IterableDataset.__init__() - Code Metrics - Inspection of "Merge pull request #345 from dmitriy-serdyuk/fix-w..." - mila-udem/fuel - Measure and Improve Code Quality continuously with Scrutinizer

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by Bart

created 2016-04-13 21:38 UTC

IterableDataset.init() C

↳ Parent: IterableDataset

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	10
dl	0
loc	20
rs	6

How to fix Complexity

import collections
from abc import ABCMeta, abstractmethod

from six import add_metaclass

from picklable_itertools import iter_, izip

from fuel.schemes import SequentialExampleScheme
from fuel.streams import DataStream
from fuel.utils import Subset


@add_metaclass(ABCMeta)
class Dataset(object):
    """A dataset.

    Dataset classes implement the interface to a particular dataset. The
    interface consists of a number of routines to manipulate so called
    "state" objects, e.g. open, reset and close them.

    Parameters
    ----------
    sources : tuple of strings, optional
        The data sources to load and return by :meth:`get_data`. By default
        all data sources are returned.
    axis_labels : dict, optional
        Maps source names to tuples of strings describing axis semantics,
        one per axis. Defaults to `None`, i.e. no information is available.

    Attributes
    ----------
    sources : tuple of strings
        The sources this dataset will provide when queried for data e.g.
        ``('features',)`` when querying only the data from MNIST.
    provides_sources : tuple of strings
        The sources this dataset *is able to* provide e.g. ``('features',
        'targets')`` for MNIST (regardless of which data the data stream
        actually requests). Any implementation of a dataset should set this
        attribute on the class (or at least before calling ``super``).
    example_iteration_scheme : :class:`.IterationScheme` or ``None``
        The iteration scheme the class uses in order to produce a stream of
        examples.
    default_transformers: It is expected to be a tuple with one element per
        transformer in the pipeline. Each element is a tuple with three
        elements:
            - the Transformer subclass to apply,
            - a list of arguments to pass to the subclass constructor, and
            - a dict of keyword arguments to pass to the subclass
              constructor.


    Notes
    -----
    Datasets should only implement the interface; they are not expected to
    perform the iteration over the actual data. As such, they are
    stateless, and can be shared by different parts of the library
    simultaneously.

    """
    provides_sources = None
    default_transformers = tuple()

    def __init__(self, sources=None, axis_labels=None):
        if not self.provides_sources:
            raise ValueError("dataset does not have `provides_sources`")
        if sources is not None:
            if not sources or not all(source in self.provides_sources
                                      for source in sources):
                raise ValueError("unable to provide requested sources")
            self.sources = sources
        self.axis_labels = axis_labels

    @property
    def sources(self):
        if not hasattr(self, '_sources'):
            return self.provides_sources
        return self._sources

    @sources.setter
    def sources(self, sources):
        self._sources = sources

    def apply_default_transformers(self, stream):
        """Applies default transformers to a stream.

        Parameters
        ----------
        stream : :class:`~.streams.AbstractDataStream`
            A data stream.

        """
        for (cls, args, kwargs) in self.default_transformers:
            args = [stream] + args
            stream = cls(*args, **kwargs)
        return stream

    @property
    def example_iteration_scheme(self):
        if not hasattr(self, '_example_iteration_scheme'):
            raise AttributeError("dataset does not provide an example "
                                 "iteration scheme")
        return self._example_iteration_scheme

    @example_iteration_scheme.setter
    def example_iteration_scheme(self, value):
        self._example_iteration_scheme = value

    def get_example_stream(self):
        return DataStream(self, iteration_scheme=self.example_iteration_scheme)

    def open(self):
        """Return the state if the dataset requires one.

        Datasets which e.g. read files from disks require open file
        handlers, and this sort of stateful information should be handled
        by the data stream.

        Returns
        -------
        state : object
            An object representing the state of a dataset.

        """
        pass

    def reset(self, state):
        """Resets the state.

