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fix tests
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@chenyangkang
chenyangkang committed Oct 25, 2024
1 parent c26e686 commit 5f878da
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Showing 2 changed files with 28 additions and 44 deletions.
48 changes: 16 additions & 32 deletions tests/make_models.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@
min_req = 1


def make_STEMClassifier(fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir=""):
def make_STEMClassifier(fold_=2, min_req=1):
model = STEMClassifier(
base_model=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1),
save_gridding_plot=True,
Expand All @@ -41,16 +41,14 @@ def make_STEMClassifier(fold_=2, min_req=1, ensemble_models_disk_saver=False, en
temporal_bin_start_jitter="adaptive",
spatio_bin_jitter_magnitude="adaptive",
use_temporal_to_train=True,
ensemble_models_disk_saver=ensemble_models_disk_saver,
ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir,
njobs=1,
n_job=1,
)

return model


def make_parallel_STEMClassifier(
fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir=""
fold_=2, min_req=1
):
model = STEMClassifier(
base_model=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1),
Expand All @@ -69,15 +67,13 @@ def make_parallel_STEMClassifier(
temporal_bin_start_jitter="adaptive",
spatio_bin_jitter_magnitude="adaptive",
use_temporal_to_train=True,
ensemble_models_disk_saver=ensemble_models_disk_saver,
ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir,
njobs=2,
n_job=2,
)

return model


def make_STEMRegressor(fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir=""):
def make_STEMRegressor(fold_=2, min_req=1):
model = STEMRegressor(
base_model=Hurdle(
classifier=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1),
Expand All @@ -98,15 +94,13 @@ def make_STEMRegressor(fold_=2, min_req=1, ensemble_models_disk_saver=False, ens
temporal_bin_start_jitter="adaptive",
spatio_bin_jitter_magnitude="adaptive",
use_temporal_to_train=True,
ensemble_models_disk_saver=ensemble_models_disk_saver,
ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir,
njobs=1,
n_job=1,
)

return model


def make_AdaSTEMClassifier(fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir=""):
def make_AdaSTEMClassifier(fold_=2, min_req=1):
model = AdaSTEMClassifier(
base_model=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1),
save_gridding_plot=True,
Expand All @@ -125,14 +119,12 @@ def make_AdaSTEMClassifier(fold_=2, min_req=1, ensemble_models_disk_saver=False,
temporal_bin_start_jitter="adaptive",
spatio_bin_jitter_magnitude="adaptive",
use_temporal_to_train=True,
ensemble_models_disk_saver=ensemble_models_disk_saver,
ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir,
njobs=1,
n_job=1,
)
return model


def make_AdaSTEMRegressor(fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir=""):
def make_AdaSTEMRegressor(fold_=2, min_req=1):
model = AdaSTEMRegressor(
base_model=Hurdle(
classifier=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1),
Expand All @@ -154,15 +146,13 @@ def make_AdaSTEMRegressor(fold_=2, min_req=1, ensemble_models_disk_saver=False,
temporal_bin_start_jitter="adaptive",
spatio_bin_jitter_magnitude="adaptive",
use_temporal_to_train=True,
ensemble_models_disk_saver=ensemble_models_disk_saver,
ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir,
njobs=1,
n_job=1,
)
return model


def make_SphereAdaSTEMRegressor(
fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir=""
fold_=2, min_req=1
):
model = SphereAdaSTEMRegressor(
base_model=Hurdle(
Expand All @@ -185,14 +175,12 @@ def make_SphereAdaSTEMRegressor(
temporal_bin_start_jitter="adaptive",
spatio_bin_jitter_magnitude="adaptive",
use_temporal_to_train=True,
ensemble_models_disk_saver=ensemble_models_disk_saver,
ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir,
njobs=1,
n_job=1,
)
return model


def make_SphereAdaClassifier(fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir=""):
def make_SphereAdaClassifier(fold_=2, min_req=1):
model = SphereAdaSTEMClassifier(
base_model=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1),
save_gridding_plot=True,
Expand All @@ -211,15 +199,13 @@ def make_SphereAdaClassifier(fold_=2, min_req=1, ensemble_models_disk_saver=Fals
temporal_bin_start_jitter="adaptive",
spatio_bin_jitter_magnitude="adaptive",
use_temporal_to_train=True,
ensemble_models_disk_saver=ensemble_models_disk_saver,
ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir,
njobs=1,
n_job=1,
)
return model


def make_parallel_SphereAdaClassifier(
fold_=2, min_req=1, ensemble_models_disk_saver=False, ensemble_models_disk_saving_dir=""
fold_=2, min_req=1
):
model = SphereAdaSTEMClassifier(
base_model=XGBClassifier(tree_method="hist", random_state=42, verbosity=0, n_jobs=1),
Expand All @@ -239,8 +225,6 @@ def make_parallel_SphereAdaClassifier(
temporal_bin_start_jitter="adaptive",
spatio_bin_jitter_magnitude="adaptive",
use_temporal_to_train=True,
ensemble_models_disk_saver=ensemble_models_disk_saver,
ensemble_models_disk_saving_dir=ensemble_models_disk_saving_dir,
njobs=2,
n_job=2,
)
return model
24 changes: 12 additions & 12 deletions tests/test_model.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ def test_STEMClassifier():
model = make_STEMClassifier()
model = model.fit(X_train, np.where(y_train > 0, 1, 0))

pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, njobs=1)
pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, n_jobs=1)
assert np.sum(~np.isnan(pred_mean)) > 0
assert np.sum(~np.isnan(pred_std)) > 0

