ML Optimization and graphs
ml optimml optimml optimml optimml mcmcml mcmcml mcmcml graph communitiesml graph pagerankml graph centrality
ml optim <method> <expression> <json_bounds>
Description
Minimize a function in a box with a metaheuristic and the default parameters.
Methods: de (differential evolution), pso (particle swarm), ga (genetic algorithm), anneal (simulated annealing), cmaes (CMA-ES).
Returns the best point, the value and the number of evaluations.
Minimize a function in a box with a metaheuristic and the default parameters.
Methods: de (differential evolution), pso (particle swarm), ga (genetic algorithm), anneal (simulated annealing), cmaes (CMA-ES).
Returns the best point, the value and the number of evaluations.
Parameters
method: de, pso, ga, anneal or cmaes - String - required
expression: The function, ex: (x-1)^2 + y^2 - String - required
json_bounds: The box, ex: {"x": [-5, 5], "y": [0, 10]} - String - required
method: de, pso, ga, anneal or cmaes - String - required
expression: The function, ex: (x-1)^2 + y^2 - String - required
json_bounds: The box, ex: {"x": [-5, 5], "y": [0, 10]} - String - required
admin
ml optim "de" "x^2 + y^2 - 10*cos(2*pi*x) - 10*cos(2*pi*y) + 20" "{\"x\": [-5.12, 5.12], \"y\": [-5.12, 5.12]}"
mentdb
{"x": {"x": -9.381357644183546E-10, "y": -2.477949455712503E-9}, "value": 0, "evaluations": 6022, "method": "de"}
Rosenbrock valley with CMA-ES
admin
ml optim "cmaes" "(1-x)^2 + 100*(y-x^2)^2" "{\"x\": [-2, 2], \"y\": [-1, 3]}"
mentdb
{"x": {"x": 1.0000000059956073, "y": 1.0000000121826966}, "value": 3.961384081139789E-17, "evaluations": 849, "method": "cmaes"}
ml optim <method> <expression> <json_bounds> <json_params>
Description
Minimize or maximize a function with parameters:
- de {population, generations: 300, f: 0.8, cr: 0.9}
- pso {particles: 30, iterations: 300, inertia: 0.72, c1: 1.49, c2: 1.49}
- ga {population: 60, generations: 300, mutation: 0.1, elite: 2}
- anneal {iterations: 20000, t0: 1, cooling: 0.9995}
- cmaes {population, max_evals: 30000}
Every method takes {"maximize": true} and {"seed": 42}.
Minimize or maximize a function with parameters:
- de {population, generations: 300, f: 0.8, cr: 0.9}
- pso {particles: 30, iterations: 300, inertia: 0.72, c1: 1.49, c2: 1.49}
- ga {population: 60, generations: 300, mutation: 0.1, elite: 2}
- anneal {iterations: 20000, t0: 1, cooling: 0.9995}
- cmaes {population, max_evals: 30000}
Every method takes {"maximize": true} and {"seed": 42}.
