Start DTWKNN tests

lib/tests/lib/classifiers/dtwknn/test_dtwknn.py

@@ -0,0 +1,87 @@
+import pytest
+import warnings
+import numpy as np
+from copy import deepcopy
+with warnings.catch_warnings():
+    warnings.filterwarnings('ignore', category=DeprecationWarning)
+    from sequentia.classifiers import DTWKNN
+from ....support import assert_equal, assert_not_equal
+
+# Set seed for reproducible randomness
+seed = 0
+np.random.seed(seed)
+rng = np.random.RandomState(seed)
+
+# Create some sample data
+X = [rng.random((10 * i, 3)) for i in range(1, 7)]
+y = ['c1', 'c1', 'c0', 'c1', 'c1', 'c0']
+x = X[0]
+
+clfs = [
+    DTWKNN(k=1, radius=1),
+    DTWKNN(k=2, radius=5),
+    DTWKNN(k=3, radius=10)
+]
+
+# ============ #
+# DTWKNN.fit() #
+# ============ #
+
+def test_fit():
+    """"""
+    clf = clfs[0]
+    clf.fit(X, y)
+    assert clf._X == X
+    assert clf._y == y
+
+def test_predict_without_fit():
+    """"""
+    pass
+
+def test_predict_single_k1_r1_verbose(capsys):
+    """Verbosely predict a single observation sequence (k=1, r=1)"""
+    clf = clfs[0]
+    clf.fit(X, y)
+    prediction = clf.predict(x, verbose=True)
+    assert 'Calculating distances' in capsys.readouterr().err
+    assert prediction == 'c1'
+
+def test_predict_single_k1_r1_no_verbose(capsys):
+    """Silently predict a single observation sequence (k=1, r=1)"""
+    clf = clfs[0]
+    clf.fit(X, y)
+    prediction = clf.predict(x, verbose=False)
+    assert 'Calculating distances' not in capsys.readouterr().err
+    assert prediction == 'c1'
+
+def test_predict_single_k2_r5_verbose(capsys):
+    """Verbosely predict a single observation sequence (k=2, r=5)"""
+    clf = clfs[1]
+    clf.fit(X, y)
+    prediction = clf.predict(x, verbose=True)
+    assert 'Calculating distances' in capsys.readouterr().err
+    assert prediction == 'c1'
+
+def test_predict_single_k2_r5_no_verbose(capsys):
+    """Silently predict a single observation sequence (k=2, r=5)"""
+    clf = clfs[1]
+    clf.fit(X, y)
+    prediction = clf.predict(x, verbose=False)
+    assert 'Calculating distances' not in capsys.readouterr().err
+    assert prediction == 'c1'
+
+def test_predict_single_k3_r10_verbose(capsys):
+    """Verbosely predict a single observation sequence (k=3, r=10)"""
+    clf = clfs[2]
+    clf.fit(X, y)
+    prediction = clf.predict(x, verbose=True)
+    assert 'Calculating distances' in capsys.readouterr().err
+    assert prediction == 'c1'
+
+def test_predict_single_k3_r10_no_verbose(capsys):
+    """Silently predict a single observation sequence (k=3, r=10)"""
+    clf = clfs[2]
+    clf.fit(X, y)
+    prediction = clf.predict(x, verbose=False)
+    assert 'Calculating distances' not in capsys.readouterr().err
+    assert prediction == 'c1'

lib/tests/lib/classifiers/hmm/test_hmm_classifier.py

@@ -2,7 +2,6 @@
 import warnings
 import numpy as np
 from copy import deepcopy
-import pomegranate as pg
 from sequentia.classifiers import HMM, HMMClassifier
 from ....support import assert_equal, assert_not_equal