From 9a9044c12a766f585b858a5aca8dab24c8c775b7 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Mon, 24 Oct 2022 16:17:06 -0400 Subject: [PATCH 01/49] Initial commit --- .../automated-machine-learning-mlnet.md | 49 ++++++++++++ .../how-to-use-the-automl-api.md | 76 +++++++++++++++++-- 2 files changed, 120 insertions(+), 5 deletions(-) create mode 100644 docs/machine-learning/automated-machine-learning-mlnet.md diff --git a/docs/machine-learning/automated-machine-learning-mlnet.md b/docs/machine-learning/automated-machine-learning-mlnet.md new file mode 100644 index 0000000000000..b00069c938d3b --- /dev/null +++ b/docs/machine-learning/automated-machine-learning-mlnet.md @@ -0,0 +1,49 @@ +--- +title: What is Automated Machine Learning (AutoML)? +description: Learn what automated machine learning is and its different components in ML.NETs +ms.date: 11/01/2022 +ms.topic: overview +ms.custom: mvc +--- + +# What is Automated Machine Learning (AutoML)? + +In this article, you learn what Automated Machine Learning (AutoML) is and its different components in ML.NET. + +Automated Machine Learning (AutoML) automates the model building process by making it easier to find the best algorithm for your scenario and dataset. + +## Load data + +When you load data into ML.NET, you usually have to define the schema. With AutoML, you can use data in a file to infer the schema. It does so by loading a subset of your data and using the data to determine what the data types are. + +For a more customized data loading experiences, see [Load data from files and other sources](how-to-guides/load-data-ml-net.md) + +## Data cleaning and preparation + +Raw data is often messy and incomplete. Machine learning algorithms expect input (features) to be represented as numbers. Similarly, the value to predict (label), especially when it's categorical data, has to be encoded. Therefore one of the goals of data cleaning and preparation is to get the data into the format expected by machine learning algorithms. + +Which transforms you apply to your data depend on the type of data you're working with (text, categorical, numerical, images). + +The process of taking raw input data and converting it into numerical data is often referred to as featurization. AutoML + +## Model training + +## Supported scenarios + +- Classification +- Regression + +For more information on machine learning tasks, [see machine elarning tasks in ML.NET](resources/tasks.md) + +## Experiments + +To train models in ML.NET, you create an experiment. + +An experiment is a collection of trials. + +## Sweepable Pipelines + +Sweepable pipelines + +## Model evaluation + diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index 45dd11ffe95de..06274af9b17ec 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -1,12 +1,78 @@ --- -title: How to use the ML.NET automated ML API -description: The ML.NET automated ML API automates the model building process and generates a model ready for deployment. Learn the options that you can use to configure automated machine learning tasks. -ms.date: 10/05/2021 +title: How to use the ML.NET Automated ML (AutoML) API +description: The ML.NET Automted ML (AutoML) API automates the model building process and generates a model ready for deployment. Learn the options that you can use to configure automated machine learning tasks. +ms.date: 11/01/2022 ms.custom: mvc,how-to ms.topic: how-to --- -# How to use the ML.NET automated machine learning API +# How to use the ML.NET Automated Machine Learning (AutoML) API + +In the article, you'll learn how to use the Automated ML (AutoML) API to: + +- Load data +- Clean and prepare data +- Create and run AutoML experiments +- Customize AutoML experiments + +## Install AutoML API + +To use the AutoML API, install the [**Microsoft.ML.AutoML**](https://www.nuget.org/packages/Microsoft.ML.AutoML) NuGet package **version 0.20.0 or later**. + +## Load data + +The first step to working with AutoML and machine learning in general is loading you data. AutoML provides the `InferColumns` method to simplify the process. + +Alternatively you can use any of the existing ML.NET APIs to load your data. For more information, see [Load data from files and other sources](load-data-ml-net.md). + +### Infer columns + +> [!NOTE] +> InferColumns only works with tabular data. + +Given a file, loads a subset of your data, inspects the contents of your file, and infers the data types for each of the columns in your dataset. + +```csharp +MLContext ctx = new MLContext(); + +var filePath = "my-data.csv"; +``` + +Given a file with the following data and a header where *NumBedrooms*, & and *Setting* are inputs (features) and *SalesPrice* is the column to predict (label): + +| NumBedrooms | NumBathrooms | Setting | SalesPrice | +| --- | --- | --- | --- | +| 1 | 1 | Urban | 100000 | +| 2 | 1.5 | Rural | 150000 | +| 3 | 2 | Suburban | 200000 | + +You can infer column information as follows: + +```csharp +var columnInferenceResults = ctx.Auto().InferColumns(path:filePath, labelColumnName: "SalesPrice", separatorChar:','); +``` + +Alternatively, if your file doesn't have headers, you can specify the label index column as follows. + +```csharp +var columnInferenceResults = ctx.Auto().InferColumns(path:filePath, labelColumnIndex: 3, separatorChar:','); +``` + +## Clean and prepare data + +Once your data is loaded, it's time to clean it and prepare it for training. + +## Create and run AutoML experiments + +## Customize AutoML experiments + +### Sweepable estimators + +### Search space + +### Tuners + + From c05b8e343478e4380f849b755e341d77b76b65cd Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Mon, 24 Oct 2022 16:21:15 -0400 Subject: [PATCH 02/49] Fix mdlint errors --- .../machine-learning/automated-machine-learning-mlnet.md | 9 +++------ .../how-to-guides/how-to-use-the-automl-api.md | 4 ++-- 2 files changed, 5 insertions(+), 8 deletions(-) diff --git a/docs/machine-learning/automated-machine-learning-mlnet.md b/docs/machine-learning/automated-machine-learning-mlnet.md index b00069c938d3b..9522883c211ea 100644 --- a/docs/machine-learning/automated-machine-learning-mlnet.md +++ b/docs/machine-learning/automated-machine-learning-mlnet.md @@ -8,7 +8,7 @@ ms.custom: mvc # What is Automated Machine Learning (AutoML)? -In this article, you learn what Automated Machine Learning (AutoML) is and its different components in ML.NET. +In this article, you learn what Automated Machine Learning (AutoML) is and its different components in ML.NET. Automated Machine Learning (AutoML) automates the model building process by making it easier to find the best algorithm for your scenario and dataset. @@ -22,7 +22,7 @@ For a more customized data loading experiences, see [Load data from files and ot Raw data is often messy and incomplete. Machine learning algorithms expect input (features) to be represented as numbers. Similarly, the value to predict (label), especially when it's categorical data, has to be encoded. Therefore one of the goals of data cleaning and preparation is to get the data into the format expected by machine learning algorithms. -Which transforms you apply to your data depend on the type of data you're working with (text, categorical, numerical, images). +Which transforms you apply to your data depend on the type of data you're working with (text, categorical, numerical, images). The process of taking raw input data and converting it into numerical data is often referred to as featurization. AutoML @@ -33,7 +33,7 @@ The process of taking raw input data and converting it into numerical data is of - Classification - Regression -For more information on machine learning tasks, [see machine elarning tasks in ML.NET](resources/tasks.md) +For more information on machine learning tasks, [see machine learning tasks in ML.NET](resources/tasks.md) ## Experiments @@ -43,7 +43,4 @@ An experiment is a collection of trials. ## Sweepable Pipelines -Sweepable pipelines - ## Model evaluation - diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index 06274af9b17ec..323b6bfa1add1 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -1,6 +1,6 @@ --- title: How to use the ML.NET Automated ML (AutoML) API -description: The ML.NET Automted ML (AutoML) API automates the model building process and generates a model ready for deployment. Learn the options that you can use to configure automated machine learning tasks. +description: The ML.NET Automated ML (AutoML) API automates the model building process and generates a model ready for deployment. Learn the options that you can use to configure automated machine learning tasks. ms.date: 11/01/2022 ms.custom: mvc,how-to ms.topic: how-to @@ -60,7 +60,7 @@ var columnInferenceResults = ctx.Auto().InferColumns(path:filePath, labelColumnI ## Clean and prepare data -Once your data is loaded, it's time to clean it and prepare it for training. +Once your data is loaded, it's time to clean it and prepare it for training. ## Create and run AutoML experiments From 086a660183e498bd47e7bd3129f327871e39a8f4 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Mon, 7 Nov 2022 08:18:11 -0500 Subject: [PATCH 03/49] Updates to TOC --- docs/machine-learning/toc.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/machine-learning/toc.yml b/docs/machine-learning/toc.yml index af9d83d387c4a..a3f0518c4e3da 100644 --- a/docs/machine-learning/toc.yml +++ b/docs/machine-learning/toc.yml @@ -89,8 +89,6 @@ href: how-to-guides/install-gpu-model-builder.md - name: Install the CLI href: how-to-guides/install-ml-net-cli.md - - name: Use the automated ML API - href: how-to-guides/how-to-use-the-automl-api.md - name: API items: - name: Install extra dependencies @@ -103,6 +101,8 @@ items: - name: Train and evaluate a model href: how-to-guides/train-machine-learning-model-ml-net.md + - name: Use the automated ML API + href: how-to-guides/how-to-use-the-automl-api.md - name: Train a model using cross-validation href: how-to-guides/train-machine-learning-model-cross-validation-ml-net.md - name: Inspect intermediate pipeline data values From dfd4c23ad36b9698cee089e09e17fa77e902ff5b Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Mon, 7 Nov 2022 10:34:26 -0500 Subject: [PATCH 04/49] Initial how-to guide --- .../how-to-use-the-automl-api.md | 603 +++++++++++++++++- 1 file changed, 571 insertions(+), 32 deletions(-) diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index 323b6bfa1add1..9b7bd4dfade30 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -8,69 +8,608 @@ ms.topic: how-to # How to use the ML.NET Automated Machine Learning (AutoML) API -In the article, you'll learn how to use the Automated ML (AutoML) API to: +## Quick Start + +AutoML provides several defaults for quickly training models. In this section you will learn how to: - Load data -- Clean and prepare data -- Create and run AutoML experiments -- Customize AutoML experiments +- Define your pipeline +- Configure your experiment +- Run your experiment +- Use the best model to make predictions -## Install AutoML API +### Defining your problem -To use the AutoML API, install the [**Microsoft.ML.AutoML**](https://www.nuget.org/packages/Microsoft.ML.AutoML) NuGet package **version 0.20.0 or later**. +Given a dataset stored in a comma-separated file called *taxi-fare.csv* that looks like the following: -## Load data +| vendor_id | rate_code | passenger_count | trip_time_in_secs | trip_distance|payment_type | fare_amount | +|---|---|---|---|---|---|---| +CMT|1|1|1271|3.8|CRD|17.5 +CMT|1|1|474|1.5|CRD|8 +CMT|1|1|637|1.4|CRD|8.5 -The first step to working with AutoML and machine learning in general is loading you data. AutoML provides the `InferColumns` method to simplify the process. +### Load data -Alternatively you can use any of the existing ML.NET APIs to load your data. For more information, see [Load data from files and other sources](load-data-ml-net.md). +To load your data, use the `InferColumn` method. -### Infer columns +```csharp +// Initialize MLContext +MLContext ctx = new MLContext(); -> [!NOTE] -> InferColumns only works with tabular data. +// Infer column information +ColumnInferenceResults columnInference = + ctx.Auto().InferColumns("taxi-fare.csv", labelColumnName: "fare_amount", groupColumns:false); +``` -Given a file, loads a subset of your data, inspects the contents of your file, and infers the data types for each of the columns in your dataset. +Then, use the `TextLoaderOptions` defined by the `ColumnInferenceResults` to create a `TextLoader` to load your data into an `IDataView`. ```csharp -MLContext ctx = new MLContext(); +// Create text loader +TextLoader loader = ctx.Data.CreateTextLoader(columnInference.TextLoaderOptions); + +// Load data into IDataView +IDataView data = loader.Load("taxi-fare.csv"); +``` + +It's often good practice to split your data into train and validation sets. Use `TrainTestSplit` to create an 80% training and 20% validation split of your dataset. + +```csharp +// Split into train (70%), validation (20%) sets +TrainTestData trainValidationData = ctx.Data.TrainTestSplit(data, testFraction:0.2); +``` + +### Define your pipeline + +Your pipeline defines the data preprocesing steps and machine learning pipeline to use for training your model. + +```csharp +SweepablePipeline pipeline = + ctx.Auto().Featurizer(data, columnInformation:columnInference.ColumnInformation) + .Append(ctx.Auto().Regression(labelColumnName:columnInference.ColumnInformation.LabelColumnName)); +``` + +A `SweepablePipeline` is a pipeline containing a colelction of `SweepableEstimators`. A `SweepableEstimator` is an ML.NET `Estimator` with a `SearchSpace`. + +The `Featurizer` is a convenience API which builds a `SweepablePipeline` of data processing sweepable estimators based on the column information you provide. Instead of building a pipeline from scratch, `Featurizer` automates the data preprocessing step. + +> [!TIP] +> Use `Featurizer` with `ColumnInferenceResults` to maximize the utility of AutoML. + +For training, AutoML provides a sweepable pipeline with default trainers and search space configurations for the following machine learning tasks: + +- Binary classification +- Multiclass classification +- Regression + +For the taxi fare prediction problem, since the goal is to predict a numerical value, use `Regression`. For more information on choosing a task, see the [machine learning tasks in ML.NET guide](/dotnet/machine-learning/resources/tasks.md). + +### Configure your experiment + +First, create an AutoML experiment. An experiment is a collection of trials. + +```csharp +// Create AutoML experiment +AutoMLExperiment experiment = ctx.Auto().CreateExperiment(); +``` + +Once your experiment is created, use the extension methods it provides to configure different settings. + +```csharp +// Configure experiment +experiment + .SetPipeline(pipeline) + .SetRegressionMetric(RegressionMetric.RSquared, labelColumn:columnInference.ColumnInformation.LabelColumnName) + .SetTrainingTimeInSeconds(60) + .SetDataset(trainValidationData); +``` + +In this example, you: + +- Set the sweepable pipeline to run during the experiment using `SetPipeline`. +- Choose `RSquared` as the metric to optimize during training using `SetRegressionMetric`. +- Set 60 seconds as the amount of time you want to train for using `SetTrainingTimeInSeconds`. +- Provide the training and validation datasets to use using `SetDataset`. + +Once your experiment is defined, you'll want some way to track its progress. The quickest way to track progress is by defining the `Log` event from `MLContext`. + +```csharp +// Log experiment trials +ctx.Log += (_,e) => { + if (e.Source.Equals("AutoMLExperiment")) + { + Console.WriteLine(e.RawMessage); + } +}; +``` + +### Run your experiment + +Now that you've defined your experiment, use the `Run` or `RunAsync` methods to start your experiment. + +```csharp +TrialResult experimentResults = await experiment.RunAsync(); +``` + +Once the time to train expires, the result is a `TrialResult` for the best model found during training. + +At this point, you can then save your model or use it for making predictions. For more information on how use an ML.NET model, see the following guides: + +- [Save and load a trained model](/dotnet/machine-learning/how-to-guides/save-load-machine-learning-models-ml-net.md) +- [Make predictions with a trained model](/dotnet/machine-learning/how-to-guides/machine-learning-model-predictions-ml-net) + +## Advanced Scenarios + +### Modify column inference results + +Because `InferColumn` only loads a subset of your data, it's possible that edge cases contained outside of the samples used to infer columns are not caught and the wrong data types are set for your columns. In those cases, you can update the properties of `ColumnInformation` and `TextLoaderOptions` to account for those cases where the column inference results are not correct. + +For example, in the taxi fare dataset, the data in the `rate_code` column is a number. However, that numerical value represents a category. By default, calling `InferColumn` will place `rate_code` in the `NumericColumnNames` property instead of `CategoricalColumnNames`. Because these properties are standard .NET collections, you can use the standard operations to add and remove items from them. + +You can do the following to update the `ColumnInformation` for `rate_code`. + +```csharp +columnInference.ColumnInformation.NumericColumnNames.Remove("rate_code"); +columnInference.ColumnInformation.CategoricalColumnNames.Add("rate_code"); +``` + +### Exclude trainers + +By default, AutoML tries multiple trainers as part of the training process to see which one works best for your data. However, throughout the training process you might discover there are some trainers that use up too many compute resources or don't provide good evaluation metrics. With AutoML, you have the option to exclude trainers from the training process. Note that which trainers are used depends on the task. For a list of supported trainers in ML.NET, see the [how to choose an ML.NET algorithm guide](/dotnet/machine-learning/how-to-choose-an-ml-net-algorithm.md). + +For example, in the taxi fare regression scenario, to exclude the LightGBM algorithm, set the `useLgbm` parameter to `false`. + +```csharp +ctx.Auto().Regression(labelColumnName: columnInference.ColumnInformation.LabelColumnName, useLgbm:false) +``` + +The process for other tasks like binary and multiclass classification and trainers works the same way. + +### Customize a sweepable estimator + +When you want to more granular customization of estimator options included as part of your sweepable pipeline, you need to: + +1. Initialize a search space +1. Use the search space to define a custom factory +1. Define your sweepable estimator +1. Add your sweepable estimator to your sweepable pipeline + +AutoML provides a set of preconfigured search spaces for trainers in the following machine learning tasks: + +- Binary classification +- Multiclass classification +- Regression + +In this example, the search space used is for the `SdcaRegressionTrainer`. + +```csharp +// Initialize default Sdca search space +var sdcaSearchSpace = new SearchSpace(); +``` + +Then, use the search space to define a custom factory method to create the `SdcaRegressionTrainer`. In this example, the values of `L1Regularization` and `L2Regularization` are both being set to something other than the default. For `L1Regularization`, the value set is determined by the tuner during each trial. The `L2Regularization` is fixed for each trial to the hard-coded value. During each trial, custom factory method's output is an `SdcaRegressionTrainer` with the configured hyperparameters. + +```csharp +// Use the search space to define a custom factory to create an SdcaRegressionTrainer +var sdcaEstimatorFactory = (MLContext ctx, SdcaOption param) => +{ + var sdcaOption = new SdcaRegressionTrainer.Options(); + sdcaOption.L1Regularization = param.L1Regularization; + sdcaOption.L2Regularization = 0.02f; + + return ctx.Regression.Trainers.Sdca(sdcaOption); +}; +``` + +A sweepable estimator is the combination of an estimator and a search space. Now that you've defined a search space and used it to create a custom factory method for generating trainers. Use the `CreateSweepableEstimator` method to create a new sweepable estimator. + +```csharp +// Define Sdca sweepable estimator (SdcaRegressionTrainer + SdcaOption search space) +var sdcaSweepableEstimator = ctx.Auto().CreateSweepableEstimator(sdcaEstimatorFactory, sdcaSearchSpace); +``` + +To use your sweepable estimator in your experiment, append it to your sweepable pipeline. + +```csharp +// Add sweepable estimator to sweepable pipeline +SweepablePipeline customEstimatorPipeline = + ctx.Auto().Featurizer(data, columnInformation: columnInference.ColumnInformation) + .Append(sdcaSweepableEstimator); +``` + +Because sweepable pipelines are a collection of sweepable estimators, you can configure and customize as many of these sweepable estimators as you need. -var filePath = "my-data.csv"; +### Customize your search space + +There are scenarios where you want to go beyond customizing the sweepable estimators used in your experiment and want control the search space range. + +```csharp +var originalSdcaSearchSpace = new SearchSpace(); +originalSdcaSearchSpace["L1Regularization"] = new UniformSingleOption(min:0.01f, max:2.0f, logBase: false, defaultValue:0.01f); ``` -Given a file with the following data and a header where *NumBedrooms*, & and *Setting* are inputs (features) and *SalesPrice* is the column to predict (label): +### Create a custom search space + +There are a few reasons where you'd want to create your own search space. However, the main one is to support ML.NET tasks that don't have predefined search spaces. These include: + +### Create your own trial runner + +By default, AutoML supports binary classification, multiclass classification, and regression. However, ML.NET supports many more scenarios such as: + +- Recommendation +- Forecasting +- Ranking +- Text classification +- Sentence similarity + +For scenarios that don't have preconfigured search spaces and sweepable estimators you can create your own and use a trial runner to enable AutoML for that scenario. + +For example, given hourly energy demand data that looks like the following: + +| load | +|---------| +| 20236 | +| 55784 | +| 179759 | +| 314597 | + +You want to use the `ForecastBySsa` trainer to forecast future demand. + +In order to do so you'll have to: + +1. Define a custom search space + + ```csharp + // Define ForecastBySsa search space + public class SSAOption + { + + [Range(2, 24 * 7 * 30)] + public int WindowSize { get; set; } = 2; + + [Range(2, 24 * 7 * 30)] + public int SeriesLength { get; set; } = 2; + + [Range(2, 24 * 7 * 30)] + public int TrainSize { get; set; } = 2; + + [Range(1, 24 * 7 * 30)] + public int Horizon { get; set; } = 1; + } + ``` + +1. Implement a custom trial runner + + To create a trial runner, implement `ITrialRunner`: + + ```csharp + public class SSARunner : ITrialRunner + { + private readonly MLContext _context; + private readonly TrainTestData _data; + private readonly IDataView _trainDataset; + private readonly IDataView _evaluateDataset; + private readonly SweepablePipeline _pipeline; + private readonly string _labelColumnName; + + public SSARunner(MLContext context, TrainTestData data, string labelColumnName, SweepablePipeline pipeline) + { + _context = context; + _data = data; + _trainDataset = data.TrainSet; + _evaluateDataset = data.TestSet; + _labelColumnName = labelColumnName; + _pipeline = pipeline; + } + + public void Dispose() + { + return; + } + + // Run trial asynchronously + public Task RunAsync(TrialSettings settings, CancellationToken ct) + { + try + { + return Task.Run(() => Run(settings)); + } + catch (Exception ex) when (ct.IsCancellationRequested) + { + throw new OperationCanceledException(ex.Message, ex.InnerException); + } + catch (Exception) + { + throw; + } + } + + // Helper function to define trial run logic + private TrialResult Run(TrialSettings settings) + { + try + { + // Initialize stop watch to measure time + var stopWatch = new Stopwatch(); + stopWatch.Start(); + + // Get pipeline parameters + var parameter = settings.Parameter["_pipeline_"]; + + // Build pipeline from parameters + var pipeline = _pipeline.BuildFromOption(_context, parameter); + + // Train model + var model = pipeline.Fit(_trainDataset); + + // Create prediction engine for single predictions + var predictEngine = model.CreateTimeSeriesEngine(_context); + + // Create a checkpoint for time series engine prediction + predictEngine.CheckPoint(_context, "origin"); + + var predictedLoad1H = new List(); + var N = _evaluateDataset.GetRowCount(); + + // Evaluate performance on a rolling basis + foreach (var load in _evaluateDataset.GetColumn(_labelColumnName)) + { + // First, get next n predictions where n is horizon, in this case, it's always 1. + var predict = predictEngine.Predict(); + + // Add prediction to list of predictions + predictedLoad1H.Add(predict.Prediction[0]); + + // Update model with true value + predictEngine.Predict(new ForecastInput() + { + Load = load, + }); + } + + // Calculate (Root Mean Squared Error) evaluation metric + var rmse = Enumerable.Zip(_evaluateDataset.GetColumn(_labelColumnName), predictedLoad1H) + .Select(x => Math.Pow(x.First - x.Second, 2)) + .Average(); + rmse = Math.Sqrt(rmse); + + return new TrialResult() + { + Metric = rmse, + Model = model, + TrialSettings = settings, + DurationInMilliseconds = stopWatch.ElapsedMilliseconds, + }; + } + catch (Exception) + { + return new TrialResult() + { + Metric = double.MinValue, + Model = null, + TrialSettings = settings, + DurationInMilliseconds = 0, + }; + } + } + + // Define input schema + private class ForecastInput + { + [ColumnName("load")] + public float Load { get; set; } + } + + // Define output schema + private class ForecastOutput + { + [ColumnName("prediction")] + public float[] Prediction { get; set; } + } + } + ``` + + `ITrialRunner` has an asynchronous run method which produces `TrialResult` information. + +1. Create a sweepable estimator and add it to your pipeline. + + ```csharp + var ssaSearchSpace = new SearchSpace(); + + var ssaFactory = (MLContext ctx, SSAOption param) => + { + return ctx.Forecasting.ForecastBySsa( + outputColumnName: "prediction", + inputColumnName: "load", + windowSize: param.WindowSize, + seriesLength: param.SeriesLength, + trainSize: param.TrainSize, + horizon: param.Horizon); + }; + + var ssaSweepableEstimator = ctx.Auto().CreateSweepableEstimator(ssaFactory, ssaSearchSpace); + + var ssaPipeline = + new EstimatorChain() + .Append(ssaSweepableEstimator); + ``` + +1. Initialize your custom trial runner + + ```csharp + var ssaRunner = new SSARunner(ssaCtx, ssaDataSplit, labelColumnName: "load", pipeline:ssaPipeline); + ``` + +1. Create and configure your experiment. Use the `SetTrialRunner` extension method to add your custom trial runner to your experiment. + + ```csharp + AutoMLExperiment ssaExperiment = ssaCtx.Auto().CreateExperiment(); + + ssaExperiment + .SetPipeline(ssaPipeline) + .SetRegressionMetric(RegressionMetric.RootMeanSquaredError, labelColumn: "load", scoreColumn: "prediction") + .SetTrainingTimeInSeconds(60) + .SetDataset(ssaDataSplit) + .SetTrialRunner(ssaRunner); + ``` + +1. Run your experiment + + ```csharp + var ssaCts = new CancellationTokenSource(); + TrialResult ssaExperimentResults = await ssaExperiment.RunAsync(ssaCts.Token); + ``` -| NumBedrooms | NumBathrooms | Setting | SalesPrice | -| --- | --- | --- | --- | -| 1 | 1 | Urban | 100000 | -| 2 | 1.5 | Rural | 150000 | -| 3 | 2 | Suburban | 200000 | +### Choose a different tuner -You can infer column information as follows: +AutoML supports a variety of tuning algorithms to iterate through the search space in search of the optimal hyperparamters. By default, it uses the Eci Cost Frugal tuner. Using experiment extension methods, you can choose another tuner that best fits your scenario. + +Use the following methods to set your tuner: + +- **SMAC** - `SetSmacTuner` +- **Grid Search** - `SetGridSearchTuner` +- **Random Search** - ``SetRandomSearchTuner` +- **Cost Frugal** - `SetCostFrugalTuner` +- **Eci Cost Frugal** - `SetEciCostFrugalTuner` + +For example, to use the SMAC tuner, your code might look like the following: + +```csharp +experiment.SetSmacTuner(); +``` + +### Configure experiment monitoring + +The quickest way to monitor the progress of an experiment is to define the `Log` event from `MLContext`. However, the `Log` event outputs a raw dump of the logs generated by AutoML during each trial. Because of the large amount of unformatted information, it's difficult. + +For a more controlled monitoring experience, implement a class with the `IMonitor` interface. + +```csharp +public class AutoMLMonitor : IMonitor +{ + private readonly SweepablePipeline _pipeline; + + public AutoMLMonitor(SweepablePipeline pipeline) + { + _pipeline = pipeline; + } + + public void ReportBestTrial(TrialResult result) + { + return; + } + + public void ReportCompletedTrial(TrialResult result) + { + var trialId = result.TrialSettings.TrialId; + var timeToTrain = result.DurationInMilliseconds; + var pipeline = _pipeline.ToString(result.TrialSettings.Parameter); + Console.WriteLine($"Trial {trialId} finished training in {timeToTrain}ms with pipeline {pipeline}"); + } + + public void ReportFailTrial(TrialSettings settings, Exception exception = null) + { + if (exception.Message.Contains("Operation was canceled.")) + { + Console.WriteLine($"{settings.TrialId} cancelled. Time budget exceeded."); + } + Console.WriteLine($"{settings.TrialId} failed with exception {exception.Message}"); + } + + public void ReportRunningTrial(TrialSettings setting) + { + return; + } +} +``` + +The `IMonitor` interface has four lifecycle events: + +- ReportBestTrial +- ReportCompletedTrial +- ReportFailTrial +- ReportRunningTrial + +> [!TIP] +> Although it's not required, include your `SweepablePipeline` in your monitor so you can inspect the pipeline that was generated for a trial using the `Parameter` property of the `TrialSettings`. + +In this example, only the `ReportCompletedTrial` and `ReportFailTrial` are implemented. + +Once you've implemented your monitor, set it as part of your experiment configuration using `SetMonitor`. + +```csharp +var monitor = new AutoMLMonitor(pipeline); +experiment.SetMonitor(monitor); +``` + +When you run the experiment with this implementation, the output should look similar to the following: + +```text +Trial 0 finished training in 5835ms with pipeline ReplaceMissingValues=>OneHotEncoding=>Concatenate=>FastForestRegression +Trial 1 finished training in 15080ms with pipeline ReplaceMissingValues=>OneHotEncoding=>Concatenate=>SdcaRegression +Trial 2 finished training in 3941ms with pipeline ReplaceMissingValues=>OneHotHashEncoding=>Concatenate=>FastTreeRegression +``` + +### Persist trials + +By default, AutoML only stores the `TrialResult` for the best model. However, if you wanted to persist each of the trials, you can do so from within your monitor. + +Inside of your monitor, define a property for your completed trials and a method for accessing them. + +```csharp +private readonly List _completedTrials; + +public IEnumerable GetCompletedTrials() => _completedTrials; +``` + +Initialize it in your constructor + +```csharp +public AutoMLMonitor(SweepablePipeline pipeline) +{ + //... + _completedTrials = new List(); + //... +} +``` + +Append each trial result inside your `ReportCompletedTrial` lifecycle method. ```csharp -var columnInferenceResults = ctx.Auto().InferColumns(path:filePath, labelColumnName: "SalesPrice", separatorChar:','); +public void ReportCompletedTrial(TrialResult result) +{ + //... + _completedTrials.Add(result); +} ``` -Alternatively, if your file doesn't have headers, you can specify the label index column as follows. +When training completes, you can access all the completed trials by calling `GetCompletedTrials` ```csharp -var columnInferenceResults = ctx.Auto().InferColumns(path:filePath, labelColumnIndex: 3, separatorChar:','); +monitor.GetCompletedTrials() ``` -## Clean and prepare data +At this point you can perform additional processing on the collection of completed trials like choosing another model other than the one selected by AutoML, logging trial results to a database, or rebuilding the pipeline from any of the completed trials. -Once your data is loaded, it's time to clean it and prepare it for training. +### Cancel asynchronous experiments -## Create and run AutoML experiments +When you run experiments asynchronously, make sure to cleanly terminate the process. To do so, use cancellation tokens. -## Customize AutoML experiments +```csharp +var cts = new CancellationTokenSource(); +TrialResult experimentResults = await experiment.RunAsync(cts.Token); +``` + +> [!WARNING] +> Cancelling an experiment will not save any of the intermediary outputs. Set a checkpoint to save intermediary outputs. -### Sweepable estimators +### Set checkpoints -### Search space +Checkpoints provide a way for you to save intermediary outputs from the training process in the event of an early termination or error. To set a checkpoint, use the `SetCheckpoint` extension method and provide a directory to store the intermediary outputs. -### Tuners +```csharp +var checkpointPath = Path.Join(Directory.GetCurrentDirectory(), "automl"); +experiment.SetCheckpoint(checkpointPath); +``` + + ```cshtml + @page + @model IndexModel + @{ + ViewData["Title"] = "Home page"; + } + +
+

Live Sentiment

+ +

+ +
+

Your sentiment is...

