def evaluation_joint():
train_x, train_y, test_x, test_y = load_data_for_rnn_new_add_noise(data_set=3, train_num=700, test_num=400, noise_percent=10)
brnn = FNNModel(time_step=12, feature_size=100)
begin = 0
process_rnn_label_list(train_y, time_step=brnn.time_step, begin=begin) # 原地修改label_list,统一维度
process_rnn_label_list(test_y, time_step=brnn.time_step, begin=begin)
# print(train_y)
train_x = trans_to_wordvec_by_word2vec(train_x, feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923, type='rnn', time_step=brnn.time_step, begin=begin)
test_x = trans_to_wordvec_by_word2vec(test_x, feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923, type='rnn',
time_step=brnn.time_step, begin=begin)
train_x, train_y = tf.constant(train_x, dtype=tf.float32), tf.constant(train_y, dtype=tf.float32)
test_x, test_y = tf.constant(test_x, dtype=tf.float32), tf.constant(test_y, dtype=tf.float32)
inputs, label_list = None, None
brnn.fit(train_x, train_y, batchsz=5, epochs=12)
ev = brnn.evaluate(test_x, test_y, choose=0)
# ev_b = brnn.evaluate(test_x, test_y, 1)
# ev_f = brnn.evaluate(test_x, test_y, 2)
template = 'test data precision:{}, recall:{}, f1-score:{}'
print(template.format(ev['precision'], ev['recall'], ev['f1-score']))
# print(template.format(ev_b['precision'], ev_b['recall'], ev_b['f1-score']))
# print(template.format(ev_f['precision'], ev_f['recall'], ev_f['f1-score']))
# model_path = ROOT_PATH + '\\fnn_11_30'
brnn.save_weights(JOINT_100_PATH)
def evaluation_mbrnn_load_model():
train_x, train_y, test_x, test_y = load_data_for_rnn_new_add_noise(
data_set=3,
train_num=100,
test_num=400,
noise_percent=10,
noise_type='shuffle')
brnn = GRUModel(time_step=TIME_STEP,
feature_size=WORD2VEC_FEATURE_NUM,
rnn_utils=RNN_UTILS,
rnn_layers_num=RNN_LAYERS_NUM)
brnn.load_weights(BRNN_700_PATH)
begin = 0
# process_rnn_label_list(train_y, time_step=brnn.time_step, begin=begin) # 原地修改label_list,统一维度
process_rnn_label_list(test_y, time_step=brnn.time_step, begin=begin)
# print(train_y)
# train_x = trans_to_wordvec_by_word2vec(train_x, feature_size=100,
# word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923, type='rnn', time_step=brnn.time_step, begin=begin)
test_x = trans_to_wordvec_by_word2vec(
test_x,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='rnn',
time_step=brnn.time_step,
begin=begin)
# train_x, train_y = tf.constant(train_x, dtype=tf.float32), tf.constant(train_y, dtype=tf.float32)
test_x, test_y = tf.constant(test_x, dtype=tf.float32), tf.constant(
test_y, dtype=tf.float32)
inputs, label_list = None, None
# brnn.fit(train_x, train_y, batchsz=10, epochs=15)
ev = brnn.evaluate(test_x, test_y)
template = 'test data precision:{}, recall:{}, f1-score:{}'
print(template.format(ev['precision'], ev['recall'], ev['f1-score']))
def evaluation_mbrnn():
test_x, test_y = load_data_for_rnn_new_add_noise(data_set=3,
train_num=1,
test_num=400,
load_train=False,
noise_percent=0)
brnn = DynamicWeightHybridModel(time_step=TIME_STEP,
feature_size=WORD2VEC_FEATURE_NUM,
rnn_utils=RNN_UTILS,
rnn_layers_num=RNN_LAYERS_NUM,
brnn_model_path=BRNN_700_PATH,
fnn_model_path=JOINT_100_PATH)
begin = 0
process_rnn_label_list(test_y, time_step=brnn.time_step, begin=begin)
# print(train_y)
test_x = trans_to_wordvec_by_word2vec(
test_x,
feature_size=WORD2VEC_FEATURE_NUM,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='rnn',
time_step=brnn.time_step,
begin=begin)
test_x, test_y = tf.constant(test_x, dtype=tf.float32), tf.constant(
