Package 'xgb2sql'

Title: Convert Trained 'XGBoost' Model to SQL Query
Description: This tool enables in-database scoring of 'XGBoost' models built in R, by translating trained model objects into SQL query. 'XGBoost' <https://github.com/dmlc/xgboost> provides parallel tree boosting (also known as gradient boosting machine, or GBM) algorithms in a highly efficient, flexible and portable way. GBM algorithm is introduced by Friedman (2001) <doi:10.1214/aos/1013203451>, and more details on 'XGBoost' can be found in Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
Authors: Chengjun Hou [aut, cre], Abhishek Bishoyi [aut]
Maintainer: Chengjun Hou <[email protected]>
License: MIT + file LICENSE
Version: 0.1.2
Built: 2024-10-30 03:30:07 UTC
Source: https://github.com/chengjunhou/xgb2sql

Help Index

Transform XGBoost model object to SQL query.

Description

This function 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.

Usage

booster2sql(xgbModel, print_progress = FALSE, unique_id = NULL,
  output_file_name = NULL, input_table_name = NULL,
  input_onehot_query = NULL)

Arguments

xgbModel

The trained model object of class xgb.Booster. 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.

print_progress

Boolean indicator controls whether the SQL generating progress should be printed to console.
unique_id

A row unique identifier is crucial for in-database scoring of XGBoost model. If not given, SQL query will be generated with id name "ROW_KEY".
output_file_name

File name that the SQL syntax will write to. It must not be empty in order for this function to run.
input_table_name

Name of raw data table in the database, that the SQL query will select from. If not given, SQL query will be generated with table name "MODREADY_TABLE".
input_onehot_query

SQL query of one-hot encoding generated by onehot2sql. When input_table_name is empty while input_onehot_query is not, the final output query will include input_onehot_query as sub-query.

Value

The SQL query will write to the file specified by output_file_name.

Examples

library(xgboost)
# load data
df = data.frame(ggplot2::diamonds)
head(df)

# data processing
out <- onehot2sql(df)
x <- out$model.matrix[,colnames(out$model.matrix)!='price']
y <- out$model.matrix[,colnames(out$model.matrix)=='price']

# model training
bst <- xgboost(data = x,
               label = y,
               max.depth = 3,
               eta = .3,
               nround = 5,
               nthread = 1,
               objective = 'reg:linear')

# generate model scoring SQL script with ROW_KEY and MODREADY_TABLE
booster2sql(bst, output_file_name='xgb.txt')

Prepare training data in R so that it is ready for XGBoost model fitting. Meta information is stored so the exact transformation can be applied to any new data. It also outputs SQL query performing the exact one-hot encoding for in-database data preparation.

Description

This function 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.

Usage

onehot2sql(data, meta = NULL, sep = "_", ws_replace = TRUE,
  ws_replace_with = "", unique_id = NULL, output_file_name = NULL,
  input_table_name = NULL)

Arguments

data

Data object of class data.frame or data.table.
meta

Optional, a list keeps track of all the transformation that has been taken on the categorical features.
sep

Separation symbol between the categorical features and their levels, which will be the column names inside the output model.matrix, default to "_".
ws_replace

Boolean indicator controls whether white-space and punctuation inside categorical feature levels should be replaced, default to TRUE.
ws_replace_with

Replacing symbol, default to ” which means all white-space and punctuation should be removed.
unique_id

A row unique identifier is crucial for in-database scoring of XGBoost model. If not given, SQL query will be generated with id name "ROW_KEY".
output_file_name

Optional, a file name that the SQL query will write to.
input_table_name

Name of raw data table in the database, that the SQL query will select from. If not given, SQL query will be generated with table name "INPUT_TABLE".

Value

A list of 1). meta data tracking the transformation; 2). matrix model.matrix is the data after processing which is ready for XGBoost fitting; 3). SQL query sql performing the exact one-hot encoding in the database.

Examples

library(data.table)
### load test data
df = data.frame(ggplot2::diamonds)
head(df)

d1 = data.frame(ggplot2::diamonds)
d1[1,2] = NA  # NA on 1st row cut
d1[2,5] = NA  # NA on 2nd row depth
head(d1)

d2 = data.table(ggplot2::diamonds)
d2[, cut:=factor(cut, ordered=FALSE)]
d2[, clarity:=as.character(clarity)]
d2[, tsdt:=as.IDate('2017-01-05')]
d2[1:3, tsdt:=tsdt-1]
head(d2)

### out is obtained for training data
out <- onehot2sql(df)
out1 <- onehot2sql(d1)  # NA is kept in the output
out2 <- onehot2sql(d2)  # all non-numeric features will be treated as categorical

### perform same transformation for new data when meta is given
# test-1: new data has column class change
newdata = df[1:5,]
newdata$cut = as.character(newdata$cut)
onehot2sql(newdata, meta=out$meta)$model.matrix

# test-2: new data has NA
newdata = df[1:5,]
newdata[1,1]=NA; newdata[2,1]=NA; newdata[3,2]=NA; newdata[3,3]=NA; newdata[5,4]=NA
onehot2sql(newdata, meta=out$meta)$model.matrix

# test-3: newdata has column with new elements
newdata = d2[1:5,]
newdata[5,clarity:='NEW']; newdata[1,tsdt:=as.IDate('2017-05-01')]
onehot2sql(newdata, meta=out2$meta)$model.matrix

# test-4: newdata has new columns
newdata = d2[1:5,]
newdata[,new_col:=1]
onehot2sql(newdata, meta=out2$meta)$model.matrix

# test-5: newdata is lacking some columns
newdata = d2[1:5,]
newdata[,cut:=NULL]
onehot2sql(newdata, meta=out2$meta)$model.matrix