- Potential Workflow
Encapsulation is bundling the data (attributes) and methods (functions) that operate on the data into a single unit or class and restricting access to some of the object's components.
- Use access specifiers (
private,protected,public) to control access to the attributes and methods. - Provide public getter and setter methods to access and update the private attributes.
This section explores essential functional programming constructs in Python, including lambda, map, filter, and reduce. Functional programming emphasizes immutability, first-class functions, and declarative code.
Functional programming techniques can lead to more concise, readable, and maintainable code. These techniques are particularly useful when working with collections of data, making it easy to apply transformations or filtering operations in a clean, declarative manner.
- Lambda Functions: Anonymous functions for short, single-use operations.
- Map: Applies a function to every element of an iterable.
- Filter: Filters elements in an iterable based on a condition.
- Reduce: Reduces an iterable to a single value through a binary operation.
- List Comprehensions: A Pythonic way to apply functions to iterables.
- Python Official Docs
- Real Python Functional Programming Guide
- Geeks for Geeks Functional Programming
Magic methods, also known as dunder methods, allow you to define how objects behave with respect to built-in operations such as arithmetic, comparison, string representation, and more.
These methods have double underscores before and after their names (e.g., __init__, __str__, __add__). Python calls these methods automatically when performing certain operations on objects, making it possible to customize object behavior in a flexible way.
__init__: Initializes an object.__str__: Defines how an object is printed as a string.__repr__: Provides an official string representation of an object.__add__: Overloads the+operator.__len__: Defines behavior for thelen()function.__getitem__,__setitem__: Enables indexing and item assignment.__eq__: Compares two objects for equality.__call__: Allows an object to be called like a function.
To explore these magic methods, define a class and implement any of the above methods. You can run each example in this section by copying the code to a .py file and running it.
Topics
Abstraction involves simplifying complex reality by modeling classes appropriate to the problem and working at the most relevant level of inheritance for a particular aspect of the problem.
- Use abstract classes and interfaces to define common methods that multiple subclasses can implement.
- Focus on what an object does rather than how it does it.
Inheritance is a mechanism where a new class is derived from an existing class. The new class inherits attributes and methods from the existing class.
- Use inheritance to promote code reusability and establish a natural hierarchy.
- Override methods in the subclass to provide specific implementations.
Polymorphism allows objects of different classes to be treated as objects of a common superclass. It is often expressed through method overriding and interface implementation.
- Use method overriding to allow different classes to provide specific implementations of a method defined in a superclass or interface.
- Leverage dynamic method binding to call the overridden methods at runtime.
A class should have one, and only one, reason to change.
Software entities should be open for extension but closed for modification.
Subtypes must be substitutable for their base types without altering the correctness of the program.
No client should be forced to depend on methods it does not use.
High-level modules should not depend on low-level modules. Both should depend on abstractions.
Favor composition (having instances of other classes as members) over inheritance (extending other classes) to achieve code reuse and flexibility.
- Use interfaces and delegate the responsibility to other classes to achieve functionality.
- Avoid deep inheritance hierarchies which can lead to complex and fragile code.
Avoid duplication of code by abstracting common functionality and reusing existing code.
- Refactor common code into reusable methods or classes.
- Use inheritance or composition to avoid code duplication.
Strive for simplicity in design and implementation, avoiding unnecessary complexity.
- Write clear, concise, and understandable code.
- Avoid over-engineering and focus on solving the problem at hand.
- Break down the workflow into modular components such as data ingestion, preprocessing, model training, evaluation, and deployment.
- Each module should be self-contained and perform a specific task.
- Efficiently load, preprocess and augment data.
- Implement data versioning to track changes in the datasets used for training and evaluation.
- Develop a pipeline for feature extraction, transformation, and selection.
- Use tools like
sklearn.pipelineor custom pipelines to automate feature engineering.
- Structure code to support hyperparameter tuning and cross-validation.
- Implement checkpoints to save and resume training.
- Define clear evaluation metrics and implement functions to compute them.
- Use cross-validation and hold-out validation to assess model performance.
- Use version control for models to keep track of different model versions and configurations.
- Implement logging to record training history and model performance.
- Write unit tests and integration tests for data processing functions and model training.
- Ensure reproducibility by setting random seeds and documenting the environment setup.
- Optimize code for performance, especially for large datasets and complex models.
- Use distributed computing frameworks like Apache Spark for large-scale data processing.
- Develop a strategy for model deployment including model serialization (e.g., using
pickleorjoblib), containerization (e.g., Docker), and serving (e.g., Flask, FastAPI). - Implement monitoring to track model performance and detect drifts in real-time.
- Document code, workflows, and decisions clearly to facilitate collaboration.
- Use tools like Jupyter Notebooks for exploratory data analysis and sharing results.
- Implement CI/CD pipelines to automate testing, validation, and deployment of ML models.
- Use MLOps tools and frameworks (e.g., MLflow, TFX, Kubeflow) to manage the end-to-end ML lifecycle.