my-python-lib-tarik 0.1.0

A modular, object-oriented framework for machine learning and data preprocessing

Machine Learning & Data Preprocessing Library

Introduction

This is a simple machine learning algorithm library consists of Linear Regression , KNN Classifier and some other data processing algorithms from scratch based on numpy and pandas libraries.

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Mapping Core Learning Outcomes

The 6 required patterns were applied appropriately in the project. Every single one is explained below.

1. Object-Oriented Programming (OOP)

• Where: In core.py and data.py.
• How:
  • Inheritance & Abstraction: Employs abstract base classes (BaseAlgorithm, RegressionStrategy, DistanceMetric, DataLoader, DataCreator, ImputeStrategy, EncodingStrategy) to enforce blueprints.
  • Polymorphism: Concrete implementations dynamically substitute base behavior. For example, LinearRegression executes .train() polymorphic actions via different assigned regression strategies without altering its own structure.
  • Encapsulation: State variables are protected internally. In data.py, the raw dataframe is hidden behind a protected attribute self._data and managed safely using the @property getter.

2. Functional Programming

• Where: In core.py and utils.py.
• How:
  • Pure Functions & Lambda: evaluate_model avoids modifying external states and relies entirely on input arguments, calculating mean squared errors via a clean pure lambda routine.
  • Higher-Order Functions & Map/Reduce:
    • reduce combined with lambda is used inside evaluate_model to sum squared errors.
    • map is used inside series_to_ndarray to cast panda series rows to float representations.
    • apply_pipeline utilizes reduce to sequentially compose list-based transformation callables across data boundaries (reduce(lambda d, func: func(d), transformations, data)).

3. Concurrency (Multi-threading)

• Where: Implemented in core.py inside the KNNClassifier class.
• How:
  • Predicting classes for massive feature maps sequentially is computationally bound. The predict method generates individual threading.Thread operations for every distinct evaluation sample.
  • The _predict_single worker calculates specific row-by-row matrix operations concurrently, storing structural outputs inside a shared pre-allocated numpy results matrix (results[index]).
  • Thread control structures utilize t.start() loops followed by systematic t.join() barriers to synchronize and block primary execution until parallel estimations conclude safely.

4. Recursion / Dynamic Programming

• Where: In core.py inside the EuclideanDistance class.
• How:
  • Distance metrics typically resolve dimensions via nested iterative syntax or high-level library functions. This implementation achieves element-wise vector difference accumulations via a custom recursive function recursive_sum_sq(a, b, idx).
  • It recursively accumulates squared parameter differences index-by-index until it reaches the base case (idx == len(a)), gracefully returning the final structural matrix sqrt reduction.

5. SOLID Principles

• Where: In core.py and data.py.
• How:
  • Single Responsibility Principle (SRP): Classes do exactly one thing. CSVLoader only ingests data streams; MeanImputer strictly provides missing value fillings; DataProcessor focuses on data manipulation.
  • Open/Closed Principle (OCP): The system is open for extension but closed for modification. Introducing a new distance metric (e.g., Cosine Distance) requires subclassing DistanceMetric without touching KNNClassifier.
  • Liskov Substitution Principle (LSP): Derived classes are completely interchangeable with their abstractions. Any encoder (LabelEncoder, OneHotEncoder, TargetEncoder) fulfills the signature constraints expected by DataProcessor.
  • Interface Segregation Principle (ISP): Interfaces remain lean and decoupled. RegressionStrategy enforces a single clear contractual point (train), avoiding bulky, unrelated structural configurations.
  • Dependency Inversion Principle (DIP): High-level objects depend on abstractions rather than low-level concrete modules. LinearRegression binds entirely against the RegressionStrategy interface, decoupling model training mechanisms from specific analytical algorithms.

6. Architectural & Design Patterns

• Where: Full design of data.py and core.py.
• How:
  • Pipeline Architecture: Managed by DataPipeline which neatly bridges file checking, concrete factory creation, loading, and structured feature preparation routines into a uniform linear API stream (run_default_preprocessing).
  • Strategy Pattern: Implemented multiple times to provide interchangeable components:
    • Optimization algorithms in LinearRegression via LeastSquaresStrategy and GradientDescentStrategy.
    • Distance formulations in KNNClassifier via EuclideanDistance and ManhattanDistance.
    • Data imputation in DataProcessor via MeanImputer, MedianImputer, and ModeImputer.
    • Variable transformations via LabelEncoder, OneHotEncoder, and TargetEncoder.
  • Factory Method Pattern: Used to create appropriate data loaders without binding to concrete files. DataCreator acts as the creator interface, declaring create_document(). Concrete implementations CSVCreator and JSONCreator override this method to instantiate and return CSVLoader or JSONLoader respectively, abstracting the instantiation process away from the main pipeline.