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trying to figure out how to refactor protocols meaninguflly

This commit is contained in:
Alejandro Moreo Fernandez 2021-12-20 11:39:44 +01:00
parent cfdf2e35bd
commit ba18d00334
4 changed files with 15 additions and 104 deletions
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2
quapy/__init__.py
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@ -10,7 +10,7 @@ from . import model_selection
from . import classification
from quapy.method.base import isprobabilistic, isaggregative
__version__ = '0.1.6'
__version__ = '0.1.7'
environ = {
'SAMPLE_SIZE': None,

85
quapy/data/base.py
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@ -3,7 +3,7 @@ from scipy.sparse import issparse
from scipy.sparse import vstack
from sklearn.model_selection import train_test_split, RepeatedStratifiedKFold
from quapy.functional import artificial_prevalence_sampling, strprev
from quapy.functional import strprev
class LabelledCollection:
@ -120,21 +120,24 @@ class LabelledCollection:
assert len(prevs) == self.n_classes, 'unexpected number of prevalences'
assert sum(prevs) == 1, f'prevalences ({prevs}) wrong range (sum={sum(prevs)})'
taken = 0
indexes_sample = []
for i, class_ in enumerate(self.classes_):
if i == self.n_classes - 1:
n_requested = size - taken
else:
n_requested = int(size * prevs[i])
# Decide how many instances should be taken for each class in order to satisfy the requested prevalence
# accurately, and the number of instances in the sample (exactly). If int(size * prevs[i]) (which is
# <= size * prevs[i]) examples are drawn from class i, there could be a remainder number of instances to take
# to satisfy the size constrain. The remainder is distributed along the classes with probability = prevs.
# (This aims at avoiding the remainder to be placed in a class for which the prevalence requested is 0.)
n_requests = {class_: int(size * prevs[i]) for i, class_ in enumerate(self.classes_)}
remainder = size - sum(n_requests.values())
for rand_class in np.random.choice(self.classes_, size=remainder, p=prevs):
n_requests[rand_class] += 1
indexes_sample = []
for class_, n_requested in n_requests.items():
n_candidates = len(self.index[class_])
index_sample = self.index[class_][
np.random.choice(n_candidates, size=n_requested, replace=(n_requested > n_candidates))
] if n_requested > 0 else []
indexes_sample.append(index_sample)
taken += n_requested
indexes_sample = np.concatenate(indexes_sample).astype(int)
@ -152,7 +155,7 @@ class LabelledCollection:
:param size: integer, the size of the uniform sample
:return: a np.ndarray of shape `(size)` with the indexes
"""
return np.random.choice(len(self), size, replace=False)
return np.random.choice(len(self), size, replace=size > len(self))
def sampling(self, size, *prevs, shuffle=True):
"""
@ -212,68 +215,6 @@ class LabelledCollection:
random_state=random_state)
return LabelledCollection(tr_docs, tr_labels), LabelledCollection(te_docs, te_labels)
def artificial_sampling_generator(self, sample_size, n_prevalences=101, repeats=1):
"""
A generator of samples that implements the artificial prevalence protocol (APP).
The APP consists of exploring a grid of prevalence values containing `n_prevalences` points (e.g.,
[0, 0.05, 0.1, 0.15, ..., 1], if `n_prevalences=21`), and generating all valid combinations of
prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], ...,
[1, 0, 0] prevalence values of size `sample_size` will be yielded). The number of samples for each valid
combination of prevalence values is indicated by `repeats`.
:param sample_size: the number of instances in each sample
:param n_prevalences: the number of prevalence points to be taken from the [0,1] interval (including the
limits {0,1}). E.g., if `n_prevalences=11`, then the prevalence points to take are [0, 0.1, 0.2, ..., 1]
:param repeats: the number of samples to generate for each valid combination of prevalence values (default 1)
:return: yield samples generated at artificially controlled prevalence values
"""
dimensions = self.n_classes
for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats):
yield self.sampling(sample_size, *prevs)
def artificial_sampling_index_generator(self, sample_size, n_prevalences=101, repeats=1):
"""
A generator of sample indexes implementing the artificial prevalence protocol (APP).
The APP consists of exploring
a grid of prevalence values (e.g., [0, 0.05, 0.1, 0.15, ..., 1]), and generating all valid combinations of
prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], ...,
[1, 0, 0] prevalence values of size `sample_size` will be yielded). The number of sample indexes for each valid
combination of prevalence values is indicated by `repeats`
:param sample_size: the number of instances in each sample (i.e., length of each index)
:param n_prevalences: the number of prevalence points to be taken from the [0,1] interval (including the
limits {0,1}). E.g., if `n_prevalences=11`, then the prevalence points to take are [0, 0.1, 0.2, ..., 1]
:param repeats: the number of samples to generate for each valid combination of prevalence values (default 1)
:return: yield the indexes that generate the samples according to APP
"""
dimensions = self.n_classes
for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats):
yield self.sampling_index(sample_size, *prevs)
def natural_sampling_generator(self, sample_size, repeats=100):
"""
A generator of samples that implements the natural prevalence protocol (NPP). The NPP consists of drawing
samples uniformly at random, therefore approximately preserving the natural prevalence of the collection.
:param sample_size: integer, the number of instances in each sample
:param repeats: the number of samples to generate
:return: yield instances of :class:`LabelledCollection`
"""
for _ in range(repeats):
yield self.uniform_sampling(sample_size)
def natural_sampling_index_generator(self, sample_size, repeats=100):
"""
A generator of sample indexes according to the natural prevalence protocol (NPP). The NPP consists of drawing
samples uniformly at random, therefore approximately preserving the natural prevalence of the collection.
:param sample_size: integer, the number of instances in each sample (i.e., the length of each index)
:param repeats: the number of indexes to generate
:return: yield `repeats` instances of np.ndarray with shape `(sample_size,)`
"""
for _ in range(repeats):
yield self.uniform_sampling_index(sample_size)
def __add__(self, other):
"""
Returns a new :class:`LabelledCollection` as the union of this collection with another collection

