semvec/pypi-packages huggingface dataset:
fluidai-sanatio 2.2.3
Run different validation tests on machine learning models.
Requires Python: >=3.6

# FluidAI - Sanatio

## Sanatio

> sanatio(Latin) OR validation(English) <br>
>> noun <br>
>> 1. the action of checking or proving the validity or accuracy of something.
>> 2. the action of making or declaring something legally or officially acceptable.
>> 3. recognition or affirmation that a person or their feelings or opinions are valid or worthwhile.

The FluidAI - Sanatio package provides functions to perform different types of validation functions, the results of
which can be used for ensuring the machine learning model trained is robust and the accuracy levels attained are
dependable. <br>

## Version 2.1.0 updates

1. Code Generation modules
2. CF model training modules
3. Correlation structure in routines

## Features in Sanatio

| Feature   |
|----------|
|Validation Reports |
|Enacting recommendation |
|Automl Module |
|Code Generation |
|Collaborative model building | 
|Fillna Module (Under Progress) |

## User Docs

https://fluid-ai.gitbook.io/fluid-ai-sanatio

### Current directory structure

```
sanatio/
├───authentication_server/
│   ├───authentication_server.py   
│   ├───utils/
│   │   ├───db_credential.json        # credential file
│   │   ├───utils.py
│   │   └───__init__.py
│   └───__init__.py
├───code_generation/
│       ├───generate_code.py        # codes to generate new cell with functions defined in the below files
│       ├───matrics_codes.py
│       └───__init__.py
├───jupyter_ui_menu/
│       ├───jupyter_menu.py          #for generating interactive dropdown menu for all sanatio features
│       ├───jupyter_codegen_menu.py  #for generating interactive dropdown menu for code generation
│       └───__init__.py
├───training/
│   ├───automl/                       # pipeline generation functions
│   │   ├───pipeline_generation.py
│   │   └───__init__.py
│   └───__init__.py
│   ├───recommsys/                       # recommendation for CF functions
│   │   ├───cf.py
│   └───__init__.py
├───validations/
│   ├───db_credential.json
│   ├───graphs/                       # plotting functions
│   │   ├───graphs.py
│   │   └───__init__.py                   
│   ├───routines/                     # folder has different routines 
│   │   ├───binary_logistic_regression_routine.py 
│   │   ├───helper_routines.py        # has the base routine and validation routine class that calls different routines
│   │   ├───linear_regression_routine.py
│   │   ├───tree_based_classification_routine.py
│   │   └───__init__.py
│   ├───sanatio_enact.py              # code to enact sanatio recommendations
│   ├───session_authentication.py     # code to authenticate with main server  
│   ├───stats/
│   │   ├───stats.py                  # plotting functions
|   |   |───correlation_structure.py
│   │   └───__init__.py
│   └───__init__.py
├───validation_tests/                 # test cases
│   ├───test_authentication.py
│   ├───test_automl.py
│   ├───test_enact.py
│   ├───test_graphs.py
│   ├───test_routines.py
│   ├───test_stats.py
│   └───__init__.py
└───__init__.py
```

*Note*: _Subject to future changes in the file structure._
httpkom 0.21
REST-like HTTP API for LysKOM
Requires Python: >=3.7

httpkom
=======

httpkom is an HTTP proxy for LysKOM protocol A servers, and exposes an
REST-like HTTP API. It can for example be used for writing LysKOM
clients in Javascript.

The source code can be found at: https://github.com/osks/httpkom

Packages are published on PyPI: https://pypi.org/project/httpkom/

The documentation can be found at: http://osks.github.io/httpkom/

httpkom uses `pylyskom <https://github.com/osks/pylyskom>`_, which
is also released under GPL.


Dependencies
------------

For required Python packages, see requirements.txt. Install them with::

    $ pip install -r requirements.txt


Development
-----------

Preparing a release
*******************

TODO: How do we update the documentation on github pages?

On master:

1. Update and check CHANGELOG.md.

2. Increment version number and remove ``+dev`` suffix
   IN BOTH ``setup.py`` AND ``httpkom/version.py``!

3. Test manually by using jskom.

4. Commit, push.

5. Tag (annotated) with ``v<version>`` (example: ``v0.1``) and push the tag::

       git tag -a v0.1 -m "Version 0.1"
       git push origin v0.1

6. Build PyPI dist: ``make dist``

7. Push to Test PyPI: ``twine upload --repository testpypi dist/*`` and check
   https://test.pypi.org/project/httpkom/ .

8. Push to PyPI: ``twine upload dist/*`` and check
   https://pypi.org/project/httpkom/ .

9. Add ``+dev`` suffix to version number, commit and push.

10. Github release: Go to https://github.com/osks/httpkom/releases
    and draft a new release. Select tag and set title to "Version
    <version>", and then publish the release.


Tools
*****

Install and update release tools with::

    pip install --upgrade setuptools wheel pip twine

Twine is used for pushing the built dist to PyPI. The examples in the
release process depends on a ``.pypirc`` file with config for the pypi
and testpypi repositories.

Example of ``.pypirc``::

    [pypi]
    username = __token__
    password = pypi-...

    [testpypi]
    repository = https://test.pypi.org/legacy/
    username = __token__
    password = pypi-...


