Learning Embedding of Dota2 Heroes and Items

Methodology

• Data collection:

  • Use dota2py wrapper of Steam Web API
  • Use get_match_history_by_sequence_num to obtain all matches following a sequence number
  • Use DotaMax to obtain match skill level
  • Check whether the math is stored or parsed by yasp. Grab the yasp data if yes.
  • In this experiment, use only All Pick games of all skill levels and at least 25 minutes.
  • In total ~80K matches
  • Run python crawler.py <start_seqnum> <end_seqnum> to crawl data
    • Current crawl to sequence number: 1663750000 on lille

• Learn low-dimension embedding of Dota2 heroes via skip-n gram model.

  • Efficient Estimation of Word Representations in Vector Space
  • Treat each team as a sentence
  • Treat each hero as a word
  • Use uniformly sampled heroes as negative instances

• Learn low-dimension embedding of Dota2 items via skip-n gram model.

  • Treat all items possessed by same player as a sentence
  • Treat each item as a word
  • We learn item embedding only from winning team
  • Use uniformly sampled heroes as negative instances

• visualization:

  • Use different methods in sklearn to project the embedding into 2D
    • TSNE
    • Isomap
    • MDS
    • Spectral
    • PCA

Usage

1. All data are stored in Mongodb in lille.

2. Run following script to learn embedding

    python learn_embedding.py [hero/item] [match filter dict]
   

   Example: use all matches with mode all pick or ranked all picked lasting at least 25 minutes

    python learn_embedding.py item "{'\$or': [{'game_mode': 1}, {'game_mode': 22}], 'duration': {'\$gt': 1500}}"
   

3. Run script to generate visualization webpage Visualize hero embedding:

    python draw_hero_embedding.py embed_weight_hero.pkl mds,tsne,isomap,spectral,pca
   

   Visualize item embedding:

    python draw_item_embedding.py embed_weight_item.pkl mds,tsne,isomap,spectral,pca
   

Hero Embedding Result

Visualization of Embedding

We briefly annotated the visualization result by PCA as follows:

Nearest Neighbor

We query the nearest neighbor of a given hero using the learned embedding.

Hero	Nearest Heroes
	<img src=result/heroes/nature'sprophet.png style="height:30px; width:30px">
	<img src=result/heroes/nature'sprophet.png style="height:30px; width:30px">
<img src=result/heroes/nature'sprophet.png style="height:30px; width:30px">
	<img src=result/heroes/nature'sprophet.png style="height:30px; width:30px">
	<img src=result/heroes/nature'sprophet.png style="height:30px; width:30px">

Item Embedding Result

Visualization of Embedding

Visualization using Isomap

Nearest Neighbor

We query the nearest neighbor of a given item using the learned embedding.

Item	Nearest Items