Windows 10: Announcing ML.NET 0.11 - Machine Learning for .NET

Brink · Mar 8, 2019

ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more!.

Today we’re announcing the release of ML.NET 0.11. (ML.NET 0.1 was released at //Build 2018). This release, and all other remaining releases before the v1.0 release, will focus on the overall stability of the framework, continuing to refine the API, fix bugs, reduce the public API surface, and improve documentation and samples.

Updates in v0.11 timeframe

Added additional ML components to the MLContext catalog, so it’s easier to find the classes and operations to use. Below you can see the experience based on IntelliSense.

Support for text input in TensorFlowTransformer so you can use TensorFlow models for text analysis (in addition to images). For instance, the following code shows ML.NET scoring a TensorFlow model for a ‘sentiment analysis’ scenario:

Code: public class TensorFlowSentiment { public string Sentiment_Text; [VectorType(600)] public int[] Features; [VectorType(2)] public float[] Prediction; } [TensorFlowFact] public void TensorFlowSentimentClassificationTest() { var mlContext = new MLContext(seed: 1, conc: 1); var data = new[] { new TensorFlowSentiment() { Sentiment_Text = "this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert is an amazing actor and now the same being director father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also to the two little boy's that played the of norman and paul they were just brilliant children are often left out of the list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all" } }; var dataView = mlContext.Data.ReadFromEnumerable(data); var lookupMap = mlContext.Data.ReadFromTextFile(@"sentiment_model/imdb_word_index.csv", columns: new[] { new TextLoader.Column("Words", DataKind.TX, 0), new TextLoader.Column("Ids", DataKind.I4, 1), }, separatorChar: ',' ); var estimator = mlContext.Transforms.Text.TokenizeWords("TokenizedWords", "Sentiment_Text") .Append(mlContext.Transforms.Conversion.ValueMap(lookupMap, "Words", "Ids", new[] { ("Features", "TokenizedWords") })); var dataPipe = estimator.Fit(dataView) .CreatePredictionEngine<TensorFlowSentiment, TensorFlowSentiment>(mlContext); string modelLocation = @"sentiment_model"; var tfEnginePipe = mlContext.Transforms.ScoreTensorFlowModel(modelLocation, new[] { "Prediction/Softmax" }, new[] { "Features" }) .Append(mlContext.Transforms.CopyColumns(("Prediction", "Prediction/Softmax"))) .Fit(dataView) .CreatePredictionEngine<TensorFlowSentiment, TensorFlowSentiment>(mlContext); //Predict the sentiment for the sample data var processedData = dataPipe.Predict(data[0]); Array.Resize(ref processedData.Features, 600); var prediction = tfEnginePipe.Predict(processedData); }
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You can see additional code example details in this code

ONNX updates: ONNX is an open and iteroperable model format that enables using models trained in one framework (i.e. scikit-learn, TensorFlow, xgboost, etc.) to use in another framework (like ML.NET).
In ML.NET 0.11 Microsoft.ML.ONNX has been renamed to Microsoft.ML.ONNXConverter and Microsoft.ML.ONNXTransorm has been renamed to Microsoft.ML.ONNXTransformer to make the distinction between ONNX conversion and transformation clearer.

Breaking changes in ML.NET 0.11

For your convenience, if you are moving your code from ML.NET v0.10 to v0.11, you can check out the breaking changes list that impacted our samples.

Explore the community samples and share yours!

As part of the ML.NET Samples repo, we also have a special Community Samples page pointing to multiple samples provided by the community. These samples are not maintained by Microsoft, but they are very interesting and cover additional scenarios not covered by us.
Here’s an screenshot of the current community samples:

We encourage you to share your ML.NET demos and samples with the community by simply submitting a brief description and URL pointing to your GitHub repo or blog posts, into this repo issue “Request for your samples!”. We’ll do the rest and publish it at the ML.NET Community Samples page.

Planning to go to production?

If you are using ML.NET in your app and looking to go into production, you can talk to an engineer on the ML.NET team to:

Get help implementing ML.NET successfully in your application.

Provide feedback about ML.NET.

Demo your app and potentially have it featured on the ML.NET homepage, .NET Blog, or other Microsoft channel.

Fill out this form and leave your contact information at the end if you’d like someone from the ML.NET team to contact you.

Get started!

If you haven’t already, get started with ML.NET here.
Next, explore further with some other resources:

Tutorials and resources at the Microsoft Docs ML.NET Guide

Code samples at the machinelearning-samples GitHub repo

Important ML.NET concepts for understanding the new API are introduced here

“How to” guides that show how to use these APIs for a variety of scenarios can be found here

We appreciate your feedback by filing issues with any suggestions or enhancements in the ML.NET GitHub repo to help us shape ML.NET and make .NET a great platform of choice for Machine Learning.

