DOC: retry to bump the doc for v0.5.0

Author: William de Vazelhes

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 89e7bf0528b77a77d89d8167f4184500
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/12b0e3dc28f1519743a1a05520733192/auto_examples_jupyter.zip

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+# -*- coding: utf-8 -*-
+"""
+Sandwich demo
+=============
+
+Sandwich demo based on code from http://nbviewer.ipython.org/6576096
+"""
+
+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.metrics import pairwise_distances
+from sklearn.neighbors import NearestNeighbors
+
+from metric_learn import LMNN, ITML_Supervised, LSML_Supervised, SDML_Supervised
+
+
+def sandwich_demo():
+  x, y = sandwich_data()
+  knn = nearest_neighbors(x, k=2)
+  ax = plt.subplot(3, 1, 1)  # take the whole top row
+  plot_sandwich_data(x, y, ax)
+  plot_neighborhood_graph(x, knn, y, ax)
+  ax.set_title('input space')
+  ax.set_aspect('equal')
+  ax.set_xticks([])
+  ax.set_yticks([])
+
+  mls = [
+      LMNN(),
+      ITML_Supervised(num_constraints=200),
+      SDML_Supervised(num_constraints=200, balance_param=0.001),
+      LSML_Supervised(num_constraints=200),
+  ]
+
+  for ax_num, ml in enumerate(mls, start=3):
+    ml.fit(x, y)
+    tx = ml.transform(x)
+    ml_knn = nearest_neighbors(tx, k=2)
+    ax = plt.subplot(3, 2, ax_num)
+    plot_sandwich_data(tx, y, axis=ax)
+    plot_neighborhood_graph(tx, ml_knn, y, axis=ax)
+    ax.set_title(ml.__class__.__name__)
+    ax.set_xticks([])
+    ax.set_yticks([])
+  plt.show()
+
+
+# TODO: use this somewhere
+def visualize_class_separation(X, labels):
+  _, (ax1,ax2) = plt.subplots(ncols=2)
+  label_order = np.argsort(labels)
+  ax1.imshow(pairwise_distances(X[label_order]), interpolation='nearest')
+  ax2.imshow(pairwise_distances(labels[label_order,None]),
+             interpolation='nearest')
+
+
+def nearest_neighbors(X, k=5):
+  knn = NearestNeighbors(n_neighbors=k)
+  knn.fit(X)
+  return knn.kneighbors(X, return_distance=False)
+
+
+def sandwich_data():
+  # number of distinct classes
+  num_classes = 6
+  # number of points per class
+  num_points = 9
+  # distance between layers, the points of each class are in a layer
+  dist = 0.7
+
+  data = np.zeros((num_classes, num_points, 2), dtype=float)
+  labels = np.zeros((num_classes, num_points), dtype=int)
+
+  x_centers = np.arange(num_points, dtype=float) - num_points / 2
+  y_centers = dist * (np.arange(num_classes, dtype=float) - num_classes / 2)
+  for i, yc in enumerate(y_centers):
+    for k, xc in enumerate(x_centers):
+      data[i, k, 0] = np.random.normal(xc, 0.1)
+      data[i, k, 1] = np.random.normal(yc, 0.1)
+    labels[i,:] = i
+  return data.reshape((-1, 2)), labels.ravel()
+
+
+def plot_sandwich_data(x, y, axis=plt, colors='rbgmky'):
+  for idx, val in enumerate(np.unique(y)):
+    xi = x[y==val]
+    axis.scatter(*xi.T, s=50, facecolors='none', edgecolors=colors[idx])
+
+
+def plot_neighborhood_graph(x, nn, y, axis=plt, colors='rbgmky'):
+  for i, a in enumerate(x):
+    b = x[nn[i,1]]
+    axis.plot((a[0], b[0]), (a[1], b[1]), colors[y[i]])
+
+
+if __name__ == '__main__':
+  sandwich_demo()