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- 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.linear_model import SGDClassifier
- 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 = SGDClassifier(loss='modified_huber', random_state=random_state)
- 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()
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