GLMNet in Python: Generalized Linear Models
R-bloggers 2024-11-19
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During the past few weeks, I’ve been adapting a Python version of the (seemingly abandoned?) official Stanford GLMNet package. Don’t try to build a programming interface on it yet, as it’s still “moving”.
GLMNet implements the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, poisson regression and the cox model. My implementation is faithful to the R Fortran-based one, but:
- uses
numpyinstead ofscipy - uses
scikit-learnstyle, with a main classGLMNethaving methodsfitandpredict
If (like me) you’re a fond a GLMNet and scikit-learn style, you may love this package. Here, I illustrate usage of this “new” package with Techtonique ecosystem, with nnetsauce and mlsauce.
!pip install git+https://github.com/Techtonique/mlsauce.git --verbose --upgrade --no-cache-dir!pip install git+https://github.com/thierrymoudiki/glmnetforpython.git --verbose --upgrade --no-cache-dir
1 – GLMNet
1 – 1 GLMNet Classification
import nnetsauce as nsimport mlsauce as msimport numpy as npimport glmnetforpython as glmnetfrom sklearn.datasets import load_breast_cancer, load_iris, load_winefrom sklearn.model_selection import train_test_splitfrom time import timedatasets = [load_iris, load_breast_cancer, load_wine]for dataset in datasets: print(f"\n\n dataset: {dataset.__name__} -------------------") X, y = dataset(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123) clf = glmnet.GLMNet(family="multinomial") print(clf.get_params()) start = time() clf.fit(X_train, y_train) print(f"elapsed: {time() - start}") #clf.print() #print(clf.score(X_test, y_test)) preds = clf.predict(X_test, ptype="class") print(preds) print("accuracy: ", np.mean(preds == y_test)) dataset: load_iris ------------------- {'alpha': 1.0, 'dfmax': 10000000000.0, 'exclude': None, 'family': 'multinomial', 'lambdau': None, 'lower_lambdau': None, 'maxit': 100000.0, 'ncores': -1, 'nlambda': 100, 'parallel': False, 'penalty_factor': None, 'pmax': 10000000000.0, 'standardize': True, 'thresh': 1e-07, 'type_measure': 1, 'upper_lambdau': None, 'verbose': False, 'weights': None} elapsed: 0.5259675979614258 [1. 2. 2. 1. 0. 2. 1. 0. 0. 1. 2. 0. 1. 2. 2. 2. 0. 0. 1. 0. 0. 1. 0. 2. 0. 0. 0. 2. 2. 0.] accuracy: 0.9666666666666667 dataset: load_breast_cancer ------------------- {'alpha': 1.0, 'dfmax': 10000000000.0, 'exclude': None, 'family': 'multinomial', 'lambdau': None, 'lower_lambdau': None, 'maxit': 100000.0, 'ncores': -1, 'nlambda': 100, 'parallel': False, 'penalty_factor': None, 'pmax': 10000000000.0, 'standardize': True, 'thresh': 1e-07, 'type_measure': 1, 'upper_lambdau': None, 'verbose': False, 'weights': None} elapsed: 1.3695988655090332 [1. 1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 0. 0. 1. 0. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 1. 0. 1. 1. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 0. 1. 0. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 0. 0. 0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] accuracy: 0.956140350877193 dataset: load_wine ------------------- {'alpha': 1.0, 'dfmax': 10000000000.0, 'exclude': None, 'family': 'multinomial', 'lambdau': None, 'lower_lambdau': None, 'maxit': 100000.0, 'ncores': -1, 'nlambda': 100, 'parallel': False, 'penalty_factor': None, 'pmax': 10000000000.0, 'standardize': True, 'thresh': 1e-07, 'type_measure': 1, 'upper_lambdau': None, 'verbose': False, 'weights': None} elapsed: 0.1249077320098877 [2. 1. 2. 1. 1. 2. 0. 2. 2. 1. 2. 2. 2. 0. 0. 2. 1. 1. 0. 1. 1. 2. 2. 2. 1. 2. 2. 1. 0. 0. 0. 0. 2. 1. 2. 1.] accuracy: 0.97222222222222221 – 2 GLMNet Regression
