def test_schema_validation(self):
trainable_16 = KNeighborsClassifier(n_neighbors=16)
with self.assertRaises(jsonschema.ValidationError):
_ = trainable_16.fit(self.train_X, self.train_y)
trainable_15 = KNeighborsClassifier(n_neighbors=15)
trained_15 = trainable_15.fit(self.train_X, self.train_y)
_ = trained_15.predict(self.test_X)
def test_predict_proba(self):
trainable = KNeighborsClassifier()
iris = sklearn.datasets.load_iris()
trained = trainable.fit(iris.data, iris.target)
#with self.assertWarns(DeprecationWarning):
predicted = trainable.predict_proba(iris.data)
predicted = trained.predict_proba(iris.data)
def test_individual_op_freeze_trained(self):
from lale.lib.sklearn import KNeighborsClassifier
trainable = KNeighborsClassifier(n_neighbors=1)
X = [[0.0], [1.0], [2.0]]
y_old = [0.0, 0.0, 1.0]
y_new = [1.0, 0.0, 0.0]
liquid_old = trainable.fit(X, y_old)
self.assertEqual(list(liquid_old.predict(X)), list(y_old))
liquid_new = liquid_old.fit(X, y_new)
self.assertEqual(list(liquid_new.predict(X)), list(y_new))
frozen_old = trainable.fit(X, y_old).freeze_trained()
self.assertFalse(liquid_old.is_frozen_trained())
self.assertTrue(frozen_old.is_frozen_trained())
self.assertEqual(list(frozen_old.predict(X)), list(y_old))
frozen_new = frozen_old.fit(X, y_new)
self.assertEqual(list(frozen_new.predict(X)), list(y_old))
def test_individual_op_freeze_trained(self):
from lale.lib.sklearn import KNeighborsClassifier
with EnableSchemaValidation():
trainable = KNeighborsClassifier(n_neighbors=1)
X = np.array([[0.0], [1.0], [2.0]])
y_old = np.array([0.0, 0.0, 1.0])
y_new = np.array([1.0, 0.0, 0.0])
liquid_old = trainable.fit(X, y_old)
self.assertEqual(list(liquid_old.predict(X)), list(y_old))
liquid_new = liquid_old.fit(X, y_new)
self.assertEqual(list(liquid_new.predict(X)), list(y_new))
frozen_old = trainable.fit(X, y_old).freeze_trained()
self.assertFalse(liquid_old.is_frozen_trained())
self.assertTrue(frozen_old.is_frozen_trained())
self.assertEqual(list(frozen_old.predict(X)), list(y_old))
frozen_new = frozen_old.fit(X, y_new)
self.assertEqual(list(frozen_new.predict(X)), list(y_old))
def test_with_multioutput_targets(self):
from sklearn.datasets import make_classification, load_iris
import numpy as np
from sklearn.utils import shuffle
X, y1 = make_classification(n_samples=10, n_features=100, n_informative=30, n_classes=3, random_state=1)
y2 = shuffle(y1, random_state=1)
y3 = shuffle(y1, random_state=2)
Y = np.vstack((y1, y2, y3)).T
trainable = KNeighborsClassifier()
trained = trainable.fit(X, Y)
predictions = trained.predict(X)
def test_trained_individual_op_freeze_trainable(self):
from lale.lib.sklearn import KNeighborsClassifier
from lale.operators import TrainedIndividualOp
trainable = KNeighborsClassifier(n_neighbors=1)
X = [[0.0], [1.0], [2.0]]
y_old = [0.0, 0.0, 1.0]
liquid = trainable.fit(X, y_old)
self.assertIsInstance(liquid, TrainedIndividualOp)
self.assertFalse(liquid.is_frozen_trainable())
self.assertIn('algorithm', liquid.free_hyperparams())
frozen = liquid.freeze_trainable()
self.assertIsInstance(frozen, TrainedIndividualOp)
self.assertTrue(frozen.is_frozen_trainable())
self.assertFalse(frozen.is_frozen_trained())
self.assertEqual(len(frozen.free_hyperparams()), 0)
def test_trained_individual_op_freeze_trainable(self):
from lale.lib.sklearn import KNeighborsClassifier
from lale.operators import TrainedIndividualOp
with EnableSchemaValidation():
trainable = KNeighborsClassifier(n_neighbors=1)
X = np.array([[0.0], [1.0], [2.0]])
y_old = np.array([0.0, 0.0, 1.0])
liquid = trainable.fit(X, y_old)
self.assertIsInstance(liquid, TrainedIndividualOp)
self.assertFalse(liquid.is_frozen_trainable())
self.assertIn("algorithm", liquid.free_hyperparams())
frozen = liquid.freeze_trainable()
self.assertIsInstance(frozen, TrainedIndividualOp)
self.assertTrue(frozen.is_frozen_trainable())
self.assertFalse(frozen.is_frozen_trained())
self.assertEqual(len(frozen.free_hyperparams()), 0)
