exp
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import numpy as np
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import quapy as qp
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import settings
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import os
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import pickle
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from glob import glob
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import itertools
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import pathlib
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qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
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resultdir = './results'
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methods = ['*']
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def evaluate_results(methods, datasets, error_name):
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results_str = []
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all = []
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error = qp.error.from_name(error_name)
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for method, dataset in itertools.product(methods, datasets):
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for experiment in glob(f'{resultdir}/{dataset}-{method}-{error_name}.pkl'):
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true_prevalences, estim_prevalences, tr_prev, te_prev, te_prev_estim, best_params = \
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pickle.load(open(experiment, 'rb'))
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result = error(true_prevalences, estim_prevalences)
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string = f'{pathlib.Path(experiment).name}: {result:.3f}'
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results_str.append(string)
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all.append(result)
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results_str = sorted(results_str)
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for r in results_str:
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print(r)
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print()
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print(f'Ave: {np.mean(all):.3f}')
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evaluate_results(methods=['epacc*mae1k'], datasets=['*'], error_name='mae')
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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 methods import *
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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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import pickle
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import itertools
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from joblib import Parallel, delayed
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import settings
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import argparse
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import torch
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import shutil
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qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
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def newLR():
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return LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1)
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__C_range = np.logspace(-4, 5, 10)
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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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def experimental_models():
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def newLR():
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return LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1)
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__C_range = np.logspace(-4, 5, 10)
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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 'PACC(5)', PACC(newLR(), val_split=5), {}
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yield 'PACC(10)', PACC(newLR(), val_split=10), {}
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def classic_models():
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# methods tested in Gao & Sebastiani 2016
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yield 'cc', CC(newLR()), lr_params
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yield 'acc', ACC(newLR()), lr_params
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yield 'pcc', PCC(newLR()), lr_params
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yield 'pacc', PACC(newLR()), lr_params
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yield 'sld', EMQ(newLR()), lr_params
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yield 'svmq', OneVsAll(SVMQ(args.svmperfpath)), svmperf_params
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yield 'svmkld', OneVsAll(SVMKLD(args.svmperfpath)), svmperf_params
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yield 'svmnkld', OneVsAll(SVMNKLD(args.svmperfpath)), svmperf_params
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# methods added
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yield 'svmmae', OneVsAll(SVMAE(args.svmperfpath)), svmperf_params
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yield 'svmmrae', OneVsAll(SVMRAE(args.svmperfpath)), svmperf_params
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yield 'hdy', OneVsAll(HDy(newLR())), lr_params
