bug fixes and improvement

tests/test_main.py

@@ -11,6 +11,7 @@
 from src.models.train_model import train_model
 from src.visualization.visualize import conf_matrix
 
+
 curr_dir = pathlib.Path(__file__)
 home_dir = curr_dir.parent.parent
 
@@ -20,72 +21,19 @@
 data_source = f'{home_dir.as_posix()}{processed_data}/train.csv'
 
 drive_ = params['load_dataset']['drive_link']
-
-# test data loading functionality 
-@pytest.fixture(params = [drive_])
-def load_dataset(request) :
-     return extract_data(request.param)
-
-def test_load_data(load_dataset) : 
-     if isinstance(load_dataset, pd.DataFrame) : 
-          assert isinstance(load_dataset, pd.DataFrame)
-     else : 
-          pytest.fail('Unable to fetch data from remote server')
-
-     # ---- OR ----
-     # assert isinstance(load_dataset, pd.DataFrame), "Failure Message"     # it will raise assertion error 
-
-# test feature generation functionality
-# 'request' is a fixture that provides information about the currently executing test.
-# It allows us to access various attributes and methods to get information about the test
-# environment, access fixtures, and more.
-# here we are accessing current parameters value using request.param
-@pytest.fixture(params = [data_source])
-def load(request) : 
-     return feat_eng(load_data(request.param))
-
-def test_build_features(load) : 
-     if load.shape[1] == 20 :
-          assert load.shape[1] == 20
-     else :
-          pytest.fail('build_feature is not working properly')
-
 parameters = params['train_model']
 TARGET = params['base']['target']
-
 train_dir = f"{home_dir.as_posix()}{params['build_features']['extended_data']}/extended_train.csv"
 test_dir = f"{home_dir.as_posix()}{params['build_features']['extended_data']}/extended_test.csv"
 model_dir = f"{home_dir.as_posix()}{parameters['model_dir']}"
 pathlib.Path(model_dir).mkdir(parents = True, exist_ok = True)
-
 train_data = load_data(train_dir)
 test_data = load_data(test_dir)
 X_train = train_data.drop(columns = [TARGET]).values
 Y = train_data[TARGET]
-
 X_test = test_data.drop(columns = [TARGET]).values
 y = test_data[TARGET]
 
-# test model training functionality
-@pytest.fixture
-def get_model() :
-     return train_model(X_train, Y, parameters['n_estimators'], parameters['criterion'], parameters['max_depth'], min_samples_leaf = parameters['min_samples_leaf'],
-                 min_samples_split = parameters['min_samples_split'], random_state = parameters['random_state'], yaml_file_obj = params)
-
-def test_train_model(get_model) :
-     if isinstance(get_model['model'], BaseEstimator) :
-          assert isinstance(get_model['model'], BaseEstimator)
-     else :
-          pytest.fail('not able to train the model')
-
-# test ploting functionality
-def test_conf_metrix(get_model) :
-     filename = conf_matrix(y_test = y, y_pred = get_model['model'].predict(X_test), labels = get_model['model'].classes_, path = './tests/trash', params_obj = params)
-     if pathlib.Path(filename).exists() :
-          assert pathlib.Path(filename).exists()
-     else :
-          pytest.fail('unable to generate confusion matrix')
-
 # test api endpoint
 # valid input
 user_input1 = {  
@@ -128,9 +76,36 @@ def test_conf_metrix(get_model) :
                 'sulphates': 0.65,
                 'alcohol': '11.4'}
 
-@pytest.fixture
-def client() :
-     return TestClient(app)
+# pass fixture as parameter
+def test_load_data(load_dataset) :
+     # load_dataset is pointing to the dataset returned by extract_data method 
+     if isinstance(load_dataset, pd.DataFrame) :            
+          assert isinstance(load_dataset, pd.DataFrame)      # checking load_dataset is instance of pandas dataframe, if yes testcase will pass
+     else : 
+          pytest.fail('Unable to fetch data from remote server')     # else test case will fail and return the message
+
+     # ---- OR ----
+     # assert isinstance(load_dataset, pd.DataFrame), "Failure Message"     # it will raise assertion error 
+
+def test_build_features(load) : 
+     if load.shape[1] == 20 :
+          assert load.shape[1] == 20
+     else :
+          pytest.fail('build_feature is not working properly')
+
+def test_train_model(get_model) :
+     if isinstance(get_model['model'], BaseEstimator) :
+          assert isinstance(get_model['model'], BaseEstimator)
+     else :
+          pytest.fail('not able to train the model')
+
+# test ploting functionality
+def test_conf_metrix(get_model) :
+     filename = conf_matrix(y_test = y, y_pred = get_model['model'].predict(X_test), labels = get_model['model'].classes_, path = './tests/trash', params_obj = params)
+     if pathlib.Path(filename).exists() :
+          assert pathlib.Path(filename).exists()
+     else :
+          pytest.fail('unable to generate confusion matrix')
 
 # test response of api
 def test_response(client) :