def test_with_randomizedsearchcv(self):
from sklearn.model_selection import RandomizedSearchCV
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score, make_scorer
from scipy.stats.distributions import uniform
import numpy as np
lr = LogisticRegression()
parameters = {'solver': ('liblinear', 'lbfgs'), 'penalty': ['l2']}
ranges, cat_idx = lr.get_param_ranges()
min_C, max_C, default_C = ranges['C']
# specify parameters and distributions to sample from
#the loguniform distribution needs to be taken care of properly
param_dist = {
"solver": ranges['solver'],
"C": uniform(min_C, np.log(max_C))
}
# run randomized search
n_iter_search = 5
with warnings.catch_warnings():
warnings.simplefilter("ignore")
random_search = RandomizedSearchCV(
lr,
param_distributions=param_dist,
n_iter=n_iter_search,
cv=5,
scoring=make_scorer(accuracy_score))
iris = load_iris()
random_search.fit(iris.data, iris.target)
def test_with_randomizedsearchcv(self):
import numpy as np
from scipy.stats.distributions import uniform
from sklearn.metrics import accuracy_score, make_scorer
from sklearn.model_selection import RandomizedSearchCV
lr = LogisticRegression()
ranges, cat_idx = lr.get_param_ranges()
# specify parameters and distributions to sample from
# the loguniform distribution needs to be taken care of properly
param_dist = {
"solver": ranges["solver"],
"C": uniform(0.03125, np.log(32768))
}
# run randomized search
n_iter_search = 5
with warnings.catch_warnings():
warnings.simplefilter("ignore")
random_search = RandomizedSearchCV(
lr,
param_distributions=param_dist,
n_iter=n_iter_search,
cv=5,
scoring=make_scorer(accuracy_score),
)
iris = load_iris()
random_search.fit(iris.data, iris.target)
def test_logisticregression(self):
from lale.lib.sklearn import LogisticRegression
ranges, dists = LogisticRegression.get_param_ranges()
expected_ranges = {
"solver": ["newton-cg", "liblinear", "sag", "saga", "lbfgs"],
"dual": (False, True, False),
"tol": (1e-08, 0.01, 0.0001),
"fit_intercept": (False, True, True),
"intercept_scaling": (0.0, 1.0, 1.0),
"max_iter": (10, 1000, 100),
"multi_class": ["ovr", "multinomial", "auto"],
}
expected_dists = {"solver": (0, 4, 4), "multi_class": (0, 2, 2)}
self.maxDiff = None
self.assertEqual(ranges, expected_ranges)
self.assertEqual(dists, expected_dists)
