if not np.isclose(noiseStrengthTM[-1], float(noise), rtol=0.1):
print "Warning: Estimated noise strength is different from the given noise"
groundTruth = np.roll(data['value'], -5)
tmTruth = np.roll(predData_TM['value'], -5)
predDataTMFiveStep = np.array(predData_TM['prediction5'])
nrmse = computeNRMSE(tmTruth, predDataTMFiveStep, startFrom)
altMAPE = computeAltMAPE(tmTruth, predDataTMFiveStep, startFrom)
mapeGroundTruth = computeAltMAPE(tmTruth, groundTruth, startFrom)
tm_prediction = np.load('./result/' + dataSet + 'TMprediction.npy')
tmTruth = np.load('./result/' + dataSet + 'TMtruth.npy')
negLL = computeLikelihood(tm_prediction, tmTruth, encoder)
negLL = np.nanmean(negLL[startFrom:])
nrmseTM = pd.concat([nrmseTM, pd.DataFrame([nrmse], columns=['TM'])])
mapeTM = pd.concat([
mapeTM,
pd.DataFrame(np.reshape(np.array([altMAPE, mapeGroundTruth]),
newshape=(1, 2)),
columns=['TM', 'GT'])
])
negLLTM = pd.concat([negLLTM, pd.DataFrame([negLL], columns=['TM'])])
lstmExptDir = 'results/nyc_taxi_experiment_continuous_likelihood_noise/'
noiseList = ['0.0', '0.020', '0.040', '0.060', '0.080', '0.10']
from plot import computeLikelihood, plotAccuracy
bucketIndex2 = []
negLL = []
minProb = 0.0001
for i in xrange(len(truth)):
bucketIndex2.append(np.where(encoder.encode(truth[i]))[0])
outOfBucketProb = 1 - sum(predictions[i, :])
prob = predictions[i, bucketIndex2[i]]
if prob == 0:
prob = outOfBucketProb
if prob < minProb:
prob = minProb
negLL.append(-np.log(prob))
negLL = computeLikelihood(predictions, truth, encoder)
negLL[:5000] = np.nan
x = range(len(negLL))
plt.figure()
plotAccuracy((negLL, x), truth, window=480, errorType='negLL')
np.save('./result/' + dataSet + classifierType + 'TMprediction.npy',
predictions)
np.save('./result/' + dataSet + classifierType + 'TMtruth.npy', truth)
plt.figure()
activeCellNumAvg = movingAverage(activeCellNum, 100)
plt.plot(np.array(activeCellNumAvg) / tm.numberOfCells())
plt.xlabel('data records')
plt.ylabel('sparsity')
plt.xlim([0, 5000])
MAPECLA = MAPE(actualData[nTrain:nTrain + nTest],
shiftedPredDataCLA[nTrain:nTrain + nTest])
MAPENN = MAPE(actualData[nTrain:nTrain + nTest],
shiftedPredDataNN[nTrain:nTrain + nTest])
print "NRMSE on test data, CLA: ", NRMSECLA
print "NRMSE on test data, NN: ", NRMSENN
# calculate neg-likelihood
encoder = NupicScalarEncoder(w=1, minval=0, maxval=40000, n=22, forced=True)
truth = np.roll(actualData, -5)
predictions = np.transpose(likelihoodsVecAll)
negLLCLA = computeLikelihood(predictions, truth, encoder)
negLLCLA[:5904] = np.nan
predictions = np.transpose(likelihoodsVecAllNN)
negLLNN = computeLikelihood(predictions, truth, encoder)
negLLNN[:5904] = np.nan
# save predicted distribution for likelihood calculation
np.save('./result/' + datasetName + 'TMprediction.npy', predictions)
np.save('./result/' + datasetName + 'TMtruth.npy', truth)
plt.figure()
plotAccuracy((negLLCLA, range(len(negLLCLA))), truth, window=480, errorType='negLL')
plotAccuracy((negLLNN, range(len(negLLNN))), truth, window=480, errorType='negLL')
window, xaxis=xaxisDate, label='continuous LSTM-3000')
(negLLLSTM6000, expResultLSTM6000negLL) = \
plotLSTMresult('results/nyc_taxi_experiment_continuous_likelihood/learning_window6001.0/',
window, xaxis=xaxisDate, label='continuous LSTM-6000')
dataSet = 'nyc_taxi'
tmPredictionLL = np.load('./result/' + dataSet + 'TMprediction.npy')
tmTruth = np.load('./result/' + dataSet + 'TMtruth.npy')
encoder = NupicScalarEncoder(w=1,
minval=0,
maxval=40000,
n=22,
forced=True)
negLL = computeLikelihood(tmPredictionLL, tmTruth, encoder)
negLL[:skipTrain] = None
negLLTM = plotAccuracy((negLL, xaxisDate),
tmTruth,
window=window,
errorType='negLL',
label='TM')
plt.legend()
plt.savefig(figPath + 'continuous_likelihood.pdf')
startFrom = skipTrain
altMAPELSTM6000 = computeAltMAPE(expResultLSTM6000.truth,
expResultLSTM6000.predictions, startFrom)
altMAPELSTM3000 = computeAltMAPE(expResultLSTM3000.truth,
expResultLSTM3000.predictions, startFrom)
altMAPELSTM1000 = computeAltMAPE(expResultLSTM1000.truth,
truth = np.roll(actual_data, -5)
from nupic.encoders.scalar import ScalarEncoder as NupicScalarEncoder
encoder = NupicScalarEncoder(w=1, minval=0, maxval=40000, n=22, forced=True)
from plot import computeLikelihood, plotAccuracy
bucketIndex2 = []
negLL = []
minProb = 0.0001
for i in xrange(len(truth)):
bucketIndex2.append(np.where(encoder.encode(truth[i]))[0])
outOfBucketProb = 1 - sum(predictions[i,:])
prob = predictions[i, bucketIndex2[i]]
if prob == 0:
prob = outOfBucketProb
if prob < minProb:
prob = minProb
negLL.append( -np.log(prob))
negLL = computeLikelihood(predictions, truth, encoder)
negLL[:5000] = np.nan
x = range(len(negLL))
plt.figure()
plotAccuracy((negLL, x), truth, window=480, errorType='negLL')
np.save('./result/'+dataSet+classifierType+'TMprediction.npy', predictions)
np.save('./result/'+dataSet+classifierType+'TMtruth.npy', truth)
predDataTMfiveStep = np.array(predDataTM['prediction5'])
iteration = predDataTM.index
tmPredPerturbTruth = np.roll(predDataTM['value'], -5)
tmPredPerturb = np.array(predDataTM['prediction5'])
filePath = './prediction/' + dataSet + '_esn_pred.csv'
predDataESN = pd.read_csv(filePath,
header=0,
skiprows=[1, 2],
names=['step', 'value', 'prediction5'])
esnPredPerturbTruth = np.roll(predDataESN['value'], -5)
esnPredPerturb = np.array(predDataESN['prediction5'])
negLLTMPerturb = computeLikelihood(tmPredictionPerturb, tmTruthPerturb,
encoder)
negLLTMPerturb[:6000] = None
nrmseTMPerturb = computeSquareDeviation(tmPredPerturb, tmPredPerturbTruth)
mapeTMPerturb = np.abs(tmPredPerturb - tmPredPerturbTruth)
mapeESNPerturb = np.abs(esnPredPerturb - esnPredPerturbTruth)
expResultPerturb1000 = ExperimentResult(
'results/nyc_taxi_experiment_continuous_perturb/learning_window' +
str(1001.0) + '/')
expResultPerturb3000 = ExperimentResult(
'results/nyc_taxi_experiment_continuous_perturb/learning_window' +
str(3001.0) + '/')
expResultPerturb6000 = ExperimentResult(
'results/nyc_taxi_experiment_continuous_perturb/learning_window' +
window, xaxis=xaxisDate, label='continuous LSTM-3000')
(negLLLSTM6000, expResultLSTM6000negLL) = plotLSTMresult(
'results/nyc_taxi_experiment_continuous_likelihood/learning_window6001.0/',
window, xaxis=xaxisDate, label='continuous LSTM-6000')
(negLLLSTMOnline, expResultLSTMOnlinenegLL) = plotLSTMresult(
'results/nyc_taxi_experiment_continuous_likelihood_online/learning_window100.0/',
