6-完整的LSTM案例

import pandas as pd
import datetime
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
import math
import matplotlib.pyplot as plt
import numpy as np

# 读取时间数据的格式化
def parser(x):
    return datetime.datetime.strptime(x, '%Y/%m/%d')

# 转换成有监督数据
def timeseries_to_supervised(data, lag=1):
    df = pd.DataFrame(data)
    tmp = [df.shift(i) for i in range(1, lag+1)]
    tmp.append(df)
    df = pd.concat(tmp, axis=1)
    df.fillna(0, inplace=True)
    return df

# 转换成差分数据
def difference(dataset, interval=1):
    diff = []
    for i in range(interval, len(dataset)):
        value = dataset[i] - dataset[i - interval]
        diff.append(value)
    return pd.Series(diff)

# 逆差分
def inverse_difference(history, yhat, interval=1):
    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.fit(train)
    train_scaled = scaler.transform(train)
    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 = np.array(new_row).reshape(1, len(new_row))
    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()
    model.add(LSTM(neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True))
    model.add(Dense(1))
    print(model.summary())
    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：", i+1)
    return model

# 1步长预测
def forecast_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
def parser(x):
    return datetime.datetime.strptime(x, '%Y/%m/%d')

ser = pd.read_csv('../LSTM系列/LSTM单变量1/data_set/shampoo-sales.csv', 
                header=0, parse_dates=[0], index_col=0, date_parser=parser).squeeze('columns')

# 稳定
raw_values = ser.values
diff_values = difference(raw_values, 1)

# 有监督
supervised = timeseries_to_supervised(diff_values, 1)
supervised_values = supervised.values

# 拆分训练集、测试集合
train, test = supervised_values[:-12], supervised_values[-12:]

# 缩放
scaler, train_scaled, test_scaled = scale(train, test)

# fit模型
lstm_model = fit_lstm(train_scaled, 1, 100, 4)

# 预测
train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)  # 训练数据转换为可输入的矩阵
lstm_model.predict(train_reshaped, batch_size=1)
predictions = []
for i in range(len(test_scaled)):
    # 1步长预测
    X, y = test_scaled[i, 0:-1], test_scaled[i, -1]
    yhat = forecast_lstm(lstm_model, 1, X)
    yhat = invert_scale(scaler, X, yhat)
    yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i)
    predictions.append(yhat)
    expected = raw_values[len(train) + i + 1]
    print('Moth=%d, Predicted=%f, Expected=%f' % (i + 1, yhat, expected))

# 性能报告
rmse = math.sqrt(mean_squared_error(raw_values[-12:], predictions))
print('Test RMSE:%.3f' % rmse)

# 绘图
plt.plot(raw_values[-12:])
plt.plot(predictions)
plt.show()