import numpy as npimport osimport sysimport glmnetforpython as glmnetfrom sklearn.datasets import load_diabetes, fetch_california_housingfrom sklearn.model_selection import train_test_splitfrom time import timedatasets = [load_diabetes, fetch_california_housing]for dataset in datasets: print(f"\n\n dataset: {dataset.__name__} -------------------") X, y = dataset(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) regr = glmnet.GLMNet() print(regr.get_params()) start = time() regr.fit(X_train, y_train) print(f"elapsed: {time() - start}") regr.print() print(regr.predict(X_test, s=0.1)) print(regr.predict(X_test, s=np.asarray([0.1, 0.5]))) print(regr.predict(X_test, s=0.5)) start = time() res_cvglmnet = regr.cvglmnet(X_train, y_train) print(f"elapsed: {time() - start}") print("\n best lambda: ", res_cvglmnet.lambda_min) print("\n best lambda std. dev: ", res_cvglmnet.lambda_1se) print("\n best coef: ", res_cvglmnet.best_coef) print("\n best GLMNet: ", res_cvglmnet.cvfit) dataset: load_diabetes ------------------- {'alpha': 1.0, 'dfmax': 10000000000.0, 'exclude': None, 'family': 'gaussian', 'lambdau': None, 'lower_lambdau': None, 'maxit': 100000.0, 'ncores': -1, 'nlambda': 100, 'parallel': False, 'penalty_factor': None, 'pmax': 10000000000.0, 'standardize': True, 'thresh': 1e-07, 'type_measure': 1, 'upper_lambdau': None, 'verbose': False, 'weights': None} elapsed: 0.003544330596923828 df %dev lambdau 0 0.000000 0.000000 44.034491 1 1.000000 0.056410 40.122588 2 2.000000 0.118800 36.558208 3 2.000000 0.173050 33.310478 4 2.000000 0.218089 30.351267 5 2.000000 0.255485 27.654944 6 2.000000 0.286528 25.198155 7 2.000000 0.312300 22.959620 8 2.000000 0.333697 20.919951 9 3.000000 0.354121 19.061480 10 4.000000 0.373003 17.368111 11 4.000000 0.390322 15.825176 12 4.000000 0.404704 14.419311 13 4.000000 0.416644 13.138339 14 4.000000 0.426556 11.971165 15 4.000000 0.434786 10.907680 16 4.000000 0.441619 9.938671 17 5.000000 0.447381 9.055747 18 5.000000 0.452319 8.251260 19 5.000000 0.456422 7.518240 20 5.000000 0.459828 6.850341 21 5.000000 0.462655 6.241775 22 5.000000 0.465003 5.687273 23 6.000000 0.468916 5.182032 24 6.000000 0.472756 4.721674 25 6.000000 0.475938 4.302214 26 6.000000 0.478579 3.920017 27 6.000000 0.480772 3.571773 28 7.000000 0.482661 3.254467 29 7.000000 0.485063 2.965349 30 7.000000 0.487080 2.701916 31 7.000000 0.488751 2.461885 32 7.000000 0.490137 2.243178 33 7.000000 0.491289 2.043900 34 7.000000 0.492244 1.862326 35 7.000000 0.493038 1.696882 36 7.000000 0.493697 1.546135 37 8.000000 0.494444 1.408781 38 8.000000 0.495256 1.283629 39 8.000000 0.495927 1.169595 40 8.000000 0.496489 1.065691 41 8.000000 0.496952 0.971018 42 8.000000 0.497335 0.884756 43 8.000000 0.497659 0.806156 44 8.000000 0.497924 0.734540 45 8.000000 0.498143 0.669285 46 8.000000 0.498329 0.609828 47 8.000000 0.498481 0.555652 48 8.000000 0.498610 0.506290 49 8.000000 0.498715 0.461312 50 8.000000 0.498805 0.420331 51 8.000000 0.498877 0.382990 52 8.000000 0.498939 0.348966 53 8.000000 0.498989 0.317965 54 8.000000 0.499032 0.289718 55 9.000000 0.499069 0.263980 56 9.000000 0.499392 0.240529 57 9.000000 0.499741 0.219161 58 9.000000 0.500032 0.199691 59 9.000000 0.500272 0.181951 60 9.000000 0.500476 0.165787 61 9.000000 0.500646 0.151059 62 9.000000 0.500787 0.137639 63 8.000000 0.500861 0.125412 64 9.000000 0.500891 0.114271 65 9.000000 0.500921 0.104119 66 9.000000 0.500946 0.094869 67 9.000000 0.500966 0.086441 68 10.000000 0.500985 0.078762 69 10.000000 0.501074 0.071765 70 10.000000 0.501148 0.065390 71 10.000000 0.501208 0.059581 72 10.000000 0.501261 0.054288 73 10.000000 0.501303 0.049465 74 10.000000 0.501340 0.045071 75 10.000000 0.501371 0.041067 76 10.000000 0.501396 0.037418 77 10.000000 0.501418 0.034094 78 10.000000 0.501436 0.031065 79 10.000000 0.501452 0.028306 80 10.000000 0.501466 0.025791 81 10.000000 0.501477 0.023500 82 10.000000 0.501486 0.021412 83 10.000000 0.501495 0.019510 84 10.000000 0.501501 0.017777 85 10.000000 0.501507 0.016198 86 10.000000 0.501512 0.014759 87 10.000000 0.501517 0.013447 [161.26225363 153.40808479 226.88078039 163.480388 158.15906743 138.70495293 252.60833458 107.20179977 107.04120812 111.4621737 123.02831339 182.46487521 161.8259466 202.19109973 222.70276584 172.29337663 108.23998068 144.9482381 176.11555866 191.67293859 163.44023323 231.8947646 140.21508949 75.13660039 129.39763652 188.26182192 100.80880331 101.63988186 157.52887579 185.93073996 85.10969035 238.43828572 208.13649047 209.71355938 198.52425274 95.48735993 93.58588193 98.38410955 225.11428814 101.19808037 193.69596077 81.44887372 102.8093431 146.00065311 110.88937281 215.06701174 79.87947637 77.58243533 101.06682798 217.30259906 70.16241913 