forked from moreo/QuaPy
96 lines
3.5 KiB
Python
96 lines
3.5 KiB
Python
import numpy as np
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from sklearn.model_selection import train_test_split
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from quapy.data import LabelledCollection
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from quapy.functional import artificial_prevalence_sampling
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class MultilabelledCollection:
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def __init__(self, instances, labels):
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assert labels.ndim==2, 'data does not seem to be multilabel'
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self.instances = instances
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self.labels = labels
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self.classes_ = np.arange(labels.shape[1])
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@classmethod
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def load(cls, path: str, loader_func: callable):
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return MultilabelledCollection(*loader_func(path))
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def __len__(self):
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return self.instances.shape[0]
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def prevalence(self):
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# return self.labels.mean(axis=0)
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pos = self.labels.mean(axis=0)
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neg = 1-pos
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return np.asarray([neg, pos]).T
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def counts(self):
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return self.labels.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 False
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def __gen_index(self):
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return np.arange(len(self))
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def sampling_multi_index(self, size, cat, prev=None):
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if prev is None: # no prevalence was indicated; returns an index for uniform sampling
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return np.random.choice(len(self), size, replace=size>len(self))
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aux = LabelledCollection(self.__gen_index(), self.labels[:,cat])
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return aux.sampling_index(size, *[1-prev, prev])
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def uniform_sampling_multi_index(self, size):
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return np.random.choice(len(self), size, replace=size>len(self))
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def uniform_sampling(self, size):
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unif_index = self.uniform_sampling_multi_index(size)
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return self.sampling_from_index(unif_index)
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def sampling(self, size, category, prev=None):
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prev_index = self.sampling_multi_index(size, category, prev)
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return self.sampling_from_index(prev_index)
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def sampling_from_index(self, index):
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documents = self.instances[index]
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labels = self.labels[index]
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return MultilabelledCollection(documents, labels)
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def train_test_split(self, train_prop=0.6, random_state=None):
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tr_docs, te_docs, tr_labels, te_labels = \
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train_test_split(self.instances, self.labels, train_size=train_prop, random_state=random_state)
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return MultilabelledCollection(tr_docs, tr_labels), MultilabelledCollection(te_docs, te_labels)
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def artificial_sampling_generator(self, sample_size, category, n_prevalences=101, repeats=1):
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dimensions = 2
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for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats).flatten():
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yield self.sampling(sample_size, category, prevs)
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def artificial_sampling_index_generator(self, sample_size, category, n_prevalences=101, repeats=1):
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dimensions = 2
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for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats).flatten():
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yield self.sampling_multi_index(sample_size, category, prevs)
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def natural_sampling_generator(self, sample_size, repeats=100):
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for _ in range(repeats):
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yield self.uniform_sampling(sample_size)
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def natural_sampling_index_generator(self, sample_size, repeats=100):
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for _ in range(repeats):
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yield self.uniform_sampling_multi_index(sample_size)
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def asLabelledCollection(self, category):
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return LabelledCollection(self.instances, self.labels[:,category])
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def genLabelledCollections(self):
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for c in self.classes_:
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yield self.asLabelledCollection(c)
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@property
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def Xy(self):
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return self.instances, self.labels |