def __getitem__(self, index):
"""
Returns the _transformed_ item from the dataset
Args:
index (int):
Returns:
(tuple):
* example (ndarray): vector representation of a training sample
* label (string): the class label
* length (int): the length (tokens) of the sentence
* index (int): the index of the dataitem in the dataset.
It is useful for getting the raw input for visualizations.
"""
sample, label = self.data[index], self.labels[index]
# transform the sample and the label,
# in order to feed them to the model
sample = vectorize(sample, self.char2idx, self.max_length)
if self.label_transformer is not None:
label = self.label_transformer.transform(label)
if isinstance(label, (list, tuple)):
label = numpy.array(label)
return sample, label, len(self.data[index]), index
def __getitem__(self, index):
"""
Returns the _transformed_ item from the dataset
Args:
index (int):
Returns:
(tuple):
* example (ndarray): vector representation of a training example
* label (int): the class label
* length (int): the length (tokens) of the sentence
Examples:
For an `index` where:
::
self.data[index] = ['this', 'is', 'really', 'simple']
self.target[index] = "neutral"
the function will have to return return:
::
example = [ 533 3908 1387 649 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0]
label = 1
"""
sample, label = self.data[index], self.labels[index]
length = min(self.max_length, len(sample))
sample = vectorize(sample, self.word2idx, self.max_length)
return sample, label, length
def __getitem__(self, index):
"""
Returns the _transformed_ item from the dataset
Args:
index (int):
Returns:
(tuple):
* example (ndarray): vector representation of a training sample
* label (string): the class label
* length (int): the length (tokens) of the sentence
* index (int): the index of the dataitem in the dataset.
It is useful for getting the raw input for visualizations.
"""
sample, label = self.data[index], self.labels[index]
# transform the sample and the label,
# in order to feed them to the model
sample = vectorize(sample, self.char2idx, self.max_length)
if self.label_transformer is not None:
label = self.label_transformer.transform(label)
if isinstance(label, (list, tuple)):
label = numpy.array(label)
return sample, label, len(self.data[index]), index