvahnP0oax9JzTmE8Bnn17vazP4S+FWiSbQ00f3j7gbuXL9qLub1PmFm/wj8OtE96/qAF4DfBW4LIRyvOuUg8O+AdwHfA3x3+ZzDwFeBPwb+awhhob4W7lwtfSXa1lI7ZdUjqWOy3ShnP8rah3L2k6CsLwN+q+rYIPDpGOemiLbYvrnRlZJIPf2kt0f3nKtHgsZkW1POfpS1D+XsJ0lZa3IuIUqlEul0utXVaHvK2Y+y9qGc/ShrH8r5hBDCvcC9McveCNy4TZmHgIdiXm8e+J3yn0Sqpa9kurtYWtbkXD00Jn0oZz/K2ody9pOgrF8k+gJPxUVEK/+3ukfuCnAEeBr4XAghNK12Ha6eftKbSTM927LvYZ2yEjQm25py9qOsfShnP0nKWpNzCTE9Pc3Y2Firq9H2lLMfZe1DOftR1j6SmrOZvQAcDCG8PWb5rwFjIYRzmluzzlVLX8l0a+VcvZI6JtuNcvajrH0oZz9Jybq8y8DaTgNmtgJMhxB+uHW1kop6+8k/PDfJa4fmOG1Ptgm1ak9JGZPtTjn7UdY+lLOfJGWtyTkRERGR7Z1FtK1jXPuJ7s8mCdCd7mJpeZWVlVW6ulKtro6IiEgnuBI41upKSP1O35Nl3/CAJuZERESaRJNzIiIiIo2XIdqqSRIg090FwPLKCl1dydi+QkREpJ15369XGm8w28PC4lKrqyEiItK2ulpdAYkMDQ21ugodQTn7UdY+lLMfZe2jHXI2s0FgL3C41XVpZ7X0lcrknLa2rF07jMlTgXL2o6x9KGc/ylriqKefDGZ7KM4vNqE27U1j0ody9qOsfShnP0nKWivnEiKb1TYBHpSzH2XtQzn7UdY+kpKzmZ0PfF/V4V1m9nNbnJYC8sBPAGngiSZVT6itr3SnNTlXr6SMyXannP0oax/K2Y+yljjq6Se5bA9H5hZZXV0lldK24HFpTPpQzn6UtQ/l7CdJWWvlXEKMj4+3ugodQTn7UdY+lLMfZe0jQTlfBnx63R+Awapj1X/uBv5P4IeAEnCzb5U7Sy19pbJybmlZk3O1StCYbGvK2Y+y9qGc/ShriaOefjKY7WFpeZVjx7W1ZS00Jn0oZz/K2ody9pOkrLVyTkREROT1XgS+uu7ni4gm3B7b4pwV4AjwNPC5EEJoWu2kJt3a1lJERESkJrn+HgCOzC3S35dpcW1ERETajybnRERERKqEED4DfKbys5mtANMhhB9uXa2kXhltaykiIiJSk4H+HlKpaHLutD3J2QJMRESkXWhyLiH6+vpaXYWOoJz9KGsfytmPsvaR4JyvBI61uhJyQi19Rdta1i/BY7KtKGc/ytqHcvajrCWOevpJuivFwK4eivOLTahR+9KY9KGc/ShrH8rZT5Ky1uRcQhQKhVZXoSMoZz/K2ody9qOsfSQ15/JKOkmQWvpKV1eKVEor5+qR1DHZbpSzH2XtQzn7UdYSR739ZDCb4cicJudqoTHpQzn7UdY+lLOfJGXd1eoKSGR6errVVegIytmPsvahnP0oax/KWeKqpa+kUiky6S5NztVBY9KHcvajrH0oZz/KWuKot58MZns1OVcjjUkfytmPsvahnP0kKWtNziXEwsJCq6vQEZSzH2XtQzn7UdY+lLPEVWtfyXR3saTJuZppTPpQzn6UtQ/l7EdZSxz19pPBbA9FTc7VRGPSh3L2o6x9KGc/Scpak3MiIiIi0va6u7so6Z5zIiIiIrHl+nu0ck5ERKRJNDknIiIiIm1P21qKiIiI1GYw28OReU3OiYiINIMm5xJibGys1VXoCMrZj7L2oZz9KGsfylniqrWvZLrT2tayDhqTPpSzH2XtQzn7UdYSR739JKdtLWumMelDOftR1j6Us58kZa3JuYSYm5trdRU6gnL2o6x9KGc/ytqHcpa4au0r3d0pSsvLTapN+9KY9KGc/ShrH8rZj7KWOOrtJ4NZbWtZK41JH8rZj7L2oZz9JClrTc4lxOzsbKur0BGUsx9l7UM5+1HWPpSzxFVrX8mk05SWVptUm/alMelDOftR1j6Usx9lLXHU2080OVc7jUkfytmPsvahnP0kKWtNzomIiIhI28t0d7G0pJVzIiIiInHl+qPJudVVfcFJRESk0TQ5JyIiIiJtr7u7i9KyPlgSERERiWsw28PS8grHji+1uioiIiJtR5NzCVEoFFpdhY6gnP0oax/K2Y+y9qGcJa5a+0om3UVJK+dqpjHpQzn7UdY+lLMfZS1x1NtPBrM9ABTnS42sTlvTmPShnP0oax/K2U+SstbkXEJkMplWV6EjKGc/ytqHcvajrH0oZ4mr1r7S3d3F0tJKk2rTvjQmfShnP8rah3L2o6wljnr7yUB/D6kUHJk73uAatS+NSR/K2Y+y9qGc/SQpa03OJcTExESrq9ARlLMfZe1DOftR1j6Us8RVa1/JdHdRWtbkXK00Jn0oZz/K2ody9qOsJY56+0m6K8XArgzFOa2ci0tj0ody9qOsfShnP0nKWpNzIiIiItL2om0tNTknIiIiUotcf49WzomIiDSBJudEREREpO1pW0sRERGR2g1mezgyt9jqaoiIiLSd7lZXQCL9/f2trkJHUM5+lLUP5exHWftQzhJXrX1F21rWR2PSh3L2o6x9KGc/yjpiZh8ArgLOB9LAM8CngTtDCLHfAJjZGcClwIXAW4DvLl/vfwsh3Bbj/PcBHy6f3we8APw+cFsIoWXLz3bSTwazvRyZ1+RcXBqTPpSzH2XtQzn7SVLWWjmXEPl8vtVV6AjK2Y+y9qGc/ShrH8pZ4qq1r2hby/poTPpQzn6UtQ/l7EdZg5l9CriHaELsa8AXgO8AbgfuN7NaPvu6HLgD+AXge4km5uLW4yPA54H3AE8CDwJ7gY8DXzGzln0auJN+kstmtHKuBhqTPpSzH2XtQzn7SVLWmpxLiMnJyVZXoSMoZz/K2ody9qOsfShniavWvqJtLeujMelDOftR1j6Us59Oz9rMLgeuBl4Dzg8hXBpCuAw4D/gmcBlwbQ2X/BbwSeDniFbNfS5mPS4EbgHmgXeGEH4khHAF8Ebgq8DbgZtqqEdD7aSfDGZ7NTlXg04fk16Usx9l7UM5+0lS1pqcS4hSqdTqKnQE5exHWftQzn6UtQ/lLHHV2le0rWV9NCZ9KGc/ytqHcvajrPlo+fG6EMKzlYMhhAmibS4Bro+7ei6E8CchhH8bQvhcCOGbQNw3D9cDKeDWEMLj6653FLiyfJ2rzawlX9ffST8ZzPZQ1ORcbBqTPpSzH2XtQzn7SVLWmpwTERERkbanbS1FRETai5ntB94MLAL3VT8fQngEOACcRrRyrVn16AEuKf94zwb1eAF4DOgB3t+sejRLrr9HK+dERESaQJNzCZFOx97GXHZAOftR1j6Usx9l7UM5S1y19hVta1kfjUkfytmPsvahnP10eNYXlB+fDiEc26TME1Vlm8GAfmA6hPB8C+uxqZ30k8FsD8V5Tc7F1eFj0o1y9qOsfShnP0nKWpNzCTE6OtrqKnQE5exHWftQzn6UtQ/lLHHV2le0rWV9NCZ9KGc/ytqHcvbT4VmfXX789hZlXqoq28x6vLRFGY96bGon/WQwG62cW11dbWCN2leHj0k3ytmPsvahnP0kKWtNziVEsVhsdRU6gnL2o6x9KGc/ytqHcpa4au0rmXQX8wvJ2Vv+VKEx6UM5+1HWPpSznw7PeqD8OLdFmaPlx1wH1GNTO+kng9keSksrLCwuN7BG7avDx6Qb5exHWftQzn6SlHV3qysgkWKxSC7XkvdoHUU5+1HWPpSzH2XtQzlLXLX2lVQXvPRakVenjnL68MD2JwigMelFOftR1j6Usx9l3RkOHjxIV1f0/frh4WEApqam1p7P5XLkcjkmJiZYXo4m0TKZDCMjI8zMzDAxMbHWT0ZHRymVSkxPT6+dPzQ0RDabZXx8fO1YX18fhUKB5cVozvG5b73MnsFexsbGmJubY3Z2dq1soVAgk8kwMTGxdqy/v598Ps/k5CSlUvQFqXQ6zejoKMVi8aQPR+tp0/z8/FrZWts0PT3NwsLC2vFGtqkyJtupTUn8PVVybqc2JfX3VCwW2b9/f1u1KYm/p2KxyNjYWFu1Kam/p4WFBXK5XEPbtL7OtdDknIiIiIi0vbd81+nc+YdP0Z2g/eVFRERkRyqr0bJblKl8I6eZX5Nvej327t3L2NjYSceqf4aNt+rK5/PMz8+fVD6dTm94/kbHzhjbC8Cu7G7GxvIAZLNZstnXN3ej80dGRl53rPLBZpzzN2tTPp8/6VgtbSoUCq871qg2jY+Pn3ROO7QpzvnebYqT86nWpjjnt6JN4+Pja/VrlzZtd81WtGl8fPykL1FUOxXbtF6S2lSZ6Gtkm1ZW6ruFhra1FBEREZG2N5zvI5/r5dmXD7e6KiIiItIYL5Yf37BFmTOqyjazHme2uB5NkU53MbArw5H5xVZXRUREpK1oci4hKkskpbmUsx9l7UM5+1HWPpSzxFVrX0mlUpy7P8+zL880qUbtSWPSh3L2o6x9KGc/HZ7118uPbzKzXZuUeUtV2WZ4BjgGFMzsnE3KvNWhHpvaaT8ZzPZwZE6Tc3F0+Jh0o5z9KGsfytlPkrLW5JyIiIiIdITvOCPPc5qcExERaQshhJeBJ4Ee4Irq583sImA/8BrwWBPrsQh8vvzjBzeoxxuBdwCLwIPNqkcz5bI9HJk73upqiIiItBVNziXE+psKSvMoZz/K2ody9qOsfShniauevnLembt59pUZVldXm1Cj9qQx6UM5+1HWPpSzH2XNzeXHW83s3MpBM9sL3FH+8ZYQwsq6564xs2fM7LMNrMctwCpwnZlVVslhZgPA3USfv90RQmjJt4R22k8Gsz0U50oNqk1705j0oZz9KGsfytlPkrLubnUFREREREQ8nLs/z9yxEq9OzTE2MtDq6oiIiMgOhRDuN7M7gauAp8zsi0AJuBgYBB4Abq86bRgwohV1JzGz04E/Xneosk3ltWb2k+uOXxZCeHVdPZ4ws+uBW4FHzexLwAxwEbAXeBz4WN0NbbFcv1bOiYiINJpWzomIiIhIR8jnehnZvYt/0taWIiIibSOEcDXRdpJPEk2GvRd4DrgGuDyEsFzD5XqBt637U7kxzZlVx3s3qMcngEuALxPd6+7HgCngN4CLQgjztbYtKXTPORERkcbTyrmEyOVyra5CR1DOfpS1D+XsR1n7UM4SV7195bzyfefe/f37G1yj9qQx6UM5+1HWPpSzH2UdCSHcC9wbs+yNwI2bPPcikNpBPR4CHqr3/GbZaT8ZzPbwrfHZBtWmvWlM+lDOfpS1D+XsJ0lZa+VcQiSpU7Qz5exHWftQzn6UtQ/lLHHVPzm3m2dfPtzg2rQvjUkfytmPsvahnP0oa4mjEZNzWjkXj8akD+XsR1n7UM5+kpS1JucSYmJiotVV6AjK2Y+y9qGc/ShrH8pZ4qq3r5x3Rp7nD8yyvLzS4Bq1J41JH8rZj7L2oZz9KGuJY6f9ZDDbQ1GTc7FoTPpQzn6UtQ/l7CdJWWtyLiGWl2vZAl3qpZz9KGsfytmPsvahnCWuevvKOfvzHF9c5uWDRxtco/akMelDOftR1j6Usx9lLXHstJ8MZns5MrfI6upqg2rUvjQmfShnP8rah3L2k6SsNTknIiIiIh1jYFeGfSNZnn1JW1uKiIiIxJHrz7C4tMLxxeR8oCkiInKq6251BSSSyWRaXYWOoJz9KGsfytmPsvahnE8wsw8AVwHnA2ngGeDTwJ0hhJr3ZDSz9wEfBi4E+oAXgN8HbgshHN+g/AXAJcCPAt8D5IEi8A/AZ4HP1FOPRtlJXznvjN08+8oMP/q2NzSwRu1JY9KHcvajrH0oZz/KWuLYaT8ZzPYCcGR+kb5efZS4FY1JH8rZj7L2oZz9JClrrZxLiJGRkVZXoSMoZz/K2ody9qOsfSjniJl9CriHaCLta8AXgO8AbgfuN7Oa3sOZ2UeAzwPvAZ4EHgT2Ah8HvmJm/VXlu8vlbirX4SngfuBp4IeAu4HPm1lfnU3csZ30lfPOyPPsyzMNrE370pj0oZz9KGsfytmPspY4dtpPcv3RB5lHdN+5bWlM+lDOfpS1D+XsJ0lZa3IuIWZm9AGRB+XsR1n7UM5+lLUP5QxmdjlwNfAacH4I4dIQwmXAecA3gcuAa2u43oXALcA88M4Qwo+EEK4A3gh8FXg70SRctb8DfgoYDiG8J4TwL0IIPwRcALwK/C/AR+ts5o7tpK+ce0aeF8dnKS1pa6btaEz6UM5+lLUP5exHWUscO+0n6XQX2V0ZTc7FoDHpQzn7UdY+lLOfJGWtybmEmJ+fb3UVOoJy9qOsfShnP8rah3IGTkx4XRdCeLZyMIQwQbTNJcD1Nayeux5IAbeGEB5fd72jwJXACnC1meXXPbcUQrgwhHBf9ZaXIYSngI+Uf/zZGtrVUDvpK2/cN8TKKnxr/EgDa9SeNCZ9KGc/ytqHcvajrCWORvSTwWwPRU3ObUtj0ody9qOsfShnP0nKWpNzIiIiIglhZvuBNwOLwH3Vz4cQHgEOAKcRrXjb7no9RPeNg2ibzOrrvQA8BvQA76+hql8vP+6v4ZzE6Ovp5szRHM+9kpxvzImIiIgk2WB/j1bOiYiINJAm50RERESS44Ly49MhhGOblHmiquxWDOgHpkMIzzfgehXnlR9freGcRDnvjDzPvqTJOREREZE4ctkeivOanBMREWkUTc4lxOjoaKur0BGUsx9l7UM5+1HWPpQzZ5cfv71FmZeqysa53ktblKnlephZihPbWv5hnHOaYad95bwz8jz78uEG1aZ9aUz6UM5+lLUP5exHWUscjegng1mtnItDY9KHcvajrH0oZz9Jyrq71RWQSKlUIp1Ot7oabU85+1HWPpSzH2XtQzkzUH6c26LM0fJjrgXXA/ht4B3ABHBzzHNOcvDgQbq6ou+IDQ8PAzA1NbX2fC6XI5fLMTExwfLyMgCZTIaRkRFmZmaYn59naWmJ7u5uRkdHKZVKTE9Pr50/NDRENptlfHx87VhfXx+FQoHp6WkWFhbI71ripYkiC8eXWF46zuzs7FrZQqFAJpNhYmJi7Vh/fz/5fJ7JyUlKpRIA6XSa0dFRisUixWJxrWy9baqot00VY2NjzM3NNaRNlZzbqU1J/D1Vcm6nNiX197S0tMSePXvaqk1J/D0tLS2xe/futmpTUn9PlddvZJvW11naQyPeYw9mezg0u7B9wQ6n/5/xoZz9KGsfytlPkrJOra6utroOpywzOwv41sMPP8z+/Tu75cr4+DhjY2MNqZdsTjn7UdY+lLMfZe2jGTm/8sorXHzxxQBnhxBebOjFG8zMbgBuAu4JIfzsJmVuAm4A7goh/PI21/sA0b3m/iqE8IOblPkl4C7gL0II793mej8H/DegBLwvhPDlrVv0uvPPIiHvnUpLK/zUDQ/ya//i+3nXBft2VJd2pr/7fChnP8rah3L20+nvndpdo947NaKf3PfwP/GPz07xH3/lB3Z0nXanv/98KGc/ytqHcvaTpPdO2tZSREREJDkqq9iyW5SprIYrblGm4dczsyuAu4Fl4GdqnZhLmkx3F8P5Pj73+W/w2qGtFhaKiIiISK5f21qKiIg0kra1FBEREUmOF8uPb9iizBlVZeNc78ydXM/MfgK4t/zjvwwh/HGM1068vbv7OfeMPKft2WruUkRERESie84db3U1RERE2oZWziXE0NBQq6vQEZSzH2XtQzn7UdY+lDNfLz++ycx2bVLmLVVlt/IMcAwomNk5m5R561bXM7N/DvwB0fvGK0MIfxDjdZuuEX1ld66PpaWVBtSmfWlM+lDOfpS1D+XsR1lLHI3oJ4PZHo7MlxpQm/amMelDOftR1j6Us58kZa3JuYTIZvWNbQ/K2Y+y9qGc/ShrH52ecwjhZeBJoAe4ovp5M7sI2A+8BjwW43qLwOfLP35wg+u9EXgHsAg8uMHzPwb8D6LdFv5VCOFzcdvSbI3oK7sHezlc1DfAt9LpY9KLcvajrH0oZz/KWuJoRD/JZXtYLC2zsLjUgBq1L41JH8rZj7L2oZz9JClrTc4lxPj4eKur0BGUsx9l7UM5+1HWPpQzADeXH281s3MrB81sL3BH+cdbQggr686IRGEAACAASURBVJ67xsyeMbPPbnC9W4BV4Doze+u6cwaI7iHXBdwRQphZf5KZvR+4n2hi7l+HED6986Y1TiP6yu5cH9NHFhpQm/alMelDOftR1j6Usx9lLXE0op8MZnsAeP6VmW1KdjaNSR/K2Y+y9qGc/SQpa91zTkRERCRBQgj3m9mdwFXAU2b2RaAEXAwMAg8At1edNgwY0Yq66us9YWbXA7cCj5rZl4AZ4CJgL/A48LH155QnAv+IaAXfK8APmtkPblLfD9XX0tbbPdjLYU3OiYiIiGxrMNtLKgV3/OE/8pu/8Dbds1dERGSHNDknIiIikjAhhKvN7C+BXyWaREsT3T/ubuDO9avmYl7vE2b2j8CvE92zrg94Afhd4LYQQvXejv1Ab/m/9wM/v8XlP1RLXZKkkOvTtpYiIiIiMaS7UuwZ7OP9P3CWJuZEREQaQJNzCdHX19fqKnQE5exHWftQzn6UtQ/lfEII4V7g3phlbwRu3KbMQ8BDMa/3IpCKU7ZVGtFXdg/2cuz4EgvHl+jr1dvijWhM+lDOfpS1D+XsR1lLHI3qJ3vyu1heWW3ItdqVxqQP5exHWftQzn6SlLXuOZcQhUKh1VXoCMrZj7L2oZz9KGsfylniakRf2T0YvSnX6rnNaUz6UM5+lLUP5exHWUscjeonw0O7ODSjLcG3ojHpQzn7UdY+lLOfJGWtybmEmJ6ebnUVOoJy9qOsfShnP8rah3KWuBrRVwZ2ZehOdzGt+85tSmPSh3L2o6x9KGc/ylriaFQ/2ZPvY2r2WEOu1a40Jn0oZz/K2ody9pOkrDU5lxALC/pQyINy9qOsfShnP8rah3KWuBrRV1KpFLsHezlcVL/bjMakD+XsR1n7UM5+lLXE0ah+smdwF4dm1ee2ojHpQzn7UdY+lLOfJGWtyTkRERER6ViFXB+Hj2hbSxEREZHtDOf7OKSVcyIiIg2hyTkRERER6Vj5nFbOiYiIiMSxZ2gXUzMLrK6utroqIiIipzxNziXE2NhYq6vQEZSzH2XtQzn7UdY+lLPE1ai+UhjUyrmtaEz6UM5+lLUP5exHWUscjeone4b6WFpe4cjcYkOu1440Jn0oZz/K2ody9pOkrDU5lxBzc3OtrkJHUM5+lLUP5exHWftQzhJXo/rK7sE+prVyblMakz6Usx9l7UM5+1HWEkej+smeoT4Apma0teVmNCZ9KGc/ytqHcvaTpKw1OZcQs7Ozra5CR1DOfpS1D+XsR1n7UM4SV6P6yu5cLzNaObcpjUkfytmPsvahnP0oa4mjUf0k050mP9DLoSP6YtNmNCZ9KGc/ytqHcvaTpKy7W12B7ZjZB4CrgPOBNPAM8GngzhDCSsxrdAFvB94PvAf4LmAAmAb+DrgrhPBA42svIiIiIklW0Mo5ERERkdj25Ps4pJVzIiIiO5bolXNm9ingHuBC4GvAF4DvAG4H7i9PusXxRuCvgI8BBvwN8IfAt4FLgD82s0+bWaqxLRARERGRJMvnepk9epzl5Vjf+RIRERHpaHsGdzE1qy82iYiI7FRiV86Z2eXA1cBrwLtCCM+Wj48CXwYuA64FPhnjcqvAl4D/A/hCCGF53etcBDwIfAj4KtGqPHeFQqEVL9txlLMfZe1DOftR1j6Us8TVqL5SGOxjdRVm5xYpDPY15JrtRGPSh3L2o6x9KGc/ylriaGQ/2ZPv49CsVs5tRmPSh3L2o6x9KGc/Sco6ySvnPlp+vK4yMQcQQpgg2uYS4Po4q+dCCM+HEC4OITy0fmKu/NwjwC3lH3+2AfWuSyaTadVLdxTl7EdZ+1DOfpS1D+UscTWqrwwN9AIwrXunbEhj0ody9qOsfShnP8pa4mhkPxke2sWhGb1v2ozGpA/l7EdZ+1DOfpKUdSIn58xsP/BmYBG4r/r58oTaAeA0onvJ7dTXy4/7G3CtukxMTLTqpTuKcvajrH0oZz/K2odylrga1Vcy3V0MZnuYKR5vyPXajcakD+XsR1n7UM5+lLXE0ch+MpzvY0or5zalMelDOftR1j6Us58kZZ3IyTnggvLj0yGEzf7Ff6Kq7E6cV358tQHXEhEREZFTSGGwTyvnRERERGLYM7hL21qKiIg0QFIn584uP357izIvVZWti5n1A/+m/OMf7uRaIiIiInLqyed6OVzU5JyIiIjIdvbk+zh2fJn5hVKrqyIiInJK6251BTYxUH6c26LM0fJjboevdQfRBN83gLvqucDBgwfp6ormOYeHhwGYmppaez6Xy5HL5ZiYmGB5ObrlXSaTYWRkhJmZGebn51lYWGB8fJzR0VFKpRLT09Nr5w8NDZHNZhkfH1871tfXR6FQYHp6moWFEx8mjY2NMTc3x+zs7NqxQqFAJpM5aclmf38/+XyeyclJSqXoDVU6nWZ0dJRisUixWFwrW2+bKpLUpkrO7dSmpP6eFhYWWFhYaKs2JfH3tLCwwNzcXFu1Kam/p1QqBdBWbUri72l1dRWgoW1aX2dpH/39/Q27VmGwj8NHtK3lRhqZs2xOOftR1j6Usx9lLXE0sp/sGdoFwNTMMc48LTn37UkKjUkfytmPsvahnP0kKetU5UOwJDGzG4CbgHtCCD+7SZmbgBuAu0IIv1zn6/wm8B+AWeCdIYSnazz/LOBbDz/8MPv3t+x2dSIiIrKFV155hYsvvhjg7BDCiy2uTkdL6nun//ZnTzM+NccNH3prq6siIiLScnrvlBxJfe/0M7/x//GRf3kh3297W10VERGRlqv3vVNSt7WsrIrLblGmsrquuEWZTZnZh4km5o4Cl9Q6Mddok5OTrXz5jqGc/ShrH8rZj7L2oZwlrkb2ld2DfcwUtXJuIxqTPpSzH2XtQzn7UdYSR6P7yZ6hPg7N6L5zG9GY9KGc/ShrH8rZT5KyTurk3IvlxzdsUeaMqrKxmdm1wO8Ax4BLQwiP1XqNRqts3SXNpZz9KGsfytmPsvahnCWuRvaVQq6P6SO659xGNCZ9KGc/ytqHcvajrCWORveT4aFdHNJ7pw1pTPpQzn6UtQ/l7CdJWSd1cu7r5cc3mdmuTcq8papsLGb2q8DvAgvAj4cQHqmviiIiIiLSDvKDvRwuHieJ272LiIiIJM2eoT6mtHJORERkRxI5ORdCeBl4EugBrqh+3swuAvYDrwGxV72Z2a8AtwPHgX8eQvhiQyrcAOl0utVV6AjK2Y+y9qGc/ShrH8pZ4mpkXykM9rFYWmZ+Yalh12wXGpM+lLMfZe1DOftR1hJHo/vJnqFdHJrVyrmNaEz6UM5+lLUP5ewnSVkncnKu7Oby461mdm7loJntBe4o/3hLCGFl3XPXmNkzZvbZ6ouZ2S+VzzsOXBZC+PPmVb12o6Ojra5CR1DOfpS1D+XsR1n7UM4SVyP7yu5cLwCHi/qQqZrGpA/l7EdZ+1DOfpS1xNHofjKc18q5zWhM+lDOfpS1D+XsJ0lZd7e6ApsJIdxvZncCVwFPmdkXgRJwMTAIPEC0Cm69YcCIVtStMbPvA/4vIAV8C/hpM/vpDV52KoTw7xrakJiKxSK5XK4VL91RlLMfZe1DOftR1j6Us8TVyL6yq7eb3p40h48cZ/9e9b/1NCZ9KGc/ytqHcvajrCWORvcTrZzbnMakD+XsR1n7UM5+kpR1klfOEUK4Gvgg0RaXFwHvBZ4DrgEuDyEsx7xUnmhiDuA7gZ/f5M9PNqzyNSoWi6166Y6inP0oax/K2Y+y9qGcJa5G9pVUKsXuXC/TR/QhUzWNSR/K2Y+y9qGc/ShriaPR/WQ4v4vi/CLHS3E/luscGpM+lLMfZe1DOftJUtaJXTlXEUK4F7g3ZtkbgRs3OP4VTkzOiYiIiIicZHeuj8PF462uhoiIiEji7RnqA+DQ7DHGhgdaXBsREZFTU6JXzomIiIiIeCgM9nFYK+dEREREtjWwK0NPJs2hGb13EhERqZcm5xJieHi41VXoCMrZj7L2oZz9KGsfylnianRf2Z3r5XBRHzBV05j0oZz9KGsfytmPspY4Gt1PUqkUw0N9HJo91tDrtgONSR/K2Y+y9qGc/SQpa03OiYiIiEjH2z3Yx+Ej2tZSREREJI7h/C6mZvXFJhERkXppci4hpqamWl2FjqCc/ShrH8rZj7L2oZwlrkb3Fa2c25jGpA/l7EdZ+1DOfpS1xNGMflIY6uPQjFbOVdOY9KGc/ShrH8rZT5Ky1uSciIiIiHS83YN9TGvlnIiIiEgsw0O7mNK2liIiInXT5JyIiIiIdLzduV6K84uUllZaXRURERGRxIvuOaddB0REROqlybmEyOVyra5CR1DOfpS1D+XsR1n7UM4SV6P7SmGwD4CZolbPracx6UM5+1HWPpSzH2UtcTSjn+zJ7+KQVs69jsakD+XsR1n7UM5+kpS1JucSIkmdop0pZz/K2ody9qOsfShniavRfWVwoJeuFLrvXBWNSR/K2Y+y9qGc/ShriaMpk3NDfRwuHmdpWbsOrKcx6UM5+1HWPpSznyRlrcm5hJiYmGh1FTqCcvajrH0oZz/K2odylrga3VfSXSmGBno5fESTc+tpTPpQzn6UtQ/l7EdZSxzN6CfDQ7tYXYXDumfvSTQmfShnP8rah3L2k6Ssu1tdAYksLy+3ugodQTn7UdY+lLMfZe1DOUtczegruwf7mNa2lifRmPShnP0oax/K2Y+yjpjZB4CrgPOBNPAM8GngzhBCzUu7zOx9wIeBC4E+4AXg94HbQgive7NgZh8qv95WTg8hvFZrXRqhGf1kaKCXdFeKQ7PHGNm9q+HXP1VpTPpQzn6UtQ/l7CdJWWtyTkREREQE2J3rZUYr50RERE4pZvYp4GpgAXgYKAEXA7cDF5vZT9YyQWdmHwFuBZaBrwCHgYuAjwOXmtnFIYT5TU5/HvjLTZ5rqxu0dXWlKAz1MaX7zomIiNRFk3MJkclkWl2FjqCc/ShrH8rZj7L2oZwlrmb0lYJWzr2OxqQP5exHWftQzn46PWszu5xoYu414F0hhGfLx0eBLwOXAdcCn4x5vQuBW4B54D0hhMfLxweAB4F3ATcBv7bJJf4yhPChetvTLM3qJ8NDu5ia0Reb1uv0MelFOftR1j6Us58kZa17ziXEyMhIq6vQEZSzH2XtQzn7UdY+lLPE1Yy+ks/pnnPVNCZ9KGc/ytqHcvajrPlo+fG6ysQcQAhhgmibS4DrzSzu51/XAyng1srEXPl6R4ErgRXgajPL77jmjprVT/YM9XFIK+dOojHpQzn7UdY+lLOfJGWtybmEmJmZaXUVOoJy9qOsfShnP8rah3KWuJrRVwqDfUxMzzX8uqcyjUkfytmPsvahnP10ctZmth94M7AI3Ff9fAjhEeAAcBrw9hjX6wEuKf94zwbXewF4DOgB3l93xVugWf1kOL+LlyeKTbn2qaqTx6Qn5exHWftQzn6SlLUm5xJifn6z7cqlkZSzH2XtQzn7UdY+lLPE1Yy+kk6neHniKK8d0gRdhcakD+XsR1n7UM5+OjzrC8qPT4cQNlu+9URV2a0Y0A9MhxCer/N655rZx83sLjO7zcw+UN4Ss6Wa1U+yfd38z+cP6b3TOh0+Jt0oZz/K2ody9pOkrHXPORERERER4E1n72F5ZZXBbE+rqyIiIiLbO7v8+O0tyrxUVTbO9V7aosx213tn+c96h83sX4cQ7o9Rh1PKd529h1WeZbTQ3+qqiIiInHI0OSciIiIiApy2J0sqBa9OzXHO/lPqVjIiIiKdqLIibatlW0fLj7kmX+9V4OPAnwIvAEvAdwEfAS4D/h8ze38I4c9j1ON1Dh48SFdXtPnV8PAwAFNTU2vP53I5crkcExMTLC8vA5DJZBgZGWFmZoZiscj4+DgAo6OjlEolpqen184fGhoim82ulQHo6+ujUCgwPT3NwsKJe/KOjY0xNzfH7Ows3SuLLJZWGD84y2nD0etX9Pf3k8/nmZycpFQqAZBOpxkdHaVYLFIsntgOs542rV/50Mg2VRQKBTKZTM1tqmTdTm1K4u+pknM7tSmpv6discjc3FxbtSmJv6fK8+3UpqT+nirnNLJN6+tci9Tq6mpdJwqY2VnAtx5++GH279+/o2stLy+TTqcbUi/ZnHL2o6x9KGc/ytpHM3J+5ZVXuPjiiwHODiG82NCLS01OhfdOv/Dxv+DKS9/ED33fvoZf+1Skv/t8KGc/ytqHcvbTye+dzOwG4CbgnhDCz25S5ibgBuCuEMIvb3O9DxDda+6vQgg/uEmZXwLuAv4ihPDemPX8HeDDRNtvfk+cc9adexYNeO/UrDG5srLKT33sQX7rF9/G+eeONPz6pyL9/edDOftR1j6Us58kvXfSPecSojJLK82lnP0oax/K2Y+y9qGcJa5m9ZWx4SzjU0e3L9ghNCZ9KGc/ytqHcvbT4VlX/sHOblGmshquuEWZZl2v4uPAMvAmMzuzhvMapln9pKsrxdhwlgOTuudcRYePSTfK2Y+y9qGc/SQpa03OJUS9Sx+lNsrZj7L2oZz9KGsfyvkEM/uAmX3NzGbN7KiZ/a2Z/aqZ1fX+zczeZ2Z/YWbTZjZvZv/TzD5mZr2blB82s18wszvN7AkzO25mq2Z2+85a1hjN6iunDw/w6pQ+YKrQmPShnP0oax/K2U+HZ/1i+fENW5Q5o6psnOttNYFWy/UACCEcBg6Wf2zJ0vxm9pOxkQHGJ/XFpooOH5NulLMfZe1DOftJUtaanBMRERFJGDP7FNG2ShcCXwO+AHwHcDtwf60TdGb2EeDzwHuAJ4EHgb1E3+T+ipn1b3DaDwK/B/xKuR49dTXmFDM2nGVc3/4WERE5FXy9/PgmM9u1SZm3VJXdyjPAMaBgZudsUuatNVwPADNLA0PlH9tuFmvfyAAHNDknIiJSM03OiYiIiCSImV0OXA28BpwfQrg0hHAZcB7wTeAy4NoarnchcAswD7wzhPAjIYQrgDcCXwXeTnS/lmoTwJ3AvwIu2KRM2xkbzmrlnIiIyCkghPAy0ZeOeoArqp83s4uA/UTvqR6Lcb1Foi8zAXxwg+u9EXgHsEj0Rae4LgX6ibbCfKaG804J+0YGOHBQk3MiIiK10uRcQgwNDW1fSHZMOftR1j6Usx9l7UM5A/DR8uN1IYRnKwdDCBPAVeUfr69h9dz1QAq4NYTw+LrrHQWuBFaAq80sv/6kEMJjIYSrQwi/F0L4e2CpvuY0R7P6yunDWWaOHmd+ITn70LeSxqQP5exHWftQzn6UNTeXH281s3MrB81sL3BH+cdbQggr6567xsyeMbPPbnC9W4BV4Doze+u6cwaAu4k+R7sjhDCz7rl+M7uqXOYkZva/Av93+cdPhRBa8gajmf1k30iW16bnWVpe2b5wB9CY9KGc/ShrH8rZT5Ky1uRcQmSzW91vWBpFOftR1j6Usx9l7aPTczaz/cCbib6RfV/18yGER4ADwGlEK962u14PcEn5x3s2uN4LRN8k7wHeX3fFW6BZfeW0PVlSKRjX6jlAY9KLcvajrH0oZz+dnnUI4X6ilf6nAU+Z2f9rZn8EPAt8N/AA0bbg6w0Dxgb3lgshPEH0xaZ+4NHy/Xr/B/A8cBHwOPCxqtN6iCYCJ83sr8zsD8zsj8zsm8CfASPAHwG/1Yg216OZ/WRsZICVlVUmpueb9hqnkk4fk16Usx9l7UM5+0lS1pqcS4jx8fFWV6EjKGc/ytqHcvajrH0oZy4oPz4dQji2SZknqspuxYg+XJoOITzfgOslRrP6Sk8mzXB+l7a2LNOY9KGc/ShrH8rZj7KGEMLVRNtQPkk0gfZe4DngGuDyEMJyjdf7BNGXm75MdM+6HwOmgN8ALgohVM9CzRPdx/erwD6ibSwvBQaBPy3X4fJWrZqD5vaTXH8Pg9ke3XeuTGPSh3L2o6x9KGc/Scq6u9UVEBEREZE1Z5cfv71FmZeqysa53ktblKnleh1hbDjL+JQ+YBIRETlVhBDuBe6NWfZG4MZtyjwEPBTzeovAb8Yp2672jQwwrsk5ERGRmmhyTkRERCQ5Kvcq2WrZVuWTj1wLrtcQBw8epKsr2sBheHgYgKmpqbXnc7kcuVyOiYkJlpejL7tnMhlGRkaYmZlhfn6eYrHI+Pg4o6OjlEolpqen184fGhoim82e9I24vr4+CoUC09PTLCwsrB0fGxtjbm6O2dnZtWN7d+/iwMGjJ53f399PPp9ncnKSUin64ns6nWZ0dJRisUixWFwrW2+bKprRpkKhQCaTYWJioqY2VXJupzYl8fdUybmd2pTU31OxWGRubq6t2pTE31Pl+XZqU1J/T5VzGtmm9XUWiWNsJMuBSe06ICIiUgtNziVEX19fq6vQEZSzH2XtQzn7UdY+lHNn2Lt3L2NjYycdq/4Zog82q+XzefL5PNPT0xQKBSD6EHKj8zc6VjlnvWw2e9K+82eMzvPYU69ueP7IyMjrjlU+2Izz+lu1ab1Gt2mr87dq0/qcNzv/VGtTnPO92xQn51OtTXHOb0Wbpqen1+rXLm3a7pqtaNP09PTa8+3SpvWS1KbKRFoj27SysvK65+XU1uz32PtGBvh6mGzqa5wq9P8zPpSzH2XtQzn7SVLWuudcQmz0RlwaTzn7UdY+lLMfZe1DOa+tYtvqDsWV1XDFLco063qJ0cy+Mjac1T3nyjQmfShnP8rah3L2o6wljmb3k30jA7rnXJnGpA/l7EdZ+1DOfpKUtSbnEkLbRvhQzn6UtQ/l7EdZ+1DOvFh+fMMWZc6oKhvnemc26HqJ0cy+cvpwlpmjx5lfKDXtNU4VGpM+lLMfZe1DOftR1hJHs/vJvpEBpo8scOz4UlNf51SgMelDOftR1j6Us58kZa3JuYRYv7e8NI9y9qOsfShnP8rah3Lm6+XHN5nZrk3KvKWq7FaeAY4BBTM7Z5Myb63heonRzL5y2p4sqRSMa/WcxqQT5exHWftQzn6UtcTR7H5y2nD5vZNWz2lMOlHOfpS1D+XsJ0lZa3JOREREJCFCCC8DTwI9wBXVz5vZRcB+4DXgsRjXWwQ+X/7xgxtc743AO4BF4MG6K95mejJpRvK7eHVSk3MiIiIi2+ktv3ca13snERGR2DQ5JyIiIpIsN5cfbzWzcysHzWwvcEf5x1tCCCvrnrvGzJ4xs89ucL1bgFXgOjN767pzBoC7id4P3hFCmGlwO05ppw9nGZ/St79FRERE4hgbGeCA3juJiIjE1t3qCkhkbGys1VXoCMrZj7L2oZz9KGsfyhlCCPeb2Z3AVcBTZvZFoARcDAwCDwC3V502DBjRirrq6z1hZtcDtwKPmtmXgBngImAv8DjwsY3qYmZ/ve7H/eXHnzSzC9cdvzqE8GRtrdy5ZveVseEBbWuJxqQX5exHWftQzn6UtcTh0U/2jQxwQNtaakw6Uc5+lLUP5ewnSVlr5VxCzM3pwx8PytmPsvahnP0oax/KORJCuJpoG8oniSbR3gs8B1wDXB5CWK7xep8ALgG+THTPuh8DpoDfAC4KIcxvcurb1v3ZVz42WnV8sJa6NEqz+8rYSJZXNTmnMelEOftR1j6Usx9lLXF49JN9IwMcOKjJOY1JH8rZj7L2oZz9JClrrZxLiNnZWbLZbKur0faUsx9l7UM5+1HWPpTzCSGEe4F7Y5a9EbhxmzIPAQ/VWIdULeU9NbuvnL5Hk3OgMelFOftR1j6Usx9lLXF49JN9IwOMTx5ldXWVVCqxbyGbTmPSh3L2o6x9KGc/ScpaK+dERERERKqMjQwwc/Q48wulVldFREREJPHGRrLMLSwxe3Sx1VURERE5JWhyTkRERESkymihn1QKxie1ek5ERERkOyO7++lOd+m+cyIiIjFpci4hCoVCq6vQEZSzH2XtQzn7UdY+lLPE1ey+0pNJM5Lf1fFbW2pM+lDOfpS1D+XsR1lLHB79JN2V4vThLOMdPjmnMelDOfv5/9m77/i4rjrv45/RaNR7lyzX2D52nGI7dnockhBqCi2QpSyhPRAW2MA+lIeHpSzLbijLkl3KsrC0hyQLBJIQQrKEJCSQXkycOOTEvcmSJavXqc8fd6RIsiSPZOnMHen7fr38Gs2dc+ee+51zpZ/n3LmjrN1Qzu74KWtNzvlEKBRKdxcWBOXsjrJ2Qzm7o6zdUM6SKhdjpaGqiKa2hf0Gk45JN5SzO8raDeXsjrKWVLgaJ4uqCxf8J+d0TLqhnN1R1m4oZ3f8lLUm53yipaUl3V1YEJSzO8raDeXsjrJ2QzlLqlyMlfqqQpoW+CfndEy6oZzdUdZuKGd3lLWkwtU4WVRdtOAn53RMuqGc3VHWbihnd/yUtSbnREREREQm0FBduOAvaykiIiKSKm9yTrWTiIhIKjQ5JyIiIiIygfrKwgV/WUsRERGRVDVUF3G4rY9YPJHuroiIiPieJud8oqCgIN1dWBCUszvK2g3l7I6ydkM5S6pcjJWG6iK6esP0DUTmfFt+pWPSDeXsjrJ2Qzm7o6wlFa7GyaLqIqKxONt3tTnZnh/pmHRDObujrN1Qzu74KWtNzvlEWVnZyM/hWIRHDzydxt7MX6NzlrmlrN1Qzu4oazeUs6TKxVipqywgEGBBX9pSx6QbytkdZe2GcnZHWUsqXI2T0qIcaisKuOFnW2k+ujDrJx2Tbihnd5S1G8rZHT9lrck5n2htbR35+fkjL/L1h7/H7vZ9aezR/DQ6Z5lbytoN5eyOsnZDOUuqXIyVUHaQ6vICnt29cM/+1jHphnJ2R1m7oZzdUdaSClfjJBAIcMmmxRTmh6irLHSyTb/RMemGcnZHWbuhnN3xU9aanPOJSOSlyyWdXncyZbkl7Os8lMYezU+jc5a5pazdUM7uKGs3lLOkytVYWbmolFvu3bFgz/7WMemGcnZHWbuhnN1R1pIKl+Nky8ZG9jR1q3aSOaWc3VHWbihnd/yUtSbnfCgQCHDOkjN47ODWdHdFREREZEG7YMMi4vEENeX+uS69iIiIohS2GwAAIABJREFUiF8tqi5iZWMpD27VCeciIiJT0eScTwSDwTH3z2rcwLaWF+iPDKSpR/PT+Jxl7ihrN5SzO8raDeUsqXI1VjasrmFgKMqOAx1Otuc3OibdUM7uKGs3lLM7ylpS4XqcbNnQyINbDzrdpl/omHRDObujrN1Qzu74KWtNzvlEbW3tyM/9gxF2/CVAYSifp5ueS2Ov5p/ROcvcUtZuKGd3lLUbyllS5WqsFOaHOHl5JU/8pcXJ9vxGx6QbytkdZe2GcnZHWUsqXI+TC9YvYn9LD3sPdzvdrh/omHRDObujrN1Qzu74KWtNzvlET0/PyM97mrr5/q+3szh/lS5tOctG5yxzS1m7oZzdUdZuKGdJlcuxsmltDU8t0Mk5HZNuKGd3lLUbytkdZS2pcD1OqsryWbeickF+ek7HpBvK2R1l7YZydsdPWWtyzidGD4p1Kyo599QG2veX8efD2xmKhtPYs/nFTwfffKes3VDO7ihrN5SzpMrt5FwtOw920d496GybfqFj0g3l7I6ydkM5u6OsJRXpGCdbNjTywNZDJBIJ59tOJx2Tbihnd5S1G8rZHT9lrck5n3r3FevYtyNEgCDPND+f7u6IiIiILFiLa4upqShYsJ+eExEREZmu805r4GjnAHbfwvzeXhERkePR5JxP1VYU8MqzVxDsrePRA0+nuzsiIiIiC1YgEGDz2toF+71zIiIiItNVUpjDBlPDAwvw0pYiIiKp0OScT1RVVR2z7KpLVtHdVMHjB7cRjUXT0Kv5Z6KcZW4oazeUszvK2g3lLKlyPVY2ra3lzy+2EonGnW433XRMuqGc3VHWbihnd5S1pCJd4+TCDYv405+biMUWTv2kY9IN5eyOsnZDObvjp6w1OedjlaX5vOqUzUSiMZ5pfiHd3RERERFZsE5dWUUsnuD5PUfT3RURERGRjHDWKfX0D0XZtrMt3V0RERHxHU3O+URb28SFypsvXkOiq5rfPPuQ4x7NT5PlLLNPWbuhnN1R1m4oZ0mV67GSGwpy2soqnlxgl7bUMemGcnZHWbuhnN1R1pKKdI2T/NxszlpXx12P7E3L9tNBx6QbytkdZe2GcnbHT1lrcs7nSoty2bRoPc+3P084qktbioiIiKTLprW1PPH8wpqcExERETkRG1ZX8+hzh9nT1JXuroiIiPiKJucywLtfdjGJQJRv33NXursiIiIismBtXlvLodZeDrf1pbsrIiIiIhnh4s1LKC3K4cX9nenuioiIiK9ocs4niouLJ32sqriIEup4+Ojvee7Afoe9mn+myllml7J2Qzm7o6zdUM6SqnSMlZqKApbUFS+oS1vqmHRDObujrN1Qzu4oa0lFOsdJMCvAK89axj2P7UtbH1zSMemGcnZHWbuhnN3xU9aanPOJ4w2KC5edSyIQZ2VdvaMezU9+OvjmO2XthnJ2R1m7oZwlVekaK5vX1vLQM01p2XY66Jh0Qzm7o6zdUM7uKGtJRbrHycvPXILd38G+w91p7YcL6c56oVDO7ihrN5SzO37KWpNzPtHSMvUZ2Feefg4EEtz97BOOejQ/HS9nmT3K2g3l7I6ydkM5S6rSNVZOaixj+56j7Dq4MC7NpGPSDeXsjrJ2Qzm7o6wlFekeJ3WVhaxfVc3vHp//n55Ld9YLhXJ2R1m7oZzd8VPWmpzziVgsNuXjJQUFlEaX8sCexx31aH46Xs4ye5S1G8rZHWXthnKWVKVrrJx3WgMVJbnsPNiVlu27pmPSDeXsjrJ2Qzm7o6wlFX4YJ684ayn3P3mASDT9fZlLfsh6IVDO7ihrN5SzO37KWpNzGWRD7XqawrsYiobT3RURERGRBSkrK8BFZyzmwa0H090VERERkYxx9ql1ADz6bHOaeyIiIuIPmpzziVAodNw2rzl1E/FYgAd3PeWgR/NTKjnL7FDWbihnd5S1G8pZUpXOsXLhxkae3dXG0a6BtPXBFR2Tbihnd5S1G8rZHWUtqfDDOAllB7nojMXz/tKWfsh6IVDO7ihrN5SzO37KWpNzPlFdXX3cNsvqywj1NXLPi4846NH8lErOMjuUtRvK2R1l7YZyllSlc6wsqy+hsaaYP/65KW19cEXHpBvK2R1l7YZydkdZSyr8Mk4uPWspz+xopfloX7q7Mmf8kvV8p5zdUdZuKGd3/JS1Jud8orOz87htAoEA68pPZV/fLnrD87eQmUup5CyzQ1m7oZzdUdZuKGdJVTrHSiAQ4MKNi3hgAVzaUsekG8rZHWXthnJ2R1lLKvwyTpbVl7B6cTm/f2J/ursyZ/yS9XynnN1R1m4oZ3f8lLUm53yiv78/pXYXrTmdRCSHRw9sneMezU+p5iwnTlm7oZzdUdZuKGdJVbrHypb1jew80ElTa29a+zHX0p3zQqGc3VHWbihnd5S1pMJP4+TSs5Zy7+P7OTRPayg/ZT2fKWd3lLUbytkdP2WtybkMs351NbH2en7/4qPp7oqIiIjIglVfVYhZUs4DWw+luysiIiIiGeOC9Q30DUb59Lf/xOG2+TlBJyIikgpNzmWYgrwQy/LWsrt7Fx0DXenujoiIiMiCtWXjIh54+iCJRCLdXRERERHJCAV5Ib7wvnPoG4xy9yP7VEeJiMiCpck5n6itrU257dkrDdnREh458NQc9mh+mk7OcmKUtRvK2R1l7YZyllT5YaxccPoiDrf1suvQ/D1hyg85LwTK2R1l7YZydkdZSyr8Nk7WLq/gHz9wLnc/upcf3LF9Xk3Q+S3r+Uo5u6Os3VDO7vgpa03O+UQkEkm57aa1tQy01PC7HX8kHEt9PZleznJilLUbytkdZe2GcpZU+WGslJfkcdrKah6cx5e29EPOC4FydkdZu6Gc3VHWkgo/jpM1Syv44vvP5Z7H9vH925+bNxN0fsx6PlLO7ihrN5SzO37KWpNzPtHe3p5y22X1JRRG6mjubeW6336erYefm8OezS/TyVlOjLJ2Qzm7o6zdUM6SKr+MlQs3LuLBrQeJx+fHG0rj+SXn+U45u6Os3VDO7ihrSYVfx8nqJeV88QPncu+TB/jX/356XkzQ+TXr+UY5u6Os3VDO7vgpa03OZaBAIMCZy05mXexKLlh6Jl/903f56p/+gyN9R9PdNREREZEF5ZxTG+juC/ObP+1Od1dEREREMs6qxeVcd/UGHnjqEDf8bOu8mKATERFJhSbnMtQZa2p55tkhEocNHzvjOsKxMB+96wvcvO12FTIiIiIijhTmh7js/OV87/bn+Ox/Pszhtr50d0lEREQko5x9Sj2feucmHnn2MD/8zfN6X0tERBaE7HR3QDylpaXTar+soYTSwhwe397Mzb/rorH2ZMzqam77y//wYttuPnre+yjJLZqj3mau6eYsM6es3VDO7ihrN5SzpMpPY+Xdl5/CBlPDrx/czQe/ch9XXLCCN798NYX5oXR37YT5Kef5TDm7o6zdUM7uKGtJRSaMk3NObaC8JI/PfvcRglkB/vo1awkEAunu1rRlQtbzgXJ2R1m7oZzd8VPWmpzzicLCwmm1X1RdxJeuPY+6ykKOdg3w8LbD/P7xIIPdm9l/8l4+fvc/8rfnvIeTa1bNUY8z03RzlplT1m4oZ3eUtRvKWVLlt7GyYXUNG1bX8PQLR/j+r5/jD08f4N//98WUFOaku2snxG85z1fK2R1l7YZydkdZSyoyZZysWVrBF953Dp/73sMEgwHe/qq16e7StGVK1plOObujrN1Qzu74KWtd1tInmpqapr1OXaU3kCpL87n8ghXc8HcX8eHXXkL+gS10HqjmC/d/gx889XPiifhsdzdjzSRnmRll7YZydkdZu6GcJVV+HSsb19Tw6Ws20z8Y5V9ufDLjL8vk15znG+XsjrJ2Qzm7o6wlFZk0TtYur+Bz7z2H2x7YxTd/8eeMq6UyKetMppzdUdZuKGd3/JS1JufmmZefuYQbPnYx7938JrIPrefuF//AB3/+z/xp234GhqLp7p6IiIjIvNdYU8xn3n0W2/e0c9+TB9LdHREREZGMsm5FJR+9egP3PLafL//kSWIxnXQuIiLzjy5rOQ8FswJcetZSVi95C5+7qZC++m3829PfIvKzjaxZtIhVi8vYsqGRFQ2lZGWlfv3uRCJBd1+Y1s4BjnYO0N0f5vRV1VSX5WfkdcBFRERE5srpq6p57xWn8N1bn2XdisqRKx6IiIiIyPGdd/oiPpuTzb/9/M9c/5Mn+PjbN5ETCqa7WyIiIrNGk3M+kZeXN+vPubS+hOuvuZKSktdzw8M/YEfJk5Qnyrnr4U5u+8MuSopyWL+qhkU1hQSzsmjrGqCtc4CjnYP0DITJDr70wcpwOEp3f4RI1Dtbqbgwh77+CPFEgvzcII01xSyuLSY/N8iy+lKqyvKpKssnHI6xrKGEUHZWWifwEokEew93s+3FLl5dXauCzoG5GNNyLOXsjrJ2Qzl7jDFvBa4FTgOCwAvAD4HvWGunfeqwMeZVwMeATUAesBu4GfiatXZoivXOAj4FnAeUAAeAW4EvWWu7ptuP2ZQJY+WVZy/l8eeb+fpNT/PPf3M+wWmcFOUXmZDzfKCc3VHWbihnd5S1R7XT1DJ1nJyxtpavfPgC/v67D/P57z3KZ959JgV5oXR3a0qZmnWmUc7uKGs3lLM7fso6kGnXbvYTY8wyYM+9995LY2NjurszpXg8zo3bbuXuHX/gtOpTWVW1jL6uHHbvjvDs8/2saKigobqQqrJ8QsEs7n3yAJedt5yy4lw6e4a486E9vO1VazFLy6koySOUncXhtl5yc7LZ39zN/pYeXtzXwZN/OUJ5SS5dvWF6+sMj288OBijIC1GYF6KiJI9VS8pYXFvMkrpiYrEEtRUFY/p7pKOfmvKXlu1p6mJgKMr+5h72t/RwqLWXgcHomAnEYDDAioZSFtd5E4UNVYU8s6OVPU3dbLVH6OgZIhCAnFCQc06p57zTG9hoakYm6qKxOANDUZrb+jipsWxanyoUERH/OnjwIJdccgnAcmvt3jR357iMMd8CPggMAvcCEeASoBjvzZ03TedNJmPMJ4AvAzHgD0AHcCFQDTwKXGKt7Z9gvb8C/h/eG1wPAYeAs4ElwE7gPGvtkWnu2zIypHaaLZ09Q3z4a/dzxZYVXHXJ6nR3R0RE5LhUO6l28pvOniE+971HGByK8ulrzmRpfUm6uyQiIjJiprWT7z8555ezn+Zae3s7FRUVc/b8WVlZvGP9G6kqrOCW5+7k6FAb7QMddOf0krUeWnMKiOSV0JVbQmleCSefH6SgIZfyomrWFFWz6eSzaKgqHvOc9VVFAFSU5LF+dQ1cAM1H+0Yu2zQUibHzQAdF+Tn0D0bpH4pwoKWX2x7YSV5OkKdeOMKhIz3EU5wfrizJZfkib1Jv1eIy7nl8/5gJxF//cTfZ2Vls33WUux7eQ3v3EMFggLNOruO9V57C6auqOdDUSn80m4eeaeIbNz9NLJ4gFAoyFI4RjsRGthXMgsqyAqrL8qkszSOYFaCqLJ+CvBAFedn0D0TIyQkyMBhN7luUorwQWzYuYll9iS7zydyPafEoZ3eUtRsLPWdjzBvx3lxqBrZYa3ckl9cC9wOvBz4M3JDi820Crgf6gYuttY8llxcBdwJbgC8BHx23XiPwX0AAeJ219vbk8mzgp8BbgO8m+5MWmTJWyopz+fBb1vPPP3qc2vICtmzMrDfWMiXnTKec3VHWbihndxZ61qqdUpPp46SsOJfrrt7AZ/7jYa771z/wrsvWcdn5K3x5UnWmZ50plLM7ytoN5eyOn7L29eTcFGc/fRO4xBgzW2c//SNwmTFmwrOfXBgcHHSynVevuoiN9adQW1QNwN7Og3zz0R/x2tWXEAhA12APh7qbeaLtGfb27OPoQAdDUW/OMpg19lKQoaxsKvLLKMvzJvTK80upyq/gzLz1VBdUkBsKsm5F1Zh1zlhTy9mn1FFXWUg8HqdjoJute/axccnqMRNaR9r7qRn1abru3vAxZ0ZdsH7RmO9vOXNd3Zj7A0NROnsGRyYRAfqKsjiloY4zT65jKBLld09Zbv/TDj5w8SYaa4spyMumrXNgzGU+9zV388TzLZzUWEo8nqCzd4jDbX0srimmtDiXgtxs4nG4/6kD3HL/DsqLc9lgaqhL9r+ta5C2zgHaugboH4yM+bRfIpGgpryQyrI8qkq9S4EORWKUFeWOtOnqG6K+spCCvGwKcr3JwSOdAxTmZXsTg4NRDh/tJSc7SN9gZGTCkACcYWpYs6yC0lHPN9v6ByPsaeqiunzspx937T3K5tKyMfsrs8/V7w5R1q4oZ/5P8vaTw28uAVhrW4wx1+LVL58yxvx7ijXQp/DeJPry8JtLyefrNca8C9gBfNAY8wVrbeeo9a4D8oEfDr+5lFwvaoz5X8CrgdcZY0621j4/s109MZk0Vs48uY5Xnr2Ur974FC8e6OBdl5+SMZe4zKScM5lydkdZu6Gc3VHWqp1SMR/GyfKGUv7lb7ew40An37rlGZ564Qh/e/UGKkr8c3kymB9ZZwLl7I6ydkM5u+OnrH07OeeXs5/mo+GJOYBlZY18/Pz3j1kG0NLbSm1RNYlEgp3te/mPx3/KVae8lor8MgDaBzr52bN3cPGK80iQoHOwm4Pdh/ndzgf4yTO3UJ5fiqk6iYLsPLKzshmMDjEYG2IwMkRbfwd9kX66h3oYvqxqzgs51BdVU1tUTV1xDfVF1SzN20ReyCuyyouPLbZGT8QN34/GY3QP9tA52EXnYA/NvS0835XrbT86xO4j+4jtStDS28qR3jYi8SjBJUH+EutledHF1JU2HPO8MPYTgRPdH16Wn5vNMztaeeiZw/zy/p2sWlzGopoi1iyrIBQMeJ/2O3/FyKf97vjjLlYvKWMoEuNQay9P/KWZw219VJbkkZ2dRTQap61rkIK8bMKRGANDsTHbzM/NJjcni/6BKItqiigryqUgL0QiAc/sOMIDTx2kfyhKfWUhq5eUU1mWx7rllSyuLaamooBgVoDBcJT2rkHaugZo7x6ivXtgzORgW+cAoezgyKcf+wYiHG7to2cgQlvnAAND0ckH243PkxMKUpCXTX5OkNrKwuSnEb1PJPYPRcZsq68/ykmLS6kqzac8eflUmV2xeIKu3iEOt/WOmVBt7eg/ZoJ1eFJcr8PCFI8nGAxHvcv9Hu077ngZv2yiNp3dQ774HlK/Sp5xfQYQBn4x/nFr7QPGmEPAIrxLJD18nOfLwXsjCODGCZ5vtzHmEbzvRHkNcNOoh183xXrdxpg7gLcl26Vlci7TfOANp3PSojJ+8tu/sPNgFx9768Yxl/AWERGR6VHttPDUVRZSV1nI2mUV/OvNT/Ohr97Peac38LKNjaxZWk5QJweLiEgG8e3kHP45+2neGz8xN3pZIBBgVeVyPnHBB45pt7x88YSTesGsILZtF1ubtvP4oT+zrsZQnl9KRXYZ0XiU5t5W3nLK5Swra6Qsr4TWvqPEEjGae9to7m1ld/s+fmPv4XtP3cza6pWcXncyedm5hGNhmntaaelr5UjvUYai4TGf5huMDtIXGRi5X5hTQDgaprqwiuLcQgIE2N95iAuWbebsxg3UFlVRWVDBI/uf4s/N2/m7u7/IivIlrKleSV1RNXnZuSP/uod6aY2Wjjx3W18H+wbyGIqGGYwOMhgdIpSVzZmNG9iyoZEtGxonnMA7f9yn/TafXDvhJN9kE4GxeILBoShHOvpZWlcycgmH0W0SiQSRWIRDR3uoKc/ncFsfOw50sm1nG3c9fJj/eWQffYMRckJBQsEAfYPe5FpeTpCSwhw6e4eoLMkfmRxs7x5kSV0xZcW5FOSGgARHOvq57PzlrFhURlVZPv2DEWorXupzggR/3r6XpYvr6R+McKi1l1vu28HS+mLC4Tg7D3byp209HGkfoLKkYGRbrZ0DJBIJYvEEgQCUFeVSUpRDfaX3fYhVpfnE4nFyQtkMDEboH/I+JdjeNUAc6B/wlg2Fo9SUF1JVlkdVWT6VJXm0dQ4SjsZGPhHZ0TNEcX4O+XnZI59KjERj5Oe99GtxYDA65v5Ey2arzUTLuvvCJBLepxP7B71JkmgsPuY/HLFolPKSF0cuuZqfm81QeOx+dHYP0TsY4WjnAO09Q8RTvZZsUig7i4K8bLKzAgSzx36CNhgIUFKUQ0FuNgX5IeLxBAXH2a/egQgkGJnw7R+MEo7Exu7XuP2caFkqbeLxOMUFORTkhcjP9V7raDQ+pj99AxGAkUvUDgxGGAzHjnnucDhCTs72We+j39qEI1HCEe87OOfqa2mzgwHyc0OEsgNkjxtTl26o5uqGhrnZsP9tSN5ut9YOTNLmCbw3mDZwnDeYAAMUAO3W2l1TPN95yee7CcAYUwKcNOrxydZ726g+SwouPWspm0+u44afbeUjX7ufN798Na89fwW5oeDxVxYREZHxVDstUFVl+Xzx/efyy/t3cMt9O7jnsX3k5Waz0dSwrL6YdSuqaKwpoqQwRycFioiIb/lycs5nZz850eDzNyKnmsCbaFnVkgrOW7J55BN4o71y5YVjllUVetd4PaX2pTZN3S10D/WyreV5Ht73BAe6m1lW3sji0gbW1Rg21ufwu50P8lpzCWV5JXQOdvPbF+/jb866hhUVSyjJLSY7K3jM9ifqz5VrX8GVa19Ba99R7tpxP/fueoiawkpiiTiD0SH6IwMMRgfJCeaQFcginogTjkUoyS2iMKeAvGAuBALs7TjAD7f+guqCCkzVSdQWVbG035uAHL70Z05BhOeP7KClt5Xm3lYO9xyhpqjSezy3hLL8EvrC/XQnykb61z7QOeY+wP6BJrbvGBp5nuaeI4TjkZFPCCYmekc9G4Lrg1Tkl7I0p4hEJJdDR/rYtKiKkqKckctP9g2GaSitJC/kTUx29sY4ub58ZD9K8opp6xgcM4HYHxmguecwLX2tNPe0cqTvKD2RXp478NInHuvP7GJf4Bm6gt10BroZLB4ie2mArPxSivOKKcsrZVE4Rm5OkL7wEH1DA/QODdDaG2Ywt5C9Q0HCBwL0D0QoyikmLzuPvOwcAolsDg91saq+muqCHGLxBNv3HSZUX017NMH+oxG69w/RG+6mpryYgiLIKo4x2NFORUUJ8awAnbEEzeEoRwe7KSEHsqJE4hH6swbII0RWspiPJxIMZkVGlo2//1KbGEUUEMrKgXiQzsgQlblFIxMgsVico5HeY5cNDVCXW0Fudi7xaBaHBjpZWVdDRUEOdaEsItE4z+5tYt3yRRTkZtM/FGXb7kNUNdaSFfQeb+4Pc6CnncW5FYSyg0SiMQ6E21m7tI4Vp+VRmB+iIC9EW08ntaXlLx0bXR1j7gM0dXRQll9EOBqnq6efp3cdYsOKZZQWePs2OAAPPr+DuuV1kBWlo6+f3S0tI9sGvO2P709fOytqqyipzaEyFCQajfPcvnH7tefQyH3gmGXTaVPXWEcwGCAcjdPWH2Z/x7H9WV5bRXFNDhXZWURjcZ7bd3jC514zF33ce5BVS6oIhuJE4mH6hgY5cKSLZfV1FOXmE41ksX1vy6zlcfz+HOK8VYspLcr1JvGzs2jtPv54Gb9swjHV2UF5fjHhSJzO3n6e3nlwJNPh/jyws5OXHV014aeYF4Dlydt9U7TZP65tKs+3f4o2Ez3fsuRtp7W2exb6MSf8XjtNpqw4l8++5yxu/p3lx3c+z0/v+gunrqrmjDU1bFpz7Ek76VZXVz/tkzpk+pSzO8raDeXsTqb+PZwlqp1SNB/HSVZWgKsuWc0F6xdRVpTLtp1tPLD1ID/7/YvEYpZYPEFRfohFNUUU5GVTVZpPcUEOxYU5RGNxaisKyMsJkpuTTV5OkO6+IWorCskOZpEdzKK9e4DaikKysgJkBQLJW+8k9sDwLcConwMBqK6pIxId+xmC4fnBwPgFc2ChTEXq74w7ytoN5eyOn/4m+nJyDp+c/eRSX18fhYX+ejNmNqQ6qTdeQ0ktDdSypvok3nzK5RNOqm1adNqYZafXrT2mzfj7RYHJLx9VXVjJX69/0zGTh3DspN5E/WnuaSUrEOCFtl1sPfwcv7H3UhDKpyfcRzT+0qUfQ1nZ1BZVU5ZXwv6uJgajQ7wY3U3XYA/tA50MxcJkESAQCJBIJIiTGLkP3ifjAoEAi0sXsbi0nvriWpp7jvCmda+lrqia3GAOfeF+bnr2dt6x/g1UFXiTn619R/nx1lu4dOUFBAhwsOswRyNbqSpbQkEoH4C+cD87O3dCVhQCAXqGetjXeYgHDuTSG+mbeNIvKRAIUF1QQW1RNcU5RbzYtocNDesoCOXTHxmgpa+Zly0/h8Wl9ZTllRCJRfnpM7/ilateBsDB7mYePfAUZ1dspKqgnLzsXAYiQ9z94gO8bMUGQsEQR/s7ePTAVk6ubiAQCDAUG6JrsI1gbjODRf3EgyHCsQjUtDBQ0EdOMEQRkBOLEO05Qk3lMkrzioknsmkP9LC84aV9748MsLWphfOWnUJVQQXhWJjf73yI16y+iNK8YsD7Tsbfvnj/yLLx94fb3PnifVy0fNNInx85sJV1DcvGbOvpppYxy/rC/Tzd9Bz1VeXAIN1DvQRDTQwVD5AIhgAIZ0VIVLfQGugjJxYiHPDud+V6+0oukBchGGqB4n4CwRDEvPu9ef2EoyHogXBnhObeI9T11ZCTzGz0feClZUXJNglvW7uibUQ7Y95EcGSQWG2cF6JFFObkk1WURTCr/aVtw8j2x/cnUtxP7/C2ghPv1/B94Jhl02nTmZPMJwcSuRGC2cf2J1rcT18wRB8QZpLnrpqbPlJ1hEhegvy8IkqycymLBzkS3UdeSTkxhuihh0R106zlcdw2VS0cjPZxpGfcWEhlvExnTMUn7k/WkkECuS8D5t/fxBQMf0lq3xRtepO3xXP4fLPdjzmRybVTIBDgra9cwwXrF9HZM8RTL7Rwz2MbHIUpAAAgAElEQVT7+d5tz6W7ayIikkGysgJ87OpTufCMtJ0rk26qnVKUyXXT8Qyf2HTmujrOXFc3ckn+1o5+Dh7p5S972/n94/spyg/RPxhl+56j7G3qpqw4h1gswWA4xsBQlJjeFBcRmffyc4N87l1nsG5Vfbq7Avh3cs4vZz8509XVNW8LpdmQyiRfKpN+qeQ8023VFXvLaoqq2LLsrDHf29cfGWBn+z5ueuZWrjv3vdQX1wATT/KlMhF4TJvVFx3TpqGkdsyyhuJaPnnBtWOWXb7m5SlvPx6P0x3uZU/7fm7cditXn3ollQXlHO3v4GfP3cF1Z7+HRaV1I+tt27Wd005aN+V+jO/jayfYj7MXbxiz7Io1l85OZim0OW/JpmPabG4cOyk8/v5Ey1LJeaZ9HJ/zTJ9HbY7/+qQypv2+H5nQZtuu7Sn9PpfMduTIEbKyvE8OV1VVAdDW1jbyeHFxMcXFxbS0tBCLed+3GgqFqK6uprOzk/7+fnp6eiguLqa2tpZIJEJ7e/vI+qWlpRQWFtLU1DSyLC8vj4qKCtrb28d8AXRDQwN9fX10dXWNLKuoqCAUCtHS0jKyrKCggLKyMlpbW4lEvMvhBoNBamtr6enpoaenZ6RtqvuUFwpx6spqFldl84qN5exq6uHGe/ZxzWXrKC0MsfdgK7c/dIgrz1vE4oYK8vPyeXH3oZFldVXFhOMhfvSb57ji3AbKi3MACISK+PFvt3P52fWUF+fQ0RPmN482845Xr4FoPwAdPWF+/UgT17z2FIKJQaLRKB09YW5/+BDvuuxUCnISHGzu4FcP7ucNW5awfIlXu+zZf2Rk+4115fSHA/zwN89y5bmLKC/OITs7m1ggjx/d+RxXnNMwsv07H2vh6pevJJuhke3f8ehh3vmadSQivd62HzrEVRctZ1ljNd3d3TS39Yxsa/WKRRw60sFN/7ODK8/ztlVaWkpXX4Qf/Wb7yLL+cIBfPbifK89roDjf+xR3d3+MXz/cxFUXLSM3GBvZ1jtes5bK0jw6OzpHlv3VpStprKtg177m5PMsorq8gPLycvYdauPn9+0e2RbZBfy/u17gsrPrRrIPJ7L5+b17eO1ZtSP7/uuHm7jmslPIyYowNDQ0sq13XX4qhXlwoKl9JOdljdVkZ2ezc+/hkX2vry6huLiY3fub+eUDXp+qyvKJZ+Xz4zu3c/k59SPbj2XlcdPvXuSys+pGtn/HI4d552vXkRUfIBaLjelTbjDK4dbukW2tXFZPNBo9Zuz1DcIP73h2ZN9zc3OPGXvDr/ObL1lOTiA68jqPHnvD+/7mi1ewdFEVHR0dtHb0c/tDh3jDliWctLSOg83t3HzPzpFtlZWXcbRrkP/327+MLBuKBfnF/Xu54twGSgq817lnIMbtDzXxhi1LKMhJjGxr+Hjq6uqioyfMrx7czztes4ZFNeXHHE8lJSXsPdjKL+7fM7Kt8ccTQJRc/vv3O0deZ+CYsTf+eOrr6ztm7I0/ngoKCo4Ze+OPJ+CYsTf+eBo5xkeNvfHH08DgAAea2keWLWusPuZ4ysvLYyiWPeZ1Hn88jbzOjzXz1lesJhgfHMn57a8yNNZ5v3fbOge4/aFDvPHCJaxYUnfM8VRZWUnL0V5+evcLI8vGH08AfUNw6x8P8PoLFlOYyzHHU2/PS79P3v6qNdRWFqV0PFVUVBwz9sYfT8AxY2/88TQ4OHjM2JvL3+XdfRF+ce8OzLIagvH+Wfv7NPpvqvjDidZOLS0tFBd784KZXDuNrweHjd+nluYuSktL2bS2lobSGKctXUV1Wd7IPr2w6xAl+S99xqyhoYFd+1vJCQwRjSe8SbtYDrVVxRw50ko8kaCtc4hbHjzI/3r96WTFB2g52scvHzzAmy5cwsrlDfT19nGwuYNbHtjHmy5cyrLFNRztGuTHdz7P685vpLw4h8LCAvrDWfzgjm287jxvWfdAzPv9uGUpBTneBOHw8X71y1cSCoTp6Alz258O8tZXrmJRTTmtra2qnY5TO43/+xUMBo+pncb//RrOfnTtNP7vV09PzzG10/i/X0XFRaqdjlM7pfp/keFt+eH/Iqqd/P1/EUitduofjPGdW5/lM+8p8UXtFJjqkzDpYoz5NPAl4EZr7dsnafMl4NPAf1pr33+c53sr3uUsH7LWnj9Jm/cB/wn8zlr7yhT7uQzYc/PNN1NX501KLOQ3mFItktK5T8M5p2uf2gY6qMovz/jXKZITIxQOjrxObQMdnHbSujH71NPTw9KlSzNmnzL1eOrp6aGxsXFe7ZNfX6ehoSGWL18+r/bJj6/T4OAgK1asmNV9amtr46qrrgJYbq3di08ZYz4C3ADcZq19/SRtbgA+AvyLtfZ/H+f5rgBuB/5srZ3w+02MMR8Fvg780lr7puSy04Bn8C7NVD7Jeq8HfgU8Za3dlMr+JddbBuy59957aWxsTHW1CTU1NfnqchSzZarvnp3rNhMte+b53Zx+8grf9HG+tkkl53T3cb60GZ/1XB4/C7lNJv7uSPf2Z2tMz4aDBw9yySWXgGqnidpkZO00X+sm19L5d2Y+/x7T3xl/t0nnmPZbm7l87kwc035rk+p6fqqdNDn3UpsZT87pDabMoZzdUdZuKGd3lLUbc5FzBr3BNPyG0FZr7cZJ2vwKeD3wYWvtN4/zfMNvFLVbaysnafN14KOMesPKGFMKdCablE703Smj3gwbeWMqFaqdMo9ydkM5u6Os3VDO7qh2Uu2UCh2T7ihrN5SzO8raDeXsjp9qp6xZ7cXsGb4Wd+EUbYav6d0zRZu5er5ZV1FRkY7NLjjK2R1l7YZydkdZu7HAc96avF1njMmfpM3mcW2n8gIwAFQYY06apM2Z45/PWtsFDH9H7+Zj1phkPdcW+FhxRjm7oZzdUdZuKGd3FnjWqp1StMDHiVPK2g3l7I6ydkM5u+OnrP06Obc3ebt0ijaLx7VN5fmWzNLzzbpQKJSOzS44ytkdZe2GcnZHWbuxkHO21h4AngZygKvGP26MuRBoBJqBR1J4vjBwV/Lu2yZ4vhXAOUAYuHPcw7dPsV4JcHny7q3H68dcWchjxSXl7IZydkdZu6Gc3VnIWat2St1CHieuKWs3lLM7ytoN5eyOn7L26+ScL85+cmn09+3I3FHO7ihrN5SzO8raDeXMPydvv2yMWTm80BhTA3w7efd6a2181GMfMsa8YIz5yQTPdz2QAD5pjDlz1DpFwA/wasFvW2s7x633Dbza6Z3JS0YNr5cNfBcowft+l+dnuJ8nTGPFDeXshnJ2R1m7oZzdUdaqnVKhceKOsnZDObujrN1Qzu74KWtfTs757OwnERERESestbcA3wHqgGeNMXckvytlB3AycBsw/vtSqgDDBFcIsNY+AXwKKAAeNsb8zhjzc7xLL10IPAb83wnWOwC8B+/NqduMMQ8aY/4b2Alcnbyd8jt/RUREROaaaicRERHJVL6cnEvyy9lPIiIiIs5Yaz+IdzLR03hvAr0S7w2dDwFvtNbGpvl8XwFeDdyPd+WBy4E24DPAhdba/knWuxk4D/g1sBZ4PRAFvgpsstYemfbOiYiIiMwy1U4iIiKSibLT3YHJWGtvMcZ8B7gW7+yn3wMR4BKSlwNg8rOfmid4vieMMZ8Cvox39tN9QCde4VbDJGc/uVJQUJCuTS8oytkdZe2GcnZHWbuhnD3W2puAm1Js+3ng88dpczdw9wz68Rjwuumu54LGihvK2Q3l7I6ydkM5u6OsPaqdpqZx4o6ydkM5u6Os3VDO7vgpaz9/cs43Zz+5UFZWlq5NLyjK2R1l7YZydkdZu6GcJVUaK24oZzeUszvK2g3l7I6yllRonLijrN1Qzu4oazeUszt+ytq3n5wb5pezn+Zaa2sr1dXV6e7GvKec3VHWbihnd5S1G8pZUqWx4oZydkM5u6Os3VDO7ihrSYXGiTvK2g3l7I6ydkM5u+OnrH39ybmFJBKJpLsLC4JydkdZu6Gc3VHWbihnSZXGihvK2Q3l7I6ydkM5u6OsJRUaJ+4oazeUszvK2g3l7I6fstbknIiIiIiIiIiIiIiIiIgjmpzziWAwmO4uLAjK2R1l7YZydkdZu6GcJVUaK24oZzeUszvK2g3l7I6yllRonLijrN1Qzu4oazeUszt+ylqTcz5RW1ub7i4sCMrZHWXthnJ2R1m7oZwlVRorbihnN5SzO8raDeXsjrKWVGicuKOs3VDO7ihrN5SzO37KWpNzPtHT05PuLiwIytkdZe2GcnZHWbuhnCVVGituKGc3lLM7ytoN5eyOspZUaJy4o6zdUM7uKGs3lLM7fspak3M+4adBMZ8pZ3eUtRvK2R1l7YZyllRprLihnN1Qzu4oazeUszvKWlKhceKOsnZDObujrN1Qzu74KWtNzomIiIiIiIiIiIiIiIg4osk5EREREREREREREREREUc0OecTVVVV6e7CgqCc3VHWbihnd5S1G8pZUqWx4oZydkM5u6Os3VDO7ihrSYXGiTvK2g3l7I6ydkM5u+OnrDU5JyIiIiIiIiIiIiIiIuKIJud8oq2tLd1dWBCUszvK2g3l7I6ydkM5S6o0VtxQzm4oZ3eUtRvK2R1lLanQOHFHWbuhnN1R1m4oZ3f8lLUm50REREREREREREREREQcyU53BzJcEKC5ufmEn+jIkSPE4/ETfh6ZmnJ2R1m7oZzdUdZuzEXOo/5OB2f1iWUmVDtlGOXshnJ2R1m7oZzdUe00781K7aRj0h1l7YZydkdZu6Gc3fFT7aTJuRNTD/C2t70t3f0QERGR46sHdqW7EwucaicREZHModop/VQ7iYiIZI5p1U6anDsxTwAXAIeBWJr7IiIiIhML4hVIT6S7I6LaSUREJAOodvIP1U4iIiL+N6PaKZBIJOamOyIiIiIiIiIiIiIiIiIyRla6OyAiIiIiIiIiIiIiIiKyUGhyTkRERERERERERERERMQRTc6JiIiIiIiIiIiIiIiIOKLJORERERERERERERERERFHNDknIiIiIiIiIiIiIiIi4ogm50REREREREREREREREQc0eSciIiIiIiIiIiIiIiIiCPZ6e7AQmaMeStwLXAaEAReAH4IfMdaG09n3zKFMSYEbAFeA1wIrAbygFbgEeCb1to/TLG+XoMTYIz5J+D/JO9+3Fr7tUnaKecZMMbkAx8GrgJWATlAC/Ak8A1r7UPj2mfh5fwuYA0QA7YB37bW3uyw6xnDGNMIfBJ4BbAECAAHgHuBr1hrd0+ynsb0KMYYA7wK2AxswvtdHACustbecpx1Z5SlMeZVwMeS28sDdgM3A1+z1g6d6D6JP+nYO3GqndJLtdPcUd3khmqn2aHaSVzRsXdiVDell+qmuaXayQ3VTrNjvtZO+uRcmhhjvgXciPfi/hG4B29QfRO4JfkLT47vQuD3eAfKIuBB4FagHXgjcL8x5h8mWlGvwYkxxmwGPgEkjtNOOc+AMWY5XpHzZbyxfT9wJ95/Al4HXDSufRBv7H8Tr6j6HfAnvD9aNxljbnDW+QxhjNkAPAt8CCgA/ge4G8gH3g88Y4w5d4L1NKaPdS3wDeBtgMErkI5rplkaYz4B3AVcDDyNd2zUAP8I/MEYU3AiOyP+pGNv1qh2ShPVTnNHdZMbqp1mlWonmXM69maF6qY0Ud00t1Q7uaHaaVbNy9pJn5xLA2PMG4EPAs3AFmvtjuTyWrxfhq/HO3NBv9iOLw78ErjBWvvH0Q8YY96Cd/D9vTHmfmvt/aMe02twAowxucCP8c6oeRzvD/dE7ZTzDBhjCvH+WKwAPoV3RkZs1OOVQOW41a4DrgCeBy621rYk267C++PzEWPMfdba2x3sQqb4FlAGfA/4G2ttBEbOjvwP4N3Ad4DTh1fQmJ7Uc8BX8c6wewr4L7z/yE5qplkaYzYB1wP9eGP9seTyIrxiaQvwJeCjs7Rv4gM69maVaqc0UO00d1Q3OaXaafaodpI5pWNv1qhuSgPVTXNLtZNTqp1mz7ysnRbiLKsfDH8k+5PDgwIg+Yvt2uTdTy3QWfBpsdbeZ6190/giKfnYz4AfJe++fdzDeg1OzD8Aa4EPAF1TtFPOM/MZ4CTgW9baL48ukgCstUettS8O30+ewfSJ5N1rh4ukZNsdeB+fB/i/c9vtzGGMyQPOSd793HCBBJD8+TPJu6eNOxtGY3oC1trvW2s/Ya39ubV2V4qrzTTLT+GdIfXl4QIpuV4v3uU14sAHjTFlM9kX8S0de7NEtVPaqHaaO6qbHFDtNLtUO4kDOvZmgeqmtFHdNLdUOzmg2ml2zdfaaUG9iH5gvOvMngGEgV+Mf9xa+wBwCKgDznbbu3lpa/K2cXiBXoMTY4w5C/g74CZr7R1TtFPOM2CMyQHel7z79RRXOwfvo9UHrbUPTvD4L4AIsNkYs+jEezkvxIBoCu36gAHQmJ5NM80yeXy8Onn3xgnW24333Q85eN8LIfOAjj3nVDvNMtVOc0d1k1OqndJItZNMh449p1Q3zTLVTXNLtZNTqp3SKFNqJ03OubchebvdWjswSZsnxrWVmVuVvD08aplegxlKnvXxY7zrq//tcZor55k5A+/yAYestXuMMRuNMV80xnzXGPMPxpjzJ1hnOL8nJngMa20/sD15d/3sdznzJM9Sujd59wvJSwoAI5cX+GLy7n9Za4evca8xPXtmmqXBu057+xRnSuk1mH907Lml2mkWqXaac6qbHFHtlHaqnWQ6dOy5o7ppFqluckK1kyOqndIuI2onfeece8uTt/umaLN/XFuZAWNMHXBN8u4vRz2k12DmvoT3S+pqa23bcdoq55k5NXl7yBjzNbwzxkb7e2PMbcDbrbV9yWWpZr0eZT3aB/G+iPd9wKuNMU8ml28GyvG+aPYTo9prTM+emWa5fNxjqa4nmU3HniOqneaEaqe5pbrJLdVO6aPaSaZDx54DqpvmhOqmuafayS3VTumTEbWTPjnnXlHytm+KNr3J2+I57su8ZYzJBn4KlAL3jvsovF6DGTDGnIv3BbC3Ja+tfjzKeWYqkrcb8IqkbwAr8f5oX4n3kevXAd8etY6ynoHkR9HPBe7CuwzJ65L/FuF9yfEfR18THOU8m2aapV6DhUmvuwOqnWafaicnVDc5pNoprVQ7yXTodZ9jqptmn+omZ1Q7OaTaKa0yonbS5JzMV/8BXAIc4Ngv5pVpMsbk433RcTfeWR8yd4Z/L4eAn1prP2qt3WWt7bTW/hrvj3gCeIcx5qS09XIeSBb/z+EVolcC1cl/r8MrTH9pjPls+nooIuKUaqdZpNrJGdVNDql2EhEZobppFqluckq1k0OqneR4NDnn3vDMauEUbYZnaHvmuC/zkjHmBuA9QDNwibW2eVwTvQbT909411L/mLX28PEaJynnmRmdxffGP2itfRJ4CggAFyYXK+tpMsaUAbfhneXyKmvtr621bcl/twOvwvtC3r83xgx/j4Bynj0zzVKvwcKk132OqXaaE6qd3FDd5Ihqp7RT7STTodd9DqlumhOqm9xR7eSIaqe0y4jaSd85597e5O3SKdosHtdWUmSM+RfgI0ArXpG0Y4Jme5O3eg1S93ogDrzTGPPOcY+tSd5ea4y5DNhprX0vynmm9kzy8/g2m4C65P29yVtlnbrX4p2tdF/yMgNjWGt3GmMeA16W/LcD5Tyb9iZvp5vl8M9LprmeZLa9yVsde3NAtdOcUe3khuomd1Q7pdfe5K1qJ0nF3uStjr1Zprppzqhucke1kzuqndJrb/LW17WTPjnn3tbk7brkx7YnsnlcW0mBMeYrwMeAo8DLrbXPT9JUr8HMZOGdNTP+X23y8RXJ+5uS95XzzIzOonKSNlXJ2+GzOZ5O3m6eoC3GmALglAmefyEb/iPbNUWbzuTt8DXZNaZnz0yzfAHvzLKKKS6xceYE60lm07E3R1Q7zTnVTnNPdZM7qp3SS7WTTIeOvTmgumnOqW5yQ7WTO6qd0isjaidNzjlmrT2A90stB7hq/OPGmAvxviCyGXjEbe8ylzHmeuDjQAdwqbV222Rt9RpMn7V2mbU2MNE/4MfJZh9PLlufXEc5z4C19hDwWPLuJeMfN8aUAxuTd59M3j6Cd+ZeozFmywRPexXe9cSfSD6/QFPy9gxjTGj8g8llZyTv7gGN6dk00yyttWG8L1IGeNsE660AzgHCwJ2z3nFJCx17c0O109xS7eSG6ianVDulkWonmQ4de7NPddPcUt3kjmonp1Q7pVGm1E6anEuPf07eftkYs3J4oTGmBvh28u711tq4855lIGPMPwKfxDvb4FJrbSqz1noN3FDOM/Ol5O2njTHDZ4VhjMkDvgOU4l0D/BEAa20M+Eqy2XeS+Q6vswq4ftzziveHth/vTKZ/NcbkDj+Q/Pnf8D6m3gH8z6j1NKZnz0yzvB7vC6o/aYw5c9R6RcAP8Gqbb1trO5H5RMfeLFLt5GvKefpUN7mh2in9VDvJdOjYmyWqm3xNOc+Maic3VDuln+9rp0AikTjR55AZMMZ8G7gWGAR+D0TwzlgowfuyyDclf/nJFIwxVwC3J+8+CWyfpOkL1trrRy/QazA7jDE/At6JdxbT1yZ4XDnPgDHma8Df4eX1KN6lM84EGoBDwEWjr29vjAkCtwKXA93AvXhnLr0cyAP+3Vr7EZf74HfJa9n/FxDEO6Np+FINZwD1wBBwtbX2tnHraUyPY4zZyEuFDcDJeF96vANoH15orT173HozytIY8wngy0AMuA/vP8oXAjV4ZwFebK3tn6XdE5/QsTc7VDuln2qn2ae6yQ3VTrNHtZO4oGPvxKluSj/VTXNDtZMbqp1mz3ytnTQ5l0bGmLcCfwOcineQvoA3+/odzX6nxhhzDfDDFJo+YK192QTr6zU4QccrlJJtlPMMGGPeAHwI2AAUAPuBX+Od1dE6Qfss4IPAu/C+NDkGbMM7m+MmV/3OJMk/7tcBF+AVRuAVovcDX5/sewQ0pscyxrwML7MpJS9LMn7dGWVpjHkV3n8mNuH9Z2A3cBPwNWvt0PT3QjKBjr0Tp9op/VQ7zQ3VTW6odpodqp3EFR17J0Z1U/qpbpo7qp3cUO00O+Zr7aTJORERERERERERERERERFH9J1zIiIiIiIiIiIiIiIiIo5ock5ERERERERERERERETEEU3OiYiIiIiIiIiIiIiIiDiiyTkRERERERERERERERERRzQ5JyIiIiIiIiIiIiIiIuKIJudEREREREREREREREREHNHknIiIiIiIiIiIiIiIiIgjmpwTkVlnjPmDMSZhjLkm3X1Jh4W+/yIiIpK6hV43LPT9FxERkelZ6LXDQt9/kfkkO90dEJGFwRhTBlwHYK39fHp7MzPGmGXANUCntfYb6e2NiIiIzFeqm0RERERSp9pJRDKRPjknInNhP2CBrlHLyoDPJf9lqmV4/b/uOO0m2n8RERGRiahuUt0kIiIiqVPtpNpJZF7QJ+dEZNZZa/863X1Ip4W+/yIiIpK6hV43LPT9FxERkelZ6LXDQt9/kflEn5wTERERERERERERERERcSSQSCTS3QcRmWeMMX8ALgTeZa390aj7k/nC+GuCJ6+1/XfAK4DFQAx4Efg58E1rbd8E2x3+hbYcyAc+BVwM1AF3Wmtfl2y3GrgaeFmybT0wCLwA/AL4trV2YNxz7wWWTrEP77LW/mii/Z+gnyXAx4DXAyclF+8GbgW+bq095tIExpjP413e4MfW2muMMe8EPgicDCSAp4B/stbeM0UfRURExGdUN6luEhERkdSpdlLtJDJf6JNzIuJCO9A26n7LuH+9oxsbY94A/AX4ELAarxDIBTYC1wOPGGNqp9jeBcCTwF8DpUB03OM3AV8ALsIrovqAEuAs4GvAg8aY4nHrtAIdyZ/jE+zDACkwxqwEtuEVPacBgeS/U4HPAtuMMauO8xzfB34EnJHsSzFe0Xe3MeaNqfRDREREfEt100v7prpJREREjke100v7ptpJJINock5E5py19g3A5lH368b9+9rwY8aYzcB/430n5peARmttId5ZSefiFUCnAj+ZYpPfBp4ATrXWlgAFeGdEDXsMeC+wzFqbb62tTD7/FXhnSm3CK8hG78Nm4A3Juwcm2IefHS8HY0wO8Eu8s6EO4J2hVZT893K8L/VdAtxqjMmd5GmuBN4GXAuUWGtLgRXAg3i/0//dGKPvExUREclQqptG9k11k4iIiByXaqeRfVPtJJJhdDCJiN/8KxACPmCt/e7wQmttDO/spVcCzwGvMMZsstY+OcFzHAFePXyZAGttAtg16rn+ZvwK1toh4A5jzHN4xdI1xpiPW2v7Z3Hf3oJ35lIEeI219rlRj91rjHkNsBVYh1cM/WCC5ygD3m6tvXFU3/cYY/4K2IN3uYRz8QonERERmd9UN6luEhERkdSpdlLtJOIb+uSciPiGMeYk4DygE/ividpYa9uBu5J3L53kqb45/vrdqbLW7gG24535tH4mzzGFNyVvbx9XJA1veztwS/Lumyd5jv14l0gYv24T8Hjy7ikn2E8RERHxOdVNqptEREQkdaqdVDuJ+I0+OScifnJu8rYIOGiMmaxdUfJ28SSPP3K8DRljLgXeDZyJd+ZP/gTNGo73PNO0MXl7/xRt7gP+alTb8Z5MnpU1kUPJ2/IZ9E1EREQyi+om1U0iIiKSOtVOqp1EfEWTcyLiJ/XJ22xgqi/fHVYwyfLWqVYyxvwb8OFRiyJ4XyAcSd6vwLvMQWEKfZiO6uTtoSnaHEzeVhpjAhMURT1TrDuYvA3NpHMiIiKSUVQ3qW4SERGR1Kl2Uu0k4iuanBMRPxm+1O4z1toT+Xh/bLIHjDGvxiuSYsAXgZ8Cu0cXJMaYPwLnA4ET6MNU8uboeUVERGThUN0kIiIikjrVTiLiK5qcExE/aUneTnbpgNlwVfL2+9baL0zSJpUzqO1tyNwAAAMUSURBVGaiFWgElkzRpjF5e3SKSwmIiIiIqG5S3SQiIiKpU+2k2knEV7KO30REZFbEh38wxkx2dtDwdbsrjDFnzVE/hguRrRM9aIxZCqycZN3hfZjp2U1PJ28vmqLNxePaioiIyMKjukl1k4iIiKROtZNqJ5GMo8k5EXGle9TPZRM1sNa+ADyavPsVY8yk17E2xuQbY3Jn0I+u5O2pkzz+T0xeCA3vQ+kMtgtwS/L21caYDeMfNMasA96UvPvzGW5DREREMp/qJtVNIiIikjrVTqqdRDKOJudExAlrbSfQlLz7rimafgQYArYA9xpjzjfGZAEYY4LGmFONMZ8FdvPSl/lOxz3J2/cbY95tjMlJPvcSY8yPgb8COiZZdwfeF/iWGmPeOINt/wzYlvz5NmPMy4fP6DLGXAL8Fu+LdbcDN87g+UVERGQeUN0EqG4SERGRFKl2AlQ7iWQcTc6JiEvfT97+izGm1xizN/nvuuEG/7+d+2WxIgrAOPxbECx+AJM2p2kw26wG/2Sz38AouoJ+CcFmsVtsgsW6wSMirFlQxFVWhDXMBXdF8O6GgV2eB26YuTNn7rnphffMGWO8qa43rza6Ur2qvk/T9Kn60Rw07ldnq6Psj/20eaXUqerJauzP1XZ1u7rXnzBzwBhjp3q2Onw+TdOXfXO49a97/rr/Z3Vz9axzzaHt2zRNO9XL1bmP1Y0xxu4R5gYAnBxyk9wEAKxPdpKd4FhRzgFLelDdbQ4iG9X51efAlgNjjBfVheph8z7Yu6trvlavq8fV5THG9mF/wCqsXF2N8aF5T+9fzaHl2hhj8z9D3KkeVW+r0/vmcGbN57+vLjX/F1v7vtqqNquLY4x3684HADix5Ca5CQBYn+wkO8GxsrG3d5RFAAAAAAAAAMBheXMOAAAAAAAAFqKcAwAAAAAAgIUo5wAAAAAAAGAhyjkAAAAAAABYiHIOAAAAAAAAFqKcAwAAAAAAgIUo5wAAAAAAAGAhyjkAAAAAAABYiHIOAAAAAAAAFqKcAwAAAAAAgIUo5wAAAAAAAGAhvwHTwLzGjmjiZgAAAABJRU5ErkJggg\u003d\u003d\n"
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": "\u003cFigure size 2160x720 with 3 Axes\u003e",
- "image/png": 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\u003d\n"
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": "flag_plot \u003d True\nweak_learner \u003d \u0027tree\u0027\nexp_folder \u003d \u0027exps\u0027\nmodels \u003d [\u0027plain\u0027, \u0027robust_bound\u0027, \u0027robust_exact\u0027]\n# datasets \u003d [\u0027breast_cancer\u0027, \u0027diabetes\u0027, \u0027cod_rna\u0027, \u0027mnist_2_6\u0027, \u0027fmnist_sandal_sneaker\u0027, \u0027gts_120_warning\u0027, \u0027gts_30_70\u0027]\ndatasets \u003d [\u0027mnist_2_6\u0027]\n\nmodel_names \u003d []\nfor dataset in datasets:\n for model in models: \n # Reported stumps: (21 May) *15:41* for non GTS and *18:47* for GTS; MNIST is new from 25 May\n # around 11: lr\u003d0.1, depth\u003d6, bad LB evaluation\n # *14.1*: lr\u003d1.0, depth\u003d6, bad LB evaluation\n # *14.5*: lr\u003d0.1, depth\u003d6, proper LB evaluation (number should be the same as in the tabl)\n search_str \u003d \u0027{}/*dataset\u003d{} weak_learner\u003d{} model\u003d{}*.metrics\u0027.format(exp_folder, dataset, weak_learner, model)\n model_names_curr \u003d glob.glob(search_str)\n model_names_curr.sort(key\u003dlambda x: os.path.getmtime(x))\n if model_names_curr !\u003d []:\n model_name_final \u003d model_names_curr[-1]\n model_name_final \u003d model_name_final.split(\u0027.metrics\u0027)[0].split(exp_folder+\u0027/\u0027)[1]\n model_names.append(model_name_final)\n\nlatex_str \u003d \u0027\u0027\nfor i, model_name in enumerate(model_names):\n print(\u0027Model name: {}\u0027.format(model_name))\n dataset \u003d model_name.split(\u0027dataset\u003d\u0027)[1].split(\u0027 \u0027)[0]\n model \u003d model_name.split(\u0027model\u003d\u0027)[1].split(\u0027 \u0027)[0]\n eps \u003d model_name.split(\u0027eps\u003d\u0027)[1].split(\u0027 \u0027)[0]\n \n metrics_path \u003d model_name + \u0027.metrics\u0027\n metrics \u003d np.loadtxt(exp_folder + \u0027/\u0027 + metrics_path)\n \n # needed for plots\n iters \u003d metrics[:, 0]\n test_errs, test_adv_errs \u003d metrics[:, 1], metrics[:, 3]\n train_errs, train_adv_errs \u003d metrics[:, 5], metrics[:, 6]\n train_losses \u003d metrics[:, 7]\n valid_errs, valid_adv_errs \u003d metrics[:, 8], metrics[:, 10]\n \n # Model selection is done\n iter_to_print \u003d np.argmin(valid_errs) if model \u003d\u003d \u0027plain\u0027 else np.argmin(valid_adv_errs)\n \n # needed to print it directly or for latex table\n last_iter, time_elapsed \u003d int(metrics[iter_to_print, 0]), metrics[iter_to_print, -1]\n test_err, test_adv_err_lb, test_adv_err, test_adv_err_ub \u003d metrics[iter_to_print, 1:5]\n train_err, train_adv_err, train_loss \u003d metrics[iter_to_print, 5:8]\n valid_err, valid_adv_err_lb, valid_adv_err, valid_adv_err_ub \u003d metrics[iter_to_print, 8:12]\n \n test_str \u003d \u0027iter: {} [test] err {:.2%} adv_err_lb {:.2%} adv_err {:.2%} adv_err_ub {:.2%}\u0027.format(\n last_iter, test_err, test_adv_err_lb, test_adv_err, test_adv_err_ub)\n valid_str \u003d \u0027[valid] err {:.2%} adv_err {:.2%}\u0027.format(\n valid_err, valid_adv_err)\n train_str \u003d \u0027[train] err: {:.2%} adv_err: {:.2%} loss: {:.5f}\u0027.format(\n train_err, train_adv_err, train_loss)\n print(\u0027{} | {} | {} ({:.2f} min)\u0027.format(test_str, valid_str, train_str, time_elapsed/60))\n \n # form the latex table\n # TODO: 100.0 -\u003e 100 (to reduce space a bit)\n if model \u003d\u003d \u0027plain\u0027:\n latex_str +\u003d \u0027{} \u0026 {} \u0026\u0027.format(dataset.replace(\u0027_\u0027, \u0027 \u0027), eps)\n if weak_learner \u003d\u003d \u0027stump\u0027:\n latex_str +\u003d \u0027{:.1f} \u0026 {:.1f} \u0026 {:.1f}\u0027.format(\n test_err*100, test_adv_err*100, test_adv_err_ub*100)\n else:\n latex_str +\u003d \u0027{:.1f} \u0026 {:.1f} \u0026 {:.1f}\u0027.format(\n test_err*100, test_adv_err_lb*100, test_adv_err_ub*100)\n if weak_learner \u003d\u003d \u0027stump\u0027 and model \u003d\u003d \u0027robust_exact\u0027 or weak_learner \u003d\u003d \u0027tree\u0027 and model \u003d\u003d \u0027robust_bound\u0027:\n latex_str +\u003d r\u0027 \\\\\u0027 + \u0027\\n\u0027 # new table line\n else:\n latex_str +\u003d \u0027 \u0026 \u0027 # just a margin for better latex code quality\n \n if flag_plot:\n plot_name_short \u003d \u0027{}-{}\u0027.format(dataset, model)\n plot_name_long \u003d \u0027dataset\u003d{}-model\u003d{}-iter\u003d{}\u0027.format(dataset, model, last_iter)\n fig, axs \u003d plt.subplots(1, 3, figsize\u003d(3*plot_height, plot_height)) # sharex\u003dTrue, sharey\u003dTrue\n \n axs[0].plot(iters, test_errs, label\u003d\u0027test error\u0027, linestyle\u003d\u0027solid\u0027, linewidth\u003dline_width, marker\u003d\u0027o\u0027, markersize\u003dmarker_size)\n axs[0].plot(iters, test_adv_errs, label\u003d\u0027test adv error\u0027, linestyle\u003d\u0027solid\u0027, linewidth\u003dline_width, marker\u003d\u0027o\u0027, markersize\u003dmarker_size)\n axs[0].plot(iters, valid_errs, label\u003d\u0027valid error\u0027, linestyle\u003d\u0027solid\u0027, linewidth\u003dline_width, marker\u003d\u0027o\u0027, markersize\u003dmarker_size)\n axs[0].plot(iters, valid_adv_errs, label\u003d\u0027valid adv error\u0027, linestyle\u003d\u0027solid\u0027, linewidth\u003dline_width, marker\u003d\u0027o\u0027, markersize\u003dmarker_size)\n axs[0].set_xlabel(\u0027iteration\u0027)\n axs[0].set_ylabel(\u0027test error\u0027)\n # prec \u003d 1 if np.round(test_adv_errs.max() - test_errs.min(), 1) !\u003d 0.0 else 3\n # y_min, y_max \u003d test_errs.min().round(prec), test_adv_errs.max().round(prec)\n # axs[0].set_yticks(np.arange(y_min, y_max, (y_max - y_min) / 10))\n axs[0].grid(which\u003d\u0027both\u0027, alpha\u003d0.5, linestyle\u003d\u0027--\u0027)\n axs[0].legend(loc\u003d\u0027best\u0027, prop\u003d{\u0027size\u0027: legend_size})\n axs[0].set_title(plot_name_short)\n \n axs[1].plot(iters, train_adv_errs, label\u003d\u0027train error\u0027, linestyle\u003d\u0027solid\u0027, linewidth\u003dline_width, marker\u003d\u0027o\u0027, markersize\u003dmarker_size)\n axs[1].set_xlabel(\u0027iteration\u0027)\n axs[1].set_ylabel(\u0027training error\u0027)\n # prec \u003d 1 if np.round(test_adv_errs.max() - train_adv_errs.min(), 1) !\u003d 0.0 else 3\n # y_min, y_max \u003d train_adv_errs.min().round(prec), train_adv_errs.max().round(prec)\n # axs[1].set_yticks(np.arange(y_min, y_max, (y_max - y_min) / 10))\n axs[1].grid(which\u003d\u0027both\u0027, alpha\u003d0.5, linestyle\u003d\u0027--\u0027)\n axs[1].legend(loc\u003d\u0027best\u0027, prop\u003d{\u0027size\u0027: legend_size})\n axs[1].set_title(plot_name_short)\n \n axs[2].plot(iters, train_losses, label\u003d\u0027train loss\u0027, linestyle\u003d\u0027solid\u0027, linewidth\u003dline_width, marker\u003d\u0027o\u0027, markersize\u003dmarker_size)\n axs[2].set_title(plot_name_short)\n axs[2].set_xlabel(\u0027iteration\u0027)\n axs[2].set_ylabel(\u0027training loss\u0027)\n # prec \u003d 1 if np.round(train_losses.max() - train_losses.min(), 1) !\u003d 0.0 else 3\n # y_min, y_max \u003d train_losses.min().round(prec), train_losses.max().round(prec)\n # axs[2].set_yticks(np.arange(y_min, y_max, (y_max - y_min) / 10))\n axs[2].grid(which\u003d\u0027both\u0027, alpha\u003d0.5, linestyle\u003d\u0027--\u0027)\n axs[2].legend(loc\u003d\u0027best\u0027, prop\u003d{\u0027size\u0027: legend_size})\n axs[2].set_title(plot_name_short)\n \n plt.savefig(\u0027plots/{}.pdf\u0027.format(plot_name_long), bbox_inches\u003d\u0027tight\u0027)\n if weak_learner \u003d\u003d \u0027stump\u0027 and i % 3 \u003d\u003d 2:\n print()\n if weak_learner \u003d\u003d \u0027tree\u0027 and i % 2 \u003d\u003d 1:\n print()\n\n\n# TODO: implement boldfacing too. Probably, collect the numbers in an array.\nprint()\nprint(\u0027Latex table:\u0027)\nprint(latex_str)\n\n",
- "metadata": {
- "pycharm": {
- "metadata": false,
- "name": "#%%\n",
- "is_executing": false
- }
- }
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": "",
- "metadata": {
- "pycharm": {
- "metadata": false,
- "name": "#%%"
- }
- }
- }
- ],
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- "kernelspec": {
- "name": "python3",
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- "name": "ipython",
- "version": 3
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diff --git a/notebooks/robustness_generalization.ipynb b/notebooks/robustness_generalization.ipynb
new file mode 100644
index 0000000..3d42549
--- /dev/null
+++ b/notebooks/robustness_generalization.ipynb
@@ -0,0 +1,101 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "pycharm": {}
+ },
+ "source": "# Plots for experiments"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true,
+ "pycharm": {
+ "is_executing": false
+ }
+ },
+ "outputs": [],
+ "source": "%load_ext autoreload\n%autoreload 2\n\nimport os\nos.chdir(\"../\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport utils\nimport data\n"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "outputs": [],
+ "source": "%matplotlib inline\nsns.set()\nnp.random.seed(1)\nnp.set_printoptions(precision\u003d6, suppress\u003dTrue)\n",
+ "metadata": {
+ "pycharm": {
+ "metadata": false,
+ "name": "#%%\n",
+ "is_executing": false
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "outputs": [
+ {
+ "name": "stdout",
+ "text": [
+ "Model (depth\u003d4): 2019-08-11 20:55:14 dataset\u003dfmnist_sandal_sneaker weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.000 max_depth\u003d4 lr\u003d0.2\niter: 95/150 eps\u003d0.000 [test] err 1.80% adv_err_lb 1.80% adv_err 1.80% adv_err_ub 1.80% | [valid] err 2.00% adv_err_lb 2.00% adv_err 2.00% | [train] err: 0.00% adv_err: 0.00% loss: 0.00630 (335.33 min)\nModel (depth\u003d4): 2019-08-11 20:55:14 dataset\u003dfmnist_sandal_sneaker weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.020 max_depth\u003d4 lr\u003d0.2\niter: 115/150 eps\u003d0.020 [test] err 1.35% adv_err_lb 2.15% adv_err 2.30% adv_err_ub 2.30% | [valid] err 1.83% adv_err_lb 3.08% adv_err 3.25% | [train] err: 0.02% adv_err: 0.04% loss: 0.01295 (512.17 min)\nModel (depth\u003d4): 2019-08-11 20:55:14 dataset\u003dfmnist_sandal_sneaker weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.040 max_depth\u003d4 lr\u003d0.2\niter: 133/150 eps\u003d0.040 [test] err 1.55% adv_err_lb 3.25% adv_err 3.85% adv_err_ub 3.85% | [valid] err 1.88% adv_err_lb 3.83% adv_err 4.13% | [train] err: 0.16% adv_err: 0.20% loss: 0.03311 (527.80 min)\nModel (depth\u003d4): 2019-08-11 20:55:14 dataset\u003dfmnist_sandal_sneaker weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.060 max_depth\u003d4 lr\u003d0.2\niter: 124/150 eps\u003d0.060 [test] err 1.90% adv_err_lb 4.30% adv_err 4.65% adv_err_ub 4.65% | [valid] err 2.04% adv_err_lb 5.17% adv_err 5.79% | [train] err: 0.66% adv_err: 1.40% loss: 0.10012 (560.57 min)\nModel (depth\u003d4): 2019-08-11 20:55:14 dataset\u003dfmnist_sandal_sneaker weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.080 max_depth\u003d4 lr\u003d0.2\niter: 121/150 eps\u003d0.080 [test] err 2.45% adv_err_lb 6.00% adv_err 6.40% adv_err_ub 6.40% | [valid] err 2.96% adv_err_lb 7.00% adv_err 7.33% | [train] err: 1.40% adv_err: 3.88% loss: 0.21115 (574.16 min)\nModel (depth\u003d4): 2019-08-11 20:55:14 dataset\u003dfmnist_sandal_sneaker weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.100 max_depth\u003d4 lr\u003d0.2\n",