116.23582088 177.21944649 195.88268542 138.92178841 198.65554716 219.68568399 169.97366232 192.47857773 189.04428441 138.71921407 121.43624221 233.40434688 202.68154217 190.88486154 42.03060013 62.01800127 159.28979811 126.65978845 86.64871155 136.58228326 76.93411617 141.41235614 199.19748035 120.79645249 173.18692022 146.96993898 139.31000819 99.86313284 83.63232759 61.45995805 159.5304213 120.28229729 225.93625573 286.05353932 165.66169186 197.95421215 70.40035793 139.89076625] [[161.26225363 160.79263694] [153.40808479 150.6281287 ] [226.88078039 225.5710481 ] [163.480388 161.80700641] [158.15906743 157.71369432] [138.70495293 144.58961694] [252.60833458 250.39569639] [107.20179977 110.67344587] [107.04120812 111.21584102] [111.4621737 107.93161795] [123.02831339 122.34617434] [182.46487521 180.55849115] [161.8259466 161.4535835 ] [202.19109973 200.49417412] [222.70276584 229.60354304] [172.29337663 170.57681745] [108.23998068 109.09703513] [144.9482381 143.71605666] [176.11555866 177.00946867] [191.67293859 194.23710327] [163.44023323 161.7697504 ] [231.8947646 229.71549579] [140.21508949 140.591871 ] [ 75.13660039 78.02802694] [129.39763652 129.5053364 ] [188.26182192 186.58248135] [100.80880331 102.6960668 ] [101.63988186 104.20365368] [157.52887579 156.12372213] [185.93073996 187.20901614] [ 85.10969035 89.82145958] [238.43828572 237.95082988] [208.13649047 207.73770948] [209.71355938 209.32169425] [198.52425274 197.67298512] [ 95.48735993 96.07154965] [ 93.58588193 95.09805607] [ 98.38410955 97.25266832] [225.11428814 220.52646948] [101.19808037 101.27641956] [193.69596077 194.77086843] [ 81.44887372 81.25151312] [102.8093431 102.64887002] [146.00065311 144.94838244] [110.88937281 110.25258101] [215.06701174 213.51721996] [ 79.87947637 79.10616278] [ 77.58243533 81.51256193] [101.06682798 103.20741885] [217.30259906 216.7643487 ] [ 70.16241913 72.0598882 ] [116.23582088 119.05445336] [177.21944649 178.45613256] [195.88268542 197.31526195] [138.92178841 137.70888526] [198.65554716 200.13140539] [219.68568399 218.50018565] [169.97366232 169.49700466] [192.47857773 188.32727388] [189.04428441 186.73052546] [138.71921407 140.07357784] [121.43624221 121.14922477] [233.40434688 231.63901622] [202.68154217 201.3077663 ] [190.88486154 189.74608267] [ 42.03060013 46.44945536] [ 62.01800127 63.00668405] [159.28979811 158.37093056] [126.65978845 126.26280796] [ 86.64871155 87.59938665] [136.58228326 136.23598795] [ 76.93411617 80.10973443] [141.41235614 140.69343212] [199.19748035 196.9680135 ] [120.79645249 119.32968814] [173.18692022 170.83211938] [146.96993898 146.07744866] [139.31000819 139.45758571] [ 99.86313284 99.37633812] [ 83.63232759 85.05298366] [ 61.45995805 64.04582025] [159.5304213 159.08368556] [120.28229729 120.78108123] [225.93625573 224.25244938] [286.05353932 287.72165668] [165.66169186 167.91861665] [197.95421215 194.94689188] [ 70.40035793 71.35611103] [139.89076625 139.15500257]] [160.79263694 150.6281287 225.5710481 161.80700641 157.71369432 144.58961694 250.39569639 110.67344587 111.21584102 107.93161795 122.34617434 180.55849115 161.4535835 200.49417412 229.60354304 170.57681745 109.09703513 143.71605666 177.00946867 194.23710327 161.7697504 229.71549579 140.591871 78.02802694 129.5053364 186.58248135 102.6960668 104.20365368 156.12372213 187.20901614 89.82145958 237.95082988 207.73770948 209.32169425 197.67298512 96.07154965 95.09805607 97.25266832 220.52646948 101.27641956 194.77086843 81.25151312 102.64887002 144.94838244 110.25258101 213.51721996 79.10616278 81.51256193 103.20741885 216.7643487 72.0598882 119.05445336 178.45613256 197.31526195 137.70888526 200.13140539 218.50018565 169.49700466 188.32727388 186.73052546 140.07357784 121.14922477 231.63901622 201.3077663 189.74608267 46.44945536 63.00668405 158.37093056 126.26280796 87.59938665 136.23598795 80.10973443 140.69343212 196.9680135 119.32968814 170.83211938 146.07744866 139.45758571 99.37633812 85.05298366 64.04582025 159.08368556 120.78108123 224.25244938 287.72165668 167.91861665 194.94689188 71.35611103 139.15500257] elapsed: 0.021459341049194336 best lambda: 1.2836287759411216 best lambda std. dev: 7.518240463343744 best coef: [ 152.36008914 0. 