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import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline


    

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from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier
from sklearn.learning_curve import validation_curve


    

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digits = load_digits()
X, y = digits.data, digits.target


    

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training_scores, validation_scores = validation_curve(RandomForestClassifier(n_estimators=50, n_jobs=-1), X, y,
                                                      param_name="max_depth", param_range=range(1, 10), cv=5)


    

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training_scores.shape


    

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def plot_validation_curve(parameter_values, train_scores, validation_scores):
    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    validation_scores_mean = np.mean(validation_scores, axis=1)
    validation_scores_std = np.std(validation_scores, axis=1)

    plt.fill_between(parameter_values, train_scores_mean - train_scores_std,
                     train_scores_mean + train_scores_std, alpha=0.1,
                     color="r")
    plt.fill_between(parameter_values, validation_scores_mean - validation_scores_std,
                     validation_scores_mean + validation_scores_std, alpha=0.1, color="g")
    plt.plot(parameter_values, train_scores_mean, 'o-', color="r",
             label="Training score")
    plt.plot(parameter_values, validation_scores_mean, 'o-', color="g",
             label="Cross-validation score")
    plt.ylim(validation_scores_mean.min() - .1, train_scores_mean.max() + .1)
    plt.legend(loc="best")


    

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plot_validation_curve(range(1, 10), training_scores, validation_scores)


    

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from sklearn.svm import LinearSVC
Cs = [0.00001, 0.0001, 0.001, 0.01, 0.1 , 1, 10]
training_scores, test_scores = validation_curve(LinearSVC(), X, y,
                                                param_name="C", param_range=Cs)


    

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plot_validation_curve(range(7), training_scores, test_scores)
plt.xticks(range(7), Cs);


    

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