mlscore 0.1.0

A comprehensive machine learning evaluation metrics library

MLScore

A comprehensive machine learning evaluation metrics library that provides a simple interface to calculate multiple evaluation metrics at once.

Installation

pip install mlscore

Usage

Basic Usage

from mlscore import score, score_c, score_r
import numpy as np

# Example 1: Classification
y_true = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1])
y_pred = np.array([0, 1, 0, 0, 0, 1, 0, 1, 0, 1])

# Using score_c for classification (automatically prints results)
score_c(y_true, y_pred)

# Example 2: Regression
y_true_reg = np.array([1.2, 2.3, 3.4, 4.5])
y_pred_reg = np.array([1.1, 2.4, 3.3, 4.6])

# Using score_r for regression (automatically prints results)
score_r(y_true_reg, y_pred_reg)

# Example 3: Automatic detection
score(y_true, y_pred)  # Will automatically detect and print results

Getting Metrics Without Printing

# Get metrics dictionary without printing
metrics = score_c(y_true, y_pred, print_results=False)

# Access specific metrics
print(metrics['Accuracy'])
print(metrics['Precision'])
print(metrics['F1 Score'])

Available Functions

1. score(y_true, y_pred, print_results=True)

   • Automatically detects if the problem is classification or regression
   • Prints results by default
   • Returns a dictionary of metrics

2. score_c(y_true, y_pred, print_results=True)

   • Specifically for classification problems
   • Prints results by default
   • Returns a dictionary of classification metrics

3. score_r(y_true, y_pred, print_results=True)

   • Specifically for regression problems
   • Prints results by default
   • Returns a dictionary of regression metrics

Available Metrics

Classification Metrics

• Accuracy
• Precision
• Recall
• F1 Score
• ROC-AUC Score (for binary classification)
• Confusion Matrix

Regression Metrics

• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• Mean Absolute Error (MAE)
• R² Score
• Adjusted R² Score

Output Format

The library provides two ways to access the metrics:

1. Printed Output (default):

==================================================
Problem Type: Classification
==================================================
Accuracy: 0.9000
Precision: 0.9167
Recall: 0.9000
F1 Score: 0.8990
ROC-AUC: 0.9000

Confusion Matrix:
  [5, 0]
  [1, 4]

2. Dictionary Output:

{
    'Accuracy': 0.9,
    'Precision': 0.9167,
    'Recall': 0.9,
    'F1 Score': 0.8990,
    'ROC-AUC': 0.9,
    'Confusion Matrix': [[5, 0], [1, 4]],
    'Problem Type': 'Classification'
}

Requirements

• Python >= 3.6
• NumPy >= 1.19.0
• scikit-learn >= 0.24.0

License

MIT License