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zilean's documentation
======================

.. toctree::
   :maxdepth: 2
   :caption: Contents:

   snapshots
   timeline_crawler
   zilean
   developer

``zilean`` is designed to facilitate data analysis of the Riot
`MatchTimelineDto <https://developer.riotgames.com/apis#match-v5/GET_getTimeline>`__.
The ``MatchTimelineDto`` is a powerful object that contains information
of a specific `League of Legends <https://leagueoflegends.com/>`__ match
at **every minute mark**. Naturally, the ``MatchTimelineDto`` became an
**ideal object for various machine learning tasks**. For example,
predicting match results using game statistics before the 16 minute
mark.

Different from traditional sports, esports such as League of Legends has
an innate advantage with respect to the data collection process. Since
every play was conducted digitally, it opened up a huge potential to
explore and perform all kinds of data analysis. ``zilean`` hopes to
explore the infinite potentials provided by the `Riot Games
API <https://developer.riotgames.com/>`__, **and through the power of
computing, make our community a better place.**

GL;HF!

Demo
----

Here is a quick look of how to do League of Legends data analysis with
``zilean``

.. code:: python

   from zilean import TimelineCrawler, SnapShots, read_api_key
   import pandas as pd

   # Use the TimelineCrawler to fetch `MatchTimelineDto`s 
   # from Riot. The `MatchTimelineDto`s have game stats 
   # at each minute mark.

   # We need a API key to fetch data. See the Riot Developer
   # Portal for more info.
   api_key = read_api_key(you_api_key_here)
   # Crawl 2000 Diamond RANKED_SOLO_5x5 timelines from the Korean server.
   crawler = TimelineCrawler(api_key, region="kr", 
                             tier="DIAMOND", queue="RANKED_SOLO_5x5")
   result = crawler.crawl(2000, match_per_id=30, file="results.json")
   # This will take a long time!

   # We will look at the player statistics at 10 and 15 minute mark.
   snaps = SnapShots(result, frames=[10, 15])

   # Store the player statistics using in a pandas DataFrame
   player_stats = snaps.summary(per_frame=True)
   data = pd.DataFrame(player_stats) 

   # Look at the distribution of totalGold difference for `player 0` (TOP player)
   # at 15 minutes mark.
   sns.displot(x="totalGold_0", data=data[data['frame'] == 15], hue="win")

.. figure:: ../demo_1.png
   :alt: demo_1.png

   demo_1.png

Here is an example of some quick machine learning.

.. code:: python

   # Do some simple modelling
   from sklearn.model_selection import train_test_split
   from sklearn.ensemble import RandomForestClassifier

   # Define X and y for training data
   train, test = train_test_split(player_stats, test_size=0.33)
   X_train = train.drop(["matchId", "win"], axis=1)
   y_train = train["win"].astype(int)

   # Build a default random forest classifier
   rf = RandomForestClassifier()
   rf.fit(X_train, y_train)
   y_fitted = rf.predict(X_train)
   print(f"Training accuracy: {mean(y_train == y_fitted)}")

Indices and tables
------------------

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`