some neurobiber PCA analysis
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Matthew Gaughan
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a96fd6db2f
commit
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18
mgaughan-rstudio-server_28911380.out
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18
mgaughan-rstudio-server_28911380.out
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1. SSH tunnel from your workstation using the following command:
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ssh -N -L 8787:n3441:47269 mjilg@klone.hyak.uw.edu
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and point your web browser to http://localhost:8787
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2. log in to RStudio Server using the following credentials:
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user: mjilg
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password: 9Qgk9UkRdmKalTKyDmH4
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When done using RStudio Server, terminate the job by:
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1. Exit the RStudio Session ("power" button in the top right corner of the RStudio window)
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2. Issue the following command on the login node:
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scancel -f 28911380
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[2025-09-05T14:55:26.103] error: *** JOB 28911380 ON n3441 CANCELLED AT 2025-09-05T14:55:26 DUE TO TIME LIMIT ***
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74
p2/quest/neurobiber_PCA_analysis.R
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p2/quest/neurobiber_PCA_analysis.R
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library(tidyverse)
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neurobiber_description_pca_csv <-"~/p2/quest/090425_description_PCA_df.csv"
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neurobiber_description_pca_df <- read.csv(neurobiber_description_pca_csv , header = TRUE)
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neurobiber_subcomment_pca_csv <-"~/p2/quest/090425_subcomment_PCA_df.csv"
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neurobiber_subcomment_pca_df <- read.csv(neurobiber_subcomment_pca_csv , header = TRUE)
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# look at correlation between PC1, PC2, and different outcome variables
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library(dplyr)
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description_anova_results <- neurobiber_description_pca_df %>%
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group_by(source) %>%
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group_map(~ summary(aov(PC2 ~ phase, data = .x)), .keep = TRUE)
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description_anova_results
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discussion_anova_results <- neurobiber_subcomment_pca_df %>%
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group_by(source) %>%
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group_map(~ summary(aov(PC2 ~ phase, data = .x)), .keep = TRUE)
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discussion_anova_results
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# look at the representative comments for PC1 and PC2
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top5 <- neurobiber_subcomment_pca_df %>%
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filter(source=="c2") |>
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arrange(desc(PC2)) %>%
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slice(15:30) %>%
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pull(text)
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bottom5 <- neurobiber_subcomment_pca_df %>%
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filter(source=="c2") |>
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arrange(PC2) %>%
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slice(15:30) %>%
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pull(text)
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cat("Top 15:30 comment_text by score:\n")
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print(top5)
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cat("\nBottom 15:30 comment_text by score:\n")
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print(bottom5)
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aggregated_neurobiber_description_pca_df <- neurobiber_description_pca_df |>
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group_by(AuthorWMFAffil, week_index, source, priority) %>%
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summarise(mean_PC1 = median(PC1),
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mean_PC2 = median(PC2),
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mean_PC3 = median(PC3),
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mean_PC4 = median(PC4),
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mean_PC5 = median(PC5))
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library(scales)
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library(ggplot2)
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affiliationColors <-
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setNames( c('#5da2d8', '#c7756a')
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,c("False", "True"))
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long_df <- aggregated_neurobiber_description_pca_df %>%
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tidyr::pivot_longer(
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cols = starts_with("mean_PC"),
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names_to = "PC",
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values_to = "PC_value"
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)
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ggplot(long_df, aes(x = week_index, y = PC_value, color = AuthorWMFAffil, group = AuthorWMFAffil)) +
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geom_line(size = 1) +
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facet_grid(PC ~ source, scales = "free_y") +
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scale_color_manual(values = affiliationColors, name = "WMF Affiliation") +
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scale_x_continuous(breaks = pretty_breaks()) +
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scale_y_continuous(limits = c(-10, 10)) +
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labs(x = "Week Index", y = "Mean PC Value",
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title = "Weekly Median PC Values by Source and PC, Colored by WMF Affiliation") +
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theme_minimal(base_size = 14) +
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theme(legend.position = "top")
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@ -1,147 +0,0 @@
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library(tidyverse)
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neurobiber_csv <-"~/p2/quest/072525_pp_biberplus_labels.csv"
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neurobiber_df <- read.csv(neurobiber_csv , header = TRUE)
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normalized_cols <- grep("^normalized_", names(neurobiber_df), value = TRUE)
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neurobiber_df$normalized_features_vec <- lapply(
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asplit(neurobiber_df[, normalized_cols], 1), as.numeric
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)
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library(dplyr)
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# duplicate, declined, invalid -> declined
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# stalled, open, progress -> open
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# resolved -> resolved
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neurobiber_df <- neurobiber_df |>
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filter(comment_type == "task_description") |>
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mutate(
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task_status = case_when(
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status %in% c("duplicate", "declined", "invalid") ~ "declined",
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status %in% c("stalled", "open", "progress") ~ "open",
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status == "resolved" ~ "resolved",
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TRUE ~ status # fallback for unexpected values
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))
