1582 lines
86 KiB
Python
1582 lines
86 KiB
Python
import os
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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import seaborn as sns
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import datetime
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from lib.tools import Graphs,mse,rmse,mae
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from lib.dataread import *
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from neuralforecast import NeuralForecast
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from neuralforecast.models import NHITS,Informer, NBEATSx,LSTM,PatchTST, iTransformer, TSMixer
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from neuralforecast.models import RNN, GRU, TCN, DeepAR, DilatedRNN, MLP, NBEATS, DLinear, NLinear, TFT, VanillaTransformer
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from neuralforecast.models import Autoformer, PatchTST, FEDformer, StemGNN, HINT, TSMixer, TSMixerx, MLPMultivariate, BiTCN, TiDE, DeepNPTS
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from tensorflow.keras.losses import MAE
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from scipy.stats import spearmanr
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.feature_selection import SelectKBest, f_classif
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from sklearn.preprocessing import StandardScaler
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from sklearn.metrics import r2_score
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from sklearn import metrics
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from lib.duojinchengpredict import testSetPredict
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from reportlab.platypus import Table, SimpleDocTemplate, Paragraph, Image # 报告内容相关类
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from reportlab.lib.pagesizes import letter # 页面的标志尺寸(8.5*inch, 11*inch)
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from reportlab.pdfbase import pdfmetrics # 注册字体
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from reportlab.pdfbase.ttfonts import TTFont # 字体类
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from reportlab.platypus import Table, SimpleDocTemplate, Paragraph, Image # 报告内容相关类
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from reportlab.lib.pagesizes import letter # 页面的标志尺寸(8.5*inch, 11*inch)
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from reportlab.lib.styles import getSampleStyleSheet # 文本样式
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from reportlab.lib import colors # 颜色模块
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from reportlab.graphics.charts.barcharts import VerticalBarChart # 图表类
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from reportlab.graphics.charts.legends import Legend # 图例类
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from reportlab.graphics.shapes import Drawing # 绘图工具
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from reportlab.lib.units import cm # 单位：cm
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# # 注册字体(提前准备好字体文件, 如果同一个文件需要多种字体可以注册多个)
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pdfmetrics.registerFont(TTFont('SimSun', 'SimSun.ttf'))
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def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patience_steps,
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is_debug,dataset,is_train,is_fivemodels,val_size,test_size,settings,now,
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etadata,modelsindex,data,is_eta):
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'''
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模型训练与预测
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:param df: 数据集
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horizon # 预测的步长
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input_size # 输入序列长度
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train_steps # 训练步数,用来限定epoch次数
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val_check_steps # 评估频率
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early_stop_patience_steps # 早停的耐心步数
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:return: 预测结果
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'''
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# 模型预测列表列名
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# columns2 = [
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# 'NHITS',
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# 'Informer',
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# 'LSTM',
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# 'iTransformer',
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# 'TSMixer',
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# 'TSMixerx',
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# 'PatchTST',
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# 'RNN',
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# 'GRU',
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# 'TCN',
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# # 'DeepAR',
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# 'DeepAR-median',
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# 'DeepAR-lo-90',
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# 'DeepAR-lo-80',
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# 'DeepAR-hi-80',
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# 'DeepAR-hi-90',
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# 'BiTCN',
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# 'DilatedRNN',
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# 'MLP',
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# 'DLinear',
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# 'NLinear',
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# 'TFT',
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# 'FEDformer',
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# 'StemGNN',
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# 'MLPMultivariate',
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# 'TiDE',
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# 'DeepNPT',
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# ]
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df= df.replace(',', '', regex=True)
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df = df.rename(columns={'date': 'ds'})
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df['y'] = pd.to_numeric(df['y'], errors='coerce')
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df['ds'] = pd.to_datetime(df['ds'], errors='coerce') # 使用errors='coerce'来处理无效日期
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# df 数值列转为 float32
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for col in df.select_dtypes(include=['int']).columns:
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df[col] = df[col].astype(np.float32)
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# 设置中文字体
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plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
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plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
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# 不筛选特征用下面的
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df_reg = df
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df_reg.sort_values('ds', inplace=True)
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if is_debug:
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df_reg = df_reg[-1000:-1]
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# 计算训练集的结束索引，占总数据的90%
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split_index = int(0.8* len(df_reg))
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# 按照时间顺序划分训练集和测试集
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df_train = df_reg[:split_index]
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df_test = df_reg[-split_index:]
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df_train['unique_id'] = 1
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df_test['unique_id'] = 1
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# 显示划分后的数据集的前几行
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logger.info("Training set head:")
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logger.info(df_train.head())
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logger.info("\nTesting set head:")
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logger.info(df_test.head())
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models = [
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NHITS (h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', activation='ReLU', early_stop_patience_steps=early_stop_patience_steps),
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Informer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps ),
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LSTM(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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iTransformer(h=horizon, input_size=input_size,n_series = 1, max_steps=train_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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TSMixer(h=horizon, input_size=input_size, n_series = 1, max_steps=train_steps, early_stop_patience_steps=early_stop_patience_steps),
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TSMixerx(h=horizon, input_size=input_size,n_series = 1, max_steps=train_steps, early_stop_patience_steps=early_stop_patience_steps),
