[Parallel(n_jobs=1)]: Done 12 out of 12 | elapsed: 0.2s finished

[zhangsirf]

[Parallel(n_jobs=1)]: Done 12 out of 12 | elapsed: 0.2s finished
➜ WeatherBot_NLU-master ./train_NLU.bash
Building prefix dict from the default dictionary ...
Loading model from cache /var/folders/cb/h27w8g6512q81xpr5wwxzhxr0000gn/T/jieba.cache
Loading model cost 0.669 seconds.
Prefix dict has been built succesfully.
2020-04-15 11:01:24 WARNING rasa_nlu.extractors.mitie_entity_extractor - Example skipped: Invalid entity {'start': 0, 'end': 5, 'value': '下个星期五', 'entity': 'date-time'} in example '下个星期五在南京': entities must span whole tokens. Wrong entity end.
2020-04-15 11:01:24 WARNING rasa_nlu.extractors.mitie_entity_extractor - Example skipped: Invalid entity {'start': 0, 'end': 2, 'value': '今天', 'entity': 'date-time'} in example '今天天气': entities must span whole tokens. Wrong entity end.
2020-04-15 11:01:24 WARNING rasa_nlu.extractors.mitie_entity_extractor - Example skipped: Invalid entity {'start': 0, 'end': 2, 'value': '今天', 'entity': 'date-time'} in example '今天天气很热耶': entities must span whole tokens. Wrong entity end.
2020-04-15 11:01:24 WARNING rasa_nlu.extractors.mitie_entity_extractor - Example skipped: Invalid entity {'start': 5, 'end': 7, 'value': '今天', 'entity': 'date-time'} in example '就让你问他今天天气几度': entities must span whole tokens. Wrong entity end.
Training to recognize 2 labels: 'address', 'date-time'
Part I: train segmenter
words in dictionary: 200000
num features: 271
now do training
C: 20
epsilon: 0.01
num threads: 1
cache size: 5
max iterations: 2000
loss per missed segment: 3
C: 20 loss: 3 0.941176
C: 35 loss: 3 0.941176
C: 20 loss: 4.5 0.941176
C: 5 loss: 3 0.941176
C: 20 loss: 1.5 0.92437
C: 20 loss: 3.75 0.932773
C: 21.5 loss: 3 0.941176
C: 20 loss: 2.8125 0.941176
C: 18.5 loss: 3 0.941176
C: 20 loss: 2.90625 0.941176
best C: 20
best loss: 3
num feats in chunker model: 4095
train: precision, recall, f1-score: 1 1 1
Part I: elapsed time: 8 seconds.

Part II: train segment classifier
now do training
num training samples: 119
C: 200 f-score: 0.983051
C: 400 f-score: 0.983051
C: 300 f-score: 0.983051
C: 100 f-score: 0.983051
C: 0.01 f-score: 0.983051
C: 50.005 f-score: 0.983051
C: 25.0075 f-score: 0.983051
C: 12.5088 f-score: 0.983051
C: 6.25938 f-score: 0.983051
C: 3.13469 f-score: 0.983051
C: 1.57234 f-score: 0.983051
C: 0.791172 f-score: 0.983051
C: 0.400586 f-score: 0.974576
best C: 0.791172
test on train:
55 0
0 64

overall accuracy: 1
Part II: elapsed time: 32 seconds.
df.number_of_classes(): 2
Fitting 2 folds for each of 6 candidates, totalling 12 fits
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
/Users/zhangfeng/anaconda3/envs/rasaweb/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/zhangfeng/anaconda3/envs/rasaweb/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/zhangfeng/anaconda3/envs/rasaweb/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/zhangfeng/anaconda3/envs/rasaweb/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/zhangfeng/anaconda3/envs/rasaweb/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/zhangfeng/anaconda3/envs/rasaweb/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/zhangfeng/anaconda3/envs/rasaweb/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/zhangfeng/anaconda3/envs/rasaweb/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/zhangfeng/anaconda3/envs/rasaweb/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/zhangfeng/anaconda3/envs/rasaweb/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/zhangfeng/anaconda3/envs/rasaweb/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/zhangfeng/anaconda3/envs/rasaweb/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
[Parallel(n_jobs=1)]: Done 12 out of 12 | elapsed: 0.2s finished
这个为题为啥啊

[howl-anderson]

问题是什么？

[Yefeiyang-luis]

F-score is ill-defined and being set to 0.0 in labels with no predicted samples.'precision', 'predicted', average, warn_for)这句和WARNING rasa_nlu.extractors.mitie_entity_extractor - Example skipped: Invalid entity {'start': 0, 'end': 5, 'value': '下个星期五', 'entity': 'date-time'} in example '下个星期五在南京': entities must span whole tokens. Wrong entity end.这句是什么错误，好像是没训练成功。我跟着步骤一步步做，到后面运行serve的时候，虽然能跑，但是浏览器localhost：5000 输句子，浏览器界面会报错。

[Yefeiyang-luis]

浏览器localhost:5000/parse?q=明天天气如何 ，会报这样的错，不知道是什么问题
{
"error": "Unable to initialize persistor"
}