Comments (6)
Updating: pymc3 is incompatible with the statsmodels kernel regression, so I'm removing the mixed independence tests, which are the only portion of the package that uses pymc 2. That'll let me get rid of that dependency, and we can upgrade to pymc3 for future features. I'll reimplement mixed variable type independence tests if/when there's a compatible kernel density estimator, or pymc removes the theano dependence (I think that's planned for pymc4)
from causality.
from causality.
Shouldn't be hard! I've been planning on it. I haven't had much time to work on it lately, but feel free to contribute a branch!
from causality.
Sounds good, Ill give it a shot this weekend.
Btw, this is a really cool library, thanks for creating it.
from causality.
Glad you like it!
from causality.
Hi,
I took a look at the code, and it seems that moving to pymc3
might require a little more work than I initially thought. The main reasons are that pymc3
doesn't have the stochastic
decorator and that causality
tests don't cover the statements that interface with pymc
. Ill try to write some tests for it later this weekend, so we can do the transition without worries. Let me know if you have any pointers to writing good tests for that. Here is a test coverage report for the file that interfaces with pymc
independence_tests.py
For now, I submitted a pull request updating the versions for all the other packages used.
from causality.
Related Issues (20)
- networkx version problem? HOT 3
- TypeError: can only concatenate list (not "dict_keys") to list HOT 1
- check statsmodels pandas.core.datetools dependency HOT 1
- Difference between analysis and estimation HOT 5
- Doesn't install for Python3 HOT 2
- Parallelize skeleton
- issues with concat of dictionaries HOT 1
- Causality installation error (cephes?) HOT 3
- issue installing on osx (no conda) HOT 2
- is there any reason you don't add (non-propensity) matching?
- categorical attributes HOT 3
- Interpretation of edges in causal graph inference HOT 1
- Symmetry in _apply_recursion_rule_2 HOT 2
- Python Package: Problems with Codeline “effect.pdf(x)” HOT 1
- Non-fully parallel jobs for causal estimation with large data sets. HOT 1
- I wonder if this package can achieve the functions shown in the picture? thanks!
- 'Graph' object has no attribute 'node' HOT 5
- Fail to import the package HOT 3
- Change `node` to `nodes` in `inference/search/__init__.py` HOT 1
- Should this line be "del matched_treated['treated_index']"? HOT 2
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from causality.