JSON : 0x7f64ee350240>处的<watson_developer_cloud.watson_service.DetailedResponse对象不可序列化
JSON : 0x7f64ee350240>处的<watson_developer_cloud.watson_service.DetailedResponse对象不可序列化
提问于 2019-05-23 02:58:59
我正在使用Ibm Watson Studio为一个机器学习项目设置一个Jupyter Notebook项目，当我试图从我的Postgresql数据库表中添加数据时，我总是得到一个TypeError不可序列化的消息。
完整的错误输出：
TypeError Traceback (most recent call last)
<ipython-input-16-e72fac39b809> in <module>()
1 classes = natural_language_classifier.classify('998520s521-nlc-1398', data_df_1.to_json())
----> 2 print(json.dumps(classes, indent=2))
/opt/conda/envs/DSX-Python35/lib/python3.5/json/__init__.py in dumps(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)
235 check_circular=check_circular, allow_nan=allow_nan, indent=indent,
236 separators=separators, default=default, sort_keys=sort_keys,
--> 237 **kw).encode(obj)
238
239
/opt/conda/envs/DSX-Python35/lib/python3.5/json/encoder.py in encode(self, o)
198 chunks = self.iterencode(o, _one_shot=True)
199 if not isinstance(chunks, (list, tuple)):
--> 200 chunks = list(chunks)
201 return ''.join(chunks)
202
/opt/conda/envs/DSX-Python35/lib/python3.5/json/encoder.py in _iterencode(o, _current_indent_level)
434 raise ValueError("Circular reference detected")
435 markers[markerid] = o
--> 436 o = _default(o)
437 yield from _iterencode(o, _current_indent_level)
438 if markers is not None:
/opt/conda/envs/DSX-Python35/lib/python3.5/json/encoder.py in default(self, o)
177
178 """
--> 179 raise TypeError(repr(o) + " is not JSON serializable")
180
181 def encode(self, o):
TypeError: <watson_developer_cloud.watson_service.DetailedResponse object at 0x7f64ee350240> is not JSON serializable这是我在Notebook中部署AI模型来分析这些数据的python代码：
from watson_developer_cloud import NaturalLanguageClassifierV1
import pandas as pd
import psycopg2
# Connecting to my database.
conn_string = 'host={} port={} dbname={} user={} password={}'.format('159.***.20.***', 5432, 'searchdb', 'lcq09', 'Mys3cr3tPass')
conn_cbedce9523454e8e9fd3fb55d4c1a52e = psycopg2.connect(conn_string)
data_df_1 = pd.read_sql('SELECT description from public."search_product"', con=conn_cbedce2drf563454e8e9fd3fb8776fgh2e)
# Connecting to the ML model.
natural_language_classifier = NaturalLanguageClassifierV1(
iam_apikey='TB97dFv8Dgug6rfi945F3***************'
)
# Apply the ML model to db datas
classes = natural_language_classifier.classify('9841d0z5a1-ncc-9076', data_df_1.to_json())
print(json.dumps(classes, indent=2))我试着运行这个：print(data_df_1.to_json())，以确保格式是Json的，并且它是正确的格式，如下所示: ps:下面的数据是随机的Lorem语句，但经过测试后将是产品描述。
{"description":{"0":"Lorem ipsum sjvh hcx bftiyf, hufcil, igfgvjuoigv gvj ifcil ,ghn fgbcggtc yfctgg h vgchbvju.","1":"Lorem ajjgvc wiufcfboitf iujcvbnb hjnkjc ivjhn oikgjvn uhnhgv 09iuvhb oiuvh boiuhb mkjhv mkiuhygv m,khbgv mkjhgv mkjhgv.","2":"Lorem aiv ibveikb jvk igvcib ok blnb v hb b hb bnjb bhb bhn bn vf vbgfc vbgv nbhgv bb nb nbh nj mjhbv mkjhbv nmjhgbv nmkn","3":"Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb vcibs j dvx","4":"Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb vcibs j dvx","5":"Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb vcibs j dvx"}}此外，我可以使用下面的代码对单个句子进行分类，但我希望对整个数据库的描述表进行分类：
classes = natural_language_classifier.classify('998260x551-nlc-1018', 'How hot will it be today?')
print(json.dumps(classes.result, indent=2))这就是为什么我用名为data_df_1的数据帧替换了这句话。
但是当我按照前面提到的方式做的时候，我有一个TypeError，
那么我应该做些什么来修复这个错误呢？
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/56263321
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