Compare commits
3 Commits
| Author | SHA1 | Date |
|---|---|---|
 Alejandro Moreo Fernandez
|
7fd32d5c5f | |
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Alejandro Moreo Fernandez
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bdbe933a41 | |
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Alejandro Moreo Fernandez
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95b21c8bc2 |
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@ -12,8 +12,8 @@ from os.path import join
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qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
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plotext='png'
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resultdir = './results'
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plotdir = './plots'
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resultdir = './results_npp'
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plotdir = './plots_npp'
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os.makedirs(plotdir, exist_ok=True)
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def gather_results(methods, error_name):
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@ -33,6 +33,12 @@ def plot_error_by_drift(methods, error_name, logscale=False, path=None):
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if path is not None:
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path = join(path, f'error_by_drift_{error_name}.{plotext}')
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method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
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method_order = ['SVM(AE)' if error_name=='ae' else 'SVM(RAE)',
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'PCC', 'SVM(KLD)', 'SVM(Q)', 'SVM(NKLD)', 'CC', 'HDy',
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'E(PACC)$_\\mathrm{Ptr}$',
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'E(PACC)$_\\mathrm{AE}$' if error_name=='ae' else 'E(PACC)$_\\mathrm{RAE}$',
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'QuaNet', 'PACC', 'ACC', 'SLD']
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qp.plot.error_by_drift(
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method_names,
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true_prevs,
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@ -43,7 +49,9 @@ def plot_error_by_drift(methods, error_name, logscale=False, path=None):
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show_std=False,
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logscale=logscale,
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title=f'Quantification error as a function of distribution shift',
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savepath=path
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savepath=path,
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vlines=[0.02, 0.1055],
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method_order=method_order
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)
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@ -52,9 +60,15 @@ def diagonal_plot(methods, error_name, path=None):
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if path is not None:
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path = join(path, f'diag_{error_name}')
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method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
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qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=0, title='Negative', legend=False, show_std=False, savepath=f'{path}_neg.{plotext}')
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qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title='Neutral', legend=False, show_std=False, savepath=f'{path}_neu.{plotext}')
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qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=2, title='Positive', legend=True, show_std=False, savepath=f'{path}_pos.{plotext}')
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method_order = ['SVM(AE)' if error_name == 'ae' else 'SVM(RAE)',
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'PCC', 'SVM(KLD)', 'SVM(Q)', 'SVM(NKLD)', 'CC', 'HDy',
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'E(PACC)$_\\mathrm{Ptr}$',
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'E(PACC)$_\\mathrm{AE}$' if error_name == 'ae' else 'E(PACC)$_\\mathrm{RAE}$',
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'QuaNet', 'PACC', 'ACC', 'SLD']
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qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=0, title='Negative', legend=False, show_std=False, savepath=f'{path}_neg.{plotext}', method_order=method_order)
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qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title='Neutral', legend=False, show_std=False, savepath=f'{path}_neu.{plotext}', method_order=method_order)
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qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=2, title='Positive', legend=False, show_std=False, savepath=f'{path}_pos.{plotext}', method_order=method_order)
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qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=2, title='Positive', legend=True, show_std=False, savepath=f'{path}_pos.legend.{plotext}', method_order=method_order)
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def binary_bias_global(methods, error_name, path=None):
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@ -84,12 +98,12 @@ new_methods_rae = ['svmmrae' , 'epaccmraeptr', 'epaccmraemrae', 'hdy', 'quanet']
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plot_error_by_drift(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
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plot_error_by_drift(gao_seb_methods+new_methods_rae, error_name='rae', logscale=True, path=plotdir)
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diagonal_plot(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
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diagonal_plot(gao_seb_methods+new_methods_rae, error_name='rae', path=plotdir)
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binary_bias_global(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
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binary_bias_global(gao_seb_methods+new_methods_rae, error_name='rae', path=plotdir)
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# diagonal_plot(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
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# diagonal_plot(gao_seb_methods+new_methods_rae, error_name='rae', path=plotdir)
