Pushing the docs for revision for branch: master, commit 2dd5a9a1b08f3b70e96dc4959af96d218a4c2c78

Author: sklearn-ci

dev/_downloads/mlcomp_sparse_document_classification.py

@@ -44,7 +44,7 @@
 import os
 import numpy as np
 import scipy.sparse as sp
-import pylab as pl
+import matplotlib.pyplot as plt
 
 from sklearn.datasets import load_mlcomp
 from sklearn.feature_extraction.text import TfidfVectorizer
@@ -121,9 +121,9 @@ def benchmark(clf_class, params, name):
     print(cm)
 
     # Show confusion matrix
-    pl.matshow(cm)
-    pl.title('Confusion matrix of the %s classifier' % name)
-    pl.colorbar()
+    plt.matshow(cm)
+    plt.title('Confusion matrix of the %s classifier' % name)
+    plt.colorbar()
 
 
 print("Testbenching a linear classifier...")
@@ -142,4 +142,4 @@ def benchmark(clf_class, params, name):
 
 benchmark(MultinomialNB, parameters, 'MultinomialNB')
 
-pl.show()
+plt.show()

dev/_downloads/plot_digits_classification.py

@@ -27,7 +27,7 @@
 # The data that we are interested in is made of 8x8 images of digits, let's
 # have a look at the first 4 images, stored in the `images` attribute of the
 # dataset.  If we were working from image files, we could load them using
-# pylab.imread.  Note that each image must have the same size. For these
+# matplotlib.pyplot.imread.  Note that each image must have the same size. For these
 # images, we know which digit they represent: it is given in the 'target' of
 # the dataset.
 images_and_labels = list(zip(digits.images, digits.target))

dev/_downloads/plot_gpc_isoprobability.py

@@ -18,7 +18,7 @@
 
 import numpy as np
 
-from matplotlib import pyplot as pl
+from matplotlib import pyplot as plt
 from matplotlib import cm
 
 from sklearn.gaussian_process import GaussianProcessClassifier
@@ -64,39 +64,39 @@ def g(x):
 y_prob = y_prob.reshape((res, res))
 
 # Plot the probabilistic classification iso-values
-fig = pl.figure(1)
+fig = plt.figure(1)
 ax = fig.gca()
 ax.axes.set_aspect('equal')
-pl.xticks([])
-pl.yticks([])
+plt.xticks([])
+plt.yticks([])
 ax.set_xticklabels([])
 ax.set_yticklabels([])
-pl.xlabel('$x_1$')
-pl.ylabel('$x_2$')
+plt.xlabel('$x_1$')
+plt.ylabel('$x_2$')
 
-cax = pl.imshow(y_prob, cmap=cm.gray_r, alpha=0.8,
-                extent=(-lim, lim, -lim, lim))
-norm = pl.matplotlib.colors.Normalize(vmin=0., vmax=0.9)
-cb = pl.colorbar(cax, ticks=[0., 0.2, 0.4, 0.6, 0.8, 1.], norm=norm)
+cax = plt.imshow(y_prob, cmap=cm.gray_r, alpha=0.8,
+                 extent=(-lim, lim, -lim, lim))
+norm = plt.matplotlib.colors.Normalize(vmin=0., vmax=0.9)
+cb = plt.colorbar(cax, ticks=[0., 0.2, 0.4, 0.6, 0.8, 1.], norm=norm)
 cb.set_label('${\\rm \mathbb{P}}\left[\widehat{G}(\mathbf{x}) \leq 0\\right]$')
-pl.clim(0, 1)
+plt.clim(0, 1)
 
-pl.plot(X[y <= 0, 0], X[y <= 0, 1], 'r.', markersize=12)
+plt.plot(X[y <= 0, 0], X[y <= 0, 1], 'r.', markersize=12)
 
-pl.plot(X[y > 0, 0], X[y > 0, 1], 'b.', markersize=12)
+plt.plot(X[y > 0, 0], X[y > 0, 1], 'b.', markersize=12)
 
-cs = pl.contour(x1, x2, y_true, [0.], colors='k', linestyles='dashdot')
+cs = plt.contour(x1, x2, y_true, [0.], colors='k', linestyles='dashdot')
 
-cs = pl.contour(x1, x2, y_prob, [0.666], colors='b',
-                linestyles='solid')
-pl.clabel(cs, fontsize=11)
+cs = plt.contour(x1, x2, y_prob, [0.666], colors='b',
+                 linestyles='solid')
+plt.clabel(cs, fontsize=11)
 
