From 805baa528e4a88d2fc3b604bbb5ecedac904e84b Mon Sep 17 00:00:00 2001 From: Genevieve Warren <24882762+gewarren@users.noreply.github.com> Date: Mon, 24 Feb 2025 13:05:40 -0800 Subject: [PATCH] fix build suggestions and broken links --- docs/azure/includes/dotnet-all.md | 14 +++++------ docs/core/install/linux-ubuntu.md | 2 +- .../testing/mstest-analyzers/mstest0039.md | 6 ++--- ...testing-mstest-writing-tests-assertions.md | 13 ++++++++-- .../unit-testing-platform-vs-vstest.md | 4 ++-- ...testing-library-with-visual-studio-code.md | 2 +- docs/fsharp/whats-new/fsharp-9.md | 2 +- .../code-analysis/quality-rules/ca2007.md | 2 +- .../includes/aspire-telemetry-overview.md | 2 +- docs/machine-learning/resources/metrics.md | 24 ++++++++----------- .../time-series-demand-forecasting.md | 2 +- 11 files changed, 39 insertions(+), 34 deletions(-) diff --git a/docs/azure/includes/dotnet-all.md b/docs/azure/includes/dotnet-all.md index e4025b7b399db..c48a58bbb3eff 100644 --- a/docs/azure/includes/dotnet-all.md +++ b/docs/azure/includes/dotnet-all.md @@ -399,7 +399,7 @@ | Common | NuGet [2.2.1](https://www.nuget.org/packages/Microsoft.Azure.Common/2.2.1) | | | | Common - Dependencies | NuGet [1.0.0](https://www.nuget.org/packages/Microsoft.Azure.Common.Dependencies/1.0.0) | | | | Computer Vision | NuGet [7.0.1](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Vision.ComputerVision/7.0.1) | | GitHub [7.0.1](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.CognitiveServices.Vision.ComputerVision_6.0.0-preview.1/sdk/cognitiveservices/Vision.ComputerVision) | -| Cosmos DB | NuGet [3.47.0](https://www.nuget.org/packages/Microsoft.Azure.Cosmos/3.47.0)
NuGet [3.48.0-preview.0](https://www.nuget.org/packages/Microsoft.Azure.Cosmos/3.48.0-preview.0) | [docs](https://learn.microsoft.com/dotnet/api/overview/azure/cosmosdb) | GitHub [3.47.0](https://github.com/Azure/azure-cosmos-dotnet-v3/tree/3.12.0/Microsoft.Azure.Cosmos) | +| Cosmos DB | NuGet [3.47.0](https://www.nuget.org/packages/Microsoft.Azure.Cosmos/3.47.0)
NuGet [3.48.0-preview.0](https://www.nuget.org/packages/Microsoft.Azure.Cosmos/3.48.0-preview.0) | [docs](/dotnet/api/overview/azure/cosmosdb) | GitHub [3.47.0](https://github.com/Azure/azure-cosmos-dotnet-v3/tree/3.12.0/Microsoft.Azure.Cosmos) | | Custom Image Search | NuGet [2.1.0-preview.1](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Search.BingCustomImageSearch/2.1.0-preview.1) | | GitHub [2.1.0-preview.1](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.CognitiveServices.Search.BingCustomImageSearch_2.1.0-preview.1/sdk/cognitiveservices/Search.BingCustomImageSearch) | | Custom Image Search | NuGet [2.0.0](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Search.CustomImageSearch/2.0.0) | | | | Custom Search | NuGet [2.1.0-preview.1](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Search.BingCustomSearch/2.1.0-preview.1) | | GitHub [2.1.0-preview.1](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.CognitiveServices.Search.BingCustomSearch_2.1.0-preview.1/sdk/cognitiveservices/Search.BingCustomSearch) | @@ -415,15 +415,15 @@ | Entity Search | NuGet [2.0.0](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Search.EntitySearch/2.0.0) | | | | Event Hubs - Service Fabric Processor | NuGet [0.5.4](https://www.nuget.org/packages/Microsoft.Azure.EventHubs.ServiceFabricProcessor/0.5.4) | | GitHub [0.5.4](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.EventHubs.ServiceFabricProcessor_0.5.4/sdk/eventhub/Microsoft.Azure.EventHubs.ServiceFabricProcessor/) | | Face | NuGet [2.8.0-preview.3](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Vision.Face/2.8.0-preview.3) | | GitHub [2.8.0-preview.3](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.CognitiveServices.Vision.Face_2.6.0-preview.1/sdk/cognitiveservices/Vision.Face) | -| Feature Management | NuGet [3.0.0](https://www.nuget.org/packages/Microsoft.FeatureManagement/3.0.0) | [docs](https://learn.microsoft.com/dotnet/api/microsoft.featuremanagement) | | +| Feature Management | NuGet [3.0.0](https://www.nuget.org/packages/Microsoft.FeatureManagement/3.0.0) | [docs](/dotnet/api/microsoft.featuremanagement) | | | Gallery | NuGet [2.6.2-preview](https://www.nuget.org/packages/Microsoft.Azure.Gallery/2.6.2-preview) | | | | HDInsight - Job | NuGet [3.0.0-preview.3](https://www.nuget.org/packages/Microsoft.Azure.HDInsight.Job/3.0.0-preview.3) | | GitHub [3.0.0-preview.3](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.HDInsight.Job_3.0.0-preview.3/sdk/hdinsight/Microsoft.Azure.HDInsight.Job/) | | Image Search | NuGet [2.1.0-preview.1](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Search.BingImageSearch/2.1.0-preview.1) | | GitHub [2.1.0-preview.1](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.CognitiveServices.Search.BingImageSearch_2.1.0-preview.1/sdk/cognitiveservices/Search.BingImageSearch) | | Image Search | NuGet [2.0.0](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Search.ImageSearch/2.0.0) | | | | IoT Edge Function | NuGet [3.5.3](https://www.nuget.org/packages/Microsoft.Azure.IoT.Edge.Function/3.5.3) | | | | IoT Plug and Play - Devices Client | NuGet [1.42.3](https://www.nuget.org/packages/Microsoft.Azure.Devices.Client/1.42.3)
NuGet [2.0.0-preview007](https://www.nuget.org/packages/Microsoft.Azure.Devices.Client/2.0.0-preview007) | | | -| Kusto Data | NuGet [9.3.1](https://www.nuget.org/packages/Microsoft.Azure.Kusto.Data/9.3.1) | [docs](https://learn.microsoft.com/azure/data-explorer/kusto/api/netfx/about-kusto-data) | GitHub [9.3.1](https://github.com/Azure/azure-kusto-dotnet) | -| Kusto Ingest | NuGet [9.3.1](https://www.nuget.org/packages/Microsoft.Azure.Kusto.Ingest/9.3.1) | [docs](https://learn.microsoft.com/azure/data-explorer/kusto/api/netfx/about-kusto-ingest) | GitHub [9.3.1](https://github.com/Azure/azure-kusto-dotnet) | +| Kusto Data | NuGet [9.3.1](https://www.nuget.org/packages/Microsoft.Azure.Kusto.Data/9.3.1) | [docs](/azure/data-explorer/kusto/api/netfx/about-kusto-data) | GitHub [9.3.1](https://github.com/Azure/azure-kusto-dotnet) | +| Kusto Ingest | NuGet [9.3.1](https://www.nuget.org/packages/Microsoft.Azure.Kusto.Ingest/9.3.1) | [docs](/azure/data-explorer/kusto/api/netfx/about-kusto-ingest) | GitHub [9.3.1](https://github.com/Azure/azure-kusto-dotnet) | | Local Search | NuGet [1.0.0-preview.1](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Search.BingLocalSearch/1.0.0-preview.1) | | GitHub [1.0.0-preview.1](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.CognitiveServices.Search.BingLocalSearch_1.0.0-preview.1/sdk/cognitiveservices/Search.BingLocalSearch) | | Local Search | NuGet [0.9.0-preview](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Search.LocalSearch/0.9.0-preview) | | | | LUIS Authoring | NuGet [3.1.0](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Language.LUIS.Authoring/3.1.0)
NuGet [3.2.0-preview.5](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Language.LUIS.Authoring/3.2.0-preview.5) | | GitHub [3.1.0](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.CognitiveServices.Language.LUIS.Authoring_3.2.0-preview.3/sdk/cognitiveservices/Language.LUIS.Authoring) | @@ -451,7 +451,7 @@ | News Search | NuGet [2.0.0](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Search.NewsSearch/2.0.0) | | | | Notification Hubs | NuGet [4.2.0](https://www.nuget.org/packages/Microsoft.Azure.NotificationHubs/4.2.0) | | GitHub [4.2.0](https://github.com/Azure/azure-notificationhubs-dotnet) | | Personalizer | NuGet [1.0.0](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Personalizer/1.0.0) | | GitHub [1.0.0](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.CognitiveServices.Personalizer_1.0.0/sdk/cognitiveservices/Personalizer) | -| Relay | NuGet [3.0.1](https://www.nuget.org/packages/Microsoft.Azure.Relay/3.0.1) | [docs](https://learn.microsoft.com/dotnet/api/overview/azure/service-bus-relay) | GitHub [3.0.1](https://github.com/Azure/azure-relay-dotnet/tree/2.0.1) | +| Relay | NuGet [3.0.1](https://www.nuget.org/packages/Microsoft.Azure.Relay/3.0.1) | [docs](/dotnet/api/overview/azure/service-bus-relay) | GitHub [3.0.1](https://github.com/Azure/azure-relay-dotnet/tree/2.0.1) | | Schema Registry - Avro | NuGet [1.0.0-beta.1](https://www.nuget.org/packages/Microsoft.Azure.Kafka.SchemaRegistry.Avro/1.0.0-beta.1) | | | | Schema Registry - JSON | NuGet [1.0.0-beta.1](https://www.nuget.org/packages/Microsoft.Azure.Kafka.SchemaRegistry.Json/1.0.0-beta.1) | | | | Search - Common | NuGet [10.1.0](https://www.nuget.org/packages/Microsoft.Azure.Search.Common/10.1.0) | | GitHub [10.1.0](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.Search.Common_10.1.0/sdk/search/Microsoft.Azure.Search.Common/) | @@ -462,7 +462,7 @@ | Speech Xamarin iOS | NuGet [1.25.0](https://www.nuget.org/packages/Microsoft.CognitiveServices.Speech.Xamarin.iOS/1.25.0) | | | | Spell Check | NuGet [4.1.0-preview.1](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Language.SpellCheck/4.1.0-preview.1) | | GitHub [4.1.0-preview.1](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.CognitiveServices.Language.SpellCheck_4.1.0-preview.1/sdk/cognitiveservices/Language.SpellCheck) | | Spring Cloud Client | NuGet [2.0.0-preview.3](https://www.nuget.org/packages/Microsoft.Azure.SpringCloud.Client/2.0.0-preview.3) | | | -| Storage - Files Data Lake | NuGet [2.0.2](https://www.nuget.org/packages/Microsoft.Azure.DataLake.Store/2.0.2) | [docs](https://learn.microsoft.com/dotnet/api/overview/azure/data-lake-store) | GitHub [2.0.2](https://github.com/Azure/azure-data-lake-store-net/tree/1.2.3-alpha) | +| Storage - Files Data Lake | NuGet [2.0.2](https://www.nuget.org/packages/Microsoft.Azure.DataLake.Store/2.0.2) | [docs](/dotnet/api/overview/azure/data-lake-store) | GitHub [2.0.2](https://github.com/Azure/azure-data-lake-store-net/tree/1.2.3-alpha) | | Synapse Analytics | NuGet [0.1.0-preview](https://www.nuget.org/packages/Microsoft.Azure.Synapse/0.1.0-preview) | | GitHub [0.1.0-preview](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.Synapse_0.1.0-preview/sdk/synapse/Microsoft.Azure.Synapse/) | | Tables | NuGet [2.1.2](https://www.nuget.org/packages/Microsoft.Azure.CosmosDB.Table/2.1.2) | | | | Video Search | NuGet [2.1.0-preview.1](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Search.BingVideoSearch/2.1.0-preview.1) | | GitHub [2.1.0-preview.1](https://github.com/Azure/azure-sdk-for-net/tree/Microsoft.Azure.CognitiveServices.Search.BingVideoSearch_2.1.0-preview.1/sdk/cognitiveservices/Search.BingVideoSearch) | @@ -487,7 +487,7 @@ | Functions extension for Azure SQL and SQL Server | NuGet [3.1.376](https://www.nuget.org/packages/Microsoft.Azure.WebJobs.Extensions.Sql/3.1.376) | | | | Functions extension for Cosmos DB | NuGet [4.9.0](https://www.nuget.org/packages/Microsoft.Azure.WebJobs.Extensions.CosmosDB/4.9.0) | | GitHub [4.9.0](https://github.com/Azure/azure-webjobs-sdk-extensions/tree/cosmos-v3.0.7/src/WebJobs.Extensions.CosmosDB) | | Functions extension for DocumentDB | NuGet [1.3.0](https://www.nuget.org/packages/Microsoft.Azure.WebJobs.Extensions.DocumentDB/1.3.0) | | GitHub [1.3.0](https://github.com/Azure/azure-webjobs-sdk-extensions) | -| Functions extension for Durable Task Framework | NuGet [3.0.3](https://www.nuget.org/packages/Microsoft.Azure.WebJobs.Extensions.DurableTask/3.0.3) | [docs](https://learn.microsoft.com/dotnet/api/overview/azure/functions) | GitHub [3.0.3](https://github.com/Azure/azure-functions-durable-extension/tree/v2.2.2/src/WebJobs.Extensions.DurableTask) | +| Functions extension for Durable Task Framework | NuGet [3.0.3](https://www.nuget.org/packages/Microsoft.Azure.WebJobs.Extensions.DurableTask/3.0.3) | [docs](/dotnet/api/overview/azure/functions) | GitHub [3.0.3](https://github.com/Azure/azure-functions-durable-extension/tree/v2.2.2/src/WebJobs.Extensions.DurableTask) | | Functions extension for Durable Task Framework - isolated worker | NuGet [1.2.2](https://www.nuget.org/packages/Microsoft.Azure.Functions.Worker.Extensions.DurableTask/1.2.2) | | | | Functions extension for HTTP | NuGet [3.2.0](https://www.nuget.org/packages/Microsoft.Azure.WebJobs.Extensions.Http/3.2.0) | | GitHub [3.2.0](https://github.com/Azure/azure-webjobs-sdk-extensions/tree/v3.0.2/src/WebJobs.Extensions.Http) | | Functions extension for IoT Edge | NuGet [1.0.7](https://www.nuget.org/packages/Microsoft.Azure.WebJobs.Extensions.EdgeHub/1.0.7) | | GitHub [1.0.7](https://github.com/Azure/iotedge/tree/1.0.7/edge-hub) | diff --git a/docs/core/install/linux-ubuntu.md b/docs/core/install/linux-ubuntu.md index d4881c3a746d9..6c76657c26f27 100644 --- a/docs/core/install/linux-ubuntu.md +++ b/docs/core/install/linux-ubuntu.md @@ -139,7 +139,7 @@ Canonical supports .NET versions in the built-in Ubuntu feed for the lifetime of ## Ubuntu .NET backports package repository -The Ubuntu .NET backports package repository provides versions of .NET, which are not available in the built-in Ubuntu feed. The [Supported distributions](#supported-distributions) section provides a table that lists which versions of .NET are available in the package feed. Canonical maintains the packages contained in this package repository and provides best-effort support, which does not extend beyond the Microsoft-provided support lifetime or the support period of the particular Ubuntu version. +The Ubuntu .NET backports package repository provides versions of .NET that aren't available in the built-in Ubuntu feed. The [Supported distributions](#supported-distributions) section provides a table that lists which versions of .NET are available in the package feed. Canonical maintains the packages contained in this package repository and provides best-effort support, which does not extend beyond the Microsoft-provided support lifetime or the support period of the particular Ubuntu version. See the [web-view of the Ubuntu .NET backports package repository](https://launchpad.net/~dotnet/+archive/ubuntu/backports) for more details. diff --git a/docs/core/testing/mstest-analyzers/mstest0039.md b/docs/core/testing/mstest-analyzers/mstest0039.md index e6f63c5d5762f..e44d4c476ef8d 100644 --- a/docs/core/testing/mstest-analyzers/mstest0039.md +++ b/docs/core/testing/mstest-analyzers/mstest0039.md @@ -26,15 +26,15 @@ ms.author: ygerges ## Cause -The use of or , which are no longer recommended. +The use of `Assert.ThrowsException` or `Assert.ThrowsExceptionAsync`, which are no longer recommended. ## Rule description - and are not recommended and might be deprecated in the future. +The `Assert.ThrowsException` and `Assert.ThrowsExceptionAsync` methods aren't recommended and might be deprecated in the future. ## How to fix violations -Use `Assert.ThrowsExactly` or `Assert.ThrowsExactlyAsync` instead of `Assert.ThrowsException` or `Assert.ThrowsExceptionAsync`. +Use or instead of `Assert.ThrowsException` or `Assert.ThrowsExceptionAsync`. ## When to suppress warnings diff --git a/docs/core/testing/unit-testing-mstest-writing-tests-assertions.md b/docs/core/testing/unit-testing-mstest-writing-tests-assertions.md index 8e3ebf3785c0d..93b1ddc16b772 100644 --- a/docs/core/testing/unit-testing-mstest-writing-tests-assertions.md +++ b/docs/core/testing/unit-testing-mstest-writing-tests-assertions.md @@ -18,16 +18,25 @@ Use the class to veri - - - +- +- +- +- - +- - +- - - - - - - -- -- +- +- +- +- +- ## The `StringAssert` class diff --git a/docs/core/testing/unit-testing-platform-vs-vstest.md b/docs/core/testing/unit-testing-platform-vs-vstest.md index 7706299be9577..a4411de942ddc 100644 --- a/docs/core/testing/unit-testing-platform-vs-vstest.md +++ b/docs/core/testing/unit-testing-platform-vs-vstest.md @@ -123,8 +123,8 @@ When using Azure DevOps tasks, you might need to update your pipeline to use the #### VSTest task -If you are using the [VSTest task](https://learn.microsoft.com/azure/devops/pipelines/tasks/reference/vstest-v3) in Azure DevOps, you can replace it with the [.NET Core task](https://learn.microsoft.com/azure/devops/pipelines/tasks/reference/dotnet-core-cli-v2). +If you're using the [VSTest task](/azure/devops/pipelines/tasks/reference/vstest-v3) in Azure DevOps, you can replace it with the [.NET Core task](/azure/devops/pipelines/tasks/reference/dotnet-core-cli-v2). #### .NET Core task -If you are using the [.NET Core task](https://learn.microsoft.com/azure/devops/pipelines/tasks/reference/dotnet-core-cli-v2), no changes are needed. +If you're using the [.NET Core task](/azure/devops/pipelines/tasks/reference/dotnet-core-cli-v2), no changes are needed. diff --git a/docs/core/tutorials/testing-library-with-visual-studio-code.md b/docs/core/tutorials/testing-library-with-visual-studio-code.md index b919c28623230..c214581142e63 100644 --- a/docs/core/tutorials/testing-library-with-visual-studio-code.md +++ b/docs/core/tutorials/testing-library-with-visual-studio-code.md @@ -67,7 +67,7 @@ The most common tests call members of the (or `Assert.Throws` and `Assert.ThrowsExactly` if using MSTest 3.8 and later) method in a test method to indicate the type of exception it's expected to throw. The test fails if the specified exception isn't thrown. +You can also use the method in a test method to indicate the type of exception it's expected to throw. The test fails if the specified exception isn't thrown. In testing the `StringLibrary.StartsWithUpper` method, you want to provide a number of strings that begin with an uppercase character. You expect the method to return `true` in these cases, so you can call the method. Similarly, you want to provide a number of strings that begin with something other than an uppercase character. You expect the method to return `false` in these cases, so you can call the method. diff --git a/docs/fsharp/whats-new/fsharp-9.md b/docs/fsharp/whats-new/fsharp-9.md index d2a0959259c52..061bf597b6550 100644 --- a/docs/fsharp/whats-new/fsharp-9.md +++ b/docs/fsharp/whats-new/fsharp-9.md @@ -304,7 +304,7 @@ let foods = [ "Apple"; "Banana"; "Carrot"; "Donut"; "Egg" ] let today'sMenu = foods |> List.randomSample 3 // [ "Donut"; "Apple"; "Egg" ] ``` -For a full list of functions and their variants, see ([RFC #1135](https://github.com/fsharp/fslang-design/blob/main/RFCs/FS-1135-random-functions-for-collections.md)). +For a full list of functions and their variants, see ([RFC #1135](https://github.com/fsharp/fslang-design/blob/main/FSharp.Core-9.0/FS-1135-random-functions-for-collections.md)). ### Parameterless constructor for `CustomOperationAttribute` diff --git a/docs/fundamentals/code-analysis/quality-rules/ca2007.md b/docs/fundamentals/code-analysis/quality-rules/ca2007.md index bde3828dcf796..f4dd1d7e58dd2 100644 --- a/docs/fundamentals/code-analysis/quality-rules/ca2007.md +++ b/docs/fundamentals/code-analysis/quality-rules/ca2007.md @@ -117,6 +117,6 @@ dotnet_code_quality.CA2007.output_kind = ConsoleApplication, DynamicallyLinkedLi ## See also - [ConfigureAwait FAQ](https://devblogs.microsoft.com/dotnet/configureawait-faq/) -- [Should I await a task with ConfigureAwait(false)?](https://github.com/Microsoft/vs-threading/blob/main/doc/cookbook_vs.md#should-i-await-a-task-with-configureawaitfalse) +- [Should I await a task with ConfigureAwait(false)?](https://github.com/microsoft/vs-threading/blob/main/docfx/docs/cookbook_vs.md#should-i-await-a-task-with-configureawaitfalse) - [CA2008: Do not create tasks without passing a TaskScheduler](ca2008.md) - [Reliability rules](reliability-warnings.md) diff --git a/docs/fundamentals/networking/telemetry/includes/aspire-telemetry-overview.md b/docs/fundamentals/networking/telemetry/includes/aspire-telemetry-overview.md index 4ee8511a97430..85af5fd92a2e1 100644 --- a/docs/fundamentals/networking/telemetry/includes/aspire-telemetry-overview.md +++ b/docs/fundamentals/networking/telemetry/includes/aspire-telemetry-overview.md @@ -2,6 +2,6 @@ A simple way to collect traces and metrics in ASP.NET applications is to use [.N The default project templates for .NET Aspire contain a `ServiceDefaults` project. Each service in the .NET Aspire solution has a reference to the Service Defaults project. The services use it to set up and configure OTel. -The Service Defaults project template includes the OTel SDK, ASP.NET, HttpClient, and Runtime Instrumentation packages. These instrumentation components are configured in the [Extensions.cs](https://github.com/dotnet/aspire/blob/main/src/Aspire.ProjectTemplates/templates/aspire-servicedefaults/Extensions.cs) file. To support telemetry visualization in Aspire Dashboard, the Service Defaults project also includes the OTLP exporter by default. +The Service Defaults project template includes the OTel SDK, ASP.NET, HttpClient, and Runtime Instrumentation packages. These instrumentation components are configured in the [Extensions.cs](https://github.com/dotnet/aspire/blob/main/src/Aspire.ProjectTemplates/templates/aspire-servicedefaults/9.1/Extensions.cs) file. To support telemetry visualization in Aspire Dashboard, the Service Defaults project also includes the OTLP exporter by default. Aspire Dashboard is designed to bring telemetry observation to the local debug cycle, which enables developers to ensure that the applications are producing telemetry. The telemetry visualization also helps to diagnose those applications locally. Being able to observe the calls between services is as useful at debug time as in production. The .NET Aspire dashboard is launched automatically when you F5 the `AppHost` Project from Visual Studio or `dotnet run` the `AppHost` project from command line. diff --git a/docs/machine-learning/resources/metrics.md b/docs/machine-learning/resources/metrics.md index 2a143fc957ee8..0b09383f7ccb3 100644 --- a/docs/machine-learning/resources/metrics.md +++ b/docs/machine-learning/resources/metrics.md @@ -11,18 +11,17 @@ Evaluation metrics are specific to the type of machine learning task that a mode For example, for the classification task, the model is evaluated by measuring how well a predicted category matches the actual category. And for clustering, evaluation is based on how close clustered items are to each other, and how much separation there is between the clusters. -## Evaluation metrics for Binary Classification +## Evaluation metrics for binary classification | Metrics | Description | Look for | |-----------|-----------------------|-----------| -| **Accuracy** | [Accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification) is the proportion of correct predictions with a test data set. It is the ratio of number of correct predictions to the total number of input samples. It works well if there are similar number of samples belonging to each class.| **The closer to 1.00, the better**. But exactly 1.00 indicates an issue (commonly: label/target leakage, over-fitting, or testing with training data). When the test data is unbalanced (where most of the instances belong to one of the classes), the dataset is small, or scores approach 0.00 or 1.00, then accuracy doesn't really capture the effectiveness of a classifier and you need to check additional metrics. | -| **AUC** | [aucROC](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) or *Area under the curve* measures the area under the curve created by sweeping the true positive rate vs. the false positive rate. | **The closer to 1.00, the better**. It should be greater than 0.50 for a model to be acceptable. A model with AUC of 0.50 or less is worthless. | +| **Accuracy** | [Accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification) is the proportion of correct predictions with a test data set. It is the ratio of number of correct predictions to the total number of input samples. It works well if there are similar number of samples belonging to each class.| **The closer to 1.00, the better**. But exactly 1.00 indicates an issue (commonly: label/target leakage, over-fitting, or testing with training data). When the test data is unbalanced (where most of the instances belong to one of the classes), the dataset is small, or scores approach 0.00 or 1.00, then accuracy doesn't really capture the effectiveness of a classifier and you need to check additional metrics. | +| **AUC** | [aucROC](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) or *Area under the curve* measures the area under the curve created by sweeping the true positive rate vs. the false positive rate. | **The closer to 1.00, the better**. It should be greater than 0.50 for a model to be acceptable. A model with AUC of 0.50 or less is worthless. | | **AUCPR** | aucPR or *Area under the curve of a Precision-Recall curve*: Useful measure of success of prediction when the classes are imbalanced (highly skewed datasets). | **The closer to 1.00, the better**. High scores close to 1.00 show that the classifier is returning accurate results (high precision), and returning a majority of all positive results (high recall). | | **F1-score** | [F1 score](https://en.wikipedia.org/wiki/F1_score) also known as *balanced F-score or F-measure*. It's the harmonic mean of the precision and recall. F1 Score is helpful when you want to seek a balance between Precision and Recall.| **The closer to 1.00, the better**. An F1 score reaches its best value at 1.00 and worst score at 0.00. It tells you how precise your classifier is. | For further details on binary classification metrics read the following articles: -- [Accuracy, Precision, Recall, or F1?](https://towardsdatascience.com/accuracy-precision-recall-or-f1-331fb37c5cb9) - [Binary Classification Metrics class](xref:Microsoft.ML.Data.BinaryClassificationMetrics) - [The Relationship Between Precision-Recall and ROC Curves](http://pages.cs.wisc.edu/~jdavis/davisgoadrichcamera2.pdf) @@ -30,8 +29,8 @@ For further details on binary classification metrics read the following articles | Metrics | Description | Look for | |-----------|-----------------------|-----------| -| **Micro-Accuracy** | [Micro-average Accuracy](xref:Microsoft.ML.Data.MulticlassClassificationMetrics.MicroAccuracy) aggregates the contributions of all classes to compute the average metric. It is the fraction of instances predicted correctly. The micro-average does not take class membership into account. Basically, every sample-class pair contributes equally to the accuracy metric. | **The closer to 1.00, the better**. In a multi-class classification task, micro-accuracy is preferable over macro-accuracy if you suspect there might be class imbalance (i.e you may have many more examples of one class than of other classes).| -| **Macro-Accuracy** | [Macro-average Accuracy](xref:Microsoft.ML.Data.MulticlassClassificationMetrics.MacroAccuracy) is the average accuracy at the class level. The accuracy for each class is computed and the macro-accuracy is the average of these accuracies. Basically, every class contributes equally to the accuracy metric. Minority classes are given equal weight as the larger classes. The macro-average metric gives the same weight to each class, no matter how many instances from that class the dataset contains. | **The closer to 1.00, the better**. It computes the metric independently for each class and then takes the average (hence treating all classes equally) | +| **Micro-Accuracy** | [Micro-average Accuracy](xref:Microsoft.ML.Data.MulticlassClassificationMetrics.MicroAccuracy) aggregates the contributions of all classes to compute the average metric. It is the fraction of instances predicted correctly. The micro-average does not take class membership into account. Basically, every sample-class pair contributes equally to the accuracy metric. | **The closer to 1.00, the better**. In a multi-class classification task, micro-accuracy is preferable over macro-accuracy if you suspect there might be class imbalance (i.e you may have many more examples of one class than of other classes).| +| **Macro-Accuracy** | [Macro-average Accuracy](xref:Microsoft.ML.Data.MulticlassClassificationMetrics.MacroAccuracy) is the average accuracy at the class level. The accuracy for each class is computed and the macro-accuracy is the average of these accuracies. Basically, every class contributes equally to the accuracy metric. Minority classes are given equal weight as the larger classes. The macro-average metric gives the same weight to each class, no matter how many instances from that class the dataset contains. | **The closer to 1.00, the better**. It computes the metric independently for each class and then takes the average (hence treating all classes equally) | | **Log-loss**| Logarithmic loss measures the performance of a classification model where the prediction input is a probability value between 0.00 and 1.00. Log-loss increases as the predicted probability diverges from the actual label. | **The closer to 0.00, the better**. A perfect model would have a log-loss of 0.00. The goal of the machine learning models is to minimize this value.| | **Log-Loss Reduction** | [Logarithmic loss reduction](xref:Microsoft.ML.Data.MulticlassClassificationMetrics.LogLossReduction) can be interpreted as the advantage of the classifier over a random prediction.| **Ranges from -inf and 1.00, where 1.00 is perfect predictions and 0.00 indicates mean predictions**. For example, if the value equals 0.20, it can be interpreted as "the probability of a correct prediction is 20% better than random guessing"| @@ -44,10 +43,7 @@ Example, for a support ticket classification task: (maps incoming tickets to sup Macro-accuracy overweights small teams in this example; a small team that gets only 10 tickets per year counts as much as a large team with 10k tickets per year. Micro-accuracy in this case correlates better with the business need of, "how much time/money can the company save by automating my ticket routing process". -For further details on multi-class classification metrics read the following articles: - -- [Micro- and Macro-average of Precision, Recall, and F-Score](https://rushdishams.blogspot.com/2011/08/micro-and-macro-average-of-precision.html) -- [Multiclass Classification with Imbalanced Dataset](https://towardsdatascience.com/machine-learning-multiclass-classification-with-imbalanced-data-set-29f6a177c1a) +For further details on multi-class classification metrics, see [Micro-average and Macro-average of Precision, Recall, and F-Score](https://rushdishams.blogspot.com/2011/08/micro-and-macro-average-of-precision.html). ## Evaluation metrics for Regression and Recommendation @@ -55,10 +51,10 @@ Both the regression and recommendation tasks predict a number. In the case of re | Metric | Description | Look for | |----------|-----------------------|-----------| -| **R-Squared** | [R-squared (R2)](https://en.wikipedia.org/wiki/Coefficient_of_determination), or *Coefficient of determination* represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative. A score of 0.00 means the model is guessing the expected value for the label. A negative R2 value indicates the fit does not follow the trend of the data and the model performs worse than random guessing. This is only possible with non-linear regression models or constrained linear regression. R2 measures how close the actual test data values are to the predicted values. | **The closer to 1.00, the better quality**. However, sometimes low R-squared values (such as 0.50) can be entirely normal or good enough for your scenario and high R-squared values are not always good and be suspicious. | -| **Absolute-loss** | [Absolute-loss](https://en.wikipedia.org/wiki/Mean_absolute_error) or *Mean absolute error (MAE)* measures how close the predictions are to the actual outcomes. It is the average of all the model errors, where model error is the absolute distance between the predicted label value and the correct label value. This prediction error is calculated for each record of the test data set. Finally, the mean value is calculated for all recorded absolute errors.| **The closer to 0.00, the better quality.** The mean absolute error uses the same scale as the data being measured (is not normalized to specific range). Absolute-loss, Squared-loss, and RMS-loss can only be used to make comparisons between models for the same dataset or dataset with a similar label value distribution. | -| **Squared-loss** | [Squared-loss](https://en.wikipedia.org/wiki/Mean_squared_error) or *Mean Squared Error (MSE)*, also called *Mean Squared Deviation (MSD)*, tells you how close a regression line is to a set of test data values by taking the distances from the points to the regression line (these distances are the errors E) and squaring them. The squaring gives more weight to larger differences. | It is always non-negative, and **values closer to 0.00 are better**. Depending on your data, it might be impossible to get a very small value for the mean squared error.| -| **RMS-loss** | [RMS-loss](https://en.wikipedia.org/wiki/Root-mean-square_deviation) or *Root Mean Squared Error (RMSE)* (also called *Root Mean Square Deviation, RMSD*), measures the difference between values predicted by a model and the values observed from the environment that is being modeled. RMS-loss is the square root of Squared-loss and has the same units as the label, similar to the absolute-loss though giving more weight to larger differences. Root mean square error is commonly used in climatology, forecasting, and regression analysis to verify experimental results. | It is always non-negative, and **values closer to 0.00 are better**. RMSD is a measure of accuracy, to compare forecasting errors of different models for a particular dataset and not between datasets, as it is scale-dependent.