0. 478.69081702 163.09825002 0. 0. -127.63723154 0. 383.45857834 14.02212484] best GLMNet: {'lambdau': array([4.40344909e+01, 4.01225881e+01, 3.65582080e+01, 3.33104775e+01, 3.03512665e+01, 2.76549436e+01, 2.51981547e+01, 2.29596201e+01, 2.09199507e+01, 1.90614799e+01, 1.73681106e+01, 1.58251755e+01, 1.44193105e+01, 1.31383387e+01, 1.19711649e+01, 1.09076796e+01, 9.93867143e+00, 9.05574725e+00, 8.25125963e+00, 7.51824046e+00, 6.85034070e+00, 6.24177531e+00, 5.68727320e+00, 5.18203152e+00, 4.72167412e+00, 4.30221361e+00, 3.92001681e+00, 3.57177332e+00, 3.25446682e+00, 2.96534896e+00, 2.70191553e+00, 2.46188480e+00, 2.24317774e+00, 2.04390001e+00, 1.86232557e+00, 1.69688170e+00, 1.54613540e+00, 1.40878100e+00, 1.28362878e+00, 1.16959473e+00, 1.06569116e+00, 9.71018095e-01, 8.84755524e-01, 8.06156282e-01, 7.34539579e-01, 6.69285108e-01, 6.09827663e-01, 5.55652254e-01, 5.06289640e-01, 4.61312263e-01, 4.20330553e-01, 3.82989545e-01, 3.48965810e-01, 3.17964649e-01, 2.89717546e-01, 2.63979838e-01, 2.40528596e-01, 2.19160699e-01, 1.99691066e-01, 1.81951062e-01, 1.65787032e-01, 1.51058969e-01, 1.37639306e-01, 1.25411810e-01, 1.14270570e-01, 1.04119088e-01, 9.48694348e-02, 8.64414957e-02, 7.87622714e-02, 7.17652483e-02, 6.53898214e-02, 5.95807699e-02, 5.42877785e-02, 4.94650019e-02, 4.50706675e-02, 4.10667136e-02, 3.74184599e-02, 3.40943071e-02, 3.10654628e-02, 2.83056927e-02, 2.57910930e-02, 2.34998834e-02, 2.14122185e-02, 1.95100160e-02, 1.77768000e-02, 1.61975581e-02, 1.47586116e-02, 1.34474973e-02]), 'cvm': array([5849.67888044, 5588.13049574, 5237.68523549, 4913.35994927, 4643.97138541, 4420.18846237, 4234.28760402, 4079.94561166, 3953.4266667 , 3843.20670735, 3742.11421001, 3650.89110401, 3567.12685974, 3496.28344973, 3438.17453542, 3390.16054415, 3350.74498364, 3318.09382761, 3291.04560008, 3268.40981966, 3249.56159518, 3235.42351215, 3224.36750318, 3210.81451391, 3195.03055142, 3182.43023807, 3170.41713324, 3160.98874011, 3153.61834888, 3147.36615655, 3140.58675366, 3134.53307657, 3126.70250974, 3121.53432349, 3118.77235699, 3117.224526 , 3116.5750121 , 3116.07320448, 3115.6035719 , 3115.63220558, 3116.16432467, 3116.61109199, 3116.30815949, 3116.14506076, 3116.23491199, 3116.51194066, 3117.07327289, 3117.66910598, 3118.28730793, 3118.94059329, 3119.58984965, 3120.29866814, 3122.45940284, 3124.65303008, 3126.52180218, 3127.45737901, 3128.23057335, 3128.3594194 , 3128.33377776, 3127.73013801, 3127.46559483, 3126.80867588, 3125.85726821, 3125.28303452, 3124.84520112, 3124.63312595, 3124.43410757, 3124.32847712, 3124.23076662, 3124.18911129, 3124.06737071, 3124.09365713, 3124.14154577, 3124.18800616, 3124.32158173, 3124.43107133, 3124.48930905, 3124.54190509, 3124.61269842, 3124.64999794, 3124.63934723, 3124.60634785, 3124.59003554, 3124.58808782, 3124.56949869, 3124.55185597, 3124.55864693, 3124.55978034]), 'cvsd': array([259.52792143, 248.75823371, 221.46767784, 196.31833834, 177.94632187, 165.15950248, 156.81416851, 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3.65582080e+01, 3.33104775e+01, 3.03512665e+01, 2.76549436e+01, 2.51981547e+01, 2.29596201e+01, 2.09199507e+01, 1.90614799e+01, 1.73681106e+01, 1.58251755e+01, 1.44193105e+01, 1.31383387e+01, 1.19711649e+01, 1.09076796e+01, 9.93867143e+00, 9.05574725e+00, 8.25125963e+00, 7.51824046e+00, 6.85034070e+00, 6.24177531e+00, 5.68727320e+00, 5.18203152e+00, 4.72167412e+00, 4.30221361e+00, 3.92001681e+00, 3.57177332e+00, 3.25446682e+00, 2.96534896e+00, 2.70191553e+00, 2.46188480e+00, 2.24317774e+00, 2.04390001e+00, 1.86232557e+00, 1.69688170e+00, 1.54613540e+00, 1.40878100e+00, 1.28362878e+00, 1.16959473e+00, 1.06569116e+00, 9.71018095e-01, 8.84755524e-01, 8.06156282e-01, 7.34539579e-01, 6.69285108e-01, 6.09827663e-01, 5.55652254e-01, 5.06289640e-01, 4.61312263e-01, 4.20330553e-01, 3.82989545e-01, 3.48965810e-01, 3.17964649e-01, 2.89717546e-01, 2.63979838e-01, 2.40528596e-01, 2.19160699e-01, 1.99691066e-01, 1.81951062e-01, 1.65787032e-01, 1.51058969e-01, 1.37639306e-01, 1.25411810e-01, 1.14270570e-01, 1.04119088e-01, 9.48694348e-02, 8.64414957e-02, 7.87622714e-02, 7.17652483e-02, 6.53898214e-02, 5.95807699e-02, 5.42877785e-02, 4.94650019e-02, 4.50706675e-02, 