{"id":33470,"date":"2022-03-30T18:56:04","date_gmt":"2022-03-30T10:56:04","guid":{"rendered":"https:\/\/swarma.org\/?p=33470"},"modified":"2022-03-30T18:56:04","modified_gmt":"2022-03-30T10:56:04","slug":"%e5%9b%a0%e6%9e%9c%e5%8f%91%e7%8e%b0%e5%b7%a5%e5%85%b7%e7%ae%b1","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=33470","title":{"rendered":"\u56e0\u679c\u53d1\u73b0\u5de5\u5177\u7bb1"},"content":{"rendered":"
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<\/strong>Causal Discovery Toolbox \uff08cdt\uff09\u662f\u4e00\u4e2a\u5e94\u7528\u4e8e\u56fe\u6a21\u578b\u548cPyhton >=3.5 \u7248\u672c\u4e2d\u7684\u56e0\u679c\u63a8\u65ad\u5de5\u5177\u7bb1\u3002\u5305\u542b\u4e86\u56fe\u7ed3\u6784\u6062\u590d\u548c\u76f8\u5173\u4f9d\u8d56\u3002\u8be5\u5de5\u5177\u7bb1\u57fa\u4e8e\u4ee5\u4e0b\u6a21\u5757\u548c\u8bed\u8a00\u6784\u5efa\uff1aNumpy\u3001Sciki-learn\u3001Pytorch\u3001R\u3002<\/span><\/strong><\/p>\n


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Causal Discovery Toolbox Documentation \u2012 Causal Discovery Toolbox 0.5.23 documentation<\/span><\/section>\n
\u6587\u6863\u94fe\u63a5\uff1a<\/span>\uff1a<\/span><\/section>\n
https:\/\/fentechsolutions.github.io\/CausalDiscoveryToolbox\/html\/index.html<\/span><\/section>\n<\/section>\n<\/blockquote>\n


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\u9700\u8981\u7248\u672c\u53f7\u4e0d\u4f4e\u4e8e 3.5 \u7684Pyhton\uff0c\u4ee5\u53ca requirements.txt \u4e2d\u6240\u5217\u4e3e\u51fa\u7684\u6a21\u5757\u3002\u5982\u679c\u9700\u8981\u4f7f\u7528\u989d\u5916\u7684\u529f\u80fd\u5219\u9700\u8981\u5b89\u88c5\u66f4\u591a\u7684\u6a21\u5757\u3002\u5728\u5b89\u88c5\u6307\u5357\u4e2d\u53ef\u4ee5\u770b\u5230\u6700\u5c0f\u5b89\u88c5\u548c\u5b8c\u5168\u5b89\u88c5\u7684\u65b9\u5f0f\u3002<\/span><\/p>\n


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\u6ce8\u610f\uff1a\u5bf9\u4e8e\u975e\u4e13\u4e1a\u7528\u6237\u800c\u8a00\uff0c(mini\/ana) conda \u6846\u67b6\u6709\u52a9\u4e8e\u7ba1\u7406\u76f8\u5173\u4f9d\u8d56\u3002\u8bf7\u67e5\u9605\u5b98\u7f51 http:\/\/pytorch.org \u83b7\u53d6\u76f8\u5173\u7684\u914d\u7f6e\u4fe1\u606f\u3002<\/span><\/p>\n


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\u53ef\u901a\u8fc7PyPi\u5b89\u88c5\u76f8\u5173\u6a21\u5757\uff1a<\/span><\/p>\n

pip install cdt<\/span><\/section><\/pre>\n

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\u4e5f\u53ef\u4ee5\u901a\u8fc7\u6e90\u4ee3\u7801\u5b89\u88c5<\/span><\/p>\n
$ git clone https:\/\/github.com\/FenTechSolutions\/CausalDiscoveryToolbox.git  # Download the package<\/span><\/p>
$ cd CausalDiscoveryToolbox<\/span><\/p>
$ pip install -r requirements.txt  # Install the requirements<\/span><\/p>
$ python setup.py install develop --user<\/span><\/p><\/pre>\n

