=[gf\zdZddlZddlmZmZmZmZmZddlZ ddl m Z ddl m Z mZddlmZmZddlmZmZmZdd lmZmZmZdd lmZd eed eed dfdZddZGddeZGddeZeeeGddeeeZ GddeZ!ee e!GddeZ"GddeZ#ee"e#y)z8Xgboost pyspark integration submodule for estimator API.N)AnyListOptionalTypeUnion) keyword_only)ParamParams)HasProbabilityColHasRawPredictionCol) XGBClassifier XGBRanker XGBRegressor)_ClassificationModel_SparkXGBEstimator_SparkXGBModel)get_class_name estimatormodelreturnc j}dtdtf fd dtdtddf fd }|j D]J}dt j d |}ttj|| }|||Lj}|j D]T}dt j d |}|d k(r|d z }ttj|| }|||Vj}|j D]J}dt j d|}ttj|| }|||Ly)zyThis function automatically infer to xgboost parameters and set them into corresponding pyspark estimators and modelsvrcXt|tjr#tj|j St|t r*|j Dcic]\}}||c}}St|tr|Dcgc] }| c}S|Scc}}wcc}wN) isinstancenpgenericarrayitemdictitemslist)rknvparam_value_converters R/var/www/html/bid-api/venv/lib/python3.12/site-packages/xgboost/spark/estimator.pyr&z?_set_pyspark_xgb_cls_param_attrs..param_value_converter s a $88A;##% % a >?ggiHiUQA,R00iH H a 89:")"-: :I:s "B! B' attr_nameparamNcH|_t||t||yr) typeConvertersetattr)r(r)rrr&s r'set_param_attrsz9_set_pyspark_xgb_cls_param_attrs..set_param_attrs*s#3 9e,y%(zRefer to XGBoost doc of z for this param )namedocz.fit() for this param callbackszThe callbacks can be arbitrary functions. It is saved using cloudpickle which is not a fully self-contained format. It may fail to load with different versions of dependencies.z.predict() for this param ) _get_xgb_params_defaultrstrr keysr_xgb_clsr _dummy_get_fit_params_default_get_predict_params_default) rr params_dictr-r/r0 param_objfit_params_dictpredict_params_dictr&s `` @r' _set_pyspark_xgb_cls_param_attrsr=s 335K)3)u))   "&i002344DTF L !tE i(# 779O$$&&~i6H6H6J'K&L$TF ,  ;  6 C &--/#> i('$??A#((*&~i6H6H6J'K&L( 0 &--/#> i( +r.c8tjdty)Nz9`use_gpu` is deprecated since 2.0.0, use `device` instead)warningswarn FutureWarningr.r'_deprecated_use_gpurCQs MMC]r.c eZdZdZedddddddddddddd d eeeefd ed ed eed eedeedeede dee deede de de de ddffdZ e deefdZe dedfdZdfd ZxZS)SparkXGBRegressoraSparkXGBRegressor is a PySpark ML estimator. It implements the XGBoost regression algorithm based on XGBoost python library, and it can be used in PySpark Pipeline and PySpark ML meta algorithms like - :py:class:`~pyspark.ml.tuning.CrossValidator`/ - :py:class:`~pyspark.ml.tuning.TrainValidationSplit`/ - :py:class:`~pyspark.ml.classification.OneVsRest` SparkXGBRegressor automatically supports most of the parameters in :py:class:`xgboost.XGBRegressor` constructor and most of the parameters used in :py:meth:`xgboost.XGBRegressor.fit` and :py:meth:`xgboost.XGBRegressor.predict` method. To enable GPU support, set `device` to `cuda` or `gpu`. SparkXGBRegressor doesn't support setting `base_margin` explicitly as well, but support another param called `base_margin_col`. see doc below for more details. SparkXGBRegressor doesn't support `validate_features` and `output_margin` param. SparkXGBRegressor doesn't support setting `nthread` xgboost param, instead, the `nthread` param for each xgboost worker will be set equal to `spark.task.cpus` config value. Parameters ---------- features_col: When the value is string, it requires the features column name to be vector type. When the value is a list of string, it requires all the feature columns to be numeric types. label_col: Label column name. Default to "label". prediction_col: Prediction column name. Default to "prediction" pred_contrib_col: Contribution prediction column name. validation_indicator_col: For params related to `xgboost.XGBRegressor` training with evaluation dataset's supervision, set :py:attr:`xgboost.spark.SparkXGBRegressor.validation_indicator_col` parameter