255 lines
9.3 KiB
Python
255 lines
9.3 KiB
Python
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.metrics import mean_squared_error
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from sklearn.model_selection import train_test_split
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import LSTM, Dense
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import datetime
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import matplotlib.pyplot as plt
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import pandas as pd
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import os
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import random
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import string
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import time
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import base64
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from hashlib import sha256
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from hmac import HMAC
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import requests
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import csv
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from numpy import concatenate
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from math import sqrt
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def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
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'''
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将时间序列数据转换为监督学习数据
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:param data:数据集
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:param n_in: 输入序列长度，默认为1
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:param n_out:输出序列长度，默认为1
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:param dropnan:
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:return:
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'''
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n_vars = 1 if type(data) is list else data.shape[1]
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df = pd.DataFrame(data)
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cols, names = list(), list()
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# input sequence (t-n, ... t-1)
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# 将3组输入数据依次向下移动3，2，1行，将数据加入cols列表（技巧：(n_in, 0, -1)中的-1指倒序循环，步长为1）
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for i in range(n_in, 0, -1):
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cols.append(df.shift(i))
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names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]
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# forecast sequence (t, t+1, ... t+n)
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# 将一组输出数据加入cols列表（技巧：其中i=0）
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for i in range(0, n_out):
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cols.append(df.shift(-i))
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if i == 0:
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names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]
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else:
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names += [('var%d(t+%d)' % (j + 1, i)) for j in range(n_vars)]
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# cols列表(list)中现在有四块经过下移后的数据(即：df(-3),df(-2),df(-1),df)，将四块数据按列 并排合并
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agg = pd.concat(cols, axis=1)
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# 给合并后的数据添加列名
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agg.columns = names
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# 删除NaN值列
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if dropnan:
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agg.dropna(inplace=True)
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return agg
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def createXY(dataset,n_past):
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dataX = []
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dataY = []
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print(dataset.shape[1])
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for i in range(n_past, len(dataset)):
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dataX.append(dataset[i - n_past:i, 0:dataset.shape[1]])
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dataY.append(dataset[i,0])
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return np.array(dataX),np.array(dataY)
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def ex_Lstm_M(df,n_days=14,out_days=7,is_debug=False,datasetpath=''):
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# dataset = pd.read_csv('brentpricepredict.csv',encoding='utf-8')
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dataset = df.copy()
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dataset.set_index('ds', inplace=True)
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values = dataset.values
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if is_debug:
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# values = values[-1000:]
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pass
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# 标准化/放缩 特征值在（0,1）之间
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scaler = MinMaxScaler(feature_range=(0, 1))
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scaled = scaler.fit_transform(values)
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# 用14天数据预测七天数据
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n_features = dataset.shape[1]
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# 构造一个14->7的监督学习型数据
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reframed = series_to_supervised(scaled, n_days, out_days)
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# 切分数据集
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values = reframed.values
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# 用80%的数据来训练，20%的数据来测试
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n_train = int(len(dataset) * 0.8)
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train = values[:n_train, :]
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test = values[n_train:, :]
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# 切分输入输出
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n_obs = n_days * n_features
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# 倒数第19列作为Y
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train_X, train_y = train[:, :n_obs], train[:, -n_features]
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test_X, test_y = test[:, :n_obs], test[:, -n_features]
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# 将数据转换为3D输入，timesteps=14，14条数据预测7条 [samples, timesteps, features]
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train_X = train_X.reshape((train_X.shape[0], n_days, n_features))
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test_X = test_X.reshape((test_X.shape[0], n_days, n_features))
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print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
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# 设计网络
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model = Sequential()
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model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
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model.add(Dense(1))
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model.compile(loss='mae', optimizer='adam')
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# 拟合网络
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history = model.fit(train_X, train_y, epochs=100, batch_size=72, validation_data=(test_X, test_y), verbose=2,
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shuffle=False)
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# 执行预测
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yhat = model.predict(test_X)
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# 将数据格式化成 n行 * 24列
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test_X = test_X.reshape((test_X.shape[0], n_days * n_features))
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# 将预测列据和后7列数据拼接，因后续逆缩放时，数据形状要符合 n行*20列 的要求
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inv_yhat = concatenate((yhat, test_X[:, -n_features+1:]), axis=1)
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# 对拼接好的数据进行逆缩放
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inv_yhat = scaler.inverse_transform(inv_yhat)
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inv_yhat = inv_yhat[:, 0]
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print(inv_yhat)
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test_y = test_y.reshape((len(test_y), 1))
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# 将真实列据和后7列数据拼接，因后续逆缩放时，数据形状要符合 n行*20列 的要求
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inv_y = concatenate((test_y, test_X[:, -n_features+1:]), axis=1)
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# 对拼接好的数据进行逆缩放
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inv_y = scaler.inverse_transform(inv_y)
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inv_y = inv_y[:, 0]
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# 计算RMSE
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rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
