comparison feature_importance.py @ 13:bf0df21a1ea3 draft default tip

planemo upload for repository https://github.com/goeckslab/gleam commit 7fc20c9ddc2b641975138c9d67b5da240af0484c

12:15707141e7da

            display_features = list(X_data.columns)
            max_display = len(display_features)

        # Background set
        bg = X_data.sample(min(len(X_data), 100), random_state=42)
        predict_fn = (
            model.predict_proba if hasattr(model, "predict_proba") else model.predict
        )

        # Optimized explainer
        explainer = None
        explainer_label = None
        if hasattr(model, "feature_importances_"):
            explainer = shap.TreeExplainer(
                model, bg, feature_perturbation="tree_path_dependent", n_jobs=-1
            )
            explainer_label = "tree_path_dependent"
        elif hasattr(model, "coef_"):
            explainer = shap.LinearExplainer(model, bg)
            explainer_label = "linear"
        else:
            explainer = shap.Explainer(predict_fn, bg)
            explainer_label = explainer.__class__.__name__

        try:
            shap_values = explainer(X_data)
            self.shap_model_name = explainer.__class__.__name__
        except Exception as e:
            error_message = str(e)
            needs_tree_fallback = (
                hasattr(model, "feature_importances_")
                and "does not cover all the leaves" in error_message.lower()
            )
            feature_name_mismatch = "feature names should match" in error_message.lower()
            if needs_tree_fallback:
                LOG.warning(

                    "SHAP computation failed due to feature-name mismatch (%s). "
                    "Falling back to model-agnostic SHAP explainer.",
                    error_message,
                )
                try:
                    agnostic_explainer = shap.Explainer(predict_fn, bg)
                    shap_values = agnostic_explainer(X_data)
                    self.shap_model_name = (
                        f"{agnostic_explainer.__class__.__name__} (fallback)"
                    )
                except Exception as fallback_exc:

    def encode_image_to_base64(self, img_path):
        with open(img_path, "rb") as img_file:
            return base64.b64encode(img_file.read()).decode("utf-8")

    def run(self):
        if (
            self.exp is None
            or not hasattr(self.exp, "is_setup")
            or not self.exp.is_setup

13:bf0df21a1ea3

            display_features = list(X_data.columns)
            max_display = len(display_features)

        # Background set
        bg = X_data.sample(min(len(X_data), 100), random_state=42)
        predict_fn = self._get_predict_fn(model)

        # Optimized explainer based on model type
        explainer, explainer_label, tree_based = self._choose_shap_explainer(
            model, bg, predict_fn
        )
        if explainer is None:
            LOG.warning("No suitable SHAP explainer for model %s; skipping SHAP.", model)
            self.shap_model_name = None
            return

        try:
            shap_values = explainer(X_data)
            self.shap_model_name = explainer.__class__.__name__
        except Exception as e:
            error_message = str(e)
            needs_tree_fallback = (
                tree_based
                and "does not cover all the leaves" in error_message.lower()
            )
            feature_name_mismatch = "feature names should match" in error_message.lower()
            if needs_tree_fallback:
                LOG.warning(

                    "SHAP computation failed due to feature-name mismatch (%s). "
                    "Falling back to model-agnostic SHAP explainer.",
                    error_message,
                )
                try:
                    agnostic_explainer = shap.Explainer(
                        predict_fn, bg, algorithm="permutation"
                    )
                    shap_values = agnostic_explainer(X_data)
                    self.shap_model_name = (
                        f"{agnostic_explainer.__class__.__name__} (fallback)"
                    )
                except Exception as fallback_exc:

    def encode_image_to_base64(self, img_path):
        with open(img_path, "rb") as img_file:
            return base64.b64encode(img_file.read()).decode("utf-8")

    def _get_predict_fn(self, model):
        if hasattr(model, "predict_proba"):
            return model.predict_proba
        if hasattr(model, "decision_function"):
            return model.decision_function
        return model.predict

    def _choose_shap_explainer(self, model, bg, predict_fn):
        """
        Select a SHAP explainer following the prescribed priority order for
        algorithms. Returns (explainer, label, is_tree_based).
        """
        if model is None:
            return None, None, False

        name = model.__class__.__name__
        lname = name.lower()
        task = getattr(self, "task_type", None)

        def _permutation(fn):
            return shap.Explainer(fn, bg, algorithm="permutation")

        if task == "classification":
            # 1) Logistic Regression
            if "logisticregression" in lname:
                return _permutation(model.predict_proba), "permutation-proba", False

            # 2) Ridge Classifier
            if "ridgeclassifier" in lname:
                fn = (
                    model.decision_function
                    if hasattr(model, "decision_function")
                    else predict_fn
                )
                return _permutation(fn), "permutation-decision_function", False

            # 3) LDA
            if "lineardiscriminantanalysis" in lname:
                return _permutation(model.predict_proba), "permutation-proba", False

            # 4) Random Forest
            if "randomforestclassifier" in lname:
                return (
                    shap.TreeExplainer(
                        model, bg, feature_perturbation="tree_path_dependent", n_jobs=-1
                    ),
                    "tree_path_dependent",
                    True,
                )

            # 5) Gradient Boosting
            if "gradientboostingclassifier" in lname:
                return (
                    shap.TreeExplainer(
                        model, bg, feature_perturbation="tree_path_dependent", n_jobs=-1
                    ),
                    "tree_path_dependent",
                    True,
                )

