update doc for 0.7.0 (#355)

Author: wdevazelhes

_downloads/7f32614e7817d99fcf5ee991f7058f36/auto_examples_python.zip renamed to _downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -1,21 +1,17 @@
 {
   "cells": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "%matplotlib inline"
+        "\n# Sandwich demo\n\nSandwich demo based on code from http://nbviewer.ipython.org/6576096\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nSandwich demo\n=============\n\nSandwich demo based on code from http://nbviewer.ipython.org/6576096\n"
+        "<div class=\"alert alert-info\"><h4>Note</h4><p>In order to show the charts of the examples you need a graphical\n    ``matplotlib`` backend installed. For intance, use ``pip install pyqt5``\n    to get Qt graphical interface or use your favorite one.</p></div>\n\n"
       ]
     },
     {
@@ -26,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom matplotlib import pyplot as plt\nfrom sklearn.metrics import pairwise_distances\nfrom sklearn.neighbors import NearestNeighbors\n\nfrom metric_learn import (LMNN, ITML_Supervised, LSML_Supervised,\n                          SDML_Supervised)\n\n\ndef sandwich_demo():\n  x, y = sandwich_data()\n  knn = nearest_neighbors(x, k=2)\n  ax = plt.subplot(3, 1, 1)  # take the whole top row\n  plot_sandwich_data(x, y, ax)\n  plot_neighborhood_graph(x, knn, y, ax)\n  ax.set_title('input space')\n  ax.set_aspect('equal')\n  ax.set_xticks([])\n  ax.set_yticks([])\n\n  mls = [\n      LMNN(),\n      ITML_Supervised(num_constraints=200),\n      SDML_Supervised(num_constraints=200, balance_param=0.001),\n      LSML_Supervised(num_constraints=200),\n  ]\n\n  for ax_num, ml in enumerate(mls, start=3):\n    ml.fit(x, y)\n    tx = ml.transform(x)\n    ml_knn = nearest_neighbors(tx, k=2)\n    ax = plt.subplot(3, 2, ax_num)\n    plot_sandwich_data(tx, y, axis=ax)\n    plot_neighborhood_graph(tx, ml_knn, y, axis=ax)\n    ax.set_title(ml.__class__.__name__)\n    ax.set_xticks([])\n    ax.set_yticks([])\n  plt.show()\n\n\n# TODO: use this somewhere\ndef visualize_class_separation(X, labels):\n  _, (ax1, ax2) = plt.subplots(ncols=2)\n  label_order = np.argsort(labels)\n  ax1.imshow(pairwise_distances(X[label_order]), interpolation='nearest')\n  ax2.imshow(pairwise_distances(labels[label_order, None]),\n             interpolation='nearest')\n\n\ndef nearest_neighbors(X, k=5):\n  knn = NearestNeighbors(n_neighbors=k)\n  knn.fit(X)\n  return knn.kneighbors(X, return_distance=False)\n\n\ndef sandwich_data():\n  # number of distinct classes\n  num_classes = 6\n  # number of points per class\n  num_points = 9\n  # distance between layers, the points of each class are in a layer\n  dist = 0.7\n\n  data = np.zeros((num_classes, num_points, 2), dtype=float)\n  labels = np.zeros((num_classes, num_points), dtype=int)\n\n  x_centers = np.arange(num_points, dtype=float) - num_points / 2\n  y_centers = dist * (np.arange(num_classes, dtype=float) - num_classes / 2)\n  for i, yc in enumerate(y_centers):\n    for k, xc in enumerate(x_centers):\n      data[i, k, 0] = np.random.normal(xc, 0.1)\n      data[i, k, 1] = np.random.normal(yc, 0.1)\n    labels[i, :] = i\n  return data.reshape((-1, 2)), labels.ravel()\n\n\ndef plot_sandwich_data(x, y, axis=plt, colors='rbgmky'):\n  for idx, val in enumerate(np.unique(y)):\n    xi = x[y == val]\n    axis.scatter(*xi.T, s=50, facecolors='none', edgecolors=colors[idx])\n\n\ndef plot_neighborhood_graph(x, nn, y, axis=plt, colors='rbgmky'):\n  for i, a in enumerate(x):\n    b = x[nn[i, 1]]\n    axis.plot((a[0], b[0]), (a[1], b[1]), colors[y[i]])\n\n\nif __name__ == '__main__':\n  sandwich_demo()"
+        "import numpy as np\nfrom matplotlib import pyplot as plt\nfrom sklearn.metrics import pairwise_distances\nfrom sklearn.neighbors import NearestNeighbors\n\nfrom metric_learn import (LMNN, ITML_Supervised, LSML_Supervised,\n                          SDML_Supervised)\n\n\ndef sandwich_demo():\n  x, y = sandwich_data()\n  knn = nearest_neighbors(x, k=2)\n  ax = plt.subplot(3, 1, 1)  # take the whole top row\n  plot_sandwich_data(x, y, ax)\n  plot_neighborhood_graph(x, knn, y, ax)\n  ax.set_title('input space')\n  ax.set_aspect('equal')\n  ax.set_xticks([])\n  ax.set_yticks([])\n\n  mls = [\n      LMNN(),\n      ITML_Supervised(n_constraints=200),\n      SDML_Supervised(n_constraints=200, balance_param=0.001),\n      LSML_Supervised(n_constraints=200),\n  ]\n\n  for ax_num, ml in enumerate(mls, start=3):\n    ml.fit(x, y)\n    tx = ml.transform(x)\n    ml_knn = nearest_neighbors(tx, k=2)\n    ax = plt.subplot(3, 2, ax_num)\n    plot_sandwich_data(tx, y, axis=ax)\n    plot_neighborhood_graph(tx, ml_knn, y, axis=ax)\n    ax.set_title(ml.__class__.__name__)\n    ax.set_xticks([])\n    ax.set_yticks([])\n  plt.show()\n\n\n# TODO: use this somewhere\ndef visualize_class_separation(X, labels):\n  _, (ax1, ax2) = plt.subplots(ncols=2)\n  label_order = np.argsort(labels)\n  ax1.imshow(pairwise_distances(X[label_order]), interpolation='nearest')\n  ax2.imshow(pairwise_distances(labels[label_order, None]),\n             interpolation='nearest')\n\n\ndef nearest_neighbors(X, k=5):\n  knn = NearestNeighbors(n_neighbors=k)\n  knn.fit(X)\n  return knn.kneighbors(X, return_distance=False)\n\n\ndef sandwich_data():\n  # number of distinct classes\n  num_classes = 6\n  # number of points per class\n  num_points = 9\n  # distance between layers, the points of each class are in a layer\n  dist = 0.7\n\n  data = np.zeros((num_classes, num_points, 2), dtype=float)\n  labels = np.zeros((num_classes, num_points), dtype=int)\n\n  x_centers = np.arange(num_points, dtype=float) - num_points / 2\n  y_centers = dist * (np.arange(num_classes, dtype=float) - num_classes / 2)\n  for i, yc in enumerate(y_centers):\n    for k, xc in enumerate(x_centers):\n      data[i, k, 0] = np.random.normal(xc, 0.1)\n      data[i, k, 1] = np.random.normal(yc, 0.1)\n    labels[i, :] = i\n  return data.reshape((-1, 2)), labels.ravel()\n\n\ndef plot_sandwich_data(x, y, axis=plt, colors='rbgmky'):\n  for idx, val in enumerate(np.unique(y)):\n    xi = x[y == val]\n    axis.scatter(*xi.T, s=50, facecolors='none', edgecolors=colors[idx])\n\n\ndef plot_neighborhood_graph(x, nn, y, axis=plt, colors='rbgmky'):\n  for i, a in enumerate(x):\n    b = x[nn[i, 1]]\n    axis.plot((a[0], b[0]), (a[1], b[1]), colors[y[i]])\n\n\nif __name__ == '__main__':\n  sandwich_demo()"
       ]
     }
   ],
@@ -46,7 +42,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.11.6"
     }
   },
   "nbformat": 4,

