gcastle 1.0.2

gCastle is the fundamental package for causal structure learning with Python.

gCastle

Introduction

gCastle is a causal structure learning toolchain developed by Huawei Noah's Ark Lab. 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	IID	A classic causal discovery algorithm based on conditional independence tests
DirectLiNGAM	IID	A direct learning algorithm for linear non-Gaussian acyclic model (LiNGAM)
ICALiNGAM	IID	An ICA-based learning algorithm for linear non-Gaussian acyclic model (LiNGAM)
NOTEARS	IID	A gradient-based algorithm for linear data models (typically with least-squares loss)
NOTEARS-MLP	IID	A gradient-based algorithm using neural network modeling for non-linear causal relationships
NOTEARS-SOB	IID	A gradient-based algorithm using Sobolev space modeling for non-linear causal relationships
NOTEARS-lOW-RANK	IID	Adapting NOTEARS for large problems with low-rank causal graphs
GOLEM	IID	A more efficient version of NOTEARS that can reduce number of optimization iterations
GraN_DAG	IID	A gradient-based algorithm using neural network modeling for non-linear additive noise data
MCSL	IID	A gradient-based algorithm for non-linear additive noise data by learning the binary adjacency matrix
GAE	IID	A gradient-based algorithm using graph autoencoder to model non-linear causal relationships
RL	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

pip install gcastle

Usage Example (PC algorithm)

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 to find more examples.

Next Up & Contributing

You can find more news about gCastle on the github website.