visualize.py

import matplotlib.pyplot as plt
import numpy as np

def plot_decision_boundary(model, X, y,cmap=plt.cm.jet):
    """
    Given a model(a function) and a set of points(X), corresponding labels(y), scatter the points in X with color coding
    according to y. Also use the model to predict the label at grid points to get the region for each label, and thus the 
    descion boundary.
    Example usage:
    say we have a function predict(x,other params) which makes 0/1 prediction for point x and we want to plot
    train set then call as:
    plot_decision_boundary(lambda x:predict(x,other params),X_train,Y_train)
    params(3): 
        model : a function which expectes the point to make 0/1 label prediction
        X : a (mx2) numpy array with the points
        y : a (mx1) numpy array with labels
    outputs(None)
    """
    # Set min and max values and give it some padding
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    h = 0.01
    # Generate a grid of points with distance h between them
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    # Predict the function value for the whole grid
    Z = model(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    # Plot the contour and training examples
    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
    plt.ylabel('x2')
    plt.xlabel('x1')
    plt.style.use("seaborn")
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.jet)
    
import itertools
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")
    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.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.tight_layout()