Select a random seed and save it

Question

I would like to select a random seed for numpy.random and store it in a variable. I can set the seed using numpy.random.seed(seed=None ), but how do you get numpy to select a random seed and tell you what it is?

The number apparently uses /dev/urandom on linux by default.

Answer 1

The full state of MT19937 PRNG underlying the RandomState cannot be contained in a single (usually, for example, 32-bit or 64-bit) integer. It has an array of 624 32-bit integers for its state. Seeing with an integer actually triggers a smaller, simpler PRNG to generate the 624 words. This is just a convenient way for people to manually set the PRNG state to a state that can be sequentially replicated. But most of the conditions that PRNG enters cannot be restored to a convenient 32-bit integer. This PRNG initializer cannot work backward this way. Instead, the entire state of RandomState contained in this array with 624 inputs. You can get this array and set it using get_state() and set_state() .

 >>> import numpy as np >>> prng = np.random.RandomState() >>> state = prng.get_state() >>> state ('MT19937', array([2310623686, 364919541, 1436109096, 1457837701, 2852017530, 562204638, 1207376362, 2290452263, 250624867, 1687514807, 3242300311, 68301227, 497650124, 3782308076, 4180165271, 3190969185, 1284472452, 2868357773, 1148940887, 433865334, 643839653, 3091921054, 2157305915, 4079505239, 1396964105, 221256094, 2789328727, 3216471912, 1782932723, 1704818545, 3880597634, 2060476197, 2599008138, 1389874875, 56765165, 1173841349, 278528026, 714062321, 3587382791, 840507318, 2086996355, 3416087866, 3081938567, 946222923, 4259369972, 868558506, 2060774692, 3239317074, 4078800142, 3833877854, 1503749328, 3821805560, 1447854235, 995535877, 3762179650, 185008825, 149218213, 3469766149, 803379340, 3971043961, 3421104633, 2287066419, 2465098532, 4088166586, 2105722956, 1451099732, 3115885598, 4240224392, 3778829453, 4059831750, 2919989511, 4092928731, 922778621, 1805422791, 3344418665, 1738799711, 1367565729, 34977430, 4008589298, 2239856842, 1717530303, 32049105, 3468621644, 2269299060, 1664083607, 3996022881, 377407365, 4070209212, 4216115381, 2124999225, 1920630572, 2011423407, 1367187092, 4158622494, 487432561, 3536187733, 931951977, 749985693, 2812437433, 3902171864, 767004922, 3807520852, 796884475, 2794577773, 1481140267, 2247603372, 1053872430, 211335743, 2997489007, 4140013480, 1601875594, 1927437737, 3349007801, 2868575676, 3474179396, 595650352, 517981041, 1947095736, 170970294, 3253183597, 2873789192, 3386930182, 2047755893, 254974719, 2747566023, 4182212825, 1934990158, 1282861435, 404005052, 3237256048, 1737335951, 386655885, 640537519, 60176882, 1825713593, 86537970, 252007523, 3674897989, 3645447766, 972417578, 1860821974, 2688102651, 2481103756, 3672142036, 2961031222, 1709451377, 134371222, 4217784577, 3792528752, 1278543741, 291978547, 1987232116, 2685749450, 948431490, 3550698848, 1384058130, 302186886, 2966159795, 1981959565, 2602891721, 1814325871, 4148300386, 1211156469, 2945951607, 4132724234, 1221821676, 3057395063, 1563869020, 3762934166, 3303914085, 1910775932, 2241726842, 3836262483, 905479357, 2974032168, 3187395363, 3071243546, 3571439927, 3756380578, 53494506, 495375628, 2149633842, 1549467921, 403773184, 3774309942, 1767528278, 421982610, 579688614, 3735062896, 2128447283, 2545877077, 3013437905, 4067651631, 26043227, 3189924699, 1882256309, 431961449, 3637287121, 1409924095, 3834921204, 3796550515, 338734970, 1632375419, 3788135288, 153287562, 2302436235, 3852961194, 2073555800, 3034065218, 1997718747, 3343015031, 3198064720, 4286393046, 3338997777, 1383744819, 1553624825, 1183357509, 1141531260, 25823987, 2951322047, 4066666075, 3687780778, 3680053857, 478734258, 3674686218, 1457141125, 3673486342, 3224971043, 2786082270, 2282591016, 1210618789, 3735610308, 587294285, 4231880327, 3702701983, 13470000, 90747549, 876795924, 1489448380, 585176585, 2398768918, 3069244786, 2901497718, 4004899727, 1992450245, 1127097566, 713011674, 2083831719, 2923291311, 315998911, 1511233310, 1515243002, 621858088, 2398475656, 3029652473, 1011396654, 1854317252, 2735915680, 1489448619, 3836317799, 1678027486, 2429831383, 170989290, 651235170, 1457126476, 3694269669, 4248613755, 3161380741, 3396304589, 26218095, 4262314194, 3090365505, 2603976562, 1742639443, 3357356842, 2527908520, 2744118109, 764708873, 608716002, 218517036, 2028062957, 123264851, 3930797933, 1358280349, 3770182726, 1475205800, 4083653367, 728440387, 578359463, 3792859449, 2660424205, 866268419, 2680711984, 1892477918, 3473675890, 5948212, 590585309, 1434154869, 4019090587, 3447601971, 3777365598, 502271900, 933280098, 551410763, 4178545332, 2426657681, 435161245, 103552671, 2751130089, 1664159723, 2124278140, 3518289293, 1397473574, 4032873848, 3104766011, 3780526375, 146118438, 3497842141, 2078614647, 1431064844, 825222639, 954382890, 3170571595, 1418867403, 4133763948, 2773874577, 459104952, 3336058631, 791669682, 79496438, 1268256964, 1327605157, 3196785479, 3094404795, 3971934915, 967528556, 1680157581, 1508139540, 3821158380, 3603819236, 593155253, 1875654417, 3734837198, 3315972391, 2450938455, 1863178045, 619766009, 1376779265, 843230528, 1818810226, 1508689309, 1353144904, 3459699509, 734863896, 1593154156, 4178196553, 559982910, 1937392142, 3328058492, 2417976146, 3197182411, 2233439700, 196920494, 3714701774, 4104568606, 850977604, 382851029, 4143478133, 3024891142, 2455897904, 28681198, 3438784382, 578301023, 2215641381, 59642080, 2913625733, 2063824530, 2113835214, 563503294, 2261300428, 1156324177, 3080988993, 1485826140, 291045970, 3740234437, 2802003429, 804278225, 1715783317, 3683156408, 2855890524, 2390104305, 172369852, 3358371994, 1184782876, 2087670358, 840924195, 2727925375, 1806621317, 2785628046, 4163132724, 3580142689, 1107366902, 809125531, 3131770778, 1922818283, 888842000, 2875999147, 2752567229, 170460348, 1952532683, 1705378473, 1784443344, 1111435234, 2373828316, 1440965774, 3986117425, 849160375, 1233392480, 4073490673, 3948548975, 2317742686, 459747729, 3981827733, 97170450, 1906613346, 2296986726, 3107045483, 3301310854, 2005065797, 1047441812, 1340913878, 1305190832, 3414530672, 2739562683, 670592573, 3517927973, 3902124497, 4085960935, 823980090, 982263838, 1807290575, 1182843877, 3543714667, 1403590968, 329717243, 1055811172, 3550329386, 3998515559, 3251582755, 2201054306, 3347834116, 1211790680, 62972368, 88227180, 2967020240, 1937245345, 524567284, 2915223835, 1039263578, 931149438, 2102426452, 4178383760, 2534760455, 3961494901, 359726861, 2377704223, 3980574430, 3941075859, 3025460765, 1087397787, 1520908724, 3979084899, 3800423495, 139799221, 644687977, 1080267251, 599331265, 379370383, 3716980301, 2450151406, 1223752702, 300351842, 295249068, 1870733374, 2986315084, 1323736886, 306347366, 2697516131, 3896227616, 2556699990, 578928278, 2356101730, 171880210, 722319049, 740054230, 3855145369, 1468149367, 311954206, 4099077708, 2941657479, 119786529, 3197372768, 2115311247, 2469241538, 2636086203, 2206369175, 374899905, 3730393440, 2288141890, 719446033, 4096038147, 4294410470, 19272682, 1964868281, 3192582061, 3934009074, 1135732985, 682697379, 3290113635, 1489105351, 347638343, 147496092, 4175447059, 341595821, 3117140389, 1003085251, 1889252416, 913732530, 3459561042, 3662473182, 3839509269, 1519115576, 677113, 597583022, 3031451769, 607339281, 55523370, 2676982537, 1238056185, 1550912054, 3112284354, 1345961520, 1541909925, 3726796822, 2696250478, 3254836471, 1362613883, 3129122359, 1550126204, 129690651, 2386622242, 407302605, 1753882614, 2376840660, 1076064874, 2449053256, 3162294193, 3779999195, 3925427556, 2601606505, 1901788890, 2217639773, 406665902, 3640687773, 2061876750, 968895635, 587973195, 2778479214, 668417883, 2226398520, 1464491431, 2792659882, 3481258691, 2339776369, 2747947338, 3000199533, 3712567952, 376206272, 2149616269, 985682501, 865295391, 1812641626, 567425379, 1468520640, 2273677177, 2267568076, 3898328230, 898149034, 3750298043, 394538907, 4101461357, 2781824777, 2719406676, 3415420393, 122661889, 1452536307, 1463257506, 2874481787, 2250093815, 1439068642, 597070280, 1439076517, 4207797347, 2579732532, 3704826787, 3847236064, 4155289003, 990963026, 2602619627, 701644802, 3629646548, 1110000288, 3609356614, 2748019645, 638526248, 3265491895, 2839687161, 913026615, 2748040592, 975131382, 83378202, 4236013846, 764917668, 1887262417], dtype=uint32), 624, 0, 0.0) >>> prng.random_sample() 0.20598058788141316 >>> prng.random_sample() 0.6864005375257146 >>> prng.random_sample() 0.08407651896523582 >>> prng.set_state(state) >>> prng.random_sample() 0.20598058788141316 >>> prng.random_sample() 0.6864005375257146 

You can also select RandomState objects. We implemented this using get_state() data so that it reliably reproduces the state of PRNG. Depending on what you want to do (you don't say), this is often the most convenient thing, rather than cheating with get_state() and set_state() manually.

 >>> import cPickle >>> pickled = cPickle.dumps(prng) >>> prng.random_sample() 0.08407651896523582 >>> prng.random_sample() 0.3501860271954601 >>> prng2 = cPickle.loads(pickled) >>> prng2.random_sample() 0.08407651896523582 >>> prng2.random_sample() 0.3501860271954601 

Answer 2

You cannot ... but there really is no good reason for this. If you are not really trying to reproduce the seed behavior, rather than putting the RNG in a repeating state, you are trying to add an additional level of indirection for no reason.

If you want to save and restore RandomState , do this using the get_state() and set_state() functions.

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If you really want to use seed instead, you can simply use np.random to generate a random seed (e.g. via random_integers(0, 255, SOME_LENGTH) ), which you can save and reuse later. But there is no reason for this.

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Or, of course, you can call Python os.urandom to create the seed just like the default NumPy. Please note that the docs explicitly state that:

If seed is None , then RandomState will try to "read the date from /dev/urandom (or an analogue of Windows), if available, or detect from the clock otherwise."

But then again, there’s nothing to do. (In addition, it is not documented how much randomness he receives from urandom , so there is always the risk that you will sow it with less random data than you usually use, or wastefully collect too much.)

Answer 3

According to docs , when seed is None , numpy tries to read from /dev/urandom , so why not just read the value from /dev/urandom , save it and pass it to numpy.random.RandomState ?

EDIT:

The internal state can be obtained and set via get_state and set_state , respectively. So, to restore the initial state, you can do something like this:

 >>> import numpy >>> r = numpy.random.RandomState() >>> saved_state = r.get_state() >>> r.rand() 0.9091545657342729 >>> r.rand() 0.9677739782319564 >>> r.rand() 0.5656156400920441 >>> r.set_state(saved_state) >>> r.rand() 0.9091545657342729 >>> r.rand() 0.9677739782319564 >>> r.rand() 0.5656156400920441 >>> 

When the seed is None , numpy does not select the "new random seed" and calls seed() with it. It reads 624 * sizeof(long) bytes ( ~ 2.5KB ) from /dev/urandom and uses these values ​​to populate the state structure. When you call seed() with no arguments, numpy never "selects" a random seed. Therefore, it cannot be restored.

Answer 4

When people need a random seed that can be recorded, people usually use system time as a random seed. This means that your program will act differently each time it is launched, but can be saved and captured. Why don't you give it a try?

If you don't want to do this for any reason, use the zero version, numpy.random.seed (seed = None), then get a random number from it, and then set the seed to this new random number.

Answer 5

If you want, you can also save it in a json file and then unzip it and then use it again. Since multi-valued material cannot be serialized, you need to serialize it yourself, but it's not so bad:

Single file:

 import json import numpy as np def put_numpy_seed_in_json_dic(results): (rnd0,rnd1,rnd2,rnd3,rnd4) = np.random.get_state() rnd1 = [int(number) for number in rnd1] rand_seed = (rnd0,rnd1,rnd2,rnd3,rnd4) results['rand_seed'] = rand_seed return results def get_numpy_seed(results): (rnd0,rnd1,rnd2,rnd3,rnd4) = results['rand_seed'] rnd1 = [np.uint32(number) for number in rnd1] rand_seed = (rnd0,rnd1,rnd2,rnd3,rnd4) return rand_seed 

then run it to save the seed:

 import json import numpy as np import my_rand_lib as mr results = {'rand_seed':None} results = mr.put_numpy_seed_in_json_dic(results) print np.random.rand(1) print np.random.rand(1) print np.random.rand(1) fpath = './rand_seed_file' with open(fpath,'w+') as f: json.dump(results,f) print '... doing other stuff' with open(fpath,'r+') as f: results2 = json.load(f) print 'other ',np.random.rand(1) print 'other ',np.random.rand(1) print 'other ',np.random.rand(1) print '... done doing stuff' rand_seed = mr.get_numpy_seed(results2) np.random.set_state(rand_seed) print np.random.rand(1) print np.random.rand(1) print np.random.rand(1) 

and if you do not want to generate a seed every time you run it, you can:

 import json import numpy as np import my_rand_lib as mr fpath = './rand_seed_file' with open(fpath,'r+') as f: results2 = json.load(f) rand_seed = mr.get_numpy_seed(results2) np.random.set_state(rand_seed) print np.random.rand(1) print np.random.rand(1) print np.random.rand(1) 

I tried this on a remote server and I always get the corresponding random numbers:

 [ 0.90741273] [ 0.6861296] [ 0.21714398] 

Not sure if this is interesting, but it was a seed (this is a tuple):

 {"rand_seed": ["MT19937", [3244492226, 4276548057, 571402114, 3235873143, 4078239958, 1440625038, 4042777784, 3400010150, 1164584760, 271139028, 1264217608, 1403324904, 234696259, 623484078, 3424719234, 3896351743, 1818071683, 3077380191, 2989066157, 3828180331, 2032001745, 1137603205, 1993713826, 873523654, 3267461254, 2964954176, 3217679339, 4079232021, 1182272168, 402998421, 968119626, 2151162455, 2550226639, 3522780791, 245256811, 2866158388, 587411937, 2836234133, 3485394274, 1767143488, 3772379711, 1244725495, 1061026769, 2544419920, 3963050848, 232749713, 2084368489, 1990090546, 2883903063, 174001222, 2569537698, 517341511, 2366955295, 1830324490, 2388090514, 1637855850, 1383101875, 2719629528, 885528387, 7941101, 2769663894, 2704541593, 3129289945, 2681434614, 3308402481, 2161196492, 2896442132, 1474561199, 156414990, 2934014108, 2740454316, 4029663532, 2903418479, 118978587, 3095335574, 1044532364, 2629619463, 623783821, 3172307947, 2539001597, 2020636966, 404303542, 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