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PatchTST(h=horizon, input_size=input_size, max_steps=train_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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RNN(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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GRU(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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TCN(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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# DeepAR(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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BiTCN(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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DilatedRNN(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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MLP(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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DLinear(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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NLinear(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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TFT(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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FEDformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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StemGNN(h=horizon, input_size=input_size,n_series = 1, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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MLPMultivariate(h=horizon, input_size=input_size,n_series = 1, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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TiDE(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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DeepNPTS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', early_stop_patience_steps=early_stop_patience_steps),
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# VanillaTransformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
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# Autoformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
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# NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
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# NBEATSx (h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ), //报错
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# HINT(h=horizon),
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]
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if is_fivemodels:
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# 获取之前存好的最好的五个模型
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with open(os.path.join(dataset,'best_modelnames.txt'), 'r',encoding='utf-8') as f:
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best_modelnames = f.readlines()[0]
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logger.info(f'获取本地最佳模型名称：{best_modelnames}')
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# 重新拼接models
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all_models = models
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models = []
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for model in all_models:
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if model._get_name() in best_modelnames:
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models.append(model)
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# 创建NeuralForecast实例并训练模型
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nf = NeuralForecast(models=models, freq="B")
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from joblib import dump, load
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if is_train:
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# 模型交叉验证
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nf_preds = nf.cross_validation(df=df_train, val_size=val_size, test_size=test_size, n_windows=None)
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nf_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False)
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nf_preds = nf_preds.reset_index()
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# 保存模型
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# 生成文件名，按时间 精确到分
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filename = f'{settings}--{now}.joblib'
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#文件名去掉冒号
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filename = filename.replace(':', '-') # 替换冒号
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# dump(nf, os.path.join(dataset,filename))
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else:
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# glob获取dataset下最新的joblib文件
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import glob
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filename = max(glob.glob(os.path.join(dataset,'*.joblib')), key=os.path.getctime)
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# logger.info('读取模型：'+ filename)
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nf = load(filename)
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# # 测试集预测
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nf_test_preds = nf.cross_validation(df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
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# 测试集预测结果保存
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nf_test_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False)
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df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce')
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#进行未来时间预测
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df_predict=nf.predict(df_test).reset_index()
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df_predict.astype({col: 'float32' for col in df_predict.columns if col not in ['ds'] })
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# 保存预测值
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df_predict.to_csv(os.path.join(dataset,"predict.csv"),index=False)
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# 把预测值上传到eta
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if is_update_eta:
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dates = df_predict['ds'].dt.strftime('%Y-%m-%d')
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for m in modelsindex.keys():
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list = []
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for date,value in zip(dates,df_predict[m].round(2)):
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list.append({'Date':date,'Value':value})
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data['DataList'] = list
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data['IndexCode'] = modelsindex[m]
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data['IndexName'] = f'价格预测{m}模型'
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data['Remark'] = m
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etadata.push_data(data)
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return nf_test_preds
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# 计算预测评估指数
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def model_losss(sqlitedb):
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global dataset
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# 预测数据处理 predict
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df_combined = loadcsv(os.path.join(dataset,"cross_validation.csv"))
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df_combined = dateConvert(df_combined)
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# 删除空列
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df_combined.dropna(axis=1,inplace=True)
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# 删除缺失值,预测过程不能有缺失值
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df_combined.dropna(inplace=True)
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# 其他列转为数值类型
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df_combined = df_combined.astype({col: 'float32' for col in df_combined.columns if col not in ['cutoff','ds'] })
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# 使用 groupby 和 transform 结合 lambda 函数来获取每个分组中 cutoff 的最小值，并创建一个新的列来存储这个最大值
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df_combined['max_cutoff'] = df_combined.groupby('ds')['cutoff'].transform('max')
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# 然后筛选出那些 cutoff 等于 max_cutoff 的行，这样就得到了每个分组中 cutoff 最大的行，并保留了其他列
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df_combined = df_combined[df_combined['cutoff'] == df_combined['max_cutoff']]
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# 删除模型生成的cutoff列
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df_combined.drop(columns=['cutoff', 'max_cutoff'], inplace=True)
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# 获取模型名称
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modelnames = df_combined.columns.to_list()[2:]
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if 'y' in modelnames:
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modelnames.remove('y')
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df_combined3 = df_combined.copy() # 备份df_combined,后面画图需要
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# 计算波动率
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df_combined3['volatility'] = df_combined3['y'].pct_change().round(4)
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# 计算近60日的波动率 10% 90%分位数
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df_combined3['quantile_10'] = df_combined3['volatility'].rolling(60).quantile(0.1)
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df_combined3['quantile_90'] = df_combined3['volatility'].rolling(60).quantile(0.9)
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df_combined3 = df_combined3.round(4)
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# 计算分位数对应的价格
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df_combined3['quantile_10_price'] = df_combined3['y'] * (1 + df_combined3['quantile_10'])
