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@ -47,8 +47,8 @@ def find_best(dataframe, criteria, ascending):
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def point_dataframe_synthetic_columns():
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return ["Model", "Order", "Scheme", "Partitions", "Size", "RMSEAVG", "RMSESTD", "SMAPEAVG", "SMAPESTD", "UAVG",
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"USTD", "TIMEAVG", "TIMESTD"]
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return ["Model", "Order", "Scheme", "Partitions", "Size", "Steps", "Method", "RMSEAVG", "RMSESTD",
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"SMAPEAVG", "SMAPESTD", "UAVG","USTD", "TIMEAVG", "TIMESTD"]
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def point_dataframe_analytic_columns(experiments):
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@ -58,11 +58,13 @@ def point_dataframe_analytic_columns(experiments):
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columns.insert(2, "Scheme")
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columns.insert(3, "Partitions")
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columns.insert(4, "Size")
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columns.insert(5, "Measure")
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columns.insert(5, "Steps")
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columns.insert(6, "Method")
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columns.insert(7, "Measure")
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return columns
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def save_dataframe_point(experiments, file, objs, rmse, save, synthetic, smape, times, u):
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def save_dataframe_point(experiments, file, objs, rmse, save, synthetic, smape, times, u, steps, method):
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"""
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Create a dataframe to store the benchmark results
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:param experiments: dictionary with the execution results
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@ -90,6 +92,8 @@ def save_dataframe_point(experiments, file, objs, rmse, save, synthetic, smape,
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mod.append(mfts.partitioner.name)
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mod.append(mfts.partitioner.partitions)
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mod.append(len(mfts))
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mod.append(steps)
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mod.append(method)
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else:
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mod.append('-')
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mod.append('-')
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@ -122,17 +126,17 @@ def save_dataframe_point(experiments, file, objs, rmse, save, synthetic, smape,
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s = '-'
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p = '-'
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l = '-'
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print([n, o, s, p, l])
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tmp = [n, o, s, p, l, 'RMSE']
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print([n, o, s, p, l, steps, method])
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tmp = [n, o, s, p, l, steps, method, 'RMSE']
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tmp.extend(rmse[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'SMAPE']
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tmp = [n, o, s, p, l, steps, method, 'SMAPE']
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tmp.extend(smape[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'U']
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tmp = [n, o, s, p, l, steps, method, 'U']
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tmp.extend(u[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'TIME']
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tmp = [n, o, s, p, l, steps, method, 'TIME']
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tmp.extend(times[k])
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ret.append(deepcopy(tmp))
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except Exception as ex:
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@ -401,7 +405,8 @@ def check_ignore_list(b, ignore):
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return flag
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def save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, synthetic, times, q05, q25, q75, q95):
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def save_dataframe_interval(coverage, experiments, file, objs, resolution, save, sharpness, synthetic, times,
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q05, q25, q75, q95, steps, method):
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ret = []
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if synthetic:
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for k in sorted(objs.keys()):
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@ -409,14 +414,19 @@ def save_dataframe_interval(coverage, experiments, file, objs, resolution, save,
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mfts = objs[k]
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mod.append(mfts.shortname)
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mod.append(mfts.order)
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l = len(mfts)
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if not mfts.benchmark_only:
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mod.append(mfts.partitioner.name)
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mod.append(mfts.partitioner.partitions)
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l = len(mfts)
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mod.append(l)
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mod.append(steps)
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mod.append(method)
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else:
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mod.append('-')
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mod.append('-')
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l = '-'
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mod.append('-')
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mod.append(steps)
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mod.append(method)
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mod.append(round(np.nanmean(sharpness[k]), 2))
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mod.append(round(np.nanstd(sharpness[k]), 2))
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mod.append(round(np.nanmean(resolution[k]), 2))
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@ -452,28 +462,28 @@ def save_dataframe_interval(coverage, experiments, file, objs, resolution, save,
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p = '-'
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l = '-'
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tmp = [n, o, s, p, l, 'Sharpness']
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tmp = [n, o, s, p, l, steps, method, 'Sharpness']
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tmp.extend(sharpness[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'Resolution']
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tmp = [n, o, s, p, l, steps, method, 'Resolution']
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tmp.extend(resolution[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'Coverage']
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tmp = [n, o, s, p, l, steps, method, 'Coverage']
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tmp.extend(coverage[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'TIME']
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tmp = [n, o, s, p, l, steps, method, 'TIME']
