Metadata-Version: 2.1
Name: labgsheet
Version: 0.1.1
Summary: A python library to note ml experiments on google sheet
Home-page: https://github.com/shotarok/labgsheet
Author: Shotaro Kohama
Author-email: khmshtr28@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: Implementation :: PyPy
Description-Content-Type: text/markdown
Requires-Dist: gspread

# labgsheet: Labnotes on Google Sheet

[![Build Status](https://travis-ci.org/shotarok/labgsheet.svg?branch=master)](https://travis-ci.org/shotarok/labgsheet)

A python library to note ml experiments on google sheet.

`labgsheet` provides an easy way to note ml experiments on Google Sheet.

## At a Glance

You can use `labgsheet` in cosole like following:

```python
# prepare for worksheet by gspread
>>> import gspread
>>> from oauth2client.service_account import ServiceAccountCredentials
>>> scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
# download credentials.json previously from Google Developers Console
>>> credentials = ServiceAccountCredentials.from_json_keyfile_name('credentials.json', scope)
>>> gc = gspread.authorize(credentials)
>>> ws = gc.create("Test for labgsheets").sheet1
# note an experiment where params and a metric are used
>>> from labgsheet import Experiment
>>> exp = Experiment(ws)
>>> exp.log_multi_params({'l1': 0.5, 'C': 10})
>>> exp.log_metric('aupr', 0.2345)
```

You can also use `labgsheet` in [Google Colaboratory](colab.research.google.com) like following:

```python
! pip install labgsheet
! pip install --upgrade -q gspread

from google.colab import auth
auth.authenticate_user()

import gspread
from oauth2client.client import GoogleCredentials

gc = gspread.authorize(GoogleCredentials.get_application_default())
ws = gc.create("Test for labgsheets").sheet1

from labgsheet import Experiment
exp = Experiment(ws)
exp.log_multi_params({'l1': 0.5, 'C': 10})
exp.log_metric('aupr', 0.2345)
```

After logging, you can get a google sheet like below:

![image](https://user-images.githubusercontent.com/1156179/39610145-551243a6-4f89-11e8-9e56-e52057e173e0.png)

## Installation

To install `labgsheet`, use pipenv (or pip):

```shell
$ pipenv install labgsheet
```

## Contribution

1. Fork
2. Create a feature branch
3. Commit your changes
4. Rebase your local changes against the master branch
5. Create new Pull Request

## License

[MIT](https://github.com/shotarok/labgsheet/blob/master/LICENSE)

## Author

[Shotaro Kohama](https://github.com/shotarok)

