cleaning new methods from master
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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=['*'], 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 NewMethods.fgsld.fgsld_quantifiers import FakeFGLSD
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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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yield 'FGSLD(3)', FakeFGLSD(newLR(), nbins=3, isomerous=False, recompute_bins=True), {}
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yield 'FGSLD(5)', FakeFGLSD(newLR(), nbins=5, isomerous=False, recompute_bins=True), {}
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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']
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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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import numpy as np
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import logging
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from collections import namedtuple
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from sklearn.metrics import brier_score_loss
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from sklearn.preprocessing import MultiLabelBinarizer
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from NewMethods.fgsld.metrics import smoothmacroF1, isometric_brier_decomposition, isomerous_brier_decomposition
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History = namedtuple('History', ('posteriors', 'priors', 'y', 'iteration', 'stopping_criterium'))
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MeasureSingleHistory = namedtuple('MeasureSingleHistory', (
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'soft_acc', 'soft_f1', 'abs_errors', 'test_priors', 'train_priors', 'predict_priors', 'brier',
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'isometric_ref_loss', 'isometric_cal_loss', 'isomerous_ref_loss', 'isomerous_cal_loss'
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))
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def get_measures_single_history(history: History, multi_class) -> MeasureSingleHistory:
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y = history.y
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y_bin = MultiLabelBinarizer(classes=list(range(history.posteriors.shape[1]))).fit_transform(np.expand_dims(y, 1))
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soft_acc = soft_accuracy(y, history.posteriors)
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f1 = smoothmacroF1(y_bin, history.posteriors)
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if multi_class:
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test_priors = np.mean(y_bin, 0)
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abs_errors = abs(test_priors - history.priors)
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train_priors = history.priors
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predict_priors = np.mean(history.posteriors, 0)
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brier = 0
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else:
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test_priors = np.mean(y_bin, 0)[1]
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abs_errors = abs(test_priors - history.priors[1])
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train_priors = history.priors[1]
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predict_priors = np.mean(history.posteriors[:, 1])
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brier = brier_score_loss(y, history.posteriors[:, 1])
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isometric_cal_loss, isometric_ref_loss = isometric_brier_decomposition(y, history.posteriors)
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isomerous_em_cal_loss, isomerous_em_ref_loss = isomerous_brier_decomposition(y, history.posteriors)
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return MeasureSingleHistory(
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soft_acc, f1, abs_errors, test_priors, train_priors, predict_priors, brier, isometric_ref_loss,
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isometric_cal_loss, isomerous_em_ref_loss, isomerous_em_cal_loss
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)
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def soft_accuracy(y, posteriors):
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return sum(posteriors[y == c][:, c].sum() for c in range(posteriors.shape[1])) / posteriors.sum()
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def soft_f1(y, posteriors):
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cont_matrix = {
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'TPM': posteriors[y == 1][:, 1].sum(),
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'TNM': posteriors[y == 0][:, 0].sum(),
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'FPM': posteriors[y == 0][:, 1].sum(),
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'FNM': posteriors[y == 1][:, 0].sum()
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}
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precision = cont_matrix['TPM'] / (cont_matrix['TPM'] + cont_matrix['FPM'])
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recall = cont_matrix['TPM'] / (cont_matrix['TPM'] + cont_matrix['FNM'])
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return 2 * (precision * recall / (precision + recall))
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def em(y, posteriors_zero, priors_zero, epsilon=1e-6, multi_class=False, return_posteriors_hist=False):
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"""
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Implements the prior correction method based on EM presented in:
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"Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure"
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Saerens, Latinne and Decaestecker, 2002
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http://www.isys.ucl.ac.be/staff/marco/Publications/Saerens2002a.pdf
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:param y: true labels of test items, to measure accuracy, precision and recall.
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:param posteriors_zero: posterior probabilities on test items, as returned by a classifier. A 2D-array with shape
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Ø(items, classes).
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:param priors_zero: prior probabilities measured on training set.
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:param epsilon: stopping threshold.
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:param multi_class: whether the algorithm is running in a multi-label multi-class context or not.
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:param return_posteriors_hist: whether posteriors for each iteration should be returned or not. If true, the returned
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posteriors_s will actually be the list of posteriors for every iteration.
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:return: posteriors_s, priors_s, history: final adjusted posteriors, final adjusted priors, a list of length s
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where each element is a tuple with the step counter, the current priors (as list), the stopping criterium value,
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accuracy, precision and recall.
