基于LSTM的单特征和多特征预测python

# coding=utf-8 from pandas import read_csv from pandas import datetime from pandas import concat from pandas import DataFrame from pandas import Series from sklearn.metrics import mean_squared_error, r2_score from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from math import sqrt from matplotlib import pyplot import numpy # 读取时间数据的格式化 def parser(x): return datetime.strptime(x, '%Y/%m/%d') # 转换成有监督数据 def timeseries_to_supervised(data, lag=1): df = DataFrame(data) columns = [df.shift(i) for i in range(1, lag + 1)] # 数据滑动一格，作为input，df原数据为output columns.append(df) df = concat(columns, axis=1) df.fillna(0, inplace=True) return df # 转换成差分数据 def difference(dataset, interval=1): diff = list() for i in range(interval, len(dataset)): value = dataset[i] - dataset[i - interval] diff.append(value) return Series(diff) # 逆差分 def inverse_difference(history, yhat, interval=1): # 历史数据，预测数据，差分间隔 return yhat + history[-interval] # 缩放 def scale(train, test): # 根据训练数据建立缩放器 scaler = MinMaxScaler(feature_range=(-1, 1)) scaler = scaler.fit(train) # 转换train data train = train.reshape(train.shape[0], train.shape[1]) train_scaled = scaler.transform(train) # 转换test data test = test.reshape(test.shape[0], test.shape[1]) test_scaled = scaler.transform(test) return scaler, train_scaled, test_scaled # 逆缩放 def invert_scale(scaler, X, value): new_row = [x for x in X] + [value] array = numpy.array(new_row) array = array.reshape(1, len(array)) inverted = scaler.inverse_transform(array) return inverted[0, -1] # fit LSTM来训练数据 def fit_lstm(train, batch_size, nb_epoch, neurons): X, y = train[:, 0:-1], train[:, -1] X = X.reshape(X.shape[0], 1, X.shape[1]) model = Sequential() # 添加LSTM层 model.add(LSTM(neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True)) model.add(Dense(1)) # 输出层1个node # 编译，损失函数mse+优化算法adam model.compile(loss='mean_squared_error', optimizer='adam') for i in range(nb_epoch): # 按照batch_size，一次读取batch_size个数据 model.fit(X, y, epochs=1, batch_size=batch_size, verbose=0, shuffle=False) model.reset_states() print("当前计算次数："+str(i)) return model # 1步长预测 def forcast_lstm(model, batch_size, X): X = X.reshape(1, 1, len(X)) yhat = model.predict(X, batch_size=batch_size) return yhat[0, 0] # 加载数据 series = read_csv('E:/bear1/1_1v_tezheng/liutezheng/pca.csv', header=None, index_col=0, squeeze=True) # 让数据变成稳定的 raw_values = series.values diff_values = difference(raw_values, 1) # 转换成差分数据 # 把稳定的数据变成有监督数据 supervised = timeseries_to_supervised(diff_values, 1) supervised_values = supervised.values # 数据拆分：训练数据、测试数据，前24行是训练集，后12行是测试集 train, test = supervised_values[0:2803], supervised_values[0:2803] # 数据缩放 scaler, train_scaled, test_scaled = scale(train, test) # fit 模型 lstm_model = fit_lstm(train_scaled, 1, 100, 4) # 训练数据，batch_size，epoche次数, 神经元个数 # 预测 train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1) # 训练数据集转换为可输入的矩阵 lstm_model.predict(train_reshaped, batch_size=1) # 用模型对训练数据矩阵进行预测 # 测试数据的前向验证，实验发现，如果训练次数很少的话，模型回简单的把数据后移，以昨天的数据作为今天的预测值，当训练次数足够多的时候 # 才会体现出来训练结果 predictions = list() P = [] E = [] for i in range(len(test_scaled)): # 根据测试数据进行预测，取测试数据的一个数值作为输入，计算出下一个预测值，以此类推 # 1步长预测 X, y = test_scaled[i, 0:-1], test_scaled[i, -1] yhat = forcast_lstm(lstm_model, 1, X) # 逆缩放 yhat = invert_scale(scaler, X, yhat) # 逆差分 yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i) predictions.append(yhat) expected = raw_values[len(train)] P.append(yhat) E.append(expected) print('Moth=%d, Predicted=%f, Expected=%f' % (i + 1, yhat, expected)) numpy.savetxt('E:/LSTM/2_6v_p_pca.txt', P) numpy.savetxt('E:/LSTM/2_6v_y_pca.txt', E) # 性能报告 rmse = sqrt(mean_squared_error(raw_values[0:2802], predictions)) print('Test RMSE:%.3f' % rmse) r2 = r2_score(raw_values[0:2802], predictions) print('Test R2:%.3f' % r2) y_data = read_csv('E:/bear2/2_6v_tezheng/liutezheng/pca_guiyihua.txt', header=None) y_predict = read_csv('E:/LSTM/2_6v_p_pca.txt', header=None) yy = numpy.sum(y_data)/(len(y_data)) R2 = 1-(numpy.sum((y_data-y_predict)**2))/(numpy.sum((yy-y_data)**2)) print(R2) # 绘图 pyplot.plot(raw_values[0:2802]) pyplot.plot(predictions) pyplot.show()