data_tes_path = '/home/chengtao/june/data/svm_pos/tes'
plot_roc.beg_plt()
best = 0.00
best_hype = ''
for sco in sorted(os.listdir(lstm_path)):
if 'info' in sco: continue
H = X_obj.SCO_obj(lstm_path+'/'+sco+'/score')
auc,fpr,tpr = H.read_report()
if auc > best:
best = auc
best_hype = sco
#plot_roc.get_plt(auc,fpr,tpr,'lstm'+sco)
H = X_obj.SCO_obj(logr_path+'/0.001/score')
auc,fpr,tpr = H.read_report()
plot_roc.get_plt(auc,fpr,tpr,'logr 0.001')
H = X_obj.SCO_obj(lstm_path+'/'+best_hype+'/score')
auc,fpr,tpr = H.read_report()
plot_roc.get_plt(auc,fpr,tpr,'lstm 16_3_0.001')
print best_hype
plot_roc.sco_plt(data_dev_path+'/targets_utt.ark',data_dev_path+'/results_hmm.pkl','hmm: 1st stage')
plot_roc.sco_plt(data_dev_path+'/targets_utt.ark',data_dev_path+'/results_svm.pkl','svm: 2nd stage')
plot_roc.sco_plt(data_dev_path+'/targets_utt.ark',data_dev_path+'/results_cld.pkl','cloud operating point',True)
plot_roc.end_plt('2015-1027-1145.png')
data_dev_path = '/home/chengtao/june/data/svm_pos/dev'
data_tes_path = '/home/chengtao/june/data/svm_pos/tes'
plot_roc.beg_plt()
best = 0.00
best_hype = ''
for sco in sorted(os.listdir(lstm_path)):
if 'info' in sco: continue
H = X_obj.SCO_obj(lstm_path+'/'+sco+'/score')
auc,fpr,tpr = H.read_report()
if auc > best:
best = auc
best_hype = sco
#plot_roc.get_plt(auc,fpr,tpr,'lstm'+sco)
H = X_obj.SCO_obj(logr_path+'/0.001/score')
auc,fpr,tpr = H.read_report()
plot_roc.get_plt(auc,fpr,tpr,'logr')
H = X_obj.SCO_obj(lstm_path+'/'+best_hype+'/score')
auc,fpr,tpr = H.read_report()
plot_roc.get_plt(auc,fpr,tpr,'lstm')
print best_hype
plot_roc.sco_plt(data_dev_path+'/targets_utt.ark',data_dev_path+'/results_svm.pkl','svm')
plot_roc.sco_plt(data_dev_path+'/targets_utt.ark',data_dev_path+'/results_hmm.pkl','hmm')
plot_roc.sco_plt(data_dev_path+'/targets_utt.ark',data_dev_path+'/results_cld.pkl','cld',True)
plot_roc.end_plt('scores.png')
data_dev_path = '/home/chengtao/june/data/svm_pos/dev'
data_tes_path = '/home/chengtao/june/data/svm_pos/tes'
plot_roc.beg_plt()
best = 0.00
best_hype = ''
for sco in sorted(os.listdir(lstm_path)):
if 'info' in sco: continue
if sys.argv[1] not in sco: continue
H = X_obj.SCO_obj(lstm_path+'/'+sco+'/score')
auc,fpr,tpr = H.read_report()
if auc > best:
best = auc
best_hype = sco
plot_roc.get_plt(auc,fpr,tpr,'lstm '+sco)
#H = X_obj.SCO_obj(logr_path+'/0.001/score')
#auc,fpr,tpr = H.read_report()
#plot_roc.get_plt(auc,fpr,tpr,'logr 0.001')
#H = X_obj.SCO_obj(lstm_path+'/'+best_hype+'/score')
#auc,fpr,tpr = H.read_report()
#plot_roc.get_plt(auc,fpr,tpr,'lstm 16_3_0.001')
print best_hype
#plot_roc.sco_plt(data_dev_path+'/targets_utt.ark',data_dev_path+'/results_hmm.pkl','hmm: 1st stage')
#plot_roc.sco_plt(data_dev_path+'/targets_utt.ark',data_dev_path+'/results_svm.pkl','svm: 2nd stage')
plot_roc.sco_plt(data_dev_path+'/targets_utt.ark',data_dev_path+'/results_cld.pkl','cloud operating point',True)
score_tra_path = "/home/chengtao/june/simple_exp/score/tra"
score_tes_path = "/home/chengtao/june/simple_exp/score/tra"
model_path = "/home/chengtao/june/simple_exp/model"
data_tra_path = "/home/chengtao/june/data/beta/tra"
data_tes_path = "/home/chengtao/june/data/beta/tes"
plot_roc.beg_plt()
best = 0.00
best_hype = ""
for sco in sorted(os.listdir(lstm_path)):
if "info" in sco:
continue
H = X_obj.SCO_obj(lstm_path + "/" + sco + "/score")
auc, fpr, tpr = H.read_report()
if auc > best:
best = auc
best_hype = sco
# plot_roc.get_plt(auc,fpr,tpr,'lstm'+sco)
H = X_obj.SCO_obj(logr_path + "/0.001/score")
auc, fpr, tpr = H.read_report()
plot_roc.get_plt(auc, fpr, tpr, "logr")
H = X_obj.SCO_obj(lstm_path + "/" + best_hype + "/score")
auc, fpr, tpr = H.read_report()
plot_roc.get_plt(auc, fpr, tpr, "single layer lstm")
plot_roc.end_plt("2015-1026-1811.png")
import X_obj import plot_roc import os import sys lstm_path = '/home/chengtao/june/hyper/lstm_negs' lstm2_path = '/home/chengtao/june/hyper/lstm' data_tra_path = '/home/chengtao/june/data/beta/tra' data_tes_path = '/home/chengtao/june/data/beta/tes' plot_roc.beg_plt() best_hype = '16_3_0.001' H = X_obj.SCO_obj(lstm_path+'/'+best_hype+'/score') auc,fpr,tpr = H.read_report() plot_roc.get_plt(auc,fpr,tpr,'lstm trained on negative, tested on positive') G = X_obj.SCO_obj(lstm_path+'/'+best_hype+'/score_dev') auc,fpr,tpr = G.read_report() plot_roc.get_plt(auc,fpr,tpr,'lstm trained on negative, tested on negative') I = X_obj.SCO_obj(lstm2_path+'/'+best_hype+'/score') auc,fpr,tpr = I.read_report() plot_roc.get_plt(auc,fpr,tpr,'lstm trained on positive, tested on positive') plot_roc.end_plt(sys.argv[0][:-3]+'.png')