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"""
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s = 0
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priors_s = np.copy(priors_zero)
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posteriors_s = np.copy(posteriors_zero)
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if return_posteriors_hist:
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posteriors_hist = [posteriors_s.copy()]
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val = 2 * epsilon
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history = list()
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history.append(get_measures_single_history(History(posteriors_zero, priors_zero, y, s, 1), multi_class))
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while not val < epsilon and s < 999:
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# M step
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priors_s_minus_one = priors_s.copy()
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priors_s = posteriors_s.mean(0)
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# E step
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ratios = priors_s / priors_zero
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denominators = 0
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for c in range(priors_zero.shape[0]):
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denominators += ratios[c] * posteriors_zero[:, c]
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for c in range(priors_zero.shape[0]):
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posteriors_s[:, c] = ratios[c] * posteriors_zero[:, c] / denominators
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# check for stop
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val = 0
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for i in range(len(priors_s_minus_one)):
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val += abs(priors_s_minus_one[i] - priors_s[i])
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logging.debug(f"Em iteration: {s}; Val: {val}")
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s += 1
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if return_posteriors_hist:
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posteriors_hist.append(posteriors_s.copy())
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history.append(get_measures_single_history(History(posteriors_s, priors_s, y, s, val), multi_class))
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if return_posteriors_hist:
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return posteriors_hist, priors_s, history
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return posteriors_s, priors_s, history
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from sklearn.calibration import CalibratedClassifierCV
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from sklearn.linear_model import LogisticRegression
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from sklearn.svm import LinearSVC
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from fgsld_quantifiers import FakeFGLSD
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from method.aggregative import EMQ, CC
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import quapy as qp
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import numpy as np
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qp.environ['SAMPLE_SIZE'] = 500
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dataset = qp.datasets.fetch_reviews('hp')
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qp.data.preprocessing.text2tfidf(dataset, min_df=5, inplace=True)
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training = dataset.training
|
||||
test = dataset.test
|
||||
|
||||
cls = CalibratedClassifierCV(LinearSVC())
|
||||
|
||||
#cls = LogisticRegression()
|
||||
|
||||
|
||||
method_names, true_prevs, estim_prevs, tr_prevs = [], [], [], []
|
||||
|
||||
for model, model_name in [
|
||||
(CC(cls), 'CC'),
|
||||
# (FakeFGLSD(cls, nbins=20, isomerous=False, recompute_bins=True), 'FGSLD-isometric-dyn-20'),
|
||||
#(FakeFGLSD(cls, nbins=11, isomerous=False, recompute_bins=True), 'FGSLD-isometric-dyn-11'),
|
||||
#(FakeFGLSD(cls, nbins=8, isomerous=False, recompute_bins=True), 'FGSLD-isometric-dyn-8'),
|
||||
#(FakeFGLSD(cls, nbins=6, isomerous=False, recompute_bins=True), 'FGSLD-isometric-dyn-6'),
|
||||
(FakeFGLSD(cls, nbins=5, isomerous=False, recompute_bins=True), 'FGSLD-isometric-dyn-5'),
|
||||
#(FakeFGLSD(cls, nbins=4, isomerous=False, recompute_bins=True), 'FGSLD-isometric-dyn-4'),
|
||||
#(FakeFGLSD(cls, nbins=3, isomerous=False, recompute_bins=True), 'FGSLD-isometric-dyn-3'),
|
||||
(FakeFGLSD(cls, nbins=1, isomerous=False, recompute_bins=True), 'FGSLD-isometric-dyn-1'),
|
||||
# (FakeFGLSD(cls, nbins=3, isomerous=False, recompute_bins=False), 'FGSLD-isometric-sta-3'),
|
||||
(EMQ(cls), 'SLD'),
|
||||
]:
|
||||
print('running ', model_name)
|
||||
model.fit(training)
|
||||
true_prev, estim_prev = qp.evaluation.artificial_sampling_prediction(
|
||||
model, test, qp.environ['SAMPLE_SIZE'], n_repetitions=5, n_prevpoints=11, n_jobs=-1
|
||||
)
|
||||
method_names.append(model_name)
|
||||
true_prevs.append(true_prev)
|
||||
estim_prevs.append(estim_prev)
|
||||
tr_prevs.append(training.prevalence())
|
||||
#if hasattr(model, 'iterations'):
|
||||
# print(f'iterations ave={np.mean(model.iterations):.3f}, min={np.min(model.iterations):.3f}, max={np.max(model.iterations):.3f}')
|
||||
|
||||
|
||||
qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, train_prev=tr_prevs[0], savepath='./plot_fglsd.png')
|
||||
|
|
@ -1,37 +0,0 @@
|
|||
from sklearn.calibration import CalibratedClassifierCV
|
||||
from sklearn.svm import LinearSVC
|
||||
|
||||
from NewMethods.fgsld.fine_grained_sld import FineGrainedSLD
|
||||
from quapy.method.aggregative import EMQ, CC, training_helper
|
||||
from quapy.data import LabelledCollection
|
||||
from quapy.method.base import BaseQuantifier
|
||||
import quapy.functional as F
|
||||
|
||||
|
||||
class FakeFGLSD(BaseQuantifier):
|
||||
def __init__(self, learner, nbins, isomerous, recompute_bins):
|
||||
self.learner = learner
|
||||
self.nbins = nbins
|
||||
self.isomerous = isomerous
|
||||
self.recompute_bins = recompute_bins
|
||||
self.iterations=[]
|
||||
|
||||
def fit(self, data: LabelledCollection):
|
||||
self.Xtr, self.ytr = data.Xy
|
||||
self.learner.fit(self.Xtr, self.ytr)
|
||||
return self
|
||||
|
||||
def quantify(self, instances):
|
||||
tr_priors = F.prevalence_from_labels(self.ytr, n_classes=2)
|
||||
fgsld = FineGrainedSLD(self.Xtr, instances, self.ytr, tr_priors, self.learner, n_bins=self.nbins)
|
||||
priors, posteriors = fgsld.run(self.isomerous, compute_bins_at_every_iter=self.recompute_bins)
|
||||
self.iterations.append(fgsld.iterations)
|
||||
return priors
|
||||
|
||||
def get_params(self, deep=True):
|
||||
pass
|
||||
|
||||
def set_params(self, **parameters):
|
||||
pass
|
||||
|
||||
|
||||
|
|
@ -1,112 +0,0 @@
|
|||
import numpy as np
|
||||
from NewMethods.fgsld.metrics import isomerous_bins, isometric_bins
|
||||
from NewMethods.fgsld.em import History, get_measures_single_history
|
||||
from sklearn.model_selection import cross_val_predict
|
||||
import math
|
||||
from scipy.special import softmax
|
||||
|
||||
class FineGrainedSLD:
|
||||
def __init__(self, x_tr, x_te, y_tr, tr_priors, clf, n_bins=10):
|
||||
self.y_tr = y_tr
|
||||
self.clf = clf
|
||||
self.tr_priors = tr_priors
|
||||
self.te_preds = clf.predict_proba(x_te)
|
||||
self.tr_preds = cross_val_predict(clf, x_tr, y_tr, method='predict_proba', n_jobs=10)
|
||||
self.n_bins = n_bins
|
||||
self.history: [History] = []
|
||||
self.multi_class = False
|
||||
|
||||
def run(self, isomerous_binning, epsilon=1e-6, compute_bins_at_every_iter=True):
|
||||
"""
|
||||
Run the FGSLD algorithm.
