Use ML.NET Model Builder in Visual Studio to train and use your first machine learning model with ML.NET.
Install the ML.NET CLI, then train and use your first machine learning model with ML.NET.
None.
10 minutes
An app that can predict whether the text from customer reviews is negative or positive sentiment.
Model Builder ships with Visual Studio version 16.6.1 and later versions when you install one of the .NET workloads.
Open the Visual Studio Installer and Modify the instance of Visual Studio that you'd like to use with Model Builder.
Check the optional ML.NET Model Builder (Preview) component.
ML.NET Model Builder is currently a Preview feature. So, you must enable this Preview feature in Visual Studio in order to use the tool.
In Visual Studio, go to Tools > Options > Environment > Preview Features.
Check Enable ML.NET Model Builder.
Note: If the tutorial screenshots do not match with what you see, you may need to update your version of Model Builder. Go to Extensions > Manage Extensions to make sure that there are no available updates for Model Builder. The most recent version is 16.3.0.2056001.
To build .NET apps, you need to download and install the .NET Core 3.1 SDK (Software Development Kit).
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:
dotnet tool install -g mlnetIf the tool installs successfully, you should see the following output message:
You can invoke the tool using the following command: mlnet
Tool 'mlnet' (version '16.1.1') was successfully installed.Note: If you're using a console other than Bash (for example, zsh, which is the new default for macOS), then you'll need to give mlnet executable permissions and include mlnet to the system path. Instructions on how to do this should appear in the terminal when you install mlnet (or any global tool).
Alternatively, you can try using the following command to run the mlnet tool:
~/.dotnet/tools/mlnetIf the command still gives you an error, use the I ran into an issue button below to report the issue and get help fixing the problem.
Open Visual Studio and create a new .NET Core console app:
myMLApp.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:
mkdir myMLApp
cd myMLApp
dotnet new console -o consumeModelAppThe 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.
To generate your model, you first need to select your machine learning scenario. Model Builder supports several scenarios:
In this case, you'll predict sentiment based on the content (text) of customer reviews.
In the Model Builder Scenario screen, select the Text classification scenario, since you're predicting which category a comment falls into (positive or negative).
After selecting the Text classification scenario, you must choose your training environment. While some scenarios support training in Azure, Classification currently only supports local training, so keep the Local environment selected and move on to the Data step.
To generate your model, you need to select your machine learning scenario.
There are several ML scenarios that are supported by the ML.NET CLI:
In this case, you'll predict sentiment based on the content (text) of customer reviews, so you'll use classification.
Download the Sentiment Labelled Sentences datasets from the UCI Machine Learning Repository. Unzip sentiment labelled sentences.zip and save the yelp_labelled.txt file to the myMLApp directory.
Each row in yelp_labelled.txt represents a different review of a restaurant left by a user on Yelp. The first column represents the comment left by the user, and the second column represents the sentiment of the text (0 is negative, 1 is positive). The columns are separated by tabs, and the dataset has no header. The data looks like the following:
Wow... Loved this place. 1
Crust is not good. 0
Not tasty and the texture was just nasty. 0In Model Builder, you can add data from a local file or connect to a SQL Server database. In this case, you'll add yelp_labelled.txt from a file.
Select File as the input data source type.
Browse for yelp_labelled.txt. Once you select your dataset, a preview of your data appears in the Data Preview section. Since your dataset does not have a header, headers are auto-generated ("col0" and "col1").
Under Column to predict (Label), select "col1." The Label is what you're predicting, which in this case is the sentiment found in the second column ("col1") of the dataset.
The columns that are used to help predict the Label are called Features. All of the columns in the dataset besides the Label are automatically selected as Features. In this case, the review comment column ("col0") is the Feature column. You can update the Feature columns and modify other data loading options in Advanced data options, but it is not necessary for this example.
After adding your data, go to the Train step.
Now you'll train your model with the yelp_labelled.txt dataset.
Model Builder evaluates many models with varying algorithms and settings based on the amount of training time given to build the best performing model.
Leave the Time to train, which is the amount of time you'd like Model Builder to explore various models, as 10 seconds. Note that for larger datasets, the training time will be longer. Model Builder automatically adjusts the training time based on the dataset size.
Select Start training to start the training process. Once training starts, you can see the time remaining.
Once training is done, you can see a summary of the training results.
