The ML.NET command line interface (CLI), provides tools for building machine learning models with ML.NET.
Note: ML.NET CLI is currently in Preview.
Once you've installed the .NET SDK, open a new terminal and run the following command:
Command prompt
dotnet tool install -g mlnet
If the tool installs successfully, you should see the following message:
Command prompt
dotnet tool install -g mlnetYou can invoke the tool using the following command: mlnetTool 'mlnet' (version '0.3.0') was successfully installed.
Note: If you are using a console other than Bash, then you will need to give mlnet executable permissions and include mlnet to the system path.
If the command still gives you an error, use the I ran into an issue button below to get help fixing the problem.
Create your app
Open Visual Studio and create a new .NET Core console app:
Select Create a new project from the Visual Studio start window (VS 2019), or File -> New -> Project (VS 2017).
Choose the C# Console App (.NET Core) project template.
Change the project name to myMLApp.
Make sure Place solution and project in the same directory is unchecked (VS 2019), or Create directory for solution is checked (VS 2017).
Create the project.
Add machine learning
Right-click on the myMLApp project in Solution Explorer and select Add -> Machine Learning.
This opens ML.NET Model Builder in a new docked tool window in Visual Studio. Model Builder will guide you through the process of building a machine learning model in the following steps.
In your terminal, run the following commands:
Command prompt
mkdir myMLAppcd myMLAppdotnet new console -o consumeModelApp
The mkdir command creates a new directory named myMLApp, and the cd myMLApp command puts you into the newly created app directory. The dotnet command creates a new C# application of type console for you. The -o parameter creates a directory named consumeModelApp where your app is stored, and populates it with the required files.
Your model training code will be generated in the upcoming steps.
Pick a scenario
To generate your model, you first need to select your machine learning scenario. Model Builder supports several scenarios:
In this case, you will predict sentiment based on the content (text) of customer reviews.
In the Model Builder Scenario screen, select the Sentiment Analysis scenario, which is a binary classification ML task.
To generate your model, you need to select your machine learning scenario and its corresponding ML task.
There are several ML tasks that are supported by the ML.NET CLI:
Binary classification - Use this when you want to analyze text and predict if it belongs in either category A or category B (e.g. analyzing sentiment of customer reviews as either positive or negative)
Multiclass classification - Use this when you want to analyze text and classify into multiple categories (e.g. labeling new GitHub issues).
Regression - Use this when you want to predict a numeric value (e.g. predicting house price). This task is called regression.
In this case, you will predict sentiment based on the content (text) of customer reviews, so you will use the binary classification ML task.
Download and add data
Download the Wikipedia detox dataset and save it as wikipedia-detox-250-line-data.tsv in the myMLApp directory you created.
Each row in wikipedia-detox-250-line-data.tsv represents a different review left by a user on Wikipedia. The first column represents the sentiment of the text (0 is non-toxic, 1 is toxic), and the second column represents the comment left by the user. The columns are separated by tabs. The data looks like the following:
wikipedia-detox-250-line-data.tsv
Sentiment SentimentText1 ==RUDE== Dude, you are rude upload that carl picture back, or else.1 == OK! == IM GOING TO VANDALIZE WILD ONES WIKI THEN!!!0 I hope this helps.
Add data
In Model Builder, you can add data from a local file or connect to a SQL Server database. In this case, you will add wikipedia-detox-250-line-data.tsv from a file.
Select File as the input data source in the drop-down, and in Select a file find and select wikipedia-detox-250-line-data.tsv.
Under Column to predict (Label), select "Sentiment." The Label is what you are predicting, which in this case is the Sentiment found in the first column of the dataset.
The columns that are used to help predict the Label are called Features. In this case, the review comment is the Feature, so leave "SentimentText" checked as the Input Column (Feature).
After adding your data, go to the Train step.
Train your model
Now you will train your model with the wikipedia-detox-250-line-data.tsv dataset.
Model Builder evaluates many models with varying algorithms and settings to give you the best performing model.
Leave the Time to train, the amount of time you would like Model Builder to explore various models, as 10 seconds. Note that for larger datasets, you should set a longer training time.
Select Start Training to start the training process.
Progress
You can keep track of the progress of model training in the Progress section.
Status - This shows you the status of the model training process; this will tell you how much time is left in the training process and will also tell you when the training process has completed.
Best accuracy - This shows you the accuracy of the best model that Model Builder has found so far. Higher accuracy means the model predicted more correctly on test data.
Best algorithm - This shows you which algorithm performed the best so far during Model Builder's exploration.
Last algorithm - This shows you the last algorithm that was explored by Model Builder.
After model training finishes, go to the Evaluate step.
In your terminal, run the following command (in your myMLApp folder):
The mlnet auto-train command runs ML.NET with AutoML to explore many iterations of models with varying combinations of data transformations, algorithms, and algorithm options and then chooses the highest performing model.
--task: You must specify the ML task, which in this case is binary classification.
--dataset: You choose wikipedia-detox-250-line-data.tsv as the dataset (internally, the CLI will split the one dataset into training and testing datasets).
--label-column: You must specify the target column you want to predict (or the Label). In this case, you want to predict the Sentiment, which is in the first column (index 0).
--max-exploration-time: You must also specify the amount of time you would like the ML.NET CLI to explore the different models, in this case 10 seconds. Note that for larger datasets, you should set a longer training time.
