🔧 fix small bug

pyproject.toml

@@ -58,6 +58,7 @@ cv = [
 ]
 cv_classification = [
     "timm",
+    "scikit-plot",
     "grad-cam>=1.4.5"
 ]
 cv_semantic = [
@@ -85,7 +86,6 @@ tabular = [
   "pandarallel>=1.6.3",
   "numpy>=1.23.4",
   "scikit-learn>=1.0.0",
-  "scikit-plot",
   "scipy>=1.7.0",
   "optuna>=3.0.5",
   "psycopg2-binary>=2.9.5",
@@ -96,7 +96,8 @@ tabular_classification = [
   "xgboost>=1.7.1",
   "catboost",
   "shap>=0.41.0",
-  "lime>=0.2.0.1"
+  "lime>=0.2.0.1",
+  "scikit-plot",
 ]
 all = [
   "theseus[cv,cv_classification,cv_semantic,cv_detection,nlp,nlp_retrieval,tabular,tabular_classification]",

theseus/base/metrics/precision_recall.py

@@ -1,6 +1,5 @@
 from typing import Any, Dict
 
-from scikitplot.metrics import plot_precision_recall_curve
 from sklearn.metrics import precision_score, recall_score
 
 from theseus.base.metrics.metric_template import Metric

theseus/base/metrics/roc_auc_score.py

@@ -1,7 +1,13 @@
 from typing import Any, Dict
 
 import torch
-from scikitplot.metrics import plot_precision_recall_curve, plot_roc_curve
+
+try:
+    from scikitplot.metrics import plot_precision_recall_curve, plot_roc_curve
+
+    has_scikitplot = True
+except:
+    has_scikitplot = False
 from sklearn.metrics import roc_auc_score
 
 from theseus.base.metrics.metric_template import Metric
@@ -41,14 +47,21 @@ def value(self):
         roc_auc_scr = roc_auc_score(
             self.targets, self.preds, average=self.average, multi_class=self.label_type
         )
-        roc_curve_fig = plot_roc_curve(self.targets, self.preds).get_figure()
-        pr_fig = plot_precision_recall_curve(self.targets, self.preds).get_figure()
 
-        return {
+        results = {
             f"{self.average}-roc_auc_score": roc_auc_scr,
-            "roc_curve": roc_curve_fig,
-            "precision_recall_curve": pr_fig,
         }
+        if has_scikitplot:
+            roc_curve_fig = plot_roc_curve(self.targets, self.preds).get_figure()
+            pr_fig = plot_precision_recall_curve(self.targets, self.preds).get_figure()
+            results.update(
+                {
+                    "roc_curve": roc_curve_fig,
+                    "precision_recall_curve": pr_fig,
+                }
+            )
+
+        return results
 
     def reset(self):
         self.targets = []