Stub script to organize schoolName data

scripts/buildSchoolGraph.coffee

@@ -0,0 +1,113 @@
+# Organize our users' schoolNames.
+
+database = require '../server/commons/database'
+mongoose = require 'mongoose'
+log = require 'winston'
+async = require 'async'
+
+### SET UP ###
+do (setupLodash = this) ->
+  GLOBAL._ = require 'lodash'
+  _.str = require 'underscore.string'
+  _.mixin _.str.exports()
+  GLOBAL.tv4 = require('tv4').tv4
+
+database.connect()
+
+UserHandler = require '../server/users/user_handler'
+User = require '../server/users/User'
+
+startDate = new Date 2015, 11, 1
+
+query = dateCreated: {$gt: startDate}, emailLower: {$exists: true}
+selection = 'name emailLower schoolName courseInstances clans ageRange dateCreated referrer points'
+User.find(query).select(selection).lean().exec (err, users) ->
+  usersWithSchools = _.filter users, 'schoolName'
+  schoolNames = _.uniq (u.schoolName for u in usersWithSchools)
+  log.info "Found #{usersWithSchools.length} users of #{users.length} users registered after #{startDate} with schools like:\n#{schoolNames.slice(0, 10).join('\n')}"
+
+  # For each user, come up with a confidence that their school is correct.
+  # For users with low confidence, look for similarities to other users with high confidence.
+  # If we have enough data, prompt to update the school.
+  # After each update, recalculate confidence to find the next user with low confidence.
+
+  # How do we come up with confidence estimate?
+  # If there are many students with the same school name, it's either correct or a rename must happen.
+  # If the school name is unique but similar to a school name with many students, it's probably incorrect.
+  #   But if we determine it is correct, how can we record this fact so it doesn't keep asking?
+
+  # How can we infer the school name when we think it's not correct?
+  # We look for users with confident schoolNames in shared courseInstances.
+  # ... in shared clans.
+  # ... with the same lastIP that doesn't cover the lastIP of students from multiple schools.
+  # If we find a school-district-formatted email domain, we could try to match to other schoolNames in that domain, but I doubt that will be helpful until we have a lot of data and a lot of time to manually look things up.
+
+  # TODO: do all this work when we actually have a bunch of schoolNames in the system, or these heuristics won't be well-calibrated.
+
+  nextPrompt users
+
+nextPrompt = (users) ->
+  return console.log('Done.') or process.exit() unless [userToSchool, suggestions] = findUserToSchool users
+  prompt "What should the school for #{JSON.stringify(userToSchool)} be?\nSuggestions: #{suggestions}\n", (answer) ->
+    return console.log('Bye.') or process.exit() if answer in ['q', 'quit']
+    console.log "You said #{answer}, so we should do something about that."
+    nextPrompt users
+
+findUserToSchool = (users) ->
+  users.sort (a, b) -> b.points - a.points
+  usersWithSchools = _.filter users, 'schoolName'
+  schoolNames = _.uniq (u.schoolName for u in usersWithSchools)
+  return [users[0], schoolNames]
+
+
+# https://github.com/joshaven/string_score
+stringScore = (_a, word, fuzziness) ->
+  return 1 if word is _a
+  return 0 if word is ""
+
+  runningScore = 0
+  string = _a
+  lString = string.toLowerCase()
+  strLength = string.length
+  lWord = word.toLowerCase()
+  wordLength = word.length
+  startAt = 0
+  fuzzies = 1
+
+  if fuzziness
+    fuzzyFactor = 1 - fuzziness
+
+  if fuzziness
+    for i in [0...wordLength]
+      idxOf = lString.indexOf lWord[i], startAt
+      if idxOf is -1
+        fuzzies += fuzzyFactor
+      else
+        if startAt is idxOf
+          charScore = 0.7
+        else
+          charScore = 0.1
+          charScore += 0.8 if string[idxOf - 1] is ' '
+        charScore += 0.1 if string[idxOf] is word[i]
+        runningScore += charScore
+        startAt = idxOf + 1
+  else
+    for i in [0...wordLength]
+      idxOf = lString.indexOf lWord[i], startAt
+    return 0 if idxOf is -1
+    if startAt is idxOf
+      charScore = 0.7
+    else
+      charScore = 0.1
+      charScore += 0.8 if string[idxOf - 1] is word[i]
+      runningScore += charScore
+      startAt = idxOf + 1
+  finalScore = 0.5 * (runningScore / strLength + runningScore / wordLength) / fuzzies
+  finalScore += 0.15 if lWord[0] is lString[0] and finalScore < 0.85
+  finalScore
+
+prompt = (question, callback) ->
+  process.stdin.resume()
+  process.stdout.write question
+  process.stdin.once 'data', (data) ->
+    callback data.toString().trim()