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# binary_bias_global(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
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# binary_bias_global(gao_seb_methods+new_methods_rae, error_name='rae', path=plotdir)
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#
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#binary_bias_bins(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
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#binary_bias_bins(gao_seb_methods+new_methods_rae, error_name='rae', path=plotdir)
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@ -10,6 +10,7 @@ from experiments import result_path
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from tabular import Table
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tables_path = './tables'
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results_path = './results'
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MAXTONE = 50 # sets the intensity of the maximum color reached by the worst (red) and best (green) results
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makedirs(tables_path, exist_ok=True)
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@ -23,8 +24,8 @@ def save_table(path, table):
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foo.write(table)
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def experiment_errors(path, dataset, method, loss):
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path = result_path(path, dataset, method, 'm'+loss if not loss.startswith('m') else loss)
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def experiment_errors(path, dataset, method, optloss, loss):
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path = result_path(path, dataset, method, 'm'+optloss if not loss.startswith('m') else optloss)
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if os.path.exists(path):
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true_prevs, estim_prevs, _, _, _, _ = pickle.load(open(path, 'rb'))
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err_fn = getattr(qp.error, loss)
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@ -35,13 +36,10 @@ def experiment_errors(path, dataset, method, loss):
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Generate tables for Tweeter Sentiment Quantification')
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parser.add_argument('results', metavar='RESULT_PATH', type=str,
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help='path to the directory where to store the results')
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args = parser.parse_args()
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datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TEST
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evaluation_measures = [qp.error.ae, qp.error.rae]
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secundary_eval_measures = [qp.error.kld.__name__, qp.error.nkld.__name__, qp.error.se.__name__]
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gao_seb_methods = ['cc', 'acc', 'pcc', 'pacc', 'sld', 'svmq', 'svmkld', 'svmnkld']
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new_methods = ['hdy', 'quanet']
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@ -52,94 +50,93 @@ if __name__ == '__main__':
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# Tables evaluation scores for AE and RAE (two tables)
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# ----------------------------------------------------
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eval_name = eval_func.__name__
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added_methods = ['svmm' + eval_name, f'epaccm{eval_name}ptr', f'epaccm{eval_name}m{eval_name}'] + new_methods
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main_eval_name = eval_func.__name__
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added_methods = ['svmm' + main_eval_name, f'epaccm{main_eval_name}ptr', f'epaccm{main_eval_name}m{main_eval_name}'] + new_methods
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methods = gao_seb_methods + added_methods
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nold_methods = len(gao_seb_methods)
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nnew_methods = len(added_methods)
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# fill data table
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table = Table(benchmarks=datasets, methods=methods)
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for dataset in datasets:
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for eval_name in [main_eval_name] + secundary_eval_measures:
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# fill data table
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table = Table(benchmarks=datasets, methods=methods)
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for dataset in datasets:
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for method in methods:
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table.add(dataset, method, experiment_errors(results_path, dataset, method, main_eval_name, eval_name))
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# write the latex table
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tabular = """
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\\resizebox{\\textwidth}{!}{%
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\\begin{tabular}{|c||""" + ('c|' * nold_methods) + '|' + ('c|' * nnew_methods) + """} \hline
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& \multicolumn{""" + str(nold_methods) + """}{c||}{Methods tested in~\cite{Gao:2016uq}} &
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\multicolumn{""" + str(nnew_methods) + """}{c|}{Newly added methods} \\\\ \hline
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"""
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rowreplace={dataset: nicename(dataset) for dataset in datasets}
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colreplace={method: nicename(method, main_eval_name, side=True) for method in methods}
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tabular += table.latexTabular(benchmark_replace=rowreplace, method_replace=colreplace)
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tabular += """
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\end{tabular}%
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}
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"""
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save_table(f'./tables/tab_results_{main_eval_name}_{eval_name}.tex', tabular)
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continue
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# Tables ranks for AE and RAE (two tables)
