# sumRedundancy.py
# Author: Jacob Schreiber <jmschreiber91@gmail.com>
import numpy
from .base import BaseGraphSelection
from tqdm import tqdm
[docs]class SumRedundancySelection(BaseGraphSelection):
"""A selector based off a sum redundancy submodular function.
The sum redundancy function is a graph-based function that penalizes
redundancy among the selected set. This approach is straightforward,
in that it simply involved a sum. It is also fast in comparison to a
facility location function because it involves only performing calculation
over the selected set as opposed to the entire ground set. Because the sum
of the similarities is not submodular, it is subtracted from the sum of
the entire similarity matrix, such that examples that are highly similar
to each other result in a lower value than examples that are not very
similar.
.. note::
All ~pairwise~ values in your data must be positive for this
selection to work.
The general form of a sum redundancy function is
.. math::
f(X, V) = \sum_{x, y \in V} \phi(x, y) - \sum_{x, y\in X} \phi(x,y)
where :math:`f` indicates the function, :math:`X` is the selected subset,
:math:`V` is the ground set, and :math:`\phi` is the similarity measure
between two examples. While sum redundancy functions involves calculating
the sum of the entire similarity matrix in principle, in practice if one
is only calculating the gains this step can be ignored.
This implementation allows users to pass in either their own symmetric
square matrix of similarity values, or a data matrix as normal and a function
that calculates these pairwise values.
Parameters
----------
n_samples : int
The number of samples to return.
metric : str, optional
The method for converting a data matrix into a square symmetric matrix
of pairwise similarities. If a string, can be any of the metrics
implemented in sklearn (see https://scikit-learn.org/stable/modules/
generated/sklearn.metrics.pairwise_distances.html), including
"precomputed" if one has already generated a similarity matrix. Note
that sklearn calculates distance matrices whereas apricot operates on
similarity matrices, and so a distances.max() - distances transformation
is performed on the resulting distances. For backcompatibility,
'corr' will be read as 'correlation'. Default is 'euclidean'.
initial_subset : list, numpy.ndarray or None, optional
If provided, this should be a list of indices into the data matrix
to use as the initial subset, or a group of examples that may not be
in the provided data should beused as the initial subset. If indices,
the provided array should be one-dimensional. If a group of examples,
the data should be 2 dimensional. Default is None.
optimizer : string or optimizers.BaseOptimizer, optional
The optimization approach to use for the selection. Default is
'two-stage', which makes selections using the naive greedy algorithm
initially and then switches to the lazy greedy algorithm. Must be
one of
'random' : randomly select elements (dummy optimizer)
'modular' : approximate the function using its modular upper bound
'naive' : the naive greedy algorithm
'lazy' : the lazy (or accelerated) greedy algorithm
'approximate-lazy' : the approximate lazy greedy algorithm
'two-stage' : starts with naive and switches to lazy
'stochastic' : the stochastic greedy algorithm
'sample' : randomly take a subset and perform selection on that
'greedi' : the GreeDi distributed algorithm
'bidirectional' : the bidirectional greedy algorithm
Default is 'two-stage'.
optimizer_kwds : dict or None
A dictionary of arguments to pass into the optimizer object. The keys
of this dictionary should be the names of the parameters in the optimizer
and the values in the dictionary should be the values that these
parameters take. Default is None.
n_neighbors : int or None
When constructing a similarity matrix, the number of nearest neighbors
whose similarity values will be kept. The result is a sparse similarity
matrix which can significantly speed up computation at the cost of
accuracy. Default is None.
reservoir : numpy.ndarray or None
The reservoir to use when calculating gains in the sieve greedy
streaming optimization algorithm in the `partial_fit` method.
Currently only used for graph-based functions. If a numpy array
is passed in, it will be used as the reservoir. If None is passed in,
will use reservoir sampling to collect a reservoir. Default is None.
max_reservoir_size : int
The maximum size that the reservoir can take. If a reservoir is passed
in, this value is set to the size of that array. Default is 1000.
n_jobs : int
The number of threads to use when performing computation in parallel.
