khannaekta commented on code in PR #581: URL: https://github.com/apache/madlib/pull/581#discussion_r897094694
########## src/ports/postgres/modules/mxgboost/madlib_xgboost.sql_in: ########## @@ -0,0 +1,714 @@ +/* ----------------------------------------------------------------------- *//** + * + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + * + * @file madlib_xgboost.sql_in + * @brief SQL functions for xgboost + * @date 05/12/2022 + * + */ + /* ----------------------------------------------------------------------- */ + + +/** +@addtogroup grp_xgboost + +@brief +This module allows you to use SQL to build gradient boosted tree +models designed in XGBoost [1]. + +<div class="toc"><b>Contents</b> +<ul> +<li><a href="#train">Training Function</a></li> +<li><a href="#predict">Prediction Function</a></li> +<li><a href="#examples">Examples</a></li> +</ul> +</div> + +XGBoost is an optimized distributed gradient boosting library. The MADlib +implementation is designed to train multiple models with different parameters +to achieve parallelism on Greenplum systems (PostgreSQL is also supported.) + +The main use case supported is classification. Regression is not currently +supported. + +Please note that the XGBoost implementation has the following limitations: +- All of the dataset is collected in a single segment, compressed by zlib and +stored in a single cells (one column of one row). PostgreSQL has a 1 GB limit +for each cell which means the supported dataset size is limited. +- The collected data is passed to the XGBoost in a single segment. This means, +without grid search, the XGBoost is using only a single segment which might +increase the run time. + +XGBoost requires the following python libraries available in every host machine: +- pandas v0.42 +- scikit-learn v0.19 +- xgboost v0.82 + +Versions are provided to ensure a python2 supported libraries can be obtained. +Other versions of these libraries might work just as well. + +@anchor train +@par Training Function +<pre class="syntax"> +SELECT xgboost( + source_table, + output_table_name, + id_column, + dependent_variable, + list_of_features, + list_of_features_to_exclude, + params_str, + sample_weights, + train_set_size, + train_set_split_var +) +</pre> + +\b Arguments +<dl class="arglist"> + <dt>source_table</dt> + <dd>TEXT. Name of the table containing the training data.</dd> + + <dt>output_table</dt> + <dd>TEXT. Name of the generated table containing the model. + If a table with the same name already exists, an + error will be returned. A summary table + named <em>\<output_table_name\>_summary</em> is also + created. + </DD> + + <DT>id_column</DT> + <DD>TEXT. Name of the column containing id information in the training data. + This is a mandatory argument and the values are expected to be unique for each + row. + </DD> + + <DT>dependent_variable</DT> + <DD>TEXT. Name of the column that contains the output (response) for + training. Boolean, integer and text types are accepted for classification. + </DD> + + <DT>list_of_features</DT> + <DD>TEXT. Comma-separated string of column names or expressions to use as predictors. + Can also be a '*' implying all columns are to be used as predictors (except for the + ones included in the next argument that lists exclusions). + + <DT>list_of_features_to_exclude (optional)</DT> + <DD>TEXT[]. Text array of column names to exclude from the predictors + list. The names in this parameter should be identical to the names used in the table and + quoted appropriately. The id_column and dependent_variable are excluded by default. + </DD> + + <DT>params_str (optional)</DT> + <DD>TEXT. Comma-separated string of key-value pairs used for initializing + XGBClassifier. Each parameter can be assigned an array of values. These values + are expanded into multiple parameter sets for grid search. + + Note that these key-value pairs are passed to the xgboost as is and