Online PHP editor | perf for hGoPT

@ 2017-07-13T19:12:37Z

<?php

class Callcenter_Flagcustomer_Model_Observer {

	/**
	 * Possibly flag an address.
	 *
	 * Matching an existing flagged address is an inexact science.  We want
	 * to catch most trivial circumventions but we don't want to incorrectly
	 * flag good orders - the latter we care about MUCH more than the former.
	 *
	 * At the end of the day, I decided to do a basic normalized levenshtein
	 * comparison, and if there are more than 3 total mistakes between the
	 * flagged address and the incoming address, we flag the order.  Region
	 * and Country are exact matches due to not being input by hand.
	 *
	 * @param  array[string] $a1
	 * @param  array[string] $a2
	 * @return boolean
	 */
	public static function shouldFlag($a1, $a2) {
		if ($a1['region'] !== $a2['region']) {
			return false;
		} elseif ($a1['country'] !== $a2['country']) {
			return false;
		}

		$score = 0;
		$score += self::sCompare($a1['street'], $a2['street']);
		$score += self::compare($a1['city'], $a2['city']);
		$score += self::pCompare($a1['postcode'], $a2['postcode'], $a1['country'], $a2['country']);

		if ($score > 3) {
			return false;
		}

		return true;
	}

	/**
	 * Compare two normalized strings via levenshtein distance.
	 *
	 * @param  string $s1 String to compare.
	 * @param  string $s2 String to compare against.
	 * @return float      Score.
	 */
	private static function compare($s1, $s2) {
		$sn1 = self::normalize($s1);
		$sn2 = self::normalize($s2);

		$score = levenshtein($sn1, $sn2);

		return $score;
	}

	/**
	 * Compare two normalized postcodes via levenshtein distance.
	 *
	 * @param  string $s1 String to compare.
	 * @param  string $s2 String to compare against.
	 * @param  string $c1 Country of $s1.
	 * @param  string $c2 Country of $s2.
	 * @return float      Score.
	 */
	private static function pCompare($s1, $s2, $c1, $c2) {
		$sn1 = self::normalize($s1);
		$sn2 = self::normalize($s2);

		if (strtoupper($c1) === 'US' && strlen($sn1) === 9) {
			$sn1 = substr($sn1, 0, 5);
		}
		if (strtoupper($c2) === 'US' && strlen($sn2) === 9) {
			$sn2 = substr($sn2, 0, 5);
		}

		$score = levenshtein($sn1, $sn2);

		return $score;
	}

	/**
	 * Compare two normalized street names via levenshtein distance.
	 *
	 * @param  string $s1 String to compare.
	 * @param  string $s2 String to compare against.
	 * @return float      Score.
	 */
	private static function sCompare($s1, $s2) {
		$sn1 = self::sNormalize($s1);
		$sn2 = self::sNormalize($s2);

		$score = levenshtein($sn1, $sn2);

		return $score;
	}

	/**
	 * Normalize a string for comparison.
	 *
	 * @param  string $s String to normalize.
	 * @return string
	 */
	private static function normalize($s) {
		// Normalize to lowercase alphanumeric.
		$s = strtolower(preg_replace('/[^a-zA-Z0-9]+/', '', $s));
		return trim($s);
	}

	/**
	 * Normalize an address for comparison.  Has a few more steps that
	 * are specific to addresses.
	 *
	 * @param  string $s String to normalize.
	 * @return string
	 */
	private static function sNormalize($s) {
		static $mappings = [
			'apt' => 'apartment',
			'ave' => 'avenue',
			'blvd' => 'boulevard',
			'ct' => 'court',
			'dr' => 'drive',
			'e' => 'east',
			'n' => 'north',
			'ne' => 'northeast',
			'nw' => 'northwest',
			'pkwy' => 'parkway',
			'pl' => 'place',
			'rd' => 'road',
			's' => 'south',
			'se' => 'southeast',
			'st' => 'street',
			'sw' => 'southwest',
			'w' => 'west',
		];