test_y, dtype=tf.float32)
ev, weights = brnn.evaluate(test_x, test_y, return_weight=True)
template = 'test data precision:{}, recall:{}, f1-score:{}'
print(template.format(ev['precision'], ev['recall'], ev['f1-score']))
brnn.save_weights(DW_HYBRID_600_PATH)
def evaluation_mbrnn_load_model():
train_x, train_y, test_x, test_y = load_data_for_rnn_new_add_noise(
data_set=3,
train_num=1,
test_num=400,
noise_type='shuffle',
noise_percent=10)
brnn = KnowledgeDistillModel(time_step=TIME_STEP,
feature_size=WORD2VEC_FEATURE_NUM,
rnn_utils=RNN_UTILS,
rnn_layers_num=RNN_LAYERS_NUM,
fnn_model_path=FNN_700_PATH)
brnn.load_weights(KNOWLEDGE_DISTILL_MODEL_700_PATH)
begin = 0
process_rnn_label_list(test_y, time_step=brnn.time_step, begin=begin)
test_x = trans_to_wordvec_by_word2vec(
test_x,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='rnn',
time_step=brnn.time_step,
begin=begin)
test_x, test_y = tf.constant(test_x, dtype=tf.float32), tf.constant(
test_y, dtype=tf.float32)
ev = brnn.evaluate(test_x, test_y)
print('b-brnn:')
template = 'test data precision:{}, recall:{}, f1-score:{}'
print(template.format(ev['precision'], ev['recall'], ev['f1-score']))
ev = brnn.evaluate(test_x, test_y, choose=1)
print('fnn:')
print(template.format(ev['precision'], ev['recall'], ev['f1-score']))
def evaluation_hbrnn():
# from tensorflow.python.client import device_lib
# print(device_lib.list_local_devices())
# set_gpu()
train_x, train_y, test_x, test_y = load_data_for_rnn_new(data_set=3,
train_num=50,
test_num=400)
h_brnn = HBRNN(time_step=12,
feature_size=100,
rnn_utils=64,
rnn_layers_num=1,
hidden_vector_size=64,
word_num=MAX_LEN)
begin = 0
process_rnn_label_list(train_y,
time_step=h_brnn.time_step,
begin=begin) # 原地修改label_list,统一维度
process_rnn_label_list(test_y, time_step=h_brnn.time_step, begin=begin)
# print(train_y)
train_x = trans_to_wordvec_by_word2vec(
train_x,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='attention',
time_step=h_brnn.time_step,
begin=begin)
test_x = trans_to_wordvec_by_word2vec(
test_x,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='attention',
time_step=h_brnn.time_step,
begin=begin)
train_x, train_y = tf.constant(train_x, dtype=tf.float32), tf.constant(
train_y, dtype=tf.float32)
test_x, test_y = tf.constant(test_x, dtype=tf.float32), tf.constant(
test_y, dtype=tf.float32)
print(train_x.shape, train_y.shape)
print(test_x.shape, test_y.shape)
inputs, label_list = None, None
# wbrnn = WBRNNLayer(rnn_utils=32, output_vector_size=32)
# output_train, output_test = wbrnn(train_x), wbrnn(test_x)
# print(output_train.shape, output_test.shape)
h_brnn.fit(train_x, train_y, batchsz=10, epochs=15)
ev = h_brnn.evaluate(test_x, test_y)
template = 'test data precision:{}, recall:{}, f1-score:{}'
print(template.format(ev['precision'], ev['recall'], ev['f1-score']))
def evaluation_mbrnn():
train_x, train_y, test_x, test_y = load_data_for_rnn_new_add_noise(
data_set=3,
train_num=600,
test_num=400,
noise_percent=10,
noise_type='swap')
brnn = GRUModel(time_step=12,
feature_size=100,
rnn_utils=64,
rnn_layers_num=2)
begin = 0
process_rnn_label_list(train_y, time_step=brnn.time_step,
begin=begin) # 原地修改label_list,统一维度
process_rnn_label_list(test_y, time_step=brnn.time_step, begin=begin)
# print(train_y)
train_x = trans_to_wordvec_by_word2vec(
train_x,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='rnn',
time_step=brnn.time_step,
begin=begin)
