def test_default_dt_space(self):
space = default_dt_space()
space.random_sample()
assert space.Module_DnnModule_1.param_values['dnn_layers'] == len(
space.DT_Module.config.dnn_params['hidden_units'])
assert space.Module_DnnModule_1.param_values['hidden_units'] == \
space.DT_Module.config.dnn_params['hidden_units'][0][
0]
assert space.Module_DnnModule_1.param_values['dnn_dropout'] == \
space.DT_Module.config.dnn_params['hidden_units'][0][
1]
assert space.Module_DnnModule_1.param_values['use_bn'] == space.DT_Module.config.dnn_params['hidden_units'][0][
2]
regularized=True,
candidates_size=30,
optimize_direction=OptimizeDirection.Maximize)
hdt = HyperDT(searcher,
callbacks=[
SummaryCallback(),
FileStorageLoggingCallback(searcher,
output_dir=f'hotexamples_com/hyn_logs')
],
reward_metric='AUC',
earlystopping_patience=1)
space = mini_dt_space()
assert space.combinations == 589824
space2 = default_dt_space()
assert space2.combinations == 3559292928
df = dsutils.load_adult()
# df.drop(['id'], axis=1, inplace=True)
df_train, df_test = train_test_split(df, test_size=0.2, random_state=42)
X = df_train
y = df_train.pop(14)
y_test = df_test.pop(14)
# dataset_id='adult_whole_data',
hdt.search(
df_train,
y,
df_test,
y_test,
max_trials=3,