diff --git a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
index c2de0f0b6b489e7c58974da80631e1b5eb084f40..cb90f5f5d7493c401662ac8ffd2db84ddc58ae9e 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -58,7 +58,6 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
CL_type = kwargs["CL_type"]
nbClass = kwargs["nbClass"]
X = getValue(X)
- print X.data.nbytes + X.indptr.nbytes + X.indices.nbytes
datasetLength = X.shape[0]
clKWARGS = kwargs[kwargs["CL_type"]+"KWARGS"]
diff --git a/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py b/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
index 2164b88ac4f6c4dfdc12dc2c22238bfc36b0c1f5..930d150da8d5d9d59f0e8a9937263d671f72af52 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
@@ -403,35 +403,36 @@ def makeSparseTotalMatrix(sortedRNASeq):
return sparseFull
-def getAdjacenceMatrix(sortedRNASeq, k=1):
- indices = np.zeros((sortedRNASeq.shape[0]*k*sortedRNASeq.shape[1]), dtype=np.int32)
- data = np.ones((sortedRNASeq.shape[0]*k*sortedRNASeq.shape[1]), dtype=bool)
- indptr = np.zeros(sortedRNASeq.shape[0]+1, dtype=np.int16)
- nbGenes = sortedRNASeq.shape[1]
+def getAdjacenceMatrix(RNASeqRanking, sotredRNASeq, k=2):
+ k=int(k)/2*2
+ indices = np.zeros((RNASeqRanking.shape[0]*k*RNASeqRanking.shape[1]), dtype=np.int32)
+ data = np.ones((RNASeqRanking.shape[0]*k*RNASeqRanking.shape[1]), dtype=bool)
+ indptr = np.zeros(RNASeqRanking.shape[0]+1, dtype=np.int16)
+ nbGenes = RNASeqRanking.shape[1]
pointer = 0
- for patientIndex, patient in enumerate(sortedRNASeq):
+ for patientIndex in range(RNASeqRanking.shape[0]):
print patientIndex
for i in range(nbGenes):
- for j in range(k):
+ for j in range(k/2):
try:
- indices[pointer]=patient[(i-(j+1))]+patient[i]*nbGenes
+ indices[pointer]=RNASeqRanking[patientIndex, (sotredRNASeq[patientIndex, i]-(j+1))]+i*nbGenes
pointer+=1
except:
pass
try:
- indices[pointer]=patient[i+(j+1)]+patient[i]*nbGenes
+ indices[pointer]=RNASeqRanking[patientIndex, (sotredRNASeq[patientIndex, i]+(j+1))]+i*nbGenes
pointer+=1
except:
pass
- # elif i<=k:
- # indices.append(patient[1]+patient[i]*nbGenes)
- # data.append(True)
- # elif i==nbGenes-1:
- # indices.append(patient[i-1]+patient[i]*nbGenes)
- # data.append(True)
+ # elif i<=k:
+ # indices.append(patient[1]+patient[i]*nbGenes)
+ # data.append(True)
+ # elif i==nbGenes-1:
+ # indices.append(patient[i-1]+patient[i]*nbGenes)
+ # data.append(True)
indptr[patientIndex+1] = pointer
- mat = sparse.csr_matrix((data, indices, indptr), shape=(sortedRNASeq.shape[0], sortedRNASeq.shape[1]*sortedRNASeq.shape[1]), dtype=bool)
+ mat = sparse.csr_matrix((data, indices, indptr), shape=(RNASeqRanking.shape[0], RNASeqRanking.shape[1]*RNASeqRanking.shape[1]), dtype=bool)
return mat
@@ -478,12 +479,16 @@ def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
logging.debug("Start:\t Getting Sorted RNASeq Data")
RNASeq = datasetFile["View2"][...]
- modifiedRNASeq = np.zeros(datasetFile.get("View2").shape, dtype=int)
+ sortedRNASeqGeneIndices = np.zeros(datasetFile.get("View2").shape, dtype=int)
+ RNASeqRanking = np.zeros(datasetFile.get("View2").shape, dtype=int)
for exampleIndex, exampleArray in enumerate(RNASeq):
- RNASeqDictionary = dict((index, value) for index, value in enumerate(exampleArray))
- sorted_x = sorted(RNASeqDictionary.items(), key=operator.itemgetter(1))
- modifiedRNASeq[exampleIndex] = np.array([index for (index, value) in sorted_x], dtype=int)
- mrnaseqDset = datasetFile.create_dataset("View4", modifiedRNASeq.shape, data=modifiedRNASeq)
+ sortedRNASeqDictionary = dict((index, value) for index, value in enumerate(exampleArray))
+ sortedRNASeqIndicesDict = sorted(sortedRNASeqDictionary.items(), key=operator.itemgetter(1))
+ sortedRNASeqIndicesArray = np.array([index for (index, value) in sortedRNASeqIndicesDict], dtype=int)
+ sortedRNASeqGeneIndices[exampleIndex] = sortedRNASeqIndicesArray
+ for geneIndex in range(RNASeq.shape[1]):
+ RNASeqRanking[exampleIndex, sortedRNASeqIndicesArray[geneIndex]] = geneIndex
+ mrnaseqDset = datasetFile.create_dataset("View4", sortedRNASeqGeneIndices.shape, data=sortedRNASeqGeneIndices)
mrnaseqDset.attrs["name"] = "SRNASeq"
mrnaseqDset.attrs["sparse"] = False
logging.debug("Done:\t Getting Sorted RNASeq Data")
