# cluster_Nos=cluster_Nos, district_Nos=district_Nos, \
# balance=True, rand_seed=r_seed, load_city=False,\
# save_format='h5',crime_type=target_crime,target='Label')
# sample_mask = loading_info['sample_mask']
# X_train, y_train = loading_info['train_data_subset']
# X_test, _ = loading_info['test_data_subset']
#
# clf = get_model_params(clf_name,rand_seed=r_seed,X=X_train)['model']
# tuning_params = get_model_params(clf_name,rand_seed=r_seed,X=X_train)['tuning_params']
# tuning_param_dicts = dict([(clf_name+'__'+key, val) for key,val in tuning_params.items()])
sample_mask = {}
if train_region == 'city':
filename_dict['train'] = train_data
X_train, y_train = ld.load_struct_data_h5(filename_dict['train'],
target_crime,
'Label',
split='train')
clf = get_model_params(clf_name, rand_seed=r_seed, X=X_train)['model']
tuning_params = get_model_params(clf_name, rand_seed=r_seed,
X=X_train)['tuning_params']
tuning_param_dicts = dict([(clf_name + '__' + key, val)
for key, val in tuning_params.items()])
# train models
fitting = model_fit(X_train, y_train, clf, clf_name,
tuning_param_dicts, CV_skf, scaling)
model, cv_results, best_params = fitting['model'], fitting[
'CV_result'], fitting['best_param']
print('CV results (' + train_region + train_region_num_str + '):')
print(cv_results)
print('Best_parameters (' + train_region + train_region_num_str + '):')
if cluster_pkl != 'NA':
filename_dict['cluster'] = cluster_pkl
if district_pkl != 'NA':
filename_dict['district'] = district_pkl
#----------------------------------------#
mask_sample_region = {'train': train_region, 'test': test_region}
start = time.time()
sample_mask = pair_train_test_sample(filename_dict, mask_sample_region,
cluster_Nos, district_Nos, chunk_size)
if train_region == 'city':
X_train, y_train = ld.load_struct_data_h5(train_data_list[0],
target_crime,
'Label',
split='train_city')
sample_mask['train'] = np.ones(len(X_train)).astype(bool)
else:
X_train_stacked = []
y_train_stacked = []
for i, fn in enumerate(train_data_list):
X_train, y_train = ld.load_struct_data_h5(fn,
target_crime,
'Label',
split='train_chunk')
X_train_stacked.append(X_train[sample_mask['train'][i], :])
y_train_stacked.append(y_train[sample_mask['train'][i]])