Is there any way to see alphas/coefs/intercept associated with *all* scenarios tested within ElasticNetCV

[cppt]

Describe the workflow you want to enable

I like that ElasticNetCV outputs the MSE path for CV folds/alphas but is there any way to similarly track associated model params (ie, coef/intercept) for each scenario and include them as part of output.

I get that it's easier to just output 'best' estimators/params but would be useful to add granularity to allow identifying a 'sweet spot', either via MSE curve or something else, which would make outputting all params additive.

Describe your proposed solution

As described, run existing scenarios as is but instead of holding only through evaluation of 'best' model, save all model params/outputs and return in an additional data object/structure.

Describe alternatives you've considered, if relevant

No response

Additional context

No response

[glemaitre]

Maybe you want to have a look at the path function: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.enet_path.html#sklearn.linear_model.enet_path

[hammad7]

@cppt , here is the code:

import numpy as np
from sklearn.linear_model import enet_path

# Create some sample data
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3

# Compute the regularization path
alphas, coefs, intercepts = enet_path(X, y, verbose = 5)
# Print the alphas, coefficients, and intercepts for all scenarios tested
for alpha, coef, intercept in zip(alphas, coefs, intercepts):
    print("Alpha:", alpha)
    print("Coefficients:", coef)
    print("Intercept:", intercept)
    print()