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\u5b89\u88c5\u5b8c\u6210\u540e\u5c31\u53ef\u4ee5\u5bfc\u5165\u8fd0\u884c\u6a21\u5757\u3002CausalDiscoveryToolbox \u4e2d\u7684\u5927\u591a\u6570\u7b97\u6cd5\u5e94\u8be5\u90fd\u53ef\u4ee5\u4f7f\u7528\uff0c\u4e0d\u53ef\u7528\u7684\u7b97\u6cd5\u4f1a\u770b\u5230\u8b66\u544a\u3002<\/span><\/p>\n

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import cdt<\/span><\/section><\/pre>\n

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https:\/\/github.com\/FenTechSolutions\/CausalDiscoveryToolbox\/blob\/master\/documentation.md<\/span><\/p>\n

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\u5982\u679c\u8981\u5728\u4f7f\u7528 cdt \u6a21\u5757\u4e2d\uff0c\u4f7f\u7528\u8bf8\u5982 nlearn\u3001kpcalg\u3001pcalg \u7b49\u7531 R \u8bed\u8a00\u7f16\u5199\u7684\u7684\u7b97\u6cd5\uff0c\u5c31\u9700\u8981\u5b89\u88c5 R \u8bed\u8a00\u3002<\/span><\/p>\n

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travis.yml \u6587\u4ef6\u7684\u9884\u5b89\u88c5\u90e8\u5206\u53ef\u4ee5\u67e5\u770b\u57fa\u4e8e Debian \u7684R\u8bed\u8a00\u5b89\u88c5\u4f9d\u8d56\u3002r-requirements \u6587\u4ef6\u5219\u5305\u542b\u4e86 cdt \u6a21\u5757\u6240\u9700\u8981\u7684 R \u8bed\u8a00\u5305\u3002<\/span><\/p>\n

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\u8be5\u5de5\u5177\u7bb1\u6709\u4e00\u4e2aSETTING\u7c7b\u53ef\u7528\u4e8e\u5bf9\u786c\u4ef6\u8fdb\u884c\u8bbe\u7f6e\u3002\u8fd9\u4e9b\u8bbe\u7f6e\u662f\u552f\u4e00\u7684\u3002\u9ed8\u8ba4\u53c2\u6570\u5b9a\u4e49\u4e8ecdt\/utils\/Settings<\/span><\/p>\n

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cdt.SETTINGS<\/span><\/p><\/pre>\n

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\u6b64\u5916\u5728\u542f\u52a8\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528 cdt.utils.Settings.autoset_settings \u65b9\u6cd5\u6765\u81ea\u52a8\u914d\u7f6e\u786c\u4ef6\u53c2\u6570<\/span>\uff08\u4f8b\u5982\uff0cGPU\uff0cCPU\u6570\u91cf\uff0c\u53ef\u9009\u8f6f\u4ef6\u5305\u7684\u6570\u91cf\uff09<\/span><\/p>\n

