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
""" Time: 2019-7-112 Description: 一些通用方法 """ from SteelDemandAnalysis.config import global_config import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import mean_absolute_error from sklearn.metrics import mean_squared_error config = global_config.PathConfig() def load_data(train_data_df, test_data_df): # 加载训练与验证数据 # 返回: # train_attr:训练集属性 # train_label:训练集标签 # test_attr:测试集属性 # test_label:测试集标签 train_data = train_data_df.values train_attr = train_data[:, :-1] train_label = train_data[:, -1] test_data = test_data_df.values test_attr = test_data[:, :-1] test_label = test_data[:, -1] return train_attr, train_label, test_attr, test_label