utils.py

import matplotlib.pyplot as plt
import itertools
import numpy as np
import os
from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer, WordNetLemmatizer
english_stopword = list(stopwords.words('english'))
import re
def plot_confusion_matrix(cm,
                          target_names,
                          title='Confusion matrix',
                          cmap=None,
                          normalize=True,
                          filename='confusion_matrix.png',
                          show=True):

    accuracy = np.trace(cm) / float(np.sum(cm))
    misclass = 1 - accuracy

    if cmap is None:
        cmap = plt.get_cmap('Blues')

    plt.figure(figsize=(8, 6))
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()

    if target_names is not None:
        tick_marks = np.arange(len(target_names))
        plt.xticks(tick_marks, target_names, rotation=45)
        plt.yticks(tick_marks, target_names)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

    thresh = cm.max() / 1.5 if normalize else cm.max() / 2
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        if normalize:
            plt.text(j, i, "{:0.2f}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")
        else:
            plt.text(j, i, "{:,}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label\naccuracy={:0.2f}; misclass={:0.2f}'.format(
        accuracy, misclass))
#     plt.show()
    if not os.path.exists("results/"):
        os.mkdir("results")
    if normalize:
        plt.savefig('results/' + 'normalize_' + filename)
    else:
        plt.savefig('results/' + filename)

    if show:
        plt.show()


def score(y_true, y_pred):
    acc = accuracy_score(y_true, y_pred)
    f1 = f1_score(y_true, y_pred, average='macro')
    precision = precision_score(y_true, y_pred, average='macro')
    recall = recall_score(y_true, y_pred, average='macro')
    print(f"Accuracy={acc:.4f}", f"  F1 score={f1:.4f}", f"  precision={precision:.4f}", f"  recall={recall:.4f}")
    return acc, f1, precision, recall