问题
Please consider the following basic reproducible example:
library(h2o)
h2o.init()
data("iris")
iris.hex = as.h2o(iris, "iris.hex")
mod = h2o.glm(y = "Sepal.Length", x = setdiff(colnames(iris), "Sepal.Length"),
training_frame = iris.hex, nfolds = 2, seed = 100,
lambda_search = T, early_stopping = F,
family = "gamma", nlambdas = 100)
When I run the above, I expect that h2o will iterate over 100 different values of lambda. However, running length(mod@allparameters$lambda) will show that only 79 values of lambda were actually tested. These 79 values are the first 79 values in the sequence:
maxLambda = max(mod@allparameters$lambda)
lambdaMinRatio = mod@allparameters$lambda_min_ratio
exp(seq(log(maxLambda), log(maxLambda*lambdaMinRatio), length.out = 100))
Could you please let me know how I can get the function to iterate over all 100 values of lambda? (I tried setting early_stopping = F to see if that would fix the issue but it does not.)
Here is my cluster info if it helps:
R is connected to the H2O cluster:
H2O cluster uptime: 11 hours 21 minutes
H2O cluster version: 3.10.5.3
H2O cluster version age: 1 month and 26 days
H2O cluster name: H2O_started_from_R_xaq943
H2O cluster total nodes: 1
H2O cluster total memory: 6.75 GB
H2O cluster total cores: 8
H2O cluster allowed cores: 4
H2O cluster healthy: TRUE
H2O Connection ip: localhost
H2O Connection port: 54321
H2O Connection proxy: NA
H2O Internal Security: FALSE
R Version: R version 3.3.3 (2017-03-06)
Thank you!
EDIT: As requested, here is the output of h2o.getGLMFullRegularizationPath(mod):
$`__meta`
$`__meta`$schema_version
[1] 3
$`__meta`$schema_name
[1] "GLMRegularizationPathV3"
$`__meta`$schema_type
[1] "RegularizationPath"
$model
NULL
$lambdas
[1] 1.434114617 1.306711827 1.190627150 1.084855115 0.988479577 0.900665776 0.820653111 0.747748550 0.681320630 0.620793983 0.565644356
[12] 0.515394071 0.469607882 0.427889212 0.389876714 0.355241141 0.323682497 0.294927436 0.268726896 0.244853939 0.223101790 0.203282042
[23] 0.185223025 0.168768322 0.153775410 0.140114426 0.127667047 0.116325458 0.105991425 0.096575439 0.087995943 0.080178626 0.073055778
[34] 0.066565704 0.060652190 0.055264017 0.050354514 0.045881158 0.041805202 0.038091343 0.034707413 0.031624102 0.028814704 0.026254885
[45] 0.023922474 0.021797267 0.019860858 0.018096474 0.016488833 0.015024011 0.013689319 0.012473198 0.011365113 0.010355468 0.009435517
[56] 0.008597291 0.007833532 0.007137622 0.006503536 0.005925779 0.005399349 0.004919686 0.004482635 0.004084410 0.003721562 0.003390949
[67] 0.003089706 0.002815225 0.002565128 0.002337249 0.002129615 0.001940426 0.001768044 0.001610975 0.001467861 0.001337460 0.001218644
[78] 0.001110383 0.001011740
$explained_deviance_train
[1] -3.294962e-08 1.278780e-01 2.352402e-01 3.253159e-01 4.008369e-01 4.641126e-01 5.170944e-01 5.614293e-01 5.985067e-01
[10] 6.294974e-01 6.553869e-01 6.770044e-01 6.950464e-01 7.100979e-01 7.226495e-01 7.331127e-01 7.418320e-01 7.490957e-01
[19] 7.551451e-01 7.687710e-01 7.815713e-01 7.921910e-01 8.010014e-01 8.083105e-01 8.143741e-01 8.194045e-01 8.235584e-01
[28] 8.270239e-01 8.298991e-01 8.322847e-01 8.342640e-01 8.359064e-01 8.372692e-01 8.384000e-01 8.393384e-01 8.401172e-01