Parameters
method: The method - String - required
expression: The function - String - required
json_bounds: The box - String - required
json_params: The parameters - String - required
method: The method - String - required
expression: The function - String - required
json_bounds: The box - String - required
json_params: The parameters - String - required
admin
ml optim "pso" "x*sin(x)" "{\"x\": [0, 10]}" "{\"maximize\": true}"
mentdb
{"x": {"x": 7.978665713503389}, "value": 7.916727371587783, "evaluations": 9032, "method": "pso"}
Genetic algorithm, 100 individuals
admin
ml optim "ga" "(x-3)^2 + (y+1)^2 + abs(z)" "{\"x\": [-10, 10], \"y\": [-10, 10], \"z\": [-10, 10]}" "{\"population\": 100, \"generations\": 200}"
mentdb
{"x": {"x": 3, "y": -1, "z": -3.56866824055463E-44}, "value": 3.56866824055463E-44, "evaluations": 19702, "method": "ga"}
ml optim <method> <json_objectives> <json_bounds>
Description
Multi-objective optimization with the method nsga2 (NSGA-II): the objectives are a JSON array of expressions to minimize; returns the Pareto front (non-dominated solutions) and its knee point
Multi-objective optimization with the method nsga2 (NSGA-II): the objectives are a JSON array of expressions to minimize; returns the Pareto front (non-dominated solutions) and its knee point
Parameters
method: nsga2 - String - required
json_objectives: The objectives, ex: ["cost", "-quality"] - String - required
json_bounds: The bounds {"x": [min, max]} - String - required
method: nsga2 - String - required
json_objectives: The objectives, ex: ["cost", "-quality"] - String - required
json_bounds: The bounds {"x": [min, max]} - String - required
admin
ml optim "nsga2" "[\"x^2\", \"(x-2)^2\"]" "{\"x\": [-5, 5]}"
mentdb
{"objectives": 2, "variables": 1, "front_size": 100, "evaluations": 20100, "knee": {"variables": {"x": 1.0057876734346567}, "objectives": [1.0116088440330997, 0.9884581502944728]}, "front": [{"variables": {"x": -3.2033717809687445E-5}, "objectives": [1.0261590767106867E-9, 4.000128135897398]}, {"variables": {"x": 0.004446482173683208}, "objectives": [1.9771203720882548E-5, 3.9822338425089883]}, {"variables": {"x": 0.0503106303287125}, "objectives": [0.0025311595240723656, 3.801288638209223]}, {"variables": {"x": 0.0654095084288886}, "objectives": [0.004278403792908848, 3.7426403700773543]}, {"variables": {"x": 0.08499146585638126}, "objectives": [0.007223549268416421, 3.667257685842891]}, {"variables": {"x": 0.10095989989228898}, "objectives": [0.010192901386261013, 3.6063533018171055]}, {"variables": {"x": 0.11914878943076396}, "objectives": [0.014196434022816529, 3.537601276299761]}, {"variables": {"x": 0.1389222478768648}, "objectives": [0.019299390955161068, 3.4636103994477017]}, {"variables": {"x": 0.15648527381827543}, "objectives": [0.024487640921980636, 3.398546545648879]}, {"variables": {"x": 0.19946304264442888}, "objectives": [0.03978550538097325, 3.241933334803258]}, {"variables": {"x": 0.2124836781951906}, "objectives": [0.045149313499357316, 3.195214600718595]}, {"variables": {"x": 0.23205444515660156}, "objectives": [0.0538492655169382, 3.125631484890532]}, {"variables": {"x": 0.2574521088844086}, "objectives": [0.06628158836902939, 3.036473152831395]}, {"variables": {"x": 0.2914224383448225}, "objectives": [0.08492703757084187, 2.919237284191552]}, {"variables": {"x": 0.31173375371223266}, "objectives": [0.09717793320351893, 2.850242918354588]}, {"variables": {"x": 0.3331994767515347}, "objectives": [0.11102189130749653, 2.778223984301358]}, {"variables": {"x": 0.35202209720728717}, "objectives": [0.12391955692221673, 2.715831168093068]}, {"variables": {"x": 0.3790688716979052}, "objectives": [0.1436932094903229, 2.627417722698702]}, {"variables": {"x": 