Add new documentation, coveralls support (#50)

* Update dev from master (#47) * Working version of insert_point * Fix bug with query and insertpoint * Cleanup disp and codisp functions * Reorder methods * Insertpoint operational, add docstrings * Small fix * Remove old files * Allow empty tree; handle duplicates in insert_point and forget_point * Account for duplicates in tree construction * Add ability to print tree * Update docstrings and minor fixes * Docstring fix * Minor fixes * Bugfix for forget_point * Add image * Update readme * Update README.md * Type check for point * Update README.md * Update README.md * Store bounding boxes * Minor changes * Fix bbox bug, add unit tests * Add support for CI * Remove Python 3.7 * Update README.md * Fix duplicate precision bug * Fix duplicates issue? * Use new indexing strategy with forget_point * Update n bug * Return 0 for codisp if leaf is root * Add efficient shingle * Add sine wave image * Update README.md * Minor cleanup * sklearn test * Add classification notebook * Updated gitignore * Edit classification notebook * taxi data test * removed swamp * taxi data 200 tree run * IF test * Add OC-SVM example with sine wave * Add OC-SVM example with taxi data * Add IF example with sine wave * Minor changes * Minor updates * Delete old sine_ocsvm_test notebook * Delete old taxi_ocsvm_test notebook * Delete old sine_if notebook * Add IF example with sine wave * Add OC-SVM example with sine wave * Add OC-SVM example with taxi data * Delete old taxi_ocsvm notebook * Add OC-SVM example with taxi data * sine wave comaprasion * table1 notebook * rrcf notebook * renamed rrcf * taxi data if * Fix shingle bug; clean up classification example * Set theme jekyll-theme-minimal * Add index labels * Update batch image * Update README.md * Update README.md * Update README.md * Create _config.yml * Create default.html * Create index.md * Update index.md * Create nav.html * Create nav.yml * Create tree-construction.html * Rename tree-construction.html to tree-construction.md * Create insert-and-delete.md * Create anomaly-scoring.md * Create batch.md * Create streaming.md * Update tree-construction.md * Update tree-construction.md * Update tree-construction.md * Update tree-construction.md * Update tree-construction.md * Update tree-construction.md * Update insert-and-delete.md * Update insert-and-delete.md * Update tree-construction.md * Update insert-and-delete.md * Update insert-and-delete.md * Update anomaly-scoring.md * Create related-work.md * Update nav.yml * Update related-work.md * Create random-cut-tree.md * Update nav.yml * Create modifying-rctree.md * Update nav.yml * Update random-cut-tree.md * Update random-cut-tree.md * Update random-cut-tree.md * Update random-cut-tree.md * Update modifying-rctree.md * Update random-cut-tree.md * Create scoring-rctree.md * Update nav.yml * Update scoring-rctree.md * Update README.md * Update README.md * Update index.md * Update index.md * Update index.md * Create paper.md * Create paper.bib * Add files via upload * Update paper.md * Update README.md * Delete figure_1.png * Add files via upload * Update paper.md * Add files via upload * Create taxi.md * Update nav.yml * Update batch.md * Update streaming.md * Update streaming.md * Update streaming.md * Update streaming.md * Update taxi.md * Update related-work.md * Update related-work.md * Update related-work.md * Update tree-construction.md * Update insert-and-delete.md * Update anomaly-scoring.md * Update related-work.md * Update tree-construction.md * Update insert-and-delete.md * Update taxi.md * Update README.md * Update batch.md * Update streaming.md * Updates to authors * Update paper * updated abhi orcid * Create rctree-api.md * Update rctree-api.md * Update rctree-api.md * Update rctree-api.md * Update rctree-api.md * Update rctree-api.md * Update rctree-api.md * Update rctree-api.md * Update nav.yml * Update rctree-api.md * Update rctree-api.md * Update docstrings * Update rctree-api.md * Update rctree-api.md * Update anomaly-scoring.md * Update random-cut-tree.md * Update rctree-api.md * Update related-work.md * Update rctree-api.md * Update setup.py * Update __init__.py * Update paper.md * Update paper.md * JOSS review updates (#48) * Working version of insert_point * Fix bug with query and insertpoint * Cleanup disp and codisp functions * Reorder methods * Insertpoint operational, add docstrings * Small fix * Remove old files * Allow empty tree; handle duplicates in insert_point and forget_point * Account for duplicates in tree construction * Add ability to print tree * Update docstrings and minor fixes * Docstring fix * Minor fixes * Bugfix for forget_point * Add image * Update readme * Update README.md * Type check for point * Update README.md * Update README.md * Store bounding boxes * Minor changes * Fix bbox bug, add unit tests * Add support for CI * Remove Python 3.7 * Update README.md * Fix duplicate