fix: hsic stat

Signed-off-by: ZhiyiHuang <huangzhiyi.chn@gmail.com>

.github/workflows/python-package.yml

@@ -25,6 +25,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           python -m pip install flake8 pytest
+          python -m pip install numpy
           if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
       - name: Lint with flake8
         run: |

causaldmir/datasets/simlulators.py

@@ -1,10 +1,11 @@
+from itertools import product
+from typing import List, Optional, Tuple, Union
+
 import networkx as nx
 import numpy as np
-from sklearn.gaussian_process import GaussianProcessRegressor
-from typing import List, Tuple, Union, Optional
 import pandas as pd
-from itertools import product
-from tick.hawkes import SimuHawkes, HawkesKernelExp
+from sklearn.gaussian_process import GaussianProcessRegressor
+from tick.hawkes import HawkesKernelExp, SimuHawkes
 
 try:
     from scipy.special import expit as sigmoid

causaldmir/datasets/tests/test_datasets_utils.py

@@ -1,7 +1,7 @@
-import numpy as np
 import networkx as nx
+import numpy as np
 
-from causaldmir.datasets.utils import erdos_renyi, np2nx, generate_lag_transitions
+from causaldmir.datasets.utils import erdos_renyi, generate_lag_transitions, np2nx
 
 
 def test_erdos_renyi():

causaldmir/datasets/tests/test_hawkes_simulator.py

@@ -1,8 +1,10 @@
 from unittest import TestCase
+
 import numpy as np
 import pandas as pd
-from ..simlulators import HawkesSimulator
-from ..utils import erdos_renyi, generate_lag_transitions
+
+from causaldmir.datasets.simlulators import HawkesSimulator
+from causaldmir.datasets.utils import erdos_renyi, generate_lag_transitions
 
 
 class TestHawkesSimulator(TestCase):

causaldmir/datasets/tests/test_iid_simulator.py

@@ -1,13 +1,12 @@
+from unittest import TestCase
+
 import numpy as np
 
 from causaldmir.datasets.simlulators import IIDSimulator
 from causaldmir.datasets.utils import erdos_renyi
 
 
-class TestIIDSimulator:
-
-    pass
-
+class TestIIDSimulator(TestCase):
     def test_simulate_linear_anm_gauss(self):
         n_nodes = 10
         n_edges = 20

causaldmir/datasets/tests/test_time_lag_simulator.py

@@ -1,4 +1,5 @@
 from unittest import TestCase
+
 import numpy as np
 
 from causaldmir.datasets.simlulators import TimeLagSimulator

causaldmir/datasets/utils.py

@@ -1,9 +1,10 @@
+import random
+from typing import List, Optional, Sequence, Tuple, Union
+
+import networkx as nx
 import numpy as np
 import pandas as pd
-import networkx as nx
 import torch
-import random
-from typing import List, Tuple, Union, Optional, Sequence
 
 
 def set_random_seed(seed, set_torch_seed=True):

causaldmir/discovery/constraint/pc.py

@@ -3,7 +3,7 @@
 from causaldmir.utils import data_form_converter_for_class_method
 from causaldmir.utils.independence import ConditionalIndependentTest
 
-from .adjacency_search import adjacency_search
+from causaldmir.discovery.constraint.adjacency_search import adjacency_search
 
 
 class PC(object):

causaldmir/discovery/constraint/tests/test_pc.py

@@ -9,7 +9,8 @@
 from scipy import stats
 
 from causaldmir.discovery.constraint import PC
-from causaldmir.graph import DiGraph, Edge, dag2cpdag
+from causaldmir.graph import DiGraph, Edge
+from causaldmir.graph.dag2cpdag import dag2cpdag
 from causaldmir.utils.independence import Dsep, FisherZ
 from causaldmir.utils.metrics.graph_evaluation import graph_equal
 

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/Causal_Discovery_in_LHM.py

@@ -1,11 +1,11 @@
-import numpy as np
-import pandas as pd
 import FindCausalCluster
+import MakeGraph
 import MergeCluster as MC
+import numpy as np
+import Orientation
+import pandas as pd
 import UpdataData
 import Utils
-import Orientation
-import MakeGraph
 
 
 

