Comments (1)
It is intentional, but may not be the best way forward. When I worked on ModelBuilder
, I was focused on getting it to be able to integrate into a scikit-learn
-based Pipeline
. It's been a while, but if I remember right, scikit-learn
transformers and estimators accept a variety of input types and then "normalize" them to numpy
arrays for the computations. So that was the idea of _validate_data
. All the base _validate_data
does is call check_array
or check_X_y
from sklearn.utils.validation
. I suppose it would be just as easy to call the validation function directly rather than through _validate_data
. In my own fork of this code, I actually don't use _validate_data
or check_array
or check_X_y
at all. In my use, the input is always pandas
objects rather than numpy arrays. I have to call set_output(transform='pandas')
on the transformers in the pipeline, but it works for me.
From comments in some other issues (#196, #246), it seems there is a desire to refactor ModelBuilder
to suit a more general use case, and then specialize it for scikit-learn
as an additional class/sub-class. Seems like a good idea. There are other API's besides scikit-learn
, for example sktime
, which also might be useful to work with ModelBuilder-based PYMC models.
from pymc-experimental.
Related Issues (20)
- Add .to_zarr to model builder save function HOT 4
- Add notebook example on how to use BlackJax SMC from pymc models
- Consider renaming to pymc-extras
- Re-working `as_model` HOT 10
- Pathfinder gives confident wrong answer with small sample prediction HOT 5
- Error message from build_statespace_graph when cycle is one of the model components.
- Add test for MarginalModel where variable depends on two marginalized variables
- including cyclic or seasonal components causes error messages from build_statespace_graph since last bug fix
- Support batched constant arguments when marginalising `DiscreteUniform` HOT 1
- use dict instead of treedict in marginalized model HOT 1
- Standard deviation parameters are incorrectly treated as variances in statespace covariance matrices HOT 24
- `statespace`: Leveraging RegressionComponent yields error HOT 6
- Error messages when using the pymc or nutpie NUTS samplers in combination with pymc-experimental HOT 8
- MarginalModel fails with Data containers
- `test_histogram_approximation` failing due to warning in newer JAX release HOT 5
- `MarginalModel.unmarginalize` doesn't accept `var_names` HOT 2
- `recover_marginals` should have a progress bar
- Support marginalization of HMM with higher lag orders
- MarginalModel freezes mutable dim lengths HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from pymc-experimental.