precision bug * Fix duplicates issue? * Use new indexing strategy with forget_point * Update n bug * Return 0 for codisp if leaf is root * Add efficient shingle * Add sine wave image * Update README.md * Minor cleanup * sklearn test * Add classification notebook * Updated gitignore * Edit classification notebook * taxi data test * removed swamp * taxi data 200 tree run * IF test * Add OC-SVM example with sine wave * Add OC-SVM example with taxi data * Add IF example with sine wave * Minor changes * Minor updates * Delete old sine_ocsvm_test notebook * Delete old taxi_ocsvm_test notebook * Delete old sine_if notebook * Add IF example with sine wave * Add OC-SVM example with sine wave * Add OC-SVM example with taxi data * Delete old taxi_ocsvm notebook * Add OC-SVM example with taxi data * sine wave comaprasion * table1 notebook * rrcf notebook * renamed rrcf * taxi data if * Fix shingle bug; clean up classification example * Set theme jekyll-theme-minimal * Add index labels * Update batch image * Update README.md * Update README.md * Update README.md * Create _config.yml * Create default.html * Create index.md * Update index.md * Create nav.html * Create nav.yml * Create tree-construction.html * Rename tree-construction.html to tree-construction.md * Create insert-and-delete.md * Create anomaly-scoring.md * Create batch.md * Create streaming.md * Update tree-construction.md * Update tree-construction.md * Update tree-construction.md * Update tree-construction.md * Update tree-construction.md * Update tree-construction.md * Update insert-and-delete.md * Update insert-and-delete.md * Update tree-construction.md * Update insert-and-delete.md * Update insert-and-delete.md * Update anomaly-scoring.md * Create related-work.md * Update nav.yml * Update related-work.md * Create random-cut-tree.md * Update nav.yml * Create modifying-rctree.md * Update nav.yml * Update random-cut-tree.md * Update random-cut-tree.md * Update random-cut-tree.md * Update random-cut-tree.md * Update modifying-rctree.md * Update random-cut-tree.md * Create scoring-rctree.md * Update nav.yml * Update scoring-rctree.md * Update README.md * Update README.md * Update index.md * Update index.md * Update index.md * Create paper.md * Create paper.bib * Add files via upload * Update paper.md * Update README.md * Delete figure_1.png * Add files via upload * Update paper.md * Add files via upload * Create taxi.md * Update nav.yml * Update batch.md * Update streaming.md * Update streaming.md * Update streaming.md * Update streaming.md * Update taxi.md * Update related-work.md * Update related-work.md * Update related-work.md * Update tree-construction.md * Update insert-and-delete.md * Update anomaly-scoring.md * Update related-work.md * Update tree-construction.md * Update insert-and-delete.md * Update taxi.md * Update README.md * Update batch.md * Update streaming.md * Updates to authors * Update paper * updated abhi orcid * Create rctree-api.md * Update rctree-api.md * Update rctree-api.md * Update rctree-api.md * Update rctree-api.md * Update rctree-api.md * Update rctree-api.md * Update rctree-api.md * Update nav.yml * Update rctree-api.md * Update rctree-api.md * Update docstrings * Update rctree-api.md * Update rctree-api.md * Update anomaly-scoring.md * Update random-cut-tree.md * Update rctree-api.md * Update related-work.md * Update rctree-api.md * Update setup.py * Update __init__.py * Update paper.md * Update paper.md * Update README.md * Update paper.bib * Update paper.md * Update paper.md * JOSS review suggested changes * Add license badge * Move installation instructions; list dependencies * Add coveralls support * Add classification and comparison to docs * Move notebook material into documentation * Add example data * Update data locations in docs
kLabUM · Mar 26, 2019 · 59c4aa6 · 59c4aa6
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diff --git a/.travis.yml b/.travis.yml
@@ -5,6 +5,10 @@ python:
 install:
   - pip install .
   - pip install -r requirements.txt
+  - pip install pytest-cov
+  - pip install coveralls
 # command to run tests
 script:
-  - pytest
+  - pytest --cov=pysheds/
+after_success:
+  - coveralls
diff --git a/docs/_data/nav.yml b/docs/_data/nav.yml
@@ -27,3 +27,7 @@ toc:
         url: /rrcf/streaming.html
       - page: Analyzing taxi data
         url: /rrcf/taxi.html
+      - page: Classification
+        url: /rrcf/classification.html
+      - page: Comparison of methods
+        url: /rrcf/comparisons.html
diff --git a/docs/classification.md b/docs/classification.md
@@ -0,0 +1,260 @@
+# Using RRCF for classification
+
+In this example, we use RRCF to classify data into one of two categories. The approach proceeds as follows:
+
+- Create two forests, each one constructed from data from one of the known classes.
+- Try inserting a new (unclassified) point into each forest. Assign the point to the class that produces the smaller CoDisp upon insertion.
