sitc/ml1/plotml.py

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.dummy import DummyClassifier

# Taken from http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#example-classification-plot-classifier-comparison-py

def plot_classifiers():
    """
    Plot classifiers in synthetic datasets, taken from http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
    A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by these examples does not necessarily carry over to real datasets.
    Particularly in high-dimensional spaces, data can more easily be separated linearly and the simplicity of classifiers such as naive Bayes and linear SVMs might lead to better generalization than is achieved by other classifiers.
    The plots show training points in solid colors and testing points semi-transparent. The lower right shows the classification accuracy on the test set.
    """
    h = .02  # step size in the mesh

    names = ["DummyClassifier", "Nearest Neighbors", "Decision Tree", "Naive Bayes", "Linear SVM", "RBF SVM", 
    "Random Forest"]
    classifiers = [
    DummyClassifier(strategy="prior"),
    KNeighborsClassifier(3),
    DecisionTreeClassifier(max_depth=5),
    GaussianNB(),
    SVC(kernel="linear", C=0.025),
    SVC(gamma=2, C=1),    
    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1)
    ]

    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]
    ds_names = ["Dataset moons", "Dataset circles", "Dataset linearly_separable"]

    figure = plt.figure(figsize=(27, 9))
    i = 1
    # iterate over datasets
    for ds_name, ds in zip(ds_names, 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)

        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)
        ax.set_title(ds_name)
        # 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, m_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(())
            ax.set_title(name)
            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
                size=15, horizontalalignment='right')
            i += 1

    figure.subplots_adjust(left=.02, right=.98)
    plt.suptitle("Comparison of Classifiers in synthetic datasets", fontsize=18)
    plt.show()