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df_combined3['quantile_90_price'] = df_combined3['y'] * (1 + df_combined3['quantile_90'])
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# 遍历行
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def find_min_max_within_quantile(row):
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# 获取分位数10%和90%的值
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q10 = row['quantile_10_price']
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q90 = row['quantile_90_price']
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# 判断flot值是否为空值
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if pd.isna(q10) or pd.isna(q90):
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return pd.Series([None, None, None, None], index=['min_within_quantile','max_within_quantile','min_model','max_model'])
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# 初始化最小和最大值为None
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min_value = None
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max_value = None
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min_value_model = ''
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max_value_model = ''
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# 遍历指定列，找出在分位数范围内的最大最小值
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for model in modelnames:
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value = row[model]
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if value >= q10 and value <= q90:
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if min_value is None or value < min_value:
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min_value = value
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min_value_model = model
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if max_value is None or value > max_value:
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max_value = value
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max_value_model = model
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# 返回最大最小值
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return pd.Series([min_value, max_value,min_value_model,max_value_model], index=['min_within_quantile', 'max_within_quantile','min_model','max_model'])
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# 应用函数到每一行
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df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
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# 去除有空值的行
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df_combined3.dropna(inplace=True)
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# 保存到数据库
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df_combined3.to_sql('testandpredict_groupby', sqlitedb.connection, if_exists='replace', index=False)
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df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False)
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# 空的列表存储每个模型的MSE、RMSE、MAE、MAPE、SMAPE
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cellText = []
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# 遍历模型名称，计算模型评估指标
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for model in modelnames:
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modelmse = mse(df_combined['y'], df_combined[model])
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modelrmse = rmse(df_combined['y'], df_combined[model])
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modelmae = mae(df_combined['y'], df_combined[model])
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# modelmape = mape(df_combined['y'], df_combined[model])
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# modelsmape = smape(df_combined['y'], df_combined[model])
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# modelr2 = r2_score(df_combined['y'], df_combined[model])
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cellText.append([model,round(modelmse, 3), round(modelrmse, 3), round(modelmae, 3)])
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model_results3 = pd.DataFrame(cellText,columns=['模型(Model)','平均平方误差(MSE)', '均方根误差(RMSE)', '平均绝对误差(MAE)'])
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# 按MSE降序排列
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model_results3 = model_results3.sort_values(by='平均平方误差(MSE)', ascending=True)
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model_results3.to_csv(os.path.join(dataset,"model_evaluation.csv"),index=False)
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modelnames = model_results3['模型(Model)'].tolist()
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allmodelnames = modelnames.copy()
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# 保存5个最佳模型的名称
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if len(modelnames) > 5:
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modelnames = modelnames[0:5]
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with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f:
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f.write(','.join(modelnames) + '\n')
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# 预测值与真实值对比图
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.figure(figsize=(15, 10))
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# 设置有5个子图的画布
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for n,model in enumerate(modelnames):
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plt.subplot(3, 2, n+1)
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plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')
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plt.plot(df_combined3['ds'], df_combined3[model], label=model)
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plt.legend()
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plt.xlabel('日期')
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plt.ylabel('价格')
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plt.title(model+'拟合')
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plt.subplots_adjust(hspace=0.5)
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plt.savefig(os.path.join(dataset,'预测值与真实值对比图.png'), bbox_inches='tight')
|
||
plt.close()
|
||
|
||
# 历史数据+预测数据
|
||
# 拼接未来时间预测
|
||
df_predict = loadcsv(os.path.join(dataset,'predict.csv'))
|
||
df_predict.drop('unique_id',inplace=True,axis=1)
|
||
df_predict.dropna(axis=1,inplace=True)
|
||
|
||
try:
|
||
df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y-%m-%d')
|
||
except ValueError :
|
||
df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')
|
||
|
||
# 取第一行数据存储到数据库中
|
||
first_row = df_predict.head(1)
|
||
first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')
|
||
# 将预测结果保存到数据库
|
||
if not sqlitedb.check_table_exists('trueandpredict'):
|
||
first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)
|
||
else:
|
||
for row in first_row.itertuples(index=False):
|
||
row_dict = row._asdict()
|
||
columns=row_dict.keys()
|
||
for col in columns:
|
||
sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
|
||
check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'")
|
||
if len(check_query) > 0:
|
||
set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
||
sqlitedb.update_data('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'")
|
||
continue
|
||
sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)
|
||
|
||
|
||
|
||
# 最多频率的模型名称
|
||
min_model_max_frequency_model = df_combined3['min_model'].value_counts().idxmax()
|
||
max_model_max_frequency_model = df_combined3['max_model'].value_counts().idxmax()
|
||
df_predict['min_model'] = min_model_max_frequency_model
|
||
df_predict['max_model'] = max_model_max_frequency_model
|
||
df_predict['min_within_quantile'] = df_predict[min_model_max_frequency_model]
|
||
df_predict['max_within_quantile'] = df_predict[max_model_max_frequency_model]
|
||
|
||
df_predict2 = df_predict.copy()
|
||
df_predict2['ds'] = df_predict2['ds'].dt.strftime('%Y-%m-%d 00:00:00')
|
||
|
||
|
||
# 将预测结果保存到数据库
|
||
# 判断表存在
|
||
if not sqlitedb.check_table_exists('testandpredict_groupby'):
|
||
df_predict2.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)
|
||
else:
|
||
for row in df_predict2.itertuples(index=False):
|
||
row_dict = row._asdict()
|
||
check_query = sqlitedb.select_data('testandpredict_groupby',where_condition = f"ds = '{row.ds}'")
|
||
if len(check_query) > 0:
|
||
set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
||
sqlitedb.update_data('testandpredict_groupby',set_clause,where_condition = f"ds = '{row.ds}'")
|
||
continue
|
||
sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())
|
||
|
||
|
||
|
||
# 计算每个预测值与真实值之间的偏差率
|
||
for model in allmodelnames:
|
||
df_combined3[f'{model}_abs_error_rate'] = abs(df_combined3['y'] - df_combined3[model]) / df_combined3['y']
|
||
|
||
# 获取每行对应的最小偏差率值
|
||
min_abs_error_rate_values = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].min(), axis=1)
|
||
# 获取每行对应的最小偏差率值对应的列名
|
||
min_abs_error_rate_column_name = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].idxmin(), axis=1)
|
||
# 将列名索引转换为列名
|
||
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
|
||
# 获取最小偏差率对应的模型的预测值
|
||
min_abs_error_rate_predictions = df_combined3.apply(lambda row: row[min_abs_error_rate_column_name[row.name]], axis=1)
|
||
# 将最小偏差率对应的模型的预测值添加到DataFrame中
|
||
df_combined3['min_abs_error_rate_prediction'] = min_abs_error_rate_predictions
|
||
df_combined3['min_abs_error_rate_column_name'] = min_abs_error_rate_column_name
|
||
df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
|
||
# 判断 df 的数值列转为float
|
||
for col in df_combined3.columns:
|
||
try:
|
||
if col != 'ds':
|
||
df_combined3[col] = df_combined3[col].astype(float)
|
||
df_combined3[col] = df_combined3[col].round(2)
|
||
except ValueError:
|
||
pass
|
||
df_combined3.to_csv(os.path.join(dataset,"df_combined3.csv"),index=False)
|
||
|
||
# 历史价格+预测价格
|
||
df_combined3 = df_combined3[-50:] # 取50个数据点画图
|
||
# 历史价格
|
||
