Methods to rescale and unify several experiments
This commit is contained in:
Petrônio Cândido de Lima e Silva
parent
9af879e195
commit
ba71e08e76
@ -200,6 +200,119 @@ def analytical_data_columns(experiments):
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return data_columns
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def scale_params(data):
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vmin = np.nanmin(data)
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vlen = np.nanmax(data) - vmin
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return (vmin, vlen)
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def scale(data, params):
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ndata = [(k-params[0])/params[1] for k in data]
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return ndata
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def unified_scaled_point(experiments, tam, save=False, file=None,
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sort_columns=['UAVG', 'RMSEAVG', 'USTD', 'RMSESTD'],
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sort_ascend=[1, 1, 1, 1],save_best=False,
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ignore=None, replace=None):
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fig, axes = plt.subplots(nrows=3, ncols=1, figsize=tam)
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axes[0].set_title('RMSE')
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axes[1].set_title('SMAPE')
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axes[2].set_title('U Statistic')
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models = {}
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for experiment in experiments:
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mdl = {}
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dat_syn = pd.read_csv(experiment[0], sep=";", usecols=point_dataframe_synthetic_columns())
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bests = find_best(dat_syn, sort_columns, sort_ascend)
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dat_ana = pd.read_csv(experiment[1], sep=";", usecols=point_dataframe_analytic_columns(experiment[2]))
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rmse = []
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smape = []
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u = []
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times = []
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data_columns = analytical_data_columns(experiment[2])
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for b in sorted(bests.keys()):
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if check_ignore_list(b, ignore):
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continue
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if b not in models:
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models[b] = {}
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models[b]['rmse'] = []
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models[b]['smape'] = []
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models[b]['u'] = []
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models[b]['times'] = []
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if b not in mdl:
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mdl[b] = {}
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mdl[b]['rmse'] = []
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mdl[b]['smape'] = []
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mdl[b]['u'] = []
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mdl[b]['times'] = []
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best = bests[b]
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print(best)
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tmp = dat_ana[(dat_ana.Model == best["Model"]) & (dat_ana.Order == best["Order"])
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& (dat_ana.Scheme == best["Scheme"]) & (dat_ana.Partitions == best["Partitions"])]
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tmpl = extract_measure(tmp,'RMSE',data_columns)
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mdl[b]['rmse'].extend( tmpl )
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rmse.extend( tmpl )
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tmpl = extract_measure(tmp, 'SMAPE', data_columns)
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mdl[b]['smape'].extend(tmpl)
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smape.extend(tmpl)
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tmpl = extract_measure(tmp, 'U', data_columns)
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mdl[b]['u'].extend(tmpl)
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u.extend(tmpl)
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tmpl = extract_measure(tmp, 'TIME', data_columns)
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mdl[b]['times'].extend(tmpl)
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times.extend(tmpl)
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models[b]['label'] = check_replace_list(best["Model"] + " " + str(best["Order"]), replace)
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rmse_param = scale_params(rmse)
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smape_param = scale_params(smape)
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u_param = scale_params(u)
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times_param = scale_params(times)
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for key in sorted(models.keys()):
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models[key]['rmse'].extend( scale(mdl[key]['rmse'], rmse_param) )