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# ----------------------------------------------------
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methods = gao_seb_methods
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table.dropMethods(added_methods)
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# fill the data table
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ranktable = Table(benchmarks=datasets, methods=methods, missing='--')
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for dataset in datasets:
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for method in methods:
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ranktable.add(dataset, method, values=table.get(dataset, method, 'rank'))
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# write the latex table
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tabular = """
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\\resizebox{\\textwidth}{!}{%
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\\begin{tabular}{|c||""" + ('c|' * len(gao_seb_methods)) + """} \hline
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& \multicolumn{""" + str(nold_methods) + """}{c|}{Methods tested in~\cite{Gao:2016uq}} \\\\ \hline
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"""
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for method in methods:
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table.add(dataset, method, experiment_errors(args.results, dataset, method, eval_name))
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tabular += ' & ' + nicename(method, eval_name, side=True)
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tabular += "\\\\\hline\n"
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# write the latex table
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# tabular = """
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# \\begin{tabularx}{\\textwidth}{|c||""" + ('Y|'*nold_methods)+ '|' + ('Y|'*nnew_methods) + """} \hline
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# & \multicolumn{"""+str(nold_methods)+"""}{c||}{Methods tested in~\cite{Gao:2016uq}} &
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# \multicolumn{"""+str(nnew_methods)+"""}{c|}{} \\\\ \hline
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# """
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tabular = """
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\\resizebox{\\textwidth}{!}{%
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\\begin{tabular}{|c||""" + ('c|' * nold_methods) + '|' + ('c|' * nnew_methods) + """} \hline
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& \multicolumn{""" + str(nold_methods) + """}{c||}{Methods tested in~\cite{Gao:2016uq}} &
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\multicolumn{""" + str(nnew_methods) + """}{c|}{} \\\\ \hline
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"""
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rowreplace={dataset: nicename(dataset) for dataset in datasets}
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colreplace={method: nicename(method, eval_name, side=True) for method in methods}
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for dataset in datasets:
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tabular += nicename(dataset) + ' '
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for method in methods:
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newrank = ranktable.get(dataset, method)
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oldrank = gao_seb_ranks[f'{dataset}-{method}-{eval_name}']
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if newrank != '--':
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newrank = f'{int(newrank)}'
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color = ranktable.get_color(dataset, method)
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if color == '--':
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color = ''
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tabular += ' & ' + f'{newrank}' + f' ({oldrank}) ' + color
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tabular += '\\\\\hline\n'
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tabular += '\hline\n'
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tabular += table.latexTabular(benchmark_replace=rowreplace, method_replace=colreplace)
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tabular += """
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\end{tabular}%
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}
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"""
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save_table(f'./tables/tab_results_{eval_name}.new.tex', tabular)
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# Tables ranks for AE and RAE (two tables)
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# ----------------------------------------------------
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methods = gao_seb_methods
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table.dropMethods(added_methods)
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# fill the data table
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ranktable = Table(benchmarks=datasets, methods=methods, missing='--')
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for dataset in datasets:
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tabular += 'Average '
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for method in methods:
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ranktable.add(dataset, method, values=table.get(dataset, method, 'rank'))
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# write the latex table
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tabular = """
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\\resizebox{\\textwidth}{!}{%
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\\begin{tabular}{|c||""" + ('c|' * len(gao_seb_methods)) + """} \hline
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& \multicolumn{""" + str(nold_methods) + """}{c|}{Methods tested in~\cite{Gao:2016uq}} \\\\ \hline
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"""
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for method in methods:
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tabular += ' & ' + nicename(method, eval_name, side=True)
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tabular += "\\\\\hline\n"
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for dataset in datasets:
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tabular += nicename(dataset) + ' '
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for method in methods:
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newrank = ranktable.get(dataset, method)
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oldrank = gao_seb_ranks[f'{dataset}-{method}-{eval_name}']
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newrank = ranktable.get_average(method)
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oldrank = gao_seb_ranks[f'Average-{method}-{eval_name}']
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if newrank != '--':
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newrank = f'{int(newrank)}'
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color = ranktable.get_color(dataset, method)
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newrank = f'{newrank:.1f}'
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oldrank = f'{oldrank:.1f}'
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color = ranktable.get_average(method, 'color')
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if color == '--':
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color = ''
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tabular += ' & ' + f'{newrank}' + f' ({oldrank}) ' + color
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tabular += '\\\\\hline\n'
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tabular += '\hline\n'
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tabular += """
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\end{tabular}%
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}
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"""
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tabular += 'Average '
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for method in methods:
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newrank = ranktable.get_average(method)
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oldrank = gao_seb_ranks[f'Average-{method}-{eval_name}']
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if newrank != '--':
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newrank = f'{newrank:.1f}'
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oldrank = f'{oldrank:.1f}'
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color = ranktable.get_average(method, 'color')
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if color == '--':
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color = ''
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tabular += ' & ' + f'{newrank}' + f' ({oldrank}) ' + color
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tabular += '\\\\\hline\n'
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tabular += """
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\end{tabular}%
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}
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"""
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save_table(f'./tables/tab_rank_{eval_name}.new.tex', tabular)
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save_table(f'./tables/tab_rank_{main_eval_name}.{eval_name}.tex', tabular)
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print("[Done]")
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@ -283,7 +283,7 @@ class Table:
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return t
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def dropMethods(self, methods):
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drop_index = [self.method_index[m] for m in methods]
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drop_index = [self.method_index[m] for m in methods if m in self.method_index]
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new_methods = np.delete(self.methods, drop_index)
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new_index = {col:j for j, col in enumerate(new_methods)}
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@ -6,6 +6,9 @@ nice = {
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'mrae':'RAE',
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'ae':'AE',
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'rae':'RAE',
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'kld':'KLD',
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'nkld':'NKLD',
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'se':'SE',
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'svmkld': 'SVM(KLD)',
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'svmnkld': 'SVM(NKLD)',
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'svmq': 'SVM(Q)',
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@ -13,7 +13,7 @@ plt.rcParams['font.size'] = 16
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def binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title=None, show_std=True, legend=True,
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train_prev=None, savepath=None):
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train_prev=None, savepath=None, method_order=None):
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fig, ax = plt.subplots()
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ax.set_aspect('equal')
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ax.grid()
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@ -21,7 +21,15 @@ def binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title=No
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method_names, true_prevs, estim_prevs = _merge(method_names, true_prevs, estim_prevs)
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for method, true_prev, estim_prev in zip(method_names, true_prevs, estim_prevs):
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order = list(zip(method_names, true_prevs, estim_prevs))
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if method_order is not None:
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table = {method_name:[true_prev, estim_prev] for method_name, true_prev, estim_prev in order}
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order = [(method_name, *table[method_name]) for method_name in method_order]
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cm = plt.get_cmap('tab20')
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NUM_COLORS = len(method_names)
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ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)])
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for method, true_prev, estim_prev in order:
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true_prev = true_prev[:,pos_class]
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estim_prev = estim_prev[:,pos_class]
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@ -44,8 +52,12 @@ def binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title=No
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if legend:
