fuel.ServerDataStream

Complexity

Total Complexity

10

Size/Duplication

Total Lines	70
Duplicated Lines	0 %

8 Methods

Rating	Name	Duplication	Size	Complexity
A	ServerDataStream.reset()	0	2	1
A	ServerDataStream.get_epoch_iterator()	0	2	1
A	ServerDataStream.next_epoch()	0	2	1
A	ServerDataStream.get_data()	0	7	3
A	ServerDataStream.__init__()	0	9	1
A	ServerDataStream.__getstate__()	0	5	1
A	ServerDataStream.close()	0	2	1
A	ServerDataStream.connect()	0	6	1

from abc import ABCMeta, abstractmethod

import zmq
from six import add_metaclass, iteritems

from fuel.iterator import DataIterator
from fuel.server import recv_arrays


@add_metaclass(ABCMeta)
class AbstractDataStream(object):
    """A stream of data separated into epochs.

    A data stream is an iterable stream of examples/minibatches. It shares
    similarities with Python file handles return by the ``open`` method.
    Data streams can be closed using the :meth:`close` method and reset
    using :meth:`reset` (similar to ``f.seek(0)``).

    Parameters
    ----------
    iteration_scheme : :class:`.IterationScheme`, optional
        The iteration scheme to use when retrieving data. Note that not all
        datasets support the same iteration schemes, some datasets require
        one, and others don't support any. In case when the data stream
        wraps another data stream, the choice of supported iteration
        schemes is typically even more limited. Be sure to read the
        documentation of the dataset or data stream in question.
    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
    ----------
    iteration_scheme : :class:`.IterationScheme`
        The iteration scheme used to retrieve data. Can be ``None`` when
        not used.
    sources : tuple of strings
        The names of the data sources returned by this data stream, as
        given by the dataset.
    produces_examples : bool
        Whether this data stream produces examples (as opposed to batches
        of examples).

    """
    def __init__(self, iteration_scheme=None, axis_labels=None):
        self.iteration_scheme = iteration_scheme
        self.axis_labels = axis_labels

    @property
    def produces_examples(self):
        if self.iteration_scheme:
            return self.iteration_scheme.requests_examples
        elif not hasattr(self, '_produces_examples'):
            raise ValueError("cannot infer type of stream for {} instance; "
                             "set the produces_examples attribute to True "
                             "(for example streams) or False (for batch "
                             "streams).".format(self.__class__.__name__))
        else:
            return self._produces_examples

    @produces_examples.setter
    def produces_examples(self, value):
        if self.iteration_scheme:
            raise ValueError("cannot set produces_examples on {} instance; "
                             "determined by iteration scheme {}".format(
                                 self.__class__.__name__,
                                 self.iteration_scheme))
        self._produces_examples = value

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

        Parameters
        ----------
        request : object
            A request fetched from the `request_iterator`.

        """

    @abstractmethod
    def reset(self):
        """Reset the data stream."""

    @abstractmethod
    def close(self):
        """Gracefully close the data stream, e.g. releasing file handles."""

    @abstractmethod
    def next_epoch(self):
        """Switch the data stream to the next epoch."""

    @abstractmethod
    def get_epoch_iterator(self, as_dict=False):
        return DataIterator(self, self.iteration_scheme.get_request_iterator()
                            if self.iteration_scheme else None,
                            as_dict=as_dict)

    def iterate_epochs(self, as_dict=False):
        """Allow iteration through all epochs.

        Notes
        -----
        This method uses the :meth:`get_epoch_iterator` method to retrieve
        the :class:`DataIterator` for each epoch. The default
        implementation of this method resets the state of the data stream
        so that the new epoch can read the data from the beginning.
        However, this behavior only works as long as the ``epochs``
        property is iterated over using e.g. ``for epoch in
        stream.epochs``. If you create the data iterators in advance (e.g.
        using ``for i, epoch in zip(range(10), stream.epochs`` in legacy
        Python) you must call the :meth:`reset` method yourself.

