^j.dZddlmZmZmZddlZddlmZm Z m Z ddl m Z m Z dede fd Zded ee de fd Zded ee efd ee de fdZddddddded ee efdeee efded ee d ee dedeej&ej&ffdZdgZy)z.Interpretability functions for XGBoost models.)OptionalTupleUnionN) ArrayLikeFloatCompatibleIterationRange)BoosterDMatrixmodelreturnct|tr|St|dd}t|s t d|}t|ts t d|S)N get_boosterzC`model` must be an xgboost.Booster or an object with get_booster().z5`model.get_booster()` must return an xgboost.Booster.) isinstancer getattrcallable TypeError)r rboosters \/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/xgboost/interpret.py _as_boosterr s[%! %5K K  Q  mG gw 'OPP Niteration_rangec:t|dd}|||S|y|S)N_get_iteration_range)rr)r)r rget_iteration_ranges rrrs4"%)?F&"?33 rXmissingc t|tr| td|St|||n t|ddt|ddt|ddt|ddS)Nz6`missing` must not be specified when `X` is a DMatrix.rn_jobs feature_typesenable_categoricalF)rnthreadr r!)rr ValueErrorr)r rrs r_as_prediction_dmatrixr$$sm!W  UV V  ".GE9d4Sx.e_d;"5*>F  rFT) X_background output_marginrrvalidate_featuresr%r&r'c | td|}t|}t|||} |j| d|t ||} | dddf} | d} | | fS)aFReturn SHAP values for an XGBoost model. .. warning:: This function is still working in progress. This function accepts either a :py:class:`xgboost.Booster` or an sklearn-style XGBoost model and returns feature contributions together with the separated bias term. Parameters ---------- model : XGBoost booster or sklearn-style XGBoost model. X : Input data. X_background : Background data for interventional SHAP values. This is reserved for a future implementation and is currently unsupported. output_margin : Accepted for API compatibility. SHAP contributions currently correspond to the model margin. iteration_range : Specifies which layer of trees are used in prediction. missing : Value in array-like ``X`` to treat as missing. When None, use the model's missing value if available, otherwise ``np.nan``. This must not be specified when ``X`` is already a DMatrix. validate_features : Validate feature names between the model and input data. Returns ------- values, bias : ``values`` contains feature SHAP values with the bias term removed. ``bias`` contains the separated bias term. For multi-target models, the output shape follows the corresponding prediction shape with the final feature dimension split into ``values`` and ``bias``. Notes ----- To use GPU algorithms, configure the model before calling this function, for example with ``booster.set_param({"device": "cuda"})``. Nz$`X_background` is not yet supported.T) pred_contribsr'r.).r*)NotImplementedErrorrr$predictr) r rr%r&rrr'_rdatacontribsvaluesbiass r shap_valuesr25sl!"HII A% G !%G 4D +,UOD Hc3B3h F G D 4<rr2)__doc__typingrrrnumpynp_typingrrr corer r objectrrr$boolndarrayr2__all__rrr>s-4))??" v '  $,^$< GY./:B?:S *9=04)-"G G Wi  G5)!345 G  G n- Go &GG 2::rzz !"GT /r