forked from moreo/QuaPy
new methods, some experiments added
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60b6fa3c12
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@ -0,0 +1,14 @@
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Things to test:
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- MultiChain for classification, MultiChain for regression?
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- Independent classifiers + independent quantifiers
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- Stacking + independent quantifiers
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- ClassifierChain + independent quantifiers
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- Independent quantifiers + cross-class regression (independent?)
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- Stacking + cross-class regression
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- ClassifierChain + cross-class regression
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- Covariates (Means, CovMatrix from samples) + multioutput regression?
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- Covariates concatented with quantifiers predictions + cross-class regression?
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- Model Selection for specific protocols?
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@ -154,15 +154,24 @@ class MultilabelNaiveAggregativeQuantifier(MultilabelNaiveQuantifier, MLAggregat
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return self.aggregate(predictions)
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class MultilabelRegressionQuantification:
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def __init__(self, base_quantifier=CC(LinearSVC()), regression='ridge', n_samples=500, sample_size=500, norm=True,
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means=True, stds=True):
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class MLRegressionQuantification:
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def __init__(self,
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mlquantifier=MultilabelNaiveQuantifier(CC(LinearSVC())),
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regression='ridge',
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protocol='npp',
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n_samples=500,
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sample_size=500,
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norm=True,
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means=True,
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stds=True):
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assert regression in ['ridge', 'svr'], 'unknown regression model'
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self.estimator = MultilabelNaiveQuantifier(base_quantifier)
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assert protocol in ['npp', 'app'], 'unknown protocol'
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self.estimator = mlquantifier
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if regression == 'ridge':
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self.reg = Ridge(normalize=norm)
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elif regression == 'svr':
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self.reg = MultiOutputRegressor(LinearSVR())
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self.protocol = protocol
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# self.reg = MultiTaskLassoCV(normalize=norm)
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# self.reg = KernelRidge(kernel='rbf')
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# self.reg = LassoLarsCV(normalize=norm)
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@ -174,25 +183,11 @@ class MultilabelRegressionQuantification:
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self.regression = regression
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self.n_samples = n_samples
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self.sample_size = sample_size
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self.norm = StandardScaler()
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# self.norm = StandardScaler()
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self.means = means
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self.stds = stds
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def fit(self, data:MultilabelledCollection):
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self.classes_ = data.classes_
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tr, te = data.train_test_split()
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self.estimator.fit(tr)
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samples_mean = []
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samples_std = []
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Xs = []
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ys = []
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for sample in te.natural_sampling_generator(sample_size=self.sample_size, repeats=self.n_samples):
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ys.append(sample.prevalence()[:,1])
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Xs.append(self.estimator.quantify(sample.instances)[:,1])
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if self.means:
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samples_mean.append(sample.instances.mean(axis=0).getA().flatten())
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if self.stds:
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samples_std.append(sample.instances.todense().std(axis=0).getA().flatten())
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def _prepare_arrays(self, Xs, ys, samples_mean, samples_std):
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Xs = np.asarray(Xs)
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ys = np.asarray(ys)
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if self.means:
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@ -201,7 +196,49 @@ class MultilabelRegressionQuantification:
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if self.stds:
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samples_std = np.asarray(samples_std)
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Xs = np.hstack([Xs, samples_std])
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Xs = self.norm.fit_transform(Xs)
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return Xs, ys
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def generate_samples_npp(self, val):
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samples_mean = []
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samples_std = []
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Xs = []
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ys = []
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for sample in val.natural_sampling_generator(sample_size=self.sample_size, repeats=self.n_samples):
