comparison base_model_trainer.py @ 9:e7dd78077b72 draft default tip

planemo upload for repository https://github.com/goeckslab/gleam commit 84d5cd0b1fa5c1ff0ad892bc39c95dad1ceb4920

8:ba45bc057d70

            )

        self.exp.setup(self.data, **self.setup_params)
        self.setup_params.update(self.user_kwargs)

    def train_model(self):
        LOG.info("Training and selecting the best model")
        if self.task_type == "classification":
            self.exp.add_metric(
                id="PR-AUC-Weighted",

            compare_kwargs["cross_validation"] = self.cross_validation

        # Respect explicit fold count
        if getattr(self, "cross_validation_folds", None) is not None:
            compare_kwargs["fold"] = self.cross_validation_folds

        LOG.info(f"compare_models kwargs: {compare_kwargs}")
        self.best_model = self.exp.compare_models(**compare_kwargs)
        self.results = self.exp.pull()
        if getattr(self, "tune_model", False):

            elif key == "Probability Threshold":
                dv = f"{v:.2f}" if v is not None else "0.5"
            else:
                dv = v if v is not None else "None"
            setup_rows.append([key, dv])
        if hasattr(self.exp, "_fold_metric"):
            setup_rows.append(["best_model_metric", self.exp._fold_metric])

        df_setup = pd.DataFrame(setup_rows, columns=["Parameter", "Value"])
        df_setup.to_csv(
            Path(self.output_dir) / "setup_params.csv", index=False
        )

9:e7dd78077b72

            )

        self.exp.setup(self.data, **self.setup_params)
        self.setup_params.update(self.user_kwargs)

    def _normalize_metric(self, m: str) -> str:
        if not m:
            return "R2" if self.task_type == "regression" else "Accuracy"
        m_low = str(m).strip().lower()
        alias = {
            "auc": "AUC", "roc_auc": "AUC", "roc-auc": "AUC",
            "accuracy": "Accuracy",
            "precision": "Precision",
            "recall": "Recall",
            "f1": "F1",
            "kappa": "Kappa",
            "logloss": "Log Loss", "log_loss": "Log Loss",
            "pr_auc": "PR-AUC-Weighted", "prauc": "PR-AUC-Weighted",
            "r2": "R2",
            "mae": "MAE",
            "mse": "MSE",
            "rmse": "RMSE",
            "rmsle": "RMSLE",
            "mape": "MAPE",
        }
        return alias.get(m_low, m)

    def train_model(self):
        LOG.info("Training and selecting the best model")
        if self.task_type == "classification":
            self.exp.add_metric(
                id="PR-AUC-Weighted",

            compare_kwargs["cross_validation"] = self.cross_validation

        # Respect explicit fold count
        if getattr(self, "cross_validation_folds", None) is not None:
            compare_kwargs["fold"] = self.cross_validation_folds

        chosen_metric = self._normalize_metric(getattr(self, "best_model_metric", None))
        if chosen_metric:
            compare_kwargs["sort"] = chosen_metric
            self.chosen_metric_label = chosen_metric
            try:
                setattr(self.exp, "_fold_metric", chosen_metric)
            except Exception as e:
                LOG.warning(f"Failed to set '_fold_metric' to '{chosen_metric}': {e}", exc_info=True)

        LOG.info(f"compare_models kwargs: {compare_kwargs}")
        self.best_model = self.exp.compare_models(**compare_kwargs)
        self.results = self.exp.pull()
        if getattr(self, "tune_model", False):

            elif key == "Probability Threshold":
                dv = f"{v:.2f}" if v is not None else "0.5"
            else:
                dv = v if v is not None else "None"
            setup_rows.append([key, dv])
        if getattr(self, "chosen_metric_label", None):
            setup_rows.append(["Best Model Metric", self.chosen_metric_label])

        df_setup = pd.DataFrame(setup_rows, columns=["Parameter", "Value"])
        df_setup.to_csv(
            Path(self.output_dir) / "setup_params.csv", index=False
        )