chore: remove preprocessing file

preprocessing.py

@@ -1,69 +0,0 @@
-"""
-Preliminary preprocessing on the data, such as:
-- correcting column names
-- encoding the target column
-"""
-
-import pandas as pd
-from sklearn import preprocessing
-
-# Files location
-TRAINING_FILE_NAME = "./data/Training.csv"
-TESTING_FILE_NAME = "./data/Testing.csv"
-
-# Columns processing
-TARGET_COLUMN = "prognosis"
-DROP_COLUMNS = ["Unnamed: 133"]
-
-RENAME_COLUMNS = {
-    "scurring": "scurving",
-    "dischromic _patches": "dischromic_patches",
-    "spotting_ urination": "spotting_urination",
-    "foul_smell_of urine": "foul_smell_of_urine",
-}
-
-RENAME_VALUES = {
-    "(vertigo) Paroymsal  Positional Vertigo": "Paroymsal Positional Vertigo",
-    "Dimorphic hemmorhoids(piles)": "Dimorphic hemmorhoids (piles)",
-    "Peptic ulcer diseae": "Peptic Ulcer",
-}
-
-if __name__ == "__main__":
-
-    # Load data
-    df_train = pd.read_csv(TRAINING_FILE_NAME)
-    df_test = pd.read_csv(TESTING_FILE_NAME)
-
-    # Remove unseless columns
-    df_train.drop(columns=DROP_COLUMNS, axis=1, errors="ignore", inplace=True)
-    df_test.drop(columns=DROP_COLUMNS, axis=1, errors="ignore", inplace=True)
-
-    # Correct some typos in some columns name
-    df_train.rename(columns=RENAME_COLUMNS, inplace=True)
-    df_test.rename(columns=RENAME_COLUMNS, inplace=True)
-
-    df_train[TARGET_COLUMN].replace(RENAME_VALUES.keys(), RENAME_VALUES.values(), inplace=True)
-    df_train[TARGET_COLUMN] = df_train[TARGET_COLUMN].apply(str.title)
-
-    df_test[TARGET_COLUMN].replace(RENAME_VALUES.keys(), RENAME_VALUES.values(), inplace=True)
-    df_test[TARGET_COLUMN] = df_test[TARGET_COLUMN].apply(str.title)
-
-    # Convert the `TARGET_COLUMN` to a numeric label
-    label_encoder = preprocessing.LabelEncoder()
-    label_encoder.fit(df_train[[TARGET_COLUMN]].values.flatten())
-
-    df_train[f"{TARGET_COLUMN}_encoded"] = label_encoder.transform(
-        df_train[[TARGET_COLUMN]].values.flatten()
-    )
-    df_test[f"{TARGET_COLUMN}_encoded"] = label_encoder.transform(
-        df_test[[TARGET_COLUMN]].values.flatten()
-    )
-
-    # Cast X features from int64 to float32
-    float_columns = df_train.columns.drop([TARGET_COLUMN])
-    df_train[float_columns] = df_train[float_columns].astype("float32")
-    df_test[float_columns] = df_test[float_columns].astype("float32")
-
-    # Save preprocessed data
-    df_train.to_csv(path_or_buf="./data/Training_preprocessed.csv", index=False)
-    df_test.to_csv(path_or_buf="./data/Testing_preprocessed.csv", index=False)