window, xaxis=xaxisDate, label='continuous LSTM-6000')
dataSet = 'nyc_taxi'
tmPredictionLL = np.load('./result/'+dataSet+'TMprediction.npy')
tmTruth = np.load('./result/' + dataSet + 'TMtruth.npy')
encoder = NupicScalarEncoder(w=1, minval=0, maxval=40000, n=22, forced=True)
negLL = computeLikelihood(tmPredictionLL, tmTruth, encoder)
negLL[:skipTrain] = None
negLLTM = plotAccuracy((negLL, xaxisDate), tmTruth,
window=window, errorType='negLL', label='TM')
plt.legend()
plt.savefig(figPath + 'continuous_likelihood.pdf')
startFrom = skipTrain
altMAPELSTM6000 = computeAltMAPE(expResultLSTM6000.truth, expResultLSTM6000.predictions, startFrom)
altMAPELSTM3000 = computeAltMAPE(expResultLSTM3000.truth, expResultLSTM3000.predictions, startFrom)
altMAPELSTM1000 = computeAltMAPE(expResultLSTM1000.truth, expResultLSTM1000.predictions, startFrom)
altMAPELSTMonline = computeAltMAPE(expResultLSTMonline.truth, expResultLSTMonline.predictions, startFrom)
altMAPETM = computeAltMAPE(tmTruth, tmPrediction, startFrom)
altMAPEARIMA = computeAltMAPE(arimaTruth, arimaPrediction, startFrom)
altMAPEESN = computeAltMAPE(esnTruth, esnPrediction, startFrom)
predData_TM_five_step = np.array(predData_TM['prediction5'])
iteration = predData_TM.index
tm_pred_perturb_truth = np.roll(predData_TM['value'], -5)
tm_pred_perturb = np.array(predData_TM['prediction5'])
filePath = './prediction/' + dataSet + '_esn_pred.csv'
predDataESN = pd.read_csv(filePath,
header=0,
skiprows=[1, 2],
names=['step', 'value', 'prediction5'])
esnPredPerturbTruth = np.roll(predDataESN['value'], -5)
esnPredPerturb = np.array(predDataESN['prediction5'])
negLL_tm_perturb = computeLikelihood(tm_prediction_perturb, tm_truth_perturb,
encoder)
negLL_tm_perturb[:6000] = None
nrmse_tm_perturb = computeSquareDeviation(tm_pred_perturb,
tm_pred_perturb_truth)
mape_tm_perturb = np.abs(tm_pred_perturb - tm_pred_perturb_truth)
mape_esn_perturb = np.abs(esnPredPerturb - esnPredPerturbTruth)
plt.figure()
plotAccuracy((negLL_LSTM3000_perturb, xaxis_datetime),
truth_LSTM3000_perturb,
window=window,
errorType='negLL',
label='LSTM3000',
train=expResult_perturb.train)
# plotAccuracy((negLL_LSTM3000_perturb_baseline, xaxis_datetime), truth_LSTM3000_perturb, window=window, errorType='negLL', label='TM')
window, xaxis=xaxis_datetime, label='continuous LSTM-1000')
(negLL_LSTM3000, expResult_LSTM3000_negLL) = \
plotLSTMresult('results/nyc_taxi_experiment_continuous_likelihood/learning_window3001.0/',
window, xaxis=xaxis_datetime, label='continuous LSTM-3000')
(negLL_LSTM6000, expResult_LSTM6000_negLL) = \
plotLSTMresult('results/nyc_taxi_experiment_continuous_likelihood/learning_window6001.0/',
window, xaxis=xaxis_datetime, label='continuous LSTM-6000')
dataSet = 'nyc_taxi'
tm_prediction = np.load('./result/'+dataSet+'TMprediction.npy')
tm_truth = np.load('./result/'+dataSet+'TMtruth.npy')
from nupic.encoders.scalar import ScalarEncoder as NupicScalarEncoder
encoder = NupicScalarEncoder(w=1, minval=0, maxval=40000, n=22, forced=True)
negLL = computeLikelihood(tm_prediction, tm_truth, encoder)
negLL[:6000] = None
negLL_TM = \
plotAccuracy((negLL, xaxis_datetime), tm_truth, window=window, errorType='negLL', label='TM')