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[1] Wang, S., Nan, B., Rosset, S., & Zhu, J. (2011). Random lasso. The annals of applied statistics, 5(1), 468.<\/span><\/p>\n
[2] Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432-441.<\/span><\/p>\n
[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.<\/span><\/p>\n
[4] Feizi, S., Marbach, D., M\u00e9dard, M., & Kellis, M. (2013). Network deconvolution as a general method to distinguish direct dependencies in networks. Nature biotechnology, 31(8), 726-733.<\/span><\/p>\n
[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.<\/span><\/p>\n
[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).<\/span><\/p>\n
[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).<\/span><\/p>\n
[8] MacKay, D. J. (1992). Bayesian interpolation. Neural Computation, 4, 415\u2013447.<\/span><\/p>\n
[9] Neal, R. M. (1996). Bayesian learning for neural networks. No. 118 in Lecture Notes in Statistics. New York: Springer.<\/span><\/p>\n
[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).<\/span><\/p>\n
[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.<\/span><\/p>\n
[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.<\/span><\/p>\n
[13] Spirtes, P., Glymour, C., Scheines, R. (2000). Causation, Prediction, and Search. MIT press.<\/span><\/p>\n
[14] Hoyer, P. O., Janzing, D., Mooij, J. M., Peters, J., & Sch\u00f6lkopf, B. (2009). Nonlinear causal discovery with additive noise models. In Advances in neural information processing systems (pp. 689-696).<\/span><\/p>\n
[15] Janzing, D., Mooij, J., Zhang, K., Lemeire, J., Zscheischler, J., Daniu\u0161is, P., … & Sch\u00f6lkopf, B. (2012). Information-geometric approach to inferring causal directions. Artificial Intelligence, 182, 1-31.<\/span><\/p>\n
[16] Lopez-Paz, D., Muandet, K., Sch\u00f6lkopf, B., & Tolstikhin, I. (2015, June). Towards a learning theory of cause-effect inference. In International Conference on Machine Learning (pp. 1452-1461).<\/span><\/p>\n
[17] Lopez-Paz, D., Nishihara, R., Chintala, S., Sch\u00f6lkopf, B., & Bottou, L. (2017, July). Discovering causal signals in images. In Proceedings of CVPR.<\/span><\/p>\n
[18] Stegle, O., Janzing, D., Zhang, K., Mooij, J. M., & Sch\u00f6lkopf, B. (2010). Probabilistic latent variable models for distinguishing between cause and effect. In Advances in Neural Information Processing Systems (pp. 1687-1695).<\/span><\/p>\n
[19] Zhang, K., & Hyv\u00e4rinen, 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.<\/span><\/p>\n
[20] Fonollosa, J. A. (2016). Conditional distribution variability measures for causality detection. arXiv preprint arXiv:1601.06680.<\/span><\/p>\n
[21] Gretton, A., Borgwardt, K. M., Rasch, M. J., Sch\u00f6lkopf, B., & Smola, A. (2012). A kernel two-sample test. Journal of Machine Learning Research, 13(Mar), 723-773.<\/span><\/p>\n
[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).<\/span><\/p>\n
[23] Margaritis D (2003). Learning Bayesian Network Model Structure from Data . Ph.D. thesis, School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA. Available as Technical Report CMU-CS-03-153<\/span><\/p>\n
[24] Tsamardinos I, Aliferis CF, Statnikov A (2003). \u201cAlgorithms for Large Scale Markov Blanket Discovery\u201d. In \u201cProceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference\u201d, pp. 376-381. AAAI Press.<\/span><\/p>\n
[25] Tsamardinos I, Aliferis CF, Statnikov A (2003). \u201cTime and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations\u201d. In \u201cKDD \u201903: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining\u201d, pp. 673-678. ACM. Tsamardinos I, Brown LE, Aliferis CF (2006). \u201cThe Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm\u201d. Machine Learning,65(1), 31-78.<\/span><\/p>\n
[26] Kalainathan, Diviyan & Goudet, Olivier & Guyon, Isabelle & Lopez-Paz, David & Sebag, Mich\u00e8le. (2018). SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning.<\/span><\/p>\n
[27] Aragam, B., & Zhou, Q. (2015). Concave penalized estimation of sparse Gaussian Bayesian networks. Journal of Machine Learning Research, 16, 2273-2328.<\/span><\/p>\n
[28] Bloebaum, P., Janzing, D., Washio, T., Shimizu, S., & Schoelkopf, B. (2018, March). Cause-Effect Inference by Comparing Regression Errors. In International Conference on Artificial Intelligence and Statistics (pp. 900-909).<\/span><\/p>\n
[29] Structural Intervention Distance (SID) for Evaluating Causal Graphs, Jonas Peters, Peter B\u00fchlmann: https:\/\/arxiv.org\/abs\/1306.1043<\/span><\/p>\n

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