@@ -496,9 +501,9 @@ def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
factorizedLeftBaseMatrix = np.genfromtxt(path+"factorLeft--n-"+str(datasetFile.get("View2").shape[1])+"--k-"+str(100)+".csv", delimiter=',')
except:
factorizedSupBaseMatrix, factorizedLeftBaseMatrix = getBaseMatrices(rnaseqData.shape[1], k)
- brnaseqDset = datasetFile.create_dataset("View5", (modifiedRNASeq.shape[0], modifiedRNASeq.shape[1]*k*2), dtype=bool)
- for patientIndex, patientSortedArray in enumerate(modifiedRNASeq):
- patientMatrix = np.zeros((modifiedRNASeq.shape[1], k * 2), dtype=bool)
+ brnaseqDset = datasetFile.create_dataset("View5", (sortedRNASeqGeneIndices.shape[0], sortedRNASeqGeneIndices.shape[1]*k*2), dtype=bool)
+ for patientIndex, patientSortedArray in enumerate(sortedRNASeqGeneIndices):
+ patientMatrix = np.zeros((sortedRNASeqGeneIndices.shape[1], k * 2), dtype=bool)
for lineIndex, geneIndex in enumerate(patientSortedArray):
patientMatrix[geneIndex]= np.concatenate((factorizedLeftBaseMatrix[lineIndex,:], factorizedSupBaseMatrix[:, lineIndex]))
brnaseqDset[patientIndex] = patientMatrix.flatten()
@@ -507,7 +512,7 @@ def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
logging.debug("Done:\t Getting Binarized RNASeq Data")
# logging.debug("Start:\t Getting Binned RNASeq Data")
- # sparseBinnedRNASeq = makeSparseTotalMatrix(modifiedRNASeq)
+ # sparseBinnedRNASeq = makeSparseTotalMatrix(sortedRNASeqGeneIndices)
# sparseBinnedRNASeqGrp = datasetFile.create_group("View6")
# dataDset = sparseBinnedRNASeqGrp.create_dataset("data", sparseBinnedRNASeq.data.shape, data=sparseBinnedRNASeq.data)
# indicesDset = sparseBinnedRNASeqGrp.create_dataset("indices", sparseBinnedRNASeq.indices.shape, data=sparseBinnedRNASeq.indices)
@@ -518,7 +523,7 @@ def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
# logging.debug("Done:\t Getting Binned RNASeq Data")
logging.debug("Start:\t Getting Adjacence RNASeq Data")
- sparseAdjRNASeq = getAdjacenceMatrix(modifiedRNASeq, k=findClosestPowerOfTwo(100)-1)
+ sparseAdjRNASeq = getAdjacenceMatrix(RNASeqRanking, sortedRNASeqGeneIndices, k=findClosestPowerOfTwo(100)-1)
sparseAdjRNASeqGrp = datasetFile.create_group("View6")
dataDset = sparseAdjRNASeqGrp.create_dataset("data", sparseAdjRNASeq.data.shape, data=sparseAdjRNASeq.data)
indicesDset = sparseAdjRNASeqGrp.create_dataset("indices", sparseAdjRNASeq.indices.shape, data=sparseAdjRNASeq.indices)
diff --git a/Code/MonoMutliViewClassifiers/utils/Dataset.py b/Code/MonoMutliViewClassifiers/utils/Dataset.py
index ad20ce5704793595e5b9a9bfb85519b1f2847931..fcc5474fc18a01ca21da0adc7477ed1c97105755 100644
--- a/Code/MonoMutliViewClassifiers/utils/Dataset.py
+++ b/Code/MonoMutliViewClassifiers/utils/Dataset.py
@@ -12,8 +12,6 @@ def getV(DATASET, viewIndex, usedIndices=None):
DATASET.get("View"+str(viewIndex)).get("indices").value,
DATASET.get("View"+str(viewIndex)).get("indptr").value),
shape=DATASET.get("View"+str(viewIndex)).attrs["shape"])[usedIndices,:]
- print sparse_mat.shape
- print sparse_mat.indptr
return sparse_mat
@@ -32,24 +30,19 @@ def getValue(DATASET):
DATASET.get("indices").value,
DATASET.get("indptr").value),
shape=DATASET.attrs["shape"])
- print sparse_mat.shape
- print sparse_mat.indptr
return sparse_mat
def extractSubset(matrix, usedIndices):
if sparse.issparse(matrix):
newIndptr = np.zeros(len(usedIndices)+1, dtype=np.int16)
oldindptr = matrix.indptr
- print oldindptr
for exampleIndexIndex, exampleIndex in enumerate(usedIndices):
newIndptr[exampleIndexIndex+1] = newIndptr[exampleIndexIndex]+(oldindptr[exampleIndex+1]-oldindptr[exampleIndex])
newData = np.ones(newIndptr[-1], dtype=bool)
newIndices = np.zeros(newIndptr[-1], dtype=np.int32)
oldIndices = matrix.indices
- print newIndptr
for exampleIndexIndex, exampleIndex in enumerate(usedIndices):
- print newIndptr[exampleIndexIndex], newIndptr[exampleIndexIndex+1]
newIndices[newIndptr[exampleIndexIndex]:newIndptr[exampleIndexIndex+1]] = oldIndices[oldindptr[exampleIndex]: oldindptr[exampleIndex+1]]
- return sparse.csr_matrix((newData, newIndices, newIndptr), shape=(len(usedIndices), matrix.shape))
+ return sparse.csr_matrix((newData, newIndices, newIndptr), shape=(len(usedIndices), matrix.shape[1]))
else:
return matrix[usedIndices]
\ No newline at end of file