		$from = [];
		$to = [];
		foreach ($mappings as $key => $value) {
			$from[] = ' '.$key.' ';
			$to[] = ' '.$value. ' ';
		}

		// Normalize to lowercase alphanumeric with spaces.
		$s = strtolower(preg_replace('/[^a-zA-Z0-9 ]+/', '', $s));

		// Ensure surrounded with spaces.
		$s = ' '.$s.' ';

		// Get rid of multiple spaces - turn into single spaces.
		$s = preg_replace('/\s{2,}/', ' ', $s);

		// Get rid of ordinal indicators.
		$s = preg_replace('/(\d+)(?:st|nd|rd|th)/', '\1', $s);

		// Replace street abbreviations.
		$s = str_replace($from, $to, $s);
		return trim($s);
	}
}

$result = Callcenter_Flagcustomer_Model_Observer::shouldFlag([
	'street' => '65 JANIS RD',
	'city' => 'WESTFIELD',
	'region' => 'MA',
	'postcode' => '01085-4016',
	'country' => 'US',
], [
	'street' => '65 JANIS RD',
	'city' => 'WESTFIELD',
	'region' => 'MA',
	'postcode' => '01085-4016',
	'country' => 'US',
]);

var_dump($result);

Here you find the average performance (time & memory) of each version. A grayed out version indicates it didn't complete successfully (based on exit-code).

