参考：
https://blog.csdn.net/u012735708/article/details/82769711
https://zybuluo.com/hanbingtao/note/581764
http://blog.sina.com.cn/s/blog_afc8730e0102xup1.html
https://blog.csdn.net/qq_30638831/article/details/80060045
执行代码：
import pandas as pd
from datetime import datetime
from matplotlib import pyplot
from sklearn.preprocessing import LabelEncoder,MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from numpy import concatenate
from math import sqrt
# load data
def parse(x):
return datetime.strptime(x, ‘%Y %m %d %H‘)
def read_raw():
dataset = pd.read_csv(‘C:/Users/cf_pc/Documents/jupyter/data/PRSA_data_2010.1.1-2014.12.31.csv‘, parse_dates = [[‘year‘, ‘month‘, ‘day‘, ‘hour‘]], index_col=0, date_parser=parse)
dataset.drop(‘No‘, axis=1, inplace=True)
# manually specify column names
dataset.columns = [‘pollution‘, ‘dew‘, ‘temp‘, ‘press‘, ‘wnd_dir‘, ‘wnd_spd‘, ‘snow‘, ‘rain‘]
dataset.index.name = ‘date‘
# mark all NA values with 0
dataset[‘pollution‘].fillna(0, inplace=True)
# drop the first 24 hours
dataset = dataset[24:]
# summarize first 5 rows
print(dataset.head(5))
# save to file
dataset.to_csv(‘C:/Users/cf_pc/Documents/jupyter/data/pollution.csv‘)
def drow_pollution():
dataset = pd.read_csv(‘C:/Users/cf_pc/Documents/jupyter/data/pollution.csv‘, header=0, index_col=0)
values = dataset.values
# specify columns to plot
groups = [0, 1, 2, 3, 5, 6, 7]
i = 1
# plot each column
pyplot.figure(figsize=(10,10))
for group in groups:
pyplot.subplot(len(groups), 1, i)
pyplot.plot(values[:, group])
pyplot.title(dataset.columns[group], y=0.5, loc=‘right‘)
i += 1
pyplot.show()
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
# convert series to supervised learning
n_vars = 1 if type(data) is list else data.shape[1]
df = pd.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 = pd.concat(cols, axis=1)
agg.columns = names
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg
def cs_to_sl():
# load dataset
dataset = pd.read_csv(‘C:/Users/cf_pc/Documents/jupyter/data/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[[9,10,11,12,13,14,15]], axis=1, inplace=True)
print(reframed.head())
return reframed,scaler
def train_test(reframed):
# split into train and test sets
values = reframed.values
n_train_hours = 365 * 24
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]))
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
return train_X,train_y,test_X,test_y
def fit_network(train_X,train_y,test_X,test_y,scaler):
model = Sequential()
model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(loss=‘mae‘, optimizer=‘adam‘)
# fit network
history = model.fit(train_X, train_y, epochs=50, batch_size=72, 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
inv_y = scaler.inverse_transform(test_X)
inv_y = inv_y[:,0]
# calculate RMSE
rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
print(‘Test RMSE: %.3f‘ % rmse)
if __name__ == ‘__main__‘:
drow_pollution()
reframed,scaler = cs_to_sl()
train_X,train_y,test_X,test_y = train_test(reframed)
fit_network(train_X,train_y,test_X,test_y,scaler)
返回信息：
