Not done reviewing ml2 yet

ml2/plot_learning_curve.py

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