0.3953665293141482}, "objectives": [0.15631469250191518, 2.5748485752453223]}, {"variables": {"x": 0.4266696706410822}, "objectives": [0.18204700784496955, 2.4753683252806407]}, {"variables": {"x": 0.4487186397715408}, "objectives": [0.2013484176784218, 2.4064738585922587]}, {"variables": {"x": 0.4877051437540538}, "objectives": [0.23785630724416232, 2.287035732227947]}, {"variables": {"x": 0.4968137515026717}, "objectives": [0.24682390368215842, 2.259568897671472]}, {"variables": {"x": 0.5295962188028533}, "objectives": [0.2804721549702797, 2.1620872797588664]}, {"variables": {"x": 0.5430828664875935}, "objectives": [0.2949389998723813, 2.1226075339220074]}, {"variables": {"x": 0.5620339786561885}, "objectives": [0.315882193164105, 2.067746278539351]}, {"variables": {"x": 0.5946568700279478}, "objectives": [0.35361679307143556, 1.9749893129596445]}, {"variables": {"x": 0.6360682305897634}, "objectives": [0.4045827939655924, 1.860309871606539]}, {"variables": {"x": 0.6497354024324108}, "objectives": [0.4221560931740068, 1.823214483444364]}, {"variables": {"x": 0.6711417105522248}, "objectives": [0.4504311956429663, 1.765864353434067]}, {"variables": {"x": 0.6885475008735187}, "objectives": [0.4740976609591682, 1.7199076574650936]}, {"variables": {"x": 0.7047427594946098}, "objectives": [0.4966623570600775, 1.677691319081638]}, {"variables": {"x": 0.7191490708305297}, "objectives": [0.5171753860764142, 1.6405791027542955]}, {"variables": {"x": 0.7509632948753675}, "objectives": [0.5639458702500681, 1.5600926907485981]}, {"variables": {"x": 0.771918349148111}, "objectives": [0.5958579377515449, 1.508184541159101]}, {"variables": {"x": 0.7847209715640033}, "objectives": [0.6157870032123534, 1.47690311695634]}, {"variables": {"x": 0.801263061909643}, "objectives": [0.6420224943808164, 1.4369702467422443]}, {"variables": {"x": 0.8109781393242188}, "objectives": [0.657685542461772, 1.4137729851648968]}, {"variables": {"x": 0.851566212136583}, "objectives": [0.7251650136526478, 1.318900165106316]}, {"variables": {"x": 0.8712751768333395}, "objectives": [0.759120433765967, 1.274019726432609]}, {"variables": {"x": 0.8979695474843246}, "objectives": [0.8063493082092026, 1.2144711182719043]}, {"variables": {"x": 0.922469881302014}, "objectives": [0.8509506819093519, 1.1610711567012957]}, {"variables": {"x": 0.9365487639694954}, "objectives": [0.8771235872927896, 1.130928531414808]}, {"variables": {"x": 0.9515344152676639}, "objectives": [0.9054177434387751, 1.0992800823681195]}, {"variables": {"x": 0.9710311417152953}, "objectives": [0.9429014781809099, 1.0587769113197287]}, {"variables": {"x": 1.0057876734346567}, "objectives": [1.0116088440330997, 0.9884581502944728]}, {"variables": {"x": 1.0173622724909264}, "objectives": [1.0350259934879018, 0.9655769035241965]}, {"variables": {"x": 1.0427547773821495}, "objectives": [1.0873375257532962, 0.9163184162246981]}, {"variables": {"x": 1.0572830939210711}, "objectives": [1.1178475406913124, 0.8887151650070281]}, {"variables": {"x": 1.0696497796771827}, "objectives": [1.1441506511634454, 0.8655515324547147]}, {"variables": {"x": 1.086517516227282}, "objectives": [1.180520313068702, 0.834450248159574]}, {"variables": {"x": 1.0909916101871593}, "objectives": [1.1902626934987706, 0.8262962527501334]}, {"variables": {"x": 1.1086929138728785}, "objectives": [1.229199977271934, 0.79442832178042]}, {"variables": {"x": 1.1287662805535117}, "objectives": [1.274113316114609, 0.7590481939005623]}, {"variables": {"x": 1.1494793366644498}, "objectives": [1.3213027454185435, 0.7233853987607444]}, {"variables": {"x": 1.1642079731519723}, "objectives": [1.3553802047506236, 0.6985483121427342]}, {"variables": {"x": 1.174508387539206}, "objectives": [1.3794699523999456, 