+

😡 😐 😍

+ +
+
+
▲
+ +
+
+
+
+ ``` 1. Next, add css styling code to the end of the *site.css* page in the *wwwroot\css* directory: - [!code-css [CssStyling](~/machinelearning-samples/samples/modelbuilder/BinaryClassification_Sentiment_Razor/SentimentRazor/wwwroot/css/site.css#L61-L105)] + + + ```css + /* Style for sentiment display */ + + .sentiment { + background-color: #eee; + position: relative; + display: inline-block; + padding: 1rem; + padding-bottom: 0; + border-radius: 1rem; + } + + .sentiment h4 { + font-size: 16px; + text-align: center; + margin: 0; + padding: 0; + } + + .sentiment p { + font-size: 50px; + } + + .sentiment .marker { + position: relative; + left: 22px; + width: calc(100% - 68px); + } + + .sentiment .marker > div { + transition: 0.3s ease-in-out; + position: absolute; + margin-left: -30px; + text-align: center; + } + + .sentiment .marker > div > div { + font-size: 50px; + line-height: 20px; + color: green; + } + + .sentiment .marker > div label { + font-size: 30px; + color: gray; + } + ``` 1. After that, add code to send inputs from the web page to the `OnGetAnalyzeSentiment` handler. 1. In the *site.js* file located in the *wwwroot\js* directory, create a function called `getSentiment` to make a GET HTTP request with the user input to the `OnGetAnalyzeSentiment` handler. - [!code-javascript [GetSentimentMethod](~/machinelearning-samples/samples/modelbuilder/BinaryClassification_Sentiment_Razor/SentimentRazor/wwwroot/js/site.js#L5-L10)] + + ```javascript + function getSentiment(userInput) { + return fetch(`Index?handler=AnalyzeSentiment&text=${userInput}`) + .then((response) => { + return response.text(); + }) + } + ``` 1. Below that, add another function called `updateMarker` to dynamically update the position of the marker on the web page as sentiment is predicted. - [!code-javascript [UpdateMarkerMethod](~/machinelearning-samples/samples/modelbuilder/BinaryClassification_Sentiment_Razor/SentimentRazor/wwwroot/js/site.js#L12-L15)] + + + ```javascript + function updateMarker(markerPosition, sentiment) { + $("#markerPosition").attr("style", `left:${markerPosition}%`); + $("#markerValue").text(sentiment); + } + ``` 1. Create an event handler function called `updateSentiment` to get the input from the user, send it to the `OnGetAnalyzeSentiment` function using the `getSentiment` function and update the marker with the `updateMarker` function. - [!code-javascript [UpdateSentimentMethod](~/machinelearning-samples/samples/modelbuilder/BinaryClassification_Sentiment_Razor/SentimentRazor/wwwroot/js/site.js#L17-L34)] + + + ```javascript + function updateSentiment() { + + var userInput = $("#Message").val(); + + getSentiment(userInput) + .then((sentiment) => { + switch (sentiment) { + case "Not Toxic": + updateMarker(100.0, sentiment); + break; + case "Toxic": + updateMarker(0.0, sentiment); + break; + default: + updateMarker(45.0, "Neutral"); + } + }); + } + ``` 1. Finally, register the event handler and bind it to the `textarea` element with the `id=Message` attribute. - [!code-javascript [UpdateSentimentEvtHandler](~/machinelearning-samples/samples/modelbuilder/BinaryClassification_Sentiment_Razor/SentimentRazor/wwwroot/js/site.js#L36)] + + $("#Message").on('change input paste', updateSentiment) ## Run the application @@ -279,8 +366,6 @@ When the application launches, enter *Model Builder is cool!* into the text area ![Running window with the predicted sentiment window](./media/sentiment-analysis-model-builder/web-app.png) -If you need to reference the Model Builder generated projects at a later time inside of another solution, you can find them inside the `C:\Users\%USERNAME%\AppData\Local\Temp\MLVSTools` directory. - ## Next steps In this tutorial, you learned how to: @@ -299,5 +384,4 @@ In this tutorial, you learned how to: To learn more about topics mentioned in this tutorial, visit the following resources: - [Model Builder Scenarios](../automate-training-with-model-builder.md#scenario) -- [Binary Classification](../resources/glossary.md#binary-classification) -- [Binary Classification Model Metrics](../resources/metrics.md#evaluation-metrics-for-binary-classification) +- [Text Classification Model Metrics](../resources/metrics.md#evaluation-metrics-for-multiclass-classification-and-text-classification) From e3773853dafdde066e41591081abe4c5cc068a4f Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Mon, 7 Nov 2022 12:15:08 -0500 Subject: [PATCH 08/49] Fix package issue --- .../tutorials/sentiment-analysis-model-builder.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md b/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md index 033b2ab7d5303..cbf9ae9380e65 100644 --- a/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md +++ b/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md @@ -130,7 +130,7 @@ To make a single prediction, you have to create a Date: Mon, 7 Nov 2022 12:20:57 -0500 Subject: [PATCH 09/49] Add conceptual AutoML doc to TOC --- docs/machine-learning/toc.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/machine-learning/toc.yml b/docs/machine-learning/toc.yml index 71bf3dc567f64..f79cdf07256b0 100644 --- a/docs/machine-learning/toc.yml +++ b/docs/machine-learning/toc.yml @@ -13,6 +13,8 @@ items: - name: The ML.NET API href: how-does-mldotnet-work.md + - name: What is Automated Machine Learning (AutoML)? + href: automated-machine-learning-mlnet.md - name: Tutorials items: - name: Model Builder & CLI From 1dd7ccdba012672cc7c2907eb190e3dbf65e3d5a Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Mon, 7 Nov 2022 12:23:52 -0500 Subject: [PATCH 10/49] Fix broken links --- docs/machine-learning/automate-training-with-cli.md | 2 +- .../tutorials/health-violation-classification-model-builder.md | 2 +- .../tutorials/sentiment-analysis-model-builder.md | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/machine-learning/automate-training-with-cli.md b/docs/machine-learning/automate-training-with-cli.md index 853cf63c34110..ffd46b3f7da3e 100644 --- a/docs/machine-learning/automate-training-with-cli.md +++ b/docs/machine-learning/automate-training-with-cli.md @@ -71,7 +71,7 @@ The following image displays the classification metrics list for the top five mo Accuracy is a popular metric for classification problems, however accuracy isn't always the best metric to select the best model from as explained in the following references. There are cases where you need to evaluate the quality of your model with additional metrics. -To explore and understand the metrics that are output by the CLI, see [Evaluation metrics for classification](resources/metrics.md#evaluation-metrics-for-multi-class-classification). +To explore and understand the metrics that are output by the CLI, see [Evaluation metrics for classification](resources/metrics.md#evaluation-metrics-for-multi-class-classification-and-text-classification). ### Metrics for Regression and Recommendation models diff --git a/docs/machine-learning/tutorials/health-violation-classification-model-builder.md b/docs/machine-learning/tutorials/health-violation-classification-model-builder.md index 8cfda621593fc..37cbf1fb5260f 100644 --- a/docs/machine-learning/tutorials/health-violation-classification-model-builder.md +++ b/docs/machine-learning/tutorials/health-violation-classification-model-builder.md @@ -179,4 +179,4 @@ To learn more about topics mentioned in this tutorial, visit the following resou - [Model Builder Scenarios](../automate-training-with-model-builder.md#scenario) - [Multiclass Classification](../resources/glossary.md#multiclass-classification) -- [Multiclass Classification Model Metrics](../resources/metrics.md#evaluation-metrics-for-multi-class-classification) +- [Multiclass Classification Model Metrics](../resources/metrics.md#evaluation-metrics-for-multi-class-classification-and-text-classification) diff --git a/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md b/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md index cbf9ae9380e65..2669afee30695 100644 --- a/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md +++ b/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md @@ -384,4 +384,4 @@ In this tutorial, you learned how to: To learn more about topics mentioned in this tutorial, visit the following resources: - [Model Builder Scenarios](../automate-training-with-model-builder.md#scenario) -- [Text Classification Model Metrics](../resources/metrics.md#evaluation-metrics-for-multiclass-classification-and-text-classification) +- [Text Classification Model Metrics](../resources/metrics.md#evaluation-metrics-for-multi-class-classification-and-text-classification) From a5dc730a35ff6eeb47dd0f0bbbfd79e2f7019989 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Mon, 7 Nov 2022 12:36:39 -0500 Subject: [PATCH 11/49] Update AutoML conceptual doc --- .../automated-machine-learning-mlnet.md | 57 ++++++++++++------- 1 file changed, 36 insertions(+), 21 deletions(-) diff --git a/docs/machine-learning/automated-machine-learning-mlnet.md b/docs/machine-learning/automated-machine-learning-mlnet.md index 9522883c211ea..a6907771709ea 100644 --- a/docs/machine-learning/automated-machine-learning-mlnet.md +++ b/docs/machine-learning/automated-machine-learning-mlnet.md @@ -1,46 +1,61 @@ --- title: What is Automated Machine Learning (AutoML)? description: Learn what automated machine learning is and its different components in ML.NETs -ms.date: 11/01/2022 +ms.date: 11/10/2022 ms.topic: overview ms.custom: mvc --- # What is Automated Machine Learning (AutoML)? -In this article, you learn what Automated Machine Learning (AutoML) is and its different components in ML.NET. +In this article you will learn what Automated machine learning (AutoML) is and how to use the AutoML API to train models in ML.NET. -Automated Machine Learning (AutoML) automates the model building process by making it easier to find the best algorithm for your scenario and dataset. +> [!NOTE] +> This topic refers to the ML.NET AutoML API, which is currently in preview. Material may be subject to change. -## Load data +## What is AutoML? -When you load data into ML.NET, you usually have to define the schema. With AutoML, you can use data in a file to infer the schema. It does so by loading a subset of your data and using the data to determine what the data types are. +Automated machine learning (AutoML) automates the process of applying machine learning to data. Given a dataset| you can run an AutoML experiment to iterate over different data featurizations, machine learning algorithms, and hyperparameters to select the best model. -For a more customized data loading experiences, see [Load data from files and other sources](how-to-guides/load-data-ml-net.md) +## When should I use AutoML? -## Data cleaning and preparation +Whether you're just getting started with machine learning or you're an experienced user, AutoML provides solutions for automating the model development process. -Raw data is often messy and incomplete. Machine learning algorithms expect input (features) to be represented as numbers. Similarly, the value to predict (label), especially when it's categorical data, has to be encoded. Therefore one of the goals of data cleaning and preparation is to get the data into the format expected by machine learning algorithms. +- **Beginners** - If you're new to machine learning, AutoML simplifies the model development process by providing a set of defaults that reduces the number of decisions you have to make when training your model. In doing so, you can focus on your data and the problem you're trying to solve and let AutoML do the rest. +- **Experienced users** - If you have some experience with machine learning, you can customize, configure, and extend the defaults provided by AutoML based on your needs while still leveraging its automation capabilities. -Which transforms you apply to your data depend on the type of data you're working with (text, categorical, numerical, images). +## AutoML in ML.NET -The process of taking raw input data and converting it into numerical data is often referred to as featurization. AutoML +- **Featurizer** - Convenience API to automate data preprocessing. +- **Trial** - A single hyperparamters optimization run. +- **Experiment** - A collection of AutoML trials. ML.NET provides a high-level API for creating experiments which sets defaults for the individual Sweepable Pipeline, Search Space, and Tuner components. +- **Search Space** - The range of available options to choose hyperparameters from. +- **Tuner** - The algorithms used to optimize hyperparameters. ML.NET supports the following tuners: + - **Cost Frugal Tuner** - Implementation of [Frugal Optimization for Cost-related Hyperparameters](https://arxiv.org/abs/2005.01571) which takes training cost into consideration + - **Eci Cost Frugal Tuner** - Implementation of Cost Frugal Tuner for hierarchical search spaces. Default tuner used by AutoML. + - **SMAC** - Tuner that uses random forests to apply Bayesian optimization. + - **Grid Search** - Tuner that works best for small search spaces. +- **Random Search** +- **Sweepable Estimator** - An ML.NET estimator that contains a search space. +- **Sweepable Pipeline** - An ML.NET pipeline that contains one or more Sweepable Estimators. +- **Trial Runner** - AutoML component that uses sweepable pipelines and trial settings to generate trial results from model training and evaluation. -## Model training +It's recommended for beginners to start with the defaults provided by the high-level experiment API. For more experienced users looking for customization options, use the sweepable estimator, sweepable pipeline, search space, trial runner and tuner components. -## Supported scenarios +For more information on getting started with the AutoML API, see the [How to use the ML.NET Automated Machine Learning (AutoML) API](how-to-guides/how-to-use-the-automl-api.md) guide. -- Classification -- Regression - -For more information on machine learning tasks, [see machine learning tasks in ML.NET](resources/tasks.md) +## Supported tasks -## Experiments +AutoML provides preconfigured defaults for the following tasks: -To train models in ML.NET, you create an experiment. +- Binary classification +- Multiclass classification +- Regression -An experiment is a collection of trials. +For other tasks, configure. For more information, see the [How to use the ML.NET Automated Machine Learning (AutoML) API](how-to-guides/how-to-use-the-automl-api.md) guide. -## Sweepable Pipelines +## Next steps -## Model evaluation +- [How to use the ML.NET Automated Machine Learning (AutoML) API](how-to-guides/how-to-use-the-automl-api.md) +- [Tutorial: Classify the severity of restaurant health violations with Model Builder](tutorials/health-violation-classification-model-builder.md) +- [Tutorial: Analyze sentiment using the ML.NET CLI](tutorials/sentiment-analysis-cli.md) From eca0eb84efead8e03bf1ddb1a389d22a93caf1f3 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Mon, 7 Nov 2022 12:56:16 -0500 Subject: [PATCH 12/49] Fix formatting --- .../automate-training-with-cli.md | 2 +- .../automate-training-with-model-builder.md | 110 ++++++++++++++++-- docs/machine-learning/resources/metrics.md | 2 +- ...-violation-classification-model-builder.md | 2 +- .../sentiment-analysis-model-builder.md | 10 +- 5 files changed, 109 insertions(+), 17 deletions(-) diff --git a/docs/machine-learning/automate-training-with-cli.md b/docs/machine-learning/automate-training-with-cli.md index ffd46b3f7da3e..07b14abe6d228 100644 --- a/docs/machine-learning/automate-training-with-cli.md +++ b/docs/machine-learning/automate-training-with-cli.md @@ -1,7 +1,7 @@ --- title: Automate model training with the ML.NET CLI description: Discover how to use the ML.NET CLI tool to automatically train the best model from the command-line. -ms.date: 06/03/2020 +ms.date: 11/10/2022 ms.custom: how-to, mlnet-tooling #Customer intent: As a developer, I want to use ML.NET CLI to automatically train the "best model" from the command-prompt. I also want to understand the output provided by the tool (metrics and output assets) --- diff --git a/docs/machine-learning/automate-training-with-model-builder.md b/docs/machine-learning/automate-training-with-model-builder.md index e6a5c85554c1c..db25d156a5484 100644 --- a/docs/machine-learning/automate-training-with-model-builder.md +++ b/docs/machine-learning/automate-training-with-model-builder.md @@ -1,7 +1,7 @@ --- title: What is Model Builder and how does it work? description: How to use the ML.NET Model Builder to automatically train a machine learning model -ms.date: 10/12/2021 +ms.date: 11/10/2022 ms.custom: overview, mlnet-tooling #Customer intent: As a developer, I want to use Model Builder to automatically train a model using a visual interface. --- @@ -47,6 +47,8 @@ Each scenario maps to a different Machine Learning Task which include: - Binary classification - Multiclass classification +- Image classification +- Text classification - Regression - Clustering - Anomaly detection @@ -62,7 +64,9 @@ For more information about the different ML Tasks supported by ML.NET see [Machi In Model Builder, you need to select a scenario. The type of scenario depends on what type of prediction you are trying to make. -#### Data classification +#### Tabular + +##### Data classification Classification is used to categorize data into categories. @@ -70,29 +74,115 @@ Classification is used to categorize data into categories. ![Examples of multiclass classification including document and product classification, support ticket routing, and customer issue prioritization](media/multiclass-classification-examples.png) -#### Value prediction +##### Value prediction Value prediction, which falls under the regression task, is used to predict numbers. ![Diagram showing regression examples such as price prediction, sales forecasting, and predictive maintenance](media/regression-examples.png) -#### Image classification +#### Recommendation + +The recommendation scenario predicts a list of suggested items for a particular user, based on how similar their likes and dislikes are to other users'. + +You can use the recommendation scenario when you have a set of users and a set of "products", such as items to purchase, movies, books, or TV shows, along with a set of users' "ratings" of those products. + +:::row::: + :::column::: + **Example Input** + :::column-end::: + :::column::: + **Example Output** + :::column-end::: +:::row-end::: +:::row::: + :::column::: + :::row::: + :::column::: + UserId + :::column-end::: + :::column::: + ProductId + :::column-end::: + :::row-end::: + :::row::: + :::column::: + 1 + :::column-end::: + :::column::: + 2 + :::column-end::: + :::row-end::: + :::column-end::: + :::column::: + :::row::: + :::column::: + Rating + :::column-end::: + :::row-end::: + :::row::: + :::column::: + 7.5 + :::column-end::: + :::row-end::: + :::column-end::: +:::row-end::: + +#### Computer vision + +##### Image classification Image classification is used to identify images of different categories. For example, different types of terrain or animals or manufacturing defects. You can use the image classification scenario if you have a set of images, and you want to classify the images into different categories. -#### Object detection +##### Object detection Object detection is used to locate and categorize entities within images. For example, locating and identifying cars and people in an image. You can use object detection when images contain multiple objects of different types. -#### Recommendation - -The recommendation scenario predicts a list of suggested items for a particular user, based on how similar their likes and dislikes are to other users'. - -You can use the recommendation scenario when you have a set of users and a set of "products", such as items to purchase, movies, books, or TV shows, along with a set of users' "ratings" of those products. +#### Language + +##### Text classification + +Text classification categorizes raw text input. + +You can use the text classification scenario if you have a set of documents or comments, and you want to classify them into different categories. + +:::row::: + :::column::: + **Example Input** + :::column-end::: + :::column::: + **Example Output** + :::column-end::: +:::row-end::: +:::row::: + :::column::: + :::row::: + :::column::: + SentimentText + :::column-end::: + :::row-end::: + :::row::: + :::column::: + I really like this steak! + :::column-end::: + :::row-end::: + :::column-end::: + :::column::: + :::row::: + :::column::: + PredictedLabel + :::column-end::: + :::row-end::: + :::row::: + :::column::: + Positive + :::column-end::: + :::row-end::: + :::column-end::: +:::row-end::: ## Environment diff --git a/docs/machine-learning/resources/metrics.md b/docs/machine-learning/resources/metrics.md index 15426262882dd..3281192bf4a9b 100644 --- a/docs/machine-learning/resources/metrics.md +++ b/docs/machine-learning/resources/metrics.md @@ -1,7 +1,7 @@ --- title: ML.NET metrics description: Understand the metrics that are used to evaluate the performance of an ML.NET model -ms.date: 12/17/2019 +ms.date: 11/10/2022 --- # Evaluate your ML.NET model with metrics diff --git a/docs/machine-learning/tutorials/health-violation-classification-model-builder.md b/docs/machine-learning/tutorials/health-violation-classification-model-builder.md index 37cbf1fb5260f..10f3456c4d880 100644 --- a/docs/machine-learning/tutorials/health-violation-classification-model-builder.md +++ b/docs/machine-learning/tutorials/health-violation-classification-model-builder.md @@ -3,7 +3,7 @@ title: 'Tutorial: Classify health violations with Model Builder' description: This tutorial illustrates how to build a multiclass classification model using ML.NET Model Builder to classify restaurant health violation severity in San Francisco. author: luisquintanilla ms.author: luquinta -ms.date: 09/20/2021 +ms.date: 11/10/2022 ms.topic: tutorial ms.custom: mvc,mlnet-tooling #Customer intent: As a non-developer, I want to use Model Builder to automatically generate a model to classify violation severity using Model Builder. diff --git a/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md b/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md index 2669afee30695..6ab75266e5e28 100644 --- a/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md +++ b/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md @@ -96,9 +96,9 @@ The machine learning task used to train the sentiment analysis model in this tut 1. Once training is complete, the results from the training process are displayed in the *Training results* section of the *Train* screen. In addition to providing training results, three code-behind files are created under the *SentimentAnalysis.mbconfig* file. - - *SentimentAnalysis.consumption.cs* - Contains code for consuming your model. - - *SentimentAnalysis.training.cs* - Contains code for retraining your model. - - **SentimentAnalysis.zip* - The serialized version of your model. + - *SentimentAnalysis.consumption.cs* - This file contains the `ModelInput` and `ModelOutput` schemas as well as the `Predict` function generated for consuming the model. + - *SentimentAnalysis.training.cs* - This file contains the training pipeline (data transforms, algorithm, algorithm hyperparameters) chosen by Model Builder to train the model. You can use this pipeline for re-training your model. + - **SentimentAnalysis.zip* - This is a serialized zip file which represents your trained ML.NET model. 1. Select the **Next step** button to move to the next step. @@ -356,7 +356,9 @@ The results returned by the `OnGetAnalyzeSentiment` will be dynamically displaye 1. Finally, register the event handler and bind it to the `textarea` element with the `id=Message` attribute. - $("#Message").on('change input paste', updateSentiment) + ```javascript + $("#Message").on('change input paste', updateSentiment) + ``` ## Run the application From 070ebf3b69db7382513f77140c241314d38bca39 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Mon, 7 Nov 2022 13:00:18 -0500 Subject: [PATCH 13/49] Add forecasting --- .../automate-training-with-model-builder.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/docs/machine-learning/automate-training-with-model-builder.md b/docs/machine-learning/automate-training-with-model-builder.md index db25d156a5484..a37b1ce540fb2 100644 --- a/docs/machine-learning/automate-training-with-model-builder.md +++ b/docs/machine-learning/automate-training-with-model-builder.md @@ -127,7 +127,13 @@ You can use the recommendation scenario when you have a set of users and a set o :::column-end::: :::row-end::: -#### Computer vision +##### Forecasting + +The forecasting scenario uses historical data with a time-series or seasonal component to it. + +You can use the forecasting scenario to forecast demand or sale for a product. + +#### Computer Vision ##### Image classification From 3d3bc7b8e3e20262216c7678d16fc84f3a95ceb7 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Mon, 7 Nov 2022 13:02:10 -0500 Subject: [PATCH 14/49] Update ML.NET supported scenarios --- .../machine-learning/how-does-mldotnet-work.md | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/docs/machine-learning/how-does-mldotnet-work.md b/docs/machine-learning/how-does-mldotnet-work.md index f12624665d7c8..b0d03280893be 100644 --- a/docs/machine-learning/how-does-mldotnet-work.md +++ b/docs/machine-learning/how-does-mldotnet-work.md @@ -1,7 +1,7 @@ --- title: What is ML.NET and how does it work? description: ML.NET gives you the ability to add machine learning to .NET applications, in either online or offline scenarios. With this capability, you can make automatic predictions using the data available to your application without having to be connected to a network to use ML.NET. This article explains the basics of machine learning in ML.NET. -ms.date: 08/12/2021 +ms.date: 11/10/2022 ms.topic: overview ms.custom: mvc #Customer intent: As a developer, I want to learn how ML.NET works so that I can leverage machine learning in my applications. @@ -67,6 +67,22 @@ Examples of the type of predictions that you can make with ML.NET: Categorize pathologies in medical images :::column-end::: :::row-end::: +:::row::: + :::column::: + **Text classification** + :::column-end::: + :::column::: + Categorize documents based on their content. + :::column-end::: +:::row-end::: +:::row::: + :::column::: + **Sentence similarity** + :::column-end::: + :::column::: + Measure how similar two sentences are. + :::column-end::: +:::row-end::: ## Hello ML.NET World From 4220008a402300137bb9f44b9737c05b23fec43c Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Mon, 7 Nov 2022 13:34:15 -0500 Subject: [PATCH 15/49] Fix column build issues --- .../automate-training-with-model-builder.md | 81 ++++++++----------- 1 file changed, 32 insertions(+), 49 deletions(-) diff --git a/docs/machine-learning/automate-training-with-model-builder.md b/docs/machine-learning/automate-training-with-model-builder.md index a37b1ce540fb2..0b04d88341c0e 100644 --- a/docs/machine-learning/automate-training-with-model-builder.md +++ b/docs/machine-learning/automate-training-with-model-builder.md @@ -80,6 +80,27 @@ Value prediction, which falls under the regression task, is used to predict numb ![Diagram showing regression examples such as price prediction, sales forecasting, and predictive maintenance](media/regression-examples.png) +:::row::: + :::column::: + **Sample Input** + :::column-end::: + :::column::: + **Sample Output** + :::column-end::: +:::row-end::: +:::row::: + :::column::: + | UserId | ProductId | + | --- | --- | + | 1 | 2 | + :::column-end::: + :::column::: + | Rating | + | --- | + | 4.5 | + :::column-end::: +:::row-end::: + #### Recommendation The recommendation scenario predicts a list of suggested items for a particular user, based on how similar their likes and dislikes are to other users'. @@ -88,42 +109,22 @@ You can use the recommendation scenario when you have a set of users and a set o :::row::: :::column::: - **Example Input** + **Sample Input** :::column-end::: :::column::: - **Example Output** + **Sample Output** :::column-end::: :::row-end::: :::row::: :::column::: - :::row::: - :::column::: - UserId - :::column-end::: - :::column::: - ProductId - :::column-end::: - :::row-end::: - :::row::: - :::column::: - 1 - :::column-end::: - :::column::: - 2 - :::column-end::: - :::row-end::: + | UserId | ProductId | + | --- | --- | + | 1 | 2 | :::column-end::: :::column::: - :::row::: - :::column::: - Rating - :::column-end::: - :::row-end::: - :::row::: - :::column::: - 7.5 - :::column-end::: - :::row-end::: + | Rating | + | --- | + | 4.5 | :::column-end::: :::row-end::: @@ -147,7 +148,7 @@ Object detection is used to locate and categorize entities within images. For e You can use object detection when images contain multiple objects of different types. -#### Language +#### Natural Language Processing ##### Text classification @@ -165,28 +166,10 @@ You can use the text classification scenario if you have a set of documents or c :::row-end::: :::row::: :::column::: - :::row::: - :::column::: - SentimentText - :::column-end::: - :::row-end::: - :::row::: - :::column::: - I really like this steak! - :::column-end::: - :::row-end::: + I really like this steak! :::column-end::: :::column::: - :::row::: - :::column::: - PredictedLabel - :::column-end::: - :::row-end::: - :::row::: - :::column::: - Positive - :::column-end::: - :::row-end::: + Positive :::column-end::: :::row-end::: From ee278057f2d7432914fffa65c665a1ffd52b63b5 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Mon, 7 Nov 2022 13:40:59 -0500 Subject: [PATCH 16/49] Fix build issues --- .../automate-training-with-model-builder.md | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/machine-learning/automate-training-with-model-builder.md b/docs/machine-learning/automate-training-with-model-builder.md index 0b04d88341c0e..7735e920eba1e 100644 --- a/docs/machine-learning/automate-training-with-model-builder.md +++ b/docs/machine-learning/automate-training-with-model-builder.md @@ -45,16 +45,16 @@ A scenario is a description of the type of prediction you want to make using you Each scenario maps to a different Machine Learning Task which include: -- Binary classification -- Multiclass classification -- Image classification -- Text classification -- Regression -- Clustering -- Anomaly detection -- Ranking -- Recommendation -- Forecasting +| Task | Scenario | +| --- | --- | +| Binary classification | Data classification | +| Multiclass classification | Data classification | +| Multiclass classification | Data classification | +| Image classification | Image classification | +| Text classification | Text classification | +| Regression | Value prediction | +| Recommendation | Recommendation | +| Forecasting | Forecasting | For example, the scenario of classifying sentiments as positive or negative would fall under the binary classification task. From 5f8a888828dafe5026c5d315461e6e2896fbf8d6 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Mon, 7 Nov 2022 13:54:04 -0500 Subject: [PATCH 17/49] Remove extra multiclass scenario --- docs/machine-learning/automate-training-with-model-builder.md | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/machine-learning/automate-training-with-model-builder.md b/docs/machine-learning/automate-training-with-model-builder.md index 7735e920eba1e..9a13ef09225ab 100644 --- a/docs/machine-learning/automate-training-with-model-builder.md +++ b/docs/machine-learning/automate-training-with-model-builder.md @@ -49,7 +49,6 @@ Each scenario maps to a different Machine Learning Task which include: | --- | --- | | Binary classification | Data classification | | Multiclass classification | Data classification | -| Multiclass classification | Data classification | | Image classification | Image classification | | Text classification | Text classification | | Regression | Value prediction | From 490c7108e8083d771021fb2f1be9d374e01626ff Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Tue, 8 Nov 2022 15:47:41 -0500 Subject: [PATCH 18/49] Add sample inputs and outputs to scenarios --- .../automate-training-with-model-builder.md | 77 +++++++++++++++--- .../dog-classification.png | Bin 0 -> 12683 bytes .../dog-object-detection-min.png | Bin 0 -> 82918 bytes .../dog-object-detection.png | Bin 0 -> 867044 bytes 4 files changed, 67 insertions(+), 10 deletions(-) create mode 100644 docs/machine-learning/media/automate-training-with-model-builder/dog-classification.png create mode 100644 docs/machine-learning/media/automate-training-with-model-builder/dog-object-detection-min.png create mode 100644 docs/machine-learning/media/automate-training-with-model-builder/dog-object-detection.png diff --git a/docs/machine-learning/automate-training-with-model-builder.md b/docs/machine-learning/automate-training-with-model-builder.md index 9a13ef09225ab..992953b2ff8d2 100644 --- a/docs/machine-learning/automate-training-with-model-builder.md +++ b/docs/machine-learning/automate-training-with-model-builder.md @@ -69,9 +69,30 @@ In Model Builder, you need to select a scenario. The type of scenario depends on Classification is used to categorize data into categories. -![Diagram showing examples of binary classification including fraud detection, risk mitigation, and application screening](media/binary-classification-examples.png) + + +:::row::: + :::column::: + **Sample Input** + :::column-end::: + :::column::: + **Sample Output** + :::column-end::: +:::row-end::: +:::row::: + :::column::: + | SepalLength | SepalWidth | Petal Length | Petal Width | Species (Label) | + | --- | --- | --- | --- | --- | + | 5.1 | 3.5 | 1.4 | 0.2 | setosa | + :::column-end::: + :::column::: + | PredictedLabel | + | --- | + | setosa | + :::column-end::: +:::row-end::: ##### Value prediction @@ -89,12 +110,12 @@ Value prediction, which falls under the regression task, is used to predict numb :::row-end::: :::row::: :::column::: - | UserId | ProductId | - | --- | --- | - | 1 | 2 | + | vendor_id | rate_code | passenger_count | trip_time_in_secs | trip_distance | payment_type | fare_amount (Label) | + | --- | --- | --- | --- | --- | --- | --- | + | CMT | 1 | 1 | 1271 | 3.8 | CRD | 17.5 | :::column-end::: :::column::: - | Rating | + | Score | | --- | | 4.5 | :::column-end::: @@ -116,12 +137,12 @@ You can use the recommendation scenario when you have a set of users and a set o :::row-end::: :::row::: :::column::: - | UserId | ProductId | - | --- | --- | - | 1 | 2 | + | UserId | ProductId | Rating (Label) | + | --- | --- | --- | + | 1 | 2 | 4.2 | :::column-end::: :::column::: - | Rating | + | PredictedLabel | | --- | | 4.5 | :::column-end::: @@ -141,12 +162,48 @@ Image classification is used to identify images of different categories. For exa You can use the image classification scenario if you have a set of images, and you want to classify the images into different categories. +:::row::: + :::column::: + **Sample Input** + :::column-end::: + :::column::: + **Sample Output** + :::column-end::: +:::row-end::: +:::row::: + :::column::: + :::image type="content" source="media/automate-training-with-model-builder/dog-classification.png" alt-text="Profile view of standing pug"::: + :::column-end::: + :::column::: + | PredictedLabel | + | --- | + | Dog | + :::column-end::: +:::row-end::: + ##### Object detection Object detection is used to locate and categorize entities within images. For example, locating and identifying cars and people in an image. You can use object detection when images contain multiple objects of different types. +:::row::: + :::column::: + **Sample Input** + :::column-end::: + :::column::: + **Sample Output** + :::column-end::: +:::row-end::: +:::row::: + :::column::: + :::image type="content" source="media/automate-training-with-model-builder/dog-classification.png" alt-text="Profile view of standing pug"::: + :::column-end::: + :::column::: + :::image type="content" source="media/automate-training-with-model-builder/dog-object-detection-min.png" alt-text="Profile view of standing pug with bounding box and dog label"::: + :::column-end::: +:::row-end::: + #### Natural Language Processing ##### Text classification diff --git a/docs/machine-learning/media/automate-training-with-model-builder/dog-classification.png b/docs/machine-learning/media/automate-training-with-model-builder/dog-classification.png new file mode 100644 index 0000000000000000000000000000000000000000..bdbf290e024f4c99925cdbc64f686ec6e6a18833 GIT binary patch literal 12683 zcmb7~Wl$W=7w#AL;O-KFdw?K8f=h6M1`8y}vOv%*?ry=|SzH1v65Kt(c3}yN^WyGy z`B&W!_v<~?RWm)O>r7A8)Tw%YPd~$-5r7W>bTqX84Rkbg40Lo1Ok6C?mmt8w#l|Hh zAR;0pAbdqkN<~IYLP_$9kerU3lA4;9mX?T&o{^r0k&1?v=6^*{&@nJDF);D5u<&V! z35jX`zvH4M7XaE4pOP&~jLBcGEDM!kpV}&K;8bp>hj04m!5`+}D#;N@#DUvk34FLwY8R5VP?mxuq0|1Bm)Cn04Pl+(c= zvtoIvUf94Yubb5Sm;6M}?euvGfRFaFh!~9+AOnaCz|z6r2e0(e<~}{a*P~sqm9wFm-1bYhWoaj4qNK8844jk!r$b|YUi~`82qV=GiSIY|k#w3&KkB>% z6=Lcdy^J}v#@3ufQFC&ipgrBYafe)iU@9jqI#3H$Z?X{Xq>C&CZpRNl7Z(zNu5#c3 zpDOy@`bAY`LsUbbhgVtK>)viFD(S9PyeBFWl2W!-v6Ef6$v-F>fyBWbR;uT1+tV|k zR;oRO^b7hAyeGo}VlMHJKW`Kro@2r#>GL|a4s!&Cb&g~Z)CrRr zFu!dVdB?;>}3RsfPn^oGl>ZuCNZGdxLeM`7NLC$4qJ zX7VtBR|LQ0W&||`+0WWV1UT3kVWR{j_aSv|H&*bts+7wFhxSBeEPRbYZq@If0UYYF zF2+ZD#ctjGz?SO`z_*GF3}J=J<-|DG78sOnO>$teDvk-Mf|Y-aq;om=AfV@kz6baF zcoW=F_&s44+n<2Y2ot8h}XJ=Ha*q^W*3D}eW=GO{_baG z+=|)v5bzbj@POB_0K3FoS?EU#b4xZl^P8VZNtgI6CUbA}8R-NV{zz*G2}Gm} zyb~L3!*=;y3hZ=C%PC0_knrT%LORo z*04=+Db4SbrtB~*Z8kp#K!ak=ee;mb5u~m7_+-XLG-uBo;&4>n_2B11{cSU7;`?AJ z%#m~mD_}12jjmN<@Y?qd-WX-4gfyayW8NiDXRu>tX zs(+;Q_|BuPx$+%-cYnlj1Yc&YZRSO;4R+z)5BB1yEnC^tzxgC_qg*OaOoE5NGVzGO zJFOTzMW)WVTi<@`j%zGOO9ZVPuQa91gp^CJ;ukTVizh`uz%AP@0;W|Q$@8JxIBB2WG>zP3xX!o-9x6seP zN4=_f*|T>iY?R9;rJjtBuE+sB+7SHWPgtki(1tV1uDqfUp*^0;5W&of|Mt0U!pl^~ z!c`Un?a-I;2#&1<4YvR3FnBFYYo)2`w$Y1P{?XAJTa{`EUa-dv#3RCZ>aeEDid-)t zm^8OHH6TwR)7NiK{iQcMWB76Iq^YgxlMf0Y5CCgF^b_r0UPRS>%N7t{lC&4YqvOw3 z94say;!AMdwJ?ZtGAlANThpi(UqLjZqiK{hk0ooMGurVP1|Z>+yf1D2S;|b#_~bC6y8z)J9y}{@?UH^LtahRJ(c)#*cP?s2i)-Go6|Nv=XD8Ar+fw z#|`ZfN;Hr0gG58W-LjqmHjc$Y>?_Ocm^c;>3TnC zl0cp?J{LO%5cBz`n>YNG7V7$INX|NjAM?Gf+tJk7uYMYbwqm|I$l42vL@+3~>O_9EBWmx3>sQmH{u5-JaReN`Ka4Nw7Le6PgX{r2udqlJFkZbKl;F8oSGFQIa$ey6 z?yvZGJuIbWp!t#OB0Es$XREjOu#c9}}KsST{v>Qlq%G z`)%LCF?Rzhc_u)a6`D@;B$98d8^FIC-jQ6GAuxx>iS`J#z!FbeVKtKq%|4KW7Iuqu ztt_uM$tv!cs}786Oq9^{@yP;lRhJ%+ao59dX9O@73e3ztzolgOy;hiK1Q({z{#`G* z%GDtLdxnd6s9<~J;1zoy7rrar-1(f|B-zDxb#e_hRrJ5P>|(mOL|~#G&=NfTLG6c0 zZE+0b8&=qdd6A^AIxG&%c4_;%>5~7x8VB6tD`*>Q{Gt}6s1L}jX?_N1XmA$4JMM2^ z;-sGZsy_GLO3U-M@h(jtq8{+DO*~o;z_N*PEywarV}fyHba2c-**9sk4+Evr8Eg$K zMSfQ2-fo}}CBDPu(NC6p>6cUY8w>R_6B8vz6avn2-TwFYgyUFN6rcDlES~7=&X#Kq zp`h^`tO4%Qy6=ncOn+F8{3+&4GB#O7t)>c=Jnv@hcETy;PB>N{r+R{*Dm>{px-bVbxEZm2^{0( zeIIM#6R(Hqnm(^>M>P7Hr$w%8X}(RDzq5DcDWm9oOsb!0;!APu+@{Y);>yhM_fpvX zRc}*pJ(ek)x|D21S0cqTz#jlXF-&K{nWqV{kTy8hakIRSji2|wd*(WNW ztU6oX_}VXZhZGaIVX|WXsknJfIkU5X>pT(FR`hKem|3{q9xQMLz^{QxmWBeIF zn5pwgS5Illnx8I??Ci!eRY_2_B(?eC^)==-a*%Oxr}(ZzU{~BNOTxF+Hil)CUPS&} z&&&ts65pBX*t);-)Imm0TmM12Zda<3qj66nwnYs)dI)#egWFH~K=Y~9GIvHbe)xXfhclP}y&!v*YtJN?=Eo%0jk!k_Sa|LI9{- zJWYRW1@hWR7(Q{KDMWa(py55QG+CpP>;?13!ixdSKWRZ@UoDuKbCIgg zfN`aEm4YRrr4dZ1RVE=7=5F7>pm}WbJ)rAq zIL9fB4Y#rYGUKz-cfW3ushvSVQzBp2Mq$C*SvQP8pu~p0(f-P9N&ZRm={Mr6XEBfm zvzWcu4ODWtbnkaU^s0W>g$lE!>htpbdd+9W)k#0D&@kG!J#Af^&j9mB0YbVV)L)IX zE*}XKteR7!&Q;3|>bl?O*NPVs^i4Ft;UO9ypN4LJk2Nyg*nWh@76E*JC%Fzj!Tolf zi|9FXXJCeUuYsg&DVKk^6S=;XnhOue? 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Although samples and code from earlier versions still work, it is highly recommended you use the APIs introduced in this version for new projects. + +For more information on installing NuGet packages, see the following guides: + +- [Install and use a NuGet package in Visual Studio](/nuget/quickstart/install-and-use-a-package-in-visual-studio) +- [Install and use a package in Visual Studio for Mac](nuget/quickstart/install-and-use-a-package-in-visual-studio-mac) +- [Install and use a package (dotnet CLI)](/nuget/quickstart/install-and-use-a-package-using-the-dotnet-cli) + ## Quick Start AutoML provides several defaults for quickly training models. In this section you will learn how to: @@ -30,7 +43,9 @@ CMT|1|1|637|1.4|CRD|8.5 ### Load data -To load your data, use the `InferColumn` method. +Start by inizializing your . `MLContext` is a starting point for all ML.NET operations. Initializing mlContext creates a new ML.NET environment that can be shared across the model creation workflow objects. It's similar, conceptually, to `DBContext` in Entity Framework. + +Then, to load your data, use the method. ```csharp // Initialize MLContext @@ -41,7 +56,19 @@ ColumnInferenceResults columnInference = ctx.Auto().InferColumns("taxi-fare.csv", labelColumnName: "fare_amount", groupColumns:false); ``` -Then, use the `TextLoaderOptions` defined by the `ColumnInferenceResults` to create a `TextLoader` to load your data into an `IDataView`. + loads a few rows from the dataset. It then inspects the data and tries to guess or infer the data type for each of the columns based on their content. + +The default behavior is to group columns of the same type into feature vectors or arrays containing the elements for each of the grouped columns. Setting `groupColumns` to `false` overrides that default behavior and only performs column inference without grouping columns. By keeping columns separate it allows you to apply different data transformations when preprocessing the data at the individual column level rather than the grouping. + +The result of a object which contains the options needed to create a as well as column information. + +For the sample dataset in *taxi-fare.csv*, column information might look like the following: + +- **LabelColumnName**: fare_amount +- **CategoricalColumnNames**: vendor_id, payment_type +- **NumericColumnNames**: rate_code, passenger_count, trip_time_in_secs, trip_distance + +Once you have your column information, use the defined by the to create a to load your data into an . ```csharp // Create text loader @@ -68,12 +95,12 @@ SweepablePipeline pipeline = .Append(ctx.Auto().Regression(labelColumnName:columnInference.ColumnInformation.LabelColumnName)); ``` -A `SweepablePipeline` is a pipeline containing a colelction of `SweepableEstimators`. A `SweepableEstimator` is an ML.NET `Estimator` with a `SearchSpace`. +A `SweepablePipeline` is a pipeline containing a colelction of `SweepableEstimators`. A `SweepableEstimator` is an ML.NET with a `SearchSpace`. The `Featurizer` is a convenience API which builds a `SweepablePipeline` of data processing sweepable estimators based on the column information you provide. Instead of building a pipeline from scratch, `Featurizer` automates the data preprocessing step. > [!TIP] -> Use `Featurizer` with `ColumnInferenceResults` to maximize the utility of AutoML. +> Use `Featurizer` with to maximize the utility of AutoML. For training, AutoML provides a sweepable pipeline with default trainers and search space configurations for the following machine learning tasks: @@ -106,11 +133,11 @@ experiment In this example, you: - Set the sweepable pipeline to run during the experiment using `SetPipeline`. -- Choose `RSquared` as the metric to optimize during training using `SetRegressionMetric`. +- Choose `RSquared` as the metric to optimize during training using `SetRegressionMetric`. For more information on evaluation metrics, see the [evaluate your ML.NET model with metrics](../resources/metrics.md) guide. - Set 60 seconds as the amount of time you want to train for using `SetTrainingTimeInSeconds`. - Provide the training and validation datasets to use using `SetDataset`. -Once your experiment is defined, you'll want some way to track its progress. The quickest way to track progress is by defining the `Log` event from `MLContext`. +Once your experiment is defined, you'll want some way to track its progress. The quickest way to track progress is by defining the event from . ```csharp // Log experiment trials @@ -124,7 +151,7 @@ ctx.Log += (_,e) => { ### Run your experiment -Now that you've defined your experiment, use the `Run` or `RunAsync` methods to start your experiment. +Now that you've defined your experiment, use the `RunAsync` method to start your experiment. ```csharp TrialResult experimentResults = await experiment.RunAsync(); @@ -179,14 +206,14 @@ AutoML provides a set of preconfigured search spaces for trainers in the followi - Multiclass classification - Regression -In this example, the search space used is for the `SdcaRegressionTrainer`. +In this example, the search space used is for the . ```csharp // Initialize default Sdca search space var sdcaSearchSpace = new SearchSpace(); ``` -Then, use the search space to define a custom factory method to create the `SdcaRegressionTrainer`. In this example, the values of `L1Regularization` and `L2Regularization` are both being set to something other than the default. For `L1Regularization`, the value set is determined by the tuner during each trial. The `L2Regularization` is fixed for each trial to the hard-coded value. During each trial, custom factory method's output is an `SdcaRegressionTrainer` with the configured hyperparameters. +Then, use the search space to define a custom factory method to create the . In this example, the values of `L1Regularization` and `L2Regularization` are both being set to something other than the default. For `L1Regularization`, the value set is determined by the tuner during each trial. The `L2Regularization` is fixed for each trial to the hard-coded value. During each trial, custom factory method's output is an with the configured hyperparameters. ```csharp // Use the search space to define a custom factory to create an SdcaRegressionTrainer @@ -238,6 +265,7 @@ By default, AutoML supports binary classification, multiclass classification, an - Recommendation - Forecasting - Ranking +- Image classification - Text classification - Sentence similarity @@ -252,7 +280,7 @@ For example, given hourly energy demand data that looks like the following: | 179759 | | 314597 | -You want to use the `ForecastBySsa` trainer to forecast future demand. +You want to use the trainer to forecast future demand. In order to do so you'll have to: @@ -461,7 +489,7 @@ In order to do so you'll have to: ### Choose a different tuner -AutoML supports a variety of tuning algorithms to iterate through the search space in search of the optimal hyperparamters. By default, it uses the Eci Cost Frugal tuner. Using experiment extension methods, you can choose another tuner that best fits your scenario. +AutoML supports a variety of tuning algorithms to iterate through the search space in search of the optimal hyperparameters. By default, it uses the Eci Cost Frugal tuner. Using experiment extension methods, you can choose another tuner that best fits your scenario. Use the following methods to set your tuner: @@ -479,7 +507,7 @@ experiment.SetSmacTuner(); ### Configure experiment monitoring -The quickest way to monitor the progress of an experiment is to define the `Log` event from `MLContext`. However, the `Log` event outputs a raw dump of the logs generated by AutoML during each trial. Because of the large amount of unformatted information, it's difficult. +The quickest way to monitor the progress of an experiment is to define the event from . However, the event outputs a raw dump of the logs generated by AutoML during each trial. Because of the large amount of unformatted information, it's difficult. For a more controlled monitoring experience, implement a class with the `IMonitor` interface. @@ -609,236 +637,4 @@ Checkpoints provide a way for you to save intermediary outputs from the training ```csharp var checkpointPath = Path.Join(Directory.GetCurrentDirectory(), "automl"); experiment.SetCheckpoint(checkpointPath); -``` - - +``` \ No newline at end of file From 2883e50a94cefe5174c9eee045d3e4a28d86a342 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Tue, 8 Nov 2022 16:37:59 -0500 Subject: [PATCH 20/49] Updates scenarios --- .../automated-machine-learning-mlnet.md | 2 +- .../how-to-guides/how-to-use-the-automl-api.md | 12 ++++++++---- .../media/model-builder-scenarios-2-0.png | Bin 0 -> 96877 bytes .../sentiment-analysis-model-builder.md | 4 ++-- 4 files changed, 11 insertions(+), 7 deletions(-) create mode 100644 docs/machine-learning/media/model-builder-scenarios-2-0.png diff --git a/docs/machine-learning/automated-machine-learning-mlnet.md b/docs/machine-learning/automated-machine-learning-mlnet.md index a6907771709ea..fcd04ca703b13 100644 --- a/docs/machine-learning/automated-machine-learning-mlnet.md +++ b/docs/machine-learning/automated-machine-learning-mlnet.md @@ -35,7 +35,7 @@ Whether you're just getting started with machine learning or you're an experienc - **Eci Cost Frugal Tuner** - Implementation of Cost Frugal Tuner for hierarchical search spaces. Default tuner used by AutoML. - **SMAC** - Tuner that uses random forests to apply Bayesian optimization. - **Grid Search** - Tuner that works best for small search spaces. -- **Random Search** + - **Random Search** - **Sweepable Estimator** - An ML.NET estimator that contains a search space. - **Sweepable Pipeline** - An ML.NET pipeline that contains one or more Sweepable Estimators. - **Trial Runner** - AutoML component that uses sweepable pipelines and trial settings to generate trial results from model training and evaluation. diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index e933cbf9c73b8..0e6d281192cb5 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -95,9 +95,13 @@ SweepablePipeline pipeline = .Append(ctx.Auto().Regression(labelColumnName:columnInference.ColumnInformation.LabelColumnName)); ``` -A `SweepablePipeline` is a pipeline containing a colelction of `SweepableEstimators`. A `SweepableEstimator` is an ML.NET with a `SearchSpace`. +A `SweepablePipeline` is a pipeline containing a collection of `SweepableEstimators`. A `SweepableEstimator` is an ML.NET with a `SearchSpace`. -The `Featurizer` is a convenience API which builds a `SweepablePipeline` of data processing sweepable estimators based on the column information you provide. Instead of building a pipeline from scratch, `Featurizer` automates the data preprocessing step. +The `Featurizer` is a convenience API which builds a sweepable pipeline of data processing sweepable estimators based on the column information you provide. Instead of building a pipeline from scratch, `Featurizer` automates the data preprocessing step. For more information on supported transforms by ML.NET, see the [data transformations guide](../resources/transforms.md). + +The resulting output is a single column containing a numerical feature vector representing the transformed data for each of the columns. This feature vector is then used as input for the algorithms used to train a machine learning model. + +If you want finer control over your data preprocessing, you can create a pipeline with each of the individual preprocessing steps. For more information, see the [prepare data for building a model guide](prepare-data-ml-net.md). > [!TIP] > Use `Featurizer` with to maximize the utility of AutoML. @@ -134,7 +138,7 @@ In this example, you: - Set the sweepable pipeline to run during the experiment using `SetPipeline`. - Choose `RSquared` as the metric to optimize during training using `SetRegressionMetric`. For more information on evaluation metrics, see the [evaluate your ML.NET model with metrics](../resources/metrics.md) guide. -- Set 60 seconds as the amount of time you want to train for using `SetTrainingTimeInSeconds`. +- Set 60 seconds as the amount of time you want to train for using `SetTrainingTimeInSeconds`. A good heuristic to determing how long to train for is the size of your data. Typically, larger datasets require longer training time. For additional guidance, see [](../automate-training-with-model-builder.md) - Provide the training and validation datasets to use using `SetDataset`. Once your experiment is defined, you'll want some way to track its progress. The quickest way to track progress is by defining the event from . @@ -495,7 +499,7 @@ Use the following methods to set your tuner: - **SMAC** - `SetSmacTuner` - **Grid Search** - `SetGridSearchTuner` -- **Random Search** - ``SetRandomSearchTuner` +- **Random Search** - `SetRandomSearchTuner` - **Cost Frugal** - `SetCostFrugalTuner` - **Eci Cost Frugal** - `SetEciCostFrugalTuner` diff --git a/docs/machine-learning/media/model-builder-scenarios-2-0.png b/docs/machine-learning/media/model-builder-scenarios-2-0.png new file mode 100644 index 0000000000000000000000000000000000000000..20057bfc5aa3ed2caa94711826b1ef4aeb2dafd8 GIT binary patch literal 96877 zcmYJabx<7L8!Vg%tkN5t* zTeqvGrq28^Qgxnr`gHe+R8f*aMbkyrhtc=n@qWmu)coDM*CIKfxQ3VE z@$yEZ(E7>EYZmzWbSb z$$Srgq_eKhxWi?@W^X$xYp!&mko#DG9vFR*@6b zYD$D)5uE6hkiLTa^V!T;^ODW>8|*JqMY^HVu!6jQDVtrYl@U$d=_?5vhekKuiwJb(f|)9Q^3%M1eL>%7 zv0x!n#?(4LN@U;A;2o%jpsi9Xp88IX8|*koEf#>cY!h7b zT1OWDnuio0F)90{B6_gVvM{G4OGl#@7CTH^GZ 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model, you need to select from the list of available machine learning scenarios provided by Model Builder. -1. For this sample, the task is text classification. In the *Scenario* step of the Model Builder extension, select the **Text classification** scenario. +For this sample, the task is text classification. 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z*O_l_-M0^<3=mTHyvu$GvV=b99v_uEaJ5LRr4AC}b1KkTLDc_6r2s;DyCpXOyLCgb zaNU|(A-M&kxw&<`6o(Vf;n7uY)&v%}*7(H3QOCDYihOq|p zWL;bFo|RBaisexV=4l>K_p}dt?_O&t@k-z7U5}ogaZ?3y>QRonG?_J5|5?j&3T^N= zD#0%v68#SFVqwcau!noge~^^7PTqm;w?>Hk zlAaeb-JZI}Yyis$L9nBVF=rqw1Jc@Qq+s9F9>}taVsTn-^McgCck6^T?RDHDJr#Q_}q_!JuHNrSBw0mtp+*N%Af_ SQ>`}$9}N|4<%$=UAO0V)9a@?I diff --git a/docs/machine-learning/tutorials/health-violation-classification-model-builder.md b/docs/machine-learning/tutorials/health-violation-classification-model-builder.md index 10f3456c4d880..b30603a67d592 100644 --- a/docs/machine-learning/tutorials/health-violation-classification-model-builder.md +++ b/docs/machine-learning/tutorials/health-violation-classification-model-builder.md @@ -69,7 +69,7 @@ When first adding Model Builder to the solution it will prompt you to create an ## Choose a scenario -![Model Builder wizard in Visual Studio](../media/model-builder-scenarios.png) +![Model Builder wizard in Visual Studio](../media/model-builder-scenarios-2-0.png) To train your model, select from the list of available machine learning scenarios provided by Model Builder. In this case, the scenario is *Data classification*. diff --git a/docs/machine-learning/tutorials/image-classification-model-builder.md b/docs/machine-learning/tutorials/image-classification-model-builder.md index bfa6a6ef5225f..0ade2070aa06f 100644 --- a/docs/machine-learning/tutorials/image-classification-model-builder.md +++ b/docs/machine-learning/tutorials/image-classification-model-builder.md @@ -69,7 +69,7 @@ When first adding Model Builder to the solution it will prompt you to create an ## Choose a scenario -![Model Builder Scenarios](../media/model-builder-scenarios.png) +![Model Builder Scenarios](../media/model-builder-scenarios-2-0.png) To train your model, you need to select from the list of available machine learning scenarios provided by Model Builder. diff --git a/docs/machine-learning/tutorials/movie-recommendation-model-builder.md b/docs/machine-learning/tutorials/movie-recommendation-model-builder.md index d471c75c674b5..467aaa19cbd0a 100644 --- a/docs/machine-learning/tutorials/movie-recommendation-model-builder.md +++ b/docs/machine-learning/tutorials/movie-recommendation-model-builder.md @@ -62,7 +62,7 @@ When first adding Model Builder to the solution it will prompt you to create an ## Choose a scenario -![Model Builder Scenarios](../media/model-builder-scenarios.png) +![Model Builder Scenarios](../media/model-builder-scenarios-2-0.png) To train your model, you need to select from the list of available machine learning scenarios provided by Model Builder. diff --git a/docs/machine-learning/tutorials/predict-prices-with-model-builder.md b/docs/machine-learning/tutorials/predict-prices-with-model-builder.md index 4ed862e325d85..7f1b29d3de5c7 100644 --- a/docs/machine-learning/tutorials/predict-prices-with-model-builder.md +++ b/docs/machine-learning/tutorials/predict-prices-with-model-builder.md @@ -76,7 +76,7 @@ When first adding Model Builder to the solution it will prompt you to create an ## Choose a scenario -![Model Builder wizard in Visual Studio](../media/model-builder-scenarios.png) +![Model Builder wizard in Visual Studio](../media/model-builder-scenarios-2-0.png) To train your model, you need to select from the list of available machine learning scenarios provided by Model Builder. In this case, the scenario is `Value prediction`. From 9fdecae561b35a955788301b2a7b963c76bfcea9 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Tue, 8 Nov 2022 17:51:51 -0500 Subject: [PATCH 22/49] Fix broken link --- .../machine-learning/how-to-guides/how-to-use-the-automl-api.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index 0e6d281192cb5..bd100cf748193 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -18,7 +18,7 @@ To use the AutoML API, install the [`Microsoft.ML.AutoML`](https://www.nuget.org For more information on installing NuGet packages, see the following guides: - [Install and use a NuGet package in Visual Studio](/nuget/quickstart/install-and-use-a-package-in-visual-studio) -- [Install and use a package in Visual Studio for Mac](nuget/quickstart/install-and-use-a-package-in-visual-studio-mac) +- [Install and use a package in Visual Studio for Mac](/nuget/quickstart/install-and-use-a-package-in-visual-studio-mac) - [Install and use a package (dotnet CLI)](/nuget/quickstart/install-and-use-a-package-using-the-dotnet-cli) ## Quick Start From bbf7909b723f74a41144bd609c16c1a365345409 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Tue, 8 Nov 2022 17:55:11 -0500 Subject: [PATCH 23/49] Fix mdlint errors --- .../how-to-guides/how-to-use-the-automl-api.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index bd100cf748193..8eb013314dae8 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -58,7 +58,7 @@ ColumnInferenceResults columnInference = loads a few rows from the dataset. It then inspects the data and tries to guess or infer the data type for each of the columns based on their content. -The default behavior is to group columns of the same type into feature vectors or arrays containing the elements for each of the grouped columns. Setting `groupColumns` to `false` overrides that default behavior and only performs column inference without grouping columns. By keeping columns separate it allows you to apply different data transformations when preprocessing the data at the individual column level rather than the grouping. +The default behavior is to group columns of the same type into feature vectors or arrays containing the elements for each of the grouped columns. Setting `groupColumns` to `false` overrides that default behavior and only performs column inference without grouping columns. By keeping columns separate it allows you to apply different data transformations when preprocessing the data at the individual column level rather than the grouping. The result of a object which contains the options needed to create a as well as column information. @@ -641,4 +641,4 @@ Checkpoints provide a way for you to save intermediary outputs from the training ```csharp var checkpointPath = Path.Join(Directory.GetCurrentDirectory(), "automl"); experiment.SetCheckpoint(checkpointPath); -``` \ No newline at end of file +``` From 90b8d505fdb49f1daf6eedf0abbbbb34337fc2f5 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Tue, 8 Nov 2022 17:55:32 -0500 Subject: [PATCH 24/49] Updates to scenario screenshot --- docs/machine-learning/automate-training-with-model-builder.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/machine-learning/automate-training-with-model-builder.md b/docs/machine-learning/automate-training-with-model-builder.md index 992953b2ff8d2..f1dd9368defe3 100644 --- a/docs/machine-learning/automate-training-with-model-builder.md +++ b/docs/machine-learning/automate-training-with-model-builder.md @@ -13,7 +13,7 @@ Model Builder uses automated machine learning (AutoML) to explore different mach You don't need machine learning expertise to use Model Builder. All you need is some data, and a problem to solve. Model Builder generates the code to add the model to your .NET application. -![Model Builder Scenarios](./media/model-builder-scenarios.png#lightbox) +![Model Builder Scenarios](./media/model-builder-scenarios-2-0.png#lightbox) > [!NOTE] > Model Builder is currently in Preview. From b52d8306a3a149c2d26392ac6a8f0d9959107a3e Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Tue, 8 Nov 2022 17:57:57 -0500 Subject: [PATCH 25/49] Add link to training time --- .../machine-learning/how-to-guides/how-to-use-the-automl-api.