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readDataset.py
executable file
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readDataset.py
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#Copyright (C) 2018 Andreas Mayr
#Licensed under GNU General Public License v3.0 (see http://www.bioinf.jku.at/research/lsc/LICENSE and https://github.com/ml-jku/lsc/blob/master/LICENSE)
import math
import numpy as np
import pandas as pd
import scipy
import scipy.io
import scipy.sparse
import sklearn
import sklearn.feature_selection
import sklearn.model_selection
import sklearn.metrics
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
import os
import sys
import argparse
#np.set_printoptions(threshold='nan')
np.set_printoptions(threshold=1000)
np.set_printoptions(linewidth=160)
np.set_printoptions(precision=4)
np.set_printoptions(edgeitems=15)
np.set_printoptions(suppress=True)
pd.set_option('display.width', 160)
pd.options.display.float_format = '{:.2f}'.format
parser = argparse.ArgumentParser()
parser.add_argument("-destPath", help="Dest Path for original data: python Format", type=str, default=os.getenv("HOME")+"/mydata/trgpred/chembl20/dataPython/")
args = parser.parse_args()
dataPathSave=args.destPath
if not os.path.exists(dataPathSave):
os.makedirs(dataPathSave)
trainInfo="train";
clusterInfo="cl1";
#targetInfo="trg1";
targetSampleInfo=trainInfo
clusterInfo=clusterInfo
#predictTargetInfo=targetInfo
projectPathname=os.getenv("HOME")+"/mydata/trgpred/chembl20"
chemPathname=os.path.join(projectPathname, "chemFeatures");
clusterPathname=os.path.join(chemPathname, "cl");
dchemPathname=os.path.join(chemPathname, "d");
schemPathname=os.path.join(chemPathname, "s");
trainPathname=os.path.join(projectPathname, "train");
runPathname=os.path.join(projectPathname, "run");
sampleIdFilename=os.path.join(chemPathname, "SampleIdTable.txt")
targetSampleFilename=os.path.join(trainPathname, targetSampleInfo+".info")
clusterSampleFilename=os.path.join(clusterPathname, clusterInfo+".info")
#predictTargetFilename=os.path.join(trainPathname, predictTargetInfo+".info")
sampleIdFilename=os.path.join(chemPathname, 'SampleIdTable.txt')
tpSampleIdToName=np.genfromtxt(sampleIdFilename, dtype=str)
clusterTab=pd.read_csv(clusterSampleFilename, header=None, index_col=False, sep=" ")
folds=[clusterTab.loc[clusterTab.iloc[:,0]==0].iloc[:,1].values, clusterTab.loc[clusterTab.iloc[:,0]==1].iloc[:,1].values, clusterTab.loc[clusterTab.iloc[:,0]==2].iloc[:,1].values]
f=open(dataPathSave+'folds0.pckl', "wb")
pickle.dump(folds, f, -1)
f.close()
lookup=clusterTab.set_index(1)
trainTab=pd.read_csv(targetSampleFilename, header=None, index_col=False, sep=" ")
trainTab=trainTab.drop_duplicates()
classTypes=np.array([ 0, 1, 2, 3, 10, 11, 13])
clusterNames=np.sort(np.unique(lookup))
targetNames=np.sort(np.unique(trainTab.iloc[:,2].values))
classNumbers=[]
for clusterName in clusterNames:
classNumbersCluster=[]
for classType in classTypes:
trainTabSel=trainTab.loc[(lookup.iloc[trainTab.iloc[:,1].values]==clusterName).values[:,0]]
eq=trainTabSel.loc[trainTabSel.iloc[:,0]==classType].iloc[:,2].value_counts()
ser=pd.Series(np.zeros(len(targetNames), dtype=np.int64))
ser.index=targetNames
ser[eq.index.values]=eq.values
classNumbersCluster.append(ser)
classNumbers.append(np.array(classNumbersCluster))
classNumbers=np.array(classNumbers)
ovTab=[classNumbers[0][np.where(classTypes==0)[0][0]], #0
classNumbers[1][np.where(classTypes==0)[0][0]], #1
classNumbers[2][np.where(classTypes==0)[0][0]], #2
classNumbers[0][np.where(classTypes==1)[0][0]], #3
classNumbers[1][np.where(classTypes==1)[0][0]], #4
classNumbers[2][np.where(classTypes==1)[0][0]], #5
classNumbers[0][np.where(classTypes==2)[0][0]], #6
