def test_import_from_sklearn_pipeline_nested_pipeline1(self):
from sklearn.decomposition import PCA as SklearnPCA
from sklearn.feature_selection import SelectKBest
from sklearn.kernel_approximation import Nystroem as SklearnNystroem
from sklearn.neighbors import KNeighborsClassifier as SklearnKNN
from sklearn.pipeline import FeatureUnion
union = FeatureUnion(
[
(
"selectkbest_pca",
sklearn.pipeline.make_pipeline(
SelectKBest(k=3),
FeatureUnion(
[
("pca", SklearnPCA(n_components=1)),
(
"nested_pipeline",
sklearn.pipeline.make_pipeline(
SelectKBest(k=2), SklearnNystroem()
),
),
]
),
),
),
("nys", SklearnNystroem(n_components=2, random_state=42)),
]
)
sklearn_pipeline = sklearn.pipeline.make_pipeline(union, SklearnKNN())
lale_pipeline = typing.cast(
lale.operators.TrainablePipeline,
import_from_sklearn_pipeline(sklearn_pipeline),
)
self.assertEqual(len(lale_pipeline.edges()), 8)
# These assertions assume topological sort, which may not be unique. So the assertions are brittle.
from lale.lib.lale.concat_features import ConcatFeatures
from lale.lib.sklearn.k_neighbors_classifier import KNeighborsClassifier
from lale.lib.sklearn.nystroem import Nystroem
from lale.lib.sklearn.pca import PCA
from lale.lib.sklearn.select_k_best import SelectKBest
self.assertIsInstance(lale_pipeline.edges()[0][0], SelectKBest) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[0][1], PCA) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[1][0], SelectKBest) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[1][1], SelectKBest) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[2][0], SelectKBest) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[2][1], Nystroem) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[3][0], PCA) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[3][1], ConcatFeatures) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[4][0], Nystroem) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[4][1], ConcatFeatures) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[5][0], ConcatFeatures) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[5][1], ConcatFeatures) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[6][0], Nystroem) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[6][1], ConcatFeatures) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[7][0], ConcatFeatures) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[7][1], KNeighborsClassifier) # type: ignore
self.assert_equal_predictions(sklearn_pipeline, lale_pipeline)
def test_import_from_sklearn_pipeline_nested_pipeline1(self):
from sklearn.pipeline import FeatureUnion
from sklearn.decomposition import PCA as SklearnPCA
from sklearn.kernel_approximation import Nystroem as SklearnNystroem
from sklearn.feature_selection import SelectKBest
from sklearn.neighbors import KNeighborsClassifier as SklearnKNN
union = FeatureUnion([
("selectkbest_pca",
sklearn.pipeline.make_pipeline(
SelectKBest(k=3),
FeatureUnion([
('pca', SklearnPCA(n_components=1)),
('nested_pipeline',
sklearn.pipeline.make_pipeline(SelectKBest(k=2),
SklearnNystroem()))
]))), ("nys", SklearnNystroem(n_components=2,
random_state=42))
])
sklearn_pipeline = sklearn.pipeline.make_pipeline(union, SklearnKNN())
lale_pipeline = import_from_sklearn_pipeline(sklearn_pipeline)
self.assertEqual(len(lale_pipeline.edges()), 8)
#These assertions assume topological sort, which may not be unique. So the assertions are brittle.
