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final v1.0
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Sejoon Lee authored and Sejoon Lee committed Jun 14, 2016
1 parent 1ce0a24 commit ffdca8e
Showing 1 changed file with 69 additions and 69 deletions.
138 changes: 69 additions & 69 deletions ncm_fastq.py
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Original file line number Diff line number Diff line change
Expand Up @@ -493,78 +493,78 @@ def classifying_test():
training_flag =0
####0715 Append

output_matrix_f = open(outdir + "/output_corr_matrix.txt","w")
output_matrix = dict()
output_matrix_f = open(outdir + "/output_corr_matrix.txt","w")
output_matrix = dict()

if out_tag!="stdout":
out_f = open(outdir + "/" + out_tag + "_all.txt","w")
out_matched = open(outdir + "/" + out_tag + "_matched.txt","w")

for i in range(0, len(keyList)):
output_matrix[keyList[i]] = dict()
for j in range(0,len(keyList)):
output_matrix[keyList[i]][keyList[j]] = 0

if training_flag == 1:
#make training set
for i in range(0,len(samples)):
trainMatrix= []
trainCategory = []
for j in range(0, len(samples)):
if i==j:
continue
else:
trainMatrix.append(samples[j])
trainCategory.append(classLabel[j])
#training samples in temp
#p0V, p1V, pAb = trainNB0(array(trainMatrix),array(trainCategory))
p1V,p1S, p0V, p0S = trainNV(array(trainMatrix),array(trainCategory))
result = classifyNV(samples[i],p0V,p0S, p1V, p1S)
if result[1] == 1:
print str(temp[i][0]) + '\tsample is matched to\t',str(temp[i][1]),'\t', samples[i]
predStrength.append(result[0])
# AUCs.append(calAUC(mat(predStrength),classLabel))
# plotROC(mat(predStrength),classLabel)
# print AUCs
else :
for i in range(0,len(samples)):
depth = min(mean_depth[temp[i][0].strip()],mean_depth[temp[i][1].strip()])
p1V,p1S, p0V, p0S = getPredefinedModel(depth)
result = classifyNV(samples[i],p0V,p0S, p1V, p1S)
if result[1] ==1:
output_matrix[temp[i][0].strip()][temp[i][1].strip()] = samples[i]
output_matrix[temp[i][1].strip()][temp[i][0].strip()] = samples[i]
if out_tag=="stdout":
print str(temp[i][0][:-4]) + '\tmatched\t',str(temp[i][1][:-4]),'\t', round(samples[i],4),'\t',round(depth,2)
else :
out_f.write(str(temp[i][0][:-4]) + '\tmatched\t' + str(temp[i][1][:-4]) + '\t'+ str(round(samples[i],4)) + '\t' + str(round(depth,2)) + '\n')
out_matched.write(str(temp[i][0][:-4]) + '\tmatched\t' + str(temp[i][1][:-4]) + '\t'+ str(round(samples[i],4)) + '\t' + str(round(depth,2)) + '\n')
if out_tag!="stdout":
out_f = open(outdir + "/" + out_tag + "_all.txt","w")
out_matched = open(outdir + "/" + out_tag + "_matched.txt","w")

for i in range(0, len(keyList)):
output_matrix[keyList[i]] = dict()
for j in range(0,len(keyList)):
output_matrix[keyList[i]][keyList[j]] = 0

if training_flag == 1:
#make training set
for i in range(0,len(samples)):
trainMatrix= []
trainCategory = []
for j in range(0, len(samples)):
if i==j:
continue
else:
if out_tag=="stdout":
print str(temp[i][0][:-4]) + '\tunmatched\t',str(temp[i][1][:-4]),'\t', round(samples[i],4),'\t',round(depth,2)
else :
out_f.write(str(temp[i][0][:-4]) + '\tunmatched\t' + str(temp[i][1][:-4]) + '\t'+ str(round(samples[i],4)) + '\t' + str(round(depth,2)) + '\n')
#print sum_file[temp[i][0]],sum_file[temp[i][1].strip()]
predStrength.append(result[0])
# AUCs.append(calAUC(mat(predStrength),classLabel))
# plotROC(mat(predStrength),classLabel)
# print AUCs
#testing sample is samples
output_matrix_f.write("sample_ID")
for key in output_matrix.keys():
output_matrix_f.write("\t" + key[0:key.index('.')])
output_matrix_f.write("\n")

for key in output_matrix.keys():
output_matrix_f.write(key[0:key.index('.')])
for otherkey in output_matrix.keys():
output_matrix_f.write("\t" + str(output_matrix[key][otherkey]))
output_matrix_f.write("\n")
trainMatrix.append(samples[j])
trainCategory.append(classLabel[j])
#training samples in temp
#p0V, p1V, pAb = trainNB0(array(trainMatrix),array(trainCategory))
p1V,p1S, p0V, p0S = trainNV(array(trainMatrix),array(trainCategory))
result = classifyNV(samples[i],p0V,p0S, p1V, p1S)
if result[1] == 1:
print str(temp[i][0]) + '\tsample is matched to\t',str(temp[i][1]),'\t', samples[i]
predStrength.append(result[0])
# AUCs.append(calAUC(mat(predStrength),classLabel))
# plotROC(mat(predStrength),classLabel)
# print AUCs
else :
for i in range(0,len(samples)):
depth = min(mean_depth[temp[i][0].strip()],mean_depth[temp[i][1].strip()])
p1V,p1S, p0V, p0S = getPredefinedModel(depth)
result = classifyNV(samples[i],p0V,p0S, p1V, p1S)
if result[1] ==1:
output_matrix[temp[i][0].strip()][temp[i][1].strip()] = samples[i]
output_matrix[temp[i][1].strip()][temp[i][0].strip()] = samples[i]
if out_tag=="stdout":
print str(temp[i][0][:-4]) + '\tmatched\t',str(temp[i][1][:-4]),'\t', round(samples[i],4),'\t',round(depth,2)
else :
out_f.write(str(temp[i][0][:-4]) + '\tmatched\t' + str(temp[i][1][:-4]) + '\t'+ str(round(samples[i],4)) + '\t' + str(round(depth,2)) + '\n')
out_matched.write(str(temp[i][0][:-4]) + '\tmatched\t' + str(temp[i][1][:-4]) + '\t'+ str(round(samples[i],4)) + '\t' + str(round(depth,2)) + '\n')
else:
if out_tag=="stdout":
print str(temp[i][0][:-4]) + '\tunmatched\t',str(temp[i][1][:-4]),'\t', round(samples[i],4),'\t',round(depth,2)
else :
out_f.write(str(temp[i][0][:-4]) + '\tunmatched\t' + str(temp[i][1][:-4]) + '\t'+ str(round(samples[i],4)) + '\t' + str(round(depth,2)) + '\n')
#print sum_file[temp[i][0]],sum_file[temp[i][1].strip()]
predStrength.append(result[0])
# AUCs.append(calAUC(mat(predStrength),classLabel))
# plotROC(mat(predStrength),classLabel)
# print AUCs
#testing sample is samples
output_matrix_f.write("sample_ID")
for key in output_matrix.keys():
output_matrix_f.write("\t" + key[0:key.index('.')])
output_matrix_f.write("\n")

for key in output_matrix.keys():
output_matrix_f.write(key[0:key.index('.')])
for otherkey in output_matrix.keys():
output_matrix_f.write("\t" + str(output_matrix[key][otherkey]))
output_matrix_f.write("\n")

output_matrix_f.close()
if out_tag!="stdout":
out_f.close()
out_matched.close()
output_matrix_f.close()
if out_tag!="stdout":
out_f.close()
out_matched.close()

def generate_R_scripts():
r_file = open(outdir + "/r_script.r","w")
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