LSTM model.py

from math import sqrt
from numpy import concatenate
from matplotlib import pyplot
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import LSTM
import numpy as np

# convert series to supervised learning
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
    n_vars = 1 if type(data) is list else data.shape[1]
    df = DataFrame(data)
    cols, names = list(), list()
    # input sequence (t-n, ... t-1)
    for i in range(n_in, 0, -1):
        cols.append(df.shift(i))
        names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]
    # forecast sequence (t, t+1, ... t+n)
    for i in range(0, n_out):
        cols.append(df.shift(-i))
        if i == 0:
            names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]
        else:
            names += [('var%d(t+%d)' % (j + 1, i)) for j in range(n_vars)]
    # put it all together
    agg = concat(cols, axis=1)
    agg.columns = names
    # drop rows with NaN values
    if dropnan:
        agg.dropna(inplace=True)
    return agg


# load dataset
dataset = read_csv('C:/Users/ZHUHO/AppData/Local/Programs/Python/Python36/pollution.csv', header=0, index_col=0)
values = dataset.values
# integer encode direction
encoder = LabelEncoder()
values[:, 4] = encoder.fit_transform(values[:, 4])
# ensure all data is float
values = values.astype('float32')
# normalize features
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
# frame as supervised learning
reframed = series_to_supervised(scaled, 1, 1)
# drop columns we don't want to predict
reframed.drop(reframed.columns[[8, 9, 10, 11, 12, 13]], axis=1, inplace=True)
print(reframed.head())

#把数据集分成训练集和测试集，然后把训练集和测试集分别分成输入和输出变量
#最后，把输入（X）重构为 LSTM 预期的 3D 格式，即 [样本，时间步，特征]
# split into train and test sets
values = reframed.values
n_train_hours = 25000
train = values[:n_train_hours, :]
test = values[n_train_hours:, :]

# split into input and outputs
train_X, train_y = train[:, :-1], train[:, -1]
test_X, test_y = test[:, :-1], test[:, -1]

# reshape input to be 3D [samples, timesteps, features]
train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))

# design network
model = Sequential()
model.add(LSTM(2, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
#添加一个全连接层，把LSTM的输出转换成想要的输出，做维度变换。
model.add(Activation('sigmoid'))
model.compile(loss='mae', optimizer='Adam')

# fit network
#当一个完整的数据集通过了神经网络一次并且返回了一次，这个过程称为一个 epoch
#在不能将数据一次性通过神经网络的时候，就需要将数据集分成几个 batch
#一个 batch 中的样本总数是batch_size
history = model.fit(train_X, train_y, epochs=2, batch_size=128, validation_data=(test_X, test_y), verbose=2,
                    shuffle=False)
# plot history
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='test')
pyplot.legend()
pyplot.show()

# make a prediction
yhat = model.predict(test_X)
test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))
# invert scaling for forecast
inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:, 0]
# invert scaling for actual
test_y = test_y.reshape((len(test_y), 1))
inv_y = concatenate((test_y, test_X[:, 1:]), axis=1)
inv_y = scaler.inverse_transform(inv_y)
inv_y = inv_y[:, 0]

# calculate RMSE
rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
print('Test RMSE: %.3f' % rmse)

A=inv_y[:-1]
B=inv_yhat[:-1]
A = A.astype(np.float64)
B = B.astype(np.float64)
print(A.dtype)
print(B.dtype)
np.save('inv_y.npy',A)
np.save('inv_yhat.npy',B)