4.10667136e-02, 3.74184599e-02, 3.40943071e-02, 3.10654628e-02, 2.83056927e-02, 2.57910930e-02, 2.34998834e-02, 2.14122185e-02, 1.95100160e-02, 1.77768000e-02, 1.61975581e-02, 1.47586116e-02, 1.34474973e-02]), 'npasses': 1211, 'jerr': 0, 'dim': array([10, 88]), 'offset': False, 'class': 'elnet'}, 'lambda_min': array([1.28362878]), 'lambda_1se': array([7.51824046]), 'class': 'cvglmnet'} dataset: fetch_california_housing ------------------- {'alpha': 1.0, 'dfmax': 10000000000.0, 'exclude': None, 'family': 'gaussian', 'lambdau': None, 'lower_lambdau': None, 'maxit': 100000.0, 'ncores': -1, 'nlambda': 100, 'parallel': False, 'penalty_factor': None, 'pmax': 10000000000.0, 'standardize': True, 'thresh': 1e-07, 'type_measure': 1, 'upper_lambdau': None, 'verbose': False, 'weights': None} elapsed: 0.0047762393951416016 df %dev lambdau 0 0.000000 0.000000 0.790539 1 1.000000 0.079846 0.720310 2 1.000000 0.146136 0.656320 3 1.000000 0.201171 0.598014 4 1.000000 0.246862 0.544888 5 1.000000 0.284796 0.496482 6 1.000000 0.316289 0.452376 7 1.000000 0.342435 0.412188 8 1.000000 0.364142 0.375570 9 1.000000 0.382163 0.342206 10 1.000000 0.397125 0.311805 11 1.000000 0.409546 0.284105 12 1.000000 0.419859 0.258866 13 1.000000 0.428421 0.235869 14 1.000000 0.435529 0.214915 15 1.000000 0.441430 0.195823 16 2.000000 0.451591 0.178426 17 2.000000 0.460828 0.162575 18 2.000000 0.468496 0.148133 19 2.000000 0.474863 0.134973 20 2.000000 0.480149 0.122982 21 3.000000 0.484680 0.112057 22 3.000000 0.489706 0.102102 23 3.000000 0.493879 0.093032 24 3.000000 0.497344 0.084767 25 3.000000 0.500220 0.077236 26 3.000000 0.502608 0.070375 27 4.000000 0.507848 0.064123 28 4.000000 0.521856 0.058427 29 4.000000 0.533472 0.053236 30 4.000000 0.543117 0.048507 31 4.000000 0.551159 0.044198 32 4.000000 0.557809 0.040271 33 6.000000 0.563606 0.036694 34 6.000000 0.569117 0.033434 35 6.000000 0.573708 0.030464 36 6.000000 0.577542 0.027757 37 6.000000 0.580708 0.025291 38 6.000000 0.583337 0.023045 39 6.000000 0.585536 0.020997 40 6.000000 0.587350 0.019132 41 7.000000 0.589628 0.017432 42 7.000000 0.591806 0.015884 43 7.000000 0.593641 0.014473 44 7.000000 0.595162 0.013187 45 7.000000 0.596442 0.012015 46 7.000000 0.597491 0.010948 47 7.000000 0.598376 0.009975 48 7.000000 0.599099 0.009089 49 7.000000 0.599711 0.008282 50 7.000000 0.600209 0.007546 51 7.000000 0.600633 0.006876 52 7.000000 0.600976 0.006265 53 7.000000 0.601269 0.005708 54 7.000000 0.601506 0.005201 55 7.000000 0.601709 0.004739 56 7.000000 0.601873 0.004318 57 7.000000 0.602014 0.003935 58 7.000000 0.602126 0.003585 59 7.000000 0.602224 0.003267 60 7.000000 0.602306 0.002976 61 7.000000 0.602371 0.002712 62 7.000000 0.602427 0.002471 63 7.000000 0.602471 0.002251 64 7.000000 0.602511 0.002051 65 7.000000 0.602544 0.001869 66 7.000000 0.602569 0.001703 67 7.000000 0.602592 0.001552 68 7.000000 0.602612 0.001414 69 7.000000 0.602626 0.001288 70 7.000000 0.602639 0.001174 71 7.000000 0.602651 0.001070 72 7.000000 0.602659 0.000975 73 7.000000 0.602668 0.000888 74 8.000000 0.602674 0.000809 75 8.000000 0.602680 0.000737 [2.15386169 1.40517538 1.75155998 ... 1.5786708 2.24914669 2.74749123] [[2.15386169 2.0965379 ] [1.40517538 1.73841308] [1.75155998 1.96630653] ... [1.5786708 1.82758546] [2.24914669 2.09450709] [2.74749123 2.33255459]] [2.0965379 1.73841308 1.96630653 ... 1.82758546 2.09450709 2.33255459] elapsed: 0.08082914352416992 best lambda: 0.0029763296520373566 best lambda std. dev: 0.015883776165844302 best coef: [-2.89480122e+01 3.87657120e-01 1.00434474e-02 -1.47638444e-02 1.56518514e-01 0.00000000e+00 -2.28921823e-03 -3.44888900e-01 -3.46534665e-01] best GLMNet: {'lambdau': array([7.90539283e-01, 7.20309952e-01, 6.56319601e-01, 5.98013976e-01, 5.44888063e-01, 4.96481709e-01, 4.52375642e-01, 4.12187837e-01, 3.75570206e-01, 3.42205584e-01, 3.11804983e-01, 2.84105088e-01, 2.58865975e-01, 2.35869035e-01, 2.14915080e-01, 1.95822617e-01, 1.78426275e-01, 1.62575377e-01, 1.48132628e-01, 1.34972934e-01, 1.22982310e-01, 1.12056901e-01, 1.02102075e-01, 9.30316077e-02, 8.47669361e-02, 7.72364751e-02, 7.03749995e-02, 6.41230785e-02, 5.84265609e-02, 5.32361063e-02, 4.85067573e-02, 4.41975507e-02, 