added kde implementation, switched to dataclass
This commit is contained in:
Lorenzo Volpi
parent
d6736d194b
commit
58661e7f93
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@ -1,26 +1,35 @@
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from dataclasses import dataclass
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from typing import List
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import numpy as np
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from quapy.method.aggregative import PACC, SLD
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from quapy.method.aggregative import PACC, SLD, BaseQuantifier
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from quapy.protocol import UPP, AbstractProtocol
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from sklearn.linear_model import LogisticRegression
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from sklearn.svm import LinearSVC
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import quacc as qc
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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 GridSearchAE
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from ..method.base import BQAE, MCAE, BaseAccuracyEstimator
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from quacc.quantification import KDEy
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_param_grid = {
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"sld": {
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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": [["isoft"], ["max_conf", "entropy"]],
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"confidence": [None, ["isoft"], ["max_conf", "entropy"]],
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},
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"pacc": {
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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": [["isoft"], ["max_conf", "entropy"]],
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"confidence": [None, ["isoft"], ["max_conf", "entropy"]],
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},
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"kde": {
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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, 5),
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"confidence": [None, ["isoft"]],
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},
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}
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@ -56,38 +65,42 @@ def evaluation_report(
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return report
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@dataclass(frozen=True)
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class EvaluationMethod:
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def __init__(self, name, q, est_c, conf=None, cf=False):
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self.name = name
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self.__name__ = name
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self.q = q
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self.est_c = est_c
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self.conf = conf
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self.cf = cf
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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
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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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)
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case "bin":
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return BQAE(c_model, self.q, confidence=self.conf)
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def __call__(self, c_model, validation, protocol) -> EvaluationReport:
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est = self.est_c(
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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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).fit(validation)
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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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def __init__(self, name, q, est_c, cf=False, pg="sld"):
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super().__init__(name, q, est_c, cf=cf)
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self.pg = pg
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pg: str = "sld"
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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=0)
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model = self.est_c(c_model, self.q, collapse_false=self.cf)
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__grid = _param_grid.get(self.pg, {})
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est = GridSearchAE(
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model=model,
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model=self.get_est(c_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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@ -108,6 +121,10 @@ def __sld_lr():
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return SLD(LogisticRegression())
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def __kde_lr():
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return KDEy(LogisticRegression())
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def __sld_lsvc():
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return SLD(LinearSVC())
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@ -119,37 +136,54 @@ def __pacc_lr():
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# fmt: off
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__methods_set = [
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# base sld
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M("bin_sld", __sld_lr(), BQAE ),
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M("mul_sld", __sld_lr(), MCAE ),
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M("m3w_sld", __sld_lr(), MCAE, cf=True),
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M("bin_sld", __sld_lr(), "bin" ),
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M("mul_sld", __sld_lr(), "mul" ),
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M("m3w_sld", __sld_lr(), "mul", cf=True),
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# max_conf + entropy sld
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M("binc_sld", __sld_lr(), BQAE, conf=["max_conf", "entropy"] ),