instead of setting the `eval_set` parameter in `xgboost.XGBRegressor` fit method. weight_col: To specify the weight of the training and validation dataset, set :py:attr:`xgboost.spark.SparkXGBRegressor.weight_col` parameter instead of setting `sample_weight` and `sample_weight_eval_set` parameter in `xgboost.XGBRegressor` fit method. base_margin_col: To specify the base margins of the training and validation dataset, set :py:attr:`xgboost.spark.SparkXGBRegressor.base_margin_col` parameter instead of setting `base_margin` and `base_margin_eval_set` in the `xgboost.XGBRegressor` fit method. num_workers: How many XGBoost workers to be used to train. Each XGBoost worker corresponds to one spark task. use_gpu: .. deprecated:: 2.0.0 Use `device` instead. device: .. versionadded:: 2.0.0 Device for XGBoost workers, available options are `cpu`, `cuda`, and `gpu`. force_repartition: Boolean value to specify if forcing the input dataset to be repartitioned before XGBoost training. repartition_random_shuffle: Boolean value to specify if randomly shuffling the dataset when repartitioning is required. enable_sparse_data_optim: Boolean value to specify if enabling sparse data optimization, if True, Xgboost DMatrix object will be constructed from sparse matrix instead of dense matrix. kwargs: A dictionary of xgboost parameters, please refer to https://xgboost.readthedocs.io/en/stable/parameter.html Note ---- The Parameters chart above contains parameters that need special handling. For a full list of parameters, see entries with `Param(parent=...` below. This API is experimental. Examples -------- >>> from xgboost.spark import SparkXGBRegressor >>> from pyspark.ml.linalg import Vectors >>> df_train = spark.createDataFrame([ ... (Vectors.dense(1.0, 2.0, 3.0), 0, False, 1.0), ... (Vectors.sparse(3, {1: 1.0, 2: 5.5}), 1, False, 2.0), ... (Vectors.dense(4.0, 5.0, 6.0), 2, True, 1.0), ... (Vectors.sparse(3, {1: 6.0, 2: 7.5}), 3, True, 2.0), ... ], ["features", "label", "isVal", "weight"]) >>> df_test = spark.createDataFrame([ ... (Vectors.dense(1.0, 2.0, 3.0), ), ... (Vectors.sparse(3, {1: 1.0, 2: 5.5}), ) ... ], ["features"]) >>> xgb_regressor = SparkXGBRegressor(max_depth=5, missing=0.0, ... validation_indicator_col='isVal', weight_col='weight', ... early_stopping_rounds=1, eval_metric='rmse') >>> xgb_reg_model = xgb_regressor.fit(df_train) >>> xgb_reg_model.transform(df_test) featureslabel predictionNrF) features_col label_colprediction_colpred_contrib_colvalidation_indicator_col weight_colbase_margin_col num_workersuse_gpudeviceforce_repartitionrepartition_random_shuffleenable_sparse_data_optimrIrJrKrLrMrNrOrPrQrRrSrTrUkwargsrc  vt||j}| r t|jdi|yNrBsuper__init__ _input_kwargsrC setParams)selfrIrJrKrLrMrNrOrPrQrRrSrTrUrV input_kwargs __class__s r'r[zSparkXGBRegressor.__init__s5& ))   !&&r.ctSrrclss r'r5zSparkXGBRegressor._xgb_clsr.SparkXGBRegressorModelctSr)rfrcs r'_pyspark_model_clsz$SparkXGBRegressor._pyspark_model_clss%%r.cnt||j|jr t dy)NzCSpark Xgboost regressor estimator does not support `qid_col` param.rZ_validate_params isDefinedqid_col ValueErrorr^r`s r'rkz"SparkXGBRegressor._validate_paramss3  " >>$,, 'U  (r.rN)__name__ __module__ __qualname____doc__rrr3rrintboolrr[ classmethodrrr5rhrk __classcell__r`s@r'rErEWsMob/9 **.26$()-"& $"'+0).'CcN+' '  ' #3- '#+3-'SM'"#''$' ' '%)'#'' !'" #''0l+&4(@#A&&r.rEc,eZdZdZedeefdZy)rfzt The model returned by :func:`xgboost.spark.SparkXGBRegressor.fit` .. Note:: This API is experimental. rctSrrbrcs r'r5zSparkXGBRegressorModel._xgb_clsrer.N)rqrrrsrtrwrrr5rBr.r'rfrfs' l+r.rfc$ eZdZdZeddddddddddddd d d d d eeeefd ed edededeedeedeedeede dee deede de de de ddf"fdZ e deefdZe