Applied Data Science Notebook in Python for Beginners to Professionals¶
Data Science Project – A Guide to Deep Learning (LSTM) Hyperparameters tuning with Keras for Time Series Forecasting in Python¶
Machine Learning for Beginners - A Guide to Deep Learning (LSTM) Hyperparameters tuning with Keras for Time Series Forecasting in Python¶
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# Suppress warnings in Jupyter Notebooks
import warnings
warnings.filterwarnings("ignore")
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
In this notebook, we will learn how to do Deep Learning (LSTM) Hyperparameters tuning with Keras for Time Series Forecasting in Python.
Python Codes¶
Load the dataset¶
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from pandas import read_csv
from matplotlib import pyplot
series = read_csv('shampoo.csv', header=0, index_col=0)
print(series.head())
series.plot(figsize = (15,8))
pyplot.show()
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Deep Learning (LSTM) model hyperparameters tuning with Keras for Time Series Forecasting in Python¶
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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
import matplotlib
# be able to save images on server
matplotlib.use('Agg')
from matplotlib import pyplot
import numpy
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# date-time parsing function for loading the dataset
def parser(x):
return datetime.strptime('190'+x, '%Y-%m')
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# frame a sequence as a supervised learning problem
def timeseries_to_supervised(data, lag=1):
df = DataFrame(data)
columns = [df.shift(i) for i in range(1, lag+1)]
columns.append(df)
df = concat(columns, axis=1)
df = df.drop(0)
return df
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# 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)
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# invert differenced value
def inverse_difference(history, yhat, interval=1):
return yhat + history[-interval]
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# scale train and test data to [-1, 1]
def scale(train, test):
# fit scaler
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler = scaler.fit(train)
# transform train
train = train.reshape(train.shape[0], train.shape[1])
train_scaled = scaler.transform(train)
# transform test
test = test.reshape(test.shape[0], test.shape[1])
test_scaled = scaler.transform(test)
return scaler, train_scaled, test_scaled
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# inverse scaling for a forecasted value
def invert_scale(scaler, X, yhat):
new_row = [x for x in X] + [yhat]
array = numpy.array(new_row)
array = array.reshape(1, len(array))
inverted = scaler.inverse_transform(array)
return inverted[0, -1]
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# fit an LSTM network to training data
def fit_lstm(train, batch_size, nb_epoch, neurons):
X, y = train[:, 0:-1], train[:, -1]
X = X.reshape(X.shape[0], 1, X.shape[1])
model = Sequential()
model.add(LSTM(neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
for i in range(nb_epoch):
model.fit(X, y, epochs=1, batch_size=batch_size, verbose=0, shuffle=False)
model.reset_states()
return model
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# run a repeated experiment
def experiment(repeats, series, epochs):
# transform data to be stationary
raw_values = series.values
diff_values = difference(raw_values, 1)
# transform data to be supervised learning
supervised = timeseries_to_supervised(diff_values, 1)
supervised_values = supervised.values
# split data into train and test-sets
train, test = supervised_values[0:-12], supervised_values[-12:]
# transform the scale of the data
scaler, train_scaled, test_scaled = scale(train, test)
# run experiment
error_scores = list()
print(); print("Number of epochs used as hyperparameters: ", epochs)
for r in range(repeats):
# fit the model
batch_size = 4
train_trimmed = train_scaled[2:, :]
lstm_model = fit_lstm(train_trimmed, batch_size, epochs, 1)
# forecast the entire training dataset to build up state for forecasting
train_reshaped = train_trimmed[:, 0].reshape(len(train_trimmed), 1, 1)
lstm_model.predict(train_reshaped, batch_size=batch_size)
# forecast test dataset
test_reshaped = test_scaled[:,0:-1]
test_reshaped = test_reshaped.reshape(len(test_reshaped), 1, 1)
output = lstm_model.predict(test_reshaped, batch_size=batch_size)
predictions = list()
for i in range(len(output)):
yhat = output[i,0]
X = test_scaled[i, 0:-1]
# invert scaling
yhat = invert_scale(scaler, X, yhat)
# invert differencing
yhat = inverse_difference(raw_values, yhat, len(test_scaled)+1-i)
# store forecast
predictions.append(yhat)
# report performance
rmse = sqrt(mean_squared_error(raw_values[-12:], predictions))
print('%d) Test RMSE: %.3f' % (r+1, rmse))
error_scores.append(rmse)
return error_scores
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# load dataset
series = read_csv('shampoo.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)
# experiment
repeats = 30
results = DataFrame()
# vary training epochs
epochs = [500, 1000, 2000, 4000, 6000]
for e in epochs:
results[str(e)] = experiment(repeats, series, e)
# summarize results
print(results.describe())
# save boxplot
results.boxplot(figsize=(14,10))
pyplot.show()
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Summary¶
In this coding recipe, we discussed how to do Deep Learning (LSTM) Hyperparameters tuning with Keras for Time Series Forecasting in Python
Specifically, we have learned the followings:
- How to do Deep Learning (LSTM) Hyperparameters tuning with Keras for Time Series Forecasting in Python.
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