Datapot | Usage | Examples | Features | Authors

Open source tool for machine learning on semi-structured data that creates numeric object-feature matrix from JSON. The idea of Datapot is to make the process of data preparation and feature extraction automatic, easy and effective.

Install Datapot:

$ git clone https://github.com/bashalex/datapot.git
$ cd datapot
$ pip install .

To create a Datapot object simply write the following:

>>> import datapot as dp 
>>> data = dp.DataPot()

• fit()
• transform()

Method fit(self, data, limit) goes through the first N objects (N = limit), passes the possible features to Transformers. Each Transformer evaluates if a feature from current field or a number of fields can be created. As a result a dict of features and Transformers is created.

To apply fit() to JSON file:

>>> f = open('data/matches_test.jsonlines', 'r')
>>> data.fit(f, limit=100)
>>> data
DataPot class instance
 - number of features without transformation: 806
 - number of new features: 315
features to transform: 
    (u'players.0.gold_t', [ComplexTransformer])
    (u'picks_bans.0.is_pick', [BoolToIntTransformer])
    (u'players.0.kills_log.0.unit', [TfidfTransformer])
    (u'players.1.xp_t', [ComplexTransformer])
    (u'picks_bans.1.is_pick', [BoolToIntTransformer])
    (u'players.1.kills_log.0.unit', [TfidfTransformer])
    ...

Method transform(self, data, verbose) generates a pandas. DataFrame with new features that were detected on the fit() call. If parameter verbose is true, progress description is printed during the feature extraction.

>>> df = data.transform(f, verbose=False)
fit transformers...OK
num of new features: 315

Look for more examples of using Datapot with different datasets and more Transformer specific.

Datapot provides many ways of extracting features from JSON-s.

Data types that can be processed:

• Boolean
• Numerical array (transform array to their sum divided by average length of array in training set)
• Time series (сalculate descriptive statistical properties of a given time series)
• Timestamp (date, time, day of week, day of month etc.)
• Text (bag of words tf-idf, word2vec)
• Categorial (one-hot encoding, dimension reduction)

• Alex Bash
• Yuriy Mokriy
• Nikita Savelyev
• Michal Rozenwald
• Peter Romov

Datapot is a course work project of the Faculty of Computer Science of the Higher School of Economics.