Output:
(array([0., 0.]), 0.0, 0.0302, 1)
(array([0. , 0.05715559]), 0.0, 0.0302, 2)
(array([0. , 0.11667845]), 5.684341886080802e-14, 0.0302, 2)
(array([0. , 0.17856487]), 0.0, 0.0302, 2)
(array([0. , 0.24279889]), -5.684341886080802e-14, 0.0302, 2)
(array([0. , 0.30935145]), 0.0, 0.0302, 2)
(array([0.01901234, 0.37443649]), 1.3194308507991082e-06, 0.0302, 4)
(array([0.06464451, 0.43584286]), 5.815761113581175e-06, 0.0302, 4)
(array([0.11104886, 0.49826743]), 1.3875526946094396e-05, 0.0302, 4)
(array([0.15812687, 0.56158046]), 1.6494158217028598e-06, 0.0302, 5)
(array([0.2057831 , 0.62564193]), 3.247510619530658e-06, 0.0302, 5)
(array([0.25390901, 0.69030648]), 5.998067450718736e-06, 0.0302, 5)
(array([0.30239422, 0.75542238]), 1.0576523891359102e-05, 0.0302, 5)
(array([0.35112521, 0.82083339]), 1.797996975483329e-05, 0.0302, 5)
(array([0.39998632, 0.88638024]), 2.9643879514651417e-05, 0.0302, 5)
(array([0.44886095, 0.95190213]), 4.75841175102687e-05, 0.0302, 5)
(array([0.4976327 , 1.01723829]), 7.456191761434638e-05, 0.0302, 5)
(array([0.54618652, 1.08222949]), 0.00011426530710423322, 0.0302, 5)
(array([0.59440985, 1.14671955]), 0.00017149506439295692, 0.0302, 5)
(array([0.64219371, 1.21055678]), 0.00025233719833295254, 0.0302, 5)
(array([0.68943375, 1.27359533]), 0.0003642978385300921, 0.0302, 5)
(array([0.73600503, 1.33570614]), 9.317236222727843e-05, 0.0302, 6)
(array([0.78185772, 1.39674312]), 0.0001400085866976042, 0.0302, 6)
(array([0.82688711, 1.45659175]), 0.00020630181253977753, 0.0302, 6)
(array([0.87101494, 1.51514063]), 0.0002981673163162668, 0.0302, 6)
(array([0.91417102, 1.5722888 ]), 0.00042285210944470464, 0.0302, 6)
(array([0.95629359, 1.6279462 ]), 0.0005886332188183019, 0.0302, 6)
(array([0.99732949, 1.68203387]), 0.0008046054945296532, 0.0302, 6)
(array([1.03718305, 1.73450754]), 0.0003165159688549579, 0.0302, 7)
(array([1.07590454, 1.78527184]), 0.0004431231468089436, 0.0302, 7)
(array([1.11342785, 1.83429845]), 0.0006085892242211344, 0.0302, 7)
(array([1.14973329, 1.88155252]), 0.0008200316172946032, 0.0302, 7)
(array([1.18480902, 1.92700936]), 0.0010844981319380054, 0.0302, 7)
(array([1.21865063, 1.97065383]), 0.001408358238293772, 0.0302, 7)
(array([1.25117521, 2.01252455]), 0.0007281750637666562, 0.0302, 8)
(array([1.28254042, 2.05254682]), 0.0009553156995636414, 0.0302, 8)
(array([1.31269417, 2.09076491]), 0.0012308004081234003, 0.0302, 8)
(array([1.34165629, 2.12719574]), 0.001556845843310839, 0.0302, 8)
(array([1.36945136, 2.16186239]), 0.0019345101170102907, 0.0302, 8)
(array([1.39599369, 2.19485863]), 0.0011828625463508047, 0.0302, 9)
(array([1.42152022, 2.22610224]), 0.0014705246886403955, 0.0302, 9)
(array([1.44597336, 2.25568279]), 0.0017984996816977628, 0.0302, 9)
(array([1.46939059, 2.28364133]), 0.002161982013863195, 0.0302, 9)
(array([1.49181143, 2.31002169]), 0.0025561466318571036, 0.0302, 9)
(array([1.51313001, 2.33495931]), 0.001755255011964607, 0.0302, 10)
(array([1.53365873, 2.35833888]), 0.002060662494283605, 0.0302, 10)
(array([1.55331617, 2.38028421]), 0.0023873322183831647, 0.0302, 10)
(array([1.57214462, 2.40084582]), 0.0027231035870052267, 0.0302, 10)
(array([1.59018678, 2.42007443]), 0.0030601661872324826, 0.0302, 10)
(array([1.60731468, 2.43812958]), 0.002267589904306533, 0.0302, 11)
(array([1.62389131, 2.45485819]), 0.0025147333974242514, 0.0302, 11)
(array([1.6398085 , 2.47040489]), 0.0027633870586996068, 0.0302, 11)
(array([1.65510415, 2.48482075]), 0.0029952686152476815, 0.0302, 11)
(array([1.66981648, 2.49815509]), 0.0032039541598951615, 0.0302, 11)
(array([1.68398224, 2.51045643]), 0.003383846876634067, 0.0302, 11)
(array([1.69763653, 2.52177235]), 0.003530214226813655, 0.0302, 11)
(array([1.7108127 , 2.53214943]), 0.003639320592169426, 0.0302, 11)
(array([1.72354233, 2.54163315]), 0.003708501889304827, 0.0302, 11)
(array([1.73585512, 2.55026788]), 0.0037361839696927746, 0.0302, 11)
(array([1.74777888, 2.5580968 ]), 0.003721852482371446, 0.0302, 11)
(array([1.75933949, 2.56516191]), 0.0036659837869166267, 0.0302, 11)
(array([1.77056088, 2.57150404]), 0.00356994707446745, 0.0302, 11)
(array([1.78146505, 2.57716285]), 0.0034358874668125594, 0.0302, 11)
(array([1.79227591, 2.58203659]), 0.003982404951348606, 0.0302, 10)
(array([1.80262024, 2.58643133]), 0.003807589819487589, 0.0302, 10)
(array([1.81289059, 2.59012413]), 0.004213254973232772, 0.0302, 9)
(array([1.82271506, 2.59341721]), 0.003930192435560365, 0.0302, 9)
(array([1.83247549, 2.59608888]), 0.004142907300813903, 0.0302, 8)
(array([1.84181918, 2.59842501]), 0.003749310136090145, 0.0302, 8)
(array([1.85130873, 2.60007179]), 0.004356432275689048, 0.0302, 6)
(array([1.86025481, 2.60154708]), 0.0039903360093003215, 0.0302, 6)
(array([1.86928474, 2.60244646]), 0.0043643070791796745, 0.0302, 4)
(array([1.87801167, 2.60307353]), 0.004425443659130224, 0.0302, 3)
(array([1.88645693, 2.60344448]), 0.004249947511216767, 0.0302, 2)
(array([1.89441145, 2.60373623]), 0.0033649108293953844, 0.0302, 2)
(array([1.90196687, 2.60391233]), 0.0020436509041052986, 0.0302, 2)
(array([1.90918874, 2.60395269]), 0.0004711652838800262, 0.0302, 2)
(array([1.91612453, 2.6038482 ]), 0.00167596941914816, 0.0302, 2)
(array([1.92280901, 2.6035971 ]), 0.004071920576677712, 0.0302, 2)
(array([1.92950366, 2.60303499]), 0.005798944142170193, 0.0302, 3)
(array([1.9365236 , 2.60195723]), 0.005998496484806548, 0.0302, 5)
(array([1.94326137, 2.60081402]), 0.006433019368176307, 0.0302, 5)
(array([1.95001382, 2.59941164]), 0.006279225706987468, 0.0302, 6)
(array([1.95646083, 2.59799461]), 0.006416734108870781, 0.0302, 6)
(array([1.96290002, 2.59636619]), 0.006047004658215727, 0.0302, 7)
(array([1.96901443, 2.59476707]), 0.006006448644048312, 0.0302, 7)
(array([1.97487238, 2.59316222]), 0.006104199110143593, 0.0302, 7)
(array([1.98050722, 2.59154059]), 0.006250992622957696, 0.0302, 7)
(array([1.98593567, 2.58990216]), 0.006404871518275179, 0.0302, 7)
(array([1.99116647, 2.58825181]), 0.006546459726492593, 0.0302, 7)
(array([1.9962046 , 2.58659636]), 0.006667379095167458, 0.0302, 7)
(array([2.00105333, 2.58494317]), 0.006764688637442617, 0.0302, 7)
(array([2.00571525, 2.58329945]), 0.006838155449508498, 0.0302, 7)
(array([2.01019279, 2.58167192]), 0.006888886693848573, 0.0302, 7)
(array([2.01448848, 2.58006668]), 0.006918629284542455, 0.0302, 7)
(array([2.01860507, 2.5784891 ]), 0.006929403744237117, 0.0302, 7)
(array([2.02254557, 2.57694391]), 0.006923308817433416, 0.0302, 7)
(array([2.0260774 , 2.57560473]), 0.007546682213824152, 0.0302, 6)
(array([2.02977259, 2.57406636]), 0.007255271415334885, 0.0302, 7)
(array([2.03303197, 2.57276514]), 0.007721195772496525, 0.0302, 6)
[1, 2, 2, 2, 2, 2, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 10, 10, 9, 9, 8, 8, 6, 6, 4, 3, 2, 2, 2, 2, 2, 2, 3, 5, 5, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 7, 6]
Alpha: 36.5
Coefficients: [0. 0. 0. 0. 0. 0.
0.01901234 0.06464451 0.11104886 0.15812687 0.2057831 0.25390901
0.30239422 0.35112521 0.39998632 0.44886095 0.4976327 0.54618652
0.59440985 0.64219371 0.68943375 0.73600503 0.78185772 0.82688711
0.87101494 0.91417102 0.95629359 0.99732949 1.03718305 1.07590454
1.11342785 1.14973329 1.18480902 1.21865063 1.25117521 1.28254042
1.31269417 1.34165629 1.36945136 1.39599369 1.42152022 1.44597336
1.46939059 1.49181143 1.51313001 1.53365873 1.55331617 1.57214462
1.59018678 1.60731468 1.62389131 1.6398085 1.65510415 1.66981648
1.68398224 1.69763653 1.7108127 1.72354233 1.73585512 1.74777888
1.75933949 1.77056088 1.78146505 1.79227591 1.80262024 1.81289059
1.82271506 1.83247549 1.84181918 1.85130873 1.86025481 1.86928474
1.87801167 1.88645693 1.89441145 1.90196687 1.90918874 1.91612453
1.92280901 1.92950366 1.9365236 1.94326137 1.95001382 1.95646083
1.96290002 1.96901443 1.97487238 1.98050722 1.98593567 1.99116647
1.9962046 2.00105333 2.00571525 2.01019279 2.01448848 2.01860507
2.02254557 2.0260774 2.02977259 2.03303197]
Intercept: 0.0