classNumbers[1][np.where(classTypes==2)[0][0]], #7
classNumbers[2][np.where(classTypes==2)[0][0]], #8
classNumbers[0][np.where(classTypes==3)[0][0]], #9
classNumbers[1][np.where(classTypes==3)[0][0]], #10
classNumbers[2][np.where(classTypes==3)[0][0]], #11
classNumbers[0][np.where(classTypes==11)[0][0]], #12
classNumbers[1][np.where(classTypes==11)[0][0]], #13
classNumbers[2][np.where(classTypes==11)[0][0]], #14
classNumbers[0][np.where(classTypes==13)[0][0]], #15
classNumbers[1][np.where(classTypes==13)[0][0]], #16
classNumbers[2][np.where(classTypes==13)[0][0]]] #17
ovTab=np.array(ovTab).T
nrOfActivities=ovTab[:,[3,4,5, 9,10,11, 12,13,14, 15,16,17, 6,7,8]].sum(1)
hasInactives=(ovTab[:,[3,4,5]].sum(1)>0.5)
hasActives=(ovTab[:,[9,10,11]].sum(1)>0.5)
allFoldsHaveInActives=(np.sum((ovTab[:,[3,4,5]])>0.5, 1)>2.5)
allFoldsHaveActives=(np.sum((ovTab[:,[9,10,11]])>0.5, 1)>2.5)
selTargetNames=targetNames[np.logical_and((nrOfActivities>=100), np.logical_and(allFoldsHaveInActives, allFoldsHaveActives))]
trainTabSel=trainTab.loc[np.in1d(trainTab.iloc[:,0].values, [1,3])]
trainTabSel=trainTabSel.loc[np.in1d(trainTabSel.iloc[:,2].values, selTargetNames)]
sampleAnnInd=pd.Series(data=np.arange(len(tpSampleIdToName)), index=np.arange(len(tpSampleIdToName)))
targetAnnInd=pd.Series(data=np.arange(len(selTargetNames)), index=selTargetNames)
annMat=scipy.sparse.coo_matrix((trainTabSel.iloc[:,0], (sampleAnnInd[trainTabSel.iloc[:,1]], targetAnnInd[trainTabSel.iloc[:,2]])), shape=(sampleAnnInd.max()+1, targetAnnInd.max()+1))
annMat.data[annMat.data<2]=-1
annMat.data[annMat.data>2]=1
annMat.eliminate_zeros()
annMat=annMat.tocsr()
annMat.sort_indices()
f=open(dataPathSave+'labelsHard.pckl', "wb")
pickle.dump(annMat, f, -1)
pickle.dump(sampleAnnInd, f, -1)
pickle.dump(targetAnnInd, f, -1)
f.close()
trainTabSel=trainTab.loc[np.in1d(trainTab.iloc[:,0].values, [1,11,3,13])]
trainTabSel=trainTabSel.loc[np.in1d(trainTabSel.iloc[:,2].values, selTargetNames)]
sampleAnnInd=pd.Series(data=np.arange(len(tpSampleIdToName)), index=np.arange(len(tpSampleIdToName)))
targetAnnInd=pd.Series(data=np.arange(len(selTargetNames)), index=selTargetNames)
annMat=scipy.sparse.coo_matrix((trainTabSel.iloc[:,0], (sampleAnnInd[trainTabSel.iloc[:,1]], targetAnnInd[trainTabSel.iloc[:,2]])), shape=(sampleAnnInd.max()+1, targetAnnInd.max()+1))
annMat.eliminate_zeros()
annMat=annMat.tocsr()
annMat.sort_indices()
f=open(dataPathSave+'labelsWeakHard.pckl', "wb")
pickle.dump(annMat, f, -1)
pickle.dump(sampleAnnInd, f, -1)
pickle.dump(targetAnnInd, f, -1)
f.close()
scipy.io.mmwrite(dataPathSave+'labelsWeakHard.mtx', annMat)
np.savetxt(dataPathSave+"labelsWeakHard.cmpNames", tpSampleIdToName[sampleAnnInd.index.values], fmt="%s")
np.savetxt(dataPathSave+"labelsWeakHard.targetNames", targetAnnInd.index.values, fmt="%s")
def readSFeature(schemPathname, sfeature):
featureIntFilename=os.path.join(schemPathname, sfeature, 'fpFeatureTableInt.bin')
featureExtFilename=os.path.join(schemPathname, sfeature, 'fpFeatureTableExt.bin')
intToExtMappingFilename=os.path.join(schemPathname, sfeature, 'IntToExtMappingTable.bin')
sampleFilename=os.path.join(schemPathname, sfeature, 'fpSampleTable.bin')
featureCountFilename=os.path.join(schemPathname, sfeature, 'fpFeatureCountTableD.bin')
featuresInt=np.fromfile(featureIntFilename, dtype=np.long, count=-1)
featuresExt=np.fromfile(featureExtFilename, dtype=np.long, count=-1)
featureMap=np.fromfile(intToExtMappingFilename, dtype=np.long, count=-1)
samples=np.fromfile(sampleFilename, dtype=np.long, count=-1)
samples=np.append(samples, featuresInt.size)
featureCounts=np.fromfile(featureCountFilename, dtype=np.float64, count=-1)
if (np.all(featureMap[featuresInt]==featuresExt))==False:
print("An error occured!")