[37] 8.407634e-01 8.411713e-01 8.420553e-01 8.434391e-01 8.445680e-01 8.454431e-01 8.462240e-01 8.468835e-01 8.476350e-01
[46] 8.481135e-01 8.497288e-01 8.513965e-01 8.528687e-01 8.541499e-01 8.551259e-01 8.560063e-01 8.566711e-01 8.572853e-01
[55] 8.578407e-01 8.583362e-01 8.586877e-01 8.590151e-01 8.593148e-01 8.595864e-01 8.596849e-01 8.599377e-01 8.600233e-01
[64] 8.602430e-01 8.603153e-01 8.605097e-01 8.605776e-01 8.608212e-01 8.608821e-01 8.610499e-01 8.611065e-01 8.611627e-01
[73] 8.612156e-01 8.616241e-01 8.616940e-01 8.617575e-01 8.617782e-01 8.617988e-01 8.618557e-01
$explained_deviance_valid
NULL
$coefficients
Species.setosa Species.versicolor Species.virginica Sepal.Width Petal.Length Petal.Width Intercept
[1,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 0.000000000 0.000000e+00 0.1711352
[2,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.001046643 0.000000e+00 0.1750882
[3,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.002009314 0.000000e+00 0.1787588
[4,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.002894275 0.000000e+00 0.1821621
[5,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.003707356 0.000000e+00 0.1853133
[6,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.004453990 0.000000e+00 0.1882274
[7,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.005139245 0.000000e+00 0.1909189
[8,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.005767843 0.000000e+00 0.1934021
[9,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.006344186 0.000000e+00 0.1956907
[10,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.006872371 0.000000e+00 0.1977980
[11,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.007356208 0.000000e+00 0.1997366
[12,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.007799235 0.000000e+00 0.2015187
[13,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.008204738 0.000000e+00 0.2031555
[14,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.008575759 0.000000e+00 0.2046579
[15,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.008915116 0.000000e+00 0.2060361
[16,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.009225414 0.000000e+00 0.2072996
[17,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.009509059 0.000000e+00 0.2084574
[18,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.009768269 0.000000e+00 0.2095177
[19,] 0.000000e+00 0.0000000000 0.000000e+00 0.000000000 -0.010005092 0.000000e+00 0.2104884
[20,] 0.000000e+00 0.0000000000 0.000000e+00 -0.001127417 -0.010319589 0.000000e+00 0.2151915
[21,] 0.000000e+00 0.0000000000 0.000000e+00 -0.002359216 -0.010623694 0.000000e+00 0.2201719
[22,] 0.000000e+00 0.0000000000 0.000000e+00 -0.003480564 -0.010900376 0.000000e+00 0.2247086
[23,] 0.000000e+00 0.0000000000 0.000000e+00 -0.004501465 -0.011152087 0.000000e+00 0.2288412
[24,] 0.000000e+00 0.0000000000 0.000000e+00 -0.005430894 -0.011381098 0.000000e+00 0.2326054
[25,] 0.000000e+00 0.0000000000 0.000000e+00 -0.006277042 -0.011589469 0.000000e+00 0.2360339
[26,] 0.000000e+00 0.0000000000 0.000000e+00 -0.007047377 -0.011779076 0.000000e+00 0.2391565
[27,] 0.000000e+00 0.0000000000 0.000000e+00 -0.007743794 -0.011951139 0.000000e+00 0.2419836
[28,] 0.000000e+00 0.0000000000 0.000000e+00 -0.008382717 -0.012108203 0.000000e+00 0.2445752