Alpha: 34.04002216123752
Coefficients: [0. 0.05715559 0.11667845 0.17856487 0.24279889 0.30935145
0.37443649 0.43584286 0.49826743 0.56158046 0.62564193 0.69030648
0.75542238 0.82083339 0.88638024 0.95190213 1.01723829 1.08222949
1.14671955 1.21055678 1.27359533 1.33570614 1.39674312 1.45659175
1.51514063 1.5722888 1.6279462 1.68203387 1.73450754 1.78527184
1.83429845 1.88155252 1.92700936 1.97065383 2.01252455 2.05254682
2.09076491 2.12719574 2.16186239 2.19485863 2.22610224 2.25568279
2.28364133 2.31002169 2.33495931 2.35833888 2.38028421 2.40084582
2.42007443 2.43812958 2.45485819 2.47040489 2.48482075 2.49815509
2.51045643 2.52177235 2.53214943 2.54163315 2.55026788 2.5580968
2.56516191 2.57150404 2.57716285 2.58203659 2.58643133 2.59012413
2.59341721 2.59608888 2.59842501 2.60007179 2.60154708 2.60244646
2.60307353 2.60344448 2.60373623 2.60391233 2.60395269 2.6038482
2.6035971 2.60303499 2.60195723 2.60081402 2.59941164 2.59799461
2.59636619 2.59476707 2.59316222 2.59154059 2.58990216 2.58825181
2.58659636 2.58494317 2.58329945 2.58167192 2.58006668 2.5784891
2.57694391 2.57560473 2.57406636 2.57276514]
Intercept: 0.0