add testcases

combo/test/test_classifier_comb.py

@@ -35,9 +35,128 @@
 # if  combo is installed, no need to use the following line
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
+from combo.models.classifier_comb import BaseClassifierAggregator
 from combo.models.classifier_comb import SimpleClassifierAggregator
 
 
+# Check sklearn\tests\test_base
+# A few test classes
+# noinspection PyMissingConstructor,PyPep8Naming
+class MyEstimator(BaseClassifierAggregator):
+
+    def __init__(self, l1=0, empty=None):
+        self.l1 = l1
+        self.empty = empty
+
+    def fit(self, X, y=None):
+        pass
+
+    def predict(self, X):
+        pass
+
+    def predict_proba(self, X):
+        pass
+
+
+# noinspection PyMissingConstructor
+class K(BaseClassifierAggregator):
+    def __init__(self, c=None, d=None):
+        self.c = c
+        self.d = d
+
+    def fit(self, X, y=None):
+        pass
+
+    def predict(self, X):
+        pass
+
+    def predict_proba(self, X):
+        pass
+
+
+# noinspection PyMissingConstructor
+class T(BaseClassifierAggregator):
+    def __init__(self, a=None, b=None):
+        self.a = a
+        self.b = b
+
+    def fit(self, X, y=None):
+        pass
+
+    def predict(self, X):
+        pass
+
+    def predict_proba(self, X):
+        pass
+
+
+# noinspection PyMissingConstructor
+class ModifyInitParams(BaseClassifierAggregator):
+    """Deprecated behavior.
+    Equal parameters but with a type cast.
+    Doesn't fulfill a is a
+    """
+
+    def __init__(self, a=np.array([0])):
+        self.a = a.copy()
+
+    def fit(self, X, y=None):
+        pass
+
+    def predict(self, X):
+        pass
+
+    def predict_proba(self, X):
+        pass
+
+
+# noinspection PyMissingConstructor
+class VargEstimator(BaseClassifierAggregator):
+    """scikit-learn estimators shouldn't have vargs."""
+
+    def __init__(self, *vargs):
+        pass
+
+    def fit(self, X, y=None):
+        pass
+
+    def predict(self, X):
+        pass
+
+    def predict_proba(self, X):
+        pass
+
+
+class TestBase(unittest.TestCase):
+    def test_repr(self):
+        # Smoke test the repr of the base estimator.
+        my_estimator = MyEstimator()
+        repr(my_estimator)
+        test = T(K(), K())
+        assert_equal(
+            repr(test),
+            "T(a=K(c=None, d=None), b=K(c=None, d=None))"
+        )
+
+        some_est = T(a=["long_params"] * 1000)
+        assert_equal(len(repr(some_est)), 415)
+
+    def test_str(self):
+        # Smoke test the str of the base estimator
+        my_estimator = MyEstimator()
+        str(my_estimator)
+
+    def test_get_params(self):
+        test = T(K(), K())
+
+        assert_true('a__d' in test.get_params(deep=True))
+        assert_true('a__d' not in test.get_params(deep=False))
+
+        test.set_params(a__d=2)
+        assert_true(test.a.d == 2)
+        assert_raises(ValueError, test.set_params, a__a=2)
+
+
 class TestAverage(unittest.TestCase):
     def setUp(self):
         self.roc_floor = 0.9
@@ -127,6 +246,12 @@ def test_parameters(self):
         assert_true(hasattr(self.clf, 'classifiers') and
                     self.clf.classifiers is not None)
 
+        # print clf details
+        self.clf
+
+        # set parameters
+        self.clf.set_params()
+
     def test_train_scores(self):
         y_train_predicted = self.clf.predict(self.X_train)
         assert_equal(len(y_train_predicted), self.X_train.shape[0])
@@ -156,13 +281,11 @@ def test_prediction_proba(self):
         n_classes = len(np.unique(self.y_train))
         assert_equal(y_test_predicted.shape, (self.X_test.shape[0], n_classes))
 
-
         # check probability sum is 1
         y_test_predicted_sum = np.sum(y_test_predicted, axis=1)
         assert_allclose(np.ones([self.X_test.shape[0], ]),
                         y_test_predicted_sum)
 
-
     def tearDown(self):
         pass