_downloads/4f011433714ef22e0c66be75bbfd08e3/plot_sandwich.ipynb

@@ -6,6 +6,13 @@
 Sandwich demo based on code from http://nbviewer.ipython.org/6576096
 """
 
+######################################################################
+# .. note::
+#
+#     In order to show the charts of the examples you need a graphical
+#     ``matplotlib`` backend installed. For intance, use ``pip install pyqt5``
+#     to get Qt graphical interface or use your favorite one.
+
 import numpy as np
 from matplotlib import pyplot as plt
 from sklearn.metrics import pairwise_distances
@@ -28,9 +35,9 @@ def sandwich_demo():
 
   mls = [
       LMNN(),
-      ITML_Supervised(num_constraints=200),
-      SDML_Supervised(num_constraints=200, balance_param=0.001),
-      LSML_Supervised(num_constraints=200),
+      ITML_Supervised(n_constraints=200),
+      SDML_Supervised(n_constraints=200, balance_param=0.001),
+      LSML_Supervised(n_constraints=200),
   ]
 
   for ax_num, ml in enumerate(mls, start=3):

_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -15,7 +15,11 @@
 ######################################################################
 # Imports
 # ^^^^^^^
+# .. note::
 #
+#     In order to show the charts of the examples you need a graphical
+#     ``matplotlib`` backend installed. For intance, use ``pip install pyqt5``
+#     to get Qt graphical interface or use your favorite one.
 
 from sklearn.manifold import TSNE
 
@@ -35,9 +39,9 @@
 # We will be using a synthetic dataset to illustrate the plotting,
 # using the function `sklearn.datasets.make_classification` from
 # scikit-learn. The dataset will contain:
-#   - 100 points in 3 classes with 2 clusters per class
-#   - 5 features, among which 3 are informative (correlated with the class
-#     labels) and two are random noise with large magnitude
+# - 100 points in 3 classes with 2 clusters per class
+# - 5 features, among which 3 are informative (correlated with the class
+# labels) and two are random noise with large magnitude
 
 X, y = make_classification(n_samples=100, n_classes=3, n_clusters_per_class=2,
                            n_informative=3, class_sep=4., n_features=5,
@@ -139,7 +143,7 @@ def plot_tsne(X, y, colormap=plt.cm.Paired):
 #
 
 # setting up LMNN
-lmnn = metric_learn.LMNN(k=5, learn_rate=1e-6)
+lmnn = metric_learn.LMNN(n_neighbors=5, learn_rate=1e-6)
 
 # fit the data!
 lmnn.fit(X, y)
@@ -310,7 +314,7 @@ def plot_tsne(X, y, colormap=plt.cm.Paired):
 # - See more in the documentation of the class :py:class:`RCA
 #   <metric_learn.RCA>`
 
-rca = metric_learn.RCA_Supervised(num_chunks=30, chunk_size=2)
+rca = metric_learn.RCA_Supervised(n_chunks=30, chunk_size=2)
 X_rca = rca.fit_transform(X, y)
 
 plot_tsne(X_rca, y)