Version	System time (s)	User time (s)	Memory (MiB)
8.3.6	0.011	0.004	18.43
8.3.5	0.012	0.006	18.23
8.3.4	0.000	0.015	19.10
8.3.3	0.011	0.004	18.92
8.3.2	0.006	0.003	20.20
8.3.1	0.008	0.000	23.70
8.3.0	0.003	0.005	19.69
8.2.18	0.016	0.006	16.75
8.2.17	0.006	0.012	22.96
8.2.16	0.010	0.005	19.17
8.2.15	0.007	0.007	24.18
8.2.14	0.008	0.000	24.66
8.2.13	0.008	0.000	19.21
8.2.12	0.006	0.009	26.35
8.2.11	0.009	0.000	20.46
8.2.10	0.004	0.008	18.03
8.2.9	0.005	0.003	19.26
8.2.8	0.005	0.003	17.97
8.2.7	0.006	0.006	17.88
8.2.6	0.003	0.006	18.04
8.2.5	0.000	0.009	18.10
8.2.4	0.003	0.006	20.30
8.2.3	0.010	0.000	19.56
8.2.2	0.004	0.004	18.24
8.2.1	0.005	0.003	18.35
8.2.0	0.000	0.007	18.27
8.1.27	0.004	0.004	23.91
8.1.26	0.000	0.008	26.35
8.1.25	0.005	0.003	28.09
8.1.24	0.009	0.000	21.16
8.1.23	0.008	0.003	19.23
8.1.22	0.009	0.000	17.91
8.1.21	0.004	0.004	18.77
8.1.20	0.003	0.006	17.60
8.1.19	0.008	0.000	17.48
8.1.18	0.003	0.005	18.10
8.1.17	0.005	0.005	18.96
8.1.16	0.004	0.004	19.07
8.1.15	0.003	0.006	20.75
8.1.14	0.004	0.004	19.64
8.1.13	0.000	0.007	18.90
8.1.12	0.004	0.004	17.58
8.1.11	0.000	0.009	17.50
8.1.10	0.000	0.008	17.66
8.1.9	0.000	0.007	17.52
8.1.8	0.007	0.000	17.63
8.1.7	0.005	0.002	17.63
8.1.6	0.006	0.003	17.60
8.1.5	0.003	0.006	17.65
8.1.4	0.006	0.003	17.68
8.1.3	0.004	0.004	17.89
8.1.2	0.000	0.008	17.74
8.1.1	0.008	0.000	17.75
8.1.0	0.004	0.004	17.56
8.0.30	0.008	0.000	18.77
8.0.29	0.004	0.004	16.88
8.0.28	0.003	0.003	18.42
8.0.27	0.003	0.003	17.31
8.0.26	0.000	0.007	17.01
8.0.25	0.005	0.003	17.15
8.0.24	0.008	0.000	17.02
8.0.23	0.003	0.003	17.10
8.0.22	0.004	0.004	17.08
8.0.21	0.003	0.003	16.98
8.0.20	0.004	0.004	17.19
8.0.19	0.003	0.006	17.19
8.0.18	0.007	0.000	17.03
8.0.17	0.004	0.004	17.07
8.0.16	0.009	0.000	16.96
8.0.15	0.004	0.004	17.10
8.0.14	0.008	0.000	17.04
8.0.13	0.003	0.003	13.44
8.0.12	0.000	0.008	17.10
8.0.11	0.004	0.003	16.96
8.0.10	0.008	0.000	17.07
8.0.9	0.000	0.007	16.96
8.0.8	0.013	0.010	17.09
8.0.7	0.004	0.004	17.09
8.0.6	0.005	0.003	17.14
8.0.5	0.004	0.004	16.90
8.0.3	0.008	0.008	17.24
8.0.2	0.014	0.008	17.44
8.0.1	0.002	0.005	17.13
8.0.0	0.013	0.010	16.79
7.4.33	0.000	0.005	15.55
7.4.32	0.006	0.000	16.74
7.4.30	0.003	0.003	16.65
7.4.29	0.007	0.000	16.84
7.4.28	0.000	0.007	16.89
7.4.27	0.003	0.003	16.74
7.4.26	0.003	0.003	16.73
7.4.25	0.005	0.003	16.83
7.4.24	0.004	0.004	16.75
7.4.23	0.007	0.000	16.65
7.4.22	0.012	0.006	16.75
7.4.21	0.007	0.007	16.86
7.4.20	0.000	0.007	16.78
7.4.16	0.013	0.003	16.70
7.4.15	0.009	0.009	17.40
7.4.14	0.013	0.005	17.86
7.4.13	0.004	0.013	16.72
7.4.12	0.011	0.011	16.92
7.4.11	0.009	0.009	16.62
7.4.10	0.015	0.003	16.70
7.4.9	0.019	0.003	16.71
7.4.8	0.004	0.014	19.39
7.4.7	0.013	0.004	16.66
7.4.6	0.007	0.010	16.64
7.4.5	0.012	0.003	16.67
7.4.4	0.006	0.012	16.66
7.4.3	0.006	0.010	16.75
7.4.0	0.007	0.006	15.23
7.3.33	0.003	0.003	13.40
7.3.32	0.000	0.006	13.46
7.3.31	0.000	0.007	16.36
