tSNE: Add tests that check for embedding correctness

Orange/tests/test_manifold.py

@@ -4,6 +4,8 @@
 import unittest
 
 import numpy as np
+from sklearn.metrics import accuracy_score
+from sklearn.neighbors import KNeighborsClassifier
 
 from Orange.data import Table
 from Orange.distance import Euclidean
@@ -12,6 +14,9 @@
 from Orange.projection.manifold import torgerson
 
 
+np.random.seed(42)
+
+
 class TestManifold(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
@@ -189,3 +194,46 @@ def test_continue_optimization_inplace(self):
 
         # The embeddings in the table should match the embedding object
         np.testing.assert_equal(new_model.embedding.X, new_model.embedding_)
+
+    def test_bh_correctness(self):
+        knn = KNeighborsClassifier(n_neighbors=5)
+
+        # Set iterations to 0 so we check that the initialization is fairly random
+        tsne = TSNE(early_exaggeration_iter=0, n_iter=0, perplexity=30,
+                    negative_gradient_method='bh', initialization='random')
+        model = tsne(self.iris)
+
+        # Evaluate KNN on the random initialization
+        knn.fit(model.embedding_, self.iris.Y)
+        predicted = knn.predict(model.embedding_)
+        self.assertTrue(accuracy_score(predicted, self.iris.Y) < 0.6)
+
+        # 100 iterations should be enough for iris
+        model.optimize(n_iter=100, inplace=True)
+
+        # Evaluate KNN on the tSNE embedding
+        knn.fit(model.embedding_, self.iris.Y)
+        predicted = knn.predict(model.embedding_)
+        self.assertTrue(accuracy_score(predicted, self.iris.Y) > 0.95)
+
+    def test_fft_correctness(self):
+        knn = KNeighborsClassifier(n_neighbors=5)
+
+        # Set iterations to 0 so we check that the initialization is fairly random
+        tsne = TSNE(early_exaggeration_iter=0, n_iter=0, perplexity=30,
+                    negative_gradient_method='fft', initialization='random')
+        model = tsne(self.iris)
+
+        # Evaluate KNN on the random initialization
+        knn.fit(model.embedding_, self.iris.Y)
+        predicted = knn.predict(model.embedding_)
+        self.assertTrue(accuracy_score(predicted, self.iris.Y) < 0.6)
+
+        # 100 iterations should be enough for iris
+        model.optimize(n_iter=100, inplace=True)
+
+        # Evaluate KNN on the tSNE embedding
+        knn.fit(model.embedding_, self.iris.Y)
+        predicted = knn.predict(model.embedding_)
+        self.assertTrue(accuracy_score(predicted, self.iris.Y) > 0.95)
+

Orange/widgets/unsupervised/tests/test_owmanifoldlearning.py

@@ -6,7 +6,7 @@
 import numpy as np
 from scipy import sparse
 
-from Orange.data import Table
+from Orange.data import Table, Domain, ContinuousVariable, DiscreteVariable
 from Orange.widgets.tests.base import WidgetTest
 from Orange.widgets.unsupervised.owmanifoldlearning import OWManifoldLearning