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def test_trainable_pipe_right(self):
from lale.lib.lale import NoOp
from lale.lib.sklearn import LogisticRegression
from sklearn.decomposition import PCA
iris = sklearn.datasets.load_iris()
pipeline = NoOp() >> PCA() >> LogisticRegression(random_state=42)
pipeline.fit(iris.data, iris.target)
def test_pipeline_2(self):
from lale.lib.lale import NoOp
from lale.lib.sklearn import Nystroem
from lale.lib.sklearn import PCA
from lale.lib.sklearn import LogisticRegression
from lale.lib.sklearn import KNeighborsClassifier
from lale.operators import make_choice, make_pipeline
from lale.json_operator import to_json, from_json
kernel_tfm_or_not = make_choice(NoOp, Nystroem)
tfm = PCA
clf = make_choice(LogisticRegression, KNeighborsClassifier)
operator = make_pipeline(kernel_tfm_or_not, tfm, clf)
json = to_json(operator)
operator_2 = from_json(json)
json_2 = to_json(operator_2)
self.assertEqual(json, json_2)
def test_planned_pipeline_2(self) :
plan = (
( MinMaxScaler() & NoOp() ) >> ConcatFeatures() >>
( NoOp() & MinMaxScaler() ) >> ConcatFeatures() >>
( LogisticRegression | KNeighborsClassifier )
)
run_hyperopt_on_planned_pipeline(plan)
def test_trained_get_pipeline_fail(self):
try:
x = NoOp().get_pipeline
self.fail("get_pipeline did not fail")
except AttributeError as e:
msg:str = str(e)
self.assertRegex(msg, "underlying operator")
def test_nested(self):
from lale.lib.sklearn import PCA
from lale.lib.sklearn import LogisticRegression as LR
from lale.lib.lale import NoOp
lr_0 = LR(C=0.09)
lr_1 = LR(C=0.19)
pipeline = PCA >> (lr_0 | NoOp >> lr_1)
expected = \
"""from lale.lib.sklearn import PCA
from lale.lib.sklearn import LogisticRegression as LR
from lale.lib.lale import NoOp
import lale
lale.wrap_imported_operators()
lr_0 = LR(C=0.09)
lr_1 = LR(C=0.19)
pipeline = PCA >> (lr_0 | NoOp >> lr_1)"""
self._roundtrip(expected, lale.pretty_print.to_string(pipeline))
def test_transform_schema_NoOp(self):
from lale.datasets.data_schemas import to_schema
for ds in [self._irisArr, self._irisDf, self._digits, self._housing, self._creditG, self._movies, self._drugRev]:
s_input = to_schema(ds['X'])
s_output = NoOp.transform_schema(s_input)
self.assertIs(s_input, s_output)
def getPipeline(self):
params = self._hyperparams
op1 = params.get('op1', None)
if op1 is None:
op1 = NoOp()
op2 = params.get('op2', None)
if op2 is None:
op2 = NoOp()
if params['order'] == 'backward':
return op2 >> op1
else:
return op1 >> op2
def getPipeline(self):
params = self._hyperparams
op1 = params.get('op1', None)
if op1 is None:
op1 = NoOp()
op2 = params.get('op2', None)
if op2 is None:
op2 = NoOp()
if params['order'] == 'backward':
return op2 >> op1
else:
return op1 >> op2