[MRG+1] Support minimal dependencies in examples (scikit-learn#10351)

* Support minimal dependencies in examples
* bins="auto" only supported for numpy >= 1.11
* viridis is default cmap in matplotlib 2
* Use signature instead of viridis cmap

Author: lesteve

.circleci/config.yml

@@ -8,7 +8,6 @@ jobs:
       - MINICONDA_PATH: ~/miniconda
       - CONDA_ENV_NAME: testenv
       - PYTHON_VERSION: 3
-      - MATPLOTLIB_VERSION: "*"
     steps:
       - checkout
       - run: ./build_tools/circle/checkout_merge_commit.sh
@@ -42,7 +41,7 @@ jobs:
       - MINICONDA_PATH: ~/miniconda
       - CONDA_ENV_NAME: testenv
       - PYTHON_VERSION: 2
-      - MATPLOTLIB_VERSION: "*"
+      - MATPLOTLIB_VERSION: "1.3"
     steps:
       - checkout
       - run: ./build_tools/circle/checkout_merge_commit.sh

examples/manifold/plot_t_sne_perplexity.py

@@ -72,7 +72,7 @@
 X, color = datasets.samples_generator.make_s_curve(n_samples, random_state=0)
 
 ax = subplots[1][0]
-ax.scatter(X[:, 0], X[:, 2], c=color, cmap=plt.cm.viridis)
+ax.scatter(X[:, 0], X[:, 2], c=color)
 ax.xaxis.set_major_formatter(NullFormatter())
 ax.yaxis.set_major_formatter(NullFormatter())
 
@@ -87,7 +87,7 @@
     print("S-curve, perplexity=%d in %.2g sec" % (perplexity, t1 - t0))
 
     ax.set_title("Perplexity=%d" % perplexity)
-    ax.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.viridis)
+    ax.scatter(Y[:, 0], Y[:, 1], c=color)
     ax.xaxis.set_major_formatter(NullFormatter())
     ax.yaxis.set_major_formatter(NullFormatter())
     ax.axis('tight')
@@ -102,7 +102,7 @@
 ])
 color = xx.ravel()
 ax = subplots[2][0]
-ax.scatter(X[:, 0], X[:, 1], c=color, cmap=plt.cm.viridis)
+ax.scatter(X[:, 0], X[:, 1], c=color)
 ax.xaxis.set_major_formatter(NullFormatter())
 ax.yaxis.set_major_formatter(NullFormatter())
 
@@ -117,7 +117,7 @@
     print("uniform grid, perplexity=%d in %.2g sec" % (perplexity, t1 - t0))
 
     ax.set_title("Perplexity=%d" % perplexity)
-    ax.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.viridis)
+    ax.scatter(Y[:, 0], Y[:, 1], c=color)
     ax.xaxis.set_major_formatter(NullFormatter())
     ax.yaxis.set_major_formatter(NullFormatter())
     ax.axis('tight')

examples/model_selection/plot_precision_recall.py

@@ -137,13 +137,17 @@
 # ................................
 from sklearn.metrics import precision_recall_curve
 import matplotlib.pyplot as plt
+from sklearn.externals.funcsigs import signature
 
 precision, recall, _ = precision_recall_curve(y_test, y_score)
 
+# In matplotlib < 1.5, plt.fill_between does not have a 'step' argument
+step_kwargs = ({'step': 'post'}
+               if 'step' in signature(plt.fill_between).parameters
+               else {})
 plt.step(recall, precision, color='b', alpha=0.2,
          where='post')
-plt.fill_between(recall, precision, step='post', alpha=0.2,
-                 color='b')
+plt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)
 
 plt.xlabel('Recall')
 plt.ylabel('Precision')
@@ -212,8 +216,8 @@
 plt.figure()
 plt.step(recall['micro'], precision['micro'], color='b', alpha=0.2,
          where='post')
-plt.fill_between(recall["micro"], precision["micro"], step='post', alpha=0.2,
-                 color='b')
+plt.fill_between(recall["micro"], precision["micro"], alpha=0.2, color='b',
+                 **step_kwargs)
 
 plt.xlabel('Recall')
 plt.ylabel('Precision')

examples/plot_kernel_ridge_regression.py

@@ -120,7 +120,7 @@
 X = 5 * rng.rand(10000, 1)
 y = np.sin(X).ravel()
 y[::5] += 3 * (0.5 - rng.rand(X.shape[0] // 5))
-sizes = np.logspace(1, 4, 7, dtype=np.int)
+sizes = np.logspace(1, 4, 7).astype(np.int)
 for name, estimator in {"KRR": KernelRidge(kernel='rbf', alpha=0.1,
                                            gamma=10),
                         "SVR": SVR(kernel='rbf', C=1e1, gamma=10)}.items():

examples/preprocessing/plot_all_scaling.py

@@ -102,6 +102,8 @@
 # scale the output between 0 and 1 for the colorbar
 y = minmax_scale(y_full)
 
+# plasma does not exist in matplotlib < 1.5
+cmap = getattr(cm, 'plasma_r', cm.hot_r)
 
 def create_axes(title, figsize=(16, 6)):
     fig = plt.figure(figsize=figsize)
@@ -153,7 +155,7 @@ def plot_distribution(axes, X, y, hist_nbins=50, title="",
     ax.set_ylabel(x1_label)
 
     # The scatter plot
-    colors = cm.plasma_r(y)
+    colors = cmap(y)
     ax.scatter(X[:, 0], X[:, 1], alpha=0.5, marker='o', s=5, lw=0, c=colors)
 
     # Removing the top and the right spine for aesthetics
@@ -209,7 +211,7 @@ def make_plot(item_idx):
                       title="Zoom-in")
 
     norm = mpl.colors.Normalize(y_full.min(), y_full.max())
-    mpl.colorbar.ColorbarBase(ax_colorbar, cmap=cm.plasma_r,
+    mpl.colorbar.ColorbarBase(ax_colorbar, cmap=cmap,
                               norm=norm, orientation='vertical',
                               label='Color mapping for values of y')
 

examples/preprocessing/plot_transformed_target.py

@@ -63,13 +63,13 @@ def exp_transform(x):
 
 f, (ax0, ax1) = plt.subplots(1, 2)
 
-ax0.hist(y, bins='auto', normed=True)
+ax0.hist(y, bins=100, normed=True)
 ax0.set_xlim([0, 2000])
 ax0.set_ylabel('Probability')
 ax0.set_xlabel('Target')
 ax0.set_title('Target distribution')
 
-ax1.hist(y_trans, bins='auto', normed=True)
+ax1.hist(y_trans, bins=100, normed=True)
 ax1.set_ylabel('Probability')
 ax1.set_xlabel('Target')
 ax1.set_title('Transformed target distribution')
@@ -148,12 +148,12 @@ def exp_transform(x):
 
 f, (ax0, ax1) = plt.subplots(1, 2)
 
-ax0.hist(y, bins='auto', normed=True)
+ax0.hist(y, bins=100, normed=True)
 ax0.set_ylabel('Probability')
 ax0.set_xlabel('Target')
 ax0.set_title('Target distribution')
 
-ax1.hist(y_trans, bins='auto', normed=True)
+ax1.hist(y_trans, bins=100, normed=True)
 ax1.set_ylabel('Probability')
 ax1.set_xlabel('Target')
 ax1.set_title('Transformed target distribution')