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QuAcc
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grid search params factorized

This commit is contained in:
Lorenzo Volpi 2023-11-06 21:26:34 +01:00
parent 2755ac7f45
commit 20a28470bd
1 changed files with 71 additions and 25 deletions
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96
quacc/evaluation/method.py
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@ -2,7 +2,7 @@ import inspect
from functools import wraps
import numpy as np
from quapy.method.aggregative import PACC, SLD
from quapy.method.aggregative import PACC, SLD, CC
from quapy.protocol import UPP, AbstractProtocol
from sklearn.linear_model import LogisticRegression
@ -13,8 +13,18 @@ from quacc.method.model_selection import BQAEgsq, GridSearchAE, MCAEgsq
from ..method.base import BQAE, MCAE, BaseAccuracyEstimator
_methods = {}
_sld_param_grid = {
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"q__recalib": [None, "bcts"],
"q__exact_train_prev": [True],
"confidence": [None, "max_conf", "entropy"],
}
_pacc_param_grid = {
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"confidence": [None, "max_conf", "entropy"],
}
def method(func):
@wraps(func)
def wrapper(c_model, validation, protocol):
@ -123,12 +133,7 @@ def bin_sld_gs(c_model, validation, protocol) -> EvaluationReport:
model = BQAE(c_model, SLD(LogisticRegression()))
est = GridSearchAE(
model=model,
param_grid={
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"q__recalib": [None, "bcts", "vs"],
"confidence": [None, "max_conf", "entropy"],
},
param_grid=_sld_param_grid,
refit=False,
protocol=UPP(v_val, repeats=100),
verbose=True,
@ -145,12 +150,7 @@ def mul_sld_gs(c_model, validation, protocol) -> EvaluationReport:
model = MCAE(c_model, SLD(LogisticRegression()))
est = GridSearchAE(
model=model,
param_grid={
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"q__recalib": [None, "bcts", "vs"],
"confidence": [None, "max_conf", "entropy"],
},
param_grid=_sld_param_grid,
refit=False,
protocol=UPP(v_val, repeats=100),
verbose=True,
@ -217,17 +217,49 @@ def mul_pacc(c_model, validation, protocol) -> EvaluationReport:
)
@method
def binmc_pacc(c_model, validation, protocol) -> EvaluationReport:
est = BQAE(c_model, PACC(LogisticRegression()), confidence="max_conf").fit(validation)
return evaluation_report(
estimator=est,
protocol=protocol,
)
@method
def mulmc_pacc(c_model, validation, protocol) -> EvaluationReport:
est = MCAE(c_model, PACC(LogisticRegression()), confidence="max_conf").fit(validation)
return evaluation_report(
estimator=est,
protocol=protocol,
)
@method
def binne_pacc(c_model, validation, protocol) -> EvaluationReport:
est = BQAE(c_model, PACC(LogisticRegression()), confidence="entropy").fit(validation)
return evaluation_report(
estimator=est,
protocol=protocol,
)
@method
def mulne_pacc(c_model, validation, protocol) -> EvaluationReport:
est = MCAE(c_model, PACC(LogisticRegression()), confidence="entropy").fit(validation)
return evaluation_report(
estimator=est,
protocol=protocol,
)
@method
def bin_pacc_gs(c_model, validation, protocol) -> EvaluationReport:
v_train, v_val = validation.split_stratified(0.6, random_state=0)
model = BQAE(c_model, PACC(LogisticRegression()))
est = GridSearchAE(
model=model,
param_grid={
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"confidence": [None, "max_conf", "entropy"],
},
param_grid=_pacc_param_grid,
refit=False,
protocol=UPP(v_val, repeats=100),
verbose=False,
@ -244,11 +276,7 @@ def mul_pacc_gs(c_model, validation, protocol) -> EvaluationReport:
model = MCAE(c_model, PACC(LogisticRegression()))
est = GridSearchAE(
model=model,
param_grid={
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"confidence": [None, "max_conf", "entropy"],
},
param_grid=_pacc_param_grid,
refit=False,
protocol=UPP(v_val, repeats=100),
verbose=False,
@ -257,3 +285,21 @@ def mul_pacc_gs(c_model, validation, protocol) -> EvaluationReport:
estimator=est,
protocol=protocol,
)
@method
def bin_cc(c_model, validation, protocol) -> EvaluationReport:
est = BQAE(c_model, CC(LogisticRegression())).fit(validation)
return evaluation_report(
estimator=est,
protocol=protocol,
)
@method
def mul_cc(c_model, validation, protocol) -> EvaluationReport:
est = MCAE(c_model, CC(LogisticRegression())).fit(validation)
return evaluation_report(
estimator=est,
protocol=protocol,
)