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models[key]['smape'].extend( scale(mdl[key]['smape'], smape_param) )
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models[key]['u'].extend( scale(mdl[key]['u'], u_param) )
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models[key]['times'].extend( scale(mdl[key]['times'], times_param) )
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rmse = []
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smape = []
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u = []
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times = []
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labels = []
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for key in sorted(models.keys()):
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rmse.append(models[key]['rmse'])
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smape.append(models[key]['smape'])
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u.append(models[key]['u'])
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times.append(models[key]['times'])
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labels.append(models[key]['label'])
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axes[0].boxplot(rmse, labels=labels, autorange=True, showmeans=True)
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axes[0].set_title("RMSE")
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axes[1].boxplot(smape, labels=labels, autorange=True, showmeans=True)
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axes[1].set_title("SMAPE")
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axes[2].boxplot(u, labels=labels, autorange=True, showmeans=True)
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axes[2].set_title("U Statistic")
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plt.tight_layout()
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Util.showAndSaveImage(fig, file, save)
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def plot_dataframe_point(file_synthetic, file_analytic, experiments, tam, save=False, file=None,
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sort_columns=['UAVG', 'RMSEAVG', 'USTD', 'RMSESTD'],
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sort_ascend=[1, 1, 1, 1],save_best=False,
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@ -425,6 +538,104 @@ def cast_dataframe_to_synthetic_interval(infile, outfile, experiments):
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dat.to_csv(outfile, sep=";", index=False)
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def unified_scaled_interval(experiments, tam, save=False, file=None,
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sort_columns=['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD'],
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sort_ascend=[True, False, True, True],save_best=False,
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ignore=None, replace=None):
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fig, axes = plt.subplots(nrows=3, ncols=1, figsize=tam)
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axes[0].set_title('Sharpness')
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axes[1].set_title('Resolution')
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axes[2].set_title('Coverage')
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models = {}
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for experiment in experiments:
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mdl = {}
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dat_syn = pd.read_csv(experiment[0], sep=";", usecols=interval_dataframe_synthetic_columns())
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bests = find_best(dat_syn, sort_columns, sort_ascend)
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dat_ana = pd.read_csv(experiment[1], sep=";", usecols=interval_dataframe_analytic_columns(experiment[2]))
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sharpness = []
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resolution = []
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coverage = []
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times = []
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data_columns = analytical_data_columns(experiment[2])
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for b in sorted(bests.keys()):
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if check_ignore_list(b, ignore):
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continue
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if b not in models:
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models[b] = {}
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models[b]['sharpness'] = []
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models[b]['resolution'] = []
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models[b]['coverage'] = []
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models[b]['times'] = []
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if b not in mdl:
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mdl[b] = {}
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mdl[b]['sharpness'] = []
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mdl[b]['resolution'] = []
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mdl[b]['coverage'] = []
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mdl[b]['times'] = []
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best = bests[b]
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print(best)
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tmp = dat_ana[(dat_ana.Model == best["Model"]) & (dat_ana.Order == best["Order"])
