# ### Figure 1: Continuous vs Batch LSTM # fig = plt.figure() # # NRMSE_StaticLSTM = plotLSTMresult('results/nyc_taxi_experiment_one_shot/', # # window, xaxis=xaxis_datetime, label='static lstm') # (nrmseLSTM6000, expResultLSTM6000) = \ # plotLSTMresult('results/nyc_taxi_experiment_continuous/learning_window6001.0/', # window, xaxis=xaxisDate, label='continuous LSTM-6000') # plt.legend() # plt.savefig(figPath + 'continuousVsbatch.pdf') # ### Figure 2: Continuous LSTM with different window size fig = plt.figure() (nrmseLSTM1000, expResultLSTM1000) = \ plotLSTMresult('results/nyc_taxi_experiment_continuous/learning_window1001.0/', window, xaxis=xaxisDate, label='continuous LSTM-1000') (nrmseLSTM3000, expResultLSTM3000) = \ plotLSTMresult('results/nyc_taxi_experiment_continuous/learning_window3001.0/', window, xaxis=xaxisDate, label='continuous LSTM-3000') (nrmseLSTM6000, expResultLSTM6000) = \ plotLSTMresult('results/nyc_taxi_experiment_continuous/learning_window6001.0/', window, xaxis=xaxisDate, label='continuous LSTM-6000') dataSet = 'nyc_taxi' filePath = './prediction/' + dataSet + '_TM_pred.csv' (tmTruth, tmPrediction) = loadExperimentResult('./prediction/' + dataSet + '_TM_pred.csv')
# ### Figure 1: Continuous vs Batch LSTM # fig = plt.figure() # # NRMSE_StaticLSTM = plotLSTMresult('results/nyc_taxi_experiment_one_shot/', # # window, xaxis=xaxis_datetime, label='static lstm') # (nrmseLSTM6000, expResultLSTM6000) = \ # plotLSTMresult('results/nyc_taxi_experiment_continuous/learning_window6001.0/', # window, xaxis=xaxisDate, label='continuous LSTM-6000') # plt.legend() # plt.savefig(figPath + 'continuousVsbatch.pdf') # ### Figure 2: Continuous LSTM with different window size fig = plt.figure() (nrmseLSTM1000, expResultLSTM1000) = plotLSTMresult( 'results/nyc_taxi_experiment_continuous/learning_window1001.0/', window, xaxis=xaxisDate, label='continuous LSTM-1000') (nrmseLSTM3000, expResultLSTM3000) = plotLSTMresult( 'results/nyc_taxi_experiment_continuous/learning_window3001.0/', window, xaxis=xaxisDate, label='continuous LSTM-3000') (nrmseLSTM6000, expResultLSTM6000) = plotLSTMresult( 'results/nyc_taxi_experiment_continuous/learning_window6001.0/', window, xaxis=xaxisDate, label='continuous LSTM-6000') (nrmseLSTMonline, expResultLSTMonline) = plotLSTMresult( 'results/nyc_taxi_experiment_continuous_online/learning_window100.0/', window, xaxis=xaxisDate, label='continuous LSTM-online') dataSet = 'nyc_taxi'
# # ### Figure 1: Continuous vs Batch LSTM # fig = plt.figure() # # NRMSE_StaticLSTM = plotLSTMresult('results/nyc_taxi_experiment_one_shot/', # # window, xaxis=xaxis_datetime, label='static lstm') # (nrmseLSTM6000, expResultLSTM6000) = \ # plotLSTMresult('results/nyc_taxi_experiment_continuous/learning_window6001.0/', # window, xaxis=xaxisDate, label='continuous LSTM-6000') # plt.legend() # plt.savefig(figPath + 'continuousVsbatch.pdf') ### Figure 2: Continuous LSTM with different window size fig = plt.figure() (nrmseLSTM1000, expResultLSTM1000) = plotLSTMresult( 'results/nyc_taxi_experiment_continuous/learning_window1001.0/', window, xaxis=xaxisDate, label='continuous LSTM-1000') # (nrmseLSTM3000, expResultLSTM3000) = plotLSTMresult( # 'results/nyc_taxi_experiment_continuous/learning_window3001.0/', # window, xaxis=xaxisDate, label='continuous LSTM-3000') # # (nrmseLSTM6000, expResultLSTM6000) = plotLSTMresult( # 'results/nyc_taxi_experiment_continuous/learning_window6001.0/', # window, xaxis=xaxisDate, label='continuous LSTM-6000') # # (nrmseLSTMonline, expResultLSTMonline) = plotLSTMresult( # 'results/nyc_taxi_experiment_continuous_online/learning_window100.0/', # window, xaxis=xaxisDate, label='continuous LSTM-online')
data = pd.read_csv(filePath, header=0, skiprows=[1, 2], names=['datetime', 'value', 'timeofday', 'dayofweek']) xaxis_datetime = pd.to_datetime(data['datetime']) def computeAltMAPE(truth, prediction, startFrom=0): return np.nanmean(np.abs(truth[startFrom:] - prediction[startFrom:]))/np.nanmean(np.abs(truth[startFrom:])) expResult = ExperimentResult('results/nyc_taxi_experiment_continuous/learning_window6001.0/') ### Figure 1: Continuous vs Batch LSTM fig = plt.figure() # NRMSE_StaticLSTM = plotLSTMresult('results/nyc_taxi_experiment_one_shot/', # window, xaxis=xaxis_datetime, label='static lstm') (NRMSE_LSTM6000, expResult_LSTM6000) = \ plotLSTMresult('results/nyc_taxi_experiment_continuous/learning_window6001.0/', window, xaxis=xaxis_datetime, label='continuous LSTM-6000') plt.legend() plt.savefig(figPath + 'continuousVsbatch.pdf') ### Figure 2: Continuous LSTM with different window size fig = plt.figure() (NRMSE_LSTM1000, expResult_LSTM1000) = \ plotLSTMresult('results/nyc_taxi_experiment_continuous/learning_window1001.0/', window, xaxis=xaxis_datetime, label='continuous LSTM-1000') (NRMSE_LSTM3000, expResult_LSTM3000) = \ plotLSTMresult('results/nyc_taxi_experiment_continuous/learning_window3001.0/', window, xaxis=xaxis_datetime, label='continuous LSTM-3000')