semvec/pypi-packages huggingface dataset:
kirznerSerializer 0.0.4
This project is my laboratory work
Requires Python: >=3.10

# Welcome to Kirzner's Serializer!
## This module supports two formats: *json* and *xml*
### Each serializer contains:
* `dump(obj, filepath)` - serialize `obj` into the file with provided `filepath`
* `dumps(obj)` - serialize `obj` and returns string result
* `load(obj, filepath)` - return the result of deserialization content from the file with provided `filepath`
* `loads(obj, s)` - return the result of deserialization content from the provided string `s`
requestRender 0.0.3
A PyPI package that provides a simple headless Selenium web browser for scraping wrapper around reactive websites.

# requestRender
A PyPI package that provides a simple headless Selenium web browser for scraping wrapper around reactive websites.

# How to install it
```
pip install requestRender
```
You also need the have Chrome installed to run this, because it uses Selenuim with Chrome as a headless Browser.

# How to use it?
```
import requests
import requestRender

# Start a session
session = requests.Session()
# Start the browser
browser = requestRender.requestRender.RequestRender(session)
# Get the response from the website
response = session.get("https://www.midjourney.com")  
# Render the response to get the render the javascript content
html = browser.renderResponse(response)
# Save the html to a file
with open("index.html", "w", encoding="utf-8") as f:
    f.write(html)
```

# What needs to Improve

1. The logging off the Browser needs be disabeld.
2. A methode to determin if a website has loaded fully. At the moment the software just waits a 3 Seconds.
constrained-lr 0.0.4
Constrained Linear Regression with sklearn-compatible API
Requires Python: >=3.8

# Constrained Linear Regression
constrained-lr is a drop-in replacement for `scikit-learn`'s `linear_model.LinearRegression` with the additional flexibility to define more complex (but linear) constraints on the model's coefficients.

### Use-cases
#### SHAP
The Kernel SHAP algorithm includes the training of a constrainted linear regression model where the sum of its coefficients is equal to the model's prediction

#### Marketing Mix Modeling
In Marketing Mix Modeling (MMM), the attribution of sales to various marketing channels can be informed by business sense or prior knowledge, by enforcing the contribution of channel variables to be positive or negative.

### Installation
```bash
pip install constrained-lr
```

### Example Usage
```python
from constrained_lr import ConstrainedLinearRegression
from sklearn.metrics import mean_squared_error

model = ConstrainedLinearRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
print(mean_squared_error(y_test, y_pred))

```


### Licence
MIT