xgboost + accepts any parameter thanks to kwargs. If there is a typo in the list, it + might get ignored by xgboost. + </DD> + + <DT>sample_weights (optional)</DT> + <DD>TEXT. Column name containing numerical weights for sample_weights parameter of + XGBClassifier fit function. + </DD> + + <DT>train_set_size (optional)</DT> + <DD>DOUBLE PRECISION. The proportion of the dataset to be used in training. + </DD> + + <DT>train_set_split_var (optional)</DT> + <DD>TEXT. Column name containing information on whether the associated row will be used for training or testing. If this parameter is set, train_set_size will be ignored. + </DD> +</DL> + +\b Output +<dl class="arglist"> +<DD> + The model table produced by the training function contains the following columns: + + <table class="output"> + <tr> + <th>model</th> + <td>BYTEA8. Trained XGBoost model stored in binary + format (not human readable).</td> + </tr> + <tr> + <th>features</th> + <td>TEXT[]. Ordered levels (values) of categorical variables + corresponding to the categorical features. Used to help + interpret the trained model.</td> + </tr> + <tr> + <th>params_index</th> + <td>INTEGER. The index of the model. During grid search, each parameter + set is given an index to distinguish different models. + </td> + </tr> + + </table> + + A summary table named <em>\<output_table\>_summary</em> is also created at + the same time, which has the following columns: + + <table class="output"> + <tr> + <th>mdl_train_ts</th> + <td>TEXT. The timestamp for the initiation of the xgboost command.</td> + </tr> + <tr> + <th>mdl_name</th> + <td>TEXT. The name of the model set to <source_table>_xgboost.</td> + </tr> + <tr> + <th>features</th> + <td>TEXT[]. The list of features inputed to the XGBoost.</td> + </tr> + <tr> + <th>params</th> + <td>TEXT. The XGBClassifier parameters used for this particular model.</td> + </tr> + <tr> + <th>fnames</th> + <td>TEXT. The list of features in sorted order. This column is used for importance</td> + </tr> + <tr> + <th>importance</th> + <td>DOUBLE PRECISION[]. Importance values for each feature in the sorted order.</td> + </tr> + <tr> + <th>recall</th> + <td>DOUBLE PRECISION[]. Recall values for each class.</td> + </tr> + <tr> + <th>fscore</th> + <td>DOUBLE PRECISION[]. fscore values for each class.</td> + </tr> + <tr> + <th>support</th> + <td>DOUBLE PRECISION[]. support values for each class.</td> + </tr> + <tr> + <th>test_ids</th> + <td>INTEGER[]. The ids of the rows used for this particular model.</td> + </tr> + <tr> + <th>params_index</th> + <td>INTEGER. The index of the parameter set used for this model.</td> + </tr> + </table> + + </DD> +</DL> + +@anchor predict +@par Prediction Function + +<pre class="syntax"> +SELECT xgboost_predict( + test_table, + model_table, + predict_output_table, + id_column, + class_label, + model_filters +); +</pre> + +\b Arguments +<dl class="arglist"> + <dt>test_table</dt> + <dd>TEXT. Name of the table containing the test data.</dd> + + <dt>model_table</dt> + <dd>TEXT. Name of the table containing the xgboost train output.</dd> + + <dt>predict_output_table</dt> + <dd>TEXT. Name of the generated table containing the prediction. + If a table with the same name already exists, an + error will be returned. A metrics table named + <em>\<output_table_name\>_metrics</em> and an roc curve table named + <em>\<output_table_name\>_roc_curve</em> are also created if class_label is + not NULL. + </DD> + + <DT>id_column</DT> + <DD>TEXT. Name of the column containing id information in the test data. + This is a mandatory argument and the values are expected to be unique for each + row. + </DD> + + <DT>class_label (optional)</DT> + <DD>TEXT. Name of the column containing class_label for the metrics and + roc_curve calculations. + </DD> + + <DT>model_filters (optional)</DT> + <DD>TEXT. The filter for the model_table in case the user wants to use a + subset of the models from the grid search. + </DD> +</DL> + +\b Output + +XGBoost prediction function creates three tables: <predict_output_table>, +<predict_output_table>_metrics and <predict_output_table>_roc_curve. +Some fields of the metrics table as well as the roc_curve table are available +only if a class_label column is provided. + +<predict_output_table> +<dl class="arglist"> +<DD> +<table class="output"> + <tr> + <th>id_column</th> + <td>INTEGER. Name of the column containing id information in the test data.