Deploy XGBoost Model as SQL Query
This pacakge enables in-database scoring of XGBoost models built in R, by translating trained model objects into SQL query.
Latest CRAN release:
install.packages("xgb2sql")Development version:
devtools::install_github("chengjunhou/xgb2sql")onehot2sql(data, meta=NULL, sep="_", ws_replace=TRUE, ws_replace_with="",
unique_id=NULL, output_file_name=NULL, input_table_name=NULL)
Function onehot2sql() performs full one-hot encoding for all the categorical features inside the training data,
with all NAs inside both categorical and numeric features preserved.
Other than outputting a matrix model.matrix which is the data after processing,
it also outputs meta information keeping track of all the transformation the function performs,
while SQL query for the transformation is kept in output sql and write to the file specified by output_file_name.
If meta is specified as input to the function, the transformation and the corresponding SQL query will
follow what is kept in meta exactly.
booster2sql(xgbModel, print_progress=FALSE, unique_id=NULL,
output_file_name=NULL, input_table_name=NULL, input_onehot_query=NULL)
Function booster2sql() generates SQL query for in-database scoring of XGBoost models,
providing a robust and efficient way of model deployment. It takes in the trained XGBoost model xgbModel,
name of the input database table input_table_name,
and name of a unique identifier within that table unique_id as input,
writes the SQL query to a file specified by output_file_name.
Note that the input database table should be generated from the raw table using the one-hot encoding query output by onehot2sql(),
or to provide the one-hot encoding query as input input_onehot_query to this function,
working as sub-query inside the final model scoring query.
Current supported booster is booster="gbtree", supported objective options are:
reg:linear: linear regression.reg:logistic: logistic regression.binary:logistic: logistic regression for binary classification, output probability.binary:logitraw: logistic regression for binary classification, output score before logistic transformation.binary:hinge: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.count:poisson: poisson regression for count data, output mean of poisson distribution.reg:gamma: gamma regression with log-link, output mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be gamma-distributed.reg:tweedie: Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be Tweedie-distributed.
### load pacakge and data
library(data.table)
library(xgboost)
library(xgb2sql)
df <- data.frame(ggplot2::diamonds)
head(df)
#> carat cut color clarity depth table price x y z
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
#> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
### data processing
out <- onehot2sql(df)
head(out$model.matrix)
#> (Intercept) carat clarity_I1 clarity_IF clarity_SI1 clarity_SI2
#> 1 1 0.23 0 0 0 1
#> 2 1 0.21 0 0 1 0
#> 3 1 0.23 0 0 0 0
#> 4 1 0.29 0 0 0 0
#> 5 1 0.31 0 0 0 1
#> 6 1 0.24 0 0 0 0
#> clarity_VS1 clarity_VS2 clarity_VVS1 clarity_VVS2 color_D color_E
#> 1 0 0 0 0 0 1
#> 2 0 0 0 0 0 1
#> 3 1 0 0 0 0 1
#> 4 0 1 0 0 0 0
#> 5 0 0 0 0 0 0
#> 6 0 0 0 1 0 0
#> color_F color_G color_H color_I color_J cut_Fair cut_Good cut_Ideal
#> 1 0 0 0 0 0 0 0 1
#> 2 0 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 1 0
#> 4 0 0 0 1 0 0 0 0
#> 5 0 0 0 0 1 0 1 0
#> 6 0 0 0 0 1 0 0 0
#> cut_Premium cut_VeryGood depth price table x y z
#> 1 0 0 61.5 326 55 3.95 3.98 2.43
#> 2 1 0 59.8 326 61 3.89 3.84 2.31
#> 3 0 0 56.9 327 65 4.05 4.07 2.31
#> 4 1 0 62.4 334 58 4.20 4.23 2.63
#> 5 0 0 63.3 335 58 4.34 4.35 2.75
#> 6 0 1 62.8 336 57 3.94 3.96 2.48
cat(out$sql)
#> SELECT ROW_KEY, [carat], [depth], [table], [price], [x], [y], [z],
