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EasyML

EasyML is a Ruby gem designed to simplify the process of building, deploying, and managing the lifecycle of machine learning models within a Ruby on Rails application. It is a plug-and-play, opinionated framework that currently supports XGBoost, with plans to expand support to a variety of models and infrastructures. EasyML aims to make deployment and lifecycle management straightforward and efficient.

Features

  • Plug-and-Play Architecture: EasyML is designed to be easily extendable, allowing for the integration of various machine learning models and data sources.
  • Opinionated Framework: Provides a structured approach to model management, ensuring best practices are followed.
  • Model Lifecycle On Rails: Seamlessly integrates with Ruby on Rails, allowing simplified deployment of models to production.

Current and Planned Features

Models Available

XGBoost LightGBM TensorFlow PyTorch
✅ ❌ ❌ ❌

Datasources Available

S3 File Polars SQL Databases REST APIs
✅ ✅ ✅ ❌ ❌

Note: Features marked with ❌ are part of the roadmap and are not yet implemented.

Quick Start:

Building a Production pipeline is as easy as 1,2,3!

1. Create Your Dataset

class MyDataset < EasyML::Data::Dataset
  datasource :s3, s3_bucket: "my-bucket" # Every time the data changes, we'll pull new data
  target "revenue" # What are we trying to predict?
  splitter :date, date_column: "created_at" # How should we partition data into training, test, and validation datasets?
  transforms DataPipeline # Class that manages data transformation, adding new columns, etc.
  preprocessing_steps({
    training: {
      annual_revenue: { median: true, clip: { min: 0, max: 500_000 } }
    }
  }) # If annual revenue is missing, use the median value, after clipping the values into the approved list
end

2. Create a Model

class MyModel < EasyML::Models::XGBoost
  dataset MyDataset
  task :regression # Or classification
  hyperparameters({
    max_depth: 5,
    learning_rate: 0.1,
    objective: "reg:squarederror"
  })
end

3. Create a Trainer

class MyTrainer < EasyML::Trainer
  model MyModel
  evaluator MyMetrics
end

class MyMetrics
  def metric_we_make_money(y_pred, y_true)
    return true if model_makes_money?
    return false if model_lose_money?
  end

  def metric_sales_team_has_enough_leads(y_pred, y_true)
    return false if sales_will_be_sitting_on_their_hands?
  end
end

Now you're ready to predict in production!

MyTrainer.train # Yay, we did it!
MyTrainer.deploy # Let the production hosts know it's live!
MyTrainer.predict(customer_data: "I am worth a lot of money")
# prediction: true!

Data Management

EasyML provides a comprehensive data management system that handles all preprocessing tasks, including splitting data into train, test, and validation sets, and avoiding data leakage. The primary abstraction for data handling is the Dataset class, which ensures data is properly managed and prepared for machine learning tasks.

Preprocessing Features

EasyML offers a variety of preprocessing features to prepare your data for machine learning models. Here's a complete list of available preprocessing steps and examples of when to use them:

  • Mean Imputation: Replace missing values with the mean of the feature. Use this when you want to maintain the average value of the data.
  annual_revenue: {
    mean: true
  }
  • Median Imputation: Replace missing values with the median of the feature. This is useful when you want to maintain the central tendency of the data without being affected by outliers.
  annual_revenue: {
    median: true
  }
  • Forward Fill (ffill): Fill missing values with the last observed value. Use this for time series data where the last known value is a reasonable estimate for missing values.
  created_date: {
    ffill: true
  }
  • Most Frequent Imputation: Replace missing values with the most frequently occurring value. This is useful for categorical data where the mode is a reasonable estimate for missing values.
  loan_purpose: {
    most_frequent: true
  }
  • Constant Imputation: Replace missing values with a constant value. Use this when you have a specific value that should be used for missing data.
  loan_purpose: {
    constant: { fill_value: 'unknown' }
  }
  • Today Imputation: Fill missing date values with the current date. Use this for features that should default to the current date.
  created_date: {
    today: true
  }
  • One-Hot Encoding: Convert categorical variables into a set of binary variables. Use this when you have categorical data that needs to be converted into a numerical format for model training.
  loan_purpose: {
    one_hot: true
  }
  • Label Encoding: Convert categorical variables into integer labels. Use this when you have categorical data that can be ordinally encoded.
  loan_purpose: {
    categorical: {
      encode_labels: true
    }
  }

Other Dataset Features

  • Data Splitting: Automatically split data into train, test, and validation sets using various strategies, such as date-based splitting.
  • Data Synchronization: Ensure data is synced from its source, such as S3 or local files.
  • Batch Processing: Process data in batches to handle large datasets efficiently.
  • Null Handling: Alert and handle null values in datasets to ensure data quality.

Installation

Install necessary Python dependencies

  1. Install Python dependencies (don't worry, all code is in Ruby, we just call through to Python)
pip install wandb
pip install optuna
  1. Install the gem:
   gem install easy_ml
  1. Run the generator to store model versions:
   rails generate easy_ml:migration
   rails db:migrate
  1. Configure CarrierWave for S3 storage:

Ensure you have CarrierWave configured to use AWS S3. If not, add the following configuration:

   # config/initializers/carrierwave.rb
   CarrierWave.configure do |config|
     config.fog_provider = 'fog/aws'
     config.fog_credentials = {
       provider: 'AWS',
       aws_access_key_id: ENV['AWS_ACCESS_KEY_ID'],
       aws_secret_access_key: ENV['AWS_SECRET_ACCESS_KEY'],
       region: ENV['AWS_REGION'],
     }
     config.fog_directory = ENV['AWS_S3_BUCKET']
     config.fog_public = false
     config.storage = :fog
   end

Usage

To use EasyML in your Rails application, follow these steps:

  1. Define your preprocessing steps in a configuration hash. For example:
   preprocessing_steps = {
     training: {
       annual_revenue: {
         median: true,
         clip: { min: 0, max: 1_000_000 }
       },
       loan_purpose: {
         categorical: {
           categorical_min: 2,
           one_hot: true
         }
       }
     }
   }

  1. Create a dataset using the EasyML::Data::Dataset class, providing necessary configurations such as data source, target, and preprocessing steps.

  2. Train a model using the EasyML::Models module, specifying the model class and configuration.

  3. Deploy the model by marking it as live and storing it in the configured S3 bucket.

Development

After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.

To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and the created tag, and push the .gem file to rubygems.org.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/[USERNAME]/easy_ml. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the code of conduct.

License

The gem is available as open source under the terms of the MIT License.

Code of Conduct

Everyone interacting in the EasyML project's codebases, issue trackers, chat rooms, and mailing lists is expected to follow the code of conduct.

Expected Future Enhancements

  • Support for Additional Models: Integration with LightGBM, TensorFlow, and PyTorch.
  • Expanded Data Source Support: Ability to pull data from SQL databases and REST APIs.
  • Enhanced Deployment Options: More flexible deployment strategies and integration with CI/CD pipelines.
  • Advanced Monitoring and Logging: Improved tools for monitoring model performance and logging.
  • User Interface Improvements: Enhanced UI components for managing models and datasets.