stackexchange-tutorial/tutorial/prepare_data.py

from . import shared
from .shared import save
import pandas as pd


def split_train_test(ratio=0.2, random_seed=42):
    from sklearn.model_selection import train_test_split

    df = pd.read_csv(shared.raw_data, encoding='utf-8')
    df[shared.col_label] = df[shared.col_tags].fillna('').str.contains('machine-learning')

    df_positive = df[df[shared.col_label]]
    df_negative = df[df[shared.col_label] != True]

    train_df, test_df = train_test_split(df, test_size=ratio, random_state=random_seed, stratify=df[shared.col_label])

    train_df.to_csv(shared.train_data, index=False)
    test_df.to_csv(shared.test_data, index=False)


from html.parser import HTMLParser

class MLStripper(HTMLParser):
    def __init__(self):
        super().__init__()
        self.reset()
        self.strict = False
        self.convert_charrefs= True
        self.fed = []
    
    def handle_data(self, d):
        self.fed.append(d)
    
    def get_data(self):
        return ''.join(self.fed)


def text_preprocess(s):
    strip = MLStripper()
    strip.feed(s.lower())
    return strip.get_data()


def text_col(df):
    return (df[shared.col_title].fillna('') + df[shared.col_body].fillna('')).astype('U', copy=False).apply(text_preprocess)


def build_pipeline():
    from sklearn.impute import SimpleImputer
    from sklearn.preprocessing import StandardScaler, FunctionTransformer
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.pipeline import make_pipeline
    from sklearn.compose import ColumnTransformer

    tfidf = TfidfVectorizer(encoding='utf-8', stop_words='english', analyzer='word', max_features=25000, ngram_range=(1, 2))
    from tutorial import prepare_data # Required for proper pickling of this pipeline
    return ColumnTransformer([
        ('passthrough', 'passthrough', [shared.col_id, shared.col_label]),
        ('num_cols', make_pipeline(SimpleImputer(),StandardScaler()), shared.extra_feature_cols),
        ('tfidf', make_pipeline(FunctionTransformer(prepare_data.text_col), tfidf), shared.text_cols)
    ])


def map_dataframe(df, pipeline):
    tfidf_cols = [f'Text_{col}' for col in pipeline.named_transformers_.tfidf[1].get_feature_names()]
    cols = [shared.col_id, shared.col_label] + shared.extra_feature_cols + tfidf_cols
    return pd.DataFrame.sparse.from_spmatrix(pipeline.transform(df), columns=cols)


def prepare_data():
    pipeline = build_pipeline()

    print("Loading train data")
    train_df = pd.read_csv(shared.train_data, encoding='utf-8')
    print("Loading test data")
    test_df = pd.read_csv(shared.test_data, encoding='utf-8')
    print("Done")
    
    print("Fitting the pipeline...")
    pipeline.fit(train_df)
    print("Done")
    print("Transforming data")
    train_df = map_dataframe(train_df, pipeline)
    test_df = map_dataframe(test_df, pipeline)
    print("Done")

    save(pipeline, shared.pipeline_pkl)
    print("Saving training data")
    train_df.to_pickle(shared.train_processed)
    print("Saving test data")
    test_df.to_pickle(shared.test_processed)
    print("Done")
    # save(train_df, shared.train_processed)
    # save(test_df, shared.test_processed)
    from dagshub import dagshub_logger
    with dagshub_logger(should_log_metrics=False, hparams_path='pipeline-params.yml') as logger:
        params = {k:v for k,v in pipeline.get_params().items() if v is None or type(v) in [str,int,float,bool]}
        print('Logging pipeline params:')
        print(params)
        logger.log_hyperparams(params)


def main():
    print("Splitting train and test...")
    split_train_test()
    print("Done")
    print("Preparing data...")
    prepare_data()
    print("Done")


if __name__ == "__main__":
    main()