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-- Lua script to perform bayes classification (multi-class)
-- This script accepts the following parameters:
-- key1 - prefix for bayes tokens (e.g. for per-user classification)
-- key2 - class labels: table of all class labels as "TABLE:label1,label2,..."
-- key3 - set of tokens encoded in messagepack array of strings
local prefix = KEYS[1]
local class_labels_arg = KEYS[2]
local input_tokens = cmsgpack.unpack(KEYS[3])
-- Parse class labels (always expect TABLE: format)
local class_labels = {}
if string.match(class_labels_arg, "^TABLE:") then
local labels_str = string.sub(class_labels_arg, 7) -- Remove "TABLE:" prefix
for label in string.gmatch(labels_str, "([^,]+)") do
table.insert(class_labels, label)
end
else
-- Legacy single class - convert to array
class_labels = { class_labels_arg }
end
-- Get learned counts for all classes (ordered)
local learned_counts = {}
for _, label in ipairs(class_labels) do
local key = 'learns_' .. string.lower(label)
-- Handle legacy keys for backward compatibility
if label == 'H' then
key = 'learns_ham'
elseif label == 'S' then
key = 'learns_spam'
end
table.insert(learned_counts, tonumber(redis.call('HGET', prefix, key)) or 0)
end
-- Get token data for all classes (ordered)
local token_results = {}
for i, _ in ipairs(class_labels) do
token_results[i] = {}
end
-- Check if we have any learning data
local has_learns = false
for _, count in ipairs(learned_counts) do
if count > 0 then
has_learns = true
break
end
end
if has_learns then
-- Process each token
for i, token in ipairs(input_tokens) do
local token_data = redis.call('HMGET', token, unpack(class_labels))
if token_data then
for j, _ in ipairs(class_labels) do
local count = token_data[j]
if count and tonumber(count) > 0 then
table.insert(token_results[j], { i, tonumber(count) })
end
end
end
end
end
-- Always return ordered arrays: [learned_counts_array, token_results_array]
return { learned_counts, token_results }
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