enable pytest . script

.github/workflows/test.yml

@@ -34,7 +34,7 @@ jobs:
           pip install --upgrade --upgrade-strategy=only-if-needed -r test-requirements.txt
       - name: Test with pytest 
         run: |
-          python -m pytest tests/test_linear_models/test_linear_models.py tests/test_exceptions/test_exceptions.py --cov=pymilo --cov-report=term
+          python -m pytest . --cov=pymilo --cov-report=term
       - name: Other tests
         run: |
           python -m vulture pymilo/ otherfiles/ setup.py --min-confidence 65 --exclude=__init__.py --sort-by-size

tests/test_exceptions/test_exceptions.py

@@ -1,22 +1,15 @@
-import unittest
-
 from test_export_exceptions import invalid_model, valid_model_invalid_structure
 from test_import_exceptions import invalid_json
 
-class TestExceptionalCases(unittest.TestCase):
-
-    EXCEPTION_TESTS = {
-        'IMPORT': [invalid_json],
-        'EXPORT': [invalid_model, valid_model_invalid_structure]
-    }
-
-    def test_full(self):
-        for category in self.EXCEPTION_TESTS.keys():
-            category_all_test_pass = True
-            for test in self.EXCEPTION_TESTS[category]:
-                category_all_test_pass = category_all_test_pass and test()
-                self.assertTrue(category_all_test_pass)
-                print("Test of Category: " + category + " with granularity of: " + test.__name__ + " executed successfully." )
-
-if __name__ == '__main__':
-    unittest.main()
+EXCEPTION_TESTS = {
+    'IMPORT': [invalid_json],
+    'EXPORT': [invalid_model, valid_model_invalid_structure]
+}
+
+def test_full():
+    for category in EXCEPTION_TESTS.keys():
+        category_all_test_pass = True
+        for test in EXCEPTION_TESTS[category]:
+            category_all_test_pass = category_all_test_pass and test()
+            assert category_all_test_pass == True 
+            print("Test of Category: " + category + " with granularity of: " + test.__name__ + " executed successfully." )

tests/test_linear_models/bayesian/test_ard_regression.py → tests/test_linear_models/bayesian/ard_regression.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Automatic-Relevance-Determination-Regression"
 
 
-def test_ard_regression():
+def ard_regression():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create ARD regression object
     ard_regression = ARDRegression()

tests/test_linear_models/bayesian/test_bayesian_regression.py → tests/test_linear_models/bayesian/bayesian_regression.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Bayesian-Ridge-Regression"
 
 
-def test_bayesian_regression():
+def bayesian_regression():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create bayesian ridge regression object
     bayesian_ridge_regression = BayesianRidge()

tests/test_linear_models/elasticnet/test_elastic_net.py → tests/test_linear_models/elasticnet/elastic_net.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Elastic-Net-Regression"
 
 
-def test_elastic_net():
+def elastic_net():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Elastic Net regression object
     elasticnet_alpha = 0.1

tests/test_linear_models/elasticnet/test_elastic_net_cv.py → tests/test_linear_models/elasticnet/elastic_net_cv.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Elastic-Net-CV-Regression"
 
 
-def test_elastic_net_cv():
+def elastic_net_cv():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Elastic Net CV regression object
     elasticnet_alphas = [1e-3, 1e-2, 1e-1, 1]

tests/test_linear_models/elasticnet/test_multi_task_elastic_net.py → tests/test_linear_models/elasticnet/multi_task_elastic_net.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Multi-Task-Elastic-Net-Regression"
 
 
-def test_multi_task_elastic_net():
+def multi_task_elastic_net():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     y_train = [[y, y**2] for y in y_train]
     y_test = [[y, y**2] for y in y_test]

tests/test_linear_models/elasticnet/test_multi_task_elastic_net_cv.py → tests/test_linear_models/elasticnet/multi_task_elastic_net_cv.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Multi-Task-Elastic-Net-CV-Regression"
 
 
-def test_multi_task_elastic_net_cv():
+def multi_task_elastic_net_cv():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     y_train = [[y, y**2] for y in y_train]
     y_test = [[y, y**2] for y in y_test]

tests/test_linear_models/glm/test_gamma_regression.py → tests/test_linear_models/glm/gamma_regression.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Gamma-Regression"
 
 
-def test_gamma_regression():
+def gamma_regression():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Gamma regression object
     gamma_alpha = 0.5

tests/test_linear_models/glm/test_poisson_regression.py → tests/test_linear_models/glm/poisson_regression.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Poisson-Regression"
 
