add conftest.py

tests/conftest.py

@@ -0,0 +1,48 @@
+import pytest
+import pathlib
+import yaml
+from fastapi.testclient import TestClient
+from prod.api import app
+from src.data.load_dataset import extract_data
+from src.features.build_features import feat_eng
+from src.data.make_dataset import load_data
+from src.models.train_model import train_model
+
+curr_dir = pathlib.Path(__file__)
+home_dir = curr_dir.parent.parent
+params = yaml.safe_load(open(f"{home_dir.as_posix()}/params.yaml"))
+drive_ = params['load_dataset']['drive_link']
+processed_data = params['make_dataset']['processed_data']
+data_source = f'{home_dir.as_posix()}{processed_data}/train.csv'
+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]
+
+# testing load_data function 
+@pytest.fixture(params = [drive_])
+def load_dataset(request) :
+     # calling extract_data method & returning its output 
+     return extract_data(request.param)
+
+@pytest.fixture(params = [data_source])
+def load(request) : 
+     return feat_eng(load_data(request.param))
+
+# 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)
+
+@pytest.fixture
+def client() :
+     return TestClient(app)