Currently, this parameter is exposed but does not actually do anything.
This will be fixed soon.
random_state : int or RandomState or None, optional
The random seed to use for the random selection process. Only used
for stochastic greedy.
verbose : bool
Whether to print output during the selection process.
Attributes
----------
n_samples : int
The number of samples to select.
pairwise_func : callable
A function that takes in a data matrix and converts it to a square
symmetric matrix.
ranking : numpy.array int
The selected samples in the order of their gain.
gains : numpy.array float
The gain of each sample in the returned set when it was added to the
growing subset. The first number corresponds to the gain of the first
added sample, the second corresponds to the gain of the second added
sample, and so forth.
"""
def __init__(self, n_samples=10, metric='euclidean',
initial_subset=None, optimizer='two-stage', n_neighbors=None,
reservoir=None, max_reservoir_size=1000, n_jobs=1,
random_state=None, optimizer_kwds={}, verbose=False):
super(SumRedundancySelection, self).__init__(n_samples=n_samples,
metric=metric, initial_subset=initial_subset, optimizer=optimizer,
n_neighbors=n_neighbors, reservoir=reservoir,
max_reservoir_size=max_reservoir_size, n_jobs=n_jobs,
random_state=random_state, optimizer_kwds=optimizer_kwds,
verbose=verbose)
[docs] def fit(self, X, y=None, sample_weight=None, sample_cost=None):
"""Run submodular optimization to select the examples.
This method is a wrapper for the full submodular optimization process.
It takes in some data set (and optionally labels that are ignored
during this process) and selects `n_samples` from it in the greedy
manner specified by the optimizer.
This method will return the selector object itself, not the transformed
data set. The `transform` method will then transform a data set to the
selected points, or alternatively one can use the ranking stored in
the `self.ranking` attribute. The `fit_transform` method will perform
both optimization and selection and return the selected items.
Parameters
----------
X : list or numpy.ndarray, shape=(n, d)
The data set to transform. Must be numeric.
y : list or numpy.ndarray or None, shape=(n,), optional
The labels to transform. If passed in this function will return
both the data and th corresponding labels for the rows that have
been selected.
sample_weight : list or numpy.ndarray or None, shape=(n,), optional
The weight of each example. Currently ignored in apricot but
included to maintain compatibility with sklearn pipelines.
sample_cost : list or numpy.ndarray or None, shape=(n,), optional
The cost of each item. If set, indicates that optimization should
be performed with respect to a knapsack constraint.
Returns
-------
self : SumRedundancySelection
The fit step returns this selector object.
"""
return super(SumRedundancySelection, self).fit(X, y=y,
sample_weight=sample_weight, sample_cost=sample_cost)
def _initialize(self, X_pairwise, idxs=None):
super(SumRedundancySelection, self)._initialize(X_pairwise, idxs=idxs)
idxs = idxs if idxs is not None else numpy.arange(X_pairwise.shape[0])
for i, idx in enumerate(idxs):
self.current_values[i] = X_pairwise[idx, idx]
if self.initial_subset is None:
return
elif self.initial_subset.ndim == 2:
raise ValueError("When using saturated coverage, the initial subset"\
" must be a one dimensional array of indices.")
elif self.initial_subset.ndim == 1:
if not self.sparse:
for i in self.initial_subset:
self.current_values += X_pairwise[i] * 2
else:
for i in self.initial_subset:
self.current_values += X_pairwise[i].toarray()[0] * 2
else:
raise ValueError("The initial subset must be either a two dimensional" \
" matrix of examples or a one dimensional mask.")
def _calculate_gains(self, X_pairwise, idxs=None):
idxs = idxs if idxs is not None else self.idxs
return -self.current_values[idxs]
def _select_next(self, X_pairwise, gain, idx):
"""This function will add the given item to the selected set."""
if self.sparse:
self.current_values += X_pairwise.toarray()[0] * 2
else:
self.current_values += X_pairwise * 2
super(SumRedundancySelection, self)._select_next(
X_pairwise, gain, idx)