def get_classification(feature, incr=False):
"""
获得分类器
:param feature:
:param incr
:return:
"""
train_datas, class_label, _ = feature.get_key_words()
train = train_datas
# 构建适合 bayes 分类的数据集
if not sp.issparse(train_datas):
train = feature.cal_weight_improve(train_datas, class_label)
if incr:
bayes = IncrBayes()
else:
bayes = Bayes()
clf = Classification(bayes=bayes, subjective=feature.subjective)
clf.get_classificator(train, class_label)
if incr:
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
if not sp.issparse(incr_train):
incr_train = feature.cal_weight_improve(incr_train, incr_class_label)
clf.get_incr_classificator(incr_train, incr_class_label, train, class_label, method="five")
return clf
def get_classification(feature, incr=False):
"""
获得分类器
:param feature:
:param incr
:return:
"""
train_datas, class_label, _ = feature.get_key_words()
train = train_datas
# 构建适合 bayes 分类的数据集
if not sp.issparse(train_datas):
train = feature.cal_weight_improve(train_datas, class_label)
if incr:
bayes = IncrBayes()
else:
bayes = Bayes()
clf = Classification(bayes=bayes, subjective=feature.subjective)
clf.get_classificator(train, class_label)
if incr:
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
if not sp.issparse(incr_train):
incr_train = feature.cal_weight_improve(incr_train,
incr_class_label)
clf.get_incr_classificator(incr_train,
incr_class_label,
train,
class_label,
method="five")
return clf
out = os.path.join(TEXT_OUT, "best_train_test_index/test_index.txt")
if not FileUtil.isexist(out) or FileUtil.isempty(out):
clf0 = Classification()
clf0.cross_validation(train, class_label, score="recall")
test_index = np.loadtxt(out, dtype=int)
test = train[test_index]
test_label = np.asanyarray(class_label)[test_index].tolist()
method_options = ("second", "four", "five")
method_options_0 = ("B", "C", "D")
linestyle = (':', '--', '-')
plot.get_instance()
for i in range(len(method_options)):
bayes = IncrBayes()
clf = Classification(bayes=bayes)
clf.get_classificator(train, class_label, iscrossvalidate=crossvalidate,
isbalance=False, minority_target=EMOTION_CLASS.keys())
# clf.get_classificator(train, class_label, isbalance=True, minority_target=["anger", "fear", "surprise"])
if(i == 0):
pred = clf.predict(test)
pred_unknow = clf.predict_unknow(test)
print "origin precision:", clf.metrics_precision(test_label, pred_unknow)
print "origin recall:", clf.metrics_recall(test_label, pred_unknow)
print "origin f1:", clf.metrics_f1(test_label, pred_unknow)
print "origin accuracy:", clf.metrics_accuracy(test_label, pred_unknow)
print "origin zero_one_loss:", clf.metrics_zero_one_loss(test_label, pred_unknow)
test_proba = clf.predict_max_proba(test)
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')