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index 8eb013314dae8..b6fc77b35453e 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -138,7 +138,7 @@ In this example, you: - Set the sweepable pipeline to run during the experiment using `SetPipeline`. - Choose `RSquared` as the metric to optimize during training using `SetRegressionMetric`. For more information on evaluation metrics, see the [evaluate your ML.NET model with metrics](../resources/metrics.md) guide. -- Set 60 seconds as the amount of time you want to train for using `SetTrainingTimeInSeconds`. A good heuristic to determing how long to train for is the size of your data. Typically, larger datasets require longer training time. For additional guidance, see [](../automate-training-with-model-builder.md) +- Set 60 seconds as the amount of time you want to train for using `SetTrainingTimeInSeconds`. A good heuristic to determine how long to train for is the size of your data. Typically, larger datasets require longer training time. For more information, see [training time guidance](../automate-training-with-model-builder.md#how-long-should-i-train-for)/. - Provide the training and validation datasets to use using `SetDataset`. Once your experiment is defined, you'll want some way to track its progress. The quickest way to track progress is by defining the event from . From f6e0af4f3899248743c9569585c00dd2aee195cf Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Tue, 8 Nov 2022 19:17:44 -0500 Subject: [PATCH 26/49] Add xrefs --- .../how-to-use-the-automl-api.md | 54 +++++++++---------- 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index b6fc77b35453e..8e292091e4e88 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -78,7 +78,7 @@ TextLoader loader = ctx.Data.CreateTextLoader(columnInference.TextLoaderOptions) IDataView data = loader.Load("taxi-fare.csv"); ``` -It's often good practice to split your data into train and validation sets. Use `TrainTestSplit` to create an 80% training and 20% validation split of your dataset. +It's often good practice to split your data into train and validation sets. Use to create an 80% training and 20% validation split of your dataset. ```csharp // Split into train (70%), validation (20%) sets @@ -95,9 +95,9 @@ SweepablePipeline pipeline = .Append(ctx.Auto().Regression(labelColumnName:columnInference.ColumnInformation.LabelColumnName)); ``` -A `SweepablePipeline` is a pipeline containing a collection of `SweepableEstimators`. A `SweepableEstimator` is an ML.NET with a `SearchSpace`. +A is a pipeline containing a collection of . A is an ML.NET with a . -The `Featurizer` is a convenience API which builds a sweepable pipeline of data processing sweepable estimators based on the column information you provide. Instead of building a pipeline from scratch, `Featurizer` automates the data preprocessing step. For more information on supported transforms by ML.NET, see the [data transformations guide](../resources/transforms.md). +The is a convenience API which builds a sweepable pipeline of data processing sweepable estimators based on the column information you provide. Instead of building a pipeline from scratch, automates the data preprocessing step. For more information on supported transforms by ML.NET, see the [data transformations guide](../resources/transforms.md). The resulting output is a single column containing a numerical feature vector representing the transformed data for each of the columns. This feature vector is then used as input for the algorithms used to train a machine learning model. @@ -136,10 +136,10 @@ experiment In this example, you: -- Set the sweepable pipeline to run during the experiment using `SetPipeline`. -- Choose `RSquared` as the metric to optimize during training using `SetRegressionMetric`. For more information on evaluation metrics, see the [evaluate your ML.NET model with metrics](../resources/metrics.md) guide. -- Set 60 seconds as the amount of time you want to train for using `SetTrainingTimeInSeconds`. A good heuristic to determine how long to train for is the size of your data. Typically, larger datasets require longer training time. For more information, see [training time guidance](../automate-training-with-model-builder.md#how-long-should-i-train-for)/. -- Provide the training and validation datasets to use using `SetDataset`. +- Set the sweepable pipeline to run during the experiment using . +- Choose `RSquared` as the metric to optimize during training using . For more information on evaluation metrics, see the [evaluate your ML.NET model with metrics](../resources/metrics.md) guide. +- Set 60 seconds as the amount of time you want to train for using . A good heuristic to determine how long to train for is the size of your data. Typically, larger datasets require longer training time. For more information, see [training time guidance](../automate-training-with-model-builder.md#how-long-should-i-train-for)/. +- Provide the training and validation datasets to use using . Once your experiment is defined, you'll want some way to track its progress. The quickest way to track progress is by defining the event from . @@ -155,13 +155,13 @@ ctx.Log += (_,e) => { ### Run your experiment -Now that you've defined your experiment, use the `RunAsync` method to start your experiment. +Now that you've defined your experiment, use the method to start your experiment. ```csharp TrialResult experimentResults = await experiment.RunAsync(); ``` -Once the time to train expires, the result is a `TrialResult` for the best model found during training. +Once the time to train expires, the result is a for the best model found during training. At this point, you can then save your model or use it for making predictions. For more information on how use an ML.NET model, see the following guides: @@ -172,11 +172,11 @@ At this point, you can then save your model or use it for making predictions. Fo ### Modify column inference results -Because `InferColumn` only loads a subset of your data, it's possible that edge cases contained outside of the samples used to infer columns are not caught and the wrong data types are set for your columns. In those cases, you can update the properties of `ColumnInformation` and `TextLoaderOptions` to account for those cases where the column inference results are not correct. +Because only loads a subset of your data, it's possible that edge cases contained outside of the samples used to infer columns are not caught and the wrong data types are set for your columns. In those cases, you can update the properties of and to account for those cases where the column inference results are not correct. For example, in the taxi fare dataset, the data in the `rate_code` column is a number. However, that numerical value represents a category. By default, calling `InferColumn` will place `rate_code` in the `NumericColumnNames` property instead of `CategoricalColumnNames`. Because these properties are standard .NET collections, you can use the standard operations to add and remove items from them. -You can do the following to update the `ColumnInformation` for `rate_code`. +You can do the following to update the for `rate_code`. ```csharp columnInference.ColumnInformation.NumericColumnNames.Remove("rate_code"); @@ -231,7 +231,7 @@ var sdcaEstimatorFactory = (MLContext ctx, SdcaOption param) => }; ``` -A sweepable estimator is the combination of an estimator and a search space. Now that you've defined a search space and used it to create a custom factory method for generating trainers. Use the `CreateSweepableEstimator` method to create a new sweepable estimator. +A sweepable estimator is the combination of an estimator and a search space. Now that you've defined a search space and used it to create a custom factory method for generating trainers. Use the method to create a new sweepable estimator. ```csharp // Define Sdca sweepable estimator (SdcaRegressionTrainer + SdcaOption search space) @@ -311,7 +311,7 @@ In order to do so you'll have to: 1. Implement a custom trial runner - To create a trial runner, implement `ITrialRunner`: + To create a trial runner, implement : ```csharp public class SSARunner : ITrialRunner @@ -440,7 +440,7 @@ In order to do so you'll have to: } ``` - `ITrialRunner` has an asynchronous run method which produces `TrialResult` information. + has an asynchronous run method which produces information. 1. Create a sweepable estimator and add it to your pipeline. @@ -497,11 +497,11 @@ AutoML supports a variety of tuning algorithms to iterate through the search spa Use the following methods to set your tuner: -- **SMAC** - `SetSmacTuner` -- **Grid Search** - `SetGridSearchTuner` -- **Random Search** - `SetRandomSearchTuner` -- **Cost Frugal** - `SetCostFrugalTuner` -- **Eci Cost Frugal** - `SetEciCostFrugalTuner` +- **SMAC** - +- **Grid Search** - +- **Random Search** - +- **Cost Frugal** - +- **Eci Cost Frugal** - For example, to use the SMAC tuner, your code might look like the following: @@ -513,7 +513,7 @@ experiment.SetSmacTuner(); The quickest way to monitor the progress of an experiment is to define the event from . However, the event outputs a raw dump of the logs generated by AutoML during each trial. Because of the large amount of unformatted information, it's difficult. -For a more controlled monitoring experience, implement a class with the `IMonitor` interface. +For a more controlled monitoring experience, implement a class with the interface. ```csharp public class AutoMLMonitor : IMonitor @@ -554,7 +554,7 @@ public class AutoMLMonitor : IMonitor } ``` -The `IMonitor` interface has four lifecycle events: +The interface has four lifecycle events: - ReportBestTrial - ReportCompletedTrial @@ -562,11 +562,11 @@ The `IMonitor` interface has four lifecycle events: - ReportRunningTrial > [!TIP] -> Although it's not required, include your `SweepablePipeline` in your monitor so you can inspect the pipeline that was generated for a trial using the `Parameter` property of the `TrialSettings`. +> Although it's not required, include your in your monitor so you can inspect the pipeline that was generated for a trial using the `Parameter` property of the . -In this example, only the `ReportCompletedTrial` and `ReportFailTrial` are implemented. +In this example, only the and are implemented. -Once you've implemented your monitor, set it as part of your experiment configuration using `SetMonitor`. +Once you've implemented your monitor, set it as part of your experiment configuration using . ```csharp var monitor = new AutoMLMonitor(pipeline); @@ -583,7 +583,7 @@ Trial 2 finished training in 3941ms with pipeline ReplaceMissingValues=>OneHotHa ### Persist trials -By default, AutoML only stores the `TrialResult` for the best model. However, if you wanted to persist each of the trials, you can do so from within your monitor. +By default, AutoML only stores the for the best model. However, if you wanted to persist each of the trials, you can do so from within your monitor. Inside of your monitor, define a property for your completed trials and a method for accessing them. @@ -604,7 +604,7 @@ public AutoMLMonitor(SweepablePipeline pipeline) } ``` -Append each trial result inside your `ReportCompletedTrial` lifecycle method. +Append each trial result inside your lifecycle method. ```csharp public void ReportCompletedTrial(TrialResult result) @@ -636,7 +636,7 @@ TrialResult experimentResults = await experiment.RunAsync(cts.Token); ### Set checkpoints -Checkpoints provide a way for you to save intermediary outputs from the training process in the event of an early termination or error. To set a checkpoint, use the `SetCheckpoint` extension method and provide a directory to store the intermediary outputs. +Checkpoints provide a way for you to save intermediary outputs from the training process in the event of an early termination or error. To set a checkpoint, use the extension method and provide a directory to store the intermediary outputs. ```csharp var checkpointPath = Path.Join(Directory.GetCurrentDirectory(), "automl"); From 60c3324be87ac23309362e9cf6ecc05e36fcb962 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Tue, 8 Nov 2022 19:40:12 -0500 Subject: [PATCH 27/49] Added lightbox to all scenarios --- .../tutorials/health-violation-classification-model-builder.md | 2 +- .../tutorials/image-classification-model-builder.md | 2 +- .../tutorials/movie-recommendation-model-builder.md | 2 +- .../tutorials/predict-prices-with-model-builder.md | 2 +- .../tutorials/sentiment-analysis-model-builder.md | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/machine-learning/tutorials/health-violation-classification-model-builder.md b/docs/machine-learning/tutorials/health-violation-classification-model-builder.md index b30603a67d592..d9b3910795fda 100644 --- a/docs/machine-learning/tutorials/health-violation-classification-model-builder.md +++ b/docs/machine-learning/tutorials/health-violation-classification-model-builder.md @@ -69,7 +69,7 @@ When first adding Model Builder to the solution it will prompt you to create an ## Choose a scenario -![Model Builder wizard in Visual Studio](../media/model-builder-scenarios-2-0.png) +![Model Builder wizard in Visual Studio](../media/model-builder-scenarios-2-0.png#lightbox) To train your model, select from the list of available machine learning scenarios provided by Model Builder. In this case, the scenario is *Data classification*. diff --git a/docs/machine-learning/tutorials/image-classification-model-builder.md b/docs/machine-learning/tutorials/image-classification-model-builder.md index 0ade2070aa06f..7c0dc91c17f9e 100644 --- a/docs/machine-learning/tutorials/image-classification-model-builder.md +++ b/docs/machine-learning/tutorials/image-classification-model-builder.md @@ -69,7 +69,7 @@ When first adding Model Builder to the solution it will prompt you to create an ## Choose a scenario -![Model Builder Scenarios](../media/model-builder-scenarios-2-0.png) +![Model Builder Scenarios](../media/model-builder-scenarios-2-0.png#lightbox) To train your model, you need to select from the list of available machine learning scenarios provided by Model Builder. diff --git a/docs/machine-learning/tutorials/movie-recommendation-model-builder.md b/docs/machine-learning/tutorials/movie-recommendation-model-builder.md index 467aaa19cbd0a..b2485f190866e 100644 --- a/docs/machine-learning/tutorials/movie-recommendation-model-builder.md +++ b/docs/machine-learning/tutorials/movie-recommendation-model-builder.md @@ -62,7 +62,7 @@ When first adding Model Builder to the solution it will prompt you to create an ## Choose a scenario -![Model Builder Scenarios](../media/model-builder-scenarios-2-0.png) +![Model Builder Scenarios](../media/model-builder-scenarios-2-0.png#lightbox) To train your model, you need to select from the list of available machine learning scenarios provided by Model Builder. diff --git a/docs/machine-learning/tutorials/predict-prices-with-model-builder.md b/docs/machine-learning/tutorials/predict-prices-with-model-builder.md index 7f1b29d3de5c7..7eac77a354d00 100644 --- a/docs/machine-learning/tutorials/predict-prices-with-model-builder.md +++ b/docs/machine-learning/tutorials/predict-prices-with-model-builder.md @@ -76,7 +76,7 @@ When first adding Model Builder to the solution it will prompt you to create an ## Choose a scenario -![Model Builder wizard in Visual Studio](../media/model-builder-scenarios-2-0.png) +![Model Builder wizard in Visual Studio](../media/model-builder-scenarios-2-0.png#lightbox) To train your model, you need to select from the list of available machine learning scenarios provided by Model Builder. In this case, the scenario is `Value prediction`. diff --git a/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md b/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md index d43657695d3d2..6f708a3317695 100644 --- a/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md +++ b/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md @@ -63,7 +63,7 @@ When first adding a machine learning model to the solution it will prompt you to ## Choose a scenario -![Model Builder wizard in Visual Studio](../media/model-builder-scenarios-2-0.png) +![Model Builder wizard in Visual Studio](../media/model-builder-scenarios-2-0.png#lightbox) To train your model, you need to select from the list of available machine learning scenarios provided by Model Builder. From 2b860374f25fd9cced88b448d994b4ccb14cc866 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Tue, 8 Nov 2022 19:41:38 -0500 Subject: [PATCH 28/49] Remove preview note --- docs/machine-learning/automate-training-with-model-builder.md | 3 --- .../tutorials/health-violation-classification-model-builder.md | 3 --- .../tutorials/image-classification-model-builder.md | 3 --- .../tutorials/movie-recommendation-model-builder.md | 3 --- .../tutorials/object-detection-model-builder.md | 3 --- .../tutorials/predict-prices-with-model-builder.md | 3 --- 6 files changed, 18 deletions(-) diff --git a/docs/machine-learning/automate-training-with-model-builder.md b/docs/machine-learning/automate-training-with-model-builder.md index f1dd9368defe3..d74beb4b13bf8 100644 --- a/docs/machine-learning/automate-training-with-model-builder.md +++ b/docs/machine-learning/automate-training-with-model-builder.md @@ -15,9 +15,6 @@ You don't need machine learning expertise to use Model Builder. All you need is ![Model Builder Scenarios](./media/model-builder-scenarios-2-0.png#lightbox) -> [!NOTE] -> Model Builder is currently in Preview. - ## Creating a Model Builder Project When you first start up Model Builder it will ask for you to name the project. This will create an `mbconfig` configuration file inside of the project. diff --git a/docs/machine-learning/tutorials/health-violation-classification-model-builder.md b/docs/machine-learning/tutorials/health-violation-classification-model-builder.md index d9b3910795fda..60d22da799280 100644 --- a/docs/machine-learning/tutorials/health-violation-classification-model-builder.md +++ b/docs/machine-learning/tutorials/health-violation-classification-model-builder.md @@ -25,9 +25,6 @@ In this tutorial, you learn how to: > - Evaluate the model > - Use the model for predictions -> [!NOTE] -> Model Builder is currently in Preview. - ## Prerequisites For a list of prerequisites and installation instructions, visit the [Model Builder installation guide](../how-to-guides/install-model-builder.md). diff --git a/docs/machine-learning/tutorials/image-classification-model-builder.md b/docs/machine-learning/tutorials/image-classification-model-builder.md index 7c0dc91c17f9e..df002bfcd706c 100644 --- a/docs/machine-learning/tutorials/image-classification-model-builder.md +++ b/docs/machine-learning/tutorials/image-classification-model-builder.md @@ -28,9 +28,6 @@ In this tutorial, you: > - Evaluate the model > - Consume the model -> [!NOTE] -> Model Builder is currently in Preview. - ## Prerequisites - For a list of pre-requisites and installation instructions, visit the [Model Builder installation guide](../how-to-guides/install-model-builder.md). diff --git a/docs/machine-learning/tutorials/movie-recommendation-model-builder.md b/docs/machine-learning/tutorials/movie-recommendation-model-builder.md index b2485f190866e..0202b54316a09 100644 --- a/docs/machine-learning/tutorials/movie-recommendation-model-builder.md +++ b/docs/machine-learning/tutorials/movie-recommendation-model-builder.md @@ -25,9 +25,6 @@ In this tutorial, you: > - Evaluate the model > - Consume the model -> [!NOTE] -> Model Builder is currently in Preview. - ## Prerequisites For a list of pre-requisites and installation instructions, visit the [Model Builder installation guide](../how-to-guides/install-model-builder.md). diff --git a/docs/machine-learning/tutorials/object-detection-model-builder.md b/docs/machine-learning/tutorials/object-detection-model-builder.md index d2fa28e4f4827..9e604039acc39 100644 --- a/docs/machine-learning/tutorials/object-detection-model-builder.md +++ b/docs/machine-learning/tutorials/object-detection-model-builder.md @@ -26,9 +26,6 @@ In this tutorial, you learn how to: > - Evaluate the model > - Use the model for predictions -> [!NOTE] -> Model Builder is currently in Preview. - ## Prerequisites For a list of prerequisites and installation instructions, visit the [Model Builder installation guide](../how-to-guides/install-model-builder.md). diff --git a/docs/machine-learning/tutorials/predict-prices-with-model-builder.md b/docs/machine-learning/tutorials/predict-prices-with-model-builder.md index 7eac77a354d00..577eb5bad1bc3 100644 --- a/docs/machine-learning/tutorials/predict-prices-with-model-builder.md +++ b/docs/machine-learning/tutorials/predict-prices-with-model-builder.md @@ -26,9 +26,6 @@ In this tutorial, you learn how to: > - Evaluate the model > - Use the model for predictions -> [!NOTE] -> Model Builder is currently in Preview. - ## Pre-requisites For a list of pre-requisites and installation instructions, visit the [Model Builder installation guide](../how-to-guides/install-model-builder.md). From 5eebd4769f3c56387896425b47edd29da9357333 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 00:13:42 -0500 Subject: [PATCH 29/49] Update code snippets --- .../how-to-use-the-automl-api.md | 330 +++++++++--------- 1 file changed, 169 insertions(+), 161 deletions(-) diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index 8e292091e4e88..f7b15e4f0e989 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -33,7 +33,7 @@ AutoML provides several defaults for quickly training models. In this section yo ### Defining your problem -Given a dataset stored in a comma-separated file called *taxi-fare.csv* that looks like the following: +Given a dataset stored in a comma-separated file called *taxi-fare-train.csv* that looks like the following: | vendor_id | rate_code | passenger_count | trip_time_in_secs | trip_distance|payment_type | fare_amount | |---|---|---|---|---|---|---| @@ -51,9 +51,12 @@ Then, to load your data, use the loads a few rows from the dataset. It then inspects the data and tries to guess or infer the data type for each of the columns based on their content. @@ -62,7 +65,7 @@ The default behavior is to group columns of the same type into feature vectors o The result of a object which contains the options needed to create a as well as column information. -For the sample dataset in *taxi-fare.csv*, column information might look like the following: +For the sample dataset in *taxi-fare-train.csv*, column information might look like the following: - **LabelColumnName**: fare_amount - **CategoricalColumnNames**: vendor_id, payment_type @@ -75,14 +78,13 @@ Once you have your column information, use the to create an 80% training and 20% validation split of your dataset. ```csharp -// Split into train (70%), validation (20%) sets -TrainTestData trainValidationData = ctx.Data.TrainTestSplit(data, testFraction:0.2); +TrainTestData trainValidationData = ctx.Data.TrainTestSplit(data, testFraction: 0.2); ``` ### Define your pipeline @@ -90,9 +92,9 @@ TrainTestData trainValidationData = ctx.Data.TrainTestSplit(data, testFraction:0 Your pipeline defines the data preprocesing steps and machine learning pipeline to use for training your model. ```csharp -SweepablePipeline pipeline = - ctx.Auto().Featurizer(data, columnInformation:columnInference.ColumnInformation) - .Append(ctx.Auto().Regression(labelColumnName:columnInference.ColumnInformation.LabelColumnName)); +SweepablePipeline pipeline = + ctx.Auto().Featurizer(data, columnInformation: columnInference.ColumnInformation) + .Append(ctx.Auto().Regression(labelColumnName: columnInference.ColumnInformation.LabelColumnName)); ``` A is a pipeline containing a collection of . A is an ML.NET with a . @@ -104,19 +106,19 @@ The resulting output is a single column containing a numerical feature vector re If you want finer control over your data preprocessing, you can create a pipeline with each of the individual preprocessing steps. For more information, see the [prepare data for building a model guide](prepare-data-ml-net.md). > [!TIP] -> Use `Featurizer` with to maximize the utility of AutoML. +> Use with to maximize the utility of AutoML. For training, AutoML provides a sweepable pipeline with default trainers and search space configurations for the following machine learning tasks: -- Binary classification -- Multiclass classification -- Regression +- +- +- For the taxi fare prediction problem, since the goal is to predict a numerical value, use `Regression`. For more information on choosing a task, see the [machine learning tasks in ML.NET guide](/dotnet/machine-learning/resources/tasks.md). ### Configure your experiment -First, create an AutoML experiment. An experiment is a collection of trials. +First, create an AutoML experiment. An is a collection of . ```csharp // Create AutoML experiment @@ -126,10 +128,9 @@ AutoMLExperiment experiment = ctx.Auto().CreateExperiment(); Once your experiment is created, use the extension methods it provides to configure different settings. ```csharp -// Configure experiment experiment .SetPipeline(pipeline) - .SetRegressionMetric(RegressionMetric.RSquared, labelColumn:columnInference.ColumnInformation.LabelColumnName) + .SetRegressionMetric(RegressionMetric.RSquared, labelColumn: columnInference.ColumnInformation.LabelColumnName) .SetTrainingTimeInSeconds(60) .SetDataset(trainValidationData); ``` @@ -145,7 +146,7 @@ Once your experiment is defined, you'll want some way to track its progress. The ```csharp // Log experiment trials -ctx.Log += (_,e) => { +ctx.Log += (_, e) => { if (e.Source.Equals("AutoMLExperiment")) { Console.WriteLine(e.RawMessage); @@ -193,7 +194,7 @@ For example, in the taxi fare regression scenario, to exclude the LightGBM algor ctx.Auto().Regression(labelColumnName: columnInference.ColumnInformation.LabelColumnName, useLgbm:false) ``` -The process for excluding trainers in other tasks like binary and multiclass classification the same way. +The process for excluding trainers in other tasks like binary and multiclass classification works the same way. ### Customize a sweepable estimator @@ -206,9 +207,9 @@ When you want to more granular customization of estimator options included as pa AutoML provides a set of preconfigured search spaces for trainers in the following machine learning tasks: -- Binary classification -- Multiclass classification -- Regression +- +- +- In this example, the search space used is for the . @@ -221,12 +222,14 @@ Then, use the search space to define a custom factory method to create the +var sdcaFactory = (MLContext ctx, SdcaOption param) => { var sdcaOption = new SdcaRegressionTrainer.Options(); sdcaOption.L1Regularization = param.L1Regularization; sdcaOption.L2Regularization = 0.02f; + sdcaOption.LabelColumnName = columnInference.ColumnInformation.LabelColumnName; + return ctx.Regression.Trainers.Sdca(sdcaOption); }; ``` @@ -235,14 +238,13 @@ A sweepable estimator is the combination of an estimator and a search space. Now ```csharp // Define Sdca sweepable estimator (SdcaRegressionTrainer + SdcaOption search space) -var sdcaSweepableEstimator = ctx.Auto().CreateSweepableEstimator(sdcaEstimatorFactory, sdcaSearchSpace); +var sdcaSweepableEstimator = ctx.Auto().CreateSweepableEstimator(sdcaFactory, sdcaSearchSpace); ``` To use your sweepable estimator in your experiment, append it to your sweepable pipeline. ```csharp -// Add sweepable estimator to sweepable pipeline -SweepablePipeline customEstimatorPipeline = +SweepablePipeline pipeline = ctx.Auto().Featurizer(data, columnInformation: columnInference.ColumnInformation) .Append(sdcaSweepableEstimator); ``` @@ -251,16 +253,29 @@ Because sweepable pipelines are a collection of sweepable estimators, you can co ### Customize your search space -There are scenarios where you want to go beyond customizing the sweepable estimators used in your experiment and want control the search space range. +There are scenarios where you want to go beyond customizing the sweepable estimators used in your experiment and want control the search space range. T ```csharp -var originalSdcaSearchSpace = new SearchSpace(); -originalSdcaSearchSpace["L1Regularization"] = new UniformSingleOption(min:0.01f, max:2.0f, logBase: false, defaultValue:0.01f); +sdcaSearchSpace["L1Regularization"] = new UniformSingleOption(min: 0.01f, max: 2.0f, logBase: false, defaultValue: 0.01f); ``` -### Create a custom search space +Depending on the data type of the hyperparameter you want to set, you can choose from the following options: -AutoML provides a set of preconfigured search spaces. +- Numerical options + - + - + - +- Boolean and strings + - + +Search spaces can contain nested search spaces as well. + +```csharp +var searchSpace = new SearchSpace(); +var nestedSearchSpace = new SearchSpace(); +nestedSearchSpace["IntOption"] = new UniformIntOption(-10, 10, false, 0); +searchSpace["Nest"] = nestedSearchSpace; +``` ### Create your own trial runner @@ -275,46 +290,62 @@ By default, AutoML supports binary classification, multiclass classification, an For scenarios that don't have preconfigured search spaces and sweepable estimators you can create your own and use a trial runner to enable AutoML for that scenario. -For example, given hourly energy demand data that looks like the following: +For example, given restaurant review data that looks like the following: -| load | -|---------| -| 20236 | -| 55784 | -| 179759 | -| 314597 | +| | | +| --- | --- | +| Wow... Loved this place. | 1 | +| Crust is not good. | 0 | -You want to use the trainer to forecast future demand. +You want to use the trainer to analyze sentiment where 0 is negative and 1 is positive. In order to do so you'll have to: -1. Define a custom search space +1. Create your own search space. You can also define using the . ```csharp - // Define ForecastBySsa search space - public class SSAOption + // Define TextClassification search space + public class TCOption { + [Range(64, 128, 32)] + public int BatchSize { get; set; } + } + ``` + + In this case, AutoML will search for different configurations of the `BatchSize` hyperparameter. + +1. Create a sweepable estimator and add it to your pipeline. + + ```csharp + // Initialize serach space + var tcSearchSpace = new SearchSpace(); - [Range(2, 24 * 7 * 30)] - public int WindowSize { get; set; } = 2; - - [Range(2, 24 * 7 * 30)] - public int SeriesLength { get; set; } = 2; + // Create factory for Text Classification trainer + var tcFactory = (MLContext ctx, TCOption param) => + { + return ctx.MulticlassClassification.Trainers.TextClassification( + sentence1ColumnName: textColumnName, + batchSize:param.BatchSize); + }; - [Range(2, 24 * 7 * 30)] - public int TrainSize { get; set; } = 2; + // Create text classification sweepable estimator + var tcEstimator = + ctx.Auto().CreateSweepableEstimator(tcFactory, tcSearchSpace); - [Range(1, 24 * 7 * 30)] - public int Horizon { get; set; } = 1; - } + // Define text classification pipeline + var pipeline = + ctx.Transforms.Conversion.MapValueToKey(columnInference.ColumnInformation.LabelColumnName) + .Append(tcEstimator); ``` -1. Implement a custom trial runner + In this example, the `TCOption` search space and a custom factory are used to create a sweepable estimator. + +1. Create a custom trial runner - To create a trial runner, implement : + To create a custom trial runner, implement : ```csharp - public class SSARunner : ITrialRunner + public class TCRunner : ITrialRunner { private readonly MLContext _context; private readonly TrainTestData _data; @@ -322,8 +353,14 @@ In order to do so you'll have to: private readonly IDataView _evaluateDataset; private readonly SweepablePipeline _pipeline; private readonly string _labelColumnName; - - public SSARunner(MLContext context, TrainTestData data, string labelColumnName, SweepablePipeline pipeline) + private readonly MulticlassClassificationMetric _metric; + + public TCRunner( + MLContext context, + TrainTestData data, + SweepablePipeline pipeline, + string labelColumnName = "Label", + MulticlassClassificationMetric metric = MulticlassClassificationMetric.MicroAccuracy) { _context = context; _data = data; @@ -331,6 +368,7 @@ In order to do so you'll have to: _evaluateDataset = data.TestSet; _labelColumnName = labelColumnName; _pipeline = pipeline; + _metric = metric; } public void Dispose() @@ -366,50 +404,26 @@ In order to do so you'll have to: // Get pipeline parameters var parameter = settings.Parameter["_pipeline_"]; - - // Build pipeline from parameters + + // Use parameters to build pipeline var pipeline = _pipeline.BuildFromOption(_context, parameter); - + // Train model var model = pipeline.Fit(_trainDataset); - // Create