| +| **R-Squared** | [R-squared (R2)](https://en.wikipedia.org/wiki/Coefficient_of_determination), or *Coefficient of determination* represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative. A score of 0.00 means the model is guessing the expected value for the label. A negative R2 value indicates the fit does not follow the trend of the data and the model performs worse than random guessing. This is only possible with non-linear regression models or constrained linear regression. R2 measures how close the actual test data values are to the predicted values. | **The closer to 1.00, the better quality**. However, sometimes low R-squared values (such as 0.50) can be entirely normal or good enough for your scenario and high R-squared values are not always good and be suspicious. | +| **Absolute-loss** | [Absolute-loss](https://en.wikipedia.org/wiki/Mean_absolute_error) or *Mean absolute error (MAE)* measures how close the predictions are to the actual outcomes. It is the average of all the model errors, where model error is the absolute distance between the predicted label value and the correct label value. This prediction error is calculated for each record of the test data set. Finally, the mean value is calculated for all recorded absolute errors.| **The closer to 0.00, the better quality.** The mean absolute error uses the same scale as the data being measured (is not normalized to specific range). Absolute-loss, Squared-loss, and RMS-loss can only be used to make comparisons between models for the same dataset or dataset with a similar label value distribution. | +| **Squared-loss** | [Squared-loss](https://en.wikipedia.org/wiki/Mean_squared_error) or *Mean Squared Error (MSE)*, also called *Mean Squared Deviation (MSD)*, tells you how close a regression line is to a set of test data values by taking the distances from the points to the regression line (these distances are the errors E) and squaring them. The squaring gives more weight to larger differences. | It is always non-negative, and **values closer to 0.00 are better**. Depending on your data, it might be impossible to get a very small value for the mean squared error.| +| **RMS-loss** | [RMS-loss](https://en.wikipedia.org/wiki/Root-mean-square_deviation) or *Root Mean Squared Error (RMSE)* (also called *Root Mean Square Deviation, RMSD*), measures the difference between values predicted by a model and the values observed from the environment that is being modeled. RMS-loss is the square root of Squared-loss and has the same units as the label, similar to the absolute-loss though giving more weight to larger differences. Root mean square error is commonly used in climatology, forecasting, and regression analysis to verify experimental results. | It is always non-negative, and **values closer to 0.00 are better**. RMSD is a measure of accuracy, to compare forecasting errors of different models for a particular dataset and not between datasets, as it is scale-dependent.| For further details on regression metrics, read the following articles: diff --git a/docs/machine-learning/tutorials/time-series-demand-forecasting.md b/docs/machine-learning/tutorials/time-series-demand-forecasting.md index e3033e5c9c089..9af88f1e6875d 100644 --- a/docs/machine-learning/tutorials/time-series-demand-forecasting.md +++ b/docs/machine-learning/tutorials/time-series-demand-forecasting.md @@ -62,7 +62,7 @@ The algorithm used in this tutorial is [Singular Spectrum Analysis(SSA)](http:// 1. Download the [*DailyDemand.mdf* database file](https://github.com/dotnet/machinelearning-samples/raw/main/samples/csharp/getting-started/Forecasting_BikeSharingDemand/BikeDemandForecasting/Data/DailyDemand.mdf) and save it to the *Data* directory. > [!NOTE] -> The data used in this tutorial comes from the [UCI Bike Sharing Dataset](http://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset). Fanaee-T, Hadi, and Gama, Joao, 'Event labeling combining ensemble detectors and background knowledge', Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg, [Web Link](https://link.springer.com/article/10.1007%2Fs13748-013-0040-3). +> The data used in this tutorial comes from the [UCI Bike Sharing Dataset](http://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset). Hadi Fanaee-T and João Gama, 'Event labeling combining ensemble detectors and background knowledge', Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg, [Web Link](https://link.springer.com/article/10.1007%2Fs13748-013-0040-3). The original dataset contains several columns corresponding to seasonality and weather. For brevity and because the algorithm used in this tutorial only requires the values from a single numerical column, the original dataset has been condensed to include only the following columns: