def get_data_label_pair(f, model_config, meta, predict_day, isShift=True):
single_stock = tv_gen._selectData2array_specialDate_v2(
f, corrDate[s][:model_config['corrDate']], model_config['corrDate'],
21, s)
labels = []
data_feature = []
for i in range(model_config['corrDate']):
single_stock_tmp, meta_v = f_extr.create_velocity(
single_stock[i], meta)
single_stock_tmp, meta_ud = f_extr.create_ud_cont_2cls(
single_stock_tmp, meta_v)
features_tmp, label_tmp = dp.get_data_from_normal(
single_stock_tmp, meta_ud, predict_day, model_config['features'],
isShift)
label_tmp = reduce_label(label_tmp)
labels += list(label_tmp)
feature_concat = []
for i in range(model_config['days']):
for k in features_tmp[i]:
feature_concat.append(features_tmp[i][k])
data_feature.append(np.concatenate(feature_concat, axis=1))
data = np.vstack(data_feature)
label = np.array(labels)
return data, label
def get_data_label_pair(single_stock, model_config, meta, isShift=True):
features, label = dp.get_data_from_normal(single_stock, meta, predict_day,
model_config['features'],
isShift)
feature_concat = []
for i in range(model_config['days']):
for k in features[i]:
feature_concat.append(features[i][k])
data_feature = np.concatenate(feature_concat, axis=1)
data = data_feature
label = label
return data, label
#***************Get train data******************
data_feature = []
labels = []
for _s in _stock_list:
single_stock = tv_gen._selectData2array_specialDate_v2(
f, corrDate[s][:corr_date], corr_date, 21, _s)
for i in range(corr_date):
single_stock_tmp, meta_v = f_extr.create_velocity(
single_stock[i], meta)
single_stock_tmp, meta_ud = f_extr.create_ud_cont_2cls(
single_stock_tmp, meta_v)
features_tmp, label_tmp = dp.get_data_from_normal(
single_stock_tmp, meta_ud, predict_day,
feature_list)
label_tmp = reduce_label(label_tmp)
labels += list(label_tmp)
feature_concat = []
for i in range(consider_lagday):
for k in features_tmp[i]:
feature_concat.append(features_tmp[i][k])
data_feature.append(
np.concatenate(feature_concat, axis=1))
train_data = np.vstack(data_feature)
train_label = np.array(labels)