test_x = trans_to_wordvec_by_word2vec(
test_x,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='rnn',
time_step=brnn.time_step,
begin=begin)
train_x, train_y = tf.constant(train_x, dtype=tf.float32), tf.constant(
train_y, dtype=tf.float32)
test_x, test_y = tf.constant(test_x, dtype=tf.float32), tf.constant(
test_y, dtype=tf.float32)
inputs, label_list = None, None
brnn.fit(train_x, train_y, batchsz=5, epochs=15)
ev = brnn.evaluate(test_x, test_y)
# ev_b = brnn.evaluate(test_x, test_y, 1)
# ev_f = brnn.evaluate(test_x, test_y, 2)
template = 'test data precision:{}, recall:{}, f1-score:{}'
print(template.format(ev['precision'], ev['recall'], ev['f1-score']))
# print(template.format(ev_b['precision'], ev_b['recall'], ev_b['f1-score']))
# print(template.format(ev_f['precision'], ev_f['recall'], ev_f['f1-score']))
# model_path = ROOT_PATH + '\\B-BRNN-IMPROVED-BY-FEATURE-INTEGRATION'
brnn.save_weights(BRNN_600_PATH)
def evaluation_load_model():
test_x, test_y = load_data_for_rnn_new_add_noise(load_train=False, data_set=3, train_num=700, test_num=400, noise_percent=10)
brnn = FNNModel(time_step=12, feature_size=100)
brnn.load_weights(JOINT_100_PATH)
begin = 0
process_rnn_label_list(test_y, time_step=brnn.time_step, begin=begin)
test_x = trans_to_wordvec_by_word2vec(test_x, feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923, type='rnn',
time_step=brnn.time_step, begin=begin)
test_x, test_y = tf.constant(test_x, dtype=tf.float32), tf.constant(test_y, dtype=tf.float32)
ev = brnn.evaluate(test_x, test_y, choose=1)
template = 'test data precision:{}, recall:{}, f1-score:{}'
print(template.format(ev['precision'], ev['recall'], ev['f1-score']))
def evaluation_mbrnn():
train_x, train_y, test_x, test_y = load_data_for_rnn_new(data_set=3,
train_num=500,
test_num=400)
brnn = GRUModel1(time_step=12,
feature_size=100,
rnn_utils=64,
rnn_layers_num=2)
begin = 0
process_rnn_label_list(train_y, time_step=brnn.time_step,
begin=begin) # 原地修改label_list,统一维度
process_rnn_label_list(test_y, time_step=brnn.time_step, begin=begin)
# print(train_y)
train_x = trans_to_wordvec_by_word2vec(
train_x,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='rnn',
time_step=brnn.time_step,
begin=begin)
test_x = trans_to_wordvec_by_word2vec(
test_x,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='rnn',
time_step=brnn.time_step,
begin=begin)
train_x, train_y = tf.constant(train_x, dtype=tf.float32), tf.constant(
train_y, dtype=tf.float32)
test_x, test_y = tf.constant(test_x, dtype=tf.float32), tf.constant(
test_y, dtype=tf.float32)
inputs, label_list = None, None
brnn.fit(train_x, train_y, batchsz=10, epochs=15)
ev = brnn.evaluate(test_x, test_y)
template = 'test data precision:{}, recall:{}, f1-score:{}'
print(template.format(ev['precision'], ev['recall'], ev['f1-score']))
model_path = ROOT_PATH + '\\wbrnn_feature_block_attention_11_24_19_30'
brnn.save_weights(model_path)
def test_model():
file_path = 'D:\\Download\\简历模板.docx'
brnn_save = FNNModel(time_step=12, feature_size=100)
brnn_save.load_weights(FNN_MODEL_PATH)
one_resume = segment_one_resume_from_file(file_path)
for module in one_resume:
print(module)
print('-------------------------------------------------------------------------')
text_list = [one_resume]
inputs = trans_to_wordvec_by_word2vec(text_list, feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923, type='rnn',
time_step=brnn_save.time_step)
