### 我的版本、环境信息

1)PaddleHub 1.8.2和PaddlePaddle版本1.8.4

2)系统环境：在Windows的AiStudio网页端跑的，python版本为3.7

复现信息：

执行的操作：

参考这里https://github.com/PaddlePaddle/PaddleHub/wiki/%E5%A6%82%E4%BD%95%E4%BF%AE%E6%94%B9Task%E5%86%85%E7%BD%AE%E6%96%B9%E6%B3%95%EF%BC%9F意图修改paddlehub中的hub.TextClassifierTask的默认的评价指标**(目前是计划先按照文档把默认指标”acc"改成“f1",进而学会怎么改成macro_f1、microf1、召回率)**，这部分代码如下：

`import numpy as np

def calculate_f1_np(preds, labels):

# 计算F1分数

# preds：预测label

# labels： 真实labels

# 返回F1分数

preds = np.array(preds)

labels = np.array(labels)

tp = np.sum((labels == 1) & (preds == 1))

tn = np.sum((labels == 0) & (preds == 0))

fp = np.sum((labels == 0) & (preds == 1))

fn = np.sum((labels == 1) & (preds == 0))

p = tp / (tp + fp) if (tp + fp) else 0

r = tp / (tp + fn) if (tp + fn) else 0

f1 = (2 * p * r) / (p + r) if p + r else 0

return f1

def calculate_metrics(self, run_states):

loss_sum = acc_sum = run_examples = 0

run_step = run_time_used = 0

all_labels = np.array([])

all_infers = np.array([])

for run_state in run_states:

run_examples += run_state.run_examples

run_step += run_state.run_step

loss_sum += np.mean(

run_state.run_results[-1]) * run_state.run_examples

acc_sum += np.mean(

run_state.run_results[2]) * run_state.run_examples

np_labels = run_state.run_results[0]

np_infers = run_state.run_results[1]

all_labels = np.hstack((all_labels, np_labels.reshape([-1])))

all_infers = np.hstack((all_infers, np_infers.reshape([-1])))

run_time_used = time.time() - run_states[0].run_time_begin

avg_loss = loss_sum / run_examples

run_speed = run_step / run_time_used

scores = OrderedDict()

f1 = calculate_f1_np(all_infers, all_labels)

scores["f1"] = f1

return scores, avg_loss, run_speed

from tb_paddle import SummaryWriter

tb_writer = SummaryWriter("PATH/TO/LOG")

def record_value(evaluation_scores, loss, s):

tb_writer.add_scalar(

tag="Loss_{}".format(self.phase),

scalar_value=loss,

global_step=self.envs['train'].current_step)

log_scores = ""

for metric in evaluation_scores:

self.tb_writer.add_scalar(

tag="{}{}".format(metric, self.phase),

scalar_value=scores[metric],

global_step=self._envs['train'].current_step)

log_scores += "%s=%.5f " % (metric, scores[metric])

print("step %d / %d: loss=%.5f %s[step/sec: %.2f]" %

(self.current_step, self.max_train_steps, avg_loss,

log_scores, run_speed))

def new_log_interval_event(self, run_states)：

print("This is the new log_interval_event!")

scores, avg_loss, run_speed = calculate_metrics(run_states)

record_value(scores, avg_loss, run_speed)

cls_task.delete_hook(hook_type="log_interval_event", name="default")

cls_task.add_hook(hook_type="log_interval_event", name="new_log_interval_event", func=new_log_interval_event)

cls_task.hook_info()`

### 报错信息如下

This is the new log_interval_event!

---------------------------------------------------------------------------TypeError Traceback (most recent call last) in

1 #run_states = cls_task.finetune_and_eval()

----> 2 cls_task.finetune_and_eval()

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlehub/finetune/task/base_task.py in finetune_and_eval(self)

943

944 def finetune_and_eval(self):

--> 945 return self.finetune(do_eval=True)

946

947 def finetune(self, do_eval=False):

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlehub/finetune/task/base_task.py in finetune(self, do_eval)

964 while self.current_epoch <= self.config.num_epoch:

965 self.config.strategy.step()

--> 966 run_states = self._run(do_eval=do_eval)

967 self.env.current_epoch += 1

968

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlehub/finetune/task/base_task.py in _run(self, do_eval)

1222 if self.is_train_phase:

1223 if self.current_step % self.config.log_interval == 0:

-> 1224 self._log_interval_event(period_run_states)

1225 global_run_states += period_run_states

1226 period_run_states = []

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddlehub/finetune/task/base_task.py in hook_function(self, *args)

708 func(*args)

709 else:

--> 710 partial(func, self)(*args)

711

712 return hook_function

in new_log_interval_event(self, run_states)

73 # 改写的事件方法，参数列表务必与PaddleHub内置的相应方法保持一致

74 print("This is the new log_interval_event!")

---> 75 scores, avg_loss, run_speed = calculate_metrics(run_states)

76 record_value(scores, avg_loss, run_speed)

77

TypeError: calculate_metrics() missing 1 required positional argument: 'run_states'