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def cuda_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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learner = PCALR(**newLR().get_params())
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yield 'quanet', QuaNet(learner, settings.SAMPLE_SIZE, checkpointdir=args.checkpointdir, device=device), lr_params
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def ensembles():
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param_mod_sel = {
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'sample_size': settings.SAMPLE_SIZE,
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'n_prevpoints': 21,
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'n_repetitions': 5,
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'verbose': False
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}
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common={
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'max_sample_size': 1000,
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'n_jobs': settings.ENSEMBLE_N_JOBS,
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'param_grid': lr_params,
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'param_mod_sel': param_mod_sel,
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'val_split': 0.4,
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'min_pos': 10
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}
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# hyperparameters will be evaluated within each quantifier of the ensemble, and so the typical model selection
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# will be skipped (by setting hyperparameters to None)
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hyper_none = None
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#yield 'epaccmaeptr', EPACC(newLR(), optim='mae', policy='ptr', **common), hyper_none
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yield 'epaccmaemae1k', EPACC(newLR(), optim='mae', policy='mae', **common), hyper_none
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# yield 'esldmaeptr', EEMQ(newLR(), optim='mae', policy='ptr', **common), hyper_none
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# yield 'esldmaemae', EEMQ(newLR(), optim='mae', policy='mae', **common), hyper_none
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#yield 'epaccmraeptr', EPACC(newLR(), optim='mrae', policy='ptr', **common), hyper_none
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#yield 'epaccmraemrae', EPACC(newLR(), optim='mrae', policy='mrae', **common), hyper_none
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#yield 'esldmraeptr', EEMQ(newLR(), optim='mrae', policy='ptr', **common), hyper_none
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#yield 'esldmraemrae', EEMQ(newLR(), optim='mrae', policy='mrae', **common), hyper_none
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def evaluate_experiment(true_prevalences, estim_prevalences):
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print('\nEvaluation Metrics:\n'+'='*22)
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for eval_measure in [qp.error.mae, qp.error.mrae]:
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err = eval_measure(true_prevalences, estim_prevalences)
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print(f'\t{eval_measure.__name__}={err:.4f}')
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print()
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def evaluate_method_point_test(true_prev, estim_prev):
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print('\nPoint-Test evaluation:\n' + '=' * 22)
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print(f'true-prev={F.strprev(true_prev)}, estim-prev={F.strprev(estim_prev)}')
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for eval_measure in [qp.error.mae, qp.error.mrae]:
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err = eval_measure(true_prev, estim_prev)
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print(f'\t{eval_measure.__name__}={err:.4f}')
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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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if dataset_name=='semeval':
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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(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(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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def run(experiment):
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optim_loss, dataset_name, (model_name, model, hyperparams) = experiment
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if is_already_computed(dataset_name, model_name, optim_loss=optim_loss):
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print(f'result for dataset={dataset_name} model={model_name} loss={optim_loss} already computed.')
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return
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elif (optim_loss == 'mae' and 'mrae' in model_name) or (optim_loss=='mrae' and 'mae' in model_name):
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print(f'skipping model={model_name} for optim_loss={optim_loss}')
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return
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else:
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print(f'running dataset={dataset_name} model={model_name} loss={optim_loss}')