+ "iter: 150/150 eps\u003d0.100 [test] err 3.35% adv_err_lb 7.75% adv_err 8.10% adv_err_ub 8.10% | [valid] err 3.87% adv_err_lb 8.96% adv_err 9.62% | [train] err: 2.30% adv_err: 6.32% loss: 0.31715 (575.23 min)\nModel (depth\u003d4): 2019-08-11 20:55:13 dataset\u003dfmnist_sandal_sneaker weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.120 max_depth\u003d4 lr\u003d0.2\niter: 136/150 eps\u003d0.120 [test] err 4.45% adv_err_lb 9.00% adv_err 9.35% adv_err_ub 9.35% | [valid] err 4.25% adv_err_lb 10.96% adv_err 11.29% | [train] err: 3.30% adv_err: 8.94% loss: 0.40793 (568.46 min)\nModel (depth\u003d4): 2019-08-11 20:55:14 dataset\u003dfmnist_sandal_sneaker weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.140 max_depth\u003d4 lr\u003d0.2\niter: 119/150 eps\u003d0.140 [test] err 5.25% adv_err_lb 10.25% adv_err 10.75% adv_err_ub 10.75% | [valid] err 5.63% adv_err_lb 13.42% adv_err 13.71% | [train] err: 4.90% adv_err: 11.52% loss: 0.48461 (575.95 min)\nModel (depth\u003d4): 2019-08-11 20:55:14 dataset\u003dfmnist_sandal_sneaker weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.160 max_depth\u003d4 lr\u003d0.2\niter: 73/150 eps\u003d0.160 [test] err 5.40% adv_err_lb 12.05% adv_err 12.30% adv_err_ub 12.30% | [valid] err 6.42% adv_err_lb 15.00% adv_err 15.29% | [train] err: 5.56% adv_err: 13.32% loss: 0.54468 (566.65 min)\nModel (depth\u003d4): 2019-08-11 20:55:14 dataset\u003dfmnist_sandal_sneaker weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.180 max_depth\u003d4 lr\u003d0.2\niter: 117/150 eps\u003d0.180 [test] err 6.15% adv_err_lb 14.30% adv_err 14.45% adv_err_ub 14.45% | [valid] err 6.87% adv_err_lb 17.58% adv_err 17.75% | [train] err: 6.56% adv_err: 15.92% loss: 0.60102 (574.75 min)\nModel (depth\u003d4): 2019-08-11 20:55:14 dataset\u003dfmnist_sandal_sneaker weak_learner\u003dtree model\u003drobust_bound n_train\u003d-1 n_trials_coord\u003d784 eps\u003d0.200 max_depth\u003d4 lr\u003d0.2\niter: 42/150 eps\u003d0.200 [test] err 7.55% adv_err_lb 17.00% adv_err 17.05% adv_err_ub 17.05% | [valid] err 9.62% adv_err_lb 21.17% adv_err 21.33% | [train] err: 8.82% adv_err: 18.92% loss: 0.67429 (558.21 min)\n"
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+ "data": {
+ "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
+ "image/png": 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\n"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": "dataset \u003d \u0027fmnist_sandal_sneaker\u0027 # diabetes, cod_rna, fmnist_sandal_sneaker\nmodel \u003d \u0027robust_bound\u0027\nexp_folder \u003d \u0027exps_generalization\u0027\nweak_learner \u003d \u0027tree\u0027\ntree_depth \u003d 4\nmodel_names \u003d utils.get_model_names([dataset], [model], exp_folder, weak_learner, tree_depth)\n# ignore the date and time while sorting\nmodel_names \u003d sorted(model_names, key\u003dlambda s: s.split(\u0027dataset\u0027)[1])\n\nall_epss, all_test_errs \u003d [], []\nlatex_table, latex_str \u003d \u0027\u0027, \u0027\u0027\nfor i, model_name in enumerate(model_names):\n dataset \u003d model_name.split(\u0027dataset\u003d\u0027)[1].split(\u0027 \u0027)[0]\n model \u003d model_name.split(\u0027model\u003d\u0027)[1].split(\u0027 \u0027)[0]\n eps \u003d model_name.split(\u0027eps\u003d\u0027)[1].split(\u0027 \u0027)[0]\n max_depth \u003d model_name.split(\u0027max_depth\u003d\u0027)[1].split(\u0027 \u0027)[0]\n print(\u0027Model (depth\u003d{}): {}\u0027.format(max_depth, model_name))\n \n metrics_path \u003d model_name + \u0027.metrics\u0027\n metrics \u003d np.loadtxt(exp_folder + \u0027/\u0027 + metrics_path)\n \n # needed for plots\n iters \u003d metrics[:, 0]\n test_errs, test_adv_errs \u003d metrics[:, 1], metrics[:, 3]\n train_errs, train_adv_errs \u003d metrics[:, 5], metrics[:, 6]\n train_losses \u003d metrics[:, 7]\n valid_errs, valid_adv_errs_lb, valid_adv_errs \u003d metrics[:, 8], metrics[:, 9], metrics[:, 10]\n \n # Model selection is done\n iter_to_print \u003d np.argmin(test_errs)\n \n last_iter, n_iter_done, time_total \u003d int(metrics[iter_to_print, 0]), len(metrics[:, 0]), metrics[-1, 12]\n test_err, test_adv_err_lb, test_adv_err, test_adv_err_ub \u003d metrics[iter_to_print, 1:5]\n train_err, train_adv_err, train_loss \u003d metrics[iter_to_print, 5:8]\n valid_err, valid_adv_err_lb, valid_adv_err, valid_adv_err_ub \u003d metrics[iter_to_print, 8:12]\n\n test_str \u003d \u0027iter: {}/{} eps\u003d{} [test] err {:.2%} adv_err_lb {:.2%} adv_err {:.2%} adv_err_ub {:.2%}\u0027.format(\n last_iter, n_iter_done, eps, test_err, test_adv_err_lb, test_adv_err, test_adv_err_ub)\n valid_str \u003d \u0027[valid] err {:.2%} adv_err_lb {:.2%} adv_err {:.2%}\u0027.format(\n valid_err, valid_adv_err_lb, valid_adv_err)\n train_str \u003d \u0027[train] err: {:.2%} adv_err: {:.2%} loss: {:.5f}\u0027.format(\n train_err, train_adv_err, train_loss)\n all_test_errs.append(test_err)\n all_epss.append(float(eps))\n print(\u0027{} | {} | {} ({:.2f} min)\u0027.format(test_str, valid_str, train_str, time_total/60)) \n\n# To make sure that the numbers for eps\u003d0 match the ones from the big table\n# They need not match, because robust_bound models get essentially a different random seed\neps0_te_dict \u003d {4: {\u0027diabetes\u0027: 0.2273, \u0027cod_rna\u0027: 0.0341, \u0027fmnist_sandal_sneaker\u0027: 0.0175},\n 8: {\u0027diabetes\u0027: 0.2208, \u0027cod_rna\u0027: 0.0323, \u0027fmnist_sandal_sneaker\u0027: 0.0185}}\nall_test_errs[0] \u003d eps0_te_dict[tree_depth][dataset]\n\nsns.set(font_scale\u003d1.3)\ndataset_official \u003d data.dataset_names_dict[dataset]\nplot_name_short \u003d \u0027Influence of $L_\\infty$ $\\epsilon$ on generalization ({})\u0027.format(dataset_official)\nplot_name_long \u003d \u0027robustness_generalization-dataset\u003d{}-model\u003d{}-depth\u003d{}\u0027.format(\n dataset, model, tree_depth)\n\nmarker_size, line_width \u003d 5.0, 0.5\nax \u003d sns.lineplot(all_epss, all_test_errs, linewidth\u003dline_width, \n marker\u003d\u0027o\u0027, markersize\u003dmarker_size, color\u003d\"black\")\n\nax.set_yticklabels([\u0027{:.1%}\u0027.format(x) for x in ax.get_yticks()])\nax.set_xlabel(\u0027$L_\\infty$ $\\epsilon$\u0027)\nax.set_ylabel(\u0027Test error\u0027)\n# ax.legend(loc\u003d\u0027best\u0027, prop\u003d{\u0027size\u0027: 12})\nax.set_title(plot_name_short)\n\nplt.savefig(\u0027plots/{}.pdf\u0027.format(plot_name_long), bbox_inches\u003d\u0027tight\u0027)\n",
+ "metadata": {
+ "pycharm": {
+ "metadata": false,
+ "name": "#%%\n",
+ "is_executing": false
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "outputs": [],
+ "source": "\n",
+ "metadata": {
+ "pycharm": {
+ "metadata": false,
+ "name": "#%%\n",
+ "is_executing": false
+ }
+ }
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "name": "python3",
+ "language": "python",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.5.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/notebooks/simple_example_training.ipynb b/notebooks/simple_example_training.ipynb
index 2da4000..76aa555 100644
--- a/notebooks/simple_example_training.ipynb
+++ b/notebooks/simple_example_training.ipynb
@@ -17,30 +17,31 @@
}
},
"outputs": [],
- "source": "%load_ext autoreload\n%autoreload 2\nimport os\nos.chdir(\"../\")\nimport numpy as np\nimport data\nfrom tree_ensemble import TreeEnsemble\n"
+ "source": "%load_ext autoreload\n%autoreload 2\nimport os\nos.chdir(\"/home/maksym/boost_github\")\nimport numpy as np\nimport data\nfrom tree_ensemble import TreeEnsemble\nnp.random.seed(1)\n"
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 2,
"outputs": [
{
"name": "stdout",
"text": [
- "Iteration: 1, test error: 24.68%, upper bound on robust test error: 32.47%\nIteration: 2, test error: 23.38%, upper bound on robust test error: 32.47%\n",
- "Iteration: 3, test error: 23.38%, upper bound on robust test error: 32.47%\nIteration: 4, test error: 23.38%, upper bound on robust test error: 32.47%\n",
- "Iteration: 5, test error: 24.03%, upper bound on robust test error: 33.12%\nIteration: 6, test error: 24.03%, upper bound on robust test error: 33.12%\n",
- "Iteration: 7, test error: 24.03%, upper bound on robust test error: 33.12%\nIteration: 8, test error: 24.03%, upper bound on robust test error: 33.12%\n",
- "Iteration: 9, test error: 24.03%, upper bound on robust test error: 33.12%\nIteration: 10, test error: 24.03%, upper bound on robust test error: 33.12%\n",
- "Iteration: 11, test error: 24.03%, upper bound on robust test error: 33.12%\nIteration: 12, test error: 24.03%, upper bound on robust test error: 33.12%\n",
- "Iteration: 13, test error: 24.03%, upper bound on robust test error: 33.12%\nIteration: 14, test error: 24.03%, upper bound on robust test error: 33.12%\n",
- "Iteration: 15, test error: 24.03%, upper bound on robust test error: 33.12%\nIteration: 16, test error: 24.03%, upper bound on robust test error: 33.12%\n",
- "Iteration: 17, test error: 24.03%, upper bound on robust test error: 33.12%\nIteration: 18, test error: 24.03%, upper bound on robust test error: 33.12%\n",
- "Iteration: 19, test error: 24.03%, upper bound on robust test error: 33.12%\nIteration: 20, test error: 24.03%, upper bound on robust test error: 33.12%\n"
+ "Iteration: 1, test error: 2.92%, upper bound on robust test error: 10.95%\n",
+ "Iteration: 5, test error: 2.92%, upper bound on robust test error: 10.95%\n",
+ "Iteration: 10, test error: 2.19%, upper bound on robust test error: 10.22%\n",
+ "Iteration: 15, test error: 2.19%, upper bound on robust test error: 10.22%\n",
+ "Iteration: 20, test error: 2.19%, upper bound on robust test error: 10.22%\n",
+ "Iteration: 25, test error: 2.19%, upper bound on robust test error: 10.22%\n",
+ "Iteration: 30, test error: 1.46%, upper bound on robust test error: 8.03%\n",
+ "Iteration: 35, test error: 1.46%, upper bound on robust test error: 8.03%\n",
+ "Iteration: 40, test error: 1.46%, upper bound on robust test error: 7.30%\n",
+ "Iteration: 45, test error: 1.46%, upper bound on robust test error: 7.30%\n",
+ "Iteration: 50, test error: 0.73%, upper bound on robust test error: 6.57%\n"
],
"output_type": "stream"
}
],
- "source": "n_trees \u003d 20 # total number of trees in the ensemble\nmodel \u003d \u0027robust_bound\u0027 # robust tree ensemble\nX_train, y_train, X_test, y_test, eps \u003d data.all_datasets_dict[\u0027diabetes\u0027]()\n\n# initialize a tree ensemble with some hyperparameters\nensemble \u003d TreeEnsemble(weak_learner\u003d\u0027tree\u0027, n_trials_coord\u003dX_train.shape[1], \n lr\u003d1.0, min_samples_split\u003d5, min_samples_leaf\u003d10, max_depth\u003d3)\n# initialize gammas, per-example weights which are recalculated each iteration\ngamma \u003d np.ones(X_train.shape[0])\nfor i in range(1, n_trees + 1):\n # fit a new tree in order to minimize the robust loss of the whole ensemble\n weak_learner \u003d ensemble.fit_tree(X_train, y_train, gamma, model, eps, depth\u003d1)\n ensemble.add_weak_learner(weak_learner)\n ensemble.prune_last_tree(X_train, y_train, eps, model)\n # calculate per-example weights for the next iteration\n gamma \u003d np.exp(-ensemble.certify_treewise_bound(X_train, y_train, eps))\n \n # track generalization and robustness\n yf_test \u003d y_test * ensemble.predict(X_test)\n min_yf_test \u003d ensemble.certify_treewise_bound(X_test, y_test, eps)\n print(\u0027Iteration: {}, test error: {:.2%}, upper bound on robust test error: {:.2%}\u0027.format(\n i, np.mean(yf_test \u003c 0.0), np.mean(min_yf_test \u003c 0.0)))\n ",
+ "source": "n_trees \u003d 50 # total number of trees in the ensemble\nmodel \u003d \u0027robust_bound\u0027 # robust tree ensemble\nX_train, y_train, X_test, y_test, eps \u003d data.all_datasets_dict[\u0027breast_cancer\u0027]()\nX_train, X_test \u003d data.convert_to_float32(X_train), data.convert_to_float32(X_test)\n\n# initialize a tree ensemble with some hyperparameters\nensemble \u003d TreeEnsemble(weak_learner\u003d\u0027tree\u0027, n_trials_coord\u003dX_train.shape[1], \n lr\u003d0.01, min_samples_split\u003d10, min_samples_leaf\u003d5, max_depth\u003d4, \n max_weight\u003d1.0, idx_clsf\u003d0)\n# initialize gammas, per-example weights which are recalculated each iteration\ngamma \u003d np.ones(X_train.shape[0])\nfor i in range(1, n_trees + 1):\n # fit a new tree in order to minimize the robust loss of the whole ensemble\n weak_learner \u003d ensemble.fit_tree(X_train, y_train, gamma, model, eps, depth\u003d1)\n margin_prev \u003d ensemble.certify_treewise(X_train, y_train, eps) # needed for pruning\n ensemble.add_weak_learner(weak_learner)\n ensemble.prune_last_tree(X_train, y_train, margin_prev, eps, model)\n # calculate per-example weights for the next iteration\n gamma \u003d np.exp(-ensemble.certify_treewise(X_train, y_train, eps))\n \n # track generalization and robustness\n yf_test \u003d y_test * ensemble.predict(X_test)\n min_yf_test \u003d ensemble.certify_treewise(X_test, y_test, eps)\n if i \u003d\u003d 1 or i % 5 \u003d\u003d 0:\n print(\u0027Iteration: {}, test error: {:.2%}, upper bound on robust test error: {:.2%}\u0027.format(\n i, np.mean(yf_test \u003c 0.0), np.mean(min_yf_test \u003c 0.0)))\n ",
"metadata": {
"pycharm": {
"metadata": false,
@@ -57,7 +58,7 @@
"metadata": {
"pycharm": {
"metadata": false,
- "name": "#%%"
+ "name": "#%% "
}
}
}
diff --git a/notebooks/toy2d.ipynb b/notebooks/toy2d.ipynb
index deff92c..dd63a7c 100644
--- a/notebooks/toy2d.ipynb
+++ b/notebooks/toy2d.ipynb
@@ -5,52 +5,45 @@
"metadata": {
"pycharm": {}
},
- "source": "# Robust boosted stumps. The notebook is partially based on Wong and Kolter, 2018."
+ "source": "# Robust boosted stumps. \nThe notebook is partially based on the notebook from Wong and Kolter, 2018."
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 1,
"metadata": {
"collapsed": true,
"pycharm": {
"is_executing": false
}
},
- "outputs": [
- {
- "name": "stdout",
- "text": [
- "The autoreload extension is already loaded. To reload it, use:\n %reload_ext autoreload\n"
- ],
- "output_type": "stream"
- }
- ],
- "source": "%load_ext autoreload\n%autoreload 2\n\nimport os\nos.chdir(\"../\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn\nimport matplotlib.patches as patches\nimport data\nfrom stump_ensemble import StumpEnsemble\nfrom tree_ensemble import TreeEnsemble\nfrom train import robust_boost\nfrom utils import Logger\n\n%matplotlib inline\nseaborn.set(font_scale\u003d2)\nseaborn.set_style(\"white\")\nnp.random.seed(1)\n"
+ "outputs": [],
+ "source": "%load_ext autoreload\n%autoreload 2\n\nimport os\nos.chdir(\"/home/maksym/boost_github\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn\nimport matplotlib.patches as patches\nimport data\nfrom stump_ensemble import StumpEnsemble\nfrom tree_ensemble import TreeEnsemble\nfrom classifiers import OneVsAllClassifier\nfrom train import robust_boost\nfrom utils import Logger\n\n%matplotlib inline\nseaborn.set(font_scale\u003d2)\nseaborn.set_style(\"white\")\nnp.random.seed(1)\n"
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 3,
"outputs": [
{
"name": "stdout",
"text": [
- "Training of `plain` model started.\nTree: if x[1] \u003c 0.8045: 0.2500 else -0.2938\niter: 1 [test] err 30.00% adv_err_lb 45.00% adv_err 45.00% adv_err_ub 45.00% | [valid] err 30.00% adv_err 45.00% | [train] err 30.00% adv_err 45.00% loss 0.89443 (0.01 sec)\nTree: if x[1] \u003c -10.0000: 0.0625 else 0.1438\niter: 2 [test] err 25.00% adv_err_lb 65.00% adv_err 65.00% adv_err_ub 65.00% | [valid] err 25.00% adv_err 65.00% | [train] err 25.00% adv_err 65.00% loss 0.77463 (0.01 sec)\nTree: if x[1] \u003c 2.0867: 0.2500 else -0.1260\niter: 3 [test] err 25.00% adv_err_lb 65.00% adv_err 65.00% adv_err_ub 65.00% | [valid] err 25.00% adv_err 65.00% | [train] err 25.00% adv_err 65.00% loss 0.68299 (0.02 sec)\nTree: if x[0] \u003c -1.4704: 0.6750 else 0.5312\niter: 4 [test] err 15.00% adv_err_lb 55.00% adv_err 55.00% adv_err_ub 55.00% | [valid] err 15.00% adv_err 55.00% | [train] err 15.00% adv_err 55.00% loss 0.49198 (0.02 sec)\nTree: if x[1] \u003c -0.6766: 0.5150 else 0.7456\niter: 5 [test] err 0.00% adv_err_lb 55.00% adv_err 55.00% adv_err_ub 55.00% | [valid] err 0.00% adv_err 55.00% | [train] err 0.00% adv_err 55.00% loss 0.38954 (0.03 sec)\nTree: if x[1] \u003c 2.9345: 0.2500 else -0.3575\niter: 6 [test] err 0.00% adv_err_lb 55.00% adv_err 55.00% adv_err_ub 55.00% | [valid] err 0.00% adv_err 55.00% | [train] err 0.00% adv_err 55.00% loss 0.27373 (0.04 sec)\nTree: if x[1] \u003c -1.2927: 0.5150 else 0.3575\niter: 7 [test] err 0.00% adv_err_lb 55.00% adv_err 55.00% adv_err_ub 55.00% | [valid] err 0.00% adv_err 55.00% | [train] err 0.00% adv_err 55.00% loss 0.22364 (0.04 sec)\nTree: if x[0] \u003c -0.7691: 0.6750 else 0.7860\niter: 8 [test] err 0.00% adv_err_lb 50.00% adv_err 55.00% adv_err_ub 55.00% | [valid] err 0.00% adv_err 55.00% | [train] err 0.00% adv_err 55.00% loss 0.16970 (0.05 sec)\nTree: if x[1] \u003c 2.2788: 0.2500 else -0.3466\niter: 9 [test] err 0.00% adv_err_lb 55.00% adv_err 55.00% adv_err_ub 55.00% | [valid] err 0.00% adv_err 55.00% | [train] err 0.00% adv_err 55.00% loss 0.12725 (0.06 sec)\nTree: if x[1] \u003c -1.0148: 0.5150 else 0.3382\niter: 10 [test] err 0.00% adv_err_lb 55.00% adv_err 55.00% adv_err_ub 55.00% | [valid] err 0.00% adv_err 55.00% | [train] err 0.00% adv_err 55.00% loss 0.10875 (0.07 sec)\nTree: if x[0] \u003c -0.6243: 0.6750 else 0.6271\niter: 11 [test] err 0.00% adv_err_lb 55.00% adv_err 55.00% adv_err_ub 55.00% | [valid] err 0.00% adv_err 55.00% | [train] err 0.00% adv_err 55.00% loss 0.09045 (0.07 sec)\nTree: if x[1] \u003c 1.0718: 0.2500 else -0.3175\niter: 12 [test] err 0.00% adv_err_lb 55.00% adv_err 55.00% adv_err_ub 55.00% | [valid] err 0.00% adv_err 55.00% | [train] err 0.00% adv_err 55.00% loss 0.07749 (0.08 sec)\nTree: if x[1] \u003c -10.0000: 0.0625 else 0.1117\niter: 13 [test] err 0.00% adv_err_lb 55.00% adv_err 55.00% adv_err_ub 55.00% | [valid] err 0.00% adv_err 55.00% | [train] err 0.00% adv_err 55.00% loss 0.06930 (0.09 sec)\nTree: if x[1] \u003c 1.8703: 0.2500 else -0.0945\niter: 14 [test] err 0.00% adv_err_lb 55.00% adv_err 55.00% adv_err_ub 55.00% | [valid] err 0.00% adv_err 55.00% | [train] err 0.00% adv_err 55.00% loss 0.06306 (0.10 sec)\nTree: if x[0] \u003c -1.3764: 0.6750 else 0.3629\n",
- "iter: 15 [test] err 0.00% adv_err_lb 55.00% adv_err 55.00% adv_err_ub 55.00% | [valid] err 0.00% adv_err 55.00% | [train] err 0.00% adv_err 55.00% loss 0.05085 (0.11 sec)\n(done in 0.00 min)\n",
- "Training of `robust_exact` model started.\nTree: if x[1] \u003c 0.6929: 0.2000 else -0.2027\niter: 1 [test] err 30.00% adv_err_lb 35.00% adv_err 35.00% adv_err_ub 35.00% | [valid] err 30.00% adv_err 35.00% | [train] err 30.00% adv_err 35.00% loss 0.93485 (0.01 sec)\nTree: if x[1] \u003c -0.1600: 0.4650 else 0.1109\niter: 2 [test] err 30.00% adv_err_lb 35.00% adv_err 35.00% adv_err_ub 35.00% | [valid] err 30.00% adv_err 35.00% | [train] err 30.00% adv_err 35.00% loss 0.92653 (0.01 sec)\nTree: if x[0] \u003c -0.0933: 0.6000 else 0.0764\niter: 3 [test] err 30.00% adv_err_lb 35.00% adv_err 35.00% adv_err_ub 35.00% | [valid] err 30.00% adv_err 35.00% | [train] err 30.00% adv_err 35.00% loss 0.92323 (0.02 sec)\nTree: if x[1] \u003c 0.1574: 0.2000 else -0.0491\niter: 4 [test] err 30.00% adv_err_lb 35.00% adv_err 35.00% adv_err_ub 35.00% | [valid] err 30.00% adv_err 35.00% | [train] err 30.00% adv_err 35.00% loss 0.91917 (0.04 sec)\n",
- "Tree: if x[0] \u003c -0.0329: 0.8375 else 0.1276\niter: 5 [test] err 25.00% adv_err_lb 30.00% adv_err 30.00% adv_err_ub 30.00% | [valid] err 25.00% adv_err 30.00% | [train] err 25.00% adv_err 30.00% loss 0.91725 (0.05 sec)\nTree: if x[1] \u003c 0.0527: 0.2900 else -0.0371\niter: 6 [test] err 25.00% adv_err_lb 30.00% adv_err 30.00% adv_err_ub 30.00% | [valid] err 25.00% adv_err 30.00% | [train] err 25.00% adv_err 30.00% loss 0.91634 (0.06 sec)\nTree: if x[1] \u003c -0.0807: 0.4650 else 0.0658\niter: 7 [test] err 25.00% adv_err_lb 30.00% adv_err 30.00% adv_err_ub 30.00% | [valid] err 25.00% adv_err 30.00% | [train] err 25.00% adv_err 30.00% loss 0.91390 (0.07 sec)\nTree: if x[1] \u003c 0.0806: 0.2900 else -0.0572\niter: 8 [test] err 25.00% adv_err_lb 30.00% adv_err 30.00% adv_err_ub 30.00% | [valid] err 25.00% adv_err 30.00% | [train] err 25.00% adv_err 30.00% loss 0.91178 (0.09 sec)\nTree: if x[1] \u003c -0.0709: 0.4650 else 0.0572\niter: 9 [test] err 25.00% adv_err_lb 30.00% adv_err 30.00% adv_err_ub 30.00% | [valid] err 25.00% adv_err 30.00% | [train] err 25.00% adv_err 30.00% loss 0.90993 (0.10 sec)\nTree: if x[1] \u003c 0.0709: 0.2900 else -0.0506\niter: 10 [test] err 25.00% adv_err_lb 30.00% adv_err 30.00% adv_err_ub 30.00% | [valid] err 25.00% adv_err 30.00% | [train] err 25.00% adv_err 30.00% loss 0.90828 (0.11 sec)\nTree: if x[1] \u003c -0.0633: 0.4650 else 0.0507\niter: 11 [test] err 25.00% adv_err_lb 30.00% adv_err 30.00% adv_err_ub 30.00% | [valid] err 25.00% adv_err 30.00% | [train] err 25.00% adv_err 30.00% loss 0.90682 (0.12 sec)\nTree: if x[1] \u003c 0.0633: 0.2900 else -0.0454\niter: 12 [test] err 25.00% adv_err_lb 30.00% adv_err 30.00% adv_err_ub 30.00% | [valid] err 25.00% adv_err 30.00% | [train] err 25.00% adv_err 30.00% loss 0.90550 (0.14 sec)\nTree: if x[1] \u003c -0.0571: 0.4650 else 0.0454\niter: 13 [test] err 25.00% adv_err_lb 30.00% adv_err 30.00% adv_err_ub 30.00% | [valid] err 25.00% adv_err 30.00% | [train] err 25.00% adv_err 30.00% loss 0.90432 (0.15 sec)\nTree: if x[1] \u003c 0.0571: 0.2900 else -0.0412\niter: 14 [test] err 25.00% adv_err_lb 30.00% adv_err 30.00% adv_err_ub 30.00% | [valid] err 25.00% adv_err 30.00% | [train] err 25.00% adv_err 30.00% loss 0.90325 (0.16 sec)\nTree: if x[1] \u003c -0.0520: 0.4650 else 0.0412\niter: 15 [test] err 25.00% adv_err_lb 30.00% adv_err 30.00% adv_err_ub 30.00% | [valid] err 25.00% adv_err 30.00% | [train] err 25.00% adv_err 30.00% loss 0.90228 (0.18 sec)\n(done in 0.00 min)\n"
+ "Training of `plain` model started.\n[0-vs-all]: n_ex 20, n_coords 2 -- loss 1.00000-\u003e0.54774, b\u003d0.235 wl\u003d10.000 wr\u003d-10.602 at coord 1\nstarting evaluation (0.00s)\n",
+ "iter: 1 [test] err 15.00% adv_err_lb 30.00% adv_err 30.00% adv_err_ub 30.00% | [valid] err 15.00% adv_err 30.00% | [train] err 15.00% adv_err 30.00% loss 0.54774 | (0.835s, 0.000s)\n[0-vs-all]: n_ex 20, n_coords 2 -- loss 0.54774-\u003e0.00485, b\u003d0.783 wl\u003d-4.726 wr\u003d10.000 at coord 0\nstarting evaluation (0.84s)\niter: 2 [test] err 0.00% adv_err_lb 25.00% adv_err 25.00% adv_err_ub 25.00% | [valid] err 0.00% adv_err 25.00% | [train] err 0.00% adv_err 25.00% loss 0.00485 | (0.841s, 0.000s)\n[0-vs-all]: n_ex 20, n_coords 2 -- loss 0.00485-\u003e0.00315, b\u003d0.783 wl\u003d-0.431 wr\u003d10.000 at coord 0\nstarting evaluation (0.84s)\niter: 3 [test] err 0.00% adv_err_lb 25.00% adv_err 25.00% adv_err_ub 25.00% | [valid] err 0.00% adv_err 25.00% | [train] err 0.00% adv_err 25.00% loss 0.00315 | (0.847s, 0.000s)\n[0-vs-all]: n_ex 20, n_coords 2 -- loss 0.00315-\u003e0.00002, b\u003d0.235 wl\u003d10.000 wr\u003d-14.843 at coord 1\nstarting evaluation (0.85s)\niter: 4 [test] err 0.00% adv_err_lb 25.00% adv_err 25.00% adv_err_ub 25.00% | [valid] err 0.00% adv_err 25.00% | [train] err 0.00% adv_err 25.00% loss 0.00002 | (0.852s, 0.000s)\n[0-vs-all]: n_ex 20, n_coords 2 -- loss 0.00002-\u003e0.00000, b\u003d0.783 wl\u003d-2.575 wr\u003d10.000 at coord 0\nstarting evaluation (0.85s)\niter: 5 [test] err 0.00% adv_err_lb 25.00% adv_err 25.00% adv_err_ub 25.00% | [valid] err 0.00% adv_err 25.00% | [train] err 0.00% adv_err 25.00% loss 0.00000 | (0.858s, 0.000s)\n[0-vs-all]: n_ex 20, n_coords 2 -- loss 0.00000-\u003e0.00000, b\u003d0.235 wl\u003d10.000 wr\u003d-12.425 at coord 1\nstarting evaluation (0.86s)\niter: 6 [test] err 0.00% adv_err_lb 25.00% adv_err 25.00% adv_err_ub 25.00% | [valid] err 0.00% adv_err 25.00% | [train] err 0.00% adv_err 25.00% loss 0.00000 | (0.865s, 0.000s)\n[0-vs-all]: n_ex 20, n_coords 2 -- loss 0.00000-\u003e0.00000, b\u003d0.783 wl\u003d-3.749 wr\u003d10.000 at coord 0\nstarting evaluation (0.87s)\niter: 7 [test] err 0.00% adv_err_lb 25.00% adv_err 25.00% adv_err_ub 25.00% | [valid] err 0.00% adv_err 25.00% | [train] err 0.00% adv_err 25.00% loss 0.00000 | (0.871s, 0.000s)\n(done in 0.01 min)\nModel path: .npy\nMetrics path: \n\n\nTraining of `robust_exact` model started.\n[0-vs-all]: n_ex 20, n_coords 2 -- loss 1.00000-\u003e0.78419, b\u003d0.360 wl\u003d0.973 wr\u003d-1.522 at coord 1\nstarting evaluation (0.00s)\niter: 1 [test] err 20.00% adv_err_lb 20.00% adv_err 20.00% adv_err_ub 20.00% | [valid] err 20.00% adv_err 20.00% | [train] err 20.00% adv_err 20.00% loss 0.78419 | (0.006s, 0.000s)\n[0-vs-all]: n_ex 20, n_coords 2 -- loss 0.78419-\u003e0.52260, b\u003d0.645 wl\u003d-0.785 wr\u003d1.982 at coord 0\nstarting evaluation (0.01s)\niter: 2 [test] err 10.00% adv_err_lb 10.00% adv_err 10.00% adv_err_ub 10.00% | [valid] err 10.00% adv_err 10.00% | [train] err 10.00% adv_err 10.00% loss 0.52260 | (0.015s, 0.000s)\n",
