def causalgraph_check():
dag = DirectedAcyclicGraph(exposure="X", outcome="Y")
dag.add_arrow(source="X", endpoint="Y")
dag.add_arrow(source="V", endpoint="Y")
dag.add_arrows(pairs=(("W", "X"), ("W", "Y")))
dag.draw_dag()
plt.show()
def test_no_mediator(self):
correct_set = [{'W', 'V'}]
dag = DirectedAcyclicGraph(exposure='X', outcome="Y")
dag.add_arrows((
("X", "Y"),
("W", "X"),
("W", "Y"),
("V", "X"),
("V", "Y"),
("X", "M"),
("M", "Y"),
))
dag.calculate_adjustment_sets()
# Making sure number of adjustment sets are equal to correct sets
assert len(dag.adjustment_sets) == len(correct_set)
# Checking no 'double' sets in adjustment sets
assert len(dag.adjustment_sets) == len(set(dag.adjustment_sets))
# Checking that all adjustment sets are in the correct
for i in dag.adjustment_sets:
assert set(i) in list(correct_set)
for i in dag.minimal_adjustment_sets:
assert set(i) in correct_set
def test_adjustment_set_1(self, arrow_list_1):
correct_set = [{"W", "Z"}, {"V", "Z"}, {"W", "V", "Z"}]
dag = DirectedAcyclicGraph(exposure='X', outcome="Y")
dag.add_arrows(arrow_list_1)
dag.calculate_adjustment_sets()
# Making sure number of adjustment sets are equal to correct sets
assert len(dag.adjustment_sets) == len(correct_set)
# Checking no 'double' sets in adjustment sets
assert len(dag.adjustment_sets) == len(set(dag.adjustment_sets))
# Checking that all adjustment sets are in the correct
for i in dag.adjustment_sets:
assert set(i) in list(correct_set)
def test_adjustment_set_3(self, arrow_list_3):
correct_set = [()]
dag = DirectedAcyclicGraph(exposure='X', outcome="Y")
dag.add_arrows(arrow_list_3)
dag.calculate_adjustment_sets()
print(dag.adjustment_sets)
# Making sure number of adjustment sets are equal to correct sets
assert len(dag.adjustment_sets) == len(correct_set)
# Checking no 'double' sets in adjustment sets
assert len(dag.adjustment_sets) == len(set(dag.adjustment_sets))
# Checking that minimal is the same
assert dag.adjustment_sets == dag.minimal_adjustment_sets
def test_butterfly(self, arrow_butterfly):
correct_set = [{'U1', 'B'}, {'B', 'U2'}, {'U1', 'B', 'U2'}]
dag = DirectedAcyclicGraph(exposure='X', outcome="Y")
dag.add_arrows(arrow_butterfly)
dag.calculate_adjustment_sets()
# Making sure number of adjustment sets are equal to correct sets
assert len(dag.adjustment_sets) == len(correct_set)
# Checking no 'double' sets in adjustment sets
assert len(dag.adjustment_sets) == len(set(dag.adjustment_sets))
# Checking that all adjustment sets are in the correct
for i in dag.adjustment_sets:
assert set(i) in list(correct_set)
for i in dag.minimal_adjustment_sets:
assert set(i) in [{'U1', 'B'}, {'B', 'U2'}]
import statsmodels.api as sm
import statsmodels.formula.api as smf
from scipy.stats import logistic
from pygam import LinearGAM
from sklearn.neural_network import MLPRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import RandomForestRegressor
from zepid.causal.causalgraph import DirectedAcyclicGraph
np.random.seed(20210316)
###############################################
# Directed Acyclic Graph
###############################################
dag = DirectedAcyclicGraph(exposure=r'$X$', outcome=r'$Y$')
dag.add_arrows([[r'$Z$', r'$X$'],
[r'$Z$', r'$Y$'],
[r'$X$', r'$M$'],
[r'$M$', r'$Y$'],
[r'$U$', r'$Z$'],
[r'$U$', r'$X$'],
])
dag.draw_dag(positions={r'$Z$': [-0.75, 0.05],
r'$X$': [-0.5, 0],
r'$M$': [-0.25, -0.02],
r'$U$': [-1, 0],
r'$Y$': [0, 0]},
fig_size=[6, 3])
plt.ylim([-0.05, 0.08])
def test_read_networkx(self):
G = nx.DiGraph()
G.add_edges_from((("X", "Y"), ("C", "Y"), ("C", "X")))
dag = DirectedAcyclicGraph(exposure='X', outcome="Y")
dag.add_from_networkx(G)
def test_error_networkx_noY(self):
G = nx.DiGraph()
G.add_edge("X", "W")
dag = DirectedAcyclicGraph(exposure='X', outcome="Y")
with pytest.raises(DAGError):
dag.add_from_networkx(G)
def test_error_read_networkx(self):
G = nx.DiGraph()
G.add_edges_from((("X", "Y"), ("Y", "C"), ("C", "X")))
dag = DirectedAcyclicGraph(exposure='X', outcome="Y")
with pytest.raises(DAGError):
dag.add_from_networkx(G)
def test_error_add_from_cyclic_arrow(self):
dag = DirectedAcyclicGraph(exposure='X', outcome="Y")
with pytest.raises(DAGError):
dag.add_arrows(pairs=(("X", "Y"), ("Y", "C"), ("C", "X")))
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#####################################################################################################################
# Causal Graphs
#####################################################################################################################
print("Running causal graphs...")
from zepid.causal.causalgraph import DirectedAcyclicGraph
dag = DirectedAcyclicGraph(exposure='X', outcome="Y")
dag.add_arrows(
(('X', 'Y'), ('U1', 'X'), ('U1', 'B'), ('U2', 'B'), ('U2', 'Y')))
pos = {"X": [0, 0], "Y": [1, 0], "B": [0.5, 0.5], "U1": [0, 1], "U2": [1, 1]}
dag.draw_dag(positions=pos)
plt.tight_layout()
plt.savefig("../images/zepid_dag_mbias.png", format='png', dpi=300)
plt.close()
dag.calculate_adjustment_sets()
print(dag.adjustment_sets)
dag.add_arrows((('X', 'Y'), ('U1', 'X'), ('U1', 'B'), ('U2', 'B'), ('U2', 'Y'),
('B', 'X'), ('B', 'Y')))
dag.draw_dag(positions=pos)
plt.tight_layout()
plt.savefig("../images/zepid_dag_bbias.png", format='png', dpi=300)
plt.close()