ben-memre 0.1.4

A special library by Emre with a few functions for AI projects.

ben-memre

This is a special demonstration library created by Memre Ozkan. It provides a few simple functions to show how a Python package can be built, and now includes several helper functions useful for common data processing tasks in AI and Machine Learning projects.

Installation

You can install this library from PyPI using pip:

pip install ben-memre

Usage

After installation, you can import and use the functions in your Python code. Note that while the package name on PyPI is ben-memre (with a hyphen), you must use an underscore (_) to import it in Python.

Basic Functions

from ben_memre import greet, add_numbers

# Use the greet function
message = greet("User")
print(message)  # Output: Hello, User! This is a message from ben-memre.

# Use the add_numbers function
result = add_numbers(10, 5)
print(f"The sum is: {result}")  # Output: The sum is: 15

AI & Data Processing Functions

These functions require numpy. It will be automatically installed as a dependency when you install the library.

import numpy as np
from ben_memre import normalize, one_hot_encode, calculate_accuracy

# --- Normalize Data ---
# Insight: Normalizing data to a 0-1 scale is crucial for many ML algorithms 
# (like neural networks and SVMs) to ensure stable and efficient training.
data = [10, 20, 30, 40, 50]
normalized_data = normalize(data)
print(f"Normalized Data: {normalized_data}")
# Output: Normalized Data: [0.   0.25 0.5  0.75 1.  ]

# --- One-Hot Encode Labels ---
# Insight: Categorical labels (like 'cat', 'dog', 'bird') must be converted
# to a numerical format for most models. One-hot encoding creates a binary
# vector for each label, preventing the model from assuming an incorrect
# ordinal relationship between categories.
labels = ['cat', 'dog', 'cat', 'bird']
encoded_labels = one_hot_encode(labels)
print(f"One-Hot Encoded Labels:\n{encoded_labels}")
# Output:
# One-Hot Encoded Labels:
# [[0. 1. 0.]
#  [0. 0. 1.]
#  [0. 1. 0.]
#  [1. 0. 0.]]

# --- Calculate Accuracy ---
# Insight: Accuracy is the most straightforward metric for evaluating a
# classification model. It measures the proportion of correct predictions.
y_true = [1, 0, 1, 1, 0]
y_pred = [1, 0, 1, 0, 0]
accuracy = calculate_accuracy(y_true, y_pred)
print(f"Accuracy: {accuracy:.2f}") # Output: Accuracy: 0.80

Version History

• 0.1.4 (Current):

  • Feature Add: Added three new functions for common AI/ML data processing tasks: normalize, one_hot_encode, and calculate_accuracy.
  • Added numpy as a project dependency.

• 0.1.3:

  • Major Fix: Restructured the project to use a src/ layout, which is the standard for modern Python packages. This resolved the persistent ModuleNotFoundError.

• 0.1.2:

  • Attempted Fix: Modified __init__.py to explicitly import functions. This was insufficient without the correct project structure.

• 0.1.1:

  • Initial Release: First version with a structural issue causing import errors.