From 8ef9e6a633c25ad06e68c426f099b956cbdaad73 Mon Sep 17 00:00:00 2001 From: Alex Moreo Date: Mon, 18 Jan 2021 10:53:22 +0100 Subject: [PATCH] bugfix --- TweetSentQuant/experiments.py | 8 ++++++-- TweetSentQuant/gen_tables.py | 2 +- quapy/method/aggregative.py | 36 ++++++++++++++++++++++------------- 3 files changed, 30 insertions(+), 16 deletions(-) diff --git a/TweetSentQuant/experiments.py b/TweetSentQuant/experiments.py index c121811..b49da43 100644 --- a/TweetSentQuant/experiments.py +++ b/TweetSentQuant/experiments.py @@ -33,6 +33,9 @@ def quantification_models(): yield 'svmmae', OneVsAll(qp.method.aggregative.SVMAE(args.svmperfpath)), svmperf_params yield 'svmmrae', OneVsAll(qp.method.aggregative.SVMRAE(args.svmperfpath)), svmperf_params + #sld = qp.method.aggregative.EMQ(newLR()) + #yield 'paccsld', qp.method.aggregative.PACC(sld), lr_params + # 'mlpe': lambda learner: MaximumLikelihoodPrevalenceEstimation(), @@ -136,8 +139,9 @@ if __name__ == '__main__': print(f'Result folder: {args.results}') np.random.seed(0) - optim_losses = ['mae', 'mrae'] - datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN + #optim_losses = ['mae', 'mrae'] + optim_losses = ['mae'] + datasets = ['hcr'] # qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN models = quantification_models() results = Parallel(n_jobs=settings.N_JOBS)( diff --git a/TweetSentQuant/gen_tables.py b/TweetSentQuant/gen_tables.py index 01d2e84..6b9adff 100644 --- a/TweetSentQuant/gen_tables.py +++ b/TweetSentQuant/gen_tables.py @@ -119,7 +119,7 @@ for i, eval_func in enumerate(evaluation_measures): # ---------------------------------------------------- eval_name = eval_func.__name__ - added_methods = ['svm' + eval_name] + new_methods + added_methods = ['svmm' + eval_name] + new_methods methods = gao_seb_methods + added_methods nold_methods = len(gao_seb_methods) nnew_methods = len(added_methods) diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py index ef233e9..4e09945 100644 --- a/quapy/method/aggregative.py +++ b/quapy/method/aggregative.py @@ -9,7 +9,7 @@ from sklearn.calibration import CalibratedClassifierCV from sklearn.metrics import confusion_matrix from sklearn.model_selection import StratifiedKFold from tqdm import tqdm - +import quapy as qp import quapy.functional as F from quapy.classification.svmperf import SVMperf from quapy.data import LabelledCollection @@ -69,8 +69,11 @@ class AggregativeProbabilisticQuantifier(AggregativeQuantifier): probabilities. """ - def posterior_probabilities(self, data): - return self.learner.predict_proba(data) + def posterior_probabilities(self, instances): + return self.learner.predict_proba(instances) + + def predict_proba(self, instances): + return self.posterior_probabilities(instances) def quantify(self, instances): classif_posteriors = self.posterior_probabilities(instances) @@ -122,7 +125,11 @@ def training_helper(learner, 'proportion, or a LabelledCollection indicating the validation split') else: train, unused = data, None - learner.fit(train.instances, train.labels) + + if isinstance(learner, BaseQuantifier): + learner.fit(train) + else: + learner.fit(train.instances, train.labels) else: if ensure_probabilistic: if not hasattr(learner, 'predict_proba'): @@ -229,10 +236,10 @@ class ACC(AggregativeQuantifier): class PCC(AggregativeProbabilisticQuantifier): - def __init__(self, learner:BaseEstimator): + def __init__(self, learner: BaseEstimator): self.learner = learner - def fit(self, data : LabelledCollection, fit_learner=True): + def fit(self, data: LabelledCollection, fit_learner=True): self.learner, _ = training_helper(self.learner, data, fit_learner, ensure_probabilistic=True) return self @@ -301,9 +308,6 @@ class PACC(AggregativeProbabilisticQuantifier): def classify(self, data): return self.pcc.classify(data) - def soft_classify(self, data): - return self.pcc.posterior_probabilities(data) - class EMQ(AggregativeProbabilisticQuantifier): @@ -319,7 +323,13 @@ class EMQ(AggregativeProbabilisticQuantifier): return self def aggregate(self, classif_posteriors, epsilon=EPSILON): - return self.EM(self.train_prevalence, classif_posteriors, epsilon) + priors, posteriors = self.EM(self.train_prevalence, classif_posteriors, epsilon) + return priors + + def predict_proba(self, instances, epsilon=EPSILON): + classif_posteriors = self.learner.predict_proba(instances) + priors, posteriors = self.EM(self.train_prevalence, classif_posteriors, epsilon) + return posteriors @classmethod def EM(cls, tr_prev, posterior_probabilities, epsilon=EPSILON): @@ -337,7 +347,7 @@ class EMQ(AggregativeProbabilisticQuantifier): # M-step: qs_pos is Ps+1(y=+1) qs = ps.mean(axis=0) - if qs_prev_ is not None and error.mae(qs, qs_prev_) < epsilon and s>10: + if qs_prev_ is not None and qp.error.mae(qs, qs_prev_) < epsilon and s>10: converged = True qs_prev_ = qs @@ -346,7 +356,7 @@ class EMQ(AggregativeProbabilisticQuantifier): if not converged: raise UserWarning('the method has reached the maximum number of iterations; it might have not converged') - return qs + return qs, ps class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier): @@ -493,7 +503,7 @@ class OneVsAll(AggregativeQuantifier): return classif_predictions_bin.T def aggregate(self, classif_predictions_bin): - assert set(np.unique(classif_predictions_bin)) == {0,1}, \ + assert set(np.unique(classif_predictions_bin)).issubset({0,1}), \ 'param classif_predictions_bin does not seem to be a valid matrix (ndarray) of binary ' \ 'predictions for each document (row) and class (columns)' prevalences = self.__parallel(self._delayed_binary_aggregate, classif_predictions_bin)