</td> + </tr> + <tr> + <th>class_label_predicted</th> + <td>TEXT. The predicted value for the given data id.</td> + </tr> + <tr> + <th>class_label_proba_predicted</th> + <td>DOUBLE PRECISION[]. The prediction probabilities for the given data id.</td> + </tr> +</table> + +<predict_output_table>_metrics +<table class="output"> + <tr> + <th>precision</th> + <td>DOUBLE PRECISION[]. Precision values for each class in the sorted order.</td> + </tr> + <tr> + <th>recall</th> + <td>DOUBLE PRECISION[]. Recall values for each class.</td> + </tr> + <tr> + <th>fscore</th> + <td>DOUBLE PRECISION[]. fscore values for each class.</td> + </tr> + <tr> + <th>support</th> + <td>DOUBLE PRECISION[]. support values for each class.</td> + </tr> + <tr> + <th>roc_auc_scores</th> + <td>DOUBLE PRECISION[]. roc_auc scores for each class.</td> + </tr> + <tr> + <th>feature_names</th> + <td>TEXT[]. The list of features in sorted order. This column is used for importance</td> + </tr> + <tr> + <th>feature_importance_scores</th> + <td>DOUBLE PRECISION[]. Importance values for each feature in the sorted order.</td> + </tr> +</table> + +<predict_output_table>_roc_curve +<table class="output"> + <tr> + <th>fpr</th> + <td>DOUBLE PRECISION[]. False positive rate for the roc curve.</td> + </tr> + <tr> + <th>tpr</th> + <td>DOUBLE PRECISION[]. True positive rate for the roc curve.</td> + </tr> + <tr> + <th>thresholds</th> + <td>DOUBLE PRECISION[]. Threshold values for the roc curve.</td> + </tr> +</table> + +@anchor examples +@par Examples + +<a href="example/madlib_xgboost_example.sql">Download the example sql file here.</a> + +-# Show the input data set. +<pre class="example"> +SELECT * FROM abalone LIMIT 10; +</pre> +<pre class="result"> + id | sex | length | diameter | height | whole_weight | shucked_weight | viscera_weight | shell_weight | rings +----+-----+--------+----------+--------+--------------+----------------+----------------+--------------+------- + 1 | M | 0.455 | 0.365 | 0.095 | 0.514 | 0.2245 | 0.101 | 0.15 | 15 + 12 | M | 0.43 | 0.35 | 0.11 | 0.406 | 0.1675 | 0.081 | 0.135 | 10 + 15 | F | 0.47 | 0.355 | 0.1 | 0.4755 | 0.1675 | 0.0805 | 0.185 | 10 + 20 | M | 0.45 | 0.32 | 0.1 | 0.381 | 0.1705 | 0.075 | 0.115 | 9 + 23 | F | 0.565 | 0.44 | 0.155 | 0.9395 | 0.4275 | 0.214 | 0.27 | 12 + 26 | F | 0.56 | 0.44 | 0.14 | 0.9285 | 0.3825 | 0.188 | 0.3 | 11 + 30 | M | 0.575 | 0.425 | 0.14 | 0.8635 | 0.393 | 0.227 | 0.2 | 11 + 31 | M | 0.58 | 0.47 | 0.165 | 0.9975 | 0.3935 | 0.242 | 0.33 | 10 + 35 | F | 0.705 | 0.55 | 0.2 | 1.7095 | 0.633 | 0.4115 | 0.49 | 13 + 36 | M | 0.465 | 0.355 | 0.105 | 0.4795 | 0.227 | 0.124 | 0.125 | 8 +</pre> + +-# Run XGBoost for a single parameter set and show the summary table +<pre class="example"> +DROP TABLE IF EXISTS xgb_out, xgb_out_summary; +SELECT xgboost( + 'abalone', -- Training table + 'id', -- Id column + 'sex', -- Class label column + '*', -- Independent variables + NULL, -- Columns to exclude from features + $$ + { + 'learning_rate': [0.01], #Regularization on weights (eta). For smaller values, increase n_estimators + 'max_depth': [9],#Larger values could lead to overfitting + 'subsample': [0.85],#introduce randomness in samples picked to prevent overfitting + 'colsample_bytree': [0.85],#introduce randomness in features picked to prevent overfitting + 'min_child_weight': [10],#larger values will prevent over-fitting + 'n_estimators':[100] #More