Minimum

Complexity

Total Complexity

2

Size/Duplication

Total Lines	20
Duplicated Lines	0 %

Importance

Changes

0

2 Methods

Rating	Name	Duplication	Size	Complexity
A	get_aggregator()	0	4	1
A	_build_aggregator()	0	13	1

"""Evaluate Theano variables on auxiliary data and during training."""
from functools import partial
import logging
from abc import ABCMeta, abstractmethod

from six import add_metaclass
from theano import tensor
from theano.ifelse import ifelse

from blocks.utils import shared_like

logger = logging.getLogger(__name__)


@add_metaclass(ABCMeta)
class AggregationScheme(object):
    """How to incrementally evaluate a Theano variable over minibatches.

    An AggregationScheme allocates :class:`Aggregator` that can
    incrementally compute the value of a Theano variable on a full dataset
    by aggregating partial results computed on multiple batches.

    The AggregationScheme should be attached via the tag
    ``aggregation_scheme`` to a Theano variable which computes the desired
    value on a single batch.

    Parameters
    ----------
    variable: :class:`~tensor.TensorVariable`
        The variable that holds the desired value on a single batch.

    """
    def __init__(self, variable):
        self.variable = variable

    @abstractmethod
    def get_aggregator(self):
        """Return a new Aggregator for this variable."""
        pass


class Aggregator(object):
    """An Aggregator incrementally evaluates a Theano variable on a dataset.

    .. warning::
        The Aggregators should never be created directly. Instead use the
        :meth:`AggregationScheme.get_aggregator` method.

    Example usages are:

    * compute the mean of some value over examples, sequence lengths etc.
    * track a parameter of a model
    * monitor a penalty

    The Aggregator maintains a set of Theano sharer values called
    accumulators and specifies how they should be initialized, and
    updated with incremental calculations. Finally, it
    provides a Theano variable that reads the accumulators
    and computes the final value.

    Parameters
    ----------
    aggregation_scheme : :class:`AggregationScheme`
        The aggregation scheme that constructed this Aggregator
    initialization_updates : list of Theano updates
        Updates that specify how to initialize shared variables of
        this Aggregator. *Can only refer to shared variables and
        constants.*
    accumulation_updates : list of Theano updates
        Updates that specify how a new batch of data gets processed
        by this Aggregator. *Can refer to model inputs.*
    readout_variable : :class:`~tensor.TensorVariable`
        Theano variable that holds the final value based on aggregated
        partial results. *readout_variable must only consist of shared
        variables and constants.*

    Attributes
    ----------
    All constructor parameters are accessible as attributes.

    """
    def __init__(self, aggregation_scheme, initialization_updates=None,
                 accumulation_updates=None, readout_variable=None):
        self.aggregation_scheme = aggregation_scheme
        self.readout_variable = readout_variable

        if initialization_updates is None:
            initialization_updates = []
        if accumulation_updates is None:
            accumulation_updates = []
        self.initialization_updates = initialization_updates
        self.accumulation_updates = accumulation_updates


class Mean(AggregationScheme):
    """Aggregation scheme which computes the mean.

    Parameters
    ----------
    numerator : :class:`~tensor.TensorVariable`
        Theano variable for the numerator e.g. the likelihood
    denominator : :class:`~tensor.TensorVariable`
        Theano variable for the denominator e.g. the batch size

    """
    def __init__(self, numerator, denominator):

The __init__ method of the super-class AggregationScheme is not called.

        self.numerator = numerator
        self.denominator = denominator

    def get_aggregator(self):
        initialized = shared_like(0.)
        numerator_acc = shared_like(self.numerator)
        denominator_acc = shared_like(self.denominator)

        # Dummy default expression to use as the previously-aggregated
        # value, that has the same shape as the new result
        numerator_zeros = tensor.as_tensor(self.numerator).zeros_like()
        denominator_zeros = tensor.as_tensor(self.denominator).zeros_like()

        conditional_update_num = self.numerator + ifelse(initialized,
                                                         numerator_acc,
                                                         numerator_zeros)
        conditional_update_den = self.denominator + ifelse(initialized,
                                                           denominator_acc,
                                                           denominator_zeros)

