from dataclasses import dataclass
from typing import Callable, List, Union
import numpy as np
from matplotlib.pylab import rand
from quapy.method.aggregative import CC, PACC, SLD, BaseQuantifier
from quapy.protocol import UPP, AbstractProtocol, OnLabelledCollectionProtocol
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
import quacc as qc
from quacc.environment import env
from quacc.evaluation.report import EvaluationReport
from quacc.method.base import BQAE, MCAE, BaseAccuracyEstimator
from quacc.method.model_selection import (
GridSearchAE,
SpiderSearchAE,
)
from quacc.quantification import KDEy
import traceback
def _param_grid(method, X_fit: np.ndarray):
match method:
case "sld_lr":
return {
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"q__recalib": [None, "bcts"],
"confidence": [
None,
["isoft"],
["max_conf", "entropy"],
["max_conf", "entropy", "isoft"],
],
}
case "sld_rbf":
_scale = 1.0 / (X_fit.shape[1] * X_fit.var())
return {
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"q__classifier__gamma": _scale * np.logspace(-2, 2, 5),
"q__recalib": [None, "bcts"],
"confidence": [
None,
["isoft"],
["max_conf", "entropy"],
["max_conf", "entropy", "isoft"],
],
}
case "pacc":
return {
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"confidence": [None, ["isoft"], ["max_conf", "entropy"]],
}
case "cc_lr":
return {
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"confidence": [
None,
["isoft"],
["max_conf", "entropy"],
["max_conf", "entropy", "isoft"],
],
}
case "kde_lr":
return {
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"q__bandwidth": np.linspace(0.01, 0.2, 20),
"confidence": [None, ["isoft"], ["max_conf", "entropy", "isoft"]],
}
case "kde_rbf":
_scale = 1.0 / (X_fit.shape[1] * X_fit.var())
return {
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"q__classifier__gamma": _scale * np.logspace(-2, 2, 5),
"q__bandwidth": np.linspace(0.01, 0.2, 20),
"confidence": [None, ["isoft"], ["max_conf", "entropy", "isoft"]],
}
def evaluation_report(
estimator: BaseAccuracyEstimator, protocol: AbstractProtocol, method_name=None
) -> EvaluationReport:
# method_name = inspect.stack()[1].function
report = EvaluationReport(name=method_name)
for sample in protocol():
try:
e_sample = estimator.extend(sample)
estim_prev = estimator.estimate(e_sample.eX)
true_prev = e_sample.e_prevalence()
acc_score = qc.error.acc(estim_prev)
row = dict(
acc_score=acc_score,
acc=abs(qc.error.acc(true_prev) - acc_score),
)
if estim_prev.can_f1():
f1_score = qc.error.f1(estim_prev)
row = row | dict(
f1_score=f1_score,
f1=abs(qc.error.f1(true_prev) - f1_score),
)
report.append_row(sample.prevalence(), **row)
except Exception as e:
print(f"sample prediction failed for method {method_name}: {e}")
traceback.print_exception(e)
report.append_row(
sample.prevalence(),
acc_score=np.nan,
acc=np.nan,
f1_score=np.nan,
f1=np.nan,
)
return report
@dataclass(frozen=True)
class EmptyMethod:
name: str
nocall: bool = True
def __call__(self, c_model, validation, protocol) -> EvaluationReport:
pass
@dataclass(frozen=True)
class EvaluationMethod:
name: str
q: BaseQuantifier
est_n: str
conf: List[str] | str = None
cf: bool = False # collapse_false
gf: bool = False # group_false
d: bool = False # dense
def get_est(self, c_model):
match self.est_n:
case "mul":
return MCAE(
c_model,
self.q,
confidence=self.conf,
collapse_false=self.cf,
group_false=self.gf,
dense=self.d,
)
case "bin":
return BQAE(
c_model,
self.q,
confidence=self.conf,
group_false=self.gf,
dense=self.d,
)
def __call__(self, c_model, validation, protocol) -> EvaluationReport:
est = self.get_est(c_model).fit(validation)
return evaluation_report(
estimator=est, protocol=protocol, method_name=self.name
)
@dataclass(frozen=True)
class EvaluationMethodGridSearch(EvaluationMethod):
pg: str = "sld"
search: str = "grid"
def get_search(self):
match self.search:
case "grid":
return (GridSearchAE, {})
case "spider" | "spider2":
return (SpiderSearchAE, dict(best_width=2))
case "spider3":
return (SpiderSearchAE, dict(best_width=3))
case _:
return GridSearchAE
def __call__(self, c_model, validation, protocol) -> EvaluationReport:
v_train, v_val = validation.split_stratified(0.6, random_state=env._R_SEED)
_model = self.get_est(c_model)
_grid = _param_grid(self.pg, X_fit=_model.extend(v_train, prefit=True).X)
_search_class, _search_params = self.get_search()
est = _search_class(
model=_model,
param_grid=_grid,
refit=False,
protocol=UPP(v_val, repeats=100),
verbose=False,
**_search_params,
).fit(v_train)
er = evaluation_report(
estimator=est,
protocol=protocol,
method_name=self.name,
)
er.fit_score = est.best_score()
return er
E = EmptyMethod
M = EvaluationMethod
G = EvaluationMethodGridSearch
def __sld_lr():
return SLD(LogisticRegression())
def __sld_rbf():
return SLD(SVC(kernel="rbf", probability=True))
def __kde_lr():
return KDEy(LogisticRegression(), random_state=env._R_SEED)
def __kde_rbf():
return KDEy(SVC(kernel="rbf", probability=True), random_state=env._R_SEED)
def __sld_lsvc():
return SLD(LinearSVC())
def __pacc_lr():
return PACC(LogisticRegression())
def __cc_lr():
return CC(LogisticRegression())
# fmt: off
__sld_lr_set = [
M("bin_sld_lr", __sld_lr(), "bin" ),
M("bgf_sld_lr", __sld_lr(), "bin", gf=True),
M("mul_sld_lr", __sld_lr(), "mul" ),
M("m3w_sld_lr", __sld_lr(), "mul", cf=True),
M("mgf_sld_lr", __sld_lr(), "mul", gf=True),
# max_conf sld
M("bin_sld_lr_mc", __sld_lr(), "bin", conf="max_conf", ),
M("bgf_sld_lr_mc", __sld_lr(), "bin", conf="max_conf", gf=True),
M("mul_sld_lr_mc", __sld_lr(), "mul", conf="max_conf", ),
