utils.py

import itertools

import matplotlib.pyplot as plt
import numpy as np


def plot_decision_boundary(model, X, y):
    X = X.T
    y = y.T

    # 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)

    ax = plt.gca()
    ax.set_aspect(1)
    # plt.axis("equal")
    #plt.contourf(xx, yy, Z, cmap=plt.cm.ocean, alpha=0.5)
    plt.contourf(xx, yy, Z, colors = ["red","royalblue"], alpha=0.2)
    # plt.contourf(xx, yy, Z, cmap=plt.cm.Pastel1, alpha=0.5)
    # plt.scatter(X[0, y==1], X[1, y==1], color="dodgerblue", edgecolors='k', label="1")
    plt.scatter(X[0, y == 1], X[1, y == 1], color="royalblue", label="1")
    plt.scatter(X[0, y == -1], X[1, y == -1], color="red", label="-1")
    plt.legend()


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]

    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() / 2.0
    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("Etiqueta correcta")
    plt.xlabel("Etiqueta predicha")