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multiple_step_LSTM
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# -*- coding: utf-8 -*-
"""
Created on Thu Apr 4 16:07:46 2019
@author: hoangd
"""
import pandas as pd
import numpy as np
import matplotlib as plt
from pandas import DataFrame
from pandas import Series
from pandas import concat
from pandas import read_csv
from pandas import datetime
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from math import sqrt
from matplotlib import pyplot
from numpy import array
#de début et fin des cycles 2,3,4,5,6
cycle_2_start_date=pd.Timestamp('2008-12-08')
cycle_2_end_date=pd.Timestamp('2011-06-02')
cycle_2_date_range = pd.date_range(start=cycle_2_start_date, end=cycle_2_end_date)
cycle_3_start_date=pd.Timestamp('2011-06-03')
cycle_3_end_date=pd.Timestamp('2013-03-18')
cycle_3_date_range=pd.date_range(start=cycle_3_start_date, end=cycle_3_end_date)
cycle_4_start_date=pd.Timestamp('2013-03-19')
cycle_4_end_date=pd.Timestamp('2015-09-17')
cycle_4_date_range=pd.date_range(start=cycle_4_start_date, end=cycle_4_end_date)
cycle_5_start_date=pd.Timestamp('2015-09-20')
cycle_5_end_date=pd.Timestamp('2018-01-31')
cycle_5_date_range = pd.date_range(start=cycle_5_start_date, end=cycle_5_end_date)
cycle_6_start_date=pd.Timestamp('2018-02-20')
cycle_6_end_date=pd.Timestamp('2018-05-21')
cycle_6_date_range = pd.date_range(start=cycle_6_start_date, end=cycle_6_end_date)
# =============================================================================
# # date-time parsing function for loading the dataset
# def parser(x):
# return datetime.strptime('190'+x, '%Y-%m')
#
# =============================================================================
# convert time series into supervised learning problem
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
# create a differenced series
def difference(dataset, interval=1):
diff = list()
for i in range(interval, len(dataset)):
value = dataset[i] - dataset[i - interval]
diff.append(value)
return Series(diff)
# transform series into train and test sets for supervised learning
def prepare_data(series, n_test, n_lag, n_seq):
# extract raw values
raw_values = series.values
# transform data to be stationary
diff_series = difference(raw_values, 1)
diff_values = diff_series.values
diff_values = diff_values.reshape(len(diff_values), 1)
# rescale values to -1, 1
scaler = MinMaxScaler(feature_range=(-1, 1))
scaled_values = scaler.fit_transform(diff_values)
scaled_values = scaled_values.reshape(len(scaled_values), 1)
# transform into supervised learning problem X, y
supervised = series_to_supervised(scaled_values, n_lag, n_seq)
supervised_values = supervised.values
# split into train and test sets
train, test = supervised_values[0:-n_test], supervised_values[-n_test:]
return scaler, train, test
# fit an LSTM network to training data
def fit_lstm(train, n_lag, n_seq, n_batch, nb_epoch, n_neurons):
# reshape training into [samples, timesteps, features]
X, y = train[:, 0:n_lag], train[:, n_lag:]
X = X.reshape(X.shape[0], 1, X.shape[1])
# design network
model = Sequential()
model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X.shape[1], X.shape[2]), stateful=True))
model.add(Dense(y.shape[1]))
model.compile(loss='mean_squared_error', optimizer='adam')
# fit network
for i in range(nb_epoch):
model.fit(X, y, epochs=1, batch_size=n_batch, verbose=0, shuffle=False)
model.reset_states()
return model
# make one forecast with an LSTM,
def forecast_lstm(model, X, n_batch):
# reshape input pattern to [samples, timesteps, features]
X = X.reshape(1, 1, len(X))
# make forecast
forecast = model.predict(X, batch_size=n_batch)
# convert to array
return [x for x in forecast[0, :]]
# evaluate the persistence model
def make_forecasts(model, n_batch, train, test, n_lag, n_seq):
forecasts = list()
for i in range(len(test)):
X, y = test[i, 0:n_lag], test[i, n_lag:]
# make forecast
forecast = forecast_lstm(model, X, n_batch)
# store the forecast
forecasts.append(forecast)
return forecasts
# invert differenced forecast
def inverse_difference(last_ob, forecast):
# invert first forecast
inverted = list()
