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/**
*
*Basic confidence score should be computed and returned for each item in the results.
* The score should range between 0-1, and take into consideration as many factors as possible.
*
* Some factors to consider:
*
* - number of results from ES
* - score of item within the range of highest-lowest scores from ES (within the returned set)
* - linguistic match of query
* - detection (or specification) of query type. i.e. an address shouldn't match an admin address.
*/
var stats = require('stats-lite');
var logger = require('pelias-logger').get('api');
var RELATIVE_SCORES = true;
function setup(peliasConfig) {
RELATIVE_SCORES = peliasConfig.hasOwnProperty('relativeScores') ? peliasConfig.relativeScores : true;
return computeScores;
}
function computeScores(req, res, next) {
// do nothing if no result data set
if (!req.results || !req.results.data || !req.results.meta) {
return next();
}
// compute standard deviation and mean from all scores
var scores = req.results.meta.scores;
var stdev = computeStandardDeviation(scores);
var mean = stats.mean(scores);
// loop through data items and determine confidence scores
req.results.data = req.results.data.map(computeConfidenceScore.bind(null, req, mean, stdev));
next();
}
/**
* Check all types of things to determine how confident we are that this result
* is correct. Score is based on overall score distribution in the result set
* as well as how closely the result matches the text parameters.
*
* @param {object} req
* @param {number} mean
* @param {number} stdev
* @param {object} hit
* @returns {object}
*/
function computeConfidenceScore(req, mean, stdev, hit) {
var dealBreakers = checkForDealBreakers(req, hit);
if (dealBreakers) {
hit.confidence = 0.5;
return hit;
}
var checkCount = 3;
hit.confidence = 0;
if (RELATIVE_SCORES) {
checkCount += 2;
hit.confidence += checkDistanceFromMean(hit._score, mean, stdev);
hit.confidence += computeZScore(hit._score, mean, stdev);
}
hit.confidence += checkName(req.clean.text, req.clean.parsed_text, hit);
hit.confidence += checkQueryType(req.clean.parsed_text, hit);
hit.confidence += checkAddress(req.clean.parsed_text, hit);
// TODO: look at categories and location
hit.confidence /= checkCount;
logger.debug('[confidence]:', hit.confidence, hit.name.default);
return hit;
}
function checkForDealBreakers(req, hit) {
if (!req.clean.parsed_text) {
return false;
}
if (req.clean.parsed_text.state && req.clean.parsed_text.state !== hit.admin1_abbr) {
logger.debug('[confidence][deal-breaker]: state !== admin1_abbr');
return true;
}
if (req.clean.parsed_text.postalcode && req.clean.parsed_text.postalcode !== hit.zip) {
logger.debug('[confidence][deal-breaker]: postalcode !== zip');
return true;
}
}
/**
* Check how statistically significant the score of this result is
* given mean and standard deviation
*
* @param {number} score
* @param {number} mean
* @param {number} stdev
* @returns {number}
*/
function checkDistanceFromMean(score, mean, stdev) {
return (score - mean) > stdev ? 1 : 0;
}
/**
* Compare text string or name component of parsed_text against
* default name in result
*
* @param {string} text
* @param {object|undefined} parsed_text
* @param {object} hit
* @returns {number}
*/
function checkName(text, parsed_text, hit) {
// parsed_text name should take precedence if available since it's the cleaner name property
if (parsed_text && parsed_text.name && hit.name.default.toLowerCase() === parsed_text.name.toLowerCase()) {
return 1;
}
// if no parsed_text check the text value as provided against result's default name
if (hit.name.default.toLowerCase() === text.toLowerCase()) {
return 1;
}
// if no matches detected, don't judge too harshly since it was a longshot anyway
return 0.7;
}
/**
* text being set indicates the query was for an address
* check if house number was specified and found in result
*
* @param {object|undefined} text
* @param {object} hit
* @returns {number}
*/
function checkQueryType(text, hit) {
if (!!text.number && (!hit.address || (hit.address && !hit.address.number))) {
return 0;
}
return 1;
}
/**
* Determine the quality of the property match
*
* @param {string|number|undefined|null} textProp
* @param {string|number|undefined|null} hitProp
* @param {boolean} expectEnriched
* @returns {number}
*/
function propMatch(textProp, hitProp, expectEnriched) {
// both missing, but expect to have enriched value in result => BAD
if (!textProp && !hitProp && expectEnriched) { return 0; }
// both missing, and no enrichment expected => GOOD
if (!textProp && !hitProp) { return 1; }
// text has it, result doesn't => BAD
if (textProp && !hitProp) { return 0; }
// text missing, result has it, and enrichment is expected => GOOD
if (!textProp && hitProp && expectEnriched) { return 1; }
// text missing, result has it, enrichment not desired => 50/50
if (!textProp && hitProp) { return 0.5; }
// both present, values match => GREAT
if (textProp && hitProp && textProp.toString().toLowerCase() === hitProp.toString().toLowerCase()) { return 1; }
// ¯\_(ツ)_/¯
return 0.7;
}
/**
* Check various parts of the parsed text address
* against the results
*
* @param {object} text
* @param {string|number} [text.number]
* @param {string} [text.street]
* @param {string} [text.postalcode]
* @param {string} [text.state]
* @param {string} [text.country]
* @param {object} hit
* @param {object} [hit.address]
* @param {string|number} [hit.address.number]
* @param {string} [hit.address.street]
* @param {string|number} [hit.zip]
* @param {string} [hit.admin1_abbr]
* @param {string} [hit.alpha3]
* @returns {number}
*/
function checkAddress(text, hit) {
var checkCount = 5;
var res = 0;
if (text && text.number && text.street) {
res += propMatch(text.number, (hit.address ? hit.address.number : null), false);
res += propMatch(text.street, (hit.address ? hit.address.street : null), false);
res += propMatch(text.postalcode, (hit.address ? hit.address.zip: null), true);
res += propMatch(text.state, hit.admin1_abbr, true);
res += propMatch(text.country, hit.alpha3, true);
res /= checkCount;
}
else {
res = 1;
}
return res;
}
/**
* z-scores have an effective range of -3.00 to +3.00.
* An average z-score is ZERO.
* A negative z-score indicates that the item/element is below
* average and a positive z-score means that the item/element
* in above average. When teachers say they are going to "curve"
* the test, they do this by computing z-scores for the students' test scores.
*
* @param {number} score
* @param {number} mean
* @param {number} stdev
* @returns {number}
*/
function computeZScore(score, mean, stdev) {
if (stdev < 0.01) {
return 0;
}
// because the effective range of z-scores is -3.00 to +3.00
// add 10 to ensure a positive value, and then divide by 10+3+3
// to further normalize to %-like result
return (((score - mean) / (stdev)) + 10) / 16;
}
/**
* Computes standard deviation given an array of values
*
* @param {Array} scores
* @returns {number}
*/
function computeStandardDeviation(scores) {
var stdev = stats.stdev(scores);
// if stdev is low, just consider it 0
return (stdev < 0.01) ? 0 : stdev;
}
module.exports = setup;