semvec/pypi-packages huggingface dataset:
mkmsdk 0.6.0
MagicKardMarket sdk
License: MIT
Keywords: mkm magickardmarket magiccardmarket sdk mtg magic the gathering card market rest tcg trading card game pokemon wow world of warcraft yugioh

# Magic Kard Market Python SDK

[![image](https://badge.fury.io/py/mkmsdk.png)](http://badge.fury.io/py/mkmsdk)

[![image](https://readthedocs.org/projects/mkm-sdk/badge/?version=latest)](http://mkm-sdk.readthedocs.org/en/latest/)

[![image](https://coveralls.io/repos/evonove/mkm-sdk/badge.svg)](https://coveralls.io/r/evonove/mkm-sdk)

[![image](https://travis-ci.org/evonove/mkm-sdk.svg)](https://travis-ci.org/evonove/mkm-sdk)

A simple SDK for dedicated and widget apps working with Magic Kard
Market.

# Contributing

Feel free to contribute! Submit [a PR following the
guidelines](https://mkm-sdk.readthedocs.io/en/latest/contributing.html#pull-request-guidelines/)
and it will be alright.

# Requirements

-   Python 3.8, 3.9, 3.10, 3.11
-   [Requests](http://docs.python-requests.org/)
-   [Requests_OAuthlib](https://github.com/requests/requests-oauthlib/)

# Setup

From the command line:

    pip install mkmsdk

For the SDK to work properly you need to create four environment
variables holding the tokens necessary to create the authorization to
make requests. You can find them in your Magic Kard Market account page
under the apps section.

-   `MKM_APP_TOKEN`
-   `MKM_APP_SECRET`
-   `MKM_ACCESS_TOKEN`
-   `MKM_ACCESS_TOKEN_SECRET`

[MKM_ACCESS_TOKEN]{.title-ref} and [MKM_ACCESS_TOKEN_SECRET]{.title-ref}
need to be set to empty string if you want to use a widget app.

# Usage

First thing to do is import the [Mkm]{.title-ref} class and the API map:

    from mkmsdk.mkm import Mkm
    from mkmsdk.api_map import _API_MAP

Instance a new instance of Mkm:

    # Using API v1.1
    mkm = Mkm(_API_MAP["1.1"]["api"], _API_MAP["1.1"]["api_root"])
    # Using API v2.0
    mkm = Mkm(_API_MAP["2.0"]["api"], _API_MAP["2.0"]["api_root"])

If you want to test on Magic Card Market\'s sandbox you must use the
sandbox root endpoint:

    mkm_sandbox = Mkm(_API_MAP["2.0"]["api"], _API_MAP["2.0"]["api_sandbox_root"])

To make a request:

    response = mkm.account_management.account()

    # Formats an endpoint
    response = mkm.market_place.user(user='SampleUser')

    # Call endpoint with specified parameters
    response = mkm.account_management.vacation(params={"onVacation": "false"})

This will return a
[Response](http://docs.python-requests.org/en/latest/api/?highlight=response#requests.Response/)
object that contains the response from the server.

Note that only [market_place]{.title-ref} requests work when using a
widget app.

To get a json you can call response.json().

# Tests

Integration tests will be skipped if the four environment variables are
not set.

-   `MKM_APP_TOKEN`
-   `MKM_APP_SECRET`
-   `MKM_ACCESS_TOKEN`
-   `MKM_ACCESS_TOKEN_SECRET`

Note that some tests will be skipped depending if
[MKM_ACCESS_TOKEN]{.title-ref} and [MKM_ACCESS_TOKEN_SECRET]{.title-ref}
are empty strings or not.
mlpj 0.3.0
Tools for machine learning projects
License: MIT

# mlpj: Tools for machine learning projects

Installation of the [PyPi package](https://pypi.org/project/mlpj):

