def split2(datafile, trfile, tfile, truefile):
allfeat = readfeats(datafile)
trfeat, tfeat, y_train, y_test = train_test_split(
allfeat,
getlabels(allfeat),
test_size=float(1) / 7,
stratify=getlabels(allfeat))
writingfile(trfile, trfeat)
writingfile(tfile, tfeat)
writingfile(truefile, y_test)
def split(datafile, trfile, vfile, tfile, vtruefile, truefile):
allfeat = readfeats(datafile)
afeat, tfeat, y_adapt, y_test = train_test_split(
allfeat,
getlabels(allfeat),
test_size=float(1) / 3,
stratify=getlabels(allfeat))
writingfile(tfile, tfeat)
writingfile(truefile, y_test)
trfeat, vfeat, y_train, y_validation = train_test_split(
afeat,
getlabels(afeat),
test_size=float(3) / 7,
stratify=getlabels(afeat))
writingfile(trfile, trfeat)
writingfile(vfile, vfeat)
writingfile(vtruefile, y_validation)
def CV2(ci, gamma, kernel, wi, trfile, CV_trfile, CV_tfile, CV_pfile, CV_truey):
feats = readfeats(trfile)
cv = StratifiedShuffleSplit(y=getlabels(feats), n_iter=5, test_size=0.2, random_state=0)
f1_list = []
p_list = []
r_list = []
count = 0
for train_index, test_index in cv:
count += 1
cv_trfile = CV_trfile+str(count)
cv_tfile = CV_tfile+str(count)
cv_pfile = CV_pfile+str(count)
cv_truey = CV_truey+str(count)
X_train = feats[train_index]
X_test = feats[test_index]
y_test = getlabels(X_test)
writingfile(cv_trfile, X_train)
writingfile(cv_tfile, X_test)
writingfile(cv_truey, y_test)
traincmd = ["../libSVM/svm-train", "-c", "0.001", "-t", "2", "-w1", "1", "-w0", "1", "-q", cv_trfile, cv_trfile.split('/')[-1]+'.model']
traincmd[2] = ci
traincmd[4] = kernel
traincmd[8] = wi
# traincmd[10] = gamma
subprocess.call(traincmd)
model = cv_trfile.split('/')[-1]+'.model'
predcmd = ["../libSVM/svm-predict", cv_tfile, model, cv_pfile]
p = subprocess.Popen(predcmd, stdout=subprocess.PIPE)
output, err = p.communicate()
y_test, y_predicted = feval(cv_truey, cv_pfile)
p_list.append(metrics.precision_score(y_test, y_predicted, average='binary'))
r_list.append(metrics.recall_score(y_test, y_predicted, average='binary'))
f1_list.append(metrics.f1_score(y_test, y_predicted, average='binary'))
recall = np.mean(np.asarray(r_list))
precision = np.mean(np.asarray(p_list))
f1 = np.mean(np.asarray(f1_list))
print "C=%s, gamma=%s and wi=%s, its F1 is %f"%(ci, gamma, wi, f1)
return [recall, precision, f1]
def CV(ci, wi, s1, s2, s3, trfile, CV_trfile, CV_tfile, CV_pfile, CV_truey,
emo):
feats = readfeats(trfile)
cv = StratifiedShuffleSplit(y=getlabels(feats),
n_iter=5,
test_size=0.2,
random_state=0)
f1_list = []
p_list = []
r_list = []
count = 0
for train_index, test_index in cv:
count += 1
cv_trfile = CV_trfile + str(count)
cv_tfile = CV_tfile + str(count)
cv_pfile = CV_pfile + str(count)
cv_truey = CV_truey + str(count)
X_train = feats[train_index]
X_test = feats[test_index]
y_test = getlabels(X_test)
writingfile(cv_trfile, X_train)
writingfile(cv_tfile, X_test)
writingfile(cv_truey, y_test)
adapt(str(ci), str(wi), str(s1), str(s2), str(s3), cv_trfile, cv_tfile,
cv_pfile, emo)
y_test, y_predicted = feval(cv_truey, cv_pfile)
p_list.append(
metrics.precision_score(y_test, y_predicted, average='binary'))
r_list.append(
metrics.recall_score(y_test, y_predicted, average='binary'))
f1_list.append(metrics.f1_score(y_test, y_predicted, average='binary'))
recall = np.mean(np.asarray(r_list))
precision = np.mean(np.asarray(p_list))
f1 = np.mean(np.asarray(f1_list))
print "C=%s, wi=%s and (s1=%s ; s2=%s ; s3=%s), its F1 is %f" % (
ci, wi, s1, s2, s3, f1)
return recall, precision, f1
def CV(ci,gamma,kernel,wi,trfile,tfile,CV_trfile,CV_tfile,CV_pfile,CV_truey):
trfeat = readfeats(trfile)
tfeat = readfeats(tfile)
cv = ShuffleSplit(n=len(tfeat), n_iter=5, test_size=0.2, random_state=0)
f1_list = []
p_list = []
r_list = []
count = 0
for train_index, test_index in cv:
count+=1
cv_tfile = CV_tfile+str(count)
cv_pfile = CV_pfile+str(count)
cv_truey = CV_truey+str(count)
X_train=trfeat
X_test=tfeat[test_index]
y_test = getlabels(X_test)
writingfile(cv_tfile, X_test)
writingfile(cv_truey, y_test)
# traincmd=["svm-train", "-c", "0.001", "-t", "2", "-g", "1", "-q", trfile]
traincmd=["svm-train", "-c", "0.001", "-t", "2", "-g", "1", "-w1", "1", "-w0", "1", "-q", trfile]
traincmd[2]=ci
traincmd[4]=kernel
traincmd[6]=gamma
traincmd[10]=wi
subprocess.call(traincmd)
model=trfile.split('/')[-1]+'.model'
predcmd=["svm-predict", cv_tfile, model, cv_pfile]
p = subprocess.Popen(predcmd, stdout=subprocess.PIPE)
output, err = p.communicate()
y_test, y_predicted = feval(cv_truey, cv_pfile)
p_list.append(metrics.precision_score(y_test, y_predicted, average='binary'))
r_list.append(metrics.recall_score(y_test, y_predicted, average='binary'))
f1_list.append(metrics.f1_score(y_test, y_predicted, average='binary'))
recall = np.mean(np.asarray(r_list))
precision = np.mean(np.asarray(p_list))
f1 = np.mean(np.asarray(f1_list))
print "C=%s, gamma=%s and wi=%s, its F1 is %f"%(ci,gamma,wi,f1)
return [recall, precision, f1]