Bug in provided SGDLasso(), SGDRidge(), and SGDLinearRegression() classes?

[th789]

🐛 Bug

For the LimeBase example provided at https://captum.ai/api/lime.html (after making the updates in #908), using the provided SGDLasso(), SGDRidge(), and SGDLinearRegression() classes for the interpretable_model argument in LimeBase() leads to the following error message: "RuntimeError: expected scalar type Float but found Double" (more info below).

To Reproduce

import torch
import torch.nn as nn
from captum.attr import LimeBase
from captum._utils.models.linear_model import SkLearnLinearModel, SGDLasso, SGDRidge, SGDLinearRegression

class SimpleClassifier(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(5, 3)
        self.sigmoid = nn.Sigmoid()
    def forward(self, x):
        out = self.linear(x)
        out = self.sigmoid(out)
        return out

net = SimpleClassifier()

def similarity_kernel(original_input, perturbed_input, perturbed_interpretable_input, **kwargs):
      # kernel_width will be provided to attribute as a kwarg
      kernel_width = kwargs["kernel_width"]
      l2_dist = torch.norm(original_input - perturbed_input)
      return torch.exp(- (l2_dist**2) / (kernel_width**2))

def perturb_func(original_input, **kwargs):
      return original_input + torch.randn_like(original_input)

def to_interp_rep_transform(curr_sample, original_inp, **kwargs):
      return curr_sample

input = torch.randn(1, 5)

lime_attr = LimeBase(net,
                     interpretable_model=SGDLinearRegression(),
                     similarity_func=similarity_kernel,
                     perturb_func=perturb_func,
                     perturb_interpretable_space=False,
                     from_interp_rep_transform=None,
                     to_interp_rep_transform=to_interp_rep_transform)

attr_coefs = lime_attr.attribute(input, target=1, kernel_width=1.1) 

The code above runs if interpretable_model=SkLearnLinearModel("linear_model.Ridge"), but does not run if interpretable_model=SGDLasso(), interpretable_model=SGDRidge(), or interpretable_model=SGDLinearRegression().

Expected behavior

There should be no error message and attr_coefs should return the feature attributions.

Environment

 - Captum version: 0.5.0
 - Pytorch version: 1.10.0+cu111
 - OS (e.g., Linux): macOS
 - How you installed Captum (`conda`, `pip`, source): 'conda' and 'pip' --> this error message arises whether I use `conda install captum -c pytorch` or `pip install captum` to install captum
 - Python version: 3.7.12

[vivekmig]

Thanks for identifying this issue @th789 ! We have made the fixes in #938 , once this change is merged, the SGD linear models should work appropriate with Lime.

[th789]

Thank you @vivekmig!