  `pip install -U mlpj`

Contents of this repository:
* Utilities and convenience functions for various libraries and purposes:
  * [python_utils](mlpj/python_utils.py): for the Python standard library
    * functions for basic datatypes
    * functions for filepaths and temporary files
    * functions on input and output streams
    * functions for printing to the console
    * date functions
  * [numpy_utils](mlpj/numpy_utils.py): for `numpy`
  * [pandas_utils](mlpj/pandas_utils.py): for `pandas`
    * functions to handle dataframe columns and their names
    * functions to handle undefined values
    * other dataframe convenience functions (e.g. for special cases)
    * fast groupby transform of multiple columns
    * functions to describe the contents of dataframes and series
    * many datetime convenience functions
      * e.g. add missing days to a multi-index
  * [stats_utils](mlpj/stats_utils.py): for statistical modeling
    * negative-binomial (Gamma-Poisson) distributions and overdispersion
      estimation
  * [plot_utils](mlpj/plot_utils.py): for `matplotlib`
    * histograms
    * profile plots
  * [timeseries_utils](mlpj/timeseries_utils.py): for timeseries models
  * [ml_utils](mlpj/ml_utils.py): for `sklearn` and other standard machine
    learning libraries
    * types (Protocols) for sklearn estimators and transformers
    * Find an enclosed estimator or transformer within a meta-estimator.
    * functions to print analyses of certain kinds of trained models
    * meta-estimators and meta-transformers
* [project_utils](mlpj/project_utils.py): project management utilities
  * [actions_looper](mlpj/actions_looper.py): Execute selected parts of your
    program based on persisted results of earlier steps. Together with the
    functionality mentioned below, this is meant as an alternative to Jupyter
    notebooks that integrates more seamlessly with reuse of code and
    test-driven development (TDD) while still being fairly interactive.
  * [result_display](mlpj/result_display.py): Collect textual and numerical
    results and plots on HTML pages.
megaladon 0.0.2
Megaladon - Pytorch
License: MIT
Requires Python: >=3.6,<4.0
Keywords: artificial intelligence,attention mechanism,transformers

# Megaladon
Sail the Seas of Data and Dive into the Depths of Computation!  
![Megaladon](megaladon.jpeg)

## _Megaladon: The Orca is no match._

Welcome aboard, shipmates! Let Megaladon be your steadfast vessel on the uncharted ocean of Big Data and Machine Learning. With our rich datasets and detailed mathematical models, there's no computational storm we can't weather together! 

> "We didn't name it 'Megaladon' for nothing. It's big, it's powerful, and it's got a heck of a bite when it comes to data crunching!" - Captain Codebeard

## Navigation Chart

```python
Dataset Sample (Row by Row) [Input] --(Feeds into)--> [Model] --(Outputs)--> Dataset Sample (Row by Row)
```

# Deck Logs (Changelog)

- **v2.0.0 - The Leviathan Update** - We've surfaced some serious computational power in this one! Modular integration of HuggingFace and OpenAI models.
- **v1.5.0 - The Kraken Patch** - Tightened up the tentacles of the code. Fewer bugs will be slipping through!
- **v1.0.0 - Maiden Voyage** - Initial launch! The Megaladon sets sail!

# Shipwright's Guide (Installation)

Batten down the hatches and ready your terminal, it's time to summon the Megaladon:

```bash
git clone https://github.com/Megaladon-ds/Megaladon.git
cd Megaladon
pip install -r requirements.txt
```

# Navigational Tools (Usage)

1. **Start your voyage with a good map.** (Load your dataset)

```python
from Megaladon import Megaladon

# Using OpenAI model
Megaladon = Megaladon(model_id="gpt-3", api_key="your-api-key", dataset="flax-sentence-embeddings/stackexchange_math_jsonl")

# Using Hugging Face model
Megaladon = Megaladon(model_id="gpt2", dataset="flax-sentence-embeddings/stackexchange_math_jsonl")
```

2. **Set sail!** (Generate explanations)

```python
explanations = Megaladon.run()
```

3. **Return to port.** (Save your results)

```python
Megaladon.save_to_huggingface(explanations, 'hf_output_dir')
```

Please replace `"your-api-key"` with your actual OpenAI API key, and `'hf_output_dir'` with your desired output directory for the Hugging Face datasets.

# Lifeboats (Support)

If you find yourself overboard in a sea of confusion, don't panic! Shoot a flare to our issue tracker on Github, and our dedicated crew will row to your rescue. 

[Create New Issue](https://github.com/Megaladon-ds/Megaladon/issues/new)

# Crow's Nest (Future Plans)

1. **New Species Detection** - We're constantly exploring unknown waters to find and integrate new algorithms and data models into Megaladon. 
2. **Crew Training** - Comprehensive documentation and examples are on the horizon to help you get the most out of your voyage with Megaladon.

Thank you for choosing to sail with Megaladon. May fair winds and calm seas guide your data journey!

Happy Sailing!

The Megaladon Team



# Todo:

* Better prompt
* More seamless model handling, plug and play with any model from OpenAI or HuggingFace.
* Save to HuggingFace after each iteration is labeled
* Potentially use Parquet for optimized storage
* Add in polymorphic or shape shifting preprocessing logic