def normalized_columns()

in fclib/fclib/feature_engineering/feature_utils.py [0:0]

• 23 lines of code
• 9 McCabe index (conditional complexity)

def normalized_columns(datetime_col, value_col, mode="log", output_colname="normalized_columns"):
    """
    Creates columns normalized to be log of input columns devided by global average of each columns,
    or normalized using maximum and minimum.
    
    Args:
        datetime_col: Datetime column.
        value_col: Value column to be normalized.
        mode: Normalization mode,
            accepted values are 'log' and 'minmax'. Default value 'log'.
    
    Returns:
        Normalized value column.
    """

    if not is_datetime_like(datetime_col):
        datetime_col = pd.to_datetime(datetime_col, format=DATETIME_FORMAT)

    df = pd.DataFrame({"Datetime": datetime_col, "value": value_col})
    df.set_index("Datetime", inplace=True)

    if not df.index.is_monotonic:
        df.sort_index(inplace=True)

    if mode == "log":
        mean_value = df["value"].mean()
        if mean_value != 0:
            df[output_colname] = np.log(df["value"] / mean_value)
        elif mean_value == 0:
            df[output_colname] = 0
    elif mode == "minmax":
        min_value = min(df["value"])
        max_value = max(df["value"])
        if min_value != max_value:
            df[output_colname] = (df["value"] - min_value) / (max_value - min_value)
        elif min_value == max_value:
            df[output_colname] = 0
    else:
        raise ValueError("Valid values for mode are 'log' and 'minmax'")

    return df[[output_colname]]