4.02711621e-02, 3.66935831e-02, 3.34338263e-02, 3.04636573e-02, 2.77573499e-02, 2.52914635e-02, 2.30446396e-02, 2.09974173e-02, 1.91320646e-02, 1.74324247e-02, 1.58837762e-02, 1.44727053e-02, 1.31869900e-02, 1.20154942e-02, 1.09480708e-02, 9.97547435e-03, 9.08928070e-03, 8.28181406e-03, 7.54608052e-03, 6.87570753e-03, 6.26488862e-03, 5.70833318e-03, 5.20122059e-03, 4.73915849e-03, 4.31814471e-03, 3.93453264e-03, 3.58499960e-03, 3.26651812e-03, 2.97632965e-03, 2.71192073e-03, 2.47100117e-03, 2.25148423e-03, 2.05146858e-03, 1.86922176e-03, 1.70316525e-03, 1.55186075e-03, 1.41399772e-03, 1.28838206e-03, 1.17392574e-03, 1.06963742e-03, 9.74613777e-04, 8.88031775e-04, 8.09141480e-04, 7.37259581e-04]), 'cvm': array([1.32794354, 1.22292703, 1.13481835, 1.06167325, 1.00095081, 0.95054152, 0.90869409, 0.87395456, 0.84511588, 0.82117596, 0.80130284, 0.78480587, 0.77111164, 0.75974414, 0.75030818, 0.74244809, 0.72908801, 0.71682279, 0.70664152, 0.69819024, 0.69117511, 0.68511138, 0.6785909 , 0.67305371, 0.66845763, 0.66464277, 0.66147643, 0.65464086, 0.63599482, 0.6205351 , 0.6076784 , 0.59699995, 0.58815497, 0.5801874 , 0.57304095, 0.56700578, 0.56200649, 0.55794318, 0.55464407, 0.55190472, 0.54967906, 0.54750774, 0.54502344, 0.54289758, 0.54115612, 0.53975023, 0.53861561, 0.53769851, 0.5369726 , 0.53638576, 0.53592615, 0.53556223, 0.53527887, 0.53506031, 0.5348938 , 0.53477021, 0.5346767 , 0.53461606, 0.53457253, 0.53454878, 0.53453534, 0.53454145, 0.53454912, 0.53456553, 0.53458344, 0.53460438, 0.53463299, 0.53466219, 0.53468341, 0.53471083, 0.53473878, 0.53475851, 0.53478052, 0.53480475, 0.53482847, 0.53484363]), 'cvsd': array([0.01178608, 0.01293004, 0.01366089, 0.01428224, 0.01477845, 0.01515521, 0.01542664, 0.0156092 , 0.01571898, 0.01577054, 0.01577649, 0.01574747, 0.0156923 , 0.01561818, 0.01553091, 0.01543565, 0.01536175, 0.01521551, 0.01507116, 0.01493074, 0.01479571, 0.01467942, 0.01454884, 0.01441053, 0.01428044, 0.01415866, 0.01404511, 0.01406737, 0.01382548, 0.01361343, 0.01342251, 0.01325314, 0.01310364, 0.01285698, 0.01259207, 0.01240794, 0.0122745 , 0.01218093, 0.01211811, 0.01209786, 0.0120965 , 0.01213827, 0.01201918, 0.0118633 , 0.01172291, 0.01160189, 0.01150054, 0.01141143, 0.01133933, 0.01127929, 0.01122912, 0.01118883, 0.01115662, 0.01113133, 0.01111179, 0.01109708, 0.01108679, 0.01107902, 0.0110729 , 0.01107114, 0.01106948, 0.01107177, 0.01107547, 0.01108005, 0.01108465, 0.0110883 , 0.01109275, 0.01109805, 0.01110229, 0.01110699, 0.01111198, 0.01111601, 0.0111207 , 0.01112377, 0.01112778, 0.01113096]), 'cvup': array([1.33972961, 1.23585706, 1.14847923, 1.07595549, 1.01572926, 0.96569674, 0.92412073, 0.88956376, 0.86083487, 0.8369465 , 0.81707933, 0.80055334, 0.78680394, 0.77536232, 0.76583909, 0.75788374, 0.74444976, 0.7320383 , 0.72171268, 0.71312098, 0.70597082, 0.6997908 , 0.69313973, 0.68746424, 0.68273806, 0.67880143, 0.67552153, 0.66870824, 0.6498203 , 0.63414852, 0.62110091, 0.61025309, 0.60125861, 0.59304439, 0.58563302, 0.57941372, 0.57428099, 0.57012411, 0.56676219, 0.56400258, 0.56177556, 0.559646 , 0.55704263, 0.55476088, 0.55287903, 0.55135213, 0.55011615, 0.54910994, 0.54831193, 0.54766505, 0.54715527, 0.54675106, 0.54643549, 0.54619164, 0.54600558, 0.5458673 , 0.54576349, 0.54569508, 0.54564544, 0.54561992, 0.54560483, 0.54561322, 0.54562459, 0.54564558, 0.54566809, 0.54569267, 0.54572575, 0.54576024, 0.5457857 , 0.54581782, 0.54585076, 0.54587452, 0.54590122, 0.54592852, 0.54595624, 0.54597458]), 'cvlo': array([1.31615746, 1.20999699, 1.12115746, 1.04739101, 0.98617236, 0.93538631, 0.89326745, 0.85834536, 0.8293969 , 0.80540542, 0.78552635, 0.7690584 , 0.75541934, 0.74412596, 0.73477727, 0.72701244, 0.71372626, 0.70160729, 0.69157036, 0.6832595 , 0.67637941, 0.67043197, 0.66404206, 0.65864319, 0.65417719, 0.65048411, 0.64743132, 0.64057349, 0.62216933, 0.60692167, 0.59425588, 0.58374681, 0.57505134, 0.56733042, 0.56044887, 0.55459784, 0.54973199, 0.54576224, 0.54252596, 0.53980686, 0.53758257, 0.53536947, 0.53300426, 0.53103428, 0.52943322, 0.52814834, 0.52711506, 0.52628708, 0.52563327, 0.52510648, 0.52469703, 0.5243734 , 0.52412225, 0.52392899, 0.52378201, 0.52367313, 0.52358991, 0.52353705, 