refactor of ensembles, launching EPACC with Ptr policy
This commit is contained in:
parent
1399125fb8
commit
482e4453a8
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@ -14,7 +14,7 @@ import torch
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parser = argparse.ArgumentParser(description='Run experiments for Tweeter Sentiment Quantification')
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parser.add_argument('results', metavar='RESULT_PATH', type=str, help='path to the directory where to store the results')
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parser.add_argument('svmperfpath', metavar='SVMPERF_PATH', type=str, help='path to the directory with svmperf')
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#parser.add_argument('svmperfpath', metavar='SVMPERF_PATH', type=str, help='path to the directory with svmperf')
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args = parser.parse_args()
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@ -25,11 +25,11 @@ def quantification_models():
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lr_params = {'C': __C_range, 'class_weight': [None, 'balanced']}
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svmperf_params = {'C': __C_range}
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#yield 'paccsld', PACCSLD(newLR()), lr_params
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#yield 'hdysld', OneVsAll(HDySLD(newLR())), lr_params # <-- promising!
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yield 'hdysld', OneVsAll(HDySLD(newLR())), lr_params # <-- promising!
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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print(f'Running QuaNet in {device}')
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yield 'quanet', QuaNet(PCALR(**newLR().get_params()), SAMPLE_SIZE, device=device), lr_params
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#device = 'cuda' if torch.cuda.is_available() else 'cpu'
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#print(f'Running QuaNet in {device}')
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#yield 'quanet', QuaNet(PCALR(**newLR().get_params()), SAMPLE_SIZE, device=device), lr_params
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if __name__ == '__main__':
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@ -38,7 +38,7 @@ if __name__ == '__main__':
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np.random.seed(0)
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optim_losses = ['mae']
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datasets = ['hcr'] # qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN
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datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN
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models = quantification_models()
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results = Parallel(n_jobs=settings.N_JOBS)(
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@ -4,7 +4,9 @@ from os import makedirs
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import sys, os
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import pickle
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from experiments import result_path
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from gen_tables import save_table, experiment_errors
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from tabular import Table
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import argparse
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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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@ -29,6 +31,7 @@ nice = {
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'svmmrae': 'SVM(RAE)',
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'quanet': 'QuaNet',
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'hdy': 'HDy',
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'hdysld': 'HDy-SLD',
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'dys': 'DyS',
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'svmperf':'',
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'sanders': 'Sanders',
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@ -44,153 +47,102 @@ 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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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 containing the results of the methods tested in Gao & Sebastiani')
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parser.add_argument('newresults', metavar='RESULT_PATH', type=str,
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help='path to the directory containing the results for the experimental methods')
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args = parser.parse_args()
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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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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'] # methods added to the Gao & Sebastiani methods
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experimental_methods = ['hdysld'] # experimental
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for i, eval_func in enumerate(evaluation_measures):
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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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# 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 + experimental_methods
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nold_methods = len(gao_seb_methods)
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nnew_methods = len(added_methods)
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nexp_methods = len(experimental_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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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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if method in experimental_methods:
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path = args.newresults
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else:
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path = args.results
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table.add(dataset, method, experiment_errors(path, 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) + '|' + ('Y|'*nexp_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|}{} &
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\multicolumn{"""+str(nexp_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 += "\n\end{tabularx}"
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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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# 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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\\begin{tabularx}{\\textwidth}{|c||""" + ('Y|'*nold_methods) + '|' + ('Y|'*nnew_methods) + '|' + ('Y|'*nexp_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|}{} &
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\multicolumn{"""+str(nexp_methods)+"""}{c|}{}\\\\ \hline
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"""
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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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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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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}' + color
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tabular += '\\\\\hline\n'
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tabular += '\hline\n'
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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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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 = []
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def experiment_errors(dataset, method, loss):
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path = result_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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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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tabular += 'Average '
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for method in methods:
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table.add(dataset, method, experiment_errors(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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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 += "\n\end{tabularx}"