Authors
-------

Oskar Skoog <oskar@osd.se>


Copyright and license
---------------------

Copyright (C) 2012-2022 Oskar Skoog

This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
MA  02110-1301, USA.
marine 0.0.6
Marine: Multi-task learning based on Japanese accent estimation

# **MARINE** : **M**ulti-task lea**R**n**I**ng-based Japa**N**ese accent **E**stimation

[![PyPI](https://img.shields.io/pypi/v/marine.svg)](https://pypi.python.org/pypi/marine)
[![Python package](https://github.com/6gsn/marine/actions/workflows/ci.yml/badge.svg)](https://github.com/6gsn/marine/actions/workflows/ci.yml)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](LICENSE)
<!-- [![DOI](https://zenodo.org/badge/#)](https://zenodo.org/badge/latestdoi/#) -->

`marine` is a tool kit for building the Japanese accent estimation model proposed in [our paper](https://www.isca-speech.org/archive/interspeech_2022/park22b_interspeech.html).

For academic use, please cite the following paper ([IEEE Xplore](https://www.isca-speech.org/archive/interspeech_2022/park22b_interspeech.html)).

```bibtex
@inproceedings{park22b_interspeech,
  author={Byeongseon Park and Ryuichi Yamamoto and Kentaro Tachibana},
  title={{A Unified Accent Estimation Method Based on Multi-Task Learning for Japanese Text-to-Speech}},
  year=2022,
  booktitle={Proc. Interspeech 2022},
  pages={1931--1935},
  doi={10.21437/Interspeech.2022-334}
}
```

## Notice

The model included in this package is trained using [JSUT corpus](https://sites.google.com/site/shinnosuketakamichi/publication/jsut), which is not the same as the dataset in [our paper](https://www.isca-speech.org/archive/interspeech_2022/park22b_interspeech.html). Therefore, the model's performance is also not equal to the performance introduced in our paper.

## Get started

### Install

```shell
$ pip install marine
```

### For develop

```shell
$ pip install -e ".[dev]"
```

### Quick demo

```python
In [1]: from marine.predict import Predictor

In [2]: nodes = [
   ...:     {
   ...:         "surface": "こんにちは",
   ...:         "pos": "感動詞:*:*:*",
   ...:         "pron": "コンニチワ",
   ...:         "c_type": "*",
   ...:         "c_form": "*",
   ...:         "accent_type": 0,
   ...:         "accent_con_type": "-1",
   ...:         "chain_flag": -1
   ...:     }
   ...: ]

In [3]: predictor = Predictor()

In [4]: predictor.predict([nodes])
Out[4]:
{'mora': [['コ', 'ン', 'ニ', 'チ', 'ワ']],
 'intonation_phrase_boundary': [[0, 0, 0, 0, 0]],
 'accent_phrase_boundary': [[0, 0, 0, 0, 0]],
 'accent_status': [[0, 0, 0, 0, 0]]}

In [5]: predictor.predict([nodes], accent_represent_mode="high_low")
Out[5]:
{'mora': [['コ', 'ン', 'ニ', 'チ', 'ワ']],
 'intonation_phrase_boundary': [[0, 0, 0, 0, 0]],
 'accent_phrase_boundary': [[0, 0, 0, 0, 0]],
 'accent_status': [[0, 1, 1, 1, 1]]}
```

### Build model your self

Coming soon

## LICENSE

- marine: Apache_2.0 license ([LICENSE](LICENSE))
- JSUT: CC-BY-SA 4.0 license (Please check [jsut-label/LICENCE.txt](https://github.com/sarulab-speech/jsut-label/blob/master/LICENCE.txt))
nso-bridge 0.1.7
Nintendo Switch Online API bridge (for Nintendo Switch Online API wrapper)
Keywords: Nintendo,Nintendo Switch,Nintendo Switch Online,Nintendo Switch Online API

# Nintendo Switch Online API Bridge

Nintendo Switch Online API bridge (for Nintendo Switch Online API wrapper)

## What is it?

Nintendo Switch Online API Bridge is a simple wrapper. It allows you to easily access the API and get the data you need.

## Installation

```bash
pip install nso-bridge
```

```bash
pip install git+https://github.com/zeroday0619/Nintendo_Switch_Online_API_Bridge.git
```

## Example Code

### GetSelf

Get information of My Nintendo Switch Account.

```python
import json
import keyring

from nso_bridge.nsa import NintendoSwitchAccount
from nso_bridge.nso import NintendoSwitchOnlineAPI

import logging

logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')

app = NintendoSwitchAccount()

# override the nso_app_version
app.nso_app_version = app.get_nso_app_version()
session_token = keyring.get_password("nso-bridge", "session_token")


nso_res = NintendoSwitchOnlineAPI(
    nso_app_version=app.nso_app_version,
    session_token=session_token,
)
nso_res.sync_login()
print(json.dumps(nso_res.getCurrentUser(), indent=4, ensure_ascii=False))

```

### GetFriends

Get information of friends registered to Nintendo Switch account.

```python
import json
import keyring

from nso_bridge.nsa import NintendoSwitchAccount
from nso_bridge.nso import NintendoSwitchOnlineAPI

import logging

logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')

app = NintendoSwitchAccount()

# override the nso_app_version
app.nso_app_version = app.get_nso_app_version()
session_token = keyring.get_password("nso-bridge", "session_token")


nso_res = NintendoSwitchOnlineAPI(
    nso_app_version=app.nso_app_version,
    session_token=session_token,
)
nso_res.sync_login()
print(json.dumps(nso_res.getFriends(), indent=4, ensure_ascii=False))
```