Expand All @@ -47,7 +47,7 @@ def test_STEMClassifier():
model.calculate_feature_importances()
assert model.feature_importances_.shape[0] > 0

importances_by_points = model.assign_feature_importances_by_points(verbosity=0, njobs=1)
importances_by_points = model.assign_feature_importances_by_points(verbosity=0, n_jobs=1)
assert importances_by_points.shape[0] > 0
assert importances_by_points.shape[1] == len(x_names) + 3

Expand Down Expand Up @@ -86,7 +86,7 @@ def test_STEMRegressor():
model = make_STEMRegressor()
model = model.fit(X_train, np.where(y_train > 0, 1, 0))

pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, njobs=1)
pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, n_jobs=1)
assert np.sum(~np.isnan(pred_mean)) > 0
assert np.sum(~np.isnan(pred_std)) > 0

Expand All @@ -107,7 +107,7 @@ def test_STEMRegressor():
model.calculate_feature_importances()
assert model.feature_importances_.shape[0] > 0

importances_by_points = model.assign_feature_importances_by_points(verbosity=0, njobs=1)
importances_by_points = model.assign_feature_importances_by_points(verbosity=0, n_jobs=1)
assert importances_by_points.shape[0] > 0
assert importances_by_points.shape[1] == len(x_names) + 3

Expand All @@ -116,7 +116,7 @@ def test_AdaSTEMClassifier():
model = make_AdaSTEMClassifier()
model = model.fit(X_train, np.where(y_train > 0, 1, 0))

pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, njobs=1)
pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, n_jobs=1)
assert np.sum(~np.isnan(pred_mean)) > 0
assert np.sum(~np.isnan(pred_std)) > 0

Expand All @@ -137,7 +137,7 @@ def test_AdaSTEMClassifier():
model.calculate_feature_importances()
assert model.feature_importances_.shape[0] > 0

importances_by_points = model.assign_feature_importances_by_points(verbosity=0, njobs=1)
importances_by_points = model.assign_feature_importances_by_points(verbosity=0, n_jobs=1)
assert importances_by_points.shape[0] > 0
assert importances_by_points.shape[1] == len(x_names) + 3

Expand All @@ -146,7 +146,7 @@ def test_AdaSTEMRegressor():
model = make_AdaSTEMRegressor()
model = model.fit(X_train, np.where(y_train > 0, 1, 0))

pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, njobs=1)
pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, n_jobs=1)
assert np.sum(~np.isnan(pred_mean)) > 0
assert np.sum(~np.isnan(pred_std)) > 0

Expand All @@ -167,7 +167,7 @@ def test_AdaSTEMRegressor():
model.calculate_feature_importances()
assert model.feature_importances_.shape[0] > 0

importances_by_points = model.assign_feature_importances_by_points(verbosity=0, njobs=1)
importances_by_points = model.assign_feature_importances_by_points(verbosity=0, n_jobs=1)
assert importances_by_points.shape[0] > 0
assert importances_by_points.shape[1] == len(x_names) + 3

Expand All @@ -176,7 +176,7 @@ def test_SphereAdaClassifier():
model = make_SphereAdaClassifier()
model = model.fit(X_train, np.where(y_train > 0, 1, 0))

pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, njobs=1)
pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, n_jobs=1)
assert np.sum(~np.isnan(pred_mean)) > 0
assert np.sum(~np.isnan(pred_std)) > 0

Expand All @@ -197,7 +197,7 @@ def test_SphereAdaClassifier():
model.calculate_feature_importances()
assert model.feature_importances_.shape[0] > 0

importances_by_points = model.assign_feature_importances_by_points(verbosity=0, njobs=1)
importances_by_points = model.assign_feature_importances_by_points(verbosity=0, n_jobs=1)
assert importances_by_points.shape[0] > 0
assert importances_by_points.shape[1] == len(x_names) + 3

Expand Down Expand Up @@ -236,7 +236,7 @@ def test_SphereAdaSTEMRegressor():
model = make_SphereAdaSTEMRegressor()
model = model.fit(X_train, np.where(y_train > 0, 1, 0))

pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, njobs=1)
pred_mean, pred_std = model.predict(X_test.reset_index(drop=True), return_std=True, verbosity=1, n_jobs=1)
assert np.sum(~np.isnan(pred_mean)) > 0
assert np.sum(~np.isnan(pred_std)) > 0

Expand All @@ -257,6 +257,6 @@ def test_SphereAdaSTEMRegressor():
model.calculate_feature_importances()
assert model.feature_importances_.shape[0] > 0

importances_by_points = model.assign_feature_importances_by_points(verbosity=0, njobs=1)
importances_by_points = model.assign_feature_importances_by_points(verbosity=0, n_jobs=1)
assert importances_by_points.shape[0] > 0
assert importances_by_points.shape[1] == len(x_names) + 3

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