Hugging Face's logo

Datasets:
piotrwilam
/
AtlasOpenCoder

DOI:
License:
Dataset card Files
xet
 Community
AtlasOpenCoder / validation /r2 /STEP6_TOKEN_pretrain.json
piotrwilam's picture
piotrwilam
AtlasOpenCoder v1: the battery, the artefacts and both papers' manifests
9b7bd53
Raw History Blame Contribute Delete
11.8 kB
{
"branch": "pretrain",
"within_group_clause": {
"status": "UNTESTABLE",
"why": "the clause compares members' STABILITY dates, and A38's spacing check leaves no construct of P1 with one on this branch: 0 dated in the product space and 0 in the verdict layer once two spacing bands must agree",
"n_dated_product": 0,
"n_dated_layer": 0,
"spacing_dependence_spearman": -0.8131468225548475
},
"exploratory_level_and_trend": {
"for": {
"members": [
"AsyncFor",
"DictComp",
"For",
"GeneratorExp",
"ListComp",
"SetComp"
],
"levels": {
"AsyncFor": 0.4846707177493174,
"DictComp": 0.5528684562618681,
"For": 0.5877919141737398,
"GeneratorExp": 0.5607898813269776,
"ListComp": 0.5651314580249929,
"SetComp": 0.5580844744638028
},
"trends": {
"AsyncFor": [
-0.5080213903743316,
0.0027
],
"DictComp": [
-0.607620320855615,
0.0006
],
"For": [
-0.4602272727272727,
0.0064
],
"GeneratorExp": [
-0.6092914438502673,
0.0004
],
"ListComp": [
-0.5471256684491979,
0.0014
],
"SetComp": [
-0.5932486631016043,
0.0005
]
},
"norm_dates": {
"AsyncFor": 1908408320000,
"DictComp": 977272832000,
"GeneratorExp": 499122176000,
"ListComp": 33554432000,
"SetComp": 624951296000
},
"role_frequency_per_file": {
"AsyncFor": 0.0014404217870701427,
"DictComp": 0.054230893692925754,
"For": 2.7936526728703464,
"GeneratorExp": 0.22663162298044973,
"ListComp": 0.8200459356427439,
"SetComp": 0.011752263238068177
},
"level_spread": 0.10312119642442236,
"levels_within_one_noise_unit": false,
"norm_dates_coincide": false,
"norm_date_spread_decades": 1.7549214096651689,
"spearman_date_vs_frequency": -0.8225427863778353
},
"with": {
"members": [
"AsyncWith",
"With"
],
"levels": {
"AsyncWith": 0.47149241117231333,
"With": 0.5116013034704975
},
"trends": {
"AsyncWith": [
-0.5858957219251337,
0.0005
],
"With": [
-0.6026069518716577,
0.0003
]
},
"norm_dates": {},
"role_frequency_per_file": {
"AsyncWith": 0.003942414699405678,
"With": 1.0171232606413625
},
"level_spread": 0.04010889229818415,
"levels_within_one_noise_unit": false
},
"if": {
"members": [
"If",
"IfExp"
],
"levels": {
"If": 0.592573118868545,
"IfExp": 0.5790821037151995
},
"trends": {
"If": [
-0.4535427807486631,
0.0087
],
"IfExp": [
-0.5745320855614974,
0.0008
]
},
"norm_dates": {
"IfExp": 1526726656000
},
"role_frequency_per_file": {
"If": 10.767073931128106,
"IfExp": 0.37722870740889825
},
"level_spread": 0.013491015153345498,
"levels_within_one_noise_unit": true
}
},
"exploratory_norm_dates": {},
"exploratory_two_predictor": {
"with_pilot|lognormal": {
"n": 162,
"n_events": 73,
"models": {
"both": {
"predictors": [
"log_frequency",
"log_exposure_multiplier"
],
"converged": true,
"log_likelihood": -547.256517906363,
"coefficients": {
"intercept": {
"beta": 27.452235058232027,
"lo": 26.888744159200733,
"hi": 27.906751640158838,
"excludes_zero": true
},
"log_frequency": {
"beta": -0.3261459125993451,
"lo": -0.4828114056205755,
"hi": -0.21853223599086474,
"excludes_zero": true
},
"log_exposure_multiplier": {
