Merge pull request #87 from bd2kccd/v.1.2.0

Push Version 1.2.0

Author: chirayukong

meta.yaml

@@ -1,6 +1,6 @@
 package:
   name: pycausal
-  version: "1.1.1"
+  version: "1.2.0"
 
 source:
   git_rev: v0.1

py-causal-fges-continuous-example.py

@@ -1,6 +1,5 @@
 #!/usr/local/bin/python
 
-
 import os
 import pandas as pd
 import pydot
@@ -14,7 +13,7 @@
 
 from pycausal import search as s
 tetrad = s.tetradrunner()
-tetrad.run(algoId = 'fges', dfs = df, scoreId = 'sem-bic-score', dataType = 'continuous',
+tetrad.run(algoId = 'fges', dfs = df, scoreId = 'sem-bic', dataType = 'continuous',
            maxDegree = -1, faithfulnessAssumed = True, verbose = True)
 
 print(tetrad.getNodes())
@@ -31,4 +30,4 @@
 graphs = pydot.graph_from_dot_data(dot_str)
 graphs[0].write_svg('fges-continuous.svg')
 
-pc.stop_vm()
+pc.stop_vm()

py-causal-fges-discrete-example.py

@@ -1,6 +1,5 @@
 #!/usr/local/bin/python
 
-
 import os
 import pandas as pd
 import pydot
@@ -14,7 +13,7 @@
 
 from pycausal import search as s
 tetrad = s.tetradrunner()
-tetrad.run(algoId = 'fges', dfs = df, scoreId = 'bdeu-score', dataType = 'discrete',
+tetrad.run(algoId = 'fges', dfs = df, scoreId = 'cg-bic-score', dataType = 'discrete',
            maxDegree = 3, faithfulnessAssumed = True, 
            symmetricFirstStep = True, verbose = True)
 
@@ -32,4 +31,4 @@
 graphs = pydot.graph_from_dot_data(dot_str)
 graphs[0].write_svg('fges-discrete.svg')
 
-pc.stop_vm()
+pc.stop_vm()

setup.py

@@ -19,7 +19,7 @@
 MA 02110-1301  USA
 
 Created on Feb 15, 2016
-Updated on Mar 14, 2019
+Updated on Aug 9, 2019
 
 @author: Chirayu Wongchokprasitti
 '''
@@ -28,7 +28,7 @@
 
 setup(
     name = "pycausal",
-    version = "1.1.1",
+    version = "1.2.0",
     description = "Python wrapper for the Tetrad Library",
     author = "Chirayu Kong Wongchokprasitti",
     author_email = '[email protected]',

src/pycausal/lib/causal-cmd-1.1.1-SNAPSHOT-jar-with-dependencies.jar renamed to src/pycausal/lib/causal-cmd-1.2.0-SNAPSHOT-jar-with-dependencies.jar

@@ -60,6 +60,11 @@ def isNodeExisting(self, nodes,node):
             print("Node {0} does not exist!".format(node))
             return False
 
+    def loadTimeSeriesData(self, tetradData, numLags = 1):
+        tetradData = javabridge.static_call('edu/cmu/tetrad/search/TimeSeriesUtils','createLagData','(Ledu/cmu/tetrad/data/DataSet;I)Ledu/cmu/tetrad/data/DataSet;', tetradData, numLags)
+        
+        return tetradData
+        
     def loadMixedData(self, df, numCategoriesToDiscretize = 4):
         tetradData = None
 

src/pycausal/pycausal.py

@@ -95,21 +95,23 @@ def listScores(self):
         _scores.sort()
         print('\n'.join(_scores))
 
-#    def getAlgorithmDescription(self, algoId):
-#        algo = self.algos[algoId]
-#        algoClass = algo.getClazz()
+    def getAlgorithmDescription(self, algoId):
+        algo = self.algos.get(algoId)
+        algoClass = algo.getClazz()
 
-#        tetradAlgorithms = javabridge.JClassWrapper("edu.pitt.dbmi.causal.cmd.tetrad.TetradAlgorithms")
-#        tetradAlgors = tetradAlgorithms.getInstance()
+        algorithmDescriptions = javabridge.JClassWrapper("edu.cmu.tetrad.util.AlgorithmDescriptions")
+        algoDescs = algorithmDescriptions.getInstance()
 
-#        if tetradAlgors.requireIndependenceTest(algoClass):
-#            print("\nIt requires the independence test.")
-#        if tetradAlgors.requireScore(algoClass):
-#            print("\nIt requires the score.")
-#        if tetradAlgors.acceptKnowledge(algoClass):
-#            print("\nIt accepts the prior knowledge.")
-#        if tetradAlgors.acceptMultipleDataset(algoClass):
-#            print("\nIt accepts multiple datasets.")
+        print(algoDescs.get(algoId))
+        algorithmAnnotations = javabridge.JClassWrapper("edu.cmu.tetrad.annotation.AlgorithmAnnotations")
+        if algorithmAnnotations.getInstance().requireIndependenceTest(algoClass):
+            print("\nIt requires the independence test.")
+        if algorithmAnnotations.getInstance().requireScore(algoClass):
+            print("\nIt requires the score.")
+        if algorithmAnnotations.getInstance().acceptKnowledge(algoClass):
+            print("\nIt accepts the prior knowledge.")
+        if algorithmAnnotations.getInstance().acceptMultipleDataset(algoClass):
+            print("\nIt accepts multiple datasets.")
 
     def getAlgorithmParameters(self, algoId, testId = None, scoreId = None):
         algo = self.algos.get(algoId)