def test_target_classifier_permute_iris(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=12)
log = LogisticRegression(n_jobs=1)
log.fit(X_train, y_train)
sc = log.score(X_test, y_test)
r2 = r2_score(y_test, log.predict(X_test))
for _ in range(10):
TransformedTargetClassifier2(classifier=None,
transformer='permute')
tt = TransformedTargetClassifier2(
classifier=LogisticRegression(n_jobs=1), transformer='permute')
try:
tt.fit(X_train, y_train)
except AttributeError as e:
if compare_module_version(sklver, "0.24") < 0:
return
raise e
sc2 = tt.score(X_test, y_test)
self.assertEqual(sc, sc2)
r22 = r2_score(y_test, tt.predict(X_test))
self.assertEqual(r2, r22)
def test_target_classifier_decision(self):
tt = TransformedTargetClassifier2(classifier=None,
transformer='permute')
X = numpy.arange(4).reshape(-1, 1)
y = numpy.array([0, 0, 1, 1], dtype=int)
tt.fit(X, y)
self.assertRaise(lambda: tt.decision_function(X), RuntimeError)
def test_target_classifier_any(self):
trans = FunctionReciprocalTransformer('log')
tt = TransformedTargetClassifier2(classifier=None, transformer=trans)
X = numpy.arange(4).reshape(-1, 1)
y = numpy.exp(2 * X).ravel()
tt.fit(X, y)
self.assertIn("TransformedTargetClassifier2", str(tt))
yp = tt.predict(X)
self.assertEqual(yp.shape, (4, ))
def test_target_classifier(self):
tt = TransformedTargetClassifier2(classifier=None,
transformer='permute')
X = numpy.arange(4).reshape(-1, 1)
y = numpy.array([0, 0, 1, 1], dtype=int)
tt.fit(X, y)
self.assertIn("TransformedTargetClassifier2", str(tt))
coef = tt.classifier_.coef_
self.assertEqual(coef.shape, (1, 1))
yp = tt.predict(X)
self.assertEqual(yp.shape, (4, ))
sc = tt.score(X, y)
self.assertLesser(sc, 1.)
def test_target_classifier_permute(self):
X = numpy.arange(4).reshape(-1, 1)
y = numpy.array([0, 0, 1, 1], dtype=int)
log = LogisticRegression()
log.fit(X, y)
sc = log.score(X, y)
tt = TransformedTargetClassifier2(classifier=None,
transformer='permute')
tt.fit(X, y)
sc2 = tt.score(X, y)
self.assertEqual(sc, sc2)
def test_target_classifier_proba(self):
tt = TransformedTargetClassifier2(classifier=None,
transformer='permute')
X = numpy.arange(4).reshape(-1, 1)
y = numpy.array([0, 0, 1, 1], dtype=int)
tt.fit(X, y)
cl = tt.classes_
self.assertEqual(cl.shape, tt.classifier_.classes_.shape)
yp2 = tt.classifier_.predict_proba(tt.transformer_.transform(X, y)[0])
if tt.transformer_.permutation_[0] == 0:
self.assertEqualArray(cl, tt.classifier_.classes_)
else:
self.assertEqualArray(cl, -(tt.classifier_.classes_ - 1))
c = yp2.copy()
yp2[:, 0] = c[:, 1]
yp2[:, 1] = c[:, 0]
yp = tt.predict_proba(X)
self.assertEqual(yp.shape, (4, 2))
self.assertEqualArray(yp, yp2)
def test_target_classifier_permute_iris(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=12)
log = LogisticRegression()
log.fit(X_train, y_train)
sc = log.score(X_test, y_test)
r2 = r2_score(y_test, log.predict(X_test))
for _ in range(10):
tt = TransformedTargetClassifier2(
classifier=None, transformer='permute')
tt.fit(X_train, y_train)
sc2 = tt.score(X_test, y_test)
self.assertEqual(sc, sc2)
r22 = r2_score(y_test, tt.predict(X_test))
self.assertEqual(r2, r22)
def test_target_classifier_err(self):
tt = TransformedTargetClassifier2(classifier=None, transformer=None)
X = numpy.arange(4).reshape(-1, 1)
y = numpy.exp(2 * X).ravel()
self.assertRaise(lambda: tt.fit(X, y), TypeError)