[29,] 0.000000e+00 0.0000000000 0.000000e+00 -0.008964450 -0.012251159 0.000000e+00 0.2469356
[30,] 0.000000e+00 0.0000000000 0.000000e+00 -0.009494120 -0.012381280 0.000000e+00 0.2490854
[31,] 0.000000e+00 0.0000000000 0.000000e+00 -0.009976404 -0.012499729 0.000000e+00 0.2510434
[32,] 0.000000e+00 0.0000000000 0.000000e+00 -0.010415558 -0.012607559 0.000000e+00 0.2528268
[33,] 0.000000e+00 0.0000000000 0.000000e+00 -0.010815455 -0.012705728 0.000000e+00 0.2544511
[34,] 0.000000e+00 0.0000000000 0.000000e+00 -0.011179617 -0.012795108 0.000000e+00 0.2559306
[35,] 0.000000e+00 0.0000000000 0.000000e+00 -0.011511250 -0.012876490 0.000000e+00 0.2572783
[36,] 0.000000e+00 0.0000000000 0.000000e+00 -0.011813271 -0.012950594 0.000000e+00 0.2585058
[37,] 0.000000e+00 0.0000000000 0.000000e+00 -0.012088333 -0.013018075 0.000000e+00 0.2596239
[38,] 0.000000e+00 0.0000000000 0.000000e+00 -0.012254270 -0.013069805 0.000000e+00 0.2603445
[39,] 0.000000e+00 -0.0001175922 0.000000e+00 -0.012623025 -0.013136288 0.000000e+00 0.2617830
[40,] 0.000000e+00 -0.0005066170 0.000000e+00 -0.013031762 -0.013198821 0.000000e+00 0.2634171
[41,] 0.000000e+00 -0.0008532154 0.000000e+00 -0.013400532 -0.013255288 0.000000e+00 0.2648907
[42,] 0.000000e+00 -0.0011428955 0.000000e+00 -0.013718316 -0.013304258 0.000000e+00 0.2661590
[43,] 0.000000e+00 -0.0014293556 0.000000e+00 -0.014023516 -0.013351005 0.000000e+00 0.2673789
[44,] 0.000000e+00 -0.0016797073 1.243120e-05 -0.014304179 -0.013396541 0.000000e+00 0.2685020
[45,] 0.000000e+00 -0.0018706468 9.790433e-05 -0.014536882 -0.013478186 8.361933e-05 0.2694643
[46,] 0.000000e+00 -0.0019698629 1.717337e-04 -0.014665554 -0.013530772 1.814935e-04 0.2699431
[47,] 0.000000e+00 -0.0021078477 2.246836e-04 -0.014925921 -0.013849890 8.489923e-04 0.2711751
[48,] 0.000000e+00 -0.0021556371 3.034315e-04 -0.015150706 -0.014237748 1.656453e-03 0.2723528
[49,] 0.000000e+00 -0.0021453273 4.458210e-04 -0.015348300 -0.014616464 2.413512e-03 0.2734328
[50,] 0.000000e+00 -0.0020839569 6.461732e-04 -0.015520020 -0.014980050 3.109852e-03 0.2744131
[51,] 0.000000e+00 -0.0020107174 8.597081e-04 -0.015660412 -0.015278421 3.659515e-03 0.2752178
[52,] 0.000000e+00 -0.0019078474 1.101906e-03 -0.015786052 -0.015572930 4.186424e-03 0.2759708
[53,] 0.000000e+00 -0.0018175109 1.323132e-03 -0.015890456 -0.015809763 4.599076e-03 0.2765883
[54,] 0.000000e+00 -0.0017094991 1.558056e-03 -0.015986195 -0.016047486 5.006251e-03 0.2771791
[55,] 0.000000e+00 -0.0015842081 1.807162e-03 -0.016071634 -0.016281094 5.397220e-03 0.2777320
[56,] 0.000000e+00 -0.0014430021 2.070103e-03 -0.016146458 -0.016507391 5.765349e-03 0.2782422
[57,] 0.000000e+00 -0.0013372850 2.282679e-03 -0.016207766 -0.016676301 6.033973e-03 0.2786413
[58,] 0.000000e+00 -0.0012235170 2.499826e-03 -0.016264638 -0.016845566 6.300372e-03 0.2790268
[59,] 0.000000e+00 -0.0011012638 2.721871e-03 -0.016315538 -0.017012360 6.558645e-03 0.2793901
[60,] 0.000000e+00 -0.0009710435 2.949010e-03 -0.016360197 -0.017174819 6.804753e-03 0.2797282
[61,] 0.000000e+00 -0.0009387214 3.037293e-03 -0.016389637 -0.017231436 6.890089e-03 0.2798895
[62,] 0.000000e+00 -0.0008039241 3.270133e-03 -0.016434978 -0.017400280 7.145560e-03 0.2802397
