Mercurial > repos > goeckslab > pycaret_predict
changeset 7:f4cb41f458fd draft default tip
planemo upload for repository https://github.com/goeckslab/gleam commit b430f8b466655878c3bf63b053655fdbf039ddb0
| author | goeckslab |
|---|---|
| date | Wed, 09 Jul 2025 01:13:01 +0000 |
| parents | a32ff7201629 |
| children | |
| files | base_model_trainer.py feature_importance.py pycaret_train.py test-data/expected_best_model_classification_customized_cross_off.csv test-data/expected_model_classification_customized_cross_off.h5 |
| diffstat | 5 files changed, 30 insertions(+), 7 deletions(-) [+] |
line wrap: on
line diff
--- a/base_model_trainer.py Wed Jul 02 19:00:03 2025 +0000 +++ b/base_model_trainer.py Wed Jul 09 01:13:01 2025 +0000 @@ -127,9 +127,11 @@ and self.cross_validation is not None and self.cross_validation is False ): - self.setup_params["cross_validation"] = self.cross_validation + logging.info( + "cross_validation is set to False. This will disable cross-validation." + ) - if hasattr(self, "cross_validation") and self.cross_validation is not None: + if hasattr(self, "cross_validation") and self.cross_validation: if hasattr(self, "cross_validation_folds"): self.setup_params["fold"] = self.cross_validation_folds @@ -182,10 +184,11 @@ ) if hasattr(self, "models") and self.models is not None: - self.best_model = self.exp.compare_models(include=self.models) + self.best_model = self.exp.compare_models(include=self.models, cross_validation=self.cross_validation) else: - self.best_model = self.exp.compare_models() + self.best_model = self.exp.compare_models(cross_validation=self.cross_validation) self.results = self.exp.pull() + if self.task_type == "classification": self.results.rename(columns={"AUC": "ROC-AUC"}, inplace=True) @@ -314,7 +317,7 @@ html_content += ( "</div>" '<div id="summary" class="tab-content">' - "<h2>Model Metrics from Cross-Validation Set</h2>" + f"<h2>Model Metrics from {'Cross-Validation Set' if self.cross_validation else 'Validation set'}</h2>" f"<h2>Best Model: {model_name}</h2>" "<h5>The best model is selected by: Accuracy (Classification)" " or R2 (Regression).</h5>"
--- a/feature_importance.py Wed Jul 02 19:00:03 2025 +0000 +++ b/feature_importance.py Wed Jul 09 01:13:01 2025 +0000 @@ -120,6 +120,9 @@ used_features = model.feature_name_ elif hasattr(model, "booster_") and hasattr(model.booster_, "feature_name"): used_features = model.booster_.feature_name() + elif hasattr(model, "feature_names_in_"): + # scikit‐learn's standard attribute for the names of features used during fit + used_features = list(model.feature_names_in_) else: used_features = X_transformed.columns @@ -130,7 +133,14 @@ plot_X = X_shap plot_title = f"SHAP Summary for {model_class_name} (TreeExplainer)" else: - sampled_X = X_transformed[used_features].sample(100, random_state=42) + logging.warning(f"len(X_transformed) = {len(X_transformed)}") + max_samples = 100 + n_samples = min(max_samples, len(X_transformed)) + sampled_X = X_transformed[used_features].sample( + n=n_samples, + replace=False, + random_state=42 + ) explainer = shap.KernelExplainer(model.predict, sampled_X) shap_values = explainer.shap_values(sampled_X) plot_X = sampled_X
--- a/pycaret_train.py Wed Jul 02 19:00:03 2025 +0000 +++ b/pycaret_train.py Wed Jul 09 01:13:01 2025 +0000 @@ -29,6 +29,9 @@ parser.add_argument("--cross_validation", action="store_true", default=None, help="Perform cross-validation for PyCaret setup") + parser.add_argument("--no_cross_validation", action="store_true", + default=None, + help="Don't perform cross-validation for PyCaret setup") parser.add_argument("--cross_validation_folds", type=int, default=None, help="Number of cross-validation folds \ @@ -62,11 +65,15 @@ args = parser.parse_args() + cross_validation = True + if args.no_cross_validation: + cross_validation = False + model_kwargs = { "train_size": args.train_size, "normalize": args.normalize, "feature_selection": args.feature_selection, - "cross_validation": args.cross_validation, + "cross_validation": cross_validation, "cross_validation_folds": args.cross_validation_folds, "remove_outliers": args.remove_outliers, "remove_multicollinearity": args.remove_multicollinearity,