utils.py

import subprocess
from tempfile import mkstemp
from itertools import cycle

import matplotlib.pyplot as plt
import numpy as np

from scipy import interp

from sklearn.tree import export_graphviz
from sklearn.metrics import roc_curve, auc

from IPython.core.display import HTML



def plot_surface(clf, X, y,
                 xlim=(140, 210), ylim=(21, 29), n_steps=250,
                 subplot=None, show=True):
    if subplot is None:
        fig = plt.figure()
    else:
        plt.subplot(*subplot)

    xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], n_steps),
                         np.linspace(ylim[0], ylim[1], n_steps))

    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]

    z = z.reshape(xx.shape)
    plt.contourf(xx, yy, z, alpha=0.8, cmap=plt.cm.RdBu_r)
    plt.scatter(X[:, 0], X[:, 1], c=y)
    plt.xlim(*xlim)
    plt.ylim(*ylim)

    if show:
        plt.show()


def draw_tree(clf, feature_names, **kwargs):
    _, name = mkstemp(suffix='.dot')
    _, svg_name = mkstemp(suffix='.svg')
    export_graphviz(clf, out_file=name,
                    feature_names=feature_names,
                    **kwargs)
    command = ["dot", "-Tsvg", name, "-o", svg_name]
    subprocess.check_call(command)
    return HTML(open(svg_name).read())


# Taken from http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc_crossval.html
def draw_roc_curve(classifier, cv, X, y):
    mean_tpr = 0.0
    mean_fpr = np.linspace(0, 1, 100)

    colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange'])
    lw = 2

    for i, (train, test) in enumerate(cv):
        probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test])
        # Compute ROC curve and area the curve
        fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
        mean_tpr += interp(mean_fpr, fpr, tpr)
        mean_tpr[0] = 0.0
        roc_auc = auc(fpr, tpr)
        plt.plot(fpr, tpr, lw=1, label='ROC fold %d (area = %0.2f)' % (i, roc_auc))

    plt.plot([0, 1], [0, 1], linestyle='--', lw=lw, color='k',
             label='Luck')

    mean_tpr /= len(cv)
    mean_tpr[-1] = 1.0
    mean_auc = auc(mean_fpr, mean_tpr)
    plt.plot(mean_fpr, mean_tpr, color='g', linestyle='--',
             label='Mean ROC (area = %0.2f)' % mean_auc, lw=lw)

    plt.xlim([-0.05, 1.05])
    plt.ylim([-0.05, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic')
    plt.legend(loc="lower right")
    plt.show()