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tmp.extend(times[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'Q05']
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tmp = [n, o, s, p, l, steps, method, 'Q05']
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tmp.extend(q05[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'Q25']
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tmp = [n, o, s, p, l, steps, method, 'Q25']
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tmp.extend(q25[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'Q75']
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tmp = [n, o, s, p, l, steps, method, 'Q75']
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tmp.extend(q75[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'Q95']
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tmp = [n, o, s, p, l, steps, method, 'Q95']
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tmp.extend(q95[k])
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ret.append(deepcopy(tmp))
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except Exception as ex:
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@ -492,18 +502,19 @@ def interval_dataframe_analytic_columns(experiments):
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columns.insert(2, "Scheme")
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columns.insert(3, "Partitions")
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columns.insert(4, "Size")
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columns.insert(5, "Measure")
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columns.insert(5, "Steps")
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columns.insert(6, "Method")
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columns.insert(7, "Measure")
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return columns
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def interval_dataframe_synthetic_columns():
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columns = ["Model", "Order", "Scheme", "Partitions", "SHARPAVG", "SHARPSTD", "RESAVG", "RESSTD", "COVAVG",
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columns = ["Model", "Order", "Scheme", "Partitions","SIZE", "Steps","Method" "SHARPAVG", "SHARPSTD", "RESAVG", "RESSTD", "COVAVG",
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"COVSTD", "TIMEAVG", "TIMESTD", "Q05AVG", "Q05STD", "Q25AVG", "Q25STD", "Q75AVG", "Q75STD", "Q95AVG", "Q95STD"]
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return columns
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def cast_dataframe_to_synthetic_interval(infile, outfile, experiments):
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columns = interval_dataframe_analytic_columns(experiments)
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dat = pd.read_csv(infile, sep=";", usecols=columns)
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@ -863,7 +874,7 @@ def plot_dataframe_interval_pinball(file_synthetic, file_analytic, experiments,
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Util.show_and_save_image(fig, file, save)
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def save_dataframe_probabilistic(experiments, file, objs, crps, times, save, synthetic):
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def save_dataframe_probabilistic(experiments, file, objs, crps, times, save, synthetic, steps, method):
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"""
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Save benchmark results for m-step ahead probabilistic forecasters
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:param experiments:
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@ -893,11 +904,15 @@ def save_dataframe_probabilistic(experiments, file, objs, crps, times, save, syn
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if not mfts.benchmark_only:
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mod.append(mfts.partitioner.name)
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mod.append(mfts.partitioner.partitions)
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l = len(mfts)
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mod.append(len(mfts))
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mod.append(steps)
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mod.append(method)
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else:
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mod.append('-')
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mod.append('-')
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l = '-'
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mod.append('-')
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mod.append(steps)
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mod.append(method)
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mod.append(np.round(np.nanmean(crps[k]), 2))
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mod.append(np.round(np.nanstd(crps[k]), 2))
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mod.append(l)
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@ -925,10 +940,10 @@ def save_dataframe_probabilistic(experiments, file, objs, crps, times, save, syn
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s = '-'
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p = '-'
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l = '-'
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tmp = [n, o, s, p, l, 'CRPS']
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tmp = [n, o, s, p, l, steps, method, 'CRPS']
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tmp.extend(crps[k])
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ret.append(deepcopy(tmp))
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tmp = [n, o, s, p, l, 'TIME']
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tmp = [n, o, s, p, l, steps, method, 'TIME']
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tmp.extend(times[k])
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ret.append(deepcopy(tmp))
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except Exception as ex:
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@ -940,7 +955,6 @@ def save_dataframe_probabilistic(experiments, file, objs, crps, times, save, syn
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return dat
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def probabilistic_dataframe_analytic_columns(experiments):
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columns = [str(k) for k in np.arange(0, experiments)]
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columns.insert(0, "Model")
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@ -948,12 +962,14 @@ def probabilistic_dataframe_analytic_columns(experiments):
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columns.insert(2, "Scheme")
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columns.insert(3, "Partitions")
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columns.insert(4, "Size")
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columns.insert(5, "Measure")
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columns.insert(5, "Steps")
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columns.insert(6, "Method")
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columns.insert(7, "Measure")
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return columns
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def probabilistic_dataframe_synthetic_columns():
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columns = ["Model", "Order", "Scheme", "Partitions", "CRPSAVG", "CRPSSTD",
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columns = ["Model", "Order", "Scheme", "Partitions","Size", "Steps", "Method", "CRPSAVG", "CRPSSTD",
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"TIMEAVG", "TIMESTD"]
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return columns
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@ -293,6 +293,9 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, **kwarg
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transformation = kwargs.get('transformation', None)
