217 lines
7.7 KiB
Python
217 lines
7.7 KiB
Python
import quapy as qp
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import numpy as np
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from os import makedirs
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import sys, os
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import pickle
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import argparse
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import settings
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from experiments import result_path
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from tabular import Table
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tables_path = './tables'
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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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qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
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nice = {
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'mae':'AE',
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'mrae':'RAE',
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'ae':'AE',
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'rae':'RAE',
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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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'svmae': 'SVM(AE)',
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'svmnae': 'SVM(NAE)',
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'svmmae': 'SVM(AE)',
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'svmmrae': 'SVM(RAE)',
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'quanet': 'QuaNet',
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'hdy': 'HDy',
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'dys': 'DyS',
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'svmperf':'',
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'sanders': 'Sanders',
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'semeval13': 'SemEval13',
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'semeval14': 'SemEval14',
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'semeval15': 'SemEval15',
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'semeval16': 'SemEval16',
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'Average': 'Average'
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}
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def nicerm(key):
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return '\mathrm{'+nice[key]+'}'
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def load_Gao_Sebastiani_previous_results():
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def rename(method):
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old2new = {
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'kld': 'svmkld',
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'nkld': 'svmnkld',
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'qbeta2': 'svmq',
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'em': 'sld'
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}
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return old2new.get(method, method)
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gao_seb_results = {}
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with open('./Gao_Sebastiani_results.txt', 'rt') as fin:
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lines = fin.readlines()
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for line in lines[1:]:
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line = line.strip()
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parts = line.lower().split()
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if len(parts) == 4:
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dataset, method, ae, rae = parts
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else:
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method, ae, rae = parts
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learner, method = method.split('-')
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method = rename(method)
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gao_seb_results[f'{dataset}-{method}-ae'] = float(ae)
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gao_seb_results[f'{dataset}-{method}-rae'] = float(rae)
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return gao_seb_results
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def get_ranks_from_Gao_Sebastiani():
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gao_seb_results = load_Gao_Sebastiani_previous_results()
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datasets = set([key.split('-')[0] for key in gao_seb_results.keys()])
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methods = np.sort(np.unique([key.split('-')[1] for key in gao_seb_results.keys()]))
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ranks = {}
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for metric in ['ae', 'rae']:
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for dataset in datasets:
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scores = [gao_seb_results[f'{dataset}-{method}-{metric}'] for method in methods]
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order = np.argsort(scores)
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sorted_methods = methods[order]
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for i, method in enumerate(sorted_methods):
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ranks[f'{dataset}-{method}-{metric}'] = i+1
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for method in methods:
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rankave = np.mean([ranks[f'{dataset}-{method}-{metric}'] for dataset in datasets])
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ranks[f'Average-{method}-{metric}'] = rankave
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return ranks, gao_seb_results
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def save_table(path, table):
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print(f'saving results in {path}')
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with open(path, 'wt') as foo:
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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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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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errors = err_fn(true_prevs, estim_prevs)
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return errors
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return None
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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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gao_seb_methods = ['cc', 'acc', 'pcc', 'pacc', 'sld', 'svmq', 'svmkld', 'svmnkld']
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new_methods = ['hdy']
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gao_seb_ranks, gao_seb_results = get_ranks_from_Gao_Sebastiani()
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for i, eval_func in enumerate(evaluation_measures):
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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] + 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(rows=datasets, cols=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(args.results, dataset, method, eval_name))
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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: nice.get(dataset, dataset.upper()) for dataset in datasets}
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colreplace={method:'\side{' + nice.get(method, method.upper()) +'$^{' + nicerm(eval_name) + '}$} ' for method in methods}
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tabular += table.latexTabular(rowreplace=rowreplace, colreplace=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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# fill the data table
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ranktable = Table(rows=datasets, cols=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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tabular += ' & \side{' + nice.get(method, method.upper()) +'$^{' + nicerm(eval_name) + '}$} '
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tabular += "\\\\\hline\n"
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for dataset in datasets:
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tabular += nice.get(dataset, dataset.upper()) + ' '
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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 += '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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print("[Done]")
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