#> (case when [cut] IS NULL then NULL when [cut] = 'Fair' then 1 else 0 end) AS [cut_Fair],
#> (case when [cut] IS NULL then NULL when [cut] = 'Good' then 1 else 0 end) AS [cut_Good],
#> (case when [cut] IS NULL then NULL when [cut] = 'Very Good' then 1 else 0 end) AS [cut_VeryGood],
#> (case when [cut] IS NULL then NULL when [cut] = 'Premium' then 1 else 0 end) AS [cut_Premium],
#> (case when [cut] IS NULL then NULL when [cut] = 'Ideal' then 1 else 0 end) AS [cut_Ideal],
#> (case when [color] IS NULL then NULL when [color] = 'D' then 1 else 0 end) AS [color_D],
#> (case when [color] IS NULL then NULL when [color] = 'E' then 1 else 0 end) AS [color_E],
#> (case when [color] IS NULL then NULL when [color] = 'F' then 1 else 0 end) AS [color_F],
#> (case when [color] IS NULL then NULL when [color] = 'G' then 1 else 0 end) AS [color_G],
#> (case when [color] IS NULL then NULL when [color] = 'H' then 1 else 0 end) AS [color_H],
#> (case when [color] IS NULL then NULL when [color] = 'I' then 1 else 0 end) AS [color_I],
#> (case when [color] IS NULL then NULL when [color] = 'J' then 1 else 0 end) AS [color_J],
#> (case when [clarity] IS NULL then NULL when [clarity] = 'I1' then 1 else 0 end) AS [clarity_I1],
#> (case when [clarity] IS NULL then NULL when [clarity] = 'SI2' then 1 else 0 end) AS [clarity_SI2],
#> (case when [clarity] IS NULL then NULL when [clarity] = 'SI1' then 1 else 0 end) AS [clarity_SI1],
#> (case when [clarity] IS NULL then NULL when [clarity] = 'VS2' then 1 else 0 end) AS [clarity_VS2],
#> (case when [clarity] IS NULL then NULL when [clarity] = 'VS1' then 1 else 0 end) AS [clarity_VS1],
#> (case when [clarity] IS NULL then NULL when [clarity] = 'VVS2' then 1 else 0 end) AS [clarity_VVS2],
#> (case when [clarity] IS NULL then NULL when [clarity] = 'VVS1' then 1 else 0 end) AS [clarity_VVS1],
#> (case when [clarity] IS NULL then NULL when [clarity] = 'IF' then 1 else 0 end) AS [clarity_IF]
#> FROM INPUT_TABLE
### model training
x <- out$model.matrix[,colnames(out$model.matrix)!='price']
y <- out$model.matrix[,colnames(out$model.matrix)=='price']
bst <- xgboost(data = x,
label = y,
max.depth = 2,
eta = .3,
nround = 2,
objective = 'reg:linear')
#> [1] train-rmse:4095.421387
#> [2] train-rmse:3074.222412
### generate XGBoost SQL script
booster2sql(bst, output_file_name='xgb.txt')
#> query is written to file with row unique id named as ROW_KEY
#> query is written to file with input table named as MODREADY_TABLE
cat(readChar('xgb.txt', file.info('xgb.txt')$size))
#> SELECT ROW_KEY , 0.5 + SUM(ONETREE) AS XGB_PRED
#> FROM (
#> SELECT ROW_KEY ,
#> (CASE WHEN [carat] < 0.995000005 THEN
#> (CASE WHEN [y] < 5.53499985 THEN 317.401001
#> WHEN [y] >= 5.53499985 THEN 922.349731
#> WHEN [y] IS NULL THEN 317.401001 END)
#> WHEN [carat] >= 0.995000005 THEN
#> (CASE WHEN [y] < 7.19499969 THEN 1841.06018
#> WHEN [y] >= 7.19499969 THEN 3696.24292
#> WHEN [y] IS NULL THEN 1841.06018 END)
#> WHEN [carat] IS NULL THEN
#> (CASE WHEN [y] < 5.53499985 THEN 317.401001
#> WHEN [y] >= 5.53499985 THEN 922.349731
#> WHEN [y] IS NULL THEN 317.401001 END) END) AS ONETREE FROM MODREADY_TABLE
#> UNION ALL
#>
#> SELECT ROW_KEY ,
#> (CASE WHEN [y] < 6.69499969 THEN
#> (CASE WHEN [carat] < 0.824999988 THEN 289.332123
#> WHEN [carat] >= 0.824999988 THEN 1056.4021
#> WHEN [carat] IS NULL THEN 289.332123 END)
#> WHEN [y] >= 6.69499969 THEN
#> (CASE WHEN [y] < 7.65499973 THEN 1814.65881
#> WHEN [y] >= 7.65499973 THEN 3217.57129
#> WHEN [y] IS NULL THEN 1814.65881 END)
#> WHEN [y] IS NULL THEN
#> (CASE WHEN [carat] < 0.824999988 THEN 289.332123
#> WHEN [carat] >= 0.824999988 THEN 1056.4021
#> WHEN [carat] IS NULL THEN 289.332123 END) END) AS ONETREE FROM MODREADY_TABLE
#> ) AS TREES_TABLE GROUP BY ROW_KEYItems under development are: