pred = clf.predict(test)
pred_unknow = clf.predict_unknow(test)
# print pred
print "precision:", clf.metrics_precision(test_label, pred_unknow)
print "recall:", clf.metrics_recall(test_label, pred_unknow)
print "f1:", clf.metrics_f1(test_label, pred_unknow)
print "accuracy:", clf.metrics_accuracy(test_label, pred_unknow)
print "zero_one_loss:", clf.metrics_zero_one_loss(test_label, pred_unknow)
test_proba = clf.predict_max_proba(test)
print "my_zero_one_loss:", clf.metrics_my_zero_one_loss(test_proba)
print
clf.metrics_correct(test_label, pred_unknow)
plot.get_instance()
plot.plot_roc(test_label,
clf.predict_proba(test),
classes=clf.bayes.classes_.tolist(),
detail=True)
plot.show()
# 加载主客观分类数据集
# feature = CHIFeature(subjective=False)
# train_datas, class_label, _ = feature.get_key_words()
# train = train_datas
# if not sp.issparse(train_datas):
# train = feature.cal_weight(train_datas)
#
# test = Load.load_test_objective_balance()
# test_datas, test_label, _ = feature.get_key_words(test)
# test = test_datas
# # 构建适合 bayes 分类的数据集
# if not sp.issparse(train_datas):
print "origin my_zero_one_loss:", clf.metrics_my_zero_one_loss(test_proba)
print
clf.metrics_correct(test_label, pred_unknow)
# plot.plot_roc(test_label, clf.predict_proba(test), classes=clf.bayes.classes_.tolist(), text='origin')
# bayes.update(c_pred[0], test_datas[0].get("sentence"))
incr_train_datas = Load.load_incr_datas()
incr_train, incr_class_label, _ = feature.get_key_words(incr_train_datas)
# 构建适合 bayes 分类的增量集
fit_incr_train = incr_train
if not sp.issparse(incr_train):
fit_incr_train = feature.cal_weight_improve(incr_train, incr_class_label)
clf.get_incr_classificator(fit_incr_train, incr_class_label, train, class_label, method=method_options[i])
pred_unknow = clf.predict_unknow(test)
print "incr precision:", clf.metrics_precision(test_label, pred_unknow)
print "incr recall:", clf.metrics_recall(test_label, pred_unknow)
print "incr f1:", clf.metrics_f1(test_label, pred_unknow)
print "incr accuracy:", clf.metrics_accuracy(test_label, pred_unknow)
print "incr zero_one_loss:", clf.metrics_zero_one_loss(test_label, pred_unknow)
test_proba = clf.predict_max_proba(test)
print "incr my_zero_one_loss:", clf.metrics_my_zero_one_loss(test_proba)
print
clf.metrics_correct(test_label, pred_unknow)
plot.plot_roc(test_label, clf.predict_proba(test),
linestyle=linestyle[i],
classes=clf.bayes.classes_.tolist(),
text='incr ' + method_options_0[i])
plot.show()
isbalance=False, minority_target=EMOTION_CLASS.keys())
pred = clf.predict(test)
pred_unknow = clf.predict_unknow(test)
# print pred
print "precision:", clf.metrics_precision(test_label, pred_unknow)
print "recall:", clf.metrics_recall(test_label, pred_unknow)
print "f1:", clf.metrics_f1(test_label, pred_unknow)
print "accuracy:", clf.metrics_accuracy(test_label, pred_unknow)
print "zero_one_loss:", clf.metrics_zero_one_loss(test_label, pred_unknow)
test_proba = clf.predict_max_proba(test)
print "my_zero_one_loss:", clf.metrics_my_zero_one_loss(test_proba)
print
clf.metrics_correct(test_label, pred_unknow)
plot.get_instance()
plot.plot_roc(test_label, clf.predict_proba(test), classes=clf.bayes.classes_.tolist(), detail=True)
plot.show()
# 加载主客观分类数据集
# feature = CHIFeature(subjective=False)
# train_datas, class_label, _ = feature.get_key_words()
# train = train_datas
# if not sp.issparse(train_datas):
# train = feature.cal_weight(train_datas)
#
# test = Load.load_test_objective_balance()
# test_datas, test_label, _ = feature.get_key_words(test)
# test = test_datas
# # 构建适合 bayes 分类的数据集
# if not sp.issparse(train_datas):
# test = feature.cal_weight(test_datas)