        Parameters
        ----------
        state : object
            The current state.

        Returns
        -------
        state : object
            A reset state.

        Notes
        -----
        The default implementation closes the state and opens a new one. A
        more efficient implementation (e.g. using ``file.seek(0)`` instead
        of closing and re-opening the file) can override the default one in
        derived classes.

        """
        self.close(state)
        return self.open()

    def next_epoch(self, state):
        """Switches the dataset state to the next epoch.

        The default implementation for this method is to reset the state.

        Parameters
        ----------
        state : object
            The current state.

        Returns
        -------
        state : object
            The state for the next epoch.

        """
        return self.reset(state)

    def close(self, state):
        """Cleanly close the dataset e.g. close file handles.

        Parameters
        ----------
        state : object
            The current state.

        """
        pass

    @abstractmethod
    def get_data(self, state=None, request=None):
        """Request data from the dataset.

        .. todo::

           A way for the dataset to communicate which kind of requests it
           accepts, and a way to communicate what kind of request is being
           sent when supporting multiple.

        Parameters
        ----------
        state : object, optional
            The state as returned by the :meth:`open` method. The dataset
            can use this to e.g. interact with files when needed.
        request : object, optional
            If supported, the request for a particular part of the data
            e.g. the number of examples to return, or the indices of a
            particular minibatch of examples.

        Returns
        -------
        tuple
            A tuple of data matching the order of :attr:`sources`.

        """

    def filter_sources(self, data):
        """Filter the requested sources from those provided by the dataset.

        A dataset can be asked to provide only a subset of the sources it
        can provide (e.g. asking MNIST only for the features, not for the
        labels). A dataset can choose to use this information to e.g. only
        load the requested sources into memory. However, in case the
        performance gain of doing so would be negligible, the dataset can
        load all the data sources and then use this method to return only
        those requested.

        Parameters
        ----------
        data : tuple of objects
            The data from all the sources i.e. should be of the same length
            as :attr:`provides_sources`.

        Returns
        -------
        tuple
            A tuple of data matching :attr:`sources`.

        Examples
        --------
        >>> import numpy
        >>> class Random(Dataset):
        ...     provides_sources = ('features', 'targets')
        ...     def get_data(self, state=None, request=None):
        ...         data = (numpy.random.rand(10), numpy.random.randn(3))
        ...         return self.filter_sources(data)
        >>> Random(sources=('targets',)).get_data() # doctest: +SKIP
        (array([-1.82436737,  0.08265948,  0.63206168]),)

        """
        return tuple([d for d, s in zip(data, self.provides_sources)
                      if s in self.sources])


class IterableDataset(Dataset):
    """Creates a dataset from a set of iterables.

    Parameters
    ----------
    iterables : :class:`~collections.OrderedDict` or iterable
        The iterable(s) to provide interface to. The iterables' `__iter__`
        method should return a new iterator over the iterable. If an
        :class:`~collections.OrderedDict` is given, its values should be
        iterables providing data, and its keys strings that are used as
        source names. If a single iterable is given, it will be given the
        source ``data``.

    Attributes
    ----------
    iterables : list
        A list of :class:`~collections.Iterable` objects.

    Notes
    -----
    Internally, this method uses picklable iterools's ``_iter``
    function, providing picklable alternatives to some iterators such as
    :func:`range`, :func:`tuple`, and even :class:`file`. However, if the
    iterable returns a different kind of iterator that is not picklable,
    you might want to consider using the :func:`.do_not_pickle_attributes`
    decorator.

    To iterate over a container in batches, combine this dataset with the
    :class:`Batch` data stream.