var1(t-1) var2(t-1) var3(t-1) var4(t-1) var5(t-1) var6(t-1) 1 0.129779 0.352941 0.245902 0.527273 0.666667 0.002290 2 0.148893 0.367647 0.245902 0.527273 0.666667 0.003811 3 0.159960 0.426471 0.229508 0.545454 0.666667 0.005332 4 0.182093 0.485294 0.229508 0.563637 0.666667 0.008391 5 0.138833 0.485294 0.229508 0.563637 0.666667 0.009912 var7(t-1) var8(t-1) var1(t) 1 0.000000 0.0 0.148893 2 0.000000 0.0 0.159960 3 0.000000 0.0 0.182093 4 0.037037 0.0 0.138833 5 0.074074 0.0 0.109658 (8760, 1, 8) (8760,) (35039, 1, 8) (35039,) WARNING:tensorflow:From C:\3rd\Anaconda2\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. WARNING:tensorflow:From C:\3rd\Anaconda2\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. Train on 8760 samples, validate on 35039 samples Epoch 1/50 - 2s - loss: 0.0578 - val_loss: 0.0562 Epoch 2/50 - 1s - loss: 0.0413 - val_loss: 0.0563 Epoch 3/50 - 1s - loss: 0.0254 - val_loss: 0.0454 Epoch 4/50 - 1s - loss: 0.0179 - val_loss: 0.0388 Epoch 5/50 - 1s - loss: 0.0158 - val_loss: 0.0237 Epoch 6/50 - 1s - loss: 0.0149 - val_loss: 0.0175 Epoch 7/50 - 1s - loss: 0.0148 - val_loss: 0.0163 Epoch 8/50 - 1s - loss: 0.0147 - val_loss: 0.0160 Epoch 9/50 - 1s - loss: 0.0148 - val_loss: 0.0155 Epoch 10/50 - 1s - loss: 0.0147 - val_loss: 0.0151 Epoch 11/50 - 1s - loss: 0.0146 - val_loss: 0.0148 Epoch 12/50 - 1s - loss: 0.0147 - val_loss: 0.0145 Epoch 13/50 - 1s - loss: 0.0146 - val_loss: 0.0143 Epoch 14/50 - 1s - loss: 0.0146 - val_loss: 0.0143 Epoch 15/50 - 1s - loss: 0.0145 - val_loss: 0.0141 Epoch 16/50 - 1s - loss: 0.0145 - val_loss: 0.0144 Epoch 17/50 - 1s - loss: 0.0147 - val_loss: 0.0140 Epoch 18/50 - 1s - loss: 0.0145 - val_loss: 0.0140 Epoch 19/50 - 1s - loss: 0.0145 - val_loss: 0.0138 Epoch 20/50 - 1s - loss: 0.0145 - val_loss: 0.0138 Epoch 21/50 - 1s - loss: 0.0144 - val_loss: 0.0138 Epoch 22/50 - 1s - loss: 0.0145 - val_loss: 0.0138 Epoch 23/50 - 1s - loss: 0.0146 - val_loss: 0.0137 Epoch 24/50 - 1s - loss: 0.0144 - val_loss: 0.0137 Epoch 25/50 - 1s - loss: 0.0144 - val_loss: 0.0137 Epoch 26/50 - 1s - loss: 0.0144 - val_loss: 0.0136 Epoch 27/50 - 1s - loss: 0.0144 - val_loss: 0.0136 Epoch 28/50 - 1s - loss: 0.0144 - val_loss: 0.0136 Epoch 29/50 - 1s - loss: 0.0145 - val_loss: 0.0137 Epoch 30/50 - 1s - loss: 0.0145 - val_loss: 0.0136 Epoch 31/50 - 1s - loss: 0.0144 - val_loss: 0.0137 Epoch 32/50 - 1s - loss: 0.0144 - val_loss: 0.0136 Epoch 33/50 - 1s - loss: 0.0144 - val_loss: 0.0136 Epoch 34/50 - 1s - loss: 0.0145 - val_loss: 0.0136 Epoch 35/50 - 1s - loss: 0.0144 - val_loss: 0.0135 Epoch 36/50 - 1s - loss: 0.0144 - val_loss: 0.0135 Epoch 37/50 - 1s - loss: 0.0144 - val_loss: 0.0135 Epoch 38/50 - 1s - loss: 0.0144 - val_loss: 0.0135 Epoch 39/50 - 1s - loss: 0.0144 - val_loss: 0.0135 Epoch 40/50 - 1s - loss: 0.0144 - val_loss: 0.0135 Epoch 41/50 - 1s - loss: 0.0143 - val_loss: 0.0135 Epoch 42/50 - 1s - loss: 0.0144 - val_loss: 0.0135 Epoch 43/50 - 1s - loss: 0.0144 - val_loss: 0.0135 Epoch 44/50 - 1s - loss: 0.0144 - val_loss: 0.0135 Epoch 45/50 - 1s - loss: 0.0144 - val_loss: 0.0137 Epoch 46/50 - 1s - loss: 0.0144 - val_loss: 0.0136 Epoch 47/50 - 1s - loss: 0.0143 - val_loss: 0.0135 Epoch 48/50 - 1s - loss: 0.0144 - val_loss: 0.0136 Epoch 49/50 - 1s - loss: 0.0143 - val_loss: 0.0135 Epoch 50/50 - 1s - loss: 0.0144 - val_loss: 0.0134
Test RMSE: 4.401
参考：
https://www.cnblogs.com/tianrunzhi/p/7825671.html
https://www.cnblogs.com/king-lps/p/7846414.html
https://www.cnblogs.com/datablog/p/6127000.html
https://www.cnblogs.com/charlotte77/p/5622325.html
https://www.cnblogs.com/bawu/p/7701810.html
原文地址：https://www.cnblogs.com/ratels/p/11186636.html
时间： 2024-11-08 23:06:07