|
||||
|
||||
:param isomerous_binning: whether to use isomerous or isometric binning.
|
||||
:param epsilon: stopping condition.
|
||||
:param compute_bins_at_every_iter: whether FGSLD should recompute the posterior bins at every iteration or not.
|
||||
:param return_posteriors_hist: whether to return posteriors at every iteration or not.
|
||||
:return: If `return_posteriors_hist` is true, the returned posteriors will be a list of numpy arrays, else a single numpy array with posteriors at last iteration.
|
||||
"""
|
||||
smoothing_tr = 1e-9 # 1 / (2 * self.tr_preds.shape[0])
|
||||
smoothing_te = 1e-9 # 1 / (2 * self.te_preds.shape[0])
|
||||
s = 0
|
||||
tr_bin_priors = np.zeros((self.n_bins, self.tr_preds.shape[1]), dtype=np.float)
|
||||
te_bin_priors = np.zeros((self.n_bins, self.te_preds.shape[1]), dtype=np.float)
|
||||
tr_bins = self.__create_bins(training=True, isomerous_binning=isomerous_binning)
|
||||
self.__compute_bins_priors(tr_bin_priors, self.tr_preds, tr_bins, smoothing_tr)
|
||||
|
||||
te_preds_cp = self.te_preds.copy()
|
||||
val = 2 * epsilon
|
||||
while not val < epsilon and s < 1000:
|
||||
if compute_bins_at_every_iter or s==0:
|
||||
te_bins = self.__create_bins(training=False, isomerous_binning=isomerous_binning)
|
||||
|
||||
if s == 0:
|
||||
te_bin_priors_prev = tr_bin_priors.copy()
|
||||
else:
|
||||
te_bin_priors_prev = te_bin_priors.copy()
|
||||
self.__compute_bins_priors(te_bin_priors, self.te_preds, te_bins, smoothing_te)
|
||||
|
||||
for label_idx, bins in te_bins.items():
|
||||
for i, bin_ in enumerate(bins):
|
||||
if bin_.shape[0] == 0:
|
||||
continue
|
||||
alpha = 1
|
||||
beta = 0.1
|
||||
local_te = te_bin_priors[i][label_idx]
|
||||
global_te = self.te_preds[:,label_idx].mean()
|
||||
te = local_te*alpha + global_te*(1-alpha)
|
||||
local_tr = tr_bin_priors[i][label_idx]
|
||||
global_tr = self.tr_priors[label_idx]
|
||||
tr = local_tr*beta + global_tr*(1-beta)
|
||||
#local_min = (math.floor(tr * self.n_bins) / self.n_bins)
|
||||
# local_max = local_min + .1
|
||||
# trans = lambda l: min(max((l - local_min) / 1, 0), 1)
|
||||
assert not isomerous_binning, 'not tested'
|
||||
#trans = lambda l: l - local_min
|
||||
# trans = lambda l: l
|
||||
# ratio = (trans(te) / trans(tr))
|
||||
#ratio = np.clip(ratio, 0.1, 2)
|
||||
#ratio = ratio**3
|
||||
#self.te_preds[:, label_idx][bin_] = (te_preds_cp[:, label_idx][bin_]) * ratio
|
||||
old_posterior = te_preds_cp[:, label_idx][bin_]
|
||||
lr = 1
|
||||
#self.te_preds[:, label_idx][bin_] = np.clip(old_posterior + (te-tr)*lr, 0, None)
|
||||
self.te_preds[:, label_idx][bin_] = np.clip(old_posterior + (te - tr) * lr, 0, None)
|
||||
#self.te_preds[:, label_idx][bin_] = (te_preds_cp[:, label_idx][bin_]) * ratio
|
||||
|
||||
# Normalization step
|
||||
self.te_preds = (self.te_preds / self.te_preds.sum(axis=1, keepdims=True))
|
||||
#self.te_preds = softmax(self.te_preds, axis=1)
|
||||
|
||||
val = np.max(np.abs(te_bin_priors / te_bin_priors_prev) - 1)
|
||||
s += 1
|
||||
|
||||
self.iterations = s
|
||||
|
||||
priors = self.te_preds.mean(axis=0)
|
||||
posteriors = self.te_preds
|
||||
|
||||
return priors, posteriors
|
||||
|
||||
def __compute_bins_priors(self, bin_priors_placeholder, posteriors, bins, smoothing):
|
||||
for label_idx, bins in bins.items():
|
||||
for i, bin_ in enumerate(bins):
|
||||
if bin_.shape[0] == 0:
|
||||
bin_priors_placeholder[i, label_idx] = smoothing
|
||||
continue
|
||||
numerator = posteriors[bin_, label_idx].mean()
|
||||
bin_prior = (numerator + smoothing) / (1 + self.n_bins * smoothing) # normalize priors
|
||||
bin_priors_placeholder[i, label_idx] = bin_prior
|
||||
|
||||
def __create_bins(self, training: bool, isomerous_binning: bool):
|
||||
bins = {}
|
||||
preds = self.tr_preds if training else self.te_preds
|
||||
if isomerous_binning:
|
||||
for label_idx in range(preds.shape[1]):
|
||||
bins[label_idx] = isomerous_bins(label_idx, preds, self.n_bins)
|
||||
else:
|
||||
intervals = np.linspace(0., 1., num=self.n_bins, endpoint=False)
|
||||
for label_idx in range(preds.shape[1]):
|
||||
bins_ = isometric_bins(label_idx, preds, intervals)
|
||||
bins[label_idx] = [bins_[i] for i in intervals]
|
||||
return bins
|
||||
|
|
@ -1,271 +0,0 @@
|
|||
import numpy as np
|
||||
|
||||
"""
|
||||
Scikit learn provides a full set of evaluation metrics, but they treat special cases differently.