    """
    example_iteration_scheme = None

    def __init__(self, iterables, **kwargs):
        if isinstance(iterables, dict):
            self.provides_sources = tuple(iterables.keys())
        else:
            self.provides_sources = ('data',)
        super(IterableDataset, self).__init__(**kwargs)
        if isinstance(iterables, dict):
            if not all(isinstance(iterable, collections.Iterable)
                       for iterable in iterables.values()):
                raise ValueError
            self.iterables = [iterables[source] for source in self.sources]
        else:
            if not isinstance(iterables, collections.Iterable):
                raise ValueError
            self.iterables = [iterables]
        try:
            if len(set(len(iterable) for iterable in self.iterables)) != 1:
                raise ValueError("iterables are of different length")
        except TypeError:
try:
    raises_exception()
except:  # Could be removed
    pass
            pass

    @property
    def num_examples(self):
        try:
            num_examples, = set(len(iterable) for iterable in self.iterables)
            return num_examples
        except TypeError:
            return float('nan')

    def open(self):
        iterators = [iter_(channel) for channel in self.iterables]
        return izip(*iterators)

    def get_data(self, state=None, request=None):
        if state is None or request is not None:
            raise ValueError
        return next(state)


class IndexableDataset(Dataset):
    """Creates a dataset from a set of indexable containers.

    Parameters
    ----------
    indexables : :class:`~collections.OrderedDict` or indexable
        The indexable(s) to provide interface to. This means it must
        support the syntax ```indexable[0]``. If an
        :class:`~collections.OrderedDict` is given, its values should be
        indexables providing data, and its keys strings that are used as
        source names. If a single indexable is given, it will be given the
        source ``data``.

    Attributes
    ----------
    indexables : list
        A list of indexable objects.

    Notes
    -----
    If the indexable data is very large, you might want to consider using
    the :func:`.do_not_pickle_attributes` decorator to make sure the data
    doesn't get pickled with the dataset, but gets reloaded/recreated
    instead.

    This dataset also uses the source names to create properties that
    provide easy access to the data.

    """
    def __init__(self, indexables, start=None, stop=None, **kwargs):
        if isinstance(indexables, dict):
            self.provides_sources = tuple(indexables.keys())
        else:
            self.provides_sources = ('data',)
        super(IndexableDataset, self).__init__(**kwargs)
        if isinstance(indexables, dict):
            self.indexables = [indexables[source][start:stop]
                               for source in self.sources]
            if not all(len(indexable) == len(self.indexables[0])
                       for indexable in self.indexables):
                raise ValueError("sources have different lengths")
        else:
            self.indexables = [indexables]

        self.example_iteration_scheme = SequentialExampleScheme(
            self.num_examples)

        self.start = start
        self.stop = stop
        self.subset = Subset(slice(start, stop), self.num_examples)

    def __getattr__(self, attr):
        if (attr not in ['sources', 'indexables', '_sources'] and
                attr in self.sources):
            return self.indexables[self.sources.index(attr)]
        raise AttributeError

    # Without explicitly defining a trivial __setstate__ method,
    # the __getattribute__ method would call the __getattr__ method,
    # which would raise an AttributeError. This causes problems
    # when unpickling.
    def __setstate__(self, dict):

        self.__dict__ = dict

    @property
    def num_examples(self):
        return len(self.indexables[0])

    def get_data(self, state=None, request=None):
        if state is not None or request is None:
            raise ValueError
        return tuple(self.subset.index_within_subset(indexable, request)
                     for indexable in self.indexables)