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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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\\begin{tabularx}{\\textwidth}{|c||""" + ('Y|' * 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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newrank = ranktable.get_average(method)
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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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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 += ' & ' + f'{newrank}' + color
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tabular += '\\\\\hline\n'
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tabular += '\hline\n'
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tabular += "\end{tabularx}"
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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 += "\end{tabularx}"
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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_{eval_name}.new.tex', tabular)
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print("[Done]")
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print("[Done]")
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@ -1,3 +1,4 @@
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import multiprocessing
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N_JOBS = -2 #multiprocessing.cpu_count()
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SAMPLE_SIZE = 100
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@ -2,7 +2,9 @@ from sklearn.linear_model import LogisticRegression
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import quapy as qp
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from classification.methods import PCALR
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from method.meta import QuaNet
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from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation
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from quapy.method.aggregative import CC, ACC, PCC, PACC, EMQ, OneVsAll, SVMQ, SVMKLD, SVMNKLD, SVMAE, SVMRAE, HDy
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from quapy.method.meta import EPACC, EEMQ
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import quapy.functional as F
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import numpy as np
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import os
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@ -14,16 +16,6 @@ import argparse
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import torch
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import shutil
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parser = argparse.ArgumentParser(description='Run experiments for Tweeter Sentiment Quantification')
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parser.add_argument('results', metavar='RESULT_PATH', type=str, help='path to the directory where to store the results')
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parser.add_argument('--svmperfpath', metavar='SVMPERF_PATH', type=str,default='./svm_perf_quantification',
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help='path to the directory with svmperf')
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parser.add_argument('--checkpointdir', metavar='PATH', type=str,default='./checkpoint',
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help='path to the directory where to dump QuaNet checkpoints')
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args = parser.parse_args()
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SAMPLE_SIZE = 100
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def quantification_models():
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def newLR():
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@ -49,13 +41,15 @@ def quantification_models():
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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print(f'Running QuaNet in {device}')
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yield 'quanet', QuaNet(PCALR(**newLR().get_params()), SAMPLE_SIZE, checkpointdir=args.checkpointdir, device=device), lr_params
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#yield 'quanet', QuaNet(PCALR(**newLR().get_params()), settings.SAMPLE_SIZE, checkpointdir=args.checkpointdir, device=device), lr_params
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# to add:
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# quapy
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# ensembles
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#
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# 'mlpe': lambda learner: MaximumLikelihoodPrevalenceEstimation(),
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param_mod_sel={'sample_size':settings.SAMPLE_SIZE, 'n_prevpoints':21, 'n_repetitions':5}
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yield 'epaccmaeptr', EPACC(newLR(), param_grid=lr_params, optim='mae', policy='ptr', param_mod_sel=param_mod_sel, n_jobs=settings.ENSEMBLE_N_JOBS), None
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# yield 'epaccmraeptr', EPACC(newLR(), param_grid=lr_params, optim='mrae', policy='ptr', param_mod_sel=param_mod_sel, n_jobs=settings.ENSEMBLE_N_JOBS), None
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# yield 'epaccmae', EPACC(newLR(), param_grid=lr_params, optim='mae', policy='mae', param_mod_sel=param_mod_sel, n_jobs=settings.ENSEMBLE_N_JOBS), None
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# yield 'epaccmrae', EPACC(newLR(), param_grid=lr_params, optim='mrae', policy='mrae', param_mod_sel=param_mod_sel, n_jobs=settings.ENSEMBLE_N_JOBS), None
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#yield 'mlpe', MaximumLikelihoodPrevalenceEstimation(), {}
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def evaluate_experiment(true_prevalences, estim_prevalences):
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@ -74,8 +68,8 @@ def evaluate_method_point_test(true_prev, estim_prev):
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print(f'\t{eval_measure.__name__}={err:.4f}')
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def result_path(dataset_name, model_name, optim_loss):
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return os.path.join(args.results, f'{dataset_name}-{model_name}-{optim_loss}.pkl')
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def result_path(path, dataset_name, model_name, optim_loss):
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return os.path.join(path, f'{dataset_name}-{model_name}-{optim_loss}.pkl')
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def is_already_computed(dataset_name, model_name, optim_loss):
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@ -83,11 +77,11 @@ def is_already_computed(dataset_name, model_name, optim_loss):
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check_datasets = ['semeval13', 'semeval14', 'semeval15']
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else:
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check_datasets = [dataset_name]
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return all(os.path.exists(result_path(name, model_name, optim_loss)) for name in check_datasets)
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return all(os.path.exists(result_path(args.results, name, model_name, optim_loss)) for name in check_datasets)
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def save_results(dataset_name, model_name, optim_loss, *results):
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rpath = result_path(dataset_name, model_name, optim_loss)
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rpath = result_path(args.results, dataset_name, model_name, optim_loss)
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qp.util.create_parent_dir(rpath)
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with open(rpath, 'wb') as foo:
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pickle.dump(tuple(results), foo, pickle.HIGHEST_PROTOCOL)
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@ -95,14 +89,14 @@ def save_results(dataset_name, model_name, optim_loss, *results):
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def run(experiment):
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qp.environ['SAMPLE_SIZE'] = SAMPLE_SIZE
|
||||
qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
|
||||
|
||||
optim_loss, dataset_name, (model_name, model, hyperparams) = experiment
|
||||
|
||||
if is_already_computed(dataset_name, model_name, optim_loss=optim_loss):
|
||||
print(f'result for dataset={dataset_name} model={model_name} loss={optim_loss} already computed.')