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/FindCausalCluster.py

@@ -1,10 +1,11 @@
-import VanishedTest as VT
-import numpy as np
-import pandas as pd
 import itertools
-import TetradMethod
+
 import MergeCluster
+import numpy as np
 import Overlap_Merge
+import pandas as pd
+import TetradMethod
+import VanishedTest as VT
 
 def FindCausalCluster(data, PureCluster, ImpureCluster, alpha=0.01):
     '''

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/HSIC.py

@@ -19,6 +19,7 @@
 #HSIC with pval return
 
 from __future__ import division
+
 import numpy as np
 from scipy.stats import gamma
 

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/Infer_Causal_Order.py

@@ -1,6 +1,6 @@
+import GIN2 as GIN  #GIN based on mutual information
 import numpy as np
 import pandas as pd
-import GIN2 as GIN #GIN based on mutual information
 
 
 # identifying causal order by identifying root node recursively.

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/MakeGraph.py

@@ -1,5 +1,5 @@
-import networkx as nx
 import matplotlib.pyplot as plt
+import networkx as nx
 
 def Make_graph(LatentIndex,order = [],ShareCommonChild = []):
     clusters = []

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/MergeCluster.py

@@ -1,9 +1,10 @@
-import VanishedTest as VT
+import itertools
+
 import numpy as np
 import pandas as pd
-import itertools
-import Utils
 import UpdataData
+import Utils
+import VanishedTest as VT
 
 
 

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/MergeCluster_backup.py

@@ -1,9 +1,10 @@
-import VanishedTest as VT
+import itertools
+
 import numpy as np
 import pandas as pd
-import itertools
-import Utils
 import UpdataData
+import Utils
+import VanishedTest as VT
 
 
 

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/Orientation.py

@@ -1,8 +1,8 @@
+import GIN2 as GIN
+import Infer_Causal_Order
 import numpy as np
 import pandas as pd
-import GIN2 as GIN
 import Utils
-import Infer_Causal_Order
 
 def Orientation_Cluster(Ora_data, LatentIndex, PureClusters, AllCausalCluster):
     pass

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/Overlap_Merge.py

@@ -1,8 +1,9 @@
-import numpy as np
-import pandas as pd
-from itertools import combinations
 import itertools
+from itertools import combinations
+
 import networkx as nx
+import numpy as np
+import pandas as pd
 
 '''Merge overlap causal cluster'''
 def merge_list(L2):

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/TetradMethod.py

@@ -8,10 +8,11 @@
 # Copyright:   (c) YY 2022
 # Licence:     <your licence>
 #-------------------------------------------------------------------------------
-import VanishedTest as VT
+import itertools
+
 import numpy as np
 import pandas as pd
-import itertools
+import VanishedTest as VT
 
 '''
 Function: Check whether a variable set C is a causal cluster

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/VanishedTest.py

@@ -1,10 +1,11 @@
 
-from scipy.stats import norm
-import scipy
-import numpy as np
-import pandas as pd
 import math
 import random
+
+import numpy as np
+import pandas as pd
+import scipy
+from scipy.stats import norm
 from sklearn.utils import resample
 
 

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/indTest/FisherTest.py

@@ -1,4 +1,5 @@
 import math
+
 from scipy.stats import chi2
 
 #independent test by Fisher'method

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/indTest/HSIC.py

@@ -19,6 +19,7 @@
 #HSIC with pval return
 
 from __future__ import division
+
 import numpy as np
 from scipy.stats import gamma
 

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/indTest/HSIC2.py

@@ -1,13 +1,13 @@
 #API for HSIC test
 import sys
 sys.path.append("./indTest")
-from kerpy.GaussianKernel import GaussianKernel
-from HSICTestObject import HSICTestObject
-from numpy import shape,savetxt,loadtxt,transpose,shape,reshape,concatenate
-from independence_testing.HSICSpectralTestObject import HSICSpectralTestObject
-from independence_testing.HSICBlockTestObject import HSICBlockTestObject
 import numpy as np
 import pandas as pd
+from HSICTestObject import HSICTestObject
+from independence_testing.HSICBlockTestObject import HSICBlockTestObject
+from independence_testing.HSICSpectralTestObject import HSICSpectralTestObject
+from kerpy.GaussianKernel import GaussianKernel
+from numpy import concatenate, loadtxt, reshape, savetxt, shape, shape, transpose
 
 #method 1:HSIC test and return boolean
 #x and y:

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/indTest/HSICBlockTestObject.py

@@ -1,8 +1,9 @@
-from TestObject import TestObject
+import time
+
 from HSICTestObject import HSICTestObject
-from numpy import mean, sum, zeros, var, sqrt
+from numpy import mean, sqrt, sum, var, zeros
 from scipy.stats import norm
-import time
+from TestObject import TestObject
 
 class HSICBlockTestObject(HSICTestObject):
     def __init__(self,num_samples, data_generator=None, kernelX=None, kernelY=None,

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/indTest/HSICPermutationTestObject.py

@@ -3,9 +3,10 @@
 
 @author: qinyi
 '''
-from HSICTestObject import HSICTestObject
 import time
 
+from HSICTestObject import HSICTestObject
+
 
 class HSICPermutationTestObject(HSICTestObject):
 

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/indTest/HSICSpectralTestObject.py

@@ -3,10 +3,11 @@
 
 @author: qinyi
 '''
-from HSICTestObject import HSICTestObject
-import numpy as np
 import time
 
+import numpy as np
+from HSICTestObject import HSICTestObject
+
 class HSICSpectralTestObject(HSICTestObject):
 
     def __init__(self, num_samples, data_generator=None, 

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/indTest/HSICTestObject.py

@@ -1,12 +1,13 @@
-from numpy import shape, fill_diagonal, zeros, mean, sqrt,identity,dot,diag
-from numpy.random import permutation, randn
-from independence_testing.TestObject import TestObject
-import numpy as np
+import time
 from abc import abstractmethod
+
+import numpy as np
+from independence_testing.TestObject import TestObject
 from kerpy.Kernel import Kernel
-import time
-from scipy.linalg import sqrtm,inv
-from numpy.linalg import eigh,svd
+from numpy import diag, dot, fill_diagonal, identity, mean, shape, sqrt, zeros
+from numpy.linalg import eigh, svd
+from numpy.random import permutation, randn
+from scipy.linalg import inv, sqrtm
 
 
 

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/indTest/HSICtestImpure.py

@@ -8,9 +8,9 @@
 # Copyright:   (c) YY 2021
 # Licence:     <your licence>
 #-------------------------------------------------------------------------------
+import GIN
 import numpy as np
 import pandas as pd
-import GIN
 import SimulationData as SD
 
 

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/indTest/TestObject.py

@@ -1,4 +1,5 @@
 from abc import abstractmethod
+
 from scipy.stats import norm as normaldist
 
 

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/indTest/fastHSIC.py

@@ -1,12 +1,12 @@
 import sys
 sys.path.append("./indTest")
-from kerpy.GaussianKernel import GaussianKernel
-from HSICTestObject import HSICTestObject
-from numpy import shape,savetxt,loadtxt,transpose,shape,reshape,concatenate
-from independence_testing.HSICSpectralTestObject import HSICSpectralTestObject
-from independence_testing.HSICBlockTestObject import HSICBlockTestObject
 import numpy as np
 import pandas as pd
+from HSICTestObject import HSICTestObject
+from independence_testing.HSICBlockTestObject import HSICBlockTestObject
+from independence_testing.HSICSpectralTestObject import HSICSpectralTestObject
+from kerpy.GaussianKernel import GaussianKernel
+from numpy import concatenate, loadtxt, reshape, savetxt, shape, shape, transpose
 
 
 def test(x,y,alph=0.05):

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/indTest/independence.py

@@ -1,6 +1,6 @@
+import mutual as MI
 import numpy as np
 import pandas as pd
-import mutual as MI
 from sklearn import metrics
 
 #estimating mutual information by sklearn

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/independence.py

@@ -1,6 +1,6 @@
+import mutual as MI
 import numpy as np
 import pandas as pd
-import mutual as MI
 from sklearn import metrics
 
 #estimating mutual information by sklearn

causaldmir/discovery/funtional_based/LearningHierarchicalStructure/mutual.py

@@ -7,11 +7,11 @@
 These computations rely on nearest-neighbor statistics
 '''
 import numpy as np
-
-from scipy.special import gamma,psi
+from numpy import pi
 from scipy import ndimage
 from scipy.linalg import det
-from numpy import pi
+
+from scipy.special import gamma, psi
 
 from sklearn.neighbors import NearestNeighbors