Metadata-Version: 2.1
Name: gcastle
Version: 1.0.2
Summary: gCastle is the fundamental package for causal structure learning with Python.
Home-page: https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle
Author: Huawei Noah's Ark Lab
Author-email: zhangkeli1@huawei.com
License: Apache License 2.0
Description: # gCastle
        
        ## Introduction
        
        `gCastle` is a causal structure learning toolchain developed by [Huawei Noah's Ark Lab](https://www.noahlab.com.hk/#/home). The package contains various functionality related to causal learning and evaluation, including:
        
        * Data generation and processing: data simulation, data reading operators, and data pre-processing operators (such as prior injection and variable selection).
        * Causal structure learning: causal structure learning methods, including both classic and recently developed methods, especially gradient-based ones that can handle large problems.
        * Evaluation metrics: various commonly used metrics for causal structure learning, including F1, SHD, FDR, TPR, FDR, NNZ, etc.
        
        ## Algorithm List
        
        | Algorithm | Category (based on data) | Description |
        | :--: | :-- | :-- |
        | [PC](https://arxiv.org/abs/math/0510436) | IID | A classic causal discovery algorithm based on conditional independence tests |
        | [DirectLiNGAM](https://arxiv.org/abs/1101.2489) | IID | A direct learning algorithm for linear non-Gaussian acyclic model (LiNGAM) |
        | [ICALiNGAM](https://dl.acm.org/doi/10.5555/1248547.1248619) | IID | An ICA-based learning algorithm for linear non-Gaussian acyclic model (LiNGAM) |
        | [NOTEARS](https://arxiv.org/abs/1803.01422) | IID | A gradient-based algorithm for linear data models (typically with least-squares loss) |
        | [NOTEARS-MLP](https://arxiv.org/abs/1909.13189) | IID | A gradient-based algorithm using neural network modeling for non-linear causal relationships |
        | [NOTEARS-SOB](https://arxiv.org/abs/1909.13189) | IID | A gradient-based algorithm using Sobolev space modeling for non-linear causal relationships |
        | [NOTEARS-lOW-RANK](https://arxiv.org/abs/2006.05691) | IID | Adapting NOTEARS for large problems with low-rank causal graphs |
        | [GOLEM](https://arxiv.org/abs/2006.10201) | IID | A more efficient version of NOTEARS that can reduce number of optimization iterations |
        | [GraN_DAG](https://arxiv.org/abs/1906.02226) | IID | A gradient-based algorithm using neural network modeling for non-linear additive noise data  |
        | [MCSL](https://arxiv.org/abs/1910.08527) | IID | A gradient-based algorithm for non-linear additive noise data by learning the binary adjacency matrix|
        | [GAE](https://arxiv.org/abs/1911.07420) | IID | A gradient-based algorithm using graph autoencoder to model non-linear causal relationships|
        | [RL](https://arxiv.org/abs/1906.04477) | IID | A RL-based algorithm that can work with flexible score functions (including non-smooth ones) |
        | CORL1 | IID | A RL- and order-based algorithm that improves the efficiency and scalability of previous RL-based approach |
        | CORL2 | IID | A RL- and order-based algorithm that improves the efficiency and scalability of previous RL-based approach |
        | TTPM | EVENT SEQUENCE | A causal structure learning algorithm based on Topological Hawkes process for spatio-temporal event sequences |
        
        ## Installation
        
        ### Dependencies
        
        gCastle requires:
        
        * python (>= 3.6)
        * tqdm (>= 4.48.2)
        * numpy (>= 1.19.1)
        * pandas (>= 0.22.0)
        * scipy (>= 1.4.1)
        * scikit-learn (>= 0.21.1)
        * matplotlib (>=2.1.2)
        * python-igraph (>= 0.8.2)
        * loguru (>= 0.5.3)
        * networkx (>= 2.5)
        * torch (>= 1.4.0)
        * tensorflow (>= 1.15.0)
        
        ### PIP installation
        
        ```bash
        pip install gcastle
        ```
        
        ## Usage Example (PC algorithm)
        
        ```python
        from castle.common import GraphDAG
        from castle.metrics import MetricsDAG
        from castle.datasets import IIDSimulation, DAG
        from castle.algorithms import PC
        
        # data simulation, simulate true causal dag and train_data.
        weighted_random_dag = DAG.erdos_renyi(n_nodes=10, n_edges=10,
                                              weight_range=(0.5, 2.0), seed=1)
        dataset = IIDSimulation(W=weighted_random_dag, n=2000, method='linear',
                                sem_type='gauss')
        true_causal_matrix, X = dataset.B, dataset.X
        
        # structure learning
        pc = PC()
        pc.learn(X)
        
        # plot predict_dag and true_dag
        GraphDAG(pc.causal_matrix, true_causal_matrix)
        
        # calculate metrics
        mt = MetricsDAG(pc.causal_matrix, true_causal_matrix)
        print(mt.metrics)
        ```
        
        You can visit [examples](https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle) to find more examples.
        
        ## Next Up & Contributing
        
        You can find more news about [gCastle](https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle) on the github website.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
