A research implementation of a concept for GPU-accelerated information theoretic causal discovery, based upon the CMIknn test for conditional independence 1. The gpucmiknn implementation can be extended to handle different CMI estimators that build upon the concept of knn searches.
- setup conda environment
- install conda
- ho to: https://rapids.ai/start.html#rapids-release-selector and configure the desired Rapids environment build command
- execute the corresponding build command
- activate the conda environment
- build cuda code:
- in parent directory of this repo:
cd pc_adjacency_search - adjust Makefile.config, e.g. (remaining lines can be commented out):
ANACONDA_HOME := $(HOME)/anaconda3/envs/rapids-21.08 PYTHON_INCLUDE := $(ANACONDA_HOME)/include \ $(ANACONDA_HOME)/include/python3.7m \ $(ANACONDA_HOME)/lib/python3.7/site-packages/numpy/core/include/ PYTHON_LIB := $(ANACONDA_HOME)/lib INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib CUDA_DIR := /usr/local/cuda BUILD_DIR := build- build the code:
make - tested with python 3.8
- in parent directory of this repo:
- install python dependencies:
pip install -r requirements.txt
In parent directory of this repo:
cd pc_adjacency_search python main.py -i ./data/coolingData.csv --permutations 100 --process_count 1 -a 0.01 --par_strategy 2 -k 7
The following options are available:
| Parameter | Default | Description |
|---|---|---|
| -i | Path to input file in .csv format. | |
| -a | 0.05 | Sets the significance level used within PC algorithm. |
| -l | None | Gives the max level for the PC algorithm (level of the pc algorithm is <= max level) |
| -k | adaptive | k-nearest neighbors during CMI estimation. Adaptive, sets the parameter to 0.2 the sample size. |
| --permutations | 50 | Number of Permutations used for the CI Test. |
| --par_strategy | Flag indicate the parallel hardware used: 1 - CPU-based execution; 2 - GPUKNNCMI-Single; 3 GPUKNNCMI-Parallel | |
| --process_count | 2 | Number of parallel processes used during adjacency search for CPU-based execution |
| -b | None | Blocks during block-wise processing of GPUKNNCMI-Parallel. Default setting calculates the blocks on encountering memory pressure due to large numbers of separation set candidates. |
GPL-3
Footnotes
-
CMIknn: J. Runge (2018): Conditional Independence Testing Based on a Nearest-Neighbor Estimator of Conditional Mutual Information. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. http://proceedings.mlr.press/v84/runge18a.html ↩