+
+The advantage of this approach compared to conventional classification algorithms is that it provides a metric of the likelihood that a point does not belong to either class (i.e. is an outlier from both classes).
+
+## Test 1: Classification using two clusters
+
+In this example, we will classify points into one of two normally-distributed clusters.
+
+#### Generate data and run RRCF classification
+
+```python
+import numpy as np
+import pandas as pd
+import scipy.io
+import rrcf
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import colors
+
+# Set parameters
+np.random.seed(0)
+n = 256
+d = 3
+num_trees = 10
+
+# Generate data
+X_0 = np.zeros((n, d))
+X_1 = np.zeros((n, d))
+X_0[:,0] = -5
+X_1[:,0] = 5
+X_0 += 0.01*np.random.randn(*X_0.shape)
+X_1 += 0.01*np.random.randn(*X_1.shape)
+
+# Create two random cut forests
+forest_0 = []
+forest_1 = []
+for _ in range(num_trees):
+    tree_0 = rrcf.RCTree(X_0)
+    tree_1 = rrcf.RCTree(X_1)
+    forest_0.append(tree_0)
+    forest_1.append(tree_1)
+
+# Generate new points from two different classes
+num_points = 500
+offsets = np.random.choice([-5, 5], size=num_points)
+labels = pd.Series({-5 : 0, 5: 1})[offsets].values
+x = 0.01*np.random.randn(num_points, d)
+x[:,0] += offsets
+
+# Compute anomaly score for each new point
+avg_codisp = np.zeros((num_points, 2))
+for index in range(num_points):
+    for tree_0, tree_1 in zip(forest_0, forest_1):
+        tree_0.insert_point(x[index], index=n + index)
+        tree_1.insert_point(x[index], index=n + index)
+        avg_codisp[index, 0] += tree_0.codisp(n + index)
+        avg_codisp[index, 1] += tree_1.codisp(n + index)
+        tree_0.forget_point(n + index)
+        tree_1.forget_point(n + index)
+
+avg_codisp /= num_trees
+```
+
+#### Compute test error
+
+```python
+predictions = np.argmin(avg_codisp, axis=1)
+test_error = 1 - ((predictions == labels).sum()/num_points)
+print("Test error: {:.1f}%".format(100*test_error))
+```
+
+> ```
+Test error: 0.0%
+```
+
+#### Plot classification results
+
+```python
+fig = plt.figure(figsize=(8,6))
+ax = fig.add_subplot(111, projection='3d')
+ax.scatter(X_0[:,0], X_0[:,1], X_0[:,2], c='0.5', alpha=0.3,
+           label='Training data')
+ax.scatter(X_1[:,0], X_1[:,1], X_1[:,2], c='0.5', alpha=0.3)
+ax.scatter(x[predictions == 0][:,0], x[predictions == 0][:,1],
+           x[predictions == 0][:,2], c='b', label='Class 0')
+ax.scatter(x[predictions == 1][:,0], x[predictions == 1][:,1],
+           x[predictions == 1][:,2], c='r', label='Class 1')
+plt.title('Classification results', size=14)
+plt.legend(frameon=True)
+plt.tight_layout()
+```
+
+![Classification 1](https://s3.us-east-2.amazonaws.com/mdbartos-img/rrcf/rrcf_classification_0.png)
+
+## Test 2: Classification using nuclear particle data
+
+In this example, we will classify points from a real-world nuclear particle energy dataset. This dataset is available in the `/resources` directory in the `rrcf` repo.
