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policy_parser_classifier_evaluation.R
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{
library(tidyverse)
library(vroom)
library(data.table)
library(openxlsx)
library(scales)
library(caret)
}
# Read results
## Twitter
### Manual Coding
# twitter_sample_1_eval <- read.xlsx("evaluation_samples/evaluated/twitter_sample_1.xlsx") %>%
# #filter(!is.na(`_source.text`)) %>% # drop unclassified due to missing text
# #bind_rows(read.xlsx("evaluation_samples/evaluated/twitter_sample_1_missing.xlsx")) %>%
# select(!c(`_source.created_at`, `_source.text`)) %>% # drop unnecessary variables
# mutate(across(!c(doc_id, intercoder_sample, policy_field), ~ case_when(!is.na(.) ~ TRUE, .default = FALSE))) %>% # TRUE/FALSE indicator
# mutate(coder = 1)
twitter_sample_2_eval <- read_csv("evaluation_samples/evaluated/twitter_sample_2_new2.csv",
col_types = list(doc_id = "c", intercoder_sample = "l")) %>%
#filter(!is.na(`_source.text`)) %>% # drop unclassified due to missing text
# bind_rows(read_csv("evaluation_samples/evaluated/twitter_sample_2_missing.csv",
# col_types = list(doc_id = "c"))) %>%
select(!c(`_source.created_at`, `_source.text`)) %>% # drop unnecessary variables
mutate(across(!c(doc_id, intercoder_sample, policy_field), ~ case_when(!is.na(.) ~ TRUE, .default = FALSE))) %>% # TRUE/FALSE indicator
mutate(coder = 2)
twitter_sample_3_eval <- read.xlsx("evaluation_samples/evaluated/twitter_sample_3_new2.xlsx") %>%
#filter(!is.na(`_source.text`)) %>% # drop unclassified due to missing text
#bind_rows(read.xlsx("evaluation_samples/evaluated/twitter_sample_3_missing.xlsx")) %>%
select(!c(`_source.created_at`, `_source.text`)) %>% # drop unnecessary variables
mutate(across(!c(doc_id, intercoder_sample, policy_field), ~ case_when(!is.na(.) ~ TRUE, .default = FALSE))) %>% # TRUE/FALSE indicator
mutate(coder = 3)
# twitter_sample_1_eval <- twitter_sample_1_eval %>% # recode intercoder for sample where different field/ID combinations lead to 2 additional intercoder rows
# mutate(id = paste0(doc_id, policy_field)) %>% # temporary ID
# mutate(intercoder_sample = case_when(
# id %in% (twitter_sample_3_eval %>%
# transmute(id = paste0(doc_id, policy_field)) %>%
# pull()) ~ TRUE,
# .default = FALSE
# )) %>% select(!id)
### process intercoder sample
twitter_intercoder_sample <- twitter_sample_3_eval %>% filter(intercoder_sample) %>%
bind_rows(twitter_sample_2_eval %>% filter(intercoder_sample)) %>%
# bind_rows(twitter_sample_3_eval %>% filter(intercoder_sample)) %>%
summarise(score = sum(correct),
score_comp = sum(correct_comp),
.by = c(doc_id, policy_field)) %>%
mutate(correct = case_when(score >= 2 ~ TRUE, # same classification
score == 1 ~ NA, # Tie
.default = FALSE),
correct_comp = case_when(score_comp >= 2 ~ TRUE, # same classification
score_comp == 1 ~ NA, # Tie
.default = FALSE))
twitter_intercoder_sample %>%
filter(!is.na(correct)) %>%
summarise(correct = sum(correct), false = (n() - sum(correct)),
percentage_correct = sum(correct)/n()) %>%
mutate(unclear_classification = 300-(correct+false))
twitter_intercoder_sample %>%
filter(!is.na(correct_comp)) %>%
summarise(correct = sum(correct_comp), false = (n() - sum(correct_comp)),
percentage = sum(correct)/n())%>%
mutate(unclear_classification = 300-(correct+false))
twitter_intercoder_sample %>%
summarise(correct = sum(correct), false = (n() - sum(correct)),
percentage = sum(correct)/n(),
.by = policy_field)
twitter_coding_sample <- bind_rows(twitter_sample_2_eval,
twitter_sample_3_eval) %>%
filter(!intercoder_sample) %>% # drop intercoder tweets
bind_rows(twitter_intercoder_sample %>% mutate(intercoder_sample = FALSE)) %>% # add processed intercoder sample
mutate(classification = case_when((!intercoder_sample & correct) ~ "correct",
(intercoder_sample & score >= 2) ~ "correct",
(intercoder_sample & score == 1) ~ "unclear",
(intercoder_sample & score == 0) ~ "incorrect",
(!intercoder_sample & !correct) ~ "incorrect"))
twitter_coding_sample %>% summarise(n = n(), .by = classification)