|
||||
return
|
||||
elif (optim_loss=='mae' and model_name=='svmmrae') or (optim_loss=='mrae' and model_name=='svmmae'):
|
||||
elif (optim_loss=='mae' and 'mrae' in model_name) or (optim_loss=='mrae' and 'mae' in model_name):
|
||||
print(f'skipping model={model_name} for optim_loss={optim_loss}')
|
||||
return
|
||||
else:
|
||||
|
|
@ -112,19 +106,24 @@ def run(experiment):
|
|||
benchmark_devel.stats()
|
||||
|
||||
# model selection (hyperparameter optimization for a quantification-oriented loss)
|
||||
model_selection = qp.model_selection.GridSearchQ(
|
||||
model,
|
||||
param_grid=hyperparams,
|
||||
sample_size=SAMPLE_SIZE,
|
||||
n_prevpoints=21,
|
||||
n_repetitions=5,
|
||||
error=optim_loss,
|
||||
refit=False,
|
||||
timeout=60*60,
|
||||
verbose=True
|
||||
)
|
||||
model_selection.fit(benchmark_devel.training, benchmark_devel.test)
|
||||
model = model_selection.best_model()
|
||||
if hyperparams is None:
|
||||
model.fit(benchmark_devel.training, benchmark_devel.test)
|
||||
best_params = {}
|
||||
else:
|
||||
model_selection = qp.model_selection.GridSearchQ(
|
||||
model,
|
||||
param_grid=hyperparams,
|
||||
sample_size=settings.SAMPLE_SIZE,
|
||||
n_prevpoints=21,
|
||||
n_repetitions=5,
|
||||
error=optim_loss,
|
||||
refit=False,
|
||||
timeout=60*60,
|
||||
verbose=True
|
||||
)
|
||||
model_selection.fit(benchmark_devel.training, benchmark_devel.test)
|
||||
model = model_selection.best_model()
|
||||
best_params=model_selection.best_params_
|
||||
|
||||
# model evaluation
|
||||
test_names = [dataset_name] if dataset_name != 'semeval' else ['semeval13', 'semeval14', 'semeval15']
|
||||
|
|
@ -137,7 +136,7 @@ def run(experiment):
|
|||
true_prevalences, estim_prevalences = qp.evaluation.artificial_sampling_prediction(
|
||||
model,
|
||||
test=benchmark_eval.test,
|
||||
sample_size=SAMPLE_SIZE,
|
||||
sample_size=settings.SAMPLE_SIZE,
|
||||
n_prevpoints=21,
|
||||
n_repetitions=25
|
||||
)
|
||||
|
|
@ -149,15 +148,23 @@ def run(experiment):
|
|||
save_results(test_name, model_name, optim_loss,
|
||||
true_prevalences, estim_prevalences,
|
||||
benchmark_eval.training.prevalence(), test_true_prevalence, test_estim_prevalence,
|
||||
model_selection.best_params_)
|
||||
best_params)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Run experiments for Tweeter Sentiment Quantification')
|
||||
parser.add_argument('results', metavar='RESULT_PATH', type=str,
|
||||
help='path to the directory where to store the results')
|
||||
parser.add_argument('--svmperfpath', metavar='SVMPERF_PATH', type=str, default='./svm_perf_quantification',
|
||||
help='path to the directory with svmperf')
|
||||
parser.add_argument('--checkpointdir', metavar='PATH', type=str, default='./checkpoint',
|
||||
help='path to the directory where to dump QuaNet checkpoints')
|
||||
args = parser.parse_args()
|
||||
|
||||
print(f'Result folder: {args.results}')
|
||||
np.random.seed(0)
|
||||
|
||||
optim_losses = ['mae', 'mrae']
|
||||
optim_losses = ['mae']#['mae', 'mrae']
|
||||
datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN
|
||||
models = quantification_models()
|
||||
|
||||
|
|
|
|||
|
|
@ -3,6 +3,9 @@ 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
|
||||
|
||||
|
|
@ -11,8 +14,7 @@ MAXTONE = 50 # sets the intensity of the maximum color reached by the worst (re
|
|||
|
||||
makedirs(tables_path, exist_ok=True)
|
||||
|
||||
sample_size = 100
|
||||
qp.environ['SAMPLE_SIZE'] = sample_size
|
||||
qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
|
||||
|
||||
|
||||
nice = {
|
||||
|
|
@ -95,14 +97,8 @@ def save_table(path, table):
|
|||
foo.write(table)
|
||||
|
||||
|
||||
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']
|
||||
|
||||
|
||||
def experiment_errors(dataset, method, loss):
|
||||
path = result_path(dataset, method, 'm'+loss if not loss.startswith('m') else loss)
|
||||
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)
|
||||
|
|
@ -111,86 +107,110 @@ def experiment_errors(dataset, method, loss):
|
|||
return None
|
||||
|
||||
|
||||
gao_seb_ranks, gao_seb_results = get_ranks_from_Gao_Sebastiani()
|
||||
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()
|
||||
|
||||
for i, eval_func in enumerate(evaluation_measures):
|
||||
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']
|
||||
|
||||
# Tables evaluation scores for AE and RAE (two tables)
|
||||
# ----------------------------------------------------
|
||||
gao_seb_ranks, gao_seb_results = get_ranks_from_Gao_Sebastiani()
|
||||
|
||||
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)
|
||||
for i, eval_func in enumerate(evaluation_measures):
|
||||
|
||||
# fill data table
|
||||
table = Table(rows=datasets, cols=methods)
|
||||
for dataset in datasets:
|
||||
# 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:
|
||||
table.add(dataset, method, experiment_errors(dataset, method, eval_name))
|
||||
tabular += ' & \side{' + nice.get(method, method.upper()) +'$^{' + nicerm(eval_name) + '}$} '
|
||||
tabular += "\\\\\hline\n"
|
||||
|
||||
# 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
|