|
||||
I.e., when the number of true positives, false positives, and false negatives ammount to 0, all
|
||||
affected metrics (precision, recall, and thus f1) output 0 in Scikit learn.
|
||||
We adhere to the common practice of outputting 1 in this case since the classifier has correctly
|
||||
classified all examples as negatives.
|
||||
"""
|
||||
|
||||
|
||||
def isometric_brier_decomposition(true_labels, predicted_labels, bin_intervals=np.arange(0., 1.1, 0.1), step=0.1):
|
||||
"""
|
||||
The Isometric Brier decomposition or score is obtained by partitioning U into intervals I_1j,...,I_bj that
|
||||
have equal length, where U is the total size of our test set (i.e., true_labels.shape[0]). This means that,
|
||||
if b=10 then I_1j = [0.0,0.1), I_2j = [0.2, 0.3),...,I_bj = [0.9,1.0).
|
||||
|
||||
bin_intervals is a numpy.array containing the range of the different intervals. Since it is a single dimensional
|
||||
array, for every interval I_n we take the posterior probabilities Pr_n(x) such that I_n <= Pr_n(x) < I_n + step.
|
||||
This variable defaults to np.arange(0., 1.0, 0.1), i.e. an array like [0.1, 0.2, ..., 1.0].
|
||||
|
||||
:return: a tuple (calibration score, refinement score)
|
||||
"""
|
||||
labels = set(true_labels)
|
||||
calibration_score, refinement_score = 0.0, 0.0
|
||||
for i in range(len(labels)):
|
||||
bins = isometric_bins(i, predicted_labels, bin_intervals, step)
|
||||
c_score, r_score = brier_decomposition(bins.values(), true_labels, predicted_labels, class_=i)
|
||||
calibration_score += c_score
|
||||
refinement_score += r_score
|
||||
return calibration_score, refinement_score
|
||||
|
||||
|
||||
def isomerous_brier_decomposition(true_labels, predicted_labels, n=10):
|
||||
"""
|
||||
The Isomerous Brier decomposition or score is obtained by partitioning U into intervals I_1j,...,I_bj such that
|
||||
the corresponding bins B_1j,...,B_bj have equal size, where U is our test set. This means that, for every x' in
|
||||
B_sj and x'' in B_tj with s < t, it holds that Pr(c_j|x') <= Pr(c_j|x'') and |B_sj| == |B_tj|, for any s,t in
|
||||
{1,...,b}.
|
||||
|
||||
The n variable holds the number of bins we want (defaults to 10). Notice that we perform a numpy.array_split on
|
||||
the predicted_labels, creating l % n sub-arrays of size l//n + 1 and the rest of size l//n, where l is the length
|
||||
of the array.
|
||||
|
||||
:return: a tuple (calibration score, refinement score)
|
||||
"""
|
||||
|
||||
labels = set(true_labels)
|
||||
calibration_score, refinement_score = 0.0, 0.0
|
||||
for i in range(len(labels)):
|
||||
bins = isomerous_bins(i, predicted_labels, n)
|
||||
c_score, r_score = brier_decomposition(bins, true_labels, predicted_labels, class_=i)
|
||||
calibration_score += c_score
|
||||
refinement_score += r_score
|
||||
return calibration_score, refinement_score
|
||||
|
||||
|
||||
def brier_decomposition(bins, true_labels, predicted_labels, class_=1):
|
||||
"""
|
||||
:param bins: must be an array of indices
|
||||
:return: a tuple (calibration_score, refinement_score)
|
||||
"""
|
||||
calibration_score = 0
|
||||
refinement_score = 0
|
||||
for bin_ in bins:
|
||||
if bin_.size <= 0:
|
||||
continue
|
||||
v_x = (bin_.shape[0] / true_labels.shape[0])
|
||||
ro_x = np.mean(true_labels[bin_] == class_)
|
||||
calibration_score += v_x * (predicted_labels[bin_, class_].mean() - ro_x)**2
|
||||
refinement_score += (v_x * ro_x) * (1 - ro_x)
|
||||
labels_len = len(set(true_labels))
|
||||
return calibration_score / (labels_len * len(bins)), refinement_score / (labels_len * len(bins))
|
||||
|
||||
|
||||
#def isometric_bins(label_index, predicted_labels, bin_intervals, step):
|
||||
# predicted_class_label = predicted_labels[:, label_index]
|
||||
# return {interv: np.where(np.logical_and(interv <= predicted_class_label, predicted_class_label < interv + step))[0]
|
||||
# for interv in bin_intervals}
|
||||
|
||||
def isometric_bins(label_index, predicted_labels, bin_intervals):
|
||||
def next_intv(i):
|
||||
return bin_intervals[i + 1] if (i + 1) < len(bin_intervals) else 1.