30
quapy/functional.py
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@ -4,36 +4,6 @@ import scipy
import numpy as np
def artificial_prevalence_sampling(dimensions, n_prevalences=21, repeat=1, return_constrained_dim=False):
"""
Generates vectors of prevalence values artificially drawn from an exhaustive grid of prevalence values. The
number of prevalence values explored for each dimension depends on `n_prevalences`, so that, if, for example,
`n_prevalences=11` then the prevalence values of the grid are taken from [0, 0.1, 0.2, ..., 0.9, 1]. Only
valid prevalence distributions are returned, i.e., vectors of prevalence values that sum up to 1. For each
valid vector of prevalence values, `repeat` copies are returned. The vector of prevalence values can be
implicit (by setting `return_constrained_dim=False`), meaning that the last dimension (which is constrained
to 1 - sum of the rest) is not returned (note that, quite obviously, in this case the vector does not sum up to 1).
:param dimensions: the number of classes
:param n_prevalences: the number of equidistant prevalence points to extract from the [0,1] interval for the grid
(default is 21)
:param repeat: number of copies for each valid prevalence vector (default is 1)
:param return_constrained_dim: set to True to return all dimensions, or to False (default) for ommitting the
constrained dimension
:return: a `np.ndarray` of shape `(n, dimensions)` if `return_constrained_dim=True` or of shape `(n, dimensions-1)`
if `return_constrained_dim=False`, where `n` is the number of valid combinations found in the grid multiplied
by `repeat`
"""
s = np.linspace(0., 1., n_prevalences, endpoint=True)
s = [s] * (dimensions - 1)
prevs = [p for p in itertools.product(*s, repeat=1) if sum(p)<=1]
if return_constrained_dim:
prevs = [p+(1-sum(p),) for p in prevs]
prevs = np.asarray(prevs).reshape(len(prevs), -1)
if repeat>1:
prevs = np.repeat(prevs, repeat, axis=0)
return prevs
def prevalence_linspace(n_prevalences=21, repeats=1, smooth_limits_epsilon=0.01):
"""

2
quapy/model_selection.py
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@ -21,7 +21,7 @@ class GridSearchQ(BaseQuantifier):
:param model: the quantifier to optimize
:type model: BaseQuantifier
:param param_grid: a dictionary with keys the parameter names and values the list of values to explore
:param sample_size: the size of the samples to extract from the validation set (ignored if protocl='gen')
:param sample_size: the size of the samples to extract from the validation set (ignored if protocol='gen')
:param protocol: either 'app' for the artificial prevalence protocol, 'npp' for the natural prevalence
protocol, or 'gen' for using a custom sampling generator function
:param n_prevpoints: if specified, indicates the number of equally distant points to extract from the interval