def test_matrix_filter_binary_classification(self):
(X_train, y_train, X_test, y_test,
_) = create_binary_classification_dataset()
feature_names = list(X_train.columns)
model_task = ModelTask.CLASSIFICATION
run_error_analyzer_on_models(X_train, y_train, X_test, y_test,
feature_names, model_task)
def test_importances_binary_classification(self):
X_train, y_train, X_test, y_test, _ = \
create_binary_classification_dataset()
feature_names = list(X_train.columns)
models = create_models_classification(X_train, y_train)
for model in models:
categorical_features = []
run_error_analyzer(model, X_test, y_test, feature_names,
categorical_features)
def test_matrix_filter_binary_classification(self):
x_train, y_train, x_test, y_test, _ = \
create_binary_classification_dataset()
feature_names = list(x_train.columns)
models = create_models(x_train, y_train)
for model in models:
categorical_features = []
run_error_analyzer(model, x_test, y_test, feature_names,
categorical_features)
def test_raianalyzer_binary(self):
x_train, y_train, x_test, y_test, classes = \
create_binary_classification_dataset()
x_train = pd.DataFrame(x_train)
x_test = pd.DataFrame(x_test)
models = create_models(x_train, y_train)
x_train[LABELS] = y_train
x_test[LABELS] = y_test
for model in models:
run_raianalyzer(model, x_train, x_test, LABELS, classes)
def test_explain_model_binary_classification_with_different_format_predictions(
self, mimic_explainer, if_predictions_as_dataframe, explainable_model):
x_train, y_train, x_test, y_test, classes = create_binary_classification_dataset()
model = LogisticRegression(random_state=42).fit(x_train, y_train)
model.fit(x_train, y_train)
model = PredictAsDataFrameClassificationTestModel(
model, return_predictions_as_dataframe=if_predictions_as_dataframe)
kwargs = {}
explainer = mimic_explainer(model, x_train, explainable_model, **kwargs)
global_explanation = explainer.explain_global(evaluation_examples=x_test)
assert global_explanation is not None
def test_large_data_surrogate_error_tree(self):
# validate tree trains quickly for large data
X_train, y_train, X_test, y_test, _ = \
create_binary_classification_dataset(100)
feature_names = list(X_train.columns)
model = create_sklearn_random_forest_regressor(X_train, y_train)
X_test, y_test = replicate_dataset(X_test, y_test)
assert X_test.shape[0] > 1000000
t0 = time.time()
categorical_features = []
model_analyzer = ModelAnalyzer(model, X_test, y_test, feature_names,
categorical_features)
max_depth = 3
num_leaves = 31
min_child_samples = 20
categories_reindexed = []
cat_ind_reindexed = []
diff = model_analyzer.get_diff()
surrogate = create_surrogate_model(model_analyzer, X_test, diff,
max_depth, num_leaves,
min_child_samples,
cat_ind_reindexed)
t1 = time.time()
execution_time = t1 - t0
print(
"creating surrogate model took {} seconds".format(execution_time))
# assert we don't take too long to train the tree on 1 million rows
# note we train on >1 million rows in ~1 second
assert execution_time < 20
model_json = surrogate._Booster.dump_model()
tree_structure = model_json["tree_info"][0]['tree_structure']
max_split_index = get_max_split_index(tree_structure) + 1
assert max_split_index == 3
cache_subtree_features(tree_structure, feature_names)
pred_y = model_analyzer.model.predict(X_test)
traversed_X_test = X_test.copy()
traversed_X_test[DIFF] = diff
traversed_X_test[TRUE_Y] = y_test
traversed_X_test[PRED_Y] = pred_y
t2 = time.time()
tree = traverse(traversed_X_test,
tree_structure,
max_split_index,
(categories_reindexed, cat_ind_reindexed), [],
feature_names,
metric=model_analyzer.metric,
classes=model_analyzer.classes)
t3 = time.time()
execution_time = t3 - t2
print("traversing tree took {} seconds".format(execution_time))
assert tree is not None
def test_model_analysis_binary(self, manager_type):
x_train, y_train, x_test, y_test, classes = \
create_binary_classification_dataset()
x_train = pd.DataFrame(x_train)
x_test = pd.DataFrame(x_test)
models = create_models_classification(x_train, y_train)
x_train[LABELS] = y_train
x_test[LABELS] = y_test
manager_args = None
for model in models:
run_model_analysis(model,
x_train,
x_test,
LABELS, [],
manager_type,
manager_args,
classes=classes)
def test_large_data_importances(self):
# mutual information can be very costly for large number of rows
# hence, assert we downsample to compute importances for large data
X_train, y_train, X_test, y_test, _ = \
create_binary_classification_dataset(100)
feature_names = list(X_train.columns)
model = create_sklearn_random_forest_regressor(X_train, y_train)
X_test, y_test = replicate_dataset(X_test, y_test)
assert X_test.shape[0] > 1000000
t0 = time.time()
categorical_features = []
model_analyzer = ModelAnalyzer(model, X_test, y_test,
feature_names,
categorical_features)
model_analyzer.compute_importances()
t1 = time.time()
execution_time = t1 - t0
print(execution_time)
# assert we don't take too long and downsample the dataset
# note execution time is in seconds
assert execution_time < 20