#
# twitter_coding_sample <- bind_rows(twitter_sample_1_eval,
# twitter_sample_2_eval,
# twitter_sample_3_eval) %>%
# filter(!intercoder_sample) %>% # drop intercoder tweets
# bind_rows(twitter_intercoder_sample) %>% # add processed intercoder sample
# #filter(intercoder_sample) %>% # testing
# filter(rowSums(select(., !c(`_id`, intercoder_sample, coder))) > 0) %>% # drop rows with all FALSE values (uncoded)
# mutate(coder = case_when(intercoder_sample == TRUE ~ "intercoder",
# .default = as.character(coder))) %>%
# pivot_longer(!c(`_id`, intercoder_sample, coder),
# names_to = "policy_field",
# values_to = "coder_score")
#
#
# # Analyse fit
#
# threshold = 0.7 # set treshold for classifier here (what value should be considered a prediction?)
# threshold_quantile = 0.75
# n_highest = 3
# threshold_method = "threshold" # set to "threshold" (fixed threshold),
# # "threshold_quantile" (quantile of non-normalized score,
# # or "highest" (top n categories per doc) to switch between methods
#
# ## Twitter
#
# twitter_eval_sample <- tweet_classification %>%
# map(\(dat) dat$classified_documents) %>%
# rbindlist()
# if (threshold_method == "threshold_quantile"){
# twitter_eval_sample <- twitter_eval_sample %>%
# mutate(classification = case_when(
# score >= quantile(score, # set quantile-based threshold classification
# probs = threshold_quantile)[[1]] ~ TRUE,
# .default = FALSE
# ))
# }
# twitter_eval_sample <- twitter_eval_sample %>% # drop documents not in the coded sample
# filter(doc_id %in% twitter_coding_sample$`_id`) # some unclassified tweets are missing here, but appear in the manually coded sample
#
# twitter_eval <- twitter_coding_sample %>%
# rename(doc_id = `_id`) %>%
# left_join(twitter_eval_sample, by = c("doc_id", "policy_field")) %>%
# mutate(across(c(score, score_norm), ~ case_when(is.na(.) ~ 0, # fill NA values for unclassified tweets
# .default = .))) %>%
# mutate(across(c(score, score_norm), ~ case_when(sum(.) == 0 & policy_field == "none" ~ 1, # set none to 1 when no scoring for any policy fields in a doc
# .default = .)), .by = doc_id)
#
# if (threshold_method == "threshold"){
# twitter_eval <- twitter_eval %>%
# mutate(classification = case_when(score_norm > threshold ~ TRUE, .default = FALSE)) # add T/F classification indicator with threshold
# }
#
# if (threshold_method == "highest"){
# twitter_eval <- twitter_eval %>%
# mutate(classification = case_when(rank(desc(score_norm)) <= n_highest ~ TRUE, # select n highest policy scores in a doc
# .default = F), .by = doc_id)
# }
#
#
# ## Precision and Recall
#
# ### total
# { cat(paste("Method:", threshold_method, "\n"))
# confusionMatrix(twitter_eval %>%
# pull(classification) %>% as.factor(),
# twitter_eval %>% pull(coder_score) %>% as.factor(),
# mode = "everything", positive = "TRUE")}
#
# ### full printout by field
# for (field in unique(twitter_eval$policy_field)){
#
# cat("\n\n")
# cat(field)
# cat("\n")
#
# dat <- twitter_eval %>% filter(policy_field == field)
#
# truth <- as.factor(dat$coder_score)
# prediction <- as.factor(dat$classification)
#
# metrics <- confusionMatrix(prediction, truth,
# mode = "everything",
# positive = "TRUE")
#
# print(metrics)
# }
#
# ### overview table
# twitter_fields <- twitter_eval %>% split(twitter_eval$policy_field) %>%
# imap(\(dat, field)
# {
# truth <- as.factor(dat$coder_score)
# prediction <- as.factor(dat$classification)
#
# metrics <- confusionMatrix(prediction, truth,
# mode = "everything",
# positive = "TRUE")
#
# out <- tibble(policy_field = field,
# metric = names(metrics$overall),
# value = metrics$overall) %>%
# bind_rows(tibble(policy_field = field,
# metric = names(metrics$byClass),
# value = metrics$byClass))
#
# return(out)
# }) %>%
# rbindlist()
#
# twitter_fields %>% pivot_wider(names_from = policy_field,
# values_from = value)
#
# twitter_fields %>% filter(metric == "F1" & !is.na(value)) %>% pull(value) %>% mean()
#
# # for (field in unique(twitter_eval$policy_field)) {
# # cat(paste("\n\n", field, "\n"))
# # twitter_eval %>% filter(policy_field == field) %>%
# # pull(score) %>% quantile() %>% print()
# # }
#
# ## Other evaluations
#
# twitter_eval %>% summarise(mean_score = mean(score_norm),
# .by = c(policy_field, coder_score)) %>%
# ggplot(aes(x = coder_score, y = mean_score)) +
# geom_col() +
# facet_wrap(vars(policy_field)) +
# labs(title = "Mean Scores of Policy Classification over Manual Coding, by Policy Field")
#
# twitter_eval %>%
# ggplot(aes(x = coder_score, y = score_norm)) +
# geom_point() +
# facet_wrap(vars(policy_field)) +
# labs(title = "Scores of Policy Classification over Manual Coding, by Policy Field")
#
# twitter_eval %>%
# ggplot(aes(x = policy_field, y = score_norm, color = coder_score)) +
# geom_point() +
# labs(title = "Scores of Policy Classification over Policy Field")
#
#
# ### number of overlapping categories (for threshold_method = "highest")
# twitter_eval %>%
# summarise(overlap = sum(coder_score & classification), .by = doc_id) %>%
# ggplot(aes(x = overlap)) +
# geom_histogram()
#
# twitter_eval %>%
# summarise(overlap = sum(coder_score & classification), .by = doc_id) %>%
# summarise(percentage = n() / nrow(.), .by = overlap)