+
+#### Load and plot original data
+
+```python
+# Load data
+nuc = scipy.io.loadmat('nuclear.mat')
+x = nuc['x'].astype(float).T
+y = nuc['y'].astype(float).T
+y = pd.Series({-1:0, 1:1})[y.ravel()].values
+
+plt.scatter(x[y == 0][:,0], x[y == 0][:,1], c='b', alpha=0.3,
+            label='Class 0')
+plt.scatter(x[y == 1][:,0], x[y == 1][:,1], c='r', alpha=0.3,
+            label='Class 1')
+plt.title('Original labeled data', size=14)
+plt.xlabel('Total energy')
+plt.ylabel('Tail energy')
+plt.legend(frameon=True)
+plt.tight_layout()
+```
+
+![Classification 2](https://s3.us-east-2.amazonaws.com/mdbartos-img/rrcf/rrcf_classification_1.png)
+
+#### Construct dual random forests
+
+```python
+# Set parameters
+np.random.seed(0)
+n = 2500
+m = 100
+d = 2
+num_trees = 60
+
+# Take random sample
+X_0 = x[np.random.choice(np.flatnonzero(y.ravel() == 0), size=n)]
+X_1 = x[np.random.choice(np.flatnonzero(y.ravel() == 1), size=n)]
+
+# Create random cut forests
+forest_0 = []
+forest_1 = []
+for _ in range(num_trees):
+    tree_0 = rrcf.RCTree(X_0)
+    tree_1 = rrcf.RCTree(X_1)
+    forest_0.append(tree_0)
+    forest_1.append(tree_1)
+
+ix = np.random.randint(0, x.shape[0], size=m)
+avg_codisp = np.zeros((m, d))
+
+for index in range(m):
+    for tree_0, tree_1 in zip(forest_0, forest_1):
+        tree_0.insert_point(x[ix[index]], index=n + index)
+        tree_1.insert_point(x[ix[index]], index=n + index)
+        avg_codisp[index,0] += tree_0.codisp(n + index)
+        avg_codisp[index,1] += tree_1.codisp(n + index)
+        tree_0.forget_point(n + index)
+        tree_1.forget_point(n + index)
+
+avg_codisp /= num_trees
+```
+
+#### Compute test error
+
+```python
+predictions = np.argmin(avg_codisp, axis=1)
+test_error = 1 - (predictions == y[ix]).sum() / m
+print("Test error: {:.1f}%".format(100*test_error))
+```
+
+>```
+Test error: 9.0%
+```
+
+#### Plot classification results
+
+```python
+plt.scatter(X_0[:,0], X_0[:,1], c='0.5', alpha=0.1)
+plt.scatter(X_1[:,0], X_1[:,1], c='0.5', alpha=0.1, label='Training data')
+plt.scatter(x[ix][predictions == 0][:,0], x[ix][predictions == 0][:,1],
+            c='b', alpha=0.4, label='Class 0')
+plt.scatter(x[ix][predictions == 1][:,0], x[ix][predictions == 1][:,1],
+            c='r', alpha=0.4, label='Class 1')
+plt.title('Classified points', size=14)
+plt.xlabel('Total energy')
+plt.ylabel('Tail energy')
+plt.legend(frameon=True)
+plt.tight_layout()
+```
+
+![Classification 3](https://s3.us-east-2.amazonaws.com/mdbartos-img/rrcf/rrcf_classification_2.png)
+
+
+#### Classify all points in search space
+
+```python
+nn = 10000
+num_trees = 10
+
+# Create random cut forests
+forest_0 = []
+forest_1 = []
+for _ in range(num_trees):
+    tree_0 = rrcf.RCTree(X_0)
+    tree_1 = rrcf.RCTree(X_1)
+    forest_0.append(tree_0)
+    forest_1.append(tree_1)
+
+points = np.vstack(np.dstack(np.meshgrid(np.linspace(0, 8, 100),
+                                         np.linspace(0, 1.4, 100))))
+avg_codisp = np.zeros((nn, d))
+
+for index in range(nn):
+    for tree_0, tree_1 in zip(forest_0, forest_1):
+        tree_0.insert_point(points[index], index=n + index)
+        tree_1.insert_point(points[index], index=n + index)
+        avg_codisp[index,0] += tree_0.codisp(n + index)
+        avg_codisp[index,1] += tree_1.codisp(n + index)
+        tree_0.forget_point(n + index)
+        tree_1.forget_point(n + index)
+
+avg_codisp /= num_trees
+```
+
+#### Plot decision boundary
+
+```python
+fig, ax = plt.subplots(figsize=(10,6))
+plt.imshow(-np.log(avg_codisp[:,1] / avg_codisp[:,0]).reshape(100, 100),
+           cmap='seismic', extent=(0, 8, 0, 1.4), origin='lower',
+           aspect='auto')
+plt.colorbar(label='Log ratio of Class 1 Codisp to Class 0 Codisp')
+plt.grid('off')
+plt.title('Decision regions', size=16)
+plt.xlabel('Total energy')
+plt.ylabel('Tail energy')
+plt.tight_layout()
+```
+
+![Classification 4](https://s3.us-east-2.amazonaws.com/mdbartos-img/rrcf/rrcf_classification_3.png)
+
+
+#### Plot outlier likelihood
+
+```python
+fig, ax = plt.subplots(figsize=(10,6))
+plt.imshow(np.log(np.min(avg_codisp, axis=1)).reshape(100, 100),
+           extent=(0, 8, 0, 1.4), origin='lower',
+           aspect='auto', cmap='cubehelix_r')
+plt.colorbar(label='$\log(\min(CoDisp(x^{(0)}), CoDisp(x^{(1)})))$')
+plt.grid('off')
+plt.title('Likelihood of belonging to neither class', size=14)
+plt.xlabel('Total energy')
+plt.ylabel('Tail energy')
+plt.tight_layout()
+```
+
+![Classification 5](https://s3.us-east-2.amazonaws.com/mdbartos-img/rrcf/rrcf_classification_4.png)
+