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Updated feature selection in modelizer function (see comment on lines 166/167)

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Erik de Vries 6 years ago
parent 65f8c26ec6
commit 919e71ac68
1 changed files with 4 additions and 2 deletions
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R/modelizer.R
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@ -163,14 +163,16 @@ modelizer <- function(dfm, cores_outer, cores_grid, cores_inner, cores_feats, se
dfm_train <- dfm_trim(dfm_train, min_termfreq = 1, min_docfreq = 0) dfm_train <- dfm_trim(dfm_train, min_termfreq = 1, min_docfreq = 0)
dfreq <- docfreq(dfm_train, scheme = "inverse", base = 10, smoothing = 0, k = 0, threshold = 0, use.names=T) dfreq <- docfreq(dfm_train, scheme = "inverse", base = 10, smoothing = 0, k = 0, threshold = 0, use.names=T)
dfm_train <- custom_tfidf(dfm_train, scheme_tf = "count", scheme_df = "inverse", base = 10, dfreq = dfreq) dfm_train <- custom_tfidf(dfm_train, scheme_tf = "count", scheme_df = "inverse", base = 10, dfreq = dfreq)
words <- unlist(mclapply(unique(docvars(dfm_train, class_type)), # Added unique to filter out duplicate words, these are caused when there are multiple categories, and a words scores higher
# than the threshold on two or more of those categories
words <- unique(unlist(mclapply(unique(docvars(dfm_train, class_type)),
feat_select, feat_select,
dfm = dfm_train, dfm = dfm_train,
class_type = class_type, class_type = class_type,
percentile = params$percentiles, percentile = params$percentiles,
measure = params$measures, measure = params$measures,
mc.cores = cores_feats mc.cores = cores_feats
)) )))
dfm_train <- dfm_keep(dfm_train, words, valuetype="fixed", verbose=T) dfm_train <- dfm_keep(dfm_train, words, valuetype="fixed", verbose=T)

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