||||
"""
|
||||
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}
|
||||
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 += table.latexTabular(rowreplace=rowreplace, colreplace=colreplace)
|
||||
tabular += "\n\end{tabularx}"
|
||||
|
||||
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:
|
||||
tabular += 'Average '
|
||||
for method in methods:
|
||||
ranktable.add(dataset, method, values=table.get(dataset, method, 'rank'))
|
||||
|
||||
# write the latex table
|
||||
tabular = """
|
||||
\\begin{tabularx}{\\textwidth}{|c||""" + ('Y|' * 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}']
|
||||
newrank = ranktable.get_average(method)
|
||||
oldrank = gao_seb_ranks[f'Average-{method}-{eval_name}']
|
||||
if newrank != '--':
|
||||
newrank = f'{int(newrank)}'
|
||||
color = ranktable.get_color(dataset, method)
|
||||
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 += '\hline\n'
|
||||
tabular += """
|
||||
\end{tabular}%
|
||||
}
|
||||
"""
|
||||
|
||||
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{tabularx}"
|
||||
save_table(f'./tables/tab_rank_{eval_name}.new.tex', tabular)
|
||||
|
||||
save_table(f'./tables/tab_rank_{eval_name}.new.tex', tabular)
|
||||
|
||||
print("[Done]")
|
||||
print("[Done]")
|
||||
|
|
|
|||
|
|
@ -1,3 +1,7 @@
|
|||
import multiprocessing
|
||||
|
||||
N_JOBS = -2 #multiprocessing.cpu_count()
|
||||
N_JOBS = 1 #multiprocessing.cpu_count()
|
||||
ENSEMBLE_N_JOBS = -2
|
||||
SAMPLE_SIZE = 100
|
||||
|
||||
assert N_JOBS==1 or ENSEMBLE_N_JOBS==1, 'general N_JOBS and ENSEMBLE_N_JOBS should not be both greater than 1'
|
||||
|
|
@ -41,17 +41,17 @@ def artificial_sampling_prediction(
|
|||
indexes = list(test.artificial_sampling_index_generator(sample_size, n_prevpoints, n_repetitions))
|
||||
|
||||
if model.aggregative: #isinstance(model, qp.method.aggregative.AggregativeQuantifier):
|
||||
print('\tinstance of aggregative-quantifier')
|
||||
# print('\tinstance of aggregative-quantifier')
|
||||
quantification_func = model.aggregate
|
||||
if model.probabilistic: # isinstance(model, qp.method.aggregative.AggregativeProbabilisticQuantifier):
|
||||
print('\t\tinstance of probabilitstic-aggregative-quantifier')
|
||||
# print('\t\tinstance of probabilitstic-aggregative-quantifier')
|
||||
preclassified_instances = model.posterior_probabilities(test.instances)
|
||||
else:
|
||||
print('\t\tinstance of hard-aggregative-quantifier')
|
||||
# print('\t\tinstance of hard-aggregative-quantifier')
|
||||
preclassified_instances = model.classify(test.instances)
|
||||
test = LabelledCollection(preclassified_instances, test.labels)
|
||||
else:
|
||||
print('\t\tinstance of base-quantifier')
|
||||
# print('\t\tinstance of base-quantifier')
|
||||
quantification_func = model.quantify
|
||||
|
||||
def _predict_prevalences(index):
|
||||
|
|
|
|||
|
|
@ -123,11 +123,11 @@ def training_helper(learner,
|
|||
if not (0 < val_split < 1):
|
||||
raise ValueError(f'train/val split {val_split} out of range, must be in (0,1)')
|
||||
train, unused = data.split_stratified(train_prop=1-val_split)
|
||||
elif isinstance(val_split, LabelledCollection):
|
||||
elif val_split.__class__.__name__ == LabelledCollection.__name__: #isinstance(val_split, LabelledCollection):
|
||||
train = data
|
||||
unused = val_split
|
||||
else:
|
||||
raise ValueError('param "val_split" not understood; use either a float indicating the split '
|
||||
raise ValueError(f'param "val_split" ({type(val_split)}) not understood; use either a float indicating the split '
|
||||
'proportion, or a LabelledCollection indicating the validation split')
|
||||
else:
|
||||
train, unused = data, None
|
||||
|
|
@ -495,7 +495,7 @@ class OneVsAll(AggregativeQuantifier):
|
|||
self.binary_quantifier = binary_quantifier
|
||||
self.n_jobs = n_jobs
|
||||
|
||||
def fit(self, data: LabelledCollection, fit_learner=True):
|
||||
def fit(self, data: LabelledCollection, fit_learner=True, val_split: Union[float, LabelledCollection]=None):
|
||||
assert not data.binary, \
|
||||
f'{self.__class__.__name__} expect non-binary data'
|
||||
assert isinstance(self.binary_quantifier, BaseQuantifier), \
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
from copy import deepcopy
|
||||
from typing import Union
|
||||
from tqdm import tqdm
|
||||
|
||||
import numpy as np
|
||||
from joblib import Parallel, delayed
|
||||
|
|
@ -6,12 +8,13 @@ from sklearn.linear_model import LogisticRegression
|
|||
from sklearn.model_selection import GridSearchCV, cross_val_predict
|
||||
|
||||
import quapy as qp
|
||||
from quapy import functional as F
|
||||
from quapy.data import LabelledCollection
|
||||
from quapy import functional as F
|
||||
from quapy.evaluation import evaluate
|
||||
from quapy.model_selection import GridSearchQ
|
||||
from . import neural
|
||||
from .base import BaseQuantifier
|
||||
from quapy.method.aggregative import CC, ACC, PCC, PACC, HDy, EMQ
|
||||
|
||||
QuaNet = neural.QuaNetTrainer
|
||||
|
||||
|
|
@ -31,7 +34,7 @@ class Ensemble(BaseQuantifier):
|
|||
Information Fusion, 45, 1-15.