plt.figure(figsize=(20, 10))
|
||
plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')
|
||
# 颜色填充
|
||
plt.fill_between(df_combined3['ds'], df_combined3['min_within_quantile'], df_combined3['max_within_quantile'], alpha=0.2)
|
||
# plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange')
|
||
# 网格
|
||
plt.grid(True)
|
||
# 显示历史值
|
||
for i, j in zip(df_combined3['ds'], df_combined3['y']):
|
||
plt.text(i, j, str(j), ha='center', va='bottom')
|
||
|
||
# 数据库查询最佳模型名称
|
||
most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]]
|
||
|
||
for model in most_model:
|
||
plt.plot(df_combined3['ds'], df_combined3[model], label=model,marker='o')
|
||
# 当前日期画竖虚线
|
||
plt.axvline(x=df_combined3['ds'].iloc[-horizon], color='r', linestyle='--')
|
||
plt.legend()
|
||
plt.xlabel('日期')
|
||
plt.ylabel('价格')
|
||
|
||
plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')
|
||
plt.close()
|
||
|
||
# 预测值表格
|
||
fig, ax = plt.subplots(figsize=(20, 6))
|
||
ax.axis('off') # 关闭坐标轴
|
||
# 数值保留2位小数
|
||
df_combined3 = df_combined3.round(2)
|
||
df_combined3 = df_combined3[-horizon:]
|
||
df_combined3['Day'] = [f'Day_{i}' for i in range(1,horizon+1)]
|
||
# Day列放到最前面
|
||
df_combined3 = df_combined3[['Day'] + list(df_combined3.columns[:-1])]
|
||
table = ax.table(cellText=df_combined3.values, colLabels=df_combined3.columns, loc='center')
|
||
#加宽表格
|
||
table.auto_set_font_size(False)
|
||
table.set_fontsize(10)
|
||
|
||
# 设置表格样式，列数据最小的用绿色标识
|
||
plt.savefig(os.path.join(dataset,'预测值表格.png'), bbox_inches='tight')
|
||
plt.close()
|
||
# plt.show()
|
||
|
||
# 可视化评估结果
|
||
plt.rcParams['font.sans-serif'] = ['SimHei']
|
||
fig, ax = plt.subplots(figsize=(20, 10))
|
||
ax.axis('off') # 关闭坐标轴
|
||
table = ax.table(cellText=model_results3.values, colLabels=model_results3.columns, loc='center')
|
||
# 加宽表格
|
||
table.auto_set_font_size(False)
|
||
table.set_fontsize(10)
|
||
|
||
# 设置表格样式，列数据最小的用绿色标识
|
||
plt.savefig(os.path.join(dataset,'模型评估.png'), bbox_inches='tight')
|
||
plt.close()
|
||
return model_results3
|
||
|
||
|
||
import matplotlib.dates as mdates
|
||
|
||
def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsize=5,dataset='dataset',time = '2024-07-30',reportname='report.pdf',sqlitedb='jbsh_yuanyou.db'):
|
||
global y
|
||
# 创建内容对应的空列表
|
||
content = list()
|
||
# 获取特征的近一月值
|
||
import pandas as pd
|
||
feature_data_df = pd.read_csv(f'dataset/指标数据添加时间特征.csv', parse_dates=['ds']).tail(20)
|
||
def draw_feature_trend(feature_data_df, features):
|
||
# 画特征近一周的趋势图
|
||
feature_df = feature_data_df[['ds','y']+features]
|
||
# 遍历X每一列，和yy画散点图 ，
|
||
|
||
for i, col in enumerate(features):
|
||
# try:
|
||
print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
|
||
if col not in ['ds', 'y']:
|
||
fig, ax1 = plt.subplots(figsize=(10, 6))
|
||
# 在第一个坐标轴上绘制数据
|
||
sns.lineplot(data=feature_df, x='ds', y='y', ax=ax1, color='b')
|
||
ax1.set_xlabel('日期')
|
||
ax1.set_ylabel('y', color='b')
|
||
ax1.tick_params('y', colors='b')
|
||
# 在 ax1 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||
for j in range(1, len(feature_df), 2):
|
||
value = feature_df['y'].iloc[j]
|
||
date = feature_df['ds'].iloc[j]
|
||
offset = 1.001
|
||
ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
|
||
# 创建第二个坐标轴
|
||
ax2 = ax1.twinx()
|
||
# 在第二个坐标轴上绘制数据
|
||
sns.lineplot(data=feature_df, x='ds', y=col, ax=ax2, color='r')
|
||
ax2.set_ylabel(col, color='r')
|
||
ax2.tick_params('y', colors='r')
|
||
# 在 ax2 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||
for j in range(0, len(feature_df), 2):
|
||
value = feature_df[col].iloc[j]
|
||
date = feature_df['ds'].iloc[j]
|
||
offset = 1.0003
|
||
ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
|
||
# 添加标题
|
||
plt.title(col)
|
||
# 设置横坐标为日期格式并自动调整
|
||
locator = mdates.AutoDateLocator()
|
||
formatter = mdates.AutoDateFormatter(locator)
|
||
ax1.xaxis.set_major_locator(locator)
|
||
ax1.xaxis.set_major_formatter(formatter)
|
||
# 文件名特殊字符处理
|
||
col = col.replace('*', '-')
|
||
col = col.replace(':', '-')
|
||
col = col.replace(r'/', '-')
|
||
plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
|
||
content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
|
||
plt.close()
|
||
# except Exception as e:
|
||
# print(f'绘制第{i+1}个特征{col}与价格散点图时出错：{e}')
|
||
|
||
|
||
|
||
### 添加标题
|
||
content.append(Graphs.draw_title(f'{y}{time}预测报告'))
|
||
|
||
### 预测结果
|
||
content.append(Graphs.draw_little_title('一、预测结果：'))
|
||
# 添加历史走势及预测价格的走势图片
|
||
content.append(Graphs.draw_img(os.path.join(dataset,'历史价格-预测值.png')))
|
||
content.append(Graphs.draw_text('图示说明：'))
|
||
content.append(Graphs.draw_text('1. 确定波动率置信区间：统计近60个交易日的真实价格波动率，找出在 10% ，90% 的分位值作为波动率置信区间;'))
|
||
content.append(Graphs.draw_text('2. 确定通道上界：在所有模型的预测结果中 <= 前一天真实价格 乘以 90%的置信波动分位数'))
|
||
content.append(Graphs.draw_text('3. 确定通道下界：在所有模型的预测结果中 >= 前一天真实价格 乘以 10%的置信波动分位数'))
|
||
content.append(Graphs.draw_text('4. 预测结果没有真实值作为参考依据，通道上界取近20个交易日内预测在上界值的模型对应的预测值，通道下界同理；'))
|
||
content.append(Graphs.draw_text('5. 预测结果选用近20个交易日内，最多接近真实值的模型的预测值对应的预测结果；'))
|
||
content.append(Graphs.draw_text('6. 预测结果在通道外的，代表最接近真实值的预测结果不在置信波动范围内。'))
|
||
|
||
|
||
# 取df中y列为空的行
|
||
import pandas as pd
|
||
df = pd.read_csv(os.path.join(dataset,'predict.csv'),encoding='gbk')
|
||
df_true = pd.read_csv(os.path.join(dataset,'指标数据添加时间特征.csv'),encoding='utf-8') # 获取预测日期对应的真实值
|
||
df_true = df_true[['ds','y']]
|
||
eval_df = pd.read_csv(os.path.join(dataset,'model_evaluation.csv'),encoding='utf-8')
|
||
# 按评估指标排序，取前五
|
||
fivemodels_list = eval_df['模型(Model)'].values # 列表形式，后面当作列名索引使用
|
||
# 取 fivemodels_list 和 ds 列
|
||
df = df[['ds'] + fivemodels_list.tolist() ]
|
||
# 拼接预测日期对应的真实值
|
||
df = pd.merge(df, df_true, on='ds', how='left')
|
||
# 删除全部为nan的列
|
||
df = df.dropna(how='all', axis=1)
|
||
# 选择除 'ds' 列外的数值列，并进行类型转换和四舍五入
|
||
num_cols = [col for col in df.columns if col!= 'ds' and pd.api.types.is_numeric_dtype(df[col])]
|
||
for col in num_cols:
|
||
df[col] = df[col].astype(float).round(2)
|
||
# 添加最大值、最小值、平均值三列
|
||
df['平均值'] = df[num_cols].mean(axis=1).round(2)
|
||
df['最大值'] = df[num_cols].max(axis=1)
|
||
df['最小值'] = df[num_cols].min(axis=1)
|
||
# df转置
|
||
df = df.T
|
||
# df重置索引
|
||
df = df.reset_index()
|
||
# 添加预测值表格
|
||
data = df.values.tolist()
|
||
col_width = 500/len(df.columns)
|
||
content.append(Graphs.draw_table(col_width,*data))
|
||
content.append(Graphs.draw_little_title('二、上一预测周期偏差率分析：'))
|
||
df = pd.read_csv(os.path.join(dataset,'testandpredict_groupby.csv'),encoding='utf-8')
|
||
df4 = df.copy() # 计算偏差率使用
|
||
# 计算模型偏差率
|
||
#计算各列对于y列的差值百分比
|
||
df3 = pd.DataFrame() # 存储偏差率
|
||
|
||
# 删除有null的行
|
||
df4 = df4.dropna()
|
||
df3['ds'] = df4['ds']
|
||
for col in fivemodels_list:
|
||
df3[col] = round(abs(df4[col] - df4['y']) / df4['y'] * 100,2)
|
||
# 找出决定系数前五的偏差率
|
||
df3 = df3[['ds']+fivemodels_list.tolist()][-inputsize:]
|
||
# 找出上一预测区间的时间
|
||
stime = df3['ds'].iloc[0]
|
||
etime = df3['ds'].iloc[-1]
|
||
# 添加偏差率表格
|
||
fivemodels = '、'.join(eval_df['模型(Model)'].values[:5]) # 字符串形式，后面写入字符串使用
|
||
content.append(Graphs.draw_text(f'预测使用了{num_models}个模型进行训练，使用评估结果MAE前五的模型分别是 {fivemodels} ，模型上一预测区间 {stime} -- {etime}的偏差率(%)分别是：'))
|
||
# # 添加偏差率表格
|
||
df3 = df3.T
|
||
df3 = df3.reset_index()
|
||
data = df3.values.tolist()
|
||
col_width = 500/len(df3.columns)
|
||
content.append(Graphs.draw_table(col_width,*data))
|
||
|
||
|
||
content.append(Graphs.draw_little_title('三、预测过程解析：'))
|
||
### 特征、模型、参数配置
|
||
content.append(Graphs.draw_little_title('模型选择：'))
|
||
content.append(Graphs.draw_text(f'本次预测使用了一个专门收集时间序列的NeuralForecast库中的{num_models}个模型：'))
|
||
content.append(Graphs.draw_text(f'使用40天的数据预测未来{inputsize}天的数据。'))
|
||
content.append(Graphs.draw_little_title('指标情况：'))
|
||
with open(os.path.join(dataset,'特征频度统计.txt'),encoding='utf-8') as f:
|
||
for line in f.readlines():
|
||
content.append(Graphs.draw_text(line))
|
||
|
||
data = pd.read_csv(os.path.join(dataset,'指标数据添加时间特征.csv'),encoding='utf-8') # 计算相关系数用
|
||
df_zhibiaofenlei = loadcsv(os.path.join(dataset,'特征处理后的指标名称及分类.csv')) # 气泡图用
|
||
df_zhibiaoshuju = data.copy() # 气泡图用
|
||
|
||
# 绘制特征相关气泡图
|
||
|
||
grouped = df_zhibiaofenlei.groupby('指标分类')
|
||
grouped_corr = pd.DataFrame(columns=['指标分类', '指标数量', '相关性总和'])
|
||
|
||
content.append(Graphs.draw_little_title('按指标分类分别与预测目标进行皮尔逊相关系数分析：'))
|
||
content.append(Graphs.draw_text('''皮尔逊相关系数说明：'''))
|
||
content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。'''))
|
||
content.append(Graphs.draw_text('''
|
||
相关系数为1：表示两个变量之间存在完全正向的线性关系，即当一个变量增加时，另一个变量也相应增加，且变化是完全一致的。'''))
|
||
content.append(Graphs.draw_text('''相关系数为-1：表示两个变量之间存在完全负向的线性关系，即当一个变量增加时，另一个变量会相应减少，且变化是完全相反的'''))
|
||
content.append(Graphs.draw_text('''相关系数接近0：表示两个变量之间不存在线性关系，即它们的变化不会随着对方的变化而变化。'''))
|
||
for name, group in grouped:
|
||
cols = group['指标名称'].tolist()
|
||
logger.info(f'开始绘制{name}类指标的相关性直方图')
|
||
cols_subset = cols
|
||
feature_names = ['y'] + cols_subset
|
||
correlation_matrix = df_zhibiaoshuju[feature_names].corr()['y']
|
||
|
||
# 绘制特征相关性直方分布图
|
||
plt.figure(figsize=(10,8))
|
||
sns.histplot(correlation_matrix.values.flatten(), bins=20, kde=True, color='skyblue')
|
||
plt.title(f'{name}类指标(共{len(cols_subset)}个)相关性直方分布图')
|
||
plt.xlabel('相关系数')
|
||
plt.ylabel('频数')
|
||
plt.savefig(os.path.join(dataset, f'{name}类指标相关性直方分布图.png'), bbox_inches='tight')
|
||
plt.close()
|
||
content.append(Graphs.draw_img(os.path.join(dataset,f'{name}类指标相关性直方分布图.png')))
|
||
content.append(Graphs.draw_text(f'{name}类指标(共{len(cols_subset)}个)的相关性直方分布图如上所示。'))
|
||
# 相关性大于0的特征
|
||
positive_corr_features = correlation_matrix[correlation_matrix > 0].sort_values(ascending=False).index.tolist()[1:]
|
||
|
||
print(f'{name}下正相关的特征值有：',positive_corr_features)
|
||
if len(positive_corr_features) > 5:
|
||
positive_corr_features = positive_corr_features[0:5]
|
||
content.append(Graphs.draw_text(f'{name}类指标中，与预测目标y正相关前五的特征有：{positive_corr_features}'))
|
||
draw_feature_trend(feature_data_df, positive_corr_features)
|
||