|
||||
predicted_class_label = predicted_labels[:, label_index]
|
||||
return {
|
||||
interv:
|
||||
np.where(np.logical_and(interv <= predicted_class_label, predicted_class_label < next_intv(i)))[
|
||||
0]
|
||||
for i, interv in enumerate(bin_intervals)
|
||||
}
|
||||
|
||||
|
||||
def isomerous_bins(label_index, predicted_labels, n):
|
||||
sorted_indices = predicted_labels[:, label_index].argsort()
|
||||
return np.array_split(sorted_indices, n)
|
||||
|
||||
|
||||
# true_labels and predicted_labels are two matrices in sklearn.preprocessing.MultiLabelBinarizer format
|
||||
def macroF1(true_labels, predicted_labels):
|
||||
return macro_average(true_labels, predicted_labels, f1)
|
||||
|
||||
|
||||
# true_labels and predicted_labels are two matrices in sklearn.preprocessing.MultiLabelBinarizer format
|
||||
def microF1(true_labels, predicted_labels):
|
||||
return micro_average(true_labels, predicted_labels, f1)
|
||||
|
||||
|
||||
# true_labels and predicted_labels are two matrices in sklearn.preprocessing.MultiLabelBinarizer format
|
||||
def macroK(true_labels, predicted_labels):
|
||||
return macro_average(true_labels, predicted_labels, K)
|
||||
|
||||
|
||||
# true_labels and predicted_labels are two matrices in sklearn.preprocessing.MultiLabelBinarizer format
|
||||
def microK(true_labels, predicted_labels):
|
||||
return micro_average(true_labels, predicted_labels, K)
|
||||
|
||||
|
||||
# true_labels is a matrix in sklearn.preprocessing.MultiLabelBinarizer format and posterior_probabilities is a matrix
|
||||
# of the same shape containing real values in [0,1]
|
||||
def smoothmacroF1(true_labels, posterior_probabilities):
|
||||
return macro_average(true_labels, posterior_probabilities, f1, metric_statistics=soft_single_metric_statistics)
|
||||
|
||||
|
||||
# true_labels is a matrix in sklearn.preprocessing.MultiLabelBinarizer format and posterior_probabilities is a matrix
|
||||
# of the same shape containing real values in [0,1]
|
||||
def smoothmicroF1(true_labels, posterior_probabilities):
|
||||
return micro_average(true_labels, posterior_probabilities, f1, metric_statistics=soft_single_metric_statistics)
|
||||
|
||||
|
||||
# true_labels is a matrix in sklearn.preprocessing.MultiLabelBinarizer format and posterior_probabilities is a matrix
|
||||
# of the same shape containing real values in [0,1]
|
||||
def smoothmacroK(true_labels, posterior_probabilities):
|
||||
return macro_average(true_labels, posterior_probabilities, K, metric_statistics=soft_single_metric_statistics)
|
||||
|
||||
|
||||
# true_labels is a matrix in sklearn.preprocessing.MultiLabelBinarizer format and posterior_probabilities is a matrix
|
||||
# of the same shape containing real values in [0,1]
|
||||
def smoothmicroK(true_labels, posterior_probabilities):
|
||||
return micro_average(true_labels, posterior_probabilities, K, metric_statistics=soft_single_metric_statistics)
|
||||
|
||||
|
||||
class ContTable:
|
||||
def __init__(self, tp=0, tn=0, fp=0, fn=0):
|
||||
self.tp = tp
|
||||
self.tn = tn
|
||||
self.fp = fp
|
||||
self.fn = fn
|
||||
|
||||
def get_d(self): return self.tp + self.tn + self.fp + self.fn
|
||||
|
||||
def get_c(self): return self.tp + self.fn
|
||||
|
||||
def get_not_c(self): return self.tn + self.fp
|
||||
|
||||
def get_f(self): return self.tp + self.fp
|
||||
|
||||
def get_not_f(self): return self.tn + self.fn
|
||||
|
||||
def p_c(self): return (1.0 * self.get_c()) / self.get_d()
|
||||
|
||||
def p_not_c(self): return 1.0 - self.p_c()
|
||||
|
||||
def p_f(self): return (1.0 * self.get_f()) / self.get_d()
|
||||
|
||||
def p_not_f(self): return 1.0 - self.p_f()
|
||||
|
||||
def p_tp(self): return (1.0 * self.tp) / self.get_d()
|
||||
|
||||
def p_tn(self): return (1.0 * self.tn) / self.get_d()
|
||||
|
||||
def p_fp(self): return (1.0 * self.fp) / self.get_d()
|
||||
|
||||
def p_fn(self): return (1.0 * self.fn) / self.get_d()
|
||||
|
||||
def tpr(self):
|
||||
c = 1.0 * self.get_c()
|
||||
return self.tp / c if c > 0.0 else 0.0
|
||||
|
||||
def fpr(self):
|
||||
_c = 1.0 * self.get_not_c()
|
||||
return self.fp / _c if _c > 0.0 else 0.0
|
||||
|
||||
def __add__(self, other):
|
||||
return ContTable(tp=self.tp + other.tp, tn=self.tn + other.tn, fp=self.fp + other.fp, fn=self.fn + other.fn)
|
||||
|
||||
|
||||
def accuracy(cell):
|
||||
return (cell.tp + cell.tn) * 1.0 / (cell.tp + cell.fp + cell.fn + cell.tn)
|
||||
|
||||
|
||||
def f1(cell):
|
||||
num = 2.0 * cell.tp
|
||||
den = 2.0 * cell.tp + cell.fp + cell.fn
|
||||
if den > 0: return num / den
|
||||
# we define f1 to be 1 if den==0 since the classifier has correctly classified all instances as negative
|
||||
return 1.0
|
||||
|
||||
|
||||
def K(cell):
|
||||
specificity, recall = 0., 0.
