Yellowbrick matploltib incompatibility fix; expanded tests; replaced …

…print with warning

.github/workflows/ci.yml

@@ -23,7 +23,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: [3.9]
+        python-version: ["3.8", "3.10", "3.12"]
     steps:
       - uses: actions/checkout@v2
 

pyproject.toml

@@ -19,6 +19,7 @@ scikit-learn = ">=0.24.1"
 yellowbrick = ">=1.3"
 scikit-plot = ">=0.3.7"
 scipy = "<1.12" # Fixes #24 (https://github.com/neptune-ai/neptune-sklearn/issues/24)
+matplotlib = { version = "<3.3", python = "<3.12"} # Fixes https://github.com/DistrictDataLabs/yellowbrick/issues/1312
 
 # dev
 pre-commit = { version = "*", optional = true }

src/neptune_sklearn/impl/__init__.py

@@ -86,6 +86,8 @@
     )
     from neptune.new.utils import stringify_unsupported
 
+from warnings import warn
+
 
 def create_regressor_summary(regressor, X_train, X_test, y_train, y_test, nrows=1000, log_charts=True):
     """Creates scikit-learn regressor summary.
@@ -455,7 +457,7 @@ def get_test_preds_proba(classifier, X_test=None, y_pred_proba=None, nrows=1000)
         try:
             y_pred_proba = classifier.predict_proba(X_test)
         except Exception as e:
-            print("This classifier does not provide predictions probabilities. Error: {}".format(e))
+            warn(f"This classifier does not provide predictions probabilities. Error: {e}")
             return
 
     df = pd.DataFrame(data=y_pred_proba, columns=classifier.classes_)
@@ -590,7 +592,7 @@ def create_learning_curve_chart(regressor, X_train, y_train):
         chart = File.as_image(fig)
         plt.close(fig)
     except Exception as e:
-        print("Did not log learning curve chart. Error: {}".format(e))
+        warn(f"Did not log learning curve chart. Error: {e}")
 
     return chart
 
@@ -633,7 +635,7 @@ def create_feature_importance_chart(regressor, X_train, y_train):
         chart = File.as_image(fig)
         plt.close(fig)
     except Exception as e:
-        print("Did not log feature importance chart. Error: {}".format(e))
+        warn(f"Did not log feature importance chart. Error: {e}")
 
     return chart
 
@@ -678,7 +680,7 @@ def create_residuals_chart(regressor, X_train, X_test, y_train, y_test):
         chart = File.as_image(fig)
         plt.close(fig)
     except Exception as e:
-        print("Did not log residuals chart. Error: {}".format(e))
+        warn(f"Did not log residuals chart. Error: {e}")
 
     return chart
 
@@ -723,7 +725,7 @@ def create_prediction_error_chart(regressor, X_train, X_test, y_train, y_test):
         chart = File.as_image(fig)
         plt.close(fig)
     except Exception as e:
-        print("Did not log prediction error chart. Error: {}".format(e))
+        warn(f"Did not log prediction error chart. Error: {e}")
 
     return chart
 
@@ -765,7 +767,7 @@ def create_cooks_distance_chart(regressor, X_train, y_train):
         chart = File.as_image(fig)
         plt.close(fig)
     except Exception as e:
-        print("Did not log cooks distance chart. Error: {}".format(e))
+        warn(f"Did not log cooks distance chart. Error: {e}")
 
     return chart
 
@@ -812,7 +814,7 @@ def create_classification_report_chart(classifier, X_train, X_test, y_train, y_t
         chart = File.as_image(fig)
         plt.close(fig)
     except Exception as e:
-        print("Did not log Classification Report chart. Error: {}".format(e))
+        warn(f"Did not log Classification Report chart. Error: {e}")
 
     return chart
 
@@ -859,7 +861,7 @@ def create_confusion_matrix_chart(classifier, X_train, X_test, y_train, y_test):
         chart = File.as_image(fig)
         plt.close(fig)
     except Exception as e:
-        print("Did not log Confusion Matrix chart. Error: {}".format(e))
+        warn(f"Did not log Confusion Matrix chart. Error: {e}")
 
     return chart
 
@@ -904,7 +906,7 @@ def create_roc_auc_chart(classifier, X_train, X_test, y_train, y_test):
         chart = File.as_image(fig)
         plt.close(fig)
     except Exception as e:
-        print("Did not log ROC-AUC chart. Error {}".format(e))
+        warn(f"Did not log ROC-AUC chart. Error {e}")
 
     return chart
 
@@ -943,9 +945,9 @@ def create_precision_recall_chart(classifier, X_test, y_test, y_pred_proba=None)
         try:
             y_pred_proba = classifier.predict_proba(X_test)
         except Exception as e:
-            print(
-                "Did not log Precision-Recall chart: this classifier does not provide predictions probabilities."
-                "Error {}".format(e)
+            warn(
+                f"""Did not log Precision-Recall chart: this classifier does not provide predictions probabilities.
+                Error {e}"""
             )
             return chart
 
@@ -955,7 +957,7 @@ def create_precision_recall_chart(classifier, X_test, y_test, y_pred_proba=None)
         chart = File.as_image(fig)
         plt.close(fig)
     except Exception as e:
-        print("Did not log Precision-Recall chart. Error {}".format(e))
+        warn(f"Did not log Precision-Recall chart. Error {e}")
 
     return chart
 
@@ -1002,7 +1004,7 @@ def create_class_prediction_error_chart(classifier, X_train, X_test, y_train, y_
         chart = File.as_image(fig)
         plt.close(fig)
     except Exception as e:
-        print("Did not log Class Prediction Error chart. Error {}".format(e))
+        warn(f"Did not log Class Prediction Error chart. Error {e}")
 
     return chart
 
@@ -1088,7 +1090,7 @@ def create_kelbow_chart(model, X, **kwargs):
         chart = File.as_image(fig)
         plt.close(fig)
     except Exception as e:
-        print("Did not log KMeans elbow chart. Error {}".format(e))
+        warn(f"Did not log KMeans elbow chart. Error {e}")
 
     return chart
 
@@ -1140,6 +1142,7 @@ def create_silhouette_chart(model, X, **kwargs):
             charts.append(File.as_image(fig))
             plt.close(fig)
         except Exception as e:
-            print("Did not log Silhouette Coefficients chart. Error {}".format(e))
+            warn(f"Did not log Silhouette Coefficients chart. Error {e}")
 
     return FileSeries(charts)
+    return FileSeries(charts)