Added tests for ScikitClassifier

bob/pad/base/algorithm/__init__.py

@@ -37,6 +37,7 @@ __appropriate__(
     SVMCascadePCA,
     Predictions,
     VideoPredictions,
+    ScikitClassifier,
     MLP,
     PadLDA
 )

bob/pad/base/test/test_algorithms.py

@@ -14,6 +14,7 @@ from bob.pad.base.algorithm import SVM
 from bob.pad.base.algorithm import OneClassGMM
 from bob.pad.base.algorithm import MLP
 from bob.pad.base.algorithm import PadLDA
+from bob.pad.base.algorithm import ScikitClassifier
 
 import random
 
@@ -219,3 +220,40 @@ def test_LDA():
     lda = PadLDA()
     lda.train_projector(training_features, '/tmp/lda.hdf5')
     assert lda.machine.shape == (2, 1)
+
+
+
+def test_ScikitClassifier():
+
+    random.seed(7)
+
+    N = 20000
+    mu = 1
+    sigma = 1
+    real_array = np.transpose(
+        np.vstack([[random.gauss(mu, sigma) for _ in range(N)],
+                   [random.gauss(mu, sigma) for _ in range(N)]]))
+
+    mu = 5
+    sigma = 1
+    attack_array = np.transpose(
+        np.vstack([[random.gauss(mu, sigma) for _ in range(N)],
+                   [random.gauss(mu, sigma) for _ in range(N)]]))
+
+    training_features = [real_array, attack_array]
+
+    from sklearn.preprocessing import StandardScaler
+    from sklearn.mixture import GaussianMixture
+
+
+    _scaler = StandardScaler()
+    _clf = GaussianMixture(n_components=10, covariance_type='full')
+
+    sk = ScikitClassifier(clf=_clf, scaler=_scaler, frame_level_scores_flag=False, one_class=True)
+    sk.train_projector(training_features, '/tmp/sk.hdf5')
+
+    # Model path `/tmp/sk_skmodel.obj`
+    # Scaler path `/tmp/sk_scaler.obj`
+
+    assert sk.clf.n_components==10
+