semvec/pypi-packages huggingface dataset:
jobmon-core 3.4.8
Shared functionality for Jobmon Namespace Packages
Requires Python: >=3.9

# JobMon Core

## Introduction

JobMon is a Python package developed by IHME's Scientific Computing team,
designed to simplify and standardize the process of job monitoring and
workflow management in computational projects.
It facilitates the tracking of job statuses, manages dependencies,
and streamlines the execution of complex workflows across various computing environments.

This package contains the code that it is shared between the client and the server, and
is deployed to the customer's worked node. 

See also the main documentation at [readthedocs](https://jobmon.readthedocs.io/en/latest/#).
Randmathcli 1.0.0
A Random Math Problem Generator

# Randmathcli

A Random Math Problem Generator

Just run `randmathcli` and you'll get a math problem.

License:

MIT License

Copyright (c) 2025 Mahdi Ruiz

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
monetary-classifier 0.1.0
A scikit-learn compatible package for classification with monetary outcomes
Requires Python: >=3.7

# Monetary Classifier

A scikit-learn compatible package for classification with monetary outcomes. This package allows you to optimize classifiers based on the actual monetary value of correct and incorrect predictions rather than just accuracy or other traditional metrics.

## Installation

```bash
# Basic installation
pip install monetary-classifier

# With gradient boosting integrations
pip install monetary-classifier[xgboost,lightgbm,catboost]

# For development
pip install monetary-classifier[dev]
```

Or install directly from the repository:

```bash
git clone https://github.com/yourusername/monetary-classifier.git
cd monetary-classifier
pip install -e .
```

## Quick Start

```python
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from monetary_classifier import MonetaryClassifier, create_lending_monetary_values

# Prepare your data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Define monetary values for each prediction outcome
monetary_values = {
    'tp': 100,    # Profit from true positive (e.g., approving a good loan)
    'tn': 0,      # No gain/loss from true negative (e.g., denying a bad loan)
    'fp': -1000,  # Loss from false positive (e.g., approving a bad loan)
    'fn': -100,   # Loss from false negative (e.g., denying a good loan)
}

# Or use the built-in function for lending scenarios
monetary_values = create_lending_monetary_values(
    loan_amount=10000,
    interest_rate=0.15,
    default_cost_factor=1.0
)

# Create and train the monetary classifier
clf = MonetaryClassifier(
    base_estimator=RandomForestClassifier(),
    monetary_values=monetary_values,
    use_monetary_weights=True  # Use monetary values as weights during training
)
clf.fit(X_train, y_train)

# Evaluate the monetary outcome
monetary_score = clf.monetary_score(X_test, y_test)
print(f"Monetary outcome: ${monetary_score:.2f}")

# Get detailed breakdown
summary = clf.get_monetary_summary(X_test, y_test)
print(summary)
```

## Key Features

1. **Monetary value-based classification**: Optimize for business value rather than just accuracy
2. **Automatic threshold optimization**: Find the optimal decision threshold to maximize monetary outcome
3. **Cost-sensitive learning**: Use monetary values as weights during model training
4. **Direct objective optimization**: Custom loss functions for gradient boosting models (XGBoost, LightGBM, CatBoost)
5. **scikit-learn compatible**: Works with existing scikit-learn pipelines and models
6. **Comprehensive evaluation**: Detailed monetary analysis of model performance

## Examples

Check the `examples` directory for detailed usage examples:

- `lending_example.py`: Basic example for a lending scenario
- `weighted_model_example.py`: Advanced example with different weighting strategies

## License

This project is licensed under the MIT License - see the LICENSE file for details.