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