return []
smat=scipy.sparse.csr_matrix((featureCounts, featuresInt, samples))
sampleInd=pd.Series(data=np.arange(smat.shape[0]), index=np.arange(smat.shape[0]))
#featureInd=pd.Series(data=np.arange(smat.shape[1]), index=np.arange(smat.shape[1]))
featureInd=pd.Series([sfeature+":"]*len(featureMap))+featureMap.astype(np.str)
return (smat, sampleInd, featureInd)
def readDFeature(dchemPathname, dfeature):
propertyFilename = os.path.join(dchemPathname, dfeature, 'properties.bin')
propertyIdFilename = os.path.join(dchemPathname, dfeature, 'PropertyIdTable.txt')
dmat=np.fromfile(propertyFilename, dtype=np.float64, count=-1)
propertyIndexToPropertyId=np.genfromtxt(propertyIdFilename, dtype=str)
dmat=dmat.reshape(-1, len(propertyIndexToPropertyId))
sampleInd=pd.Series(data=np.arange(dmat.shape[0]), index=np.arange(dmat.shape[0]))
#featureInd=pd.Series(data=np.arange(dmat.shape[1]), index=np.arange(dmat.shape[1]))
featureInd=pd.Series([dfeature+":"]*len(propertyIndexToPropertyId))+propertyIndexToPropertyId
return (dmat, sampleInd, featureInd)
dMat, dMatSampleInd, dMatSFeatureInd=readDFeature(dchemPathname, "dense")
f=open(dataPathSave+'static.pckl', "wb")
pickle.dump(dMat, f, -1)
pickle.dump(dMatSampleInd, f, -1)
pickle.dump(dMatSFeatureInd, f, -1)
f.close()
sMat, sMatSampleInd, sMatFeatureInd=readSFeature(schemPathname, "semisparse")
f=open(dataPathSave+'semi.pckl', "wb")
pickle.dump(sMat, f, -1)
pickle.dump(sMatSampleInd, f, -1)
pickle.dump(sMatFeatureInd, f, -1)
f.close()
sMat, sMatSampleInd, sMatFeatureInd=readSFeature(schemPathname, "ECFC6_ES")
f=open(dataPathSave+'ecfp6.pckl', "wb")
pickle.dump(sMat, f, -1)
pickle.dump(sMatSampleInd, f, -1)
pickle.dump(sMatFeatureInd, f, -1)
f.close()
sMat, sMatSampleInd, sMatFeatureInd=readSFeature(schemPathname, "DFS8_ES")
f=open(dataPathSave+'dfs8.pckl', "wb")
pickle.dump(sMat, f, -1)
pickle.dump(sMatSampleInd, f, -1)
pickle.dump(sMatFeatureInd, f, -1)
f.close()
sMat, sMatSampleInd, sMatFeatureInd=readSFeature(schemPathname, "toxicophores")
f=open(dataPathSave+'tox.pckl', "wb")
pickle.dump(sMat, f, -1)
pickle.dump(sMatSampleInd, f, -1)
pickle.dump(sMatFeatureInd, f, -1)
f.close()