 
-def test_poisson_regression():
+def poisson_regression():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Poisson regression object
     poisson_alpha = 0.5

tests/test_linear_models/glm/test_tweedie_regression.py → tests/test_linear_models/glm/tweedie_regression.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Tweedie-Regression"
 
 
-def test_tweedie_regression():
+def tweedie_regression():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Tweedie Regression object
     tweedie_alpha = 0.5

tests/test_linear_models/lasso_lars/test_lasso.py → tests/test_linear_models/lasso_lars/lasso.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Lasso-Regression"
 
 
-def test_lasso():
+def lasso():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Lasso regression object
     lasso_alpha = 0.2

tests/test_linear_models/lasso_lars/test_lasso_cv.py → tests/test_linear_models/lasso_lars/lasso_cv.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Lasso-Regression-CV"
 
 
-def test_lasso_cv():
+def lasso_cv():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Lasso CV regression object
     lasso_alphas = [1e-3, 1e-2, 1e-1, 1]

tests/test_linear_models/lasso_lars/test_lasso_lars.py → tests/test_linear_models/lasso_lars/lasso_lars.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Lasso-Lars-Regression"
 
 
-def test_lasso_lars():
+def lasso_lars():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Lasso Lars regression object
     lasso_alpha = 0.2

tests/test_linear_models/lasso_lars/test_lasso_lars_cv.py → tests/test_linear_models/lasso_lars/lasso_lars_cv.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Lasso-Lars-CV-Regression"
 
 
-def test_lasso_lars_cv():
+def lasso_lars_cv():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Lasso Lars CV regression object
     lasso_cv = 5

tests/test_linear_models/lasso_lars/test_lasso_lars_ic.py → tests/test_linear_models/lasso_lars/lasso_lars_ic.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Lasso-Lars-IC-Regression"
 
 
-def test_lasso_lars_ic():
+def lasso_lars_ic():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Lasso Lars IC regression object
     lasso_criterian = "bic"

tests/test_linear_models/lasso_lars/test_multi_task_lasso.py → tests/test_linear_models/lasso_lars/multi_task_lasso.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Multi-Task-Lasso-Regression"
 
 
-def test_multi_task_lasso():
+def multi_task_lasso():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     y_train = [[y, y**2] for y in y_train]
     y_test = [[y, y**2] for y in y_test]

tests/test_linear_models/lasso_lars/test_multi_task_lasso_cv.py → tests/test_linear_models/lasso_lars/multi_task_lasso_cv.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Multi-Task-Lasso-CV-Regression"
 
 
-def test_multi_task_lasso_cv():
+def multi_task_lasso_cv():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     y_train = [[y, y**2] for y in y_train]
     y_test = [[y, y**2] for y in y_test]

tests/test_linear_models/linear_regression/test_linear_regression.py → tests/test_linear_models/linear_regression/linear_regression.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Linear-Regression"
 
 
-def test_linear_regression():
+def linear_regression():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create linear regression object
     linear_regression = LinearRegression()

tests/test_linear_models/logistic/test_logistic_regression.py → tests/test_linear_models/logistic/logistic_regression.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Logistic-Regression"
 
 
-def test_logistic_regression():
+def logistic_regression():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Logistic regression object
     logistic_regression_random_state = 4

tests/test_linear_models/logistic/test_logistic_regression_cv.py → tests/test_linear_models/logistic/logistic_regression_cv.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Logistic-Regression-CV"
 
 
-def test_logistic_regression_cv():
+def logistic_regression_cv():
     x_train, y_train, x_test, y_test = prepare_logistic_regression_datasets()
     # Create Logistic regression cv object
     logistic_regression_cv = 5

tests/test_linear_models/omp/test_omp.py → tests/test_linear_models/omp/omp.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Orthogonal-Matching-Pursuit-Regression"
 
 
-def test_omp():
+def omp():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Orthogonal Matching Pursuit regression object
     omp_n_nonzero_coefs = 10

tests/test_linear_models/omp/test_omp_cv.py → tests/test_linear_models/omp/omp_cv.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Orthogonal-Matching-Pursuit-CV-Regression"
 
 
-def test_omp_cv():
+def omp_cv():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Orthogonal Matching Pursuit CV regression object
     omp_cv = 5

tests/test_linear_models/passive_aggressive/test_passive_aggressive_classifier.py → tests/test_linear_models/passive_aggressive/passive_aggressive_classifier.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Passive-Aggressive-Classifier"
 
