# featureBased.py
# Author: Jacob Schreiber <jmschreiber91@gmail.com>
import math
import numpy
from .base import BaseSelection
from ..optimizers import LazyGreedy
from ..optimizers import ApproximateLazyGreedy
from tqdm import tqdm
from numba import njit
from numba import prange
@njit('float64[:](float64[:])', fastmath=True)
def sigmoid(X):
return X / (1. + X)
dtypes = 'void(float64[:,:], float64[:], float64[:], int64[:])'
sparse_dtypes = 'void(float64[:], int32[:], int32[:], float64[:],' \
'float64[:], float64[:], int64[:])'
sieve_dtypes = 'void(float64[:,:], int64, float64[:,:], int64[:,:],' \
'float64[:,:], float64[:], float64[:], int64[:], int64[:])'
sieve_sparse_dtypes = 'void(float64[:], int32[:], int32[:], int64,' \
'float64[:,:], int64[:,:], float64[:,:], float64[:], float64[:],' \
'int64[:], int64[:])'
def calculate_gains(func, dtypes, parallel, fastmath, cache):
@njit(dtypes, parallel=parallel, fastmath=fastmath, cache=cache)
def calculate_gains_(X, gains, current_values, idxs):
for i in prange(idxs.shape[0]):
idx = idxs[i]
gains[i] = func(current_values + X[idx]).sum()
return calculate_gains_
def calculate_gains_sparse(func, dtypes, parallel, fastmath, cache):
@njit(dtypes, parallel=parallel, fastmath=fastmath, cache=cache)
def calculate_gains_sparse_(X_data, X_indices, X_indptr, gains,
current_values, current_concave_values, idxs):
for i in prange(idxs.shape[0]):
idx = idxs[i]
start = X_indptr[idx]
end = X_indptr[idx+1]
for j in range(start, end):
k = X_indices[j]
gains[i] += (func(X_data[j] + current_values[k])
- current_concave_values[k])
return calculate_gains_sparse_
def calculate_gains_sieve(func, dtypes, parallel, fastmath, cache):
@njit(dtypes, parallel=parallel, fastmath=fastmath, cache=cache)
def calculate_gains_sieve_(X, k, current_values, selections, gains,
total_gains, max_values, n_selected, idxs):
n = X.shape[0]
d = max_values.shape[0]
for j in prange(d):
for i in range(n):
if n_selected[j] == k:
break
idx = idxs[i]
threshold = (max_values[j] / 2. - total_gains[j]) / (k - n_selected[j])
gain = func(current_values[j] + X[i]).sum() - total_gains[j]
if gain > threshold:
current_values[j] += X[i]
total_gains[j] += gain
selections[j, n_selected[j]] = idx
gains[j, n_selected[j]] = gain
n_selected[j] += 1
return calculate_gains_sieve_
def calculate_gains_sieve_sparse(func, dtypes, parallel, fastmath, cache):
@njit(dtypes, parallel=parallel, fastmath=fastmath, cache=cache)
def calculate_gains_sieve_sparse_(X_data, X_indices, X_indptr, k,
current_values, selections, gains, total_gains, max_values,
n_selected, idxs):
d = max_values.shape[0]
for j in prange(d):
for i in range(idxs.shape[0]):
if n_selected[j] == k:
break
idx = idxs[i]
start = X_indptr[i]
end = X_indptr[i+1]
threshold = (max_values[j] / 2. - total_gains[j]) / (k - n_selected[j])
gain = 0.0
for l in range(start, end):
m = X_indices[l]
gain += (func(current_values[j, m] + X_data[l]) -
func(current_values[j, m]))
if gain > threshold:
for l in range(start, end):
m = X_indices[l]
current_values[j, m] += X_data[l]
total_gains[j] += gain
selections[j, n_selected[j]] = idx
gains[j, n_selected[j]] = gain
n_selected[j] += 1
return calculate_gains_sieve_sparse_
[docs]class FeatureBasedSelection(BaseSelection):
"""A selector based off a feature based submodular function.
Feature-based functions are those that operate on the feature values of
examples directly, rather than on a similarity matrix (or graph) derived
from those features, as graph-based functions do. Because these functions
do not require calculating and storing a :math:`\\mathcal{O}(n^{2})` sized
matrix of similarities, they can easily scale to data sets with millions of
examples.
.. note::
All values in your data must be positive for this selection to work.
The general form of a feature-based function is:
.. math::
f(X) = \\sum\\limits_{d=1}^{D}\\phi\\left(\\sum\\limits_{i=1}^{N} X_{i, d}\\right)
where :math:`f` indicates the function that uses the concave function
:math:`\\phi` and is operating on a subset :math:`X` that has :math:`N`
examples and :math:`D` dimensions. Importantly, :math:`X` is the subset
and not the ground set, meaning that the time it takes to evaluate this
function is proportional only to the size of the selected subset and not
the size of the full data set, like it is for graph-based functions.
See https://ieeexplore.ieee.org/document/6854213 for more details on
feature based functions.