If you want, you can view more information about the training session in the Machine Learning Output window.
After model training finishes, go to the Evaluate step.
In your terminal, run the following command (in your myMLApp folder):
mlnet classification --dataset "yelp_labelled.txt" --has-header false --label-col 1 --train-time 20
The mlnet classification command runs ML.NET with AutoML to explore many iterations of classification models in the given amount of train time with varying combinations of data transformations, algorithms, and algorithm options and then chooses the highest performing model.
yelp_labelled.txt as the dataset (internally, the CLI will split the one dataset into training and testing datasets).While the ML.NET CLI is exploring different models, it displays the following data:
If you want, you can view more information about the training session in the log file generated by the CLI.
The Evaluate step shows you the best-performing algorithm and the best accuracy and lets you try out the model in the UI.
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.
In this case, 0 means negative sentiment and 1 means positive sentiment.
After evaluating and trying out your model, move on to the Consume step.
After the ML.NET CLI selects the best model, it will display the training Summary, which shows you a summary of the exploration process, including how many models were explored in the given training time.
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. For more information, see ML.NET metrics.
In the Consume step in Model Builder, select Add to solution.
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 Core console app that 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 that contains ModelInput.cs and ModelOutput.cs (input/output classes for model training and consumption), ConsumeModel.cs (class that contains the method for model consumption), and MLModel.zip (the 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:
SampleClassification.ConsoleApp), which contains ModelBuilder.cs (used to build/train the model) and Program.cs (used to run the model).SampleClassification.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 (SampleClassification.ConsoleApp) to predict the sentiment of a single statement with the 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.
You can see an example of how to consume the model in the Program.cs file in the myMLAppML.ConsoleApp project.
Replace the Program.cs code in your myMLApp project with the following code:
using System;
using MyMLAppML.Model;
namespace myMLApp
{
class Program
{
static void Main(string[] args)
{
// Add input data
var input = new ModelInput()
{
Col0 = "This restaurant was wonderful."
};
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
// If Prediction is 1, sentiment is "Positive"; otherwise, sentiment is "Negative"
string sentiment = result.Prediction == "1" ? "Positive" : "Negative";
Console.WriteLine($"Text: {input.Col0}\nSentiment: {sentiment}");
}
}
}Run myMLApp (select Ctrl+F5 or Debug > Start Without Debugging). You should see the following output, predicting whether the input statement is positive or negative.
The ML.NET CLI has generated the trained model and code for you, so you can now use the model in your other .NET applications (for example, your consumeModelApp console app) by following these steps:
MLModel.zip file from the SampleClassification.Model folder to the consumeModelApp folder.consumeModelApp directory.
cd consumeModelAppAdd a reference to the generated class library that contains the trained model and consumption code (SampleClassification.Model) in your consumeModelApp project by running the following command:
dotnet add reference "../SampleClassification/SampleClassification.Model/SampleClassification.Model.csproj"Open the consumeModelApp Program.cs in any code editor and replace the code with the following:
using System;
using SampleClassification.Model;
namespace consumeModelApp
{
class Program
{
static void Main(string[] args)
{
// Add input data
var input = new ModelInput()
{
Col0 = "This restaurant was wonderful."
};
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
// If Prediction is 1, sentiment is "Positive"; otherwise, sentiment is "Negative"
string sentiment = result.Prediction == "1" ? "Positive" : "Negative";
Console.WriteLine($"Text: {input.Col0}\nSentiment: {sentiment}");
}
}
}Run your consumeModelApp. You can do this by running the following command in the terminal (make sure you are in the consumeModelApp directory):
dotnet runThe output should look something like this:
Congratulations, you've built your first machine learning model with ML.NET Model Builder!
Now that you've used Model Builder for Classification (specifically, sentiment analysis), you can try other scenarios. Try out the Value Prediction scenario (specifically price prediction) in Model Builder using the Taxi Fare dataset to keep building ML.NET models with Model Builder:
Download the Taxi Fare dataset
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Congratulations, you've built your first machine learning model with the ML.NET CLI!
Now that you've used the ML.NET CLI for Classification (specifically sentiment analysis), you can try other scenarios. Try out a Regression scenario (specifically price prediction) using the Taxi Fare dataset to keep building ML.NET models with the ML.NET CLI.
Download the Taxi Fare dataset
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