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mw-lifecycle-analysis/p2/quest/neurobiber_cosine.R
2025-07-29 14:25:19 -07:00

62 lines
2.0 KiB
R

library(tidyverse)
neurobiber_csv <-"~/p2/quest/072525_pp_biberplus_labels.csv"
neurobiber_df <- read.csv(neurobiber_csv , header = TRUE)
normalized_cols <- grep("^normalized_", names(neurobiber_df), value = TRUE)
neurobiber_df$normalized_features_vec <- lapply(
asplit(neurobiber_df[, normalized_cols], 1), as.numeric
)
library(dplyr)
# duplicate, declined, invalid -> declined
# stalled, open, progress -> open
# resolved -> resolved
neurobiber_df <- neurobiber_df |>
filter(comment_type == "task_description") |>
mutate(
task_status = case_when(
status %in% c("duplicate", "declined", "invalid") ~ "declined",
status %in% c("stalled", "open", "progress") ~ "open",
status == "resolved" ~ "resolved",
TRUE ~ status # fallback for unexpected values
))
X <- do.call(rbind, neurobiber_df$normalized_features_vec)
library(coop)
#cos_sim1 <- coop::cosine(t(X))
register_means <- aggregate(
X,
by = list(
priority = neurobiber_df$priority,
outcome= neurobiber_df$task_status,
phase = neurobiber_df$phase,
source = neurobiber_df$source,
affiliation = neurobiber_df$AuthorWMFAffil
),
FUN = mean
)
feature_mat <- as.matrix(register_means[, -(1:5)])
cos_sim_matrix <- coop::cosine(t(feature_mat))
rownames(cos_sim_matrix) <- apply(register_means[, 1:5], 1, paste, collapse = "_")
colnames(cos_sim_matrix) <- rownames(cos_sim_matrix)
scaled_mat <- scale(cos_sim_matrix)
#pheatmap(scaled_mat, symm = TRUE)
#heatmap(cos_sim_matrix, col=heat.colors(256), breaks=seq(-1, 1, length.out=257))
library(viridis)
library(pheatmap)
pheatmap(cos_sim_matrix,
cluster_rows = FALSE, # Now features are clustered (rows)
cluster_cols = FALSE,
scale='none',
color = viridis(100)) # Standardize featu
diag(cos_sim_matrix) <- NA
which(cos_sim_matrix == max(cos_sim_matrix, na.rm = TRUE), arr.ind = TRUE) # Most similar
which(cos_sim_matrix == min(cos_sim_matrix, na.rm = TRUE), arr.ind = TRUE) # Least similar