print(brnn_save.predict(inputs), brnn_save.predict(inputs, number2label=number2label))
def evaluation_mbrnn_best_params():
test_xs, test_ys = [], []
for i in range(0, 11): # 异常比例从0到100%
test_x, test_y = load_data_for_rnn_new_add_noise(data_set=3,
train_num=1,
test_num=400,
noise_percent=i,
load_train=False)
test_xs.append(test_x)
test_ys.append(test_y)
begin = 0
for i in range(len(test_ys)):
process_rnn_label_list(test_ys[i],
time_step=TIME_STEP,
begin=begin)
test_xs[i] = trans_to_wordvec_by_word2vec(
test_xs[i],
feature_size=WORD2VEC_FEATURE_NUM,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='rnn',
time_step=TIME_STEP,
begin=begin)
test_xs[i], test_ys[i] = tf.constant(
test_xs[i], dtype=tf.float32), tf.constant(test_ys[i],
dtype=tf.float32)
print('-----------------------------------------------')
model = KnowledgeDistillModel(time_step=TIME_STEP,
feature_size=WORD2VEC_FEATURE_NUM,
rnn_utils=RNN_UTILS,
rnn_layers_num=RNN_LAYERS_NUM)
model.load_weights(KNOWLEDGE_DISTILL_MODEL_700_PATH)
res = 0
length = len(test_xs)
for i, test_x in enumerate(test_xs):
ev = model.evaluate(test_x, test_ys[i])
print(i * 10, '%:', ev['f1-score'])
res += ev['f1-score']
res /= length
print('brnn mean f1-score:', res)
res = 0
for i, test_x in enumerate(test_xs):
ev = model.evaluate(test_x, test_ys[i], choose=1)
print(i * 10, '%:', ev['f1-score'])
res += ev['f1-score']
res /= length
print('fnn mean f1-score:', res)
def test_model():
file_path = 'D:\\Download\\简历模板.docx'
brnn_save = DynamicWeightHybridModel(time_step=TIME_STEP,
feature_size=WORD2VEC_FEATURE_NUM,
rnn_utils=RNN_UTILS,
rnn_layers_num=RNN_LAYERS_NUM)
brnn_save.load_weights(DW_HYBRID_700_PATH)
# one_resume = segment_one_resume_from_file(file_path)
one_resume = [
"""计算机中级 英语""", """ """,
"""姓 名：孙XX 性 别：男 出生年月：1992.07
籍 贯：广东湛江 身 高：170cm 政治面貌：团员
学 历：高技/专科 专 业：室内设计
手 机：13XXXXXXXX94
电子邮箱：[email protected]
在读院校：广州市XXXXXXXXX术学院
""", """
计算机中级 英语
""", """
深圳印刷玩具兼职开机员，
一味餐厅兼职后厨；
河源精雕装饰材料店兼职；
泰康人寿职员；
""", """
在学校担任班干部-
“橘阳话剧社”社员；
加入“英语爱好者学会”成为了一名英语爱好者;
""", """
掌握WORD、EXCEL、POWERPOINT、AutoCAD、3ds max、
精通AutoCAD绘图与建模;
在大学期间，培养了我较强的组织能力和较强的责任心。课余时间一直在腾讯课堂增强专业知识，完善各个方面的能力。"""
]
for module in one_resume:
print(module)
print(
'-------------------------------------------------------------------------'
)
text_list = [one_resume]
inputs = trans_to_wordvec_by_word2vec(
text_list,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='rnn',
time_step=brnn_save.time_step)
print(brnn_save.predict(inputs),
brnn_save.predict(inputs, number2label=number2label))
def evaluation_mbrnn1():
train_x, train_y, test_x, test_y = load_data_for_rnn_new_add_noise(
data_set=1, train_num=1, test_num=400)
brnn = TeacherModel(time_step=TIME_STEP,
feature_size=WORD2VEC_FEATURE_NUM,
rnn_utils=RNN_UTILS,
rnn_layers_num=RNN_LAYERS_NUM,
brnn_model_path=BBRNN_MODEL_PATH,
fnn_model_path=FNN_MODEL_PATH)
begin = 0
process_rnn_label_list(test_y, time_step=brnn.time_step, begin=begin)
test_x = trans_to_wordvec_by_word2vec(
test_x,
feature_size=WORD2VEC_FEATURE_NUM,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='rnn',
time_step=brnn.time_step,
begin=begin)
test_x, test_y = tf.constant(test_x, dtype=tf.float32), tf.constant(
test_y, dtype=tf.float32)
inputs, label_list = None, None
brnn.fit(test_x, test_y, batchsz=10, epochs=10)
def segment_one_resume_list_format(txt_list, nn_model_w=None, word2vec_model=None):
"""
:param txt_list:DataFrame [ [txt,font1,font2,...,label,title], [txt,font1,font2,...,label,title], ...]