 
-def test_passive_aggressive_classifier():
+def passive_aggressive_classifier():
     x_train, y_train, x_test, y_test = prepare_simple_classification_datasets()
     # Create ridge regression object
     pac_max_iter = 1000

tests/test_linear_models/passive_aggressive/test_passive_aggressive_regressor.py → tests/test_linear_models/passive_aggressive/passive_aggressive_regressor.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Passive-Aggressive-Regressor"
 
 
-def test_passive_agressive_regressor():
+def passive_agressive_regressor():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Passive Aggressive Regression object
     par_random_state = 2

tests/test_linear_models/perceptron/test_perception.py → tests/test_linear_models/perceptron/perception.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Perceptron"
 
 
-def test_perceptron():
+def perceptron():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create perceptron regression object
 

tests/test_linear_models/quantile/test_quantile.py → tests/test_linear_models/quantile/quantile.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Quantile-Regressor"
 
 
-def test_quantile_regressor():
+def quantile_regressor():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Quantile regression object
     quantile_regression = QuantileRegressor(quantile=0.8, solver="highs")

tests/test_linear_models/ridge/test_ridge_classifier.py → tests/test_linear_models/ridge/ridge_classifier.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Ridge-Classifier"
 
 
-def test_ridge_classifier():
+def ridge_classifier():
     x_train, y_train, x_test, y_test = prepare_simple_classification_datasets()
     # Create ridge classifier object
     ridge_alpha = 0.4

tests/test_linear_models/ridge/test_ridge_classifier_cv.py → tests/test_linear_models/ridge/ridge_classifier_cv.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Ridge-Classifier-CV"
 
 
-def test_ridge_classifier_cv():
+def ridge_classifier_cv():
     x_train, y_train, x_test, y_test = prepare_simple_classification_datasets()
     # Create ridge classifier cv object
     ridge_cv_alphas = [1e-3, 1e-2, 1e-1, 1]

tests/test_linear_models/ridge/test_ridge_regression.py → tests/test_linear_models/ridge/ridge_regression.py

@@ -4,7 +4,7 @@
 MODEL_NAME = "Ridge-Regression"
 
 
-def test_ridge_regression():
+def ridge_regression():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create ridge regression object
     ridge_alpha = 0.5

tests/test_linear_models/ridge/test_ridge_regression_cv.py → tests/test_linear_models/ridge/ridge_regression_cv.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Ridge-Regression-CV"
 
 
-def test_ridge_regression_cv():
+def ridge_regression_cv():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create ridgeCV regression object
     ridge_cv_alphas = [1e-3, 1e-2, 1e-1, 1]

tests/test_linear_models/robustness/test_huber_regression.py → tests/test_linear_models/robustness/huber_regression.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Huber-Regressor"
 
 
-def test_huber_regression():
+def huber_regression():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create Huber regression object
     huber_regresion = HuberRegressor(max_iter=300)

tests/test_linear_models/robustness/test_ransac_regression.py → tests/test_linear_models/robustness/ransac_regression.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "RANSAC-Regressor"
 
 
-def test_ransac_regression():
+def ransac_regression():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create ransac regression object
     ransac_random_state = 3

tests/test_linear_models/robustness/test_theil_sen_regression.py → tests/test_linear_models/robustness/theil_sen_regression.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "Theil-Sen-Regressor"
 
 
-def test_theil_sen_regression():
+def theil_sen_regression():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create TheilSen Regression object
     theilsen_random_state = 4

tests/test_linear_models/sgd/test_sgd_classifier.py → tests/test_linear_models/sgd/sgd_classifier.py

@@ -4,7 +4,7 @@
 MODEL_NAME = "SGD-Classifier"
 
 
-def test_sgd_classifier():
+def sgd_classifier():
     x_train, y_train, x_test, y_test = prepare_simple_classification_datasets()
     # Create SGDClassifier regression object
     sgd_max_iter = 100000

tests/test_linear_models/sgd/test_sgd_oneclass_svm.py → tests/test_linear_models/sgd/sgd_oneclass_svm.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "SGD-OneClass-Regression"
 
 
-def test_sgd_oneclass_svm():
+def sgd_oneclass_svm():
     x_train, _, x_test, y_test = prepare_simple_regression_datasets()
     # Create SGDOneClassSVM regression object
     sgd_random_state = 34

tests/test_linear_models/sgd/test_sgd_regression.py → tests/test_linear_models/sgd/sgd_regression.py

@@ -5,7 +5,7 @@
 MODEL_NAME = "SGD-Regression"
 
 
-def test_sgd_regression():
+def sgd_regression():
     x_train, y_train, x_test, y_test = prepare_simple_regression_datasets()
     # Create SGD Regression object
     sgd_max_iter = 100000