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M("mulc_sld", __sld_lr(), MCAE, conf=["max_conf", "entropy"] ),
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M("m3wc_sld", __sld_lr(), MCAE, conf=["max_conf", "entropy"], cf=True),
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M("binc_sld", __sld_lr(), "bin", conf=["max_conf", "entropy"] ),
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M("mulc_sld", __sld_lr(), "mul", conf=["max_conf", "entropy"] ),
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M("m3wc_sld", __sld_lr(), "mul", conf=["max_conf", "entropy"], cf=True),
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# max_conf sld
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M("binmc_sld", __sld_lr(), BQAE, conf="max_conf", ),
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M("mulmc_sld", __sld_lr(), MCAE, conf="max_conf", ),
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M("m3wmc_sld", __sld_lr(), MCAE, conf="max_conf", cf=True),
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M("binmc_sld", __sld_lr(), "bin", conf="max_conf", ),
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M("mulmc_sld", __sld_lr(), "mul", conf="max_conf", ),
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M("m3wmc_sld", __sld_lr(), "mul", conf="max_conf", cf=True),
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# entropy sld
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M("binne_sld", __sld_lr(), BQAE, conf="entropy", ),
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M("mulne_sld", __sld_lr(), MCAE, conf="entropy", ),
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M("m3wne_sld", __sld_lr(), MCAE, conf="entropy", cf=True),
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M("binne_sld", __sld_lr(), "bin", conf="entropy", ),
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M("mulne_sld", __sld_lr(), "mul", conf="entropy", ),
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M("m3wne_sld", __sld_lr(), "mul", conf="entropy", cf=True),
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# inverse softmax sld
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M("binis_sld", __sld_lr(), BQAE, conf="isoft", ),
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M("mulis_sld", __sld_lr(), MCAE, conf="isoft", ),
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M("m3wis_sld", __sld_lr(), MCAE, conf="isoft", cf=True),
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# inverse softmax sld
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M("binis_pacc", __pacc_lr(), BQAE, conf="isoft", ),
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M("mulis_pacc", __pacc_lr(), MCAE, conf="isoft", ),
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M("m3wis_pacc", __pacc_lr(), MCAE, conf="isoft", cf=True),
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M("binis_sld", __sld_lr(), "bin", conf="isoft", ),
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M("mulis_sld", __sld_lr(), "mul", conf="isoft", ),
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M("m3wis_sld", __sld_lr(), "mul", conf="isoft", cf=True),
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# gs sld
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G("bin_sld_gs", __sld_lr(), BQAE, pg="sld" ),
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G("mul_sld_gs", __sld_lr(), MCAE, pg="sld" ),
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G("m3w_sld_gs", __sld_lr(), MCAE, pg="sld", cf=True),
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# gs pacc
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G("bin_pacc_gs", __pacc_lr(), BQAE, pg="pacc" ),
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G("mul_pacc_gs", __pacc_lr(), MCAE, pg="pacc" ),
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G("m3w_pacc_gs", __pacc_lr(), MCAE, pg="pacc", cf=True),
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G("bin_sld_gs", __sld_lr(), "bin", pg="sld" ),
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G("mul_sld_gs", __sld_lr(), "mul", pg="sld" ),
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G("m3w_sld_gs", __sld_lr(), "mul", pg="sld", cf=True),
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# base kde
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M("bin_kde", __kde_lr(), "bin" ),
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M("mul_kde", __kde_lr(), "mul" ),
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M("m3w_kde", __kde_lr(), "mul", cf=True),
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# max_conf + entropy kde
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M("binc_kde", __kde_lr(), "bin", conf=["max_conf", "entropy"] ),
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M("mulc_kde", __kde_lr(), "mul", conf=["max_conf", "entropy"] ),
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M("m3wc_kde", __kde_lr(), "mul", conf=["max_conf", "entropy"], cf=True),
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# max_conf kde
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M("binmc_kde", __kde_lr(), "bin", conf="max_conf", ),
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M("mulmc_kde", __kde_lr(), "mul", conf="max_conf", ),
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M("m3wmc_kde", __kde_lr(), "mul", conf="max_conf", cf=True),
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# entropy kde
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M("binne_kde", __kde_lr(), "bin", conf="entropy", ),
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M("mulne_kde", __kde_lr(), "mul", conf="entropy", ),
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M("m3wne_kde", __kde_lr(), "mul", conf="entropy", cf=True),
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# inverse softmax kde
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M("binis_kde", __kde_lr(), "bin", conf="isoft", ),
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M("mulis_kde", __kde_lr(), "mul", conf="isoft", ),
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M("m3wis_kde", __kde_lr(), "mul", conf="isoft", cf=True),
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# gs kde
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G("bin_kde_gs", __kde_lr(), "bin", pg="kde", ),
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G("mul_kde_gs", __kde_lr(), "mul", pg="kde", ),
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G("m3w_kde_gs", __kde_lr(), "mul", pg="kde", cf=True),
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]
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# fmt: on
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