semvec/pypi-packages huggingface dataset:
chef-recipes 2.5.5
Personal recipe management app.
License: MIT
Requires Python: <4.0,>=3.8
Keywords: food, recipes, culinary

# 🍽️ Chef

A simple, customizable recipe management app.

A simple FastAPI + SQLAlchemy app + VueJS frontend.
Frontend app is built and bundled together with the python build downloadable from PyPI -> the entire app can be run with a single command.
It's also built as a single container and can be easily hosted (with little tweaks) on GCP Cloud Run.

## Installation

```shell
curl https://raw.githubusercontent.com/xyzjonas/chef/master/install.sh | bash
```

or install the package yourself

```shell
pip install chef-recipes
chef --help
```


or run with docker:

```shell
docker run -p 8000:8000 -v ~/.chef:/chef/data scotch3840/chef:latest
```


## Build & Development
### Migrations
migrations are included in the build package, symbolic link to the alembic.ini is in the repository root
```shell
# schema changes
alembic revision --autogenerate -m "<... message ...>"

# upgrade the target database - accoring to $DATABASE_URI
alembic upgrade head

# ...or can be executed from the built script - i.e. without the sources
chef migrate-db
```
### Build
1. Poetry: build the standalone Python package
```shell
cd ./src/js/chef
npm run build
cd -
poetry build
```
```shell
poetry publish
```
2. Build the docker image
```shell
docker build . -t scotch3840/chef
```
```shell
docker push scotch3840/chef
```

---------

...or run on GCP using `scotch3840/misc:chef-gcp` image (PostgreSQL):
> ! second generation execution environment required.
- `BUCKET`: name of your GCP/CloudStorage bucket.
- `MNT_DIR`: target where the bucket will be mounted.
- `IMAGES_FOLDER`: where the app will be looking for and storing uploaded images, has to be somewhere inside your bucket.
- `DATABASE_URI`: specify your posgres or possibly put an sqlite file in the bucket as well.
binary-classification-ratios 0.3.0
Binary classification ratios gathered in one package.
License: MIT
Requires Python: <4.0,>=3.8

# Binary classification ratios

<img src="https://raw.githubusercontent.com/kovalp/binary_classification_ratios/main/assets/logo-binary-classification-ratios.svg" alt="logo-binary-classification-ratios" width="96">

The package helps computing the quality metrics (ratios) arising in the binary classification.
The binary classification is given by the confusion matrix. The confusion matrix is given
by number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN)
generated by an algorithm or procedure.

The ratios are easier to interpret than the confusion matrix.

For example, the `Accuracy` is given by the ratio of all accurate responses to the 
total number of responses

$$
\mathrm{Accuracy} = \frac{\mathrm{TP} + \mathrm{TN}}{\mathrm{TP} + \mathrm{TN} + \mathrm{FP} + \mathrm{FN}}.
$$

Another popular ratio is `Recall`

$$
\mathrm{Recall} = \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN}}.
$$

## Usage

There is a command-line utility `binary-classification-ratios`. The utility takes the 
optional arguments &nbsp;-tp, &nbsp;-tn, &nbsp;-fp, and &nbsp;-fn,
computes the popular binary-classification ratios such as Accuracy, Recall, Precision and 
F1-score and prints them to terminal.

```shell

binary-classification-ratios -tp 10 -tn 20 -fp 30 -fn 40
Confusion matrix TP 10 TN 20 FP 30 FN 40
     accuracy 0.30000
    precision 0.250
       recall 0.200
     f1-score 0.222
```

The package is designed to be useful in other Python projects where the elements of the confusion matrix
are known

```python
from binary_classification_ratios import BinaryClassificationRatios

ratios = BinaryClassificationRatios(tp=10, tn=20, fp=30, fn=40)
print(ratios.get_summary())

ratios.assert_min(0.9, 0.8, 0.7)
```

## Install

```shell

pip install binary-classification-ratios
```
linear-model-Surya-Sudhaharan 0.1.0
A lightweight Python library for Linear Regression with multiple training methods and regularization options.
License: MIT
Requires Python: >=3.7

# 📦 linear_model

A lightweight and easy-to-use Python library for **Linear Regression**.  
Supports multiple training methods, regularization options, and sample weighting.

✨ Features:
- 📐 Normal Equation
- 🔁 Gradient Descent
- 🛡️ Ridge Regression (L2 regularization)
- ✂️ Lasso Regression (L1 regularization)
- ⚖️ Sample weights for weighted regression
- 🎯 Optional intercept fitting

---

## 🚀 Installation

Clone the repository and install in editable mode:

```bash
git clone https://github.com/<your-username>/linear_model.git
cd linear_model
pip install -e .

Or install directly from GitHub (no clone needed):

pip install git+https://github.com/<your-username>/linear_model.git
```
---
## 📌 Quick Start
```python
import numpy as np
from linear_model import LinearRegression

# Training data
X = np.array([[1], [2], [3], [4]])
y = np.array([2, 4, 6, 8])

# Initialize model
model = LinearRegression(method="ridge", alpha=0.01, lambd=0.1)

# Fit model
model.fit(X, y, sample_weight=None)

# Predictions
print("Predictions:", model.predict([[5], [6]]))

# Model details
print("Coef:", model.coef_())
print("Intercept:", model.intercept_())

# R² score
print("R²:", model.score(X, y))
```

## ⚙️ API Overview

- **`fit(X, y, sample_weight=None)`** → Train the model  
- **`predict(X)`** → Predict target values  
- **`score(X, y, sample_weight=None)`** → Compute R² score  
- **`coef_()`** → Get feature coefficients  
- **`intercept_()`** → Get model intercept  

### Parameters
- `method`: `"normal_equation" | "gradient_descent" | "ridge" | "lasso"`  
- `fit_intercept`: `True` (default) | `False`  
- `n_iter`: Number of iterations for gradient methods (default: `1000`)  
- `alpha`: Learning rate for gradient methods (default: `0.01`)  
- `lambd`: Regularization strength (default: `0.1`)  

---

## 🧪 Testing

Run unit tests with:

```bash
pytest tests/
```
## 📂 Project Structure
```arduino
linear_model/
│── __init__.py
tests/
│── test_linear_regression.py
setup.py
README.md
LICENSE
```
## 📜 License
This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.