"beta": 0.030549133816755142,
"lo": -0.36644300354099607,
"hi": 0.6398129257140309,
"excludes_zero": false
}
},
"n_bootstrap": 200
},
"frequency_alone": {
"predictors": [
"log_frequency"
],
"converged": true,
"log_likelihood": -547.2689294905076,
"coefficients": {
"intercept": {
"beta": 27.4698687356487,
"lo": 27.053663837718,
"hi": 27.899301423222123,
"excludes_zero": true
},
"log_frequency": {
"beta": -0.32354409186761746,
"lo": -0.4468756888215359,
"hi": -0.20720410578129608,
"excludes_zero": true
}
},
"n_bootstrap": 200
},
"exposure_alone": {
"predictors": [
"log_exposure_multiplier"
],
"converged": true,
"log_likelihood": -566.7040175938164,
"coefficients": {
"intercept": {
"beta": 28.744101915668807,
"lo": 28.43571082305455,
"hi": 29.082685453270177,
"excludes_zero": true
},
"log_exposure_multiplier": {
"beta": -0.4207998476464216,
"lo": -0.819690215804176,
"hi": -0.01814055908952038,
"excludes_zero": true
}
},
"n_bootstrap": 200
}
},
"exposure_total_coefficient": {
"value": 0.030549133816755142,
"note": "in the (log frequency, log multiplier) parameterisation the multiplier's coefficient IS the coefficient on log total exposure holding frequency fixed, since log exposure = log frequency + log multiplier"
}
},
"with_pilot|weibull": {
"n": 162,
"n_events": 73,
"models": {
"both": {
"predictors": [
"log_frequency",
"log_exposure_multiplier"
],
"converged": true,
"log_likelihood": -544.7742205347744,
"coefficients": {
"intercept": {
"beta": 27.964159727961206,
"lo": 27.548494530140953,
"hi": 28.309324423281183,
"excludes_zero": true
},
"log_frequency": {
"beta": -0.243489861227487,
"lo": -0.3913851785189969,
"hi": -0.15439659406482714,
"excludes_zero": true
},
"log_exposure_multiplier": {
"beta": 0.11926604484257766,
"lo": -0.17169139747501083,
"hi": 0.5580170539613779,
"excludes_zero": false
}
},
"n_bootstrap": 200
},
"frequency_alone": {
"predictors": [
"log_frequency"
],
"converged": true,
"log_likelihood": -545.0650239551546,
"coefficients": {
"intercept": {
"beta": 28.05694070199219,
"lo": 27.800171961875062,
"hi": 28.29547132894031,
"excludes_zero": true
},
"log_frequency": {
"beta": -0.22552302065350452,
"lo": -0.316517099045804,
"hi": -0.1473092469049648,
"excludes_zero": true
}
},
"n_bootstrap": 200
},
"exposure_alone": {
"predictors": [
"log_exposure_multiplier"
],
"converged": true,
"log_likelihood": -561.0559614677179,
"coefficients": {
"intercept": {
"beta": 28.93062939793549,
"lo": 28.702876137135586,
"hi": 29.270869209763056,
"excludes_zero": true
},
"log_exposure_multiplier": {
"beta": -0.3780833837070209,
"lo": -0.6104188056988531,
"hi": -0.13188371177148822,
"excludes_zero": true
}
},
"n_bootstrap": 200
}
},
"exposure_total_coefficient": {
"value": 0.11926604484257766,
"note": "in the (log frequency, log multiplier) parameterisation the multiplier's coefficient IS the coefficient on log total exposure holding frequency fixed, since log exposure = log frequency + log multiplier"
}
},
"without_pilot|lognormal": {
"n": 149,
"n_events": 65,
"models": {
"both": {
"predictors": [
"log_frequency",
"log_exposure_multiplier"
],
"converged": true,
"log_likelihood": -483.16782717370194,
"coefficients": {
"intercept": {
"beta": 27.16920168461071,
"lo": 26.575790567551035,
"hi": 27.602113936379176,
"excludes_zero": true
},
"log_frequency": {
"beta": -0.3874442116103855,
"lo": -0.5727239900399403,
"hi": -0.2591748080795826,
"excludes_zero": true
},
"log_exposure_multiplier": {
"beta": 0.100734752048378,
"lo": -0.3051406983080099,
"hi": 0.868479948845614,
"excludes_zero": false
}
},
"n_bootstrap": 200
},
"frequency_alone": {
"predictors": [
"log_frequency"
],
"converged": true,
"log_likelihood": -483.2448968290138,
"coefficients": {
"intercept": {
"beta": 27.193038467240115,
"lo": 26.755310696829177,
"hi": 27.608774987677844,
"excludes_zero": true
},
"log_frequency": {
"beta": -0.38547785880080515,
"lo": -0.5092048502531483,
"hi": -0.24924767137831835,
"excludes_zero": true
}
},
"n_bootstrap": 200
},
"exposure_alone": {
"predictors": [
"log_exposure_multiplier"
],
"converged": true,
"log_likelihood": -508.6728911020474,
"coefficients": {
"intercept": {
"beta": 28.71285334233304,
"lo": 28.441944483373714,
"hi": 29.05100647364153,
"excludes_zero": true
},
"log_exposure_multiplier": {
"beta": -0.24144778294660219,
"lo": -0.5311939066506085,
"hi": 0.12028595775034973,
"excludes_zero": false
}
},
"n_bootstrap": 200
}
},
"exposure_total_coefficient": {
"value": 0.100734752048378,
"note": "in the (log frequency, log multiplier) parameterisation the multiplier's coefficient IS the coefficient on log total exposure holding frequency fixed, since log exposure = log frequency + log multiplier"
}
},
"without_pilot|weibull": {
"n": 149,
"n_events": 65,
"models": {
"both": {
"predictors": [
"log_frequency",
"log_exposure_multiplier"
],
"converged": true,
"log_likelihood": -481.1503656103173,
"coefficients": {
"intercept": {
"beta": 27.769151920942797,
"lo": 27.37620154862592,
"hi": 28.107416203105704,
"excludes_zero": true
},
"log_frequency": {
"beta": -0.29045977610707574,
"lo": -0.43460058385626643,
"hi": -0.19034008625411813,
"excludes_zero": true
},
"log_exposure_multiplier": {
"beta": 0.11341103175916134,
"lo": -0.27835982028427764,
"hi": 0.5019005757138669,
"excludes_zero": false
}
},
"n_bootstrap": 200
},
"frequency_alone": {
"predictors": [
"log_frequency"
],
"converged": true,
"log_likelihood": -481.32933083719195,
"coefficients": {
"intercept": {
"beta": 27.816491642958844,
"lo": 27.486945549471585,
"hi": 28.06628372139525,
"excludes_zero": true
},
"log_frequency": {
"beta": -0.28195677314282863,
"lo": -0.39246646042161204,
"hi": -0.17932301114977048,
"excludes_zero": true
}
},
"n_bootstrap": 200
},
"exposure_alone": {
"predictors": [
"log_exposure_multiplier"
],
"converged": true,
"log_likelihood": -503.76743882475506,
"coefficients": {
"intercept": {
"beta": 28.900026808597318,
"lo": 28.676437873786966,
"hi": 29.204911535795745,
"excludes_zero": true
},
"log_exposure_multiplier": {
"beta": -0.3414021405939167,
"lo": -0.6136916516554873,
"hi": -0.04623076651821903,
"excludes_zero": true
}
},
"n_bootstrap": 200
}
},
"exposure_total_coefficient": {
"value": 0.11341103175916134,
"note": "in the (log frequency, log multiplier) parameterisation the multiplier's coefficient IS the coefficient on log total exposure holding frequency fixed, since log exposure = log frequency + log multiplier"
}
}
},
"marked": "both forms below are EXPLORATORY forms of P3 and are never its verdict (the Settle chat, 15 Oct)"
}