elif len(positive_corr_features) == 0:
|
||
pass
|
||
else:
|
||
positive_corr_features = positive_corr_features
|
||
content.append(Graphs.draw_text(f'其中，与预测目标y正相关的特征有：{positive_corr_features}'))
|
||
draw_feature_trend(feature_data_df, positive_corr_features)
|
||
|
||
# 相关性小于0的特征
|
||
negative_corr_features = correlation_matrix[correlation_matrix < 0].sort_values(ascending=True).index.tolist()
|
||
|
||
print(f'{name}下负相关的特征值有：',negative_corr_features)
|
||
if len(negative_corr_features) > 5:
|
||
negative_corr_features = negative_corr_features[:5]
|
||
content.append(Graphs.draw_text(f'与预测目标y负相关前五的特征有：{negative_corr_features}'))
|
||
draw_feature_trend(feature_data_df, negative_corr_features)
|
||
elif len(negative_corr_features) == 0:
|
||
pass
|
||
else:
|
||
content.append(Graphs.draw_text(f'{name}类指标中，与预测目标y负相关的特征有：{negative_corr_features}'))
|
||
draw_feature_trend(feature_data_df, negative_corr_features)
|
||
|
||
|
||
# 计算correlation_sum 第一行的相关性的绝对值的总和
|
||
correlation_sum = correlation_matrix.abs().sum()
|
||
logger.info(f'{name}类指标的相关性总和为：{correlation_sum}')
|
||
# 分组的相关性总和拼接到grouped_corr
|
||
goup_corr = pd.DataFrame({'指标分类': [name], '指标数量': [len(cols_subset)], '相关性总和': [correlation_sum]})
|
||
grouped_corr = pd.concat([grouped_corr, goup_corr], axis=0, ignore_index=True)
|
||
|
||
# 绘制相关性总和的气泡图
|
||
logger.info(f'开始绘制相关性总和的气泡图')
|
||
plt.figure(figsize=(10, 10))
|
||
sns.scatterplot(data=grouped_corr, x='相关性总和', y='指标数量', size='相关性总和', sizes=(grouped_corr['相关性总和'].min()*5, grouped_corr['相关性总和'].max()*5), hue='指标分类', palette='viridis')
|
||
plt.title('指标分类相关性总和的气泡图')
|
||
plt.ylabel('数量')
|
||
plt.savefig(os.path.join(dataset, '指标分类相关性总和的气泡图.png'), bbox_inches='tight')
|
||
plt.close()
|
||
content.append(Graphs.draw_img(os.path.join(dataset,'指标分类相关性总和的气泡图.png')))
|
||
content.append(Graphs.draw_text('气泡图中，横轴为指标分类，纵轴为指标分类下的特征数量，气泡的面积越大表示该分类中特征的相关系数和越大。'))
|
||
logger.info(f'绘制相关性总和的气泡图结束')
|
||
|
||
|
||
|
||
# # 计算特征相关性
|
||
# data.rename(columns={y: 'y'}, inplace=True)
|
||
# data['ds'] = pd.to_datetime(data['ds'])
|
||
# data.drop(columns=['ds'], inplace=True)
|
||
# # 创建一个空的 DataFrame 来保存相关系数
|
||
# correlation_df = pd.DataFrame(columns=['Feature', 'Correlation'])
|
||
# # 计算各特征与目标列的皮尔逊相关系数，并保存到新的 Data 中
|
||
# for col in data.columns:
|
||
# if col!= 'y':
|
||
# pearson_correlation = np.corrcoef(data[col], data['y'])[0, 1]
|
||
# spearman_correlation, _ = spearmanr(data[col], data['y'])
|
||
# new_row = {'Feature': col, 'Pearson_Correlation': round(pearson_correlation,3), 'Spearman_Correlation': round(spearman_correlation,2)}
|
||
# correlation_df = correlation_df._append(new_row, ignore_index=True)
|
||
|
||
# correlation_df.drop('Correlation', axis=1, inplace=True)
|
||
# correlation_df.dropna(inplace=True)
|
||
# correlation_df.to_csv(os.path.join(dataset,'指标相关性分析.csv'), index=False)
|
||
|
||
# data = correlation_df['Pearson_Correlation'].values.tolist()
|
||
# # 生成 -1 到 1 的 20 个区间
|
||
# bins = np.linspace(-1, 1, 21)
|
||
# # 计算每个区间的统计数（这里是区间内数据的数量）
|
||
# hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
|
||
|
||
# #设置画布大小
|
||
# plt.figure(figsize=(10, 6))
|
||
# # 绘制直方图
|
||
# plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
|
||
|
||
# # 添加标题和坐标轴标签
|
||
# plt.title('皮尔逊相关系数分布图')
|
||
# plt.xlabel('区间')
|
||
# plt.ylabel('统计数')
|
||
# plt.savefig(os.path.join(dataset, '皮尔逊相关性系数.png'))
|
||
# plt.close()
|
||
|
||
|
||
# #设置画布大小
|
||
# plt.figure(figsize=(10, 6))
|
||
# data = correlation_df['Spearman_Correlation'].values.tolist()
|
||
# # 计算每个区间的统计数（这里是区间内数据的数量）
|
||
# hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
|
||
|
||
# # 绘制直方图
|
||
# plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
|
||
|
||
# # 添加标题和坐标轴标签
|
||
# plt.title('斯皮尔曼相关系数分布图')
|
||
# plt.xlabel('区间')
|
||
# plt.ylabel('统计数')
|
||
# plt.savefig(os.path.join(dataset, '斯皮尔曼相关性系数.png'))
|
||
# plt.close()
|
||
# content.append(Graphs.draw_text(f'指标相关性分析--皮尔逊相关系数：'))
|
||
# # 皮尔逊正相关 不相关 负相关 的表格
|
||
# content.append(Graphs.draw_img(os.path.join(dataset,'皮尔逊相关性系数.png')))
|
||
# content.append(Graphs.draw_text('''皮尔逊相关系数说明：'''))
|
||
# content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。'''))
|
||
# content.append(Graphs.draw_text('''
|
||
# 相关系数为1：表示两个变量之间存在完全正向的线性关系，即当一个变量增加时，另一个变量也相应增加，且变化是完全一致的。'''))
|
||
# content.append(Graphs.draw_text('''当前特征中正相关前十的有：'''))
|
||
# top10_columns = correlation_df.sort_values(by='Pearson_Correlation',ascending=False).head(10)['Feature'].to_list()
|
||
# top10 = ','.join(top10_columns)
|
||
# content.append(Graphs.draw_text(f'''{top10}'''))
|
||
|
||
# feature_df = feature_data_df[['ds','y']+top10_columns]
|
||
# # 遍历X每一列，和yy画散点图 ，
|
||
# for i, col in enumerate(feature_df.columns):
|
||
# print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
|
||
# if col not in ['ds', 'y']:
|
||
# fig, ax1 = plt.subplots(figsize=(10, 6))
|
||
# # 在第一个坐标轴上绘制数据
|
||
# ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
|
||
# ax1.set_xlabel('日期')
|
||
# ax1.set_ylabel('y', color='b')
|
||
# ax1.tick_params('y', colors='b')
|
||
# # 在 ax1 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||
# for j in range(1,len(feature_df),2):
|
||
# value = feature_df['y'].iloc[j]
|
||
# date = feature_df['ds'].iloc[j]
|
||
# offset = 1.001
|
||
# ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
|
||
# # 创建第二个坐标轴
|
||
# ax2 = ax1.twinx()
|
||
# # 在第二个坐标轴上绘制数据
|
||
# line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
|
||
# ax2.set_ylabel(col, color='r')
|
||
# ax2.tick_params('y', colors='r')
|
||
# # 在 ax2 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||
# for j in range(0,len(feature_df),2):
|
||
# value = feature_df[col].iloc[j]
|
||
# date = feature_df['ds'].iloc[j]
|
||
# offset = 1.001
|
||
# ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
|
||
# # 添加标题
|
||
# plt.title(col)
|
||
# # 设置横坐标为日期格式并自动调整
|
||
# locator = mdates.AutoDateLocator()
|
||
# formatter = mdates.AutoDateFormatter(locator)
|
||
# ax1.xaxis.set_major_locator(locator)
|
||
# ax1.xaxis.set_major_formatter(formatter)
|
||
# # 文件名特殊字符处理
|
||
# col = col.replace('*', '-')
|
||
# col = col.replace(':', '-')
|
||
# plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
|
||
# content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
|
||
# plt.close()
|
||
|
||
|
||
# content.append(Graphs.draw_text(f'指标相关性分析--斯皮尔曼相关系数：'))
|
||
# # 皮尔逊正相关 不相关 负相关 的表格
|
||
# content.append(Graphs.draw_img(os.path.join(dataset,'斯皮尔曼相关性系数.png')))
|
||
# content.append(Graphs.draw_text('斯皮尔曼相关系数（Spearmans rank correlation coefficient）是一种用于衡量两个变量之间的单调关系（不一定是线性关系）的统计指标。'))
|
||
# content.append(Graphs.draw_text('它的计算基于变量的秩次（即变量值的排序位置）而非变量的原始值。'))
|
||
# content.append(Graphs.draw_text('斯皮尔曼相关系数的取值范围在 -1 到 1 之间。'))
|
||
# content.append(Graphs.draw_text('当系数为 1 时，表示两个变量之间存在完全正的单调关系；'))
|
||
# content.append(Graphs.draw_text('''当前特征中正单调关系前十的有：'''))
|
||
# top10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=False).head(10)['Feature'].to_list()
|
||
# top10 = ','.join(top10_columns)
|
||
# content.append(Graphs.draw_text(f'''{top10}'''))
|
||
|
||
# feature_df = feature_data_df[['ds','y']+top10_columns]
|
||
# # 遍历X每一列，和yy画散点图 ，
|
||
# for i, col in enumerate(feature_df.columns):
|
||
# print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
|
||
# if col not in ['ds', 'y']:
|
||
# fig, ax1 = plt.subplots(figsize=(10, 6))
|
||
# # 在第一个坐标轴上绘制数据
|
||
# ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
|
||
# ax1.set_xlabel('日期')
|
||
# ax1.set_ylabel('y', color='b')
|
||
# ax1.tick_params('y', colors='b')
|
||
# # 在 ax1 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||
# for j in range(1,len(feature_df),2):
|
||
# value = feature_df['y'].iloc[j]
|
||
# date = feature_df['ds'].iloc[j]
|
||
# offset = 1.001
|
||
# ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
|
||
# # 创建第二个坐标轴
|
||
# ax2 = ax1.twinx()
|
||
# # 在第二个坐标轴上绘制数据
|
||
# line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
|
||
# ax2.set_ylabel(col, color='r')
|
||
# ax2.tick_params('y', colors='r')
|
||
# # 在 ax2 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||
# for j in range(0,len(feature_df),2):
|
||
# value = feature_df[col].iloc[j]
|
||
# date = feature_df['ds'].iloc[j]
|
||
# offset = 1.001
|
||
# ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
|
||
# # 添加标题
|
||
# plt.title(col)
|
||
# # 设置横坐标为日期格式并自动调整
|
||
# locator = mdates.AutoDateLocator()
|
||
# formatter = mdates.AutoDateFormatter(locator)
|
||
# ax1.xaxis.set_major_locator(locator)
|
||
# ax1.xaxis.set_major_formatter(formatter)
|
||
# # 文件名特殊字符处理
|
||
# col = col.replace('*', '-')
|
||
# col = col.replace(':', '-')
|
||
# plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
|
||
# content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
|
||
# plt.close()
|
||
|
||
# content.append(Graphs.draw_text('当系数为 -1 时，表示存在完全负的单调关系；'))
|
||
# content.append(Graphs.draw_text('''当前特征中负单调关系前十的有：'''))
|
||
# tail10_columns = correlation_df.sort_values(by='Spearman_Correlation',ascending=True).head(10)['Feature'].to_list()
|
||
# top10 = ','.join(tail10_columns)
|
||
# content.append(Graphs.draw_text(f'''{top10}'''))
|
||
# # 获取特征的近一周值
|
||
# feature_df = feature_data_df[['ds','y']+tail10_columns]
|
||
# # 遍历X每一列，和yy画散点图 ，
|
||
# for i, col in enumerate(feature_df.columns):
|
||
# print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
|
||
# if col not in ['ds', 'y']:
|
||
# fig, ax1 = plt.subplots(figsize=(10, 6))
|
||
# # 在第一个坐标轴上绘制数据
|
||
# ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
|
||
# ax1.set_xlabel('日期')
|
||
# ax1.set_ylabel('y', color='b')
|
||
# ax1.tick_params('y', colors='b')
|
||
# # 在 ax1 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||
# for j in range(len(feature_df)):
|
||
# if j%2 == 1:
|
||
# value = feature_df['y'].iloc[j]
|
||
# date = feature_df['ds'].iloc[j]
|
||
# offset = 1.001
|
||
# ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
|
||
# # 创建第二个坐标轴
|
||
# ax2 = ax1.twinx()
|
||
# # 在第二个坐标轴上绘制数据
|
