utils.py

from __future__ import print_function
import os, sys, itertools, struct, gzip
import numpy as np
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt 
from sklearn.metrics import confusion_matrix
from sys import platform


# Plot Metrics
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`.
    """
    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)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, 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')
    
def classification_report(y_true, y_pred):
    print(metrics.classification_report(y_true, y_pred))

    # Compute confusion matrix
    cnf_matrix = confusion_matrix(y_true, y_pred)
    np.set_printoptions(precision=2)

    # Plot non-normalized confusion matrix
    plt.figure()
    class_names = sorted(set(y_true))
    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='Normalized confusion matrix')

    plt.show()


# load compressed MNIST gz files and return numpy arrays
def load_data(filename, label=False):
    with gzip.open(filename) as gz:
        struct.unpack('I', gz.read(4))
        n_items = struct.unpack('>I', gz.read(4))
        if not label:
            n_rows = struct.unpack('>I', gz.read(4))[0]
            n_cols = struct.unpack('>I', gz.read(4))[0]
            res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8)
            res = res.reshape(n_items[0], n_rows * n_cols)
        else:
            res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8)
            res = res.reshape(n_items[0], 1)
    return res


# one-hot encode a 1-D array
def one_hot_encode(array, num_of_classes):
    return np.eye(num_of_classes)[array.reshape(-1)]