prediction engine for single predictions - var predictEngine = model.CreateTimeSeriesEngine(_context); - - // Create a checkpoint for time series engine prediction - predictEngine.CheckPoint(_context, "origin"); - - var predictedLoad1H = new List(); - var N = _evaluateDataset.GetRowCount(); - - // Evaluate performance on a rolling basis - foreach (var load in _evaluateDataset.GetColumn(_labelColumnName)) - { - // First, get next n predictions where n is horizon, in this case, it's always 1. - var predict = predictEngine.Predict(); + // Evaluate the model + var predictions = model.Transform(_evaluateDataset); - // Add prediction to list of predictions - predictedLoad1H.Add(predict.Prediction[0]); - - // Update model with true value - predictEngine.Predict(new ForecastInput() - { - Load = load, - }); - } - - // Calculate (Root Mean Squared Error) evaluation metric - var rmse = Enumerable.Zip(_evaluateDataset.GetColumn(_labelColumnName), predictedLoad1H) - .Select(x => Math.Pow(x.First - x.Second, 2)) - .Average(); - rmse = Math.Sqrt(rmse); + // Get metrics + var evaluationMetrics = _context.MulticlassClassification.Evaluate(predictions, labelColumnName: _labelColumnName); + var chosenMetric = GetMetric(evaluationMetrics); return new TrialResult() { - Metric = rmse, + Metric = chosenMetric, Model = model, TrialSettings = settings, - DurationInMilliseconds = stopWatch.ElapsedMilliseconds, + DurationInMilliseconds = stopWatch.ElapsedMilliseconds }; } catch (Exception) @@ -424,71 +438,54 @@ In order to do so you'll have to: } } - // Define input schema - private class ForecastInput - { - [ColumnName("load")] - public float Load { get; set; } - } - - // Define output schema - private class ForecastOutput + // Helper function to choose metric used by experiment + private double GetMetric(MulticlassClassificationMetrics metric) { - [ColumnName("prediction")] - public float[] Prediction { get; set; } + return _metric switch + { + MulticlassClassificationMetric.MacroAccuracy => metric.MacroAccuracy, + MulticlassClassificationMetric.MicroAccuracy => metric.MicroAccuracy, + MulticlassClassificationMetric.LogLoss => metric.LogLoss, + MulticlassClassificationMetric.LogLossReduction => metric.LogLossReduction, + MulticlassClassificationMetric.TopKAccuracy => metric.TopKAccuracy, + _ => throw new NotImplementedException(), + }; } } ``` - has an asynchronous run method which produces information. - -1. Create a sweepable estimator and add it to your pipeline. - - ```csharp - var ssaSearchSpace = new SearchSpace(); - - var ssaFactory = (MLContext ctx, SSAOption param) => - { - return ctx.Forecasting.ForecastBySsa( - outputColumnName: "prediction", - inputColumnName: "load", - windowSize: param.WindowSize, - seriesLength: param.SeriesLength, - trainSize: param.TrainSize, - horizon: param.Horizon); - }; - - var ssaSweepableEstimator = ctx.Auto().CreateSweepableEstimator(ssaFactory, ssaSearchSpace); - - var ssaPipeline = - new EstimatorChain() - .Append(ssaSweepableEstimator); - ``` + The `TCRunner` implementation in this example: + - Extracts the hyperparameters chosen for that trial + - Uses the hyperparameters to create an ML.NET pipeline + - Uses the ML.NET pipeline to train a model + - Evaluates the model + - Returns a object with the information for that trial 1. Initialize your custom trial runner ```csharp - var ssaRunner = new SSARunner(ssaCtx, ssaDataSplit, labelColumnName: "load", pipeline:ssaPipeline); + var tcRunner = new TCRunner(context: ctx, data: trainValidationData, pipeline: pipeline); ``` -1. Create and configure your experiment. Use the `SetTrialRunner` extension method to add your custom trial runner to your experiment. +1. Create and configure your experiment. Use the extension method to add your custom trial runner to your experiment. ```csharp - AutoMLExperiment ssaExperiment = ssaCtx.Auto().CreateExperiment(); + AutoMLExperiment experiment = ctx.Auto().CreateExperiment(); - ssaExperiment - .SetPipeline(ssaPipeline) - .SetRegressionMetric(RegressionMetric.RootMeanSquaredError, labelColumn: "load", scoreColumn: "prediction") - .SetTrainingTimeInSeconds(60) - .SetDataset(ssaDataSplit) - .SetTrialRunner(ssaRunner); + // Configure AutoML experiment + experiment + .SetPipeline(pipeline) + .SetMulticlassClassificationMetric(MulticlassClassificationMetric.MicroAccuracy, labelColumn: columnInference.ColumnInformation.LabelColumnName) + .SetTrainingTimeInSeconds(120) + .SetDataset(trainValidationData) + .SetTrialRunner(tcRunner); ``` 1. Run your experiment ```csharp - var ssaCts = new CancellationTokenSource(); - TrialResult ssaExperimentResults = await ssaExperiment.RunAsync(ssaCts.Token); + var tcCts = new CancellationTokenSource(); + TrialResult textClassificationExperimentResults = await experiment.RunAsync(tcCts.Token); ``` ### Choose a different tuner @@ -503,10 +500,10 @@ Use the following methods to set your tuner: - **Cost Frugal** - - **Eci Cost Frugal** - -For example, to use the SMAC tuner, your code might look like the following: +For example, to use the grid search tuner, your code might look like the following: ```csharp -experiment.SetSmacTuner(); +experiment.SetGridSearchTuner(); ``` ### Configure experiment monitoring @@ -525,6 +522,8 @@ public class AutoMLMonitor : IMonitor _pipeline = pipeline; } + public IEnumerable GetCompletedTrials() => _completedTrials; + public void ReportBestTrial(TrialResult result) { return; @@ -556,13 +555,13 @@ public class AutoMLMonitor : IMonitor The interface has four lifecycle events: -- ReportBestTrial -- ReportCompletedTrial -- ReportFailTrial -- ReportRunningTrial +- +- +- +- > [!TIP] -> Although it's not required, include your in your monitor so you can inspect the pipeline that was generated for a trial using the `Parameter` property of the . +> Although it's not required, include your in your monitor so you can inspect the pipeline that was generated for a trial using the property of the . In this example, only the and are implemented. @@ -573,6 +572,13 @@ var monitor = new AutoMLMonitor(pipeline); experiment.SetMonitor(monitor); ``` +Then, run your experiment: + +```csharp +var cts = new CancellationTokenSource(); +TrialResult experimentResults = await experiment.RunAsync(cts.Token); +``` + When you run the experiment with this implementation, the output should look similar to the following: ```text @@ -585,25 +591,27 @@ Trial 2 finished training in 3941ms with pipeline ReplaceMissingValues=>OneHotHa By default, AutoML only stores the for the best model. However, if you wanted to persist each of the trials, you can do so from within your monitor. -Inside of your monitor, define a property for your completed trials and a method for accessing them. +Inside of your monitor: -```csharp -private readonly List _completedTrials; +1. Define a property for your completed trials and a method for accessing them. -public IEnumerable GetCompletedTrials() => _completedTrials; -``` - -Initialize it in your constructor + ```csharp + private readonly List _completedTrials; + + public IEnumerable GetCompletedTrials() => _completedTrials; + ``` -```csharp -public AutoMLMonitor(SweepablePipeline pipeline) -{ - //... - _completedTrials = new List(); - //... -} -``` +1. Initialize it in your constructor + ```csharp + public AutoMLMonitor(SweepablePipeline pipeline) + { + //... + _completedTrials = new List(); + //... + } + ``` + Append each trial result inside your lifecycle method. ```csharp @@ -617,14 +625,14 @@ public void ReportCompletedTrial(TrialResult result) When training completes, you can access all the completed trials by calling `GetCompletedTrials` ```csharp -monitor.GetCompletedTrials() +var completedTrials = monitor.GetCompletedTrials(); ``` At this point you can perform additional processing on the collection of completed trials like choosing another model other than the one selected by AutoML, logging trial results to a database, or rebuilding the pipeline from any of the completed trials. ### Cancel asynchronous experiments -When you run experiments asynchronously, make sure to cleanly terminate the process. To do so, use cancellation tokens. +When you run experiments asynchronously, make sure to cleanly terminate the process. To do so, use a . ```csharp var cts = new CancellationTokenSource(); From bbef5fa4ddf19e2f41502e80f2b14b14299ff160 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 00:55:09 -0500 Subject: [PATCH 30/49] Updates to sentiment analysis MB tutorial --- .../sentiment-analysis-model-builder.md | 57 ++++++++----------- 1 file changed, 25 insertions(+), 32 deletions(-) diff --git a/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md b/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md index 6f708a3317695..1df0ff24d81df 100644 --- a/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md +++ b/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md @@ -35,12 +35,12 @@ For a list of pre-requisites and installation instructions, visit the [Model Bui ## Create a Razor Pages application -1. Create an **ASP.NET Core Razor Pages Application**. +Create an **ASP.NET Core Razor Pages Application**. - 1. Open Visual Studio and select **File > New > Project** from the menu bar. - 1. In the New Project dialog, select the **ASP.NET Core Web App** project template. - 1. In the **Name** text box, type "SentimentRazor" and select the **Next** button. - 1. In the Additional information dialog, leave all the defaults as is and select the **Create** button. +1. In Visual Studio open the **Create a new project** dialog. +1. In the "Create a new project" dialog, select the **ASP.NET Core Web App** project template. +1. In the **Name** text box, type "SentimentRazor" and select the **Next** button. +1. In the Additional information dialog, leave all the defaults as is and select the **Create** button. ## Prepare and understand the data @@ -59,7 +59,7 @@ Each row in the *wikipedia-detox-250-line-data.tsv* dataset represents a differe When first adding a machine learning model to the solution it will prompt you to create an `mbconfig` file. The `mbconfig` file keeps track of everything you do in Model Builder to allow you to reopen the session. 1. In **Solution Explorer**, right-click the *SentimentRazor* project, and select **Add** > **Machine Learning Model...**. -1. In the dialog, name the Model Builder project **SentimentAnalysis**, and click **Add**. +1. In the dialog, name the Model Builder project **SentimentAnalysis.mbconfig**, and select **Add**. ## Choose a scenario @@ -71,7 +71,7 @@ For this sample, the task is text classification. In the *Scenario* step of the ## Select an environment -Model Builder can run the training on different environments depending on the scenario that was selected. +Model Builder can train on different environments depending on the selected scenario. Select **Local (GPU)** as your environment and click the **Next step** button. @@ -90,29 +90,31 @@ Model Builder accepts data from two sources, a SQL Server database or a local fi ## Train the model -The machine learning task used to train the sentiment analysis model in this tutorial is text classification. During the model training process, Model Builder trains a NAS-BERT model for your dataset. +The machine learning task used to train the sentiment analysis model in this tutorial is text classification. During the model training process, Model Builder trains a text classification model for your dataset using the [NAS-BERT](https://arxiv.org/abs/2105.14444) neural network architecture. 1. Select **Start Training**. 1. Once training is complete, the results from the training process are displayed in the *Training results* section of the *Train* screen. In addition to providing training results, three code-behind files are created under the *SentimentAnalysis.mbconfig* file. - *SentimentAnalysis.consumption.cs* - This file contains the `ModelInput` and `ModelOutput` schemas as well as the `Predict` function generated for consuming the model. - - *SentimentAnalysis.training.cs* - This file contains the training pipeline (data transforms, algorithm, algorithm hyperparameters) chosen by Model Builder to train the model. You can use this pipeline for re-training your model. + - *SentimentAnalysis.training.cs* - This file contains the training pipeline (data transforms, trainer, trainer hyperparameters) chosen by Model Builder to train the model. You can use this pipeline for re-training your model. - **SentimentAnalysis.zip* - This is a serialized zip file which represents your trained ML.NET model. 1. Select the **Next step** button to move to the next step. ## Evaluate the model -The result of the training step will be one model that has the best performance. In the evaluate step of the Model Builder tool, the output section will contain the algorithm used by the best-performing model in the **Best Model** entry along with metrics in **Best Model Accuracy**. Additionally, a summary table containing the top five models and their metrics is shown. +The result of the training step will be one model that has the best performance. In the evaluate step of the Model Builder tool, the output section will contain the trainer used by the best-performing model in the as well as evaluation metrics. -If you're not satisfied with your evaluation metrics, some easy ways to try to improve model accuracy are to increase the amount of time to train the model or use more data. Otherwise, select the **Next step** button to move to the *Consume* step in Model Builder. +If you're not satisfied with your evaluation metrics, some easy ways to try to improve model performance are to use more data. + +Otherwise, select the **Next step** button to move to the *Consume* step in Model Builder. ## Add consumption project templates (Optional) -In the *Consume* step, Model Builder provides project templates that you can use to consume the model. This step is optional and you can choose the method that best suits your needs on how to serve the model. +In the *Consume* step, Model Builder provides project templates that you can use to consume the model. This step is optional and you can choose the method that best fits your needs for using the model. -- Console App +- Console application - Web API ## Add the code to make predictions @@ -130,8 +132,6 @@ To make a single prediction, you have to create a for your application in the *Program.cs* file: ```csharp builder.Services.AddPredictionEnginePool() @@ -149,7 +149,7 @@ To make a single prediction, you have to create a to return a prediction needs to be added. 1. Open the *Index.cshtml.cs* file located in the *Pages* directory and add the following using statements: @@ -158,15 +158,15 @@ Predictions will be made inside the main page of the application. Therefore, a m using static SentimentRazor.SentimentAnalysis; ``` - In order to use the `PredictionEnginePool` configured in the `Program` class, you have to inject it into the constructor of the model where you want to use it. + In order to use the configured in the *Program.cs* file, you have to inject it into the constructor of the model where you want to use it. -1. Add a variable to reference the `PredictionEnginePool` inside the `IndexModel` class. +1. Add a variable to reference the inside the `IndexModel` class inside the *Pages/Index.cshtml.cs* file. ```csharp private readonly PredictionEnginePool _predictionEnginePool; ``` -1. Modify the constructor in the `IndexModel` class and inject the `PredictionEnginePool` service into it. +1. Modify the constructor in the `IndexModel` class and inject the service into it. ```csharp public IndexModel(ILogger logger, PredictionEnginePool predictionEnginePool) @@ -199,7 +199,7 @@ Predictions will be made inside the main page of the application. Therefore, a m var input = new ModelInput { SentimentText = text }; ``` - 1. Use the `PredictionEnginePool` to predict sentiment. + 1. Use the to predict sentiment. ```csharp var prediction = _predictionEnginePool.Predict(input); @@ -223,8 +223,6 @@ The results returned by the `OnGetAnalyzeSentiment` will be dynamically displaye 1. Open the *Index.cshtml* file in the *Pages* directory and replace its contents with the following code: - - ```cshtml @page @model IndexModel @@ -253,8 +251,6 @@ The results returned by the `OnGetAnalyzeSentiment` will be dynamically displaye 1. Next, add css styling code to the end of the *site.css* page in the *wwwroot\css* directory: - - ```css /* Style for sentiment display */ @@ -307,7 +303,6 @@ The results returned by the `OnGetAnalyzeSentiment` will be dynamically displaye 1. In the *site.js* file located in the *wwwroot\js* directory, create a function called `getSentiment` to make a GET HTTP request with the user input to the `OnGetAnalyzeSentiment` handler. - ```javascript function getSentiment(userInput) { return fetch(`Index?handler=AnalyzeSentiment&text=${userInput}`) @@ -319,8 +314,6 @@ The results returned by the `OnGetAnalyzeSentiment` will be dynamically displaye 1. Below that, add another function called `updateMarker` to dynamically update the position of the marker on the web page as sentiment is predicted. - - ```javascript function updateMarker(markerPosition, sentiment) { $("#markerPosition").attr("style", `left:${markerPosition}%`); @@ -330,8 +323,6 @@ The results returned by the `OnGetAnalyzeSentiment` will be dynamically displaye 1. Create an event handler function called `updateSentiment` to get the input from the user, send it to the `OnGetAnalyzeSentiment` function using the `getSentiment` function and update the marker with the `updateMarker` function. - - ```javascript function updateSentiment() { @@ -355,7 +346,6 @@ The results returned by the `OnGetAnalyzeSentiment` will be dynamically displaye 1. Finally, register the event handler and bind it to the `textarea` element with the `id=Message` attribute. - ```javascript $("#Message").on('change input paste', updateSentiment) ``` @@ -364,10 +354,13 @@ The results returned by the `OnGetAnalyzeSentiment` will be dynamically displaye Now that your application is set up, run the application, which should launch in your browser. -When the application launches, enter *Model Builder is cool!* into the text area. The predicted sentiment displayed should be *Not Toxic*. +When the application launches, enter *This model doesn't have enough data!* into the text area. The predicted sentiment displayed should be *Toxic*. ![Running window with the predicted sentiment window](./media/sentiment-analysis-model-builder/web-app.png) +> [!NOTE] +> creates multiple instances of . Because of the size of the model, the first time you use it to make a prediction, it can take a couple of seconds. Subsequent predictions should be instantaneous. + ## Next steps In this tutorial, you learned how to: From a83155903078407aa9763b6794e331320f6da42a Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 01:12:29 -0500 Subject: [PATCH 31/49] Updated GPU doc to list hw reqs and troubleshoot --- .../install-gpu-model-builder.md | 29 ++++++++++++++++--- 1 file changed, 25 insertions(+), 4 deletions(-) diff --git a/docs/machine-learning/how-to-guides/install-gpu-model-builder.md b/docs/machine-learning/how-to-guides/install-gpu-model-builder.md index 275d5761e5220..587197f1eb95a 100644 --- a/docs/machine-learning/how-to-guides/install-gpu-model-builder.md +++ b/docs/machine-learning/how-to-guides/install-gpu-model-builder.md @@ -1,7 +1,7 @@ --- title: How to install GPU support in Model Builder description: Learn how to install GPU support in Model Builder -ms.date: 09/02/2021 +ms.date: 11/10/2022 author: luisquintanilla ms.author: luquinta ms.topic: how-to @@ -12,14 +12,21 @@ ms.topic: how-to Learn how to install the GPU drivers to use your GPU with Model Builder. -## Prerequisites +## Hardware requirements -- Model Builder Visual Studio extension. The extension is built into Visual Studio as of version 16.6.1. -- [Model Builder Visual Studio GPU support extension](https://marketplace.visualstudio.com/items?itemName=MLNET.ModelBuilderGPU2022). - At least one CUDA compatible GPU. For a list of compatible GPUs, see [NVIDIA's guide](https://developer.nvidia.com/cuda-gpus). +- At least 6GB of dedicated GPU memory. + +## Prerequisites + +- [Model Builder Visual Studio extension](install-model-builder.md). The extension is built into Visual Studio as of version 16.6.1. - NVIDIA developer account. If you don't have one, [create a free account](https://developer.nvidia.com/developer-program). - Make sure the appropriate [driver](https://www.nvidia.com/drivers) is installed for the GPU. +**Image classification only** + +- [Model Builder Visual Studio GPU support extension](https://marketplace.visualstudio.com/items?itemName=MLNET.ModelBuilderGPU2022) + ## Install dependencies 1. Install [CUDA v10.1](https://developer.nvidia.com/cuda-10.1-download-archive-update2). Make sure you install CUDA v10.1, not any other newer version. @@ -27,6 +34,14 @@ Learn how to install the GPU drivers to use your GPU with Model Builder. ## Troubleshooting +**What if I don't have a GPU installed locally?** + +Deep learning scenarios tend to run faster on GPUs. + +Some scenarios like image classification support training on Azure GPU VMs. + +However, if local GPUs or Azure are not an option for you, these scenarios also run on CPU. Note however that training times are significantly longer. + **How do I know what GPU I have?** Follow instructions provided: @@ -47,3 +62,9 @@ Follow instructions provided: 1. Open a PowerShell or command line window 1. Type in `nvcc --version` + +**cuda is not available, please confirm you have a cuda-supported gpu** + +1. Open GeForce Experience app. +1. The application should show installed and available driver updates. If you have trouble seeing updates, you can get the latest drivers from https://www.nvidia.com/en-in/geforce/drivers/. +1. Install the latest drivers. \ No newline at end of file From e50655f73a6bd14b8bba4ad35eafb3a0e937000e Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 01:15:04 -0500 Subject: [PATCH 32/49] Addtl updates --- .../how-to-guides/install-gpu-model-builder.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/machine-learning/how-to-guides/install-gpu-model-builder.md b/docs/machine-learning/how-to-guides/install-gpu-model-builder.md index 587197f1eb95a..25db567e738a7 100644 --- a/docs/machine-learning/how-to-guides/install-gpu-model-builder.md +++ b/docs/machine-learning/how-to-guides/install-gpu-model-builder.md @@ -44,8 +44,6 @@ However, if local GPUs or Azure are not an option for you, these scenarios also **How do I know what GPU I have?** -Follow instructions provided: - 1. Right-click on desktop 1. If you see "NVIDIA Control Panel" or "NVIDIA Display" in the pop-up window, you have an NVIDIA GPU 1. Click on "NVIDIA Control Panel" or "NVIDIA Display" in the pop-up window @@ -65,6 +63,6 @@ Follow instructions provided: **cuda is not available, please confirm you have a cuda-supported gpu** -1. Open GeForce Experience app. +1. Open the [GeForce Experience](https://www.nvidia.com/en-us/geforce/geforce-experience/) app. 1. The application should show installed and available driver updates. If you have trouble seeing updates, you can get the latest drivers from https://www.nvidia.com/en-in/geforce/drivers/. 1. Install the latest drivers. \ No newline at end of file From b874b3ed3117c4ce83ecb3786747fc0a2f637df2 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 01:18:33 -0500 Subject: [PATCH 33/49] Remove localization --- .../machine-learning/how-to-guides/install-gpu-model-builder.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/machine-learning/how-to-guides/install-gpu-model-builder.md b/docs/machine-learning/how-to-guides/install-gpu-model-builder.md index 25db567e738a7..4220d3a104fba 100644 --- a/docs/machine-learning/how-to-guides/install-gpu-model-builder.md +++ b/docs/machine-learning/how-to-guides/install-gpu-model-builder.md @@ -64,5 +64,5 @@ However, if local GPUs or Azure are not an option for you, these scenarios also **cuda is not available, please confirm you have a cuda-supported gpu** 1. Open the [GeForce Experience](https://www.nvidia.com/en-us/geforce/geforce-experience/) app. -1. The application should show installed and available driver updates. If you have trouble seeing updates, you can get the latest drivers from https://www.nvidia.com/en-in/geforce/drivers/. +1. The application should show installed and available driver updates. If you have trouble seeing updates, you can get the latest drivers from https://www.nvidia.com/geforce/drivers/. 1. Install the latest drivers. \ No newline at end of file From e10a337af8b575018545155bfc84a03959795b55 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 01:21:28 -0500 Subject: [PATCH 34/49] Fix md lint errors --- .../how-to-guides/how-to-use-the-automl-api.md | 10 +++++----- .../how-to-guides/install-gpu-model-builder.md | 8 ++++---- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index f7b15e4f0e989..28100ff3f332a 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -292,10 +292,10 @@ For scenarios that don't have preconfigured search spaces and sweepable estimato For example, given restaurant review data that looks like the following: -| | | +| | | | --- | --- | -| Wow... Loved this place. | 1 | -| Crust is not good. | 0 | +| Wow... Loved this place. | 1 | +| Crust is not good. | 0 | You want to use the trainer to analyze sentiment where 0 is negative and 1 is positive. @@ -591,7 +591,7 @@ Trial 2 finished training in 3941ms with pipeline ReplaceMissingValues=>OneHotHa By default, AutoML only stores the for the best model. However, if you wanted to persist each of the trials, you can do so from within your monitor. -Inside of your monitor: +Inside your monitor: 1. Define a property for your completed trials and a method for accessing them. @@ -611,7 +611,7 @@ Inside of your monitor: //... } ``` - + Append each trial result inside your lifecycle method. ```csharp diff --git a/docs/machine-learning/how-to-guides/install-gpu-model-builder.md b/docs/machine-learning/how-to-guides/install-gpu-model-builder.md index 4220d3a104fba..3cd1e5096be9c 100644 --- a/docs/machine-learning/how-to-guides/install-gpu-model-builder.md +++ b/docs/machine-learning/how-to-guides/install-gpu-model-builder.md @@ -40,7 +40,7 @@ Deep learning scenarios tend to run faster on GPUs. Some scenarios like image classification support training on Azure GPU VMs. -However, if local GPUs or Azure are not an option for you, these scenarios also run on CPU. Note however that training times are significantly longer. +However, if local GPUs or Azure are not an option for you, these scenarios also run on CPU. Note however that training times are significantly longer. **How do I know what GPU I have?** @@ -63,6 +63,6 @@ However, if local GPUs or Azure are not an option for you, these scenarios also **cuda is not available, please confirm you have a cuda-supported gpu** -1. Open the [GeForce Experience](https://www.nvidia.com/en-us/geforce/geforce-experience/) app. -1. The application should show installed and available driver updates. If you have trouble seeing updates, you can get the latest drivers from https://www.nvidia.com/geforce/drivers/. -1. Install the latest drivers. \ No newline at end of file +1. Open the [GeForce Experience](https://www.nvidia.com/geforce/geforce-experience/) app. +1. The application should show installed and available driver updates. If you have trouble seeing updates, you can get the latest drivers from [https://www.nvidia.com/geforce/drivers/](https://www.nvidia.com/geforce/drivers/). +1. Install the latest drivers. From 3fafffc6e07ab8e6f251978a67fe1d6065aa2378 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 01:34:59 -0500 Subject: [PATCH 35/49] Update intro automl doc --- .../automated-machine-learning-mlnet.md | 18 ++++++++++++++---- .../media/ml-automl-workflow.png | Bin 0 -> 171367 bytes 2 files changed, 14 insertions(+), 4 deletions(-) create mode 100644 docs/machine-learning/media/ml-automl-workflow.png diff --git a/docs/machine-learning/automated-machine-learning-mlnet.md b/docs/machine-learning/automated-machine-learning-mlnet.md index fcd04ca703b13..7c18ab2a54b20 100644 --- a/docs/machine-learning/automated-machine-learning-mlnet.md +++ b/docs/machine-learning/automated-machine-learning-mlnet.md @@ -8,14 +8,24 @@ ms.custom: mvc # What is Automated Machine Learning (AutoML)? -In this article you will learn what Automated machine learning (AutoML) is and how to use the AutoML API to train models in ML.NET. +Automated machine learning (AutoML) automates the process of applying machine learning to data. Given a dataset, you can run AutoML to iterate over different data transformations, machine learning algorithms, and hyperparameters to select the best model. > [!NOTE] > This topic refers to the ML.NET AutoML API, which is currently in preview. Material may be subject to change. -## What is AutoML? +## How does AutoML work? -Automated machine learning (AutoML) automates the process of applying machine learning to data. Given a dataset| you can run an AutoML experiment to iterate over different data featurizations, machine learning algorithms, and hyperparameters to select the best model. +In general, the workflow to train machine learning models is as follows: + +- Define a problem +- Collect data +- Preprocess data +- Train a model +- Evaluate the model + +:::image type="content" source="media/ml-automl-workflow.png" alt-text="Traditional ML and AutoML training workflow" lightbox="media/ml-automl-workflow.png"::: + +Preprocessing, training, and evaluation are an experimental and iterative process that requires multiple trials until you achieve satisfactory results. Because these tasks tend to be repetitive, AutoML can help automate these steps. In addition to automation, optimization techniques are used during the training and evaluation process to find and select algorithms and hyperparameters. ## When should I use AutoML? @@ -52,7 +62,7 @@ AutoML provides preconfigured defaults for the following tasks: - Multiclass classification - Regression -For other tasks, configure. For more information, see the [How to use the ML.NET Automated Machine Learning (AutoML) API](how-to-guides/how-to-use-the-automl-api.md) guide. +For other tasks, you can build your own trial runner to enable those scenarios. 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Date: Wed, 9 Nov 2022 02:22:01 -0500 Subject: [PATCH 36/49] Final pass AutoML API how-to --- .../how-to-use-the-automl-api.md | 141 +++++++++++------- 1 file changed, 87 insertions(+), 54 deletions(-) diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index 28100ff3f332a..260b8d3e95338 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -23,9 +23,9 @@ For more information on installing NuGet packages, see the following guides: ## Quick Start -AutoML provides several defaults for quickly training models. In this section you will learn how to: +AutoML provides several defaults for quickly training machine learning models. In this section you will learn how to: -- Load data +- Load your data - Define your pipeline - Configure your experiment - Run your experiment @@ -61,9 +61,9 @@ ColumnInferenceResults columnInference = loads a few rows from the dataset. It then inspects the data and tries to guess or infer the data type for each of the columns based on their content. -The default behavior is to group columns of the same type into feature vectors or arrays containing the elements for each of the grouped columns. Setting `groupColumns` to `false` overrides that default behavior and only performs column inference without grouping columns. By keeping columns separate it allows you to apply different data transformations when preprocessing the data at the individual column level rather than the grouping. +The default behavior is to group columns of the same type into feature vectors or arrays containing the elements for each of the individual columns. Setting `groupColumns` to `false` overrides that default behavior and only performs column inference without grouping columns. By keeping columns separate it allows you to apply different data transformations when preprocessing the data at the individual column level rather than the column grouping. -The result of a object which contains the options needed to create a as well as column information. +The result of is a object which contains the options needed to create a as well as column information. For the sample dataset in *taxi-fare-train.csv*, column information might look like the following: @@ -89,7 +89,7 @@ TrainTestData trainValidationData = ctx.Data.TrainTestSplit(data, testFraction: ### Define your pipeline -Your pipeline defines the data preprocesing steps and machine learning pipeline to use for training your model. +Your pipeline defines the data processing steps and machine learning pipeline to use for training your model. ```csharp SweepablePipeline pipeline = @@ -97,11 +97,11 @@ SweepablePipeline pipeline = .Append(ctx.Auto().Regression(labelColumnName: columnInference.ColumnInformation.LabelColumnName)); ``` -A is a pipeline containing a collection of . A is an ML.NET with a . +A is a collection of . A is an ML.NET with a . The is a convenience API which builds a sweepable pipeline of data processing sweepable estimators based on the column information you provide. Instead of building a pipeline from scratch, automates the data preprocessing step. For more information on supported transforms by ML.NET, see the [data transformations guide](../resources/transforms.md). -The resulting output is a single column containing a numerical feature vector representing the transformed data for each of the columns. This feature vector is then used as input for the algorithms used to train a machine learning model. +The output is a single column containing a numerical feature vector representing the transformed data for each of the columns. This feature vector is then used as input for the algorithms used to train a machine learning model. If you want finer control over your data preprocessing, you can create a pipeline with each of the individual preprocessing steps. For more information, see the [prepare data for building a model guide](prepare-data-ml-net.md). @@ -121,7 +121,6 @@ For the taxi fare prediction problem, since the goal is to predict a numerical v First, create an AutoML experiment. An is a collection of . ```csharp -// Create AutoML experiment AutoMLExperiment experiment = ctx.Auto().CreateExperiment(); ``` @@ -139,10 +138,10 @@ In this example, you: - Set the sweepable pipeline to run during the experiment using . - Choose `RSquared` as the metric to optimize during training using . For more information on evaluation metrics, see the [evaluate your ML.NET model with metrics](../resources/metrics.md) guide. -- Set 60 seconds as the amount of time you want to train for using . A good heuristic to determine how long to train for is the size of your data. Typically, larger datasets require longer training time. For more information, see [training time guidance](../automate-training-with-model-builder.md#how-long-should-i-train-for)/. +- Set 60 seconds as the amount of time you want to train for using . A good heuristic to determine how long to train for is the size of your data. Typically, larger datasets require longer training time. For more information, see [training time guidance](../automate-training-with-model-builder.md#how-long-should-i-train-for). - Provide the training and validation datasets to use using . -Once your experiment is defined, you'll want some way to track its progress. The quickest way to track progress is by defining the event from . +Once your experiment is defined, you'll want some way to track its progress. The quickest way to track progress is by modifying the event from . ```csharp // Log experiment trials @@ -164,7 +163,7 @@ TrialResult experimentResults = await experiment.RunAsync(); Once the time to train expires, the result is a for the best model found during training. -At this point, you can then save your model or use it for making predictions. For more information on how use an ML.NET model, see the following guides: +At this point, you can save your model or use it for making predictions. For more information on how use an ML.NET model, see the following guides: - [Save and load a trained model](/dotnet/machine-learning/how-to-guides/save-load-machine-learning-models-ml-net.md) - [Make predictions with a trained model](/dotnet/machine-learning/how-to-guides/machine-learning-model-predictions-ml-net) @@ -173,9 +172,9 @@ At this point, you can then save your model or use it for making predictions. Fo ### Modify column inference results -Because only loads a subset of your data, it's possible that edge cases contained outside of the samples used to infer columns are not caught and the wrong data types are set for your columns. In those cases, you can update the properties of and to account for those cases where the column inference results are not correct. +Because only loads a subset of your data, it's possible that edge cases contained outside of the samples used to infer columns are not caught and the wrong data types are set for your columns. In those cases, you can update the properties of to account for those cases where the column inference results are not correct. -For example, in the taxi fare dataset, the data in the `rate_code` column is a number. However, that numerical value represents a category. By default, calling `InferColumn` will place `rate_code` in the `NumericColumnNames` property instead of `CategoricalColumnNames`. Because these properties are standard .NET collections, you can use the standard operations to add and remove items from them. +For example, in the taxi fare dataset, the data in the `rate_code` column is a number. However, that numerical value represents a category. By default, calling will place `rate_code` in the `NumericColumnNames` property instead of `CategoricalColumnNames`. Because these properties are .NET collections, you can use standard operations to add and remove items from them. You can do the following to update the for `rate_code`. @@ -186,7 +185,7 @@ columnInference.ColumnInformation.CategoricalColumnNames.Add("rate_code"); ### Exclude trainers -By default, AutoML tries multiple trainers as part of the training process to see which one works best for your data. However, throughout the training process you might discover there are some trainers that use up too many compute resources or don't provide good evaluation metrics. With AutoML, you have the option to exclude trainers from the training process. Note that which trainers are used depends on the task. For a list of supported trainers in ML.NET, see the [how to choose an ML.NET algorithm guide](/dotnet/machine-learning/how-to-choose-an-ml-net-algorithm.md). +By default, AutoML tries multiple trainers as part of the training process to see which one works best for your data. However, throughout the training process you might discover there are some trainers that use up too many compute resources or don't provide good evaluation metrics. You have the option to exclude trainers from the training process. Note that which trainers are used depends on the task. For a list of supported trainers in ML.NET, see the [Machine learning tasks in ML.NET guide](../resources/tasks.md). For example, in the taxi fare regression scenario, to exclude the LightGBM algorithm, set the `useLgbm` parameter to `false`. @@ -202,7 +201,7 @@ When you want to more granular customization of estimator options included as pa 1. Initialize a search space 1. Use the search space to define a custom factory -1. Define your sweepable estimator +1. Create a sweepable estimator 1. Add your sweepable estimator to your sweepable pipeline AutoML provides a set of preconfigured search spaces for trainers in the following machine learning tasks: @@ -211,14 +210,13 @@ AutoML provides a set of preconfigured search spaces for trainers in the followi - - -In this example, the search space used is for the . +In this example, the search space used is for the . Initialize it by using . ```csharp -// Initialize default Sdca search space var sdcaSearchSpace = new SearchSpace(); ``` -Then, use the search space to define a custom factory method to create the . In this example, the values of `L1Regularization` and `L2Regularization` are both being set to something other than the default. For `L1Regularization`, the value set is determined by the tuner during each trial. The `L2Regularization` is fixed for each trial to the hard-coded value. During each trial, custom factory method's output is an with the configured hyperparameters. +Then, use the search space to define a custom factory method to create the . In this example, the values of `L1Regularization` and `L2Regularization` are both being set to something other than the default. For `L1Regularization`, the value set is determined by the tuner during each trial. The `L2Regularization` is fixed for each trial to the hard-coded value. During each trial, the custom factory's output is an with the configured hyperparameters. ```csharp // Use the search space to define a custom factory to create an SdcaRegressionTrainer @@ -234,14 +232,14 @@ var sdcaFactory = (MLContext ctx, SdcaOption param) => }; ``` -A sweepable estimator is the combination of an estimator and a search space. Now that you've defined a search space and used it to create a custom factory method for generating trainers. Use the method to create a new sweepable estimator. +A sweepable estimator is the combination of an estimator and a search space. Now that you've defined a search space and used it to create a custom factory method for generating trainers, use the method to create a new sweepable estimator. ```csharp // Define Sdca sweepable estimator (SdcaRegressionTrainer + SdcaOption search space) var sdcaSweepableEstimator = ctx.Auto().CreateSweepableEstimator(sdcaFactory, sdcaSearchSpace); ``` -To use your sweepable estimator in your experiment, append it to your sweepable pipeline. +To use your sweepable estimator in your experiment, add it to your sweepable pipeline. ```csharp SweepablePipeline pipeline = @@ -253,7 +251,7 @@ Because sweepable pipelines are a collection of sweepable estimators, you can co ### Customize your search space -There are scenarios where you want to go beyond customizing the sweepable estimators used in your experiment and want control the search space range. T +There are scenarios where you want to go beyond customizing the sweepable estimators used in your experiment and want control the search space range. You can do so by accessing the search space properties using keys. In this case, the `L1Regularization` parameter is a `float`. Therefore, to customize the search range, use . ```csharp sdcaSearchSpace["L1Regularization"] = new UniformSingleOption(min: 0.01f, max: 2.0f, logBase: false, defaultValue: 0.01f); @@ -261,22 +259,45 @@ sdcaSearchSpace["L1Regularization"] = new UniformSingleOption(min: 0.01f, max: 2 Depending on the data type of the hyperparameter you want to set, you can choose from the following options: -- Numerical options +- Numbers - - - -- Boolean and strings +- Booleans and strings - Search spaces can contain nested search spaces as well. ```csharp var searchSpace = new SearchSpace(); +searchSpace["SingleOption"] = new UniformSingleOption(min:-10f, max:10f, defaultValue=0f) var nestedSearchSpace = new SearchSpace(); -nestedSearchSpace["IntOption"] = new UniformIntOption(-10, 10, false, 0); +nestedSearchSpace["IntOption"] = new UniformIntOption(min:-10, max:10, defaultValue=0); searchSpace["Nest"] = nestedSearchSpace; ``` +Another option for customizing search ranges is by extending them. For example, only provides the `L1Regularization` and `L2Regularization` parameters. However, has more parameters you can set such as `BiasLearningRate`. + +To extend the search space, create a new class such as `SdcaExtendedOption` which inherits from . + +```csharp +public class SdcaExtendedOption : SdcaOption +{ + [Range(0.10f, 1f, 0.01f)] + public float BiasLearningRate {get;set;} +} +``` + +To specify the search space range, use which is equivalent to . + +Then, anywhere you use your search space, reference the `SdcaExtendedOption` instead of . + +For example, when you initialize your search space, you can do so as follows: + +```csharp +var sdcaSearchSpace = new SearchSpace(); +``` + ### Create your own trial runner By default, AutoML supports binary classification, multiclass classification, and regression. However, ML.NET supports many more scenarios such as: @@ -292,16 +313,28 @@ For scenarios that don't have preconfigured search spaces and sweepable estimato For example, given restaurant review data that looks like the following: -| | | -| --- | --- | -| Wow... Loved this place. | 1 | -| Crust is not good. | 0 | - -You want to use the trainer to analyze sentiment where 0 is negative and 1 is positive. - -In order to do so you'll have to: - -1. Create your own search space. You can also define using the . +:::row::: + :::column::: + Wow... Loved this place. + :::column-end::: + :::column::: + 1 + :::column-end::: +:::row-end::: +:::row::: + :::column::: + Crust is not good. + :::column-end::: + :::column::: + 0 + :::column-end::: +:::row-end::: + +You want to use the trainer to analyze sentiment where 0 is negative and 1 is positive. However, there is no `ctx.Auto().TextClassification()` configuration. + +In order to use AutoML with the text classification trainer you'll have to: + +1. Create your own search space. ```csharp // Define TextClassification search space @@ -455,11 +488,11 @@ In order to do so you'll have to: ``` The `TCRunner` implementation in this example: - - Extracts the hyperparameters chosen for that trial - - Uses the hyperparameters to create an ML.NET pipeline - - Uses the ML.NET pipeline to train a model - - Evaluates the model - - Returns a object with the information for that trial + - Extracts the hyperparameters chosen for that trial + - Uses the hyperparameters to create an ML.NET pipeline + - Uses the ML.NET pipeline to train a model + - Evaluates the model + - Returns a object with the information for that trial 1. Initialize your custom trial runner @@ -612,21 +645,21 @@ Inside your monitor: } ``` -Append each trial result inside your lifecycle method. +1. Append each trial result inside your lifecycle method. -```csharp -public void ReportCompletedTrial(TrialResult result) -{ - //... - _completedTrials.Add(result); -} -``` + ```csharp + public void ReportCompletedTrial(TrialResult result) + { + //... + _completedTrials.Add(result); + } + ``` -When training completes, you can access all the completed trials by calling `GetCompletedTrials` +1. When training completes, you can access all the completed trials by calling `GetCompletedTrials` -```csharp -var completedTrials = monitor.GetCompletedTrials(); -``` + ```csharp + var completedTrials = monitor.GetCompletedTrials(); + ``` At this point you can perform additional processing on the collection of completed trials like choosing another model other than the one selected by AutoML, logging trial results to a database, or rebuilding the pipeline from any of the completed trials. @@ -634,14 +667,14 @@ At this point you can perform additional processing on the collection of complet When you run experiments asynchronously, make sure to cleanly terminate the process. To do so, use a . +> [!WARNING] +> Cancelling an experiment will not save any of the intermediary outputs. Set a checkpoint to save intermediary outputs. + ```csharp var cts = new CancellationTokenSource(); TrialResult experimentResults = await experiment.RunAsync(cts.Token); ``` -> [!WARNING] -> Cancelling an experiment will not save any of the intermediary outputs. Set a checkpoint to save intermediary outputs. - ### Set checkpoints Checkpoints provide a way for you to save intermediary outputs from the training process in the event of an early termination or error. To set a checkpoint, use the extension method and provide a directory to store the intermediary outputs. From 8beb3314e030b3d207cd86a8a8ba5a1c15828d87 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 02:28:47 -0500 Subject: [PATCH 37/49] Fix mdlint errors --- .../how-to-guides/how-to-use-the-automl-api.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index 260b8d3e95338..f41941006cb1c 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -292,7 +292,7 @@ To specify the search space range, use . -For example, when you initialize your search space, you can do so as follows: +For example, when you initialize your search space, you can do so as follows: ```csharp var sdcaSearchSpace = new SearchSpace(); @@ -330,7 +330,7 @@ For example, given restaurant review data that looks like the following: :::column-end::: :::row-end::: -You want to use the trainer to analyze sentiment where 0 is negative and 1 is positive. However, there is no `ctx.Auto().TextClassification()` configuration. +You want to use the trainer to analyze sentiment where 0 is negative and 1 is positive. However, there is no `ctx.Auto().TextClassification()` configuration. In order to use AutoML with the text classification trainer you'll have to: From bcac2444a3e9596bea59b7f6d8ef953ccb620910 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 02:47:59 -0500 Subject: [PATCH 38/49] Fix MB doc --- .../automate-training-with-model-builder.md | 51 +++++++++++++++---- 1 file changed, 40 insertions(+), 11 deletions(-) diff --git a/docs/machine-learning/automate-training-with-model-builder.md b/docs/machine-learning/automate-training-with-model-builder.md index d74beb4b13bf8..4ab3fd720b76d 100644 --- a/docs/machine-learning/automate-training-with-model-builder.md +++ b/docs/machine-learning/automate-training-with-model-builder.md @@ -85,7 +85,7 @@ Classification is used to categorize data into categories. | 5.1 | 3.5 | 1.4 | 0.2 | setosa | :::column-end::: :::column::: - | PredictedLabel | + | Predicted species | | --- | | setosa | :::column-end::: @@ -112,7 +112,7 @@ Value prediction, which falls under the regression task, is used to predict numb | CMT | 1 | 1 | 1271 | 3.8 | CRD | 17.5 | :::column-end::: :::column::: - | Score | + | Predicted Fare | | --- | | 4.5 | :::column-end::: @@ -139,7 +139,7 @@ You can use the recommendation scenario when you have a set of users and a set o | 1 | 2 | 4.2 | :::column-end::: :::column::: - | PredictedLabel | + | Predicted rating | | --- | | 4.5 | :::column-end::: @@ -151,6 +151,27 @@ The forecasting scenario uses historical data with a time-series or seasonal com You can use the forecasting scenario to forecast demand or sale for a product. +:::row::: + :::column::: + **Sample Input** + :::column-end::: + :::column::: + **Sample Output** + :::column-end::: +:::row-end::: +:::row::: + :::column::: + | Date | SaleQty | + | --- | --- | + | 1/1/1970 | 1000 | + :::column-end::: + :::column::: + | 3 Day Forecast | + | --- | + | [1000,1001,1002] | + :::column-end::: +:::row-end::: + #### Computer Vision ##### Image classification @@ -172,7 +193,7 @@ You can use the image classification scenario if you have a set of images, and y :::image type="content" source="media/automate-training-with-model-builder/dog-classification.png" alt-text="Profile view of standing pug"::: :::column-end::: :::column::: - | PredictedLabel | + | Predicted Label | | --- | | Dog | :::column-end::: @@ -219,10 +240,14 @@ You can use the text classification scenario if you have a set of documents or c :::row-end::: :::row::: :::column::: - I really like this steak! + | Review | + | --- | + | I really like this steak!| :::column-end::: :::column::: - Positive + | Sentiment | + | --- | + | Positive | :::column-end::: :::row-end::: @@ -232,11 +257,15 @@ You can train your machine learning model locally on your machine or in the clou When you train locally, you work within the constraints of your computer resources (CPU, memory, and disk). When you train in the cloud, you can scale up your resources to meet the demands of your scenario, especially for large datasets. -Local CPU training is supported for all scenarios except Object Detection. - -Local GPU training is supported for Image Classification. - -Azure training is supported for Image Classification and Object Detection. +| Scenario | Local CPU | Local GPU | Azure | +|-----------------------|------------|------------|--------| +| Data classification | ✔️ | ❌ | ❌ | +| Value prediction | ✔️ | ❌ | ❌ | +| Recommendation | ✔️ | ❌ | ❌ | +| Forecasting | ✔️ | ❌ | ❌ | +| Image classification | ✔️ | ✔️ | ✔️ | +| Object detection | ❌ | ❌ | ✔️ | +| Text classification | ✔️ | ✔️ | ❌ | ## Data From b887c04093a900e2e2719588437ad2e2c5f2bf81 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 02:57:27 -0500 Subject: [PATCH 39/49] Fix table formatting --- .../automate-training-with-model-builder.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/machine-learning/automate-training-with-model-builder.md b/docs/machine-learning/automate-training-with-model-builder.md index 4ab3fd720b76d..7d4caa61fb07a 100644 --- a/docs/machine-learning/automate-training-with-model-builder.md +++ b/docs/machine-learning/automate-training-with-model-builder.md @@ -80,7 +80,7 @@ Classification is used to categorize data into categories. :::row-end::: :::row::: :::column::: - | SepalLength | SepalWidth | Petal Length | Petal Width | Species (Label) | + | SepalLength | SepalWidth | Petal Length | Petal Width | Species | | --- | --- | --- | --- | --- | | 5.1 | 3.5 | 1.4 | 0.2 | setosa | :::column-end::: @@ -107,7 +107,7 @@ Value prediction, which falls under the regression task, is used to predict numb :::row-end::: :::row::: :::column::: - | vendor_id | rate_code | passenger_count | trip_time_in_secs | trip_distance | payment_type | fare_amount (Label) | + | vendor_id | rate_code | passenger_count | trip_time_in_secs | trip_distance | payment_type | fare_amount | | --- | --- | --- | --- | --- | --- | --- | | CMT | 1 | 1 | 1271 | 3.8 | CRD | 17.5 | :::column-end::: @@ -134,7 +134,7 @@ You can use the recommendation scenario when you have a set of users and a set o :::row-end::: :::row::: :::column::: - | UserId | ProductId | Rating (Label) | + | UserId | ProductId | Rating | | --- | --- | --- | | 1 | 2 | 4.2 | :::column-end::: From 3f1d082b81b00996c274484c2a101a587ecebe79 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 03:01:12 -0500 Subject: [PATCH 40/49] Promote advanced scenario headings --- .../how-to-use-the-automl-api.md | 21 +++++++++---------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index f41941006cb1c..f615062dd23f9 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -168,9 +168,8 @@ At this point, you can save your model or use it for making predictions. For mor - [Save and load a trained model](/dotnet/machine-learning/how-to-guides/save-load-machine-learning-models-ml-net.md) - [Make predictions with a trained model](/dotnet/machine-learning/how-to-guides/machine-learning-model-predictions-ml-net) -## Advanced Scenarios -### Modify column inference results +## Modify column inference results Because only loads a subset of your data, it's possible that edge cases contained outside of the samples used to infer columns are not caught and the wrong data types are set for your columns. In those cases, you can update the properties of to account for those cases where the column inference results are not correct. @@ -183,7 +182,7 @@ columnInference.ColumnInformation.NumericColumnNames.Remove("rate_code"); columnInference.ColumnInformation.CategoricalColumnNames.Add("rate_code"); ``` -### Exclude trainers +## Exclude trainers By default, AutoML tries multiple trainers as part of the training process to see which one works best for your data. However, throughout the training process you might discover there are some trainers that use up too many compute resources or don't provide good evaluation metrics. You have the option to exclude trainers from the training process. Note that which trainers are used depends on the task. For a list of supported trainers in ML.NET, see the [Machine learning tasks in ML.NET guide](../resources/tasks.md). @@ -195,7 +194,7 @@ ctx.Auto().Regression(labelColumnName: columnInference.ColumnInformation.LabelCo The process for excluding trainers in other tasks like binary and multiclass classification works the same way. -### Customize a sweepable estimator +## Customize a sweepable estimator When you want to more granular customization of estimator options included as part of your sweepable pipeline, you need to: @@ -249,7 +248,7 @@ SweepablePipeline pipeline = Because sweepable pipelines are a collection of sweepable estimators, you can configure and customize as many of these sweepable estimators as you need. -### Customize your search space +## Customize your search space There are scenarios where you want to go beyond customizing the sweepable estimators used in your experiment and want control the search space range. You can do so by accessing the search space properties using keys. In this case, the `L1Regularization` parameter is a `float`. Therefore, to customize the search range, use . @@ -298,7 +297,7 @@ For example, when you initialize your search space, you can do so as follows: var sdcaSearchSpace = new SearchSpace(); ``` -### Create your own trial runner +## Create your own trial runner By default, AutoML supports binary classification, multiclass classification, and regression. However, ML.NET supports many more scenarios such as: @@ -521,7 +520,7 @@ In order to use AutoML with the text classification trainer you'll have to: TrialResult textClassificationExperimentResults = await experiment.RunAsync(tcCts.Token); ``` -### Choose a different tuner +## Choose a different tuner AutoML supports a variety of tuning algorithms to iterate through the search space in search of the optimal hyperparameters. By default, it uses the Eci Cost Frugal tuner. Using experiment extension methods, you can choose another tuner that best fits your scenario. @@ -539,7 +538,7 @@ For example, to use the grid search tuner, your code might look like the followi experiment.SetGridSearchTuner(); ``` -### Configure experiment monitoring +## Configure experiment monitoring The quickest way to monitor the progress of an experiment is to define the event from . However, the event outputs a raw dump of the logs generated by AutoML during each trial. Because of the large amount of unformatted information, it's difficult. @@ -620,7 +619,7 @@ Trial 1 finished training in 15080ms with pipeline ReplaceMissingValues=>OneHotE Trial 2 finished training in 3941ms with pipeline ReplaceMissingValues=>OneHotHashEncoding=>Concatenate=>FastTreeRegression ``` -### Persist trials +## Persist trials By default, AutoML only stores the for the best model. However, if you wanted to persist each of the trials, you can do so from within your monitor. @@ -663,7 +662,7 @@ Inside your monitor: At this point you can perform additional processing on the collection of completed trials like choosing another model other than the one selected by AutoML, logging trial results to a database, or rebuilding the pipeline from any of the completed trials. -### Cancel asynchronous experiments +## Cancel experiments When you run experiments asynchronously, make sure to cleanly terminate the process. To do so, use a . @@ -675,7 +674,7 @@ var cts = new CancellationTokenSource(); TrialResult experimentResults = await experiment.RunAsync(cts.Token); ``` -### Set checkpoints +## Set checkpoints Checkpoints provide a way for you to save intermediary outputs from the training process in the event of an early termination or error. To set a checkpoint, use the extension method and provide a directory to store the intermediary outputs. From 59d2ddac2616042ca3732acc3fcccba8f1efe4d9 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 03:01:35 -0500 Subject: [PATCH 41/49] Fix mdlint error --- docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index f615062dd23f9..1ac6c8327df14 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -168,7 +168,6 @@ At this point, you can save your model or use it for making predictions. For mor - [Save and load a trained model](/dotnet/machine-learning/how-to-guides/save-load-machine-learning-models-ml-net.md) - [Make predictions with a trained model](/dotnet/machine-learning/how-to-guides/machine-learning-model-predictions-ml-net) - ## Modify column inference results Because only loads a subset of your data, it's possible that edge cases contained outside of the samples used to infer columns are not caught and the wrong data types are set for your columns. In those cases, you can update the properties of to account for those cases where the column inference results are not correct. From c77c7af6be425cb478fe02c1dea9df4ab7117831 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 03:11:37 -0500 Subject: [PATCH 42/49] Fix lightbox --- docs/machine-learning/automate-training-with-model-builder.md | 2 +- .../tutorials/health-violation-classification-model-builder.md | 2 +- .../tutorials/image-classification-model-builder.md | 2 +- .../tutorials/movie-recommendation-model-builder.md | 2 +- .../tutorials/predict-prices-with-model-builder.md | 2 +- .../tutorials/sentiment-analysis-model-builder.md | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/machine-learning/automate-training-with-model-builder.md b/docs/machine-learning/automate-training-with-model-builder.md index 7d4caa61fb07a..b44719edbe507 100644 --- a/docs/machine-learning/automate-training-with-model-builder.md +++ b/docs/machine-learning/automate-training-with-model-builder.md @@ -13,7 +13,7 @@ Model Builder uses automated machine learning (AutoML) to explore different mach You don't need machine learning expertise to use Model Builder. All you need is some data, and a problem to solve. Model Builder generates the code to add the model to your .NET application. -![Model Builder Scenarios](./media/model-builder-scenarios-2-0.png#lightbox) +:::image type="content" source="../media/model-builder-scenarios-2-0.png" alt-text="Model Builder Scenario Screen" lightbox="../media/model-builder-scenarios-2-0.png"::: ## Creating a Model Builder Project diff --git a/docs/machine-learning/tutorials/health-violation-classification-model-builder.md b/docs/machine-learning/tutorials/health-violation-classification-model-builder.md index 60d22da799280..bf1843ab80e8a 100644 --- a/docs/machine-learning/tutorials/health-violation-classification-model-builder.md +++ b/docs/machine-learning/tutorials/health-violation-classification-model-builder.md @@ -66,7 +66,7 @@ When first adding Model Builder to the solution it will prompt you to create an ## Choose a scenario -![Model Builder wizard in Visual Studio](../media/model-builder-scenarios-2-0.png#lightbox) +:::image type="content" source="../media/model-builder-scenarios-2-0.png" alt-text="Model Builder Scenario Screen" lightbox="../media/model-builder-scenarios-2-0.png"::: To train your model, select from the list of available machine learning scenarios provided by Model Builder. In this case, the scenario is *Data classification*. diff --git a/docs/machine-learning/tutorials/image-classification-model-builder.md b/docs/machine-learning/tutorials/image-classification-model-builder.md index df002bfcd706c..ef2e9c9712607 100644 --- a/docs/machine-learning/tutorials/image-classification-model-builder.md +++ b/docs/machine-learning/tutorials/image-classification-model-builder.md @@ -66,7 +66,7 @@ When first adding Model Builder to the solution it will prompt you to create an ## Choose a scenario -![Model Builder Scenarios](../media/model-builder-scenarios-2-0.png#lightbox) +:::image type="content" source="../media/model-builder-scenarios-2-0.png" alt-text="Model Builder Scenario Screen" lightbox="../media/model-builder-scenarios-2-0.png"::: To train your model, you need to select from the list of available machine learning scenarios provided by Model Builder. diff --git a/docs/machine-learning/tutorials/movie-recommendation-model-builder.md b/docs/machine-learning/tutorials/movie-recommendation-model-builder.md index 0202b54316a09..f7d52ab4461eb 100644 --- a/docs/machine-learning/tutorials/movie-recommendation-model-builder.md +++ b/docs/machine-learning/tutorials/movie-recommendation-model-builder.md @@ -59,7 +59,7 @@ When first adding Model Builder to the solution it will prompt you to create an ## Choose a scenario -![Model Builder Scenarios](../media/model-builder-scenarios-2-0.png#lightbox) +:::image type="content" source="../media/model-builder-scenarios-2-0.png" alt-text="Model Builder Scenario Screen" lightbox="../media/model-builder-scenarios-2-0.png"::: To train your model, you need to select from the list of available machine learning scenarios provided by Model Builder. diff --git a/docs/machine-learning/tutorials/predict-prices-with-model-builder.md b/docs/machine-learning/tutorials/predict-prices-with-model-builder.md index 577eb5bad1bc3..49b3172486b59 100644 --- a/docs/machine-learning/tutorials/predict-prices-with-model-builder.md +++ b/docs/machine-learning/tutorials/predict-prices-with-model-builder.md @@ -73,7 +73,7 @@ When first adding Model Builder to the solution it will prompt you to create an ## Choose a scenario -![Model Builder wizard in Visual Studio](../media/model-builder-scenarios-2-0.png#lightbox) +:::image type="content" source="../media/model-builder-scenarios-2-0.png" alt-text="Model Builder Scenario Screen" lightbox="../media/model-builder-scenarios-2-0.png"::: To train your model, you need to select from the list of available machine learning scenarios provided by Model Builder. In this case, the scenario is `Value prediction`. diff --git a/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md b/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md index 1df0ff24d81df..9ae2190766606 100644 --- a/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md +++ b/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md @@ -63,7 +63,7 @@ When first adding a machine learning model to the solution it will prompt you to ## Choose a scenario -![Model Builder wizard in Visual Studio](../media/model-builder-scenarios-2-0.png#lightbox) +:::image type="content" source="../media/model-builder-scenarios-2-0.png" alt-text="Model Builder Scenario Screen" lightbox="../media/model-builder-scenarios-2-0.png"::: To train your model, you need to select from the list of available machine learning scenarios provided by Model Builder. From 9c9a29d4cdda5ad61e9658e5dcabac9eaae75f24 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 03:16:59 -0500 Subject: [PATCH 43/49] Fix broken link --- docs/machine-learning/automate-training-with-model-builder.