OOPs Design Patterns did it again!

Speed up Machine Learning model training with MLFlow and Object-Oriented-Programming Design Patterns.

Data

The Higgs dataset is a dataset of proton-proton collision events collected at the Large Hadron Collider (LHC). The dataset contains information about the properties of the particles produced in the collisions, such as their momentum, energy, and charge. The Higgs dataset is used to study the properties of the Higgs boson, a fundamental particle that was discovered at the LHC in 2012. The Higgs boson is a key component of the Standard Model of particle physics, and its discovery was a major breakthrough in physics.

The Higgs dataset is a valuable resource for physicists who are studying the Higgs boson and its properties. The dataset is large and complex, but it can be used to learn a great deal about the Higgs boson. The Higgs dataset has been used to make important discoveries about the Higgs boson, and it will continue to be used to study the Higgs boson in the years to come.

Here are some of the key features of the Higgs dataset:

• The dataset contains information about the properties of millions of proton-proton collision events.
• The dataset is large and complex, making it difficult to analyze.
• The dataset has been used to make important discoveries about the Higgs boson.
• The dataset will continue to be used to study the Higgs boson in the years to come.

MLFlow and Strategy Pattern for Machine Learning Experimentation

This project demonstrates how to use MLFlow and the Strategy pattern to speed up machine learning experimentation.

MLFlow

MLFlow is an open-source platform for managing the ML lifecycle, including experiment tracking, model packaging, and model serving. MLFlow can be used to track the progress of machine learning experiments, store and share models, and deploy models to production.

Factory Pattern

The Factory Pattern is a creational design pattern that provides an interface for creating objects of a superclass or an interface while allowing the subclasses to decide which class to instantiate. It encapsulates the object creation logic, providing a way to create objects without exposing the instantiation logic to the client.

The primary goal of the Factory Pattern is to abstract the process of object creation, making it more flexible and easier to manage. It promotes loose coupling between the client code and the concrete classes, allowing for code that is more maintainable and extensible.

The key components of the Factory Pattern are:

• Product: This refers to the superclass or the interface that defines the common set of methods that the concrete classes must implement.
• Concrete Products: These are the specific classes that implement the Product interface. Each concrete product provides a different implementation for the common set of methods.
• Factory: This is an interface or an abstract class that declares the factory method for creating objects of the Product type. The factory method returns an instance of the Product.
• Concrete Factory: These are the subclasses of the Factory that implement the factory method. Each concrete factory is responsible for creating a specific type of product.

The typical flow of the Factory Pattern involves the following steps:

1. The client code requests an object creation by invoking the factory method on the Factory interface or abstract class.
2. The concrete Factory associated with the requested object type creates an instance of the concrete product.
3. The concrete Product is returned to the client code through the factory method, but the client code is unaware of the specific class that was instantiated.

By utilizing the Factory Pattern, you can centralize the object creation logic and decouple the client code from the specific implementation classes. This makes it easier to introduce new types of products or modify the existing ones without modifying the client code.

Overall, the Factory Pattern provides a flexible and extensible way to create objects, promoting code re-usability, maintainability, and separation of concerns.

The following diagram shows the Factories which have been implemented in the current project.

Model

The UML diagram is shown below.

classDiagram-v2
      class Model{
        <<interface>>
        Model: +preprocess()
        Model: +fit()
      }
      Model <|.. LogisticRegression
      Model <|.. RandomForest
      Model <|.. LightGBM
      Model <|.. NeuralNetwork

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Fine Tuner

The UML diagram is shown below.

classDiagram-v2
      class FineTuner{
        <<interface>>
        FineTuner: +fit()
      }
      FineTuner <|.. BayesSearchCV
      FineTuner <|.. GridSearchCV
      FineTuner <|.. RandomizedSearchCV

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Strategy Pattern

The Strategy pattern is a design pattern that allows you to decouple the algorithm from the context in which it is used. This can be useful for machine learning experimentation, as it allows you to easily experiment with different algorithms without having to modify the code that loads and prepares the data.