dedfdZd!fd ZxZS)"SparkXGBClassifieraSparkXGBClassifier is a PySpark ML estimator. It implements the XGBoost classification algorithm based on XGBoost python library, and it can be used in PySpark Pipeline and PySpark ML meta algorithms like - :py:class:`~pyspark.ml.tuning.CrossValidator`/ - :py:class:`~pyspark.ml.tuning.TrainValidationSplit`/ - :py:class:`~pyspark.ml.classification.OneVsRest` SparkXGBClassifier automatically supports most of the parameters in :py:class:`xgboost.XGBClassifier` constructor and most of the parameters used in :py:meth:`xgboost.XGBClassifier.fit` and :py:meth:`xgboost.XGBClassifier.predict` method. To enable GPU support, set `device` to `cuda` or `gpu`. SparkXGBClassifier doesn't support setting `base_margin` explicitly as well, but support another param called `base_margin_col`. see doc below for more details. SparkXGBClassifier doesn't support setting `output_margin`, but we can get output margin from the raw prediction column. See `raw_prediction_col` param doc below for more details. SparkXGBClassifier doesn't support `validate_features` and `output_margin` param. SparkXGBClassifier doesn't support setting `nthread` xgboost param, instead, the `nthread` param for each xgboost worker will be set equal to `spark.task.cpus` config value. Parameters ---------- features_col: When the value is string, it requires the features column name to be vector type. When the value is a list of string, it requires all the feature columns to be numeric types. label_col: Label column name. Default to "label". prediction_col: Prediction column name. Default to "prediction" probability_col: Column name for predicted class conditional probabilities. Default to probabilityCol raw_prediction_col: The `output_margin=True` is implicitly supported by the `rawPredictionCol` output column, which is always returned with the predicted margin values. pred_contrib_col: Contribution prediction column name. validation_indicator_col: For params related to `xgboost.XGBClassifier` training with evaluation dataset's supervision, set :py:attr:`xgboost.spark.SparkXGBClassifier.validation_indicator_col` parameter instead of setting the `eval_set` parameter in `xgboost.XGBClassifier` fit method. weight_col: To specify the weight of the training and validation dataset, set :py:attr:`xgboost.spark.SparkXGBClassifier.weight_col` parameter instead of setting `sample_weight` and `sample_weight_eval_set` parameter in `xgboost.XGBClassifier` fit method. base_margin_col: To specify the base margins of the training and validation dataset, set :py:attr:`xgboost.spark.SparkXGBClassifier.base_margin_col` parameter instead of setting `base_margin` and `base_margin_eval_set` in the `xgboost.XGBClassifier` fit method. num_workers: How many XGBoost workers to be used to train. Each XGBoost worker corresponds to one spark task. use_gpu: .. deprecated:: 2.0.0 Use `device` instead. device: .. versionadded:: 2.0.0 Device for XGBoost workers, available options are `cpu`, `cuda`, and `gpu`. force_repartition: Boolean value to specify if forcing the input dataset to be repartitioned before XGBoost training. repartition_random_shuffle: Boolean value to specify if randomly shuffling the dataset when repartitioning is required. enable_sparse_data_optim: Boolean value to specify if enabling sparse data optimization, if True, Xgboost DMatrix object will be constructed from sparse matrix instead of dense matrix. kwargs: A dictionary of xgboost parameters, please refer to https://xgboost.readthedocs.io/en/stable/parameter.html Note ---- The Parameters chart above contains parameters that need special handling. For a full list of parameters, see entries with `Param(parent=...