semvec/pypi-packages huggingface dataset:
networkd 0.1.14
A package to quickly embed and analyze abstract data types!
License: MIT
Requires Python: <4.0,>=3.9

# networkd

Hi! Thanks for checking out networkd. networkd seamlessly builds bipartite networks/graphs. Examples of data in bipartite form include songs in playlists or users and the movies they watch. These data are very popular in studying consumer trends, building recommendation engines, and other research related to network science. However, no package in Python seems to seamlessly and efficiently embed such data into a graph/network in order to summarize the relationship between categories (songs, users) with respect to their entities (playlists, movies). networkd fills this gap by making the network/graph building process efficient and seamless!

See Usage below for an example case of how to quickly embed the data into a co-occurence matrix using the embed class. More functionality to come! 

## Installation

```bash
$ pip install networkd
```

## Usage

import networkd as nd
import pandas as pd

#create pandas data frame of bipartite data. (also accepts dictionary)

data = pd.DataFrame({
    0: ['cat1', 'cat1', 'cat2', 'cat3', 'cat2', 'cat3'],
    1: ['ent1', 'ent2', 'ent1', 'ent3', 'ent2', 'ent1'],
    2: [2, 3, 4, 5, 7, 9]
}) 

#create co-occurrence matrix (output is pandas df)

nd.embed(data, self_loops = False)

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`networkd` was created by Lorenzo Giamartino. It is licensed under the terms of the MIT license.

## Credits

`networkd` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
agi 0.2.1
A Python client library for interacting with the General Reasoning platform
License: MIT
Requires Python: >=3.7

# AGI Client

A Python client library for interacting with the General Reasoning platform API.

The documentation is available [here](https://gr.inc/docs).

## Installation

You can install the package using pip:

```bash
pip install agi
```

## Making API Calls

Obtain an API key from [the website](https://gr.inc). Then:

```python
import agi

client = agi.Client(api_key=YOUR_API_KEY)

# Download reasoning traces and verifications as a .jsonl
client.data.get(task='math-word-problems', model='DeepSeek-R1')
```

## Evaluating your reasoning model

The full evaluation guide is available [here](https://gr.inc/docs/evaluations). A boilerplate example is shown below:

```python
import agi
                
client = agi.Client("YOUR_API_KEY")
model = "USERNAME/MODEL_NAME"

# Retrieve test questions
data = client.evals.get(
    task='mathematical-brainteasers', 
    split='test'
)
question = data['questions']

# Submit model evaluations
for question in questions:
    reasoning_trace, answer = MyModel(
        system_prompt=question['system_prompt'],
        question=question['text']
    ) # Replace with your model logic
    client.evals.submit(
        id=question['id'],
        model=model,
        reasoning_trace=reasoning_trace,
        answer=answer
    )
```

## Running our verification and grading models

<!-- TODO: Add link to ther verifier and grader blogpost -->
Read more about our grader and verifier [here](https://gr.inc/)

```python
import agi 
 
client = agi.Client(api_key=YOUR_API_KEY)
 
response = client.verify.create(
  model="endeavour-preview",
  question="Are Dyson spheres feasible to build?",
  model_answer="Yes"
)
```

```python
import agi 
 
client = agi.Client(api_key=YOUR_API_KEY)
 
response = client.grade.create(
  model="endeavour-preview",
  question="Are Dyson spheres feasible to build?",
  model_answer="Yes",
  ground_truth_answer="No",
)
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.