adjust LabelTensor (inheritance)
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@@ -1,64 +1,119 @@
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""" Module for LabelTensor """
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import torch
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class LabelTensor():
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def __init__(self, x, labels):
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if len(labels) != x.shape[1]:
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print(len(labels), x.shape[1])
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raise ValueError
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self.__labels = labels
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self.tensor = x
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def __getitem__(self, key):
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if isinstance(key, (tuple, list)):
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indeces = [self.labels.index(k) for k in key]
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return LabelTensor(self.tensor[:, indeces], [self.labels[idx] for idx in indeces])
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if key in self.labels:
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return self.tensor[:, self.labels.index(key)]
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else:
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return self.tensor.__getitem__(key)
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def __repr__(self):
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return self.tensor
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def __str__(self):
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return '{}\n {}\n'.format(self.labels, self.tensor)
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@property
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def shape(self):
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return self.tensor.shape
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@property
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def dtype(self):
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return self.tensor.dtype
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@property
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def device(self):
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return self.tensor.device
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@property
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def labels(self):
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return self.__labels
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class LabelTensor(torch.Tensor):
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"""Torch tensor with a label for any column."""
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@staticmethod
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def hstack(labeltensor_list):
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concatenated_tensor = torch.cat([lt.tensor for lt in labeltensor_list], axis=1)
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concatenated_label = sum([lt.labels for lt in labeltensor_list], [])
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return LabelTensor(concatenated_tensor, concatenated_label)
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def __new__(cls, x, labels, *args, **kwargs):
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return super().__new__(cls, x, *args, **kwargs)
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def __init__(self, x, labels):
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'''
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Construct a `LabelTensor` by passing a tensor and a list of column
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labels. Such labels uniquely identify the columns of the tensor,
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allowing for an easier manipulation.
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:param torch.Tensor x: the data tensor.
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:param labels: the labels of the columns.
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:type labels: str or iterable(str)
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if __name__ == "__main__":
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import numpy as np
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a = np.random.uniform(size=(20, 3))
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a = np.random.uniform(size=(20, 3))
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p = torch.from_numpy(a)
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t = LabelTensor(p, labels=['u', 'p', 't'])
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print(t)
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print(t['u'])
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t *= 2
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print(t['u'])
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print(t[:, 0])
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:Example:
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>>> from pina import LabelTensor
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>>> tensor = LabelTensor(torch.rand((2000, 3)), ['a', 'b', 'c'])
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>>> tensor
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tensor([[6.7116e-02, 4.8892e-01, 8.9452e-01],
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[9.2392e-01, 8.2065e-01, 4.1986e-04],
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[8.9266e-01, 5.5446e-01, 6.3500e-01],
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...,
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[5.8194e-01, 9.4268e-01, 4.1841e-01],
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[1.0246e-01, 9.5179e-01, 3.7043e-02],
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[9.6150e-01, 8.0656e-01, 8.3824e-01]])
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>>> tensor.extract('a')
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tensor([[0.0671],
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[0.9239],
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[0.8927],
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...,
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[0.5819],
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[0.1025],
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[0.9615]])
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>>> tensor.extract(['a', 'b'])
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tensor([[0.0671, 0.4889],
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[0.9239, 0.8207],
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[0.8927, 0.5545],
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...,
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[0.5819, 0.9427],
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[0.1025, 0.9518],
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[0.9615, 0.8066]])
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>>> tensor.extract(['b', 'a'])
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tensor([[0.4889, 0.0671],
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[0.8207, 0.9239],
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[0.5545, 0.8927],
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...,
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[0.9427, 0.5819],
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[0.9518, 0.1025],
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[0.8066, 0.9615]])
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'''
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if isinstance(labels, str):
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labels = [labels]
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if len(labels) != x.shape[1]:
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raise ValueError(
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'the tensor has not the same number of columns of '
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'the passed labels.'
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)
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self.labels = labels
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def clone(self, *args, **kwargs):
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"""
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Clone the LabelTensor. For more details, see
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:meth:`torch.Tensor.clone`.
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:return: a copy of the tensor
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:rtype: LabelTensor
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"""
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return LabelTensor(super().clone(*args, **kwargs), self.labels)
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def to(self, *args, **kwargs):
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"""
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Performs Tensor dtype and/or device conversion. For more details, see
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:meth:`torch.Tensor.to`.
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"""
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new_obj = LabelTensor([], self.labels)
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tempTensor = super().to(*args, **kwargs)
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new_obj.data = tempTensor.data
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new_obj.requires_grad = tempTensor.requires_grad
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return new_obj
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def extract(self, label_to_extract):
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"""
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Extract the subset of the original tensor by returning all the columns
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corresponding to the passed `label_to_extract`.
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:param label_to_extract: the label(s) to extract.
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:type label_to_extract: str or iterable(str)
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:raises TypeError: labels are not str
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:raises ValueError: label to extract is not in the labels list
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"""
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if isinstance(label_to_extract, str):
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label_to_extract = [label_to_extract]
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elif isinstance(label_to_extract, (tuple, list)): # TODO
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pass
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else:
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raise TypeError(
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'`label_to_extract` should be a str, or a str iterator')
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try:
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indeces = [self.labels.index(f) for f in label_to_extract]
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except ValueError:
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raise ValueError('`label_to_extract` not in the labels list')
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extracted_tensor = LabelTensor(
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self[:, indeces],
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[self.labels[idx] for idx in indeces]
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)
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return extracted_tensor
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