plt.legend()
plt.savefig(figPath + 'continuous_likelihood.pdf')
### Figure 4: Continuous LSTM with different window size using the likelihood metric
fig = plt.figure()
# negLL_StaticLSTM = \
# plotLSTMresult('results/nyc_taxi_experiment_one_shot_likelihood/',
# window, xaxis=xaxis_datetime, label='static LSTM ')
plt.clf()
predDataTMfiveStep = np.array(predDataTM['prediction5'])
iteration = predDataTM.index
tmPredPerturbTruth = np.roll(predDataTM['value'], -5)
tmPredPerturb = np.array(predDataTM['prediction5'])
filePath = './prediction/' + dataSet + '_esn_pred.csv'
predDataESN = pd.read_csv(filePath, header=0, skiprows=[1, 2],
names=['step', 'value', 'prediction5'])
esnPredPerturbTruth = np.roll(predDataESN['value'], -5)
esnPredPerturb = np.array(predDataESN['prediction5'])
negLLTMPerturb = computeLikelihood(tmPredictionPerturb, tmTruthPerturb, encoder)
negLLTMPerturb[:6000] = None
nrmseTMPerturb = computeSquareDeviation(tmPredPerturb, tmPredPerturbTruth)
mapeTMPerturb = np.abs(tmPredPerturb - tmPredPerturbTruth)
mapeESNPerturb = np.abs(esnPredPerturb - esnPredPerturbTruth)
expResultPerturb1000 = ExperimentResult(
'results/nyc_taxi_experiment_continuous_perturb/learning_window'+str(1001.0)+'/')
expResultPerturb3000 = ExperimentResult(
'results/nyc_taxi_experiment_continuous_perturb/learning_window'+str(3001.0)+'/')
expResultPerturb6000 = ExperimentResult(
'results/nyc_taxi_experiment_continuous_perturb/learning_window'+str(6001.0)+'/')
dataSet = 'nyc_taxi_perturb'
tm_prediction_perturb = np.load('./result/'+dataSet+'TMprediction.npy')
tm_truth_perturb = np.load('./result/'+dataSet+'TMtruth.npy')
from nupic.encoders.scalar import ScalarEncoder as NupicScalarEncoder
encoder = NupicScalarEncoder(w=1, minval=0, maxval=40000, n=22, forced=True)
filePath = './prediction/' + dataSet + '_TM_pred.csv'
predData_TM = pd.read_csv(filePath, header=0, skiprows=[1, 2], names=['step', 'value', 'prediction5'])
predData_TM_five_step = np.array(predData_TM['prediction5'])
iteration = predData_TM.index
tm_pred_perturb_truth = np.roll(predData_TM['value'], -5)
tm_pred_perturb = np.array(predData_TM['prediction5'])
negLL_tm_perturb = computeLikelihood(tm_prediction_perturb, tm_truth_perturb, encoder)
negLL_tm_perturb[:6000] = None
nrmse_tm_perturb = computeSquareDeviation(tm_pred_perturb, tm_pred_perturb_truth)
mape_tm_perturb = np.abs(tm_pred_perturb - tm_pred_perturb_truth)
plt.figure()
window = 480
plotAccuracy((negLL_LSTM3000_perturb, xaxis_datetime), truth_LSTM3000_perturb,
window=window, errorType='negLL', label='LSTM3000', train=expResult_perturb.train)
# plotAccuracy((negLL_LSTM3000_perturb_baseline, xaxis_datetime), truth_LSTM3000_perturb, window=window, errorType='negLL', label='TM')
plotAccuracy((negLL_LSTM6000_perturb, xaxis_datetime), truth_LSTM6000_perturb, window=window, errorType='negLL', label='LSTM6000')
# plotAccuracy((negLL_LSTM6000_perturb_baseline, xaxis_datetime), truth_LSTM3000_perturb, window=window, errorType='negLL', label='TM')
plotAccuracy((negLL_tm_perturb, xaxis_datetime), tm_truth_perturb, window=window, errorType='negLL', label='TM')
plt.axvline(xaxis_datetime[13152], color='black', linestyle='--')