Benefits of Using MLFlow and the Strategy Pattern There are several benefits to using MLFlow and the Strategy pattern for machine learning experimentation:

MLFlow provides a centralized platform for tracking experiments, storing models, and deploying models. This can help you to keep track of your experiments, share your models with others, and deploy your models to production.

On the other hand, the Strategy pattern allows you to easily experiment with different algorithms without having to modify the code that loads and prepares the data. This can save you time and effort when experimenting with different machine learning algorithms.

Search Algorithm

The Search Algorithm takes the FineTuner factory. The UML diagram is shown below.

classDiagram-v2
    class SearchAlgorithm{
        SearchAlgorithm: +fine_tuner FineTuner
        SearchAlgorithm: +fit()
    }
    class FineTuner{
        <<interface>>
        FineTuner: +fit()
    }
    SearchAlgorithm ..> FineTuner
    FineTuner <|.. BayesSearchCV
    FineTuner <|.. GridSearchCV
    FineTuner <|.. RandomizedSearchCV

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Pipeline

The Pipeline takes both factories: FineTuner & Model concrete implementations by composition. The UML diagram is shown below.

classDiagram-v2
    class Pipeline{
        Pipeline: +model Model
        Pipeline: +search_algorithm SearchAlgorithm
        Pipeline: +train()
        Pipeline: -preprocess()
    }
    class Model{
        <<interface>>
        Model: +preprocess()
        Model: +fit()
    }
    class SearchAlgorithm{
        SearchAlgorithm: +finetuner FineTuner
        SearchAlgorithm: +fit()
    }
    class FineTuner{
        <<interface>>
        FineTuner: +fit()
    }

    Pipeline ..> Model
    Pipeline ..> SearchAlgorithm

    Model <|.. LogisticRegression
    Model <|.. RandomForest
    Model <|.. LightGBM
    Model <|.. NeuralNetwork

    SearchAlgorithm ..> FineTuner

    FineTuner <|.. BayesSearchCV
    FineTuner <|.. GridSearchCV
    FineTuner <|.. RandomizedSearchCV

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Requirements

To use this project, you will need to have Python 3.9 or greater, and install the following dependencies.

pip install -r requirements_dev.txt

Once you have installed the necessary dependencies, you can run the following command to start a MLFlow server:

export MLFLOW_PORT=5000 &&
export MLFLOW_ARTIFACT_ROOT=mlruns
export MLFLOW_TRACKING_URI=sqlite:///database/mlruns.db &&
mlflow server --backend-store-uri=$MLFLOW_TRACKING_URI --default-artifact-root=file:$MLFLOW_ARTIFACT_ROOT --host 0.0.0.0 --port $MLFLOW_PORT

Modify config/config.yaml according to your needs. Then, you can start a training loop as follows:

python -m src.main [options]

The options are listed below:

usage: [-h] [-f FILEPATH] [-d DATASET] [-l LOCATION] [-e EXPERIMENT_NAME]

options:
  -h, --help            show this help message and exit
  -f FILEPATH, --filepath FILEPATH
                        Path to configuration file
  -d DATASET, --dataset DATASET
                        Dataset name
  -l LOCATION, --location LOCATION
                        Folder to store data
  -e EXPERIMENT_NAME, --experiment_name EXPERIMENT_NAME
                        MLFlow's experiment name

The project will then load the Higgs dataset, train a model, and track the experiment using MLFlow. You can then view the results of the experiment in the MLFlow UI in your web browser in localhost

Conclusion

This project demonstrates how to use MLFlow, the Strategy pattern, and the Factory pattern to speed up machine learning experimentation. MLFlow provides a powerful framework for managing experiments, tracking model performance, and sharing models with others. The Strategy pattern allows for flexible and interchangeable strategies for fine-tuning machine learning models. Additionally, the Factory pattern facilitates the creation of these strategies without exposing the instantiation logic to the client code.

By combining MLFlow, the Strategy pattern, and the Factory pattern, you can effectively manage and optimize your machine learning workflows. MLFlow enables easy experiment tracking and model management, while the Strategy pattern and the Factory pattern provide a structured approach to fine-tuning models and encapsulating object creation logic.