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& (dat_ana.Scheme == best["Scheme"]) & (dat_ana.Partitions == best["Partitions"])]
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tmpl = extract_measure(tmp, 'Sharpness', data_columns)
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mdl[b]['sharpness'].extend(tmpl)
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sharpness.extend(tmpl)
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tmpl = extract_measure(tmp, 'Resolution', data_columns)
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mdl[b]['resolution'].extend(tmpl)
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resolution.extend(tmpl)
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tmpl = extract_measure(tmp, 'Coverage', data_columns)
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mdl[b]['coverage'].extend(tmpl)
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coverage.extend(tmpl)
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tmpl = extract_measure(tmp, 'TIME', data_columns)
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mdl[b]['times'].extend(tmpl)
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times.extend(tmpl)
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models[b]['label'] = check_replace_list(best["Model"] + " " + str(best["Order"]), replace)
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sharpness_param = scale_params(sharpness)
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resolution_param = scale_params(resolution)
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coverage_param = scale_params(coverage)
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times_param = scale_params(times)
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for key in sorted(models.keys()):
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models[key]['sharpness'].extend(scale(mdl[key]['sharpness'], sharpness_param))
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models[key]['resolution'].extend(scale(mdl[key]['resolution'], resolution_param))
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models[key]['coverage'].extend(scale(mdl[key]['coverage'], coverage_param))
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models[key]['times'].extend(scale(mdl[key]['times'], times_param))
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sharpness = []
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resolution = []
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coverage = []
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times = []
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labels = []
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for key in sorted(models.keys()):
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sharpness.append(models[key]['sharpness'])
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resolution.append(models[key]['resolution'])
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coverage.append(models[key]['coverage'])
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times.append(models[key]['times'])
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labels.append(models[key]['label'])
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axes[0].boxplot(sharpness, labels=labels, autorange=True, showmeans=True)
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axes[1].boxplot(resolution, labels=labels, autorange=True, showmeans=True)
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axes[2].boxplot(coverage, labels=labels, autorange=True, showmeans=True)
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plt.tight_layout()
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Util.showAndSaveImage(fig, file, save)
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def plot_dataframe_interval(file_synthetic, file_analytic, experiments, tam, save=False, file=None,
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sort_columns=['COVAVG', 'SHARPAVG', 'COVSTD', 'SHARPSTD'],
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sort_ascend=[True, False, True, True],save_best=False,
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@ -480,6 +691,102 @@ def plot_dataframe_interval(file_synthetic, file_analytic, experiments, tam, sav
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Util.showAndSaveImage(fig, file, save)
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def unified_scaled_interval_pinball(experiments, tam, save=False, file=None,
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sort_columns=['COVAVG','SHARPAVG','COVSTD','SHARPSTD'],
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sort_ascend=[True, False, True, True], save_best=False,
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ignore=None, replace=None):
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fig, axes = plt.subplots(nrows=1, ncols=4, figsize=tam)
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axes[0].set_title(r'$\tau=0.05$')
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axes[1].set_title(r'$\tau=0.25$')
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axes[2].set_title(r'$\tau=0.75$')
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axes[3].set_title(r'$\tau=0.95$')
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models = {}
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for experiment in experiments:
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mdl = {}
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dat_syn = pd.read_csv(experiment[0], sep=";", usecols=interval_dataframe_synthetic_columns())
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bests = find_best(dat_syn, sort_columns, sort_ascend)
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dat_ana = pd.read_csv(experiment[1], sep=";", usecols=interval_dataframe_analytic_columns(experiment[2]))
