def calculateError(actual, predict):
actual = DataFrameParser.h2oToNumpyArray(actual)
predict = DataFrameParser.h2oToNumpyArray(predict)
error = 0
for i in range(len(actual)):
d = predict[i] - actual[i]
if d > 0:
error += math.exp(d / 10.0)
elif d < 0:
error += math.exp(d / 13.0)
return error
training_columns = list(pTrain.columns)
training_columns.remove('UnitNumber')
training_columns.remove('Time')
training_columns.remove('RUL')
response_column = 'RUL'
print("OK")
model = H2OGradientBoostingEstimator(distribution='poisson', histogram_type='QuantilesGlobal', fold_assignment='auto')
#model = H2ORandomForestEstimator(ntrees=50, max_depth=20, nbins=100, seed=12345)
model.train(x=training_columns, y=response_column, training_frame=hTrain, validation_frame=hValidate)
print(model.model_performance(test_data=hTest))
predict = DataFrameParser.h2oToNumpyArray(model.predict(test_data=hTest))
actual = DataFrameParser.h2oToNumpyArray(hTest['RUL'])
Chart.residual_histogram(actual, predict)
Chart.residual_vs_estimated(actual, predict)
Chart.acutal_and_predict(actual, predict)