Deletes Rows from Tables Other than wflow_tbl
rpwf_db_del_entry.RdThe deletion of workflows from wflow_tbl is specified separately to
avoid mistakes.
Arguments
- tbls
vector of character of table names, i.e., "df_tbl", "model_type_tbl", "r_grid_tbl", "r_grid_tbl", "wflow_result_tbl".
- id
vector of ids to be deleted from a particular table.
- db_con
an
rpwf_connect_db()object.
Examples
board <- pins::board_temp()
tmp_dir <- tempdir()
db_con <- rpwf_connect_db(paste(tmp_dir, "db.SQLite", sep = "/"), board)
# Before deleting
DBI::dbGetQuery(db_con$con, "SELECT * FROM model_type_tbl;")
#> model_type_id py_module py_base_learner r_engine
#> 1 1 sklearn.linear_model LogisticRegression glmnet
#> 2 2 sklearn.linear_model ElasticNet glmnet
#> 3 3 sklearn.svm SVC kernlab
#> 4 4 sklearn.svm SVR kernlab
#> 5 5 xgboost XGBClassifier xgboost
#> 6 6 sklearn.ensemble RandomForestClassifier rpart
#> hyper_par_rename
#> 1 {"penalty":["C"],"mixture":["l1_ratio"]}
#> 2 {"penalty":["alpha"],"mixture":["l1_ratio"]}
#> 3 {"cost":["C"],"degree":["degree"],"scale_factor":["gamma"],"rbf_sigma":["gamma"],"kernel_offset":["coef0"]}
#> 4 {"cost":["C"],"degree":["degree"],"scale_factor":["gamma"],"rbf_sigma":["gamma"],"kernel_offset":["coef0"],"margin":["epsilon"]}
#> 5 {"mtry":["colsample_bytree"],"trees":["n_estimators"],"min_n":["min_child_weight"],"tree_depth":["max_depth"],"learn_rate":["learning_rate"],"loss_reduction":["gamma"],"sample_size":["subsample"]}
#> 6 {"cost_complexity":["ccp_alpha"],"tree_depth":["max_depth"],"min_n":["min_samples_split"]}
#> model_mode
#> 1 classification
#> 2 regression
#> 3 classification
#> 4 regression
#> 5 classification
#> 6 classification
rpwf_db_del_entry("model_type_tbl", 1, db_con)
# After deleting
DBI::dbGetQuery(db_con$con, "SELECT * FROM model_type_tbl;")
#> model_type_id py_module py_base_learner r_engine
#> 1 2 sklearn.linear_model ElasticNet glmnet
#> 2 3 sklearn.svm SVC kernlab
#> 3 4 sklearn.svm SVR kernlab
#> 4 5 xgboost XGBClassifier xgboost
#> 5 6 sklearn.ensemble RandomForestClassifier rpart
#> hyper_par_rename
#> 1 {"penalty":["alpha"],"mixture":["l1_ratio"]}
#> 2 {"cost":["C"],"degree":["degree"],"scale_factor":["gamma"],"rbf_sigma":["gamma"],"kernel_offset":["coef0"]}
#> 3 {"cost":["C"],"degree":["degree"],"scale_factor":["gamma"],"rbf_sigma":["gamma"],"kernel_offset":["coef0"],"margin":["epsilon"]}
#> 4 {"mtry":["colsample_bytree"],"trees":["n_estimators"],"min_n":["min_child_weight"],"tree_depth":["max_depth"],"learn_rate":["learning_rate"],"loss_reduction":["gamma"],"sample_size":["subsample"]}
#> 5 {"cost_complexity":["ccp_alpha"],"tree_depth":["max_depth"],"min_n":["min_samples_split"]}
#> model_mode
#> 1 regression
#> 2 classification
#> 3 regression
#> 4 classification
#> 5 classification