DagsHubBasicTutorial/main.py

import pandas as pd
import argparse
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import roc_auc_score, average_precision_score, accuracy_score, precision_score, recall_score, \
    f1_score
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import joblib
import dagshub


CLASS_LABEL = 'MachineLearning'
train_df_path = 'data/train.csv'
test_df_path = 'data/test.csv'


def feature_engineering(raw_df):
    df = raw_df.copy()
    df['CreationDate'] = pd.to_datetime(df['CreationDate'])
    df['CreationDate_Epoch'] = df['CreationDate'].astype('int64') // 10 ** 9
    df['MachineLearning'] = df['Tags'].str.contains('machine-learning').fillna(False)
    df = df.drop(columns=['Id', 'Tags'])
    df['Title_Len'] = df.Title.str.len()
    df['Body_Len'] = df.Body.str.len()
    # Drop the correlated features
    df = df.drop(columns=['FavoriteCount'])
    df['Text'] = df['Title'].fillna('') + ' ' + df['Body'].fillna('')
    return df


def fit_tfidf(train_df, test_df):
    tfidf = TfidfVectorizer(max_features=25000)
    tfidf.fit(train_df['Text'])
    train_tfidf = tfidf.transform(train_df['Text'])
    test_tfidf = tfidf.transform(test_df['Text'])
    return train_tfidf, test_tfidf, tfidf


def fit_model(train_X, train_y, random_state=42):
    clf_tfidf = RandomForestClassifier(random_state=random_state, class_weight='balanced', max_depth=50)
    clf_tfidf.fit(train_X, train_y)
    return clf_tfidf


def eval_model(clf, X, y):
    y_proba = clf.predict_proba(X)[:, 1]
    y_pred = clf.predict(X)
    return {
        'roc_auc': roc_auc_score(y, y_proba),
        'average_precision': average_precision_score(y, y_proba),
        'accuracy': accuracy_score(y, y_pred),
        'precision': precision_score(y, y_pred),
        'recall': recall_score(y, y_pred),
        'f1': f1_score(y, y_pred),
    }


def split(random_state=42):
    print('Loading data...')
    df = pd.read_csv('data/CrossValidated-Questions.csv')
    df[CLASS_LABEL] = df['Tags'].str.contains('machine-learning').fillna(False)
    train_df, test_df = train_test_split(df, random_state=random_state, stratify=df[CLASS_LABEL])

    print('Saving split data...')
    train_df.to_csv(train_df_path)
    test_df.to_csv(test_df_path)


def train():
    print('Loading data...')
    train_df = pd.read_csv(train_df_path)
    test_df = pd.read_csv(test_df_path)

    print('Engineering features...')
    train_df = feature_engineering(train_df)
    test_df = feature_engineering(test_df)

    with dagshub.dagshub_logger() as logger:
        print('Fitting TFIDF...')
        train_tfidf, test_tfidf, tfidf = fit_tfidf(train_df, test_df)

        print('Saving TFIDF object...')
        joblib.dump(tfidf, 'outputs/tfidf.joblib')
        logger.log_hyperparams({'tfidf': tfidf.get_params()})

        print('Training model...')
        train_y = train_df[CLASS_LABEL]
        model = fit_model(train_tfidf, train_y)

        print('Saving trained model...')
        joblib.dump(model, 'outputs/model.joblib')
        logger.log_hyperparams(model_class=type(model).__name__)
        logger.log_hyperparams({'model': model.get_params()})

        print('Evaluating model...')
        train_metrics = eval_model(model, train_tfidf, train_y)
        print('Train metrics:')
        print(train_metrics)
        logger.log_metrics({f'train__{k}': v for k,v in train_metrics.items()})

        test_metrics = eval_model(model, test_tfidf, test_df[CLASS_LABEL])
        print('Test metrics:')
        print(test_metrics)
        logger.log_metrics({f'test__{k}': v for k,v in test_metrics.items()})


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    subparsers = parser.add_subparsers(title='Split or Train step:', dest='step')
    subparsers.required = True
    split_parser = subparsers.add_parser('split')
    split_parser.set_defaults(func=split)
    train_parser = subparsers.add_parser('train')
    train_parser.set_defaults(func=train)
    parser.parse_args().func()