Thanks and happy coding with ML.NET!

-The ML.NET Team-

This blog was authored by Bri Achtman and Cesar de la Torre, plus additional contributions of the ML.NET team

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Source: .NET Blog | Announcing ML.NET 0.11 - Machine Learning for .NET

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Brink · Mar 8, 2019

Announcing ML.NET 0.8 - Machine Learning for .NET

ML.NET is an open-source and cross-platform framework (Windows, Linux, macOS) which makes machine learning accessible for .NET developers.

ML.NET allows you to create and use machine learning models targeting scenarios to achieve common tasks such as sentiment analysis, issue classification, forecasting, recommendations, fraud detection, image classification and more. You can check out these common tasks at our GitHub repo with ML.NET samples.

Today we’re happy to announce the release of ML.NET 0.8. (ML.NET 0.1 was released at //Build 2018). This release focuses on adding improved support for recommendation scenarios, model explainability in the form of feature importance, debuggability by previewing your in-memory datasets, API improvements such as caching, filtering, and more.

This blog post provides details about the following topics in the ML.NET 0.8 release:

New Recommendation scenarios

Improved debuggability

Model explainability

Additional API improvements

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Read more: Announcing ML.NET 0.8 Machine Learning for .NET | .NET Blog

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Brink · Mar 8, 2019

Announcing ML.NET 0.10 - Machine Learning for .NET

ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models.

ML.NET allows you to create and use machine learning models targeting common tasks such as classification, regression, clustering, ranking, recommendations and anomaly detection. It also supports the broader open source ecosystem by proving integration with popular deep-learning frameworks like TensorFlow and interoperability through ONNX. Some common use cases of ML.NET are scenarios like Sentiment Analysis, Recommendations, Image Classification, Sales Forecast, etc. Please see our samples for more scenarios.

Today we’re announcing the release of ML.NET 0.10. (ML.NET 0.1 was released at //Build 2018). Note that ML.NET follows a semantic versioning pattern, so this preview version is 0.10. There will be additional versions such as 0.11 and 0.12 before we release v1.0.

This release focuses on the overall stability of the framework, continuing to refine the API, increase test coverage and as an strategic milestone, we have moved the IDataView components into a new and separated assembly under Microsoft.Data.DataView namespace so it will favor interoperability in the future.

The main highlights for this blog post are described below in further details:

IDataView as a shared type across libraries in the .NET ecosystem

Support for multiple ‘feature columns’ in recommendations (FFM based)

Additional updates in v0.10 timeframe

Explore the community samples and share yours!

Planning to go to production?

Get Started!

Click to expand...

Read more: Announcing ML.NET 0.10 - Machine Learning for .NET | .NET Blog

Brink · Mar 8, 2019

Announcing ML.NET 0.7 (Machine Learning .NET)

We’re excited to announce today the release of ML.NET 0.7 – the latest release of the cross-platform and open source machine learning framework for .NET developers (ML.NET 0.1 was released at //Build 2018). This release focuses on enabling better support for recommendation based ML tasks, enabling anomaly detection, enhancing the customizability of the machine learning pipelines, enabling using ML.NET in x86 apps, and more.

This blog post provides details about the following topics in the ML.NET 0.7 release:

Enhanced support for recommendation tasks with Matrix Factorization

Enabled anomaly detection scenarios – detecting unusual events rel

Improved customizability of ML pipelines

x86 support

NimbusML – experimental Python bindings for ML.NET

Get started with ML.NET v07

Enhanced support for recommendation tasks with Matrix Factorization

Recommender systems enable producing a list of recommendations for products in a catalog, songs, movies, and more. We have improved support for creating recommender systems in ML.NET by adding Matrix factorization (MF), a common approach to recommendations when you have data on how users rated items in your catalog. For example, you might know how users rated some movies and want to recommend which other movies they are likely to watch next.

We added MF to ML.NET because it is often significantly faster than Field-Aware Factorization Machines (which we added in ML.NET 0.3) and it can support ratings which are continuous number ratings (e.g. 1-5 stars) instead of boolean values (“liked” or “didn’t like”). Even though we just added MF, you might still want to use FFM if you want to take advantage of other information beyond the rating a user assigns to an item (e.g. movie genre, movie release date, user profile). A more in-depth discussion of the differences can be found here.

