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Add invariant ratio estimators.

This commit is contained in:
Paweł Czyż 2024-03-15 18:14:42 +01:00
parent d34b086a76
commit 5cdd158fcc
2 changed files with 92 additions and 55 deletions
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9
quapy/functional.py
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@ -466,11 +466,12 @@ def solve_adjustment(
if method == "inversion":
pass # We leave A and B unchanged
elif method == "invariant-ratio":
# Change the last set of equations
raise NotImplementedError
# Change the last equation to replace
# it with the normalization condition
A[-1, :] = 1.0
B[-1] = 1.0
else:
raise ValueError(f"Flavour {method} not known.")
raise ValueError(f"Method {method} not known.")
if solver == "minimize":
def loss(prev):

138
quapy/method/aggregative.py
View File

@ -1,6 +1,6 @@
from abc import ABC, abstractmethod
from copy import deepcopy
from typing import Callable, Union
from typing import Callable, Literal, Union
import numpy as np
from abstention.calibration import NoBiasVectorScaling, TempScaling, VectorScaling
from scipy import optimize
@ -367,28 +367,50 @@ class ACC(AggregativeCrispQuantifier):
Alternatively, this set can be specified at fit time by indicating the exact set of data
on which the predictions are to be generated.
:param n_jobs: number of parallel workers
:param solver: indicates the method to be used for obtaining the final estimates. The choice
'exact' comes down to solving the system of linear equations :math:`Ax=B` where `A` is a
matrix containing the class-conditional probabilities of the predictions (e.g., the tpr and fpr in
binary) and `B` is the vector of prevalence values estimated via CC, as :math:`x=A^{-1}B`. This solution
might not exist for degenerated classifiers, in which case the method defaults to classify and count
(i.e., does not attempt any adjustment).
Another option is to search for the prevalence vector that minimizes the L2 norm of :math:`|Ax-B|`. The latter
is achieved by indicating solver='minimize'. This one generally works better, and is the default parameter.
More details about this can be consulted in `Bunse, M. "On Multi-Class Extensions of Adjusted Classify and
Count", on proceedings of the 2nd International Workshop on Learning to Quantify: Methods and Applications
(LQ 2022), ECML/PKDD 2022, Grenoble (France) <https://lq-2022.github.io/proceedings/CompleteVolume.pdf>`_.
:param method: adjustment method to be used:
'inversion': matrix inversion method based on the matrix equality :math:`P(C)=P(C|Y)P(Y)`,
which tries to invert `P(C|Y)` matrix.
'invariant-ratio': invariant ratio estimator of `Vaz et al. <https://jmlr.org/papers/v20/18-456.html>`_,
which replaces the last equation with the normalization condition.
:param solver: the method to use for solving the system of linear equations. Valid options are:
'exact-raise': tries to solve the system using matrix inversion. Raises an error if the matrix has
rank strictly less than `n_classes`.
'exact-cc': if the matrix is not of full rank, returns `p_c` as the estimates, which corresponds
to no adjustment (i.e., the classify and count method. See :class:`quapy.method.aggregative.CC`)
'exact': deprecated, defaults to 'exact-cc'
'minimize': minimizes the L2 norm of :math:`|Ax-B|`. This one generally works better, and is the default parameter.
More details about this can be consulted in `Bunse, M. "On Multi-Class Extensions of Adjusted Classify and
Count", on proceedings of the 2nd International Workshop on Learning to Quantify: Methods and Applications
(LQ 2022), ECML/PKDD 2022, Grenoble (France) <https://lq-2022.github.io/proceedings/CompleteVolume.pdf>`_.
:param clipping: the method to use for normalization.
If `None` or `"none"`, no normalization is performed.
If `"clip"`, the values are clipped to the range [0,1] and normalized, so they sum up to 1.
If `"project"`, the values are projected onto the probability simplex.
"""
def __init__(self, classifier: BaseEstimator, val_split=5, n_jobs=None, solver='minimize'):
def __init__(
self,
classifier: BaseEstimator,
val_split=5,
n_jobs=None,
solver: Literal['minimize', 'exact', 'exact-raise', 'exact-cc'] = 'minimize',
method: Literal['inversion', 'invariant-ratio'] = 'inversion',
clipping: Literal['clip', 'none', 'project'] = 'clip',
) -> None:
self.classifier = classifier
self.val_split = val_split
self.n_jobs = qp._get_njobs(n_jobs)
self.solver = solver
self.method = method
self.clipping = clipping
def _check_init_parameters(self):
if self.solver not in ['exact', 'minimize']:
raise ValueError("unknown solver; valid ones are 'exact', 'minimize'")
if self.solver not in ['exact', 'minimize', 'exact-raise', 'exact-cc']:
raise ValueError("unknown solver; valid ones are 'exact', 'minimize', 'exact-raise', 'exact-cc'")
if self.method not in ['inversion', 'invariant-ratio']:
raise ValueError("unknown method; valid ones are 'inversion', 'invariant-ratio'")
if self.clipping not in ['clip', 'none', 'project', None]:
raise ValueError("unknown clipping; valid ones are 'clip', 'none', 'project' or None")
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
"""
@ -418,30 +440,13 @@ class ACC(AggregativeCrispQuantifier):
def aggregate(self, classif_predictions):
prevs_estim = self.cc.aggregate(classif_predictions)
return ACC.solve_adjustment(self.Pte_cond_estim_, prevs_estim, solver=self.solver)
@classmethod
def solve_adjustment(cls, PteCondEstim, prevs_estim, solver='exact'):
"""
Solves the system linear system :math:`Ax = B` with :math:`A` = `PteCondEstim` and :math:`B` = `prevs_estim`
