Upload preprocessing_utils.py

preprocessing_utils.py

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+""" Utils functions for preprocessing"""
+import pandas as pd
+from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
+
+
+def aggregate_transform_df(original_df, transformed_df, transformed_cols):
+    """
+    Helper function to aggregate the columns transformed with the original dataset
+    """
+    print(original_df.shape)
+    print(transformed_df.shape)
+    df_final = original_df.drop(columns=transformed_cols)
+    df_final = df_final.merge(transformed_df, left_index=True, right_index=True)
+    print(df_final.shape)
+    return df_final
+
+
+def encode_categorical(df, categorical_cols, method="OneHot", encoder=None, fit=True):
+    """
+    Returns the dataframe where the categorical columns have been replaced
+    according to the method selected
+
+    Right now only OneHot is supported
+    """
+    print(f"Running {method} encoding")
+    if fit:
+        encoder = OneHotEncoder()
+        encoder.fit(df[categorical_cols])
+    array_transformed = encoder.transform(df[categorical_cols]).toarray()
+    df_encoded = pd.DataFrame(array_transformed, columns=encoder.get_feature_names_out(), index=df.index)
+    df_final = aggregate_transform_df(df, df_encoded, categorical_cols)
+    if fit:
+        return df_final, encoder
+    else:
+        return df_final
+
+
+def scale_numerical(df, numerical_cols, method="MinMax", scaler=None, fit=True):
+    """
+    Returns the dataframe where the numerical columns have been scaled
+    according to the method selected
+
+    Right now only MinMax is supported
+    """
+    print(f"Running {method} scaling")
+    if fit:
+        scaler = MinMaxScaler()
+        scaler.fit(df[numerical_cols])
+    array_transformed = scaler.transform(df[numerical_cols])
+    df_transformed = pd.DataFrame(array_transformed, columns=numerical_cols, index=df.index)
+    df_final = aggregate_transform_df(df, df_transformed, numerical_cols)
+    if fit:
+        return df_final, scaler
+    else:
+        return df_final
+
+
+def fill_nans(df, cols, method="mean"):
+    df_filled = df.copy()
+    print(f"Fill nans in {cols} with the {method} method")
+    for col in cols:
+        if method == "mean":
+            df_filled[col] = df_filled[col].fillna(df[col].mean())
+        elif method == "mode":
+            df_filled[col] = df_filled[col].fillna(df[col].mode())
+    return df_filled