` below. This API is experimental. Examples -------- >>> from xgboost.spark import SparkXGBClassifier >>> from pyspark.ml.linalg import Vectors >>> df_train = spark.createDataFrame([ ... (Vectors.dense(1.0, 2.0, 3.0), 0, False, 1.0), ... (Vectors.sparse(3, {1: 1.0, 2: 5.5}), 1, False, 2.0), ... (Vectors.dense(4.0, 5.0, 6.0), 0, True, 1.0), ... (Vectors.sparse(3, {1: 6.0, 2: 7.5}), 1, True, 2.0), ... ], ["features", "label", "isVal", "weight"]) >>> df_test = spark.createDataFrame([ ... (Vectors.dense(1.0, 2.0, 3.0), ), ... ], ["features"]) >>> xgb_classifier = SparkXGBClassifier(max_depth=5, missing=0.0, ... validation_indicator_col='isVal', weight_col='weight', ... early_stopping_rounds=1, eval_metric='logloss') >>> xgb_clf_model = xgb_classifier.fit(df_train) >>> xgb_clf_model.transform(df_test).show() rFrGrH probability rawPredictionNrF)rIrJrKprobability_colraw_prediction_colrLrMrNrOrPrQrRrSrTrUrIrJrKrrrLrMrNrOrPrQrRrSrTrUrVrc t||j}| r t|jdi||j dy)N) objectiverB)rZr[r\rCr] _setDefault)r^rIrJrKrrrLrMrNrOrPrQrRrSrTrUrVr_r`s r'r[zSparkXGBClassifier.__init__{sH*  ))   !&& 4(r.ctSrr rcs r'r5zSparkXGBClassifier._xgb_clsr.SparkXGBClassifierModelctSr)rrcs r'rhz%SparkXGBClassifier._pyspark_model_clss&&r.ct||j|jr t d|j dr t dy)NzDSpark Xgboost classifier estimator does not support `qid_col` param.rzHSetting custom 'objective' param is not allowed in 'SparkXGBClassifier'.)rZrkrlrmrn getOrDefaultros r'rkz#SparkXGBClassifier._validate_paramssS  " >>$,, 'V    [ )Z  *r.rp)rqrrrsrtrrr3rrrurvrr[rwrr r5rhrkrxrys@r'r}r}sgwr/9 *,"1*.26$()-"& $"'+0).#)CcN+) )  )  ) )#3-)#+3-)SM)"#))$) ) ) %)!)"#'#)$%)& '))>m,'4(A#B''  r.r}c,eZdZdZedeefdZy)rzu The model returned by :func:`xgboost.spark.SparkXGBClassifier.fit` .. Note:: This API is experimental. rctSrrrcs r'r5z SparkXGBClassifierModel._xgb_clsrr.N)rqrrrsrtrwrr r5rBr.r'rrs' m,r.rc" eZdZdZedddddddddddddddd eeeefd ed ed eed eedeedeedeede dee deede de de de ddf fdZ e deefdZe dedfdZdfd ZxZS)SparkXGBRankeraSparkXGBRanker is a PySpark ML estimator. It implements the XGBoost ranking algorithm based on XGBoost python library, and it can be used in PySpark Pipeline and PySpark ML meta algorithms like :py:class:`~pyspark.ml.tuning.CrossValidator`/ :py:class:`~pyspark.ml.tuning.TrainValidationSplit`/ :py:class:`~pyspark.ml.classification.OneVsRest` SparkXGBRanker automatically supports most of the parameters in :py:class:`xgboost.XGBRanker` constructor and most of the parameters used in :py:meth:`xgboost.XGBRanker.fit` and :py:meth:`xgboost.XGBRanker.predict` method. To enable GPU support, set `device` to `cuda` or `gpu`. SparkXGBRanker doesn't support setting `base_margin` explicitly as well, but support another param called `base_margin_col`. see doc below for more details. SparkXGBRanker doesn't support setting `output_margin`, but we can get output margin from the raw prediction column. See `raw_prediction_col` param doc below for more details. SparkXGBRanker doesn't support `validate_features` and `output_margin` param. SparkXGBRanker doesn't support setting `nthread` xgboost param, instead, the `nthread` param for each xgboost worker will be set equal to `spark.task.cpus` config value. Parameters ---------- features_col: When the value is string, it requires the features column name to be vector type. When the value is a list of string, it requires all the feature columns to be numeric types. label_col: Label column name. Default to "label". prediction_col: Prediction column name. Default to "prediction" pred_contrib_col: Contribution prediction column name. validation_indicator_col: For params related to `xgboost.XGBRanker` training