def test_pipeline_1(self):
self.maxDiff = None
from lale.json_operator import from_json, to_json
from lale.lib.lale import ConcatFeatures, NoOp
from lale.lib.sklearn import PCA
from lale.lib.sklearn import LogisticRegression as LR
operator = (PCA & NoOp) >> ConcatFeatures >> LR
json_expected = {
"class": "lale.operators.PlannedPipeline",
"state": "planned",
"edges": [
["pca", "concat_features"],
["no_op", "concat_features"],
["concat_features", "lr"],
],
"steps": {
"pca": {
"class": PCA.class_name(),
"state": "planned",
"operator": "PCA",
"label": "PCA",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.pca.html",
},
"no_op": {
"class": NoOp.class_name(),
"state": "trained",
"operator": "NoOp",
"label": "NoOp",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.lale.no_op.html",
"hyperparams": None,
"coefs": None,
"is_frozen_trainable": True,
"is_frozen_trained": True,
},
"concat_features": {
"class": ConcatFeatures.class_name(),
"state": "trained",
"operator": "ConcatFeatures",
"label": "ConcatFeatures",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.lale.concat_features.html",
"hyperparams": None,
"coefs": None,
"is_frozen_trainable": True,
"is_frozen_trained": True,
},
"lr": {
"class": LR.class_name(),
"state": "planned",
"operator": "LogisticRegression",
"label": "LR",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.logistic_regression.html",
},
},
}
json = to_json(operator)
self.assertEqual(json, json_expected)
operator_2 = from_json(json)
json_2 = to_json(operator_2)
self.assertEqual(json, json_2)
def test_operator_choice(self):
self.maxDiff = None
from lale.json_operator import from_json, to_json
from lale.lib.sklearn import PCA
from lale.lib.sklearn import MinMaxScaler as Scl
operator = PCA | Scl
json_expected = {
"class": "lale.operators.OperatorChoice",
"operator": "OperatorChoice",
"state": "planned",
"steps": {
"pca": {
"class": PCA.class_name(),
"state": "planned",
"operator": "PCA",
"label": "PCA",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.pca.html",
},
"scl": {
"class": Scl.class_name(),
"state": "planned",
"operator": "MinMaxScaler",
"label": "Scl",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.min_max_scaler.html",
},
},
}
json = to_json(operator)
self.assertEqual(json, json_expected)
operator_2 = from_json(json)
json_2 = to_json(operator_2)
self.assertEqual(json_2, json_expected)
def test_higher_order_1(self):
from lale.json_operator import from_json
from lale.lib.lale import Both
from lale.lib.sklearn import PCA, Nystroem
operator = Both(op1=PCA(n_components=2), op2=Nystroem)
json_expected = {
"class": Both.class_name(),
"state": "trainable",
"operator": "Both",
"label": "Both",
"documentation_url":
"https://lale.readthedocs.io/en/latest/modules/lale.lib.lale.both.html",
"hyperparams": {
"op1": {
"$ref": "../steps/pca"
},
"op2": {
"$ref": "../steps/nystroem"
},
},
"steps": {
"pca": {
"class": PCA.class_name(),
"state": "trainable",
"operator": "PCA",
"label": "PCA",
"documentation_url":
"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.pca.html",
"hyperparams": {
"n_components": 2
},
"is_frozen_trainable": False,
},
"nystroem": {
"class":
Nystroem.class_name(),
"state":
"planned",
"operator":
"Nystroem",
"label":
"Nystroem",
"documentation_url":
"https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.nystroem.html",
},
},
"is_frozen_trainable": False,
}
json = operator.to_json()
self.assertEqual(json, json_expected)
operator_2 = from_json(json)
json_2 = operator_2.to_json()
self.assertEqual(json, json_2)
def test_nested(self):
self.maxDiff = None
from lale.json_operator import from_json, to_json
from lale.lib.lale import NoOp
from lale.lib.sklearn import PCA
from lale.lib.sklearn import LogisticRegression as LR
operator = PCA >> (LR(C=0.09) | NoOp >> LR(C=0.19))
json_expected = {
"class": "lale.operators.PlannedPipeline",
"state": "planned",
"edges": [["pca", "choice"]],
"steps": {
"pca": {
"class": PCA.class_name(),
"state": "planned",
"operator": "PCA",
"label": "PCA",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.pca.html",
},
"choice": {
"class": "lale.operators.OperatorChoice",
"state": "planned",
"operator": "OperatorChoice",
"steps": {
"lr_0": {
"class": LR.class_name(),
"state": "trainable",
"operator": "LogisticRegression",
"label": "LR",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.logistic_regression.html",
"hyperparams": {"C": 0.09},
"is_frozen_trainable": False,
},
"pipeline_1": {
"class": "lale.operators.TrainablePipeline",
"state": "trainable",
"edges": [["no_op", "lr_1"]],
"steps": {
"no_op": {
"class": NoOp.class_name(),
"state": "trained",
"operator": "NoOp",
"label": "NoOp",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.lale.no_op.html",
"hyperparams": None,
"coefs": None,
"is_frozen_trainable": True,
"is_frozen_trained": True,
},
"lr_1": {
"class": LR.class_name(),
"state": "trainable",
"operator": "LogisticRegression",
"label": "LR",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.logistic_regression.html",
"hyperparams": {"C": 0.19},
"is_frozen_trainable": False,
},
},
},
},
},
},
}
json = to_json(operator)
self.assertEqual(json, json_expected)
operator_2 = from_json(json)
json_2 = to_json(operator_2)
self.assertEqual(json, json_2)
def test_higher_order_2(self):
self.maxDiff = None
from lale.json_operator import from_json
from lale.lib.sklearn import PCA
from lale.lib.sklearn import KNeighborsClassifier as KNN
from lale.lib.sklearn import LogisticRegression as LR
from lale.lib.sklearn import VotingClassifier as Vote
operator = Vote(
estimators=[("knn", KNN), ("pipeline", PCA() >> LR)], voting="soft"
)
json_expected = {
"class": Vote.class_name(),
"state": "trainable",
"operator": "VotingClassifier",
"is_frozen_trainable": True,
"label": "Vote",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.voting_classifier.html",
"hyperparams": {
"estimators": [
("knn", {"$ref": "../steps/knn"}),
("pipeline", {"$ref": "../steps/pipeline"}),
],
"voting": "soft",
},
"steps": {
"knn": {
"class": KNN.class_name(),
"state": "planned",
"operator": "KNeighborsClassifier",
"label": "KNN",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.k_neighbors_classifier.html",
},
"pipeline": {
"class": "lale.operators.PlannedPipeline",
"state": "planned",
"edges": [["pca", "lr"]],
"steps": {
"pca": {
"class": PCA.class_name(),
"state": "trainable",
"operator": "PCA",
"label": "PCA",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.pca.html",
"hyperparams": {},
"is_frozen_trainable": False,
},
"lr": {
"class": LR.class_name(),
"state": "planned",
"operator": "LogisticRegression",
"label": "LR",
"documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.logistic_regression.html",
},
},
},
},
}
json = operator.to_json()
self.assertEqual(json, json_expected)
operator_2 = from_json(json)
json_2 = operator_2.to_json()
self.assertEqual(json, json_2)
