46 lines
1.1 KiB
Python
46 lines
1.1 KiB
Python
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import numpy as np
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import pandas as pd
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# Erro quadrático médio
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def rmse(targets, forecasts):
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return np.sqrt(np.nanmean((forecasts - targets) ** 2))
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def rmse_interval(targets, forecasts):
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fmean = [np.mean(i) for i in forecasts]
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return np.sqrt(np.nanmean((fmean - targets) ** 2))
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# Erro Percentual médio
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def mape(targets, forecasts):
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return np.mean(abs(forecasts - targets) / forecasts) * 100
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def mape_interval(targets, forecasts):
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fmean = [np.mean(i) for i in forecasts]
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return np.mean(abs(fmean - targets) / fmean) * 100
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# Sharpness - Mean size of the intervals
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def sharpness(forecasts):
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tmp = [i[1] - i[0] for i in forecasts]
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return np.mean(tmp)
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# Resolution - Standard deviation of the intervals
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def resolution(forecasts):
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shp = sharpness(forecasts)
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tmp = [abs((i[1] - i[0]) - shp) for i in forecasts]
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return np.mean(tmp)
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# Percent of
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def coverage(targets, forecasts):
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preds = []
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for i in np.arange(0, len(forecasts)):
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if targets[i] >= forecasts[i][0] and targets[i] <= forecasts[i][1]:
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preds.append(1)
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else:
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preds.append(0)
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return np.mean(preds)
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