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Fix the bug for dropping highly correlated features

data_cleaning
Meng Li 2021-10-19 14:32:40 +00:00
parent 9f0db3bedd
commit 3e7b9260d2
1 changed files with 13 additions and 9 deletions
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22
src/features/utils/clean_sensor_features.R
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@ -14,7 +14,7 @@ data_yielded_hours_ratio_threshold <- as.numeric(snakemake@params[["data_yielded
corr_valid_pairs_threshold <- as.numeric(snakemake@params[["corr_valid_pairs_threshold"]]) corr_valid_pairs_threshold <- as.numeric(snakemake@params[["corr_valid_pairs_threshold"]])
corr_threshold <- as.numeric(snakemake@params[["corr_threshold"]]) corr_threshold <- as.numeric(snakemake@params[["corr_threshold"]])
# drop rows with the value of "phone_data_yield_rapids_ratiovalidyieldedhours" column less than data_yielded_hours_ratio_threshold # drop rows with the value of "phone_data_yield_rapids_ratiovalidyieldedhours" column less or equal than data_yielded_hours_ratio_threshold
clean_features <- clean_features %>% clean_features <- clean_features %>%
filter(phone_data_yield_rapids_ratiovalidyieldedhours > data_yielded_hours_ratio_threshold) filter(phone_data_yield_rapids_ratiovalidyieldedhours > data_yielded_hours_ratio_threshold)
@ -32,19 +32,23 @@ features_for_corr <- clean_features %>%
valid_pairs <- crossprod(!is.na(features_for_corr)) >= corr_valid_pairs_threshold * nrow(features_for_corr) valid_pairs <- crossprod(!is.na(features_for_corr)) >= corr_valid_pairs_threshold * nrow(features_for_corr)
highly_correlated_features <- features_for_corr %>% if((dim(features_for_corr)[1] != 0) & (dim(features_for_corr)[2] != 0)){
correlate(use = "pairwise.complete.obs", method = "spearman") %>%
column_to_rownames(., var = "term") %>%
as.matrix() %>%
replace(!valid_pairs | is.na(.), 0) %>%
findCorrelation(., cutoff = corr_threshold, verbose = F, names = T)
clean_features <- clean_features[, !names(clean_features) %in% highly_correlated_features] highly_correlated_features <- features_for_corr %>%
correlate(use = "pairwise.complete.obs", method = "spearman") %>%
column_to_rownames(., var = "term") %>%
as.matrix() %>%
replace(!valid_pairs | is.na(.), 0) %>%
findCorrelation(., cutoff = corr_threshold, verbose = F, names = T)
clean_features <- clean_features[, !names(clean_features) %in% highly_correlated_features]
}
# drop rows with a percentage of NA values above rows_nan_threshold # drop rows with a percentage of NA values above rows_nan_threshold
clean_features <- clean_features %>% clean_features <- clean_features %>%
mutate(percentage_na = rowSums(is.na(.)) / ncol(.)) %>% mutate(percentage_na = rowSums(is.na(.)) / ncol(.)) %>%
filter(percentage_na < rows_nan_threshold) %>% filter(percentage_na <= rows_nan_threshold) %>%
select(-percentage_na) select(-percentage_na)
write.csv(clean_features, snakemake@output[[1]], row.names = FALSE) write.csv(clean_features, snakemake@output[[1]], row.names = FALSE)