0.6814364022431219]}, {"variables": {"x": 1.2046734090942475}, "objectives": [1.4512380225787562, 0.6325443862017662]}, {"variables": {"x": 1.2331992015618338}, "objectives": [1.5207802707327445, 0.5879834644854092]}, {"variables": {"x": 1.2416475812858627}, "objectives": [1.541688716113033, 0.5750983909695822]}, {"variables": {"x": 1.267309940081875}, "objectives": [1.6060744842303256, 0.5368347239028256]}, {"variables": {"x": 1.3086927264920571}, "objectives": [1.7126766523732142, 0.47790574640498573]}, {"variables": {"x": 1.3250637136693713}, "objectives": [1.7557938452832655, 0.45553899060578046]}, {"variables": {"x": 1.353021406379883}, "objectives": [1.8306669261221968, 0.41858130060266435]}, {"variables": {"x": 1.3714115541159775}, "objectives": [1.8807696507628007, 0.3951234342988907]}, {"variables": {"x": 1.385196990430464}, "objectives": [1.9187707022976148, 0.37798274057575904]}, {"variables": {"x": 1.3928324005298167}, "objectives": [1.9399820959656517, 0.36865249384638493]}, {"variables": {"x": 1.40877456861142}, "objectives": [1.9846457851662922, 0.34954751072061263]}, {"variables": {"x": 1.4272136509542979}, "objectives": [2.0369388054702964, 0.3280842016531049]}, {"variables": {"x": 1.4493467430779856}, "objectives": [2.1006059816707645, 0.303219009358822]}, {"variables": {"x": 1.4559550461231157}, "objectives": [2.1198050963313637, 0.2959849118389012]}, {"variables": {"x": 1.4716442415094821}, "objectives": [2.165736773568019, 0.27915980753009045]}, {"variables": {"x": 1.4862423210289732}, "objectives": [2.2089162368175894, 0.2639469527016966]}, {"variables": {"x": 1.5096803500858818}, "objectives": [2.2791347594354305, 0.24041335909190348]}, {"variables": {"x": 1.5369116152662528}, "objectives": [2.362097313140322, 0.21445085207531112]}, {"variables": {"x": 1.5708770182739193}, "objectives": [2.4676546065411595, 0.18414653344548215]}, {"variables": {"x": 1.5958750950672764}, "objectives": [2.5468173190559886, 0.16331693878688286]}, {"variables": {"x": 1.6181109741378894}, "objectives": [2.6182831246254694, 0.14583922807391178]}, {"variables": {"x": 1.6526361662139497}, "objectives": [2.731206297878342, 0.12066163302254278]}, {"variables": {"x": 1.679883037825218}, "objectives": [2.822007020772883, 0.10247486947201076]}, {"variables": {"x": 1.6851550629277756}, "objectives": [2.839747586111115, 0.09912733440001294]}, {"variables": {"x": 1.7024215585801155}, "objectives": [2.8982391631183493, 0.08855292879788766]}, {"variables": {"x": 1.718723617640259}, "objectives": [2.954010873834419, 0.07911640327338325]}, {"variables": {"x": 1.7382099599539405}, "objectives": [3.0213738648830795, 0.06853402506731746]}, {"variables": {"x": 1.7579573984830432}, "objectives": [3.090414214881269, 0.058584620949096326]}, {"variables": {"x": 1.7746937400827019}, "objectives": [3.1495378710887287, 0.0507629107579211]}, {"variables": {"x": 1.7902492412969604}, "objectives": [3.2049923459643423, 0.043995380776500746]}, {"variables": {"x": 1.811542188703557}, "objectives": [3.2816851014528736, 0.035516346638645724]}, {"variables": {"x": 1.823667703548511}, "objectives": [3.3257638929658997, 0.031093078771855825]}, {"variables": {"x": 1.8372044810459098}, "objectives": [3.375320305175171, 0.02650238099153153]}, {"variables": {"x": 1.8513350827390966}, "objectives": [3.427441588580578, 0.022101257624191243]}, {"variables": {"x": 1.8640922387301255}, "objectives": [3.4748398744938913, 0.018470919573389193]}, {"variables": {"x": 1.8824356553838917}, "objectives": [3.543563996660582, 0.013821375125015077]}, {"variables": {"x": 1.910243823362314}, "objectives": [3.6490314646938717, 0.008056171244615487]}, {"variables": {"x": 1.9294007565288194}, "objectives": [3.7225872792939807, 0.00498425317870304]}, {"variables": {"x": 1.9435335950910881}, "objectives": [3.7773228352476895, 0.0031884548833371854]}, {"variables": {"x": 1.962584473693265}, "objectives": [3.8517378163818696, 0.0013999216088099833]}, {"variables": {"x": 1.980144592531688}, "objectives": [3.920972607332485, 3.942372057326974E-4]}, {"variables": {"x": 1.9941745092506988}, "objectives": [3.9767319733452657, 3.3936342470193455E-5]}, {"variables": {"x": 1.999990444879367}, "objectives": [3.9999617796087685, 9.130033031008356E-11]}]}
