plot_confusion_matrix.py

import itertools
import numpy as np
import matplotlib.pyplot as plt

from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix

# # import some data to play with
# iris = datasets.load_iris()
# X = iris.data
# y = iris.target
# class_names = iris.target_names

# # Split the data into a training set and a test set
# X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# # Run classifier, using a model that is too regularized (C too low) to see
# # the impact on the results
# classifier = svm.SVC(kernel='linear', C=0.01)
# y_pred = classifier.fit(X_train, y_train).predict(X_test)


def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix - Inflection Category Identification")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

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

    plt.tight_layout()
    plt.ylabel('True Inflection Category')
    plt.xlabel('Predicted Inflection Category')

avg_gold = [line.split()[0].strip() for line in open("proto_infl_id_preds.txt").readlines()]
avg_pred = [line.split()[1].strip() for line in open("proto_infl_id_preds.txt").readlines()]
# Compute confusion matrix
cnf_matrix = confusion_matrix(avg_gold, avg_pred)
np.set_printoptions(precision=2)

class_names = ['PL', '3S', 'PRESP', 'PAST', 'PASTP', 'IRR_PAST', 'CP']
# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names,
                      title='Confusion matrix, without normalization')

# Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
                      title="Confusion Matrix - Inflection Category Identification (Proto Centroid)")

plt.show()