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indexer = kwargs.get('indexer', None)
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steps_ahead = kwargs.get('steps_ahead', 1)
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method = kwargs.get('method', None)
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if mfts.benchmark_only:
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_key = mfts.shortname + str(mfts.order if mfts.order is not None else "")
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else:
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@ -313,7 +316,8 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, **kwarg
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_end = time.time()
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times += _end - _start
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ret = {'key': _key, 'obj': mfts, 'rmse': _rmse, 'smape': _smape, 'u': _u, 'time': times, 'window': window_key}
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ret = {'key': _key, 'obj': mfts, 'rmse': _rmse, 'smape': _smape, 'u': _u, 'time': times, 'window': window_key,
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'steps': steps_ahead, 'method': method}
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return ret
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@ -346,6 +350,9 @@ def run_interval(mfts, partitioner, train_data, test_data, window_key=None, **kw
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transformation = kwargs.get('transformation', None)
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indexer = kwargs.get('indexer', None)
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steps_ahead = kwargs.get('steps_ahead', 1)
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method = kwargs.get('method', None)
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if mfts.benchmark_only:
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_key = mfts.shortname + str(mfts.order if mfts.order is not None else "") + str(mfts.alpha)
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else:
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@ -367,7 +374,8 @@ def run_interval(mfts, partitioner, train_data, test_data, window_key=None, **kw
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times += _end - _start
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ret = {'key': _key, 'obj': mfts, 'sharpness': _sharp, 'resolution': _res, 'coverage': _cov, 'time': times,
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'Q05': _q05, 'Q25': _q25, 'Q75': _q75, 'Q95': _q95, 'window': window_key}
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'Q05': _q05, 'Q25': _q25, 'Q75': _q75, 'Q95': _q95, 'window': window_key,
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'steps': steps_ahead, 'method': method}
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return ret
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@ -403,6 +411,9 @@ def run_probabilistic(mfts, partitioner, train_data, test_data, window_key=None,
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transformation = kwargs.get('transformation', None)
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indexer = kwargs.get('indexer', None)
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steps_ahead = kwargs.get('steps_ahead', 1)
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method = kwargs.get('method', None)
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if mfts.benchmark_only:
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_key = mfts.shortname + str(mfts.order if mfts.order is not None else "") + str(mfts.alpha)
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else:
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@ -429,7 +440,8 @@ def run_probabilistic(mfts, partitioner, train_data, test_data, window_key=None,
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_crps1 = np.nan
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_t1 = np.nan
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ret = {'key': _key, 'obj': mfts, 'CRPS': _crps1, 'time': _t1, 'window': window_key}
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ret = {'key': _key, 'obj': mfts, 'CRPS': _crps1, 'time': _t1, 'window': window_key,
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'steps': steps_ahead, 'method': method}
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return ret
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@ -466,6 +478,8 @@ def process_point_jobs(jobs, experiments, save=False, file=None, sintetic=False)
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smape = {}
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u = {}
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times = {}
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steps = None
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method = None
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for job in jobs:
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_key = job['key']
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@ -475,6 +489,8 @@ def process_point_jobs(jobs, experiments, save=False, file=None, sintetic=False)
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smape[_key] = []
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u[_key] = []
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times[_key] = []
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steps[_key] = job['steps']
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method[_key] = job['method']
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rmse[_key].append(job['rmse'])
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smape[_key].append(job['smape'])
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u[_key].append(job['u'])
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@ -89,17 +89,17 @@ class FTS(object):
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steps_ahead = kwargs.get("steps_ahead", None)
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if type == 'point' and steps_ahead == None:
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if type == 'point' and (steps_ahead == None or steps_ahead == 1):
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ret = self.forecast(ndata, **kwargs)
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elif type == 'point' and steps_ahead != None:
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elif type == 'point' and steps_ahead > 1:
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ret = self.forecast_ahead(ndata, steps_ahead, **kwargs)
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elif type == 'interval' and steps_ahead == None:
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elif type == 'interval' and (steps_ahead == None or steps_ahead == 1):
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ret = self.forecast_interval(ndata, **kwargs)
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elif type == 'interval' and steps_ahead != None:
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elif type == 'interval' and steps_ahead > 1:
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ret = self.forecast_ahead_interval(ndata, steps_ahead, **kwargs)
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elif type == 'distribution' and steps_ahead == None:
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elif type == 'distribution' and (steps_ahead == None or steps_ahead == 1):
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ret = self.forecast_distribution(ndata, **kwargs)
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elif type == 'distribution' and steps_ahead != None:
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elif type == 'distribution' and steps_ahead > 1:
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ret = self.forecast_ahead_distribution(ndata, steps_ahead, **kwargs)
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else:
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raise ValueError('The argument \'type\' has an unknown value.')
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