hierarchical data generation for causal discovery and abstraction
Project description
ProblemSetApproximateCausalDiscovery
from causalspyne import gen_partially_observed
gen_partially_observed(size_micro_node_dag=4,
num_macro_nodes=4,
degree=2, # average vertex/node degree
list_confounder2hide=[0.5, 0.9], # choie of confounder to hide: percentile or index of all toplogically sorted confounders
num_sample=200)
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
causalspyne-0.1.3.tar.gz
(11.1 kB
view details)
Built Distribution
File details
Details for the file causalspyne-0.1.3.tar.gz.
File metadata
- Download URL: causalspyne-0.1.3.tar.gz
- Upload date:
- Size: 11.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.2.0 CPython/3.9.12 Linux/5.15.0-117-generic
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 54f986320cb7e590292db739b5d902e27ec277b0dbc1ed4e4be99a8d09e229af |
|
| MD5 | 4e7b28dc64fcbc1c780959e04e777bb9 |
|
| BLAKE2b-256 | ecd53e3f0fdf6914187e366b9fdeea10cd31fafe8b7c247d18aa30f09ef87b99 |
File details
Details for the file causalspyne-0.1.3-py3-none-any.whl.
File metadata
- Download URL: causalspyne-0.1.3-py3-none-any.whl
- Upload date:
- Size: 15.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.2.0 CPython/3.9.12 Linux/5.15.0-117-generic
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 7f42725c4c3c257882d6d6ac05987f38614108b63a614f7b0874b9b0cd2d1f72 |
|
| MD5 | c9f2eddede78bc6d65cb71f94f079b21 |
|
| BLAKE2b-256 | 79e9e2b57ea49df9a2f27dc6a906dcb24ddda354017b61f681fc0cfb09c00b19 |
