Ability to have a multivariate X

[dirknbr]

I would like to model a multivariate X, where each X column is a metric
X = sim(theta)

At the moment I have to flatten X into a long vector

Is any NPE model able to learn (X, theta) pairs where X has 2 dimensions?

[manuelgloeckler]

Hey @dirknbr ,

this seems like a classic case for using an embedding net i.e. maybe checkout this howto guide.

Or e..g if your X is image-like see e.g. this tutorial.

Effectively, you can write any neural net that embeds X as a fixed size vector (which can of a different dimension than X i.e. usually smaller). What is the best embedding net for your data depends on associated structure of the data.

Hope this help!

[dirknbr]

I am not sure this works, I used

embedding_net = CNNEmbedding(input_shape=(100,), in_channels=2)

but then later I get an error with

samples_cnn = posterior_cnn.sample((5000,), x=x_o_mv)
      5 
      6 # from sbi.analysis import pairplot

11 frames
[/usr/local/lib/python3.12/dist-packages/sbi/neural_nets/embedding_nets/cnn.py](https://localhost:8080/#) in forward(self, x)
    168 
    169         # reshape to account for single channel data.
--> 170         x = self.cnn_subnet(x.view(batch_size, *self.input_shape))
    171         # flatten for linear layers.
    172         x = x.view(batch_size, -1)

RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.

[manuelgloeckler]

Hey @dirknbr,

Mhh. What is the dimension of your original data? Could you also give some more details on what kind of data it is i.e. if a CNNEmbeddingNet would even make sense.

As far as I can see the embedding nets input shape is defined as input_shape = (in_channels, *input_shape) which would mean that in your example the data should be (2, 100) without batch dimension. Is this the shape of your data?

Notably this also would run a 1d convolution over 100, if you instead intended to do a 2d convolution then use input_shape=(100,100) for e.g. a 100x100 grey scale (1 channel) image.

[dirknbr]

The data from Y = sim() is (T, 2), where T = 100 is the time dimension, Y has autocorrelation in the time dimension and correlation across the 2 columns.

[manuelgloeckler]

Mhh, could you try to transpose Y i.e (1,2, T) (with batch dim (B,2,T)). This is what currently CNNEmbeddingNet expects i.e. channel first format (this probably should be documented better). I think one limitation here is that the current implementation always expects a batch_dim as Y.shape[0]. So to input a single Y you have to use [1,2,T].

If this doesn't work I will try to figure out whats going on there + produce a small working example for this format.

I think your parmeterization does make sense for this kind of data.