added docstrings to functions and tests

src/pynyairbnb/pynyairbnb.py

@@ -79,6 +79,22 @@ def build_clf_model(model, preprocessor, tbl_out_dir, X_train, y_train, X_test,
 
 
 def knn_param_optimization(knn_model, tbl_out_dir, X_train, y_train, X_test, y_test,replacement_dict):
+    """_summary_
+    Performs Hyperparameter optimization for KNN model 
+    Args:
+        knn_model (_type_): KNN Model Built with Sklearn Library 
+        tbl_out_dir (_type_): Data Input
+        X_train (_type_): training data input features
+        y_train (_type_): training data target variable
+        X_test (_type_): testing data input features
+        y_test (_type_): testing data target variable
+        replacement_dict (_type_): Dictionary with proper Formatting for classification report 
+        output_file_name (str): The name to save the output as, will be a .csv file 
+        
+        
+    Output:
+    Saves output of model to csv file defined in output_file_name
+    """
     # KNN Hyperparameter Optimization
     param_dist = {
         'kneighborsclassifier__n_neighbors': randint(1, 30),

tests/test_pynyairbnb.py

@@ -33,6 +33,10 @@ def test_transformer_assignment():
     assert transformers.get('countvectorizer') == CountVectorizer
 
 def test_preprocessor():
+    """_summary_
+    Tests the functionality of build preprocessor method
+    
+    """
     # Create an artificial dataset
     np.random.seed(0)  # For reproducibility
     data = pd.DataFrame({
@@ -73,6 +77,9 @@ def test_preprocessor():
 
 
 def test_build_clf_model():
+    """_summary_
+    Tests the build clf model and ensures output file is correctly created
+    """
     preprocessor = StandardScaler()
     model = KNeighborsClassifier()
     tbl_out_dir = "./test_outputs"