[63,] 0.000000e+00 -0.0007660395 3.357739e-03 -0.016459753 -0.017456898 7.230221e-03 0.2803861
[64,] 0.000000e+00 -0.0006199118 3.595215e-03 -0.016496474 -0.017622417 7.475811e-03 0.2807040
[65,] 0.000000e+00 -0.0005768718 3.683036e-03 -0.016516321 -0.017676868 7.554952e-03 0.2808322
[66,] -3.476645e-05 -0.0004234267 3.926329e-03 -0.016530611 -0.017839966 7.778584e-03 0.2811053
[67,] -6.891206e-05 -0.0003785185 4.015034e-03 -0.016531687 -0.017896456 7.848434e-03 0.2812048
[68,] -2.509409e-04 -0.0001852860 4.347389e-03 -0.016506532 -0.018118743 8.088206e-03 0.2815679
[69,] -3.133552e-04 -0.0001451602 4.438172e-03 -0.016500367 -0.018173876 8.139393e-03 0.2816729
[70,] -5.214018e-04 -0.0000198928 4.695164e-03 -0.016468891 -0.018337882 8.275330e-03 0.2819765
[71,] -6.024159e-04 0.0000000000 4.785344e-03 -0.016466374 -0.018391875 8.314552e-03 0.2821158
[72,] -6.921978e-04 0.0000000000 4.869432e-03 -0.016471686 -0.018446669 8.353211e-03 0.2822946
[73,] -7.920269e-04 0.0000000000 4.942796e-03 -0.016472703 -0.018501428 8.391136e-03 0.2824681
[74,] -2.055117e-03 0.0000000000 5.491157e-03 -0.016310937 -0.018964797 8.523048e-03 0.2838078
[75,] -2.353043e-03 0.0000000000 5.606834e-03 -0.016260884 -0.019047344 8.505333e-03 0.2840483
[76,] -2.644396e-03 0.0000000000 5.720820e-03 -0.016211592 -0.019126493 8.483952e-03 0.2842812
[77,] -2.743107e-03 0.0000000000 5.760265e-03 -0.016195310 -0.019153151 8.477521e-03 0.2843592
[78,] -2.843096e-03 0.0000000000 5.800458e-03 -0.016179171 -0.019181275 8.473083e-03 0.2844411
[79,] -3.135365e-03 0.0000000000 5.915736e-03 -0.016130870 -0.019263792 8.457283e-03 0.2846831
$coefficients_std
Species.setosa Species.versicolor Species.virginica Sepal.Width Petal.Length Petal.Width Intercept
[1,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 0.000000000 0.0000000000 0.1711352
[2,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.001847636 0.0000000000 0.1711550
[3,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.003547039 0.0000000000 0.1712078
[4,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.005109259 0.0000000000 0.1712854
[5,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.006544589 0.0000000000 0.1713811
[6,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.007862621 0.0000000000 0.1714893
[7,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.009072299 0.0000000000 0.1716056
[8,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.010181963 0.0000000000 0.1717265
[9,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.011199381 0.0000000000 0.1718492
[10,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.012131784 0.0000000000 0.1719716
[11,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.012985900 0.0000000000 0.1720920
[12,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.013767976 0.0000000000 0.1722091
[13,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.014483810 0.0000000000 0.1723221
[14,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.015138773 0.0000000000 0.1724302
[15,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.015737839 0.0000000000 0.1725331
[16,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.016285607 0.0000000000 0.1726305
[17,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.016786324 0.0000000000 0.1727224
