Getting started

Dependencies

gCastle requires:

• python (>= 3.6, <=3.9)

• tqdm (>= 4.48.2)

• numpy (>= 1.19.1)

• pandas (>= 0.22.0)

• scipy (>= 1.7.3)

• scikit-learn (>= 0.21.1)

• matplotlib (>=2.1.2)

• networkx (>= 2.5)

• torch (>= 1.9.0)

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, 'result')

# calculate metrics
mt = MetricsDAG(pc.causal_matrix, true_causal_matrix)
print(mt.metrics)

You can visit examples to find more examples.