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

adv_ex is already a numpy array, so you can’t call .numpy() again on it (which is a tensor method). Store adv_ex as a tensor or avoid calling numpy on it: adv_ex = perturbed_data.squeeze().detach().cpu() adv_examples.append( (init_pred.item(), final_pred.item(), adv_ex) )

Your question leaves a lot unexplained, but the error you're receiving probably came from the last line vectorized_text = features.transform("Heathcare is good").toarray().
The variable features is itself an array (it came from features = ... .toarray()).
I'm assuming you meant to replace it with
vectorized_text = tfidf.transform("Heathcare is good").toarray()?

np.load() converts CSR matrix to an object, so you cannot use toarray() to see the matrix. You need to first use tolist() and then toarray() to see the data. · See the value of X with and without tolist(). · >>> Xarray(<3x3 sparse matrix of type ''        with 6 stored elements in Compressed Sparse Row format>, dtype=object) · Here dtype is object, so toarray() will give error. · >>> X=np.load('rr.npy').tolist()>>> X<3x3 sparse matrix of type ''        with 6 stored elements in Compressed Sparse Row format>  · Using tolist(), you can convert object to a list and then use toarray() to see the data. · >>> X.toarray()array([[1, 0, 2],       [0, 0, 3],       [4, 5, 6]])

You are using the variable name image in your code, while you also import a module named image. This conflict results in trying to find img_to_array method in the variable rather than in the module.

Use a different name for you image variable, or directly import the img_to_array function:

from keras.preprocessing.image import img_to_array
[...]
x = img_to_array(img1)

The problem is that train_test_split(X, y, ...) returns numpy arrays and not pandas dataframes. Numpy arrays have no attribute named columns

If you want to see what features SelectFromModel kept, you need to substitute X_train (which is a numpy.array) with X which is a pandas.DataFrame.

selected_feat= X.columns[(sel.get_support())]

This will return a list of the columns kept by the feature selector.

If you wanted to see how many features were kept you can just run this:

sel.get_support().sum()  # by default this will count 'True' as 1 and 'False' as 0

I think I'm using the wrong notation to append to the empty arrays: The best way to find out is to read the documentation https://numpy.org/doc/stable/reference/generated/numpy.append.html And yes you are. Numpy arrays don't have an append method, it's a function from the library. And there is no such thing as object.library.function(...) which is what you are doing here similarity.np.append(...) num_interactions.np.append(...) It should be similarity = np.append(similarity, values_to_append) num_interactions = np.append(num_interactions, values_to_append) And should avoid appending data to a numpy array. I'm almost sure there is much better alternative. But since you didn't explain what you are trying to do, I can't help you with that.

You should not be accessing dataframe columns with the dot notation. This is one of the reasons why. Use uplift_df['MESSAGE_A'].