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/machine-learning/automate-training-with-model-builder.md b/docs/machine-learning/automate-training-with-model-builder.md index b44719edbe507..5171fc7870852 100644 --- a/docs/machine-learning/automate-training-with-model-builder.md +++ b/docs/machine-learning/automate-training-with-model-builder.md @@ -13,7 +13,7 @@ Model Builder uses automated machine learning (AutoML) to explore different mach You don't need machine learning expertise to use Model Builder. All you need is some data, and a problem to solve. Model Builder generates the code to add the model to your .NET application. -:::image type="content" source="../media/model-builder-scenarios-2-0.png" alt-text="Model Builder Scenario Screen" lightbox="../media/model-builder-scenarios-2-0.png"::: +:::image type="content" source="media/model-builder-scenarios-2-0.png" alt-text="Model Builder scenario screen" lightbox="media/model-builder-scenarios-2-0.png"::: ## Creating a Model Builder Project From 6737130ad837da724fde7ed2c0bdb095204eec21 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 12:03:32 -0500 Subject: [PATCH 44/49] Added deep learning doc --- .../deep-learning-overview.md | 185 ++++++++++++++++++ docs/machine-learning/toc.yml | 6 +- 2 files changed, 189 insertions(+), 2 deletions(-) create mode 100644 docs/machine-learning/deep-learning-overview.md diff --git a/docs/machine-learning/deep-learning-overview.md b/docs/machine-learning/deep-learning-overview.md new file mode 100644 index 0000000000000..177276f7456f0 --- /dev/null +++ b/docs/machine-learning/deep-learning-overview.md @@ -0,0 +1,185 @@ +--- +title: Deep Learning overview +description: Learn what deep learning is and components in ML.NET +ms.date: 11/10/2022 +ms.topic: overview +ms.custom: mvc +--- + +# What is Deep Learning? + +Deep Learning is an umbrella term for an approach to Machine Learning +that makes use of "deep" Neural Networks, a kind of models originally +inspired by the function of biological brains. These days, Deep +Learning is probably the most visible area of Machine Learning, and it +has seen amazing successes in areas like Computer Vision, Natural +Language Processing and, in combination with Reinforcement Learning, +more complicated settings such as game playing, decision making and +simulation. + +A crucial element of the success of Deep Learning ("DL" in what +follows) has been the existence of software frameworks and runtimes +that facilitate the creation of Neural Network models and their +execution for inference. Examples of such frameworks include +Tensorflow, (Py)Torch and onnx. ML.NET provides access to some of +these frameworks, while maintaining the familiar pipeline interface. +In this way, users of ML.NET can take advantage of some +state-of-the-art models and applications of DL at a lower cost than +the steep learning curve learning that other DL frameworks require. + +## Deep Learning vs Machine Learning? + +As mentioned above, DL relies on "Neural Network" models, in contrast +with "traditional" Machine Learning techniques (which use a wider +variety of architectures, such as, for example, generalized linear +models, decision trees or Support Vector Machines). The most +immediate, practical implication of this difference is that DL methods +may be better or worse suited for some kind of data. The performance +of DL methods on images, on textual and on other non- or +less-structured data has been well documented in the literature. +Traditional Machine Learning methods such as gradient-boosted trees +(XGBoost, LightGBM and CatBoost) seem to still have an edge when it +comes to tabular data. The best approach is always to experiment with +your particular data source and use case and determine for yourself, +and ML.NET makes this experimentation relatively straightforward and +pain-free. + +## Neural Network architectures + +A crucial differentiating characteristic of DL from other classes (or +schools) of ML is the use of artificial Neural Networks as models. At +a high-level, one can think of a Neural Network as a configuration of +"processing units" where the output of each unit constitutes the input +of another. Each of these units can take one or many inputs, and +essentially carries out a weighted sum of its inputs, applies an +offset (or "bias") and then a non-linear transformation function +(called "activation"). Different arrangements of these relatively +simple components have been proven surprisingly rich to describe +decision boundaries in classification, regression functions and other +structures central to ML tasks. + +The past decade has seen an explosion of use cases, applications and +techniques of DL, each more impressive than the last, pushing the +boundaries of what functionalities we thought a computer program could +feature. This expansion is fueled by an increasing variety of +operations that can be incorporated into Neural Networks, by a richer +set of arrangments that these operations can be configured in and by +improved computational support for these improvements. In general, we +can categorize these new Neural Architectures, and their use cases +they enable, in (a more complete description can be found [here](https://learn.microsoft.com/azure/machine-learning/concept-deep-learning-vs-machine-learning#artificial-neural-networks) ): + +* Feed-forward Neural Network +* Convolutional Neural Network +* Recurrent Neural Network +* Generative Adversarial Network +* Transformers + +## What can I use deep learning for? + +As stated above, the scope of application of DL techniques is rapidly +expanding. DL architectures, however, have shown amazing +(close-to-human in some cases) performance in tasks having to do with +"unstructured data": images, audio, free-form text and the like. In +this way, DL is constantly featured in image/audio classification and +generation applications. When it comes to text processing, more +generally Natural Language Processing, DL methods have shown amazing +results in tasks like translation, classification, generation and +similar. Some of the more spectacular, recent applications of ML, +such as "[Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion)" are powered by sophisticated, large Neural +Network architectures. + +## Deep learning in ML.NET + +A central concern of DL is what Neural Network architecture (specific configuration of operations) will the model have, and to this end, DL frameworks like Tensorflow and Pytorch feature expressive Domain-Specific Languages to describe in detail such architectures. ML.NET departs from this practice and concentrates on the consumption of pre-trained models (i.e., architectures that have been specified *and* trained in other frameworks). + +## Train custom models + +Many Deep Learning frameworks include facilities to build up a Deep Learning architecture out of individual operations, and to construct a training loop. Training Deep Learning models usually requires a significant amount of training data and compute, which is why ML.NET has implemented a "separable" training/inference API, that allows training a model (in, for example, [the Cloud](https://devblogs.microsoft.com/dotnet/training-a-ml-dotnet-model-with-azure-ml/) to make use of bigger and more plentiful resources) and consuming the result in a client/edge or other deployment scenarios with more limited resources. + +## Image classification + +Add section on image classification + +## Object detection + +Add section on object detection + +## Text classification + +As stated above, Deep Learning has proven to be especially useful in tasks involving non-structured data, like audio, video, images or text. ML.NET provides a rich API for working with text, powered by state-of-the-art Deep Learning models (specifically, [NAS-BERT](https://dl.acm.org/doi/abs/10.1145/3447548.3467262)). The API is high-level, though, so you can treat the actual models powering it as an implementation detail, and concentrate of the functionality of your application. + +An end-to-end example of this API can be found [here](https://github.com/dotnet/csharp-notebooks/blob/main/machine-learning/E2E-Text-Classification-API-with-Yelp-Dataset.ipynb), but a snippet is shown below as an illustration. Here, the input (for the training task) is assumed as a `IDataView` that includes phrases with their corresponding label (the strings "positive" or "negative" to describe the sentiment they express): + +```csharp +var pipeline = + mlContext.Transforms.Conversion.MapValueToKey("Label","Sentiment") + .Append(mlContext.MulticlassClassification.Trainers.TextClassification(sentence1ColumnName: "Text")) + .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel")); + +var model = pipeline.Fit(trainingData); +``` + +As you can see, the pipeline recodes the sentiment category, and configures the `TextClassification` trainer to use the relevant column in the input. The call to `Fit` trains the model, that then can be used to predict the labels on unseen data with a call like: + +```csharp +var predictedData = model.Transform(newData); +``` + +## Sentence Similarity + +Many important tasks in NLP (for example, text summarization or semantic search) rely on the determination of how similar two sentences or passages of text are to each other. Deep Learning models in general, and Transformers in particular, are especially well-suited to carry out this operation since they represent sentence in a mathematical space that captures the *meaning* of each sentence (in contrast with other ways to measure similarity that concentrate on the *form* -- for example, methods like the [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance)). ML.NET provides a high-level API (also powered by NAS-BERT) that takes, for its training input, a set of sentence pairs and their associated similarity, coded as a single-precision floating-point number, in the range 0-1, where closer to 1 indicates higher similarity between the sentences. With this on hand, an ML.NET pipeline (for training/scoring) would look like: + +```csharp +var pipeline = mlContext.Regression.Trainers.SentenceSimilarity(sentence1ColumnName: "Sentence", sentence2ColumnName: "Sentence2"); + +var model = pipeline.Fit(trainingData); +var score = model.Transform(newData).GetColumn("Score"); +``` + +## Consume pretrained models + +ML.NET provides access to many specialized libraries for Machine Learning. This is evident in many "traditional" Machine Learning tasks, like gradient boosted trees, but is especially useful in Deep Learning, where it allows users to build applications from the great variety of pre-trained models that are available as open-source. In the following subsections, we describe this workflow for solving Image Classification problems, but this functionality applies to many other scenarios. + +### TensorFlow + +[Tensorflow](https://www.tensorflow.org/) is one of best-known and most mature Deep Learning frameworks in existence today. It has a rich ecosystem, including a great variety of pre-trained models in the "[Tensorflow Hub](https://www.tensorflow.org/hub)". ML.NET makes it straightforward to make use of those models, define appropriate pre- and post-processing, and assemble a pipeline that can drive the ML tasks of your app. + +You can find a "Getting Started" guide, [here](https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow) for example (and there are more references cited below). At the heart of these examples, you'll find pipelines like: + +```csharp +var pipeline = mlContext.Transforms.LoadImages(outputColumnName: "input", imageFolder: imagesFolder, inputColumnName: nameof(ImageNetData.ImagePath)) + .Append(mlContext.Transforms.ResizeImages(outputColumnName: "input", imageWidth: ImageNetSettings.imageWidth, imageHeight: ImageNetSettings.imageHeight, inputColumnName: "input")) + .Append(mlContext.Transforms.ExtractPixels(outputColumnName: "input", interleavePixelColors: ImageNetSettings.channelsLast, offsetImage: ImageNetSettings.mean)) + .Append(mlContext.Model.LoadTensorFlowModel(modelLocation) + .ScoreTensorFlowModel(outputColumnNames: new[] { "softmax2" }, inputColumnNames: new[] { "input" }, + addBatchDimensionInput:true)); +``` + +where, taking an input a csv file that lists images (providing the path to their location on disk), the pipeline carries out some preprocessing (here illustrated by the `ResizeImages` and `ExtractPixels` transformations), applies a TensorFlow model downloaded from the Hub (and referenced in the code by the variable `modelLocation`, which contains its path in disk) and outputs a `IDataView` that provides the probability of the corresponding image being associated with a label (using a "[softmax](https://en.wikipedia.org/wiki/Softmax_function)" operation, a common practice in DL classification tasks). + +The pipeline is somewhat verbose, owing to the many configuration knobs that pre-trained models have (which give them their flexibility), but relatively easy to read and understand. Moreover, the `ImageAnalytics` preprocessing operation ML.NET includes make the very non-trivial task of pre-processing much easier. + +### References + +* [https://learn.microsoft.com/dotnet/machine-learning/tutorials/text-classification-tf](https://learn.microsoft.com/dotnet/machine-learning/tutorials/text-classification-tf) +* [https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow](https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow) + +### ONNX + +[ONNX](https://onnx.ai/), or "Open Neural Network Exchange" is an industry-wide format designed to make Deep Learning frameworks interoperable. Throughout the years, the scope of ONNX has expanded beyond Neural Network models, and it has recently also acquired support for training (it was originally only for inference). Also, and very much like TensorFlow, it is at the center of a growing ecosystem, which also includes a repository for pre-trained models. The [ONNX Model Zoo](https://github.com/onnx/models) hosts many models pre-trained in a great variety of datasets, and optimized for a variety of scenarios (including, for example models that support specialized precisions like BFloat or INT8, or that have been quantized). + +Like it does with TensorFlow, ML.NET makes it straightforward to consume pre-trained models in the ONNX format. The pipeline is very similar (this is a snippet from the sample scenario referenced below): + +```csharp +var pipeline = mlContext.Transforms.LoadImages(outputColumnName: "image", imageFolder: "", inputColumnName: nameof(ImageNetData.ImagePath)) + .Append(mlContext.Transforms.ResizeImages(outputColumnName: "image", imageWidth: ImageNetSettings.imageWidth, imageHeight: ImageNetSettings.imageHeight, inputColumnName: "image")) + .Append(mlContext.Transforms.ExtractPixels(outputColumnName: "image")) + .Append(mlContext.Transforms.ApplyOnnxModel(modelFile: modelLocation, outputColumnNames: new[] { TinyYoloModelSettings.ModelOutput }, inputColumnNames: new[] { TinyYoloModelSettings.ModelInput })); + +``` + +Note that the pre-processing stages are effectively the same as those in the TensorFlow example, variations only introduced to accommodate the requirements of the model. + +### References + +* [https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx](https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx) diff --git a/docs/machine-learning/toc.yml b/docs/machine-learning/toc.yml index f79cdf07256b0..425b66fc3bd76 100644 --- a/docs/machine-learning/toc.yml +++ b/docs/machine-learning/toc.yml @@ -69,10 +69,12 @@ items: - name: ML.NET tasks href: resources/tasks.md - - name: Algorithms - href: how-to-choose-an-ml-net-algorithm.md - name: Data transforms href: resources/transforms.md + - name: Algorithms + href: how-to-choose-an-ml-net-algorithm.md + - name: Deep learning overview + href: deep-learning-overview.md - name: Model evaluation metrics href: resources/metrics.md - name: Improve model accuracy From 26a2759550fd71496d11ac6914b2bea994d525e2 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 12:15:42 -0500 Subject: [PATCH 45/49] Fix mdlint errors --- docs/fundamentals/code-analysis/quality-rules/ca2109.md | 2 +- docs/machine-learning/deep-learning-overview.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/fundamentals/code-analysis/quality-rules/ca2109.md b/docs/fundamentals/code-analysis/quality-rules/ca2109.md index cb94d249700d6..a5162973c242e 100644 --- a/docs/fundamentals/code-analysis/quality-rules/ca2109.md +++ b/docs/fundamentals/code-analysis/quality-rules/ca2109.md @@ -26,7 +26,7 @@ A public or protected event-handling method was detected. ## Rule description -An externally visible event-handling method presents a security issue that requires review. +An externally visible event-handling method presents a security issue that requires review. Do not expose event-handling methods unless absolutely necessary. An event handler, a delegate type, that invokes the exposed method can be added to any event as long as the handler and event signatures match. Events can potentially be raised by any code, and are frequently raised by highly trusted system code in response to user actions such as clicking a button. Adding a security check to an event-handling method does not prevent code from registering an event handler that invokes the method. diff --git a/docs/machine-learning/deep-learning-overview.md b/docs/machine-learning/deep-learning-overview.md index 177276f7456f0..b11f79cf7249b 100644 --- a/docs/machine-learning/deep-learning-overview.md +++ b/docs/machine-learning/deep-learning-overview.md @@ -8,7 +8,7 @@ ms.custom: mvc # What is Deep Learning? -Deep Learning is an umbrella term for an approach to Machine Learning +Deep learning is an umbrella term for an approach to machine learning that makes use of "deep" Neural Networks, a kind of models originally inspired by the function of biological brains. These days, Deep Learning is probably the most visible area of Machine Learning, and it From f55d8e6367637a2214ba33e27c916ba97914f304 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 12:21:17 -0500 Subject: [PATCH 46/49] Fix lint errors --- docs/fundamentals/code-analysis/quality-rules/ca2109.md | 2 +- docs/machine-learning/deep-learning-overview.md | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/fundamentals/code-analysis/quality-rules/ca2109.md b/docs/fundamentals/code-analysis/quality-rules/ca2109.md index a5162973c242e..cb94d249700d6 100644 --- a/docs/fundamentals/code-analysis/quality-rules/ca2109.md +++ b/docs/fundamentals/code-analysis/quality-rules/ca2109.md @@ -26,7 +26,7 @@ A public or protected event-handling method was detected. ## Rule description -An externally visible event-handling method presents a security issue that requires review. +An externally visible event-handling method presents a security issue that requires review. Do not expose event-handling methods unless absolutely necessary. An event handler, a delegate type, that invokes the exposed method can be added to any event as long as the handler and event signatures match. Events can potentially be raised by any code, and are frequently raised by highly trusted system code in response to user actions such as clicking a button. Adding a security check to an event-handling method does not prevent code from registering an event handler that invokes the method. diff --git a/docs/machine-learning/deep-learning-overview.md b/docs/machine-learning/deep-learning-overview.md index b11f79cf7249b..16b924ce0ddd7 100644 --- a/docs/machine-learning/deep-learning-overview.md +++ b/docs/machine-learning/deep-learning-overview.md @@ -6,12 +6,12 @@ ms.topic: overview ms.custom: mvc --- -# What is Deep Learning? +# What is deep learning? Deep learning is an umbrella term for an approach to machine learning -that makes use of "deep" Neural Networks, a kind of models originally -inspired by the function of biological brains. These days, Deep -Learning is probably the most visible area of Machine Learning, and it +that makes use of "deep" neural networks, a kind of model originally +inspired by the function of biological brains. Today, deep +learning is probably the most visible area of Machine Learning, and it has seen amazing successes in areas like Computer Vision, Natural Language Processing and, in combination with Reinforcement Learning, more complicated settings such as game playing, decision making and From 8bb7791a4e0b19694fc5a4e24da6a9cbab34d116 Mon Sep 17 00:00:00 2001 From: Luis Quintanilla <46974588+luisquintanilla@users.noreply.github.com> Date: Wed, 9 Nov 2022 16:51:06 -0500 Subject: [PATCH 47/49] Update docs/fundamentals/code-analysis/quality-rules/ca2109.md Co-authored-by: Cam Soper --- docs/fundamentals/code-analysis/quality-rules/ca2109.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/fundamentals/code-analysis/quality-rules/ca2109.md b/docs/fundamentals/code-analysis/quality-rules/ca2109.md index a5162973c242e..cb94d249700d6 100644 --- a/docs/fundamentals/code-analysis/quality-rules/ca2109.md +++ b/docs/fundamentals/code-analysis/quality-rules/ca2109.md @@ -26,7 +26,7 @@ A public or protected event-handling method was detected. ## Rule description -An externally visible event-handling method presents a security issue that requires review. +An externally visible event-handling method presents a security issue that requires review. Do not expose event-handling methods unless absolutely necessary. An event handler, a delegate type, that invokes the exposed method can be added to any event as long as the handler and event signatures match. Events can potentially be raised by any code, and are frequently raised by highly trusted system code in response to user actions such as clicking a button. Adding a security check to an event-handling method does not prevent code from registering an event handler that invokes the method. From 17a872e830d5e78b335c3a3c699f9db01327c72f Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 16:57:17 -0500 Subject: [PATCH 48/49] More DL --- .../deep-learning-overview.md | 80 ++++--------------- 1 file changed, 16 insertions(+), 64 deletions(-) diff --git a/docs/machine-learning/deep-learning-overview.md b/docs/machine-learning/deep-learning-overview.md index 16b924ce0ddd7..4f5a796523452 100644 --- a/docs/machine-learning/deep-learning-overview.md +++ b/docs/machine-learning/deep-learning-overview.md @@ -8,65 +8,26 @@ ms.custom: mvc # What is deep learning? -Deep learning is an umbrella term for an approach to machine learning -that makes use of "deep" neural networks, a kind of model originally -inspired by the function of biological brains. Today, deep -learning is probably the most visible area of Machine Learning, and it -has seen amazing successes in areas like Computer Vision, Natural -Language Processing and, in combination with Reinforcement Learning, -more complicated settings such as game playing, decision making and -simulation. - -A crucial element of the success of Deep Learning ("DL" in what -follows) has been the existence of software frameworks and runtimes -that facilitate the creation of Neural Network models and their -execution for inference. Examples of such frameworks include -Tensorflow, (Py)Torch and onnx. ML.NET provides access to some of -these frameworks, while maintaining the familiar pipeline interface. -In this way, users of ML.NET can take advantage of some -state-of-the-art models and applications of DL at a lower cost than -the steep learning curve learning that other DL frameworks require. +Deep learning is an umbrella term for machine learning techniques that make use of "deep" neural networks. Deep neural networks are a type of model originally +inspired by the function of biological brains. Today, deep learning is one of the most visible areas of machine learning because of its success in areas like Computer Vision, Natural Language Processing, and when applied to reinforcement learning, scenarios like game playing, decision making and simulation. + +A crucial element to the success of deep learning has been the availability of data, compute, software frameworks, and runtimes that facilitate the creation of neural network models and their execution for inference. Examples of such frameworks include Tensorflow, (Py)Torch and ONNX. ML.NET provides access to some of these frameworks. As a result, ML.NET users can take advantage of deep learning models without having to build them from scratch. ## Deep Learning vs Machine Learning? -As mentioned above, DL relies on "Neural Network" models, in contrast -with "traditional" Machine Learning techniques (which use a wider -variety of architectures, such as, for example, generalized linear -models, decision trees or Support Vector Machines). The most -immediate, practical implication of this difference is that DL methods -may be better or worse suited for some kind of data. The performance -of DL methods on images, on textual and on other non- or -less-structured data has been well documented in the literature. -Traditional Machine Learning methods such as gradient-boosted trees -(XGBoost, LightGBM and CatBoost) seem to still have an edge when it -comes to tabular data. The best approach is always to experiment with -your particular data source and use case and determine for yourself, -and ML.NET makes this experimentation relatively straightforward and -pain-free. +Deep learning relies on neural network algorithms. This is in contrast with traditional or classical machine learning techniques which use a wider variety of algorithms such as generalized linear models, decision trees or Support Vector Machines (SVM). The most immediate, practical implication of this difference is that deep learning methods may be better or worse suited for some kind of data. In some cases, classical machine learning techniques such as gradient-boosted trees (XGBoost, LightGBM and CatBoost) seem to still have an edge for tabular data. For less structured data like text and images, neural networks tend to perform better. The best approach is always to experiment with your particular data source and use case and determine for yourself which techniques work best for your problem. + +For classical machine learning tasks, ML.NET simplifies this experimentation process through Automated Machine Learning (AutoML). For more information on AutoML, see the article [what is Automated Machine Learning (AutoML)?](automated-machine-learning-mlnet.md). ## Neural Network architectures -A crucial differentiating characteristic of DL from other classes (or -schools) of ML is the use of artificial Neural Networks as models. At -a high-level, one can think of a Neural Network as a configuration of -"processing units" where the output of each unit constitutes the input -of another. Each of these units can take one or many inputs, and -essentially carries out a weighted sum of its inputs, applies