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@ -1,3 +1,5 @@
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from typing import List, Union
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import numpy as np
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from sklearn.model_selection import train_test_split
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@ -93,4 +95,102 @@ class MultilabelledCollection:
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@property
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def Xy(self):
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return self.instances, self.labels
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return self.instances, self.labels
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class MultilingualLabelledCollection:
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def __init__(self, langs:List[str], labelledCollections:List[Union[LabelledCollection, MultilabelledCollection]]):
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assert len(langs) == len(labelledCollections), 'length mismatch for langs and labelledCollection lists'
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assert all(isinstance(lc, LabelledCollection) or all(isinstance(lc, MultilabelledCollection)) for lc in labelledCollections), \
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'unexpected type for labelledCollections'
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assert all(labelledCollections[0].classes_ == lc_i.classes_ for lc_i in labelledCollections[1:]), \
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'inconsistent classes found for some labelled collections'
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self.llc = {l: lc for l, lc in zip(langs, labelledCollections)}
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self.classes_=labelledCollections[0].classes_
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@classmethod
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def fromLangDict(cls, lang_labelledCollection:dict):
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return MultilingualLabelledCollection(*list(zip(*list(lang_labelledCollection.items()))))
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def langs(self):
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return list(sorted(self.llc.keys()))
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def __getitem__(self, lang)->LabelledCollection:
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return self.llc[lang]
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@classmethod
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def load(cls, path: str, loader_func: callable):
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return MultilingualLabelledCollection(*loader_func(path))
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def __len__(self):
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return sum(map(len, self.llc.values()))
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def prevalence(self):
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prev = np.asarray([lc.prevalence() * len(lc) for lc in self.llc.values()]).sum(axis=0)
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return prev / prev.sum()
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def language_prevalence(self):
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lang_count = np.asarray([len(self.llc[l]) for l in self.langs()])
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return lang_count / lang_count.sum()
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def counts(self):
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return np.asarray([lc.counts() for lc in self.llc.values()]).sum(axis=0)
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@property
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def n_classes(self):
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return len(self.classes_)
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@property
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def binary(self):
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return self.n_classes == 2
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def __check_langs(self, l_dict:dict):
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assert len(l_dict)==len(self.langs()), 'wrong number of languages'
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assert all(l in l_dict for l in self.langs()), 'missing languages in l_sizes'
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def __check_sizes(self, l_sizes: Union[int,dict]):
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assert isinstance(l_sizes, int) or isinstance(l_sizes, dict), 'unexpected type for l_sizes'
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if isinstance(l_sizes, int):
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return {l:l_sizes for l in self.langs()}
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self.__check_langs(l_sizes)
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return l_sizes
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def sampling_index(self, l_sizes: Union[int,dict], *prevs, shuffle=True):
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l_sizes = self.__check_sizes(l_sizes)
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return {l:lc.sampling_index(l_sizes[l], *prevs, shuffle=shuffle) for l,lc in self.llc.items()}
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def uniform_sampling_index(self, l_sizes: Union[int, dict]):
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l_sizes = self.__check_sizes(l_sizes)
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return {l: lc.uniform_sampling_index(l_sizes[l]) for l,lc in self.llc.items()}
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def uniform_sampling(self, l_sizes: Union[int, dict]):
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l_sizes = self.__check_sizes(l_sizes)
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return MultilingualLabelledCollection.fromLangDict(
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{l: lc.uniform_sampling(l_sizes[l]) for l,lc in self.llc.items()}
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)
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def sampling(self, l_sizes: Union[int, dict], *prevs, shuffle=True):
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l_sizes = self.__check_sizes(l_sizes)
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return MultilingualLabelledCollection.fromLangDict(
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{l: lc.sampling(l_sizes[l], *prevs, shuffle=shuffle) for l,lc in self.llc.items()}
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)
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def sampling_from_index(self, l_index:dict):
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self.__check_langs(l_index)
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return MultilingualLabelledCollection.fromLangDict(