|
||||
"""
|
||||
|
||||
def __init__(self, quantifier: BaseQuantifier, size=50, min_pos=1, red_size=25, policy='ave', n_jobs=1):
|
||||
def __init__(self, quantifier: BaseQuantifier, size=50, min_pos=1, red_size=25, policy='ave', n_jobs=1, verbose=False):
|
||||
assert policy in Ensemble.VALID_POLICIES, f'unknown policy={policy}; valid are {Ensemble.VALID_POLICIES}'
|
||||
self.base_quantifier = quantifier
|
||||
self.size = size
|
||||
|
|
@ -40,8 +43,14 @@ class Ensemble(BaseQuantifier):
|
|||
self.policy = policy
|
||||
self.n_jobs = n_jobs
|
||||
self.post_proba_fn = None
|
||||
self.verbose = verbose
|
||||
|
||||
def fit(self, data: LabelledCollection):
|
||||
def sout(self, msg):
|
||||
if self.verbose:
|
||||
print('[Ensemble]' + msg)
|
||||
|
||||
def fit(self, data: qp.data.LabelledCollection, val_split:Union[qp.data.LabelledCollection, float]=None):
|
||||
self.sout('Fit')
|
||||
if self.policy=='ds' and not data.binary:
|
||||
raise ValueError(f'ds policy is only defined for binary quantification, but this dataset is not binary')
|
||||
|
||||
|
|
@ -57,14 +66,15 @@ class Ensemble(BaseQuantifier):
|
|||
is_static_policy = (self.policy in qp.error.QUANTIFICATION_ERROR_NAMES)
|
||||
self.ensemble = Parallel(n_jobs=self.n_jobs)(
|
||||
delayed(_delayed_new_instance)(
|
||||
self.base_quantifier, data, prev, posteriors, keep_samples=is_static_policy
|
||||
) for prev in prevs
|
||||
self.base_quantifier, data, val_split, prev, posteriors, keep_samples=is_static_policy, verbose=self.verbose
|
||||
) for prev in tqdm(prevs, desc='fitting ensamble')
|
||||
)
|
||||
|
||||
# static selection policy (the name of a quantification-oriented error function to minimize)
|
||||
if self.policy in qp.error.QUANTIFICATION_ERROR_NAMES:
|
||||
self.accuracy_policy(error_name=self.policy)
|
||||
|
||||
self.sout('Fit [Done]')
|
||||
return self
|
||||
|
||||
def quantify(self, instances):
|
||||
|
|
@ -82,8 +92,9 @@ class Ensemble(BaseQuantifier):
|
|||
|
||||
def set_params(self, **parameters):
|
||||
raise NotImplementedError(f'{self.__class__.__name__} should not be used within GridSearchQ; '
|
||||
f'instead, use GridSearchQ within Ensemble, or GridSearchCV whithin the '
|
||||
f'base quantifier if it is an aggregative one.')
|
||||
f'instead, use Ensemble(GridSearchQ(q),...), with q a Quantifier (recommended), '
|
||||
f'or Ensemble(Q(GridSearchCV(l))) with Q a quantifier class that has a learner '
|
||||
f'l optimized for classification (not recommended).')
|
||||
|
||||
def get_params(self, deep=True):
|
||||
raise NotImplementedError()
|
||||
|
|
@ -158,11 +169,13 @@ class Ensemble(BaseQuantifier):
|
|||
|
||||
@property
|
||||
def aggregative(self):
|
||||
raise NotImplementedError('aggregative functionality not yet supported for Ensemble')
|
||||
return False
|
||||
#raise NotImplementedError('aggregative functionality not yet supported for Ensemble')
|
||||
|
||||
@property
|
||||
def probabilistic(self):
|
||||
raise NotImplementedError('probabilistic functionality not yet supported for Ensemble')
|
||||
return False
|
||||
#raise NotImplementedError('probabilistic functionality not yet supported for Ensemble')
|
||||
#return self.base_quantifier.probabilistic
|
||||
|
||||
|
||||
|
|
@ -177,20 +190,32 @@ def select_k(elements, order, k):
|
|||
return [elements[idx] for idx in order[:k]]
|
||||
|
||||
|
||||
def _delayed_new_instance(base_quantifier, data:LabelledCollection, prev, posteriors, keep_samples):
|
||||
def _delayed_new_instance(base_quantifier,
|
||||
data: LabelledCollection,
|
||||
val_split: Union[LabelledCollection, float],
|
||||
prev,
|
||||
posteriors,
|
||||
keep_samples,
|
||||
verbose):
|
||||
if verbose:
|
||||
print(f'\tfit-start for prev {F.strprev(prev)}')
|
||||
model = deepcopy(base_quantifier)
|
||||
sample_index = data.sampling_index(len(data), *prev)
|
||||
sample = data.sampling_from_index(sample_index)
|
||||
model.fit(sample)
|
||||
if val_split is None:
|
||||
model.fit(sample)
|
||||
else:
|
||||
if isinstance(val_split, float):
|
||||
assert 0<val_split<1, 'val_split should be in (0,1)'
|
||||
sample, val_split = sample.split_stratified(train_prop=1-val_split)
|
||||