M("m3w_sld_lr_mc", __sld_lr(), "mul", conf="max_conf", cf=True),
M("mgf_sld_lr_mc", __sld_lr(), "mul", conf="max_conf", gf=True),
# entropy sld
M("bin_sld_lr_ne", __sld_lr(), "bin", conf="entropy", ),
M("bgf_sld_lr_ne", __sld_lr(), "bin", conf="entropy", gf=True),
M("mul_sld_lr_ne", __sld_lr(), "mul", conf="entropy", ),
M("m3w_sld_lr_ne", __sld_lr(), "mul", conf="entropy", cf=True),
M("mgf_sld_lr_ne", __sld_lr(), "mul", conf="entropy", gf=True),
# inverse softmax sld
M("bin_sld_lr_is", __sld_lr(), "bin", conf="isoft", ),
M("bgf_sld_lr_is", __sld_lr(), "bin", conf="isoft", gf=True),
M("mul_sld_lr_is", __sld_lr(), "mul", conf="isoft", ),
M("m3w_sld_lr_is", __sld_lr(), "mul", conf="isoft", cf=True),
M("mgf_sld_lr_is", __sld_lr(), "mul", conf="isoft", gf=True),
# max_conf + entropy sld
M("bin_sld_lr_c", __sld_lr(), "bin", conf=["max_conf", "entropy"] ),
M("bgf_sld_lr_c", __sld_lr(), "bin", conf=["max_conf", "entropy"], gf=True),
M("mul_sld_lr_c", __sld_lr(), "mul", conf=["max_conf", "entropy"] ),
M("m3w_sld_lr_c", __sld_lr(), "mul", conf=["max_conf", "entropy"], cf=True),
M("mgf_sld_lr_c", __sld_lr(), "mul", conf=["max_conf", "entropy"], gf=True),
# sld all
M("bin_sld_lr_a", __sld_lr(), "bin", conf=["max_conf", "entropy", "isoft"], ),
M("bgf_sld_lr_a", __sld_lr(), "bin", conf=["max_conf", "entropy", "isoft"], gf=True),
M("mul_sld_lr_a", __sld_lr(), "mul", conf=["max_conf", "entropy", "isoft"], ),
M("m3w_sld_lr_a", __sld_lr(), "mul", conf=["max_conf", "entropy", "isoft"], cf=True),
M("mgf_sld_lr_a", __sld_lr(), "mul", conf=["max_conf", "entropy", "isoft"], gf=True),
# gs sld
G("bin_sld_lr_gs", __sld_lr(), "bin", pg="sld_lr" ),
G("bgf_sld_lr_gs", __sld_lr(), "bin", pg="sld_lr", gf=True),
G("mul_sld_lr_gs", __sld_lr(), "mul", pg="sld_lr" ),
G("m3w_sld_lr_gs", __sld_lr(), "mul", pg="sld_lr", cf=True),
G("mgf_sld_lr_gs", __sld_lr(), "mul", pg="sld_lr", gf=True),
]
__dense_sld_lr_set = [
M("d_bin_sld_lr", __sld_lr(), "bin", d=True, ),
M("d_bgf_sld_lr", __sld_lr(), "bin", d=True, gf=True),
M("d_mul_sld_lr", __sld_lr(), "mul", d=True, ),
M("d_m3w_sld_lr", __sld_lr(), "mul", d=True, cf=True),
M("d_mgf_sld_lr", __sld_lr(), "mul", d=True, gf=True),
# max_conf sld
M("d_bin_sld_lr_mc", __sld_lr(), "bin", d=True, conf="max_conf", ),
M("d_bgf_sld_lr_mc", __sld_lr(), "bin", d=True, conf="max_conf", gf=True),
M("d_mul_sld_lr_mc", __sld_lr(), "mul", d=True, conf="max_conf", ),
M("d_m3w_sld_lr_mc", __sld_lr(), "mul", d=True, conf="max_conf", cf=True),
M("d_mgf_sld_lr_mc", __sld_lr(), "mul", d=True, conf="max_conf", gf=True),
# entropy sld
M("d_bin_sld_lr_ne", __sld_lr(), "bin", d=True, conf="entropy", ),
M("d_bgf_sld_lr_ne", __sld_lr(), "bin", d=True, conf="entropy", gf=True),
M("d_mul_sld_lr_ne", __sld_lr(), "mul", d=True, conf="entropy", ),