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q05 = []
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q25 = []
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q75 = []
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q95 = []
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data_columns = analytical_data_columns(experiment[2])
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for b in sorted(bests.keys()):
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if check_ignore_list(b, ignore):
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continue
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if b not in models:
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models[b] = {}
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models[b]['q05'] = []
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models[b]['q25'] = []
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models[b]['q75'] = []
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models[b]['q95'] = []
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if b not in mdl:
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mdl[b] = {}
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mdl[b]['q05'] = []
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mdl[b]['q25'] = []
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mdl[b]['q75'] = []
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mdl[b]['q95'] = []
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best = bests[b]
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print(best)
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tmp = dat_ana[(dat_ana.Model == best["Model"]) & (dat_ana.Order == best["Order"])
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& (dat_ana.Scheme == best["Scheme"]) & (dat_ana.Partitions == best["Partitions"])]
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tmpl = extract_measure(tmp, 'Q05', data_columns)
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mdl[b]['q05'].extend(tmpl)
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q05.extend(tmpl)
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tmpl = extract_measure(tmp, 'Q25', data_columns)
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mdl[b]['q25'].extend(tmpl)
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q25.extend(tmpl)
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tmpl = extract_measure(tmp, 'Q75', data_columns)
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mdl[b]['q75'].extend(tmpl)
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q75.extend(tmpl)
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tmpl = extract_measure(tmp, 'Q95', data_columns)
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mdl[b]['q95'].extend(tmpl)
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q95.extend(tmpl)
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models[b]['label'] = check_replace_list(best["Model"] + " " + str(best["Order"]), replace)
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q05_param = scale_params(q05)
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q25_param = scale_params(q25)
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q75_param = scale_params(q75)
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q95_param = scale_params(q95)
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for key in sorted(models.keys()):
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models[key]['q05'].extend(scale(mdl[key]['q05'], q05_param))
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models[key]['q25'].extend(scale(mdl[key]['q25'], q25_param))
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models[key]['q75'].extend(scale(mdl[key]['q75'], q75_param))
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models[key]['q95'].extend(scale(mdl[key]['q95'], q95_param))
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q05 = []
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q25 = []
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q75 = []
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q95 = []
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labels = []
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for key in sorted(models.keys()):
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q05.append(models[key]['q05'])
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q25.append(models[key]['q25'])
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q75.append(models[key]['q75'])
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q95.append(models[key]['q95'])
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labels.append(models[key]['label'])
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axes[0].boxplot(q05, labels=labels, vert=False, autorange=True, showmeans=True)
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axes[1].boxplot(q25, labels=labels, vert=False, autorange=True, showmeans=True)
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axes[2].boxplot(q75, labels=labels, vert=False, autorange=True, showmeans=True)
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axes[3].boxplot(q95, labels=labels, vert=False, autorange=True, showmeans=True)
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plt.tight_layout()
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Util.showAndSaveImage(fig, file, save)
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def plot_dataframe_interval_pinball(file_synthetic, file_analytic, experiments, tam, save=False, file=None,
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sort_columns=['COVAVG','SHARPAVG','COVSTD','SHARPSTD'],
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@ -675,6 +982,82 @@ def cast_dataframe_to_synthetic_ahead(infile, outfile, experiments):
