Marcin Elantkowski

Looping over tensor / using symbolic variable.

3

Hello, if I'm understanding the code correctly, currently there is no way of doing loops with symbolic number of iterations (or looping over leading dimension of tensor like in ScanOP)....

Incorrect results when sampling from the prior

13

While going through Statistical Rethinking I wanted to execute a prior-predictive simulation, but the results did not match the textbook example, see below. What's more, I played with some other...

Implement a do operator

1

I thought I would open an issue to follow up on https://github.com/rlouf/mcx/issues/1#issuecomment-812733048 , even though I'm not sure if I would be able to contribute a PR. @rlouf Do I...

MRR score uses a "mean" instead of "min"

6

If I'm not mistaken, MRR score is a mean of `reciprocal rank` scores over a set of examples, where `reciprocal rank` is given as `1 / (rank of the first...

Solves issue #91 I wasn't sure if this should be called `min` mode, since we take `min(ranks)`, or `max` mode, since we take `max(1 / ranks)`.

Getting multiple predictions seems broken

6

I'm playing with implicit models using default `BilinearNet` as representation. Given interactions `test` and some model `model`, one would expect ```python model.predict(test.user_ids) ``` will work, but it raises ```python RuntimeError:...

Hi, do you think it would be worth trying to transform the current `evaluation` code to compute scores in mini-batches, instead of one user at a time as it is...

pmap seems to drastically improve performance in the example notebook

4

# Description I am running `blackjax` in `WSL2` on a 32 core CPU. I was playing with the example notebook (https://blackjax-devs.github.io/blackjax/examples/Introduction.html#), and noticed that the CPU utilization is actually quite...

Hashing a Task to include its dependencies

2

Let's say I have the following code: ```python def log_transform(s: pd.Series): return np.log(s) @task() def transform_features(df: pd.DataFrame) -> pd.DataFrame: for c in df.columns: df[c] = log_transform(df[c]) return df ``` Where...