estimators, lesser variance (better fit on test set) + } + $$, -- XGBoost grid search parameters + 'xgb_out', -- Grid search results table. + '', -- Class weights + 0.8, -- Training set size ratio + NULL -- Variable used to do the test/train split. +); +\\x on +SELECT * FROM xgb_out_summary; +</pre> +<pre class="result"> +-[ RECORD 1 ]+--------------------------------------------- +mdl_train_ts | 2022-05-16 17:42:45.033789+03 +mdl_name | abalone_xgboost +features | {length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings} +params | ('colsample_bytree=0.85', 'learning_rate=0.01', 'min_child_weight=10', 'n_estimators=100', 'subsample=0.85', 'max_depth=9') +fnames | {viscera_weight,whole_weight,shucked_weight,shell_weight,length,rings,diameter,height} +importance | {1206,1189,1184,867,766,622,589,526} +precision | {0.4295774647887324,0.6858006042296072,0.4318181818181818} +recall | {0.46387832699619774,0.8021201413427562,0.328719723183391} +fscore | {0.4460694698354662,0.739413680781759,0.3732809430255403} +support | {263.0,283.0,289.0} +test_ids | {486,3627,432,2766,132,2397,3313,2346...} +</pre> + +-# Run XGBoost prediction. For this example we are using the same abalone table for prediction as well. +<pre class="example"> +DROP TABLE IF EXISTS xgb_score_out, xgb_score_out_metrics, xgb_score_out_roc_curve; +SELECT xgboost_predict( + 'abalone', -- test_table + 'xgb_out', -- model_table + 'xgb_score_out', -- predict_output_table + 'id', -- id_column + 'sex' -- class_label +); +\\x off +SELECT * FROM xgb_score_out LIMIT 10; +</pre> +<pre class="result"> + id | sex_predicted | sex_proba_predicted +----+---------------+------------------------------------------------ + 5 | I | {0.168330997229,0.632858633995,0.198810324073} + 6 | I | {0.202547758818,0.574552714825,0.222899526358} + 9 | I | {0.255484640598,0.44379144907,0.300723910332} + 10 | M | {0.347418963909,0.242429286242,0.410151779652} + 11 | F | {0.4157371521,0.316571623087,0.267691165209} + 13 | F | {0.338543832302,0.32665386796,0.334802359343} + 14 | F | {0.400314897299,0.293721526861,0.305963635445} + 17 | I | {0.175603896379,0.608917593956,0.215478509665} + 21 | M | {0.280931055546,0.333337903023,0.385731071234} + 25 | F | {0.498989373446,0.185877665877,0.315133005381} +</pre> + +-# Show roc curve and metrics tables +<pre class="example"> +SELECT * FROM xgb_score_out_roc_curve limit 10;</pre> +<pre class="result"> + fpr | tpr | thresholds +---------------------------+---------------------------+--------------------------- + {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352} + {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352} + {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352} + {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352} + {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352} + {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352} + {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352} + {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352} + {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352} + {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352} +</pre> +<pre class="example"> +\\x on +SELECT * FROM xgb_score_out_metrics; +</pre> +<pre class="result"> +-[ RECORD 1 ]-------------+--------------------------------------------------------------------------------------- +precision | {0.549047282992237,0.70062893081761,0.598290598290598} +recall | {0.595256312165264,0.830104321907601,0.458115183246073} +fscore | {0.571218795888399,0.759890859481583,0.518902891030393} +support | {1307,1342,1528} +roc_auc_scores | {0.77659,0.9091,0.74816} +feature_names | {viscera_weight,whole_weight,shucked_weight,shell_weight,length,rings,diameter,height} +feature_importance_scores | {1206,1189,1184,867,766,622,589,526} +</pre> + +-# Run XGBoost grid search with parameter options +<pre class="example"> +DROP TABLE IF EXISTS