From 493fb931a92c62038b33eb5c30c32dde17095986 Mon Sep 17 00:00:00 2001
From: Lorenzo Volpi <lorenzo.volpi@outlook.com>
Date: Fri, 3 Nov 2023 23:29:05 +0100
Subject: [PATCH] pacc added to methods

---
 quacc/evaluation/method.py | 65 +++++++++++++++++++++++++++++++++++++-
 1 file changed, 64 insertions(+), 1 deletion(-)

diff --git a/quacc/evaluation/method.py b/quacc/evaluation/method.py
index d50ccab..ac6b624 100644
--- a/quacc/evaluation/method.py
+++ b/quacc/evaluation/method.py
@@ -2,7 +2,7 @@ import inspect
 from functools import wraps
 
 import numpy as np
-from quapy.method.aggregative import SLD
+from quapy.method.aggregative import PACC, SLD
 from quapy.protocol import UPP, AbstractProtocol
 from sklearn.linear_model import LogisticRegression
 
@@ -127,3 +127,66 @@ def mul_sld_gs(c_model, validation, protocol) -> EvaluationReport:
         estimator=est,
         protocol=protocol,
     )
+
+
+@method
+def bin_pacc(c_model, validation, protocol) -> EvaluationReport:
+    est = BQAE(c_model, PACC(LogisticRegression(), recalib="bcts"))
+    est.fit(validation)
+    return evaluation_report(
+        estimator=est,
+        protocol=protocol,
+    )
+
+
+@method
+def mul_pacc(c_model, validation, protocol) -> EvaluationReport:
+    est = MCAE(c_model, PACC(LogisticRegression(), recalib="bcts"))
+    est.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"],
+            "q__recalib": [None, "bcts", "vs"],
+        },
+        refit=False,
+        protocol=UPP(v_val, repeats=100),
+        verbose=False,
+    ).fit(v_train)
+    return evaluation_report(
+        estimator=est,
+        protocol=protocol,
+    )
+
+
+@method
+def mul_pacc_gs(c_model, validation, protocol) -> EvaluationReport:
+    v_train, v_val = validation.split_stratified(0.6, random_state=0)
+    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"],
+            "q__recalib": [None, "bcts", "vs"],
+        },
+        refit=False,
+        protocol=UPP(v_val, repeats=100),
+        verbose=False,
+    ).fit(v_train)
+    return evaluation_report(
+        estimator=est,
+        protocol=protocol,
+        method_name="bin_sld_gs",
+    )