Segmentation fault (core dump) at the prediction

[phenric]

I'm using the WARP to create my model. To fit it, I've no problem. But for the prediction, I've a segmentation fault (core dump).

How can I solve it ?

Thx

I'm using Ubuntu 16.04 with python 2.7.

[maciejkula]

How did you install LightFM? Can you post the inputs you are using and the functions you are calling?

[phenric]

Thank you for your responsiveness.
I installed LightFM with pip.
I use scores = model.predict(user_id, np.arange(n_items), data['items'], data['users'], 2)
where data['items'], data['users'] are float matrices and np.arange(n_items) is a int matrix.

[maciejkula]

data['items'] and data['users'] are dense numpy matrices? Or sparse matrices? If sparse, in what format?

[maciejkula]

Can you experiment and try to narrow down with what arguments, or in what conditions, does the problem happen?

[phenric]

Yes data['items'] and data['users'] are spares.csr_matrix build with:
`def csv_to_csr(file_given):

with open(file_given, 'r') as f:

    lines = list(csv.reader(f, delimiter=";"))
    lines = np.array(lines[0:], dtype='float32')

return sparse.csr_matrix(lines, dtype='float32m')`

[maciejkula]

What shape is lines?

[phenric]

The shape of lines is (100, 8)

[maciejkula]

OK, so you have a 100 users/items in your dataset? Is it a dataset you can share so that I could reproduce the problem?

[phenric]

Yes, that's right. My datasets are not real. I generated them for the test. So, I can give them. Do you have an email address where I can send the files ?

[maciejkula]

Can you please make a gist or a github repo with the full code that I can run?

[phenric]

Here you can find the repo https://github.com/phenric/reco
Feel free to add modifications
Thank you

[maciejkula]

Your code doesn't actually work from line 34 onwards: https://github.com/phenric/reco/blob/master/recommander.py#L34

[phenric]

When I comment those lines, I still have a segmentation fault. Do you have any idea about the issue ? What am I wrong ?

[maciejkula]

I know what the issue is. You're passing a ludicrously large user_id into the predict function. Am I right?

[maciejkula]

#256

For future reference, LightFM expects that user and item ids be contiguous and start at zero. This means that if you have 10 users the largest possible user index you should be passing in to predict is 9.

[phenric]

Thank you very much for your help !
You're right. When the ID is smaller, I don't have Segmentation fault anymore but Exception: Number of user feature rows does not equal the number of users. So, I supposed it was my error ?

[maciejkula]

Yes, it was your error, but the library should never segfault. So thank you for the report!