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{l: lc.sampling_from_index(l_index[l]) for l,lc in self.llc.items()}
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)
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def split_stratified(self, train_prop=0.6, random_state=None):
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train, test = list(zip(*[self[l].split_stratified(train_prop, random_state) for l in self.langs()]))
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return MultilingualLabelledCollection(self.langs(), train), MultilingualLabelledCollection(self.langs(), test)
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def asLabelledCollection(self, return_langs=False):
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lXy_list = [([l]*len(lc),*lc.Xy) for l, lc in self.llc.items()] # a list with (lang_i, Xi, yi)
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ls,Xs,ys = list(zip(*lXy_list))
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ls = np.concatenate(ls)
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vertstack = vstack if issparse(Xs[0]) else np.vstack
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Xs = vertstack(Xs)
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ys = np.concatenate(ys)
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lc = LabelledCollection(Xs, ys, classes_=self.classes_)
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# return lc, ls if return_langs else lc
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@ -31,28 +31,28 @@ n_samples = 5000
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def models():
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# yield 'NaiveCC', MultilabelNaiveAggregativeQuantifier(CC(cls()))
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# yield 'NaivePCC', MultilabelNaiveAggregativeQuantifier(PCC(cls()))
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# yield 'NaiveACC', MultilabelNaiveAggregativeQuantifier(ACC(cls()))
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# yield 'NaivePACC', MultilabelNaiveAggregativeQuantifier(PACC(cls()))
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yield 'NaiveCC', MultilabelNaiveAggregativeQuantifier(CC(cls()))
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yield 'NaivePCC', MultilabelNaiveAggregativeQuantifier(PCC(cls()))
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yield 'NaiveACC', MultilabelNaiveAggregativeQuantifier(ACC(cls()))
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yield 'NaivePACC', MultilabelNaiveAggregativeQuantifier(PACC(cls()))
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# yield 'EMQ', MultilabelQuantifier(EMQ(calibratedCls()))
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# yield 'StackCC', MLCC(MultilabelStackedClassifier(cls()))
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# yield 'StackPCC', MLPCC(MultilabelStackedClassifier(cls()))
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# yield 'StackACC', MLACC(MultilabelStackedClassifier(cls()))
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# yield 'StackPACC', MLPACC(MultilabelStackedClassifier(cls()))
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yield 'StackCC', MLCC(MultilabelStackedClassifier(cls()))
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yield 'StackPCC', MLPCC(MultilabelStackedClassifier(cls()))
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yield 'StackACC', MLACC(MultilabelStackedClassifier(cls()))
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yield 'StackPACC', MLPACC(MultilabelStackedClassifier(cls()))
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# yield 'ChainCC', MLCC(ClassifierChain(cls(), cv=None, order='random'))
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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, '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-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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@ -63,8 +63,11 @@ def models():
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# yield 'MRQ-ChainPACC', MLRegressionQuantification(MLPACC(ClassifierChain(cls())), **common)
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dataset = 'reuters21578'
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picklepath = '/home/moreo/word-class-embeddings/pickles'
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# dataset = 'reuters21578'
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# dataset = 'ohsumed'
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dataset = 'jrcall'
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# picklepath = '/home/moreo/word-class-embeddings/pickles'
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picklepath = './pickles'
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data = Dataset.load(dataset, pickle_path=f'{picklepath}/{dataset}.pickle')
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Xtr, Xte = data.vectorize()
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@ -72,7 +75,8 @@ ytr = data.devel_labelmatrix.todense().getA()
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yte = data.test_labelmatrix.todense().getA()
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# remove categories with < 10 training documents
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to_keep = np.logical_and(ytr.sum(axis=0)>=50, yte.sum(axis=0)>=50)
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# to_keep = np.logical_and(ytr.sum(axis=0)>=50, yte.sum(axis=0)>=50)
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to_keep = np.argsort(ytr.sum(axis=0))[-10:]
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ytr = ytr[:, to_keep]
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yte = yte[:, to_keep]
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print(f'num categories = {ytr.shape[1]}')
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@ -176,104 +176,6 @@ class LabelledCollection:
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yield train, test
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class MultilingualLabelledCollection:
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def __init__(self, langs:List[str], labelledCollections:List[LabelledCollection]):
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assert len(langs) == len(labelledCollections), 'length mismatch for langs and labelledCollection lists'
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assert all(isinstance(lc, LabelledCollection) for lc in labelledCollections), 'unexpected type for labelledCollections'
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assert all(labelledCollections[0].classes_ == lc_i.classes_ for lc_i in labelledCollections[1:]), \
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'inconsistent classes found for some labelled collections'
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self.llc = {l: lc for l, lc in zip(langs, labelledCollections)}
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self.classes_=labelledCollections[0].classes_