-cs = pl.contour(x1, x2, y_prob, [0.5], colors='k',
-                linestyles='dashed')
-pl.clabel(cs, fontsize=11)
+cs = plt.contour(x1, x2, y_prob, [0.5], colors='k',
+                 linestyles='dashed')
+plt.clabel(cs, fontsize=11)
 
-cs = pl.contour(x1, x2, y_prob, [0.334], colors='r',
-                linestyles='solid')
-pl.clabel(cs, fontsize=11)
+cs = plt.contour(x1, x2, y_prob, [0.334], colors='r',
+                 linestyles='solid')
+plt.clabel(cs, fontsize=11)
 
-pl.show()
+plt.show()

dev/_downloads/plot_gpr_noisy_targets.py

@@ -26,7 +26,7 @@
 # Licence: BSD 3 clause
 
 import numpy as np
-from matplotlib import pyplot as pl
+from matplotlib import pyplot as plt
 
 from sklearn.gaussian_process import GaussianProcessRegressor
 from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
@@ -61,18 +61,18 @@ def f(x):
 
 # Plot the function, the prediction and the 95% confidence interval based on
 # the MSE
-fig = pl.figure()
-pl.plot(x, f(x), 'r:', label=u'$f(x) = x\,\sin(x)$')
-pl.plot(X, y, 'r.', markersize=10, label=u'Observations')
-pl.plot(x, y_pred, 'b-', label=u'Prediction')
-pl.fill(np.concatenate([x, x[::-1]]),
-        np.concatenate([y_pred - 1.9600 * sigma,
-                       (y_pred + 1.9600 * sigma)[::-1]]),
-        alpha=.5, fc='b', ec='None', label='95% confidence interval')
-pl.xlabel('$x$')
-pl.ylabel('$f(x)$')
-pl.ylim(-10, 20)
-pl.legend(loc='upper left')
+fig = plt.figure()
+plt.plot(x, f(x), 'r:', label=u'$f(x) = x\,\sin(x)$')
+plt.plot(X, y, 'r.', markersize=10, label=u'Observations')
+plt.plot(x, y_pred, 'b-', label=u'Prediction')
+plt.fill(np.concatenate([x, x[::-1]]),
+         np.concatenate([y_pred - 1.9600 * sigma,
+                        (y_pred + 1.9600 * sigma)[::-1]]),
+         alpha=.5, fc='b', ec='None', label='95% confidence interval')
+plt.xlabel('$x$')
+plt.ylabel('$f(x)$')
+plt.ylim(-10, 20)
+plt.legend(loc='upper left')
 
 # ----------------------------------------------------------------------
 # now the noisy case
@@ -97,17 +97,17 @@ def f(x):
 
 # Plot the function, the prediction and the 95% confidence interval based on
 # the MSE
-fig = pl.figure()
-pl.plot(x, f(x), 'r:', label=u'$f(x) = x\,\sin(x)$')
-pl.errorbar(X.ravel(), y, dy, fmt='r.', markersize=10, label=u'Observations')
-pl.plot(x, y_pred, 'b-', label=u'Prediction')
-pl.fill(np.concatenate([x, x[::-1]]),
-        np.concatenate([y_pred - 1.9600 * sigma,
-                       (y_pred + 1.9600 * sigma)[::-1]]),
-        alpha=.5, fc='b', ec='None', label='95% confidence interval')
-pl.xlabel('$x$')
-pl.ylabel('$f(x)$')
-pl.ylim(-10, 20)
-pl.legend(loc='upper left')
-
-pl.show()
+fig = plt.figure()
+plt.plot(x, f(x), 'r:', label=u'$f(x) = x\,\sin(x)$')
+plt.errorbar(X.ravel(), y, dy, fmt='r.', markersize=10, label=u'Observations')
+plt.plot(x, y_pred, 'b-', label=u'Prediction')
+plt.fill(np.concatenate([x, x[::-1]]),
+         np.concatenate([y_pred - 1.9600 * sigma,
+                        (y_pred + 1.9600 * sigma)[::-1]]),
+         alpha=.5, fc='b', ec='None', label='95% confidence interval')
+plt.xlabel('$x$')
+plt.ylabel('$f(x)$')
+plt.ylim(-10, 20)
+plt.legend(loc='upper left')
+
+plt.show()

dev/_downloads/plot_sparse_coding.py

@@ -17,7 +17,7 @@
 print(__doc__)
 
 import numpy as np
-import matplotlib.pylab as plt
+import matplotlib.pyplot as plt
 
 from sklearn.decomposition import SparseCoder