M("d_m3w_sld_lr_ne", __sld_lr(), "mul", d=True, conf="entropy", cf=True),
M("d_mgf_sld_lr_ne", __sld_lr(), "mul", d=True, conf="entropy", gf=True),
# inverse softmax sld
M("d_bin_sld_lr_is", __sld_lr(), "bin", d=True, conf="isoft", ),
M("d_bgf_sld_lr_is", __sld_lr(), "bin", d=True, conf="isoft", gf=True),
M("d_mul_sld_lr_is", __sld_lr(), "mul", d=True, conf="isoft", ),
M("d_m3w_sld_lr_is", __sld_lr(), "mul", d=True, conf="isoft", cf=True),
M("d_mgf_sld_lr_is", __sld_lr(), "mul", d=True, conf="isoft", gf=True),
# max_conf + entropy sld
M("d_bin_sld_lr_c", __sld_lr(), "bin", d=True, conf=["max_conf", "entropy"] ),
M("d_bgf_sld_lr_c", __sld_lr(), "bin", d=True, conf=["max_conf", "entropy"], gf=True),
M("d_mul_sld_lr_c", __sld_lr(), "mul", d=True, conf=["max_conf", "entropy"] ),
M("d_m3w_sld_lr_c", __sld_lr(), "mul", d=True, conf=["max_conf", "entropy"], cf=True),
M("d_mgf_sld_lr_c", __sld_lr(), "mul", d=True, conf=["max_conf", "entropy"], gf=True),
# sld all
M("d_bin_sld_lr_a", __sld_lr(), "bin", d=True, conf=["max_conf", "entropy", "isoft"], ),
M("d_bgf_sld_lr_a", __sld_lr(), "bin", d=True, conf=["max_conf", "entropy", "isoft"], gf=True),
M("d_mul_sld_lr_a", __sld_lr(), "mul", d=True, conf=["max_conf", "entropy", "isoft"], ),
M("d_m3w_sld_lr_a", __sld_lr(), "mul", d=True, conf=["max_conf", "entropy", "isoft"], cf=True),
M("d_mgf_sld_lr_a", __sld_lr(), "mul", d=True, conf=["max_conf", "entropy", "isoft"], gf=True),
# gs sld
G("d_bin_sld_lr_gs", __sld_lr(), "bin", d=True, pg="sld_lr" ),
G("d_bgf_sld_lr_gs", __sld_lr(), "bin", d=True, pg="sld_lr", gf=True),
G("d_mul_sld_lr_gs", __sld_lr(), "mul", d=True, pg="sld_lr" ),
G("d_m3w_sld_lr_gs", __sld_lr(), "mul", d=True, pg="sld_lr", cf=True),
G("d_mgf_sld_lr_gs", __sld_lr(), "mul", d=True, pg="sld_lr", gf=True),
]
__dense_sld_rbf_set = [
M("d_bin_sld_rbf", __sld_rbf(), "bin", d=True, ),
M("d_bgf_sld_rbf", __sld_rbf(), "bin", d=True, gf=True),
M("d_mul_sld_rbf", __sld_rbf(), "mul", d=True, ),
M("d_m3w_sld_rbf", __sld_rbf(), "mul", d=True, cf=True),
M("d_mgf_sld_rbf", __sld_rbf(), "mul", d=True, gf=True),
# max_conf sld
M("d_bin_sld_rbf_mc", __sld_rbf(), "bin", d=True, conf="max_conf", ),
M("d_bgf_sld_rbf_mc", __sld_rbf(), "bin", d=True, conf="max_conf", gf=True),
M("d_mul_sld_rbf_mc", __sld_rbf(), "mul", d=True, conf="max_conf", ),
M("d_m3w_sld_rbf_mc", __sld_rbf(), "mul", d=True, conf="max_conf", cf=True),
M("d_mgf_sld_rbf_mc", __sld_rbf(), "mul", d=True, conf="max_conf", gf=True),
# entropy sld
M("d_bin_sld_rbf_ne", __sld_rbf(), "bin", d=True, conf="entropy", ),
M("d_bgf_sld_rbf_ne", __sld_rbf(), "bin", d=True, conf="entropy", gf=True),
M("d_mul_sld_rbf_ne", __sld_rbf(), "mul", d=True, conf="entropy", ),
M("d_m3w_sld_rbf_ne", __sld_rbf(), "mul", d=True, conf="entropy", cf=True),
