The input risk factors should be a data.frame or a
tibble, which contains all raw risk factors
value and should not contians user_id and others. The
score_config_list should be a list, *_values should be in front
of *_scores.
rf2score(rf_df, score_config_list)
| rf_df | Risk factors data frame(exclude user_id) |
|---|---|
| score_config_list | A List contains all riskFactors value and score index, read from .RDS file. |
a tibble with every risk factor score
You should carefull about the risk facotrs rf_df names and the
score_config_list names. *_values/*_scores and the risk
factors names should be the same.
# NOT RUN { require(dplyr) score_config_list <- readRDS("result/score_config_list.RDS") pj_rf <- readr::read_csv("data/pingjia/pingjia_result.csv",col_names = FALSE) nameTmp <- c("acc_count_phk","act_radius","dec_count_phk","high_curv_tr","holiday_tr", "interstate_r","lane_change_phk","late_night_tr","long_tr","main_act_prov" , "mileage","speeding_lvl", "speeding_phk", "trip_dis_e", "turn_count_phk","user_id") names(pj_rf) <- nameTmp risk_factor_names <- setdiff(nameTmp, c("user_id","main_act_prov")) pj_rf <- pj_rf %>% select(!!risk_factor_names) rf2score(pj_rf, score_config_list) # }