Merge branch 'master' of source.ijs.si:pkraljnovak/DM_course

9_neural_nets-4-time_series_Metod.py

-from pandas import read_csv
-from matplotlib import pyplot
-from sklearn.preprocessing import LabelEncoder
-from sklearn.preprocessing import MinMaxScaler
-from pandas import DataFrame
-from pandas import concat
-from keras import Sequential
-from keras.layers import Dense
-from keras.layers import LSTM
-from sklearn.metrics import mean_squared_error
-from numpy import concatenate
-from math import sqrt
-
-#load and plot dataset
-#file = r'd:\tmp\technical_analysis_test_eth_usd_bitstamp_900_2018-12-01T00-00-00_2019-02-15T00-00-00_5minfuture.csv'
-file = r"d:\tmp\technical_tmp.csv "
-
-# load dataset
-dataset = read_csv(file , header=0, index_col=0)
-values = dataset.values
-# specify columns to plot
-groups = range(len(dataset.columns))
-i = 1
-# plot each column
-pyplot.figure()
-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()
-
-#---------------------------------------------------
-# convert series to supervised learning
-
-print (dataset.head)
-
-# load dataset
-#dataset = read_csv('.\Datasets\pollution_clean.csv', header=0, index_col=0)
-#values = dataset.values
-# ensure all data is float
-values = values.astype('float32')
-# normalize features
-scaler = MinMaxScaler(feature_range=(0, 1))
-
-
-scaled = scaler.fit_transform(values)
-print ("shape ", scaled.shape)
-
-
-# drop columns we don't want to predict
-
-
-# split into train and test sets
-#values = reframed.values
-n_train_hours = 5000
-train = values[:n_train_hours, :]
-print ("tainn ", train.shape)
-test = values[n_train_hours:, :]
-print ("test ", test.shape)
-# 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()
-
-print ("make prediction")
-# make a prediction
-yhat = model.predict(test_X)
-print ("yhat", yhat.shape)
-test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))
-print ("test_X", test_X.shape)
-
-# invert scaling for actual
-#test_y = test_y.reshape((len(test_y), 1))
-inv_y = concatenate((test_y, test_X[:, 1:]), axis=1)
-print ("inv_y", inv_y.shape)
-inv_y = scaler.inverse_transform(inv_y)
-inv_y = inv_y[:,0]
-
-
-
-# invert scaling for forecast
-inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
-print ("inv_yhat conc", inv_yhat.shape)
-inv_yhat = scaler.inverse_transform(inv_yhat)
-inv_yhat = inv_yhat[:,0]
-
-
-# calculate RMSE
-rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
-print('Test RMSE: %.3f' % rmse)
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