from lale.lib.sklearn.pca import PCAImpl
from lale.lib.sklearn.nystroem import NystroemImpl
from lale.lib.lale.concat_features import ConcatFeaturesImpl
from lale.lib.sklearn.k_neighbors_classifier import KNeighborsClassifierImpl
from lale.lib.sklearn.select_k_best import SelectKBestImpl
self.assertEqual(lale_pipeline.edges()[0][0]._impl_class(),
SelectKBestImpl)
self.assertEqual(lale_pipeline.edges()[0][1]._impl_class(), PCAImpl)
self.assertEqual(lale_pipeline.edges()[1][0]._impl_class(),
SelectKBestImpl)
self.assertEqual(lale_pipeline.edges()[1][1]._impl_class(),
SelectKBestImpl)
self.assertEqual(lale_pipeline.edges()[2][0]._impl_class(),
SelectKBestImpl)
self.assertEqual(lale_pipeline.edges()[2][1]._impl_class(),
NystroemImpl)
self.assertEqual(lale_pipeline.edges()[3][0]._impl_class(), PCAImpl)
self.assertEqual(lale_pipeline.edges()[3][1]._impl_class(),
ConcatFeaturesImpl)
self.assertEqual(lale_pipeline.edges()[4][0]._impl_class(),
NystroemImpl)
self.assertEqual(lale_pipeline.edges()[4][1]._impl_class(),
ConcatFeaturesImpl)
self.assertEqual(lale_pipeline.edges()[5][0]._impl_class(),
ConcatFeaturesImpl)
self.assertEqual(lale_pipeline.edges()[5][1]._impl_class(),
ConcatFeaturesImpl)
self.assertEqual(lale_pipeline.edges()[6][0]._impl_class(),
NystroemImpl)
self.assertEqual(lale_pipeline.edges()[6][1]._impl_class(),
ConcatFeaturesImpl)
self.assertEqual(lale_pipeline.edges()[7][0]._impl_class(),
ConcatFeaturesImpl)
self.assertEqual(lale_pipeline.edges()[7][1]._impl_class(),
KNeighborsClassifierImpl)
self.assert_equal_predictions(sklearn_pipeline, lale_pipeline)
def test_import_from_sklearn_pipeline_feature_union(self):
from sklearn.decomposition import PCA as SklearnPCA
from sklearn.kernel_approximation import Nystroem as SklearnNystroem
from sklearn.neighbors import KNeighborsClassifier as SklearnKNN
from sklearn.pipeline import FeatureUnion
union = FeatureUnion([
("pca", SklearnPCA(n_components=1)),
("nys", SklearnNystroem(n_components=2, random_state=42)),
])
sklearn_pipeline = sklearn.pipeline.make_pipeline(union, SklearnKNN())
lale_pipeline = typing.cast(
lale.operators.TrainablePipeline,
import_from_sklearn_pipeline(sklearn_pipeline),
)
self.assertEqual(len(lale_pipeline.edges()), 3)
from lale.lib.lale.concat_features import ConcatFeatures
from lale.lib.sklearn.k_neighbors_classifier import KNeighborsClassifier
from lale.lib.sklearn.nystroem import Nystroem
from lale.lib.sklearn.pca import PCA
self.assertIsInstance(lale_pipeline.edges()[0][0], PCA) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[0][1],
ConcatFeatures) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[1][0],
Nystroem) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[1][1],
ConcatFeatures) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[2][0],
ConcatFeatures) # type: ignore
self.assertIsInstance(lale_pipeline.edges()[2][1],
KNeighborsClassifier) # type: ignore
self.assert_equal_predictions(sklearn_pipeline, lale_pipeline)
def test_import_from_sklearn_pipeline_feature_union(self):
from sklearn.decomposition import PCA as SklearnPCA
from sklearn.kernel_approximation import Nystroem as SklearnNystroem
from sklearn.neighbors import KNeighborsClassifier as SklearnKNN
from sklearn.pipeline import FeatureUnion
union = FeatureUnion([
("pca", SklearnPCA(n_components=1)),
("nys", SklearnNystroem(n_components=2, random_state=42)),
])
sklearn_pipeline = sklearn.pipeline.make_pipeline(union, SklearnKNN())
lale_pipeline = import_from_sklearn_pipeline(sklearn_pipeline)
self.assertEqual(len(lale_pipeline.edges()), 3)
from lale.lib.lale.concat_features import ConcatFeaturesImpl
from lale.lib.sklearn.k_neighbors_classifier import KNeighborsClassifierImpl
from lale.lib.sklearn.nystroem import NystroemImpl
from lale.lib.sklearn.pca import PCAImpl