||
# line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
|
||
# ax2.set_ylabel(col, color='r')
|
||
# ax2.tick_params('y', colors='r')
|
||
# # 在 ax2 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||
# for j in range(1,len(feature_df),2):
|
||
# value = feature_df[col].iloc[j]
|
||
# date = feature_df['ds'].iloc[j]
|
||
# offset = 1.001
|
||
# ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
|
||
# # 添加标题
|
||
# plt.title(col)
|
||
# # 设置横坐标为日期格式并自动调整
|
||
# locator = mdates.AutoDateLocator()
|
||
# formatter = mdates.AutoDateFormatter(locator)
|
||
# ax1.xaxis.set_major_locator(locator)
|
||
# ax1.xaxis.set_major_formatter(formatter)
|
||
# # 文件名特殊字符处理
|
||
# col = col.replace('*', '-')
|
||
# col = col.replace(':', '-')
|
||
# plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
|
||
# content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
|
||
# plt.close()
|
||
# content.append(Graphs.draw_text('当系数为 0 时，表示两个变量之间不存在单调关系。'))
|
||
# content.append(Graphs.draw_text('与皮尔逊相关系数相比，斯皮尔曼相关系数对于数据中的异常值不敏感，更适用于处理非线性关系或存在极端值的数据。'))
|
||
content.append(Graphs.draw_little_title('模型选择：'))
|
||
content.append(Graphs.draw_text(f'预测使用了{num_models}个模型进行训练拟合，通过评估指标MAE从小到大排列，前5个模型的简介如下：'))
|
||
|
||
### 读取模型简介
|
||
with open(os.path.join(dataset,'model_introduction.txt'), 'r', encoding='utf-8') as f:
|
||
for line in f:
|
||
line_split = line.strip().split('--')
|
||
if line_split[0] in fivemodels_list:
|
||
for introduction in line_split:
|
||
content.append(Graphs.draw_text(introduction))
|
||
|
||
content.append(Graphs.draw_little_title('模型评估：'))
|
||
|
||
df = pd.read_csv(os.path.join(dataset,'model_evaluation.csv'),encoding='utf-8')
|
||
# 判断 df 的数值列转为float
|
||
for col in eval_df.columns:
|
||
if col not in ['模型(Model)']:
|
||
eval_df[col] = eval_df[col].astype(float)
|
||
eval_df[col] = eval_df[col].round(3)
|
||
# 筛选 fivemodels_list.tolist() 的行
|
||
eval_df = eval_df[eval_df['模型(Model)'].isin(fivemodels_list)]
|
||
# df转置
|
||
eval_df = eval_df.T
|
||
# df重置索引
|
||
eval_df = eval_df.reset_index()
|
||
eval_df = eval_df.T
|
||
# # 添加表格
|
||
data = eval_df.values.tolist()
|
||
col_width = 500/len(eval_df.columns)
|
||
content.append(Graphs.draw_table(col_width,*data))
|
||
content.append(Graphs.draw_text('评估指标释义：'))
|
||
content.append(Graphs.draw_text('1. 均方根误差(RMSE)：均方根误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||
content.append(Graphs.draw_text('2. 平均绝对误差(MAE)：平均绝对误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||
content.append(Graphs.draw_text('3. 平均平方误差(MSE)：平均平方误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||
content.append(Graphs.draw_text('模型拟合：'))
|
||
# 添加图片
|
||
content.append(Graphs.draw_img(os.path.join(dataset,'预测值与真实值对比图.png')))
|
||
|
||
# 附1，特征列表
|
||
content.append(Graphs.draw_little_title('附1、特征列表：'))
|
||
df_fuyi = pd.read_csv(os.path.join(dataset,'特征频度统计.csv'),encoding='utf-8')
|
||
for col in df_fuyi.columns:
|
||
fuyi = df_fuyi[col]
|
||
fuyi = fuyi.dropna()
|
||
content.append(Graphs.draw_text(f'{col}：'))
|
||
for i in range(len(fuyi)):
|
||
content.append(Graphs.draw_text(f'{i+1}、{fuyi[i]}'))
|
||
|
||
|
||
|
||
### 生成pdf文件
|
||
doc = SimpleDocTemplate(os.path.join(dataset,reportname), pagesize=letter)
|
||
# doc = SimpleDocTemplate(os.path.join(dataset,'reportname.pdf'), pagesize=letter)
|
||
doc.build(content)
|
||
# pdf 上传到数字化信息平台
|
||
# 读取pdf并转为base64
|
||
try:
|
||
if is_update_report:
|
||
with open(os.path.join(dataset,reportname), 'rb') as f:
|
||
base64_data = base64.b64encode(f.read()).decode('utf-8')
|
||
upload_data["data"]["fileBase64"] = base64_data
|
||
upload_data["data"]["fileName"] = reportname
|
||
token = get_head_auth_report()
|
||
upload_report_data(token, upload_data)
|
||
except TimeoutError as e:
|
||
print(f"请求超时: {e}")
|
||
|
||
|
||
|
||
def pp_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inputsize=5,dataset='dataset',time = '2024-07-30',reportname='report.pdf'):
|
||
global y
|
||
# 创建内容对应的空列表
|
||
content = list()
|
||
|
||
### 添加标题
|
||
content.append(Graphs.draw_title(f'{y}{time}预测报告'))
|
||
|
||
### 预测结果
|
||
content.append(Graphs.draw_little_title('一、预测结果：'))
|
||
# 添加图片
|
||
# 找出后缀是历史价格-预测值.png的图片
|
||
# import glob
|
||
# imgs = glob.glob(os.path.join(dataset,'*历史价格-预测值.png'))
|
||
# for img in imgs:
|
||
# content.append(Graphs.draw_img(img))
|
||
content.append(Graphs.draw_img(os.path.join(dataset,'历史价格-预测值.png')))
|
||
|
||
# 取df中y列为空的行
|
||
import pandas as pd
|
||
df = pd.read_csv(os.path.join(dataset,'predict.csv'),encoding='gbk')
|
||
df_true = pd.read_csv(os.path.join(dataset,'指标数据添加时间特征.csv'),encoding='utf-8') # 获取预测日期对应的真实值
|
||
df_true = df_true[['ds','y']]
|
||
eval_df = pd.read_csv(os.path.join(dataset,'model_evaluation.csv'),encoding='utf-8')
|
||
# 按评估指标排序，取前五
|
||
fivemodels_list = eval_df['模型(Model)'].values # 列表形式，后面当作列名索引使用
|
||
# 取 fivemodels_list 和 ds 列
|
||
df = df[['ds'] + fivemodels_list.tolist() ]
|
||
# 拼接预测日期对应的真实值
|
||
df = pd.merge(df, df_true, on='ds', how='left')
|
||
# 删除全部为nan的列
|
||
df = df.dropna(how='all', axis=1)
|
||
# 选择除 'ds' 列外的数值列，并进行类型转换和四舍五入
|
||
num_cols = [col for col in df.columns if col!= 'ds' and pd.api.types.is_numeric_dtype(df[col])]
|
||
for col in num_cols:
|
||
df[col] = df[col].astype(float).round(2)
|
||
# 添加最大值、最小值、平均值三列
|
||
df['平均值'] = df[num_cols].mean(axis=1).round(2)
|
||
df['最大值'] = df[num_cols].max(axis=1)
|
||
df['最小值'] = df[num_cols].min(axis=1)
|
||
# df转置
|
||
df = df.T
|
||
# df重置索引
|
||
df = df.reset_index()
|
||
# 添加预测值表格
|
||
data = df.values.tolist()
|
||
col_width = 500/len(df.columns)
|
||
content.append(Graphs.draw_table(col_width,*data))
|
||
content.append(Graphs.draw_little_title('二、上一预测周期偏差率分析：'))
|
||
df = pd.read_csv(os.path.join(dataset,'testandpredict_groupby.csv'),encoding='utf-8')
|
||
df4 = df.copy() # 计算偏差率使用
|
||
# 计算模型偏差率
|
||
#计算各列对于y列的差值百分比
|
||
df3 = pd.DataFrame() # 存储偏差率
|
||
|
||
# 删除有null的行
|
||
df4 = df4.dropna()
|
||
df3['ds'] = df4['ds']
|
||
for col in df.columns:
|
||
if col not in ['y','ds','index']:
|
||
df3[col] = round(abs(df4[col] - df4['y']) / df4['y'] * 100,2)
|
||
# 找出决定系数前五的偏差率
|
||
df3 = df3[['ds']+fivemodels_list.tolist()][-inputsize:]
|
||
# 找出上一预测区间的时间
|
||
stime = df3['ds'].iloc[0]
|
||
etime = df3['ds'].iloc[-1]
|
||
# 添加偏差率表格
|
||
fivemodels = '、'.join(eval_df['模型(Model)'].values[:5]) # 字符串形式，后面写入字符串使用
|
||
content.append(Graphs.draw_text(f'预测使用了{num_models}个模型进行训练，使用评估结果MAE前五的模型分别是 {fivemodels} ，模型上一预测区间 {stime} -- {etime}的偏差率(%)分别是：'))
|
||
# # 添加偏差率表格
|
||
df3 = df3.T
|
||
df3 = df3.reset_index()
|
||
data = df3.values.tolist()
|
||
col_width = 500/len(df3.columns)
|
||
content.append(Graphs.draw_table(col_width,*data))
|
||
|
||
|
||
content.append(Graphs.draw_little_title('三、预测过程解析：'))
|
||
### 特征、模型、参数配置
|
||
content.append(Graphs.draw_little_title('模型选择：'))
|
||
content.append(Graphs.draw_text(f'本次预测使用了一个专门收集时间序列的NeuralForecast库中的{num_models}个模型：'))
|
||
content.append(Graphs.draw_text(f'使用40天的数据预测未来{inputsize}天的数据。'))
|
||
content.append(Graphs.draw_little_title('指标情况：'))
|
||
with open(os.path.join(dataset,'特征频度统计.txt'),encoding='utf-8') as f:
|
||
for line in f.readlines():
|
||
content.append(Graphs.draw_text(line))
|
||
|
||
|
||
|
||
### 特征工程
|
||
# 计算特征相关性
|
||
# 读取数据
|
||
from scipy.stats import spearmanr
|
||
data = pd.read_csv(os.path.join(dataset,'指标数据添加时间特征.csv'),encoding='utf-8')
|
||
# 重命名预测列
|
||
data.rename(columns={y: 'y'}, inplace=True) # 修改
|
||
data['ds'] = pd.to_datetime(data['ds']) # 修改
|
||
# 去掉ds列
|
||
data.drop(columns=['ds'], inplace=True)
|
||
# 创建一个空的 DataFrame 来保存相关系数
|
||
correlation_df = pd.DataFrame(columns=['Feature', 'Correlation'])
|
||
# 计算各特征与目标列的皮尔逊相关系数，并保存到新的 DataFrame 中
|
||
for col in data.columns:
|
||
if col!= 'y':
|
||
pearson_correlation = np.corrcoef(data[col], data['y'])[0, 1]
|
||
spearman_correlation, _ = spearmanr(data[col], data['y'])
|
||
new_row = {'Feature': col, 'Pearson_Correlation': round(pearson_correlation,3), 'Spearman_Correlation': round(spearman_correlation,2)}
|
||
correlation_df = correlation_df._append(new_row, ignore_index=True)
|
||
|
||
# 删除空列
|
||
correlation_df.drop('Correlation', axis=1, inplace=True)
|
||
correlation_df.dropna(inplace=True)
|
||
correlation_df.to_csv(os.path.join(dataset,'指标相关性分析.csv'), index=False)
|
||
|
||
data = correlation_df['Pearson_Correlation'].values.tolist()
|
||
# 生成 -1 到 1 的 20 个区间
|
||
bins = np.linspace(-1, 1, 21)
|
||
# 计算每个区间的统计数（这里是区间内数据的数量）
|
||
hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
|
||
|
||
#设置画布大小
|
||
plt.figure(figsize=(10, 6))
|
||
# 绘制直方图
|
||
plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
|
||
|
||
# 添加标题和坐标轴标签
|
||
plt.title('皮尔逊相关系数分布图')
|
||
plt.xlabel('区间')
|
||
plt.ylabel('统计数')
|
||
plt.savefig(os.path.join(dataset, '皮尔逊相关性系数.png'))
|
||
plt.close()
|
||
|
||
|
||
#设置画布大小
|
||
plt.figure(figsize=(10, 6))
|
||
data = correlation_df['Spearman_Correlation'].values.tolist()
|
||
# 计算每个区间的统计数（这里是区间内数据的数量）
|
||
hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
|
||
|
||
# 绘制直方图
|
||
plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
|
||
|
||
# 添加标题和坐标轴标签
|
||
plt.title('斯皮尔曼相关系数分布图')
|
||
plt.xlabel('区间')
|
||
plt.ylabel('统计数')
|
||
plt.savefig(os.path.join(dataset, '斯皮尔曼相关性系数.png'))
|
||
plt.close()
|
||
content.append(Graphs.draw_text(f'指标相关性分析--皮尔逊相关系数：'))
|
||
# 皮尔逊正相关 不相关 负相关 的表格
|
||
content.append(Graphs.draw_img(os.path.join(dataset,'皮尔逊相关性系数.png')))
|
||
content.append(Graphs.draw_text('''皮尔逊相关系数说明：'''))
|
||
content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。'''))
|
||
content.append(Graphs.draw_text('''
|
||
相关系数为1：表示两个变量之间存在完全正向的线性关系，即当一个变量增加时，另一个变量也相应增加，且变化是完全一致的。'''))
|
||
content.append(Graphs.draw_text('''当前特征中正相关前十的有：'''))
|
||
top10_columns = correlation_df.sort_values(by='Pearson_Correlation',ascending=False).head(10)['Feature'].to_list()
|
||
top10 = ','.join(top10_columns)
|
||
content.append(Graphs.draw_text(f'''{top10}'''))
|
||
# 获取特征的近一月值
|
||
feature_data_df = pd.read_csv(f'dataset/填充后的特征数据.csv', parse_dates=['ds']).tail(20)
|
||
feature_df = feature_data_df[['ds','y']+top10_columns]
|
||
# feature_df['ds'] = pd.to_datetime(df['ds'], format = '%Y-%m-%d' )
|
||
# 遍历X每一列，和yy画散点图 ，
|
||
for i, col in enumerate(feature_df.columns):
|
||
print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
|
||
if col not in ['ds', 'y']:
|
||
fig, ax1 = plt.subplots(figsize=(10, 6))
|
||
# 在第一个坐标轴上绘制数据
|
||
ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
|
||
ax1.set_xlabel('日期')
|
||
ax1.set_ylabel('y', color='b')
|
||
ax1.tick_params('y', colors='b')
|
||
# 在 ax1 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||
for j in range(1,len(feature_df),2):
|
||
value = feature_df['y'].iloc[j]
|
||
date = feature_df['ds'].iloc[j]
|
||
offset = 1.001
|
||
ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
|
||
# 创建第二个坐标轴
|
||
ax2 = ax1.twinx()
|
||
# 在第二个坐标轴上绘制数据
|
||
line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
|
||
ax2.set_ylabel(col, color='r')
|
||
ax2.tick_params('y', colors='r')
|
||
# 在 ax2 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||
for j in range(0,len(feature_df),2):
|
||
value = feature_df[col].iloc[j]
|
||
date = feature_df['ds'].iloc[j]
|
||
offset = 1.001
|
||
ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
|
||
# 添加标题
|
||
plt.title(col)
|
||
# 设置横坐标为日期格式并自动调整
|
||
locator = mdates.AutoDateLocator()
|
||
formatter = mdates.AutoDateFormatter(locator)
|
||
ax1.xaxis.set_major_locator(locator)
|
||
ax1.xaxis.set_major_formatter(formatter)
|
||
# 文件名特殊字符处理
|
||
col = col.replace('*', '-')
|
||
col = col.replace(':', '-')
|
||
plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
|
||
content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
|
||
plt.close()
|
||
|
||
content.append(Graphs.draw_text('''相关系数为-1：表示两个变量之间存在完全负向的线性关系，即当一个变量增加时，另一个变量会相应减少，且变化是完全相反的'''))
|
||
content.append(Graphs.draw_text('''当前特征中负相关前十的有：'''))
|
||
tail10_columns = correlation_df.sort_values(by='Pearson_Correlation',ascending=True).head(10)['Feature'].to_list()
|
||
top10 = ','.join(tail10_columns)
|
||
content.append(Graphs.draw_text(f'''{top10}'''))
|
||
# 获取特征的近一周值
|
||
feature_df = feature_data_df[['ds','y']+tail10_columns]
|
||
# 遍历X每一列，和yy画散点图 ，
|
||
for i, col in enumerate(feature_df.columns):
|
||
print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
|
||
if col not in ['ds', 'y']:
|
||
fig, ax1 = plt.subplots(figsize=(10, 6))
|
||
# 在第一个坐标轴上绘制数据
|
||
ax1.plot(feature_df['ds'], feature_df['y'], 'b-')
|
||
ax1.set_xlabel('日期')
|
||
ax1.set_ylabel('y', color='b')
|
||
ax1.tick_params('y', colors='b')
|
||
# 在 ax1 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||
for j in range(len(feature_df)):
|
||
if j%2 == 1:
|
||
value = feature_df['y'].iloc[j]
|
||
date = feature_df['ds'].iloc[j]
|
||
offset = 1.001
|
||
ax1.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='b', fontsize=10)
|
||
# 创建第二个坐标轴
|
||
ax2 = ax1.twinx()
|
||
# 在第二个坐标轴上绘制数据
|
||
line2 = ax2.plot(feature_df['ds'], feature_df[col], 'r-')
|
||
ax2.set_ylabel(col, color='r')
|
||
ax2.tick_params('y', colors='r')
|
||
# 在 ax2 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||
for j in range(1,len(feature_df),2):
|
||
value = feature_df[col].iloc[j]
|
||
date = feature_df['ds'].iloc[j]
|
||
offset = 1.001
|
||
ax2.text(date, value * offset, str(round(value, 2)), ha='center', va='bottom', color='r', fontsize=10)
|
||
# 添加标题
|
||
plt.title(col)
|
||
# 设置横坐标为日期格式并自动调整
|
||
locator = mdates.AutoDateLocator()
|
||
formatter = mdates.AutoDateFormatter(locator)
|
||
ax1.xaxis.set_major_locator(locator)
|
||
ax1.xaxis.set_major_formatter(formatter)
|
||
# 文件名特殊字符处理
|
||
col = col.replace('*', '-')
|
||
col = col.replace(':', '-')
|
||
plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
|
||
content.append(Graphs.draw_img(os.path.join(dataset, f'{col}与价格散点图.png')))
|
||
plt.close()
|
||
content.append(Graphs.draw_text('''相关系数接近0：表示两个变量之间不存在线性关系，即它们的变化不会随着对方的变化而变化。'''))
|
||
content.append(Graphs.draw_text(f'指标相关性分析--斯皮尔曼相关系数：'))
|
||
# 皮尔逊正相关 不相关 负相关 的表格
|
||
content.append(Graphs.draw_img(os.path.join(dataset,'斯皮尔曼相关性系数.png')))
|
||
content.append(Graphs.draw_text('斯皮尔曼相关系数（Spearmans rank correlation coefficient）是一种用于衡量两个变量之间的单调关系（不一定是线性关系）的统计指标。'))
|
||
content.append(Graphs.draw_text('它的计算基于变量的秩次（即变量值的排序位置）而非变量的原始值。'))
|
||
content.append(Graphs.draw_text('斯皮尔曼相关系数的取值范围在 -1 到 1 之间。'))
|
||
content.append(Graphs.draw_text('当系数为 1 时，表示两个变量之间存在完全正的单调关系；'))
|
||
content.append(Graphs.draw_text('''当前特征中正单调关系前十的有：'''))
|
||
top10 = ','.join(correlation_df.sort_values(by='Spearman_Correlation',ascending=False).head(10)['Feature'])
|
||
content.append(Graphs.draw_text(f'''{top10}'''))
|
||
content.append(Graphs.draw_text('当系数为 -1 时，表示存在完全负的单调关系；'))
|
||
content.append(Graphs.draw_text('''当前特征中负单调关系前十的有：'''))
|
||
top10 = ','.join(correlation_df.sort_values(by='Spearman_Correlation',ascending=True).head(10)['Feature'])
|
||
content.append(Graphs.draw_text(f'''{top10}'''))
|
||
content.append(Graphs.draw_text('当系数为 0 时，表示两个变量之间不存在单调关系。'))
|
||
content.append(Graphs.draw_text('与皮尔逊相关系数相比，斯皮尔曼相关系数对于数据中的异常值不敏感，更适用于处理非线性关系或存在极端值的数据。'))
|
||
content.append(Graphs.draw_little_title('模型选择：'))
|
||
content.append(Graphs.draw_text(f'预测使用了{num_models}个模型进行训练拟合，通过评估指标MAE从小到大排列，前5个模型的简介如下：'))
|
||
|
||
### 读取模型简介
|
||
with open(os.path.join(dataset,'model_introduction.txt'), 'r', encoding='utf-8') as f:
|
||
for line in f:
|
||
line_split = line.strip().split('--')
|
||
if line_split[0] in fivemodels_list:
|
||
for introduction in line_split:
|
||
content.append(Graphs.draw_text(introduction))
|
||
|
||
content.append(Graphs.draw_little_title('模型评估：'))
|
||
|
||
df = pd.read_csv(os.path.join(dataset,'model_evaluation.csv'),encoding='utf-8')
|
||
# 判断 df 的数值列转为float
|
||
for col in eval_df.columns:
|
||
if col not in ['模型(Model)']:
|
||
eval_df[col] = eval_df[col].astype(float)
|
||
eval_df[col] = eval_df[col].round(3)
|
||
# 筛选 fivemodels_list.tolist() 的行
|
||
eval_df = eval_df[eval_df['模型(Model)'].isin(fivemodels_list)]
|
||
# df转置
|
||
eval_df = eval_df.T
|
||
# df重置索引
|
||
eval_df = eval_df.reset_index()
|
||
eval_df = eval_df.T
|
||
# # 添加表格
|
||
data = eval_df.values.tolist()
|
||
col_width = 500/len(eval_df.columns)
|
||
content.append(Graphs.draw_table(col_width,*data))
|
||
content.append(Graphs.draw_text('评估指标释义：'))
|
||
content.append(Graphs.draw_text('1. 均方根误差(RMSE)：均方根误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||
content.append(Graphs.draw_text('2. 平均绝对误差(MAE)：平均绝对误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||
content.append(Graphs.draw_text('3. 平均平方误差(MSE)：平均平方误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||
content.append(Graphs.draw_text('模型拟合：'))
|
||
# 添加图片
|
||
content.append(Graphs.draw_img(os.path.join(dataset,'预测值与真实值对比图.png')))
|
||
|
||
# 附1，特征列表
|
||
content.append(Graphs.draw_little_title('附1、特征列表：'))
|
||
df_fuyi = pd.read_csv(os.path.join(dataset,'特征频度统计.csv'),encoding='utf-8')
|
||
for col in df_fuyi.columns:
|
||
fuyi = df_fuyi[col]
|
||
fuyi = fuyi.dropna()
|
||
content.append(Graphs.draw_text(f'{col}：'))
|
||
for i in range(len(fuyi)):
|
||
content.append(Graphs.draw_text(f'{i+1}、{fuyi[i]}'))
|
||
|
||
### 生成pdf文件
|
||
doc = SimpleDocTemplate(os.path.join(dataset,reportname), pagesize=letter)
|
||
# doc = SimpleDocTemplate(os.path.join(dataset,'reportname.pdf'), pagesize=letter)
|
||
doc.build(content)
|
||
# pdf 上传到数字化信息平台
|
||
# 读取pdf并转为base64
|
||
try:
|
||
if is_update_report:
|
||
with open(os.path.join(dataset,reportname), 'rb') as f:
|
||
base64_data = base64.b64encode(f.read()).decode('utf-8')
|
||
upload_data["data"]["fileBase64"] = base64_data
|
||
upload_data["data"]["fileName"] = reportname
|
||
token = get_head_auth_report()
|
||
upload_report_data(token, upload_data)
|
||
except TimeoutError as e:
|
||
print(f"请求超时: {e}")
|
||
|
||
|
||
|
||
|
||
def tansuanli_export_pdf(num_indicators=475,num_models=22, num_dayindicator=202,inputsize=5,dataset='dataset',y='电碳价格',end_time='2024-07-30',reportname='tansuanli.pdf'):
|
||
# 创建内容对应的空列表
|
||
content = list()
|
||
### 添加标题
|
||
content.append(Graphs.draw_title(f'{y}{end_time}预测报告'))
|
||
### 预测结果
|
||
content.append(Graphs.draw_little_title('一、预测结果：'))
|
||
content.append(Graphs.draw_img(os.path.join(dataset,'历史价格-预测值.png')))
|
||
# 取df中y列为空的行
|
||
from lib.dataread import loadcsv
|
||
df = loadcsv(os.path.join(dataset,'predict.csv'))
|
||
df_true = loadcsv(os.path.join(dataset,'指标数据添加时间特征.csv')) # 获取预测日期对应的真实值
|
||
df_true = df_true[['ds','y']]
|
||
eval_df = loadcsv(os.path.join(dataset,'model_evaluation.csv'))
|
||
# 按评估指标排序，取前五
|
||
fivemodels_list = eval_df['模型(Model)'].values[:5] # 列表形式，后面当作列名索引使用
|
||
# 取 fivemodels_list 和 ds 列
|
||
df = df[['ds'] + fivemodels_list.tolist() ]
|
||
# 拼接预测日期对应的真实值
|
||
df = pd.merge(df, df_true, on='ds', how='left')
|
||
# 删除全部为nan的列
|
||
df = df.dropna(how='all', axis=1)
|
||
# 选择除 'ds' 列外的数值列，并进行类型转换和四舍五入
|
||
num_cols = [col for col in df.columns if col!= 'ds' and pd.api.types.is_numeric_dtype(df[col])]
|
||
for col in num_cols:
|
||
df[col] = df[col].astype(float).round(2)
|
||
# 添加预测每日的最大值、最小值、平均值三列
|
||
df['平均值'] = df[num_cols].mean(axis=1).round(2)
|
||
df['最大值'] = df[num_cols].max(axis=1)
|
||
df['最小值'] = df[num_cols].min(axis=1)
|
||
# 添加模型预测周期内的最大值、最小值、平均值三行
|
||
# 计算列的统计值
|
||
mean_values = df[num_cols].mean(axis=0).round(2)
|
||
max_values = df[num_cols].max(axis=0)
|
||
min_values = df[num_cols].min(axis=0)
|
||
# 创建一个新的 DataFrame 来存储统计行
|
||
stats_row = pd.DataFrame([mean_values, max_values, min_values], index=[0,1,2])
|
||
stats_row['ds'] = ['平均值', '最大值', '最小值']
|
||
# 将统计行添加到原始 DataFrame
|
||
df = pd.concat([df, stats_row], axis=0)
|
||
# df替换nan 为 '--'
|
||
df = df.fillna('--')
|
||
# df转置
|
||
df = df.T
|
||
# df重置索引
|
||
df = df.reset_index()
|
||
# 添加预测值表格
|
||
data = df.values.tolist()
|
||
col_width = 500/len(df.columns)
|
||
content.append(Graphs.draw_table(col_width,*data))
|
||
content.append(Graphs.draw_little_title('二、上一预测周期偏差率分析：'))
|
||
df = loadcsv(os.path.join(dataset,'testandpredict_groupby.csv'))
|
||
df4 = df.copy() # 计算偏差率使用
|
||
# 计算模型偏差率
|
||
#计算各列对于y列的差值百分比
|
||
df3 = pd.DataFrame() # 存储偏差率
|
||
|
||
# 删除有null的行
|
||
df4 = df4.dropna()
|
||
df3['ds'] = df4['ds']
|
||
for col in df.columns:
|
||
if col not in ['y','ds','index']:
|
||
df3[col] = round(abs(df4[col] - df4['y']) / df4['y'] * 100,2)
|
||
# 找出决定系数前五的偏差率
|
||
df3 = df3[['ds']+fivemodels_list.tolist()][-inputsize:]
|
||
# 找出上一预测区间的时间
|
||
stime = df3['ds'].iloc[0]
|
||
etime = df3['ds'].iloc[-1]
|
||
# 添加偏差率表格
|
||
fivemodels = '、'.join(eval_df['模型(Model)'].values[:5]) # 字符串形式，后面写入字符串使用
|
||
content.append(Graphs.draw_text(f'预测使用了{num_models}个模型进行训练，使用评估结果MAE前五的模型分别是 {fivemodels} ，模型上一预测区间 {stime} -- {etime}的偏差率(%)分别是：'))
|
||
# # 添加偏差率表格
|
||
df3 = df3.T
|
||
df3 = df3.reset_index()
|
||
df3 = df3.T
|
||
data = df3.values.tolist()
|
||
col_width = 500/len(df3.columns)
|
||
content.append(Graphs.draw_table(col_width,*data))
|
||
content.append(Graphs.draw_little_title('三、预测过程解析：'))
|
||
### 特征、模型、参数配置
|
||
content.append(Graphs.draw_text(f'本次预测使用了给定的28个指标（列名重复的排除后）作为特征，应用了一个专门收集时间序列的NeuralForecast库中的{num_models}个模型。'))
|
||
content.append(Graphs.draw_text(f'使用10天的数据预测未来{inputsize}天的数据。'))
|
||
content.append(Graphs.draw_little_title('指标情况：'))
|
||
content.append(Graphs.draw_text(' 指标频度包括'))
|
||
# 添加频度统计表格
|
||
pindu_df = loadcsv(os.path.join(dataset,'特征频度统计.csv'))
|
||
pindu_df.fillna('-', inplace=True)
|
||
pindu_df = pindu_df.T
|
||
pindu_df = pindu_df.reset_index()
|
||
pindu_df = pindu_df.T
|
||
data = pindu_df.values.tolist()
|
||
col_width = 500/len(pindu_df.columns)
|
||
content.append(Graphs.draw_table(col_width,*data))
|
||
content.append(Graphs.draw_text(f'从指标特征的频度信息来看，月度指标占比最高，而我们需要进行预测的指标为日度的，所以本数据集中月度和周度指标需要进行插值处理。'))
|
||
content.append(Graphs.draw_text(' 数据特征工程：'))
|
||
content.append(Graphs.draw_text('1. 数据日期排序，新日期在最后'))
|
||
content.append(Graphs.draw_text('2. 删除空列，特征数据列没有值，就删除'))
|
||
content.append(Graphs.draw_text('3. 周度、月度特征填充为日度数据，填充规则：'))
|
||
content.append(Graphs.draw_text(' -- 向后填充，举例：假设周五出现一个周度指标数据，那么在这之前的数据用上周五的数据'))
|
||
content.append(Graphs.draw_text(' -- 向前填充，举例：采集数据开始日期为2018年1月1日，那么周度数据可能是2018年1月3日，那么3日的数据向前填充，使1日2日都有数值'))
|
||
content.append(Graphs.draw_text(f'以上处理其实并不合理，但结合我们想要的结果，我们选择了这种处理方式。'))
|
||
content.append(Graphs.draw_text(f'一般来讲，指标数据的频度和预测列是一致的，我们可以考虑预测月度的目标列，不过这样的话，月度数据太少了，不足以用来训练模型。'))
|
||
|
||
### 特征工程
|
||
# 预测列分析
|
||
content.append(Graphs.draw_text(' 电碳价格自相关ACF和偏自相关PACF分析：'))
|
||
content.append(Graphs.draw_img(os.path.join(dataset,'指标数据自相关图.png')))
|
||
content.append(Graphs.draw_img(os.path.join(dataset,'指标数据偏自相关图.png')))
|
||
content.append(Graphs.draw_text(' 解读：'))
|
||
content.append(Graphs.draw_text(' 自相关函数的取值范围为 [-1, 1]。正值表示信号在不同时间点之间具有正相关性，负值表示信号具有负相关性，而 0 表示信号在不同时间点之间不相关。 '))
|
||
content.append(Graphs.draw_text(' 偏自相关函数（PACF）则是在控制了中间的滞后项影响后，特定滞后项与当前项的相关性。 '))
|
||
content.append(Graphs.draw_text(' 当前目标列表现出的 ACF 呈现出拖尾的特征，而 PACF 在1个滞后阶数后截尾，这说明目标值适合使用自回归（AR）模型 '))
|
||
content.append(Graphs.draw_text(' 数据特征可视化分析：'))
|
||
# 找出所有后缀为散点图.png的文件
|
||
import glob
|
||
scatter_files = glob.glob(os.path.join(dataset,'*散点图.png'))
|
||
for file in scatter_files:
|
||
content.append(Graphs.draw_img(file))
|
||
content.append(Graphs.draw_text(' 解读：'))
|
||
content.append(Graphs.draw_text(' 观察特征与目标列的散点图，我们可以直观的感受到特征与我们要预测的列没有明显的趋势相关，需要考虑选取的特征合理。 '))
|
||
content.append(Graphs.draw_text(' 数据特征相关性分析：'))
|
||
# 计算特征相关性
|
||
# 读取数据
|
||
from scipy.stats import spearmanr
|
||
data = loadcsv(os.path.join(dataset,'指标数据添加时间特征.csv'))
|
||
# 重命名预测列
|
||
data.rename(columns={y: 'y'}, inplace=True) # 修改
|
||
from lib.tools import dateConvert
|
||
data = dateConvert(data) # 修改
|
||
# 去掉ds列
|
||
data.drop(columns=['ds'], inplace=True)
|
||
# 创建一个空的 DataFrame 来保存相关系数
|
||
correlation_df = pd.DataFrame(columns=['Feature', 'Correlation'])
|
||
# 计算各特征与目标列的皮尔逊相关系数，并保存到新的 DataFrame 中
|
||
for col in data.columns:
|
||
if col!= 'y':
|
||
pearson_correlation = np.corrcoef(data[col], data['y'])[0, 1]
|
||
spearman_correlation, _ = spearmanr(data[col], data['y'])
|
||
new_row = {'Feature': col, 'Pearson_Correlation': round(pearson_correlation,3), 'Spearman_Correlation': round(spearman_correlation,2)}
|
||
correlation_df = correlation_df._append(new_row, ignore_index=True)
|
||
|
||
# 删除空列
|
||
correlation_df.drop('Correlation', axis=1, inplace=True)
|
||
correlation_df.dropna(inplace=True)
|
||
correlation_df.to_csv(os.path.join(dataset,'指标相关性分析.csv'), index=False)
|
||
data = correlation_df['Pearson_Correlation'].values.tolist()
|
||
# 生成 -1 到 1 的 20 个区间
|
||
bins = np.linspace(-1, 1, 21)
|
||
# 计算每个区间的统计数（这里是区间内数据的数量）
|
||
hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
|
||
#设置画布大小
|
||
plt.figure(figsize=(10, 6))
|
||
# 绘制直方图
|
||
plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
|
||
# 添加标题和坐标轴标签
|
||
plt.title('皮尔逊相关系数分布图')
|
||
plt.xlabel('区间')
|
||
plt.ylabel('统计数')
|
||
plt.savefig(os.path.join(dataset, '皮尔逊相关性系数.png'))
|
||
plt.close()
|
||
#设置画布大小
|
||
plt.figure(figsize=(10, 6))
|
||
data = correlation_df['Spearman_Correlation'].values.tolist()
|
||
# 计算每个区间的统计数（这里是区间内数据的数量）
|
||
hist_values = [np.sum((data >= bins[i]) & (data < bins[i + 1])) for i in range(len(bins) - 1)]
|
||
# 绘制直方图
|
||
plt.bar(bins[:-1], hist_values, width=(bins[1] - bins[0]))
|
||
# 添加标题和坐标轴标签
|
||
plt.title('斯皮尔曼相关系数分布图')
|
||
plt.xlabel('区间')
|
||
plt.ylabel('统计数')
|
||
plt.savefig(os.path.join(dataset, '斯皮尔曼相关性系数.png'))
|
||
plt.close()
|
||
content.append(Graphs.draw_text(f'指标相关性分析--皮尔逊相关系数：'))
|
||
# 皮尔逊正相关 不相关 负相关 的表格
|
||
content.append(Graphs.draw_img(os.path.join(dataset,'皮尔逊相关性系数.png')))
|
||
content.append(Graphs.draw_text('''皮尔逊相关系数说明：'''))
|
||
content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。'''))
|
||
content.append(Graphs.draw_text('''
|
||
相关系数为1：表示两个变量之间存在完全正向的线性关系，即当一个变量增加时，另一个变量也相应增加，且变化是完全一致的。'''))
|
||
content.append(Graphs.draw_text('''当前特征中正相关前十的有：'''))
|
||
top10 = ','.join(correlation_df.sort_values(by='Pearson_Correlation',ascending=False).head(10)['Feature'])
|
||
content.append(Graphs.draw_text(f'''{top10}'''))
|
||
content.append(Graphs.draw_text('''相关系数为-1：表示两个变量之间存在完全负向的线性关系，即当一个变量增加时，另一个变量会相应减少，且变化是完全相反的'''))
|
||
content.append(Graphs.draw_text('''当前特征中负相关前十的有：'''))
|
||
top10 = ','.join(correlation_df.sort_values(by='Pearson_Correlation',ascending=True).head(10)['Feature'])
|
||
content.append(Graphs.draw_text(f'''{top10}'''))
|
||
content.append(Graphs.draw_text('''相关系数接近0：表示两个变量之间不存在线性关系，即它们的变化不会随着对方的变化而变化。'''))
|
||
content.append(Graphs.draw_text(f'指标相关性分析--斯皮尔曼相关系数：'))
|
||
# 皮尔逊正相关 不相关 负相关 的表格
|
||
content.append(Graphs.draw_img(os.path.join(dataset,'斯皮尔曼相关性系数.png')))
|
||
content.append(Graphs.draw_text('斯皮尔曼相关系数（Spearmans rank correlation coefficient）是一种用于衡量两个变量之间的单调关系（不一定是线性关系）的统计指标。'))
|
||
content.append(Graphs.draw_text('它的计算基于变量的秩次（即变量值的排序位置）而非变量的原始值。'))
|
||
content.append(Graphs.draw_text('斯皮尔曼相关系数的取值范围在 -1 到 1 之间。'))
|
||
content.append(Graphs.draw_text('当系数为 1 时，表示两个变量之间存在完全正的单调关系；'))
|
||
content.append(Graphs.draw_text('''当前特征中正单调关系前十的有：'''))
|
||
top10 = ','.join(correlation_df.sort_values(by='Spearman_Correlation',ascending=False).head(10)['Feature'])
|
||
content.append(Graphs.draw_text(f'''{top10}'''))
|
||
content.append(Graphs.draw_text('当系数为 -1 时，表示存在完全负的单调关系；'))
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||
content.append(Graphs.draw_text('''当前特征中负单调关系前十的有：'''))
|
||
top10 = ','.join(correlation_df.sort_values(by='Spearman_Correlation',ascending=True).head(10)['Feature'])
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||
content.append(Graphs.draw_text(f'''{top10}'''))
|
||
content.append(Graphs.draw_text('当系数为 0 时，表示两个变量之间不存在单调关系。'))
|
||
content.append(Graphs.draw_text('与皮尔逊相关系数相比，斯皮尔曼相关系数对于数据中的异常值不敏感，更适用于处理非线性关系或存在极端值的数据。'))
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content.append(Graphs.draw_little_title('模型选择：'))
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||
content.append(Graphs.draw_text(f'预测使用了{num_models}个模型进行训练拟合，模型的简介如下：'))
|
||
|
||
### 读取模型简介
|
||
with open(os.path.join(dataset,'model_introduction.txt'), 'r', encoding='utf-8') as f:
|
||
for line in f:
|
||
line_split = line.strip().split('--')
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||
# if line_split[0] in fivemodels_list:
|
||
for introduction in line_split:
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||
content.append(Graphs.draw_text(introduction))
|
||
|
||
content.append(Graphs.draw_little_title('模型评估：'))
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||
content.append(Graphs.draw_text(f'通过评估指标MAE从小到大排列，前5个模型的评估详情如下：'))
|
||
df = loadcsv(os.path.join(dataset,'model_evaluation.csv'))
|
||
# 判断 df 的数值列转为float
|
||
for col in eval_df.columns:
|
||
if col not in ['模型(Model)']:
|
||
eval_df[col] = eval_df[col].astype(float)
|
||
eval_df[col] = eval_df[col].round(3)
|
||
# 筛选 fivemodels_list.tolist() 的行
|
||
eval_df = eval_df[eval_df['模型(Model)'].isin(fivemodels_list)]
|
||
# df转置
|
||
eval_df = eval_df.T
|
||
# df重置索引
|
||
eval_df = eval_df.reset_index()
|
||
eval_df = eval_df.T
|
||
# # 添加表格
|
||
data = eval_df.values.tolist()
|
||
col_width = 500/len(eval_df.columns)
|
||
content.append(Graphs.draw_table(col_width,*data))
|
||
content.append(Graphs.draw_text('评估指标释义：'))
|
||
content.append(Graphs.draw_text('1. 均方根误差(RMSE)：均方根误差是衡量预测值与实际值之间误差的一种方法，先计算预测值与真实值的差值的平方，然后对这些平方差求平均值，最后取平均值的平方根。取值越小，误差越小，预测效果越好。'))
|
||
content.append(Graphs.draw_text('2. 平均绝对误差(MAE)：平均绝对误差是衡量预测值与实际值之间误差的一种方法，对预测值与真实值之间差值的绝对值进行求和，然后除以样本数量。取值越小，误差越小，预测效果越好。'))
|
||
content.append(Graphs.draw_text('3. 平均平方误差(MSE)：平均平方误差是衡量预测值与实际值之间误差的一种方法，先计算预测值与真实值之差的平方，然后对这些平方差求平均值。取值越小，误差越小，预测效果越好。'))
|
||
content.append(Graphs.draw_text('模型拟合：'))
|
||
# 添加图片
|
||
content.append(Graphs.draw_img(os.path.join(dataset,'预测值与真实值对比图.png')))
|
||
### 生成pdf文件
|
||
doc = SimpleDocTemplate(os.path.join(dataset,reportname), pagesize=letter)
|
||
doc.build(content)
|