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print('Test RMSE: %.3f' % rmse)
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# 可视化结果
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# 保留n天历史数据
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n = len(inv_y) - 7
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# 设置要可视化的值
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time_axis_data = np.array(range(n))
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time_axis_future = np.array(range(n + 7))
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inv_y = inv_y[-n:]
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inv_yhat = inv_yhat[-n-7:]
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# Plot data and future predictions
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fig, ax = plt.subplots(2, 1, gridspec_kw={'height_ratios': [5, 4]})
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# 设置画布大小
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fig.set_size_inches(6, 6)
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# 第一个子图画历史价格和预测价格
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ax[0].plot(time_axis_data, inv_y, label='历史价格')
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ax[0].plot(time_axis_future, inv_yhat, linestyle='dashed', label='预测价格')
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ax[0].set_xlabel('时间')
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ax[0].set_ylabel('价格')
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ax[0].legend()
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# 设置标题
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ax[0].set_title('布伦特_多价格预测')
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# 设置y轴范围
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ax[0].set_ylim(50, 120)
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# 第二个子图画表格，展示预测价格
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ax[1].axis('off')
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table_data = [[f"Day {i + 1}", "{:.2f}".format(val)] for i, val in enumerate(inv_yhat[-7:])]
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table = ax[1].table(cellText=table_data, colLabels=['Day', 'Prediction'], loc='center')
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# 设置表格内容居中
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table.auto_set_font_size(True)
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# 保存图片
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filename = os.path.basename(__file__).split('.')[0]
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plt.savefig(os.path.join(datasetpath,filename + '_M.png'))
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# plt.show()
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def ex_Lstm(df,input_seq_len=50, output_seq_len=7,is_debug=False,dataset=''):
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# 将日期列转换为 datetime 类型（如果尚未转换）
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df['ds'] = pd.to_datetime(df['ds'])
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# 分离出数值列（排除日期列）
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numeric_df = df.select_dtypes(include=['int64', 'float64'])
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prices = df
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# prices = df
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# print(data1)
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# Remove any NaN values
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df = df.drop('ds', axis=1)
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prices = np.array(df, dtype=float) # convert to NumPy array of floats
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prices = prices[~np.isnan(prices)]
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if is_debug:
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prices = prices[-300:]
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# Prepare input sequences
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inputs = []
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for i in range(len(prices)-input_seq_len-output_seq_len+1):
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inputs.append(prices[i:i+input_seq_len])
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inputs = np.array(inputs)
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# Prepare output sequences
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outputs = []
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for i in range(input_seq_len, len(prices)-output_seq_len+1):
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outputs.append(prices[i:i+output_seq_len])
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outputs = np.array(outputs)
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# Split dataset into training and testing sets
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X_train, X_test, y_train, y_test = train_test_split(inputs, outputs, test_size=0.2)
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# Normalize data
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scaler_in = MinMaxScaler()
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X_train = scaler_in.fit_transform(X_train)
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X_test = scaler_in.transform(X_test)
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scaler_out = MinMaxScaler()
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y_train = scaler_out.fit_transform(y_train)
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y_test = scaler_out.transform(y_test)
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# Define LSTM model
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model = Sequential()
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model.add(LSTM(128, activation='relu', input_shape=(input_seq_len, 1)))
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model.add(Dense(output_seq_len))
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model.compile(optimizer='adam', loss='mse')
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# Train LSTM model
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model.fit(X_train.reshape(-1, input_seq_len, 1), y_train, epochs=100, batch_size=64, validation_data=(X_test.reshape(-1, input_seq_len, 1), y_test))
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# Evaluate LSTM model
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mse = model.evaluate(X_test.reshape(-1, input_seq_len, 1), y_test)
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# Make future predictions
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future_inputs = np.array([prices[-input_seq_len:]])
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future_inputs = scaler_in.transform(future_inputs)
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future_predictions = model.predict(future_inputs.reshape(-1, input_seq_len, 1))
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future_predictions = scaler_out.inverse_transform(future_predictions)[0]
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# Print results
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print("MSE: ", mse)
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print("Future predictions: ", future_predictions)
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# Generate time axis for data and future predictions
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time_axis_data = np.arange(len(prices))
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time_axis_future = np.arange(len(prices), len(prices) + len(future_predictions))
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# Concatenate time axis and data
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time_axis = np.concatenate((time_axis_data, time_axis_future))
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# Concatenate data and future predictions
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data_and_predictions = np.concatenate((prices, future_predictions))
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# Plot data and future predictions
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fig, ax = plt.subplots(2, 1, gridspec_kw={'height_ratios': [3, 1]})
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# First subplot: Data and Future Predictions
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ax[0].plot(time_axis, data_and_predictions, label='Data and Future Predictions')
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ax[0].plot(time_axis_future, future_predictions, linestyle='dashed', label='Future Predictions')
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ax[0].set_xlabel('Time')
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ax[0].set_ylabel('Price')
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ax[0].legend()
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# Second subplot: Table for Future Predictions
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ax[1].axis('off')
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table_data = [[f"Day {i+1}", "{:.2f}".format(val)] for i, val in enumerate(future_predictions)]
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table = ax[1].table(cellText=table_data, colLabels=['Day', 'Prediction'], loc='center')
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plt.savefig(os.path.join(dataset,'lstmmodels.png'))
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# plt.show()
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