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dat.to_csv(outfile, sep=";", index=False)
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def unified_scaled_ahead(experiments, tam, save=False, file=None,
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sort_columns=['CRPS1AVG', 'CRPS2AVG', 'CRPS1STD', 'CRPS2STD'],
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sort_ascend=[True, True, True, True], save_best=False,
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ignore=None, replace=None):
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fig, axes = plt.subplots(nrows=2, ncols=1, figsize=tam)
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axes[0].set_title('CRPS Interval Ahead')
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axes[1].set_title('CRPS Distribution Ahead')
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models = {}
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for experiment in experiments:
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mdl = {}
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dat_syn = pd.read_csv(experiment[0], sep=";", usecols=ahead_dataframe_synthetic_columns())
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bests = find_best(dat_syn, sort_columns, sort_ascend)
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dat_ana = pd.read_csv(experiment[1], sep=";", usecols=ahead_dataframe_analytic_columns(experiment[2]))
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crps1 = []
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crps2 = []
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data_columns = analytical_data_columns(experiment[2])
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for b in sorted(bests.keys()):
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if check_ignore_list(b, ignore):
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continue
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if b not in models:
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models[b] = {}
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models[b]['crps1'] = []
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models[b]['crps2'] = []
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if b not in mdl:
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mdl[b] = {}
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mdl[b]['crps1'] = []
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mdl[b]['crps2'] = []
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best = bests[b]
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tmp = dat_ana[(dat_ana.Model == best["Model"]) & (dat_ana.Order == best["Order"])
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& (dat_ana.Scheme == best["Scheme"]) & (dat_ana.Partitions == best["Partitions"])]
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tmpl = extract_measure(tmp, 'CRPS_Interval', data_columns)
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mdl[b]['crps1'].extend(tmpl)
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crps1.extend(tmpl)
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tmpl = extract_measure(tmp, 'CRPS_Distribution', data_columns)
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mdl[b]['crps2'].extend(tmpl)
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crps2.extend(tmpl)
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models[b]['label'] = check_replace_list(best["Model"] + " " + str(best["Order"]), replace)
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crps1_param = scale_params(crps1)
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crps2_param = scale_params(crps2)
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for key in sorted(mdl.keys()):
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print(key)
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models[key]['crps1'].extend(scale(mdl[key]['crps1'], crps1_param))
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models[key]['crps2'].extend(scale(mdl[key]['crps2'], crps2_param))
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crps1 = []
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crps2 = []
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labels = []
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for key in sorted(models.keys()):
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crps1.append(models[key]['crps1'])
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crps2.append(models[key]['crps2'])
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labels.append(models[key]['label'])
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axes[0].boxplot(crps1, labels=labels, autorange=True, showmeans=True)
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axes[1].boxplot(crps2, labels=labels, autorange=True, showmeans=True)
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plt.tight_layout()
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Util.showAndSaveImage(fig, file, save)
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def plot_dataframe_ahead(file_synthetic, file_analytic, experiments, tam, save=False, file=None,
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sort_columns=['CRPS1AVG', 'CRPS2AVG', 'CRPS1STD', 'CRPS2STD'],
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sort_ascend=[True, True, True, True],save_best=False,
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@ -56,9 +56,9 @@ DATASETS
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#sonda = np.array(sondapd["glo_avg"])
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#del(sondapd)
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bestpd = pd.read_csv("DataSets/BEST_TAVG.csv", sep=";")