with evaluation dataset's supervision, set :py:attr:`xgboost.spark.SparkXGBRanker.validation_indicator_col` parameter instead of setting the `eval_set` parameter in :py:class:`xgboost.XGBRanker` fit method. weight_col: To specify the weight of the training and validation dataset, set :py:attr:`xgboost.spark.SparkXGBRanker.weight_col` parameter instead of setting `sample_weight` and `sample_weight_eval_set` parameter in :py:class:`xgboost.XGBRanker` fit method. base_margin_col: To specify the base margins of the training and validation dataset, set :py:attr:`xgboost.spark.SparkXGBRanker.base_margin_col` parameter instead of setting `base_margin` and `base_margin_eval_set` in the :py:class:`xgboost.XGBRanker` fit method. qid_col: Query id column name. num_workers: How many XGBoost workers to be used to train. Each XGBoost worker corresponds to one spark task. use_gpu: .. deprecated:: 2.0.0 Use `device` instead. device: .. versionadded:: 2.0.0 Device for XGBoost workers, available options are `cpu`, `cuda`, and `gpu`. force_repartition: Boolean value to specify if forcing the input dataset to be repartitioned before XGBoost training. repartition_random_shuffle: Boolean value to specify if randomly shuffling the dataset when repartitioning is required. enable_sparse_data_optim: Boolean value to specify if enabling sparse data optimization, if True, Xgboost DMatrix object will be constructed from sparse matrix instead of dense matrix. kwargs: A dictionary of xgboost parameters, please refer to https://xgboost.readthedocs.io/en/stable/parameter.html .. Note:: The Parameters chart above contains parameters that need special handling. For a full list of parameters, see entries with `Param(parent=...` below. .. Note:: This API is experimental. Examples -------- >>> from xgboost.spark import SparkXGBRanker >>> from pyspark.ml.linalg import Vectors >>> ranker = SparkXGBRanker(qid_col="qid") >>> df_train = spark.createDataFrame( ... [ ... (Vectors.dense(1.0, 2.0, 3.0), 0, 0), ... (Vectors.dense(4.0, 5.0, 6.0), 1, 0), ... (Vectors.dense(9.0, 4.0, 8.0), 2, 0), ... (Vectors.sparse(3, {1: 1.0, 2: 5.5}), 0, 1), ... (Vectors.sparse(3, {1: 6.0, 2: 7.5}), 1, 1), ... (Vectors.sparse(3, {1: 8.0, 2: 9.5}), 2, 1), ... ], ... ["features", "label", "qid"], ... ) >>> df_test = spark.createDataFrame( ... [ ... (Vectors.dense(1.5, 2.0, 3.0), 0), ... (Vectors.dense(4.5, 5.0, 6.0), 0), ... (Vectors.dense(9.0, 4.5, 8.0), 0), ... (Vectors.sparse(3, {1: 1.0, 2: 6.0}), 1), ... (Vectors.sparse(3, {1: 6.0, 2: 7.0}), 1), ... (Vectors.sparse(3, {1: 8.0, 2: 10.5}), 1), ... ], ... ["features", "qid"], ... ) >>> model = ranker.fit(df_train) >>> model.transform(df_test).show() rFrGrHNrF)rIrJrKrLrMrNrOrmrPrQrRrSrTrUrIrJrKrLrMrNrOrmrPrQrRrSrTrUrVrc vt||j}| r t|jdi|yrXrY)r^rIrJrKrLrMrNrOrmrPrQrRrSrTrUrVr_r`s r'r[zSparkXGBRanker.__init__9s5( ))   !&&r.ctSrrrcs r'r5zSparkXGBRanker._xgb_clsSr.SparkXGBRankerModelctSr)rrcs r'rhz!SparkXGBRanker._pyspark_model_clsWs""r.cnt||j|js t dy)Nz@Spark Xgboost ranker estimator requires setting `qid_col` param.rjros r'rkzSparkXGBRanker._validate_params[s3  "~~dll+R ,r.rp)rqrrrsrtrrr3rrrurvrr[rwrrr5rhrkrxrys@r'rrs]xt/9 **.26$()-!%"& $"'+0).!'CcN+' '  ' #3- '#+3-'SM'"#'#''$' ' '%)' #'!'"#'$ %''2i#4(=#>##r.rc,eZdZdZedeefdZy)rzq The model returned by :func:`xgboost.spark.SparkXGBRanker.fit` .. Note:: This API is experimental. rctSrrrcs r'r5zSparkXGBRankerModel._xgb_clsjrr.N)rqrrrsrtrwrrr5rBr.r'rrcs& ir.rrp)$rtr?typingrrrrrnumpyrpysparkrpyspark.ml.paramr r pyspark.ml.param.sharedr r xgboostr rrcorerrrutilsrr=rCrErfr}rrrrBr.r'rs> 33 *J:: "5)&'5)04^0D5) 5)p X*Xv ^ !!24JKk+->@Sk\ 2 !!35LMb'bJ . !1DEr.