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Update ANM to be compatible with the latest version of sklearn #113

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Merged
merged 1 commit into from
Jul 6, 2023
Merged

Update ANM to be compatible with the latest version of sklearn #113

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7 changes: 1 addition & 6 deletions causallearn/search/FCMBased/ANM/ANM.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,3 @@
import os
import sys

BASE_DIR = os.path.join(os.path.dirname(__file__), '..')
sys.path.append(BASE_DIR)
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF
from sklearn.gaussian_process.kernels import ConstantKernel as C
Expand Down Expand Up @@ -49,7 +44,7 @@ def fit_gp(self, X, y):

# fit Gaussian process, including hyperparameter optimization
gpr.fit(X, y)
pred_y = gpr.predict(X)
pred_y = gpr.predict(X).reshape(-1, 1)
return pred_y

def cause_or_effect(self, data_x, data_y):
Expand Down
19 changes: 5 additions & 14 deletions tests/TestANM.py
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Original file line number Diff line number Diff line change
@@ -1,14 +1,5 @@
import os
import sys

BASE_DIR = os.path.join(os.path.dirname(__file__), '..')
sys.path.append(BASE_DIR)
import sys
import unittest
from pickle import load

import numpy as np

from causallearn.search.FCMBased.ANM.ANM import ANM


Expand All @@ -33,7 +24,7 @@ class TestANM(unittest.TestCase):
# Test ANM by some simulated data
def test_anm_simulation_1(self):
# simulated data y = x + 2x^3 + e
simulated_dataset_1 = np.loadtxt('TestData/anm_simulation_1.txt', delimiter=',')
simulated_dataset_1 = np.loadtxt('tests/TestData/anm_simulation_1.txt', delimiter=',')
simulated_dataset_1_p_value_forward, simulated_dataset_1_p_value_backward = 0.99541, 0.0 # round(value, 5) results
x_1 = simulated_dataset_1[:, 0].reshape(-1, 1)
y_1 = simulated_dataset_1[:, 1].reshape(-1, 1)
Expand All @@ -46,7 +37,7 @@ def test_anm_simulation_1(self):

def test_anm_simulation_2(self):
# simulated data y = 5 * exp(x) + e
simulated_dataset_2 = np.loadtxt('TestData/anm_simulation_2.txt', delimiter=',')
simulated_dataset_2 = np.loadtxt('tests/TestData/anm_simulation_2.txt', delimiter=',')
simulated_dataset_2_p_value_forward, simulated_dataset_2_p_value_backward = 0.99348, 0.0 # round(value, 5) results
x_2 = simulated_dataset_2[:, 0].reshape(-1, 1)
y_2 = simulated_dataset_2[:, 1].reshape(-1, 1)
Expand All @@ -59,7 +50,7 @@ def test_anm_simulation_2(self):

def test_anm_simulation_3(self):
# simulated data y = 3^x + e
simulated_dataset_3 = np.loadtxt('TestData/anm_simulation_3.txt', delimiter=',')
simulated_dataset_3 = np.loadtxt('tests/TestData/anm_simulation_3.txt', delimiter=',')
simulated_dataset_3_p_value_forward, simulated_dataset_3_p_value_backward = 0.65933, 0.0 # round(value, 5) results
x_3 = simulated_dataset_3[:, 0].reshape(-1, 1)
y_3 = simulated_dataset_3[:, 1].reshape(-1, 1)
Expand All @@ -73,8 +64,8 @@ def test_anm_simulation_3(self):
# data pair from the Tuebingen cause-effect pair dataset.
def test_anm_pair(self):

dataset = np.loadtxt('TestData/pair0001.txt')
dataset_p_value_forward, dataset_p_value_backward = 0.14736, 0.0 # round(value, 5) results
dataset = np.loadtxt('tests/TestData/pair0001.txt')
dataset_p_value_forward, dataset_p_value_backward = 0.14773, 0.0 # round(value, 5) results
p_value_forward, p_value_backward = self.anm.cause_or_effect(dataset[:, 0].reshape(-1, 1), dataset[:, 1].reshape(-1, 1))
self.assertTrue(round(p_value_forward, 5) == dataset_p_value_forward)
self.assertTrue(round(p_value_backward, 5) == dataset_p_value_backward)
Expand Down