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best = np.array(bestpd["Anomaly"])
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del(bestpd)
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#bestpd = pd.read_csv("DataSets/BEST_TAVG.csv", sep=";")
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#best = np.array(bestpd["Anomaly"])
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#del(bestpd)
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#print(lag)
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#print(a)
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@ -164,17 +164,74 @@ from pyFTS import pwfts
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from pyFTS.common import Transformations
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from pyFTS.partitioners import Grid
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diff = Transformations.Differential(1)
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fs = Grid.GridPartitioner(best, 190) #, transformation=diff)
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#diff = Transformations.Differential(1)
|
||||
#fs = Grid.GridPartitioner(best, 190) #, transformation=diff)
|
||||
|
||||
|
||||
model = pwfts.ProbabilisticWeightedFTS("FTS 1")
|
||||
#model = pwfts.ProbabilisticWeightedFTS("FTS 1")
|
||||
#model.appendTransformation(diff)
|
||||
model.train(best[0:1600],fs.sets, order=3)
|
||||
#model.train(best[0:1600],fs.sets, order=3)
|
||||
|
||||
#bchmk.plot_compared_intervals_ahead(best[1600:1700],[model], ['blue','red'],
|
||||
# distributions=[True], save=True, file="pictures/best_ahead_forecasts",
|
||||
# time_from=40, time_to=60, resolution=100)
|
||||
'''
|
||||
experiments = [
|
||||
["experiments/taiex_point_synthetic_diff.csv","experiments/taiex_point_analytic_diff.csv",16],
|
||||
["experiments/nasdaq_point_synthetic_diff.csv","experiments/nasdaq_point_analytic_diff.csv", 11],
|
||||
["experiments/sp500_point_synthetic_diff.csv","experiments/sp500_point_analytic_diff.csv", 21],
|
||||
["experiments/best_point_synthetic_diff.csv","experiments/best_point_analytic_diff.csv", 13],
|
||||
["experiments/sondasun_point_synthetic_diff.csv","experiments/sondasun_point_analytic_diff.csv", 15],
|
||||
["experiments/sondawind_point_synthetic_diff.csv","experiments/sondawind_point_analytic_diff.csv", 8],
|
||||
["experiments/gauss_point_synthetic_diff.csv","experiments/gauss_point_analytic_diff.csv", 16]
|
||||
]
|
||||
|
||||
Util.unified_scaled_point(experiments,tam=[15,8],save=True,file="pictures/unified_experiments_point.png",
|
||||
ignore=['ARIMA(1,0,0)','ARIMA(2,0,0)','ARIMA(2,0,1)','ARIMA(2,0,2)','QAR(2)'],
|
||||
replace=[['ARIMA','ARIMA'],['QAR','QAR']])
|
||||
|
||||
'''
|
||||
|
||||
'''
|
||||
experiments = [
|
||||
["experiments/taiex_interval_synthetic.csv","experiments/taiex_interval_analytic.csv",16],
|
||||
["experiments/nasdaq_interval_synthetic_diff.csv","experiments/nasdaq_interval_analytic_diff.csv",11],
|
||||
["experiments/sp500_interval_synthetic_diff.csv","experiments/sp500_interval_analytic_diff.csv", 11],
|
||||
["experiments/best_interval_synthetic_diff.csv","experiments/best_interval_analytic_diff.csv",13],
|
||||
["experiments/sondasun_interval_synthetic_diff.csv","experiments/sondasun_interval_analytic_diff.csv",8],
|
||||
["experiments/sondawind_interval_synthetic_diff.csv","experiments/sondawind_interval_analytic_diff.csv",8],
|
||||
["experiments/gauss_interval_synthetic_diff.csv","experiments/gauss_interval_analytic_diff.csv", 8]
|
||||
]
|
||||
|
||||
Util.unified_scaled_interval(experiments,tam=[15,8],save=True,file="pictures/unified_experiments_interval.png",
|
||||
ignore=['ARIMA(1,0,0)', 'ARIMA(2,0,0)', 'ARIMA(2,0,1)', 'ARIMA(2,0,2)', 'QAR(2)'],
|
||||
replace=[['ARIMA(1,0,1) - 0.05', 'ARIMA 0.05'], ['ARIMA(1,0,1) - 0.25', 'ARIMA 0.25'],
|
||||
['QAR(1) - 0.05', 'QAR 0.05'], ['QAR(1) - 0.25', 'QAR 0.25']])
|
||||
|
||||
Util.unified_scaled_interval_pinball(experiments,tam=[15,8],save=True,file="pictures/unified_experiments_interval_pinball.png",
|
||||
ignore=['ARIMA(1,0,0)', 'ARIMA(2,0,0)', 'ARIMA(2,0,1)', 'ARIMA(2,0,2)', 'QAR(2)'],
|
||||
replace=[['ARIMA(1,0,1) - 0.05', 'ARIMA 0.05'], ['ARIMA(1,0,1) - 0.25', 'ARIMA 0.25'],
|
||||
['QAR(1) - 0.05', 'QAR 0.05'], ['QAR(1) - 0.25', 'QAR 0.25']])
|
||||
|
||||
'''
|
||||
|
||||
experiments = [
|
||||
["experiments/taiex_ahead_synthetic.csv","experiments/taiex_ahead_analytic.csv",16],
|
||||
["experiments/nasdaq_ahead_synthetic.csv","experiments/nasdaq_ahead_analytic.csv",11],
|
||||
["experiments/sp500_ahead_synthetic.csv","experiments/sp500_ahead_analytic.csv", 21],
|
||||
["experiments/best_ahead_synthetic.csv","experiments/best_ahead_analytic.csv", 24],
|
||||
["experiments/sondasun_ahead_synthetic.csv","experiments/sondasun_ahead_analytic.csv",13],
|
||||
["experiments/sondawind_ahead_synthetic.csv","experiments/sondawind_ahead_analytic.csv", 13],
|
||||
["experiments/gauss_ahead_synthetic_diff.csv","experiments/gauss_ahead_analytic_diff.csv",16]
|
||||
]
|
||||
|
||||
Util.unified_scaled_ahead(experiments,tam=[15,8],save=True,file="pictures/unified_experiments_ahead.png",
|
||||
ignore=['ARIMA(1,0,0)', 'ARIMA(0,0,1)', 'ARIMA(2,0,0)', 'ARIMA(2,0,1)',
|
||||
'ARIMA(2,0,2)', 'QAR(2)', 'ARIMA0.05'],
|
||||
replace=[['ARIMA(1,0,1) - 0.05', 'ARIMA 0.05'], ['ARIMA(1,0,1) - 0.25', 'ARIMA 0.25'],
|
||||
['QAR(1) - 0.05', 'QAR 0.05'], ['QAR(1) - 0.25', 'QAR 0.25']])
|
||||
|
||||
|
||||
bchmk.plot_compared_intervals_ahead(best[1600:1700],[model], ['blue','red'],
|
||||
distributions=[True], save=True, file="pictures/best_ahead_forecasts",
|
||||
time_from=40, time_to=60, resolution=100)
|
||||
|
||||
"""
|
||||
from pyFTS.partitioners import Grid
|
||||
|
||||
Loading…
Reference in New Issue
Block a user