gs params updated, methods added

quacc/evaluation/method.py

@@ -17,7 +17,7 @@ _sld_param_grid = {
     "q__classifier__C": np.logspace(-3, 3, 7),
     "q__classifier__class_weight": [None, "balanced"],
     "q__recalib": [None, "bcts"],
-    "confidence": [["max_conf", "entropy"]],
+    "confidence": [["max_conf"], ["entropy"], ["max_conf", "entropy"]],
 }
 _pacc_param_grid = {
     "q__classifier__C": np.logspace(-3, 3, 7),
@@ -151,6 +151,20 @@ def mulmc_sld(c_model, validation, protocol) -> EvaluationReport:
     )
 
 
+@method
+def mul3wmc_sld(c_model, validation, protocol) -> EvaluationReport:
+    est = MCAE(
+        c_model,
+        SLD(LogisticRegression()),
+        confidence="max_conf",
+        collapse_false=True,
+    ).fit(validation)
+    return evaluation_report(
+        estimator=est,
+        protocol=protocol,
+    )
+
+
 @method
 def binne_sld(c_model, validation, protocol) -> EvaluationReport:
     est = BQAE(
@@ -177,6 +191,20 @@ def mulne_sld(c_model, validation, protocol) -> EvaluationReport:
     )
 
 
+@method
+def mul3wne_sld(c_model, validation, protocol) -> EvaluationReport:
+    est = MCAE(
+        c_model,
+        SLD(LogisticRegression()),
+        confidence="entropy",
+        collapse_false=True,
+    ).fit(validation)
+    return evaluation_report(
+        estimator=est,
+        protocol=protocol,
+    )
+
+
 @method
 def bin_sld_gs(c_model, validation, protocol) -> EvaluationReport:
     v_train, v_val = validation.split_stratified(0.6, random_state=0)