xgb_out, xgb_out_summary; +SELECT xgboost( + 'abalone', -- Training table + 'id', -- Id column + 'sex', -- Class label column + '*', -- Independent variables + NULL, -- Columns to exclude from features + $$ + { + 'learning_rate': [0.01,0.1], #Regularization on weights (eta). For smaller values, increase n_estimators + 'max_depth': [9,12],#Larger values could lead to overfitting + 'subsample': [0.85],#introduce randomness in samples picked to prevent overfitting + 'colsample_bytree': [0.85],#introduce randomness in features picked to prevent overfitting + 'min_child_weight': [10],#larger values will prevent over-fitting + 'n_estimators':[100] #More estimators, lesser variance (better fit on test set) + } + $$, -- XGBoost grid search parameters + 'xgb_out', -- Grid search results table. + '', -- Class weights + 0.8, -- Training set size ratio + NULL -- Variable used to do the test/train split. +); +\\x on +SELECT * FROM xgb_out_summary; +</pre> +<pre class="result"> +-[ RECORD 1 ]+--------------------------------------------- +mdl_train_ts | 2022-05-16 17:56:11.488767+03 +mdl_name | abalone_xgboost +features | {length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings} +params | ('colsample_bytree=0.85', 'learning_rate=0.01', 'min_child_weight=10', 'n_estimators=100', 'subsample=0.85', 'max_depth=12') +fnames | {viscera_weight,whole_weight,shucked_weight,shell_weight,length,rings,diameter,height} +importance | {1276,1205,1200,906,864,822,580,415} +precision | {0.4090909090909091,0.752411575562701,0.5} +recall | {0.45188284518828453,0.8153310104529616,0.42071197411003236} +fscore | {0.42942345924453285,0.782608695652174,0.45694200351493847} +support | {239.0,287.0,309.0} +test_ids | {3975,35,1759,1469,3437,3951...} +-[ RECORD 2 ]+--------------------------------------------- +mdl_train_ts | 2022-05-16 17:56:11.488767+03 +mdl_name | abalone_xgboost +features | {length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings} +params | ('colsample_bytree=0.85', 'learning_rate=0.01', 'min_child_weight=10', 'n_estimators=100', 'subsample=0.85', 'max_depth=9') +fnames | {viscera_weight,whole_weight,shucked_weight,shell_weight,length,rings,diameter,height} +importance | {1268,1196,1182,860,832,668,595,461} +precision | {0.46096654275092935,0.6566265060240963,0.5213675213675214} +recall | {0.4901185770750988,0.8288973384030418,0.3824451410658307} +fscore | {0.475095785440613,0.7327731092436974,0.4412296564195299} +support | {253.0,263.0,319.0} +test_ids | {2988,2303,2034,3085,2465,2887...} +-[ RECORD 3 ]+--------------------------------------------- +mdl_train_ts | 2022-05-16 17:56:11.488767+03 +mdl_name | abalone_xgboost +features | {length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings} +params | ('colsample_bytree=0.85', 'learning_rate=0.1', 'min_child_weight=10', 'n_estimators=100', 'subsample=0.85', 'max_depth=9') +fnames | {shucked_weight,whole_weight,viscera_weight,shell_weight,length,diameter,height,rings} +importance | {998,948,862,765,629,489,441,383} +precision | {0.4635036496350365,0.6986301369863014,0.45724907063197023} +recall | {0.4847328244274809,0.75,0.40863787375415284} +fscore | {0.47388059701492535,0.7234042553191489,0.43157894736842106} +support | {262.0,272.0,301.0} +test_ids | {396,2150,809,2846,1108,1841...} +-[ RECORD 3 ]+--------------------------------------------- +mdl_train_ts | 2022-05-16 17:56:11.488767+03 +mdl_name | abalone_xgboost +features | {length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings} +params | ('colsample_bytree=0.85', 'learning_rate=0.1', 'min_child_weight=10', 'n_estimators=100', 'subsample=0.85', 'max_depth=12') +fnames | {shucked_weight,viscera_weight,whole_weight,shell_weight,length,diameter,height,rings} +importance | {1101,1056,1045,958,680,621,458,458} +precision | {0.40484429065743943,0.7016949152542373,0.43824701195219123} +recall | {0.4517374517374517,0.75,0.36666666666666664} +fscore | {0.42700729927007297,0.7250437828371278,0.3992740471869329} +support | {259.0,276.0,300.0} +test_ids | {1740,2777,1907,581,3525,1022...