M("d_mgf_sld_rbf_ne", __sld_rbf(), "mul", d=True, conf="entropy", gf=True),
# inverse softmax sld
M("d_bin_sld_rbf_is", __sld_rbf(), "bin", d=True, conf="isoft", ),
M("d_bgf_sld_rbf_is", __sld_rbf(), "bin", d=True, conf="isoft", gf=True),
M("d_mul_sld_rbf_is", __sld_rbf(), "mul", d=True, conf="isoft", ),
M("d_m3w_sld_rbf_is", __sld_rbf(), "mul", d=True, conf="isoft", cf=True),
M("d_mgf_sld_rbf_is", __sld_rbf(), "mul", d=True, conf="isoft", gf=True),
# max_conf + entropy sld
M("d_bin_sld_rbf_c", __sld_rbf(), "bin", d=True, conf=["max_conf", "entropy"] ),
M("d_bgf_sld_rbf_c", __sld_rbf(), "bin", d=True, conf=["max_conf", "entropy"], gf=True),
M("d_mul_sld_rbf_c", __sld_rbf(), "mul", d=True, conf=["max_conf", "entropy"] ),
M("d_m3w_sld_rbf_c", __sld_rbf(), "mul", d=True, conf=["max_conf", "entropy"], cf=True),
M("d_mgf_sld_rbf_c", __sld_rbf(), "mul", d=True, conf=["max_conf", "entropy"], gf=True),
# sld all
M("d_bin_sld_rbf_a", __sld_rbf(), "bin", d=True, conf=["max_conf", "entropy", "isoft"], ),
M("d_bgf_sld_rbf_a", __sld_rbf(), "bin", d=True, conf=["max_conf", "entropy", "isoft"], gf=True),
M("d_mul_sld_rbf_a", __sld_rbf(), "mul", d=True, conf=["max_conf", "entropy", "isoft"], ),
M("d_m3w_sld_rbf_a", __sld_rbf(), "mul", d=True, conf=["max_conf", "entropy", "isoft"], cf=True),
M("d_mgf_sld_rbf_a", __sld_rbf(), "mul", d=True, conf=["max_conf", "entropy", "isoft"], gf=True),
# gs sld
G("d_bin_sld_rbf_gs", __sld_rbf(), "bin", d=True, pg="sld_rbf", search="grid", ),
G("d_bgf_sld_rbf_gs", __sld_rbf(), "bin", d=True, pg="sld_rbf", search="grid", gf=True),
G("d_mul_sld_rbf_gs", __sld_rbf(), "mul", d=True, pg="sld_rbf", search="grid", ),
G("d_m3w_sld_rbf_gs", __sld_rbf(), "mul", d=True, pg="sld_rbf", search="grid", cf=True),
G("d_mgf_sld_rbf_gs", __sld_rbf(), "mul", d=True, pg="sld_rbf", search="grid", gf=True),
]
__kde_lr_set = [
# base kde
M("bin_kde_lr", __kde_lr(), "bin" ),
M("mul_kde_lr", __kde_lr(), "mul" ),
M("m3w_kde_lr", __kde_lr(), "mul", cf=True),
# max_conf kde
M("bin_kde_lr_mc", __kde_lr(), "bin", conf="max_conf", ),
M("mul_kde_lr_mc", __kde_lr(), "mul", conf="max_conf", ),
M("m3w_kde_lr_mc", __kde_lr(), "mul", conf="max_conf", cf=True),
# entropy kde
M("bin_kde_lr_ne", __kde_lr(), "bin", conf="entropy", ),
M("mul_kde_lr_ne", __kde_lr(), "mul", conf="entropy", ),
M("m3w_kde_lr_ne", __kde_lr(), "mul", conf="entropy", cf=True),
# inverse softmax kde
M("bin_kde_lr_is", __kde_lr(), "bin", conf="isoft", ),
M("mul_kde_lr_is", __kde_lr(), "mul", conf="isoft", ),
M("m3w_kde_lr_is", __kde_lr(), "mul", conf="isoft", cf=True),
# max_conf + entropy kde
M("bin_kde_lr_c", __kde_lr(), "bin", conf=["max_conf", "entropy"] ),
M("mul_kde_lr_c", __kde_lr(), "mul", conf=["max_conf", "entropy"] ),
M("m3w_kde_lr_c", __kde_lr(), "mul", conf=["max_conf", "entropy"], cf=True),
