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109 lines
4.1 KiB
Python
109 lines
4.1 KiB
Python
"""
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Taken from http://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html
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========================
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Plotting Learning Curves
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========================
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On the left side the learning curve of a naive Bayes classifier is shown for
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the digits dataset. Note that the training score and the cross-validation score
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are both not very good at the end. However, the shape of the curve can be found
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in more complex datasets very often: the training score is very high at the
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beginning and decreases and the cross-validation score is very low at the
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beginning and increases. On the right side we see the learning curve of an SVM
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with RBF kernel. We can see clearly that the training score is still around
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the maximum and the validation score could be increased with more training
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samples.
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"""
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#print(__doc__)
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.naive_bayes import GaussianNB
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from sklearn.svm import SVC
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from sklearn.datasets import load_digits
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from sklearn.model_selection import learning_curve
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def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
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n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
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"""
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Generate a simple plot of the test and traning learning curve.
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Parameters
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----------
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estimator : object type that implements the "fit" and "predict" methods
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An object of that type which is cloned for each validation.
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title : string
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Title for the chart.
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X : array-like, shape (n_samples, n_features)
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Training vector, where n_samples is the number of samples and
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n_features is the number of features.
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y : array-like, shape (n_samples) or (n_samples, n_features), optional
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Target relative to X for classification or regression;
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None for unsupervised learning.
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ylim : tuple, shape (ymin, ymax), optional
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Defines minimum and maximum yvalues plotted.
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cv : integer, cross-validation generator, optional
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If an integer is passed, it is the number of folds (defaults to 3).
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Specific cross-validation objects can be passed, see
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sklearn.model_selection module for the list of possible objects
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n_jobs : integer, optional
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Number of jobs to run in parallel (default 1).
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"""
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plt.figure()
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plt.title(title)
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if ylim is not None:
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plt.ylim(*ylim)
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plt.xlabel("Training examples")
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plt.ylabel("Score")
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train_sizes, train_scores, test_scores = learning_curve(
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estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
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train_scores_mean = np.mean(train_scores, axis=1)
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train_scores_std = np.std(train_scores, axis=1)
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test_scores_mean = np.mean(test_scores, axis=1)
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test_scores_std = np.std(test_scores, axis=1)
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plt.grid()
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plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
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train_scores_mean + train_scores_std, alpha=0.1,
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color="r")
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plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
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test_scores_mean + test_scores_std, alpha=0.1, color="g")
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plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
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label="Training score")
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plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
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label="Cross-validation score")
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plt.legend(loc="best")
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return plt
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#digits = load_digits()
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#X, y = digits.data, digits.target
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#title = "Learning Curves (Naive Bayes)"
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# Cross validation with 100 iterations to get smoother mean test and train
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# score curves, each time with 20% data randomly selected as a validation set.
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#cv = cross_validation.ShuffleSplit(digits.data.shape[0], n_iter=100,
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# test_size=0.2, random_state=0)
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#estimator = GaussianNB()
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#plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=cv, n_jobs=4)
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#title = "Learning Curves (SVM, RBF kernel, $\gamma=0.001$)"
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# SVC is more expensive so we do a lower number of CV iterations:
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#cv = cross_validation.ShuffleSplit(digits.data.shape[0], n_iter=10,
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# test_size=0.2, random_state=0)
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#estimator = SVC(gamma=0.001)
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#plot_learning_curve(estimator, title, X, y, (0.7, 1.01), cv=cv, n_jobs=4)
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#plt.show()
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