def model_predict():
path_config = global_config.PathConfig()
param_config = global_config.ParamConfig()
param_config.set_begin_time('2012-01')
param_config.set_end_time('2019-5')
param_config.set_predict_period(1)
fca = feature_correlation_analysis.FeatureCorrelationAnalysis(
path_config, param_config)
fca.feature_correlation_analysis()
data_coversion.DataCoversion(path_config, param_config).data_coversion()
data_partitioning.DataPartition(path_config,
param_config).data_partitioning()
feature_df = fca.initial_attr_data()
predict_time = datetime.datetime.strptime('2019-6', '%Y-%m')
data_attr = []
with open(path_config.attr_intro, mode='r') as f:
for line in f:
attr, period = line.split()
data_attr.append(
feature_df[attr][predict_time -
dateutil.relativedelta.relativedelta(
months=int(period))])
data_array = np.array(data_attr)
data_array = data_array[np.newaxis, :]
model = BaggingRegression_model.final_Bagging_regression()
data_value = model.predict(data_array)
print(data_value)
def initial_data(data_type=1):
# 用于初始化数据集
path_config = global_config.PathConfig()
param_config = global_config.ParamConfig()
param_config.data_type = data_type # 数据类型
param_config.predict_period = 1 # 预测期
feature_correlation_analysis.FeatureCorrelationAnalysis(
path_config, param_config).feature_correlation_analysis()
data_coversion.DataCoversion(path_config, param_config).data_coversion()
def __init__(self,
path_config=global_config.PathConfig(),
param_config=global_config.ParamConfig()):
self.path_config = path_config
self.param_config = param_config
def __init__(self, path_config=global_config.PathConfig(), param_config=global_config.ParamConfig()):
self.path_config = path_config
self.param_config = param_config
self.MAX_PERIOD = self.param_config.corr_max_period
self.PREDICT_PERIOD = self.param_config.predict_period