7.3.30	0.004	0.004	16.49
7.3.29	0.009	0.006	16.56
7.3.28	0.009	0.010	16.53
7.3.27	0.014	0.007	17.40
7.3.26	0.009	0.008	16.54
7.3.25	0.011	0.009	16.66
7.3.24	0.009	0.009	16.85
7.3.23	0.009	0.009	16.63
7.3.21	0.012	0.010	16.54
7.3.20	0.008	0.008	16.56
7.3.19	0.010	0.013	16.75
7.3.18	0.009	0.014	16.92
7.3.17	0.012	0.006	16.54
7.3.16	0.013	0.003	16.79
7.3.12	0.003	0.016	15.21
7.3.11	0.006	0.006	14.89
7.3.10	0.003	0.013	15.23
7.3.9	0.008	0.004	15.26
7.3.8	0.009	0.006	14.70
7.3.7	0.007	0.007	14.93
7.3.6	0.007	0.011	15.13
7.3.5	0.007	0.007	15.18
7.3.4	0.000	0.013	15.13
7.3.3	0.000	0.009	15.13
7.3.2	0.007	0.007	16.60
7.3.1	0.006	0.009	16.63
7.3.0	0.011	0.007	16.86
7.2.33	0.009	0.009	17.03
7.2.32	0.011	0.006	17.12
7.2.31	0.011	0.007	16.72
7.2.30	0.007	0.010	16.95
7.2.29	0.003	0.013	17.06
7.2.25	0.007	0.014	15.29
7.2.24	0.000	0.016	15.12
7.2.23	0.006	0.009	15.05
7.2.22	0.004	0.011	15.44
7.2.21	0.003	0.007	15.36
7.2.20	0.004	0.008	15.42
7.2.19	0.009	0.009	15.33
7.2.18	0.012	0.003	15.28
7.2.17	0.009	0.006	15.37
7.2.11	0.010	0.014	16.89
7.2.9	0.347	0.003	14.87
7.2.8	0.256	0.003	15.19
7.2.7	0.062	0.010	15.36
7.2.6	0.015	0.008	15.45
7.2.5	0.090	0.006	15.38
7.2.4	0.017	0.010	15.18
7.2.3	0.019	0.005	15.18
7.2.2	0.289	0.013	15.35
7.2.1	0.205	0.013	15.38
7.2.0	0.012	0.006	15.42
7.1.33	0.006	0.009	15.64
7.1.32	0.003	0.007	16.16
7.1.31	0.004	0.011	16.02
7.1.30	0.006	0.003	15.73
7.1.29	0.008	0.008	15.98
7.1.28	0.009	0.000	16.01
7.1.27	0.007	0.007	15.94
7.1.26	0.005	0.005	15.84
7.1.21	0.061	0.012	14.30
7.1.20	0.236	0.003	14.20
7.1.19	0.018	0.000	13.98
7.1.18	0.018	0.007	14.33
7.1.17	0.017	0.000	14.30
7.1.16	0.022	0.006	14.19
7.1.15	0.012	0.008	14.09
7.1.14	0.262	0.007	14.29
7.1.13	0.311	0.010	14.20
7.1.12	0.009	0.009	14.09
7.1.11	0.010	0.010	14.38
7.1.10	0.047	0.003	14.26
7.1.9	0.055	0.003	14.27
7.1.8	0.031	0.008	14.24
7.1.7	0.018	0.000	14.13
7.1.6	0.111	0.012	32.44
7.1.5	0.033	0.015	32.32
7.1.4	0.021	0.024	32.35
7.1.3	0.027	0.010	32.34
7.1.2	0.142	0.000	32.23
7.1.1	0.086	0.003	14.07
7.1.0	0.116	0.003	14.04
7.0.31	0.061	0.010	13.79
7.0.30	0.009	0.010	13.80
7.0.29	0.013	0.008	13.77
7.0.28	0.015	0.003	13.87
7.0.27	0.025	0.011	13.50
7.0.26	0.009	0.012	13.67
7.0.25	0.015	0.004	13.89
7.0.24	0.019	0.010	14.01
7.0.23	0.197	0.004	13.97
7.0.22	0.215	0.007	13.93
7.0.21	0.135	0.009	13.88
7.0.20	0.204	0.010	13.82
7.0.19	0.179	0.007	13.77
7.0.18	0.135	0.012	13.97
7.0.17	0.023	0.003	14.05
7.0.16	0.161	0.007	13.76
7.0.15	0.024	0.000	13.87
7.0.14	0.109	0.013	13.91
7.0.13	0.070	0.006	13.79
7.0.12	0.097	0.007	14.04
7.0.11	0.085	0.004	13.98
7.0.10	0.028	0.006	13.69
7.0.9	0.222	0.010	13.77
7.0.8	0.019	0.006	13.77
7.0.7	0.014	0.008	13.73
7.0.6	0.052	0.004	13.80
7.0.5	0.010	0.010	14.12
7.0.4	0.023	0.008	13.84
7.0.3	0.034	0.006	14.11
7.0.2	0.014	0.005	13.98
7.0.1	0.104	0.004	13.83
7.0.0	0.013	0.006	13.79

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