hate-speech-detector/main.py

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
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

def feature_engineering(raw_df):
    df = raw_df.copy()
    df['len'] = df.comment.str.len()
    df['comment'] = df['comment'].fillna('')
    df['label'] = df.isHate.apply(lambda x: float(x>=0.5))
    df = df.drop(columns=['isHate'])
    return df

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


def fit_model(train_X, train_y):
    clf_tfidf = LogisticRegression(solver='sag')
    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),
    }

if __name__ == '__main__':
    print('Loading data...')
    df = pd.read_csv('data/Ethos_Dataset_Binary.csv', delimiter=';')
    train_df, test_df = train_test_split(df)

    train_df = feature_engineering(train_df)
    test_df = feature_engineering(test_df)

    print('Fitting TFIDF...')
    train_tfidf, test_tfidf, tfidf = fit_tfidf(train_df, test_df)

    print('Fitting classifier...')
    train_y = train_df['label']
    model = fit_model(train_tfidf, train_y)

    train_metrics = eval_model(model, train_tfidf, train_y)
    print('Train metrics:')
    print(train_metrics)

    test_metrics = eval_model(model, test_tfidf, test_df['label'])
    print('Test metrics:')
    print(test_metrics)