Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
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 fond of GLMNet and scikit-learn style, you may love this package. Here, I illustrate the usage of this “new” package, along with its use within the Techtonique ecosystem (nnetsauce and mlsauce).
!pip install nnetsauce
!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 ns
import mlsauce as ms
import numpy as np
import glmnetforpython as glmnet
from sklearn.datasets import load_breast_cancer, load_iris, load_wine
from sklearn.model_selection import train_test_split
from time import time
datasets = [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: 0.9722222222222222
1 - 2 GLMNet Regression
import numpy as np
import os
import sys
import glmnetforpython as glmnet
from sklearn.datasets import load_diabetes, fetch_california_housing
from sklearn.model_selection import train_test_split
from time import time
datasets = [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, 151.85850454,
149.5689093 , 150.20248582, 149.67202475, 146.56506657,
143.00853209, 140.60041047, 138.55782035, 136.8510055 ,
135.41484385, 134.23857211, 133.26532408, 132.2940369 ,
131.49659758, 130.83158262, 130.54053885, 130.02800433,
129.85450444, 130.97322891, 133.21473862, 135.58414859,
138.00196431, 140.50827777, 143.37107165, 145.89654308,
148.34884887, 150.24805302, 152.2145693 , 154.10961339,
155.93288424, 157.76716921, 159.63011092, 161.33532503,
162.88557649, 164.32062807, 165.470281 , 166.54562968,
167.54645567, 168.49635186, 169.37581278, 170.17848841,
170.94950123, 171.65270522, 172.31624765, 172.88013735,
172.80109307, 172.76638875, 172.75274784, 173.02037182,
173.2886591 , 173.4866617 , 173.69450463, 173.79827944,
173.43628185, 172.75611616, 172.21856656, 171.70858949,
171.08574087, 170.55631258, 170.09854581, 169.68475556,
169.32167531, 168.99748594, 168.74915863, 168.50227099,
168.28568613, 168.08332938, 167.91550145, 167.77407306,
167.65195892, 167.54167529, 167.44368359, 167.34059865,
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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
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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
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36 6.000000 0.577542 0.027757
37 6.000000 0.580708 0.025291
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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
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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
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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,
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6.26488862e-03, 5.70833318e-03, 5.20122059e-03, 4.73915849e-03,
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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,
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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,
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0.72701244, 0.71372626, 0.70160729, 0.69157036, 0.6832595 ,
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0.55459784, 0.54973199, 0.54576224, 0.54252596, 0.53980686,
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0.5236268 , 0.5236425 , 0.52365982, 0.52368098, 0.52370069,
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0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
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0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, -4.56049861e-08,
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[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
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-4.28408964e-01]]), 'dev': array([0. , 0.07984633, 0.14613615, 0.20117112, 0.24686213,
0.2847956 , 0.31628864, 0.34243471, 0.36414164, 0.38216311,
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0.60263938, 0.6026508 , 0.60265903, 0.60266799, 0.60267415,
0.60267971]), 'nulldev': array([0.71665039, 1.22201514, 1.94477545, ..., 0.48518478, 1.44347715,
0.36306899]), 'df': 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]), '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,
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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 glmnet
import mlsauce as ms
import nnetsauce as ns
from sklearn.datasets import load_breast_cancer, load_wine, load_iris
from sklearn.model_selection import train_test_split
from time import time
for 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: 0.9333333333333333
from sklearn.datasets import load_diabetes, fetch_california_housing
for 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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