# kde all
M("bin_kde_lr_a", __kde_lr(), "bin", conf=["max_conf", "entropy", "isoft"], ),
M("mul_kde_lr_a", __kde_lr(), "mul", conf=["max_conf", "entropy", "isoft"], ),
M("m3w_kde_lr_a", __kde_lr(), "mul", conf=["max_conf", "entropy", "isoft"], cf=True),
# gs kde
G("bin_kde_lr_gs", __kde_lr(), "bin", pg="kde_lr", search="grid" ),
G("mul_kde_lr_gs", __kde_lr(), "mul", pg="kde_lr", search="grid" ),
G("m3w_kde_lr_gs", __kde_lr(), "mul", pg="kde_lr", search="grid", cf=True),
]
__dense_kde_lr_set = [
# base kde
M("d_bin_kde_lr", __kde_lr(), "bin", d=True, ),
M("d_mul_kde_lr", __kde_lr(), "mul", d=True, ),
M("d_m3w_kde_lr", __kde_lr(), "mul", d=True, cf=True),
# max_conf kde
M("d_bin_kde_lr_mc", __kde_lr(), "bin", d=True, conf="max_conf", ),
M("d_mul_kde_lr_mc", __kde_lr(), "mul", d=True, conf="max_conf", ),
M("d_m3w_kde_lr_mc", __kde_lr(), "mul", d=True, conf="max_conf", cf=True),
# entropy kde
M("d_bin_kde_lr_ne", __kde_lr(), "bin", d=True, conf="entropy", ),
M("d_mul_kde_lr_ne", __kde_lr(), "mul", d=True, conf="entropy", ),
M("d_m3w_kde_lr_ne", __kde_lr(), "mul", d=True, conf="entropy", cf=True),
# inverse softmax kde d=True,
M("d_bin_kde_lr_is", __kde_lr(), "bin", d=True, conf="isoft", ),
M("d_mul_kde_lr_is", __kde_lr(), "mul", d=True, conf="isoft", ),
M("d_m3w_kde_lr_is", __kde_lr(), "mul", d=True, conf="isoft", cf=True),
# max_conf + entropy kde
M("d_bin_kde_lr_c", __kde_lr(), "bin", d=True, conf=["max_conf", "entropy"] ),
M("d_mul_kde_lr_c", __kde_lr(), "mul", d=True, conf=["max_conf", "entropy"] ),
M("d_m3w_kde_lr_c", __kde_lr(), "mul", d=True, conf=["max_conf", "entropy"], cf=True),
# kde all
M("d_bin_kde_lr_a", __kde_lr(), "bin", d=True, conf=["max_conf", "entropy", "isoft"], ),
M("d_mul_kde_lr_a", __kde_lr(), "mul", d=True, conf=["max_conf", "entropy", "isoft"], ),
M("d_m3w_kde_lr_a", __kde_lr(), "mul", d=True, conf=["max_conf", "entropy", "isoft"], cf=True),
# gs kde
G("d_bin_kde_lr_gs", __kde_lr(), "bin", d=True, pg="kde_lr", search="grid" ),
G("d_mul_kde_lr_gs", __kde_lr(), "mul", d=True, pg="kde_lr", search="grid" ),
G("d_m3w_kde_lr_gs", __kde_lr(), "mul", d=True, pg="kde_lr", search="grid", cf=True),
]
__dense_kde_rbf_set = [
# base kde
M("d_bin_kde_rbf", __kde_rbf(), "bin", d=True, ),
M("d_mul_kde_rbf", __kde_rbf(), "mul", d=True, ),
M("d_m3w_kde_rbf", __kde_rbf(), "mul", d=True, cf=True),
# max_conf kde
M("d_bin_kde_rbf_mc", __kde_rbf(), "bin", d=True, conf="max_conf", ),
M("d_mul_kde_rbf_mc", __kde_rbf(), "mul", d=True, conf="max_conf", ),
M("d_m3w_kde_rbf_mc", __kde_rbf(), "mul", d=True, conf="max_conf", cf=True),
# entropy kde
M("d_bin_kde_rbf_ne", __kde_rbf(), "bin", d=True, conf="entropy", ),
M("d_mul_kde_rbf_ne", __kde_rbf(), "mul", d=True, conf="entropy", ),
M("d_m3w_kde_rbf_ne", __kde_rbf(), "mul", d=True, conf="entropy", cf=True),
