Building a Sentiment Classifier using Scikit-Learn

3.choosing_data_format.py

from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split
from scipy.sparse import csr_matrix
import numpy as np

def train_and_show_scores(X: csr_matrix, y: np.array, title: str) -> None:
    X_train, X_valid, y_train, y_valid = train_test_split(
        X, y, train_size=0.75, stratify=y
    )

    clf = SGDClassifier()
    clf.fit(X_train, y_train)
    train_score = clf.score(X_train, y_train)
    valid_score = clf.score(X_valid, y_valid)
    print(f'{title}\nTrain score: {round(train_score, 2)} ; Validation score: {round(valid_score, 2)}\n')

y_train = imdb_train['label'].values

train_and_show_scores(X_train_unigram, y_train, 'Unigram Counts')
train_and_show_scores(X_train_unigram_tf_idf, y_train, 'Unigram Tf-Idf')
train_and_show_scores(X_train_bigram, y_train, 'Bigram Counts')
train_and_show_scores(X_train_bigram_tf_idf, y_train, 'Bigram Tf-Idf')