# Source code for apricot.functions.sumRedundancy

# sumRedundancy.py
# Author: Jacob Schreiber <jmschreiber91@gmail.com>

"""
This code implements the graph cut function.
"""

try:
import cupy
except:
import numpy as cupy

import numpy

from .base import BaseGraphSelection

from tqdm import tqdm

[docs]class SumRedundancySelection(BaseGraphSelection):
"""A selector based off a sum redundancy submodular function.

NOTE: All ~pairwise~ values in your data must be positive for this
selection to work.

This selector uses a sum redundancy function to perform selection. The
sum redundancy function is based on maximizing the difference between
the

This selector uses a sum redundancy submodular function to perform
selection. The sum redundancy function is based on maximizing the
pairwise similarities between the points in the data set and their nearest
chosen point. The similarity function can be species by the user but must
be non-negative where a higher value indicates more similar.

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'.

n_naive_samples : int, optional
The number of samples to perform the naive greedy algorithm on
before switching to the lazy greedy algorithm. The lazy greedy
algorithm is faster once features begin to saturate, but is slower
in the initial few selections. This is, in part, because the naive
greedy algorithm is parallelized whereas the lazy greedy
algorithm currently is not. Default is 1.

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

'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
'greedi' : the GreeDi distributed algorithm
'bidirectional' : the bidirectional greedy algorithm

Default is 'naive'.

epsilon : float, optional
The inverse of the sampling probability of any particular point being
included in the subset, such that 1 - epsilon is the probability that
a point is included. Only used for stochastic greedy. Default is 0.9.

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, 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, 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[i, 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)