def test_classification(feature, incr=False):
clf = get_classification(feature, incr)
# 加载测试数据集
if feature.subjective:
test = Load.load_test_balance()
else:
test = Load.load_test_objective_balance()
test_datas, c_true, _ = feature.get_key_words(test)
test = test_datas
# 构建适合 bayes 分类的数据集
if not sp.issparse(test_datas):
test = feature.cal_weight_improve(test_datas, c_true)
c_pred_unknow = clf.predict_unknow(test)
print c_pred_unknow
print "precision:", clf.metrics_precision(c_true, c_pred_unknow)
print "recall:", clf.metrics_recall(c_true, c_pred_unknow)
print "f1:", clf.metrics_f1(c_true, c_pred_unknow)
print "origin accuracy:", clf.metrics_accuracy(c_true, c_pred_unknow)
print "zero_one_loss:", clf.metrics_zero_one_loss(c_true, c_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(c_true, c_pred_unknow)
# l = [sentence]
# fit_sentence = Feature_Hasher.transform(l).toarray()
b_matrix = sentence.toarray()
np.power(correct_row + b_matrix, 1, correct_row)
np.power(copy_feature_count, 1, out)
return copy_feature_count
if __name__ == "__main__":
# 加载情绪分类数据集
feature = CHIFeature()
train_datas, class_label, _ = feature.get_key_words()
train = train_datas
if not sp.issparse(train_datas):
train = feature.cal_weight_improve(train_datas, class_label)
test = Load.load_test_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(test_datas):
test = feature.cal_weight_improve(test_datas, test_label)
crossvalidate = False
# 若不交叉验证 记得修改 load_sample.py 中加载 train 的比例
if crossvalidate:
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]
