comparing models

utils.py

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+"""
+Saeed Khosravi - 27 Nov 2025
+"""
+
+
+def split_path(full_path):
+    import os
+
+    directory = os.path.dirname(full_path)
+    filename = os.path.splitext(os.path.basename(full_path))[0]
+    return directory, filename
+
+
+def write_textfile(path, data_list):
+    with open(path, "w") as file:
+        for data in data_list:
+            file.write(f"{data} \n")
+
+
+def missing_value_handler(data_path):
+    import pandas
+    from sklearn.impute import KNNImputer
+
+    data_directory, data_filename = split_path(data_path)
+
+    data_frame = pandas.read_csv(data_path)
+
+    columns = list(data_frame.head(0))
+    # remove column id
+    if "id" in columns:
+        data_frame = data_frame.drop("id", axis="columns")
+
+    columns = list(data_frame.head(0))
+    write_textfile(f"{data_directory}/columns.txt", columns)
+
+    # find missing values
+    missing_value_counts = data_frame.isna().sum()
+    write_textfile(f"{data_directory}/missing.txt", missing_value_counts)
+
+    # fill missing values - KNNImputer
+
+    imputer = KNNImputer(n_neighbors=5)
+    data_imputed = imputer.fit_transform(data_frame)
+    data_frame_imputed = pandas.DataFrame(data_imputed, columns=columns)
+
+    missing_value_counts = data_frame_imputed.isna().sum()
+    write_textfile(f"{data_directory}/no_missing.txt", missing_value_counts)
+    return data_frame_imputed
+
+
+def scaling_handler(data_frame, method="robust_scaling"):
+    if method == "robust_scaling":
+        import pandas
+        from sklearn.preprocessing import RobustScaler
+
+        labels = data_frame["label"]
+        scaler = RobustScaler()
+        x = data_frame.drop("label", axis=1)
+        x_scale = scaler.fit_transform(x)
+        data_frame_scaled = pandas.DataFrame(x_scale, columns=x.columns)
+        data_frame_scaled["label"] = labels.values
+        return data_frame_scaled