framework3 1.0.29

A flexible framework for machine learning pipelines

LabChain

LabChain is an innovative platform designed to simplify and accelerate the development of machine learning models. It provides data scientists and machine learning engineers with a flexible and powerful tool to create, experiment with, and deploy models efficiently and in a structured manner. https://manucouto1.github.io/LabChain

Key Features

• Modular and flexible architecture
• Customizable pipelines for ML workflows
• Extensible plugin system for filters, metrics, and storage
• Support for distributed processing with MapReduce
• Integrated model evaluation and optimization tools

Diagram

      classDiagram
      class BasePlugin {
            <<abstract>>
            +item_dump() : Dict
            +build_from_dump(dump_dict: Dict, factory: BaseFactory) : BasePlugin
      }

      class BaseFilter {
            <<abstract>>
            +fit(x: XYData, y: XYData|None) : float|None
            +predict(x: XYData) : XYData
            +evaluate(x_data: XYData, y_true: XYData|None, y_pred: XYData) : Dict[str, Any]
      }

      class BasePipeline {
            <<abstract>>
            -filters: List[BaseFilter]
            +evaluate(x_data: XYData, y_true: XYData|None, y_pred: XYData) : Dict[str, Any]
            +start(x: XYData, y: XYData|None, X_: XYData|None) : XYData|None
            +init()
            +get_types() : List[Type[BaseFilter]]
            +optimizer(optimizer: BaseOptimizer) : BaseOptimizer|None
            +splitter(splitter: BaseSplitter) : BaseSplitter|None
      }

      class BaseSplitter {
            <<abstract>>
            -pipeline: BasePipeline
            +split(pipeline: BasePipeline)
            +evaluate(x_data: XYData, y_true: XYData|None, y_pred: XYData) : Dict[str, Any]
            +start(x: XYData, y: XYData|None, X_: XYData|None) : XYData|None
            +unwrap() : BasePipeline
      }

      class BaseOptimizer {
            <<abstract>>
            -pipeline: BasePipeline
            +start(x: XYData, y: XYData|None, X_: XYData|None) : XYData|None
            +optimize(pipeline: BasePipeline)
      }

      class BaseMetric {
            <<abstract>>
            +evaluate(x_data: XYData, y_true: XYData|None, y_pred: XYData) : float
      }

      class BaseStorer {
            <<abstract>>
            +get_root_path() : str
            +upload_file(file_path: str, destination_path: str)
            +list_stored_files() : List[str]
            +get_file_by_hashcode(hashcode: str) : str
            +check_if_exists(file_path: str) : bool
            +download_file(file_path: str, destination_path: str)
            +delete_file(file_path: str)
      }

      class ParallelPipeline {
            <<abstract>>
      }

      class SequentialPipeline {
            <<abstract>>
      }

      class MonoPipeline {
      }

      class LocalThreadPipeline {
      }

      class HPCPipeline {
      }

      class F3Pipeline {
      }

      class KFoldSplitter {
      }

      class OptunaOptimizer {
      }

      class SklearnOptimizer {
      }

      class WandbOptimizer {
      }

      class LocalStorer {
      }

      class S3Storer {
      }

      class Container {
            +bind()
            +get()
      }

      BasePlugin <|-- BaseFilter
      BasePlugin <|-- BaseMetric
      BaseFilter <|-- BasePipeline
      BaseFilter <|-- BaseSplitter
      BaseFilter <|-- BaseOptimizer
      BasePlugin <|-- BaseStorer
      BasePipeline <|-- ParallelPipeline
      BasePipeline <|-- SequentialPipeline
      ParallelPipeline <|-- MonoPipeline
      ParallelPipeline <|-- LocalThreadPipeline
      ParallelPipeline <|-- HPCPipeline
      SequentialPipeline <|-- F3Pipeline
      BaseSplitter <|-- KFoldSplitter
      BaseOptimizer <|-- OptunaOptimizer
      BaseOptimizer <|-- SklearnOptimizer
      BaseOptimizer <|-- WandbOptimizer
      BaseStorer <|-- LocalStorer
      BaseStorer <|-- S3Storer

      BasePipeline "1" *-- "1..*" BaseFilter : contains
      BaseSplitter "1" *-- "1" BaseFilter : contains
      BaseOptimizer "1" *-- "1" BaseFilter : contains
      BasePipeline "1" *-- "0..*" BaseMetric : uses

      Container --> BasePlugin : contains

Prerequisites

Before installing LabChain, ensure you have the following prerequisites:

1. Python 3.11 or higher
2. pip (Python package installer)

Installation Options

You have two options to install LabChain:

Option 1: Install from PyPI

The easiest way to install LabChain is directly from PyPI using pip:

pip install framework3

This will install the latest stable version of LabChain and its dependencies.

Option 2: Install from Source

2. Clone the repository:

   git clone https://github.com/manucouto1/LabChain.git
   

3. Navigate to the project directory:

   cd LabChain
   

4. Install the dependencies using pip:

   pip install -r requirements.txt
   

Basic Usage

Here's a basic example of how to use LabChain:

from framework3.plugins.pipelines import F3Pipeline
from framework3.plugins.filters import KnnFilter
from framework3.plugins.metrics import F1, Precision, Recall

# Create a pipeline
pipeline = F3Pipeline(
    plugins=[KnnFilter()],
    metrics=[F1(), Precision(), Recall()]
)

# Fit the model
pipeline.fit(X_train, y_train)

# Make predictions
predictions = pipeline.predict(X_test)

# Evaluate the model
evaluation = pipeline.evaluate(X_test, y_test, y_pred=predictions)
print(evaluation)

Documentation

For more detailed information on how to use Framework3, check out our complete documentation at:

https://manucouto1.github.io/LabChain

Contributing

Contributions are welcome. Please read our contribution guidelines before submitting pull requests.

License

This project is licensed under the AGPL-3.0 license. See the LICENSE file for more details.

Contact

If you have any questions or suggestions, don't hesitate to open an issue in this repository or contact the development team.

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Thank you for your interest in LabChain! We hope this tool will be useful in your machine learning projects.