# inverse softmax kde
M("d_bin_kde_rbf_is", __kde_rbf(), "bin", d=True, conf="isoft", ),
M("d_mul_kde_rbf_is", __kde_rbf(), "mul", d=True, conf="isoft", ),
M("d_m3w_kde_rbf_is", __kde_rbf(), "mul", d=True, conf="isoft", cf=True),
# max_conf + entropy kde
M("d_bin_kde_rbf_c", __kde_rbf(), "bin", d=True, conf=["max_conf", "entropy"] ),
M("d_mul_kde_rbf_c", __kde_rbf(), "mul", d=True, conf=["max_conf", "entropy"] ),
M("d_m3w_kde_rbf_c", __kde_rbf(), "mul", d=True, conf=["max_conf", "entropy"], cf=True),
# kde all
M("d_bin_kde_rbf_a", __kde_rbf(), "bin", d=True, conf=["max_conf", "entropy", "isoft"], ),
M("d_mul_kde_rbf_a", __kde_rbf(), "mul", d=True, conf=["max_conf", "entropy", "isoft"], ),
M("d_m3w_kde_rbf_a", __kde_rbf(), "mul", d=True, conf=["max_conf", "entropy", "isoft"], cf=True),
# gs kde
G("d_bin_kde_rbf_gs", __kde_rbf(), "bin", d=True, pg="kde_rbf", search="spider" ),
G("d_mul_kde_rbf_gs", __kde_rbf(), "mul", d=True, pg="kde_rbf", search="spider" ),
G("d_m3w_kde_rbf_gs", __kde_rbf(), "mul", d=True, pg="kde_rbf", search="spider", cf=True),
]
__cc_lr_set = [
# base cc
M("bin_cc_lr", __cc_lr(), "bin" ),
M("mul_cc_lr", __cc_lr(), "mul" ),
M("m3w_cc_lr", __cc_lr(), "mul", cf=True),
# max_conf cc
M("bin_cc_lr_mc", __cc_lr(), "bin", conf="max_conf", ),
M("mul_cc_lr_mc", __cc_lr(), "mul", conf="max_conf", ),
M("m3w_cc_lr_mc", __cc_lr(), "mul", conf="max_conf", cf=True),
# entropy cc
M("bin_cc_lr_ne", __cc_lr(), "bin", conf="entropy", ),
M("mul_cc_lr_ne", __cc_lr(), "mul", conf="entropy", ),
M("m3w_cc_lr_ne", __cc_lr(), "mul", conf="entropy", cf=True),
# inverse softmax cc
M("bin_cc_lr_is", __cc_lr(), "bin", conf="isoft", ),
M("mul_cc_lr_is", __cc_lr(), "mul", conf="isoft", ),
M("m3w_cc_lr_is", __cc_lr(), "mul", conf="isoft", cf=True),
# max_conf + entropy cc
M("bin_cc_lr_c", __cc_lr(), "bin", conf=["max_conf", "entropy"] ),
M("mul_cc_lr_c", __cc_lr(), "mul", conf=["max_conf", "entropy"] ),
M("m3w_cc_lr_c", __cc_lr(), "mul", conf=["max_conf", "entropy"], cf=True),
# cc all
M("bin_cc_lr_a", __cc_lr(), "bin", conf=["max_conf", "entropy", "isoft"], ),
M("mul_cc_lr_a", __cc_lr(), "mul", conf=["max_conf", "entropy", "isoft"], ),
M("m3w_cc_lr_a", __cc_lr(), "mul", conf=["max_conf", "entropy", "isoft"], cf=True),
# gs cc
G("bin_cc_lr_gs", __cc_lr(), "bin", pg="cc_lr", search="grid" ),
G("mul_cc_lr_gs", __cc_lr(), "mul", pg="cc_lr", search="grid" ),
G("m3w_cc_lr_gs", __cc_lr(), "mul", pg="cc_lr", search="grid", cf=True),
]
__ms_set = [
E("cc_lr_gs"),
E("sld_lr_gs"),
E("kde_lr_gs"),
E("QuAcc"),
]
# fmt: on
__methods_set = (
__sld_lr_set
+ __dense_sld_lr_set
+ __dense_sld_rbf_set
+ __kde_lr_set
+ __dense_kde_lr_set
+ __dense_kde_rbf_set
+ __cc_lr_set
+ __ms_set
)
_methods = {m.name: m for m in __methods_set}