:param PteCondEstim: a `np.ndarray` of shape `(n_classes,n_classes,)` with entry `(i,j)` being the estimate
of :math:`P(y_i|y_j)`, that is, the probability that an instance that belongs to :math:`y_j` ends up being
classified as belonging to :math:`y_i`
:param prevs_estim: a `np.ndarray` of shape `(n_classes,)` with the class prevalence estimates
:param solver: indicates the method to use for solving the system of linear equations. Valid options are
'exact' (tries to solve the system --may fail if the misclassificatin matrix has rank < n_classes) or
'optim_minimize' (minimizes a norm --always exists).
:return: an adjusted `np.ndarray` of shape `(n_classes,)` with the corrected class prevalence estimates
"""
estimate = F.solve_adjustment(
p_c_y=PteCondEstim,
p_c_y=self.Pte_cond_estim_,
p_c=prevs_estim,
solver=solver,
method='inversion',
solver=self.solver,
method=self.method,
)
return F.clip_prevalence(estimate, method="clip")
return F.clip_prevalence(estimate, method=self.clipping)
class PCC(AggregativeSoftQuantifier):
@ -481,28 +486,51 @@ class PACC(AggregativeSoftQuantifier):
for `k`). Alternatively, this set can be specified at fit time by indicating the exact set of data
on which the predictions are to be generated.
:param n_jobs: number of parallel workers
:param solver: indicates the method to be used for obtaining the final estimates. The choice
'exact' comes down to solving the system of linear equations :math:`Ax=B` where `A` is a
matrix containing the class-conditional probabilities of the predictions (e.g., the tpr and fpr in
binary) and `B` is the vector of prevalence values estimated via CC, as :math:`x=A^{-1}B`. This solution
might not exist for degenerated classifiers, in which case the method defaults to classify and count
(i.e., does not attempt any adjustment).
Another option is to search for the prevalence vector that minimizes the L2 norm of :math:`|Ax-B|`. The latter
is achieved by indicating solver='minimize'. This one generally works better, and is the default parameter.
More details about this can be consulted in `Bunse, M. "On Multi-Class Extensions of Adjusted Classify and
Count", on proceedings of the 2nd International Workshop on Learning to Quantify: Methods and Applications
(LQ 2022), ECML/PKDD 2022, Grenoble (France) <https://lq-2022.github.io/proceedings/CompleteVolume.pdf>`_.
:param method: adjustment method to be used:
'inversion': matrix inversion method based on the matrix equality :math:`P(C)=P(C|Y)P(Y)`,
which tries to invert `P(C|Y)` matrix.
'invariant-ratio': invariant ratio estimator of `Vaz et al. <https://jmlr.org/papers/v20/18-456.html>`_,
which replaces the last equation with the normalization condition.
:param solver: the method to use for solving the system of linear equations. Valid options are:
'exact-raise': tries to solve the system using matrix inversion. Raises an error if the matrix has
rank strictly less than `n_classes`.
'exact-cc': if the matrix is not of full rank, returns `p_c` as the estimates, which corresponds
to no adjustment (i.e., the classify and count method. See :class:`quapy.method.aggregative.CC`)
'exact': deprecated, defaults to 'exact-cc'
'minimize': minimizes the L2 norm of :math:`|Ax-B|`. This one generally works better, and is the default parameter.
More details about this can be consulted in `Bunse, M. "On Multi-Class Extensions of Adjusted Classify and
Count", on proceedings of the 2nd International Workshop on Learning to Quantify: Methods and Applications
(LQ 2022), ECML/PKDD 2022, Grenoble (France) <https://lq-2022.github.io/proceedings/CompleteVolume.pdf>`_.
:param clipping: the method to use for normalization.
If `None` or `"none"`, no normalization is performed.
If `"clip"`, the values are clipped to the range [0,1] and normalized, so they sum up to 1.
If `"project"`, the values are projected onto the probability simplex.
"""
def __init__(self, classifier: BaseEstimator, val_split=5, n_jobs=None, solver='minimize'):
def __init__(
self,
classifier: BaseEstimator,
val_split=5,
n_jobs=None,
solver: Literal['minimize', 'exact', 'exact-raise', 'exact-cc'] = 'minimize',
method: Literal['inversion', 'invariant-ratio'] = 'inversion',
clipping: Literal['clip', 'none', 'project'] = 'clip',
) -> None:
self.classifier = classifier
self.val_split = val_split
self.n_jobs = qp._get_njobs(n_jobs)
self.solver = solver
self.method = method
self.clipping = clipping
def _check_init_parameters(self):
assert self.solver in ['exact', 'minimize'], "unknown solver; valid ones are 'exact', 'minimize'"
if self.solver not in ['exact', 'minimize', 'exact-raise', 'exact-cc']:
raise ValueError("unknown solver; valid ones are 'exact', 'minimize', 'exact-raise', 'exact-cc'")
if self.method not in ['inversion', 'invariant-ratio']:
raise ValueError("unknown method; valid ones are 'inversion', 'invariant-ratio'")
if self.clipping not in ['clip', 'none', 'project', None]:
raise ValueError("unknown clipping; valid ones are 'clip', 'none', 'project' or None")
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
"""
@ -518,7 +546,15 @@ class PACC(AggregativeSoftQuantifier):
def aggregate(self, classif_posteriors):
prevs_estim = self.pcc.aggregate(classif_posteriors)
return ACC.solve_adjustment(self.Pte_cond_estim_, prevs_estim, solver=self.solver)
estimate = F.solve_adjustment(
p_c_y=self.Pte_cond_estim_,
p_c=prevs_estim,
solver=self.solver,
method=self.method,
)
return F.clip_prevalence(estimate, method=self.clipping)
@classmethod
def getPteCondEstim(cls, classes, y, y_):