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benchmark_devel = qp.datasets.fetch_twitter(dataset_name, for_model_selection=True, min_df=5, pickle=True)
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benchmark_devel.stats()
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# model selection (hyperparameter optimization for a quantification-oriented loss)
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if hyperparams is not None:
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model_selection = qp.model_selection.GridSearchQ(
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model,
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param_grid=hyperparams,
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sample_size=settings.SAMPLE_SIZE,
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n_prevpoints=21,
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n_repetitions=5,
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error=optim_loss,
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refit=False,
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timeout=60*60,
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verbose=True
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)
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model_selection.fit(benchmark_devel.training, benchmark_devel.test)
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model = model_selection.best_model()
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best_params = model_selection.best_params_
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else:
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best_params = {}
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# model evaluation
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test_names = [dataset_name] if dataset_name != 'semeval' else ['semeval13', 'semeval14', 'semeval15']
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for test_no, test_name in enumerate(test_names):
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benchmark_eval = qp.datasets.fetch_twitter(test_name, for_model_selection=False, min_df=5, pickle=True)
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if test_no == 0:
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print('fitting the selected model')
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# fits the model only the first time
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model.fit(benchmark_eval.training)
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true_prevalences, estim_prevalences = qp.evaluation.artificial_sampling_prediction(
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model,
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test=benchmark_eval.test,
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sample_size=settings.SAMPLE_SIZE,
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n_prevpoints=21,
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n_repetitions=25,
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n_jobs=-1 if isinstance(model, qp.method.meta.Ensemble) else 1
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)
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test_estim_prevalence = model.quantify(benchmark_eval.test.instances)
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test_true_prevalence = benchmark_eval.test.prevalence()
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evaluate_experiment(true_prevalences, estim_prevalences)
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evaluate_method_point_test(test_true_prevalence, test_estim_prevalence)
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save_results(test_name, model_name, optim_loss,
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true_prevalences, estim_prevalences,
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benchmark_eval.training.prevalence(), test_true_prevalence, test_estim_prevalence,
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best_params)
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#if isinstance(model, QuaNet):
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#model.clean_checkpoint_dir()
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if __name__ == '__main__':
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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,
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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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print(f'Result folder: {args.results}')
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np.random.seed(0)
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optim_losses = ['mae', 'mrae']
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datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN
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qp.util.parallel(run, itertools.product(optim_losses, datasets, experimental_models()), n_jobs=settings.N_JOBS)
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# qp.util.parallel(run, itertools.product(optim_losses, datasets, classic_models()), n_jobs=settings.N_JOBS)
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# qp.util.parallel(run, itertools.product(optim_losses, datasets, cuda_models()), n_jobs=settings.CUDA_N_JOBS)
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# qp.util.parallel(run, itertools.product(optim_losses, datasets, ensembles()), n_jobs=1)
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from sklearn.calibration import CalibratedClassifierCV
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from sklearn.svm import LinearSVC