model.fit(sample, val_split=val_split)
|
||||
tr_prevalence = sample.prevalence()
|
||||
tr_distribution = get_probability_distribution(posteriors[sample_index]) if (posteriors is not None) else None
|
||||
if verbose:
|
||||
print(f'\t\--fit-ended for prev {F.strprev(prev)}')
|
||||
return (model, tr_prevalence, tr_distribution, sample if keep_samples else None)
|
||||
|
||||
|
||||
def _delayed_fit(quantifier, data):
|
||||
quantifier.fit(data)
|
||||
|
||||
|
||||
def _delayed_quantify(quantifier, instances):
|
||||
return quantifier[0].quantify(instances)
|
||||
|
||||
|
|
@ -219,7 +244,7 @@ def _draw_simplex(ndim, min_val, max_trials=100):
|
|||
f'>= {min_val} is unlikely (it failed after {max_trials} trials)')
|
||||
|
||||
|
||||
def _instantiate_ensemble(learner, base_quantifier_class, param_grid, optim, sample_size, eval_budget, **kwargs):
|
||||
def _instantiate_ensemble(learner, base_quantifier_class, param_grid, optim, param_model_sel, **kwargs):
|
||||
if optim is None:
|
||||
base_quantifier = base_quantifier_class(learner)
|
||||
elif optim in qp.error.CLASSIFICATION_ERROR:
|
||||
|
|
@ -228,8 +253,7 @@ def _instantiate_ensemble(learner, base_quantifier_class, param_grid, optim, sam
|
|||
elif optim in qp.error.QUANTIFICATION_ERROR:
|
||||
base_quantifier = GridSearchQ(base_quantifier_class(learner),
|
||||
param_grid=param_grid,
|
||||
sample_size=sample_size,
|
||||
eval_budget=eval_budget,
|
||||
**param_model_sel,
|
||||
error=optim)
|
||||
else:
|
||||
raise ValueError(f'value optim={optim} not understood')
|
||||
|
|
@ -237,74 +261,49 @@ def _instantiate_ensemble(learner, base_quantifier_class, param_grid, optim, sam
|
|||
return Ensemble(base_quantifier, **kwargs)
|
||||
|
||||
|
||||
class EnsembleFactory(BaseQuantifier):
|
||||
def _check_error(error):
|
||||
if error is None:
|
||||
return None
|
||||
if error in qp.error.QUANTIFICATION_ERROR or error in qp.error.CLASSIFICATION_ERROR:
|
||||
return error
|
||||
elif isinstance(error, str):
|
||||
assert error in qp.error.ERROR_NAMES, \
|
||||
f'unknown error name; valid ones are {qp.error.ERROR_NAMES}'
|
||||
return getattr(qp.error, error)
|
||||
else:
|
||||
raise ValueError(f'unexpected error type; must either be a callable function or a str representing\n'
|
||||
f'the name of an error function in {qp.error.ERROR_NAMES}')
|
||||
|
||||
def __init__(self, learner, base_quantifier_class, param_grid=None, optim=None, sample_size=None, eval_budget=None,
|
||||
|
||||
def ensembleFactory(learner, base_quantifier_class, param_grid=None, optim=None,
|
||||
param_model_sel:dict=None,
|
||||
size=50, min_pos=1, red_size=25, policy='ave', n_jobs=1):
|
||||
if param_grid is None and optim is not None:
|
||||
raise ValueError(f'param_grid is None but optim was requested.')
|
||||
error = self._check_error(optim)
|
||||
self.model = _instantiate_ensemble(learner, base_quantifier_class, param_grid, error, sample_size,
|
||||
eval_budget, size=size, min_pos=min_pos, red_size=red_size,
|
||||
policy=policy, n_jobs=n_jobs)
|
||||
|
||||
def fit(self, data):
|
||||
return self.model.fit(data)
|
||||
|
||||
def quantify(self, instances):
|
||||
return self.model.quantify(instances)
|
||||
|
||||
def set_params(self, **parameters):
|
||||
return self.model.set_params(**parameters)
|
||||
|
||||
def get_params(self, deep=True):
|
||||
return self.model.get_params(deep)
|
||||
|
||||
def _check_error(self, error):
|
||||
if error is None:
|
||||
return None
|
||||
if error in qp.error.QUANTIFICATION_ERROR or error in qp.error.CLASSIFICATION_ERROR:
|
||||
return error
|
||||
elif isinstance(error, str):
|
||||
assert error in qp.error.ERROR_NAMES, \
|
||||
f'unknown error name; valid ones are {qp.error.ERROR_NAMES}'
|
||||
return getattr(qp.error, error)
|
||||
else:
|
||||
raise ValueError(f'unexpected error type; must either be a callable function or a str representing\n'
|
||||
f'the name of an error function in {qp.error.ERROR_NAMES}')
|
||||
if optim is not None:
|
||||
if param_grid is None:
|
||||
raise ValueError(f'param_grid is None but optim was requested.')
|
||||
if param_model_sel is None:
|
||||
raise ValueError(f'param_model_sel is None but optim was requested.')