1			import collections
2			from abc import ABCMeta, abstractmethod
3
4			from six import add_metaclass
5
6			from picklable_itertools import iter_, izip
7
8			from fuel.schemes import SequentialExampleScheme
9			from fuel.streams import DataStream
10			from fuel.utils import Subset
11
12
13			@add_metaclass(ABCMeta)
14			class Dataset(object):
15			"""A dataset.
16
17			Dataset classes implement the interface to a particular dataset. The
18			interface consists of a number of routines to manipulate so called
19			"state" objects, e.g. open, reset and close them.
20
21			Parameters
22			----------
23			sources : tuple of strings, optional
24			The data sources to load and return by :meth:`get_data`. By default
25			all data sources are returned.
26			axis_labels : dict, optional
27			Maps source names to tuples of strings describing axis semantics,
28			one per axis. Defaults to `None`, i.e. no information is available.
29
30			Attributes
31			----------
32			sources : tuple of strings
33			The sources this dataset will provide when queried for data e.g.
34			``('features',)`` when querying only the data from MNIST.
35			provides_sources : tuple of strings
36			The sources this dataset is able to provide e.g. ``('features',
37			'targets')`` for MNIST (regardless of which data the data stream
38			actually requests). Any implementation of a dataset should set this
39			attribute on the class (or at least before calling ``super``).
40			example_iteration_scheme : :class:`.IterationScheme` or ``None``
41			The iteration scheme the class uses in order to produce a stream of
42			examples.
43			default_transformers: It is expected to be a tuple with one element per
44			transformer in the pipeline. Each element is a tuple with three
45			elements:
46			- the Transformer subclass to apply,
47			- a list of arguments to pass to the subclass constructor, and
48			- a dict of keyword arguments to pass to the subclass
49			constructor.
50
51
52			Notes
53			-----
54			Datasets should only implement the interface; they are not expected to
55			perform the iteration over the actual data. As such, they are
56			stateless, and can be shared by different parts of the library
57			simultaneously.
58
59			"""
60			provides_sources = None
61			default_transformers = tuple()
62
63			def __init__(self, sources=None, axis_labels=None):
64			if not self.provides_sources:
65			raise ValueError("dataset does not have `provides_sources`")
66			if sources is not None:
67			if not sources or not all(source in self.provides_sources
68			for source in sources):
69			raise ValueError("unable to provide requested sources")
70			self.sources = sources
71			self.axis_labels = axis_labels
72
73			@property
74			def sources(self):
75			if not hasattr(self, '_sources'):
76			return self.provides_sources
77			return self._sources
78
79			@sources.setter
80			def sources(self, sources):
81			self._sources = sources
82
83			def apply_default_transformers(self, stream):
84			"""Applies default transformers to a stream.
85
86			Parameters
87			----------
88			stream : :class:`~.streams.AbstractDataStream`
89			A data stream.
90
91			"""
92			for (cls, args, kwargs) in self.default_transformers:
93			args = [stream] + args
94			stream = cls(args, *kwargs)
95			return stream
96
97			@property
98			def example_iteration_scheme(self):
99			if not hasattr(self, '_example_iteration_scheme'):
100			raise AttributeError("dataset does not provide an example "
101			"iteration scheme")
102			return self._example_iteration_scheme
103
104			@example_iteration_scheme.setter
105			def example_iteration_scheme(self, value):
106			self._example_iteration_scheme = value
107
108			def get_example_stream(self):
109			return DataStream(self, iteration_scheme=self.example_iteration_scheme)
110
111			def open(self):
112			"""Return the state if the dataset requires one.
113
114			Datasets which e.g. read files from disks require open file
115			handlers, and this sort of stateful information should be handled
116			by the data stream.
117
118			Returns
119			-------
120			state : object
121			An object representing the state of a dataset.
122
123			"""
124			pass
125
126			def reset(self, state):
127			"""Resets the state.
128
129			Parameters
130			----------
131			state : object
132			The current state.
133
134			Returns
135			-------
136			state : object
137			A reset state.
138
139			Notes
140			-----
141			The default implementation closes the state and opens a new one. A