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from NewMethods.fgsld.fine_grained_sld import FineGrainedSLD
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from quapy.method.aggregative import EMQ, CC, training_helper
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from quapy.data import LabelledCollection
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from quapy.method.base import BaseQuantifier
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import quapy.functional as F
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class FakeFGLSD(BaseQuantifier):
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def __init__(self, learner, nbins, isomerous, recompute_bins):
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self.learner = learner
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self.nbins = nbins
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self.isomerous = isomerous
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self.recompute_bins = recompute_bins
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def fit(self, data: LabelledCollection):
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self.Xtr, self.ytr = data.Xy
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self.learner.fit(self.Xtr, self.ytr)
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return self
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def quantify(self, instances):
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tr_priors = F.prevalence_from_labels(self.ytr, n_classes=2)
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fgsld = FineGrainedSLD(self.Xtr, instances, self.ytr, tr_priors, self.learner, n_bins=self.nbins)
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priors, posteriors = fgsld.run(self.isomerous, compute_bins_at_every_iter=self.recompute_bins)
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return priors
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def get_params(self, deep=True):
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pass
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def set_params(self, **parameters):
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pass
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import quapy as qp
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import settings
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import os
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import pathlib
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import pickle
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from glob import glob
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import sys
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from TweetSentQuant.util import nicename
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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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os.makedirs(plotdir, exist_ok=True)
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def gather_results(methods, error_name):
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method_names, true_prevs, estim_prevs, tr_prevs = [], [], [], []
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for method in methods:
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for experiment in glob(f'{resultdir}/*-{method}-m{error_name}.pkl'):
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true_prevalences, estim_prevalences, tr_prev, te_prev, te_prev_estim, best_params = pickle.load(open(experiment, 'rb'))
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method_names.append(nicename(method))
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true_prevs.append(true_prevalences)
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estim_prevs.append(estim_prevalences)
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tr_prevs.append(tr_prev)
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return method_names, true_prevs, estim_prevs, tr_prevs
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def plot_error_by_drift(methods, error_name, logscale=False, path=None):
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print('plotting error by drift')
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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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qp.plot.error_by_drift(
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method_names,
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true_prevs,
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estim_prevs,
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tr_prevs,
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n_bins=20,
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error_name=error_name,
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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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)
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def diagonal_plot(methods, error_name, path=None):
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print('plotting diagonal plots')
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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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def binary_bias_global(methods, error_name, path=None):
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print('plotting bias global')
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if path is not None:
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path = join(path, f'globalbias_{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_bias_global(method_names, true_prevs, estim_prevs, pos_class=0, title='Negative', savepath=f'{path}_neg.{plotext}')
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qp.plot.binary_bias_global(method_names, true_prevs, estim_prevs, pos_class=1, title='Neutral', savepath=f'{path}_neu.{plotext}')
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qp.plot.binary_bias_global(method_names, true_prevs, estim_prevs, pos_class=2, title='Positive', savepath=f'{path}_pos.{plotext}')
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def binary_bias_bins(methods, error_name, path=None):
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print('plotting bias local')
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if path is not None:
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path = join(path, f'localbias_{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_bias_bins(method_names, true_prevs, estim_prevs, pos_class=0, title='Negative', legend=False, savepath=f'{path}_neg.{plotext}')
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qp.plot.binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=1, title='Neutral', legend=False, savepath=f'{path}_neu.{plotext}')
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qp.plot.binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=2, title='Positive', legend=True, savepath=f'{path}_pos.{plotext}')
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gao_seb_methods = ['cc', 'acc', 'pcc', 'pacc', 'sld', 'svmq', 'svmkld', 'svmnkld']
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new_methods_ae = ['svmmae' , 'epaccmaeptr', 'epaccmaemae', 'hdy', 'quanet']
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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)
|
||||
|
||||
#binary_bias_bins(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
|
||||
#binary_bias_bins(gao_seb_methods+new_methods_rae, error_name='rae', path=plotdir)
|
||||
|
||||
|
|
@ -0,0 +1,26 @@
|
|||
from glob import glob
|
||||
import pickle
|
||||
import numpy as np
|
||||
|
||||
results = './results'
|
||||
|
||||
method_choices = {}
|
||||
for file in glob(f'{results}/*'):
|
||||
hyper = pickle.load(open(file, 'rb'))[-1]
|
||||
if hyper:
|
||||
dataset,method,optim = file.split('/')[-1].split('-')
|
||||
key = str(hyper)
|
||||
if method not in method_choices:
|
||||
method_choices[method] = {}
|
||||
if key not in method_choices[method]:
|
||||
method_choices[method][key] = 0
|
||||
method_choices[method][key] = method_choices[method][key]+1
|
||||
|
||||
for method, hyper_count_dict in method_choices.items():
|
||||
hyper, counts = zip(*list(hyper_count_dict.items()))
|
||||
order = np.argsort(counts)
|
||||
counts = np.asarray(counts)[order][::-1]
|
||||
hyper = np.asarray(hyper)[order][::-1]
|
||||
print(method)
|
||||
for hyper_i, count_i in zip(hyper, counts):
|
||||
print('\t', hyper_i, count_i)
|
||||
|
|
@ -0,0 +1,318 @@
|
|||
import numpy as np
|
||||
import itertools
|
||||
from scipy.stats import ttest_ind_from_stats, wilcoxon
|
||||
|
||||
|
||||
class Table:
|
||||
VALID_TESTS = [None, "wilcoxon", "ttest"]
|
||||
|
||||
def __init__(self, benchmarks, methods, lower_is_better=True, ttest='ttest', prec_mean=3,
|
||||
clean_zero=False, show_std=False, prec_std=3, average=True, missing=None, missing_str='--', color=True):
|
||||
assert ttest in self.VALID_TESTS, f'unknown test, valid are {self.VALID_TESTS}'
|
||||
|
||||
self.benchmarks = np.asarray(benchmarks)
|
||||
self.benchmark_index = {row:i for i, row in enumerate(benchmarks)}
|
||||
|
||||
self.methods = np.asarray(methods)
|
||||
self.method_index = {col:j for j, col in enumerate(methods)}
|
||||
|
||||
self.map = {}
|
||||
# keyed (#rows,#cols)-ndarrays holding computations from self.map['values']
|
||||
self._addmap('values', dtype=object)
|
||||
self.lower_is_better = lower_is_better
|
||||
self.ttest = ttest
|
||||
self.prec_mean = prec_mean
|
||||
self.clean_zero = clean_zero
|
||||
self.show_std = show_std
|
||||
self.prec_std = prec_std
|
||||
self.add_average = average
|
||||
self.missing = missing
|
||||
self.missing_str = missing_str
|
||||
self.color = color
|
||||
|
||||
self.touch()
|
||||
|
||||
@property
|
||||
def nbenchmarks(self):
|
||||
return len(self.benchmarks)
|
||||
|
||||
@property
|
||||
def nmethods(self):
|
||||
return len(self.methods)
|
||||
|
||||
def touch(self):
|
||||
self._modif = True
|
||||
|
||||
def update(self):
|
||||
if self._modif:
|
||||
self.compute()
|
||||
|
||||
def _getfilled(self):
|
||||
return np.argwhere(self.map['fill'])
|
||||
|
||||
@property
|
||||
def values(self):
|
||||
return self.map['values']
|
||||
|
||||
def _indexes(self):
|
||||
return itertools.product(range(self.nbenchmarks), range(self.nmethods))
|
||||
|
||||
def _addmap(self, map, dtype, func=None):
|
||||
self.map[map] = np.empty((self.nbenchmarks, self.nmethods), dtype=dtype)
|
||||
if func is None:
|
||||
return
|
||||
m = self.map[map]
|
||||
f = func
|
||||
indexes = self._indexes() if map == 'fill' else self._getfilled()
|
||||
for i, j in indexes:
|
||||
m[i, j] = f(self.values[i, j])
|
||||
|
||||
def _addrank(self):
|
||||
for i in range(self.nbenchmarks):
|
||||
filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
|
||||
col_means = [self.map['mean'][i,j] for j in filled_cols_idx]
|
||||
ranked_cols_idx = filled_cols_idx[np.argsort(col_means)]
|
||||
if not self.lower_is_better:
|
||||
ranked_cols_idx = ranked_cols_idx[::-1]
|
||||
self.map['rank'][i, ranked_cols_idx] = np.arange(1, len(filled_cols_idx)+1)
|
||||
|
||||
def _addcolor(self):
|
||||
for i in range(self.nbenchmarks):
|
||||
filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
|
||||
if filled_cols_idx.size==0:
|
||||
continue
|
||||
col_means = [self.map['mean'][i,j] for j in filled_cols_idx]
|
||||
minval = min(col_means)
|
||||
maxval = max(col_means)
|
||||
for col_idx in filled_cols_idx:
|
||||
val = self.map['mean'][i,col_idx]
|
||||
norm = (maxval - minval)
|
||||
if norm > 0:
|
||||
normval = (val - minval) / norm
|
||||
else:
|
||||
normval = 0.5
|
||||
if self.lower_is_better:
|
||||
normval = 1 - normval
|
||||
self.map['color'][i, col_idx] = color_red2green_01(normval)
|
||||
|
||||
def _run_ttest(self, row, col1, col2):
|
||||
mean1 = self.map['mean'][row, col1]
|
||||
std1 = self.map['std'][row, col1]
|
||||
nobs1 = self.map['nobs'][row, col1]
|
||||
mean2 = self.map['mean'][row, col2]
|
||||
std2 = self.map['std'][row, col2]
|
||||
nobs2 = self.map['nobs'][row, col2]
|
||||
_, p_val = ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2)
|
||||
return p_val
|
||||
|
||||
def _run_wilcoxon(self, row, col1, col2):
|
||||
values1 = self.map['values'][row, col1]
|
||||
values2 = self.map['values'][row, col2]
|
||||
_, p_val = wilcoxon(values1, values2)
|
||||
return p_val
|
||||
|
||||
def _add_statistical_test(self):
|
||||
if self.ttest is None:
|
||||
return
|
||||
self.some_similar = [False]*self.nmethods
|
||||
for i in range(self.nbenchmarks):
|
||||
filled_cols_idx = np.argwhere(self.map['fill'][i]).flatten()
|
||||
if len(filled_cols_idx) <= 1:
|
||||
continue
|
||||
col_means = [self.map['mean'][i,j] for j in filled_cols_idx]
|
||||
best_pos = filled_cols_idx[np.argmin(col_means)]
|
||||
|
||||
for j in filled_cols_idx:
|
||||
if j==best_pos:
|
||||
continue
|
||||
if self.ttest == 'ttest':
|
||||
p_val = self._run_ttest(i, best_pos, j)
|
||||
else:
|
||||
p_val = self._run_wilcoxon(i, best_pos, j)
|
||||
|
||||
pval_outcome = pval_interpretation(p_val)
|
||||
self.map['ttest'][i, j] = pval_outcome
|
||||
if pval_outcome != 'Diff':
|
||||
self.some_similar[j] = True
|
||||
|
||||
def compute(self):
|
||||
self._addmap('fill', dtype=bool, func=lambda x: x is not None)
|
||||
self._addmap('mean', dtype=float, func=np.mean)
|
||||
self._addmap('std', dtype=float, func=np.std)
|
||||
self._addmap('nobs', dtype=float, func=len)
|
||||
self._addmap('rank', dtype=int, func=None)
|
||||
self._addmap('color', dtype=object, func=None)
|
||||
self._addmap('ttest', dtype=object, func=None)
|
||||
self._addmap('latex', dtype=object, func=None)
|
||||
self._addrank()
|
||||
self._addcolor()
|
||||
self._add_statistical_test()
|
||||
if self.add_average:
|
||||
self._addave()
|
||||
self._modif = False
|
||||
|
||||
def _is_column_full(self, col):
|
||||
return all(self.map['fill'][:, self.method_index[col]])
|
||||
|
||||
def _addave(self):
|
||||
ave = Table(['ave'], self.methods, lower_is_better=self.lower_is_better, ttest=self.ttest, average=False,
|
||||
missing=self.missing, missing_str=self.missing_str)
|
||||
for col in self.methods:
|
||||
values = None
|
||||
if self._is_column_full(col):
|
||||
if self.ttest == 'ttest':
|
||||
values = np.asarray(self.map['mean'][:, self.method_index[col]])
|
||||
else: # wilcoxon
|
||||
values = np.concatenate(self.values[:, self.method_index[col]])
|
||||
ave.add('ave', col, values)
|
||||
self.average = ave
|
||||
|
||||
def add(self, benchmark, method, values):
|
||||
if values is not None:
|
||||
values = np.asarray(values)
|
||||
if values.ndim==0:
|
||||
values = values.flatten()
|
||||
rid, cid = self._coordinates(benchmark, method)
|
||||
self.map['values'][rid, cid] = values
|
||||
self.touch()
|
||||
|
||||
def get(self, benchmark, method, attr='mean'):
|
||||
self.update()
|
||||
assert attr in self.map, f'unknwon attribute {attr}'
|
||||
rid, cid = self._coordinates(benchmark, method)
|
||||
if self.map['fill'][rid, cid]:
|
||||
v = self.map[attr][rid, cid]
|
||||
if v is None or (isinstance(v,float) and np.isnan(v)):
|
||||
return self.missing
|
||||
return v
|
||||
else:
|
||||
return self.missing
|
||||
|
||||
def _coordinates(self, benchmark, method):
|
||||
assert benchmark in self.benchmark_index, f'benchmark {benchmark} out of range'
|
||||
assert method in self.method_index, f'method {method} out of range'
|
||||
rid = self.benchmark_index[benchmark]
|
||||
cid = self.method_index[method]
|
||||
return rid, cid
|
||||
|
||||
def get_average(self, method, attr='mean'):
|
||||
self.update()
|
||||
if self.add_average:
|
||||
return self.average.get('ave', method, attr=attr)
|
||||
return None
|
||||
|
||||
def get_color(self, benchmark, method):
|
||||
color = self.get(benchmark, method, attr='color')
|
||||
if color is None:
|
||||
return ''
|
||||
return color
|
||||
|
||||
def latex(self, benchmark, method):
|
||||
self.update()
|
||||
i,j = self._coordinates(benchmark, method)
|
||||
if self.map['fill'][i,j] == False:
|
||||
return self.missing_str
|
||||
|
||||
mean = self.map['mean'][i,j]
|
||||
l = f" {mean:.{self.prec_mean}f}"
|
||||
if self.clean_zero:
|
||||
l = l.replace(' 0.', '.')
|
||||
|
||||
isbest = self.map['rank'][i,j] == 1
|
||||
if isbest:
|
||||
l = "\\textbf{"+l.strip()+"}"
|
||||
|
||||
stat = ''
|
||||
if self.ttest is not None and self.some_similar[j]:
|
||||
test_label = self.map['ttest'][i,j]
|
||||
if test_label == 'Sim':
|
||||
stat = '^{\dag\phantom{\dag}}'
|
||||
elif test_label == 'Same':
|
||||
stat = '^{\ddag}'
|
||||
elif isbest or test_label == 'Diff':
|
||||
stat = '^{\phantom{\ddag}}'
|
||||
|
||||
std = ''
|
||||
if self.show_std:
|
||||
std = self.map['std'][i,j]
|
||||
std = f" {std:.{self.prec_std}f}"
|
||||
if self.clean_zero:
|
||||
std = std.replace(' 0.', '.')