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box = ax.get_position()
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ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
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ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
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# ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
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# ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
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# ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
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ax.legend(loc='lower center',
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bbox_to_anchor=(1, -0.5),
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ncol=(len(method_names)+1)//2)
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save_or_show(savepath)
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@ -158,10 +170,19 @@ def _merge(method_names, true_prevs, estim_prevs):
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return method_order, true_prevs_, estim_prevs_
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def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, error_name='ae', show_std=True,
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def _set_colors(ax, n_methods):
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NUM_COLORS = n_methods
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cm = plt.get_cmap('tab20')
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ax.set_prop_cycle(color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)])
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def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, error_name='ae', show_std=False,
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show_density=True,
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logscale=False,
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title=f'Quantification error as a function of distribution shift',
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savepath=None):
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savepath=None,
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vlines=None,
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method_order=None):
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fig, ax = plt.subplots()
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ax.grid()
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|
@ -171,7 +192,10 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, e
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# join all data, and keep the order in which the methods appeared for the first time
|
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data = defaultdict(lambda:{'x':np.empty(shape=(0)), 'y':np.empty(shape=(0))})
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method_order = []
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||||
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if method_order is None:
|
||||
method_order = []
|
||||
|
||||
for method, test_prevs_i, estim_prevs_i, tr_prev_i in zip(method_names, true_prevs, estim_prevs, tr_prevs):
|
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tr_prev_i = np.repeat(tr_prev_i.reshape(1,-1), repeats=test_prevs_i.shape[0], axis=0)
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|
||||
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|
@ -184,9 +208,12 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, e
|
|||
if method not in method_order:
|
||||
method_order.append(method)
|
||||
|
||||
_set_colors(ax, n_methods=len(method_order))
|
||||
|
||||
bins = np.linspace(0, 1, n_bins+1)
|
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binwidth = 1 / n_bins
|
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min_x, max_x = None, None
|
||||
min_x, max_x, min_y, max_y = None, None, None, None
|
||||
npoints = np.zeros(len(bins), dtype=float)
|
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for method in method_order:
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tr_test_drifts = data[method]['x']
|
||||
method_drifts = data[method]['y']
|
||||
|
|
@ -194,33 +221,49 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=20, e
|
|||
method_drifts=np.log(1+method_drifts)
|
||||
|
||||
inds = np.digitize(tr_test_drifts, bins, right=True)
|
||||
|
||||
xs, ys, ystds = [], [], []
|
||||
for ind in range(len(bins)):
|
||||
for p,ind in enumerate(range(len(bins))):
|
||||
selected = inds==ind
|
||||
if selected.sum() > 0:
|
||||
xs.append(ind*binwidth)
|
||||
xs.append(ind*binwidth-binwidth/2)
|
||||
ys.append(np.mean(method_drifts[selected]))
|
||||
ystds.append(np.std(method_drifts[selected]))
|
||||
npoints[p] += len(method_drifts[selected])
|
||||
|
||||
xs = np.asarray(xs)
|
||||
ys = np.asarray(ys)
|
||||
ystds = np.asarray(ystds)
|
||||
|
||||
min_x_method, max_x_method = xs.min(), xs.max()
|
||||
min_x_method, max_x_method, min_y_method, max_y_method = xs.min(), xs.max(), ys.min(), ys.max()
|
||||
min_x = min_x_method if min_x is None or min_x_method < min_x else min_x
|
||||
max_x = max_x_method if max_x is None or max_x_method > max_x else max_x
|
||||
max_y = max_y_method if max_y is None or max_y_method > max_y else max_y
|
||||
min_y = min_y_method if min_y is None or min_y_method < min_y else min_y
|
||||
max_y = max_y_method if max_y is None or max_y_method > max_y else max_y
|
||||
|
||||
ax.errorbar(xs, ys, fmt='-', marker='o', color='w', markersize=8, linewidth=4, zorder=1)
|
||||
ax.errorbar(xs, ys, fmt='-', marker='o', label=method, markersize=6, linewidth=2, zorder=2)
|
||||
|
||||
ax.errorbar(xs, ys, fmt='-', marker='o', label=method, markersize=3, zorder=2)
|
||||
if show_std:
|
||||
ax.fill_between(xs, ys-ystds, ys+ystds, alpha=0.25)
|
||||
|
||||
if show_density:
|
||||
ax.bar([ind * binwidth-binwidth/2 for ind in range(len(bins))],
|
||||
max_y*npoints/np.max(npoints), alpha=0.15, color='g', width=binwidth, label='density')
|
||||
|
||||
ax.set(xlabel=f'Distribution shift between training set and test sample',
|
||||
ylabel=f'{error_name.upper()} (true distribution, predicted distribution)',
|
||||
title=title)
|
||||
box = ax.get_position()
|
||||
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
|
||||
if vlines:
|
||||
for vline in vlines:
|
||||
ax.axvline(vline, 0, 1, linestyle='--', color='k')
|
||||
# ax.axvline(0.02, 0, 1, linestyle='--', color='k')
|
||||
# ax.axvline(0.1055, 0, 1, linestyle='--', color='k')
|
||||
ax.set_xlim(0, max_x)
|
||||
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
|
||||
ax.set_xlim(min_x, max_x)
|
||||
|
||||
save_or_show(savepath)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue