def test_cross_val_two_model_classifier_testing_binary():
df, column_names, target_name, clf1, clf2 = generate_binary_classification_data_and_models(
)
test_suite = classification_tests.ClassifierComparison(
clf1, clf2, df, target_name, column_names)
try:
test_suite.cross_val_per_class_two_model_classifier_testing()
assert True
except:
assert False
def test_two_model_classifier_testing_multiclass():
df, column_names, target_name, clf1, clf2 = generate_multiclass_classification_data_and_models(
)
test_suite = classification_tests.ClassifierComparison(
clf1, clf2, df, target_name, column_names)
try:
test_suite.two_model_classifier_testing(average="micro")
assert True
except:
assert False
def test_two_model_prediction_run_time_stress_test():
df, column_names, target_name, clf1, clf2 = generate_binary_classification_data_and_models(
)
test_suite = classification_tests.ClassifierComparison(
clf1, clf2, df, target_name, column_names)
sample_sizes = [i for i in range(100, 1000, 100)]
try:
test_suite.two_model_prediction_run_time_stress_test(sample_sizes)
assert True
except:
assert False
def test_two_model_prediction_run_time_stress_test():
df, column_names, target_name, clf1, clf2 = generate_binary_classification_data_and_models(
)
test_suite = classification_tests.ClassifierComparison(
clf1, clf2, df, target_name, column_names)
performance_boundary = []
for i in range(100, 1000, 100):
performance_boundary.append({"sample_size": i, "max_run_time": 100})
try:
test_suite.two_model_prediction_run_time_stress_test(
performance_boundary)
assert True
except:
assert False
