Added unit test for transformer-metric conversion

Author: Björn Barz

test/test_transformer_metric_conversion.py

@@ -0,0 +1,80 @@
+import unittest
+import numpy as np
+from sklearn.datasets import load_iris
+from numpy.testing import assert_array_almost_equal
+
+from metric_learn import (
+    LMNN, NCA, LFDA, Covariance, MLKR,
+    LSML_Supervised, ITML_Supervised, SDML_Supervised, RCA_Supervised)
+
+
+class TestTransformerMetricConversion(unittest.TestCase):
+  @classmethod
+  def setUpClass(self):
+    # runs once per test class
+    iris_data = load_iris()
+    self.X = iris_data['data']
+    self.y = iris_data['target']
+
+  def test_cov(self):
+    cov = Covariance()
+    cov.fit(self.X)
+    L = cov.transformer()
+    assert_array_almost_equal(L.T.dot(L), cov.metric())
+
+  def test_lsml_supervised(self):
+    seed = np.random.RandomState(1234)
+    lsml = LSML_Supervised(num_constraints=200)
+    lsml.fit(self.X, self.y, random_state=seed)
+    L = lsml.transformer()
+    assert_array_almost_equal(L.T.dot(L), lsml.metric())
+
+  def test_itml_supervised(self):
+    seed = np.random.RandomState(1234)
+    itml = ITML_Supervised(num_constraints=200)
+    itml.fit(self.X, self.y, random_state=seed)
+    L = itml.transformer()
+    assert_array_almost_equal(L.T.dot(L), itml.metric())
+
+  def test_lmnn(self):
+    lmnn = LMNN(k=5, learn_rate=1e-6, verbose=False)
+    lmnn.fit(self.X, self.y)
+    L = lmnn.transformer()
+    assert_array_almost_equal(L.T.dot(L), lmnn.metric())
+
+  def test_sdml_supervised(self):
+    seed = np.random.RandomState(1234)
+    sdml = SDML_Supervised(num_constraints=1500)
+    sdml.fit(self.X, self.y, random_state=seed)
+    L = sdml.transformer()
+    assert_array_almost_equal(L.T.dot(L), sdml.metric())
+
+  def test_nca(self):
+    n = self.X.shape[0]
+    nca = NCA(max_iter=(100000//n), learning_rate=0.01)
+    nca.fit(self.X, self.y)
+    L = nca.transformer()
+    assert_array_almost_equal(L.T.dot(L), nca.metric())
+
+  def test_lfda(self):
+    lfda = LFDA(k=2, num_dims=2)
+    lfda.fit(self.X, self.y)
+    L = lfda.transformer()
+    assert_array_almost_equal(L.T.dot(L), lfda.metric())
+
+  def test_rca_supervised(self):
+    seed = np.random.RandomState(1234)
+    rca = RCA_Supervised(num_dims=2, num_chunks=30, chunk_size=2)
+    rca.fit(self.X, self.y, random_state=seed)
+    L = rca.transformer()
+    assert_array_almost_equal(L.T.dot(L), rca.metric())
+
+  def test_mlkr(self):
+    mlkr = MLKR(num_dims=2)
+    mlkr.fit(self.X, self.y)
+    L = mlkr.transformer()
+    assert_array_almost_equal(L.T.dot(L), mlkr.metric())
+
+
+if __name__ == '__main__':
+  unittest.main()