Add plot_choose_n_optimal example [ci skip]

tguillemot · tguillemot · commit 5f0415a648a9 · 2016-06-29T18:06:21.000+02:00
diff --git a/examples/cluster/plot_optimal_n_clusters.py b/examples/cluster/plot_optimal_n_clusters.py
@@ -0,0 +1,73 @@
+"""Plot the results of the gap criterium."""
+
+# Authors: Thierry Guillemot <thierry.guillemot.work@gmail.com>
+
+import time
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.cluster import KMeans, OptimalNClusterSearch
+from sklearn.datasets import make_blobs
+from sklearn.metrics import calinski_harabaz_score, fowlkes_mallows_score
+from sklearn.metrics import silhouette_score
+from sklearn.utils import check_random_state
+
+
+n_samples, n_features, random_state = 1000, 2, 1
+parameters = {'n_clusters': np.arange(1, 7)}
+
+rng = check_random_state(random_state)
+datasets = [
+    ('3 clusters', make_blobs(n_samples=n_samples, n_features=2,
+                              random_state=random_state, centers=3)),
+    ('5 clusters', make_blobs(n_samples=n_samples, n_features=2,
+                              random_state=random_state, centers=5)),
+    ('random', (rng.rand(n_samples, n_features),
+                np.zeros(n_samples, dtype=int))),
+]
+
+estimator = KMeans(n_init=10, random_state=0)
+searchers = [
+    ('Silhouette', OptimalNClusterSearch(
+        estimator=estimator, parameters=parameters,
+        fitting_process='unsupervised', metric=silhouette_score)),
+    ('Calinski', OptimalNClusterSearch(
+        estimator=estimator, parameters=parameters,
+        fitting_process='unsupervised', metric=calinski_harabaz_score)),
+    ('Stability', OptimalNClusterSearch(
+        estimator=estimator, parameters=parameters, random_state=0,
+        fitting_process='stability', metric=fowlkes_mallows_score)),
+    ('Distortion jump', OptimalNClusterSearch(
+        estimator=estimator, parameters=parameters,
+        fitting_process='distortion_jump')),
+    ('Gap', OptimalNClusterSearch(
+        estimator=estimator, parameters=parameters, random_state=0,
+        fitting_process='gap')),
+    ('Pham', OptimalNClusterSearch(
+        estimator=estimator, parameters=parameters, fitting_process='pham')),
+]
+
+color = 'bgrcmyk'
+plt.figure(figsize=(13, 9.5))
+plt.subplots_adjust(left=.001, right=.999, bottom=.001, top=.96, wspace=.05,
+                    hspace=.01)
+for k, (data_name, data) in enumerate(datasets):
+    X, _ = data
+    for l, (search_name, search) in enumerate(searchers):
+        t0 = time.time()
+        y = search.fit(X).predict(X)
+        t1 = time.time()
+
+        colors = np.array([color[k] for k in y])
+        plt.subplot(len(datasets), len(searchers),
+                    len(searchers) * k + l + 1)
+        if k == 0:
+            plt.title(search_name, size=18)
+        plt.scatter(X[:, 0], X[:, 1], color=colors, alpha=.6)
+        plt.xticks(())
+        plt.yticks(())
+        plt.axis('equal')
+        plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'),
+                 transform=plt.gca().transAxes, size=15,
+                 horizontalalignment='right')
+plt.show()
diff --git a/sklearn/cluster/optimal_nclusters_search.py b/sklearn/cluster/optimal_nclusters_search.py
@@ -202,6 +202,10 @@ def _initialized(self, X, y):
 
     def _estimator_fit(self, estimator, X, y, parameters):
         estimator.set_params(**parameters)
+        if parameters['n_clusters'] == 1:
+            warnings.warn('Put a warning.')
+            return np.nan, parameters
+
         draw_scores = np.empty(self.n_draws)
         for l, d in enumerate(self.data_):
             p1, p2 = np.split(d, 2)
@@ -284,6 +288,9 @@ def _compute_results(self, X, out):
         if self.n_clusters_values[0] == 0:
             scores[0, :] = 1.
 
+        # XXX change that to not modify the score
+        scores[scores > 0.85] = 1.
+
         return {
             'score': -scores.ravel(),
             'params': parameters[self._index].ravel(),
@@ -333,7 +340,7 @@ def _compute_results(self, X, out):
         safety = np.array(safety).reshape(gap.shape)
 
         scores = (gap[self._index] - gap[1:][self._index[:-1]] +
-                  safety[1:][self._index[:-1]])
+                  safety[1:][self._index[:-1]]) >= 0
 
         return {
             'gap': gap[self._index].ravel(),
@@ -382,4 +389,4 @@ def fit(self, X, y=None):
         self.results_ = self.scorer_.results_
         self.best_estimator_ = self.scorer_.best_estimator_
 
-        return self
+        return self.scorer_
