diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 79e24c0..dd926d0 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -2,6 +2,8 @@ + + @@ -50,7 +52,7 @@ - + @@ -60,7 +62,7 @@ - + @@ -91,11 +93,11 @@ - - + + - + @@ -104,8 +106,8 @@ - - + + @@ -180,6 +182,8 @@ timeseries_to_supervised batch_index 12 + 32 + forecast_lstm @@ -195,11 +199,11 @@ @@ -243,7 +247,7 @@ - + @@ -284,15 +288,16 @@ - + @@ -412,17 +417,6 @@ - - - - - - - - - - - @@ -433,10 +427,21 @@ + + + + + + + + + + + - - + + diff --git a/serve.py b/serve.py index 3a50801..46c1e9b 100644 --- a/serve.py +++ b/serve.py @@ -11,6 +11,17 @@ import os from sklearn.model_selection import train_test_split from model_tensorflow import train, predict import csv +from pandas import DataFrame +from pandas import Series +from pandas import concat +from pandas import read_csv +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 numpy class Config: diff --git a/train.py b/train.py index 6e73192..1959902 100644 --- a/train.py +++ b/train.py @@ -9,7 +9,6 @@ from keras.layers import Dense from keras.layers import LSTM from math import sqrt import numpy -from keras.optimizers import Adam # frame a sequence as a supervised learning problem @@ -75,21 +74,14 @@ def fit_lstm(train, batch_size2, nb_epoch, neurons): return model -# make a one-step forecast -def forecast_lstm(model, batch_size, X): - X = X.reshape(1, 1, len(X)) - yhat = model.predict(X, batch_size=batch_size) - return yhat[0, 0] - - -# run a repeated experiment -def experiment(repeats, series, seed): +# run a experiment +def experiment(series): # transform data to be stationary raw_values = series.values diff_values = difference(raw_values, 1) # transform data to be supervised learning - lag2 = 4 - supervised = timeseries_to_supervised(diff_values, lag2) + lag = 4 + supervised = timeseries_to_supervised(diff_values, lag) supervised_values = supervised.values batch_size = 32 @@ -102,59 +94,40 @@ def experiment(repeats, series, seed): # split data into train and test-sets train, test = supervised_values[0:-test_data_num], supervised_values[-test_data_num:] # transform the scale of the data - print(test) + scaler, train_scaled, test_scaled = scale(train, test) - print(test_scaled) + # run experiment error_scores = list() - for r in range(repeats): - # fit the model - t1 = train.shape[0] % batch_size - t2 = test.shape[0] % batch_size + # fit the model + t1 = train.shape[0] % batch_size + t2 = test.shape[0] % batch_size - train_trimmed = train_scaled[t1:, :] - lstm_model = fit_lstm(train_trimmed, batch_size, 30, 4) - # forecast the entire training dataset to build up state for forecasting - print(train_trimmed) - print(train_trimmed[:, 0]) - print(train_trimmed[:, :-1]) - # if seed: - # train_reshaped = train_trimmed[:, :-1].reshape(len(train_trimmed), 1, lag2) - # 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, lag2) - 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[-test_data_num:], predictions)) - print(predictions, raw_values[-test_data_num:]) - print('%d) Test RMSE: %.3f' % (r + 1, rmse)) - error_scores.append(rmse) + train_trimmed = train_scaled[t1:, :] + lstm_model = fit_lstm(train_trimmed, batch_size, 30, 4) + + # forecast the entire training dataset to build up state for forecasting + test_reshaped = test_scaled[:, 0:-1] + test_reshaped = test_reshaped.reshape(len(test_reshaped), 1, lag) + 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[-test_data_num:], predictions)) + print(predictions, raw_values[-test_data_num:]) + error_scores.append(rmse) return error_scores # load dataset series = read_csv('data.csv', header=0, index_col=0, squeeze=True) -# experiment -repeats = 1 -results = DataFrame() -# with seeding -with_seed = experiment(repeats, series, True) -results['with-seed'] = with_seed -# without seeding -# without_seed = experiment(repeats, series, False) -# results['without-seed'] = without_seed -# summarize results -print(results.describe()) -# save boxplot -# results.boxplot() + +with_seed = experiment(series)