|
||||
|
||||
AN = cell.tn + cell.fp
|
||||
if AN != 0:
|
||||
specificity = cell.tn * 1. / AN
|
||||
|
||||
AP = cell.tp + cell.fn
|
||||
if AP != 0:
|
||||
recall = cell.tp * 1. / AP
|
||||
|
||||
if AP == 0:
|
||||
return 2. * specificity - 1.
|
||||
elif AN == 0:
|
||||
return 2. * recall - 1.
|
||||
else:
|
||||
return specificity + recall - 1.
|
||||
|
||||
|
||||
# computes the (hard) counters tp, fp, fn, and tn fron a true and predicted vectors of hard decisions
|
||||
# true_labels and predicted_labels are two vectors of shape (number_documents,)
|
||||
def hard_single_metric_statistics(true_labels, predicted_labels):
|
||||
assert len(true_labels) == len(predicted_labels), "Format not consistent between true and predicted labels."
|
||||
nd = len(true_labels)
|
||||
tp = np.sum(predicted_labels[true_labels == 1])
|
||||
fp = np.sum(predicted_labels[true_labels == 0])
|
||||
fn = np.sum(true_labels[predicted_labels == 0])
|
||||
tn = nd - (tp + fp + fn)
|
||||
return ContTable(tp=tp, tn=tn, fp=fp, fn=fn)
|
||||
|
||||
|
||||
# computes the (soft) contingency table where tp, fp, fn, and tn are the cumulative masses for the posterioir
|
||||
# probabilitiesfron with respect to the true binary labels
|
||||
# true_labels and posterior_probabilities are two vectors of shape (number_documents,)
|
||||
def soft_single_metric_statistics(true_labels, posterior_probabilities):
|
||||
assert len(true_labels) == len(posterior_probabilities), "Format not consistent between true and predicted labels."
|
||||
pos_probs = posterior_probabilities[true_labels == 1]
|
||||
neg_probs = posterior_probabilities[true_labels == 0]
|
||||
tp = np.sum(pos_probs)
|
||||
fn = np.sum(1. - pos_probs)
|
||||
fp = np.sum(neg_probs)
|
||||
tn = np.sum(1. - neg_probs)
|
||||
return ContTable(tp=tp, tn=tn, fp=fp, fn=fn)
|
||||
|
||||
|
||||
# if the classifier is single class, then the prediction is a vector of shape=(nD,) which causes issues when compared
|
||||
# to the true labels (of shape=(nD,1)). This method increases the dimensions of the predictions.
|
||||
def __check_consistency_and_adapt(true_labels, predictions):
|
||||
if predictions.ndim == 1:
|
||||
return __check_consistency_and_adapt(true_labels, np.expand_dims(predictions, axis=1))
|
||||
if true_labels.ndim == 1:
|
||||
return __check_consistency_and_adapt(np.expand_dims(true_labels, axis=1), predictions)
|
||||
if true_labels.shape != predictions.shape:
|
||||
raise ValueError("True and predicted label matrices shapes are inconsistent %s %s."
|
||||
% (true_labels.shape, predictions.shape))
|
||||
_, nC = true_labels.shape
|
||||
return true_labels, predictions, nC
|
||||
|
||||
|
||||
def macro_average(true_labels, predicted_labels, metric, metric_statistics=hard_single_metric_statistics):
|
||||
true_labels, predicted_labels, nC = __check_consistency_and_adapt(true_labels, predicted_labels)
|
||||
return np.mean([metric(metric_statistics(true_labels[:, c], predicted_labels[:, c])) for c in range(nC)])
|
||||
|
||||
|
||||
def micro_average(true_labels, predicted_labels, metric, metric_statistics=hard_single_metric_statistics):
|
||||
true_labels, predicted_labels, nC = __check_consistency_and_adapt(true_labels, predicted_labels)
|
||||
|
||||
accum = ContTable()
|
||||
for c in range(nC):
|
||||
other = metric_statistics(true_labels[:, c], predicted_labels[:, c])
|
||||
accum = accum + other
|
||||
|
||||
return metric(accum)
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 179 KiB |
|
|
@ -1,95 +0,0 @@
|
|||
import quapy as qp
|
||||
import settings
|
||||
import os
|
||||
import pathlib
|
||||
import pickle
|
||||
from glob import glob
|
||||
import sys
|
||||
from TweetSentQuant.util import nicename
|
||||
from os.path import join
|
||||
|
||||
|
||||
qp.environ['SAMPLE_SIZE'] = settings.SAMPLE_SIZE
|
||||
plotext='png'
|
||||
|
||||
resultdir = './results'
|
||||
plotdir = './plots'
|
||||
os.makedirs(plotdir, exist_ok=True)
|
||||
|
||||
def gather_results(methods, error_name):
|
||||
method_names, true_prevs, estim_prevs, tr_prevs = [], [], [], []
|
||||
for method in methods:
|
||||
for experiment in glob(f'{resultdir}/*-{method}-m{error_name}.pkl'):
|
||||
true_prevalences, estim_prevalences, tr_prev, te_prev, te_prev_estim, best_params = pickle.load(open(experiment, 'rb'))
|
||||
method_names.append(nicename(method))
|
||||
true_prevs.append(true_prevalences)
|
||||
estim_prevs.append(estim_prevalences)
|
||||
tr_prevs.append(tr_prev)
|
||||
return method_names, true_prevs, estim_prevs, tr_prevs
