Handling NeptuneUnsupportedType error if expected metadata not found

CHANGELOG.md

@@ -1,3 +1,11 @@
+## [UNRELEASED] neptune-sklearn 2.2.0
+
+### Fixes
+- `create_*_summary()` now does not throw a `NeptuneUnsupportedType` error if expected metadata is not found
+
+### Changes
+- Bumped minimum supported Python version to 3.8
+
 ## neptune-sklearn 2.1.0
 
 ### Changes

pyproject.toml

@@ -9,7 +9,7 @@ style = "semver"
 pattern = "default-unprefixed"
 
 [tool.poetry.dependencies]
-python = "^3.7"
+python = "^3.8"
 
 # Python lack of functionalities from future versions
 importlib-metadata = { version = "*", python = "<3.8" }
@@ -45,7 +45,7 @@ name = "neptune-sklearn"
 readme = "README.md"
 version = "0.0.0"
 classifiers = [
- "Development Status :: 4 - Beta",
+ "Development Status :: 5 - Production/Stable",
  "Environment :: Console",
  "Intended Audience :: Developers",
  "Intended Audience :: Science/Research",

src/neptune_sklearn/impl/__init__.py

@@ -144,17 +144,20 @@ def create_regressor_summary(regressor, X_train, X_test, y_train, y_test, nrows=
  "scores": get_scores(regressor, X_test, y_test, y_pred=y_pred),
  }
 
- if log_charts:
- reg_summary["diagnostics_charts"] = {
- "learning_curve": create_learning_curve_chart(regressor, X_train, y_train),
- "feature_importance": create_feature_importance_chart(regressor, X_train, y_train),
- "residuals": create_residuals_chart(regressor, X_train, X_test, y_train, y_test),
- "prediction_error": create_prediction_error_chart(regressor, X_train, X_test, y_train, y_test),
- "cooks_distance": create_cooks_distance_chart(regressor, X_train, y_train),
- }
-
  reg_summary["integration/about/neptune-sklearn"] = __version__
 
+ if log_charts:
+ if learning_curve := create_learning_curve_chart(regressor, X_train, y_train):
+ reg_summary["diagnostics_charts/learning_curve"] = learning_curve
+ if feature_importance := create_feature_importance_chart(regressor, X_train, y_train):
+ reg_summary["diagnostics_charts/feature_importance"] = feature_importance
+ if residuals := create_residuals_chart(regressor, X_train, X_test, y_train, y_test):
+ reg_summary["diagnostics_charts/residuals"] = residuals
+ if prediction_error := create_prediction_error_chart(regressor, X_train, X_test, y_train, y_test):
+ reg_summary["diagnostics_charts/prediction_error"] = prediction_error
+ if cooks_distance := create_cooks_distance_chart(regressor, X_train, y_train):
+ reg_summary["diagnostics_charts/cooks_distance"] = cooks_distance
+
  return reg_summary
 
 
@@ -217,17 +220,20 @@ def create_classifier_summary(classifier, X_train, X_test, y_train, y_test, nrow
  "scores": get_scores(classifier, X_test, y_test, y_pred=y_pred),
  }
 
- if log_charts:
- cls_summary["diagnostics_charts"] = {
- "classification_report": create_classification_report_chart(classifier, X_train, X_test, y_train, y_test),
- "confusion_matrix": create_confusion_matrix_chart(classifier, X_train, X_test, y_train, y_test),
- "ROC_AUC": create_roc_auc_chart(classifier, X_train, X_test, y_train, y_test),
- "precision_recall": create_precision_recall_chart(classifier, X_test, y_test),
- "class_prediction_error": create_class_prediction_error_chart(classifier, X_train, X_test, y_train, y_test),
- }
-
  cls_summary["integration/about/neptune-sklearn"] = __version__
 
+ if log_charts:
+ if classification_report := create_classification_report_chart(classifier, X_train, X_test, y_train, y_test):
+ cls_summary["diagnostics_charts/classification_report"] = classification_report
+ if confusion_matrix := create_confusion_matrix_chart(classifier, X_train, X_test, y_train, y_test):
+ cls_summary["diagnostics_charts/confusion_matrix"] = confusion_matrix
+ if ROC_AUC := create_roc_auc_chart(classifier, X_train, X_test, y_train, y_test):
+ cls_summary["diagnostics_charts/ROC_AUC"] = ROC_AUC
+ if precision_recall := create_precision_recall_chart(classifier, X_test, y_test):
+ cls_summary["diagnostics_charts/precision_recall"] = precision_recall
+ if class_prediction_error := create_class_prediction_error_chart(classifier, X_train, X_test, y_train, y_test):
+ cls_summary["diagnostics_charts/class_prediction_error"] = class_prediction_error
+
  return cls_summary
 
 
@@ -272,13 +278,13 @@ def create_kmeans_summary(model, X, nrows=1000, **kwargs):
  kmeans_summary["all_params"] = stringify_unsupported(get_estimator_params(model))
  kmeans_summary["pickled_model"] = get_pickled_model(model)
  kmeans_summary["cluster_labels"] = get_cluster_labels(model, X, nrows=nrows, **kwargs)
- kmeans_summary["diagnostics_charts"] = {
- "kelbow": create_kelbow_chart(model, X, **kwargs),
- "silhouette": create_silhouette_chart(model, X, **kwargs),
- }
-
  kmeans_summary["integration/about/neptune-sklearn"] = __version__
 
+ if kelbow := create_kelbow_chart(model, X, **kwargs):
+ kmeans_summary["diagnostics_charts/kelbow"] = kelbow
+ if silhouette := create_silhouette_chart(model, X, **kwargs):
+ kmeans_summary["diagnostics_charts/silhouette"] = silhouette
+
  return kmeans_summary