From 39379b5b7f1171def5dc9832f1b53b167789f307 Mon Sep 17 00:00:00 2001
From: Markus Semmler <markus.semmler@unternehmertum.de>
Date: Fri, 20 Oct 2023 18:22:23 +0200
Subject: [PATCH] Add balanced models.

---
 params.yaml                       |  9 ++++++++-
 scripts/evaluate_metrics.py       |  6 ++----
 src/re_classwise_shapley/model.py | 11 ++++++-----
 3 files changed, 16 insertions(+), 10 deletions(-)

diff --git a/params.yaml b/params.yaml
index 74b69a1e..182cd506 100644
--- a/params.yaml
+++ b/params.yaml
@@ -156,29 +156,36 @@ datasets:
 
 models:
   logistic_regression:
+    model: logistic_regression
     solver: liblinear
 
   logistic_regression_balanced:
+    model: logistic_regression
     solver: liblinear
     class_weight: balanced
     
   gradient_boosting_classifier:
+    model: gradient_boosting_classifier
     n_estimators: 40
     min_samples_split: 6
     max_depth: 2
 
   knn:
+    model: knn
     n_neighbors: 5
     weights: uniform
 
   svm:
+    model: svm
     kernel: rbf
   
   svm_balanced:
+    model: svm
     kernel: rbf
     class_weight: balanced
 
-  mlp: {}
+  mlp:
+    model: mlp
 
 valuation_methods:
   random:
diff --git a/scripts/evaluate_metrics.py b/scripts/evaluate_metrics.py
index 7047c49e..8661e4e3 100644
--- a/scripts/evaluate_metrics.py
+++ b/scripts/evaluate_metrics.py
@@ -97,10 +97,8 @@ def _evaluate_metrics(
         / str(repetition_id)
         / valuation_method_name
     )
-    if (
-        False
-        and os.path.exists(output_dir / f"{metric_name}.csv")
-        and os.path.exists(output_dir / f"{metric_name}.curve.csv")
+    if os.path.exists(output_dir / f"{metric_name}.csv") and os.path.exists(
+        output_dir / f"{metric_name}.curve.csv"
     ):
         return logger.info(f"Metric data exists in '{output_dir}'. Skipping...")
 
diff --git a/src/re_classwise_shapley/model.py b/src/re_classwise_shapley/model.py
index d1a79091..b2bd6491 100644
--- a/src/re_classwise_shapley/model.py
+++ b/src/re_classwise_shapley/model.py
@@ -33,26 +33,27 @@ def instantiate_model(
         The instantiated model.
     """
     random_state = np.random.RandomState(seed)
+    model = model_kwargs.pop("model")
 
-    if model_name == "gradient_boosting_classifier":
+    if model == "gradient_boosting_classifier":
         model = make_pipeline(
             GradientBoostingClassifier(**model_kwargs, random_state=random_state)
         )
-    elif model_name == "logistic_regression":
+    elif model == "logistic_regression":
         model = make_pipeline(
             StandardScaler(),
             LogisticRegression(**model_kwargs, random_state=random_state),
         )
-    elif model_name == "knn":
+    elif model == "knn":
         model = make_pipeline(
             StandardScaler(),
             KNeighborsClassifier(**model_kwargs),
         )
-    elif model_name == "svm":
+    elif model == "svm":
         model = make_pipeline(
             StandardScaler(), SVC(**model_kwargs, random_state=random_state)
         )
-    elif model_name == "mlp":
+    elif model == "mlp":
         model = make_pipeline(
             StandardScaler(), MLPClassifier(**model_kwargs, random_state=random_state)
         )