Progress
While the ML.NET CLI is exploring different models, the progress bar will indicate how much time is left in the training process. As new models are explored, the Best Accuracy, Best Algorithm, Last Algorithm, and time duration will change.
Best accuracy - This shows you the accuracy of the best model so far. Higher accuracy means the model predicted more correctly on test data.
Best algorithm - This shows you which algorithm has performed the best so far.
Last algorithm - This shows you the last algorithm that was explored.
Evaluate your model
After Model Builder trains and selects the best model, you can move on to the Evaluate step, which shows you various output (like the best-performing algorithm, how many models were explored, and the ML task, in this case binary classification) and lets you try out the model in the UI.
You can toggle between the Overview and Details to see more information about the training session and the top models explored (up to 5), including several evaluation metrics for each of those top models.
Try out your model
You can make predictions on sample input in the Try your model section. The textbox is pre-filled with the first line of data from your dataset, but you can change the input and hit Predict to try out different Sentiment predictions.
After evaluating and trying out your model, move on to the Code step.
After the ML.NET CLI selects the best model, it will display the Experiment Results, which shows you a summary of the exploration process, including how many models were explored in the given training time.
Top models
While the ML.NET CLI generates code for the highest performing model, it also displays the top models (up to 5) with the highest accuracy that it found in the given exploration time. It displays several evaluation metrics for those top models, including AUC, AUPRC, and F1-score, which you can learn more about here.
Generate code
In the Code step in Model Builder, select Add Projects.
Model Builder adds both the machine learning model and the projects for training and consuming the model to your solution. In the Solution Explorer, you should see the code files that were generated by Model Builder.
myMLAppML.ConsoleApp is a .NET console app which contains ModelBuilder.cs (used to build/train the model) and Program.cs (used to test run the model).
myMLAppML.Model is a .NET Standard class library which contains ModelInput.cs and ModelOutput.cs (input/output classes for model training and consumption), ConsumeModel.cs (class that contains method for model consumption), and MLModel.zip (trained serialized ML model).
The ML.NET CLI adds both the machine learning model and the projects for training and consuming the model to your solution, including:
A .NET console app (SampleBinaryClassification.ConsoleApp), which contains ModelBuilder.cs (used to build/train the model) and Program.cs (used to run the model).
A .NET Standard class library (SampleBinaryClassification.Model), which contains ModelInput.cs and ModelOutput.cs (input/output classes for model training and consumption) and MLModel.zip (generated serialized ML model).
To try the model, you can run the console app (SampleBinaryClassification.ConsoleApp) to predict the sentiment of a single statement with the model.
Consume your model
Model Builder generates the trained model and code for you, including the next steps for model consumption, so you can easily use the model in your end-user .NET application.
Replace the Program.cs code in your myMLApp with the following code:
Program.cs
using System;using MyMLAppML.Model;namespace myMLApp{ class Program { static void Main(string[] args) { // Add input data var input = new ModelInput(); input.SentimentText = "That is rude."; // Load model and predict output of sample data ModelOutput result = ConsumeModel.Predict(input); Console.WriteLine($"Text: {input.SentimentText}\nIs Toxic: {result.Prediction}"); } }}
Run myMLApp. You should see the following output, predicting whether the input statement is toxic (true) or non-toxic (false).
The ML.NET CLI has generated the trained model and code for you, so you can now use the model in your other .NET applications (e.g. your consumeModelApp console app) by following these steps:
From your consumeModelApp, add a reference to the generated library project (SampleBinaryClassification.Model).
In your terminal, run the following command (make sure you are in your consumeModelApp directory):
Command prompt
cd consumeModelAppdotnet add reference ../SampleBinaryClassification/SampleBinaryClassification.Model/
Copy the model (MLModel.zip) from SampleBinaryClassification.Model and paste it into the consumeModelApp directory.
Replace the Program.cs code in your consumeModelApp with the following code:
Program.cs
using System;using SampleBinaryClassification.Model.DataModels;using Microsoft.ML;namespace consumeModelApp{ class Program { static void Main(string[] args) { ConsumeModel(); } public static void ConsumeModel() { // Load the model MLContext mlContext = new MLContext(); ITransformer mlModel = mlContext.Model.Load("MLModel.zip", out var modelInputSchema); var predEngine = mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel); // Use the code below to add input data var input = new ModelInput(); input.SentimentText = "That is rude"; // Try model on sample data // True is toxic, false is non-toxic ModelOutput result = predEngine.Predict(input); Console.WriteLine($"Text: {input.SentimentText} | Prediction: {(Convert.ToBoolean(result.Prediction) ? "Toxic" : "Non-toxic")} sentiment"); } }}
In your consumeModelApp, modify the sample text (input.SentimentText) in Program.cs.
Program.cs
input.SentimentText = "That is rude.";
Run your consumeModelApp.
In your terminal, run the following command (make sure you are in your consumeModelApp directory):
Command prompt
dotnet run
Next steps
Congratulations, you've built your first machine learning model with ML.NET Model Builder!
Now that you've used Model Builder for Sentiment Analysis, you can try other scenarios. Try out the Price Prediction scenario in Model Builder using the Taxi Fare dataset to keep building ML.NET models with Model Builder:
Congratulations, you've built your first machine learning model with the ML.NET CLI!
Now that you've used the ML.NET CLI for Sentiment Analysis, you can try other scenarios. Try out a Price Prediction scenario (e.g. the regression task in the ML.NET CLI) using the Taxi Fare dataset to keep building ML.NET models with the ML.NET CLI.