[18,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.017243908 0.0000000000 0.1728086
[19,] 0.000000e+00 0.0000000000 0.000000e+00 0.0000000000 -0.017661971 0.0000000000 0.1728892
[20,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0004914029 -0.018217153 0.0000000000 0.1729636
[21,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0010283025 -0.018753989 0.0000000000 0.1730351
[22,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0015170607 -0.019242414 0.0000000000 0.1731038
[23,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0019620369 -0.019686760 0.0000000000 0.1731692
[24,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0023671437 -0.020091032 0.0000000000 0.1732312
[25,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0027359511 -0.020458869 0.0000000000 0.1732897
[26,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0030717141 -0.020793582 0.0000000000 0.1733446
[27,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0033752589 -0.021097325 0.0000000000 0.1733959
[28,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0036537438 -0.021374590 0.0000000000 0.1734438
[29,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0039073015 -0.021626949 0.0000000000 0.1734884
[30,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0041381667 -0.021856652 0.0000000000 0.1735299
[31,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0043483781 -0.022065749 0.0000000000 0.1735682
[32,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0045397906 -0.022256101 0.0000000000 0.1736038
[33,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0047140920 -0.022429399 0.0000000000 0.1736366
[34,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0048728180 -0.022587182 0.0000000000 0.1736668
[35,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0050173658 -0.022730846 0.0000000000 0.1736947
[36,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0051490065 -0.022861661 0.0000000000 0.1737203
[37,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0052688970 -0.022980784 0.0000000000 0.1737439
[38,] 0.000000e+00 0.0000000000 0.000000e+00 -0.0053412234 -0.023072104 0.0000000000 0.1737627
[39,] 0.000000e+00 -0.0001175922 0.000000e+00 -0.0055019511 -0.023189466 0.0000000000 0.1738240
[40,] 0.000000e+00 -0.0005066170 0.000000e+00 -0.0056801058 -0.023299856 0.0000000000 0.1739735
[41,] 0.000000e+00 -0.0008532154 0.000000e+00 -0.0058408402 -0.023399537 0.0000000000 0.1741074
[42,] 0.000000e+00 -0.0011428955 0.000000e+00 -0.0059793516 -0.023485982 0.0000000000 0.1742201
[43,] 0.000000e+00 -0.0014293556 0.000000e+00 -0.0061123779 -0.023568506 0.0000000000 0.1743313
[44,] 0.000000e+00 -0.0016797073 1.243120e-05 -0.0062347093 -0.023648890 0.0000000000 0.1744252
[45,] 0.000000e+00 -0.0018706468 9.790433e-05 -0.0063361369 -0.023793017 0.0000637378 0.1744695
[46,] 0.000000e+00 -0.0019698629 1.717337e-04 -0.0063922205 -0.023885848 0.0001383412 0.1744746
[47,] 0.000000e+00 -0.0021078477 2.246836e-04 -0.0065057056 -0.024449186 0.0006471339 0.1745119
[48,] 0.000000e+00 -0.0021556371 3.034315e-04 -0.0066036820 -0.025133872 0.0012626110 0.1745132
[49,] 0.000000e+00 -0.0021453273 4.458210e-04 -0.0066898067 -0.025802418 0.0018396700 0.1744739
