def test_warm_start_detection(self):
parameter_grid = {"alpha": Real(1e-4, 1e-1, 1)}
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(max_depth=2, random_state=0)
tune_search = TuneSearchCV(clf,
parameter_grid,
n_jobs=1,
max_iters=10,
local_dir="./test-result")
self.assertFalse(tune_search._can_early_stop())
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(random_state=0)
tune_search2 = TuneSearchCV(clf,
parameter_grid,
n_jobs=1,
max_iters=10,
local_dir="./test-result")
self.assertFalse(tune_search2._can_early_stop())
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
tune_search3 = TuneSearchCV(clf,
parameter_grid,
n_jobs=1,
max_iters=10,
local_dir="./test-result")
self.assertTrue(tune_search3._can_early_stop())
def test_warm_start_error(self):
parameter_grid = {"alpha": Real(1e-4, 1e-1, 1)}
from sklearn.ensemble import VotingClassifier, RandomForestClassifier
clf = VotingClassifier(estimators=[(
"rf", RandomForestClassifier(n_estimators=50, random_state=0))])
tune_search = TuneSearchCV(
clf,
parameter_grid,
n_jobs=1,
early_stopping=False,
max_iters=10,
local_dir="./test-result")
self.assertFalse(tune_search._can_early_stop())
with self.assertRaises(ValueError):
tune_search = TuneSearchCV(
clf,
parameter_grid,
n_jobs=1,
early_stopping=True,
max_iters=10,
local_dir="./test-result")
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
with self.assertRaises(ValueError):
parameter_grid = {"max_iter": [1, 2]}
TuneSearchCV(
clf,
parameter_grid,
early_stopping=True,
n_jobs=1,
max_iters=10,
local_dir="./test-result")
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
with self.assertRaises(ValueError):
parameter_grid = {"n_estimators": [1, 2]}
TuneSearchCV(
clf,
parameter_grid,
early_stopping=True,
n_jobs=1,
max_iters=10,
local_dir="./test-result")
def test_warm_start_error(self):
parameter_grid = {"alpha": Real(1e-4, 1e-1, 1)}
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(max_depth=2, random_state=0)
tune_search = TuneSearchCV(clf,
parameter_grid,
n_jobs=1,
early_stopping=False,
max_iters=10,
local_dir="./test-result")
self.assertFalse(tune_search._can_early_stop())
with self.assertRaises(ValueError):
tune_search = TuneSearchCV(clf,
parameter_grid,
n_jobs=1,
early_stopping=True,
max_iters=10,
local_dir="./test-result")
