import quapy as qp import numpy as np from os import makedirs import sys, os import pickle import argparse import settings from experiments import result_path from tabular import Table tables_path = './tables' MAXTONE = 50 # sets the intensity of the maximum color reached by the worst (red) and best (green) results makedirs(tables_path, exist_ok=True) qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE nice = { 'mae':'AE', 'mrae':'RAE', 'ae':'AE', 'rae':'RAE', 'svmkld': 'SVM(KLD)', 'svmnkld': 'SVM(NKLD)', 'svmq': 'SVM(Q)', 'svmae': 'SVM(AE)', 'svmnae': 'SVM(NAE)', 'svmmae': 'SVM(AE)', 'svmmrae': 'SVM(RAE)', 'quanet': 'QuaNet', 'hdy': 'HDy', 'dys': 'DyS', 'svmperf':'', 'sanders': 'Sanders', 'semeval13': 'SemEval13', 'semeval14': 'SemEval14', 'semeval15': 'SemEval15', 'semeval16': 'SemEval16', 'Average': 'Average' } def nicerm(key): return '\mathrm{'+nice[key]+'}' def load_Gao_Sebastiani_previous_results(): def rename(method): old2new = { 'kld': 'svmkld', 'nkld': 'svmnkld', 'qbeta2': 'svmq', 'em': 'sld' } return old2new.get(method, method) gao_seb_results = {} with open('./Gao_Sebastiani_results.txt', 'rt') as fin: lines = fin.readlines() for line in lines[1:]: line = line.strip() parts = line.lower().split() if len(parts) == 4: dataset, method, ae, rae = parts else: method, ae, rae = parts learner, method = method.split('-') method = rename(method) gao_seb_results[f'{dataset}-{method}-ae'] = float(ae) gao_seb_results[f'{dataset}-{method}-rae'] = float(rae) return gao_seb_results def get_ranks_from_Gao_Sebastiani(): gao_seb_results = load_Gao_Sebastiani_previous_results() datasets = set([key.split('-')[0] for key in gao_seb_results.keys()]) methods = np.sort(np.unique([key.split('-')[1] for key in gao_seb_results.keys()])) ranks = {} for metric in ['ae', 'rae']: for dataset in datasets: scores = [gao_seb_results[f'{dataset}-{method}-{metric}'] for method in methods] order = np.argsort(scores) sorted_methods = methods[order] for i, method in enumerate(sorted_methods): ranks[f'{dataset}-{method}-{metric}'] = i+1 for method in methods: rankave = np.mean([ranks[f'{dataset}-{method}-{metric}'] for dataset in datasets]) ranks[f'Average-{method}-{metric}'] = rankave return ranks, gao_seb_results def save_table(path, table): print(f'saving results in {path}') with open(path, 'wt') as foo: foo.write(table) def experiment_errors(path, dataset, method, loss): path = result_path(path, dataset, method, 'm'+loss if not loss.startswith('m') else loss) if os.path.exists(path): true_prevs, estim_prevs, _, _, _, _ = pickle.load(open(path, 'rb')) err_fn = getattr(qp.error, loss) errors = err_fn(true_prevs, estim_prevs) return errors return None if __name__ == '__main__': parser = argparse.ArgumentParser(description='Generate tables for Tweeter Sentiment Quantification') parser.add_argument('results', metavar='RESULT_PATH', type=str, help='path to the directory where to store the results') args = parser.parse_args() datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TEST evaluation_measures = [qp.error.ae, qp.error.rae] gao_seb_methods = ['cc', 'acc', 'pcc', 'pacc', 'sld', 'svmq', 'svmkld', 'svmnkld'] new_methods = ['hdy'] gao_seb_ranks, gao_seb_results = get_ranks_from_Gao_Sebastiani() for i, eval_func in enumerate(evaluation_measures): # Tables evaluation scores for AE and RAE (two tables) # ---------------------------------------------------- eval_name = eval_func.__name__ added_methods = ['svmm' + eval_name] + new_methods methods = gao_seb_methods + added_methods nold_methods = len(gao_seb_methods) nnew_methods = len(added_methods) # fill data table table = Table(rows=datasets, cols=methods) for dataset in datasets: for method in methods: table.add(dataset, method, experiment_errors(args.results, dataset, method, eval_name)) # write the latex table # tabular = """ # \\begin{tabularx}{\\textwidth}{|c||""" + ('Y|'*nold_methods)+ '|' + ('Y|'*nnew_methods) + """} \hline # & \multicolumn{"""+str(nold_methods)+"""}{c||}{Methods tested in~\cite{Gao:2016uq}} & # \multicolumn{"""+str(nnew_methods)+"""}{c|}{} \\\\ \hline # """ tabular = """ \\resizebox{\\textwidth}{!}{% \\begin{tabular}{|c||""" + ('c|' * nold_methods) + '|' + ('c|' * nnew_methods) + """} \hline & \multicolumn{""" + str(nold_methods) + """}{c||}{Methods tested in~\cite{Gao:2016uq}} & \multicolumn{""" + str(nnew_methods) + """}{c|}{} \\\\ \hline """ rowreplace={dataset: nice.get(dataset, dataset.upper()) for dataset in datasets} colreplace={method:'\side{' + nice.get(method, method.upper()) +'$^{' + nicerm(eval_name) + '}$} ' for method in methods} tabular += table.latexTabular(rowreplace=rowreplace, colreplace=colreplace) tabular += """ \end{tabular}% } """ save_table(f'./tables/tab_results_{eval_name}.new.tex', tabular) # Tables ranks for AE and RAE (two tables) # ---------------------------------------------------- methods = gao_seb_methods # fill the data table ranktable = Table(rows=datasets, cols=methods, missing='--') for dataset in datasets: for method in methods: ranktable.add(dataset, method, values=table.get(dataset, method, 'rank')) # write the latex table tabular = """ \\resizebox{\\textwidth}{!}{% \\begin{tabular}{|c||""" + ('c|' * len(gao_seb_methods)) + """} \hline & \multicolumn{""" + str(nold_methods) + """}{c|}{Methods tested in~\cite{Gao:2016uq}} \\\\ \hline """ for method in methods: tabular += ' & \side{' + nice.get(method, method.upper()) +'$^{' + nicerm(eval_name) + '}$} ' tabular += "\\\\\hline\n" for dataset in datasets: tabular += nice.get(dataset, dataset.upper()) + ' ' for method in methods: newrank = ranktable.get(dataset, method) oldrank = gao_seb_ranks[f'{dataset}-{method}-{eval_name}'] if newrank != '--': newrank = f'{int(newrank)}' color = ranktable.get_color(dataset, method) if color == '--': color = '' tabular += ' & ' + f'{newrank}' + f' ({oldrank}) ' + color tabular += '\\\\\hline\n' tabular += '\hline\n' tabular += 'Average ' for method in methods: newrank = ranktable.get_average(method) oldrank = gao_seb_ranks[f'Average-{method}-{eval_name}'] if newrank != '--': newrank = f'{newrank:.1f}' oldrank = f'{oldrank:.1f}' color = ranktable.get_average(method, 'color') if color == '--': color = '' tabular += ' & ' + f'{newrank}' + f' ({oldrank}) ' + color tabular += '\\\\\hline\n' tabular += """ \end{tabular}% } """ save_table(f'./tables/tab_rank_{eval_name}.new.tex', tabular) print("[Done]")