Merge pull request #92 from wean2016/main

Fix Typo for GES Algorithm

Author: kunwuz

causallearn/search/ScoreBased/GES.py

@@ -60,7 +60,7 @@ def ges(X: ndarray, score_func: str = 'local_score_BIC', maxP: Optional[float] =
         if parameters is None:
             parameters = {'kfold': 10, 'lambda': 0.01, 'dlabel': {}}  # regularization parameter
             for i in range(X.shape[1]):
-                parameters['dlabel']['{}'.format(i)] = i
+                parameters['dlabel'][i] = i
         if maxP is None:
             maxP = len(parameters['dlabel']) / 2
         N = len(parameters['dlabel'])
@@ -71,7 +71,7 @@ def ges(X: ndarray, score_func: str = 'local_score_BIC', maxP: Optional[float] =
         if parameters is None:
             parameters = {'dlabel': {}}
             for i in range(X.shape[1]):
-                parameters['dlabel']['{}'.format(i)] = i
+                parameters['dlabel'][i] = i
         if maxP is None:
             maxP = len(parameters['dlabel']) / 2
         N = len(parameters['dlabel'])

tests/TestLocalScore.py

@@ -0,0 +1,89 @@
+import unittest
+from causallearn.utils.GESUtils import *
+from causallearn.score.LocalScoreFunctionClass import LocalScoreClass
+
+class TestLocalScore(unittest.TestCase):
+
+    np.random.seed(10)
+    X = np.random.randn(300, 1)
+    X_prime = np.random.randn(300, 1)
+    Y = X + 0.5 * np.random.randn(300, 1)
+    Z = Y + 0.5 * np.random.randn(300, 1)
+    data = np.mat(np.hstack((X, X_prime, Y, Z)))
+
+    np.random.seed(10)
+    X_slash = np.random.randint(0, 10, (300, 1))
+    X_prime_slash = np.random.randint(0, 10, (300, 1))
+    Y_slash = X_slash + np.random.randint(0, 10, (300, 1))
+    Z_slash = Y_slash + np.random.randint(0, 10, (300, 1))
+    data_slash = np.mat(np.hstack((X_slash, X_prime_slash, Y_slash, Z_slash)))
+
+    def test_local_score_marginal_multi(self):
+        parameters = {'dlabel': {}}
+        for i in range(self.data.shape[1]):
+            parameters['dlabel'][i] = i
+
+        localScoreClass = LocalScoreClass(data=self.data, local_score_fun=local_score_marginal_multi, parameters=parameters)
+
+        q = localScoreClass.score(0, [0])
+        p = localScoreClass.score(0, [1])
+        v = localScoreClass.score(0, [2])
+
+        assert q < v < p
+
+    def test_local_score_CV_multi(self):
+        parameters = {'kfold': 10, 'lambda': 0.01, 'dlabel': {}}  # regularization parameter
+        for i in range(self.data.shape[1]):
+            parameters['dlabel'][i] = i
+
+        localScoreClass = LocalScoreClass(data=self.data, local_score_fun=local_score_cv_multi, parameters=parameters)
+
+        q = localScoreClass.score(0, [0])
+        p = localScoreClass.score(0, [1])
+        v = localScoreClass.score(0, [2])
+
+        assert q < v < p
+
+    def test_local_score_BIC(self):
+        parameters = {}
+        parameters["lambda_value"] = 2
+
+        localScoreClass = LocalScoreClass(data=self.data, local_score_fun=local_score_BIC, parameters=parameters)
+
+        q = localScoreClass.score(0, [0])
+        p = localScoreClass.score(0, [1])
+        v = localScoreClass.score(0, [2])
+
+        assert q < v < p
+
+    def test_local_score_CV_general(self):
+        parameters = {'kfold': 10,  # 10 fold cross validation
+                      'lambda': 0.01}  # regularization parameter
+
+        localScoreClass = LocalScoreClass(data=self.data, local_score_fun=local_score_cv_general, parameters=parameters)
+
+        q = localScoreClass.score(0, [0])
+        p = localScoreClass.score(0, [1])
+        v = localScoreClass.score(0, [2])
+
+        assert q < v < p
+
+    def test_local_score_marginal_general(self):
+        parameters = {}
+
+        localScoreClass = LocalScoreClass(data=self.data, local_score_fun=local_score_marginal_general, parameters=parameters)
+
+        q = localScoreClass.score(0, [0])
+        p = localScoreClass.score(0, [1])
+        v = localScoreClass.score(0, [2])
+
+        assert q < v < p
+
+    def test_local_score_BDeu(self):
+        localScoreClass = LocalScoreClass(data=self.data_slash, local_score_fun=local_score_BDeu, parameters=None)
+
+        q = localScoreClass.score(0, [0])
+        p = localScoreClass.score(0, [1])
+        v = localScoreClass.score(0, [2])
+
+        assert q < v < p