        initialization_updates = [(numerator_acc,
                                   tensor.zeros_like(numerator_acc)),
                                  (denominator_acc,
                                   tensor.zeros_like(denominator_acc)),
                                  (initialized,
                                   tensor.zeros_like(initialized))]
        accumulation_updates = [(numerator_acc,
                                 conditional_update_num),
                                (denominator_acc,
                                 conditional_update_den),
                                (initialized, tensor.ones_like(initialized))]
        aggregator = Aggregator(aggregation_scheme=self,
                                initialization_updates=initialization_updates,
                                accumulation_updates=accumulation_updates,
                                readout_variable=(numerator_acc /
                                                  denominator_acc))
        return aggregator


def mean(numerator, denominator=1.):
    """Mean of quantity (numerator) over a number (denominator) values."""
    variable = numerator / denominator
    variable.tag.aggregation_scheme = Mean(numerator, denominator)
    variable.name = numerator.name
    return variable


class _DataIndependent(AggregationScheme):
    """Dummy aggregation scheme for values that don't depend on data."""
    def get_aggregator(self):
        return Aggregator(aggregation_scheme=self,
                          initialization_updates=[],
                          accumulation_updates=[],
                          readout_variable=self.variable)


class TakeLast(AggregationScheme):
    """Aggregation scheme which remembers only the last value."""
    def get_aggregator(self):
        self.storage = shared_like(self.variable)
        return Aggregator(aggregation_scheme=self,
                          initialization_updates=[
                              (self.storage,
                               tensor.zeros_like(self.storage))],
                          accumulation_updates=[(self.storage, self.variable)],
                          readout_variable=self.storage)


def _simple_aggregation(scheme, variable):
    variable = variable.copy(variable.name)
    variable.tag.aggregation_scheme = scheme(variable)
    return variable


take_last = partial(_simple_aggregation, TakeLast)

The name take_last does not conform to the constant naming conventions ((([A-Z_][A-Z0-9_]*)|(__.*__))$).

class Minimum(AggregationScheme):
    """Aggregation scheme which remembers only the minimum value."""
    def _build_aggregator(self, accumulate_update):
        initialized = shared_like(0.)
        accumulate = ifelse(initialized, accumulate_update, self.variable)
        return Aggregator(aggregation_scheme=self,
                          initialization_updates=[
                              (self.storage, tensor.zeros_like(self.storage)),
                              (initialized, tensor.zeros_like(initialized))
                          ],
                          accumulation_updates=[
                              (self.storage, accumulate),
                              (initialized, tensor.ones_like(initialized))
                          ],
                          readout_variable=self.storage)

    def get_aggregator(self):
        self.storage = shared_like(self.variable)
        return self._build_aggregator(tensor.minimum(self.storage,
                                                     self.variable))

minimum = partial(_simple_aggregation, Minimum)

The name minimum does not conform to the constant naming conventions ((([A-Z_][A-Z0-9_]*)|(__.*__))$).

class Maximum(Minimum):
    """Aggregation scheme which remembers only the maximum value."""
    def get_aggregator(self):
        self.storage = shared_like(self.variable)
        return self._build_aggregator(tensor.maximum(self.storage,
                                                     self.variable))

maximum = partial(_simple_aggregation, Maximum)

The name maximum does not conform to the constant naming conventions ((([A-Z_][A-Z0-9_]*)|(__.*__))$).

@add_metaclass(ABCMeta)

This abstract class does not seem to be used anywhere.

class MonitoredQuantity(object):
    """The base class for monitored-quantities.

    To monitor a non-Theano quantity in Blocks you have to implement this
    interface for it. The initialize method initializes accumulators and
    the parameters needed to compute this quantity, aggregate method
    aggregates results for every batch, and finally readout is called
    to get the aggregated results.

    Attributes
    ----------
    requires : list
        List of Theano variables needed to calculate this quantity.
    name : str
        The name of monitored quantity which appears in the log.

    See Also
    --------
    :class:`~blocks.monitoring.evaluators.DatasetEvaluator`
    :class:`~blocks.extensions.DataStreamMonitoring`

    """
    def __init__(self, requires=None, name=None):
        if requires is None:
            requires = []
        self.requires = requires
        self.name = name

    @abstractmethod
    def initialize(self):
        """Initialize accumulators for this monitored quantity."""
        pass

    @abstractmethod
    def aggregate(self, *args):
        r"""Aggregate results for every batch.

        \*args : list of :class:`~numpy.ndarray`
            The values of the variables required to aggregate the
            value of the quantity.

        """
        pass

    @abstractmethod
    def get_aggregated_value(self):
        """Obtain the result of aggregation."""
        pass