an -offset (or "bias") and then a non-linear transformation function -(called "activation"). Different arrangements of these relatively -simple components have been proven surprisingly rich to describe -decision boundaries in classification, regression functions and other -structures central to ML tasks. - -The past decade has seen an explosion of use cases, applications and -techniques of DL, each more impressive than the last, pushing the -boundaries of what functionalities we thought a computer program could -feature. This expansion is fueled by an increasing variety of -operations that can be incorporated into Neural Networks, by a richer -set of arrangments that these operations can be configured in and by -improved computational support for these improvements. In general, we -can categorize these new Neural Architectures, and their use cases -they enable, in (a more complete description can be found [here](https://learn.microsoft.com/azure/machine-learning/concept-deep-learning-vs-machine-learning#artificial-neural-networks) ): +One of the main differentiating characteristics of deep learning is the use of artificial neural network algorithms. At a high-level, you can think of neural networks as a configuration of "processing units" where the output of each unit constitutes the input of another. Each of these units can take one or many inputs, and essentially carries out a weighted sum of its inputs, applies an offset (or "bias") and then a non-linear transformation function (called "activation"). Different arrangements of these relatively simple components have been proven surprisingly rich to describe decision boundaries in classification, regression functions and other structures central to ML tasks. + + + +The past decade has seen an explosion of use cases, applications and techniques of DL, each more impressive than the last, pushing the boundaries of what functionalities we thought a computer program could feature. This expansion is fueled by an increasing variety of operations that can be incorporated into Neural Networks, by a richer set of arrangements that these operations can be configured in and by improved computational support for these improvements. In general, we +can categorize these new Neural Architectures, and their use cases they enable, in (a more complete description can be found [here](https://learn.microsoft.com/azure/machine-learning/concept-deep-learning-vs-machine-learning#artificial-neural-networks)): * Feed-forward Neural Network * Convolutional Neural Network @@ -76,17 +37,8 @@ they enable, in (a more complete description can be found [here](https://learn.m ## What can I use deep learning for? -As stated above, the scope of application of DL techniques is rapidly -expanding. DL architectures, however, have shown amazing -(close-to-human in some cases) performance in tasks having to do with -"unstructured data": images, audio, free-form text and the like. In -this way, DL is constantly featured in image/audio classification and -generation applications. When it comes to text processing, more -generally Natural Language Processing, DL methods have shown amazing -results in tasks like translation, classification, generation and -similar. Some of the more spectacular, recent applications of ML, -such as "[Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion)" are powered by sophisticated, large Neural -Network architectures. +As stated above, the scope of application of DL techniques is rapidly expanding. DL architectures, however, have shown amazing (close-to-human in some cases) performance in tasks having to do with "unstructured data": images, audio, free-form text and the like. In this way, DL is constantly featured in image/audio classification and +generation applications. When it comes to text processing, more generally Natural Language Processing, DL methods have shown amazing results in tasks like translation, classification, generation and similar. Some of the more spectacular, recent applications of ML, such as "[Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion)" are powered by sophisticated, large Neural Network architectures. ## Deep learning in ML.NET From 7ead54cb5d136c08ae648653c1b99a9b09190050 Mon Sep 17 00:00:00 2001 From: luisquintanilla Date: Wed, 9 Nov 2022 19:08:16 -0500 Subject: [PATCH 49/49] Deep learning doc complete --- .../deep-learning-overview.md | 166 ++++++++++++------ .../how-to-use-the-automl-api.md | 8 +- .../media/single-layer-neural-net.png | Bin 0 -> 14324 bytes .../sentiment-analysis-model-builder.md | 4 +- 4 files changed, 117 insertions(+), 61 deletions(-) create mode 100644 docs/machine-learning/media/single-layer-neural-net.png diff --git a/docs/machine-learning/deep-learning-overview.md b/docs/machine-learning/deep-learning-overview.md index 4f5a796523452..8c3a866371b16 100644 --- a/docs/machine-learning/deep-learning-overview.md +++ b/docs/machine-learning/deep-learning-overview.md @@ -8,26 +8,25 @@ ms.custom: mvc # What is deep learning? -Deep learning is an umbrella term for machine learning techniques that make use of "deep" neural networks. Deep neural networks are a type of model originally -inspired by the function of biological brains. Today, deep learning is one of the most visible areas of machine learning because of its success in areas like Computer Vision, Natural Language Processing, and when applied to reinforcement learning, scenarios like game playing, decision making and simulation. +Deep learning is an umbrella term for machine learning techniques that make use of "deep" neural networks. Today, deep learning is one of the most visible areas of machine learning because of its success in areas like Computer Vision, Natural Language Processing, and when applied to reinforcement learning, scenarios like game playing, decision making and simulation. -A crucial element to the success of deep learning has been the availability of data, compute, software frameworks, and runtimes that facilitate the creation of neural network models and their execution for inference. Examples of such frameworks include Tensorflow, (Py)Torch and ONNX. ML.NET provides access to some of these frameworks. As a result, ML.NET users can take advantage of deep learning models without having to build them from scratch. +A crucial element to the success of deep learning has been the availability of data, compute, software frameworks, and runtimes that facilitate the creation of neural network models and their execution for inference. Examples of such frameworks include Tensorflow, (Py)Torch and ONNX. + +ML.NET provides access to some of these frameworks. As a result, ML.NET users can take advantage of deep learning models without having to start from scratch. ## Deep Learning vs Machine Learning? -Deep learning relies on neural network algorithms. This is in contrast with traditional or classical machine learning techniques which use a wider variety of algorithms such as generalized linear models, decision trees or Support Vector Machines (SVM). The most immediate, practical implication of this difference is that deep learning methods may be better or worse suited for some kind of data. In some cases, classical machine learning techniques such as gradient-boosted trees (XGBoost, LightGBM and CatBoost) seem to still have an edge for tabular data. For less structured data like text and images, neural networks tend to perform better. The best approach is always to experiment with your particular data source and use case and determine for yourself which techniques work best for your problem. +Deep learning relies on neural network algorithms. This is in contrast with traditional or classical machine learning techniques which use a wider variety of algorithms such as generalized linear models, decision trees or Support Vector Machines (SVM). The most immediate, practical implication of this difference is that deep learning methods may be better suited for some kind of data. In some cases, classical machine learning techniques such as gradient-boosted trees (XGBoost, LightGBM and CatBoost) seem to have an edge for tabular data. For less structured data like text and images, neural networks tend to perform better. The best approach is always to experiment with your particular data source and use case to determine for yourself which techniques work best for your problem. For classical machine learning tasks, ML.NET simplifies this experimentation process through Automated Machine Learning (AutoML). For more information on AutoML, see the article [what is Automated Machine Learning (AutoML)?](automated-machine-learning-mlnet.md). ## Neural Network architectures -One of the main differentiating characteristics of deep learning is the use of artificial neural network algorithms. At a high-level, you can think of neural networks as a configuration of "processing units" where the output of each unit constitutes the input of another. Each of these units can take one or many inputs, and essentially carries out a weighted sum of its inputs, applies an offset (or "bias") and then a non-linear transformation function (called "activation"). Different arrangements of these relatively simple components have been proven surprisingly rich to describe decision boundaries in classification, regression functions and other structures central to ML tasks. +One of the main differentiating characteristics of deep learning is the use of artificial neural network algorithms. At a high-level, you can think of neural networks as a configuration of "processing units" where the output of each unit constitutes the input of another. Each of these units can take one or many inputs, and essentially carries out a weighted sum of its inputs, applies an offset (or "bias") and then a non-linear transformation function (called "activation"). Different arrangements of these components have been used to describe decision boundaries in classification, regression functions and other structures central to machine learning tasks. - +:::image type="content" source="media/single-layer-neural-net.png" alt-text="Diagram representing single layer in neural network" lightbox="media/single-layer-neural-net.png"::: -The past decade has seen an explosion of use cases, applications and techniques of DL, each more impressive than the last, pushing the boundaries of what functionalities we thought a computer program could feature. This expansion is fueled by an increasing variety of operations that can be incorporated into Neural Networks, by a richer set of arrangements that these operations can be configured in and by improved computational support for these improvements. In general, we -can categorize these new Neural Architectures, and their use cases they enable, in (a more complete description can be found [here](https://learn.microsoft.com/azure/machine-learning/concept-deep-learning-vs-machine-learning#artificial-neural-networks)): +The past decade has seen an increase in cases, applications and techniques of deep learning. This increase is driven in part by an increasing variety of operations that can be incorporated into neural networks, a richer set of arrangements that these operations can be configured in and improved computational support for these improvements. In general, neural network architectures can be grouped into the following categories: * Feed-forward Neural Network * Convolutional Neural Network @@ -35,103 +34,156 @@ can categorize these new Neural Architectures, and their use cases they enable, * Generative Adversarial Network * Transformers +For more details, see the [artificial neural networks guide](/azure/machine-learning/concept-deep-learning-vs-machine-learning). + ## What can I use deep learning for? -As stated above, the scope of application of DL techniques is rapidly expanding. DL architectures, however, have shown amazing (close-to-human in some cases) performance in tasks having to do with "unstructured data": images, audio, free-form text and the like. In this way, DL is constantly featured in image/audio classification and -generation applications. When it comes to text processing, more generally Natural Language Processing, DL methods have shown amazing results in tasks like translation, classification, generation and similar. Some of the more spectacular, recent applications of ML, such as "[Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion)" are powered by sophisticated, large Neural Network architectures. +Deep learning architectures, have shown good performance in tasks involving "unstructured data" such as images, audio, and free-form text. As a result, deep learning has been used to solve problems like: + +* Image classification +* Audio classification +* Translation +* Text generation +* Text classification ## Deep learning in ML.NET -A central concern of DL is what Neural Network architecture (specific configuration of operations) will the model have, and to this end, DL frameworks like Tensorflow and Pytorch feature expressive Domain-Specific Languages to describe in detail such architectures. ML.NET departs from this practice and concentrates on the consumption of pre-trained models (i.e., architectures that have been specified *and* trained in other frameworks). +Training a deep learning model from scratch requires setting several parameters, a large amount of labeled training data, and a vast amount of compute resources (hundreds of GPU hours). ML.NET enables you to shortcut this process by using pretrained models and knowledge transfer techniques such as transfer learning and fine-tuning. + +ML.NET also enables you to import models trained in other frameworks and consume them within your .NET applications. + +Depending on the scenario, you can use local GPU as well as Azure GPU compute resources to train and consume deep learning models. + +### Train custom models + +ML.NET provides APIs to train custom deep learning models and use them to make predictions inside your .NET application. + +These APIs are powered by [TorchSharp](https://github.com/dotnet/TorchSharp) and [TensorFlow.NET](https://github.com/SciSharp/TensorFlow.NET). + +#### Image classification + +In ML.NET you can use the set of APIs to train custom image classification models. + +An image classification training pipeline in ML.NET might look like the following: + +```csharp +//Append ImageClassification trainer to your pipeline containing any preprocessing transforms +pipeline + .Append(mlContext.MulticlassClassification.Trainers.ImageClassification(featureColumnName: "Image") + .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"); + +// Train your model +var model = pipeline.Fit(trainingData); + +// Use your model for inferencing +var predictedData = model.Transform(newData).GetColumn("PredictedLabel"); +``` + +To get started training custom image classification models in ML.NET, see the [Train an image classification model in Azure using Model Builder](tutorials/image-classification-model-builder.md) -## Train custom models +#### Object detection -Many Deep Learning frameworks include facilities to build up a Deep Learning architecture out of individual operations, and to construct a training loop. Training Deep Learning models usually requires a significant amount of training data and compute, which is why ML.NET has implemented a "separable" training/inference API, that allows training a model (in, for example, [the Cloud](https://devblogs.microsoft.com/dotnet/training-a-ml-dotnet-model-with-azure-ml/) to make use of bigger and more plentiful resources) and consuming the result in a client/edge or other deployment scenarios with more limited resources. +ML.NET enables you to train custom object detection models in Model Builder using Azure Machine Learning. All you need is a labelled dataset containing bounding box information and the categories the objects in the bounding boxes belong to. -## Image classification +The result of the training process is an ONNX model which can then be used with the transform for to make predictions. -Add section on image classification +At this time, there is no local support for object detection in ML.NET. -## Object detection +To train custom object detection models with ML.NET, see the [Detect stop signs in images with Model Builder tutorial](tutorials/object-detection-model-builder.md) -Add section on object detection +#### Text classification -## Text classification +Classifying free-form text, whether that's customer reviews or business memos is an important part of many processes. -As stated above, Deep Learning has proven to be especially useful in tasks involving non-structured data, like audio, video, images or text. ML.NET provides a rich API for working with text, powered by state-of-the-art Deep Learning models (specifically, [NAS-BERT](https://dl.acm.org/doi/abs/10.1145/3447548.3467262)). The API is high-level, though, so you can treat the actual models powering it as an implementation detail, and concentrate of the functionality of your application. +In ML.NET, you can use the set of APIs to train custom text classification models. The technique used to train custom text classification models in ML.NET is known as fine-tuning. Fine-tuning enables you to take a pre-trained model and retrain the layers specific to your domain or problem using your own data. This gives you the benefit of having a model that’s more tailored to solve your problem without having to go through the process of training the entire model from scratch. The pretrained model used by the Text Classification API is a TorchSharp implementation of [NAS-BERT](https://dl.acm.org/doi/abs/10.1145/3447548.3467262). -An end-to-end example of this API can be found [here](https://github.com/dotnet/csharp-notebooks/blob/main/machine-learning/E2E-Text-Classification-API-with-Yelp-Dataset.ipynb), but a snippet is shown below as an illustration. Here, the input (for the training task) is assumed as a `IDataView` that includes phrases with their corresponding label (the strings "positive" or "negative" to describe the sentiment they express): +A text classification training pipeline in ML.NET might look like the following: ```csharp +// Define training pipeline using TextClassification trainer var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label","Sentiment") .Append(mlContext.MulticlassClassification.Trainers.TextClassification(sentence1ColumnName: "Text")) .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel")); - + +// Train your model var model = pipeline.Fit(trainingData); + +// Use your model to make predictions +var predictedData = model.Transform(newData).GetColumn("PredictedLabel"); ``` -As you can see, the pipeline recodes the sentiment category, and configures the `TextClassification` trainer to use the relevant column in the input. The call to `Fit` trains the model, that then can be used to predict the labels on unseen data with a call like: +To get started training text classification models with ML.NET, see the [Analyze sentiment of website comments in a web application using ML.NET Model Builder tutorial](tutorials/sentiment-analysis-model-builder.md). -```csharp -var predictedData = model.Transform(newData); -``` +#### Sentence Similarity -## Sentence Similarity +Tasks such as semantic search rely on the determination of how similar two sentences or passages of text are to each other. -Many important tasks in NLP (for example, text summarization or semantic search) rely on the determination of how similar two sentences or passages of text are to each other. Deep Learning models in general, and Transformers in particular, are especially well-suited to carry out this operation since they represent sentence in a mathematical space that captures the *meaning* of each sentence (in contrast with other ways to measure similarity that concentrate on the *form* -- for example, methods like the [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance)). ML.NET provides a high-level API (also powered by NAS-BERT) that takes, for its training input, a set of sentence pairs and their associated similarity, coded as a single-precision floating-point number, in the range 0-1, where closer to 1 indicates higher similarity between the sentences. With this on hand, an ML.NET pipeline (for training/scoring) would look like: +ML.NET provides the set of APIs which use the same underlying model and fine-tuning techniques as the . However, instead of producing a category as output, it produces a numerical value representing how similar two passages are. + +A training and inference pipeline for sentence similarity in ML.NET might look like the following: ```csharp +// Define your pipeline var pipeline = mlContext.Regression.Trainers.SentenceSimilarity(sentence1ColumnName: "Sentence", sentence2ColumnName: "Sentence2"); +// Train your model var model = pipeline.Fit(trainingData); + +// Use the model to make predictions and extract their similarity values var score = model.Transform(newData).GetColumn("Score"); ``` -## Consume pretrained models +To get started with sentence similarity, see the samples in the [dotnet/machinelearning-samples repo](https://aka.ms/mlnet-2-samples). + +### Consume pretrained models + +ML.NET provides APIs to consume models in other formats like TensorFlow and ONNX and use them to make predictions inside your .NET application. -ML.NET provides access to many specialized libraries for Machine Learning. This is evident in many "traditional" Machine Learning tasks, like gradient boosted trees, but is especially useful in Deep Learning, where it allows users to build applications from the great variety of pre-trained models that are available as open-source. In the following subsections, we describe this workflow for solving Image Classification problems, but this functionality applies to many other scenarios. +These APIs are powered by [TensorFlow.NET](https://github.com/SciSharp/TensorFlow.NET) and the [ONNX Runtime](https://onnxruntime.ai/). -### TensorFlow +#### TensorFlow -[Tensorflow](https://www.tensorflow.org/) is one of best-known and most mature Deep Learning frameworks in existence today. It has a rich ecosystem, including a great variety of pre-trained models in the "[Tensorflow Hub](https://www.tensorflow.org/hub)". ML.NET makes it straightforward to make use of those models, define appropriate pre- and post-processing, and assemble a pipeline that can drive the ML tasks of your app. +[TensorFlow](https://www.tensorflow.org/) is a deep learning framework with a rich ecosystem and a variety of pre-trained models available in the [Tensorflow Hub](https://www.tensorflow.org/hub). -You can find a "Getting Started" guide, [here](https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow) for example (and there are more references cited below). At the heart of these examples, you'll find pipelines like: +With ML.NET, you can take these pretrained TensorFlow models and use them for inferencing inside your .NET applications. + +An inference pipeline using a pretrained TensorFlow model might look like the following: ```csharp -var pipeline = mlContext.Transforms.LoadImages(outputColumnName: "input", imageFolder: imagesFolder, inputColumnName: nameof(ImageNetData.ImagePath)) - .Append(mlContext.Transforms.ResizeImages(outputColumnName: "input", imageWidth: ImageNetSettings.imageWidth, imageHeight: ImageNetSettings.imageHeight, inputColumnName: "input")) - .Append(mlContext.Transforms.ExtractPixels(outputColumnName: "input", interleavePixelColors: ImageNetSettings.channelsLast, offsetImage: ImageNetSettings.mean)) - .Append(mlContext.Model.LoadTensorFlowModel(modelLocation) - .ScoreTensorFlowModel(outputColumnNames: new[] { "softmax2" }, inputColumnNames: new[] { "input" }, - addBatchDimensionInput:true)); -``` +// Load TensorFlow model +TensorFlowModel tensorFlowModel = mlContext.Model.LoadTensorFlowModel(_modelPath); -where, taking an input a csv file that lists images (providing the path to their location on disk), the pipeline carries out some preprocessing (here illustrated by the `ResizeImages` and `ExtractPixels` transformations), applies a TensorFlow model downloaded from the Hub (and referenced in the code by the variable `modelLocation`, which contains its path in disk) and outputs a `IDataView` that provides the probability of the corresponding image being associated with a label (using a "[softmax](https://en.wikipedia.org/wiki/Softmax_function)" operation, a common practice in DL classification tasks). +//Append ScoreTensorFlowModel transform to your pipeline containing any preprocessing transforms +pipeline.Append(tensorFlowModel.ScoreTensorFlowModel(outputColumnName: "Prediction/Softmax", inputColumnName:"Features")) -The pipeline is somewhat verbose, owing to the many configuration knobs that pre-trained models have (which give them their flexibility), but relatively easy to read and understand. Moreover, the `ImageAnalytics` preprocessing operation ML.NET includes make the very non-trivial task of pre-processing much easier. +// Create ML.NET model from pipeline +ITransformer model = pipeline.Fit(dataView); -### References +var predictions = model.Transform(dataView).GetColumn("Prediction/Softmax"); +``` -* [https://learn.microsoft.com/dotnet/machine-learning/tutorials/text-classification-tf](https://learn.microsoft.com/dotnet/machine-learning/tutorials/text-classification-tf) -* [https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow](https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow) +To get started consuming pretrained TensorFlow models with ML.NET, see the [movie reviews sentiment analysis using a pre-trained TensorFlow model in ML.NET tutorial](tutorials/text-classification-tf.md) -### ONNX +#### ONNX -[ONNX](https://onnx.ai/), or "Open Neural Network Exchange" is an industry-wide format designed to make Deep Learning frameworks interoperable. Throughout the years, the scope of ONNX has expanded beyond Neural Network models, and it has recently also acquired support for training (it was originally only for inference). Also, and very much like TensorFlow, it is at the center of a growing ecosystem, which also includes a repository for pre-trained models. The [ONNX Model Zoo](https://github.com/onnx/models) hosts many models pre-trained in a great variety of datasets, and optimized for a variety of scenarios (including, for example models that support specialized precisions like BFloat or INT8, or that have been quantized). +The [Open Neural Network Exchange(ONNX)](https://onnx.ai/) is an open-source format designed to enable interoperability between machine learning and deep learning frameworks. This means you can train a model in one of the many popular machine learning frameworks like PyTorch, convert it into ONNX format and consume the ONNX model in a different framework like ML.NET. -Like it does with TensorFlow, ML.NET makes it straightforward to consume pre-trained models in the ONNX format. The pipeline is very similar (this is a snippet from the sample scenario referenced below): +The [ONNX model repository](https://github.com/onnx/models) hosts several pre-trained ONNX models you can use for inferencing in a wide variety of tasks. -```csharp -var pipeline = mlContext.Transforms.LoadImages(outputColumnName: "image", imageFolder: "", inputColumnName: nameof(ImageNetData.ImagePath)) - .Append(mlContext.Transforms.ResizeImages(outputColumnName: "image", imageWidth: ImageNetSettings.imageWidth, imageHeight: ImageNetSettings.imageHeight, inputColumnName: "image")) - .Append(mlContext.Transforms.ExtractPixels(outputColumnName: "image")) - .Append(mlContext.Transforms.ApplyOnnxModel(modelFile: modelLocation, outputColumnNames: new[] { TinyYoloModelSettings.ModelOutput }, inputColumnNames: new[] { TinyYoloModelSettings.ModelInput })); +With ML.NET, you can take these pretrained ONNX models and use them for inferencing inside your .NET applications. -``` +An inference pipeline using a pretrained ONNX model might look like the following: + +```csharp +// Append ApplyOnnxModel transform to pipeline containing any preprocessing transforms +pipeline.Append((modelFile: modelLocation, outputColumnNames: new[] { TinyYoloModelSettings.ModelOutput }, inputColumnNames: new[] { TinyYoloModelSettings.ModelInput }) -Note that the pre-processing stages are effectively the same as those in the TensorFlow example, variations only introduced to accommodate the requirements of the model. +// Create ML.NET model from pipeline +var model = pipeline.Fit(data); -### References +// Use the model to make predictions +var predictions = pipeline.Fit(data).GetColumn(TinyYoloModelSettings.ModelOutput); +``` -* [https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx](https://github.com/dotnet/machinelearning-samples/tree/main/samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx) +To get started consuming pretrained ONNX models with ML.NET, see the [object detection using ONNX in ML.NET tutorial](tutorials/object-detection-onnx.md) diff --git a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md index 1ac6c8327df14..0db64d647a720 100644 --- a/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md +++ b/docs/machine-learning/how-to-guides/how-to-use-the-automl-api.md @@ -8,6 +8,10 @@ ms.topic: how-to # How to use the ML.NET Automated Machine Learning (AutoML) API +In this article, you learn how to use the ML.NET Automated ML (AutoML API). + +Samples for the AutoML API can be found in the [dotnet/machinelearning-samples](https://aka.ms/mlnet-2-samples) repo. + ## Installation To use the AutoML API, install the [`Microsoft.ML.AutoML`](https://www.nuget.org/packages/Microsoft.ML.AutoML) NuGet package in the .NET project you want to reference it in. @@ -31,7 +35,7 @@ AutoML provides several defaults for quickly training machine learning models. I - Run your experiment - Use the best model to make predictions -### Defining your problem +### Define your problem Given a dataset stored in a comma-separated file called *taxi-fare-train.csv* that looks like the following: @@ -41,7 +45,7 @@ CMT|1|1|1271|3.8|CRD|17.5 CMT|1|1|474|1.5|CRD|8 CMT|1|1|637|1.4|CRD|8.5 -### Load data +### Load your data Start by inizializing your . `MLContext` is a starting point for all ML.NET operations. Initializing mlContext creates a new ML.NET environment that can be shared across the model creation workflow objects. 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b/docs/machine-learning/tutorials/sentiment-analysis-model-builder.md @@ -1,6 +1,6 @@ --- -title: 'Tutorial: Analyze sentiment - binary classification' -description: This tutorial shows you how to create a Razor Pages application that classifies sentiment from website comments and takes the appropriate action. The binary sentiment classifier uses Model Builder in Visual Studio. +title: 'Tutorial: Analyze sentiment - Text Classification' +description: This tutorial shows you how to create a Razor Pages application that classifies sentiment from website comments and takes the appropriate action. The classification uses the Text Classification scenario in the Model Builder Visual Studio extension. ms.date: 11/10/2022 author: luisquintanilla ms.author: luquinta

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