ri8 dZddlZddlmZddlmZddlmZmZm Z m Z m Z m Z m Z mZddlZddlZddlmZmZmZmZmZmZmZmZmZmZmZmZm Z m!Z!m"Z"ddl#m$Z$m%Z%dd l&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5dd l6m7Z7erdd l&m8Z8gd Z9ee4e ee e:e e;fej<e5ej=j>fZ?ee e ej<ej<ge ej<ej<ffe e ej<ej<e ej<ge ej<ej<ffe e ej<ej<e ej<e ej<ge ej<ej<fffZ@ee e ej<ej<gefe e ej<ej<ge efe e ej<ej<e ej<gefe e ej<ej<e ej<ge efe e ej<ej<e ej<e ej<gefe e ej<ej<e ej<e ej<ge effZAeeBeAe eeBeAffZCe e?e fZDd ede ej<fdZEd edej<fdZFd ede ej<fdZGGddZHGddZIdZJdZKdZLde ee eefe efdeBde;de efdZMGdde0ZNGdd e1eNZOGd!d"e,eNZPGd#d$eNZQdS)%z,Scikit-learn wrapper interface for LightGBM.N) signature)Path) TYPE_CHECKINGAnyCallableDictListOptionalTupleUnion)_MULTICLASS_OBJECTIVESBoosterDataset LightGBMError_choose_param_value_ConfigAliases_LGBM_BoosterBestScoreType%_LGBM_CategoricalFeatureConfiguration_LGBM_EvalFunctionResultType_LGBM_FeatureNameConfiguration_LGBM_GroupType_LGBM_InitScoreType_LGBM_LabelType_LGBM_WeightType _log_warning)_EvalResultDictrecord_evaluation)SKLEARN_INSTALLEDLGBMNotFittedError_LGBMAssertAllFinite_LGBMCheckClassificationTargets_LGBMCheckSampleWeight_LGBMClassifierBase_LGBMComputeSampleWeight _LGBMCpuCount_LGBMLabelEncoder_LGBMModelBase_LGBMRegressorBase_LGBMValidateData_sklearn_version dt_DataTable pd_DataFrame)train) _sklearn_Tags)LGBMClassifier LGBMModel LGBMRanker LGBMRegressordatasetreturnc~|}d}|$t|tjs J||S)NzEstimators in lightgbm.sklearn should only retrieve query groups from a constructed Dataset. If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/microsoft/LightGBM/issues.) get_group isinstancenpndarray)r4group error_msgs f/var/www/html/pippo.cuttalo.com/services/trading/venv/lib/python3.11/site-packages/lightgbm/sklearn.py#_get_group_from_constructed_datasetr>ysD     E C =Jubj99==9=== Lcz|}d}t|tjs J||S)NzEstimators in lightgbm.sklearn should only retrieve labels from a constructed Dataset. If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/microsoft/LightGBM/issues.) get_labelr8r9r:)r4labelr<s r=#_get_label_from_constructed_datasetrCsE     E C eRZ ( (33)333 Lr?c~|}d}|$t|tjs J||S)NzEstimators in lightgbm.sklearn should only retrieve weights from a constructed Dataset. If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/microsoft/LightGBM/issues.) get_weightr8r9r:)r4weightr<s r=$_get_weight_from_constructed_datasetrGsD    ! !F C >Z ;;>>Y>>> Mr?c`eZdZdZdefdZdejdede ejejffdZ dS) _ObjectiveFunctionWrapperz#Proxy class for objective function.funcc||_dS)aJ Construct a proxy class. This class transforms objective function to match objective function with signature ``new_func(preds, dataset)`` as expected by ``lightgbm.engine.train``. Parameters ---------- func : callable Expects a callable with following signatures: ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)`` or ``func(y_true, y_pred, weight, group)`` and returns (grad, hess): y_true : numpy 1-D array of shape = [n_samples] The target values. y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The predicted values. Predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task. weight : numpy 1-D array of shape = [n_samples] The weight of samples. Weights should be non-negative. group : numpy 1-D array Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape [n_samples, n_classes] (for multi-class task) The value of the first order derivative (gradient) of the loss with respect to the elements of y_pred for each sample point. hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The value of the second order derivative (Hessian) of the loss with respect to the elements of y_pred for each sample point. .. note:: For multi-class task, y_pred is a numpy 2-D array of shape = [n_samples, n_classes], and grad and hess should be returned in the same format. NrJselfrJs r=__init__z"_ObjectiveFunctionWrapper.__init__sR r?predsr4r5ct|}tt|jj}|dkr|||\}}||fSt |}|dkr||||\}}||fS|dkr't |}|||||Std|)aCall passed function with appropriate arguments. Parameters ---------- preds : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The predicted values. dataset : Dataset The training dataset. Returns ------- grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The value of the first order derivative (gradient) of the loss with respect to the elements of preds for each sample point. hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The value of the second order derivative (Hessian) of the loss with respect to the elements of preds for each sample point. zESelf-defined objective function should have 2, 3 or 4 arguments, got rClenrrJ parametersrGr> TypeError) rNrPr4labelsargcgradhessrFr;s r=__call__z"_ObjectiveFunctionWrapper.__call__s.5W==9TY''233 1996511JD$: 5g>> 19965&99JD$:  1997@@E99VUFE:: :f`dffgggr?N) __name__ __module__ __qualname____doc__#_LGBM_ScikitCustomObjectiveFunctionrOr9r:rr r]r?r=rIrIs--)@))))V&hz&h&h rz2:% & &h&h&h&h&h&hr?rIc XeZdZdZdefdZdejdede e e e ffdZ dS) _EvalFunctionWrapperz$Proxy class for evaluation function.rJc||_dS)aConstruct a proxy class. This class transforms evaluation function to match evaluation function with signature ``new_func(preds, dataset)`` as expected by ``lightgbm.engine.train``. Parameters ---------- func : callable Expects a callable with following signatures: ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)`` or ``func(y_true, y_pred, weight, group)`` and returns (eval_name, eval_result, is_higher_better) or list of (eval_name, eval_result, is_higher_better): y_true : numpy 1-D array of shape = [n_samples] The target values. y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array shape = [n_samples, n_classes] (for multi-class task) The predicted values. In case of custom ``objective``, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. weight : numpy 1-D array of shape = [n_samples] The weight of samples. Weights should be non-negative. group : numpy 1-D array Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. NrLrMs r=rOz_EvalFunctionWrapper.__init__sJ r?rPr4r5czt|}tt|jj}|dkr|||St |}|dkr||||S|dkr't |}|||||Std|)aaCall passed function with appropriate arguments. Parameters ---------- preds : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The predicted values. dataset : Dataset The training dataset. Returns ------- eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. rRrSrTz@Self-defined eval function should have 2, 3 or 4 arguments, got rU)rNrPr4rYrZrFr;s r=r]z_EvalFunctionWrapper.__call__s.5W==9TY''233 19999VU++ +5g>> 19999VUF33 3 1997@@E99VUFE:: :a[_aabbbr?N) r^r_r`ra_LGBM_ScikitCustomEvalFunctionrOr9r:rr rr r]rcr?r=reres..%;%%%%N$cz$c$c +T2N-OO P $c$c$c$c$c$cr?reaX Build a gradient boosting model from the training set (X, y). Parameters ---------- X : {X_shape} Input feature matrix. y : {y_shape} The target values (class labels in classification, real numbers in regression). sample_weight : {sample_weight_shape} Weights of training data. Weights should be non-negative. init_score : {init_score_shape} Init score of training data. group : {group_shape} Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. eval_set : list or None, optional (default=None) A list of (X, y) tuple pairs to use as validation sets. eval_names : list of str, or None, optional (default=None) Names of eval_set. eval_sample_weight : {eval_sample_weight_shape} Weights of eval data. Weights should be non-negative. eval_class_weight : list or None, optional (default=None) Class weights of eval data. eval_init_score : {eval_init_score_shape} Init score of eval data. eval_group : {eval_group_shape} Group data of eval data. eval_metric : str, callable, list or None, optional (default=None) If str, it should be a built-in evaluation metric to use. If callable, it should be a custom evaluation metric, see note below for more details. If list, it can be a list of built-in metrics, a list of custom evaluation metrics, or a mix of both. In either case, the ``metric`` from the model parameters will be evaluated and used as well. Default: 'l2' for LGBMRegressor, 'logloss' for LGBMClassifier, 'ndcg' for LGBMRanker. feature_name : list of str, or 'auto', optional (default='auto') Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of str or int, or 'auto', optional (default='auto') Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0. callbacks : list of callable, or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. init_model : str, pathlib.Path, Booster, LGBMModel or None, optional (default=None) Filename of LightGBM model, Booster instance or LGBMModel instance used for continue training. Returns ------- self : LGBMModel Returns self. a9 Note ---- Custom eval function expects a callable with following signatures: ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)`` or ``func(y_true, y_pred, weight, group)`` and returns (eval_name, eval_result, is_higher_better) or list of (eval_name, eval_result, is_higher_better): y_true : numpy 1-D array of shape = [n_samples] The target values. y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The predicted values. In case of custom ``objective``, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case. weight : numpy 1-D array of shape = [n_samples] The weight of samples. Weights should be non-negative. group : numpy 1-D array Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. eval_name : str The name of evaluation function (without whitespace). eval_result : float The eval result. is_higher_better : bool Is eval result higher better, e.g. AUC is ``is_higher_better``. a_ {description} Parameters ---------- X : {X_shape} Input features matrix. raw_score : bool, optional (default=False) Whether to predict raw scores. start_iteration : int, optional (default=0) Start index of the iteration to predict. If <= 0, starts from the first iteration. num_iteration : int or None, optional (default=None) Total number of iterations used in the prediction. If None, if the best iteration exists and start_iteration <= 0, the best iteration is used; otherwise, all iterations from ``start_iteration`` are used (no limits). If <= 0, all iterations from ``start_iteration`` are used (no limits). pred_leaf : bool, optional (default=False) Whether to predict leaf index. pred_contrib : bool, optional (default=False) Whether to predict feature contributions. .. note:: If you want to get more explanations for your model's predictions using SHAP values, like SHAP interaction values, you can install the shap package (https://github.com/slundberg/shap). Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra column, where the last column is the expected value. validate_features : bool, optional (default=False) If True, ensure that the features used to predict match the ones used to train. Used only if data is pandas DataFrame. **kwargs Other parameters for the prediction. Returns ------- {output_name} : {predicted_result_shape} The predicted values. X_leaves : {X_leaves_shape} If ``pred_leaf=True``, the predicted leaf of every tree for each sample. X_SHAP_values : {X_SHAP_values_shape} If ``pred_contrib=True``, the feature contributions for each sample. collectionnameic|dSt|trt||kr||SdSt|tr||dSt |d)z?Try to extract the ith element of one of the ``eval_*`` inputs.Nz should be dict or list)r8listrVdictgetrXrirjrks r=_extract_evaluation_meta_datarqst J % % : z??Q  a= 4 J % %:~~a&&&4888999r?c)eZdZdZddddddddd d d d d d d d dddddededededededeeee fdeee efdedededededededed eeee j j e j jfd!eed"ed#ef(d$Zd%e eeffd&Zed'd(d)e eefd%d(fd*Zd%ed(ffd+ Zd%efd,Zdnd.ed%e eefffd/ Zd0ed%dfd1Zd2ed%e eeffd3Zd!eed%efd4Z dod6ed7ed8eed9eed:eed;ee e!dee ed?ee ed@ee edAee"dBe#dCe$dDee e%dEeeee&e'dfd%df"dFZ(e)*dGdHdIdJdKdLdMdNOdPze+ze(_ dpd6edRedSedTeedUedVedWed#efdXZ,e-*dYdGdZd[d\d]^e,_e.d%efd_Z/e.d%efd`Z0e0j1daed%dfdbZ0e.d%e2fdcZ3e.d%efddZ4e.d%eee ffdeZ5e.d%efdfZ6e.d%efdgZ7e.d%e'fdhZ8e.d%e9fdiZ:e.d%e j;fdjZe>j?dqdmZ>xZ@S)rr1z4Implementation of the scikit-learn API for LightGBM.gbdt皙?d@ NMbP??rsplit boosting_type num_leaves max_depth learning_rate n_estimatorssubsample_for_bin objective class_weightmin_split_gainmin_child_weightmin_child_samples subsamplesubsample_freqcolsample_bytree reg_alpha reg_lambda random_staten_jobsimportance_typerrrrrrrrrrrrrrrrrrrkwargsc tstd||_||_||_||_||_||_||_| |_ | |_ | |_ | |_ | |_ ||_||_||_||_||_||_d|_i|_i|_d|_i|_||_||_d|_d|_d|_d|_d|_d|_ |j!di|dS)aConstruct a gradient boosting model. Parameters ---------- boosting_type : str, optional (default='gbdt') 'gbdt', traditional Gradient Boosting Decision Tree. 'dart', Dropouts meet Multiple Additive Regression Trees. 'rf', Random Forest. num_leaves : int, optional (default=31) Maximum tree leaves for base learners. max_depth : int, optional (default=-1) Maximum tree depth for base learners, <=0 means no limit. If setting this to a positive value, consider also changing ``num_leaves`` to ``<= 2^max_depth``. learning_rate : float, optional (default=0.1) Boosting learning rate. You can use ``callbacks`` parameter of ``fit`` method to shrink/adapt learning rate in training using ``reset_parameter`` callback. Note, that this will ignore the ``learning_rate`` argument in training. n_estimators : int, optional (default=100) Number of boosted trees to fit. subsample_for_bin : int, optional (default=200000) Number of samples for constructing bins. objective : str, callable or None, optional (default=None) Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below). Default: 'regression' for LGBMRegressor, 'binary' or 'multiclass' for LGBMClassifier, 'lambdarank' for LGBMRanker. class_weight : dict, 'balanced' or None, optional (default=None) Weights associated with classes in the form ``{class_label: weight}``. Use this parameter only for multi-class classification task; for binary classification task you may use ``is_unbalance`` or ``scale_pos_weight`` parameters. Note, that the usage of all these parameters will result in poor estimates of the individual class probabilities. You may want to consider performing probability calibration (https://scikit-learn.org/stable/modules/calibration.html) of your model. The 'balanced' mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``. If None, all classes are supposed to have weight one. Note, that these weights will be multiplied with ``sample_weight`` (passed through the ``fit`` method) if ``sample_weight`` is specified. min_split_gain : float, optional (default=0.) Minimum loss reduction required to make a further partition on a leaf node of the tree. min_child_weight : float, optional (default=1e-3) Minimum sum of instance weight (Hessian) needed in a child (leaf). min_child_samples : int, optional (default=20) Minimum number of data needed in a child (leaf). subsample : float, optional (default=1.) Subsample ratio of the training instance. subsample_freq : int, optional (default=0) Frequency of subsample, <=0 means no enable. colsample_bytree : float, optional (default=1.) Subsample ratio of columns when constructing each tree. reg_alpha : float, optional (default=0.) L1 regularization term on weights. reg_lambda : float, optional (default=0.) L2 regularization term on weights. random_state : int, RandomState object or None, optional (default=None) Random number seed. If int, this number is used to seed the C++ code. If RandomState or Generator object (numpy), a random integer is picked based on its state to seed the C++ code. If None, default seeds in C++ code are used. n_jobs : int or None, optional (default=None) Number of parallel threads to use for training (can be changed at prediction time by passing it as an extra keyword argument). For better performance, it is recommended to set this to the number of physical cores in the CPU. Negative integers are interpreted as following joblib's formula (n_cpus + 1 + n_jobs), just like scikit-learn (so e.g. -1 means using all threads). A value of zero corresponds the default number of threads configured for OpenMP in the system. A value of ``None`` (the default) corresponds to using the number of physical cores in the system (its correct detection requires either the ``joblib`` or the ``psutil`` util libraries to be installed). .. versionchanged:: 4.0.0 importance_type : str, optional (default='split') The type of feature importance to be filled into ``feature_importances_``. If 'split', result contains numbers of times the feature is used in a model. If 'gain', result contains total gains of splits which use the feature. **kwargs Other parameters for the model. Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters. .. warning:: \*\*kwargs is not supported in sklearn, it may cause unexpected issues. Note ---- A custom objective function can be provided for the ``objective`` parameter. In this case, it should have the signature ``objective(y_true, y_pred) -> grad, hess``, ``objective(y_true, y_pred, weight) -> grad, hess`` or ``objective(y_true, y_pred, weight, group) -> grad, hess``: y_true : numpy 1-D array of shape = [n_samples] The target values. y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The predicted values. Predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task. weight : numpy 1-D array of shape = [n_samples] The weight of samples. Weights should be non-negative. group : numpy 1-D array Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The value of the first order derivative (gradient) of the loss with respect to the elements of y_pred for each sample point. hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) The value of the second order derivative (Hessian) of the loss with respect to the elements of y_pred for each sample point. For multi-class task, y_pred is a numpy 2-D array of shape = [n_samples, n_classes], and grad and hess should be returned in the same format. zyscikit-learn is required for lightgbm.sklearn. You must install scikit-learn and restart your session to use this module.Nrurc)"rrrrrrrrrrrrrrrrrrrr_Booster _evals_result _best_score_best_iteration _other_params _objectiver _class_weight _class_map _n_features_n_features_in_classes _n_classes set_params)rNrrrrrrrrrrrrrrrrrrrrs r=rOzLGBMModel.__init__s1\! ]  +"$"*(!2, 0!2", 0"$( .+/ .079$&-/#(9=48 "#%.2 !!!&!!!!!r?r5c d}dgdd|||ddS)NaIn LightGBM, setting a sample's weight to 0 can produce a different result than omitting the sample. Such samples intentionally still affect count-based measures like 'min_data_in_leaf' (https://github.com/microsoft/LightGBM/issues/5626#issuecomment-1712706678) and the estimated distribution of features for Dataset construction (see https://github.com/microsoft/LightGBM/issues/5553).T)2darraysparse1dlabelszscikit-learn incorrectly asserts that private attributes cannot be set in __init__: (see https://github.com/microsoft/LightGBM/issues/2628))check_no_attributes_set_in_initcheck_sample_weight_equivalence-check_sample_weight_equivalence_on_dense_data.check_sample_weight_equivalence_on_sparse_data) allow_nanX_types _xfail_checksrc)rNcheck_sample_weight_strs r= _more_tagszLGBMModel._more_tagssF l 8884J4KAXBY    r?tagsr/ tags_dictcz|d|j_d|dv|j_d|dv|j_|S)aSUpdate ``sklearn.utils.Tags`` inherited from ``scikit-learn`` base classes. ``scikit-learn`` 1.6 introduced a dataclass-based interface for estimator tags. ref: https://github.com/scikit-learn/scikit-learn/pull/29677 This method handles updating that instance based on the value in ``self._more_tags()``. rrrr) input_tagsrr target_tags one_d_labelsrrs r=_update_sklearn_tags_from_dictz(LGBMModel._update_sklearn_tags_from_dictsA%.k$:!!)Yy-A!A(2i 6J(J% r?cttdsdt}t||t |S)N__sklearn_tags__zW__sklearn_tags__() should not be called when using scikit-learn<1.6. Detected version: r)hasattrr(r+AttributeErrorrsuperrr)rNerr_msg __class__s r=rzLGBMModel.__sklearn_tags__s~~'9:: *8%588 !)) )22))++oo''3   r?c$t|ddS)Nfitted_F)getattrrNs r=__sklearn_is_fitted__zLGBMModel.__sklearn_is_fitted__stY...r?Tdeepct|}tj|}tt |jjdddr|jjd|_n|jjd|_||j||||j|S)agGet parameters for this estimator. Parameters ---------- deep : bool, optional (default=True) If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : dict Parameter names mapped to their values. )rr get_paramsNr ) rrcopycallablerr __bases__updater)rNrparamscprs r=rzLGBMModel.get_paramssB###.. Yt__ GBL215|TJJ K K 5<1!4BLL<1!4BL bl--b$77888 d())) r?rc |D]G\}}t|||t|d|rt|d||||j|<H|S)zSet the parameters of this estimator. Parameters ---------- **params Parameter names with their new values. Returns ------- self : object Returns self. _)itemssetattrrr)rNrkeyvalues r=rzLGBMModel.set_params sv!,,.. , ,JC D#u % % %tYYY'' 0i#ii///&+D s # # r?stagec|dvsJ|}|ddtjdD];}||vr5||}t d|d|dkr||_<|dkr|jyt |trd|_n\t |tr|j dkrd |_n4d |_n,t |trd |_ntd t|jr$|dkrt|j|d<nd |d<n |j|d<|dd|dd|ddt |dtjjr@|dtjtjj|d<nqt |dtjjrLt-|dtjtjj|d<|j dkr7tjdD]}||d|j |d<t1|drO|j}tjdD].}||vr(t d|d||}/||d<t |jt4r|jnd}|Tt |trd}nfitpredictrNzFound 'z8' in params. Will use it instead of 'objective' argumentr regressionrR multiclassbinary lambdarankzUnknown LGBMModel type.Nonerrrr num_class_eval_ateval_atz6' in params. Will use it instead of 'eval_at' argumentl2 multi_loglossbinary_loglossndcgmetric num_threads)rpoprrorrr8r3r0rr2 ValueErrorrrIr9random RandomStaterandintiinfoint32max Generatorintintegersrrstrrr_process_n_jobs)rNrraliasobjroriginal_metrics r=_process_paramszLGBMModel._process_params!s*****"" ;%%%#' 44 * *Ejj''fufffgggE>>&)DO E>>&dM22 @&2DOOn55@***6*2j11@&2DOO$%>??? DO $ $ 2~~&?&P&P{##&,{##"&/F;  $d+++ >4((( >4((( f^,bi.C D D b%+N%;%C%CBHRXDVDVDZ%[%[F> " " ~. 0C D D b%()?)H)HRTRZI[I[I_)`)`%a%aF> " ?Q  '+K88 ( ( 5$''''"&/F;  4 $ $ (mG'+I66 0 0F?? !h5!h!h!hiii$jj//G 'F9 .8-M-MW$//SW  "$ .. )"&D.11 )59_q5H5H//N^D*-- )"(%XvGG E>>( LLF$($8$8 9N$O$OF= ! r?c|td}n*|dkr$ttddz|zd}|S)aConvert special values of n_jobs to their actual values according to the formulas that apply. Parameters ---------- n_jobs : int or None The original value of n_jobs, potentially having special values such as 'None' or negative integers. Returns ------- n_jobs : int The value of n_jobs with special values converted to actual number of threads. 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T * * <#}[99   $ k : :; !c bBR b b bRR+;RRR2>#0         [[]]    , ,E   ud + + + +f%%%-]NDKXX(,(<(<^M=Z([([}%$t}$   +'%/      r?z+Return the predicted value for each sample.predicted_resultCarray-like of shape = [n_samples] or shape = [n_samples, n_classes]Varray-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]array-like of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or list with n_classes length of such objects descriptionr@ output_namepredicted_result_shapeX_leaves_shapeX_SHAP_values_shapecV|std|jS)3:obj:`int`: The number of features of fitted model.z1No n_features found. Need to call fit beforehand.)rr rrs r= n_features_zLGBMModel.n_features_1))++ Z$%XYY Yr?cV|std|jS)r]z4No n_features_in found. Need to call fit beforehand.)rr rrs r=r'zLGBMModel.n_features_in_s1))++ ]$%[\\ \""r?rc||_dS)aSet number of features found in passed-in dataset. Starting with ``scikit-learn`` 1.6, ``scikit-learn`` expects to be able to directly set this property in functions like ``validate_data()``. .. note:: Do not call ``estimator.n_features_in_ = some_int`` or anything else that invokes this method. It is only here for compatibility with ``scikit-learn`` validation functions used internally in ``lightgbm``. N)r)rNrs r=r'zLGBMModel.n_features_in_s$r?cV|std|jS)z,:obj:`dict`: The best score of fitted model.z1No best_score found. Need to call fit beforehand.)rr rrs r= best_score_zLGBMModel.best_score_r_r?cV|std|jS)zc:obj:`int`: The best iteration of fitted model if ``early_stopping()`` callback has been specified.zRNo best_iteration found. Need to call fit with early_stopping callback beforehand.)rr rrs r=best_iteration_zLGBMModel.best_iteration_s8))++ $d ##r?cV|std|jS)zT:obj:`str` or :obj:`callable`: The concrete objective used while fitting this model.z0No objective found. Need to call fit beforehand.)rr rrs r= objective_zLGBMModel.objective_s0))++ Y$%WXX Xr?cz|std|jS):obj:`int`: True number of boosting iterations performed. This might be less than parameter ``n_estimators`` if early stopping was enabled or if boosting stopped early due to limits on complexity like ``min_gain_to_split``. .. versionadded:: 4.0.0 z3No n_estimators found. Need to call fit beforehand.rr rcurrent_iterationrs r= n_estimators_zLGBMModel.n_estimators_s<))++ \$%Z[[ [}..000r?cz|std|jS)riz-No n_iter found. Need to call fit beforehand.rjrs r=n_iter_zLGBMModel.n_iter_s<))++ V$%TUU U}..000r?cV|std|jS)z.Booster: The underlying Booster of this model.z.No booster found. Need to call fit beforehand.)rr rrs r=r,zLGBMModel.booster_0))++ W$%UVV V}r?cV|std|jS)zK:obj:`dict`: The evaluation results if validation sets have been specified.z> $"!'OS37 #"&!#"%Y] $&+s"s"s"s" s"  s"  s"s"s"E#'J"JKLs"uT3Y/0s"s" s"s"s"s"  !s""#s"$%s"&uS")*?AT%TUV's"( )s"*+s",-s"s"s"s"r DcN    .S>  \" (?";      "/t////,,t,tCH~,,,,,,\3;(TSTT#s(^TTTTlhsm05948+/9=*.?C37?C6:<@7=EK.2GK#ii !i i 01 i 01 i ( i4 456iT#Y'i%T*:%;<i$DK0i"$':";<iT/23i89i5iCi DN+!i"U3g{#BCD#i$ %iiiiX !!ho!KMl&B"{q "    * *K$  '+""'< < !< <  <  } <  < <  < < < < < |-33Ad&dos 4GO S   X ####X#  $C $D $ $ $ $ 7   X $$$$X$E#'J"JKX  1s 1 1 1X 1 1 1 1 1X 1'X """"X"  Vbj V V VX V ,tCy , , ,X ,,2:,,,X,        r?r1c+:eZdZdZddddddddd d d d d d d d dddddededededededeeee fdeee efdedededededededed eeee j j e j jfd!eed"ed#ed$df*fd%Zejje_d$e eeffd&Zd@fd( Z dAd*ed+ed,eed-eed.eeed/eeed0eeed1eeed2eed3ed4ed5eeed6eeeeeefd$dffd7 Z ej j!d8d9Z"e"de"#d:e"e"#d;dzZ"e"de"#d<e"e"#d=dzZ"e"de"#d>e"e"#d?dze _xZ$S)Br3zLightGBM regressor.rsrtrurvrwrxNryrzr{r|rr}r~rrrrrrrrrrrrrrrrrrrrr5c tjdid|d|d|d|d|d|d|d|d | d | d | d | d | d|d|d|d|d|d||dSNrrrrrrrrrrrrrrrrrrrrcrrOrNrrrrrrrrrrrrrrrrrrrrrs r=rOzLGBMRegressor.__init__'0    '- !z  i (-  &  0/   i & *> .- 0/  i *> .-  i "z! "&# $6% &,O)     r?ctttddrtj|}ni}|t ||SNr)rrr)rrr1rNrs r=rzLGBMRegressor._more_tagsXs[ G. dCC D D %066DDD I((../// r?r/cDtSr)rr)rNrs r=rzLGBMRegressor.__sklearn_tags__bsww'')))r?rrrrrrrrrrrrrrcdt||||||||| | | | |  |S)*Docstring is inherited from the LGBMModel.) rrrrrrrrrrr)rr)rNrrrrrrrrrrrrrrs r=rzLGBMRegressor.fitesR"  '!!1+#% 3!     r?self : LGBMModelzself : LGBMRegressorgroup : eval_set :eval_class_weight :eval_init_score : eval_group : eval_metric :r5r/) NNNNNNNrrNN)%r^r_r`rarrr}r r rbrr9rrrrrOr1rrrrrrr rrrrrrrrreplace _base_docfindrrs@r=r3r3"s$"!'OS37 #"&!#"%Y] $&+- - - -  -  -  - - - E#'J"JKL- uT3Y/0- -  - - - -  !- "#- $%- &uS")*?AT%TUV'- ( )- *+- ,-- . /- - - - - - ^!)1HDcN******59489=*.?C?C<@7=EK.2EI   !    01  01  4 456 T#Y' %T*:%;< "$':";< 89 5 C DN+ U3gy#@AB        D %--.@BXYYI-INN9---. INN<0022 3 4AINN+@AAABYy~~^qOrOrOtOtEuuIdze _ dTd*ed@e$dAedBeedCe$dDe$dEe$d#efdFZ%ej%je%_ dTd*ed@e$dAedBeedCe$dDe$dEe$d#effdG Z&e'(dHdIdJdKdLdMNe&_e)d$e j*fdOZ+e)d$efdPZ,e)d$e$fdQZ-xZ.S)Ur0zLightGBM classifier.rsrtrurvrwrxNryrzr{r|rr}r~rrrrrrrrrrrrrrrrrrrrr5c tjdid|d|d|d|d|d|d|d|d | d | d | d | d | d|d|d|d|d|d||dSrrrs r=rOzLGBMClassifier.__init__rr?ctttddrtj|}ni}|t ||Sr)rrr$rrr1rs r=rzLGBMClassifier._more_tagss[ G/tDD E E &1$77DDD I((../// r?r/cxt}d|j_d|j_|S)NTF)rrclassifier_tags multi_class multi_label)rNrrs r=rzLGBMClassifier.__sklearn_tags__s3ww''))+/(+0( r?rrrrrrrrrrrrrrrct|t|t|_j|}t tjjjjj_ tj t r*fdj D_ jj_tj_jd_t'| st| t(r| }nt| t*r| g}ng}jr)t/|D]\}}|dvrd||<|dvrd||<n(t/|D]\}}|dvrd||<|d vrd ||<|} d}|qt|t0r|g}g}|D]T\}}||ur||ur|||f%||j|fUt5||||||||| | | | | | S) rc2i|]\}}j||Srcrrs r=rz&LGBMClassifier.fit..s&!^!^!^DAq$/!"4a!^!^!^r?N>loglossrr>error binary_error multi_error>rrr>rrr) rrrrrrrrrrrr)r!r"r'r_le transformrnzipclasses_rr8rrrrrVrrrrrmr_LGBMClassifier__is_multiclassr*r)r+r)rNrrrrrrrrrrrrrrr5r1indexrr!valid_xvalid_yrs` r=rzLGBMClassifier.fits$ Q'***$&&**1-- X   " "s48#4dh6H6HIZ6[6[\\]] d' . . _!^!^!^!^DDUD[D[D]D]!^!^!^D ) dm,, > !"DO $$ ++t,, &#.  K-- &$/=  #% # A%./?%@%@@@ME6!>>>2A(//#<<<2?(/ @ &//?%@%@AAME6!===2B(//#;;;2@(/*K<@  (E** &$:J$, N N a<>@@ @r?z@Return the predicted probability for each class for each sample.r?predicted_probabilityrSrTrUrVcV|std|jS)z;:obj:`array` of shape = [n_classes]: The class label array..No classes found. Need to call fit beforehand.)rr rrs r=rzLGBMClassifier.classes_zrpr?cV|std|jS)z":obj:`int`: The number of classes.r)rr rrs r= n_classes_zLGBMClassifier.n_classes_s0))++ W$%UVV Vr?ch|jdkp't|jto |jtvS)zI:obj:`bool`: Indicator of whether the classifier is used for multiclass.rR)rr8rrrrs r=__is_multiclasszLGBMClassifier.__is_multiclasss/"vz$/3'G'G'uDO_uLuvr?r) NNNNNNNNrrNNr|)/r^r_r`rarrr}r r rbrr9rrrrrOr1rrrrrrr rrrrrrrrrrrrrrrrrr:rrrrrs@r=r0r0s$"!'OS37 #"&!#"%Y] $&+- - - -  -  -  - - - E#'J"JKL- uT3Y/0- -  - - - -  !- "#- $%- &uS")*?AT%TUV'- ( )- *+- ,-- . /- - - - - - ^!)1HDcN59489=*.?C37?C<@7=EK.2EIRR !R R 01 R 01 R 4 456 RT#Y'R%T*:%;<R$DK0R"$':";<R89R5RCRDN+RU3gy#@ABR !RRRRRRh %--.@BYZZI-INN9---. INN<0022 3 4\Z%Z&Z'e%d?e&e'e _xZ(S)Ar2a!LightGBM ranker. .. warning:: scikit-learn doesn't support ranking applications yet, therefore this class is not really compatible with the sklearn ecosystem. Please use this class mainly for training and applying ranking models in common sklearnish way. rsrtrurvrwrxNryrzr{r|rr}r~rrrrrrrrrrrrrrrrrrrrr5c tjdid|d|d|d|d|d|d|d|d | d | d | d | d | d|d|d|d|d|d||dSrrrs r=rOzLGBMRanker.__init__rr?r rRrSrTrrrrrr;rrrrrrr.rrrrc, |td|lj tdt t|krtdt tr5t fdt t Ds.t t r(t d Drtd| |_t ||||||||| | | ||||S) rNz!Should set group for ranking taskz3Eval_group cannot be None when eval_set is not Nonez0Length of eval_group should be equal to eval_setc34K|]}|vp |duVdSrrc)r rkrs r= z!LGBMRanker.fit..s6ee+Dz!}/Deeeeeer?c3K|]}|duV dSrrc)r r;s r=rz!LGBMRanker.fit..s&>>% >>>>>>r?zgShould set group for all eval datasets for ranking task; if you use dict, the index should start from 0) rrr;rrrrrrrrrr) rrVr8rnanyrangermrrr)rNrrrrr;rrrrrrrrrrrrs ` r=rzLGBMRanker.fitsO* =@AA A  ! !VWWWZCMM11 !STTT:t,, eeeeeTWXbTcTcNdNdeeeee j$// >>:>>>>>  !E     '!!1+!#% 3!    " r?rzself : LGBMRankerrrzfeature_name :zeval_at : list or tuple of int, optional (default=(1, 2, 3, 4, 5)) The evaluation positions of the specified metric. )NNNNNNNNNrrrNN))r^r_r`rarrr}r r rbrr9rrrrrOr1rrrrrr rrr rrrrrrrrr partition_before_feature_name _feature_name_after_feature_namerrs@r=r2r2s$"!'OS37 #"&!#"%Y] $&+- - - -  -  -  - - - E#'J"JKL- uT3Y/0- -  - - - -  !- "#- $%- &uS")*?AT%TUV'- ( )- *+- ,-- . /- - - - - - ^!)1H 5948+/9=*.?C?C6:<@5D7=EK.2EI#:: !: : 01 : 01 : ( :4 456:T#Y':%T*:%;<:"$':";<:T/23:89:tCy%S/12:5:C: DN+!:"U3gy#@AB#:$ %::::::x %--.@BUVVI9INN#8999: INN#67799 : ;K I?H?R?RSc?d?d<-)<+,,,',,CKKKKKr?r2)Rrarinspectrpathlibrtypingrrrrr r r r numpyr9 scipy.sparsescipybasicrrrrrrrrrrrrrrrcallbackrrcompatrr r!r"r#r$r%r&r'r(r)r*r+r,r-enginer.r/__all__r}rr:rspmatrixrrbrhrrrr>rCrGrIrerrrrqr1r3r0r2rcr?r=rs22 SSSSSSSSSSSSSSSSSSSS"98888888"&%%%%%%   tE{DI% &'J L ', "* rz* bj"*$% '  "* rz8BJ+?@ bj"*$% '  "* rz8BJ+?"*AUV bj"*$% ' '#""' "* rz*$ & "* rz* )* ,  "* rz8BJ+?@$ & "* rz8BJ+?@ )* ,  "* rz8BJ+?"*AUV$ & "* rz8BJ+?"*AUV )* ,-":#"s22 346 3_DEXbj=QRZ'hrz>RThThThThThThThThnNcNcNcNcNcNcNcNch<|#>,^:tCH~tCy89:: : : c] ::::0y  y  y  y  y  y  y  y  xkmkmkmkmkm& kmkmkm\{w{w{w{w{w(){w{w{w|B,B,B,B,B,B,B,B,B,B,r?