|
||||
error = _check_error(optim)
|
||||
return _instantiate_ensemble(learner, base_quantifier_class, param_grid, error, param_model_sel,
|
||||
size=size, min_pos=min_pos, red_size=red_size,
|
||||
policy=policy, n_jobs=n_jobs)
|
||||
|
||||
|
||||
class ECC(EnsembleFactory):
|
||||
def __init__(self, learner, param_grid=None, optim=None, sample_size=None, eval_budget=None,
|
||||
size=50, min_pos=1, red_size=25, policy='ave', n_jobs=1):
|
||||
super().__init__(
|
||||
learner, qp.method.aggregative.CC, param_grid, optim, sample_size, eval_budget, size, min_pos,
|
||||
red_size, policy, n_jobs
|
||||
)
|
||||
def ECC(learner, param_grid=None, optim=None, param_mod_sel=None, size=50, min_pos=1, red_size=25, policy='ave', n_jobs=1):
|
||||
return ensembleFactory(learner, CC, param_grid, optim, param_mod_sel, size, min_pos, red_size, policy, n_jobs)
|
||||
|
||||
|
||||
class EACC(EnsembleFactory):
|
||||
def __init__(self, learner, param_grid=None, optim=None, sample_size=None, eval_budget=None,
|
||||
size=50, min_pos=1, red_size=25, policy='ave', n_jobs=1):
|
||||
super().__init__(
|
||||
learner, qp.method.aggregative.ACC, param_grid, optim, sample_size, eval_budget, size, min_pos,
|
||||
red_size, policy, n_jobs
|
||||
)
|
||||
def EACC(learner, param_grid=None, optim=None, param_mod_sel=None, size=50, min_pos=1, red_size=25, policy='ave', n_jobs=1):
|
||||
return ensembleFactory(learner, ACC, param_grid, optim, param_mod_sel, size, min_pos, red_size, policy, n_jobs)
|
||||
|
||||
|
||||
class EHDy(EnsembleFactory):
|
||||
def __init__(self, learner, param_grid=None, optim=None, sample_size=None, eval_budget=None,
|
||||
size=50, min_pos=1, red_size=25, policy='ave', n_jobs=1):
|
||||
super().__init__(
|
||||
learner, qp.method.aggregative.HDy, param_grid, optim, sample_size, eval_budget, size, min_pos,
|
||||
red_size, policy, n_jobs
|
||||
)
|
||||
def EPACC(learner, param_grid=None, optim=None, param_mod_sel=None, size=50, min_pos=1, red_size=25, policy='ave', n_jobs=1):
|
||||
return ensembleFactory(learner, PACC, param_grid, optim, param_mod_sel, size, min_pos, red_size, policy, n_jobs)
|
||||
|
||||
|
||||
class EEMQ(EnsembleFactory):
|
||||
def __init__(self, learner, param_grid=None, optim=None, sample_size=None, eval_budget=None,
|
||||
size=50, min_pos=1, red_size=25, policy='ave', n_jobs=1):
|
||||
super().__init__(
|
||||
learner, qp.method.aggregative.EMQ, param_grid, optim, sample_size, eval_budget, size, min_pos,
|
||||
red_size, policy, n_jobs
|
||||
)
|
||||
def EHDy(learner, param_grid=None, optim=None, param_mod_sel=None, size=50, min_pos=1, red_size=25, policy='ave', n_jobs=1):
|
||||
return ensembleFactory(learner, HDy, param_grid, optim, param_mod_sel, size, min_pos, red_size, policy, n_jobs)
|
||||
|
||||
|
||||
def EEMQ(learner, param_grid=None, optim=None, param_mod_sel=None, size=50, min_pos=1, red_size=25, policy='ave', n_jobs=1):
|
||||
return ensembleFactory(learner, EMQ, param_grid, optim, param_mod_sel, size, min_pos, red_size, policy, n_jobs)
|
||||
|
|
@ -278,6 +278,7 @@ class QuaNetModule(torch.nn.Module):
|
|||
# the shape should be (1, number-of-instances, embedding-size + 1)
|
||||
embeded_posteriors = embeded_posteriors.unsqueeze(0)
|
||||
|
||||
self.lstm.flatten_parameters()
|
||||
_, (rnn_hidden,_) = self.lstm(embeded_posteriors, self.init_hidden())
|
||||
rnn_hidden = rnn_hidden.view(self.nlayers, self.ndirections, -1, self.hidden_size)
|
||||
quant_embedding = rnn_hidden[0].view(-1)
|
||||
|
|
|
|||
|
|
@ -118,17 +118,18 @@ class GridSearchQ(BaseQuantifier):
|
|||
raise ValueError(f'unexpected error type; must either be a callable function or a str representing\n'
|
||||
f'the name of an error function in {qp.error.QUANTIFICATION_ERROR_NAMES}')
|
||||
|
||||
def fit(self, training: LabelledCollection, validation: Union[LabelledCollection, float]=0.4):
|
||||
def fit(self, training: LabelledCollection, val_split: Union[LabelledCollection, float]=0.4):
|
||||
"""
|
||||
:param training: the training set on which to optimize the hyperparameters
|
||||
:param validation: either a LabelledCollection on which to test the performance of the different settings, or
|
||||
:param val_split: either a LabelledCollection on which to test the performance of the different settings, or
|
||||
a float in [0,1] indicating the proportion of labelled data to extract from the training set
|
||||
"""
|
||||
training, validation = self.__check_training_validation(training, validation)
|
||||
training, val_split = self.__check_training_validation(training, val_split)
|
||||
assert isinstance(self.sample_size, int) and self.sample_size > 0, 'sample_size must be a positive integer'
|
||||
self.__check_num_evals(self.n_prevpoints, self.eval_budget, self.n_repetitions, training.n_classes)