[50,] 0.000000e+00 -0.0020839569 6.461732e-04 -0.0067646533 -0.026444255 0.0023704464 0.1743980
[51,] 0.000000e+00 -0.0020107174 8.597081e-04 -0.0068258458 -0.026970969 0.0027894199 0.1743114
[52,] 0.000000e+00 -0.0019078474 1.101906e-03 -0.0068806078 -0.027490866 0.0031910501 0.1742055
[53,] 0.000000e+00 -0.0018175109 1.323132e-03 -0.0069261140 -0.027908946 0.0035055892 0.1741087
[54,] 0.000000e+00 -0.0017094991 1.558056e-03 -0.0069678433 -0.028328598 0.0038159527 0.1740017
[55,] 0.000000e+00 -0.0015842081 1.807162e-03 -0.0070050834 -0.028740986 0.0041139641 0.1738844
[56,] 0.000000e+00 -0.0014430021 2.070103e-03 -0.0070376966 -0.029140468 0.0043945661 0.1737569
[57,] 0.000000e+00 -0.0013372850 2.282679e-03 -0.0070644186 -0.029438645 0.0045993214 0.1736560
[58,] 0.000000e+00 -0.0012235170 2.499826e-03 -0.0070892073 -0.029737447 0.0048023811 0.1735510
[59,] 0.000000e+00 -0.0011012638 2.721871e-03 -0.0071113928 -0.030031888 0.0049992462 0.1734416
[60,] 0.000000e+00 -0.0009710435 2.949010e-03 -0.0071308582 -0.030318677 0.0051868390 0.1733278
[61,] 0.000000e+00 -0.0009387214 3.037293e-03 -0.0071436900 -0.030418624 0.0052518855 0.1732887
[62,] 0.000000e+00 -0.0008039241 3.270133e-03 -0.0071634528 -0.030716683 0.0054466147 0.1731721
[63,] 0.000000e+00 -0.0007660395 3.357739e-03 -0.0071742512 -0.030816631 0.0055111469 0.1731316
[64,] 0.000000e+00 -0.0006199118 3.595215e-03 -0.0071902568 -0.031108822 0.0056983444 0.1730097
[65,] 0.000000e+00 -0.0005768718 3.683036e-03 -0.0071989073 -0.031204944 0.0057586692 0.1729675
[66,] -3.476645e-05 -0.0004234267 3.926329e-03 -0.0072051358 -0.031492861 0.0059291294 0.1728522
[67,] -6.891206e-05 -0.0003785185 4.015034e-03 -0.0072056052 -0.031592582 0.0059823720 0.1728199
[68,] -2.509409e-04 -0.0001852860 4.347389e-03 -0.0071946407 -0.031984985 0.0061651351 0.1727122
[69,] -3.133552e-04 -0.0001451602 4.438172e-03 -0.0071919537 -0.032082312 0.0062041522 0.1726902
[70,] -5.214018e-04 -0.0000198928 4.695164e-03 -0.0071782342 -0.032371830 0.0063077682 0.1726367
[71,] -6.024159e-04 0.0000000000 4.785344e-03 -0.0071771375 -0.032467144 0.0063376645 0.1726279
[72,] -6.921978e-04 0.0000000000 4.869432e-03 -0.0071794528 -0.032563872 0.0063671323 0.1726309
[73,] -7.920269e-04 0.0000000000 4.942796e-03 -0.0071798959 -0.032660538 0.0063960401 0.1726409
[74,] -2.055117e-03 0.0000000000 5.491157e-03 -0.0071093875 -0.033478523 0.0064965885 0.1728921
[75,] -2.353043e-03 0.0000000000 5.606834e-03 -0.0070875711 -0.033624243 0.0064830849 0.1729542
[76,] -2.644396e-03 0.0000000000 5.720820e-03 -0.0070660864 -0.033763964 0.0064667874 0.1730147
[77,] -2.743107e-03 0.0000000000 5.760265e-03 -0.0070589896 -0.033811024 0.0064618858 0.1730346
[78,] -2.843096e-03 0.0000000000 5.800458e-03 -0.0070519551 -0.033860671 0.0064585028 0.1730548
[79,] -3.135365e-03 0.0000000000 5.915736e-03 -0.0070309023 -0.034006338 0.0064464596 0.1731155
$coefficient_names
[1] "Species.setosa" "Species.versicolor" "Species.virginica" "Sepal.Width" "Petal.Length" "Petal.Width"
[7] "Intercept"
EDIT #2: In response to @Darren's answer. I am now seeing the following on my actual (confidential) dataset. The cross-validated models have selected smaller lambda's, yet the main model stops at a very large lambda.
