def test_pipeline_parameters(self):
pgo = PGO.load_pgo_file(example_pgo_fp)
trainable = PCA() >> LogisticRegression()
parameters = get_grid_search_parameter_grids(trainable,
num_samples=2,
pgo=pgo)
def test_lr_parameters(self):
pgo = PGO.load_pgo_file(example_pgo_fp)
lr = LogisticRegression()
parameters = get_grid_search_parameter_grids(lr,
num_samples=2,
pgo=pgo)
def test_lr_run(self):
pgo = PGO.load_pgo_file(example_pgo_fp)
from lale.lib.lale import Hyperopt
from sklearn.datasets import load_iris
lr = LogisticRegression()
clf = Hyperopt(estimator=lr, max_evals=5, pgo=pgo)
iris = load_iris()
clf.fit(iris.data, iris.target)
def test_lr_run(self):
pgo = PGO.load_pgo_file(example_pgo_fp)
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score, make_scorer
lr = LogisticRegression()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
clf = lale.lib.lale.GridSearchCV(
estimator=lr, lale_num_samples=2, lale_num_grids=5,
cv=5, pgo=pgo, scoring=make_scorer(accuracy_score))
iris = load_iris()
clf.fit(iris.data, iris.target)
def test_lr_parameters(self):
pgo = PGO.load_pgo_file(example_pgo_fp)
lr = LogisticRegression()
parameters: SearchSpace = hyperopt_search_space(lr, pgo=pgo)
def test_pgo_sample(self):
pgo = PGO.load_pgo_file(example_pgo_fp)
lr_c = pgo["LogisticRegression"]["C"]
dist = PGO.FrequencyDistribution.asIntegerValues(lr_c.items())
samples: List[str] = dist.samples(10)
def test_pgo_load(self):
pgo = PGO.load_pgo_file(example_pgo_fp)
lr_c = pgo["LogisticRegression"]["C"]