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@classmethod
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def fromLangDict(cls, lang_labelledCollection:dict):
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return MultilingualLabelledCollection(*list(zip(*list(lang_labelledCollection.items()))))
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def langs(self):
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return list(sorted(self.llc.keys()))
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def __getitem__(self, lang)->LabelledCollection:
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return self.llc[lang]
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@classmethod
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def load(cls, path: str, loader_func: callable):
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return MultilingualLabelledCollection(*loader_func(path))
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def __len__(self):
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return sum(map(len, self.llc.values()))
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def prevalence(self):
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prev = np.asarray([lc.prevalence() * len(lc) for lc in self.llc.values()]).sum(axis=0)
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return prev / prev.sum()
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def language_prevalence(self):
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lang_count = np.asarray([len(self.llc[l]) for l in self.langs()])
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return lang_count / lang_count.sum()
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def counts(self):
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return np.asarray([lc.counts() for lc in self.llc.values()]).sum(axis=0)
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@property
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def n_classes(self):
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return len(self.classes_)
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@property
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def binary(self):
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return self.n_classes == 2
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def __check_langs(self, l_dict:dict):
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assert len(l_dict)==len(self.langs()), 'wrong number of languages'
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assert all(l in l_dict for l in self.langs()), 'missing languages in l_sizes'
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def __check_sizes(self, l_sizes: Union[int,dict]):
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assert isinstance(l_sizes, int) or isinstance(l_sizes, dict), 'unexpected type for l_sizes'
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if isinstance(l_sizes, int):
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return {l:l_sizes for l in self.langs()}
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self.__check_langs(l_sizes)
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return l_sizes
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def sampling_index(self, l_sizes: Union[int,dict], *prevs, shuffle=True):
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l_sizes = self.__check_sizes(l_sizes)
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return {l:lc.sampling_index(l_sizes[l], *prevs, shuffle=shuffle) for l,lc in self.llc.items()}
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def uniform_sampling_index(self, l_sizes: Union[int, dict]):
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l_sizes = self.__check_sizes(l_sizes)
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return {l: lc.uniform_sampling_index(l_sizes[l]) for l,lc in self.llc.items()}
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def uniform_sampling(self, l_sizes: Union[int, dict]):
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l_sizes = self.__check_sizes(l_sizes)
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return MultilingualLabelledCollection.fromLangDict(
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{l: lc.uniform_sampling(l_sizes[l]) for l,lc in self.llc.items()}
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)
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def sampling(self, l_sizes: Union[int, dict], *prevs, shuffle=True):
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l_sizes = self.__check_sizes(l_sizes)
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return MultilingualLabelledCollection.fromLangDict(
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{l: lc.sampling(l_sizes[l], *prevs, shuffle=shuffle) for l,lc in self.llc.items()}
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)
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def sampling_from_index(self, l_index:dict):
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self.__check_langs(l_index)
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return MultilingualLabelledCollection.fromLangDict(
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{l: lc.sampling_from_index(l_index[l]) for l,lc in self.llc.items()}
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)
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def split_stratified(self, train_prop=0.6, random_state=None):
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train, test = list(zip(*[self[l].split_stratified(train_prop, random_state) for l in self.langs()]))
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return MultilingualLabelledCollection(self.langs(), train), MultilingualLabelledCollection(self.langs(), test)
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def asLabelledCollection(self, return_langs=False):
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lXy_list = [([l]*len(lc),*lc.Xy) for l, lc in self.llc.items()] # a list with (lang_i, Xi, yi)
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ls,Xs,ys = list(zip(*lXy_list))
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ls = np.concatenate(ls)
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vertstack = vstack if issparse(Xs[0]) else np.vstack
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Xs = vertstack(Xs)
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ys = np.concatenate(ys)
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lc = LabelledCollection(Xs, ys, classes_=self.classes_)
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# return lc, ls if return_langs else lc
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#
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#
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#
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class Dataset:
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def __init__(self, training: LabelledCollection, test: LabelledCollection, vocabulary: dict = None, name=''):
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