def test_sample_model_param_list(self):
import random
random.seed(0)
model_param_search_range = {'norm_type':['normalize', 'clip_0to1'],
'n_estimators':[10, 50], 'random_state': [0]}
dicts = ModelCrossValidation._sample_model_param_list(
model_param_search_range, 4)
expected_dicts = [
{'norm_type':'clip_0to1', 'n_estimators':50, 'random_state':0},
{'norm_type':'clip_0to1', 'n_estimators':10, 'random_state':0},
{'norm_type':'normalize', 'n_estimators':50, 'random_state':0},
{'norm_type':'clip_0to1', 'n_estimators':50, 'random_state':0},
]
self.assertEquals(dicts, expected_dicts)
model_param_search_range = {'norm_type':['normalize', 'clip_0to1'],
'n_estimators':{'low':10, 'high':50, 'decimal':0},
'random_state': [0]}
dicts = ModelCrossValidation._sample_model_param_list(
model_param_search_range, 4)
expected_dicts = [
{'norm_type':'clip_0to1', 'n_estimators':21, 'random_state':0},
{'norm_type':'clip_0to1', 'n_estimators':20, 'random_state':0},
{'norm_type':'clip_0to1', 'n_estimators':42, 'random_state':0},
{'norm_type':'clip_0to1', 'n_estimators':39, 'random_state':0},
]
self.assertEquals(dicts, expected_dicts)
def test_sample_model_param_list(self):
import random
random.seed(0)
model_param_search_range = {'norm_type':['normalize', 'clip_0to1'],
'n_estimators':[10, 50], 'random_state': [0]}
dicts = ModelCrossValidation._sample_model_param_list(
model_param_search_range, 4)
expected_dicts = [
{'norm_type':'clip_0to1', 'n_estimators':50, 'random_state':0},
{'norm_type':'clip_0to1', 'n_estimators':10, 'random_state':0},
{'norm_type':'normalize', 'n_estimators':50, 'random_state':0},
{'norm_type':'clip_0to1', 'n_estimators':50, 'random_state':0},
]
self.assertEquals(dicts, expected_dicts)
model_param_search_range = {'norm_type':['normalize', 'clip_0to1'],
'n_estimators':{'low':10, 'high':50, 'decimal':0},
'random_state': [0]}
dicts = ModelCrossValidation._sample_model_param_list(
model_param_search_range, 4)
expected_dicts = [
{'norm_type':'clip_0to1', 'n_estimators':21, 'random_state':0},
{'norm_type':'clip_0to1', 'n_estimators':20, 'random_state':0},
{'norm_type':'clip_0to1', 'n_estimators':42, 'random_state':0},
{'norm_type':'clip_0to1', 'n_estimators':39, 'random_state':0},
]
self.assertEquals(dicts, expected_dicts)