Sample usage of MF can be found here. The example is general but you can imagine that the matrix rows correspond to users, matrix columns correspond to movies, and matrix values correspond to ratings. This matrix would be quite sparse as users have only rated a small subset of the catalog.

ML.NET’s MF uses LIBMF.

Enabled anomaly detection scenarios – detecting unusual events

Anomaly detection enables identifying unusual values or events. It is used in scenarios such as fraud detection (identifying suspicious credit card transactions) and server monitoring (identifying unusual activity).

ML.NET 0.7 enables detecting two types of anomalous behavior:

Spike detection: spikes are attributed to sudden yet temporary bursts in values of the input data. These could be outliers due to outages, cyber-attacks, viral web content, etc.

Change point detection: change points mark the beginning of more persistent deviations in the behavior of the data. For example, if product sales are relatively consistent and become more popular (monthly sales double), there is a change point when the trend changes.

These anomalies can be detected on two types of data using different ML.NET components:

IidSpikeDetector and IidChangePointDetector are used on data assumed to be from one stationary distribution (each data point is independent of previous data, such as the number of retweets of each tweet).

SsaSpikeDetector and SsaChangePointDetector are used on data that has a season/trend components (perhaps ordered by time, such as product sales)

Sample code using anomaly detection with ML.NET can be found here.

Improved customizability of ML.NET pipelines

ML.NET offers a variety of data transformations (e.g. processing text, images, categorical features, etc.). However, some use cases require application-specific transformations, such as calculating cosine similarity between two text columns. We have now added support for custom transforms so you can easily include custom business logic.

The CustomMappingEstimator allows you to write your own methods to process data and bring them into the ML.NET pipeline. Here is what it would look like in the pipeline:

var estimator = mlContext.Transforms.CustomMapping<MyInput, MyOutput>(MyLambda.MyAction, "MyLambda")
.Append(...)
.Append(...)

Below is the definition of what this custom mapping will do. In this example, we convert the text label (“spam” or “ham”) to a boolean label (true or false).

public class MyInput
{
public string Label { get; set; }
}
public class MyOutput
{
public bool Label { get; set; }
}
public class MyLambda
{
[Export("MyLambda")]
public ITransformer MyTransformer => ML.Transforms.CustomMappingTransformer<MyInput, MyOutput>(MyAction, "MyLambda");
[Import]
public MLContext ML { get; set; }
public static void MyAction(MyInput input, MyOutput output)
{
output.Label= input.Label == "spam" ? true : false;
}
}

A more complete example of the CustomMappingEstimator can be found here.

x86 support in addition to x64

With this release of ML.NET you can now train and use machine learning models on x86 / 32-bit architecture devices. Previously, ML.NET was limited to x64 devices.

Note that some components that are based on external dependencies (e.g. TensorFlow) will not be available in x86.

NimbusML – experimental Python bindings for ML.NET

NimbusML provides experimental Python bindings for ML.NET. We have seen feedback from the external community and internal teams regarding the use of multiple programming languages. We wanted to enable as many people as possible to benefit from ML.NET and help teams to work together more easily. ML.NET not only enables data scientists to train and use machine learning models in Python (with components that can also be used in scikit-learn pipelines), but it also enables saving models which can be easily used in .NET applications through ML.NET (see here for more details).

In case you missed it: provide your feedback on the new API

ML.NET 0.6 introduced a new set of APIs for ML.NET that provide enhanced flexibility. These APIs in 0.7 and upcoming versions are still evolving and we would love to get your feedback so you can help shape the long-term API for ML.NET.

Want to get involved? Start by providing feedback through issues at the ML.NET GitHub repo!

Additional resources

The most important ML.NET concepts for understanding the new API are introduced here.

A cookbook (How to guides) that shows how to use these APIs for a variety of existing and new scenarios can be found here.

A ML.NET API Reference with all the documented APIs can be found here.

Get started!

If you haven’t already, get started with ML.NET here Next, explore some other great resources:

Tutorials and resources at the Microsoft Docs ML.NET Guide

Code samples at the machinelearning-samples GitHub repo

We look forward to your feedback and welcome you to file issues with any suggestions or enhancements in the ML.NET GitHub repo.

This blog was authored by Gal Oshri and Cesar de la Torre

Thanks,

The ML.NET Team
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Source: Announcing ML.NET 0.7 (Machine Learning .NET) | .NET Blog

Windows 10: Announcing ML.NET 0.11 - Machine Learning for .NET

Announcing ML.NET 0.11 - Machine Learning for .NET

Announcing ML.NET 0.11 - Machine Learning for .NET

Announcing ML.NET 0.11 - Machine Learning for .NET

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