semvec/pypi-packages huggingface dataset:
gitlab_registry_async_cleaner 1.0.6
Gitlab registry async cleaner
License: MIT
Requires Python: <4.0,>=3.10

# gitlab_registry_cleaner

A project can remove tags from the gitlab registry by configuration file.

Supported time:
1. **days**
2. **hours**
3. **minutes**

Environments:
1. GITLAB_TOKEN - personal gitlab token. Override from CLI **-t** or **--token**.
2. GITLAB_URL - general gitlab domain, https://gitlab.com for example. Override from CLI **--url**.
3. REPOSITORY_CONFIG_PATH - path to json config file. Override from CLI **-c** or **--config**.
4. SSL_PATH - path to SSL certificate. Override from CLI **--ssl_path**.
5. TIMEOUT - timeout to all HTTP requests. Override from CLI **--timeout**.

Also, you can set **--dry-run** flag from CLI. In this mode found tags not be deleted.

# Install
```bash
pip install gitlab_registry_async_cleaner
```

# Usage
```bash
gitlab_async_cleaner
```
You can create an .env file in your startup directory or set environment 
variables any way you like. The CLI takes precedence and arguments passed
through the CLI will change the environment variables.

JSON config example
```json
{
  "gitlab_project_1":
  {
    "id": 1,
    "delete_older_than": {
      "days": 2
    }
  },
  "gitlab_project_2":
  {
    "id": 2,
    "delete_older_than": {
      "hours": 5
    }
  },
  "gitlab_project_3":
  {
    "id": 3,
    "delete_older_than": {
      "minutes": 60
    }
  }
}
```
gcrnet 1.1.0
A Python library for sparse-input neural networks using group concave regularization
License: MIT

# GCRNet: Sparse-Input Neural Networks with Group Concave Regularization

[Project Page](https://github.com/r08in/GCRNN)|[Paper](https://arxiv.org/abs/2307.00344)

GCRNet is a Python library that implements a novel framework for sparse-input neural networks using group concave regularization. Leveraging the power of concave penalties, specifically MCP and SCAD, GCRNet provides a comprehensive approach for simultaneous feature selection and non-linear function estimation. The proposed framework considers all outgoing connections from a single input neuron as a group and applies an appropriate concave penalty to the $l_2$ norm of weights within each group. By selectively shrinking the weights of certain groups to exact zeros, GCRNet constructs a neural network that utilizes only a small subset of variables, enhancing both the accuracy and interpretability of the feature selection process. GCRNet offers versatile functionality, supporting regression or classification tasks with continuous, binary, or time-to-event outcomes.

### Installation

#### Installation with pip

To install with `pip`, run the following command:
```
pip install --user gcrnet
```

#### Installation from GitHub

You can also clone the repository and install manually:
```
git clone 
cd gcrnet/python
python setup.py install --user
```

### Usage

Once you install the library, you can import `GCRNet` to create a model instance:
```
from gcrnet import GCRNet
lam=np.exp(np.linspace(np.log(0.01),np.log(0.5), 50))
gmcp_net = GCRNet(task_type='regression',device=torch.device("cpu"), input_dim=train_data['X'].shape[1], output_dim=1, hidden_dims=[10,5], activation="relu",
    optimizer="Adam",learning_rate=0.001, batch_size=train_data['X'].shape[0], 
    alpha=0.01, lam=lam, penalty="MCP",drop_input=True)

# fit model and tune parameters
param_grid={"alpha":[0.03]}
gmcp_net= gmcp_net.fit_and_validate(X=train_data['X'], y=train_data['y'], 
                              param_grid=param_grid, init_num_epochs=2000, num_epochs=200, verbose=True, print_interval=200)
```

For more details, please see our Colab notebooks:

- [Regression example](https://colab.research.google.com/github/r08in/GCRNN/blob/main/python/examples/Regression-example.ipynb)
- [Classification example](https://colab.research.google.com/github/r08in/GCRNN/blob/main/python/examples/Classification-example.ipynb)
- [Cox example](https://colab.research.google.com/github/r08in/GCRNN/blob/main/python/examples/Cox-example.ipynb)

### Acknowledgements and References

Some of our codebase and its structure are inspired by https://github.com/runopti/stg. 

If you find our library useful in your research, please consider citing us:

```
@misc{luo2023sparseinput,
      title={Sparse-Input Neural Network using Group Concave Regularization}, 
      author={Bin Luo and Susan Halabi},
      year={2023},
      eprint={2307.00344},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}
```
kegg-ingest 0.0.1
kegg-ingest
License: MIT
Requires Python: <4.0,>=3.9

```markdown
# kegg-ingest CLI

`kegg-ingest` is a command line interface for interacting with the KEGG database. This tool allows you to fetch, process, and manage data from KEGG.

## Installation

To install `kegg-ingest`, use pip:

```sh
pip install kegg-ingest
```

## Commands

### `get`

Fetch and process data from KEGG.

**Usage:**

```sh
kegg-ingest get --db <database> [--batch-size <size>] [--use-kegg/--no-use-kegg] [--output <file>]
```

**Options:**

- `--db`: Database to use (required).
- `--batch-size, -b`: Batch size for processing (default: 10, max: 10).
- `--use-kegg/--no-use-kegg`: Use KEGG API to fetch data (default: True). Alternatively uses [`bioservices`](https://github.com/cokelaer/bioservices)
- `--output, -o`: Output file to write to (tsv format).

**Example:**

```sh
kegg-ingest get --db pathway --batch-size 5 --use-kegg --output output.tsv
```

### `clear-db`

Clear the entire database.

**Usage:**

```sh
kegg-ingest clear-db
```

### `drop`

Drop a specific table from the database.

**Usage:**

```sh
kegg-ingest drop <table_name>
```

**Arguments:**

- `table_name`: Name of the table to drop.

**Example:**

```sh
kegg-ingest drop pathway_table
```

### `preview`

Show the contents of a table.

**Usage:**

```sh
kegg-ingest preview <table_name> [--limit <number>]
```

**Arguments:**

- `table_name`: Name of the table to preview.

**Options:**

- `--limit`: Number of rows to preview (default: 5).

**Example:**

```sh
kegg-ingest preview pathway_table --limit 10
```

### `overview`

Print an overview of the database.

**Usage:**

```sh
kegg-ingest overview
```

### `query`

Run a query on the database.

**Usage:**

```sh
kegg-ingest query <query_text>
```

**Arguments:**

- `query_text`: SQL query to run.

**Example:**

```sh
kegg-ingest query "SELECT * FROM pathway_table WHERE description LIKE '%metabolism%'"
```

## License

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


---
# Acknowledgements

This [cookiecutter](https://cookiecutter.readthedocs.io/en/stable/README.html) project was developed from the [monarch-project-template](https://github.com/monarch-initiative/monarch-project-template) template and will be kept up-to-date using [cruft](https://cruft.github.io/cruft/).