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X <- do.call(rbind, neurobiber_df$normalized_features_vec)
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library(coop)
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#cos_sim1 <- coop::cosine(t(X))
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register_means <- aggregate(
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X,
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by = list(
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outcome= neurobiber_df$task_status,
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source = neurobiber_df$source,
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affiliation = neurobiber_df$AuthorWMFAffil
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),
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FUN = mean
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)
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feature_mat <- as.matrix(register_means[, -(1:3)])
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cos_sim_matrix <- coop::cosine(t(feature_mat))
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rownames(cos_sim_matrix) <- apply(register_means[, 1:3], 1, paste, collapse = "_")
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colnames(cos_sim_matrix) <- rownames(cos_sim_matrix)
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#finding the most dissimilar pairs
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compare_feature_vectors <- function(
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pair1, pair2,
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cos_sim_matrix,
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feature_mat,
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normalized_cols,
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top_n = 5
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) {
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# Allow for both index and name input
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if (is.character(pair1)) row_idx <- which(rownames(cos_sim_matrix) == pair1) else row_idx <- pair1
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if (is.character(pair2)) col_idx <- which(colnames(cos_sim_matrix) == pair2) else col_idx <- pair2
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# Get feature vectors
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vec1 <- feature_mat[row_idx, ]
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vec2 <- feature_mat[col_idx, ]
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# Feature-wise absolute differences
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feature_diff <- abs(vec1 - vec2)
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top_features_idx <- order(feature_diff, decreasing = TRUE)[1:top_n]
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top_features <- names(feature_diff)[top_features_idx]
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top_diffs <- feature_diff[top_features_idx]
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# Map Vxx to normalized column names
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feature_nums <- as.integer(sub("V", "", top_features))
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feature_colnames <- normalized_cols[feature_nums]
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# Determine which vector is larger for each feature
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larger_in <- ifelse(vec1[top_features_idx] > vec2[top_features_idx],
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rownames(cos_sim_matrix)[row_idx],
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colnames(cos_sim_matrix)[col_idx])
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# Assemble results
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top_features_df <- data.frame(
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feature = top_features,
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normalized_colname = feature_colnames,
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vec1_value = vec1[top_features_idx],
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vec2_value = vec2[top_features_idx],
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abs_difference = top_diffs,
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larger_in = larger_in
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)
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# Print pair and return
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cat("Comparing:", rownames(cos_sim_matrix)[row_idx], "and", colnames(cos_sim_matrix)[col_idx], "\n")
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print(top_features_df)
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invisible(top_features_df)
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}
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compare_feature_vectors("resolved_c1_True", "resolved_c2_True", cos_sim_matrix, feature_mat, normalized_cols, top_n = 10)
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#plotting stuff beneath here
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annotation_row <- data.frame(
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affiliation = register_means$affiliation,
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source = register_means$source
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)
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rownames(annotation_row) <- rownames(cos_sim_matrix)
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annotation_col <- data.frame(
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affiliation = register_means$affiliation,
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source = register_means$source
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)
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rownames(annotation_col) <- colnames(cos_sim_matrix)
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annotation_row <- annotation_row |>
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mutate(affil = case_when(
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affiliation == "True" ~ "WMF",
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affiliation == "False" ~ "non-WMF"
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)) |> select(-affiliation)
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annotation_col <- annotation_col |>
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mutate(affil = case_when(
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affiliation == "True" ~ "WMF",
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affiliation == "False" ~ "non-WMF"
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)) |> select(-affiliation)
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my_annotation_colors = list(
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affil = c("WMF" = "green", "non-WMF" = "purple"),
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source = c(c1 = "lightgrey", c2 = "grey", c3 = "black")
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)
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cos_sim_matrix[lower.tri(cos_sim_matrix)] <- NA
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#pheatmap(scaled_mat, symm = TRUE)
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#heatmap(cos_sim_matrix, col=heat.colors(256), breaks=seq(-1, 1, length.out=257))
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library(viridis)
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library(pheatmap)
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fossy_heatmap <- pheatmap(cos_sim_matrix,
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cluster_rows = FALSE,
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cluster_cols = FALSE,
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scale='none',
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annotation_row = annotation_row,
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annotation_col = annotation_col,
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annotation_colors = my_annotation_colors,
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na_col = "white")
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#ggsave(filename = "073125_FOSSY_comm_heatmap.png", plot = fossy_heatmap, width = 9, height = 9, dpi = 800)
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#diag(cos_sim_matrix) <- NA
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#which(cos_sim_matrix == max(cos_sim_matrix, na.rm = TRUE), arr.ind = TRUE) # Most similar
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#which(cos_sim_matrix == min(cos_sim_matrix, na.rm = TRUE), arr.ind = TRUE) # Least similar
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