        """
        while True:
            yield self.get_epoch_iterator(as_dict=as_dict)


class DataStream(AbstractDataStream):
    """A stream of data from a dataset.

    Parameters
    ----------
    dataset : instance of :class:`Dataset`
        The dataset from which the data is fetched.

    """
    def __init__(self, dataset, **kwargs):
        if dataset.axis_labels:
            kwargs.setdefault('axis_labels', dataset.axis_labels.copy())
        super(DataStream, self).__init__(**kwargs)
        # A DataStream with no iteration scheme is considered an example stream
        # by default
        if not self.iteration_scheme:
            self.produces_examples = True
        # If the data stream produces examples, remove 'batch' from axis labels
        if self.produces_examples and self.axis_labels:
            for source, labels in iteritems(self.axis_labels):
                self.axis_labels[source] = tuple(
                    label for label in labels if label != 'batch')
        self.dataset = dataset
        self.data_state = self.dataset.open()
        self._fresh_state = True

    @property
    def sources(self):
        if hasattr(self, '_sources'):
            return self._sources
        return self.dataset.sources

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

    def close(self):
        self.data_state = self.dataset.close(self.data_state)

    def reset(self):
        self.data_state = self.dataset.reset(self.data_state)
        self._fresh_state = True

    def next_epoch(self):
        self.data_state = self.dataset.next_epoch(self.data_state)

    def get_data(self, request=None):
        """Get data from the dataset."""
        return self.dataset.get_data(self.data_state, request)

    def get_epoch_iterator(self, **kwargs):
        """Get an epoch iterator for the data stream."""
        if not self._fresh_state:
            self.next_epoch()
        else:
            self._fresh_state = False
        return super(DataStream, self).get_epoch_iterator(**kwargs)

    @classmethod
    def default_stream(cls, dataset, **kwargs):
        data_stream = cls(dataset, **kwargs)
        return dataset.apply_default_transformers(data_stream)


class ServerDataStream(AbstractDataStream):
    """A data stream that receives batches from a Fuel server.

    Parameters
    ----------
    sources : tuple of strings
        The names of the data sources returned by this data stream.
    produces_examples : bool
        Whether this data stream produces examples (as opposed to batches
        of examples).
    host : str, optional
        The host to connect to. Defaults to ``localhost``.
    port : int, optional
        The port to connect on. Defaults to 5557.
    hwm : int, optional
        The `ZeroMQ high-water mark (HWM)
        <http://zguide.zeromq.org/page:all#High-Water-Marks>`_ on the
        receiving socket. Increasing this increases the buffer, which can
        be useful if your data preprocessing times are very random.
        However, it will increase memory usage. There is no easy way to
        tell how many batches will actually be queued with a particular
        HWM. Defaults to 10. Be sure to set the corresponding HWM on the
        server's end as well.
    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.

    """
    def __init__(self, sources, produces_examples, host='localhost', port=5557,
                 hwm=10, axis_labels=None):
        super(ServerDataStream, self).__init__(axis_labels=axis_labels)
        self.sources = sources
        self.produces_examples = produces_examples
        self.host = host
        self.port = port
        self.hwm = hwm
        self.connect()

    def connect(self):
        context = zmq.Context()
        self.socket = socket = context.socket(zmq.PULL)
        socket.set_hwm(self.hwm)
        socket.connect("tcp://{}:{}".format(self.host, self.port))
        self.connected = True

    def get_data(self, request=None):
        if request is not None:
            raise ValueError
        if not self.connected:
            self.connect()
        data = recv_arrays(self.socket)
        return tuple(data)

    def get_epoch_iterator(self, **kwargs):
        return super(ServerDataStream, self).get_epoch_iterator(**kwargs)

    def close(self):
        pass

    def next_epoch(self):
        pass

    def reset(self):
        pass

    def __getstate__(self):
        state = self.__dict__.copy()
        state['connected'] = False
        del state['socket']
        return state