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QuaPy
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launching experiments

This commit is contained in:
Alejandro Moreo Fernandez 2021-08-29 11:03:51 +02:00
parent 13eb682e53
commit dc2fa05cf8
2 changed files with 18 additions and 16 deletions
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32
MultiLabel/main.py
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@ -38,28 +38,28 @@ n_samples = 5000
def models():
yield 'NaiveCC', MultilabelNaiveAggregativeQuantifier(CC(cls()))
# yield 'NaivePCC', MultilabelNaiveAggregativeQuantifier(PCC(cls()))
# yield 'NaiveACC', MultilabelNaiveAggregativeQuantifier(ACC(cls()))
# yield 'NaivePACC', MultilabelNaiveAggregativeQuantifier(PACC(cls()))
yield 'NaivePCC', MultilabelNaiveAggregativeQuantifier(PCC(cls()))
yield 'NaiveACC', MultilabelNaiveAggregativeQuantifier(ACC(cls()))
yield 'NaivePACC', MultilabelNaiveAggregativeQuantifier(PACC(cls()))
# yield 'NaiveHDy', MultilabelNaiveAggregativeQuantifier(HDy(cls()))
# yield 'NaiveSLD', MultilabelNaiveAggregativeQuantifier(EMQ(calibratedCls()))
# yield 'StackCC', MLCC(MultilabelStackedClassifier(cls()))
# yield 'StackPCC', MLPCC(MultilabelStackedClassifier(cls()))
# yield 'StackACC', MLACC(MultilabelStackedClassifier(cls()))
# yield 'StackPACC', MLPACC(MultilabelStackedClassifier(cls()))
yield 'StackCC', MLCC(MultilabelStackedClassifier(cls()))
yield 'StackPCC', MLPCC(MultilabelStackedClassifier(cls()))
yield 'StackACC', MLACC(MultilabelStackedClassifier(cls()))
yield 'StackPACC', MLPACC(MultilabelStackedClassifier(cls()))
# yield 'ChainCC', MLCC(ClassifierChain(cls(), cv=None, order='random'))
# yield 'ChainPCC', MLPCC(ClassifierChain(cls(), cv=None, order='random'))
# yield 'ChainACC', MLACC(ClassifierChain(cls(), cv=None, order='random'))
# yield 'ChainPACC', MLPACC(ClassifierChain(cls(), cv=None, order='random'))
common={'sample_size':sample_size, 'n_samples': n_samples, 'norm': True, 'means':False, 'stds':False, 'regression':'svr'}
# yield 'MRQ-CC', MLRegressionQuantification(MultilabelNaiveQuantifier(CC(cls())), **common)
# yield 'MRQ-PCC', MLRegressionQuantification(MultilabelNaiveQuantifier(PCC(cls())), **common)
# yield 'MRQ-ACC', MLRegressionQuantification(MultilabelNaiveQuantifier(ACC(cls())), **common)
# yield 'MRQ-PACC', MLRegressionQuantification(MultilabelNaiveQuantifier(PACC(cls())), **common)
# yield 'MRQ-StackCC', MLRegressionQuantification(MLCC(MultilabelStackedClassifier(cls())), **common)
# yield 'MRQ-StackPCC', MLRegressionQuantification(MLPCC(MultilabelStackedClassifier(cls())), **common)
# yield 'MRQ-StackACC', MLRegressionQuantification(MLACC(MultilabelStackedClassifier(cls())), **common)
# yield 'MRQ-StackPACC', MLRegressionQuantification(MLPACC(MultilabelStackedClassifier(cls())), **common)
yield 'MRQ-CC', MLRegressionQuantification(MultilabelNaiveQuantifier(CC(cls())), **common)
yield 'MRQ-PCC', MLRegressionQuantification(MultilabelNaiveQuantifier(PCC(cls())), **common)
yield 'MRQ-ACC', MLRegressionQuantification(MultilabelNaiveQuantifier(ACC(cls())), **common)
yield 'MRQ-PACC', MLRegressionQuantification(MultilabelNaiveQuantifier(PACC(cls())), **common)
yield 'MRQ-StackCC', MLRegressionQuantification(MLCC(MultilabelStackedClassifier(cls())), **common)
yield 'MRQ-StackPCC', MLRegressionQuantification(MLPCC(MultilabelStackedClassifier(cls())), **common)
yield 'MRQ-StackACC', MLRegressionQuantification(MLACC(MultilabelStackedClassifier(cls())), **common)
yield 'MRQ-StackPACC', MLRegressionQuantification(MLPACC(MultilabelStackedClassifier(cls())), **common)
# yield 'MRQ-StackCC-app', MLRegressionQuantification(MLCC(MultilabelStackedClassifier(cls())), protocol='app', **common)
# yield 'MRQ-StackPCC-app', MLRegressionQuantification(MLPCC(MultilabelStackedClassifier(cls())), protocol='app', **common)
# yield 'MRQ-StackACC-app', MLRegressionQuantification(MLACC(MultilabelStackedClassifier(cls())), protocol='app', **common)
@ -176,6 +176,8 @@ def run_experiment(dataset_name, model_name, model):
print(f'runing experiment {dataset_name} x {model_name}')
train, test = get_dataset(dataset_name)
if train.n_classes>100:
return
print_info(train, test)

2
MultiLabel/mldata.py
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@ -64,7 +64,7 @@ class MultilabelledCollection:
return MultilabelledCollection(documents, labels)
def train_test_split(self, train_prop=0.6, random_state=None):
raise ValueError('use the scikit-multilearn implementation')
#raise ValueError('use the scikit-multilearn implementation')
tr_docs, te_docs, tr_labels, te_labels = \
train_test_split(self.instances, self.labels, train_size=train_prop, random_state=random_state)
return MultilabelledCollection(tr_docs, tr_labels), MultilabelledCollection(te_docs, te_labels)