inverted.append(forecast[0] + last_ob)
# propagate difference forecast using inverted first value
for i in range(1, len(forecast)):
inverted.append(forecast[i] + inverted[i-1])
return inverted
# inverse data transform on forecasts
def inverse_transform(series, forecasts, scaler, n_test):
inverted = list()
for i in range(len(forecasts)):
# create array from forecast
forecast = array(forecasts[i])
forecast = forecast.reshape(1, len(forecast))
# invert scaling
inv_scale = scaler.inverse_transform(forecast)
inv_scale = inv_scale[0, :]
# invert differencing
index = len(series) - n_test + i - 1
last_ob = series.values[index]
inv_diff = inverse_difference(last_ob, inv_scale)
# store
inverted.append(inv_diff)
return inverted
# evaluate the RMSE for each forecast time step
def evaluate_forecasts(test, forecasts, n_lag, n_seq):
for i in range(n_seq):
actual = [row[i] for row in test]
predicted = [forecast[i] for forecast in forecasts]
rmse = sqrt(mean_squared_error(actual, predicted))
print('t+%d RMSE: %f' % ((i+1), rmse))
# plot the forecasts in the context of the original dataset
def plot_forecasts(series, forecasts, n_test):
# plot the entire dataset in blue
pyplot.plot(series.values)
# plot the forecasts in red
for i in range(len(forecasts)):
off_s = len(series) - n_test + i - 1
off_e = off_s + len(forecasts[i]) + 1
xaxis = [x for x in range(off_s, off_e)]
yaxis = [series.values[off_s]] + forecasts[i]
pyplot.plot(xaxis, yaxis, color='red')
# show the plot
#pyplot.subplot(figsize = (24,24))
pyplot.show()
# load dataset
#series = read_csv('C:/Users/hoangd/Downloads/sales-of-shampoo-over-a-three-ye.csv', header=0, nrows=36, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)
series = read_csv('C:/Users/hoangd/Desktop/Projet_Kiwi-master/visualition/imputed_data.csv', header = [0,1], index_col = 0, parse_dates=True)
series = series['PROCESS_CORR_k0']
# Mask Cycle
mask_Cycle2 = (series.index >= cycle_2_start_date) & (series.index <= cycle_2_end_date)
mask_Cycle3 = (series.index >= cycle_3_start_date) & (series.index <= cycle_3_end_date)
mask_Cycle4 = (series.index >= cycle_4_start_date) & (series.index <= cycle_4_end_date)
mask_Cycle5 = (series.index >= cycle_5_start_date) & (series.index <= cycle_5_end_date)
mask_Cycle6 = (series.index >= cycle_6_start_date) & (series.index <= cycle_6_end_date)
series_Cycle2=series.loc[mask_Cycle2].copy()
series_Cycle3=series.loc[mask_Cycle3].copy()
series_Cycle4=series.loc[mask_Cycle4].copy()
series_Cycle5=series.loc[mask_Cycle5].copy()
series_Cycle6=series.loc[mask_Cycle6].copy()
#train5 = series_Cycle5[:int(0.7*len(series_Cycle5))]
#test5 = series_Cycle5[int(0.7*len(series_Cycle5)):]
#test5.rename({'Unnamed: 38_level_1':'True Test'},axis=1)
# configure
n_lag = 70
#n_seq = len(series_Cycle5) - int(0.7*len(series_Cycle5))
n_test = 1
n_epochs = 100
n_batch = 1
n_neurons = 240
for series_Cycle in [#series_Cycle2
#series_Cycle3
series_Cycle4
#series_Cycle5
]:
train_Cycle = series_Cycle[:int(0.7*len(series_Cycle))]
test_Cycle = series_Cycle[int(0.7*len(series_Cycle)):]
test_Cycle = test_Cycle.rename({'Unnamed: 38_level_1':'True Test'},axis=1)
n_seq = len(series_Cycle) - int(0.7*len(series_Cycle))
# prepare data
scaler, train, test = prepare_data(train_Cycle, n_test, n_lag, n_seq)
# fit model
model = fit_lstm(train, n_lag, n_seq, n_batch, n_epochs, n_neurons)
# make forecasts
forecasts = make_forecasts(model, n_batch, train, test, n_lag, n_seq)
# inverse transform forecasts and test
forecasts = inverse_transform(train_Cycle, forecasts, scaler, n_test+2)
#forecasts = pd.DataFrame(forecasts, index = test5.index,columns=['Predicted Test'])
actual = [row[n_lag:] for row in test]
actual = inverse_transform(train_Cycle, actual, scaler, n_test+2)
# evaluate forecasts
evaluate_forecasts(actual, forecasts, n_lag, n_seq)
# plot forecasts
plot_forecasts(train_Cycle, forecasts, n_test+2)
forecasts = np.ravel(forecasts)
forecasts = pd.DataFrame(forecasts, index = test_Cycle.index,columns=['Predicted Test'])
pd.concat([test_Cycle,forecasts],axis=1).plot(figsize=(24,24))
print("The RMSE score:", np.sqrt(mean_squared_error(test_Cycle, forecasts)))