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ys.append(sample.prevalence()[:, 1])
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Xs.append(self.estimator.quantify(sample.instances)[:, 1])
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if self.means:
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samples_mean.append(sample.instances.mean(axis=0).getA().flatten())
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if self.stds:
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samples_std.append(sample.instances.todense().std(axis=0).getA().flatten())
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return self._prepare_arrays(Xs, ys, samples_mean, samples_std)
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def generate_samples_app(self, val):
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samples_mean = []
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samples_std = []
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Xs = []
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ys = []
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ncats = len(self.classes_)
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nprevs = 21
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repeats = max(self.n_samples // (ncats * nprevs), 1)
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for cat in self.classes_:
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for sample in val.artificial_sampling_generator(sample_size=self.sample_size, category=cat, n_prevalences=nprevs, repeats=repeats):
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ys.append(sample.prevalence()[:, 1])
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Xs.append(self.estimator.quantify(sample.instances)[:, 1])
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if self.means:
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samples_mean.append(sample.instances.mean(axis=0).getA().flatten())
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if self.stds:
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samples_std.append(sample.instances.todense().std(axis=0).getA().flatten())
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return self._prepare_arrays(Xs, ys, samples_mean, samples_std)
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def fit(self, data:MultilabelledCollection):
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self.classes_ = data.classes_
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tr, val = data.train_test_split()
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self.estimator.fit(tr)
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if self.protocol == 'npp':
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Xs, ys = self.generate_samples_npp(val)
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elif self.protocol == 'app':
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Xs, ys = self.generate_samples_app(val)
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# Xs = self.norm.fit_transform(Xs)
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self.reg.fit(Xs, ys)
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return self
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@ -213,9 +250,9 @@ class MultilabelRegressionQuantification:
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if self.stds:
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sample_std = instances.todense().std(axis=0).getA()
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Xs = np.hstack([Xs, sample_std])
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Xs = self.norm.transform(Xs)
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# Xs = self.norm.transform(Xs)
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Xs = self.reg.predict(Xs)
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Xs = self.norm.inverse_transform(Xs)
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# Xs = self.norm.inverse_transform(Xs)
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adjusted = np.clip(Xs, 0, 1)
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adjusted = adjusted.flatten()
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neg_prevs = 1-adjusted
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@ -6,7 +6,7 @@ from tqdm import tqdm
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import quapy as qp
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from MultiLabel.mlclassification import MultilabelStackedClassifier
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from MultiLabel.mldata import MultilabelledCollection
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from MultiLabel.mlquantification import MultilabelNaiveQuantifier, MLCC, MLPCC, MultilabelRegressionQuantification, \
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from MultiLabel.mlquantification import MultilabelNaiveQuantifier, MLCC, MLPCC, MLRegressionQuantification, \
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MLACC, \
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MLPACC, MultilabelNaiveAggregativeQuantifier
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from method.aggregative import PACC, CC, EMQ, PCC, ACC, HDy
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@ -44,11 +44,23 @@ def models():
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# yield 'ChainPCC', MLPCC(ClassifierChain(cls(), cv=None, order='random'))
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# yield 'ChainACC', MLACC(ClassifierChain(cls(), cv=None, order='random'))
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# yield 'ChainPACC', MLPACC(ClassifierChain(cls(), cv=None, order='random'))
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common={'sample_size':sample_size, 'n_samples': n_samples, 'norm': True, 'means':False, 'stds':False}
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yield 'MRQ-CC', MultilabelRegressionQuantification(base_quantifier=CC(cls()), regression='svr', **common)
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yield 'MRQ-PCC', MultilabelRegressionQuantification(base_quantifier=PCC(cls()), regression='svr', **common)
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yield 'MRQ-ACC', MultilabelRegressionQuantification(base_quantifier=ACC(cls()), regression='svr', **common)
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yield 'MRQ-PACC', MultilabelRegressionQuantification(base_quantifier=PACC(cls()), regression='svr', **common)
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common={'sample_size':sample_size, 'n_samples': n_samples, 'norm': True, 'means':False, 'stds':False, 'regression':'svr'}
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# yield 'MRQ-CC', MLRegressionQuantification(MultilabelNaiveQuantifier(CC(cls())), **common)
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# yield 'MRQ-PCC', MLRegressionQuantification(MultilabelNaiveQuantifier(PCC(cls())), **common)
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# yield 'MRQ-ACC', MLRegressionQuantification(MultilabelNaiveQuantifier(ACC(cls())), **common)
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# yield 'MRQ-PACC', MLRegressionQuantification(MultilabelNaiveQuantifier(PACC(cls())), **common)
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# yield 'MRQ-StackCC', MLRegressionQuantification(MLCC(MultilabelStackedClassifier(cls())), **common)
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# yield 'MRQ-StackPCC', MLRegressionQuantification(MLPCC(MultilabelStackedClassifier(cls())), **common)
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# yield 'MRQ-StackACC', MLRegressionQuantification(MLACC(MultilabelStackedClassifier(cls())), **common)
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# yield 'MRQ-StackPACC', MLRegressionQuantification(MLPACC(MultilabelStackedClassifier(cls())), **common)
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yield 'MRQ-StackCC-app', MLRegressionQuantification(MLCC(MultilabelStackedClassifier(cls())), protocol='app', **common)
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yield 'MRQ-StackPCC-app', MLRegressionQuantification(MLPCC(MultilabelStackedClassifier(cls())), protocol='app', **common)
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yield 'MRQ-StackACC-app', MLRegressionQuantification(MLACC(MultilabelStackedClassifier(cls())), protocol='app', **common)
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yield 'MRQ-StackPACC-app', MLRegressionQuantification(MLPACC(MultilabelStackedClassifier(cls())), protocol='app', **common)
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# yield 'MRQ-ChainCC', MLRegressionQuantification(MLCC(ClassifierChain(cls())), **common)
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# yield 'MRQ-ChainPCC', MLRegressionQuantification(MLPCC(ClassifierChain(cls())), **common)
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# yield 'MRQ-ChainACC', MLRegressionQuantification(MLACC(ClassifierChain(cls())), **common)
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# yield 'MRQ-ChainPACC', MLRegressionQuantification(MLPACC(ClassifierChain(cls())), **common)
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dataset = 'reuters21578'
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@ -0,0 +1,79 @@
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num categories = 10
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Train-counts: [1650 181 389 2877 433 347 538 197 369 212]
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Test-counts: [ 719 56 189 1087 149 131 179 89 117 71]
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MLPE: 0.01101
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NPP:
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NaiveCC mae=0.01718
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NaivePCC mae=0.00898
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NaiveACC mae=0.01560
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NaivePACC mae=0.01062
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StackCC mae=0.00790
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StackPCC mae=0.00659 **
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StackACC mae=0.00913
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StackPACC mae=0.00771
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ChainCC mae=0.01644
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ChainPCC mae=0.00924
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ChainACC mae=0.01767
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ChainPACC mae=0.01140
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MRQ-CC mae=0.01130
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MRQ-PCC mae=0.00941
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MRQ-ACC mae=0.01153
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MRQ-PACC mae=0.01000
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MRQ-StackCC mae=0.00757
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MRQ-StackPCC mae=0.00652 **
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MRQ-StackACC mae=0.00799
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MRQ-StackPACC mae=0.00763
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MRQ-StackCC-app mae=0.00791
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MRQ-StackPCC-appmae=0.00840
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MRQ-StackACC-appmae=0.00910
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MRQ-StackPACC-apmae=0.00941
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MRQ-ChainCC mae=0.00989
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MRQ-ChainPCC mae=0.00916
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MRQ-ChainACC mae=0.01251
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MRQ-ChainPACC mae=0.00954
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APP:
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NaiveCC mae=0.04120
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NaivePCC mae=0.03741
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NaiveACC mae=0.03202
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NaivePACC mae=0.02293
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StackCC mae=0.01969
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StackPCC mae=0.01871
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StackACC mae=0.01386 **
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StackPACC mae=0.01267 **
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ChainCC mae=0.04136
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ChainPCC mae=0.03571
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ChainACC mae=0.03622
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ChainPACC mae=0.02659
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MRQ-CC mae=0.04356
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MRQ-PCC mae=0.02532
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MRQ-ACC mae=0.05716
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MRQ-PACC mae=0.02936
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MRQ-StackCC mae=0.02448
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MRQ-StackPCC mae=0.02090
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MRQ-StackACC mae=0.02579
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MRQ-StackPACC mae=0.02388
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MRQ-StackCC-app mae=0.01535
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MRQ-StackPCC-appmae=0.01457
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MRQ-StackACC-appmae=0.01441
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MRQ-StackPACC-apmae=0.01633
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MRQ-ChainCC mae=0.04874
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MRQ-ChainPCC mae=0.02537
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MRQ-ChainACC mae=0.06262
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MRQ-ChainPACC mae=0.02906
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