Implement error handling for data_preprocessing_core()

[ETA444]

Error Handling in data_preprocessing_core()

Type Validations

• DataFrame Type Check
  • Ensures that the input df is a pandas DataFrame. This is crucial because the function performs operations specific to pandas DataFrames.

if not isinstance(df, pd.DataFrame):
    raise TypeError("data_preprocessing_core(): The 'df' parameter must be a pandas DataFrame.")

• List and String Type Checks for Columns
  • Verifies that x_cols is a list of strings, as these are expected to be column names in the DataFrame. It also checks that y_col is a string since it specifies the target column.

if not isinstance(x_cols, list) or not all(isinstance(col, str) for col in x_cols):
    raise TypeError("data_preprocessing_core(): The 'x_cols' parameter must be a list of strings representing column names.")
if not isinstance(y_col, str):
    raise TypeError("data_preprocessing_core(): The 'y_col' parameter must be a string representing the target column name.")

• String Type Check for Data State
  • Ensures data_state is a string. This parameter indicates whether the data is already preprocessed or needs preprocessing.

if not isinstance(data_state, str):
    raise TypeError("data_preprocessing_core(): The 'data_state' parameter must be a string.")

• Float and Integer Type Checks
  • Checks the types for test_size and random_state to ensure they are a float and an integer, respectively. This is important for correctly setting up the data split configuration.

if not isinstance(test_size, float):
    raise TypeError("data_preprocessing_core(): The 'test_size' parameter must be a float.")
if not isinstance(random_state, int):
    raise TypeError("data_preprocessing_core(): The 'random_state' parameter must be an integer.")

• Integer Type Check for Verbose
  • Confirms that verbose is an integer, which is used to control the level of output detail.

if not isinstance(verbose, int):
    raise TypeError("data_preprocessing_core(): 'verbose' must be an integer value.")

• Validation for Transformers and Vectorizers
  • Ensures that all transformers and vectorizers have the required methods (fit_transform, fit, and transform) to be used in the preprocessing pipeline.

if not (hasattr(numeric_imputer, 'fit_transform') or (hasattr(numeric_imputer, 'fit') and hasattr(numeric_imputer, 'transform'))):
    raise TypeError("data_preprocessing_core(): The 'numeric_imputer' must support 'fit_transform' or both 'fit' and 'transform' methods.")
# Similar checks for other transformers and vectorizers...

Value Validations

• Data Presence Check
  • Ensures that the DataFrame is not empty to guarantee that there is data available for preprocessing.

if df.empty:
    raise ValueError("data_preprocessing_core(): The input DataFrame is empty.")

• Data State Validation
  • Confirms that data_state matches either 'unprocessed' or 'preprocessed'. This is crucial for determining whether to apply preprocessing steps.

if data_state.lower() not in ['unprocessed', 'preprocessed']:
    raise ValueError("data_preprocessing_core(): The 'data_state' parameter must be either 'unprocessed' or 'preprocessed'.")

• Test Size Range Check
  • Ensures test_size is between 0 and 1 to correctly partition the dataset into training and testing sets.

if not 0 < test_size < 1:
    raise ValueError("data_preprocessing_core(): The 'test_size' parameter must be a float between 0 and 1.")

• Column Presence Check
  • Verifies that y_col and all x_cols are present in the DataFrame. This is essential for selecting the right data for preprocessing.

if y_col not in df.columns:
    raise ValueError(f"data_preprocessing_core(): The specified target column '{y_col}' is not present in the DataFrame.")
missing_cols = set(x_cols) - set(df.columns)
if missing_cols:
    raise ValueError(f"data_preprocessing_core(): The following feature columns are not present in the DataFrame: {', '.join(missing_cols)}")