0.52349963, 0.52347764, 0.52346586, 0.52346967, 0.52347365, 0.52348548, 0.52349879, 0.52351608, 0.52354024, 0.52356414, 0.52358113, 0.52360384, 0.5236268 , 0.5236425 , 0.52365982, 0.52368098, 0.52370069, 0.52371267]), 'nzero': array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8]), 'name': 'Mean-Squared Error', 'glmnet_fit': {'a0': array([ 2.07155206, 1.92869743, 1.79853362, 1.6799332 , 1.57186891, 1.47340475, 1.38368788, 1.30194121, 1.22745669, 1.15958917, 1.09775081, 1.041406 , 0.99006671, 0.94328826, 0.90066548, 0.86182919, 0.78419117, 0.70670286, 0.6360984 , 0.57176624, 0.51314918, 0.47506172, 0.58273794, 0.68084849, 0.77024317, 0.85169627, 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array([7.90539283e-01, 7.20309952e-01, 6.56319601e-01, 5.98013976e-01, 5.44888063e-01, 4.96481709e-01, 4.52375642e-01, 4.12187837e-01, 3.75570206e-01, 3.42205584e-01, 3.11804983e-01, 2.84105088e-01, 2.58865975e-01, 2.35869035e-01, 2.14915080e-01, 1.95822617e-01, 1.78426275e-01, 1.62575377e-01, 1.48132628e-01, 1.34972934e-01, 1.22982310e-01, 1.12056901e-01, 1.02102075e-01, 9.30316077e-02, 8.47669361e-02, 7.72364751e-02, 7.03749995e-02, 6.41230785e-02, 5.84265609e-02, 5.32361063e-02, 4.85067573e-02, 4.41975507e-02, 4.02711621e-02, 3.66935831e-02, 3.34338263e-02, 3.04636573e-02, 2.77573499e-02, 2.52914635e-02, 2.30446396e-02, 2.09974173e-02, 1.91320646e-02, 1.74324247e-02, 1.58837762e-02, 1.44727053e-02, 1.31869900e-02, 1.20154942e-02, 1.09480708e-02, 9.97547435e-03, 9.08928070e-03, 8.28181406e-03, 7.54608052e-03, 6.87570753e-03, 6.26488862e-03, 5.70833318e-03, 5.20122059e-03, 4.73915849e-03, 4.31814471e-03, 3.93453264e-03, 3.58499960e-03, 3.26651812e-03, 2.97632965e-03, 2.71192073e-03, 2.47100117e-03, 2.25148423e-03, 2.05146858e-03, 1.86922176e-03, 1.70316525e-03, 1.55186075e-03, 1.41399772e-03, 1.28838206e-03, 1.17392574e-03, 1.06963742e-03, 9.74613777e-04, 8.88031775e-04, 8.09141480e-04, 7.37259581e-04]), 'npasses': 800, 'jerr': 0, 'dim': array([ 8, 76]), 'offset': False, 'class': 'elnet'}, 'lambda_min': array([0.00297633]), 'lambda_1se': array([0.01588378]), 'class': 'cvglmnet'}2 – GLMNet + nnetsauce
import glmnetforpython as glmnetimport mlsauce as msimport nnetsauce as nsfrom sklearn.datasets import load_breast_cancer, load_wine, load_irisfrom sklearn.model_selection import train_test_splitfrom time import timefor dataset in [load_breast_cancer, load_wine, load_iris]: print(f"\n\n dataset: {dataset.__name__} -----") X, y = dataset(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123) regr = ms.MultiTaskRegressor(glmnet.GLMNet(lambdau=1000)) model = ms.GenericBoostingClassifier(regr, tolerance=1e-2) # Train the model on the training datac start_time = time() model.fit(X_train, y_train) end_time = time() print(f"Training time: {end_time - start_time} seconds") # Evaluate the model's performance (e.g., using accuracy) accuracy = model.score(X_test, y_test) print(f"Accuracy: {accuracy}") clf = ns.CustomClassifier(ns.MultitaskClassifier(glmnet.GLMNet(lambdau=1000)), n_hidden_features=10) # Train the model on the training datac start_time = time() model.fit(X_train, y_train) end_time = time() print(f"Training time: {end_time - start_time} seconds") # Evaluate the model's performance (e.g., using accuracy) accuracy = model.score(X_test, y_test) print(f"Accuracy: {accuracy}") clf = ns.CustomClassifier(ns.SimpleMultitaskClassifier(glmnet.GLMNet(lambdau=1000))) # Train the model on the training datac start_time = time() model.fit(X_train, y_train) end_time = time() print(f"Training time: {end_time - start_time} seconds") # Evaluate the model's performance (e.g., using accuracy) accuracy = model.score(X_test, y_test) print(f"Accuracy: {accuracy}") clf = ns.DeepClassifier(ns.MultitaskClassifier(glmnet.GLMNet(lambdau=1000))) # Train the model on the training datac start_time = time() model.fit(X_train, y_train) end_time = time() print(f"Training time: {end_time - start_time} seconds") # Evaluate the model's performance (e.g., using accuracy) accuracy = model.score(X_test, y_test) print(f"Accuracy: {accuracy}") clf = ns.DeepClassifier(ns.SimpleMultitaskClassifier(glmnet.GLMNet(lambdau=1000))) # Train the model on the training