ml optim <method> <json_objectives> <json_bounds> <json_params>
Description
NSGA-II with parameters: population (100, even), generations (200), constraints (expressions g <= 0), maximize (one true / false per objective), seed
NSGA-II with parameters: population (100, even), generations (200), constraints (expressions g <= 0), maximize (one true / false per objective), seed
Parameters
method: nsga2 - String - required
json_objectives: The objectives - String - required
json_bounds: The bounds - String - required
json_params: The parameters - String - required
method: nsga2 - String - required
json_objectives: The objectives - String - required
json_bounds: The bounds - String - required
json_params: The parameters - String - required
admin
ml optim "nsga2" "[\"x\", \"(1+y)/x\"]" "{\"x\": [0.1, 1], \"y\": [0, 5]}" "{\"constraints\": [\"6 - (y+9*x)\", \"1 - (9*x-y)\"], \"generations\": 100}"
mentdb
{"objectives": 2, "variables": 2, "front_size": 100, "evaluations": 10100, "knee": {"variables": {"x": 0.5982145387724666, "y": 0.6193753437053338}, "objectives": [0.5982145387724666, 2.7070143547970003]}, "front": [{"variables": {"x": 0.390502381817038, "y": 2.4913025707622163}, "objectives": [0.390502381817038, 8.940541039767577]}, {"variables": {"x": 0.390502381817038, "y": 2.4913025707622163}, "objectives": [0.390502381817038, 8.940541039767577]}, {"variables": {"x": 0.3911991977009646, "y": 2.4913025707622163}, "objectives": [0.3911991977009646, 8.924615876720157]}, {"variables": {"x": 0.3945638538537516, "y": 2.452093220634288}, "objectives": [0.3945638538537516, 8.74913702032583]}, {"variables": {"x": 0.39909651676597174, "y": 2.4758149159349414}, "objectives": [0.39909651676597174, 8.709208850282055]}, {"variables": {"x": 0.4009171893812429, "y": 2.39218349429755}, "objectives": [0.4009171893812429, 8.461057754926621]}, {"variables": {"x": 0.4019122569790034, "y": 2.39218349429755}, "objectives": [0.4019122569790034, 8.440109589578313]}, {"variables": {"x": 0.4046467630473584, "y": 2.368370028870373}, "objectives": [0.4046467630473584, 8.324223338655871]}, {"variables": {"x": 0.40802487288096195, "y": 2.37107465767249}, "objectives": [0.40802487288096195, 8.261934214623173]}, {"variables": {"x": 0.4086593035318771, "y": 2.3235063338191733}, "objectives": [0.4086593035318771, 8.132706890790082]}, {"variables": {"x": 0.4128823818092859, "y": 2.3028692186855038}, "objectives": [0.4128823818092859, 7.9995402182385416]}, {"variables": {"x": 0.41586350345567147, "y": 2.3028692186855038}, "objectives": [0.41586350345567147, 7.942195434896031]}, {"variables": {"x": 0.4182271958553442, "y": 2.2456271765906086}, "objectives": [0.4182271958553442, 7.760440279242867]}, {"variables": {"x": 0.4231021709940811, "y": 2.2727835883866394}, "objectives": [0.4231021709940811, 7.735208686585594]}, {"variables": {"x": 0.42597390555932985, "y": 2.2456271765906086}, 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Used in use cases (1 recipe)
Process & reactor engineer › Fit Arrhenius parameters with a global optimizer
Process & reactor engineer › Fit Arrhenius parameters with a global optimizer
ml mcmc <log_density> <json_init>
Description
