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multilabel_generator.py
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multilabel_generator.py
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import numpy as np
from skmultiflow.data.base_stream import Stream
from sklearn.datasets import make_multilabel_classification
from skmultiflow.utils import check_random_state
class MultilabelGenerator(Stream):
""" Creates a multi-label stream.
This generator creates a stream of samples for a multi-label problem.
It uses the make_multi-label_classification function from scikit-learn,
which creates a batch setting multi-label classification problem. These
samples are then sequentially yield by the next_sample method.
Parameters
----------
n_samples: int (Default: 40000)
Total amount of samples to generate.
n_features: int (Default: 100)
Number of features to generate.
n_targets: int (Default: 1)
Number of targets to generate.
n_labels: int (Default: 2)
Average number of labels per instance.
random_state: int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Notes
-----
This is a wrapper for scikit-lean's `make_multilabel_classification`
Examples
--------
>>> # Imports
>>> from skmultiflow.data.multilabel_generator import MultilabelGenerator
>>> # Setting up the stream
>>> stream = MultilabelGenerator(n_samples=100, n_features=20, n_targets=4, n_labels=4,
... random_state=0)
>>> # Retrieving one sample
>>> stream.next_sample()
(array([[3., 0., 1., 3., 6., 2., 5., 0., 5., 6., 3., 5., 1., 2., 0., 3.,
3., 2., 2., 1.]]), array([[0, 1, 1, 1]]))
>>> # Retrieving 10 samples
>>> stream.next_sample(10)
(array([[4., 0., 2., 6., 2., 2., 1., 1., 3., 1., 3., 0., 1., 4., 0., 1.,
2., 2., 1., 1.],
[2., 2., 1., 6., 4., 0., 3., 1., 2., 4., 2., 2., 1., 2., 2., 1.,
3., 2., 1., 1.],
[7., 3., 3., 5., 6., 1., 4., 3., 3., 1., 1., 1., 1., 1., 1., 1.,
3., 2., 1., 8.],
[1., 5., 1., 3., 4., 2., 2., 0., 4., 3., 2., 2., 2., 2., 3., 1.,
5., 0., 2., 0.],
[7., 3., 2., 7., 4., 6., 2., 1., 4., 1., 1., 0., 1., 0., 1., 0.,
1., 1., 1., 4.],
[0., 2., 1., 1., 6., 3., 4., 2., 5., 3., 0., 3., 0., 1., 3., 0.,
3., 3., 2., 3.],
[5., 1., 2., 3., 4., 1., 0., 3., 3., 3., 8., 0., 0., 2., 0., 0.,
0., 2., 1., 1.],
[2., 5., 6., 0., 5., 2., 5., 2., 5., 4., 1., 1., 4., 1., 1., 0.,
1., 8., 3., 4.],
[2., 4., 6., 2., 3., 8., 2., 2., 3., 3., 5., 1., 0., 0., 1., 4.,
0., 1., 0., 3.],
[4., 2., 2., 2., 6., 5., 3., 3., 6., 1., 1., 0., 2., 2., 1., 2.,
3., 5., 1., 5.]]), array([[1, 1, 1, 1],
[0, 1, 1, 0],
[0, 1, 0, 1],
[1, 0, 1, 0],
[0, 1, 0, 1],
[1, 0, 1, 1],
[0, 1, 0, 0],
[1, 1, 1, 0],
[0, 1, 0, 0],
[1, 1, 1, 1]]))
>>> stream.n_remaining_samples()
89
>>> stream.has_more_samples()
True
"""
def __init__(self, n_samples=40000, n_features=20, n_targets=5, n_labels=2, random_state=None):
super().__init__()
self.X = None
self.y = None
self.n_samples = n_samples
self.n_features = n_features
self.n_targets = n_targets
self.n_labels = n_labels
self.n_classes = 2
self.n_num_features = n_features
self.random_state = random_state
self._random_state = None # This is the actual random_state object used internally
self.name = "Multilabel Generator"
self._prepare_for_use()
def _prepare_for_use(self):
self._random_state = check_random_state(self.random_state)
self.X, self.y = make_multilabel_classification(n_samples=self.n_samples,
n_features=self.n_features,
n_classes=self.n_targets,
n_labels=self.n_labels,
random_state=self._random_state)
self.target_names = ["target_" + str(i) for i in range(self.n_targets)]
self.feature_names = ["att_num_" + str(i) for i in range(self.n_num_features)]
self.target_values = np.unique(self.y).tolist() if self.n_targets == 1 else \
[np.unique(self.y[:, i]).tolist() for i in range(self.n_targets)]
def next_sample(self, batch_size=1):
""" Returns next sample from the stream.
Parameters
----------
batch_size: int (optional, default=1)
The number of samples to return.
Returns
-------
tuple or tuple list
Return a tuple with the features matrix and the labels matrix for
the batch_size samples that were requested.
"""
self.sample_idx += batch_size
try:
self.current_sample_x = self.X[self.sample_idx - batch_size:self.sample_idx, :]
self.current_sample_y = self.y[self.sample_idx - batch_size:self.sample_idx, :]
if self.n_targets < 2:
self.current_sample_y = self.current_sample_y.flatten()
except IndexError:
self.current_sample_x = None
self.current_sample_y = None
return self.current_sample_x, self.current_sample_y
def restart(self):
""" Restarts the stream
"""
# Note: No need to regenerate the data, just reset the idx
self.sample_idx = 0
self.current_sample_x = None
self.current_sample_y = None
def n_remaining_samples(self):
"""
Returns
-------
int
Number of remaining samples.
"""
return self.n_samples - self.sample_idx