|
||||
|
||||
print(f'training size={len(training)}')
|
||||
print(f'validation size={len(validation)}')
|
||||
# print(f'training size={len(training)}')
|
||||
# print(f'validation size={len(val_split)}')
|
||||
params_keys = list(self.param_grid.keys())
|
||||
params_values = list(self.param_grid.values())
|
||||
|
||||
|
|
@ -146,7 +147,7 @@ class GridSearchQ(BaseQuantifier):
|
|||
self.best_score_ = None
|
||||
some_timeouts = False
|
||||
for values in itertools.product(*params_values):
|
||||
params = {k: values[i] for i, k in enumerate(params_keys)}
|
||||
params = dict({k: values[i] for i, k in enumerate(params_keys)})
|
||||
|
||||
if self.timeout > 0:
|
||||
signal.alarm(self.timeout)
|
||||
|
|
@ -156,8 +157,8 @@ class GridSearchQ(BaseQuantifier):
|
|||
model.set_params(**params)
|
||||
model.fit(training)
|
||||
true_prevalences, estim_prevalences = artificial_sampling_prediction(
|
||||
model, validation, self.sample_size, self.n_prevpoints, self.n_repetitions, n_jobs, self.random_seed,
|
||||
verbose=True
|
||||
model, val_split, self.sample_size, self.n_prevpoints, self.n_repetitions, n_jobs, self.random_seed,
|
||||
verbose=False
|
||||
)
|
||||
|
||||
score = self.error(true_prevalences, estim_prevalences)
|
||||
|
|
@ -184,7 +185,7 @@ class GridSearchQ(BaseQuantifier):
|
|||
|
||||
if self.refit:
|
||||
self.sout(f'refitting on the whole development set')
|
||||
self.best_model_.fit(training + validation)
|
||||
self.best_model_.fit(training + val_split)
|
||||
|
||||
return self
|
||||
|
||||
|
|
|
|||
26
test.py
26
test.py
|
|
@ -17,7 +17,7 @@ from quapy.model_selection import GridSearchQ
|
|||
qp.environ['SAMPLE_SIZE'] = 500
|
||||
#param_grid = {'C': np.logspace(-3,3,7), 'class_weight': ['balanced', None]}
|
||||
param_grid = {'C': np.logspace(0,3,4), 'class_weight': ['balanced']}
|
||||
max_evaluations = 5000
|
||||
max_evaluations = 500
|
||||
|
||||
sample_size = qp.environ['SAMPLE_SIZE']
|
||||
binary = False
|
||||
|
|
@ -29,7 +29,7 @@ if binary:
|
|||
|
||||
else:
|
||||
dataset = qp.datasets.fetch_twitter('hcr', for_model_selection=False, min_df=10, pickle=True)
|
||||
#dataset.training = dataset.training.sampling(sample_size, 0.2, 0.5, 0.3)
|
||||
dataset.training = dataset.training.sampling(sample_size, 0.2, 0.5, 0.3)
|
||||
|
||||
print(f'dataset loaded: #training={len(dataset.training)} #test={len(dataset.test)}')
|
||||
|
||||
|
|
@ -52,8 +52,14 @@ print(f'dataset loaded: #training={len(dataset.training)} #test={len(dataset.tes
|
|||
|
||||
#learner = GridSearchCV(LogisticRegression(max_iter=1000), param_grid=param_grid, n_jobs=-1, verbose=1)
|
||||
learner = LogisticRegression(max_iter=1000)
|
||||
model = qp.method.aggregative.ClassifyAndCount(learner)
|
||||
#model = qp.method.meta.ECC(learner, size=20, red_size=10, param_grid=None, optim=None, policy='ds')
|
||||
# model = qp.method.aggregative.ClassifyAndCount(learner)
|
||||
|
||||
|
||||
model = qp.method.meta.EPACC(learner, size=10, red_size=5,
|
||||
param_grid={'C':[1,10,100]},
|
||||
optim='mae', param_mod_sel={'sample_size':100, 'n_prevpoints':21, 'n_repetitions':5},
|
||||
policy='ptr', n_jobs=1)
|
||||
|
||||
#model = qp.method.meta.EHDy(learner, param_grid=param_grid, optim='mae',
|
||||
# sample_size=sample_size, eval_budget=max_evaluations//10, n_jobs=-1)
|
||||
#model = qp.method.aggregative.ClassifyAndCount(learner)
|
||||
|
|
@ -69,10 +75,10 @@ if qp.isbinary(model) and not qp.isbinary(dataset):
|
|||
print(f'fitting model {model.__class__.__name__}')
|
||||
#train, val = dataset.training.split_stratified(0.6)
|
||||
#model.fit(train, val_split=val)
|
||||
model.fit(dataset.training)
|
||||
#for i,e in enumerate(model.ensemble):
|
||||
#print(i, e.learner.best_estimator_)
|
||||
# print(i, e.best_model_.learner)
|
||||
model.fit(dataset.training, val_split=dataset.test)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# estimating class prevalences
|
||||
|
|
@ -106,7 +112,7 @@ for error in qp.error.QUANTIFICATION_ERROR:
|
|||
score = error(true_prev, estim_prev)
|
||||
print(f'{error.__name__}={score:.5f}')
|
||||
|
||||
|
||||
sys.exit(0)
|
||||
# Model selection and Evaluation according to the artificial sampling protocol
|
||||
# ----------------------------------------------------------------------------
|
||||
|
||||
|
|
@ -119,7 +125,7 @@ model_selection = GridSearchQ(model,
|
|||
verbose=True,
|
||||
timeout=4)
|
||||
|
||||
model = model_selection.fit(dataset.training, validation=0.3)
|
||||
model = model_selection.fit(dataset.training, val_split=0.3)
|
||||
#model = model_selection.fit(train, validation=val)
|
||||
print(f'Model selection: best_params = {model_selection.best_params_}')
|
||||
print(f'param scores:')
|
||||
|
|
|
|||
Loading…
Reference in New Issue