semvec/pypi-packages huggingface dataset:
shapinteractions 0.1
Build a comprehensive interaction graph visualization
Requires Python: >=3.8

# README

This package allows you to create a comprehensive visualization graph of feature interactions for machine learning models explained with Local Additive Explanation methods. It should work with any 3-dimensional array of explanation interactions but is particularly well suited for the output of the `TreeExplainer` SHAP interaction extractor from the *shap* Python library.

### Get started

To get started, simply run:

```bash
pip install shapinteractions
```

You should be ready to go. You can now import `ShapInteractions` from `shapinteractions` and use it in your Python environment and scripts:

```python
from shapinteractions import ShapInteractions
```


### How to use the package?

Please head over to our [GitHub repository](https://github.com/fefurger/Interactions) for an example notebook.

### How to read the graph?

You can interact with our interaction graph example [right here](https://fefurger.github.io/Interactions/).

Each **feature** is represented by a **node**:
- its color informs about whether the feature is positively correlated (red) or negatively correlated (blue) with the model predictions
- its size relates to the average absolute SHAP value of the feature's main effect (the contribution of the feature alone, without accounting for its interactions)

Each **interaction** is represented by an **arrow**:
- its color informs about whether the interaction reinforces (red) or attenuates (blue) the main effect of the pointed feature (regardless of that feature's color)
- its width relates to the average absolute SHAP interaction value
- the top slider allows you to hide/reveal interactions based on their strength (average absolute SHAP interaction value)

A node/arrow is rendered black if its Spearman's and Pearson's coefficients are of opposite signs, suggesting that the relationship is more complex than it appears. Similarly, if Spearman's coefficient for a given interaction is smaller than `spearmans_threshold` (default value of 0.3), the arrow is rendered dashed, suggesting that the correlation is weak.

You can hover over a node/arrow to display the corresponding correlation coefficients and average absolute SHAP value.
caldav2ics 0.1.1
Cache CalDAV calendars in static .ICS files.
Requires Python: >=3.9

# CalDAV2ICS

A simple script to download all calendars from a CalDAV server and save them as .ics files.

# Installation

```bash
pip install caldav2ics
```

# Usage
- Create a config .toml file
```toml
[server_a]
url = "https://caldav.server_a.com/calendar/remote.php/dav/calendars/user/"
username = "user"
save-dir = "calendars/server_a"

[calendar_b]
...
```
- Run the script
```bash
caldav2ics -c config.toml
```
or without to be prompted for the password
```bash
C2I_PASSWORD=$(cat .password) caldav2ics -c config.toml
```

# TODO
- tests