|
||||
std = f" \pm {std:{self.prec_std}}"
|
||||
|
||||
if stat!='' or std!='':
|
||||
l = f'{l}${stat}{std}$'
|
||||
|
||||
if self.color:
|
||||
l += ' ' + self.map['color'][i,j]
|
||||
|
||||
return l
|
||||
|
||||
def latexTabular(self, benchmark_replace={}, method_replace={}, average=True):
|
||||
tab = ' & '
|
||||
tab += ' & '.join([method_replace.get(col, col) for col in self.methods])
|
||||
tab += ' \\\\\hline\n'
|
||||
for row in self.benchmarks:
|
||||
rowname = benchmark_replace.get(row, row)
|
||||
tab += rowname + ' & '
|
||||
tab += self.latexRow(row)
|
||||
|
||||
if average:
|
||||
tab += '\hline\n'
|
||||
tab += 'Average & '
|
||||
tab += self.latexAverage()
|
||||
return tab
|
||||
|
||||
def latexRow(self, benchmark, endl='\\\\\hline\n'):
|
||||
s = [self.latex(benchmark, col) for col in self.methods]
|
||||
s = ' & '.join(s)
|
||||
s += ' ' + endl
|
||||
return s
|
||||
|
||||
def latexAverage(self, endl='\\\\\hline\n'):
|
||||
if self.add_average:
|
||||
return self.average.latexRow('ave', endl=endl)
|
||||
|
||||
def getRankTable(self):
|
||||
t = Table(benchmarks=self.benchmarks, methods=self.methods, prec_mean=0, average=True)
|
||||
for rid, cid in self._getfilled():
|
||||
row = self.benchmarks[rid]
|
||||
col = self.methods[cid]
|
||||
t.add(row, col, self.get(row, col, 'rank'))
|
||||
t.compute()
|
||||
return t
|
||||
|
||||
def dropMethods(self, methods):
|
||||
drop_index = [self.method_index[m] for m in methods]
|
||||
new_methods = np.delete(self.methods, drop_index)
|
||||
new_index = {col:j for j, col in enumerate(new_methods)}
|
||||
|
||||
self.map['values'] = self.values[:,np.asarray([self.method_index[m] for m in new_methods], dtype=int)]
|
||||
self.methods = new_methods
|
||||
self.method_index = new_index
|
||||
self.touch()
|
||||
|
||||
|
||||
def pval_interpretation(p_val):
|
||||
if 0.005 >= p_val:
|
||||
return 'Diff'
|
||||
elif 0.05 >= p_val > 0.005:
|
||||
return 'Sim'
|
||||
elif p_val > 0.05:
|
||||
return 'Same'
|
||||
|
||||
|
||||
def color_red2green_01(val, maxtone=50):
|
||||
if np.isnan(val): return None
|
||||
assert 0 <= val <= 1, f'val {val} out of range [0,1]'
|
||||
|
||||
# rescale to [-1,1]
|
||||
val = val * 2 - 1
|
||||
if val < 0:
|
||||
color = 'red'
|
||||
tone = maxtone * (-val)
|
||||
else:
|
||||
color = 'green'
|
||||
tone = maxtone * val
|
||||
return '\cellcolor{' + color + f'!{int(tone)}' + '}'
|
||||
|
||||
|
|
@ -0,0 +1,89 @@
|
|||
import numpy as np
|
||||
|
||||
|
||||
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',
|
||||
'epaccmaeptr': 'E(PACC)$_\mathrm{Ptr}$',
|
||||
'epaccmaemae': 'E(PACC)$_\mathrm{AE}$',
|
||||
'epaccmraeptr': 'E(PACC)$_\mathrm{Ptr}$',
|
||||
'epaccmraemrae': 'E(PACC)$_\mathrm{RAE}$',
|
||||
'svmperf':'',
|
||||
'sanders': 'Sanders',
|
||||
'semeval13': 'SemEval13',
|
||||
'semeval14': 'SemEval14',
|
||||
'semeval15': 'SemEval15',
|
||||
'semeval16': 'SemEval16',
|
||||
'Average': 'Average'
|
||||
}
|
||||
|
||||
|
||||
def nicerm(key):
|
||||
return '\mathrm{'+nice[key]+'}'
|
||||
|
||||
|
||||
def nicename(method, eval_name=None, side=False):
|
||||
m = nice.get(method, method.upper())
|
||||
if eval_name is not None:
|
||||
o = '$^{' + nicerm(eval_name) + '}$'
|
||||
m = (m+o).replace('$$','')
|
||||
if side:
|
||||
m = '\side{'+m+'}'
|
||||
return m
|
||||
|
||||
|
||||
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
|
||||
Loading…
Reference in New Issue