def test_pandas(self):
X = pandas.DataFrame(numpy.array([[0.1, 0.2], [-0.2, 0.3]]))
Y = numpy.array([0, 1])
clq = DecisionTreeLogisticRegression(fit_improve_algo=None)
clq.fit(X, Y)
pred2 = clq.predict(X)
self.assertEqual(numpy.array([0, 1]), pred2)
def test_classifier_simple_perpendicular(self):
X = [[0.1, 0.2], [0.2, 0.3], [-0.2, -0.3], [0.4, 0.3]]
Y = numpy.array([0, 1, 0, 1])
dtlr = DecisionTreeLogisticRegression(fit_improve_algo=None,
strategy='perpendicular')
self.assertRaise(lambda: dtlr.fit(X, Y), TypeError)
X = numpy.array(X)
Y = numpy.array(Y)
self.assertRaise(lambda: dtlr.fit(X, Y), NotImplementedError)
def test_classifier_grid_search(self):
X = random(100)
Y = X > 0.5 # pylint: disable=W0143
X = X.reshape((100, 1)) # pylint: disable=E1101
self.assertRaise(lambda: test_sklearn_grid_search_cv(
lambda: DecisionTreeLogisticRegression(fit_improve_algo=None), X, Y), ValueError)
res = test_sklearn_grid_search_cv(lambda: DecisionTreeLogisticRegression(fit_improve_algo=None),
X, Y, max_depth=[2, 3])
self.assertIn('model', res)
self.assertIn('score', res)
self.assertGreater(res['score'], 0)
self.assertLesser(res['score'], 1)
def test_iris_fit_improve(self):
data = load_iris()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
random_state=11)
self.assertRaise(
lambda: DecisionTreeLogisticRegression(
fit_improve_algo='fit_improve_algo'), ValueError)
dtlr = DecisionTreeLogisticRegression(
fit_improve_algo='intercept_sort_always')
self.assertRaise(lambda: dtlr.fit(X_train, y_train), RuntimeError)
y = y % 2
X_train, X_test, y_train, y_test = train_test_split(X,
y,
random_state=11)
dtlr.fit(X_train, y_train)
depth = dtlr.tree_depth_
self.assertGreater(depth, 2)
sc = dtlr.score(X_test, y_test)
lr = LogisticRegression()
lr.fit(X_train, y_train)
sc2 = lr.score(X_test, y_test)
self.assertGreater(sc, sc2)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
sc3 = dt.score(X_test, y_test)
self.assertGreater(sc3, sc2)
sc = dtlr.score(X_train, y_train)
sc2 = lr.score(X_train, y_train)
sc3 = dt.score(X_train, y_train)
self.assertGreater(sc, sc2)
self.assertGreater(sc3, sc2)
def test_classifier_pickle(self):
X = random(100)
Y = X > 0.5 # pylint: disable=W0143
X = X.reshape((100, 1)) # pylint: disable=E1101
test_sklearn_pickle(lambda: LogisticRegression(), X, Y)
test_sklearn_pickle(lambda: DecisionTreeLogisticRegression(
fit_improve_algo=None), X, Y)
def test_classifier_simple(self):
X = [[0.1, 0.2], [0.2, 0.3], [-0.2, -0.3], [0.4, 0.3]]
Y = numpy.array([0, 1, 0, 1])
dtlr = DecisionTreeLogisticRegression(fit_improve_algo=None)
self.assertRaise(lambda: dtlr.fit(X, Y), TypeError)
X = numpy.array(X)
Y = numpy.array(Y)
dtlr.fit(X, Y)
prob = dtlr.predict_proba(X)
self.assertEqual(prob.shape, (4, 2))
dtlr.fit(X, Y, sample_weight=numpy.array([1, 1, 1, 1]))
def test_decision_path(self):
data = load_iris()
X, y = data.data, data.target
y = y % 2
X_train, X_test, y_train, _ = train_test_split(X, y, random_state=11)
dtlr = DecisionTreeLogisticRegression()
dtlr.fit(X_train, y_train)
path = dtlr.decision_path(X_test)
self.assertEqual(path.shape[0], X_test.shape[0])
self.assertGreater(path.shape[1], X_test.shape[1])
indices = dtlr.get_leaves_index()
self.assertGreater(indices.shape[0], 3)
leaves = predict_leaves(dtlr, X_test)
self.assertEqual(leaves.shape[0], X_test.shape[0])
def test_classifier_clone(self):
test_sklearn_clone(
lambda: DecisionTreeLogisticRegression(fit_improve_algo=None))
def test_classifier_list(self):
X = [[0.1, 0.2], [0.2, 0.3]]
Y = numpy.array([0, 1])
clq = DecisionTreeLogisticRegression(fit_improve_algo=None)
self.assertRaise(lambda: clq.fit(X, Y), TypeError)
def test_classifier_strat(self):
X = numpy.array([[0.1, 0.2], [0.2, 0.3], [-0.2, -0.3], [0.4, 0.3]])
Y = numpy.array([0, 1, 0, 1])
dtlr = DecisionTreeLogisticRegression(fit_improve_algo=None,
strategy='')
self.assertRaise(lambda: dtlr.fit(X, Y), ValueError)