Parameters
----------
n_samples : int
The number of samples to return.
concave_func : str or callable
The type of concave function to apply to the feature values. You can
pass in your own function to apply. Otherwise must be one of the
following:
'log' : log(1 + X)
'sqrt' : sqrt(X)
'sigmoid' : X / (1 + X)
initial_subset : list, numpy.ndarray or None
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.
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.
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.
ranking : numpy.array int
The selected samples in the order of their gain with the first number in
the ranking corresponding to the index of the first sample that was
selected by the greedy procedure.
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, concave_func='sqrt', initial_subset=None,
optimizer='two-stage', optimizer_kwds={}, reservoir=None,
max_reservoir_size=1000, n_jobs=1, random_state=None,
verbose=False):
self.concave_func_name = concave_func
if concave_func == 'log':
self.concave_func = numpy.log1p
elif concave_func == 'sqrt':
self.concave_func = numpy.sqrt
elif concave_func == 'sigmoid':
self.concave_func = sigmoid
elif callable(concave_func):
self.concave_func = concave_func
else:
raise KeyError("Must be one of 'log', 'sqrt', 'sigmoid', or a any other numpy, scipy, or numba-compiled function.")
super(FeatureBasedSelection, self).__init__(n_samples=n_samples,
initial_subset=initial_subset, optimizer=optimizer,
optimizer_kwds=optimizer_kwds, reservoir=reservoir,
max_reservoir_size=max_reservoir_size, n_jobs=n_jobs,
random_state=random_state, 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 : FeatureBasedSelection
The fit step returns this selector object.
"""
return super(FeatureBasedSelection, self).fit(X, y=y,
sample_weight=sample_weight, sample_cost=sample_cost)
def _initialize(self, X):
super(FeatureBasedSelection, self)._initialize(X)
if self.initial_subset is None:
pass
elif self.initial_subset.ndim == 2:
self.current_values = self.initial_subset.sum(axis=0).astype('float64')
elif self.initial_subset.ndim == 1:
self.current_values = X[self.initial_subset].sum(axis=0).astype('float64')
else:
raise ValueError("The initial subset must be either a two dimensional" \
" matrix of examples or a one dimensional mask.")
self.current_concave_values = self.concave_func(self.current_values)
self.current_concave_values_sum = self.current_concave_values.sum()
calculate_gains_ = calculate_gains_sparse if self.sparse else calculate_gains
dtypes_ = sparse_dtypes if self.sparse else dtypes
if self.optimizer in (LazyGreedy, ApproximateLazyGreedy):
self.calculate_gains_ = calculate_gains_(self.concave_func,
dtypes_, False, True, False)
elif self.optimizer in ('lazy', 'approimate-lazy'):
self.calculate_gains_ = calculate_gains_(self.concave_func,
dtypes_, False, True, False)
else:
self.calculate_gains_ = calculate_gains_(self.concave_func,
dtypes_, True, True, False)
calculate_sieve_gains_ = calculate_gains_sieve_sparse if self.sparse else calculate_gains_sieve
dtypes_ = sieve_sparse_dtypes if self.sparse else sieve_dtypes
self.calculate_sieve_gains_ = calculate_sieve_gains_(self.concave_func,
dtypes_, True, True, False)
def _calculate_gains(self, X, idxs=None):
"""This function will return the gain that each example would give.
This function will return the gains that each example would give if
added to the selected set. When a matrix of examples is given, a
vector will be returned showing the gain for each example. When
a single element is passed in, it will return a singe value."""
idxs = idxs if idxs is not None else self.idxs
gains = numpy.zeros(idxs.shape[0], dtype='float64')
if self.sparse:
self.calculate_gains_(X.data, X.indices, X.indptr, gains,
self.current_values, self.current_concave_values, idxs)
else:
self.calculate_gains_(X, gains, self.current_values, idxs)
gains -= self.current_concave_values_sum
return gains
def _calculate_sieve_gains(self, X, thresholds, idxs):
"""This function will update the internal statistics from a stream.
This function will update the various internal statistics that are a
part of the sieve algorithm for streaming submodular optimization. This
function does not directly return gains but it updates the values
used by a streaming optimizer.
"""
super(FeatureBasedSelection, self)._calculate_sieve_gains(X,
thresholds, idxs)
if self.sparse:
self.calculate_sieve_gains_(X.data, X.indices, X.indptr,
self.n_samples, self.sieve_current_values_,
self.sieve_selections_, self.sieve_gains_,
self.sieve_total_gains_, thresholds,
self.sieve_n_selected_, idxs)
else:
self.calculate_sieve_gains_(X, self.n_samples,
self.sieve_current_values_, self.sieve_selections_,
self.sieve_gains_, self.sieve_total_gains_, thresholds,
self.sieve_n_selected_, idxs)
def _select_next(self, X, gain, idx):
"""This function will add the given item to the selected set."""
if self.sparse:
self.current_values += X.toarray()[0]
else:
self.current_values += X
self.current_concave_values = self.concave_func(self.current_values)
self.current_concave_values_sum = self.current_concave_values.sum()
super(FeatureBasedSelection, self)._select_next(
X, gain, idx)