Sample a posterior with MCMC (adaptive random walk Metropolis-Hastings, 4 chains of 5000 draws after 2000 of warm-up): the log density is an expression in the parameters (same syntax as ml optim, ln = natural logarithm); mean, sd, median, 95 % credible interval, split R-hat (< 1.05) and effective sample size of each parameter
Sample a posterior with MCMC (adaptive random walk Metropolis-Hastings, 4 chains of 5000 draws after 2000 of warm-up): the log density is an expression in the parameters (same syntax as ml optim, ln = natural logarithm); mean, sd, median, 95 % credible interval, split R-hat (< 1.05) and effective sample size of each parameter
Parameters
log_density: The log posterior (up to a constant) - String - required
json_init: The initial values {"name": value} - String - required
log_density: The log posterior (up to a constant) - String - required
json_init: The initial values {"name": value} - String - required
admin
ml mcmc "-21*ln(s) - (76 + 20*(m-5)^2)/(2*s^2)" "{\"m\": 4, \"s\": 1}" "{\"bounds\": {\"s\": [0, null]}}"
mentdb
{"parameters": [{"parameter": "s", "mean": 2.095942992368577, "sd": 0.3628258958553108, "median": 2.0431875728536957, "lower": 1.5417615591765041, "upper": 2.9507669125464377, "rhat": 1.0012095199984568, "ess": 2057.3683271401496}, {"parameter": "m", "mean": 4.99707151660001, "sd": 0.46471191558126196, "median": 4.994923661386166, "lower": 4.072259239193639, "upper": 5.884783561375852, "rhat": 1.0006907197061314, "ess": 2496.6456593822436}], "acceptance": 0.2617, "chains": 4, "draws": 20000, "converged": true}
ml mcmc <log_density> <json_init> <json_params>
Description
MCMC with parameters: chains (4), iterations (5000), burn_in (2000), thin (1), bounds {"name": [min, max]} (null = no limit), level, seed, samples (number of draws returned)
MCMC with parameters: chains (4), iterations (5000), burn_in (2000), thin (1), bounds {"name": [min, max]} (null = no limit), level, seed, samples (number of draws returned)
Parameters
log_density: The log posterior - String - required
json_init: The initial values - String - required
json_params: The parameters - String - required
log_density: The log posterior - String - required
json_init: The initial values - String - required
json_params: The parameters - String - required
admin
ml mcmc "2*(a+b*1) - 20*ln(1+exp(a+b*1)) + 8*(a+b*2) - 20*ln(1+exp(a+b*2)) + 17*(a+b*3) - 20*ln(1+exp(a+b*3))" "{\"a\": 0, \"b\": 0}" "{\"iterations\": 4000, \"samples\": 3}"
mentdb
{"parameters": [{"parameter": "a", "mean": -4.56786909898477, "sd": 1.1217755929420508, "median": -4.474129247812157, "lower": -6.955034088089458, "upper": -2.540138920758467, "rhat": 1.0022951421838078, "ess": 2158.8062419124763}, {"parameter": "b", "mean": 2.1133067385740034, "sd": 0.5064875881673842, "median": 2.070598467618345, "lower": 1.2083323050935233, "upper": 3.192129340169593, "rhat": 1.0017959229560722, "ess": 2228.0255418194292}], "acceptance": 0.2930625, "chains": 4, "draws": 16000, "converged": true, "samples": [[-4.987180230664025, 2.6115746269592846], [-8.020412334287773, 3.5576870718337874], [-4.477884366880547, 2.4790781760144345]]}
ml mcmc <log_density> <json_init> <json_params>
Description
MCMC with the No-U-Turn sampler: {"method": "nuts"} (Hoffman and Gelman, exact gradients of the expression, step size by dual averaging, 1000 draws after 1000 of warm-up): much larger effective sample sizes on correlated posteriors; max_depth (10), target_accept (0.8); divergent transitions reported
MCMC with the No-U-Turn sampler: {"method": "nuts"} (Hoffman and Gelman, exact gradients of the expression, step size by dual averaging, 1000 draws after 1000 of warm-up): much larger effective sample sizes on correlated posteriors; max_depth (10), target_accept (0.8); divergent transitions reported
Parameters
log_density: The log posterior - String - required
json_init: The initial values - String - required
json_params: The parameters - String - required
log_density: The log posterior - String - required
json_init: The initial values - String - required
json_params: The parameters - String - required
admin
ml mcmc "-0.5*(x^2 + (y/10)^2) - 0.5*((x+y/10)^2)" "{\"x\": 0, \"y\": 0}" "{\"method\": \"nuts\", \"chains\": 2}"
mentdb
{"parameters": [{"parameter": "x", "mean": -0.02683366646795284, "sd": 0.7999993366575558, "median": -0.014109147025500868, "lower": -1.6007893167377594, "upper": 1.5338771180765702, "rhat": 0.9999529593885311, "ess": 1061.7095386946817}, {"parameter": "y", "mean": 0.14042155321776068, "sd": 8.280891955235793, "median": 0.06151259496861991, "lower": -15.620968718313279, "upper": 16.63528149232199, "rhat": 1.0016366798815968, "ess": 387.4727948337872}], "acceptance": 0.862792, "chains": 2, "draws": 2000, "converged": true, "method": "nuts", "divergent_transitions": 0}
ml graph communities <json_edges> <method>
Description
Communities of an undirected graph: louvain (maximum of the modularity) or label_propagation; returns the community of each node, the sizes and the modularity
Communities of an undirected graph: louvain (maximum of the modularity) or label_propagation; returns the community of each node, the sizes and the modularity
Parameters
json_edges: The edges [[a, b], [a, b, weight], ...] - String - required
method: louvain or label_propagation - String - required
json_edges: The edges [[a, b], [a, b, weight], ...] - String - required
method: louvain or label_propagation - String - required
admin
ml graph communities "[[\"a\", \"b\"], [\"b\", \"c\"], [\"a\", \"c\"], [\"c\", \"d\"], [\"d\", \"e\"], [\"e\", \"f\"], [\"d\", \"f\"]]" "louvain"
mentdb
{"communities": {"a": 0, "b": 0, "c": 0, "d": 1, "e": 1, "f": 1}, "n_communities": 2, "sizes": [3, 3], "modularity": 0.3571428571428571}
ml graph pagerank <json_edges> <damping>
Description
PageRank of the nodes of a directed graph (the dangling nodes spread their rank uniformly)
PageRank of the nodes of a directed graph (the dangling nodes spread their rank uniformly)
Parameters
json_edges: The directed edges [[from, to], ...] - String - required
damping: The damping factor, ex: 0.85 - Number - required
json_edges: The directed edges [[from, to], ...] - String - required
damping: The damping factor, ex: 0.85 - Number - required
admin
ml graph pagerank "[[\"a\", \"b\"], [\"b\", \"c\"], [\"c\", \"a\"], [\"d\", \"c\"]]" 0.85
mentdb
{"a": 0.3202137998054778, "b": 0.30968172983465614, "c": 0.33260447035986596, "d": 0.037500000000000006}
ml graph centrality <json_edges> <type>
Description
Centrality of the nodes of an undirected graph: degree (normalized), weighted_degree, betweenness (Brandes, normalized) or closeness (Wasserman and Faust)
Centrality of the nodes of an undirected graph: degree (normalized), weighted_degree, betweenness (Brandes, normalized) or closeness (Wasserman and Faust)
Parameters
json_edges: The edges - String - required
type: degree, weighted_degree, betweenness or closeness - String - required
json_edges: The edges - String - required
type: degree, weighted_degree, betweenness or closeness - String - required
admin
ml graph centrality "[[\"a\", \"b\"], [\"b\", \"c\"], [\"c\", \"d\"], [\"d\", \"e\"]]" "betweenness"
mentdb
{"a": 0, "b": 0.5, "c": 0.6666666666666666, "d": 0.5, "e": 0}
admin
ml graph centrality "[[\"a\", \"b\"], [\"b\", \"c\"], [\"a\", \"c\"], [\"c\", \"d\"], [\"d\", \"e\"], [\"e\", \"f\"], [\"d\", \"f\"]]" "closeness"
mentdb
{"a": 0.5, "b": 0.5, "c": 0.7142857142857143, "d": 0.7142857142857143, "e": 0.5, "f": 0.5}