:param title_list :title key list
:return: [[key,txt], [key,txt],...]
"""""
length = len(txt_list)
modules = []
key_loc = 0
last_key = 'base_info'
one_module = ''
if nn_model_w is None:
nn_model_w = load_models(muti_textcnn_api_model_update2_path_zhwiki_corpus_word2vec)
# nn_model_w.build((None, 350, 100))
nn_model_w.summary()
if word2vec_model is None:
word2vec_model = gensim.models.word2vec.Word2Vec.load(word2vec_model_path_zhwiki_rnn_update_20_923)
for i in range(length):
if txt_list.iloc[i, :]['Label'] == 0:
one_module += '\n' + txt_list.iloc[i, :]['Text']
else:
text_array = trans_to_wordvec_by_word2vec([one_module], feature_size=WORD2VEC_FEATURE_NUM, type='cnn', max_len=MAX_LEN,
word2vec_model=word2vec_model) # 转为词向量
res = nn_model_w.predict(text_array)
# print(res[0])
res_list = list(res[0])
# print(res_list)
tag = number2label[res_list.index(max(res[0]))]
# print(tag)
# print(one_module[1:])
modules.append([tag, one_module[1:]])
one_module = ''
key_loc = i
last_key = txt_list.iloc[i, :]['Title']
if key_loc != length-1:
modules.append([last_key, one_module[1:]])
return modules
def test_model():
file_path = 'D:\\Download\\简历模板.docx'
model_path = ROOT_PATH + '\\wbrnn_feature_h_brnn_固定投票权重_11_24_15_05'
brnn_save = GRUModel(time_step=12,
feature_size=100,
rnn_utils=64,
rnn_layers_num=1)
brnn_save.load_weights(model_path)
one_resume = segment_one_resume_from_file(file_path)
for module in one_resume:
print(module)
print(
'-------------------------------------------------------------------------'
)
text_list = [one_resume]
inputs = trans_to_wordvec_by_word2vec(
text_list,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='rnn',
time_step=brnn_save.time_step)
print(brnn_save.predict(inputs),
brnn_save.predict(inputs, number2label=number2label))
def test_svm():
tests1 = [100, 200, 300, 400, 500, 600, 700]
tests = [5, 10, 20, 50, 60, 70, 100]
for train_num in tests:
train_x, train_y, test_x, test_y = load_data_for_single_muti_classification(
data_set=3, train_num=train_num, test_num=400)
print(len(train_x), len(train_y))
train_x = trans_to_wordvec_by_word2vec(
train_x,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='full')
test_x = trans_to_wordvec_by_word2vec(
test_x,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='full')
# print(train_y)
# train_x, train_y = np.array(train_x), np.array(train_y)
# print(np.isnan(train_x).all())
model = SVC()
# model = GradientBoostingClassifier()
model.fit(train_x, train_y)
pre = model.predict(test_x)
total = len(test_y)
trues = 0
total_p, total_p_t = 0, 0
total_r, total_r_p = 0, 0
for i in range(total):
if pre[i] == test_y[i]:
trues += 1
acc = trues / total
# print('accuracy is:', acc)
pre_c = [[], [], [], [], [], [], [], [], [], []] # 每一个类别对应一个P
recall_c = [[], [], [], [], [], [], [], [], [], []] # 每一个类别对应一个R
p_arr = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
c_arr = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
for i in range(total):
recall_c[test_y[i]].append(i)
pre_c[pre[i]].append(i)
weight_r = [len(recall_c[i]) / total for i in range(len(recall_c))]
# print(weight_r)
for i in range(len(pre_c)):
total_p = len(pre_c[i])
total_r = len(recall_c[i])
total_p_t = 0
# print('numer', str(i), 'pre and actual:', total_p, total_r)
for ele in pre_c[i]:
if test_y[ele] == i:
total_p_t += 1
if total_p != 0:
p_arr[i] = total_p_t / total_p * weight_r[i]
precision = sum(p_arr)
# print('precision is:', precision)
# print(p_arr)
for i in range(len(recall_c)):
total_r = len(recall_c[i])
total_r_p = 0
for ele in recall_c[i]:
if pre[ele] == i:
total_r_p += 1
if total_r != 0:
c_arr[i] = total_r_p / total_r * weight_r[i]
recall = sum(c_arr)
# print('recall is:', recall)
# print(c_arr)
print('')
print('')
print('')
print('sklearn-precision-score:',
precision_score(test_y, pre, average='weighted'))
print('sklearn-recall-score:',
recall_score(test_y, pre, average='weighted'))
print('sklearn-f1-score:', f1_score(test_y,
pre,
average='weighted'))
print('')
print('')
print('')
def evaluation_mbrnn_load_model():
test_x, test_y = load_data_for_rnn_new_add_noise(data_set=3,
train_num=1,
test_num=400,
noise_type='shuffle',
noise_percent=0,
load_train=False)
test_x_noise, test_y_noise = load_data_for_rnn_new_add_noise(
data_set=3,
train_num=1,
test_num=400,
noise_type='shuffle',
noise_percent=10,
load_train=False)
brnn = DynamicWeightHybridModel(time_step=TIME_STEP,
feature_size=WORD2VEC_FEATURE_NUM,
rnn_utils=RNN_UTILS,
rnn_layers_num=RNN_LAYERS_NUM)
brnn.load_weights(DW_HYBRID_600_PATH)
begin = 0
process_rnn_label_list(test_y, time_step=brnn.time_step, begin=begin)
test_x = trans_to_wordvec_by_word2vec(
test_x,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='rnn',
time_step=brnn.time_step,
begin=begin)
test_x, test_y = tf.constant(test_x, dtype=tf.float32), tf.constant(
test_y, dtype=tf.float32)
process_rnn_label_list(test_y_noise,
time_step=brnn.time_step,
begin=begin)
test_x_noise = trans_to_wordvec_by_word2vec(
test_x_noise,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='rnn',
time_step=brnn.time_step,
begin=begin)
test_x_noise, test_y_noise = tf.constant(
test_x_noise, dtype=tf.float32), tf.constant(test_y_noise,
dtype=tf.float32)
ev_noise, weight_noise = brnn.evaluate(test_x_noise,
test_y_noise,
return_weight=True)
ev, weight = brnn.evaluate(test_x, test_y, return_weight=True)
template = 'test data precision:{}, recall:{}, f1-score:{}'
print(template.format(ev['precision'], ev['recall'], ev['f1-score']))
print(
template.format(ev_noise['precision'], ev_noise['recall'],
ev_noise['f1-score']))
x = [i for i in range(0, 400)]
# colors = []
# for i in range(400):
# colors.append('r')
# for i in range(400, 800):
# colors.append('g')
# plt.scatter(x, tf.concat([weight, weight_noise], axis=-1), c=colors, s=20)
# plt.scatter(x, weight_noise, c=['g'], s=20)
plt.scatter(x,
weight_noise,
c="r",
alpha=0.5,
label="abnormal test set",
s=15,
marker='^')
# 第二个散点图，颜色为蓝色，透明度50%，图例为散点图2
plt.scatter(x,
weight,
c="g",
alpha=0.5,
label="normal test set",
s=15,
marker='*')
plt.xlabel('index of resume sample')
plt.ylabel('total weight value of each time step ')
plt.legend(loc='best')
plt.title('weight value distribution')
plt.savefig(ROOT_PATH + '\\lspd_weight_distribution.pdf')
plt.show()
# coding=utf-8
"""
@File : text_clustering.py
@Author: Xu Qiqiang
@Date : 2020/11/11 0011
"""
from load_resume_data import load_data_for_single_muti_classification
from feature_engineer import trans_to_wordvec_by_word2vec
from special_string import *
from sklearn.cluster import KMeans
from sklearn.metrics import adjusted_mutual_info_score, silhouette_score
from sklearn.decomposition import PCA
if __name__ == '__main__':
train_x, train_y, test_x, test_y = load_data_for_single_muti_classification(
data_set=1, train_num=100, test_num=10)
train_x = trans_to_wordvec_by_word2vec(
train_x,
feature_size=100,
word2vec_model=word2vec_model_path_zhwiki_rnn_update_20_923,
type='full')
# pca = PCA(n_components=10)
# train_x = pca.fit_transform(train_x)
model = KMeans(n_clusters=10)
model.fit(train_x)
res = model.predict(train_x)
# print(train_y)
# print(adjusted_mutual_info_score(train_y, res))
print(silhouette_score(train_x, res))