self.assertEqual(lale_pipeline.edges()[0][0]._impl_class(), PCAImpl)
self.assertEqual(lale_pipeline.edges()[0][1]._impl_class(),
ConcatFeaturesImpl)
self.assertEqual(lale_pipeline.edges()[1][0]._impl_class(),
NystroemImpl)
self.assertEqual(lale_pipeline.edges()[1][1]._impl_class(),
ConcatFeaturesImpl)
self.assertEqual(lale_pipeline.edges()[2][0]._impl_class(),
ConcatFeaturesImpl)
self.assertEqual(lale_pipeline.edges()[2][1]._impl_class(),
KNeighborsClassifierImpl)
self.assert_equal_predictions(sklearn_pipeline, lale_pipeline)
def test_comparison_with_scikit(self):
import warnings
warnings.filterwarnings("ignore")
from lale.lib.sklearn import PCA
import sklearn.datasets
from lale.helpers import cross_val_score
pca = PCA(n_components=3, random_state=42, svd_solver='arpack')
nys = Nystroem(n_components=10, random_state=42)
concat = ConcatFeatures()
lr = LogisticRegression(random_state=42, C=0.1)
trainable = (pca & nys) >> concat >> lr
digits = sklearn.datasets.load_digits()
X, y = sklearn.utils.shuffle(digits.data, digits.target, random_state=42)
cv_results = cross_val_score(trainable, X, y)
cv_results = ['{0:.1%}'.format(score) for score in cv_results]
from sklearn.pipeline import make_pipeline, FeatureUnion
from sklearn.decomposition import PCA as SklearnPCA
from sklearn.kernel_approximation import Nystroem as SklearnNystroem
from sklearn.linear_model import LogisticRegression as SklearnLR
from sklearn.model_selection import cross_val_score
union = FeatureUnion([("pca", SklearnPCA(n_components=3, random_state=42, svd_solver='arpack')),
("nys", SklearnNystroem(n_components=10, random_state=42))])
lr = SklearnLR(random_state=42, C=0.1)
pipeline = make_pipeline(union, lr)
scikit_cv_results = cross_val_score(pipeline, X, y, cv = 5)
scikit_cv_results = ['{0:.1%}'.format(score) for score in scikit_cv_results]
self.assertEqual(cv_results, scikit_cv_results)
warnings.resetwarnings()
def test_import_from_sklearn_pipeline_nested_pipeline2(self):
from sklearn.decomposition import PCA as SklearnPCA
from sklearn.feature_selection import SelectKBest
from sklearn.kernel_approximation import Nystroem as SklearnNystroem
from sklearn.neighbors import KNeighborsClassifier as SklearnKNN
from sklearn.pipeline import FeatureUnion
union = FeatureUnion([
(
"selectkbest_pca",
sklearn.pipeline.make_pipeline(
SelectKBest(k=3),
sklearn.pipeline.make_pipeline(SelectKBest(k=2),
SklearnPCA()),
),
),
("nys", SklearnNystroem(n_components=2, random_state=42)),
])
sklearn_pipeline = sklearn.pipeline.make_pipeline(union, SklearnKNN())
lale_pipeline = typing.cast(
lale.operators.TrainablePipeline,
import_from_sklearn_pipeline(sklearn_pipeline),
)
self.assertEqual(len(lale_pipeline.edges()), 5)
from lale.lib.lale.concat_features import ConcatFeaturesImpl
from lale.lib.sklearn.k_neighbors_classifier import KNeighborsClassifierImpl
from lale.lib.sklearn.nystroem import NystroemImpl
from lale.lib.sklearn.pca import PCAImpl
from lale.lib.sklearn.select_k_best import SelectKBestImpl
self.assertEqual(lale_pipeline.edges()[0][0]._impl_class(),
SelectKBestImpl)
self.assertEqual(lale_pipeline.edges()[0][1]._impl_class(),
SelectKBestImpl)
self.assertEqual(lale_pipeline.edges()[1][0]._impl_class(),
SelectKBestImpl)
self.assertEqual(lale_pipeline.edges()[1][1]._impl_class(), PCAImpl)
self.assertEqual(lale_pipeline.edges()[2][0]._impl_class(), PCAImpl)
self.assertEqual(lale_pipeline.edges()[2][1]._impl_class(),
ConcatFeaturesImpl)
self.assertEqual(lale_pipeline.edges()[3][0]._impl_class(),
NystroemImpl)
self.assertEqual(lale_pipeline.edges()[3][1]._impl_class(),
ConcatFeaturesImpl)
self.assertEqual(lale_pipeline.edges()[4][0]._impl_class(),
ConcatFeaturesImpl)
self.assertEqual(lale_pipeline.edges()[4][1]._impl_class(),
KNeighborsClassifierImpl)
self.assert_equal_predictions(sklearn_pipeline, lale_pipeline)