clean-up the tests

bob/learn/em/cluster/k_means.py

@@ -152,7 +152,7 @@ class KMeansMachine(BaseEstimator):
         """
         if trainer is None:
             logger.info("Using default k-means trainer.")
-            trainer = KMeansTrainer(init_method="k-means||")
+            trainer = KMeansTrainer(init_method="k-means||", random_state=self.random_state)
 
         logger.debug(f"Initializing trainer.")
         trainer.initialize(

bob/learn/em/test/test_kmeans.py

@@ -8,7 +8,7 @@
 """Tests the KMeans machine
 """
 
-import numpy
+import numpy as np
 
 from bob.learn.em.cluster import KMeansMachine
 from bob.learn.em.cluster import KMeansTrainer
@@ -16,45 +16,39 @@ from bob.learn.em.cluster import KMeansTrainer
 import dask.array as da
 
 
-def equals(x, y, epsilon):
-    return abs(x - y) < epsilon
-
-
 def test_KMeansMachine():
     # Test a KMeansMachine
 
-    means = numpy.array([[3, 70, 0], [4, 72, 0]], "float64")
-    mean = numpy.array([3, 70, 1], "float64")
+    means = np.array([[3, 70, 0], [4, 72, 0]], "float64")
+    mean = np.array([3, 70, 1], "float64")
 
     # Initializes a KMeansMachine
     km = KMeansMachine(2)
     km.centroids_ = means
 
     # Distance and closest mean
-    eps = 1e-10
-
-    assert equals(km.transform(mean)[0], 1, eps), km.transform(mean)[0].compute()
-    assert equals(km.transform(mean)[1], 6, eps), km.transform(mean)[1].compute()
+    np.testing.assert_almost_equal(km.transform(mean)[0], 1)
+    np.testing.assert_almost_equal(km.transform(mean)[1], 6)
 
     (index, dist) = km.get_closest_centroid(mean)
 
-    assert index == 0
-    assert equals(dist, 1, eps)
-    assert equals(km.get_min_distance(mean), 1, eps)
+    assert index == 0, index
+    np.testing.assert_almost_equal(dist, 1.0)
+    np.testing.assert_almost_equal(km.get_min_distance(mean), 1)
 
 
 def test_KMeansMachine_var_and_weight():
     kmeans = KMeansMachine(2)
-    kmeans.centroids_ = numpy.array([[1.2, 1.3], [0.2, -0.3]])
+    kmeans.centroids_ = np.array([[1.2, 1.3], [0.2, -0.3]])
 
-    data = numpy.array([[1.0, 1], [1.2, 3], [0, 0], [0.3, 0.2], [0.2, 0]])
+    data = np.array([[1.0, 1], [1.2, 3], [0, 0], [0.3, 0.2], [0.2, 0]])
     variances, weights = kmeans.get_variances_and_weights_for_each_cluster(data)
 
-    variances_result = numpy.array([[0.01, 1.0], [0.01555556, 0.00888889]])
-    weights_result = numpy.array([0.4, 0.6])
+    variances_result = np.array([[0.01, 1.0], [0.01555556, 0.00888889]])
+    weights_result = np.array([0.4, 0.6])
 
-    assert equals(weights_result, weights, 1e-3).all()
-    assert equals(variances_result, variances, 1e-3).all()
+    np.testing.assert_almost_equal(variances, variances_result)
+    np.testing.assert_almost_equal(weights, weights_result)
 
 
 def test_kmeans_fit():
@@ -62,12 +56,9 @@ def test_kmeans_fit():
     data1 = da.random.normal(loc=1, size=(2000, 3))
     data2 = da.random.normal(loc=-1, size=(2000, 3))
     data = da.concatenate([data1, data2], axis=0)
-    machine = KMeansMachine(2).fit(data)
-    expected = da.array(
-        [[-0.99262315, -1.05226141, -1.00525245], [1.00426431, 1.00359693, 1.05996704]]
-    )
-    centroids = machine.centroids_.compute()
-    numpy.testing.assert_array_almost_equal(centroids, expected)
+    machine = KMeansMachine(2, random_state=0).fit(data)
+    expected = [[1.00426431, 1.00359693, 1.05996704], [-0.99262315, -1.05226141, -1.00525245]]
+    np.testing.assert_almost_equal(machine.centroids_, expected)
 
 
 def test_kmeans_fit_init_pp():
@@ -75,12 +66,10 @@ def test_kmeans_fit_init_pp():
     data1 = da.random.normal(loc=1, size=(2000, 3))
     data2 = da.random.normal(loc=-1, size=(2000, 3))
     data = da.concatenate([data1, data2], axis=0)
-    trainer = KMeansTrainer(init_method="k-means++")
+    trainer = KMeansTrainer(init_method="k-means++", random_state=0)
     machine = KMeansMachine(2).fit(data, trainer=trainer)
-    expected = da.array(
-        [[-0.99262315, -1.05226141, -1.00525245], [1.00426431, 1.00359693, 1.05996704]]
-    )
-    assert da.isclose(machine.centroids_, expected).all(), machine.centroids_.compute()
+    expected = [[-0.99262315, -1.05226141, -1.00525245], [1.00426431, 1.00359693, 1.05996704]]
+    np.testing.assert_almost_equal(machine.centroids_, expected)
 
 
 def test_kmeans_fit_init_random():
@@ -90,7 +79,5 @@ def test_kmeans_fit_init_random():
     data = da.concatenate([data1, data2], axis=0)
     trainer = KMeansTrainer(init_method="random", random_state=0)
     machine = KMeansMachine(2).fit(data, trainer=trainer)
-    expected = da.array(
-        [[-0.99433738, -1.05561588, -1.01236246], [0.99800688, 0.99873325, 1.05879539]]
-    )
-    assert da.isclose(machine.centroids_, expected).all(), machine.centroids_.compute()
+    expected = [[-0.99433738, -1.05561588, -1.01236246], [0.99800688, 0.99873325, 1.05879539]]
+    np.testing.assert_almost_equal(machine.centroids_, expected)