python - Python 和 Node.js 的不同 WPI v3 结果

Question

我正在使用 Watson Personality Insights 从文本正文中获取结果。我从 Node.js Personality Insights 演示中获得的结果与使用 Python SDK 时获得的结果不同。

Python 脚本:

with open('input_file.txt', encoding='utf-8') as input_file:
    profile = personality_insights.profile(
        input_file.read(), content_type='text/plain;charset=utf-8',
        raw_scores=True, consumption_preferences=True)

    print(profile)

Python 输出:(仅添加宜人性分数以保持在字符限制之内)

{
        "trait_id": "big5_agreeableness",
        "name": "Agreeableness",
        "category": "personality",
        "percentile": 0.2641097108346445,
        "raw_score": 0.717124182764663,
        "children": [{
                "trait_id": "facet_altruism",
                "name": "Altruism",
                "category": "personality",
                "percentile": 0.5930367181429955,
                "raw_score": 0.7133462509414262
            },
            {
                "trait_id": "facet_cooperation",
                "name": "Cooperation",
                "category": "personality",
                "percentile": 0.49207238025136585,
                "raw_score": 0.5781918028043768
            },
            {
                "trait_id": "facet_modesty",
                "name": "Modesty",
                "category": "personality",
                "percentile": 0.7504251965616365,
                "raw_score": 0.4840369062774408
            },
            {
                "trait_id": "facet_morality",
                "name": "Uncompromising",
                "category": "personality",
                "percentile": 0.4144135962141314,
                "raw_score": 0.6156094284542545
            },
            {
                "trait_id": "facet_sympathy",
                "name": "Sympathy",
                "category": "personality",
                "percentile": 0.8204286367393345,
                "raw_score": 0.6984933017082747
            },
            {
                "trait_id": "facet_trust",
                "name": "Trust",
                "category": "personality",
                "percentile": 0.5357101531393991,
                "raw_score": 0.5894943830064112
            }
        ]
    }

Node.js 脚本:

fs.readFile('input_file.txt', 'utf-8', function (err,data) {
   var params={};
   params.text=data;
   params.content_type='text/plain; charset=utf-8';
   params.raw_scores=true;
   params.consumption_preferences=true;

   personality_insights.profile(params, function(error, response) {
    console.log(JSON.stringify(response));
   });
});

Node.js 输出:

{
                "id": "Agreeableness",
                "name": "Agreeableness",
                "category": "personality",
                "percentage": 0.2798027409516949,
                "sampling_error": 0.101059064,
                "children": [{
                    "id": "Altruism",
                    "name": "Altruism",
                    "category": "personality",
                    "percentage": 0.597937110939136,
                    "sampling_error": 0.07455418080000001
                }, {
                    "id": "Cooperation",
                    "name": "Cooperation",
                    "category": "personality",
                    "percentage": 0.46813215597029234,
                    "sampling_error": 0.0832951302
                }, {
                    "id": "Modesty",
                    "name": "Modesty",
                    "category": "personality",
                    "percentage": 0.7661123497302398,
                    "sampling_error": 0.0594182198
                }, {
                    "id": "Morality",
                    "name": "Uncompromising",
                    "category": "personality",
                    "percentage": 0.42178661415240626,
                    "sampling_error": 0.0662383546
                }, {
                    "id": "Sympathy",
                    "name": "Sympathy",
                    "category": "personality",
                    "percentage": 0.8252000440378008,
                    "sampling_error": 0.1022423736
                }, {
                    "id": "Trust",
                    "name": "Trust",
                    "category": "personality",
                    "percentage": 0.5190032062613837,
                    "sampling_error": 0.0600995984
                }]
            }

两者的输入文件是相同的:

Operations at ports in the U.S. Southeast are shut as the region copes with the changing path of one hurricane even as another is churning toward the region. Hurricane Irma was downgraded to a Category 1 storm as it pushed up through western and central Florida, the WSJ’s Arian Campo-Flores and Joseph De Avila report. That put the Port Tampa Bay in its path but left major trade gateways on the Atlantic coast, including Jacksonville, Georgia’s Port of Savannah and South Carolina Port of Charleston largely outside the storm’s strongest force. The second Category 4 storm to reach the U.S. this season lashed the Miami area with powerful winds and sheets of rain, and both Florida coasts were preparing for severe storm surges and flooding as it headed north and likely toward Georgia. With the storm following so soon after Hurricane Harvey hit the Gulf Coast and a third storm, Jose, heading north, the U.S. issued a rare waiver of the Jones Act, the federal law that prohibits foreign ships from operating in domestic sea routes, the WSJ’s Costas Paris reports. The action will allow foreign tankers to distribute fuel to hurricane-stricken areas.

从两种方法收到的值不匹配。当 content_type=text/plain 添加 charset=utf-8 属性似乎对通过 Python 代码接收的结果没有影响时，两个脚本的值是相同的。

Answer 1

如您所见，当参数设置为 text/plain 时，Watson API 建议使用参数:Accept: application/json，因为这是 response 所需的内容类型，您可以选择 CSV 或 JSON 格式。

例如:

profile(text, content_type='text/plain', content_language=None,
  accept='application/json', accept_language=None, raw_scores=False,
  consumption_preferences=False, csv_headers=False)

重要提示:在使用 Python 的示例中，您仅设置打印配置文件，而不是使用缩进来获取基本的 pretty-print 结果(如 Node 返回)。 我认为可能是您的返回问题。

所以尝试使用print(json.dumps(profile, indent=2))而不是print(profile)

我使用 Python 完成了 API 引用中的示例和 Node ，并且我得到了相同的结果。

使用 Personality Insight 的 Python 示例:

personality_insights = PersonalityInsightsV3(
  version='2016-10-20',
  username='{username}',
  password='{password}')

with open(join(dirname(__file__), './profile.json')) as profile_json:
  profile = personality_insights.profile(
    profile_json.read(), content_type='application/json',
    raw_scores=True, consumption_preferences=True)

print(json.dumps(profile, indent=2))

使用 Personality Insight 的 Node 示例:

var PersonalityInsightsV3 = require('watson-developer-cloud/personality-insights/v3');
var personality_insights = new PersonalityInsightsV3({
  username: '{username}',
  password: '{password}',
  version_date: '2016-10-20'
});

var params = {
  // Get the content items from the JSON file.
  content_items: require('./profile.json').contentItems,
  consumption_preferences: true,
  raw_scores: true,
  headers: {
    'accept-language': 'en',
    'accept': 'application/json'
  }
};

personality_insights.profile(params, function(error, response) {
  if (error)
    console.log('Error:', error);
  else
    console.log(JSON.stringify(response, null, 2));
  }
);

• 引用JSON返回 Python .
• API Reference用于使用个性洞察。