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11
serve.py
11
serve.py
@ -11,6 +11,17 @@ import os
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from sklearn.model_selection import train_test_split
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from model_tensorflow import train, predict
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import csv
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from pandas import DataFrame
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from pandas import Series
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from pandas import concat
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from pandas import read_csv
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from sklearn.metrics import mean_squared_error
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from sklearn.preprocessing import MinMaxScaler
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from keras.models import Sequential
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from keras.layers import Dense
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from keras.layers import LSTM
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from math import sqrt
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import numpy
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class Config:
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train.py
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train.py
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from keras.layers import LSTM
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from math import sqrt
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import numpy
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from keras.optimizers import Adam
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# frame a sequence as a supervised learning problem
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@ -75,21 +74,14 @@ def fit_lstm(train, batch_size2, nb_epoch, neurons):
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return model
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# make a one-step forecast
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def forecast_lstm(model, batch_size, X):
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X = X.reshape(1, 1, len(X))
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yhat = model.predict(X, batch_size=batch_size)
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return yhat[0, 0]
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# run a repeated experiment
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def experiment(repeats, series, seed):
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# run a experiment
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def experiment(series):
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# transform data to be stationary
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raw_values = series.values
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diff_values = difference(raw_values, 1)
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# transform data to be supervised learning
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lag2 = 4
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supervised = timeseries_to_supervised(diff_values, lag2)
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lag = 4
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supervised = timeseries_to_supervised(diff_values, lag)
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supervised_values = supervised.values
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batch_size = 32
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@ -102,59 +94,40 @@ def experiment(repeats, series, seed):
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# split data into train and test-sets
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train, test = supervised_values[0:-test_data_num], supervised_values[-test_data_num:]
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# transform the scale of the data
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print(test)
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scaler, train_scaled, test_scaled = scale(train, test)
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print(test_scaled)
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# run experiment
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error_scores = list()
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for r in range(repeats):
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# fit the model
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t1 = train.shape[0] % batch_size
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t2 = test.shape[0] % batch_size
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# fit the model
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t1 = train.shape[0] % batch_size
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t2 = test.shape[0] % batch_size
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train_trimmed = train_scaled[t1:, :]
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lstm_model = fit_lstm(train_trimmed, batch_size, 30, 4)
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# forecast the entire training dataset to build up state for forecasting
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print(train_trimmed)
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print(train_trimmed[:, 0])
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print(train_trimmed[:, :-1])
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# if seed:
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# train_reshaped = train_trimmed[:, :-1].reshape(len(train_trimmed), 1, lag2)
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# lstm_model.predict(train_reshaped, batch_size=batch_size)
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# forecast test dataset
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test_reshaped = test_scaled[:, 0:-1]
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test_reshaped = test_reshaped.reshape(len(test_reshaped), 1, lag2)
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output = lstm_model.predict(test_reshaped, batch_size=batch_size)
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predictions = list()
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for i in range(len(output)):
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yhat = output[i, 0]
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X = test_scaled[i, 0:-1]
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# invert scaling
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yhat = invert_scale(scaler, X, yhat)
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# invert differencing
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yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i)
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# store forecast
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predictions.append(yhat)
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# report performance
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rmse = sqrt(mean_squared_error(raw_values[-test_data_num:], predictions))
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print(predictions, raw_values[-test_data_num:])
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print('%d) Test RMSE: %.3f' % (r + 1, rmse))
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error_scores.append(rmse)
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train_trimmed = train_scaled[t1:, :]
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lstm_model = fit_lstm(train_trimmed, batch_size, 30, 4)
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# forecast the entire training dataset to build up state for forecasting
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test_reshaped = test_scaled[:, 0:-1]
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test_reshaped = test_reshaped.reshape(len(test_reshaped), 1, lag)
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output = lstm_model.predict(test_reshaped, batch_size=batch_size)
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predictions = list()
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for i in range(len(output)):
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yhat = output[i, 0]
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X = test_scaled[i, 0:-1]
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# invert scaling
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yhat = invert_scale(scaler, X, yhat)
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# invert differencing
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yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i)
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# store forecast
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predictions.append(yhat)
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# report performance
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rmse = sqrt(mean_squared_error(raw_values[-test_data_num:], predictions))
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print(predictions, raw_values[-test_data_num:])
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error_scores.append(rmse)
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return error_scores
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# load dataset
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series = read_csv('data.csv', header=0, index_col=0, squeeze=True)
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# experiment
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repeats = 1
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results = DataFrame()
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# with seeding
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with_seed = experiment(repeats, series, True)
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results['with-seed'] = with_seed
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# without seeding
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# without_seed = experiment(repeats, series, False)
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# results['without-seed'] = without_seed
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# summarize results
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print(results.describe())
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# save boxplot
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# results.boxplot()
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with_seed = experiment(series)
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