            # 6) AdaBoost
            if "adaboostclassifier" in lname:
                return (
                    shap.TreeExplainer(
                        model, bg, feature_perturbation="tree_path_dependent", n_jobs=-1
                    ),
                    "tree_path_dependent",
                    True,
                )

            # 7) Extra Trees
            if "extratreesclassifier" in lname:
                return (
                    shap.TreeExplainer(
                        model, bg, feature_perturbation="tree_path_dependent", n_jobs=-1
                    ),
                    "tree_path_dependent",
                    True,
                )

            # 8) LightGBM
            if "lgbmclassifier" in lname:
                return (
                    shap.TreeExplainer(
                        model,
                        bg,
                        model_output="raw",
                        feature_perturbation="tree_path_dependent",
                        n_jobs=-1,
                    ),
                    "tree_path_dependent",
                    True,
                )

            # 9) XGBoost
            if "xgbclassifier" in lname:
                return (
                    shap.TreeExplainer(
                        model, bg, feature_perturbation="tree_path_dependent", n_jobs=-1
                    ),
                    "tree_path_dependent",
                    True,
                )

            # 10) CatBoost (classifier)
            if "catboost" in lname:
                return (
                    shap.TreeExplainer(
                        model, bg, feature_perturbation="tree_path_dependent", n_jobs=-1
                    ),
                    "tree_path_dependent",
                    True,
                )

            # 11) KNN
            if "kneighborsclassifier" in lname:
                return _permutation(model.predict_proba), "permutation-proba", False

            # 12) SVM - linear kernel
            if "svc" in lname or "svm" in lname:
                kernel = getattr(model, "kernel", None)
                if kernel == "linear":
                    return shap.LinearExplainer(model, bg), "linear", False
                return _permutation(predict_fn), "permutation-svm", False

            # 13) Decision Tree
            if "decisiontreeclassifier" in lname:
                return (
                    shap.TreeExplainer(
                        model, bg, feature_perturbation="tree_path_dependent", n_jobs=-1
                    ),
                    "tree_path_dependent",
                    True,
                )

            # 14) Naive Bayes
            if "naive_bayes" in lname or lname.endswith("nb"):
                fn = model.predict_proba if hasattr(model, "predict_proba") else predict_fn
                return _permutation(fn), "permutation-proba", False

            # 15) QDA
            if "quadraticdiscriminantanalysis" in lname:
                return _permutation(model.predict_proba), "permutation-proba", False

            # 16) Dummy
            if "dummyclassifier" in lname:
                return None, None, False

            # Default classification: permutation on predict_fn
            return _permutation(predict_fn), "permutation-default", False

        # Regression path
        # Linear family
        linear_keys = [
            "linearregression",
            "lasso",
            "ridge",
            "elasticnet",
            "lars",
            "lassolars",
            "orthogonalmatchingpursuit",
            "bayesianridge",
            "ardregression",
            "passiveaggressiveregressor",
            "theilsenregressor",
            "huberregressor",
        ]
        if any(k in lname for k in linear_keys):
            return shap.LinearExplainer(model, bg), "linear", False

        # Kernel ridge / SVR / KNN / MLP / RANSAC (model-agnostic)
        if "kernelridge" in lname:
            return _permutation(predict_fn), "permutation-kernelridge", False
        if "svr" in lname or "svm" in lname:
            kernel = getattr(model, "kernel", None)
            if kernel == "linear":
                return shap.LinearExplainer(model, bg), "linear", False
            return _permutation(predict_fn), "permutation-svr", False
        if "kneighborsregressor" in lname:
            return _permutation(predict_fn), "permutation-knn", False
        if "mlpregressor" in lname:
            return _permutation(predict_fn), "permutation-mlp", False
        if "ransacregressor" in lname:
            return _permutation(predict_fn), "permutation-ransac", False

        # Tree-based regressors
        tree_class_names = [
            "decisiontreeregressor",
            "randomforestregressor",
            "extratreesregressor",
            "adaboostregressor",
            "gradientboostingregressor",
        ]
        if any(k in lname for k in tree_class_names):
            return (
                shap.TreeExplainer(
                    model, bg, feature_perturbation="tree_path_dependent", n_jobs=-1
                ),
                "tree_path_dependent",
                True,
            )

        # Boosting libraries
        if "lgbmregressor" in lname or "lightgbm" in lname:
            return (
                shap.TreeExplainer(
                    model,
                    bg,
                    model_output="raw",
                    feature_perturbation="tree_path_dependent",
                    n_jobs=-1,
                ),
                "tree_path_dependent",
                True,
            )
        if "xgbregressor" in lname or "xgboost" in lname:
            return (
                shap.TreeExplainer(
                    model, bg, feature_perturbation="tree_path_dependent", n_jobs=-1
                ),
                "tree_path_dependent",
                True,
            )
        if "catboost" in lname:
            return (
                shap.TreeExplainer(
                    model, bg, feature_perturbation="tree_path_dependent", n_jobs=-1
                ),
                "tree_path_dependent",
                True,
            )

        # Default regression: model-agnostic permutation explainer
        return _permutation(predict_fn), "permutation-default", False

    def run(self):
        if (
            self.exp is None
            or not hasattr(self.exp, "is_setup")
            or not self.exp.is_setup