def learn_fci(self, df, tabu_edges):
"""This function is used to learn model using FCI"""
from pycausal.pycausal import pycausal as pc
from pycausal import search as s
from pycausal import prior as p
pc = pc()
pc.start_vm()
forbid = [list(i) for i in tabu_edges]
prior = p.knowledge(forbiddirect=forbid)
tetrad = s.tetradrunner()
tetrad.getAlgorithmParameters(algoId='fci', testId='fisher-z-test')
tetrad.run(algoId='fci',
dfs=df,
testId='fisher-z-test',
depth=-1,
maxPathLength=-1,
completeRuleSetUsed=False,
verbose=False)
edges = tetrad.getEdges()
dot_str = pc.tetradGraphToDot(tetrad.getTetradGraph())
graph = pydot.graph_from_dot_data(dot_str)
# graph[0].write_pdf(fname)
pc.stop_vm()
return edges
df = pd.read_table(data_dir, sep="\t")
from pycausal.pycausal import pycausal as pc
pc = pc()
pc.start_vm()
from pycausal import search as s
tetrad = s.tetradrunner()
tetrad.run(algoId='fges',
dfs=df,
scoreId='sem-bic',
dataType='continuous',
maxDegree=-1,
faithfulnessAssumed=True,
verbose=True)
print(tetrad.getNodes())
print(tetrad.getEdges())
graph = tetrad.getTetradGraph()
print('Graph BIC: {}'.format(graph.getAttribute('BIC')))
nodes = graph.getNodes()
for i in range(nodes.size()):
node = nodes.get(i)
print('Node {} BIC: {}'.format(node.getName(), node.getAttribute('BIC')))
dot_str = pc.tetradGraphToDot(graph)
graphs = pydot.graph_from_dot_data(dot_str)
graphs[0].write_svg('fges-continuous.svg')
pc.stop_vm()
#!/usr/local/bin/python
import os
import pandas as pd
import pydot
from IPython.display import SVG
data_dir = os.path.join(os.getcwd(), 'data', 'audiology.txt')
df = pd.read_table(data_dir, sep="\t")
from pycausal.pycausal import pycausal as pc
pc = pc()
pc.start_vm(java_max_heap_size = '100M')
from pycausal import search as s
tetrad = s.tetradrunner()
tetrad.run(algoId = 'fges', dfs = df, scoreId = 'bdeu', dataType = 'discrete',
structurePrior = 1.0, samplePrior = 1.0, maxDegree = 3, faithfulnessAssumed = True, verbose = True)
tetrad.getNodes()
tetrad.getEdges()
dot_str = pc.tetradGraphToDot(tetrad.getTetradGraph())
graphs = pydot.graph_from_dot_data(dot_str)
graphs[0].write_svg('fges-discrete.svg')
pc.stop_vm()
import pandas as pd
import pydot
from IPython.display import SVG
data_dir = os.path.join(os.getcwd(), 'data', 'charity.txt')
df = pd.read_table(data_dir, sep="\t")
from pycausal.pycausal import pycausal as pc
pc = pc()
pc.start_vm()
from pycausal import search as s
tetrad = s.tetradrunner()
tetrad.run(algoId='fges',
dfs=df,
scoreId='sem-bic',
dataType='continuous',
penaltyDiscount=2,
maxDegree=-1,
faithfulnessAssumed=True,
verbose=True)
tetrad.getNodes()
tetrad.getEdges()
dot_str = pc.tetradGraphToDot(tetrad.getTetradGraph())
graphs = pydot.graph_from_dot_data(dot_str)
graphs[0].write_svg('fges-continuous.svg')
pc.stop_vm()