142			more efficient implementation (e.g. using ``file.seek(0)`` instead
143			of closing and re-opening the file) can override the default one in
144			derived classes.
145
146			"""
147			self.close(state)
148			return self.open()
149
150			def next_epoch(self, state):
151			"""Switches the dataset state to the next epoch.
152
153			The default implementation for this method is to reset the state.
154
155			Parameters
156			----------
157			state : object
158			The current state.
159
160			Returns
161			-------
162			state : object
163			The state for the next epoch.
164
165			"""
166			return self.reset(state)
167
168			def close(self, state):
169			"""Cleanly close the dataset e.g. close file handles.
170
171			Parameters
172			----------
173			state : object
174			The current state.
175
176			"""
177			pass
178
179			@abstractmethod
180			def get_data(self, state=None, request=None):
181			"""Request data from the dataset.
182
183			.. todo::
184
185			A way for the dataset to communicate which kind of requests it
186			accepts, and a way to communicate what kind of request is being
187			sent when supporting multiple.
188
189			Parameters
190			----------
191			state : object, optional
192			The state as returned by the :meth:`open` method. The dataset
193			can use this to e.g. interact with files when needed.
194			request : object, optional
195			If supported, the request for a particular part of the data
196			e.g. the number of examples to return, or the indices of a
197			particular minibatch of examples.
198
199			Returns
200			-------
201			tuple
202			A tuple of data matching the order of :attr:`sources`.
203
204			"""
205
206			def filter_sources(self, data):
207			"""Filter the requested sources from those provided by the dataset.
208
209			A dataset can be asked to provide only a subset of the sources it
210			can provide (e.g. asking MNIST only for the features, not for the
211			labels). A dataset can choose to use this information to e.g. only
212			load the requested sources into memory. However, in case the
213			performance gain of doing so would be negligible, the dataset can
214			load all the data sources and then use this method to return only
215			those requested.
216
217			Parameters
218			----------
219			data : tuple of objects
220			The data from all the sources i.e. should be of the same length
221			as :attr:`provides_sources`.
222
223			Returns
224			-------
225			tuple
226			A tuple of data matching :attr:`sources`.
227
228			Examples
229			--------
230			>>> import numpy
231			>>> class Random(Dataset):
232			... provides_sources = ('features', 'targets')
233			... def get_data(self, state=None, request=None):
234			... data = (numpy.random.rand(10), numpy.random.randn(3))
235			... return self.filter_sources(data)
236			>>> Random(sources=('targets',)).get_data() # doctest: +SKIP
237			(array([-1.82436737, 0.08265948, 0.63206168]),)
238
239			"""
240			return tuple([d for d, s in zip(data, self.provides_sources)
241			if s in self.sources])
242
243
244			class IterableDataset(Dataset):
245			"""Creates a dataset from a set of iterables.
246
247			Parameters
248			----------
249			iterables : :class:`~collections.OrderedDict` or iterable
250			The iterable(s) to provide interface to. The iterables' `__iter__`
251			method should return a new iterator over the iterable. If an
252			:class:`~collections.OrderedDict` is given, its values should be
253			iterables providing data, and its keys strings that are used as
254			source names. If a single iterable is given, it will be given the
255			source ``data``.
256
257			Attributes
258			----------
259			iterables : list
260			A list of :class:`~collections.Iterable` objects.
261
262			Notes
263			-----
264			Internally, this method uses picklable iterools's ``_iter``
265			function, providing picklable alternatives to some iterators such as
266			:func:`range`, :func:`tuple`, and even :class:`file`. However, if the
267			iterable returns a different kind of iterator that is not picklable,
268			you might want to consider using the :func:`.do_not_pickle_attributes`
269			decorator.
270
271			To iterate over a container in batches, combine this dataset with the
272			:class:`Batch` data stream.
273
274			"""
275			example_iteration_scheme = None
276
277			def __init__(self, iterables, **kwargs):
278			if isinstance(iterables, dict):
279			self.provides_sources = tuple(iterables.keys())
280			else:
281			self.provides_sources = ('data',)
282			super(IterableDataset, self).__init__(**kwargs)
283			if isinstance(iterables, dict):
284			if not all(isinstance(iterable, collections.Iterable)
285			for iterable in iterables.values()):
286			raise ValueError
287			self.iterables = [iterables[source] for source in self.sources]
288			else:
289			if not isinstance(iterables, collections.Iterable):
290			raise ValueError
291			self.iterables = [iterables]
292			try:
293			if len(set(len(iterable) for iterable in self.iterables)) != 1:
294			raise ValueError("iterables are of different length")
295			except TypeError:
			0 ignored issues – show Unused Code introduced 2015-12-02 16:30 UTC by Report Bug Copy Issue Report This except handler seems to be unused and could be removed. Except handlers which only contain `pass` and do not have an `else` clause can usually simply be removed: try: raises_exception() except: # Could be removed pass Loading history...
296			pass
297
298			@property
299			def num_examples(self):
300			try:
301			num_examples, = set(len(iterable) for iterable in self.iterables)
302			return num_examples
303			except TypeError:
304			return float('nan')
305
306			def open(self):
307			iterators = [iter_(channel) for channel in self.iterables]
308			return izip(*iterators)
309
310			def get_data(self, state=None, request=None):
311			if state is None or request is not None:
312			raise ValueError
313			return next(state)
314
315
316			class IndexableDataset(Dataset):
317			"""Creates a dataset from a set of indexable containers.
318
319			Parameters
320			----------
321			indexables : :class:`~collections.OrderedDict` or indexable
322			The indexable(s) to provide interface to. This means it must
323			support the syntax ```indexable[0]``. If an
324			:class:`~collections.OrderedDict` is given, its values should be
325			indexables providing data, and its keys strings that are used as
326			source names. If a single indexable is given, it will be given the
327			source ``data``.
328
329			Attributes
330			----------
331			indexables : list
332			A list of indexable objects.
333
334			Notes
335			-----
336			If the indexable data is very large, you might want to consider using
337			the :func:`.do_not_pickle_attributes` decorator to make sure the data
338			doesn't get pickled with the dataset, but gets reloaded/recreated
339			instead.
340
341			This dataset also uses the source names to create properties that
342			provide easy access to the data.
343
344			"""
345			def __init__(self, indexables, start=None, stop=None, **kwargs):
346			if isinstance(indexables, dict):
347			self.provides_sources = tuple(indexables.keys())
348			else:
349			self.provides_sources = ('data',)
350			super(IndexableDataset, self).__init__(**kwargs)
351			if isinstance(indexables, dict):
352			self.indexables = [indexables[source][start:stop]
353			for source in self.sources]
354			if not all(len(indexable) == len(self.indexables[0])
355			for indexable in self.indexables):
356			raise ValueError("sources have different lengths")
357			else:
358			self.indexables = [indexables]
359
360			self.example_iteration_scheme = SequentialExampleScheme(
361			self.num_examples)
362
363			self.start = start
364			self.stop = stop
365			self.subset = Subset(slice(start, stop), self.num_examples)
366
367			def __getattr__(self, attr):
368			if (attr not in ['sources', 'indexables', '_sources'] and
369			attr in self.sources):
370			return self.indexables[self.sources.index(attr)]
371			raise AttributeError
372
373			# Without explicitly defining a trivial __setstate__ method,
374			# the __getattribute__ method would call the __getattr__ method,
375			# which would raise an AttributeError. This causes problems
376			# when unpickling.
377			def __setstate__(self, dict):
			0 ignored issues – show Bug Best Practice introduced 2015-12-02 16:30 UTC by Report Bug Copy Issue Report This seems to re-define the built-in `dict`. It is generally discouraged to redefine built-ins as this makes code very hard to read. Loading history...
378			self.__dict__ = dict
379
380			@property
381			def num_examples(self):
382			return len(self.indexables[0])
383
384			def get_data(self, state=None, request=None):
385			if state is not None or request is None:
386			raise ValueError
387			return tuple(self.subset.index_within_subset(indexable, request)
388			for indexable in self.indexables)
389

mila-udem / fuel

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IterableDataset.__init__() C

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IterableDataset.init() C