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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 from functools import wraps
import numpy as np 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 quapy.protocol import UPP, AbstractProtocol
from sklearn.linear_model import LogisticRegression 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 from ..method.base import BQAE, MCAE, BaseAccuracyEstimator
_methods = {} _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): def method(func):
@wraps(func) @wraps(func)
def wrapper(c_model, validation, protocol): 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())) model = BQAE(c_model, SLD(LogisticRegression()))
est = GridSearchAE( est = GridSearchAE(
model=model, model=model,
param_grid={ param_grid=_sld_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"],
},
refit=False, refit=False,
protocol=UPP(v_val, repeats=100), protocol=UPP(v_val, repeats=100),
verbose=True, verbose=True,
@ -145,12 +150,7 @@ def mul_sld_gs(c_model, validation, protocol) -> EvaluationReport:
model = MCAE(c_model, SLD(LogisticRegression())) model = MCAE(c_model, SLD(LogisticRegression()))
est = GridSearchAE( est = GridSearchAE(
model=model, model=model,
param_grid={ param_grid=_sld_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"],
},
refit=False, refit=False,
protocol=UPP(v_val, repeats=100), protocol=UPP(v_val, repeats=100),
verbose=True, 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 @method
def bin_pacc_gs(c_model, validation, protocol) -> EvaluationReport: def bin_pacc_gs(c_model, validation, protocol) -> EvaluationReport:
v_train, v_val = validation.split_stratified(0.6, random_state=0) v_train, v_val = validation.split_stratified(0.6, random_state=0)
model = BQAE(c_model, PACC(LogisticRegression())) model = BQAE(c_model, PACC(LogisticRegression()))
est = GridSearchAE( est = GridSearchAE(
model=model, model=model,
param_grid={ param_grid=_pacc_param_grid,
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"confidence": [None, "max_conf", "entropy"],
},
refit=False, refit=False,
protocol=UPP(v_val, repeats=100), protocol=UPP(v_val, repeats=100),
verbose=False, verbose=False,
@ -244,11 +276,7 @@ def mul_pacc_gs(c_model, validation, protocol) -> EvaluationReport:
model = MCAE(c_model, PACC(LogisticRegression())) model = MCAE(c_model, PACC(LogisticRegression()))
est = GridSearchAE( est = GridSearchAE(
model=model, model=model,
param_grid={ param_grid=_pacc_param_grid,
"q__classifier__C": np.logspace(-3, 3, 7),
"q__classifier__class_weight": [None, "balanced"],
"confidence": [None, "max_conf", "entropy"],
},
refit=False, refit=False,
protocol=UPP(v_val, repeats=100), protocol=UPP(v_val, repeats=100),
verbose=False, verbose=False,
@ -257,3 +285,21 @@ def mul_pacc_gs(c_model, validation, protocol) -> EvaluationReport:
estimator=est, estimator=est,
protocol=protocol, 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,
)