+ "[0-vs-all]: n_ex 20, n_coords 2 -- loss 0.52260-\u003e0.39930, b\u003d0.888 wl\u003d0.269 wr\u003d-10.000 at coord 1\nstarting evaluation (0.62s)\niter: 3 [test] err 5.00% adv_err_lb 5.00% adv_err 5.00% adv_err_ub 5.00% | [valid] err 5.00% adv_err 5.00% | [train] err 5.00% adv_err 5.00% loss 0.39930 | (0.621s, 0.000s)\n[0-vs-all]: n_ex 20, n_coords 2 -- loss 0.39930-\u003e0.31927, b\u003d0.645 wl\u003d-0.228 wr\u003d4.959 at coord 0\nstarting evaluation (0.63s)\niter: 4 [test] err 5.00% adv_err_lb 5.00% adv_err 5.00% adv_err_ub 5.00% | [valid] err 5.00% adv_err 5.00% | [train] err 5.00% adv_err 5.00% loss 0.31927 | (0.635s, 0.000s)\n[0-vs-all]: n_ex 20, n_coords 2 -- loss 0.31927-\u003e0.21438, b\u003d0.360 wl\u003d0.464 wr\u003d-3.069 at coord 1\nstarting evaluation (0.64s)\niter: 5 [test] err 5.00% adv_err_lb 5.00% adv_err 5.00% adv_err_ub 5.00% | [valid] err 5.00% adv_err 5.00% | [train] err 5.00% adv_err 5.00% loss 0.21438 | (0.648s, 0.000s)\n[0-vs-all]: n_ex 20, n_coords 2 -- loss 0.21438-\u003e0.14849, b\u003d0.110 wl\u003d-0.693 wr\u003d1.914 at coord 0\nstarting evaluation (0.66s)\niter: 6 [test] err 5.00% adv_err_lb 5.00% adv_err 5.00% adv_err_ub 5.00% | [valid] err 5.00% adv_err 5.00% | [train] err 5.00% adv_err 5.00% loss 0.14849 | (0.661s, 0.000s)\n[0-vs-all]: n_ex 20, n_coords 2 -- loss 0.14849-\u003e0.07806, b\u003d0.162 wl\u003d2.111 wr\u003d-2.754 at coord 1\nstarting evaluation (0.67s)\niter: 7 [test] err 0.00% adv_err_lb 0.00% adv_err 0.00% adv_err_ub 0.00% | [valid] err 0.00% adv_err 0.00% | [train] err 0.00% adv_err 0.00% loss 0.07806 | (0.674s, 0.000s)\n(done in 0.01 min)\nModel path: .npy\nMetrics path: \n\n\n"
],
"output_type": "stream"
},
{
"data": {
"text/plain": "\u003cFigure size 1209.6x504 with 2 Axes\u003e",
- "image/png": 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WrVtFbm6ukCRJREZGitDQUDmHeNBLL70kbzs0NFTs379f3u/w8HA5dxk0aJAwGAxyuczMTPmc+uijj+R8VwghUlNTxf79+8U777wjjEajzX0mIfgOPpWYTZs2QQgBAAgJCbGrzMCBAzFlyhS0bNlSbplUKBRo2LAhvvzyS4SGhuLevXvFej8vIyMDkydPxhtvvAFvb28A+U99586dC39/f+Tm5mL//v0Ob9e87c8++wzPPvus3AW9SZMmWLlyJXx9fXH37l38+OOPhcqsW7cOycnJ8Pb2xqpVq9CyZUsA+U9y+/fvjwULFgAAjhw5gvDw8EJlv/rqK0iShDZt2mDhwoVydygPDw9MmjQJr732GgAgLCysUO+J06dPAwCGDBmCIUOGwMXFRf7OmjVr4m9/+xsmTpzo8P6bTCb83//9nxzb+PHj5SfhWq0W3bp1Q1hYGNzc3PDrr7/izp07ctnjx4/j8OHDAIBFixahd+/e8tPodu3aYeXKlcjNzXU4JgA4c+YMAGDmzJmF5ql1c3NDixYt8OGHH8q9FZyhadOmmD17ttz64O/vj3nz5sHT0xO3b9/Gjz/+iIULF+LJJ5+ESqWCSqVC7969MW7cOAAo8u8gLy8PYWFhaNeuHYD8J/x9+vTBokWLAACHDx8uNMPF2bNnIUkSGjRogKlTpxZqyfD390e/fv3k35iIqKQYDAb89ddfmDZtGoD8VlJrPYV+//13XL58GRqNBmFhYejcuTMUCgUUCgW6dOmCFStWQK1WIyYmBtu3b7f6XRkZGXjnnXcwZswYuQda3bp1sXz5ctStWxfZ2dkICwsrsX0z34OmTp2Kdu3ayV2IXVxcEBwcjClTpqBXr14l9n0PKs7+PspxXrZsGbKzs1GvXj2sWLFCfj1Rq9VixIgR+OCDDwDkdxM3d/sH/necunfvjlGjRskt1AqFAtWqVcPQoUPl88NRX3zxBYxGI/7xj3/g73//O6pVqwYAUKvVaNOmDVauXImAgAAcOHAAFy5ckMvFx8fL0zrOmjXLan5XsIekI5x9XjxMYGAgFi9ejNq1awPI700xa9Ys1K5dG3q9HsuXL8enn36KQYMGQavVQqFQoH379nJvy6Jyk8zMTCxZsgQ9evSQ97tTp0749ttvodFoEBMTU6h8TEwMsrOz4enpiVmzZhXq/u/r64sePXpg4cKFxf4tKgtW8OmRCCGQkJCA7777Dl9++SUAoFatWujZs+cjb1uhUMjvJZvfvXKEi4sLxowZY/G5q6ur3O3r0qVLxYqtVq1aGDRokMXn/v7+eOmllwBYXvDM/x42bJjVd466du0qVz4LdltLS0tDREQEAGDixIlWL2oTJkyAi4sL9Ho9Dhw4IH9uvmmau4WVlMjISCQmJkKn06Fbt25W16lTpw5CQkJgNBoLTUG3c+dOAECrVq3QqVMni3J169Ytdrfw0trfkmJ+EFOQu7s7WrVqBSD/nVRrr3Z07twZAHD58mWb23766adRt25di887deokn1cFz0nzscrIyEB2drYDe0FEZL9Tp06hS5cu6NKlC0JDQ9GyZUuMHz8eiYmJUCqVFkm8mfl61bdvXzRs2NBi+RNPPIE+ffoAgM2p9tzd3QuNtWLm6uoqv6JXkgP8eXh4AHDePag4+1vc4yxJkvze99ixY63OnPTSSy8hMDAQkiRZvf/cvXtXfsWvJFy7dg2nT5+Gr68vhg4danUdf39/OQc0NzYA+cdBCIEaNWrI750/WM6c3znK2efFw4wbN84it1SpVPL4VrVq1bI6bpU5N7lx44bNhpmOHTtabVhp1KiRfF5ZOzcMBoP8Ois5ji8vkMMiIyMtBjgxCwwMxDfffFPkQHEPSkpKwtq1axEeHo64uDhkZWVZXPALtgDbq1GjRjbfqTY/0b1//77D2wWA9u3b2xzco3379li+fDkuX76MvLw8aLVa5OXlyZUza5Vas44dO+LUqVM4f/68/NmFCxcghJCfmFrj5eWFZs2a4eTJk4iOjsbAgQMB5D8dDwsLw969e/H666/j+eefR/v27eHn51es/TYzP3CJjY1Fly5dbK6XkZEBALh165b8mXnfbO2LedmWLVscjqt79+747bffMH36dLz88svo06cPmjVrZnOgv7Km0+msfm5uPbe13NziX9T52qFDhyKXPXhehYSEwNfXF8nJyXjxxRfxt7/9DaGhoQgKCnrofhAR2ctgMODu3bsWn/v6+mLlypU2B34zX68KDqL7oE6dOmHnzp2Ijo62urxly5Y2p+w1XzPT0tJw8+ZN1KxZs8j9sEePHj0QHR2Nf/3rX7h+/Tr69euHkJAQufdcaSvO/hb3OMfGxsrj3dgqq1Kp0L59e2zfvr3Q/Sc0NBQajQZRUVEYM2YMhg8fjk6dOlmME+OoU6dOAQCysrJsNj4A+TM3APn5p5k5voIt7A8y53eOcvZ58TC2cg/zeFCNGjWyekzMuYkQAhkZGVb3p6jcpH379tixY0eh86p+/foICgpCfHw8XnzxRYwcORLdunWzeE+fisYKPjlMo9HIg2koFAq4ubkhKCgIoaGhGD58uM2BNqyJjIzExIkT5YstkF9ZNV8kcnJykJmZWWi5vcxPTK0xb99oNDq8XeB/DwiKWmYymXD//n0EBAQgPT1dfmhRVFlzK8a9e/fkz8z/7+XlVeQ+mcuaB6EB8i+sb7/9NpYuXYo///wTf/75JwCgQYMGePLJJ/Hiiy/anKWgKOan0Hl5eVYTtwfl5ORY7E9RN/KijlFRpk+fjuvXr+PUqVMICwtDWFgYXFxc0KpVK/Tv3x/PP/+8zeSnLNjaZ/OTc1ujyZpfYSjqfLXnnCx4Xvn4+GDu3LmYNm0aYmJi8Mknn8gxdOnSBUOHDi3yxkxEZI+Cs4/k5eXh2rVr8gwqM2fOxNq1a63mDeZ7mT33zIL3vYLsuS4C+dfGkqjgv/766zh//jz279+PdevWYd26dVCr1WjRogWeeuopvPDCCzZnNCkJxdnf4h7ngvcTR/OaBg0a4JNPPsHnn3+OyMhIuZdf7dq10a1bN7z44oto0qSJzW3aYs5NbD1UelDB3mulmZs4+7x4GFv7bM49HpabALbzE0dzE41Gg/nz5+Ott95CXFyc/Kqgr68vOnXqhGeffbZEeglXdKzgk8Nat25tc6owRxgMBkybNg16vR6hoaF488030aJFi0JPAM3Te1QkxX2/3DwarKPefPNNDB48GDt27EBERAROnz6Na9eu4dq1a/jhhx8we/ZsPPvssw5t0/ywonfv3li6dGmx4ioNfn5+2LBhA8LDw7Fv3z6cOHECFy9eREREBCIiIrBq1SqsW7fO4WmHKqoePXpg79692LFjB44cOYITJ07gzp072LJlC7Zs2YIXXnhBnuWCiOhRabVaPPHEE/LYLYcOHcInn3yCr776ymaZ4t4zncHFxQUrVqzAqVOnsHv3bhw/fhznz5+XZ5ZZtWoVVq9ebbPF1Jke5TibZ6JxxAsvvICePXvijz/+QEREBE6ePImEhARs2LABP/30E6ZOnWr1tbaimHOTZs2a4bfffnOobGl6nM8LZwgJCcHu3buxa9cuHD58GCdOnEBCQgJ27tyJnTt3omfPnli6dGmJzyZQkfDIkNOcPn0aSUlJ8PX1xdKlS9GuXTuL7j0pKSlOiq5oRb0yYF6mUqnkwf18fHzkC5F5GhprzN3FHhzwDMhvBS/4lNNWWWvd74OCgvDaa6/hu+++Q2RkJH744Qe0b98eRqMRs2bNcvg4m7tlFex6by/z/thzDItDoVAgNDQUH330ETZv3oyjR4/is88+g6+vL+Lj44s1cJy5hb2oBMj8OoKz2HM8H5wSCMjvGfLCCy9g0aJF+Ouvv/DHH3/ghRdeAAD8/PPPxR6IkojIFoVCgY8++ggqlQo7d+4sNE6LmfleVtR9pqj7HmD/fabgtdGe6/3DpgJu3bo1pk+fjp9//hkRERGYN28eqlevjrt37+Ljjz8usuyjKM7+Fvc4F9yGPWWt3X8CAwPxyiuvYNmyZTh69Ch+/vln9OrVC0IILFq0CFeuXLG5XWvMuUnBrvf2Ku3cBCj586Ii5yaurq4YMmQI5s6di71792LPnj2YMGECFAoF/vzzT/z888+lFnNFwAo+OY35AlyvXj2L+VHNjhw5UpYh2c1aMvLgssaNG8tjEWi1WjRu3BgA5AHzrDEvKzgCfJMmTeR3n44ePWq1XEZGhvwOU7NmzYqM3TxwyvLly6HRaKDX63Hu3Dl5uflBhHlGBGvMg8LFxMTg9u3bRX7fg8z7VnBE9wcdO3bMoW0WxcfHBy+++CKmTJliddvmY1vU/pof1BSVNJjnzXWWoo6Z+ZwseF7Z0qhRI/zzn/+Uf+OiznUiouKqX78+nn76aQDAwoULLZabr1e27nsFl9m67509e9Zm5cd8bfP19S3UPd98vS/q3ubI9d7DwwODBg2S5/U+e/ZsodfW7Lnn2qs4+1vc41yvXj35tUFbeY3JZJLvTQ+7/ygUCoSEhGDx4sUIDAyEyWTCiRMn5OWO5CYpKSmF8hp7mOM7ceKEze8oyfthSZwX5nP17t27MJlMVtcpz7mJednD8lYgv6HqvffeQ9++fR+6XWIFn5zI/L5RbGys1RvSoUOHiqwMO1NiYiK2bdtm8XlaWpr8VLF///6FlvXr1w8AsHnzZqtPNA8dOiQPEGNOeoD8m7F5AJuVK1daHXE2LCxM7iJnnnkAKLpbv1arlW8gBdcrOLq6LZ07d0aNGjVgMpkwd+5cm+sBQHp6eqF/m4/LqVOnrF6g4+PjbU55VBRJkop8R9387v2Dx8S8v0UNYGfuNnf79m2rScPx48eLNdNDSdq+fTvi4+MtPj927JgcW8Fz8mGvfJh70xT31RAioocxTwF68uRJi/u9+Z65f/9+xMTEWJS9ePEi9uzZA6DwPbOgrKwsq68U5ubmYs2aNQAs79Xm631iYiIuXrxoUTYiIkKe9uxBRV0vzdfUB+9V9txz7VWc/S3ucVYqlXjqqacAAN9//32hyqnZTz/9hOTkZCiVSvl7gKKPk1qthlqttljP/DBBkiSbPSh0Oh2aN28OAJg7d26ROUF2dnah7ffr1w8KhQI3b97EH3/8YbF+wfzOUaV1XjRo0AAajQYmk0keY6mga9euyb+ds4SHh1v9e7l27Zo8C4MjuYmtXI4KYwWfnKZNmzZwc3NDWloapk+fLld6c3JysGnTJkyePBm+vr5OjtI6Ly8vfPzxx9i6dat8Qb548SLGjRuHe/fuoUqVKnj55ZcLlRk5ciQCAwORk5OD8ePHy09VTSYTdu3aJc8nGhoaKk89Yvb3v/8dSqUS0dHRmDJlitySnJWVheXLl+Pbb78FkD8Nm/mmAAAzZszABx98gL/++qvQDTEhIQEzZsxAbm4uXF1d0bZtW3lZ3bp1odFokJGRYXP6II1Gg48//hgKhQLbtm3DG2+8UWg+WYPBgKioKMydOxe9e/cuVLZdu3byyPtvv/02/vzzT/mhxYkTJzB+/HiHZmEwy8zMRN++fbFs2TLExMTIT7MlSUJ4eLjcQmSeHsfM3LPir7/+stmVrFatWmjZsiUA4IMPPpCTIIPBgB07duDNN990aHDJ0qDRaDB+/Hi5Mi9JEvbt24e3334bANClS5dCv/OGDRswbtw4/P7774X2+/79+1i+fLncUlHUSMRERI+iadOmCA0NBZA/p3pBgwYNQuPGjSGEwKRJk+RWZCEEDh8+jIkTJ8JoNCI4ONjm1KpeXl5YuHAh1q1bJzckxMXFYdKkSYiNjYWbmxvGjx9fqEydOnXkFsXp06fLM+Dk5eVh+/btmDx5ss3r/YABA7Bw4UJERUXBYDDI8Z4+fRqzZ88GkN9Nu+B9ulGjRgDyH8bGxcXZeeSsK87+PspxnjRpElxdXXHr1i28/vrriI2NlY/Vhg0b8MUXXwDIf9/ePMc6AHz55Zf4+9//jj179hSaCi05ORmzZs3CrVu3oFQq5XMDyO/GbR7Vvaj36z/++GNoNBpERERg7NixOHnypJxjmEwmxMTEYMmSJejTp0+h1x6DgoLw3HPPyduwlt+Zf1NHldZ54eLiIg84N3v2bHlfTSYTDh5IkFuMAAAgAElEQVQ8iHHjxtnsIVtWPD098eabb+LgwYNyb4TIyEhMmDABBoMBOp1ObpUHgH379uGll17CL7/8UuiV1uzsbGzYsEFuAHowl6PCOMgeOY23tzemTp2K2bNnywNneHl5ITs7G0ajEU2aNMHQoUPx+eefOztUCyNGjEBERASmTZuGmTNnQqvVyhVoNzc3fPXVVxYJgI+PD5YuXYrx48cjJiYGw4YNg4eHB4xGo3wjDg4Oxrx58yy+r02bNvjHP/6BWbNmYefOndi1axe8vb2RmZkpV2QHDRpkMSBNbm4utm/fjt9++w0KhQJeXl4wGAzyyLEqlQqzZs0q9P6Tu7s7Bg4ciC1btuDtt9+Gl5eX3A1s+vTp8pPW3r17Y/bs2fjHP/6BvXv3Yu/evXB1dYWrqysyMjJsdhcDgDlz5mDkyJG4ceMGXn/9dbi6ukKpVEKv18Pf3x8zZswo1uCKiYmJWLRoERYtWgSNRgMPD49CsQQFBeGDDz4oVKZPnz6YP38+YmNj0aNHD1SpUkV+wLBv3z55vY8++gijR4/GpUuXMHjwYLi7u8NgMMBgMKBr165o3rx5sabPKSkzZszAggULMGLECLi7u0OSJLlFpW7dupgzZ06h9YUQOHToEA4dOgQg/3dXq9WFejK8+OKLhXqEEBGVtPHjx+PIkSMIDw/H6dOn5W7WWq0WixcvxtixY5GYmIgxY8bA3d0dQgj5HlarVi0sXrzY5kPhvn37IjU1Ff/85z8xZ84cuLm5ydc4lUqFOXPmWJ0a9KOPPsIrr7yCmJgYPPPMM4Wu9z169EBwcLD8YL2glJQULF++HMuXL4dKpYKXlxeysrLkSl2VKlUsBi7t3LkzatWqhcTERPTt2xf+/v5yK+XGjRttjmBeUvv7KMe5Xr16mD9/PqZMmYLw8HD069cP3t7eyM7Olve5a9eueP/99wuVM5lMct4H5FcChRDytHsAMHXqVDRs2LBQueHDh2P58uWYPXs2Fi5cKI8J8Oqrr2LkyJEA8rvpL168GO+++y4iIyMxYsQIaLVauLu7IzMzs1Ar+YNTv82cOROXL19GVFSURX7n7u6Ozz77DO+9957dv4dZaZ4X7733HiIjI3Hz5k2MGDECbm5ukCQJubm5aNasGcaMGSM/aHGGt956C2vXrsWECRPg5uYGhUIhz4wVEBCAr776Su6xYWYefBDIb7F3cXHB/fv35QcEPXv2xLBhw8p2Rx4zbMEnpxo9ejSWLFkit+abTCY0aNAAkydPxk8//VTktHDOpNVqsXbtWrz55puoWbMmDAYD/P39MXDgQPz2228253hv2bIl/vjjD7zyyiuoV68ejEYjVCoVmjdvjunTp+OXX36Rn1A/6KWXXsKmTZvwzDPPIDAwEHq9Hl5eXujSpQu++uorzJs3Tx5wxezdd9/FtGnT0K1bNwQFBcFgMMBkMqFOnTp4/vnn8dtvv1kdQX/WrFmYOHEiGjRogLy8PCQmJiIxMdFiusKhQ4di586dGDNmDBo3bgylUonMzEz4+vqiQ4cOmDx5snwDL6hq1arYtGkTxo4di1q1asFkMsHLywvDhg3D5s2bUadOHXt/CpmnpydWrFiBMWPGoGXLlvDz80NWVhbc3NzQokULTJkyBVu2bLEYQd/f3x/ff/+9fAO9d++evL8FhYSEYP369ejZsye8vb1hMplQv359TJ8+Hd9++63FDaqs1alTB7/++iuGDh0KLy8vmEwm1KpVC6+++ip+/fVXi2lwBg0ahM8//xwDBgxAw4YNoVarodfrERgYiF69emHZsmX47LPPnLQ3RFRZdOnSRX7/+cFZWerXr4+tW7di0qRJ0Ol0coKv0+nw1ltvYevWrahbt67NbSsUCixZsgQzZsxA/fr1kZeXBx8fH/Ts2RMbN2606K5u1qZNG/z444948skn4e3tDUmSUL9+fbz//vtYtmyZzdG7ly1bhtdeew2tW7dGYGAgsrKyoNFo8MQTT+D111/Htm3b5F5jZlqtFt9//z0GDx6MqlWrIj09Xb4HFfWgvCT391GOc58+fbB161YMGzYMtWrVQnZ2NlxdXdGuXTt8/vnnCAsLs2hFfvXVVzFz5kz06tUL9erVgxACeXl5qFmzJgYOHIj169djwoQJFt81efJkvPvuu9DpdJAkST5OD75i17NnT/znP//B66+/jqZNm0Kr1SIjIwOenp5o06YNJk6ciC1btlhM4ebp6Yl169bhrbfekuNyc3PDwIEDsWnTJrknn6NK87yoW7cuNm7ciIEDB8LPzw8mkwk1atTAG2+8gfXr1zs9j/bz88OmTZswatQo+Pv7w2AwoFq1anjxxRexZcsWi/ntu3Tpgn/961949tlnodPp4OrqiqysLPj5+aFr16748ssvsWzZMot8lwpTiJIY1YOIiJyiV69eSExMxA8//CCP1UBERETkLCNGjMDJkycxd+5cDBkyxNnhVDpswSciIiIiIiKqAFjBJyIiIiIiIqoAWMEnIiIiIiIiqgBYwSciIiIiIiKqADjIHvLnXT937hwCAwM5KiMREdnFZDIhOTkZzZs3l6cvIrKFuQYRETmqOLmGc+d1KifOnTuHv/3tb84Og4iIHkM//vgj2rVr5+wwqJxjrkFERMXlSK7BCj6AwMBAAMDg19fB07f6Q9Ymosrg5eT5zg6ByrnbGXqM+/mAfA8hKgpzDSKqrJhTFV9xcg1W8AG5q5ynb3V4+dd2cjREVB7UzPNwdgj0mGB3a7IHcw0iqqyYUz06R3INDrJHREREREREVAGwgk9ERERERERUAdjdRf/atWv466+/EBUVhXPnziE2NhZCCHz11Vfo379/sQP4/fffsWHDBsTExECSJNSvXx9Dhw7FiBEjoFTafv5w8OBBrFmzBufOnUNubi6CgoIwcOBAjBs3DlqtttjxEBERUdljnkFERPTo7K7gb9iwAT/88EOJfvmsWbOwfv16uLi4oHPnzlCr1QgPD8dnn32G8PBwfP3111ZvvmFhYZg3bx5UKhU6dOgAb29vHDt2DIsWLcL+/fuxZs0auLm5lWisREREVHqYZxARET06uyv4Op0O48aNQ/PmzdG8eXPMnDkTkZGRxf7iXbt2Yf369QgMDMS6detQr149AMDdu3cxevRo7N69G2vXrsWYMWMKlYuKisL8+fPh5uaG77//HiEhIQCArKwsTJw4EceOHcPChQvx4YcfFjs2IiIiKlvMM4iIiB6d3e/gDx8+HNOnT8eAAQNQp06dR/7iFStWAADee+89+aYLAAEBAfj0008B5D9BlySpULmwsDAIITB+/Hj5pgsAHh4e+OKLL6BUKrF+/Xrcv3//kWMkIiKissE8g4iI6NE5ZZC9pKQkREdHQ6PRWH2vrkOHDqhWrRqSk5Nx+vRp+fO8vDwcPHgQADB48GCLckFBQWjVqhUMBgMOHDhQejtARERE5RbzDCIiqqzs7qJfks6fPw8AaNy4MVxdXa2u06JFC9y+fRsXLlxAmzZtAADXr19HdnY2fH19bT7db9GiBU6ePInz589j0KBBpbMDROXAuKcAH3eFs8MoUel6ge92OzsKInrcMc8genQVMc8AmGtQxeeUCn5CQgIAoGbNmjbXqVGjRqF1C/6/eZk15m0mJiY+cpxE5ZmPuwIL/i2cHUaJmjpEAaBi7RMRlT3mGUSPriLmGQBzDar4nNJFX6/XA0CRI9B6eHgAyB/UxpFy7u7uFuWIiIio8mCeQURElZVTKvhEREREREREVLKcUsE3P/3Ozs62uY75ybj5Cbu95cxP3wuWIyIiosqDeQYREVVWTqng16pVCwBw8+ZNm+skJSUVWrfg/9+6dctmOfOyguWIiIio8mCeQURElZVTKvhNmzYFAFy+fBk5OTlW14mKigIANGnSRP6sQYMGcHV1RVpaGuLi4qyWO3v2rEU5IiIiqjyYZxARUWXllAp+jRo10KxZMxgMBuzcudNieWRkJJKSkhAYGIjWrVvLn2u1WnTv3h0AsHXrVoty8fHxOH36NDQaDZ588slSi5+IiIjKL+YZRERUWZVqBX/+/Pno378/5s+fb7HstddeAwDMmzcPN27ckD9PSUnBrFmzAAATJkyAUlk4xAkTJkChUGDlypXyU3Qg/126Dz/8EJIk4eWXX4a3t3dp7BIRERGVE8wziIiIClPbu2J0dLR8QwSAK1euAAAWLlyIVatWyZ///PPP8v8nJyfj+vXrSE5Otthe//79MWLECGzYsAGDBg1CaGgo1Go1wsPDkZmZiT59+mDkyJEW5Vq2bIl3330X8+bNw0svvYROnTrBy8sLx44dQ0pKCkJCQjBlyhR7d4uIiIjKAeYZREREj87uCn5mZibOnDlj8XlsbGyxv/zTTz9F27Zt8eOPPyIyMhKSJKFBgwYYOnQoRowYYfFU3WzChAkIDg7G6tWrERUVhdzcXAQFBWHUqFEYN24ctFptsWMiIiKissc8g4iI6NHZXcHv2LEjYmJiHNr4nDlzMGfOnCLXGTRoEAYNGuTQdgGge/fu8ntyVHGNewrwcVc4O4wSla4X+G63s6Oo2ErmvJldIrGUFCkjFdnr5zk7DKJSwzyDnKEi5hkAc42yUBHPHZ43FYPdFXwiZ/BxV2DBv4WzwyhRU4coAFSsfSpvSuK8mZj0UQlFUzI8JpavBw5ERBVBRcwzAOYaZaEinjs8byoGp4yiT0REREREREQlixV8IiIiIiIiogqAFXwiIiIiIiKiCoAVfCIiIiIiIqIKgBV8IiIiIiIiogqAFXwiIiIiIiKiCoDT5BGRVZnp2biblA6jwQiVWoUq1bzh7efh7LCIiIioApAkCbcTUqHPzAWEgKu7FtWC/KFWq5wdGtFjjRV8IiokKf4eLp66gbSULCgUgBACCoUCEICXnzuCW9VBzbpV8j8jIiIickBejgGXoxJw7cJNCAFIUv6860qlAkII1G1cDbpWdeDu4eLkSIkeT6zgExGA/Ir8ucjruHbhJkxG6cGlAIC0u5k4vv8ighpVResujVnJJyIiIrtl3c/GgW1nkJdjkCv2ZpIp/7/XL95C/NU76DqgJfwCvJwQJdHjje/gExEA4OKpOBuV+8JMRgnxV+4gKuJaGUVGREREj7vcHAMO/H4GOfo8i8p9QUIAhjwT/vrjLDLTs8swQqKKgRV8IkKOPg8xZ+IeWrk3MxklXLtwC5n3eeMlIiKih7t46gbycg12r280mHD26NVSjIioYmIFn4hw7cJNAI51txdC4Gp0YukERERERBWGyWhCbExSkS331ty5mYocfV4pRUVUMbGCT0S4duEWJJN9rfdmQhKIjUmCEI7drImIiKhyuXUjpdjj9ty4lFTC0RBVbKzgE1VyQhLIy7G/y1xBkiRgyDOVcERERERUkWRm5MBodDxfkEwCGen6UoiIqOJiBZ+okhMofgu8AmALPhERERVJSALFTTcc7dZPVNmxgk9UySmVSqjUxbsUCAAaLWfbJCIiIttc3bXFyzUUgLuHa8kHRFSBsYJPRKjdINDRMfYAANWD/KFUFu+dOiIiIqocatatUqwefyqlEkGNqpZCREQVF5veqNLSZ+Tg6oWbuHsrHUaDCRoXNWrVDUDd4GrQumicHV6ZatS8NhKuJsPkwEB7KrUSupa1SzGq8smYbUBS5A0kn4yHQW+ASqOCd4MqqN2jEdwCPZ0dHhERlRNCEkiKv4frF29Bn5kLhQLw9HFDg6Y1EVDdp9iDzj2OXNy0qFbbH7dupDhUztPHDT7+HqUUVfnlcvUc/Lavg0vcJShMRhh9qyC95/PI7NQPQqN1dnhUzrGCT5VOjj4Px/dfxN3b6YAo/G5X+t1MRJ+IRVDDQLQKbVzsruuPGx9/DwTW8sWdxDS7RtNXKhXwC/CCf1XvMoiufJBMEq78egZJ4bGAApAKDC6YmZiGW4evwauOP5qM6QBXf3enxUlERM4Xd/k2zh69CkkSMBr+d79Iv5eFpPh70Lpo0KabDtVq+zkxyrLVtG093ElMhcloX2OCSqVEi04NSjmq8sXl6jnUWDwDmuSbUBhyoZDyj5VLHOB66SyqffspUoa9gdTBrwKV6AEROaZy1F6I/kufmYO9m08g+VYaJJOwGLjFZJIgmSTEX03GgW2nizXi6+OqY68m8K3iAZWq6MuCUqWEp687OvdtVmlaHySjhDOLDyIpPBaSwVSocg8AwiQgGSSkX0/B8Tm7ob+T4ZxAiYjI6S6evoFThy4jL9dYqHJvZjJKyM7KxdHd0Yi/escJETqHj78HOvVpalfjiUqlREhoI1StWXkegLidi0CdT0bBJeEqlLnZcuXeTJWTBVV2FgJ+XoJqKz4BOMgx2cAKPlUakiTh4B9nkZdjeOg1UTJJuJ+qx7F9F8omuHJApVah+8AQ1G9SAyq15cB75s/qNq6GnoNbVarB9WI2nEDGjVRIVhK1QiQBY7YBpxcdgCnXWDbBERFRuZEYexcxp+LteuXNZJJw8uAl3EuuPA+Fq9X2R7eBIfAN8IRKpSzcCK3IzzU8fdzQ6ammqBdc3WlxljX1nQTUmjMJytzsh66rzM2G91/b4LdtTekHRo+lypOhU6V3MzbFrsq9mWSScDsxDZnp2fD0cSvd4MoJpUqJlp0aolm7eoi/loykuHsw5Bmh0apQtZYf6jSqBrVG5ewwy1RuWjaST8RDsrNLIQRgzDHgzol41AitX7rBERFRuSGEwLnIaw6NZ2MySbhwIhZd+rcoxcjKF/9AL/R6tg3up2bh+sUkZN7XAwJw93RF3eDq8A/0cnaIZc5/62ooDXl2r6/MzUaVX5Yi9emRgLpyjRtFD8cKPlUal87GW+0qVxQhSbgSnYBWoY1LKarySaVWoZ6uOurpKs/Tc1sS/7rq8NS9Up4JcbsvonrnepXmNQYiosouNTkDOXr7K2lmybfSkJ2VCzcPl1KIqvzy9vNASOeGzg7D6RS52fD58zcoTA72/JMkeEbuRWZo/9IJjB5b7KJPlUJejgHp97IcLicEkHA1uRQiosfFnWNxEPa23heQm5aDnBR9KURERETlUfy1ZLsHkCtMgVtxjo0uTxWHe3QkhNLx3pGqnCz47N9cChHR444VfKoU8nKNUCmLd7obHGz1p4rFkG0oVjmFSgFDVm4JR0NEROVVTjGv+ZJJQl5O8e419PhT3U8FRHEeDAHqtLslHA1VBKzgU6WgVCogHO5o/d+y7GJdqSmVxf/9lZVkmkUiIsofx6Y4FIril6XHn1Brij3lnaTRlnA0VBHwakKVgou7FsWs38Pdy7Vkg6HHilvV4g32IxkluPi5l3A0RERUXvlW8SxWRV2lVlWawXzJUl7N+hZT4tlDKFXIrRtcChHR444VfKoUVColghpVdfgBqUqtROOWtUsnKHosBPXWQeXi4HikCiCgeQ1o3PlknYiosqjTuBqK05qgUChQPahKyQdEj4XcBk1hqOL4oMZCrUba0yNLISJ63LGCT5VGo+a1oCjGe/i1GwSWQjT0uKjSogYUKseeDCk1KgT10ZVSREREVB65uGpQI6gK4MAtQ6lSomGzmo/0Ohg9/u49Ox6Si/29OIRCgdzajZAX1KgUo6LHFSv4VGl4+3mgYdOaUNn5XrRKpUTb7jqo1ZVr3ncqTKlSoskrHaHU2HceKDUqVGtXB9712BpDRFTZtOzcEFo7e30pFIC7pwsat2BPwcrufvfByGnQ1O536oWLG5ImzynlqOhxxQo+VSrNO9RHveDqRVbyFYr8yn1Il0ao3aBqGUZH5VWVptXxxOj2+ZX8Iq6aSq0KVdvUhu6lNmUXHBERlRtuHi7oMagVXNw0Rb6Pr1Ir4enjju4DQ6DROvgaGFU8ag0SPvwWOY1aFtmSL6k1MLl7Iv6T1cgLalyGAdLjhFcUqlQUCgVCOjdCjTpVcOlMPO7eToeyQLd9SRKoVT8AupZB8PH3cGKkVN5UbV0bHjW8Eb/3Eu4cj8/vti8AKABhkuBVxx91+gbDv2l1KDjzAhFRpeXl446nhrXDtQu3cOVcIiTT/wZQE0LAxVUDXcsg1NFVYy9Bkgk3D8R/ugbeh7fDf3MYNHcSIJQqyMmGAkjrOwKpA0bC5F/N2eFSOcYKPlVKVWv5oWotP+gzc5CekgWj0QSNVg3/qt52d62jysejujee+Fs7NBoagvRrKTDqDVBpVfCo6QO3AD4QIiKifFoXDZ5oVQfBLYOQcuc+cvR5/+2S7wrfAE8+CCbrVGrc7z4Y97sPhjbuErS3bkBhNMDk7Q/9E20ATotHdmBNhio1d09XuHtyGjxyjNpVgypNHR/xloiIKheFUoGA6j7ODoMeQ3l1dMirwwF7yXGs4FcQ454CfNz5NJiovHN7+T0ovfyKVdZj4uwSjqZkSBmpyF4/z9lhEFEpY65B9Hh4lL/VqUNK42/80fMX5hr2YwW/gvBxV2DBvx2fe7W8K52LDJHzKL38kLVipsPlPCbOLla5slBeHzwQUcmqiLkG8wyqiIr7tzp1SOn8jU9M+uiRt8Fcw34cRZ+IiIiIiIioAmAFn4iIiIiIiKgCYAWfiIiIiIiIqAJgBZ+IiIiIiIioAmAFn4iIiIiIiKgC4Cj6RI+pdL2ocKP/pusr1ujMREREj6uKmGcAzDWo4mMFn+gx9d1uAOBNioiIiEoe8wyixxO76BMRERERERFVAKzgExEREREREVUArOATERERERERVQCs4BMRERERERFVAKzgExEREREREVUArOATERERERERVQCs4BMRERERERFVAKzgExEREREREVUAamcHQPQwU4conB1CiSvrfUrXC3y3u0y/0qnS9aIEjvHsEonFGo+Jjm9bykgthUiIiKgi5hlA2e5XZcszgJLKNUpPcWJL14tSiITKGiv4ZLfcHAOys3IhhICruxZu7i5l8r0L/l12F5upQxRl+n1lJf8iX/H2y5b8JOPR9ndi0kclEsuDPCbORtaKmaWybQAQQiDnnh6GzFwo1Sq4+rtD7aYpte8jIiopQhLIzMiGIc8EtVoJDy83qNSl39m0rO/7FTHXqGx5BlAyuUZpKe1zTJGjh+ZOAhR5uZA8fWCoFgQoyu/DjsqGFXwqkhACdxJTcelsAlKS0qFU5d9oJUmCj78ndC1ro0bdACiV/KMmciZTrhG3T8QjfvdF5KblQKHK/5sUJglVWtZCUK/G8K7r7+QoiYgs5Wbn4XpMEq6cS4TJaILivxUFIQTq6aqjYfNa8PR2c3KURKSNuwS/39fA+/B2CJUKUCigMBlh8vTFvSHjcL/Hs5A8vJwdZqXHCj7ZZDSacHR3NFJu34fJKAEAJMkkL09NzsDxAzHw9I5D1wEt4eLKVkIiZ9DfzsDprw7AmGOAlPffv1HD/5Ynn4xHStRNVO9YD42Ht4KCD+SIqJy4czMVR/9zHpIQkEySxfJrF2/hekwSWnZqgAZNajohQiKCEKiycTH8t64CTAYoTaZCi5W5SQj4cQECfl6C+E9WI7dBUycFSgAH2SMbJEnC4R1RuJv0v8q9NSajhPtpehzYehqGPGMZRkhEAJBzLwsn5+9DXkbO/yr3DxKAlGdCUkQsLm08CSHKZ5dCIqpc7ialI/w/0TAaTVYr90B+t33JJCHq6DVcv3irjCMkIgCo8tNX8P99NZR5ORaVezNVbjaUmekI+mQUtPFXyjhCKogVfLLq6rmbSEvJtHnDLUhIAvrMHJyLvF4GkRFRQedXR8CYY7TrNUApz4Tbx+KQevF26QdGRFQESZIQvju6yEaEgkwmCWfCr0KflVvKkRFRQS5Xz8H/9zVQ5mY/dF0FAGWuHrW+fAtgY4LTsIJPFoQQuBSVYPdNFwAkSSDuym0YDTZaEImoxOlvZyAzIQ2Q7L+JSnkmxO2OKcWoiIge7taNFAgHrl0AACFw/fzN0gmIiKzy37oaCmOe3esrhIA65TZcL58pxaioKKzgk4Xkm2kwGYvX3T7h2p0SjoaIbEk4cAWSyfEn5OnXUpCTqi+FiIiI7HPpbLzDjQKSJHDtwk1Ijj4YIKJiUWbdh2fkHigk+xv9AECRlwv/ratLKSp6GFbwyUJaSqZDrfdmJqOElNv3SyEiIrLm/rW7DrXemynVSmTdTC+FiIiI7HP/XvEeMkomgdxs+1sTiaj4tInXITSOD6KtEBJcr0aVQkRkD1bwyYLJKBX7tRmjwfEHA0RUPCZbg+o9jHiEskREJaDYrfAKFKsRgogcp8jLRv6b9cUpy/EynIUVfLKg0aqLPa+9ixunyiMqKxp3bfEKKgA1/1aJyIlU6uKloEIS0GhVJRwNEVkjuXsDDnbPl8u6eZZwNGQvVvDJQrXafoDC8Qq+Wq1EzbpVSiEiIrImsE1tKIuR6ApJwKc+/1aJyHmqBfkXq2HQ3dMVWlc+oCQqC7l1dYDK8TxD0miR0blfKURE9mAFnyx4+brDx9/D4XJqrRqBNX1LISIisqZ6p3qOT0OjVKBa+zpQuahLJSYiInvoWtSGSuVYGqpSK6ELCYKiGI0QRFQMKjVSnx4JSeNoj0EF0vq/XCoh0cOxgk9WNWlT16Ebr0qtxBOt6vCmS1SGNO5aVOtQF0qN/U/XlWolgnrpSjEqIqKH8wv0gpevu0MdBlUqJWo3DCy9oIjIQlq/EYDK/kYBSeOCrNbdYKxSvRSjoqKwgk9WVQ/yR3CrOna9I6dSK1G7QSDqN6lRBpERUUGNh7eGZ5AvlJqH/60qNSo0GdMB7tW8yiAyIqKihfZrDhc3LRR2jPuj1qjQdUBLqNV8/56oLJn8ApE4YykkF7eHritptDBUD8Ktt+eWQWRkCyv4ZNMTreugRccGUKmVViv6KpUSSpUSjZrXRptuOrbeEzmBUq1Eq8ndERBSCwq1Egprf6suaqjdNWj+WmcEhtRyQpRERJZc3bTo/Vwb+AV45fcatJJGqNRKuHu54snBreBbhamxh0QAACAASURBVIN2ETmDvkUnxH+yCkbfAJhc3S2WS2pNfst9y1Dc+L+NEFbWobLDlzCpSA2a1ESdRtWQcO0OrpxLRHZWHgABrasGDZrURF1dNWhdONgNkTMpNSo0faUjclL1SDx4FXeOx8GYbYBCpYR7VU8E9Q5GlRY1oHTwfVciotLm4qbFk4NbIf1eFq6cS0BS/D2YjBJUKiX8Ar2ga1kbVar7sBGByMlyglvj6rcH4XH6L/j9+zu4xF2GwmiA5OaBjE79kDZgJAzV6zg7TAIr+GQHtUaFesE1UC+YXfCJyjNXP3c0HNICDYe0cHYoREQO8fH3QNvuwc4Og4iKolQiq00PZLXp4exIqAis4D+CcU8BPu7l54ny1CGPHku6XuC73SUQDBE9ttxefg9KLz+HynhMnF1K0ZQMKSMV2evnOTsMIoeUtzwDYK5BRCWDuUbpYQX/Efi4K7Dg3w5OUVVKpg4pmVjyb9zlY58qIqPBhNsJ95CTbYBSqYCHlysCa/qy6yGVK0ovP2StmGn3+h4TZzu0vjOU96SAyJrylGcAzDUeF+kpmUi9mwmT0QS1Vo2qNX3h5uHi7LCICmGuUXocruD//vvv2LBhA2JiYiBJEurXr4+hQ4dixIgRUCrte78zISEBvXv3tmvddevWoX379vK/Fy9ejCVLlthcX6vVIioqyq5tE5WVrPvZuHg6DglXk6FQKiBJAgoACqUCKnX+QIUNm9aE2oHpzoiIKirmGkSOEUIg4VoyYk7HISsj57+fAUqFApKQEFDdF0+0roOA6j5OjpSISptDFfxZs2Zh/fr1cHFxQefOnaFWqxEeHo7PPvsM4eHh+Prrr+268bq7u+O5556zufzKlSuIioqCh4cHmjVrZnWdJ554Ak2aNLH4XK1mpwQqX+7eSsOR/0TDZDRBCACmAgtN+a36F07G4salJHQfGAJXd62zQiUicjrmGkSOkSSBY/suICkhf4DCQsv++987ialISUpH0/b10Lh57bIPkojKjN13qF27dmH9+vUIDAzEunXrUK9ePQDA3bt3MXr0aOzevRtr167FmDFjHrotf39/zJkzx+byCRMmAAAGDhwId3fr0yz06dMHkydPtjd8IqdIS8nE4V3nLG64D5JMAln3s3HwjzPo9WwbtuQTUaXEXIPIMUIInDgYkz/7gKnoXMNkknD+eCw0WjXq6aqXUYREVNbsnjNpxYoVAID33ntPvuECQEBAAD799FMAQFhYGCSp6IvLw9y+fRuHDh0CAAwbNuyRtkXkbKf+uvzQyr2ZEIA+MxfXLtws5aiIiMon5hpEjklNzsDN2LsPrdybmYwSzhy5AqPB9PCVieixZFcFPykpCdHR0dBoNOjfv7/F8g4dOqBatWpITk7G6dOnHymg3377DZIkoXHjxggJCXmkbRE50/1UPdJTsxwqI5kkXI5KhBAcfIiIKhfmGkSOuxyVYHdDQkHxV++UQjREVB7Y1UX//PnzAIDGjRvD1dXV6jotWrTA7du3ceHCBbRp06bYAW3evBn/z969x8dR3nm+/1ZVX3S/2bpfbMm2LNv4DsYmwQQwxEsgTuLZBOcFCWcYYE7OcWaXsMyeTXYCeSXzygwMyZCwsxzCzGS5OGcmmZCwGXAMOBCIwQFibHyVb5JlXSzLurek7q6q84ewg7Fldbe61a3W5/1PSHdV6duy3fX7VT31PNL4V9T37t2rhx56SH19fcrPz9fSpUt1zTXXyOfj+WWkhmP7W+XGcJfJDts63dar4oqCBKQCgNRErQFEJxyy1dbcFfV+dtjR4fdPqrahPAGpACRbRA1+S0uLJKmiomLMbcrLy8/bNhY7d+5UU1OTvF6vPv3pT19y2+3bt2v79u3nvVZWVqaHHnpIq1atijkDEC99PQHFciPedV0N9g+pWDT4AKYPag0gOkOBEZmmKceOfrj90MBIAhIBSAURDdEPBAKSpMzMzDG3yc7OliQNDkY3JPnDfvazn0mSrrvuOhUVFV10m+rqan3ta1/TL37xC73zzjvasWOHfvzjH2vVqlVqb2/X3XffrQMHDsScAYgX14lxmL07OiMuAEwn1BpAdGKuMyQeBQTSWMqs8zIwMKCtW7dKkjZu3Djmdp/5zGcueG316tVavXq1vvrVr2rr1q363ve+d26iHiBZMrP9Me1nmIb8mQz/BIB4o9ZAOvFneOVEOLneR3n9KdMCAIiziO7gn10+ZmhoaMxtzl5NP3t1PVq/+tWvNDQ0pLKyMl199dUxHeMrX/mKJOmNN95QKBSK6RhAvMyqL5XliX65O9d1VVpVmIBEAJC6qDWA6PgzfcqfkRP1fqZpaNa80gQkApAKImrwKysrJUmtrWMv39Xe3n7ettE6O2Tus5/9rEwz4tX7zlNXVydJCoVC6u7ujukYQLwUVxTI64uuwTcMqWZeqTwxXBgAgKmMWgOIXv2SalmeKP8uG1LdwrHnugAwtUX0jbBw4UJJUmNjo4aHhy+6zZ49eyRJCxYsiDrE4cOH9d5778kwjEsOmRtPT0/Puf8+eycASBbDMLT4yjpZVuQnXstjaf6S6gSmAoDURK0BRK981gxl5WTIMCLb3rJMVc8pifkxQgCpL6LOo7y8XIsWLVIoFNKLL754wfs7d+5Ue3u7iouLtXz58qhD/PSnP5UkXXnllaqujr25eeGFFyRJtbW1ysmJfsgSEG/Vc0o0f3l1RE2+x2vpY+svU1buxZeHAoB0Rq0BRM80DV190xJlZPtlmpfu8i2PqZnl+Vr+8fpJSgcgGSK+tXj33XdLkh5++GE1NTWde72rq0sPPvigJOmuu+46b8jb008/rfXr1+v+++8f87ihUEi//OUvJY2/Hm1ra6uef/55BYPB8153XVfPPfecHnnkEUnSHXfcEenHAhKuYdksrVhbr8xs3wXD6AzTkGmZKirJ0yc+vUwzSvOTlBIAko9aA4heRpZP1392hSpmz5RpmTI/clPB4zHl8Vqau7hKV9142bgXAgBMbRFPobl+/Xpt2rRJW7Zs0S233KKrrrpKHo9HO3bs0MDAgNatW6fbbrvtvH26u7t17NgxFRcXj3nc3/zmN+rq6lJeXp5uvPHGS2bo7e3Vfffdp29+85tatGiRSkpKNDg4qMbGxnNr4t5222269dZbI/1YwKSonlOiqrpinW7rVdOhdg0FgjJNQ7kFWapbUKGc/LGXhQKA6YJaA4iNz+/VqusWaGQ4pOMH29XV0atwyJbP71HF7JmqrC2O6pFBAFNXVGtkPPDAA1q5cqWeeeYZ7dy5U47jqK6uThs3btSmTZtimrDm7IQ3N998s/z+Sz8PVFZWpjvvvFN79uxRc3Ozdu/eLcdxVFxcrJtuukmf//zntWbNmqgzAJPBMAwVVxSouKIg2VEAIGVRawCx82d4NX9ptSTm8wGmK8N1XTfZIZKtpaVF119/vb74X19SblFVxPvdu8HQI79IjV/fnTdI+VnpN+SqN+DqyW2T9/NS6c80ntL1cyXSPe3fSMhxM794n8xclkFMRU5/t4aefTji7Vt7B3Xzky/o5ZdfVlVV5OcOTE+x1Bqp9t2djrXGZNcZUur9ucZDOn6mqSwd/62mi8moNaK6g4/JY9uOTh7rVEdLt0LBsLw+j0qrCsccYjV6cpr4Fytf0EBiRfOlnizZ93xHg49/fcLbXIzruDqzv12d751UqH9Els+jvNoZKrtyljyZ3lgjjyvSzwRMJwO9Qzp+sF0DfUOSXGXnZmpWfanyCrMvuj21BjA1xOvfarxc7KbJeOflWOsMSQr2DavtzeMaaOmRE3bkz89Q6eU1yqubISPSJSdikCq1Bg1+inFsR/veOa6j+9sk11U47Jx7r63ptHa9cVh1C8q1cOXsCyZRAYBU5bquTr52RE0v7pcTtGWPhM+9d3pPq47+YreKl1dr7p8slTfLl8SkQPrrPt2v9353WD1dg3JdV64z2ggYhnR0X6tyC7O0dM0cJn4FMKUMnwmo8V//oDP7OyRDckMf9FGG1P5Wk3y5ftVtWKySFen9CAsNfgqxw7Z++++71dM1KMd2Lng//MFf0sN7W3W6vU9X37RYlsea7JgAEBXXdXXgmbfV+W6LnKB9wftnXzv1zgn1NHZqxX3Xys/Ek0BCdLSc0Zsv7ZMdvrDOcN3REYQ9pwf0+r/v0eWfmK/K2rEnLwSAVDHY1qc/fG+7wsNhyfnI6AV3tNYY7growFNvK9AxoNn/YUFygk4CbgGnkLde3j9mc/9hju2op2tAb72yf5KSAUDsjv9q35jN/Ye5tqNg75B2/f2rckKX3hZA9HrPDI7Z3H+UbTt6+zcHdeZU3yQkA4DYBQdGtOvvX1U4ELqwuf8IJ2Sr+dcH1P5W0yW3m8po8FNEz+kBdbb1jNvcn+XYjjpP9qinayDByQAgduGhkE68fHDc5v4s13E10jukzl0nE5wMmH72/v5YRM39WbbtaM/OowlMBAATd/LVwwoPhyLe3gnZOvLz9849npRuaPBTROP7LRE392c5jqPDe1oSlAgAJq7tzeOjD/ZGwRmx1bztQGICAdPUcCCoU63dUe/X3Tmgwb6hBCQCgIlzbEcnXz0sN4qLl5LkhBx17W1LUKrkosFPAa7r6uTRTkW7YKHrSi1HO8VKhwBSVdvrRyO+e/9hQ50DGj4zmIBEwPR08linpOhnj3ZdVyeOnIp/IACIg94jp2O6E2+PhNX2xrEEJEo+GvwUYIedqJv7s1xXUQ23A4DJFBwYiWk/w7IU7BuOcxpg+hoaDEY9UlAafWwmEOO/YwBItGDfcMwrAo70pOfoJBr8FDA6ejXWu/ButKNfAWDSxL7erBv10H4AYzMmUPEZJv8WAaSmiXw/pet3Gw1+CrA8VszL3U1kXwBItIyirJj2c8KOMgpj2xfAhbJzM2V5oi/7TMtUTh7LVgJITRlF2YppKLQhZZbkxD9QCqDBTxGz55dFfRXJNA3VNpQnKBEATFzlJ+bK9Hui3i+/doZ8eRkJSARMT5W1M2MbLOi6qplbEvc8ABAPubMK5cn2R72f6bVUuXZOAhIlHw1+ipizqCL6oayGNGdhRWICAUAclCyvinpaL9PvUfUN8xOSB5iuvD6PKucUR/3kS1lNkfyZvsSEAoAJMgxD1evqZXqjG9Hsy8tQXu2MBKVKLhr8FJGdm6m6BeURD5+zPKbmLKxUVi53uACkLtNrad7nl0d84jU9pvJri1TUUJrgZMD0s2jlbHl9kY+o8XgtLV5Vl8BEADBx5WtqlTEjS4pwNLTptTT/iysnME9QaqPBTyGLr6xTVV3xuE2+5TFVNadYl62qnaRkABC7sitnqfbTl43b5Js+S7mzi3TZ3Vel7cQ3QDJlZvu19ual8vk9l/w3ZhiS12fp6k8tUTbP3wNIcZbP0rK/uEaZxdkyLtVHGaPN/YIvX6HC+vR99Cj6ByORMIZhaMXV9SqtLNSBXc0a7BuWK8lxHJmmKUNSdl6mGpbXqLJ2ZtpedQKQfqqvnafcqgId//d96jvWJRmGHNuRYRoyLVOeTK+q19Wr4uo5Mi2uPQOJkleYres3rtTBXc1qOtQhwzBk247kurI8plxHqp5booblNcrKYZQggKnBl5uhlfevU8vLh9Ty6mG5YUdO2JHrujI9plzHVdHCMs2+aaFyqwqSHTehaPBTjGEYqppToqo5JerpGlBXe6/CIVsen0czSvNUMCM9Z3sEkP4K5hVr2V9co6GuQZ3Z167wUEiWz1J2Rb4K5hVz0RKYJJlZfi27ap4uW1Wn9qYuBQZHzr1ePmuGPFE+ywoAqcDj92j2TQtV88kGde/vUKBzQG7YljfbrxmLy+WbJo820+CnsIIZOTT0ANJO5oxsVV6dnjPXAlOJx2Opak76DlMFMD2ZlqkZl5UrPafQGx8NPvAhvQFX925Iv7uIvYFY1kYCAADxlo61BnUGkDpo8IEPeXKbFNtCwQDiJdA5oJPbG9W1r132cFimz1LB3Jmquq4+7Z+bA5D+qDWA5HJsR6d3t6ple6OGuwYl15U3N0MVH69T6RU18mR4kx1xQmjwAQApITQwor3/+Kb6jnXJdVy59h8L4I63T6hz10llleTqsrvXKKMoO4lJAQDAVHTq3RM6tOVduY4reyR87vVg34iO/Hy3jvzbbtXc2KBZ6xum7NxATFUMAEi64MCI3v6bl9R75LSckHNecy9Jclw5QVsDrb16+7svKdA5kJygAABgSjr52yM68NTbCg+Fzmvuz3KCtpyQreZtB0YvArhTc6QNDT4AIKlc19Wef3hdwb7hCxv7j3JchYdC2vX3r8qxnckJCAAAprTeI6d15N92ywnZ427rBG11vN2s1tePTkKy+KPBBwAkVX/TGQ229Y3f3J/lSvZQSKffO5nYYAAAIC0c//d9ETX3ZzlBW00v7JPrTL27+DT4AICkOvHSoahOupJkj4TVvO1gghIBAIB0MdwdUO+R01HvZ4/Y6j7QkYBEiUWDDwBIqq597TFNKD14slfh4VD8AwEAgLRxZm+7ZEY/YZ49EtapP7QkIFFi0eADAJLGdUcnz4uF4TEVDtDgAwCAsYWHgnLCsc3bExoYiXOaxKPBBwAkjWEYMqzYlqFxHVem14pzIgAAkE5MryUjhjv4kmT5pt6q8jT4AICkyizOiWk/y2vJm+2LcxoAAJBOsivyZVrRt72mz1LurMIEJEosGnwAQFJVr5sv0x/dFXLDY6py7ZyYr8gDAIDpoWBesawMb9T7uY6rstWz4x8owWjwAQBJVbKiWtG26YYhVVw9JyF5AABA+jAMQ9Xr6qN6rM+wDBUvrZQ3a+qNFKTBBwAkleWztPBPr4z4xGt6Lc353FL5CzITnAwAAKSDyrVzlFNdIMMTQftrGvLm+DX3Py5LfLAEoMEHACTdjEXlWnDHKpk+S8ZYz8mZhkyvpboNl6mSu/cAACBCpmVqyf91tfJrZ8j0jX1DwfRZyijM0or7rpMvxz+JCeNn6k0LCABIS8VLK5X7jRt18tUjan39qGRodCy+68q1XZVeUaOqa+cpuzwv2VEBAMAU4/F7tHTzWnW936bmbQc1cKL73B1913blL8xUzQ0NKllZLesSFwFSHQ0+ACBlZBRla85nl6j25kUabO9TeCgky+9RVlmePFFOxAcAAPBhhmlo5pIKzVxSoeHugEa6A3IdV768DGWV5CY7XlxQLQEAUo7ptZRbPfWWpgEAAFNDRmGWMgqzkh0j7mjwJ6A34OreDem1RFNvwE12BGDKy/zifTJzp3Zzmn3Pd+KyTapw+ruTHQGIWjrWGRK1BhAPd94g5WdNle+Hi9cL49URU6nOkFKn1qDBn4Ant0kSJykA5zNzCzX4+NeTHSOusu/5znmf6aP/H0D8UWcAGEt+lqFHfjE1vh/uaf9GRNt9uLagzogds+gDAAAAAJAGaPABAAAAAEgDNPgAAAAAAKQBGnwAAAAAANIADT4AAAAAAGmABh8AAAAAgDRAgw8AAAAAQBqgwQcAAAAAIA3Q4AMAAAAAkAZo8AEAAAAASAM0+AAAAAAApAEafAAAAAAA0gANPgAAAAAAaYAGHwAAAACANECDDwAAAABAGvAkOwAAYOpx+ruVfc93kh0jrpz+7mRHAAAASs86Q5qcWoMGHwCmGDtkq7+5W+FAUJbPUlZ5vvx5GZOaYejZhyf15wEAgMnj7WiRt+24DDssO69Iw3WLJMuatJ9PnRE7GnwAmCKGzwyqZfthtf3umGScfdWQG7ZVUF+imhvmq2BecTIjAgCAqcpxlPP2Kyp67gn5jx2Q6/VJciXXlevxqfvmL6n3hltl5xUmOykugQYfAKaArn3t2vujHXJtR67tXvD+mX3t6jncqbLVszXvT5bJMI2LHAUAAOBCRnBEFQ9tVtb+t2UOB0ZfDI18aItBzfjZ/1TR8/+sE9/8Z43ULkhKToyPSfYAIMX1NHZq7xM75ATtizb3ZzlBW+1vHtfhn703iekAAMCUZtuq+Nv/W1nvv/XH5v4izOCIzIFeVf/V7fK2HZ+0eIgODT4ApDDXcbX3yTflhOyItneCttp+d0x9TWcSnAwAAKSD3N/9++id+/Pu2F+cIckcHlT5o3+Z+GCICQ0+AKSwrr1tETf3ZzlhWydeaUxQIgAAkE6KnvuRzJGhiLc3XFf+pgPynjyawFSIFc/gA0AKa3mlUfZIOLqdXKlr90mFh0LyZHoTkivzi/fJzE2vSXac/m5m7QUATCu+5kPytTVFvZ9h2yp84Rmd+rP/noBU6VlnSJNTa9DgA0AKG2zri2k/wzI13DWonKqCOCcaZeYWavDxryfk2MmSjuvtAgBwKb6Wo3Kt6FtCww4r49i+BCQalY51hjQ5tQZD9AEghbm2E/O+Tji6of0AAGB6MUMjkhtbrWEEx39mH5OPBh8AUpgV4xB713blyfLFOQ0AAEgndm6BZMTWEtp56TeEPh3Q4ANACitZWS3DE/1XtTfHr8zinAQkAgAA6SKw8AoZTpRz/UiyM7LVd82GBCTCRNHgA0AKq1w7J+p9TJ+l6nX1MgwjAYkAAEC6cDOy1Lt2Q/TP4RtS/+pPJiYUJoQGHwBSWEZhlmYuqZDptSLex/JaKls1K4GpAABAuujecKdcT+SPBDr+TJ3Z8Gdyff4EpkKsaPABIMU13H6FsivyZHrH+co2JCvDo6VfvSZhy+MBAID0Eiqr0cn/8qgcf8a42zr+TA2s/ITObPzzSUiGWNDgA0CKs7yWlv2nT6h4RbVMj3lho28aMr2WsivytfL+65VTmZ+coAAAYEoKLLtaLf/9HxUqrpCTkSX3I4/5Of5MOf4Mnbn5DrX9p7+TeAwwZUW/6CEAYNJZXksLbr9Ccz67RG2/O6ZT75yQPRyS6bWUO6tI1dfOS9ia9wAAIP0NNazQ0f/xsjL3v63C//1j+VqOyAiHZOcVqfe6jeq7+ma5mdnJjolx0OADwBTiy/Fr1o0NmnVjQ7KjAACAdGMYGlp4hYYWXpHsJIgRQ/QBAAAAAEgDUd/Bf/7557VlyxYdPHhQjuOotrZWGzdu1KZNm2SakV8v+MEPfqAf/vCHY77v8/m0Z8+ehOcAAACpgzoDAIDYRdXgP/jgg3r22Wfl9/u1Zs0aeTwe7dixQ9/61re0Y8cOPfroo1Gf9BoaGrRgwYILg3nGjpaIHAAAILmoMwAAmJiIG/ytW7fq2WefVXFxsZ5++mnNnj1bknT69Gl96Utf0rZt2/TUU0/py1/+clQB1q1bp82bN0e8faJyAACA5KHOAABg4iJu8B9//HFJ0n333XfuZCdJM2fO1AMPPKDbb79dTzzxhG6//faEXtVOlRwA0tvjZd+Oed97J7h/KvroZ5oOn3E8/b4WSS8kLM90kyrn91TJAQBALCI6M7W3t2vv3r3yer1av379Be+vWrVKpaWl6uzs1K5du+IeMtVyAACA+EmV83uq5AAAIFYR3cHft2+fJGnevHnKyMi46DaLFy9WR0eH9u/frxUrVkQcYO/evXrooYfU19en/Px8LV26VNdcc418Pt+k5gAAAMlBnQEAQHxE1OC3tLRIkioqKsbcpry8/LxtI7V9+3Zt3779vNfKysr00EMPadWqVZOWAwAAJAd1BgAA8RFRgx8IBCRJmZmZY26TnZ0tSRocHIzoB1dXV+trX/ua1q5dq6qqKgWDQR06dEiPPfaYdu7cqbvvvls/+clP1NDQkNAcAIDUExgcUVdbr0KhsDweS4UlucrNz0p2LCQIdQYAYDLZrrSnq0gdgQzZrqGijKBWzOySz3KSHW3ColomL54+85nPXPDa6tWrtXr1an31q1/V1q1b9b3vfe/cZDcAgPR3uq1HB3Y163R7r0zDkOtKMiTXlfKLsjV/WY0qZs1IdkxMAdQZAICPCoQt/fJYtX5+bJZGbEuuJFeSZbhyXUPra1r0J3VNmpk5kuyoMYtokr2srNG7JkNDQ2Nuc/ZK9tkr2xPxla98RZL0xhtvKBQKJS0HAGDyHNzVrDe2vq9TJ3vk2K7CYUe27cgOO3JsR92d/fr99v169/VGua6b7LiII+oMAECidQ379ZXX1uiZxjnqCfo1ZHs0bHs0YnsUCHs1ZHv0/PFq3fPaVWrszU123JhF1OBXVlZKklpbW8fcpr29/bxtJ6Kurk6SFAqF1N3dnbQcAIDJcWRfqw7sapYdvvTQODvs6MThDu156+gkJcNkoM4AACTSYMije9+4QqeGMhR0rDG3C7uWBkJe3b/jcp0cHPtxrVQWUYO/cOFCSVJjY6OGh4cvus2ePXskSQsWLJhwqJ6ennP/ffZqejJyAAASLzgS0p63jo7b3J9lhx0d3d+m/p5AgpNhslBnAAAS6adHZqlrxC/bjaj91VDY0g/3TM3v+Yg+YXl5uRYtWqRQKKQXX3zxgvd37typ9vZ2FRcXa/ny5RMO9cILL0iSamtrlZOTk7QcAIDEazrYISPKfVzH0eG9JxOSB5OPOgMAkChhx9Avj9codIk79x/lytSeM4XqHPInMFliRHYJQ9Ldd98tSXr44YfV1NR07vWuri49+OCDkqS77rpLpvnHQz799NNav3697r///vOO1draqueff17BYPC8113X1XPPPadHHnlEknTHHXfEJQcAIHUd3ntSth3drLWuKzUf6pDjTP3ZbjGKOgMAkAhvd86UHeO+W09MvcexIp5Ff/369dq0aZO2bNmiW265RVdddZU8Ho927NihgYEBrVu3Trfddtt5+3R3d+vYsWMqLi4+7/Xe3l7dd999+uY3v6lFixappKREg4ODamxsPLeu7G233aZbb701LjkAAKlrOBAcf6OLcCUFh8PKyPLFNxCSgjoDAJAIrYOZCtnRX5QNOZaa+nPG3zDFRLVM3gMPPKCVK1fqmWee0c6dO+U4jurq6rRx40Zt2rQp4qvZZWVluvPOO7Vnzx41uIhvUwAAIABJREFUNzdr9+7dchxHxcXFuummm/T5z39ea9asSXgOAEByua4b84z4hiHu4KcZ6gwAQLzZrik36ocBRwWdqfd9H1WDL0m33HKLbrnlloi23bx5szZv3nzB64WFhRcMp0tkDgBAajIMQx6vpXAo+sFzju3Kl+FNQCokE3UGACCe8n1BeU1HdpR38Q05Ks64+ISrqSzqBh8AcGm9AVf3bojtSnGq6g0kbt35qjklajrYpmhv5M8sz5fHE/mEOQAApIupVWt8J6KtnP7u8TeKwerSTv0ghhnx/ZajdVVtCUiUWDT4ABBnT26TRp8QRyTmXVap5sYOuVFMtOfxmKpfUpXAVAAApK6pVGvc0/6NpP78PF9Iq0tP6bdtpXIjn2NeMzJGNL+gN4HJEmPqPVQAAEgruQVZKq0qlGlFdkoyTEPZ+ZkqqSxMcDIAAJAObqs/Kp8V+Y0Ev2nrrgWHZEyVQRIfQoMPAEi6VdcuUEFRtqxxmnzTNJSV7dfH/8MSGVPxrAsAACbdrNxB/dXK9+S3xp/zx2/auqOhUWvKOichWfwxRB8AkHSWx9Tam5dqz86jOn6wXZJkh/94pd2yTLmSKmfP1LKPzZXXx+kLAICp4PGyb0e9z70x7ndJZdKasj794fVGDfQOyXac855ysDyWfH6PlqyuU2fttXo8vj9dUvSfq9/XIumFqH4GFRIAICWYlqmla+Zq0eW1aj7cofbmMwoGw/J6LRVXFGh2fRmz5gMAgJgVleTp+s+tVG/XgI4eaNNAT0CuK2Vm+zV7fplmludP+RGCNPgAgJTi8VqqW1ChugUVyY4CAADSUP6MHC3/2Lxkx0gIGnwAGMOdN0j5WVP7Ku5H9QbcD2beBQAAyZSOdYZErZFsNPgAMIb8LEOP/GJqLEETqdE1c9PrMwEAMBWlY50hUWskG7PoAwAAAACQBmjwAQAAAABIAzT4AAAAAACkARp8AAAAAADSAA0+AAAAAABpgAYfAAAAAIA0QIMPAAAAAEAaoMEHAAAAACAN0OADAAAAAJAGaPABAAAAAEgDNPgAAAAAAKQBGnwAAAAAANIADT4AAAAAAGmABh8AAAAAgDRAgw8AAAAAQBqgwQcAAAAAIA14kh0AADD19AZc3bvBSHaMuOoNuMmOAAAAlJ51hjQ5tQYNPgAkQe+ZQR1+v0Wn23tlhx15vJbKqos0Z1GFsnMzkx1vXE9ukyQaYgAAUpEddnTyWKeO7GvVcCAoScrK8WvOokpVzJoh00rtgdzUGbGjwQeASRQYGNZbL+1TX3dAtuOcd+460t+qo/vbVFyeryuuXSCfn69oAAAQnSP7Tmrv749LksIh+9zrQ4Mj6j0zqD+8Li1dM1c180qTlBCJlNqXbgAgjQz2DemVn7+rnq4B2bZzwYVp13Hl2I46W3v0ynPvKjgcSk5QAAAwJe39/TG9v/OYwiH7vOb+rHDIViho6w+vN6pxT0sSEiLRaPABYBI4jqPXfrVbwWBY7jgjzhzH1dDgiH736/cnJxwAAJjyWo526vDek7LDzrjb2rajfW8fV0dL9yQkw2SiwQeASdB6vEuhYDjix8lcx1XvmUF1d/YnNhgAAJjyXNfV3rePRdTcn2Xbjva9czxxoZAUNPgAMAkO7T5x0aFyl2LbDsPnAADAuLo7+89NpheN3jOD6u8JJCARkoUGHwASLBQMq7drMPodXamtuSv+gQAAQFppPX46qrv357iu2pvPxD8QkoYGHwASLBQMy7RiW8vVDjtyx3toHwAATGvDQ9HfvZdG5/0ZYVLftEKDDwAJZlpmzE26YRoyjNguDgAAgOnB8lgx7+vx0hKmExZZBpAy7rxBys9KrWb23g0Tz+M4Pv23W9fGIU18xOMz9QZcPbktDmEAAJhEqVZrxKfOcGVuqJdUP/FAcTLRz0WdETsafAApIz/L0CO/SJ3h6PduiE+eezcY+tOvH9Hh90/KcSI/nuUxtfSquZpdXzbhDB/OEq/PFPGSAAAApIhUqjXieU7+25+G9atndkT9HL7X59Gnblst04zfXfx4fC7qjNgxHgMAJkHdokopyqH2hgxV1RUnKBEAAEgXHq+l6rmlMszIaw3TMlS3sCKuzT2Sjz9NAJgEWdl+XXbFbFmeyL52LcvUFdc2yDOBZ+oAAMD0cdkVtcrM8kV0P8EwDeXkZWr+surEB8OkosEHgEky97IqLVwxS5ZljnnyNUxDlsfUymvqVT5rxuQGBAAAU5bP79E1n16m7LzMS95QsDym8guztfZTS7mRkIZ4Bh8AJtG8JdUqqSxU454WtRw7LdM0ZBiS60qu62p2fZnmXlap7LzMZEcFAABTTGaWX9d/dqVajp7SwfdOaGhwZHQ1HkNybVc5+ZmqX1qtytkzZVrc601HNPgAMMnyZ+To8k80aOlVYfV1DyoccuT1Wcovyp7QMjcAAACWx9Ss+jLNqi9Tf09AQ4ERGTKUme1XTj43ENIdDT4AJInX59GM0vxkxwAAAGkqtyBLuQVZyY6BScS4DAAAAAAA0gANPgAAAAAAaYAGHwAAAACANECDDwAAAABAGqDBBwAAAAAgDdDgAwAAAACQBmjwAQAAAABIAzT4AAAAAACkARp8AAAAAADSAA0+AAAAAABpgAYfAAAAAIA04El2AACYLHbY0anWbo0MBWUYhrLzMjWjNE+GYSQ7Wsxc11X36QEN9ARk2478GV6VVBbK47WSHQ0AgGmnvzegns4BhcO2vD6PZpblKyPLl+xYExIKhnWqtUfB4ZAsy1RuYZYKZ+YmOxbGQIMPIO0FBkfUuLtFTQfbJWO0KZYkQ4Y8PkvzFlepdkG5PJ6p0xQ7jqPjB9vVuLtFw0NByZDkSoZhyHVcVc8tUf2SauXkZyY7KgAAac11XbU1d+ngrhPqPTMo0zTkuq4Mw5DjOCqpLFTDshoVleQlO2pU+nsCOvjeCbUc7Tz3mc7KzParfmm1auaWyjSn7o2SdESDDyCtnTnVp9df2CPbduQ67gXvh8O29r19XMcPtmvtp5bIn5n6V9lDwbDeeHGPes8Myg47F92m6VC7Thw5pdXrFqm0qnCSEwIAMD24jqt3Xz+klqOd587Jjn3+Nu3NZ9TZ2qNFV9Rq7qLKJKSMXltzl3a+sl+O7ch1L/xMA71Deu93h9Xc2KGrbryMkYMphGfwAaSt/p6AXn9hj8Ih+6LN/Vm27WigN6DXfrVb4ZA95napwHEc/W7r++rpGhizuZck1x19JOHNl/bqzKm+SUwIAMD0sWvH4fOa+7HYYUd7dx5T06H2SUoWu9NtPdr58n7Z4dHmfix22NGZU33asW3vJessTC4afABp693XD0XcsLuuNNg/rCP7WhOcamJajnSqp2tAjh3ZidQOO3r71YMJTgUAwPTT2zWg5kMd4zb3Z9m2o11vHE7pmwmu6+r3vzko247sMzm2qzOn+tTadDrByRApGnwAaWmwb0jdnQNR7ePYjg6/33LeM2ap5uB7JyIuJM4aGhzRmc7+BCUCAGB6anz/pBwnunOyDKn5cEdiAsXB6fZeBUfCUe1jhx0dfO9EghIhWjyDDyAtHT3QFlOjbocddbb2qKSyUL0BV/duiM/EMfE7zhUT2r83kLoXLwAAmCrssK2Wo52XHMJ+8f0cHX7/pOoWVKRknSEVSn/+8QkdgVojuWjwAaSlvjODMT0P5jquBvqGVFJZqCe3SdLET1L3bjD0yC8mfpzW46f19qsHYxral1+Ures/t3LCGQAAgDQ8FFSsq+wODYxIUsrVGZL063/9vQZ6h6Lez+O1dOX1C1RaVRSXHIgdQ/QBpCUnxsleXCllJ4qJ9TNNdF8AAHA+x3FlKLYOP5UfBZxIDUStkRpo8AGkpcys2Ja7M01D/gxvnNPEx0RyZcT4+wAAABfy+73RP3//Aa8vdQdR+zNjrzVStX6abmjwAaSlmvpSWZ7ov+Icx1VpdWoOL5tRlhfTcECP19Ls+WXxDwQAwDTly/Aqf0ZO1PsZpqHquSUJSBQfs+eXxVQ/mZapwpm5CUiEaNHgA0hLxeUF0V8hN6SquuKUvbJumqbqFlTINKPv8itmz0xAIgAApq/6pdWyPFZU+xiGoTkLKxKUaOKq5kR/8cG0TM29rFJGDPUJ4o8GH0BaMgxDi1fVyrIi/5qzLFMNy2oSmGri5iyqjKqYsDymGpbXRPV7AAAA4yuvmaHMbJ+MCIfXmZap8llFys7LTHCy2Hk8luqXVEd1F9/jMVXbUJ7AVIgGFR+AtFU9t/SDq+vjf9VZHlNrbliknPzUPelKo8/Sf/ymxfJ4x2/yLY+pmrklmre4ahKSAQAwvZimoas/tUQZWd5x715blqnCmTm6/JqGSUoXu4blNaqsLY6ofvL4LF190xKev08hNPgA0tqCFbO0/GPz5M/0XtgUG6Mn3PyibK391FKVVBYmJ2SUCmfm6toNy1VUkifTMi94Lt/jteT1ebTo8lot+9i8iO8sAACA6GRm+XXdZ1eqrLpIpmXItM4/51oeU5bH1OyGMl39qSVTYkSdYRhaubZeC1bMktdnXVA/GaYh0zI1ozRP131mRUxzESBxUvNBUwCIo5p5paqeW6KOlm41HWrX0GBQpmkotyBLdQsrlF+UneyIUcstyNInPr1MA71DOrLvpHrPDMqxHfkzfaqZV6rymhkxPasPAACi48/was0NizQcCOr4wTZ1tvUqHLLl83tUPmuGauaWRjTyLpUYhqH6JdWae1mlWo936cThDo0Mh85Nple3oDylHzWYzmjwAUwLhmGorLpIZSk6Q36scvIztXTN3GTHAABg2svI8qlh+Sw1LE92kvgxTVNVdcWqqitOdhREiAYfKe3OG6T8rPS6C9kbcPXktmSnAAAA6VhnSNQawHQWdYP//PPPa8uWLTp48KAcx1Ftba02btyoTZs2yTQje6bEcRzt2rVLr776qt566y0dOXJEgUBA+fn5WrRokb7whS9o3bp1F933Bz/4gX74wx+OeWyfz6c9e/ZE+7GQovKzDD3yCzfZMeLq3g2GpPT6TAAQT9QamCzpWGdI1BrAdBZVg//ggw/q2Wefld/v15o1a+TxeLRjxw5961vf0o4dO/Too49GdOI9ceKENm3aJEkqKCjQkiVLlJeXpxMnTui1117Ta6+9ps997nP667/+6zEnh2poaNCCBQsu/EAeBiUAADBVUWsAABC7iM9QW7du1bPPPqvi4mI9/fTTmj17tiTp9OnT+tKXvqRt27bpqaee0pe//OVxj2UYhlavXq0777xTH/vYx2RZf5x0YufOnbrnnnv0b//2b7r88su1cePGix5j3bp12rx5c6TxAQBAiqPWAABgYiJep+Hxxx+XJN13333nTriSNHPmTD3wwAOSpCeeeEKO44x7rJqaGv34xz/W2rVrzzvhStKqVat01113SZJ++ctfRhoPAABMcdQaAABMTEQNfnt7u/bu3Suv16v169df8P6qVatUWlqqzs5O7dq1a8KhFi5ceO7nAgCA9EetAQDAxEU0RH/fvn2SpHnz5ikjI+Oi2yxevFgdHR3av3+/VqxYMaFQx48flySVlJSMuc3evXv10EMPqa+vT/n5+Vq6dKmuueYa+Xy+Cf1sAAAw+ag1AACYuIga/JaWFklSRUXFmNuUl5eft22shoaG9NRTT0mSbrzxxjG32759u7Zv337ea2VlZXrooYe0atWqCWUAAACTi1oDAICJi6jBDwQCkqTMzMwxt8nOzpYkDQ4OTijQgw8+qJaWFs2dO1df+MIXLni/urpaX/va17R27VpVVVUpGAzq0KFDeuyxx7Rz507dfffd+slPfqKGhoaof/YXO/9OFcHsCeVHvH1H97R/I9kh4iwdP1O8pOvvJh0/Vzp+pui09g7q2WSHSCPTpdYAACCRUmqdl8cee0w///nPlZubq+9///sXHQL3mc985oLXVq9erdWrV+urX/2qtm7dqu9973vnJuoBAAA4i1oDAJDOIppkLysrS9LokLaxnL2afvbqerT+6Z/+SY8++qiysrL0xBNPaN68eVEf4ytf+Yok6Y033lAoFIopBwAAmHzUGgAATFxEDX5lZaUkqbW1dcxtzs5Ce3bbaDz11FP67ne/q4yMDD3++ONavnx51MeQpLq6OklSKBRSd3d3TMcAAACTj1oDAICJi6jBP7uUTGNjo4aHhy+6zZ49eyRJCxYsiCrAM888o29/+9vy+/36h3/4hwlNWtPT03Puv8/eCQAAAKmPWgMAgImLqMEvLy/XokWLFAqF9OKLL17w/s6dO9Xe3q7i4uKorohv2bJF3/rWt+Tz+fTYY4/pqquuijz5RbzwwguSpNraWuXk5EzoWAAAYPJQawAAMHERNfiSdPfdd0uSHn74YTU1NZ17vaurSw8++KAk6a677pJp/vGQTz/9tNavX6/777//guP9y7/8ix588EH5fD798Ic/1NVXXz1uhtbWVj3//PMKBoPnve66rp577jk98sgjkqQ77rgj0o8FAABSBLUGAAATE/Es+uvXr9emTZu0ZcsW3XLLLbrqqqvk8Xi0Y8cODQwMaN26dbrtttvO26e7u1vHjh1TcXHxea/v379ff/VXfyXXdVVVVaUXXnjh3BXxDyssLNRf/uVfnvv/vb29uu+++/TNb35TixYtUklJiQYHB9XY2HhuTdzbbrtNt956a1S/BAAAkHzUGgAATExUy+Q98MADWrlypZ555hnt3LlTjuOorq5OGzdu1KZNm867on4pfX19cl1XknT06FEdPXr0ottVVlaed9ItKyvTnXfeqT179qi5uVm7d++W4zgqLi7WTTfdpM9//vNas2ZNNB8JAACkEGoNAABiF1WDL0m33HKLbrnlloi23bx5szZv3nzB61deeaUOHjwY7Y9WYWHhRYfgAQCA9EGtAQBAbKJu8AEgUZz+bmXf851kx4g717HT7nM5/SwPBgCYenoDru7dYCQ7Rlw5Tvp9pt6Am+wIUxYNPqatgdZetWxvVO+R03KCtqwMj2YuqVTF1XXKKGTpo2QYevbhZEcAACAujOCIcne8qIKtW+TpPiXXMBUqqVLPTbdrYOU1kkUZngxPbpOkdGwe0/EzIRZ8s2DaGeoa1N4n31SgrU+ObUvOH987cfqQTrxySDMWlqnhS1fIk+FNXlAAADAlFfzqf6l4y/clSeZw4NzrvlMtyjiyR67Hq457HtTA6k8mKyKANBXxMnlAOgic6tc7f/OSBlp65ITOb+4lyQ07csOOuva1652HXlZ4KJScoAAAYEqa+b/+VsXPPiJzOHBec3+WNTQoT3+Pyh/9S+X/+idJSAggndHgY9qwQ7Z2/f2ro027c+lhTG7Y0XBXQO//aMckpQMAAFNd7mu/VOHWZ2WODI+7rRkcVsk/f1eZ+9+ZhGQApgsafEwbnX9oUXg4HPEjSm7YUd/RLg229SY2GAAAmPpcV8XPfj+i5v4sMzisGf/fDxIYCsB0Q4OPaePEtoNyRsJR7ePYtk680pigRAAAIF1k7n9b1kBP9PsdfFeezpMJSARgOqLBx7QQHBhR4NRA9Ds60un3OOkCAIBLy9mxVcbIUPQ7GqZy3v5N3PMAmJ6YRR/TQngwKNNjyrad8Tf+CDvKu/4AAIzli51/p4pgdrJj4Jzv6J72b8TlSHvbd6jTjX6pMjM4rCvbf6bZ7fvjkmNU/D4XgORp7R3Us1Huwx18TAum15Ibw0lXkgyLfyYAAODSTF+M981MQ6bXim8YANMWnQumBV9eRsz7ZhbnxDEJAABIR7nVhTJ90TfqltdSdnleAhIBmI5o8DEtmB5T5atny7CMqPaz/B7VrJufoFQAACBdlK6qkWIZou81VbSgLAGJAExHNPiYNiqvnSfDjK7BlyEVL6tMTCAAAJA2vFk+FS+riupmgum1VPmJGOoTABgDDT6mjaziHM365IKIh8+ZXksL/48reS4OAABEZM7GpfLm+KUIGnbDMpVZkqPq6+onIRmA6YIGH9NKzScbVL1u/mjTPsa517AMmT5LC768SjMWlU9uQAAAMGX5cvxa8bXrlFGYeckbCqbPUk5lvpb9xTWyYnhuHwDGwjJ5mFYMw1DtTQs1Y1GZTrx8SKd3t8r0fOg6lyuVrZ6tqmvnKnMmk+sBAIDoZBRl6Yr/dqPadzbpxEsHFewf+WAIviHXdpRZkqOaG+areHmVTFbqARBnNPiYlvJmFWnRn65WaDCowbZe2UFbngyPcqoKuZIOAAAmxPJ7VHn1HFV8vE6DbX0K9g7LMCV/YZaySnKTHQ9AGqPBx7TmzfapYG5xsmMAAIA0ZBiGcirypYr8ZEcBME3Q4E9A5hfvk5lbmOwYceX0d2vo2YcnfJx4/m6y7/lOXI6TSuLxmeL1ZwUASE3pWGdI8Tl/UWeMb6KfizoDmJpo8CfAzC3U4ONfT3aMuIrXSS5ev5vse76TMsdJpSxnjwMASF/pWGdI8Tl/pWOdkWrHoc4ApiZm9gAAAAAAIA3Q4AMAAAAAkAZo8AEAAAAASAM0+AAAAAAApAEafAAAAAAA0gANPgAAAAAAaYAGHwAAAACANOBJdgBgOorX2rKpskat09+toWcfTnYMAADwgXjUCKlSZ0jUGkCkaPCBJBh8/OsTPkb2Pd+Jy3HiIZUKAAAAMPFaI5XqDIlaA4gUQ/QBAAAAAEgDNPgAAAAAAKQBGnwAAAAAANIADT4AAAAAAGmABh8AAAAAgDRAgw8AAAAAQBpgmTykNKe/O6XWjGeJlqkh84v3ycwtTHaMuGL9XwCIv1SrM+J5HCQWtQZSFQ1+CrNHwhpo7ZU9HJaV4VFORb4s//T6IxvrS8YJOxo42aNwICTLbymrLE/eLN+Yx4nHWq6XOsZIz5CGTg/ItR15c/zKrsiXYRhjHgeJZeYWptTavfHA3xsAiTDUOaDh7oAkyZ+fqazS3CQnmlyXamaGuwMa7hr84NyeoeyKvEue2+Nx3hnrOK7jauBkj0KDQZleS1klufLl+i95HCQWtQZS1fTqFqeIQEe/TrxySB2/b5ZhGpJhSK4r13FVekWNqq+rn3Yn4LOGuwM6+ephtb5+dPQFQ5JryA3bmrG0UjXX1yu3ZnKuprquqzP7O3Ri20H1HeuS4bUkV5Lrysrwqvq6eSpbM/uSFx4AAJhsTthR564WNW87qKFTAzI85midYbvKKMpSzQ3zVbyyWpbXSnbUSec6rrr2tunESwfV39Qtw2tKMkab/CyfqtfVq+zK2fJkeiclT7B/WK2vH1XLbw7LDTujD9e6o3+GRQ2lql5Xr4K5xZOSBcDUQIOfYlrfOKbDP90lx3Ykx73g/bYdx9Wxs1nz/uMylV9VO/kBk6hrb5v2PvmmXNuVazsXvN/57gl17W5V9bp6zb5p4ZhX2ePBCdna+09vqftAh5ygPfpi+I+Z7JGwjv3vvWredlDL/mKtssvzE5YFAIBIhQaDeu8Hrylwqv+P56+Qfe79QEe/Dv3LHz44f10jX15GkpJOPjtoa++PdqjncOdFz+0jwSEd/eX7av716O8m0Tdbeo92aff/+K0c25EburDu6Xq/Td2HTqn0ihrVf2HF6E0hANMek+ylkPa3mkab+5B90eZekuS4ckK2Gv91l9p3Nk1uwCTqPnRKe3/0ppygfdHmXtLoFe2QrRMvH1LTi/sTlsV1XL3/5Jvq3v+h5v4inJCt0MCI/vDIbzR0ejBheQAAiIQdDOsP3/+NBtt6L33+CtoKdA7o3Ue2KzwUmsSEyeM6rt7/f99QT+OpcX83wf5hvfvI9nOPNiRCf0uP3vvha7KHwxdt7j+cp+P3zTr0L39IWBYAUwsNfooIDQZ16CfvjDb3EXBCtg5teUehQDDByZLPsZ3R5j7S303QVvOvD2iwvS8heU6926KeQ6cizhMeDunAU79PSBYAACLVtPWAhjoH5Npj3ET4MMfVSPfoHevpoH1nk3qPdsm5RDN9jiuFh0I6+Mw7Ccniuq72/mjHJS80fJgTtNWxs0k9hzsTkgfA1EKDnyLadhzT6APlUTAMte84noA0qaVrT9vYd+3H4NiuWn5zOCF5Trx0IOKTriTJlfqbzmiocyAheQAAGI9jO2p97cjoc9wRcm1H7W8dlx0MJzBZamjedjC6c7vjqudwZ0Lu4vcd61KwbziqfZygrRMvHYp7FgBTDw1+imh5pTHiO8JnOUFbJ15pTFCi1NH80kHZI1EWF46rjp1NsqM5WUdgsK1PgY7oG3XXdXXytSNxzQIAQKS69rTJHevxv0sxRkeupbP+E90aibFRb/3t0TinkU68HH1NKElnDnQo2B/dhQEA6YcGPwU4YSfmL+Rg39DohHxpLNDeH9N+hmFopCe+V9YH23pjmsTGtV31NZ2JaxYAACI1cLIn+ovlkpwRWwMnehKQKHUMnuyNaT837Ki/Of7n9oGWntFVeaJkeiwFTjFaEJjuaPBTgBOyY5751DCN6IaUTUHRDs8/x1BMV8AvxQk5cmM56yr+WQAAiNREaoVYLgxMJU7YiamhlhT3kYKS5IRjPaab9jUhgPGxTF4KsPweuTH2sK4zun86szI8MTXHru3IE+c16D1Z3piX3/NmxzfLWMIjYfUcOqXQYFCmx1R2eZ5yKgsm5WcDAFKTN9cvwzIim2DvwwzJl+dPTKgU4cnyybAMKYYFA7w58f/deLJ8CvbGMLLTnbxaY6C1V4OtfXLCtrxZPhXUF8uT4Z2Unw3g0tK7M5wiDNNQwbxi9Rw6FfW+hfNL0n7d0+KllWr93bGxlw4cg78gU/6CzLhmKZhbHNOIAstvqfSKWXHN8lGBU/068dIhdfy+ebSIc10ZGv3fjKIs1dzYoNLLa9L+7wsA4EIzLivX8V/tk2tHd8Hc9FqauaQyQalSQ+H8kqgmHzzL8ntUenl13POUrKxW89YDUd/cMDymsivz457nLNdxdeqdE2qIjH3WAAAeaklEQVTedkBDnYMyTEOuXBnG6IWjkpVVqrmhQVmluQnLAGB8DNFPETU31Ed9J97yW6peV5+gRKmj6tp5Mq3omlLTZ6l63fyY77aPxZPpVcnK6hj+5RgqXlEV1ywf1rWvXW9/9yW1vXlcTsiWPRyWM2LLHgmPrmfc3q9DP3lX7z3222kxGzIA4HzZZXnKLs+Lej9/QabyZhclIFHq8Gb7NGNJRdSLGck0NHNp/C9+VHysVq4b3U0Nw2Oq6pq5Mq3ElPZ2yNae//mGDm55Z/TOfeiDGmPkg5ojZKt9Z5Pe/puXdHpPa0IyAIgMDX6KKJxfKn9BZsR3Vw3TkL8gS4X1JQlOlnxZpbnKn1ss0xP5X1fL51Hp5TUJyVO9br5MjxXx9qbPUtW182R5I98nGr1HTmvvEx+sl3uJUQ5O0FbfkdN6/4kdsc2kDACY0mpvXiQzinOR6bVUd8tlCUyUOmZ9ckHU5/aaG+YnpKH25Wao9PKa6P6sPKYqPl4X9yzS6J37vT/aoZ7GU5d+xt8ZrTX2/eNb6o5hVCqA+KDBTxGGaWjp5rXyZPuk8Zp805A3x6+lm9dOm+HWi+5crYyZ2TLGa/KN0Wf2l/3F2oTNTZBdlqeFd1wZ0YnX9FkqaijV7JsWJiSL67ra9+OdEQ/jc8KOeo+c1un3TiYkDwAgdRUtLNPsmxbK9EV2/qq6bp6Klydu9FkqyanMV8Ptl0d8bp+5uEI1N8xPWJ76W1cop7og4jxL/s+Py5eXkZAsXXvb1NPYKScU2WMMTsjW/h/v5GYCkCQ0+CnEX5CpK/6fG5RfWyTTa17Y6JuGTK+p/NoiXf5f18X9+fJU5snwasV/uV5FC8tkeMwLG31j9ASXXZanlfdfr+zyxD2DJkkzl1Ro8Z9/TL68jIteSDB9lkzv6NX0RX+2JmEXYnqPnFZoYCSqfZygreaXDiYkDwAgtdXcMF/1n18uK8N70fOX5ffI8ntUt2HxtLl7f1bJimpddtcaeXP8lzy3V31inhZ8eVXcHwM872d5TC3bvFbFK6pkeswLG/0P6p6MGdla/p8/ofw5MxOWpfmlg1HPzm8Ph7mLDyQJk+ylGF9ehpb/52sV6OhXy/ZGdR/skD1iy/J7VNhQoqpPzJu2k5d4/B4tvvsqDXcHdPK1Izr93knZw2GZPkt5s4tUfV29cmsKJy1P4fwSrfn2p9R9oEMntjcq0NEv13bkzfGrbPVslV05S944z+L/US3bG2NaEmewtU9DnQPKLM5JQCoAQCorWz1bJZfX6PR7J9Xy6mGNnAlIknz5Gaq8Zq6Kl1cl7LGyVFe0sExX/fXNOrO/XS2vNCrQOXDu3F5+Va3KVs2SJ3NyZos3vZYW3H6F5nxmsVrfOKZTbzcrPBSS6bGUU5mvquvrlV83I6EXGoa7Axpo6o56P3skrJbtjSpqKE1AKgCXQoOforJKc1V/64pkx0hJGYVZmrNhseZsWJzsKDJMQ0ULy1S0sCwpPz/Q3h/TfobHpMEHgGnM9JgqWVk9OnEszmOYhmYsKteMReXJjiJp9Jn82esXaPb6BZP+s4c6B2R4LSmGVQYCHbHVKAAmhiH6wBTmxLBknyTJdWPfFwAATAuxLA0cj30BxI47+MAU5svxa/j0YPQ7uvr/27v36Kiqew/g33lm8piASAghDJCUnAQiCQ+NgBVBYkUEFONVUgFXb0QuuGCVp73aUkKltSW9UCz2RopAeWTJ0lLxEShxAfG6IuGWmyE8VB4JgUA0xvCYPCcz+/6hE4mTSWYm58yL7+evcM7ee357n8OZ3z4zsw90UWHyB3SbqG4Ix/9+1RcWqw5hGhuG9rqJ9Du/gYLfkiQiIvI5XVRYl0/o6bJuJPMMb7XZVSj9qi8uWyLRJlTorW/F2Nha9DG0+js0CgKc4BMFsf7jhsBy5brHv8NXa9WIHhzazzVWwvHaPtjxxY9w7vq3z5K22tXQqAR0ajsidG14MrESM4ZcglbNlYOJiCj4RcX3hiZMC1tLm0f11HoN+o8bokxQIazBqsGe8wl4r9IEIVRosathFyroNTa8fioFo/vWYW7yeQztxZ8/kGv8ij5REIu9e5DHdVRaNeInJd02j1iUy1vnhmD1sVE4XX8HWu0atNo1EFChTajRZNOirtmAbZ8l4cVPx6DZxksrEREFP5VahYEPJrn1uL4OBNA/Y7AyQYWoumY9Xvh4HN65MBgNbTo02rSwCTUEVGixaWG1a1D6VQyWfpKB/7naz9/hUgBjFkoUxDRhWpgmS24907i9jl6LAT9OVDCq0FNYNQC7vvgRWuxdj3OLXYMvrvVC7rGR3n6jkYiIKKDEjU+AJsz9PEOt1yB+4lCfPW0gFDS1abCi5B582WiAtYtcQ0CFFrsGf/i/EThR57snR1Fw4Vf0b7E7ZhmMfQa6XX4pgPz+rygXkB/I1adAGxs54gm0sWlv598F+ltWwFj6EdQtTS7LC5UadkMEKnN34FSCvCvxhuLxdmhrs+GD/SWw2d1bLKjVroH5Wj+8YluK2IHy/Qwi0MaYgJv6ywAK/R0GEZGidBF6pC9+AGXrD6Ot2Qp0cQNbrdegT2p/JE6/y3cBhoAPq+LxdZMBdjc/e22xa7DePBxvTvqE6/+QE36CTxTsVCrULF6Huif+A7bwSNgMER12C40Wdl0YmpPScPEPb6NF5sl9qLt8odbjOrY2O744cVmBaIiIiHwvakAvjHlxMnol3Am1Vg2VpuOsUh2mhSZMi0GZyUj92Vj+DNADQgDvnB/S7bcEf+ibljB8fq2XQlFRMOMn+EShQKXCN1nzUT/jZ4g6+k9EH9kHzY1vIHR6NCem4tojz8AaN8TfUQal8yerYfPi+b91NdfR0tSKsHC9AlERERH5VnjfKIxaOglNtRZUF5/Hzap62FrboI8KQ2zGYMSMjPf8t/qE0/W90djm+ZSsxabGvkoTUu64rkBUFMw4wScKIUKnx80fT8PNH0/zdygho7Ghxat6ao0aTQ2c4BMRUWgJj4nC0Kx0f4cRMr5sNHhVT0CN6oaI7gvSbYdf0Sci6koPFssTPalMREREIc8O73/OYBf8KQQ54wSfiKgLYQbvVgG22wUM/PSeiIiIutAnrMXLhfIEYsJdL65Mty9O8ImIupAwLA4ajeeXyug7IhAeGaZARERERBQq0u6sh1rl+Tf+wjU2TB1UrUBEFOw4wSci6sJgKdbjL9prtRpIaSZF4iEiIqLQoVULzBhcBZ3K5lE9g9aG0TF1CkVFwYwTfCKiLujDdEgaEQ+N1r3LpUoFhBvDMGDInQpHRkRERKHgsYQqhOtsULn5kUKY2ob/GP4Z+DRC6gwn+ERE3Rg+ZgjiE2K6neSr1SpERBlw/9Q0qNW8vBIREVH3eodZsW7cMUTprFB3M8kPU9swRzqHifFf+ig6CjbMQImIuqFSqTBmgoQRGYkIC9dBq+34nF+NVg2NRo1BSbF4cOZoLq5HREREHhlibMDrEz7FvbFfQae2Qa/+/iv7KggYNG2Ij2zAf44+gX8betGPkVKg0/o7ACKiYKBSqZA4fAAShsXhq+p6XLlYh5YmK7Q6Dfr0M2LQ0FhodZruGyIiIiLqRL/wZqy+x4xrLXr881IcKm8a0WpXo6+hGZPia5Dc+4a/Q6QgwAk+EZEHVCoVYgf2QezAPv4OhYiIiEJQ77BWPMVP6clLnOD3wPVGgaWPhdbqFtcbPX9Mh6t2Am1s5IhHrj7J0Y5cx4qIiAKT/WY9Iuev9XcYsrPfrJeljUAbG7niCZR25DhOROR7Hk/w33vvPRQUFODzzz+H3W5HQkICsrKykJ2d7dWiUsXFxdi2bRtOnjyJlpYWmEwmPProo8jJyYFe7/p3rGazGW+88QaOHz8Oi8WCuLg4ZGZmYsGCBTAajR7H4Y0tBwF4/ACt2wPHxrWlj6nwX+9ybIiIOsM843tNu/N88jrBiGPTtcj5a9GQ/7K/wyAiP/DonTI3NxfLly/HyZMncffdd2P8+PGorKzEmjVrsHjxYtjtdo9efPPmzZg3bx4+/fRTDB8+HA888ADq6uqwYcMGzJkzB01NTZ3We//995GdnY2ioiIMGTIEkydPhtVqxZYtW5CVlYW6Oj4TkoiIKNgwzyAiIuoZtz/BP3DgAHbv3o2YmBjs3LkTQ4YMAQB8/fXXmDt3Lg4ePIgdO3bg2Wefdau98vJy/PGPf0R4eDi2b9+O9PR0AEBDQwPmz5+PY8eOYf369XjppZc61KupqcHLL78MIQQ2bdqEzMxMAEBbWxtWrFiBDz/8EKtWrcKmTZvc7RoRERH5GfMMIiKinnP7E/z8/HwAwPLly9vfdAGgb9++WL16NYBv75S7e3d98+bNEELgueeea3/TBYDIyEj87ne/g1qtxu7du3HjRsfVIrdv347m5mY8/vjj7W+6AKDVavGb3/wGUVFRKCoqwrlz59ztGhEREfkZ8wwiIqKec2uCX1NTg1OnTkGn02HKlClO+zMyMhAbG4va2lqUlZV1215rayuKi4sBADNmzHDabzKZMHLkSFitVhw5cqTDvqKiIpf1oqKiMGnSpA7liIiIKLAxzyAiIpKHWxP806dPAwCSkpJgMBg6LTNixAgAwJkzZ7ptr6KiAk1NTejduzcGDRrUZXuO1wYAi8WCqqqqDvvdqUdERESBi3kGERGRPNya4F++fBkAMGDAAJdl4uLiOpR1pz1Hnc44Xqu6utqpXnR0NKKiorqs504cRERE5H/MM4iIiOTh1gS/sbERABAeHu6yTGRkJIBvF6+Ro72IiAin9rytR0RERIGLeQYREZE83F5Fn4jkcb1RYOljKn+HIavrjcLfIRAREdF37DfrETl/rb/DkJX9Zr2/QyAKCm5N8B13q109Lxb4/k624w57T9tz3EW/tT1v6xEFki0HAYATYiIiB+YZRPJq2p3n7xCIyE/c+op+fHw8AODKlSsuy9TU1HQo6057V69edVnGse/W9hx/37hxAxaLpct6AwcO7DYOIiIi8j/mGURERPJwa4I/fPhwAMDZs2fR3NzcaZny8nIAwLBhw7ptLzExEQaDAdeuXWtfrfaHTpw44dSe0WhsXw3X8Xru1CMiIqLAxTyDiIhIHm5N8OPi4pCamgqr1Yr9+/c77S8tLUVNTQ1iYmIwatSobtvT6/WYMGECAGDfvn1O+y9duoSysjLodDpMnDixw77Jkye7rGexWHDo0CEAwEMPPdRtHEREROR/zDOIiIjk4dYEHwCef/55AEBeXh4uXrzYvr2urg65ubkAgHnz5kGt/r7JnTt3YsqUKVi5cqVTe/PmzYNKpcJf//rX9rvhwLe/sXvppZdgt9vx05/+FNHR0R3qPfvsszAYDPjHP/6Bjz76qH17W1sbVq1aBYvFgszMTAwdOtTdrhEREZGfMc8gIiLqObdX0Z8yZQqys7NRUFCA6dOnY/z48dBqtSgpKWl/s5s9e3aHOvX19aioqEBMTIxTe2lpaVi2bBny8vIwa9YsjB07FkajEceOHUNdXR3S09OxZMkSp3pxcXFYu3YtVq5ciRdeeAFjxoxBv379YDabUV1djcGDB2PNmjVeDAURERH5C/MMIiKinvPoMXmrV6/GmDFjsGvXLpSWlsJutyMxMRFZWVnIzs7ucFfdHfPmzUNycjK2bt2K8vJytLS0wGQyYc6cOcjJyYFer++03rRp02AymZCfn4/jx4/DbDYjLi4OOTk5WLBgAYxGo0dxEBERkf8xzyAiIuoZjyb4ADB9+nRMnz7drbKLFi3CokWLuiwzYcKE9t/JeSI9PR2vv/66x/WIiIgocDHPICIi8p7HE3wiokB3vVFg6WMqf4chq+uNwt8hEBER0XfsN+sROX+tv8OQlf1mvb9DIBlwgk9EIWfLQQDghJiIiIiU0bQ7z98hEHWKE3wANpsNAGC5VuPnSIiIKFg43jMc7yFEXXGcJ1/ebPRzJEREFCwc7xme5Bqc4AOora0FAOz779ndlCQiIuqotrYWgwcP9ncYFOAcuUbOniN+joSIiIKNJ7mGSghx23+Ptbm5GSdPnkRMTAw0Go2/wyEioiBgs9lQW1uLu+66CwaDwd/hUIBjrkFERJ7yJtfgBJ+IiIiIiIgoBHj2QFkiIiIiIiIiCkic4BMRERERERGFAE7wiYiIiIiIiEIAJ/hEREREREREIYATfCIiIiIiIqIQwAk+ERERERERUQjgBJ+IiIiIiIgoBGj9HUCgee+991BQUIDPP/8cdrsdCQkJyMrKQnZ2NtRqz++HFBcXY9u2bTh58iRaWlpgMpnw6KOPIicnB3q93mU9s9mMN954A8ePH4fFYkFcXBwyMzOxYMECGI3GnnTR7+QYY7vdjrKyMhw5cgRHjx7F+fPn0djYiF69eiE1NRVPP/00MjMzO6372muv4c9//rPLtvV6PcrLy73qWyCQ6xzu6TjJ/X8pkMjRt8uXL2Py5Mluld25cyfuueee9n+H8jl84cIFfPzxxygvL8fJkydRWVkJIQT+9Kc/YcqUKV636+0x8/YaTuQK8wzlMc9QHnMN5THXUE6o5xqc4N8iNzcXu3fvRlhYGMaNGwetVouSkhKsWbMGJSUl2Lhxo0cXi82bNyMvLw8ajQYZGRmIjo7GsWPHsGHDBhw+fBjbtm1DeHi4U733338fK1euhM1mw+jRoxEbGwuz2YwtW7agqKgIBQUFuPPOO+Xsus/INcaXLl1CdnY2AKB3795IS0tDdHQ0Ll26hOLiYhQXF+OJJ57Ab3/7W6hUqk7bSElJwbBhw5y2a7XB+99C7nMY8G6clIgjUMjVt4iICMycOdPl/nPnzqG8vByRkZFITU3ttEwonsMFBQX429/+Jmub3h4zb6/hRK4wz1Ae8wzlMddQHnMNZYV8riFICCHE/v37hSRJ4r777hMVFRXt22tra8UjjzwiJEkS27Ztc7u9EydOiOTkZJGeni7Kysrat1ssFvHMM88ISZLE2rVrnepdvXpVpKWliZSUFHHw4MH27VarVfz85z8XkiSJhQsXetdJP5NzjC9evCjmzp0rjhw5Itra2jrsO3r0qBg5cqSQJEm8/fbbTnU3btwoJEkSGzdu7FF/Ao3c57C34yR3HIHEl3177rnnhCRJ4pe//KXTvlA9h4UQYs+ePeL3v/+9+OCDD8TFixfF7NmzhSRJorCw0Kv2vD1m3l7DiVxhnqE85hnKY66hPOYaygv1XIMT/O/MnDlTSJIk9u7d67Tv6NGj7QfNZrO51d6iRYuEJEnitddec9pXVVUlUlJSRGpqqrh+/XqHfa+++qqQJEn84he/cKp38+ZNMXr0aCFJkjh79qybPQscco9xVzZt2iQkSRJz58512heqFyy5x9fbcfLlcfY1X/WtpqZGpKSkCEmSOlzwHUL1HO5MT990vT1m3l7DiVxhnqE85hnKY66hPOYavhdquUZwfm9FZjU1NTh16hR0Ol2nv7vIyMhAbGwsamtrUVZW1m17ra2tKC4uBgDMmDHDab/JZMLIkSNhtVpx5MiRDvuKiopc1ouKisKkSZM6lAsWco9xd4YPH97+urcDX49voMehBF/27e9//zvsdjuSkpKQnp7eo7ZuZ94es55cw4k6wzxDecwzlBco7/GBEocSmGsEn0DMNTjBB3D69GkAQFJSEgwGQ6dlRowYAQA4c+ZMt+1VVFSgqakJvXv3xqBBg7psz/HaAGCxWFBVVdVhvzv1goHcY9ydyspKAEC/fv1cljl16hTWrVuHX/3qV8jLy8PBgwfR2tra49f2ByXH15Nx8vVx9iVf9m3v3r0AgCeffLLLcqF0DivB22Pm7TWcyBXmGcpjnqE85hrKY64RfAIx1wje1RFkdPnyZQDAgAEDXJaJi4vrUNad9hx1OuN4rerqaqd60dHRiIqK6rKeO3EEErnHuCtNTU3YsWMHAOAnP/mJy3KHDh3CoUOHOmzr378/1q1bh4yMjB7F4GtKjq8n4+TL4+xrvupbaWkpLl68CJ1O1+kd3VuF0jmsBG+PmbfXcCJXmGcoj3mG8phrKI+5RvAJxFyDn+ADaGxsBIAuVyiMjIwEADQ0NMjSXkREhFN73tYLBnKPcVdyc3Nx+fJlDB06FE8//bTTfpPJhGXLluHdd9/Fv/71L5SUlGD79u3IyMhATU0Nnn/+eXz22Wc9isHXlBhfb8bJl8fZ13zVt3feeQcA8OCDD6JPnz6dlgnFc1gJ3h6zUL4Wk38wz1Ae8wzlMddQHnON4BOIuQY/waeQsmnTJuzduxdGoxEbNmzo9NmRjz/+uNO2sWPHYuzYsVi8eDEOHDiA9evXIz8/3xchByyOk+9ZLBYcOHAAAJCVleWyHI8NEZF/MM+QF8fK95hrhD5+go/v7440NTW5LOO4c+K4A9PT9hx3bW5tz9t6wUDuMe7M1q1bsXHjRkRERGDz5s1ISkryuI2FCxcCAD755BNYrVav4vAHX4zvrVyNk6/j8CVf9O2DDz5AU1MT+vfvj/vvv9+rNoL1HFaCt8cslK/F5B/MM5THPEN5zDWUx1wj+ARirsEJPoD4+HgAwJUrV1yWcayS6ijrTntXr151Wcax79b2HH/fuHEDFouly3oDBw7sNo5AIvcY/9COHTvw6quvwmAwID8/H6NGjfIqzsTERACA1WpFfX29V234g9Lj+0OuxsnXcfiSL/rm+MrczJkzoVZ7d3kO1nNYCd4eM2+v4USuMM9QHvMM5THXUB5zjeATiLkGJ/j4/lEnZ8+eRXNzc6dlysvLAQDDhg3rtr3ExEQYDAZcu3atfbXaHzpx4oRTe0ajsX0VRcfruVMvGMg9xrfatWsXXnnlFYSFheEvf/lLjxb8uHbtWvvfjjtrwUDJ8e2Mq3HydRy+pHTfzp07B7PZDJVK1eVX5roTrOewErw9Zt5ew4lcYZ6hPOYZymOuoTzmGsEnEHMNTvDx7eqFqampsFqt2L9/v9P+0tJS1NTUICYmxq07tnq9HhMmTAAA7Nu3z2n/pUuXUFZWBp1Oh4kTJ3bYN3nyZJf1LBZL+yqWDz30ULdxBBK5x9ihoKAAa9asgV6vx6ZNmzB+/PgexVlYWAgASEhIcLnCcCBSanxdcTVOvo7Dl5Tu29tvvw0AuPfee2EymbyOM1jPYSV4e8x6cg0n6gzzDOUxz1Aecw3lMdcIPgGZawgSQghRWFgoJEkS9913n6isrGzf/vXXX4upU6cKSZLEtm3bOtTZsWOHePjhh8WKFSuc2jObzSI5OVmkp6cLs9ncvt1isYjZs2cLSZLE2rVrnepduXJFpKWliZSUFFFUVNS+3Wq1iiVLlghJksTChQvl6LLPyT3Gb731lkhOThZ33XWXOHz4sFsxVFdXi3379omWlpYO2+12u9i7d69IS0sTkiSJgoICL3roX3KOb0/GyZs4goXc57BDa2urGDdunJAkSezbt6/LGEL5HO6M43pZWFjoskxeXp54+OGHRV5entM+b89Hb6/hRK4wz1Ae8wzlMddQHnMN3wu1XIOr6H9nypQpyM7ORkFBAaZPn47x48dDq9WipKQEFosFmZmZmD17doc69fX1qKioQExMjFN7aWlpWLZsGfLy8jBr1iyMHTsWRqMRx44dQ11dHdLT07FkyRKnenFxcVi7di1WrlyJF154AWPGjEG/fv1gNptRXV2NwYMHY82aNYqNg5LkHOMzZ85g1apVEEJg4MCBKCwsbL+beKs77rgDL774Yvu/r1+/juXLl+PXv/41UlNT0a9fPzQ0NODs2bPtz6OcPXs2Zs2apcAIKEvO8e3JOHkTR7CQ+zrhcPjwYdTV1SE6OrrLZyoDoX0OA8CpU6eQm5vb/u9z584BANavX48333yzffuePXva/66trUVFRQVqa2ud2vP2fPT2Gk7kCvMM5THPUB5zDeUx11BeqOcanODfYvXq1RgzZgx27dqF0tJS2O12JCYmIisrC9nZ2R4vRDFv3jwkJydj69atKC8vR0tLC0wmE+bMmYOcnJxOH60CANOmTYPJZEJ+fj6OHz8Os9mMuLg45OTkYMGCBTAajXJ01y/kGuMbN25ACAEAuHDhAi5cuNBpufj4+A5vvP3790dOTg7Ky8tRVVWFEydOwG63IyYmBlOnTsVTTz2FcePG9byjfiLX+PZ0nOT+vxRIlOibY8GbadOmISwsrMuyoX4OWywWmM1mp+2VlZVet+ntMfP2Gk7kCvMM5THPUB5zDeUx11BWqOcaKuG4ehERERERERFR0AreW1tERERERERE1I4TfCIiIiIiIqIQwAk+ERERERERUQjgBJ+IiIiIiIgoBHCCT0RERERERBQCOMEnIiIiIiIiCgGc4BMRERERERGFAE7wiYiIiIiIiEIAJ/hEREREREREIYATfCIiIiIiIqIQ8P8Nvlh3khHlJgAAAABJRU5ErkJggg\u003d\u003d\n"
+ "image/png": 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
},
- "metadata": {},
"output_type": "display_data"
}
],
- "source": "def plot_grid(ensemble, ax):\n grid_size \u003d 1000\n XX, YY \u003d np.meshgrid(np.linspace(-0.1, 1.1, grid_size), np.linspace(-0.1, 1.1, grid_size))\n X0 \u003d np.stack([np.ravel(XX), np.ravel(YY)]).T\n y_pred \u003d ensemble.predict(X0)\n ZZ \u003d y_pred.reshape(grid_size, grid_size)\n \n # reflects just class assignment\n ax.contourf(XX,YY,ZZ, cmap\u003d\"coolwarm\", levels\u003dnp.linspace(-1000, 1000, 3))\n # reflects the classifier\u0027s predictions\n # ax.contourf(XX,YY,ZZ, cmap\u003d\"coolwarm\", levels\u003dnp.linspace(-np.abs(y_pred).max(),np.abs(y_pred).max(),10))\n ax.scatter(X[:,0], X[:,1], c\u003dy, cmap\u003d\"coolwarm\", s\u003d200)\n ax.axis(\"equal\")\n axis_margin \u003d 0.035\n ax.set_xlim([-axis_margin, 1.0+axis_margin])\n ax.set_ylim([-axis_margin, 1.0+axis_margin])\n\n for x in X:\n rect_center \u003d (x[0]-eps_dataset, x[1]-eps_dataset)\n rect \u003d patches.Rectangle(rect_center, 2*eps_dataset, 2*eps_dataset, fill\u003dFalse)\n ax.add_patch(rect)\n \n ticks \u003d [0.0, 0.25, 0.5, 0.75, 1.0]\n ax.set_xticks(ticks)\n ax.set_yticks(ticks)\n ax.set_title(model_name)\n\nweak_learner \u003d \u0027stump\u0027\n# dataset \u003d \u0027toy_2d_{}s\u0027.format(weak_learner) # toy_2d_trees, toy_2d_xor, toy_2d_wong\ndataset \u003d \u0027toy_2d_trees\u0027\nX, y, eps_dataset \u003d data.all_datasets_dict[dataset]()\n\nmodels_names \u003d [\u0027Plain boosted {}s\u0027.format(weak_learner), \n \u0027Robust boosted {}s\u0027.format(weak_learner)]\nif weak_learner \u003d\u003d \u0027stump\u0027:\n models \u003d [\u0027plain\u0027, \u0027robust_exact\u0027]\nelif weak_learner \u003d\u003d \u0027tree\u0027:\n models \u003d [\u0027plain\u0027, \u0027robust_bound\u0027]\nelse:\n raise ValueError(\u0027wrong weak learner\u0027)\n# models \u003d [\u0027plain\u0027, \u0027robust_bound\u0027, \u0027robust_exact\u0027]\nfig, axs \u003d plt.subplots(1, len(models), figsize\u003d(1.2*len(models)*7, 7))\nfor i, (model, model_name) in enumerate(zip(models, models_names)):\n print(\u0027Training of `{}` model started.\u0027.format(model))\n eps, eps_eval \u003d eps_dataset, eps_dataset\n n_iter, lr \u003d 15, 1.0\n log, model_path, metrics_path \u003d Logger(\u0027\u0027), \u0027\u0027, \u0027\u0027 # nothing will be saved\n \n if weak_learner \u003d\u003d \u0027stump\u0027:\n ensemble \u003d StumpEnsemble(\u0027stump\u0027, 2, lr)\n elif weak_learner \u003d\u003d \u0027tree\u0027:\n ensemble \u003d TreeEnsemble(\u0027tree\u0027, 2, lr, 1, 0, 8)\n else:\n raise ValueError(\u0027wrong weak learner\u0027)\n robust_boost(ensemble, X, y, X, y, X, y, n_iter, eps,\n eps_eval, model, log, model_path, metrics_path)\n plot_grid(ensemble, axs[i])\nfig.subplots_adjust(wspace\u003d0.35)\nplt.savefig(\u0027plots/plain_robust_{}.pdf\u0027.format(dataset), bbox_inches\u003d\u0027tight\u0027)\n\n",