|
||||
|
||||
|
||||
def plot_error_by_drift(methods, error_name, logscale=False, path=None):
|
||||
print('plotting error by drift')
|
||||
if path is not None:
|
||||
path = join(path, f'error_by_drift_{error_name}.{plotext}')
|
||||
method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
|
||||
qp.plot.error_by_drift(
|
||||
method_names,
|
||||
true_prevs,
|
||||
estim_prevs,
|
||||
tr_prevs,
|
||||
n_bins=20,
|
||||
error_name=error_name,
|
||||
show_std=False,
|
||||
logscale=logscale,
|
||||
title=f'Quantification error as a function of distribution shift',
|
||||
savepath=path
|
||||
)
|
||||
|
||||
|
||||
def diagonal_plot(methods, error_name, path=None):
|
||||
print('plotting diagonal plots')
|
||||
if path is not None:
|
||||
path = join(path, f'diag_{error_name}')
|
||||
method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
|
||||
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}')
|
||||
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}')
|
||||
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}')
|
||||
|
||||
|
||||
def binary_bias_global(methods, error_name, path=None):
|
||||
print('plotting bias global')
|
||||
if path is not None:
|
||||
path = join(path, f'globalbias_{error_name}')
|
||||
method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
|
||||
qp.plot.binary_bias_global(method_names, true_prevs, estim_prevs, pos_class=0, title='Negative', savepath=f'{path}_neg.{plotext}')
|
||||
qp.plot.binary_bias_global(method_names, true_prevs, estim_prevs, pos_class=1, title='Neutral', savepath=f'{path}_neu.{plotext}')
|
||||
qp.plot.binary_bias_global(method_names, true_prevs, estim_prevs, pos_class=2, title='Positive', savepath=f'{path}_pos.{plotext}')
|
||||
|
||||
|
||||
def binary_bias_bins(methods, error_name, path=None):
|
||||
print('plotting bias local')
|
||||
if path is not None:
|
||||
path = join(path, f'localbias_{error_name}')
|
||||
method_names, true_prevs, estim_prevs, tr_prevs = gather_results(methods, error_name)
|
||||
qp.plot.binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=0, title='Negative', legend=False, savepath=f'{path}_neg.{plotext}')
|
||||
qp.plot.binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=1, title='Neutral', legend=False, savepath=f'{path}_neu.{plotext}')
|
||||
qp.plot.binary_bias_bins(method_names, true_prevs, estim_prevs, pos_class=2, title='Positive', legend=True, savepath=f'{path}_pos.{plotext}')
|
||||
|
||||
|
||||
gao_seb_methods = ['cc', 'acc', 'pcc', 'pacc', 'sld', 'svmq', 'svmkld', 'svmnkld']
|
||||
new_methods_ae = ['svmmae' , 'epaccmaeptr', 'epaccmaemae', 'hdy', 'quanet']
|
||||
new_methods_rae = ['svmmrae' , 'epaccmraeptr', 'epaccmraemrae', 'hdy', 'quanet']
|
||||
|
||||
plot_error_by_drift(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
|
||||
plot_error_by_drift(gao_seb_methods+new_methods_rae, error_name='rae', logscale=True, path=plotdir)
|
||||
|
||||
diagonal_plot(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
|
||||
diagonal_plot(gao_seb_methods+new_methods_rae, error_name='rae', path=plotdir)
|
||||
|
||||
binary_bias_global(gao_seb_methods+new_methods_ae, error_name='ae', path=plotdir)
|
||||
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)
|
||||
|
||||
|
|
@ -1,161 +0,0 @@
|
|||
import quapy as qp
|
||||
import numpy as np
|
||||
from os import makedirs
|
||||
import sys, os
|
||||
import pickle
|
||||
from experiments import result_path
|
||||
from tabular import Table
|
||||
import argparse
|
||||
|
||||
tables_path = './tables'
|
||||
MAXTONE = 50 # sets the intensity of the maximum color reached by the worst (red) and best (green) results
|
||||
|
||||
makedirs(tables_path, exist_ok=True)
|
||||
|
||||
sample_size = 100
|
||||
qp.environ['SAMPLE_SIZE'] = sample_size
|
||||
|
||||
|
||||
nice = {
|
||||
'mae':'AE',
|
||||
'mrae':'RAE',
|
||||
'ae':'AE',
|
||||
'rae':'RAE',
|
||||
'svmkld': 'SVM(KLD)',
|
||||
'svmnkld': 'SVM(NKLD)',
|
||||
'svmq': 'SVM(Q)',
|
||||
'svmae': 'SVM(AE)',
|
||||
'svmnae': 'SVM(NAE)',
|
||||
'svmmae': 'SVM(AE)',
|
||||
'svmmrae': 'SVM(RAE)',
|
||||
'quanet': 'QuaNet',
|
||||
'hdy': 'HDy',
|
||||
'hdysld': 'HDy-SLD',
|
||||
'dys': 'DyS',
|
||||
'svmperf':'',
|
||||
'sanders': 'Sanders',
|
||||
'semeval13': 'SemEval13',
|
||||
'semeval14': 'SemEval14',
|
||||
'semeval15': 'SemEval15',
|
||||
'semeval16': 'SemEval16',
|
||||
'Average': 'Average'
|
||||
}
|
||||
|
||||
def save_table(path, table):
|
||||
print(f'saving results in {path}')
|
||||
with open(path, 'wt') as foo:
|
||||
foo.write(table)
|
||||
|
||||
|
||||
def experiment_errors(path, dataset, method, loss):
|
||||
path = result_path(path, dataset, method, 'm'+loss if not loss.startswith('m') else loss)
|
||||
if os.path.exists(path):
|
||||
true_prevs, estim_prevs, _, _, _, _ = pickle.load(open(path, 'rb'))
|
||||
err_fn = getattr(qp.error, loss)
|
||||
errors = err_fn(true_prevs, estim_prevs)
|
||||
return errors
|
||||
return None
|
||||
|
||||
def nicerm(key):
|
||||
return '\mathrm{'+nice[key]+'}'
|
||||
|
||||
|
||||
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 containing the results of the methods tested in Gao & Sebastiani')
|
||||
parser.add_argument('newresults', metavar='RESULT_PATH', type=str,
|
||||
help='path to the directory containing the results for the experimental methods')
|
||||
args = parser.parse_args()
|
||||
|
||||
datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TEST
|
||||
evaluation_measures = [qp.error.ae, qp.error.rae]
|
||||
gao_seb_methods = ['cc', 'acc', 'pcc', 'pacc', 'sld', 'svmq', 'svmkld', 'svmnkld']
|
||||
new_methods = ['hdy'] # methods added to the Gao & Sebastiani methods
|
||||
experimental_methods = ['hdysld'] # experimental
|
||||
|
||||
for i, eval_func in enumerate(evaluation_measures):
|
||||
|
||||
# Tables evaluation scores for AE and RAE (two tables)
|
||||
# ----------------------------------------------------
|
||||
|
||||
eval_name = eval_func.__name__
|
||||
|
||||
added_methods = ['svmm' + eval_name] + new_methods
|
||||
methods = gao_seb_methods + added_methods + experimental_methods
|
||||
nold_methods = len(gao_seb_methods)
|
||||
nnew_methods = len(added_methods)
|
||||
nexp_methods = len(experimental_methods)
|
||||
|
||||
# fill data table
|
||||
table = Table(benchmarks=datasets, methods=methods)
|
||||
for dataset in datasets:
|
||||
for method in methods:
|
||||
if method in experimental_methods:
|
||||
path = args.newresults
|
||||
else:
|
||||
path = args.results
|
||||
table.add(dataset, method, experiment_errors(path, dataset, method, eval_name))
|
||||
|
||||
# write the latex table
|
||||
tabular = """
|
||||
\\begin{tabularx}{\\textwidth}{|c||""" + ('Y|'*nold_methods) + '|' + ('Y|'*nnew_methods) + '|' + ('Y|'*nexp_methods) + """} \hline
|
||||
& \multicolumn{"""+str(nold_methods)+"""}{c||}{Methods tested in~\cite{Gao:2016uq}} &
|
||||
\multicolumn{"""+str(nnew_methods)+"""}{c|}{} &
|
||||
\multicolumn{"""+str(nexp_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(benchmark_replace=rowreplace, method_replace=colreplace)
|
||||
tabular += "\n\end{tabularx}"
|
||||
|
||||
save_table(f'./tables/tab_results_{eval_name}.new.tex', tabular)
|
||||
|
||||
# Tables ranks for AE and RAE (two tables)
|
||||
# ----------------------------------------------------
|
||||
# fill the data table
|
||||
ranktable = Table(benchmarks=datasets, methods=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 = """
|
||||
\\begin{tabularx}{\\textwidth}{|c||""" + ('Y|'*nold_methods) + '|' + ('Y|'*nnew_methods) + '|' + ('Y|'*nexp_methods) + """} \hline
|
||||
& \multicolumn{"""+str(nold_methods)+"""}{c||}{Methods tested in~\cite{Gao:2016uq}} &
|
||||
\multicolumn{"""+str(nnew_methods)+"""}{c|}{} &
|
||||
\multicolumn{"""+str(nexp_methods)+"""}{c|}{}\\\\ \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)
|
||||
if newrank != '--':
|
||||
newrank = f'{int(newrank)}'
|
||||
color = ranktable.get_color(dataset, method)
|
||||
if color == '--':
|
||||
color = ''
|
||||
tabular += ' & ' + f'{newrank}' + color
|
||||
tabular += '\\\\\hline\n'
|
||||
tabular += '\hline\n'
|
||||
|
||||
tabular += 'Average '
|
||||
for method in methods:
|
||||
newrank = ranktable.get_average(method)
|
||||
if newrank != '--':
|
||||
newrank = f'{newrank:.1f}'
|
||||
color = ranktable.get_average(method, 'color')
|
||||
if color == '--':
|
||||
color = ''
|
||||
tabular += ' & ' + f'{newrank}' + color
|
||||
tabular += '\\\\\hline\n'
|
||||
tabular += "\end{tabularx}"
|
||||
|
||||
save_table(f'./tables/tab_rank_{eval_name}.new.tex', tabular)
|
||||
|
||||
print("[Done]")
|
||||
|
|
@ -1,5 +0,0 @@
|
|||
import multiprocessing
|
||||
|
||||
N_JOBS = -2 #multiprocessing.cpu_count()
|
||||
ENSEMBLE_N_JOBS=1
|
||||
SAMPLE_SIZE = 100
|
||||
|
|
@ -1,26 +0,0 @@
|
|||
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)
|
||||
|
|
@ -1,318 +0,0 @@
|
|||
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)}' + '}'
|
||||
|
||||
|
|
@ -1,89 +0,0 @@
|
|||
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