我已经建立了一个Jupyter Notebook，它从PostgreSQL数据库中的表中调用我的描述列，并将来自Ibm watson studio API的机器学习模型应用于此数据。
我能够正确地获得预测的响应，但是问题是我的数据都被显示并作为单个对象而不是每一行的单个对象读取。
我的目标是将模型应用于每个描述行，但是正如您在下面看到的那样，预测将应用于列本身，而不是行：
{
"collection": [
{
"top_class": "hot",
"text": "{\"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\",\"6\":\"Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb vcibs j dvx\",\"7\":\"Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb vcibs j dvx\",\"8\":\"Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb vcibs j dvx\",\"9\":\"Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb vcibs j dvx\",\"10\":\"lorem sivbnogc hbiuygv bnjiuygv bmkjygv nmjhgv.\"}}",
"classes": [
{
"confidence": 0.40859634691282776,
"class_name": "hot"
},
{
"confidence": 0.2325080584859929,
"class_name": "cold"
}
]
}
],
"classifier_id": "7818d2s519-nlc-1311",
"url": "https://gateway.watsonplatform.net/natural-language-classifier/api/v1/classifiers/7818d2s519-nlc-1311"
}
为清楚起见，预期输出应如下所示：
{
"classifier_id": "7818d2s519-nlc-1311",
"url": "https://gateway.watsonplatform.net/natural-language-classifier/api/v1/classifiers/7818d2s519-nlc-1311",
"collection": [
{
"text": "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.",
"top_class": "cold",
"classes": [
{
"confidence": 0.89084859929,
"class_name": "cold"
},
{
"confidence": 0.23250805848,
"class_name": "hot"
}
]
},
{
"text": "Lorem ajjgvc wiufcfboitf iujcvbnb hjnkjc ivjhn oikgjvn uhnhgv 09iuvhb oiuvh boiuhb mkjhv mkiuhygv m,khbgv mkjhgv mkjhgv.",
"top_class": "hot",
"classes": [
{
"confidence": 0.1084859929,
"class_name": "cold"
},
{
"confidence": 0.99250805848,
"class_name": "hot"
}
]
},
{
"text": "Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb vcibs j dvx.",
"top_class": "cold",
"classes": [
{
"confidence": 0.7084859929,
"class_name": "cold"
},
{
"confidence": 0.19250805848,
"class_name": "hot"
}
]
}
etc.....
这是我在笔记本中的python代码：
from watson_developer_cloud import NaturalLanguageClassifierV1
import pandas as pd
import psycopg2
import json
# connect to the database
conn_string = 'host={} port={} dbname={} user={} password={}'.format('119.203.10.242', 5432, 'mydb', 'locq', 'Mypass***')
conn_cbedce9523454e8e9fd3fb55d4c1a52e = psycopg2.connect(conn_string)
# select the description column
data_df_1 = pd.read_sql('SELECT description from public."search_product"', con=conn_cbedce9523454e8e9fd3fb55d4c1a52e)
# connect to the Watson Studio API
natural_language_classifier = NaturalLanguageClassifierV1(
iam_apikey='F76ugy8hv1s3sr87buhb7564vb7************'
)
# apply the model to the datas
classes = natural_language_classifier.classify_collection('7818d2s519-nlc-1311', [{'text':data_df_1.to_json()}]).get_result()
# print the result in json
print(json.dumps(classes, indent=2))
还有一个数据结构的示例：
Id description
0 Lorem ipsum sjvh hcx bftiyf, hufcil, igfgvju...
1 Lorem ajjgvc wiufcfboitf iujcvbnb hjnkjc ivjh...
2 Lorem aiv ibveikb jvk igvcib ok blnb v hb b h...
3 Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb...
4 Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb...
5 Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb...
6 Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb...
7 Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb...
8 Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb...
9 Lorem jsvc smc cbd ciecdbbc d vd bcvdvbj obcvb...
10 Lorem sivbnogc hbiuygv bnjiuygv bmkjygv nmjhgv...
有什么办法可以通过一些python代码实现这一目标吗？ 还是有一种方法可以调用列中的行而不是列本身？
已应用@Peter解决方案，数据格式正确，但现在出现此错误：
为清晰起见，完整的输出：
---------------------------------------------------------------------------
JSONDecodeError Traceback (most recent call last)
<ipython-input-114-9d8e7cf98a41> in <module>()
1 import json
2
----> 3 classes = natural_language_classifier.classify_collection('7818d2s519-nlc-1311', reshaped).get_result()
4
5 print(json.dumps(classes, indent=2))
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/watson_developer_cloud/natural_language_classifier_v1.py in classify_collection(self, classifier_id, collection, **kwargs)
152 if collection is None:
153 raise ValueError('collection must be provided')
--> 154 collection = [self._convert_model(x, ClassifyInput) for x in collection]
155
156 headers = {}
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/watson_developer_cloud/natural_language_classifier_v1.py in <listcomp>(.0)
152 if collection is None:
153 raise ValueError('collection must be provided')
--> 154 collection = [self._convert_model(x, ClassifyInput) for x in collection]
155
156 headers = {}
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/watson_developer_cloud/watson_service.py in _convert_model(val, classname)
461 if classname is not None and not hasattr(val, "_from_dict"):
462 if isinstance(val, str):
--> 463 val = json_import.loads(val)
464 val = classname._from_dict(dict(val))
465 if hasattr(val, "_to_dict"):
/opt/conda/envs/DSX-Python35/lib/python3.5/json/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
317 parse_int is None and parse_float is None and
318 parse_constant is None and object_pairs_hook is None and not kw):
--> 319 return _default_decoder.decode(s)
320 if cls is None:
321 cls = JSONDecoder
/opt/conda/envs/DSX-Python35/lib/python3.5/json/decoder.py in decode(self, s, _w)
337
338 """
--> 339 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
340 end = _w(s, end).end()
341 if end != len(s):
/opt/conda/envs/DSX-Python35/lib/python3.5/json/decoder.py in raw_decode(self, s, idx)
353 """
354 try:
--> 355 obj, end = self.scan_once(s, idx)
356 except StopIteration as err:
357 raise JSONDecodeError("Expecting value", s, err.value) from None
JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
答案 0 :(得分：1)
基于classify_collection方法（link）的Watson文档，您需要使用单个顶级密钥"collection"将短语打包到JSON对象中，该顶级密钥的值是对象列表，每个对象的键= {"text"和值=短语。
尝试这样的事情：
# package phrases into format required by Watson
reshaped = json.dumps({'collection': [{'text' : t} for t in data_df_1['description']]})
# apply the model to the datas
classes = natural_language_classifier.classify_collection('7818d2s519-nlc-1311', reshaped).get_result()