c# - .NET-Core GoogleCloudMlV1PredictRequest 执行方法返回空响应

Question

在 .NET-Core C# 中，我使用 Google 的 ml API 与引擎交互。我的预测方法代码在这里

string credPath = @".\appkey.json";
var json = File.ReadAllText(credPath);
PersonalServiceAccountCred cr = JsonConvert.DeserializeObject(json);

// Create an explicit ServiceAccountCredential credential
var xCred = new ServiceAccountCredential(new 
ServiceAccountCredential.Initializer(cr.ClientEmail)
{
    Scopes = new [] {
        CloudMachineLearningEngineService.Scope.CloudPlatform
    }
}.FromPrivateKey(cr.PrivateKey));

var service = new CloudMachineLearningEngineService(new BaseClientService.Initializer {
    HttpClientInitializer = xCred
});

ProjectsResource.PredictRequest req = new ProjectsResource.PredictRequest(service, new GoogleCloudMlV1PredictRequest {
    HttpBody = new GoogleApiHttpBody {
        Data = "{\"instances\": [{\"age\": 25, \"workclass\": \" Private\", \"education\": \" 11th\", \"education_num\": 7, \"marital_status\": \" Never - married\", \"occupation\": \" Machine - op - inspct\", \"relationship\": \" Own - child\", \"race\": \" Black\", \"gender\": \" Male\", \"capital_gain\": 0, \"capital_loss\": 0, \"hours_per_week\": 40, \"native_country\": \" United - States\"}]}"
}, "projects/{project_name}/models/census/versions/v1");

GoogleApiHttpBody body = req.Execute();

但是，我在 GoogleApiHttpBody 对象上收到此响应:

有人知道这是怎么回事吗？

Answer 1

在检查了 Google API 并看到请求通过后，我认为库可能有问题。我已经在我自己的博客上发布了详细信息:.NET-Core GoogleCloudMlV1PredictRequest Execture Method Returns null Response

发生的事情是 GoogleApiHttpBody 没有序列化 :predict 端点期望它的“实例”对象。当我阅读流并看到此响应时，我发现了这一点:

{"error": "<strong>Missing \"instances\" field in request body</strong>: {\"httpBody\":{\"data\":\"{\\\"instances\\\":[{\\\"age\\\":25,\\\"workclass\\\":\\\" Private\\\",\\\"education\\\":\\\" 11th\\\",\\\"education_num\\\":7,\\\"marital_status\\\":\\\" Never - married\\\",\\\"occupation\\\":\\\" Machine - op - inspct\\\",\\\"relationship\\\":\\\" Own - child\\\",\\\"race\\\":\\\" Black\\\",\\\"gender\\\":\\\" Male\\\",\\\"capital_gain\\\":0,\\\"capital_loss\\\":0,\\\"hours_per_week\\\":40,\\\"native_country\\\":\\\" United - States\\\"}]}\"}}"}

所以，我简单地改变了我的代码如下，现在我得到了正确的预测结果

string credPath = @".\appkey.json";
var json = File.ReadAllText(credPath);
PersonalServiceAccountCred cr = JsonConvert.DeserializeObject(json);

// Create an explicit ServiceAccountCredential credential
var xCred = new ServiceAccountCredential(new ServiceAccountCredential.Initializer(cr.ClientEmail) {
    Scopes = new [] {
        CloudMachineLearningEngineService.Scope.CloudPlatform
    }
}.FromPrivateKey(cr.PrivateKey));

var service = new CloudMachineLearningEngineService(new BaseClientService.Initializer {
    HttpClientInitializer = xCred
});

ProjectsResource.PredictRequest req = new ProjectsResource.PredictRequest(service, new GoogleCloudMlV1PredictRequest(), "projects/{project-name}/models/census/versions/v1");

string requestPath = req.Service.BaseUri + 
CloudMachineLearningEngineService.Version + "/" + req.Name + ":" + req.MethodName;
Task result = service.HttpClient.PostAsync(requestPath, new StringContent("{\"instances\": [{\"age\": 25, \"workclass\": \" Private\", \"education\": \" 11th\", \"education_num\": 7, \"marital_status\": \" Never - married\", \"occupation\": \" Machine - op - inspct\", \"relationship\": \" Own - child\", \"race\": \" Black\", \"gender\": \" Male\", \"capital_gain\": 0, \"capital_loss\": 0, \"hours_per_week\": 40, \"native_country\": \" United - States\"}]}"));
Task.WaitAll(result);

HttpResponseMessage responseMessage = result.Result;

Task responseStreamTask = responseMessage.Content.ReadAsStringAsync();
Task.WaitAll(responseStreamTask);

string responseText = responseStreamTask.Result;