} +</pre> + +-# Run XGBoost prediction using params_index=2 +<pre class="example"> +DROP TABLE IF EXISTS xgb_score_out, xgb_score_out_metrics, xgb_score_out_roc_curve; +SELECT xgboost_predict( + 'abalone', -- test_table + 'xgb_out', -- model_table + 'xgb_score_out', -- predict_output_table + 'id', -- id_column + 'sex', -- class_label + 2 -- params_index +); +\\x off +SELECT * FROM xgb_score_out LIMIT 10; +</pre> +<pre class="result"> + id | sex_predicted | sex_proba_predicted +----+---------------+------------------------------------------------ + 5 | I | {0.178420484066,0.599636971951,0.221942588687} + 6 | I | {0.185853347182,0.602131128311,0.212015494704} + 9 | I | {0.253592431545,0.440728843212,0.30567869544} + 10 | M | {0.374555230141,0.249226689339,0.376218110323} + 11 | F | {0.402999937534,0.336779236794,0.260220855474} + 13 | I | {0.338803291321,0.36541134119,0.295785397291} + 14 | F | {0.377499818802,0.301990658045,0.320509523153} + 17 | I | {0.179169252515,0.602536559105,0.218294218183} + 21 | M | {0.27216938138,0.33275142312,0.395079195499} + 25 | F | {0.449239164591,0.187060594559,0.36370024085} +</pre> + + + + +@anchor literature +@literature + +[1] Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. +In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge +Discovery and Data Mining (pp. 785–794). New York, NY, USA: ACM. +https://doi.org/10.1145/2939672.2939785 + +*/ + +m4_include(`SQLCommon.m4') + +DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__serialize_pandas_dframe_as_bytea__( + TEXT, + TEXT, + TEXT, + TEXT[] +); + +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__serialize_pandas_dframe_as_bytea__( + source_table TEXT, + id_column TEXT, + class_label TEXT, + features TEXT +) +RETURNS BYTEA +AS +$$ + PythonFunctionBodyOnly(mxgboost, madlib_xgboost) + with AOControl(False): + return madlib_xgboost.serialize_pandas_dframe_as_bytea(schema_madlib, source_table, id_column, class_label, features) +$$ LANGUAGE plpythonu VOLATILE +m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `'); + + + +DROP TYPE IF EXISTS MADLIB_SCHEMA.xgb_gridsearch_train_results_type CASCADE; +CREATE TYPE MADLIB_SCHEMA.xgb_gridsearch_train_results_type +AS +( + features TEXT[], + mdl BYTEA, + params TEXT, + fnames TEXT[], + importance TEXT[], + precision TEXT[], + recall TEXT[], + fscore TEXT[], + support TEXT[], + test_ids INTEGER[] +); + +DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__xgboost_train_parallel__( + BYTEA, + TEXT[], + TEXT, + TEXT, + TEXT, + NUMERIC +); + +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__xgboost_train_parallel__( + dframe BYTEA, + features_all TEXT[], + class_label TEXT, + params TEXT, + class_weights TEXT, + train_set_size NUMERIC, + id_column TEXT, + train_set_split_var TEXT +) +RETURNS MADLIB_SCHEMA.xgb_gridsearch_train_results_type +AS +$$ + PythonFunctionBodyOnly(mxgboost, madlib_xgboost) + with AOControl(False): + return madlib_xgboost.xgboost_train(schema_madlib, dframe, features_all, class_label, params, class_weights, train_set_size, id_column, train_set_split_var) +$$ LANGUAGE plpythonu VOLATILE +m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `'); + +DROP FUNCTION IF EXISTS MADLIB_SCHEMA.xgboost( + TEXT, + TEXT, + TEXT, + TEXT[], + TEXT, + TEXT, + TEXT, + TEXT, + NUMERIC, + TEXT +); + +CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost( Review Comment: Since the params after `list_of_features` are optional, we probably should support the following (AFAIk we do it for the other modules): ``` SELECT xgboost( 'abalone', -- Training table 'id', -- Id column 'sex', -- Class label column 'rings'); -- Independent variables ``` -- This is an automated message from the Apache Git Service. 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