This approach empowers data scientists and machine learning engineers to iterate quickly, compare different strategies, and make informed decisions based on experiment results. Furthermore, the modularity and extensibility offered by the Strategy pattern and the Factory pattern make it easier to incorporate new fine-tuning strategies or modify existing ones without impacting the overall system.

In summary, leveraging MLFlow, the Strategy pattern, and the Factory pattern can greatly enhance the efficiency and effectiveness of machine learning experimentation, model management, and deployment processes.

Future work

In order to enhance the existing system, the future work will involve implementing two additional Factory patterns: FeatureEnricher and Dataset. These new patterns will complement the already present Strategy Pattern, consisting of the Pipeline and SearchAlgorithm, which includes the Model and FineTuner factories, respectively.

Dataset Factory

The Dataset Factory will facilitate the collection of data used for training or evaluating machine learning models. By utilizing the Dataset Factory, we can abstract away the creation details and handle the creation of various types of datasets, such as image datasets, text datasets, or audio datasets, with ease. Also, will facilitate switching between datasets such as Iris, Higgs, Wine, between others. The UML diagram is shown below:

classDiagram-v2
    class Dataset{
        <<interface>>
        Dataset: +read()
        Dataset: +train_test_split()
    }

    Dataset <|.. IrisDataset : implements
    Dataset <|.. HiggsDataset : implements
    Dataset <|.. WineDataset : implements

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Feature Enricher Factory

Similarly, the FeatureEnricher factory will provide a flexible way to processes and enrich the input data with additional features before it is fed into the Pipeline. By employing the FeatureEnricher, we can encapsulate the creation logic and easily switch between different enricher implementations (such as LogTransformation) based on specific requirements or configurations. The UML diagram is shown below:

classDiagram-v2
    class FeatureEnricher{
        <<interface>>
        FeatureEnricher: +read()
        FeatureEnricher: +train_test_split()
    }
    FeatureEnricher <|.. LogTransformation
    FeatureEnricher <|.. ZScale
    FeatureEnricher <|.. Automated

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Final System

These new factory patterns will seamlessly integrate with the existing Strategy Pattern, which already includes the Pipeline and SearchAlgorithm strategies. The Pipeline strategy defines the pipeline of operations to be applied to the training data and subsequent training. On the other hand, the SearchAlgorithm encapsulates the algorithmic choices for performing hyperparameter search and optimization of the Model at hand.

By incorporating the FeatureEnricher and Dataset factories alongside the existing Strategy pattern and Factory Pattern, the system will gain increased modularity, extensibility, and maintainability.

A proposed final product is shown below:

classDiagram-v2
    class Pipeline{
        Pipeline: +dataset Dataset
        Pipeline: +feature_enricher FeatureEnricher
        Pipeline: +model Model
        Pipeline: +search_algorithm SearchAlgorithm
        Pipeline: +train()
        Pipeline: -preprocess()
    }

    class Dataset{
        <<interface>>
        Dataset: +read()
        Dataset: +train_test_split()

    }

    class FeatureEnricher{
        <<interface>>
        FeatureEnricher: +read()
        FeatureEnricher: +train_test_split()

    }

    class Model{
        <<interface>>
        Model: +preprocess()
        Model: +fit()
    }

    class SearchAlgorithm{
        SearchAlgorithm: +finetuner FineTuner
        SearchAlgorithm: +fit()
      }

    class FineTuner{
        <<interface>>
        FineTuner: +fit()
    }


    Pipeline ..> Dataset : has a
    Pipeline ..> FeatureEnricher : has a
    Pipeline ..> Model : has a
    Pipeline ..> SearchAlgorithm : has a

    SearchAlgorithm ..> FineTuner : has a

    Dataset <|.. ConcreteDataset : implements

    FeatureEnricher <|.. ConcreteFeatureEnricher : implements

    Model <|.. ConcreteModel : implements

    FineTuner <|.. Concrete FineTuner : implements

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