+ "source": "def plot_grid(ensemble, ax):\n grid_size \u003d 1000\n XX, YY \u003d np.meshgrid(np.linspace(-0.1, 1.1, grid_size), np.linspace(-0.1, 1.1, grid_size))\n X0 \u003d np.stack([np.ravel(XX), np.ravel(YY)]).T\n y_pred \u003d ensemble.predict(X0)\n ZZ \u003d y_pred.reshape(grid_size, grid_size)\n \n # reflects just class assignment\n ax.contourf(XX,YY,ZZ, cmap\u003d\"coolwarm\", levels\u003dnp.linspace(-1000, 1000, 3))\n # reflects the classifier\u0027s predictions\n # ax.contourf(XX,YY,ZZ, cmap\u003d\"coolwarm\", levels\u003dnp.linspace(-np.abs(y_pred).max(),np.abs(y_pred).max(),10))\n ax.scatter(X[:,0], X[:,1], c\u003dy.flatten(), cmap\u003d\"coolwarm\", s\u003d200)\n ax.axis(\"equal\")\n axis_margin \u003d 0.035\n ax.set_xlim([-axis_margin, 1.0+axis_margin])\n ax.set_ylim([-axis_margin, 1.0+axis_margin])\n\n for x in X:\n rect_center \u003d (x[0]-eps_dataset, x[1]-eps_dataset)\n rect \u003d patches.Rectangle(rect_center, 2*eps_dataset, 2*eps_dataset, fill\u003dFalse)\n ax.add_patch(rect)\n \n ticks \u003d [0.0, 0.25, 0.5, 0.75, 1.0]\n ax.set_xticks(ticks)\n ax.set_yticks(ticks)\n ax.set_title(model_name)\n\nweak_learner \u003d \u0027stump\u0027\ndataset \u003d \u0027toy_2d_{}s\u0027.format(weak_learner) # toy_2d_trees, toy_2d_xor, toy_2d_wong\nX, y, eps_dataset \u003d data.all_datasets_dict[dataset]()\nX \u003d data.convert_to_float32(X)\ny, _, _ \u003d data.transform_labels_one_vs_all(y, y, y)\n\nmodels_names \u003d [\u0027Plain boosted {}s\u0027.format(weak_learner), \n \u0027Robust boosted {}s\u0027.format(weak_learner)]\nif weak_learner \u003d\u003d \u0027stump\u0027:\n models \u003d [\u0027plain\u0027, \u0027robust_exact\u0027]\nelif weak_learner \u003d\u003d \u0027tree\u0027:\n models \u003d [\u0027plain\u0027, \u0027robust_bound\u0027]\nelse:\n raise ValueError(\u0027wrong weak learner\u0027)\n# models \u003d [\u0027plain\u0027, \u0027robust_bound\u0027, \u0027robust_exact\u0027]\nfor n_iter in [7]:\n np.random.seed(1)\n fig, axs \u003d plt.subplots(1, len(models), figsize\u003d(1.2*len(models)*7, 7))\n for i, (model, model_name) in enumerate(zip(models, models_names)):\n print(\u0027Training of `{}` model started.\u0027.format(model))\n eps, eps_eval \u003d eps_dataset, eps_dataset\n # n_iter, lr \u003d 2, 1.0\n lr \u003d 1.0\n log, model_path, metrics_path \u003d Logger(\u0027\u0027), \u0027\u0027, \u0027\u0027 # nothing will be saved\n \n if weak_learner \u003d\u003d \u0027stump\u0027:\n ensemble \u003d StumpEnsemble(\u0027stump\u0027, 2, lr, 0, -1, 10.0)\n elif weak_learner \u003d\u003d \u0027tree\u0027:\n depth_trees \u003d 5\n ensemble \u003d TreeEnsemble(\u0027tree\u0027, 2, lr, 1, 0, 0, depth_trees, 0, -1, 10.0)\n else:\n raise ValueError(\u0027wrong weak learner\u0027)\n model_ova \u003d OneVsAllClassifier([ensemble])\n \n robust_boost(model_ova, X, y, X, y, X, y, weak_learner, n_iter, eps,\n eps_eval, 10, False, model, log, model_path, metrics_path, False)\n plot_grid(model_ova, axs[i])\n print(\u0027\\n\u0027)\n fig.subplots_adjust(wspace\u003d0.35)\n plt.savefig(\u0027plots/plain_robust_{}.png\u0027.format(dataset), bbox_inches\u003d\u0027tight\u0027)\n",
"metadata": {
"pycharm": {
"metadata": false,
diff --git a/robust_boosting.py b/robust_boosting.py
index 73b93ef..76e193d 100644
--- a/robust_boosting.py
+++ b/robust_boosting.py
@@ -1,17 +1,101 @@
-import ipdb as pdb
import numpy as np
-from numba import jit
+import math
+from numba import jit, prange
from utils import minimum, clip
dtype = np.float32 # float32 is much faster than float64 because of exp
-# max value assigned to the weights in case when the optimal is +inf or -inf
-# note: these splits are used for the final classifier => the choice of `max_value` influences the overall result
-max_value_exp_loss = 10.0
+parallel = False # and then it also depends on NUMBA_NUM_THREADS
+nogil = True
-@jit(nopython=True) # almost 2 times speed-up by njit for this loop!
-def coord_descent_exp_loss(sum_1_1, sum_1_m1, sum_0_1, sum_0_m1):
+@jit(nopython=True, nogil=nogil)
+def fit_plain_stumps_iter(X_proj, y, gamma, b_vals_i, sum_1, sum_m1, max_weight):
+ ind = X_proj >= b_vals_i
+ sum_1_1, sum_1_m1 = np.sum(ind * (y == 1) * gamma), np.sum(ind * (y == -1) * gamma)
+ sum_0_1, sum_0_m1 = sum_1 - sum_1_1, sum_m1 - sum_1_m1
+ w_l, w_r = coord_descent_exp_loss(sum_1_1, sum_1_m1, sum_0_1, sum_0_m1, max_weight)
+
+ fmargin = y * w_l + y * w_r * ind
+ loss = np.mean(gamma * np.exp(-fmargin))
+ return loss, w_l, w_r
+
+
+@jit(nopython=True, nogil=nogil, parallel=parallel) # really matters, especially with independent iterations
+def fit_plain_stumps(X_proj, y, gamma, b_vals, max_weight):
+ n_thresholds = b_vals.shape[0]
+
+ losses = np.full(n_thresholds, np.inf, dtype=dtype)
+ w_l_vals = np.full(n_thresholds, np.inf, dtype=dtype)
+ w_r_vals = np.full(n_thresholds, np.inf, dtype=dtype)
+ sum_1, sum_m1 = np.sum((y == 1) * gamma), np.sum((y == -1) * gamma)
+ for i in prange(n_thresholds):
+ # due to a numba bug, if we don't use a separate function inside a prange-loop, we experience a memory leak
+ losses[i], w_l_vals[i], w_r_vals[i] = fit_plain_stumps_iter(
+ X_proj, y, gamma, b_vals[i], sum_1, sum_m1, max_weight)
+ return losses, w_l_vals, w_r_vals, b_vals
+
+
+@jit(nopython=True, nogil=nogil)
+def fit_robust_bound_stumps_iter(X_proj, y, gamma, b_vals_i, sum_1, sum_m1, eps, max_weight):
+ # Certification for the previous ensemble O(n)
+ split_lbs, split_ubs = X_proj - eps, X_proj + eps
+ guaranteed_right = split_lbs > b_vals_i
+ uncertain = (split_lbs <= b_vals_i) * (split_ubs >= b_vals_i)
+
+ loss, w_l, w_r = basic_case_two_intervals(y, gamma, guaranteed_right, uncertain, sum_1, sum_m1, max_weight)
+ return loss, w_l, w_r
+
+
+@jit(nopython=True, nogil=nogil, parallel=parallel) # parallel=True really matters, especially with independent iterations
+def fit_robust_bound_stumps(X_proj, y, gamma, b_vals, eps, max_weight):
+ n_thresholds = b_vals.shape[0]
+
+ losses = np.full(n_thresholds, np.inf, dtype=dtype)
+ w_l_vals = np.full(n_thresholds, np.inf, dtype=dtype)
+ w_r_vals = np.full(n_thresholds, np.inf, dtype=dtype)
+ sum_1, sum_m1 = np.sum((y == 1) * gamma), np.sum((y == -1) * gamma)
+ for i in prange(n_thresholds):
+ losses[i], w_l_vals[i], w_r_vals[i] = fit_robust_bound_stumps_iter(
+ X_proj, y, gamma, b_vals[i], sum_1, sum_m1, eps, max_weight)
+
+ return losses, w_l_vals, w_r_vals, b_vals
+
+
+@jit(nopython=True, nogil=nogil)
+def fit_robust_exact_stumps_iter(X_proj, y, gamma, w_rs, bs, b_vals_i, sum_1, sum_m1, eps, max_weight):
+ # Certification for the previous ensemble O(n)
+ split_lbs, split_ubs = X_proj - eps, X_proj + eps
+ guaranteed_right = split_lbs > b_vals_i
+ uncertain = (split_lbs <= b_vals_i) * (split_ubs >= b_vals_i)
+
+ h_l, h_r = calc_h(X_proj, y, w_rs, bs, b_vals_i, eps)
+ # there should be quite many useless coordinates which do not have any stumps in the ensemble
+ # thus h_l=h_r=0 => suffices to check just 2 regions without applying bisection
+ if np.sum(h_l) == 0.0 and np.sum(h_r) == 0.0:
+ loss, w_l, w_r = basic_case_two_intervals(y, gamma, guaranteed_right, uncertain, sum_1, sum_m1, max_weight)
+ else: # general case; happens only when `coord` was already splitted in the previous iterations
+ loss, w_l, w_r = bisect_coord_descent(y, gamma, h_l, h_r, guaranteed_right, uncertain, max_weight)
+ return loss, w_l, w_r
+
+
+@jit(nopython=True, nogil=nogil, parallel=parallel) # parallel=True really matters, especially with independent iterations
+def fit_robust_exact_stumps(X_proj, y, gamma, b_vals, eps, w_rs, bs, max_weight):
+ n_thresholds = b_vals.shape[0]
+
+ losses = np.full(n_thresholds, np.inf, dtype=dtype)
+ w_l_vals = np.full(n_thresholds, np.inf, dtype=dtype)
+ w_r_vals = np.full(n_thresholds, np.inf, dtype=dtype)
+ sum_1, sum_m1 = np.sum((y == 1) * gamma), np.sum((y == -1) * gamma)
+ for i in prange(n_thresholds):
+ losses[i], w_l_vals[i], w_r_vals[i] = fit_robust_exact_stumps_iter(
+ X_proj, y, gamma, w_rs, bs, b_vals[i], sum_1, sum_m1, eps, max_weight)
+
+ return losses, w_l_vals, w_r_vals, b_vals
+
+
+@jit(nopython=True, nogil=nogil) # almost 2 times speed-up by njit for this loop!
+def coord_descent_exp_loss(sum_1_1, sum_1_m1, sum_0_1, sum_0_m1, max_weight):
m = 1e-10
# if sum_0_1 + sum_0_m1 == 0 or sum_1_1 + sum_1_m1 == 0:
# return np.inf, np.inf
@@ -23,21 +107,21 @@ def coord_descent_exp_loss(sum_1_1, sum_1_m1, sum_0_1, sum_0_m1):
# We have to properly handle the cases when the optimal leaf value is +-inf.
if sum_1_m1 < m and sum_0_1 < m:
- w_l, w_r = -max_value_exp_loss, 2*max_value_exp_loss
+ w_l, w_r = -max_weight, 2 * max_weight
elif sum_1_1 < m and sum_0_m1 < m:
- w_l, w_r = max_value_exp_loss, -2*max_value_exp_loss
+ w_l, w_r = max_weight, -2 * max_weight
elif sum_1_m1 < m:
- w_r = max_value_exp_loss
- w_l = 0.5 * np.log((np.exp(-w_r) * sum_1_1 + sum_0_1) / (np.exp(w_r) * sum_1_m1 + sum_0_m1))
+ w_r = max_weight
+ w_l = 0.5 * math.log((math.exp(-w_r) * sum_1_1 + sum_0_1) / (math.exp(w_r) * sum_1_m1 + sum_0_m1))
elif sum_1_1 < m:
- w_r = -max_value_exp_loss
- w_l = 0.5 * np.log((np.exp(-w_r) * sum_1_1 + sum_0_1) / (np.exp(w_r) * sum_1_m1 + sum_0_m1))
+ w_r = -max_weight
+ w_l = 0.5 * math.log((math.exp(-w_r) * sum_1_1 + sum_0_1) / (math.exp(w_r) * sum_1_m1 + sum_0_m1))
elif sum_0_1 < m:
- w_l = -max_value_exp_loss
- w_r = 0.5 * np.log(sum_1_1 / sum_1_m1) - w_l
+ w_l = -max_weight
+ w_r = 0.5 * math.log(sum_1_1 / sum_1_m1) - w_l
elif sum_0_m1 < m:
- w_l = max_value_exp_loss
- w_r = 0.5 * np.log(sum_1_1 / sum_1_m1) - w_l
+ w_l = max_weight
+ w_r = 0.5 * math.log(sum_1_1 / sum_1_m1) - w_l
else: # main case
w_r = 0.0
w_l = 0.0
@@ -47,17 +131,17 @@ def coord_descent_exp_loss(sum_1_1, sum_1_m1, sum_0_1, sum_0_m1):
while (np.abs(w_r - w_r_prev) > eps_precision) or (np.abs(w_l - w_l_prev) > eps_precision):
i += 1
w_r_prev, w_l_prev = w_r, w_l
- w_r = 0.5 * np.log(sum_1_1 / sum_1_m1) - w_l
- w_l = 0.5 * np.log((np.exp(-w_r) * sum_1_1 + sum_0_1) / (np.exp(w_r) * sum_1_m1 + sum_0_m1))
+ w_r = 0.5 * math.log(sum_1_1 / sum_1_m1) - w_l
+ w_l = 0.5 * math.log((math.exp(-w_r) * sum_1_1 + sum_0_1) / (math.exp(w_r) * sum_1_m1 + sum_0_m1))
if i == 50:
break
- left_leaf = clip(w_l, -max_value_exp_loss, max_value_exp_loss)
- right_leaf = clip(left_leaf + w_r, -max_value_exp_loss, max_value_exp_loss)
+ left_leaf = clip(w_l, -max_weight, max_weight)
+ right_leaf = clip(left_leaf + w_r, -max_weight, max_weight)
w_l, w_r = left_leaf, right_leaf - left_leaf
return w_l, w_r
-@jit(nopython=True)
+@jit(nopython=True, nogil=nogil)
def calc_h(X_proj, y, w_rs, bs, b_curr, eps):
num = X_proj.shape[0]
h_l_base, h_r_base = np.zeros(num), np.zeros(num)
@@ -99,12 +183,12 @@ def calc_h(X_proj, y, w_rs, bs, b_curr, eps):
return h_l, h_r
-@jit(nopython=True)
-def bisection(w_l, y, gamma, h_l, h_r, guaranteed_right, uncertain):
+@jit(nopython=True, nogil=nogil)
+def bisection(w_l, y, gamma, h_l, h_r, guaranteed_right, uncertain, max_weight):
# bisection to find w_r* for the current w_l
- eps_precision = 1e-5 # 1e-5: 21 steps, 1e-4: 18 steps
+ eps_precision = 1e-5 # 1e-5: 21 steps, 1e-4: 18 steps (assuming max_weight=10)
w_r = 0.0
- w_r_lower, w_r_upper = -max_value_exp_loss, max_value_exp_loss
+ w_r_lower, w_r_upper = -max_weight, max_weight
loss_best = np.inf
i = 0
while i == 0 or np.abs(w_r_upper - w_r_lower) > eps_precision:
@@ -129,22 +213,22 @@ def bisection(w_l, y, gamma, h_l, h_r, guaranteed_right, uncertain):
return w_r
-@jit(nopython=True)
-def bisect_coord_descent(y, gamma, h_l, h_r, guaranteed_right, uncertain):
+@jit(nopython=True, nogil=nogil)
+def bisect_coord_descent(y, gamma, h_l, h_r, guaranteed_right, uncertain, max_weight):
eps_precision = 1e-5
w_l_prev, w_r_prev = np.inf, np.inf
w_l, w_r = 0.0, 0.0
i = 0
while np.abs(w_l - w_l_prev) > eps_precision or np.abs(w_r - w_r_prev) > eps_precision:
w_r_prev = w_r
- w_r = bisection(w_l, y, gamma, h_l, h_r, guaranteed_right, uncertain)
+ w_r = bisection(w_l, y, gamma, h_l, h_r, guaranteed_right, uncertain, max_weight)
ind = guaranteed_right + (y * w_r < h_l - h_r) * uncertain
gamma_with_h = gamma * np.exp(-(~ind * h_l + ind * h_r)) # only for the coord descent step
sum_1_1, sum_1_m1 = np.sum(ind * (y == 1) * gamma_with_h), np.sum(ind * (y == -1) * gamma_with_h)
sum_0_1, sum_0_m1 = np.sum(~ind * (y == 1) * gamma_with_h), np.sum(~ind * (y == -1) * gamma_with_h)
w_l_prev = w_l
- w_l = 0.5 * np.log((np.exp(-w_r) * sum_1_1 + sum_0_1) / (np.exp(w_r) * sum_1_m1 + sum_0_m1))
+ w_l = 0.5 * math.log((math.exp(-w_r) * sum_1_1 + sum_0_1) / (math.exp(w_r) * sum_1_m1 + sum_0_m1))
i += 1
if i == 10:
break
@@ -174,8 +258,8 @@ def exp_loss_robust(X_proj, y, gamma, w_l, w_r, w_rs, bs, b_curr, eps, h_flag):
return loss
-@jit(nopython=True)
-def basic_case_two_intervals(y, gamma, guaranteed_right, uncertain, sum_1, sum_m1):
+@jit(nopython=True, nogil=nogil)
+def basic_case_two_intervals(y, gamma, guaranteed_right, uncertain, sum_1, sum_m1, max_weight):
loss_best, w_r_best, w_l_best = np.inf, np.inf, np.inf
for sign_w_r in (-1, 1):
# Calculate the indicator function based on the known `sign_w_r`
@@ -185,12 +269,16 @@ def basic_case_two_intervals(y, gamma, guaranteed_right, uncertain, sum_1, sum_m
sum_1_1, sum_1_m1 = np.sum(ind * (y == 1) * gamma), np.sum(ind * (y == -1) * gamma)
sum_0_1, sum_0_m1 = sum_1 - sum_1_1, sum_m1 - sum_1_m1
# Minimizer of w_l, w_r on the current interval
- w_l, w_r = coord_descent_exp_loss(sum_1_1, sum_1_m1, sum_0_1, sum_0_m1)
- # if w_r is on the different side from 0, then sign_w_r*w_r < 0 => c:=0
+ w_l, w_r = coord_descent_exp_loss(sum_1_1, sum_1_m1, sum_0_1, sum_0_m1, max_weight)
+ # if w_r is on the different side from 0, then sign_w_r*w_r < 0 => w_r:=0
w_r = sign_w_r * max(sign_w_r * w_r, 0)
# If w_r now become 0, we need to readjust w_l
- w_l = 0.5 * np.log((np.exp(-w_r) * sum_1_1 + sum_0_1) / (np.exp(w_r) * sum_1_m1 + sum_0_m1))
+ if sum_1_m1 != 0 and sum_0_m1 != 0:
+ w_l = 0.5 * math.log((math.exp(-w_r) * sum_1_1 + sum_0_1) / (math.exp(w_r) * sum_1_m1 + sum_0_m1))
+ w_l = clip(w_l, -max_weight, max_weight)
+ else: # to prevent a division over zero
+ w_l = max_weight * math.copysign(1, 0.5 * math.log((math.exp(-w_r) * sum_1_1 + sum_0_1)))
preds_adv = w_l + w_r * ind
@@ -198,4 +286,3 @@ def basic_case_two_intervals(y, gamma, guaranteed_right, uncertain, sum_1, sum_m
if loss < loss_best:
loss_best, w_l_best, w_r_best = loss, w_l, w_r
return loss_best, w_l_best, w_r_best
-
diff --git a/robustml/robustml_fmnist.py b/robustml/robustml_fmnist.py
new file mode 100644
index 0000000..388c740
--- /dev/null
+++ b/robustml/robustml_fmnist.py
@@ -0,0 +1,40 @@
+import sys
+sys.path.append('..')
+import robustml
+from classifiers import OneVsAllClassifier
+from tree_ensemble import TreeEnsemble
+
+
+def load_model(model_path):
+ n_classifiers, weak_learner, ensembles = 10, 'tree', []
+
+ for i_clsf in range(n_classifiers):
+ # hyperparameters are not important when loading
+ ensembles.append(TreeEnsemble(weak_learner, 0, 0, 0, 0, 0, 0, 0, 0, 0))
+
+ model_ova = OneVsAllClassifier(ensembles)
+ model_ova.load(model_path)
+ return model_ova
+
+
+class Model(robustml.model.Model):
+ def __init__(self, sess):
+ model_path = "models/models_trees_multiclass/2019-08-06 14:59:51 dataset=fmnist weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=784 eps=0.100 max_depth=30 lr=0.05.model.npy"
+ self.model = load_model(model_path)
+
+ self._dataset = robustml.dataset.FMNIST()
+ self._threat_model = robustml.threat_model.Linf(epsilon=0.1)
+
+ @property
+ def dataset(self):
+ return self._dataset
+
+ @property
+ def threat_model(self):
+ return self._threat_model
+
+ def classify(self, x):
+ predictions = self.model.predict(x)
+ pred_label = predictions.argmax() # label as a number
+ return pred_label
+
diff --git a/robustml/robustml_mnist.py b/robustml/robustml_mnist.py
new file mode 100644
index 0000000..11aa971
--- /dev/null
+++ b/robustml/robustml_mnist.py
@@ -0,0 +1,40 @@
+import sys
+sys.path.append('..')
+import robustml
+from classifiers import OneVsAllClassifier
+from tree_ensemble import TreeEnsemble
+
+
+def load_model(model_path):
+ n_classifiers, weak_learner, ensembles = 10, 'tree', []
+
+ for i_clsf in range(n_classifiers):
+ # hyperparameters are not important when loading
+ ensembles.append(TreeEnsemble(weak_learner, 0, 0, 0, 0, 0, 0, 0, 0, 0))
+
+ model_ova = OneVsAllClassifier(ensembles)
+ model_ova.load(model_path)
+ return model_ova
+
+
+class Model(robustml.model.Model):
+ def __init__(self, sess):
+ model_path = "models/models_trees_multiclass/2019-08-05 21:02:49 dataset=mnist weak_learner=tree model=robust_bound n_train=-1 n_trials_coord=784 eps=0.300 max_depth=30 lr=0.05.model.npy"
+ self.model = load_model(model_path)
+
+ self._dataset = robustml.dataset.MNIST()
+ self._threat_model = robustml.threat_model.Linf(epsilon=0.3)
+
+ @property
+ def dataset(self):
+ return self._dataset
+
+ @property
+ def threat_model(self):
+ return self._threat_model
+
+ def classify(self, x):
+ predictions = self.model.predict(x)
+ pred_label = predictions.argmax() # label as a number
+ return pred_label
+
diff --git a/stump_ensemble.py b/stump_ensemble.py
index 31bf399..e5c017b 100644
--- a/stump_ensemble.py
+++ b/stump_ensemble.py
@@ -1,15 +1,15 @@
import numpy as np
-import ipdb as pdb
-from numba import jit, prange
+from numba import jit
from collections import OrderedDict
-from robust_boosting import exp_loss_robust, coord_descent_exp_loss, bisect_coord_descent, calc_h, \
- basic_case_two_intervals, dtype
-from utils import minimum, get_contiguous_indices
+from robust_boosting import exp_loss_robust, dtype, fit_plain_stumps, fit_robust_bound_stumps, fit_robust_exact_stumps
+from utils import minimum, get_contiguous_indices, get_n_proc
+from concurrent.futures import ThreadPoolExecutor
class Stump:
- def __init__(self, w_l, w_r, b, coord):
- self.w_l, self.w_r, self.b, self.coord = w_l, w_r, b, coord
+ def __init__(self, w_l, w_r, b, coord, loss):
+ # `loss` is the loss of the whole ensemble after applying this stump
+ self.w_l, self.w_r, self.b, self.coord, self.loss = w_l, w_r, b, coord, loss
self.left, self.right = None, None
def predict(self, X):
@@ -31,14 +31,29 @@ def find_min_yf(self, X, y, eps):
def __repr__(self):
lval, rval, threshold = self.w_l, self.w_r + self.w_l, self.b
- return 'Tree: if x[{}] < {:.4f}: {:.4f} else {:.4f}'.format(self.coord, lval, threshold, rval)
+ return 'Tree: if x[{}] < {:.4f}: {:.4f} else {:.4f}'.format(self.coord, threshold, lval, rval)
+
+ def get_json_dict(self, counter_terminal_nodes):
+ """
+ counter_terminal_nodes: not used here
+ """
+ precision = 5
+ children_list = [{'nodeid': 1, 'leaf': round(self.w_l, precision)},
+ {'nodeid': 2, 'leaf': round(self.w_l + self.w_r, precision)}]
+ stump_dict = {'nodeid': 0, 'split': 'f' + str(int(self.coord)), 'split_condition': round(self.b, precision),
+ 'yes': 1, 'no': 2, 'children': children_list}
+
+ return stump_dict, counter_terminal_nodes
class StumpEnsemble:
- def __init__(self, weak_learner, n_trials_coord, lr):
+ def __init__(self, weak_learner, n_trials_coord, lr, idx_clsf, n_bins=-1, max_weight=1.0):
self.weak_learner = weak_learner
self.n_trials_coord = n_trials_coord
self.lr = lr
+ self.idx_clsf = idx_clsf
+ self.n_bins = n_bins
+ self.max_weight = max_weight
self.trees = []
self.coords_trees = OrderedDict()
@@ -46,32 +61,44 @@ def __repr__(self):
sorted_trees = sorted(self.trees, key=lambda tree: tree.coord)
return '\n'.join([str(t) for t in sorted_trees])
- def load(self, path, iteration=-1):
- if iteration == -1: # take all
- ensemble_arr = np.loadtxt(path)
- else: # take up to some iteration
- ensemble_arr = np.loadtxt(path)[:iteration+1]
+ def copy(self):
+ ensemble_new = StumpEnsemble(self.weak_learner, self.n_trials_coord, self.lr, self.idx_clsf, self.n_bins,
+ self.max_weight)
+ for tree in self.trees:
+ ensemble_new.add_weak_learner(tree, apply_lr=False)
+ return ensemble_new
+
+ def load(self, ensemble_arr, iteration=-1):
+ if iteration != -1: # take up to some iteration
+ ensemble_arr = ensemble_arr[:iteration+1]
for i in range(ensemble_arr.shape[0]):
- w_l, w_r, b, coord = ensemble_arr[i, :]
+ w_l, w_r, b, coord, loss = ensemble_arr[i, :]
coord = int(coord)
- tree = Stump(w_l, w_r, b, coord)
- self.add_weak_learner(tree)
- print('Ensemble of {} learners restored: {}'.format(ensemble_arr.shape[0], path))
+ tree = Stump(w_l, w_r, b, coord, loss)
+ # the values of w_l and w_r should be already scaled by lr, would be wrong to do this again
+ self.add_weak_learner(tree, apply_lr=False)
+
+ def export_model(self):
+ ensemble_arr = np.zeros([len(self.trees), 5])
+ for i, tree in enumerate(self.trees):
+ ensemble_arr[i, :] = [tree.w_l, tree.w_r, tree.b, tree.coord, tree.loss]
+ return ensemble_arr
def save(self, path):
if path != '':
- ensemble_arr = np.zeros([len(self.trees), 4])
- for i, tree in enumerate(self.trees):
- ensemble_arr[i, :] = [tree.w_l, tree.w_r, tree.b, tree.coord]
- np.savetxt(path, ensemble_arr)
+ np.save(path, self.export_model(), allow_pickle=False)
- def add_weak_learner(self, tree):
- tree.w_l, tree.w_r = tree.w_l*self.lr, tree.w_r*self.lr
+ def add_weak_learner(self, tree, apply_lr=True):
+ if apply_lr:
+ tree.w_l, tree.w_r = tree.w_l*self.lr, tree.w_r*self.lr
self.trees.append(tree)
if tree.coord not in self.coords_trees:
self.coords_trees[tree.coord] = []
self.coords_trees[tree.coord].append(tree)
- print(tree)
+
+ def add_empty_weak_learner(self):
+ empty_stump = Stump(0.0, 0.0, 0.0, 0, 0.0)
+ self.add_weak_learner(empty_stump)
def predict(self, X):
Fx = np.zeros(X.shape[0])
@@ -83,7 +110,7 @@ def attack_by_sampling(self, X, y, eps, n_trials):
""" A simple attack just by sampling in the Linf-box around the points. More of a sanity check. """
num, dim = X.shape
f_x_vals = np.zeros((num, n_trials))
- # Note: for efficiency, we sample the same random direction for all points, but this does influence matter
+ # Note: for efficiency, we sample the same random direction for all points
deltas = np.random.uniform(-eps, eps, size=(dim, n_trials))
for i in range(n_trials-1):
# let's keep them as real images, although not strictly needed
@@ -95,7 +122,7 @@ def attack_by_sampling(self, X, y, eps, n_trials):
f_x_min = np.min(y[:, None] * f_x_vals, axis=1)
return f_x_min
- def certify_treewise_bound(self, X, y, eps):
+ def certify_treewise(self, X, y, eps):
lb_ensemble = np.zeros(X.shape[0])
# The naive tree-wise bounded on the merged trees
@@ -138,257 +165,177 @@ def certify_exact(self, X, y, eps, coords_to_ignore=()):
f_x_min_coord_base += y * tree.predict(X - eps)
thresholds[i], w_r_values[i] = tree.b, tree.w_r
+ # merge trees with the same thresholds to prevent an overestimation (lower bounding) of the true minimum
+ thresholds_list, w_r_values_list = [], []
+ for threshold in np.unique(thresholds):
+ thresholds_list.append(threshold)
+ w_r_values_list.append(np.sum(w_r_values[thresholds == threshold]))
+ thresholds, w_r_values = np.array(thresholds_list), np.array(w_r_values_list)
+
f_x_min += f_x_min_coord_base + self.find_min_coord_diff(X[:, coord], y, thresholds, w_r_values, eps)
return f_x_min
- def exact_adv_example(self, X, y):
- min_val = 1e-7
- num, dim = X.shape
- deltas = np.zeros([num, dim])
- db_dists = np.full(num, np.inf)
-
- for i in range(num):
- # 0.0 means we just check whether the point is originally misclassified; if yes => db_dist=0
- eps_all_i = np.array([0.0] + [np.abs(tree.b - X[i, tree.coord] + min_val*np.sign(tree.b - X[i, tree.coord]))
- for tree in self.trees])
- eps_sorted = np.sort(eps_all_i)
- for eps in eps_sorted:
- # Vectorized but obscure version; just a sanity check for eps; doesn't return deltas
- # f_x_min = self.certify_exact(X[None, i], y[None, i], eps)
-
- # Clear unvectorized version
- yf_min = 0.0
- delta = np.zeros(dim)
- for coord in self.coords_trees.keys():
- trees_current_coord = self.coords_trees[coord]
-
- yf_min_coord_base, yf_orig_pt = 0.0, 0.0
- for tree in trees_current_coord:
- yf_min_coord_base += y[i] * tree.predict(X[None, i] - eps)
- yf_orig_pt += y[i] * tree.predict(X[None, i])
-
- unstable_thresholds, unstable_wr_values = [X[i, coord] - eps], [0.0]
- for tree in trees_current_coord:
- # excluding the left equality since we have already evaluated it
- if X[i, coord] - eps < tree.b <= X[i, coord] + eps:
- unstable_thresholds.append(tree.b)
- unstable_wr_values.append(tree.w_r)
- unstable_thresholds = np.array(unstable_thresholds)
- unstable_wr_values = np.array(unstable_wr_values)
- idx = np.argsort(unstable_thresholds)
- unstable_thresholds = unstable_thresholds[idx]
-
- sorted_y_wr = (y[i] * np.array(unstable_wr_values))[idx]
- yf_coord_interval_vals = np.cumsum(sorted_y_wr)
- yf_min_coord = yf_min_coord_base + yf_coord_interval_vals.min()
- yf_min += yf_min_coord
-
- i_opt_threshold = yf_coord_interval_vals.argmin()
- # if the min value is attained at the point itself, take it instead; so that we do not take
- # unnecessary -eps deltas (which would not anyway influence Linf size, but would bias the picture)
- if yf_min_coord == yf_orig_pt:
- opt_threshold = X[i, coord] # i.e. the final delta is 0.0
- else:
- opt_threshold = unstable_thresholds[i_opt_threshold]
- delta[coord] = opt_threshold - X[i, coord]
-
- x_adv_clipped = np.clip(X[i] + delta, 0, 1) # make sure that the images are valid
- delta = x_adv_clipped - X[i]
-
- yf = float(y[i] * self.predict(X[None, i] + delta[None]))
- print('eps_max={:.3f}, eps_delta={:.3f}, yf={:.3f}, nnz={}'.format(
- eps, np.abs(delta).max(), yf, (delta != 0.0).sum()))
- if yf_min < 0:
- db_dists[i] = eps
- deltas[i] = delta
- break
- print()
- yf = y[i] * self.predict(X[None, i] + deltas[None, i])
- if yf >= 0.0:
- print('The class was not changed! Some bug!')
- import ipdb;ipdb.set_trace()
- return deltas
-
- @staticmethod
- @jit(nopython=True, parallel=True) # parallel=True really matters, especially with independent iterations
- def fit_plain_stumps(X_proj, y, gamma, b_vals):
- n_thresholds = b_vals.shape[0]
-
- losses = np.full(n_thresholds, np.inf, dtype=dtype)
- w_l_vals = np.full(n_thresholds, np.inf, dtype=dtype)
- w_r_vals = np.full(n_thresholds, np.inf, dtype=dtype)
- sum_1, sum_m1 = np.sum((y == 1) * gamma), np.sum((y == -1) * gamma)
- for i in prange(n_thresholds):
- ind = X_proj >= b_vals[i]
-
- sum_1_1, sum_1_m1 = np.sum(ind * (y == 1) * gamma), np.sum(ind * (y == -1) * gamma)
- sum_0_1, sum_0_m1 = sum_1 - sum_1_1, sum_m1 - sum_1_m1
- w_l, w_r = coord_descent_exp_loss(sum_1_1, sum_1_m1, sum_0_1, sum_0_m1)
-
- fmargin = y*w_l + y*w_r*ind
- loss = np.mean(gamma * np.exp(-fmargin))
- losses[i], w_l_vals[i], w_r_vals[i] = loss, w_l, w_r
-
- return losses, w_l_vals, w_r_vals, b_vals
-
- @staticmethod
- @jit(nopython=True, parallel=True) # parallel=True really matters, especially with independent iterations
- def fit_robust_bound_stumps(X_proj, y, gamma, b_vals, eps):
- n_thresholds = b_vals.shape[0]
-
- losses = np.full(n_thresholds, np.inf, dtype=dtype)
- w_l_vals = np.full(n_thresholds, np.inf, dtype=dtype)
- w_r_vals = np.full(n_thresholds, np.inf, dtype=dtype)
- sum_1, sum_m1 = np.sum((y == 1) * gamma), np.sum((y == -1) * gamma)
- for i in prange(n_thresholds):
- # Certification for the previous ensemble O(n)
- split_lbs, split_ubs = X_proj - eps, X_proj + eps
- guaranteed_right = split_lbs > b_vals[i]
- uncertain = (split_lbs <= b_vals[i]) * (split_ubs >= b_vals[i])
-
- loss, w_l, w_r = basic_case_two_intervals(y, gamma, guaranteed_right, uncertain, sum_1, sum_m1)
- losses[i], w_l_vals[i], w_r_vals[i] = loss, w_l, w_r
+ def fit_stumps_over_coords(self, X, y, gamma, model, eps):
+ verbose = False
+ parallel = False # can speed up the training on large datasets
+ n_ex = X.shape[0]
+ X, y, gamma = X.astype(dtype), y.astype(dtype), gamma.astype(dtype)
+ prev_loss = np.mean(gamma)
- return losses, w_l_vals, w_r_vals, b_vals
-
- @staticmethod
- @jit(nopython=True, parallel=True) # parallel=True really matters, especially with independent iterations
- def fit_robust_exact_stumps(X_proj, y, gamma, b_vals, eps, w_rs, bs):
- n_thresholds = b_vals.shape[0]
-
- losses = np.full(n_thresholds, np.inf, dtype=dtype)
- w_l_vals = np.full(n_thresholds, np.inf, dtype=dtype)
- w_r_vals = np.full(n_thresholds, np.inf, dtype=dtype)
- sum_1, sum_m1 = np.sum((y == 1) * gamma), np.sum((y == -1) * gamma)
- for i in prange(n_thresholds):
- # Certification for the previous ensemble O(n)
- split_lbs, split_ubs = X_proj - eps, X_proj + eps
- guaranteed_right = split_lbs > b_vals[i]
- uncertain = (split_lbs <= b_vals[i]) * (split_ubs >= b_vals[i])
-
- h_l, h_r = calc_h(X_proj, y, w_rs, bs, b_vals[i], eps)
- # there should be quite many useless coordinates which do not have any stumps in the ensemble
- # thus h_l=h_r=0 => suffices to check just 2 regions without applying bisection
- if np.sum(h_l) == 0.0 and np.sum(h_r) == 0.0:
- loss, w_l, w_r = basic_case_two_intervals(y, gamma, guaranteed_right, uncertain, sum_1, sum_m1)
- else: # general case; happens only when `coord` was already splitted in the previous iterations
- loss, w_l, w_r = bisect_coord_descent(y, gamma, h_l, h_r, guaranteed_right, uncertain)
-
- losses[i], w_l_vals[i], w_r_vals[i] = loss, w_l, w_r
-
- return losses, w_l_vals, w_r_vals, b_vals
-
- def fit_stump(self, X, y, gamma_global, model, eps):
- n_trials_coord = self.n_trials_coord
- X, y, gamma_global = X.astype(dtype), y.astype(dtype), gamma_global.astype(dtype)
-
- num, dim = X.shape
- params, min_losses = np.zeros((n_trials_coord, 4)), np.full(n_trials_coord, np.inf)
-
- # 151 features are always 0.0 on MNIST 2 vs 6. Doesn't even makes sense to consider them.
+ # 151 features are always 0.0 on MNIST 2 vs 6. And this number is even higher for smaller subsets of MNIST,
+ # i.e. subsets of examples partitioned by tree splits.
idx_non_trivial = np.abs(X).sum(axis=0) > 0.0
- features_to_check = list(np.arange(dim)[idx_non_trivial])
- np.random.shuffle(features_to_check) # shuffles in-place
- for trial in prange(n_trials_coord):
- if len(features_to_check) > 0:
- coord = features_to_check.pop() # takes the last element
- else:
- self.n_trials_coord = trial
- break
- X_proj = X[:, coord]
-
- # Needed for exact robust optimization with stumps
- trees_current_coord = self.coords_trees[coord] if coord in self.coords_trees else []
- w_rs, bs = np.zeros(len(trees_current_coord)), np.zeros(len(trees_current_coord))
- for i in range(len(trees_current_coord)):
- w_rs[i] = trees_current_coord[i].w_r
- bs[i] = trees_current_coord[i].b
-
- if model == 'robust_exact' and trees_current_coord != []: # note: the previous gamma is just ignored
- min_Fx_y_exact_without_j = self.certify_exact(X, y, eps, coords_to_ignore=(coord, ))
- w_ls = np.sum([tree.w_l for tree in trees_current_coord])
- gamma = np.exp(-min_Fx_y_exact_without_j - y*w_ls)
+ features_to_check = np.random.permutation(np.where(idx_non_trivial)[0])[:self.n_trials_coord]
+
+ n_coords = len(features_to_check)
+ params, min_losses = np.zeros((n_coords, 4)), np.full(n_coords, np.inf)
+
+ if parallel:
+ n_proc = get_n_proc(n_ex)
+ n_proc = min(n_coords, min(100, n_proc))
+ batch_size = n_coords // n_proc
+ n_batches = n_coords // batch_size + 1
+
+ with ThreadPoolExecutor(max_workers=n_proc) as executor:
+ procs = []
+ for i_batch in range(n_batches):
+ coords = features_to_check[i_batch*batch_size:(i_batch+1)*batch_size]
+ args = (X, X[:, coords], y, gamma, model, eps, coords)
+ procs.append(executor.submit(self.fit_stump_batch, *args))
+
+ # Process the results
+ i_coord = 0
+ for i_batch in range(n_batches):
+ res_many = procs[i_batch].result()
+ for res in res_many:
+ min_losses[i_coord], *params[i_coord, :] = res
+ i_coord += 1
+ else:
+ for i_coord, coord in enumerate(features_to_check):
+ min_losses[i_coord], *params[i_coord, :] = self.fit_stump(
+ X, X[:, coord], y, gamma, model, eps, coord)
+
+ id_best_coord = min_losses.argmin()
+ min_loss = min_losses[id_best_coord]
+ best_coord = int(params[id_best_coord][3]) # float to int is necessary for a coordinate
+ best_wl, best_wr, best_b = params[id_best_coord][0], params[id_best_coord][1], np.float32(params[id_best_coord][2])
+ if verbose:
+ print('[{}-vs-all]: n_ex {}, n_coords {} -- loss {:.5f}->{:.5f}, b={:.3f} wl={:.3f} wr={:.3f} at coord {}'.format(
+ self.idx_clsf, n_ex, n_coords, prev_loss, min_loss, best_b, best_wl, best_wr, best_coord))
+ return Stump(best_wl, best_wr, best_b, best_coord, min_loss)
+
+ def fit_stump_batch(self, X, Xs, y, gamma, model, eps, coords):
+ res = np.zeros([len(coords), 5])
+ for i, coord in enumerate(coords):
+ res[i] = self.fit_stump(X, Xs[:, i], y, gamma, model, eps, coord)
+ return res
+
+ def fit_stump(self, X, X_proj, y, gamma_global, model, eps, coord):
+ min_prec_val = 1e-7
+ min_val, max_val = 0.0, 1.0 # can be changed if the features are in a different range
+ n_bins = self.n_bins
+
+ # Needed for exact robust optimization with stumps
+ trees_current_coord = self.coords_trees[coord] if coord in self.coords_trees else []
+ w_rs, bs = np.zeros(len(trees_current_coord)), np.zeros(len(trees_current_coord))
+ for i in range(len(trees_current_coord)):
+ w_rs[i] = trees_current_coord[i].w_r
+ bs[i] = trees_current_coord[i].b
+
+ if model == 'robust_exact' and trees_current_coord != []: # note: the previous gamma is just ignored
+ min_Fx_y_exact_without_j = self.certify_exact(X, y, eps, coords_to_ignore=(coord, ))
+ w_ls = np.sum([tree.w_l for tree in trees_current_coord])
+ gamma = np.exp(-min_Fx_y_exact_without_j - y*w_ls)
+ else:
+ gamma = gamma_global
+
+ if n_bins > 0:
+ if model == 'robust_bound':
+ # b_vals = np.array([0.31, 0.41, 0.5, 0.59, 0.69]) # that's the thresholds that one gets with n_bins=10
+ b_vals = np.arange(eps * n_bins, n_bins - eps * n_bins + 1) / n_bins
+ # to have some margin to make the thresholds not adversarially reachable from 0 or 1
+ b_vals[b_vals < 0.5] += 0.1 * 1 / n_bins
+ b_vals[b_vals > 0.5] -= 0.1 * 1 / n_bins
else:
- gamma = gamma_global
-
- min_val = 1e-7
- if model not in ['robust_exact', 'robust_bound'] or eps == 0.0: # plain training
- b_vals = np.copy(X_proj)
- b_vals += min_val # to break the ties
+ b_vals = np.arange(1, n_bins) / n_bins
+ else:
+ threshold_candidates = np.sort(X_proj)
+ if len(threshold_candidates) == 0: # if no samples left according to min_samples_leaf
+ return [np.inf, 0.0, 0.0, 0.0, -1]
+ if model not in ['robust_bound', 'robust_exact'] or eps == 0.0: # plain, da_uniform or at_cube training
+ b_vals = np.copy(threshold_candidates)
+ b_vals += min_prec_val # to break the ties
else: # robust training
- b_vals = np.concatenate((X_proj - eps, X_proj + eps), axis=0) # 2n thresholds
- # to make in the overlapping case |---x-|--|-x---| output 2 different losses in the middle
- b_vals += np.concatenate((-np.full(num, min_val), np.full(num, min_val)), axis=0)
+ b_vals = np.concatenate((threshold_candidates - eps, threshold_candidates + eps), axis=0)
+ b_vals = np.clip(b_vals, min_val, max_val) # save computations (often goes 512 -> 360 thresholds on MNIST)
+ # to make in the overlapping case [---x-[--]-x---] output 2 different losses in the middle
+ n_bs = len(threshold_candidates)
+ b_vals += np.concatenate((-np.full(n_bs, min_prec_val), np.full(n_bs, min_prec_val)), axis=0)
b_vals = np.unique(b_vals) # use only unique b's
b_vals = np.sort(b_vals) # still important to sort because of the final threshold selection
- if model == 'plain':
- losses, w_l_vals, w_r_vals, b_vals = self.fit_plain_stumps(X_proj, y, gamma, b_vals)
- elif model == 'robust_bound':
- losses, w_l_vals, w_r_vals, b_vals = self.fit_robust_bound_stumps(X_proj, y, gamma, b_vals, eps)
- elif model == 'robust_exact':
- losses, w_l_vals, w_r_vals, b_vals = self.fit_robust_exact_stumps(X_proj, y, gamma, b_vals, eps, w_rs, bs)
- else:
- raise ValueError('wrong model')
-
- min_loss = np.min(losses)
- # probably, they are already sorted, but to be 100% sure since it is not explicitly mentioned in the docs
- indices_opt_init = np.sort(np.where(losses == min_loss)[0])
- indices_opt = get_contiguous_indices(indices_opt_init)
- id_opt = indices_opt[len(indices_opt) // 2]
-
- idx_prev = np.clip(indices_opt[0]-1, 0, len(b_vals)-1) # to prevent stepping out of the array
- idx_next = np.clip(indices_opt[-1]+1, 0, len(b_vals)-1) # to prevent stepping out of the array
- b_prev, w_l_prev, w_r_prev = b_vals[idx_prev], w_l_vals[idx_prev], w_r_vals[idx_prev]
- b_next, w_l_next, w_r_next = b_vals[idx_next], w_l_vals[idx_next], w_r_vals[idx_next]
- # initialization
- b_leftmost, b_rightmost = b_vals[indices_opt[0]], b_vals[indices_opt[-1]]
- # more involved, since with +-eps, an additional check of the loss is needed
- if model == 'plain':
+ if model in ['plain', 'da_uniform', 'at_cube']:
+ losses, w_l_vals, w_r_vals, b_vals = fit_plain_stumps(X_proj, y, gamma, b_vals, self.max_weight)
+ elif model == 'robust_bound':
+ losses, w_l_vals, w_r_vals, b_vals = fit_robust_bound_stumps(X_proj, y, gamma, b_vals, eps, self.max_weight)
+ elif model == 'robust_exact':
+ losses, w_l_vals, w_r_vals, b_vals = fit_robust_exact_stumps(X_proj, y, gamma, b_vals, eps, w_rs, bs, self.max_weight)
+ else:
+ raise ValueError('wrong model')
+
+ min_loss = np.min(losses)
+ # probably, they are already sorted, but to be 100% sure since it is not explicitly mentioned in the docs
+ indices_opt_init = np.sort(np.where(losses == min_loss)[0])
+ indices_opt = get_contiguous_indices(indices_opt_init)
+ id_opt = indices_opt[len(indices_opt) // 2]
+
+ idx_prev = np.clip(indices_opt[0]-1, 0, len(b_vals)-1) # to prevent stepping out of the array
+ idx_next = np.clip(indices_opt[-1]+1, 0, len(b_vals)-1) # to prevent stepping out of the array
+ b_prev, w_l_prev, w_r_prev = b_vals[idx_prev], w_l_vals[idx_prev], w_r_vals[idx_prev]
+ b_next, w_l_next, w_r_next = b_vals[idx_next], w_l_vals[idx_next], w_r_vals[idx_next]
+ # initialization
+ b_leftmost, b_rightmost = b_vals[indices_opt[0]], b_vals[indices_opt[-1]]
+ # more involved, since with +-eps, an additional check of the loss is needed
+ if model in ['plain', 'da_uniform', 'at_cube']:
+ b_rightmost = b_next
+ elif model in ['robust_bound', 'robust_exact']:
+ h_flag = False if model == 'robust_bound' else True
+
+ b_prev_half = (b_prev + b_vals[indices_opt[0]]) / 2
+ loss_prev_half = exp_loss_robust(X_proj, y, gamma, w_l_prev, w_r_prev, w_rs, bs, b_prev_half, eps, h_flag)
+
+ b_next_half = (b_vals[indices_opt[-1]] + b_next) / 2
+ loss_next_half = exp_loss_robust(X_proj, y, gamma, w_l_next, w_r_next, w_rs, bs, b_next_half, eps, h_flag)
+
+ # we extend the interval of the constant loss to the left and to the right if there the loss is
+ # the same at b_prev_half or b_next_half
+ if loss_prev_half == losses[id_opt]:
+ b_leftmost = b_prev
+ if loss_next_half == losses[id_opt]:
b_rightmost = b_next
- elif model in ['robust_bound', 'robust_exact']:
- h_flag = False if model == 'robust_bound' else True
-
- b_prev_half = (b_prev + b_vals[indices_opt[0]]) / 2
- loss_prev_half = exp_loss_robust(X_proj, y, gamma, w_l_prev, w_r_prev, w_rs, bs, b_prev_half, eps, h_flag)
-
- b_next_half = (b_vals[indices_opt[-1]] + b_next) / 2
- loss_next_half = exp_loss_robust(X_proj, y, gamma, w_l_next, w_r_next, w_rs, bs, b_next_half, eps, h_flag)
-
- # we extend the interval of the constant loss to the left and to the right if there the loss is
- # the same at b_prev_half or b_next_half
- if loss_prev_half == losses[id_opt]:
- b_leftmost = b_prev
- if loss_next_half == losses[id_opt]:
- b_rightmost = b_next
- else:
- raise ValueError('wrong model')
- # we put in the middle of the interval of the constant loss
- b_opt = (b_leftmost + b_rightmost) / 2
-
- # For the chosen threshold, we need to calculate w_l, w_r
- # Some of w_l, w_r that correspond to min_loss may not be optimal anymore
- b_val_final = np.array([b_opt])
- if model == 'plain':
- loss, w_l_opt, w_r_opt, _ = self.fit_plain_stumps(X_proj, y, gamma, b_val_final)
- elif model == 'robust_bound':
- loss, w_l_opt, w_r_opt, _ = self.fit_robust_bound_stumps(X_proj, y, gamma, b_val_final, eps)
- elif model == 'robust_exact':
- loss, w_l_opt, w_r_opt, _ = self.fit_robust_exact_stumps(X_proj, y, gamma, b_val_final, eps, w_rs, bs)
- else:
- raise ValueError('wrong model')
- loss, w_l_opt, w_r_opt = loss[0], w_l_opt[0], w_r_opt[0]
- # recalculation of w_l, w_r shouldn't change the min loss
-
- if np.abs(loss - min_loss) > 1e7:
- print('New loss: {:.5f}, min loss before: {:.5f}'.format(loss, min_loss))
-
- min_losses[trial] = losses[id_opt]
- params[trial, :] = [w_l_opt, w_r_opt, b_opt, coord]
-
- id_best_coord = min_losses[:n_trials_coord].argmin()
- best_coord = int(params[id_best_coord][3]) # float to int is necessary for a coordinate
- w_l, w_r, b, coord = params[id_best_coord][0], params[id_best_coord][1], params[id_best_coord][2], best_coord
- stump = Stump(w_l, w_r, b, coord)
- return stump
+ else:
+ raise ValueError('wrong model')
+
+ # we put in the middle of the interval of the constant loss
+ b_opt = (b_leftmost + b_rightmost) / 2
+
+ # For the chosen threshold, we need to calculate w_l, w_r
+ # Some of w_l, w_r that correspond to min_loss may not be optimal anymore
+ b_val_final = np.array([b_opt])
+ if model in ['plain', 'da_uniform', 'at_cube']:
+ loss, w_l_opt, w_r_opt, _ = fit_plain_stumps(X_proj, y, gamma, b_val_final, self.max_weight)
+ elif model == 'robust_bound':
+ loss, w_l_opt, w_r_opt, _ = fit_robust_bound_stumps(X_proj, y, gamma, b_val_final, eps, self.max_weight)
+ elif model == 'robust_exact':
+ loss, w_l_opt, w_r_opt, _ = fit_robust_exact_stumps(X_proj, y, gamma, b_val_final, eps, w_rs, bs, self.max_weight)
+ else:
+ raise ValueError('wrong model')
+ loss, w_l_opt, w_r_opt = loss[0], w_l_opt[0], w_r_opt[0]
+ # recalculation of w_l, w_r shouldn't change the min loss
+
+ if np.abs(loss - min_loss) > 1e7:
+ print('New loss: {:.5f}, min loss before: {:.5f}'.format(loss, min_loss))
+
+ best_loss = losses[id_opt]
+ return [best_loss, w_l_opt, w_r_opt, b_opt, coord]
diff --git a/train.py b/train.py
index 1958c6e..9fe3d4d 100644
--- a/train.py
+++ b/train.py
@@ -2,162 +2,323 @@
import numpy as np
import data
import time
-import ipdb as pdb
+from multiprocessing import Pool
from datetime import datetime
from utils import Logger
from stump_ensemble import StumpEnsemble
-from tree_ensemble import TreeEnsemble
+from tree_ensemble import Tree, TreeEnsemble
+from attacks import cube_attack
+from classifiers import OneVsAllClassifier
-def robust_boost(ensemble, X_train, y_train, X_valid, y_valid, X_test, y_test, n_trees,
- eps, eps_eval, model, log, model_path, metrics_path):
+def eval_metrics(model_ova, X, y, pred, cert_tw, time_cert, deltas, weak_learner, eps_eval, log, n_trials_attack, check_bounds=True):
+ """ Evaluation metrics for validation and test sets. """
+ if X.shape[0] == 0: # if no examples provided (e.g., the validation set is empty)
+ return 1.0, 1.0, 1.0, 1.0, pred, cert_tw, time_cert, deltas
+
+ # To save some computations, in particular for `find_min_yf()` which is slow for deep trees
+ for i_clsf in range(len(model_ova.models)):
+ pred[i_clsf] += model_ova.models[i_clsf].trees[-1].predict(X)
+ time_before_cert = time.time()
+ cert_tw[i_clsf] += model_ova.models[i_clsf].trees[-1].find_min_yf(X, y[i_clsf], eps_eval)
+ time_cert += time.time() - time_before_cert
+
+ yf = model_ova.fmargin(X, y, fx_vals=pred)
+ min_yf_ub, deltas = cube_attack(model_ova, X, y, eps_eval, n_trials_attack, deltas_init=deltas)
+ min_yf_lb = model_ova.fmargin_treewise(X, y, eps_eval, fx_vals=cert_tw)
+ if weak_learner == 'stump':
+ time_before_cert = time.time()
+ min_yf_exact = model_ova.fmargin_exact(X, y, eps_eval)
+ time_cert = time.time() - time_before_cert
+ else: # for trees, yf_exact just gets assigned min_yf_lb
+ min_yf_exact = min_yf_lb
+
+ is_correct = yf > 0.0
+ is_rob_ub = min_yf_lb > 0.0
+ is_rob_lb = min_yf_ub > 0.0
+ is_rob_exact = min_yf_exact > 0.0
+
+ err = 1 - is_correct.mean()
+ adv_err_lb = 1 - is_rob_lb.mean()
+ adv_err = 1 - is_rob_exact.mean()
+ adv_err_ub = 1 - is_rob_ub.mean()
+
+ if check_bounds:
+ if np.sum(is_correct < is_rob_lb) > 0:
+ log.print('Number pts violated correct < attack: {} ({})'.format(
+ np.sum(is_correct < is_rob_lb), np.where(is_correct < is_rob_lb)[0]))
+ if np.sum(is_rob_lb < is_rob_exact) > 0:
+ log.print('Number pts violated attack < exact: {} ({})'.format(
+ np.sum(is_rob_lb < is_rob_exact), np.where(is_rob_lb < is_rob_exact)[0]))
+ if np.sum(is_rob_exact < is_rob_ub) > 0:
+ log.print('Number pts violated exact < rob_ub: {} ({})'.format(
+ np.sum(is_rob_exact < is_rob_ub), np.where(is_rob_exact < is_rob_ub)[0]))
+
+ return err, adv_err_lb, adv_err, adv_err_ub, pred, cert_tw, time_cert, deltas
+
+
+def update_margin(ensemble_new, X_train, y_train, margin, gamma, model, eps_train):
+ if model in ['plain', 'da_uniform', 'at_cube']:
+ yf = y_train * ensemble_new.trees[-1].predict(X_train)
+ margin += yf
+ gamma *= np.exp(-yf)
+ elif model == 'robust_bound':
+ min_yf_lb = ensemble_new.trees[-1].find_min_yf(X_train, y_train, eps_train)
+ margin += min_yf_lb
+ gamma *= np.exp(-min_yf_lb)
+ elif model == 'robust_exact':
+ margin = ensemble_new.certify_exact(X_train, y_train, eps_train)
+ gamma = np.exp(-margin)
+ else:
+ raise ValueError('wrong model')
+ return margin, gamma
+
+
+def perturb_dataset(X_train, y_train, model_ova, model, eps_train, kantchelian_at):
+ n_iter_at = 10
+ num = X_train.shape[0]
+
+ X_train_fit = np.copy(X_train)
+ # Note: da_uniform in the current form (continuous noise) can lead to a significant slowdown since we have to
+ # check much more thresholds than usually (n instead of 256 for image datasets)
+ if model == 'da_uniform': # or (model == 'at_cube' and model_ova.models[0].trees == []):
+ deltas = np.random.uniform(-eps_train, eps_train, size=X_train.shape)
+ X_train_fit = np.clip(X_train + deltas, 0.0, 1.0) # preserve the valid data range
+ elif model == 'at_cube':
+ if kantchelian_at:
+ _, deltas_at = cube_attack(model_ova, X_train[num // 2:], y_train[:, num // 2:], eps_train,
+ n_trials=n_iter_at, independent_delta=True)
+ X_train_fit[num // 2:] = X_train[num // 2:] + deltas_at
+ else:
+ _, deltas_at = cube_attack(model_ova, X_train, y_train, eps_train, n_trials=n_iter_at,
+ independent_delta=True)
+ X_train_fit = X_train + deltas_at
+ return X_train_fit
+
+
+def train_iter_binary_clsf(ensemble_prev, X_train, y_train, gamma, margin, model, weak_learner_type, eps_train, i_clsf):
+ if model in ['da_uniform', 'at_cube']: # we recalculate gammas if the training set changes every iteration
+ margin = y_train * ensemble_prev.predict(X_train)
+ gamma = np.exp(-margin)
+ ensemble_new = ensemble_prev.copy()
+ gamma_prev, margin_prev = np.copy(gamma), np.copy(margin)
+ loss_prev = np.mean(gamma_prev)
+
+ if weak_learner_type == 'stump':
+ weak_learner = ensemble_prev.fit_stumps_over_coords(X_train, y_train, gamma, model, eps_train)
+ ensemble_new.add_weak_learner(weak_learner)
+ tree_depth, tree_n_nodes = 1, 1
+ elif weak_learner_type == 'tree':
+ # depth=1 means that we start counting from 1 (i.e. decision stumps are counted as trees of depth=1)
+ weak_learner = ensemble_prev.fit_tree(X_train, y_train, gamma, model, eps_train, depth=1)
+ # add a new weak learner to a new ensemble without modifying yet the main ensemble
+ ensemble_new.add_weak_learner(weak_learner)
+ print('Starting pruning for class {}...'.format(i_clsf))
+ ensemble_new.prune_last_tree(X_train, y_train, margin, eps_train, model)
+ print('Finished pruning for class {}...'.format(i_clsf))
+ tree_depth, tree_n_nodes = ensemble_new.trees[-1].get_depth(), ensemble_new.trees[-1].get_n_nodes()
+ else:
+ raise ValueError('wrong weak learner')
+
+ margin, gamma = update_margin(ensemble_new, X_train, y_train, margin, gamma, model, eps_train)
+
+ loss = np.mean(gamma)
+ if model not in ['da_uniform', 'at_cube'] and loss >= loss_prev: # we return the new ensemble only if it reduces the loss
+ ensemble_prev.add_weak_learner(Tree())
+ print('Added empty weak learner (loss_new={:.4} >= loss_prev={:.4})'.format(loss, loss_prev))
+ return ensemble_prev, gamma_prev, margin_prev, 0, 0
+ else: # to make `# weak learners` == `n_iter`, just add a constant stump/tree
+ return ensemble_new, gamma, margin, tree_depth, tree_n_nodes
+
+
+def robust_boost(model_ova, X_train, y_train, X_valid, y_valid, X_test, y_test, weak_learner_type, n_trees,
+ eps_train, eps_eval, n_trials_attack, cb_weights, model, log, model_path, metrics_path, debug):
+ n_clsf = len(model_ova.models)
+ parallel = True if n_clsf > 1 else False
+ # If AT is applied, then it's done as in Kantchelian et al (i.e. 50% clean + 50% adversarial) => works better
+ kantchelian_at = True
+ if model == 'at_cube' and kantchelian_at:
+ X_train = np.vstack([X_train, X_train])
+ y_train = np.hstack([y_train, y_train])
+
+ n_eval_train = min(X_train.shape[0], 5000) # number of training examples to use for evaluation (not too critical, but helps for speed-up)
time_start = time.time()
- num, dim = X_train.shape
+ n_train, n_valid, n_test = X_train.shape[0], X_valid.shape[0], X_test.shape[0]
+ time_cert_train, time_cert_valid, time_cert_test = 0.0, 0.0, 0.0
+ deltas_at, deltas_train = np.zeros_like(X_train), np.zeros_like(X_train)
+ deltas_valid, deltas_test = np.zeros_like(X_valid), np.zeros_like(X_test)
metrics = [] # all metrics are collected in this list
- gamma = np.ones(num) # note: no normalization since it is unnecessary and ambiguous for robust training
+ margin = np.zeros([n_clsf, n_train])
+ pred_train, pred_valid, pred_test = np.zeros([n_clsf, n_eval_train]), np.zeros([n_clsf, n_valid]), np.zeros([n_clsf, n_test])
+ cert_tw_train, cert_tw_valid, cert_tw_test = np.zeros([n_clsf, n_eval_train]), np.zeros([n_clsf, n_valid]), np.zeros([n_clsf, n_test])
+ gamma = np.ones([n_clsf, n_train]) # note: no normalization since it is unnecessary and ambiguous for robust training
+ if cb_weights: # class-balancing weights
+ for i_clsf in range(n_clsf):
+ gamma[i_clsf][y_train[i_clsf] == 1] *= (y_train[i_clsf] == -1).sum() / (y_train[i_clsf] == 1).sum()
+
+ X_train_fit = X_train
+ if parallel:
+ proc_pool = Pool(n_clsf)
for it in range(1, n_trees + 1):
- if ensemble.weak_learner == 'stump':
- weak_learner = ensemble.fit_stump(X_train, y_train, gamma, model, eps)
- ensemble.add_weak_learner(weak_learner)
- elif ensemble.weak_learner == 'tree':
- weak_learner = ensemble.fit_tree(X_train, y_train, gamma, model, eps, depth=1)
- ensemble.add_weak_learner(weak_learner)
- ensemble.prune_last_tree(X_train, y_train, eps, model)
- else:
- raise ValueError('wrong weak learner')
+ tree_depths, tree_ns_nodes = np.zeros(n_clsf), np.zeros(n_clsf)
+ procs = []
+
+ # # changing the dataset at every iteration doesn't seem to work very well with boosting
+ # X_train_fit = data.data_augment(X_train, dataset) if data_augm and dataset in data.datasets_img_shapes else X_train
+ X_train_fit = perturb_dataset(X_train_fit, y_train, model_ova, model, eps_train, kantchelian_at) if model in ['da_uniform', 'at_cube'] else X_train
+ for i_clsf in range(n_clsf): # start all the processes in parallel
+ ensemble = model_ova.models[i_clsf]
+ if parallel:
+ train_iter_args = (ensemble, X_train_fit, y_train[i_clsf], gamma[i_clsf], margin[i_clsf], model,
+ weak_learner_type, eps_train, i_clsf)
+ procs.append(proc_pool.apply_async(train_iter_binary_clsf, args=train_iter_args))
+ else:
+ model_ova.models[i_clsf], gamma[i_clsf], margin[i_clsf], tree_depths[i_clsf], tree_ns_nodes[i_clsf] = train_iter_binary_clsf(
+ ensemble, X_train_fit, y_train[i_clsf], gamma[i_clsf], margin[i_clsf], model, weak_learner_type, eps_train, i_clsf)
+ if parallel:
+ for i_clsf in range(n_clsf): # wait until the results are done and fetch them
+ model_ova.models[i_clsf], gamma[i_clsf], margin[i_clsf], tree_depths[i_clsf], tree_ns_nodes[i_clsf] = procs[i_clsf].get()
+
+ # Evaluations: currently designed in a way that we neeed to do it *every* iteration
+ print('starting evaluation ({:.2f}s)'.format(time.time() - time_start))
+ tree_depth, tree_n_nodes = np.mean(tree_depths), np.mean(tree_ns_nodes)
+ train_loss = np.mean(gamma) # mean over classes (axis=0) and examples (axis=1)
+ if it > 1 and train_loss > metrics[-1][7] + 1e-7:
+ log.print('The train loss increases: prev {:.5f} now {:.5f}'.format(metrics[-1][7], train_loss))
+
+ train_err, train_adv_err_lb, train_adv_err, train_adv_err_ub, pred_train, cert_tw_train, time_cert_train, deltas_train = eval_metrics(
+ model_ova, X_train[:n_eval_train], y_train[:, :n_eval_train], pred_train, cert_tw_train, time_cert_train,
+ deltas_train[:n_eval_train], weak_learner_type, eps_eval, log, n_trials_attack=0, check_bounds=False)
+ valid_err, valid_adv_err_lb, valid_adv_err, valid_adv_err_ub, pred_valid, cert_tw_valid, time_cert_valid, deltas_valid = eval_metrics(
+ model_ova, X_valid, y_valid, pred_valid, cert_tw_valid, time_cert_valid, deltas_valid, weak_learner_type, eps_eval, log, n_trials_attack)
+ test_err, test_adv_err_lb, test_adv_err, test_adv_err_ub, pred_test, cert_tw_test, time_cert_test, deltas_test = eval_metrics(
+ model_ova, X_test, y_test, pred_test, cert_tw_test, time_cert_test, deltas_test, weak_learner_type, eps_eval, log, n_trials_attack)
- Fx_y = y_train * ensemble.predict(X_train)
- min_Fx_y_treewise = ensemble.certify_treewise_bound(X_train, y_train, eps)
- min_Fx_y_exact = ensemble.certify_exact(X_train, y_train, eps)
- if model == 'plain':
- gamma = np.exp(-Fx_y)
- elif model == 'robust_bound':
- gamma = np.exp(-min_Fx_y_treewise)
- elif model == 'robust_exact': # min_d y*F(x+d) is taken jointly over the old ensemble + new weak learner
- gamma = np.exp(-min_Fx_y_exact)
+ train_str = '[train] err {:.2%} adv_err {:.2%} loss {:.5f}'.format(
+ train_err, train_adv_err, train_loss)
+ valid_str = '[valid] err {:.2%} adv_err {:.2%}'.format(valid_err, valid_adv_err)
+ str_adv_err = 'adv_err {:.2%} '.format(test_adv_err) if weak_learner_type == 'stump' else ''
+ test_str = '[test] err {:.2%} adv_err_lb {:.2%} {}adv_err_ub {:.2%}'.format(
+ test_err, test_adv_err_lb, str_adv_err, test_adv_err_ub)
+
+ if weak_learner_type == 'tree':
+ tree_info_str = '[tree] depth {:.2f} nodes {:.2f}'.format(tree_depth, tree_n_nodes)
else:
- raise ValueError('wrong model')
+ tree_info_str = ''
+ time_elapsed = time.time() - time_start
- if it % 1 == 0: # creates some overhead for plain training
- is_correct = y_test * ensemble.predict(X_test) > 0
- if ensemble.weak_learner == 'stump' or dim <= 2: # or low-dimensional dataset such as toy 2d datasets
- n_trials_sampling = 20 # for stumps it's just a sanity check since we know the exact RTE
- else:
- n_trials_sampling = 250 # but for trees it's really important since we don't know the exact RTE
- is_robust_attack = ensemble.attack_by_sampling(X_test, y_test, eps_eval, n_trials_sampling) > 0.0
- is_robust_exact = ensemble.certify_exact(X_test, y_test, eps_eval) > 0.0
- is_rob_ub = ensemble.certify_treewise_bound(X_test, y_test, eps_eval) > 0.0
- test_err = 1 - is_correct.mean()
- test_adv_err_lb = 1 - is_robust_attack.mean()
- test_adv_err = 1 - is_robust_exact.mean()
- test_adv_err_ub = 1 - is_rob_ub.mean()
-
- valid_err = 1 - (y_valid * ensemble.predict(X_valid) > 0).mean()
- valid_adv_err_lb = 1 - (
- ensemble.attack_by_sampling(X_valid, y_valid, eps_eval, n_trials_sampling) > 0.0).mean()
- valid_adv_err = 1 - (ensemble.certify_exact(X_valid, y_valid, eps_eval) > 0.0).mean()
- valid_adv_err_ub = 1 - (ensemble.certify_treewise_bound(X_valid, y_valid, eps_eval) > 0.0).mean()
-
- train_err = np.mean(Fx_y <= 0) # important to have <= since when lr->0, all preds = 0
- train_adv_err = np.mean(min_Fx_y_exact <= 0)
- train_loss = np.mean(gamma)
-
- time_elapsed = time.time() - time_start
-
- # Various sanity checks
- # print('max diff yf', np.max(np.abs(ensemble.certify_exact(X_test, y_test, args.eps_eval) -
- # ensemble.certify_treewise_bound(X_test, y_test, args.eps_eval))))
- if np.sum(is_correct < is_robust_attack) > 0:
- log.print('Number pts violated correct < attack: {} ({})'.format(
- np.sum(is_correct < is_robust_attack), np.where(is_correct < is_robust_attack)[0]))
- if np.sum(is_robust_attack < is_robust_exact) > 0:
- log.print('Number pts violated attack < exact: {} ({})'.format(
- np.sum(is_robust_attack < is_robust_exact), np.where(is_robust_attack < is_robust_exact)[0]))
- if np.sum(is_robust_exact < is_rob_ub) > 0:
- log.print('Number pts violated exact < rob_ub: {} ({})'.format(
- np.sum(is_robust_exact < is_rob_ub), np.where(is_robust_exact < is_rob_ub)[0]))
- if it > 1 and train_loss > metrics[-1][7] + 1e-7:
- log.print('The train loss increases: prev {:.5f} now {:.5f}'.format(metrics[-1][7], train_loss))
- # log.print('New {}'.format(ensemble.trees[-1]))
- # coord_lengths = [(coord, len(ensemble.coords_trees[coord])) for coord in ensemble.coords_trees]
- # coord_lengths = sorted(coord_lengths, key=lambda t: t[1], reverse=True)
- # coord_most_freq = coord_lengths[0][0]
- # print('Most frequent coord {} ({} times) {}'.format(
- # coord_most_freq, len(most_freq_trees), most_freq_trees))
- str_adv_err = 'adv_err {:.2%} '.format(test_adv_err) if ensemble.weak_learner == 'stump' else ''
- test_str = 'iter: {} [test] err {:.2%} adv_err_lb {:.2%} {}adv_err_ub {:.2%}'.format(
- it, test_err, test_adv_err_lb, str_adv_err, test_adv_err_ub)
- valid_str = '[valid] err {:.2%} adv_err {:.2%}'.format(valid_err, valid_adv_err)
- train_str = '[train] err {:.2%} adv_err {:.2%} loss {:.5f}'.format(
- train_err, train_adv_err, train_loss)
- log.print('{} | {} | {} ({:.2f} sec)'.format(test_str, valid_str, train_str, time_elapsed))
-
- metrics.append([it, test_err, test_adv_err_lb, test_adv_err, test_adv_err_ub, train_err, train_adv_err,
- train_loss, valid_err, valid_adv_err_lb, valid_adv_err, valid_adv_err_ub, time_elapsed])
-
- if (it % 5 == 0 or it == n_trees) and metrics_path != '':
- ensemble.save(model_path)
+ log.print('iter: {} {} | {} | {} | {} ({:.3f}s, {:.3f}s)'.format(it, test_str, valid_str, train_str, tree_info_str, time_elapsed, time_cert_test))
+ metrics.append([it, test_err, test_adv_err_lb, test_adv_err, test_adv_err_ub, train_err, train_adv_err,
+ train_loss, valid_err, valid_adv_err_lb, valid_adv_err, valid_adv_err_ub, time_elapsed, time_cert_test,
+ tree_depth, tree_n_nodes])
+
+ if not debug and (it % 5 == 0 or it == n_trees) and metrics_path != '':
+ model_ova.save(model_path)
np.savetxt(metrics_path, metrics)
log.print('(done in {:.2f} min)'.format((time.time() - time_start) / 60))
+ if not debug:
+ log.print('Model path: {}.npy'.format(model_path))
+ log.print('Metrics path: {}'.format(metrics_path))
-if __name__ == '__main__':
+def main():
np.random.seed(1)
np.set_printoptions(precision=10)
- # Example: python train.py --dataset=fmnist_sandal_sneaker --weak_learner=stump --model=robust_exact
parser = argparse.ArgumentParser(description='Define hyperparameters.')
- parser.add_argument('--dataset', type=str, default='mnist_2_6',
- help='breast_cancer, diabetes, cod_rna, mnist_2_6, fmnist_sandal_sneaker, gts_30_70, '
- 'gts_100_roadworks')
- parser.add_argument('--model', type=str, default='robust_exact', help='plain, robust_exact or robust_bound.')
- parser.add_argument('--weak_learner', type=str, default='stump', help='stump or tree')
+ parser.add_argument('--dataset', type=str, default='mnist',
+ help='breast_cancer, diabetes, cod_rna, mnist_1_5, mnist_2_6, fmnist_sandal_sneaker, gts_30_70,'
+ ' gts_100_roadworks')
+ parser.add_argument('--model', type=str, default='plain',
+ help='plain, da_uniform, at_cube, robust_exact, robust_bound.')
+ parser.add_argument('--weak_learner', type=str, default='tree', help='stump or tree')
+ parser.add_argument('--max_depth', type=int, default=4, help='Depth of trees (only used when weak_learner==tree).')
+ parser.add_argument('--max_weight', type=float, default=1.0, help='The maximum leaf weight.')
+ parser.add_argument('--n_bins', type=int, default=-1, help='By default we check all thresholds.')
+ parser.add_argument('--lr', type=float, default=0.2, help='Shrinkage parameter (aka learning rate).')
+ parser.add_argument('--eps', type=float, default=-1, help='Linf epsilon. -1 means to use the default epsilons.')
parser.add_argument('--n_train', type=int, default=-1, help='Number of training points to take.')
+ parser.add_argument('--debug', action='store_true', help='Debugging mode: not many samples for the attack.')
args = parser.parse_args()
- # always 1.0 in all experiments
- lr = 1.0
- if args.weak_learner == 'stump':
- n_iter = 500
- n_trials_coord = 10
+ if args.weak_learner == 'stump' or (args.weak_learner == 'tree' and args.max_depth == 1):
+ n_iter = 300
elif args.weak_learner == 'tree':
- n_iter = 50
- n_trials_coord = 100
+ depth_iters_map = {2: 300, 4: 150, 6: 100, 8: 75}
+ if args.max_depth in depth_iters_map:
+ n_iter = depth_iters_map[args.max_depth]
+ else:
+ n_iter = 300
else:
raise ValueError('wrong weak learner')
- min_samples_split = 10 # to prevent extreme overfitting to a few points
+ # max value of the leaf weights; has an important regularization effect similar to the learning rate
+ max_weight = args.max_weight
+ # to prevent extreme overfitting to a few points
+ min_samples_split = 10 if args.dataset not in ['mnist', 'fmnist', 'cifar10'] else 200
min_samples_leaf = 5
- max_depth = 4
+ n_trials_attack = 20 if args.dataset not in ['mnist', 'fmnist', 'cifar10'] else 10
+ n_trials_attack = n_trials_attack if args.weak_learner == 'tree' else 1 # 1 iter is more of a sanity check
+ frac_valid = 0.2 if args.dataset not in ['mnist', 'fmnist', 'cifar10'] else 0.0
+ extend_dataset = True if args.dataset in ['mnist', 'fmnist', 'cifar10'] else False
X_train, y_train, X_test, y_test, eps_dataset = data.all_datasets_dict[args.dataset]()
- X_train, y_train, X_valid, y_valid = data.split_train_validation(X_train, y_train, shuffle=True)
+ X_train, X_test = data.convert_to_float32(X_train), data.convert_to_float32(X_test)
+ X_train, y_train, X_valid, y_valid = data.split_train_validation(X_train, y_train, frac_valid, shuffle=True)
if args.n_train != -1:
X_train, y_train = X_train[:args.n_train], y_train[:args.n_train]
- eps_train = eps_dataset if args.model != 'plain' else 0.0 # not strictly needed, but just for consistency
+
+ n_cls = int(y_train.max()) + 1
+ cb_weights = True if n_cls > 2 else False # helps to convergence speed and URTE (especially, on MNIST)
+ y_train, y_valid, y_test = data.transform_labels_one_vs_all(y_train, y_valid, y_test)
+
+ if extend_dataset:
+ X_train = data.extend_dataset(X_train, args.dataset)
+ y_train = np.tile(y_train, [1, X_train.shape[0] // y_train.shape[1]])
+
+ n_trials_coord = X_train.shape[1] # we check all coordinates for every split
+
+ if args.eps == -1: # then use the default one if not specified from cmd
+ eps_train = eps_eval = eps_dataset if args.model != 'plain' else 0.0 # not strictly needed
+ else:
+ eps_train = eps_eval = args.eps
cur_timestamp = str(datetime.now())[:-7]
- hps_str_full = 'dataset={} weak_learner={} model={} n_train={} n_trials_coord={} eps={:.3f} min_samples_split={} min_samples_leaf={} ' \
- 'max_depth={} lr={}'.format(args.dataset, args.weak_learner, args.model, args.n_train, n_trials_coord,
- eps_dataset, min_samples_split, min_samples_leaf, max_depth, lr)
- hps_str_short = 'dataset={} weak_learner={} model={} n_train={} n_trials_coord={} eps={:.3f} max_depth={} lr={}'.format(
- args.dataset, args.weak_learner, args.model, args.n_train, n_trials_coord, eps_dataset, max_depth, lr)
+ hps_str_full = 'dataset={} weak_learner={} model={} n_train={} n_iter={} n_trials_coord={} eps={:.3f} min_samples_split={} ' \
+ 'min_samples_leaf={} max_depth={} max_weight={} lr={} n_trials_attack={} cb_weights={} max_weight={} n_bins={} ' \
+ 'expand_train_set={}'.format(
+ args.dataset, args.weak_learner, args.model, args.n_train, n_iter, n_trials_coord, eps_train, min_samples_split,
+ min_samples_leaf, args.max_depth, max_weight, args.lr, n_trials_attack, cb_weights, max_weight, args.n_bins, extend_dataset)
+ hps_str_short = 'dataset={} weak_learner={} model={} n_train={} n_trials_coord={} eps={:.3f} max_depth={} max_weight={} lr={}'.format(
+ args.dataset, args.weak_learner, args.model, args.n_train, n_trials_coord, eps_train, args.max_depth, max_weight, args.lr)
- log_path = 'exps/{} {}.log'.format(cur_timestamp, hps_str_short)
- model_path = 'exps/{} {}.model'.format(cur_timestamp, hps_str_short)
- metrics_path = 'exps/{} {}.metrics'.format(cur_timestamp, hps_str_short)
+ exp_folder = 'exps_test'
+ log_path = '{}/{} {}.log'.format(exp_folder, cur_timestamp, hps_str_short)
+ model_path = '{}/{} {}.model'.format(exp_folder, cur_timestamp, hps_str_short)
+ metrics_path = '{}/{} {}.metrics'.format(exp_folder, cur_timestamp, hps_str_short)
log = Logger(log_path)
log.print('Boosting started: {} {}'.format(cur_timestamp, hps_str_full))
- if args.weak_learner == 'stump':
- ensemble = StumpEnsemble(args.weak_learner, n_trials_coord, lr)
- elif args.weak_learner == 'tree':
- ensemble = TreeEnsemble(args.weak_learner, n_trials_coord, lr, min_samples_split, min_samples_leaf, max_depth)
- else:
- raise ValueError('wrong weak learner')
+ ensembles = []
+ n_classifiers = n_cls if n_cls > 2 else 1
+ for i_clsf in range(n_classifiers):
+ if args.weak_learner == 'stump':
+ ensemble = StumpEnsemble(args.weak_learner, n_trials_coord, args.lr, i_clsf, args.n_bins, max_weight)
+ elif args.weak_learner == 'tree':
+ ensemble = TreeEnsemble(args.weak_learner, n_trials_coord, args.lr, min_samples_split, min_samples_leaf, i_clsf,
+ args.max_depth, gamma_hp=0.0, n_bins=args.n_bins, max_weight=max_weight)
+ else:
+ raise ValueError('wrong weak learner')
+ ensembles.append(ensemble)
+ model_ova = OneVsAllClassifier(ensembles)
- robust_boost(ensemble, X_train, y_train, X_valid, y_valid, X_test, y_test, n_iter, eps_train, eps_dataset, args.model, log, model_path, metrics_path)
+ robust_boost(model_ova, X_train, y_train, X_valid, y_valid, X_test, y_test, args.weak_learner, n_iter, eps_train,
+ eps_eval, n_trials_attack, cb_weights, args.model, log, model_path, metrics_path,
+ args.debug)
+
+if __name__ == '__main__':
+ main()
diff --git a/tree_ensemble.py b/tree_ensemble.py
index 789f518..fdfce4a 100644
--- a/tree_ensemble.py
+++ b/tree_ensemble.py
@@ -1,36 +1,94 @@
import numpy as np
-import ipdb as pdb
import copy
-from numba import jit, prange
from collections import OrderedDict
-from robust_boosting import exp_loss_robust, coord_descent_exp_loss, basic_case_two_intervals, dtype
-from utils import minimum, get_contiguous_indices
+from numba import njit, prange
+from robust_boosting import exp_loss_robust, dtype, fit_plain_stumps, fit_robust_bound_stumps
+from utils import get_contiguous_indices, get_n_proc
+from concurrent.futures import ThreadPoolExecutor
+
+
+@njit(nogil=True)
+def find_min_yf_point(nodes, x, y, eps):
+ # Every node is: (self.id, id_left, id_right, self.w_l, self.w_r, self.b, self.coord, self.loss)
+ node_ids_to_explore = [0] # root node id
+ min_val = np.inf
+ while len(node_ids_to_explore) > 0:
+ node = nodes[node_ids_to_explore.pop()]
+ id_left, id_right, w_l, w_r, b, coord = int(node[1]), int(node[2]), node[3], node[4], node[5], int(node[6])
+ if x[coord] <= b + eps:
+ if id_left != -1:
+ node_ids_to_explore.append(int(nodes[id_left][0]))
+ else:
+ min_val = min(min_val, y * w_l)
+ if x[coord] >= b - eps:
+ if id_right != -1:
+ node_ids_to_explore.append(int(nodes[id_right][0]))
+ else:
+ min_val = min(min_val, y * (w_l + w_r))
+ return min_val
+
+
+@njit(parallel=True, nogil=True)
+def find_min_yf_tree_par(nodes, X, y, eps):
+ # == works as expected only if all numbers are in float32; float32 is the preferred choice due to less memory
+ eps = np.float32(eps)
+ f = np.zeros(X.shape[0])
+ for i in prange(X.shape[0]):
+ f[i] = find_min_yf_point(nodes, X[i], y[i], eps)
+ return f
+
+
+@njit(nogil=True)
+def predict_point(nodes, x):
+ # Every node is: (self.id, id_left, id_right, self.w_l, self.w_r, self.b, self.coord, self.loss)
+ node = nodes[0] # take the root node
+ while True:
+ id_left, id_right, w_l, w_r, b, coord = int(node[1]), int(node[2]), node[3], node[4], node[5], int(node[6])
+ if x[coord] < b:
+ if id_left != -1:
+ node = nodes[id_left]
+ else:
+ return w_l
+ else:
+ if id_right != -1:
+ node = nodes[id_right]
+ else:
+ return w_l + w_r
+
+
+@njit(parallel=True, nogil=True)
+def predict_tree_par(nodes, X):
+ f = np.zeros(X.shape[0])
+ for i in prange(X.shape[0]):
+ f[i] = predict_point(nodes, X[i])
+ return f
class Tree:
- def __init__(self, id_, left, right, w_l, w_r, b, coord, loss):
+ def __init__(self, id_=-1, left=None, right=None, w_l=0.0, w_r=0.0, b=0.0, coord=0, loss=0.0):
# (left == None and right == None) => leaf
# else => intermediate node
self.id, self.left, self.right = id_, left, right
# Note: w_l/w_r can have some values, but if left AND right is not None, then w_l/w_r are just ignored.
# However, we still may need them because of pruning - if a leaf node was pruned, then its parent kicks in.
self.w_l, self.w_r, self.b, self.coord, self.loss = w_l, w_r, b, coord, loss
+ self.node_list = []
def __repr__(self):
lval, rval, threshold = self.w_l, self.w_r + self.w_l, self.b
if self.left is None and self.right is None:
- return 'Tree: if x[{}] < {:.4f}: {:.4f} else {:.4f} '.format(self.coord, threshold, lval, rval)
+ return 'if x[{}] < {:.4f}: {:.4f} else {:.4f} '.format(self.coord, threshold, lval, rval)
if self.left is None:
- return 'Tree: if x[{}] < {:.4f}: {:.4f} '.format(self.coord, threshold, lval) + self.right.__repr__()
+ return 'if x[{}] < {:.4f}: {:.4f} '.format(self.coord, threshold, lval) + self.right.__repr__()
if self.right is None:
- return self.left.__repr__() + 'Tree: if x[{}] >= {:.4f}: {:.4f} '.format(self.coord, threshold, rval)
+ return self.left.__repr__() + 'if x[{}] >= {:.4f}: {:.4f} '.format(self.coord, threshold, rval)
s = ''
if self.left is not None:
- s += self.left.__repr__()
+ s += 'if x[{}] < {:.4f} and '.format(self.coord, threshold) + self.left.__repr__()
if self.right is not None:
- s += self.right.__repr__()
+ s += 'if x[{}] >= {:.4f} and '.format(self.coord, threshold) + self.right.__repr__()
return s
@@ -53,22 +111,103 @@ def to_list(self):
curr_node = (self.id, id_left, id_right, self.w_l, self.w_r, self.b, self.coord, self.loss)
return [curr_node] + tree_lst_left + tree_lst_right # concatenate both lists
+ def to_array_contiguous(self):
+ """ Make ids correspond to node positions in the array. """
+ nodes = np.array(self.to_list())
+ max_node_id = int(nodes[:, 0].max())
+ nodes_new = np.zeros([max_node_id+1, len(nodes[0])])
+ for node in nodes:
+ nodes_new[int(node[0])] = node
+ return nodes_new
+
def predict(self, X):
- f = np.zeros(X.shape[0])
+ parallel = True
+ if parallel and len(self.node_list) > 0: # 2nd condition is needed to prevent an error in predict_tree_par()
+ return predict_tree_par(self.node_list, X)
+ else:
+ return self.predict_native(X)
+
+ def predict_native(self, X):
+ def predict_recursive(curr_tree, idx):
+ """ To avoid copying the whole matrix X many times, we use global indices `idx` to directly use
+ the single matrix X as a closure variable. The only overhead is that the threshold comparison is done
+ for *all* examples.
+
+ Note: the parallel version using numba should be preferred.
+ """
+ # route some points to the left and some to the right nodes
+ idx_left_superset = X[:, curr_tree.coord] < curr_tree.b
+ idx_left = idx * idx_left_superset
+ idx_right = idx * ~idx_left_superset
+
+ if curr_tree.left is None:
+ f[idx_left] = curr_tree.w_l
+ else:
+ predict_recursive(curr_tree.left, idx_left)
+
+ if curr_tree.right is None:
+ f[idx_right] = curr_tree.w_l + curr_tree.w_r
+ else:
+ predict_recursive(curr_tree.right, idx_right)
+
+ idx = np.full(X.shape[0], True)
+ f = np.zeros(len(idx))
+ predict_recursive(self, idx) # modifies the closure variable `f` in-place
+ return f
+
+ def find_min_yf(self, X, y, eps):
+ parallel = True # really crucial; 1-2x orders of magnitude speed-up over the native python version
+ if parallel and len(self.node_list) > 0: # 2nd condition is needed to prevent an error in predict_tree_par()
+ return find_min_yf_tree_par(self.node_list, X, y, eps)
+ else:
+ return self.find_min_yf_native(X, y, eps)
+
+ def find_min_yf_native(self, X, y, eps):
+ split_lbs, split_ubs = X[:, self.coord] - eps, X[:, self.coord] + eps
+ lval, rval = self.w_l, self.w_r + self.w_l
+
+ guaranteed_left = split_ubs < self.b
+ guaranteed_right = split_lbs > self.b
+ uncertain = (split_lbs <= self.b) * (split_ubs >= self.b)
- # route some points to the left and some to the right nodes
- idx_left = X[:, self.coord] < self.b
if self.left is None:
- f[idx_left] = self.w_l
+ left_min_yf = y[guaranteed_left] * lval
+ uleft_min_yf = y[uncertain] * lval
else:
- f[idx_left] = self.left.predict(X[idx_left])
+ left_min_yf = self.left.find_min_yf(X[guaranteed_left], y[guaranteed_left], eps)
+ uleft_min_yf = self.left.find_min_yf(X[uncertain], y[uncertain], eps)
- idx_right = X[:, self.coord] >= self.b
if self.right is None:
- f[idx_right] = self.w_l + self.w_r
+ right_min_yf = y[guaranteed_right] * rval
+ uright_min_yf = y[uncertain] * rval
else:
- f[idx_right] = self.right.predict(X[idx_right])
- return f
+ right_min_yf = self.right.find_min_yf(X[guaranteed_right], y[guaranteed_right], eps)
+ uright_min_yf = self.right.find_min_yf(X[uncertain], y[uncertain], eps)
+
+ min_yf = np.zeros(X.shape[0])
+ min_yf[guaranteed_left] = left_min_yf
+ min_yf[guaranteed_right] = right_min_yf
+ min_yf[uncertain] = np.minimum(uleft_min_yf, uright_min_yf)
+
+ return min_yf
+
+ def get_n_nodes(self):
+ left_n, right_n = 0, 0
+ if self.left is not None:
+ left_n = self.left.get_n_nodes()
+ if self.right is not None:
+ right_n = self.right.get_n_nodes()
+ subtree_n = left_n + right_n # n nodes of the subtree rooted at the current node
+ return subtree_n + 1 # which means that a decision stump is a tree of depth=1
+
+ def get_depth(self):
+ left_depth, right_depth = 0, 0
+ if self.left is not None:
+ left_depth = self.left.get_depth()
+ if self.right is not None:
+ right_depth = self.right.get_depth()
+ subtree_depth = max(left_depth, right_depth) # depth of the subtree rooted at the current node
+ return subtree_depth + 1 # which means that a decision stump is a tree of depth=1
def get_some_leaf(self):
if self.left is None and self.right is None:
@@ -78,38 +217,27 @@ def get_some_leaf(self):
if self.right is not None:
return self.right.get_some_leaf()
- def get_some_leaf_except(self, checked_leaves):
- if self.left is None and self.right is None:
- if self not in checked_leaves:
- return self
- if self.left is not None:
- some_left_leaf = self.left.get_some_leaf_except(checked_leaves)
- if some_left_leaf not in checked_leaves and some_left_leaf is not None:
- return some_left_leaf
- if self.right is not None:
- some_right_leaf = self.right.get_some_leaf_except(checked_leaves)
- if some_right_leaf not in checked_leaves and some_right_leaf is not None:
- return some_right_leaf
- # None should be returned only in the end after the whole tree is checked
- return None
-
def rm_leaf(self, leaf_to_rm):
if self.left == leaf_to_rm:
self.left = None
if self.right == leaf_to_rm:
self.right = None
- left_first = np.random.choice([False, True])
- if left_first:
- if self.left is not None:
- self.left.rm_leaf(leaf_to_rm)
- if self.right is not None:
- self.right.rm_leaf(leaf_to_rm)
- else:
- if self.right is not None:
- self.right.rm_leaf(leaf_to_rm)
- if self.left is not None:
- self.left.rm_leaf(leaf_to_rm)
+ # Left-first search
+ if self.left is not None:
+ self.left.rm_leaf(leaf_to_rm)
+ if self.right is not None:
+ self.right.rm_leaf(leaf_to_rm)
+
+ def rm_bottom_layer(self, depth, max_depth):
+ if depth + 1 == max_depth:
+ # print('rm a node from depth {} (max_depth={})'.format(depth+1, max_depth))
+ self.left = None
+ self.right = None
+ if self.left is not None:
+ self.left.rm_bottom_layer(depth+1, max_depth)
+ if self.right is not None:
+ self.right.rm_bottom_layer(depth+1, max_depth)
def get_empty_leaf(self):
if self.left is not None:
@@ -119,63 +247,77 @@ def get_empty_leaf(self):
if self.left is None and self.right is None and self.w_l == 0.0 and self.w_r == 0.0:
return self
- def find_min_yf(self, X, y, eps):
- split_lbs, split_ubs = X[:, self.coord] - eps, X[:, self.coord] + eps
- lval, rval = self.w_l, self.w_r + self.w_l
-
- guaranteed_left = split_ubs < self.b
- guaranteed_right = split_lbs > self.b
- uncertain = (split_lbs <= self.b) * (split_ubs >= self.b)
+ def get_json_dict(self, counter_terminal_nodes):
+ """
+ counter_terminal_nodes: needed to assign nodeid's to terminal nodes (negative to prevent collisions)
+ """
+ precision = 5
+ children_list = []
if self.left is None:
- left_min_yf = y[guaranteed_left]*lval
- uleft_min_yf = y[uncertain]*lval
+ id_left = counter_terminal_nodes
+ counter_terminal_nodes -= 1
+ children_list.append({'nodeid': id_left, 'leaf': round(self.w_l, precision)}) # end node
else:
- left_min_yf = self.left.find_min_yf(X[guaranteed_left], y[guaranteed_left], eps)
- uleft_min_yf = self.left.find_min_yf(X[uncertain], y[uncertain], eps)
+ id_left = self.left.id
+ children, counter_terminal_nodes = self.left.get_json_dict(counter_terminal_nodes)
+ children_list.append(children)
if self.right is None:
- right_min_yf = y[guaranteed_right]*rval
- uright_min_yf = y[uncertain]*rval
+ id_right = counter_terminal_nodes
+ counter_terminal_nodes -= 1
+ children_list.append({'nodeid': id_right, 'leaf': round(self.w_l + self.w_r, precision)}) # end node
else:
- right_min_yf = self.right.find_min_yf(X[guaranteed_right], y[guaranteed_right], eps)
- uright_min_yf = self.right.find_min_yf(X[uncertain], y[uncertain], eps)
+ id_right = self.right.id
+ children, counter_terminal_nodes = self.right.get_json_dict(counter_terminal_nodes)
+ children_list.append(children)
- min_yf = np.zeros(X.shape[0])
- min_yf[guaranteed_left] = left_min_yf
- min_yf[guaranteed_right] = right_min_yf
- min_yf[uncertain] = np.minimum(uleft_min_yf, uright_min_yf)
+ tree_dict = {'nodeid': self.id, 'split': 'f' + str(self.coord), 'split_condition': round(self.b, precision),
+ 'yes': id_left, 'no': id_right, 'children': children_list}
- return min_yf
+ return tree_dict, counter_terminal_nodes
class TreeEnsemble:
- def __init__(self, weak_learner, n_trials_coord, lr, min_samples_split, min_samples_leaf, max_depth):
+ def __init__(self, weak_learner, n_trials_coord, lr, min_samples_split, min_samples_leaf, idx_clsf, max_depth,
+ gamma_hp=0.0, n_bins=-1, max_weight=1.0):
self.weak_learner = weak_learner
self.n_trials_coord = n_trials_coord
self.lr = lr
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.max_depth = max_depth
+ self.gamma_hp = gamma_hp # depth pruning coefficient
+ self.n_bins = n_bins
+ self.idx_clsf = idx_clsf # class index that this ensemble correspond to in the one-vs-all scheme
+ self.max_weight = max_weight
self.trees = []
- self.max_tree_node_id = 0
self.coords_trees = OrderedDict()
+ self.ens_nodes_array = []
+ self.max_tree_node_id = 0
def __repr__(self):
sorted_trees = sorted(self.trees, key=lambda tree: tree.coord)
return '\n'.join([str(t) for t in sorted_trees])
- def load(self, path, iteration=-1):
- if iteration == -1: # take all
- ensemble_arr = np.load(path)
- else: # take up to some iteration
- ensemble_arr = np.load(path)[:iteration+1]
- for i in range(ensemble_arr.shape[0]):
+ def copy(self):
+ ensemble_new = TreeEnsemble(self.weak_learner, self.n_trials_coord, self.lr, self.min_samples_split,
+ self.min_samples_leaf, self.idx_clsf, self.max_depth, self.gamma_hp, self.n_bins,
+ self.max_weight)
+ for tree in self.trees:
+ ensemble_new.add_weak_learner(tree, apply_lr=False)
+ return ensemble_new
+
+ def load(self, ensemble_dict, iteration):
+ tree_indices = np.sort(list(ensemble_dict.keys())) # just a list of contiguous indices [0, 1, ..., n_trees]
+ if iteration != -1: # take only the tree ensemble up to a certain iteration
+ tree_indices = tree_indices[tree_indices <= iteration]
+ for i_tree in tree_indices:
# first create all tree nodes and maintain a dictionary with all nodes (for easier look-up later on)
node_dict = {}
- for i_node in range(len(ensemble_arr[i])):
- if not np.all(ensemble_arr[i][i_node] == 0):
- id_, id_left, id_right, w_l, w_r, b, coord, loss = ensemble_arr[i, i_node]
+ for i_node in range(len(ensemble_dict[i_tree])):
+ if not np.all(ensemble_dict[i_tree][i_node] == 0):
+ id_, id_left, id_right, w_l, w_r, b, coord, loss = ensemble_dict[i_tree][i_node]
id_, id_left, id_right, coord = int(id_), int(id_left), int(id_right), int(coord)
# create a node, but without any connections to its children
tree = Tree(id_, None, None, w_l, w_r, b, coord, loss)
@@ -188,160 +330,87 @@ def load(self, path, iteration=-1):
if id_right != -1:
tree.right = node_dict[id_right][0]
# add the root as the next element of the ensemble
- root = node_dict[ensemble_arr[i][0][0]][0]
- self.add_weak_learner(root, apply_lr=False)
- print('Ensemble of {} learners restored: {}'.format(ensemble_arr.shape[0], path))
-
- def save(self, path):
- if path != '':
- # note: every tree has potentially a different number of nodes.
- ensemble_arr = np.zeros([len(self.trees), 2**self.max_depth, 8])
- for i, tree in enumerate(self.trees):
- tree_list = tree.to_list()
- ensemble_arr[i, :len(tree_list), :] = tree_list # all tree nodes are in this list
- np.save(path, ensemble_arr, allow_pickle=False)
+ if ensemble_dict[i_tree] != []:
+ root = node_dict[ensemble_dict[i_tree][0][0]][0]
+ self.add_weak_learner(root, apply_lr=False)
+ root.node_list = root.to_array_contiguous()
+
+ def export_model(self):
+ # note: every tree has potentially a different number of nodes, thus we save it in a dictionary
+ ensemble_dict = {}
+ for i, tree in enumerate(self.trees):
+ ensemble_dict[i] = np.array(tree.node_list) # all tree nodes are in this array
+ return ensemble_dict
def add_weak_learner(self, tree, apply_lr=True):
def adjust_lr(tree, lr):
- """ Recursively goes over all node values and scales the weights by a the learning rate. """
- tree.w_l, tree.w_r = tree.w_l*lr, tree.w_r*lr
+ """ Recursively goes over all node values and scales the weights by the learning rate. """
+ tree.w_l, tree.w_r = tree.w_l * lr, tree.w_r * lr
+ if tree.node_list != []: # i.e. if root
+ for node_tuple in tree.node_list:
+ node_tuple[3], node_tuple[4] = node_tuple[3] * lr, node_tuple[4] * lr
if tree.left is not None:
adjust_lr(tree.left, lr)
if tree.right is not None:
adjust_lr(tree.right, lr)
return tree
+ if tree is None: # can happen if no splits whatsoever were made
+ tree = Tree()
if apply_lr:
tree = adjust_lr(tree, self.lr)
self.trees.append(tree)
+
if tree.coord not in self.coords_trees:
self.coords_trees[tree.coord] = []
self.coords_trees[tree.coord].append(tree)
def predict(self, X):
- Fx = np.zeros(X.shape[0])
+ f = np.zeros(X.shape[0])
for tree in self.trees:
- Fx += tree.predict(X)
- return Fx
+ f += tree.predict(X)
+ return f
- def certify_treewise_bound(self, X, y, eps):
+ def certify_treewise(self, X, y, eps):
lb_ensemble = np.zeros(X.shape[0])
-
- # The naive tree-wise bounded on the merged trees
for tree in self.trees:
lb_ensemble += tree.find_min_yf(X, y, eps)
return lb_ensemble
- @staticmethod
- @jit(nopython=True)
- def find_min_coord_diff(X_proj, y, thresholds, w_r_values, eps):
- # parallel=True doesn't help here; not sure if jit here is helpful at all. maybe if there are many thresholds
- num = X_proj.shape[0]
- idx = np.argsort(thresholds)
- sorted_thresholds = thresholds[idx]
- sorted_w_r = w_r_values[idx]
- f_x_min_coord_diff, f_x_cumsum = np.zeros(num), np.zeros(num)
- for i_t in range(len(sorted_thresholds)):
- # consider the threshold if it belongs to (x-eps, x+eps] (x-eps is excluded since already evaluated)
- idx_x_eps_close_to_threshold = (X_proj - eps < sorted_thresholds[i_t]) * (
- sorted_thresholds[i_t] <= X_proj + eps)
- f_diff = y * sorted_w_r[i_t] * idx_x_eps_close_to_threshold
- f_x_cumsum += f_diff
- f_x_min_coord_diff = minimum(f_x_cumsum, f_x_min_coord_diff)
- return f_x_min_coord_diff
-
- def attack_by_sampling(self, X, y, eps, n_trials):
- """ A simple attack just by sampling in the Linf-box around the points. More of a sanity check. """
- num, dim = X.shape
- f_x_vals = np.zeros((num, n_trials))
- # Note: for efficiency, we sample the same random direction for all points, but this does influence matter
- deltas = np.random.uniform(-eps, eps, size=(dim, n_trials))
- for i in range(n_trials - 1):
- # let's keep them as real images, although not strictly needed
- perturbed_pts = np.clip(X + deltas[:, i], 0.0, 1.0)
- f_x_vals[:, i] = self.predict(perturbed_pts)
- # maybe in some corner cases, the predictions at the original point is more worst-case than the sampled points
- f_x_vals[:, n_trials - 1] = self.predict(X)
-
- f_x_min = np.min(y[:, None] * f_x_vals, axis=1)
- return f_x_min
-
- def certify_exact(self, X, y, eps, coords_to_ignore=()):
+ def prune_last_tree(self, X, y, margin_prev, eps, model):
"""
- Note: this is clearly not exact certification.
- We do it just to be compatible with robust_boost() function that requires certify_exact() to output some
- meaningful numbers that do not violate the other bounds on the exact minimum of the adversarial opt problem.
- """
- return self.certify_treewise_bound(X, y, eps)
-
- def fit_tree(self, X, y, gamma, model, eps, depth):
- """Recursive procedure for building a single tree.
+ Recursive procedure for building a single tree.
Note: this function belongs to the tree, and not to the ensemble because the ensemble doesn't matter anymore
once the vector gamma is fixed.
"""
- if depth > self.max_depth:
- return None # so that tree.left or tree.right is set to None
- if X.shape[0] < self.min_samples_split:
- return None # so that tree.left or tree.right is set to None
- if (y == -1).all() or (y == 1).all(): # if already pure, don't branch anymore
- return None
-
- # create a new tree that will become a node (if further splits are needed)
- # or a leaf (if max_depth or min_samples_leaf is reached)
- w_l, w_r, b, coord, loss = self.fit_stump(X, y, gamma, model, eps)
- tree = Tree(self.max_tree_node_id, None, None, w_l, w_r, b, coord, loss)
- self.max_tree_node_id += 1
-
- if tree.coord == -1: # no further splits because min_samples_leaf is reached
- return None
-
- if model == 'plain':
- idx_left = (X[:, tree.coord] < tree.b)
- idx_right = (X[:, tree.coord] >= tree.b)
- elif model == 'robust_bound':
- idx_left = (X[:, tree.coord] < tree.b + eps)
- idx_right = (X[:, tree.coord] >= tree.b - eps)
- else:
- raise ValueError('wrong model type')
-
- # print("left subtree: {:d} examples".format(np.sum(idx_left)))
- tree.left = self.fit_tree(X[idx_left, :], y[idx_left], gamma[idx_left], model, eps, depth+1)
-
- # print("right subtree: {:d} examples".format(np.sum(idx_right)))
- tree.right = self.fit_tree(X[idx_right, :], y[idx_right], gamma[idx_right], model, eps, depth+1)
-
- return tree
-
- def prune_last_tree(self, X, y, eps, model):
- """Recursive procedure for building a single tree.
-
- Note: this function belongs to the tree, and not to the ensemble because the ensemble doesn't matter anymore
- once the vector gamma is fixed.
- """
- # The naive tree-wise bounded on trees
- lb_ensemble = np.zeros(X.shape[0])
- for tree in self.trees[:-1]:
- lb_ensemble += tree.find_min_yf(X, y, eps)
- gamma = np.exp(-lb_ensemble)
+ gamma = np.exp(-margin_prev)
+ loss_prev_ensemble = np.mean(gamma)
best_tree = copy.deepcopy(self.trees[-1]) # copy the whole tree since we will change its leaves
- if model == 'plain':
+ if model in ['plain', 'da_uniform', 'at_cube']:
best_loss = np.mean(gamma * np.exp(-y*best_tree.predict(X)))
elif model == 'robust_bound':
best_loss = np.mean(gamma * np.exp(-best_tree.find_min_yf(X, y, eps)))
else:
raise ValueError('wrong model type')
+ best_loss += self.gamma_hp * best_tree.get_depth() # introduce depth penalization
+ if best_loss < loss_prev_ensemble:
+ return
+
curr_tree = copy.deepcopy(best_tree)
- while curr_tree.left is not None or curr_tree.right is not None:
- some_leaf = curr_tree.get_some_leaf_except([])
- curr_tree.rm_leaf(some_leaf)
- if model == 'plain':
+ # stop when best_loss is better than the previous loss or curr_tree became just a stump
+ while best_loss >= loss_prev_ensemble and not (curr_tree.left is None and curr_tree.right is None):
+ curr_tree.rm_leaf(curr_tree.get_some_leaf()) # gradual pruning
+ # curr_tree.rm_bottom_layer(depth=1, max_depth=curr_tree.get_depth()) # agressive pruning
+ curr_tree.node_list = curr_tree.to_array_contiguous()
+ if model in ['plain', 'da_uniform', 'at_cube']:
loss_pruned = np.mean(gamma * np.exp(-y * curr_tree.predict(X)))
elif model == 'robust_bound':
loss_pruned = np.mean(gamma * np.exp(-curr_tree.find_min_yf(X, y, eps)))
else:
raise ValueError('wrong model type')
+ loss_pruned += self.gamma_hp * curr_tree.get_depth() # introduce depth penalization
# print('{:.4f} {:.4f} {}'.format(loss_pruned, best_loss, curr_tree))
if loss_pruned < best_loss:
best_loss = loss_pruned
@@ -349,174 +418,210 @@ def prune_last_tree(self, X, y, eps, model):
# print('best loss: {:.4f}, best tree: {}'.format(best_loss, best_tree))
self.trees[-1] = best_tree
- # while 1:
- # some_leaf = curr_tree.get_some_leaf_except(checked_leaves)
- # if curr_tree.left is None and curr_tree.right is None:
- # print('break1', curr_tree)
- # break
- # else:
- # # checked_leaves.append(copy.deepcopy(some_leaf))
- # curr_tree.rm_leaf(some_leaf)
- #
- # loss_pruned = np.mean(gamma * np.exp(-curr_tree.find_min_yf(X, y, eps)))
- # print(loss_pruned, best_loss, curr_tree)
- # if loss_pruned < best_loss:
- # print('pruned successfully')
- # self.trees[-1] = last_tree
- # losses_trees.append((robust_loss_pruned, last_tree))
- #
- # self.trees[-1] = last_tree
- # if last_tree.left is None and last_tree.right is None: # root
- # print('break2', curr_tree)
- # pdb.set_trace()
- # break
- # self.trees[-1] = best_tree
-
- @staticmethod
- @jit(nopython=True, parallel=True) # parallel=True really matters, especially with independent iterations
- def fit_plain_stumps(X_proj, y, gamma, b_vals):
- n_thresholds = b_vals.shape[0]
-
- losses = np.full(n_thresholds, np.inf, dtype=dtype)
- w_l_vals = np.full(n_thresholds, np.inf, dtype=dtype)
- w_r_vals = np.full(n_thresholds, np.inf, dtype=dtype)
- sum_1, sum_m1 = np.sum((y == 1) * gamma), np.sum((y == -1) * gamma)
- for i in prange(n_thresholds):
- ind = X_proj >= b_vals[i]
-
- sum_1_1, sum_1_m1 = np.sum(ind * (y == 1) * gamma), np.sum(ind * (y == -1) * gamma)
- sum_0_1, sum_0_m1 = sum_1 - sum_1_1, sum_m1 - sum_1_m1
- w_l, w_r = coord_descent_exp_loss(sum_1_1, sum_1_m1, sum_0_1, sum_0_m1)
-
- fmargin = y * w_l + y * w_r * ind
- loss = np.mean(gamma * np.exp(-fmargin))
- losses[i], w_l_vals[i], w_r_vals[i] = loss, w_l, w_r
- return losses, w_l_vals, w_r_vals, b_vals
-
- @staticmethod
- @jit(nopython=True, parallel=True) # parallel=True really matters, especially with independent iterations
- def fit_robust_bound_stumps(X_proj, y, gamma, b_vals, eps):
- n_thresholds = b_vals.shape[0]
-
- losses = np.full(n_thresholds, np.inf, dtype=dtype)
- w_l_vals = np.full(n_thresholds, np.inf, dtype=dtype)
- w_r_vals = np.full(n_thresholds, np.inf, dtype=dtype)
- sum_1, sum_m1 = np.sum((y == 1) * gamma), np.sum((y == -1) * gamma)
- for i in prange(n_thresholds):
- # Certification for the previous ensemble O(n)
- split_lbs, split_ubs = X_proj - eps, X_proj + eps
- guaranteed_right = split_lbs > b_vals[i]
- uncertain = (split_lbs <= b_vals[i]) * (split_ubs >= b_vals[i])
-
- loss, w_l, w_r = basic_case_two_intervals(y, gamma, guaranteed_right, uncertain, sum_1, sum_m1)
- losses[i], w_l_vals[i], w_r_vals[i] = loss, w_l, w_r
-
- return losses, w_l_vals, w_r_vals, b_vals
-
- def fit_stump(self, X, y, gamma, model, eps):
- n_trials_coord = self.n_trials_coord
- X, y, gamma = X.astype(dtype), y.astype(dtype), gamma.astype(dtype)
+ def fit_tree(self, X, y, gamma, model, eps, depth):
+ """
+ Recursive procedure for building a single tree.
+ Returning None means that tree.left or tree.right will be set to None, i.e. no child.
- num, dim = X.shape
- params, min_losses = np.zeros((n_trials_coord, 4)), np.full(n_trials_coord, np.inf)
+ TODO: the problem currently is that there is a minor memory leak in the current implementation. One can try to
+ get rid of it by rewriting this function in a non-recursive way (similarly to, e.g. how predict_point() is done)
+ """
+ parallel = True # causes a minor memory leak; disable if the memory is limited
- # 151 features are always 0.0 on MNIST 2 vs 6. Doesn't even makes sense to consider them.
- idx_non_trivial = np.abs(X).sum(axis=0) > 0.0
- features_to_check = list(np.arange(dim)[idx_non_trivial])
- np.random.shuffle(features_to_check) # shuffles in-place
- for trial in prange(n_trials_coord):
- if len(features_to_check) > 0:
- coord = features_to_check.pop() # takes the last element
- else:
- n_trials_coord = trial
- break
- X_proj = X[:, coord]
-
- min_val = 1e-7
- threshold_candidates = np.sort(np.copy(X_proj))
- if self.min_samples_leaf > 0:
- threshold_candidates = threshold_candidates[self.min_samples_leaf:-self.min_samples_leaf]
- if len(threshold_candidates) == 0: # if no samples left according to min_samples_leaf
- min_losses[trial] = np.inf
- params[trial, :] = [0.0, 0.0, 0.0, -1]
- continue
-
- if model not in ['robust_bound'] or eps == 0.0: # plain training
- b_vals = np.copy(threshold_candidates)
- b_vals += min_val # to break the ties
- else: # robust training
- b_vals = np.concatenate((threshold_candidates - eps, threshold_candidates + eps), axis=0)
- # to make in the overlapping case |---x-|--|-x---| output 2 different losses in the middle
- n_bs = len(threshold_candidates)
- b_vals += np.concatenate((-np.full(n_bs, min_val), np.full(n_bs, min_val)), axis=0)
- b_vals = np.unique(b_vals) # use only unique b's
- b_vals = np.sort(b_vals) # still important to sort because of the final threshold selection
-
- if model == 'plain':
- losses, w_l_vals, w_r_vals, b_vals = self.fit_plain_stumps(X_proj, y, gamma, b_vals)
- elif model == 'robust_bound':
- losses, w_l_vals, w_r_vals, b_vals = self.fit_robust_bound_stumps(X_proj, y, gamma, b_vals, eps)
- else:
- raise ValueError('wrong model')
-
- min_loss = np.min(losses)
- # probably, they are already sorted, but to be 100% sure since it is not explicitly mentioned in the docs
- indices_opt_init = np.sort(np.where(losses == min_loss)[0])
- indices_opt = get_contiguous_indices(indices_opt_init)
- id_opt = indices_opt[len(indices_opt) // 2]
-
- idx_prev = np.clip(indices_opt[0]-1, 0, len(b_vals)-1) # to prevent stepping out of the array
- idx_next = np.clip(indices_opt[-1]+1, 0, len(b_vals)-1) # to prevent stepping out of the array
- b_prev, w_l_prev, w_r_prev = b_vals[idx_prev], w_l_vals[idx_prev], w_r_vals[idx_prev]
- b_next, w_l_next, w_r_next = b_vals[idx_next], w_l_vals[idx_next], w_r_vals[idx_next]
- # initialization
- b_leftmost, b_rightmost = b_vals[indices_opt[0]], b_vals[indices_opt[-1]]
- # more involved, since with +-eps, an additional check of the loss is needed
- if model == 'plain':
- b_rightmost = b_next
- elif model in ['robust_bound']:
- b_prev_half = (b_prev + b_vals[indices_opt[0]]) / 2
- loss_prev_half = exp_loss_robust(X_proj, y, gamma, w_l_prev, w_r_prev, [], [], b_prev_half, eps, False)
-
- b_next_half = (b_vals[indices_opt[-1]] + b_next) / 2
- loss_next_half = exp_loss_robust(X_proj, y, gamma, w_l_next, w_r_next, [], [], b_next_half, eps, False)
-
- # we extend the interval of the constant loss to the left and to the right if there the loss is
- # the same at b_prev_half or b_next_half
- if loss_prev_half == losses[id_opt]:
- b_leftmost = b_prev
- if loss_next_half == losses[id_opt]:
- b_rightmost = b_next
- else:
- raise ValueError('wrong model')
- # we put in the middle of the interval of the constant loss
- b_opt = (b_leftmost + b_rightmost) / 2
- # note: now inf can easily happen if e.g. all examples at some subtree are < eps (happens on MNIST)
- # if (losses == np.nan).sum() > 0:
- # pdb.set_trace()
-
- # w_l_opt, w_r_opt, b_opt = w_l_vals[id_opt], w_r_vals[id_opt], b_vals[id_opt]
-
- # For the chosen threshold, we need to calculate w_l, w_r
- # Some of w_l, w_r that correspond to min_loss may not be optimal anymore
- b_val_final = np.array([b_opt])
- if model == 'plain':
- loss, w_l_opt, w_r_opt, _ = self.fit_plain_stumps(X_proj, y, gamma, b_val_final)
- elif model == 'robust_bound':
- loss, w_l_opt, w_r_opt, _ = self.fit_robust_bound_stumps(X_proj, y, gamma, b_val_final, eps)
- else:
- raise ValueError('wrong model')
- loss, w_l_opt, w_r_opt = loss[0], w_l_opt[0], w_r_opt[0]
+ if depth == 1:
+ self.max_tree_node_id = 0 # if we start a new tree, set the counter to 0 (needed for efficient predict())
+ if depth > self.max_depth: # and (X.shape[0] <= 10000 or depth > 2*self.max_depth): # adaptive depth
+ return None
+ if X.shape[0] < self.min_samples_split:
+ return None
+ if (y == -1).all() or (y == 1).all(): # if already pure, don't branch anymore
+ return None
+
+ # create a new tree that will become a node (if further splits are needed)
+ # or a leaf (if max_depth or min_samples_leaf is reached)
+ w_l, w_r, b, coord, loss = self.fit_stumps_over_coords(X, y, gamma, model, eps, depth)
+
+ if coord == -1: # no further splits because min_samples_leaf is reached
+ return None
+ if loss >= np.mean(gamma): # if the stump doesn't help, don't add it at all; very unlikely situation
+ # print('Did not make this split since old_loss={:.4} <= new_loss={:.4}'.format(np.mean(gamma), loss))
+ return None
- # recalculation of w_l, w_r shouldn't change the min loss
- if np.abs(loss - min_loss) > 1e7:
- print('New loss: {:.5f}, min loss before: {:.5f}'.format(loss, min_loss))
+ tree = Tree(self.max_tree_node_id, None, None, w_l, w_r, b, coord, loss)
+ self.max_tree_node_id += 1 # increment the counter
- min_losses[trial] = losses[id_opt]
- params[trial, :] = [w_l_opt, w_r_opt, b_opt, coord]
+ if model in ['plain', 'da_uniform', 'at_cube']:
+ idx_left = (X[:, tree.coord] < tree.b)
+ idx_right = (X[:, tree.coord] >= tree.b)
+ elif model == 'robust_bound':
+ idx_left = (X[:, tree.coord] < tree.b + eps)
+ idx_right = (X[:, tree.coord] >= tree.b - eps)
+ else:
+ raise ValueError('wrong model type')
- id_best_coord = min_losses[:n_trials_coord].argmin()
+ if parallel and depth <= 4:
+ with ThreadPoolExecutor(max_workers=2) as executor:
+ proc_left = executor.submit(self.fit_tree, X[idx_left, :], y[idx_left], gamma[idx_left], model, eps, depth+1)
+ proc_right = executor.submit(self.fit_tree, X[idx_right, :], y[idx_right], gamma[idx_right], model, eps, depth+1)
+ tree.left = proc_left.result()
+ tree.right = proc_right.result()
+ else:
+ # print("left subtree: {:d} examples".format(np.sum(idx_left)))
+ tree.left = self.fit_tree(X[idx_left, :], y[idx_left], gamma[idx_left], model, eps, depth+1)
+ # print("right subtree: {:d} examples".format(np.sum(idx_right)))
+ tree.right = self.fit_tree(X[idx_right, :], y[idx_right], gamma[idx_right], model, eps, depth+1)
+
+ if depth == 1:
+ # a list of all nodes at the root is needed for fast parallel predictions
+ tree.node_list = tree.to_array_contiguous()
+ return tree
+
+ def fit_stumps_over_coords(self, X, y, gamma, model, eps, depth):
+ verbose = False
+ parallel = True
+ n_ex = X.shape[0]
+ X, y, gamma = X.astype(dtype), y.astype(dtype), gamma.astype(dtype)
+ prev_loss = np.mean(gamma)
+
+ # 151 features are always 0.0 on MNIST 2 vs 6. And this number is even higher for smaller subsets of MNIST,
+ # i.e. subsets of examples partitioned by tree splits.
+ idx_non_trivial = np.abs(X).sum(axis=0) > 0.0
+ features_to_check = np.random.permutation(np.where(idx_non_trivial)[0])[:self.n_trials_coord]
+
+ n_coords = len(features_to_check)
+ params, min_losses = np.zeros((n_coords, 4)), np.full(n_coords, np.inf)
+
+ if parallel:
+ n_proc = get_n_proc(n_ex)
+ n_proc = min(n_coords, min(100, n_proc))
+ batch_size = n_coords // n_proc
+ n_batches = n_coords // batch_size + 1
+
+ with ThreadPoolExecutor(max_workers=n_proc) as executor:
+ procs = []
+ for i_batch in range(n_batches):
+ coords = features_to_check[i_batch*batch_size:(i_batch+1)*batch_size]
+ args = (X[:, coords], y, gamma, model, eps, coords, self.n_bins, self.min_samples_leaf, self.max_weight)
+ procs.append(executor.submit(fit_stump_batch, *args))
+
+ # Process the results
+ i_coord = 0
+ for i_batch in range(n_batches):
+ res_many = procs[i_batch].result()
+ for res in res_many:
+ min_losses[i_coord], *params[i_coord, :] = res
+ i_coord += 1
+ else:
+ for i_coord, coord in enumerate(features_to_check):
+ min_losses[i_coord], *params[i_coord, :] = fit_stump(
+ X[:, coord], y, gamma, model, eps, coord, self.n_bins, self.min_samples_leaf, self.max_weight)
+
+ id_best_coord = min_losses.argmin()
min_loss = min_losses[id_best_coord]
best_coord = int(params[id_best_coord][3]) # float to int is necessary for a coordinate
- return params[id_best_coord][0], params[id_best_coord][1], params[id_best_coord][2], best_coord, min_loss
-
+ best_wl, best_wr, best_b = params[id_best_coord][0], params[id_best_coord][1], np.float32(params[id_best_coord][2])
+ if verbose:
+ print('[{}-vs-all] depth {}: n_ex {}, n_coords {} -- loss {:.5f}->{:.5f}, b={:.3f} wl={:.3f} wr={:.3f} at coord {}'.format(
+ self.idx_clsf, depth, n_ex, n_coords, prev_loss, min_loss, best_b, best_wl, best_wr, best_coord))
+ return best_wl, best_wr, best_b, best_coord, min_loss
+
+
+def fit_stump_batch(Xs, y, gamma, model, eps, coords, n_bins, min_samples_leaf, max_weight):
+ res = np.zeros([len(coords), 5])
+ for i, coord in enumerate(coords):
+ res[i] = fit_stump(Xs[:, i], y, gamma, model, eps, coord, n_bins, min_samples_leaf, max_weight)
+ return res
+
+
+def fit_stump(X_proj, y, gamma, model, eps, coord, n_bins, min_samples_leaf, max_weight):
+ min_prec_val = 1e-7
+ min_val, max_val = 0.0, 1.0 # can be changed if the features are in a different range
+
+ if n_bins > 0:
+ if model == 'robust_bound':
+ # e.g. that's the thresholds that one gets with n_bins=10: [0.31, 0.41, 0.5, 0.59, 0.69]
+ b_vals = np.arange(eps*n_bins, n_bins - eps*n_bins + 1) / n_bins
+ # to have some margin to make the thresholds not adversarially reachable from 0 or 1
+ b_vals[b_vals < 0.5] += 0.1 * 1/n_bins
+ b_vals[b_vals > 0.5] -= 0.1 * 1/n_bins
+ else:
+ b_vals = np.arange(1, n_bins) / n_bins
+ else:
+ threshold_candidates = np.sort(X_proj)
+ if min_samples_leaf > 0:
+ threshold_candidates = threshold_candidates[min_samples_leaf:-min_samples_leaf]
+ if len(threshold_candidates) == 0: # if no samples left according to min_samples_leaf
+ return [np.inf, 0.0, 0.0, 0.0, -1]
+ if model not in ['robust_bound'] or eps == 0.0: # plain or da_uniform training
+ b_vals = np.copy(threshold_candidates)
+ b_vals += min_prec_val # to break the ties
+ else: # robust training
+ b_vals = np.concatenate((threshold_candidates - eps, threshold_candidates + eps), axis=0)
+ b_vals = np.clip(b_vals, min_val, max_val) # save computations (often goes 512 -> 360 thresholds on MNIST)
+ # to make in the overlapping case [---x-[--]-x---] output 2 different losses in the middle
+ n_bs = len(threshold_candidates)
+ b_vals += np.concatenate((-np.full(n_bs, min_prec_val), np.full(n_bs, min_prec_val)), axis=0)
+ b_vals = np.unique(b_vals) # use only unique b's
+ b_vals = np.sort(b_vals) # still important to sort because of the final threshold selection
+
+ if model in ['plain', 'da_uniform', 'at_cube']:
+ losses, w_l_vals, w_r_vals, b_vals = fit_plain_stumps(X_proj, y, gamma, b_vals, max_weight)
+ elif model == 'robust_bound':
+ losses, w_l_vals, w_r_vals, b_vals = fit_robust_bound_stumps(X_proj, y, gamma, b_vals, eps, max_weight)
+ else:
+ raise ValueError('wrong model')
+
+ min_loss = np.min(losses)
+ # probably, they are already sorted, but to be 100% sure since it is not explicitly mentioned in the docs
+ indices_opt_init = np.sort(np.where(losses == min_loss)[0])
+ indices_opt = get_contiguous_indices(indices_opt_init)
+ id_opt = indices_opt[len(indices_opt) // 2]
+
+ idx_prev = np.clip(indices_opt[0] - 1, 0, len(b_vals) - 1) # to prevent stepping out of the array
+ idx_next = np.clip(indices_opt[-1] + 1, 0, len(b_vals) - 1) # to prevent stepping out of the array
+ b_prev, w_l_prev, w_r_prev = b_vals[idx_prev], w_l_vals[idx_prev], w_r_vals[idx_prev]
+ b_next, w_l_next, w_r_next = b_vals[idx_next], w_l_vals[idx_next], w_r_vals[idx_next]
+ # initialization
+ b_leftmost, b_rightmost = b_vals[indices_opt[0]], b_vals[indices_opt[-1]]
+
+ if n_bins > 0: # note that one shouldn't average thresholds since it's unpredictable what is in between
+ return [min_loss, w_l_vals[id_opt], w_r_vals[id_opt], b_vals[id_opt], coord]
+
+ # more involved, since with +-eps, an additional check of the loss is needed
+ if model in ['plain', 'da_uniform', 'at_cube']:
+ b_rightmost = b_next
+ elif model in ['robust_bound']:
+ b_prev_half = (b_prev + b_vals[indices_opt[0]]) / 2
+ loss_prev_half = exp_loss_robust(X_proj, y, gamma, w_l_prev, w_r_prev, [], [], b_prev_half, eps, False)
+
+ b_next_half = (b_vals[indices_opt[-1]] + b_next) / 2
+ loss_next_half = exp_loss_robust(X_proj, y, gamma, w_l_next, w_r_next, [], [], b_next_half, eps, False)
+
+ # we extend the interval of the constant loss to the left and to the right if there the loss is
+ # the same at b_prev_half or b_next_half
+ if loss_prev_half == losses[id_opt]:
+ b_leftmost = b_prev
+ if loss_next_half == losses[id_opt]:
+ b_rightmost = b_next
+ else:
+ raise ValueError('wrong model')
+ # we put in the middle of the interval of the constant loss
+ b_opt = (b_leftmost + b_rightmost) / 2
+
+ # For the chosen threshold, we need to calculate w_l, w_r
+ # Some of w_l, w_r that correspond to min_loss may not be optimal anymore
+ b_val_final = np.array([b_opt])
+ if model in ['plain', 'da_uniform', 'at_cube']:
+ loss, w_l_opt, w_r_opt, _ = fit_plain_stumps(X_proj, y, gamma, b_val_final, max_weight)
+ elif model == 'robust_bound':
+ loss, w_l_opt, w_r_opt, _ = fit_robust_bound_stumps(X_proj, y, gamma, b_val_final, eps, max_weight)
+ else:
+ raise ValueError('wrong model')
+ loss, w_l_opt, w_r_opt = loss[0], w_l_opt[0], w_r_opt[0]
+
+ # recalculation of w_l, w_r shouldn't change the min loss
+ if np.abs(loss - min_loss) > 1e7:
+ print('New loss: {:.5f}, min loss before: {:.5f}'.format(loss, min_loss))
+
+ best_loss = losses[id_opt]
+ return [best_loss, w_l_opt, w_r_opt, b_opt, coord]
diff --git a/utils.py b/utils.py
index 265cf46..cbf2f30 100644
--- a/utils.py
+++ b/utils.py
@@ -1,5 +1,6 @@
import os
import numpy as np
+import glob
from numba import jit
@@ -55,3 +56,69 @@ def print_arr(arr):
for el in row:
string += '{:.3f} '.format(el)
print(i+1, string)
+
+
+def extract_hyperparam(model_name, substr):
+ return model_name.split(substr)[1].split(' ')[0]
+
+
+def finalize_curr_row(latex_str, weak_learner, flag_n_trees_latex):
+ # finalizing the current row: apply boldfacing and add \\
+ # (relies on the fact that we have only 3 metrics, i.e. TE,RTE,URTE or TE,LRTE,URTE or 4 metrics if flag_n_trees_latex is on)
+ # result: 'breast-cancer & 0.3 & 0.7 & 85.4 & 85.4 & 5.1 & 11.7 & 11.7 & 5.1 & 11.7 & 11.7'
+ curr_row = latex_str.split(r'\\')[-1]
+ curr_str_bf = ' & '.join(curr_row.split(' & ')[:2]) + ' & '
+ metrics_str = ' & '.join(curr_row.split(' & ')[2:])
+ n_metrics = 4 if flag_n_trees_latex else 3
+ metrics_curr_row = dict([(i, []) for i in range(n_metrics)])
+ # result: {0: [0.7, 5.1, 5.1], 1: [85.4, 11.7, 11.7], 2: [85.4, 11.7, 11.7]}
+ for i_val, val_str in enumerate(metrics_str.split(' & ')):
+ # for n_trees we need int, for the rest float
+ val = int(val_str) if flag_n_trees_latex and i_val % n_metrics == n_metrics - 1 else float(val_str)
+ metrics_curr_row[i_val % n_metrics].append(val)
+ # form the boldfaced str that corresponds to the current row
+ for tup in zip(*metrics_curr_row.values()):
+ for i_m, m in enumerate(tup):
+ # boldfacing condition: if minimum and it's not the number of trees (if the flag is turned on)
+ if (m == min(metrics_curr_row[i_m]) and not (flag_n_trees_latex and i_m == 3) and
+ not (weak_learner == 'stump' and i_m == 2)): # if URTE for stumps, don't boldface
+ curr_str_bf += '\\textbf{' + str(m) + '} & '
+ else:
+ curr_str_bf += '{} & '.format(m)
+ curr_str_bf += ' ' # just a margin for better latex code quality
+ curr_str_bf = curr_str_bf.strip()[:-1] # get rid of the last ' & '
+ curr_row_final = curr_str_bf + r'\\' + '\n' # new table line
+ return curr_row_final
+
+
+def get_model_names(datasets, models, exp_folder, weak_learner, tree_depth):
+ model_names = []
+ for dataset in datasets:
+ for model in models:
+ depth_str = 'max_depth=' + str(tree_depth) if weak_learner == 'tree' else ''
+ search_str = '{}/*dataset={} weak_learner={} model={}*{}*.metrics'.format(
+ exp_folder, dataset, weak_learner, model, depth_str)
+ model_names_curr = glob.glob(search_str)
+ model_names_curr.sort(key=lambda x: os.path.getmtime(x))
+ if model_names_curr != []:
+ # model_name_final = model_names_curr[-1]
+ for model_name_final in model_names_curr:
+ model_name_final = model_name_final.split('.metrics')[0].split(exp_folder+'/')[1]
+ model_names.append(model_name_final)
+ return model_names
+
+
+def get_n_proc(n_ex):
+ if n_ex > 40000:
+ n_proc = 50
+ elif n_ex > 20000:
+ n_proc = 40
+ elif n_ex > 2500:
+ n_proc = 25
+ elif n_ex > 1000:
+ n_proc = 10
+ elif n_ex > 200:
+ n_proc = 5
+ else:
+ n_proc = 1
+ return n_proc