plot_classifiers.py

# Code source: Gaël Varoquaux
#              Andreas Müller
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis

def plot_classifiers(names, classifiers, figuresize):
    h = .02  # step size in the mesh
    X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
                           random_state=1, n_clusters_per_class=1)
    rng = np.random.RandomState(2)
    X += 2 * rng.uniform(size=X.shape)
    linearly_separable = (X, y)

    datasets = [make_moons(noise=0.3, random_state=0),
            make_circles(noise=0.2, factor=0.5, random_state=1),
            linearly_separable
            ]
    figure = plt.figure(figsize=figuresize)
    i = 1
    
    # iterate over datasets
    for ds_cnt, ds in enumerate(datasets):
        # preprocess dataset, split into training and test part
        X, y = ds
        X = StandardScaler().fit_transform(X)
        X_train, X_test, y_train, y_test = \
            train_test_split(X, y, test_size=.4, random_state=42)

        x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
        y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
        xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                             np.arange(y_min, y_max, h))

        # just plot the dataset first
        cm = plt.cm.RdBu
        cm_bright = ListedColormap(['#FF0000', '#0000FF'])
        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
        if ds_cnt == 0:
            ax.set_title("Input data")
        # Plot the training points
        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
        # and testing points
        ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
        ax.set_xlim(xx.min(), xx.max())
        ax.set_ylim(yy.min(), yy.max())
        ax.set_xticks(())
        ax.set_yticks(())
        i += 1
    
        # iterate over classifiers
        for name, clf in zip(names, classifiers):
            ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
            clf.fit(X_train, y_train)
            score = clf.score(X_test, y_test)

            # Plot the decision boundary. For that, we will assign a color to each
            # point in the mesh [x_min, x_max]x[y_min, y_max].
            if hasattr(clf, "decision_function"):
                Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
            else:
                Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]

            # Put the result into a color plot
            Z = Z.reshape(xx.shape)
            ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)

            # Plot also the training points
            ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
            # and testing points
            ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,
                       alpha=0.6)

            ax.set_xlim(xx.min(), xx.max())
            ax.set_ylim(yy.min(), yy.max())
            ax.set_xticks(())
            ax.set_yticks(())
            if ds_cnt == 0:
                ax.set_title(name)
            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
                    size=15, horizontalalignment='right')
            i += 1
        
    plt.tight_layout()
    plt.show()