The method name is all lowercase: tolist.

So you need to change the offending line to:

x.append(df.values.tolist())

TruncatedSVD.transform returns an array, not a sparse matrix. In fact, in the present version of scikit-learn, only the vectorizers return sparse matrices.

It would be helpful if you could post the full stack trace, so that we can see which line your error occurs at. In general, the more information you can provide in a question, the better.

In this case, it looks like your full_model_pipeline may somehow become a numpy array. Since you have a one-element pipeline, you could try changing

full_model_pipeline = Pipeline(steps =[
                        ('full_pipeline',full_pipeline),
                        ('model',LinearRegression())
                    ])

full_model_pipeline.fit(X_train,y_train)

to

model = LinearRegression()
model.fit(X_train, y_train)

You can use mxnet.ndarray.squeeze to remove unused nested dimensions, then convert to numpy array with mxnet.ndarray.asnumpy to extract the list of values, like this:

box_ids.squeeze().asnumpy().tolist()

As mentioned in the comment above, please note that even though MXNet NDArrays have by design an API extremely similar to Numpy arrays, they are actually totally different libraries with different internals. See in this post more details about MXNet NDArray specificities.

NDArray really shines at doing things in a vectorized fashion over CPU or GPU, so before implementing a manual for loop over an NDArray consider if you can implement things in native mxnet NDArray only. This will likely be much faster since mxnet commands are ran asynchronously in C++

If you check the source code, you will see that right after tolist() code there is line to_list = tolist, so to_list is just alias for tolist

EDIT: to_list() was added in ver. 0.24.0, see issue#8826