uo 0.0.6

pipeline computing

uo

pipeline computing

To install: pip install uo

Overview

The uo package provides a framework for creating and managing computation pipelines. This functionality is particularly useful for scenarios where a sequence of data transformations and computations need to be applied in a specific order. The core of the package is the ComputationPipeline class which allows users to compose a pipeline of callable objects (functions, methods, etc.) that are executed sequentially.

Features

• Composable Pipelines: Easily compose multiple callable objects into a single callable pipeline.
• Flexible Step Definition: Each step in the pipeline can be defined with a callable and its associated method to call, allowing for great flexibility in how operations are performed.
• Partial Argument Binding: Steps can include partial argument binding using the partial function from Python's functools, providing more control over how functions are called within the pipeline.

ComputationPipeline Class

The ComputationPipeline class is designed to chain a sequence of operations together. Each step in the pipeline is defined by a tuple that specifies the name of the step, the object (function or class instance), and optionally, the method name to call on the object (default is __call__). The pipeline can be executed by calling the instance with the appropriate arguments.

Constructor

def __init__(self, steps):
    """
    Initializes a new instance of the ComputationPipeline.

    :param steps: A list of tuples. Each tuple should contain:
        - func_name: A string representing the name of the function or method.
        - obj: The function or object that will be called.
        - call_method (optional): The method name to call on the object. If not provided, '__call__' is assumed.
    """

Usage Example

# Define a simple pipeline with two steps
f = ComputationPipeline(steps=[
    ('increment', lambda x: x + 2),
    ('multiply', lambda x: x * 10)
])

# Execute the pipeline
result = f(1)  # (1 + 2) * 10 = 30
print(result)  # Output: 30

Advanced Usage

You can also specify methods of objects or use partial functions to pre-specify some arguments:

from functools import partial

# Assuming an object with methods that take additional parameters
class MathOperations:
    def multiply(self, x, factor):
        return x * factor

    def add(self, x, increment):
        return x + increment

math_ops = MathOperations()

# Create a pipeline using methods from the MathOperations class
g = ComputationPipeline(steps=[
    ('add', math_ops, 'add'),
    ('multiply', partial(math_ops.multiply, factor=5))
])

# Execute the pipeline
result = g(3)  # (3 + 0) * 5 = 15
print(result)  # Output: 15

This package is ideal for scenarios where data needs to be processed in distinct, sequential steps, such as data preprocessing, feature engineering, or even in a machine learning inference pipeline.