> tail(mx@allparameters$lambda)
[1] 0.1536665 0.1400152 0.1275767 0.1162431 0.1059164
> mx@model$lambda_best
[1] 0.1059164
>
> lapply(mx@model$cross_validation_models, function(m_cv){
+ m <- h2o.getModel(m_cv$name)
+ list( tail(m@allparameters$lambda), m@model$lambda_best )
+ })
[[1]]
[[1]][[1]]
[1] 2.446806e-05 2.229438e-05 2.031381e-05 1.850919e-05 1.686488e-05 1.536665e-05
[[1]][[2]]
[1] 0.01135707
[[2]]
[[2]][[1]]
[1] 2.446806e-05 2.229438e-05 2.031381e-05 1.850919e-05 1.686488e-05 1.536665e-05
[[2]][[2]]
[1] 0.01808366
[[3]]
[[3]][[1]]
[1] 2.446806e-05 2.229438e-05 2.031381e-05 1.850919e-05 1.686488e-05 1.536665e-05
[[3]][[2]]
[1] 0.01647716
回答1:
Short answer: you've found a bug and we've opened a ticket here. The early stopping flag is not being honored when nfolds > 0. In the meantime, if you don't set nfolds, you should get 100 lambdas.
回答2:
What is happening is it learns from the cross-validation models, to optimize the parameters used for the final run. (BTW, you are using nfolds=2 which is fairly unusual for a small data set: learn on just 75 records, then test on the other 75. So you are going to have a lot of noise in what it learns from CV.)
Following on from your code:
tail(mod@allparameters$lambda)
mod@model$lambda_best
I'm using 3.14.0.1, so here is what I get:
[1] 0.002129615 0.001940426 0.001768044 0.001610975 0.001467861 0.001337460
and:
[1] 0.001610975
Then if we go look at the same for the 2 CV models:
lapply(mod@model$cross_validation_models, function(m_cv){
m <- h2o.getModel(m_cv$name)
list( tail(m@allparameters$lambda), m@model$lambda_best )
})
I get:
[[1]]
[[1]][[1]]
[1] 0.0002283516 0.0002080655 0.0001895815 0.0001727396 0.0001573939 0.0001434115
[[1]][[2]]
[1] 0.002337249
[[2]]
[[2]][[1]]
[1] 0.0002283516 0.0002080655 0.0001895815 0.0001727396 0.0001573939 0.0001434115
[[2]][[2]]
[1] 0.00133746
I.e. it seems the lowest best lambda found in the CV models was 0.00133, so it has used that as early stopping for the final model.
BTW, if you poke around in those cv models you will see they both tried 100 values for lambda. It is only the final model that does the extra optimization.
(I'm thinking of it as a time optimization, but reading p.26/27 of the Generalized Linear Models booklet (free download from https://www.h2o.ai/resources/), I think it is mainly about using the cv data to avoid over-fitting.)
You can explicitly specify a set of lambda values to try. BUT, the cross-validation learning will still take priority for the final model. E.g. in the following the final model only tried the first 4 of the 6 lambda values I suggested, because both CV models liked 0.001 best.
mx = h2o.glm(y = "Sepal.Length", x = setdiff(colnames(iris), "Sepal.Length"),
training_frame = iris.hex, nfolds = 2, seed = 100,
lambda = c(1.0, 0.1, 0.01, 0.001, 0.0001, 0), lambda_search = T,
family = "gamma")
tail(mx@allparameters$lambda)
mx@model$lambda_best
lapply(mx@model$cross_validation_models, function(m_cv){
m <- h2o.getModel(m_cv$name)
list( tail(m@allparameters$lambda), m@model$lambda_best )
})
来源：https://stackoverflow.com/questions/45890985/h2o-glm-lambda-search-not-appearing-to-iterate-over-all-lambdas