DSIC-Bayes-continuous/datapreparator.py

from sklearn.model_selection import train_test_split
from copy import deepcopy
import pandas as pd
import typing

class DataPreparator:
    genre_dict = {
        "blues" : 1,
        "classical" : 2,
        "country" : 3,
        "disco" : 4,
        "hiphop" : 5,
        "jazz" : 6,
        "metal" : 7,
        "pop" : 8,
        "reggae" : 9,
        "rock" : 10
    }


    def prepare_data(df: pd.DataFrame) -> pd.DataFrame:
        data = deepcopy(df)
        column = df["label"].apply(lambda x: DataPreparator.genre_dict[x])
        data.insert(0, 'genre', column, 'int')
        data = data.drop(columns=['filename', 'label', 'length'])
        return data

    
    def train_test_split(df: pd.DataFrame) -> typing.Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]:
        #X = df.drop(["genre"], axis=1)
        X = df[["chroma_stft_mean","chroma_stft_var","rms_mean"]]
        Y = df["genre"]
        return train_test_split(X, Y, test_size = 0.20, random_state = False)


    def print_df_info(df: pd.DataFrame) -> None:
        for key in DataPreparator.genre_dict.keys():
            count = len(df[df["genre"]==DataPreparator.genre_dict[key]])
            print(f"Key: {key}\tCount: {count}")