原文鏈接

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 from keras.layers import LSTM # convert series to supervised learningfrom pandas import read_csv from datetime import datetime # load data def parse(x):return datetime.strptime(x, '%Y %m %d %H') dataset = read_csv('lg.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('pollution.csv')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 togetheragg = concat(cols, axis=1)agg.columns = names# drop rows with NaN valuesif dropnan:agg.dropna(inplace=True)return agg # load dataset dataset = read_csv('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()) # 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) # design network 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)

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