cdt 0.3.1

A Toolbox for causal graph inference

# Causal Discovery Toolbox

Package for causal inference in graphs and in the pairwise settings for Python>=3.5. Tools for graph structure recovery and dependencies are included. The package is based on Numpy, Scikit-learn, Pytorch and R.

[](https://travis-ci.org/Diviyan-Kalainathan/CausalDiscoveryToolbox)
[](https://codecov.io/gh/Diviyan-Kalainathan/CausalDiscoveryToolbox)
[](https://raw.githubusercontent.com/Diviyan-Kalainathan/CausalDiscoveryToolbox/master/LICENSE.md)
[](#)
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It implements lots of algorithms for graph structure recovery (including algorithms from the __bnlearn__, __pcalg__ packages), mainly based out of observational data.

## [Check out the DOCS here !](https://diviyan-kalainathan.github.io/CausalDiscoveryToolbox/html/index.html)

An example of application of the toolbox on the LUCAS dataset (on Lung cancer) using CGNNs can be found here : [jupyter-notebook](https://github.com/Diviyan-Kalainathan/CausalDiscoveryToolbox/blob/master/examples/Discovery_LUCAS.ipynb)

Install it using pip: (See more details on installation below)
```sh
pip install cdt
```
## Docker images
Docker images are available, including all the dependencies, and enabled functionalities:

| python ver. | cpu | gpu |
|-------------- |----------------------------------------------------------------------------------------------------------------------------- |---------------------------------------------------------------------------------------------------------------------------------------- |
| 3.6 | [](https://hub.docker.com/r/divkal/cdt-py3.6/) | [](https://hub.docker.com/r/divkal/nv-cdt-py3.6/) |
| 3.7 | [](https://hub.docker.com/r/divkal/cdt-py3.7/) | [](#) |

## Installation

The packages requires a python version >=3.5, as well as some libraries listed in [requirements file](https://github.com/Diviyan-Kalainathan/CausalDiscoveryToolbox/blob/master/requirements.txt). For some additional functionalities, more libraries are needed for these extra functions and options to become available. Here is a quick install guide of the package, starting off with the minimal install up to the full installation.

**Note** : A (mini/ana)conda framework would help installing all those packages and therefore could be recommended for non-expert users.

### Install PyTorch
As some of the key algorithms in the _cdt_ package use the PyTorch package, it is required to install it.
Check out their website to install the PyTorch version suited to your hardware configuration: http://pytorch.org

### Install the CausalDiscoveryToolbox package
The package is available on PyPi:
```sh
pip install cdt
```
Or you can also install it from source.
```sh
$ git clone https://github.com/Diviyan-Kalainathan/CausalDiscoveryToolbox.git # Download the package
$ cd CausalDiscoveryToolbox
$ pip install -r requirements.txt # Install the requirements
$ python setup.py install develop --user
```
**The package is then up and running ! You can run most of the algorithms in the CausalDiscoveryToolbox, you might get warnings: some additional features are not available**

From now on, you can import the library using :
```python
import cdt
```
Check out the package structure and more info on the package itself [here](https://github.com/Diviyan-Kalainathan/CausalDiscoveryToolbox/blob/master/documentation.md).

### Additional : R and R libraries
In order to have access to additional algorithms from various R packages such as bnlearn, kpcalg, pcalg, ... while using the _cdt_ framework, it is required to install R.

Check out how to install all R dependencies in the before-install section of the [travis.yml](https://github.com/Diviyan-Kalainathan/CausalDiscoveryToolbox/blob/master/.travis.yml) file for debian based distributions.
The [r-requirements file](https://github.com/Diviyan-Kalainathan/CausalDiscoveryToolbox/blob/master/r_requirements.txt) notes all the R packages used by the toolbox.


## Overview
### General package structure
The following figure shows how the package and its algorithms are structured


```
cdt package
|
|- independence
| |- skeleton (Infering the skeleton from data, and removing spurious connections)
| | |- Lasso variants (Randomized Lasso[1], Glasso[2], HSICLasso[3])
| | |- FSGNN (CGNN variant for feature selection)
| | |- Network deconvolution[4]
| | |- Skeleton recovery using feature selection algorithms (RFECV[5], LinearSVR[6], RRelief[7], ARD[8,9], DecisionTree)
| |- stats (pairwise methods for dependency)
| |- Correlation (Pearson, Spearman, KendallTau)
| |- Kernel based (NormalizedHSIC[10])
| |- Mutual information based (MIRegression, Adjusted Mutual Information[11], Normalized mutual information[11])
|
|- generators
| |- RandomGraphFromData (Generate a random graph similar to inputdata)
| |- RandomGraphGenerator (Generates a random graph, can generate pairs of variables)
| |- generate_graph_with_structure (generates a graph with a fixed structure)
|
|- causality
| |- graph (methods for graph inference)
| | |- CGNN[12] method (In tensorflow, pytorch version needs revision)
| | |- PC[13]
| | |- GES[13]
| | |- GIES[13]
| | |- LiNGAM[13]
| | |- CAM[13]
| |- pairwise (methods for pairwise inference)
| |- ANM[14] (Additive Noise Model)
| |- IGCI[15] (Information Geometric Causal Inference)
| |- RCC[16] (Randomized Causation Coefficient)
| |- NCC[17] (Neural Causation Coefficient)
| |- GNN[12] (Generative Neural Network -- Part of CGNN )
| |- Bivariate fit (Baseline method of regression)
| |- GPI[18], PNL[19], Jarfo[20] to implement
|
|- utils
|- Settings -> CGNN_SETTINGS, SETTINGS (hardware settings)
|- Loss -> MMD loss [21, 22] & various other loss functions
|- metrics -> Implements the metrics for graph scoring
|- Formats -> for importing data formats
|- Graph -> defines the DirectedGraph and UndirectedGraph class (see below)


```

### Hardware and algorithm settings
The toolbox has a SETTINGS class that defines the hardware settings . Those settings are unique and their default parameters are defined in **_cdt/utils/Settings_**.

These parameters are accessible and overridable via accessing the class :

```python
import cdt
cdt.SETTINGS
```

Moreover, the hardware parameters are detected and defined automatically (including number of GPUs, CPUs, available optional packages) at the **import** of the package using the **cdt.utils.Settings.autoset_settings** method, run at startup.

### The graph class
The whole package revolves around using the **DiGraph** and **Graph** classes from the **networkx** package.

### References

- [1] Wang, S., Nan, B., Rosset, S., & Zhu, J. (2011). Random lasso. The annals of applied statistics, 5(1), 468.
- [2] Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432-441.
- [3] Yamada, M., Jitkrittum, W., Sigal, L., Xing, E. P., & Sugiyama, M. (2014). High-dimensional feature selection by feature-wise kernelized lasso. Neural computation, 26(1), 185-207.
- [4] Feizi, S., Marbach, D., Médard, M., & Kellis, M. (2013). Network deconvolution as a general method to distinguish direct dependencies in networks. Nature biotechnology, 31(8), 726-733.
- [5] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46(1), 389-422.
- [6] Vapnik, V., Golowich, S. E., & Smola, A. J. (1997). Support vector method for function approximation, regression estimation and signal processing. In Advances in neural information processing systems (pp. 281-287).
- [7] Kira, K., & Rendell, L. A. (1992, July). The feature selection problem: Traditional methods and a new algorithm. In Aaai (Vol. 2, pp. 129-134).
- [8] MacKay, D. J. (1992). Bayesian interpolation. Neural Computation, 4, 415–447.
- [9] Neal, R. M. (1996). Bayesian learning for neural networks. No. 118 in Lecture Notes in Statistics. New York: Springer.
- [10] Gretton, A., Bousquet, O., Smola, A., & Scholkopf, B. (2005, October). Measuring statistical dependence with Hilbert-Schmidt norms. In ALT (Vol. 16, pp. 63-78).
- [11] Vinh, N. X., Epps, J., & Bailey, J. (2010). Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. Journal of Machine Learning Research, 11(Oct), 2837-2854.
- [12] Goudet, O., Kalainathan, D., Caillou, P., Lopez-Paz, D., Guyon, I., Sebag, M., ... & Tubaro, P. (2017). Learning functional causal models with generative neural networks. arXiv preprint arXiv:1709.05321.
- [13] Spirtes, P., Glymour, C., Scheines, R. (2000). Causation, Prediction, and Search. MIT press.
- [14] Hoyer, P. O., Janzing, D., Mooij, J. M., Peters, J., & Schölkopf, B. (2009). Nonlinear causal discovery with additive noise models. In Advances in neural information processing systems (pp. 689-696).
- [15] Janzing, D., Mooij, J., Zhang, K., Lemeire, J., Zscheischler, J., Daniušis, P., ... & Schölkopf, B. (2012). Information-geometric approach to inferring causal directions. Artificial Intelligence, 182, 1-31.
- [16] Lopez-Paz, D., Muandet, K., Schölkopf, B., & Tolstikhin, I. (2015, June). Towards a learning theory of cause-effect inference. In International Conference on Machine Learning (pp. 1452-1461).
- [17] Lopez-Paz, D., Nishihara, R., Chintala, S., Schölkopf, B., & Bottou, L. (2017, July). Discovering causal signals in images. In Proceedings of CVPR.
- [18] Stegle, O., Janzing, D., Zhang, K., Mooij, J. M., & Schölkopf, B. (2010). Probabilistic latent variable models for distinguishing between cause and effect. In Advances in Neural Information Processing Systems (pp. 1687-1695).
- [19] Zhang, K., & Hyvärinen, A. (2009, June). On the identifiability of the post-nonlinear causal model. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence (pp. 647-655). AUAI Press.
- [20] Fonollosa, J. A. (2016). Conditional distribution variability measures for causality detection. arXiv preprint arXiv:1601.06680.
- [21] Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., & Smola, A. (2012). A kernel two-sample test. Journal of Machine Learning Research, 13(Mar), 723-773.
- [22] Li, Y., Swersky, K., & Zemel, R. (2015). Generative moment matching networks. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15) (pp. 1718-1727).