datac start_time = time() model.fit(X_train, y_train) end_time = time() print(f"Training time: {end_time - start_time} seconds") # Evaluate the model's performance (e.g., using accuracy) accuracy = model.score(X_test, y_test) print(f"Accuracy: {accuracy}") /usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. and should_run_async(code) dataset: load_breast_cancer ----- 100%|██████████| 100/100 [00:18<00:00, 5.46it/s] Training time: 18.33358597755432 seconds Accuracy: 0.9649122807017544 100%|██████████| 100/100 [00:18<00:00, 5.31it/s] Training time: 18.904021501541138 seconds Accuracy: 0.9649122807017544 100%|██████████| 100/100 [00:12<00:00, 8.24it/s] Training time: 12.280655860900879 seconds Accuracy: 0.9649122807017544 100%|██████████| 100/100 [00:23<00:00, 4.32it/s] Training time: 23.297285318374634 seconds Accuracy: 0.9649122807017544 100%|██████████| 100/100 [00:24<00:00, 4.08it/s] Training time: 24.91062593460083 seconds Accuracy: 0.9649122807017544 dataset: load_wine ----- 100%|██████████| 100/100 [00:03<00:00, 28.64it/s] Training time: 3.5058298110961914 seconds Accuracy: 1.0 100%|██████████| 100/100 [00:05<00:00, 16.76it/s] Training time: 6.019681453704834 seconds Accuracy: 1.0 100%|██████████| 100/100 [00:08<00:00, 11.76it/s] Training time: 8.692431688308716 seconds Accuracy: 1.0 100%|██████████| 100/100 [00:20<00:00, 4.85it/s] Training time: 20.893232583999634 seconds Accuracy: 1.0 100%|██████████| 100/100 [00:13<00:00, 7.42it/s] Training time: 13.870125532150269 seconds Accuracy: 1.0 dataset: load_iris ----- 14%|█▍ | 14/100 [00:00<00:05, 16.97it/s] Training time: 0.8306210041046143 seconds Accuracy: 0.9333333333333333 100%|██████████| 14/14 [00:00<00:00, 35.76it/s] Training time: 0.40160202980041504 seconds Accuracy: 0.9333333333333333 100%|██████████| 14/14 [00:00<00:00, 30.18it/s] Training time: 0.47559595108032227 seconds Accuracy: 0.9333333333333333 100%|██████████| 14/14 [00:00<00:00, 30.39it/s] Training time: 0.4738032817840576 seconds Accuracy: 0.9333333333333333 100%|██████████| 14/14 [00:00<00:00, 26.63it/s] Training time: 0.5447156429290771 seconds Accuracy: 0.9333333333333333from sklearn.datasets import load_diabetes, fetch_california_housingfor dataset in [load_diabetes, fetch_california_housing]: print(f"\n\n dataset: {dataset.__name__} -----") X, y = dataset(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123) regr = glmnet.GLMNet(lambdau=1000) model = ms.GenericBoostingRegressor(regr, backend="cpu", tolerance=1e-2) # Train the model on the training datac start_time = time() model.fit(X_train, y_train) end_time = time() print(f"Training time: {end_time - start_time} seconds") # Evaluate the model's performance (e.g., using accuracy) preds = model.predict(X_test) rmse = ((preds - y_test)**2).mean()**0.5 print(f"RMSE: {rmse}") model = ns.CustomRegressor(regr) # Train the model on the training datac start_time = time() model.fit(X_train, y_train) end_time = time() print(f"Training time: {end_time - start_time} seconds") # Evaluate the model's performance (e.g., using accuracy) preds = model.predict(X_test) rmse = ((preds - y_test)**2).mean()**0.5 print(f"RMSE: {rmse}") model = ns.DeepRegressor(regr) # Train the model on the training datac start_time = time() model.fit(X_train, y_train) end_time = time() print(f"Training time: {end_time - start_time} seconds") # Evaluate the model's performance (e.g., using accuracy) preds = model.predict(X_test) rmse = ((preds - y_test)**2).mean()**0.5 print(f"RMSE: {rmse}") /usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. and should_run_async(code) dataset: load_diabetes ----- 57%|█████▋ | 57/100 [00:00<00:00, 230.67it/s] Training time: 0.25351572036743164 seconds RMSE: 50.47735955241068 Training time: 0.04386782646179199 seconds RMSE: 51.2098185574396 Training time: 0.09994053840637207 seconds RMSE: 51.02354464725009 dataset: fetch_california_housing ----- 52%|█████▏ | 52/100 [00:00<00:00, 58.32it/s] Training time: 0.9048025608062744 seconds RMSE: 0.8216935762732704 Training time: 0.1747438907623291 seconds RMSE: 0.8218417233321206 Training time: 0.512531042098999 seconds RMSE: 0.8218417233321208
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