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dotastuff's Issues

Improve performance of column shuffle

Right now we use a crazyshuffle() method that uses numpy and Python for-loops. Can we make this faster using jnp? It's considerably slowing down our training pipeline.

When building dataset, do not use str()

Python's str() function ruins the JSON and formats the string in a Python-friendly way (eg. single quotes). I think we're supposed to use json.dump() or json.dumps()?

Look at match seq num 5125140861 with the Dire team name I'm bored

Create one embedding table for heroes and one for accounts

Currently I'm accidentally creating 10 embedding tables for heroes and 10 embedding tables for accounts.

This is incorrect and we probably waste a lot of time (10x!) training each separate table instead of learning jointly. We should figure out how to fix this.

Investigate missing account numbers

The default anonymous account number is 4294967295 (0xFFFFFFFF). For some reason, some players have no account number at all. Currently we're just assigning them the default and moving on but we should try to better understand why we're seeing this.

Example:
https://www.dotabuff.com/matches/6123672329

Error: 'account_id'
{"players": [{"account_id": 4294967295, "player_slot": 0, "hero_id": 121, "leaver_status": 0}, {"account_id": 4294967295, "player_slot": 1, "hero_id": 14, "leaver_status": 0}, {"account_id": 4294967295, "player_slot": 2, "hero_id": 114, "leaver_status": 0}, {"account_id": 4294967295, "player_slot": 3, "hero_id": 8, "leaver_status": 0}, {"account_id": 4294967295, "player_slot": 128, "hero_id": 50, "leaver_status": 0}, {"account_id": 301406649, "player_slot": 129, "hero_id": 101, "leaver_status": 0}, {"account_id": 200676327, "player_slot": 130, "hero_id": 11, "leaver_status": 0}, {"account_id": 4294967295, "player_slot": 131, "hero_id": 59, "leaver_status": 0}, {"account_id": 4294967295, "player_slot": 132, "hero_id": 120, "leaver_status": 0}, {"player_slot": 4, "hero_id": 35, "hero_damage": 0, "tower_damage": 0, "hero_healing": 0, "gold": 0, "gold_spent": 0, "scaled_hero_damage": 0, "scaled_tower_damage": 0, "scaled_hero_healing": 0}], "radiant_win": true, "duration": 1946, "pre_game_duration": 90, "start_time": 1628309333, "match_id": 6123672329, "match_seq_num": 5125154990, "cluster": 117, "first_blood_time": 2, "lobby_type": 0, "human_players": 10, "leagueid": 0, "game_mode": 22, "flags": 1, "engine": 1}

Consider shuffling hero columns during training.

We could shuffle the heros on each team (or even shuffle the teams and invert radiant_win). This could be a useful form of regularization. It's unclear how much this will be needed if we just get more data.

Investigate `float64` in account embedding columns

For some reason, one of the tables is incorrectly float64.

df.dtypes

hero0              int64
hero1              int64
hero2              int64
hero3              int64
hero4              int64
hero5              int64
hero6              int64
hero7              int64
hero8              int64
hero9              int64
account0           int64
account1           int64
account2           int64
account3           int64
account4           int64
account5           int64
account6           int64
account7           int64
account8           int64
account9           int64
start_time         int64
radiant_win      float64
account_emb_0      int64
account_emb_1      int64
account_emb_2      int64
account_emb_3      int64
account_emb_4      int64
account_emb_5      int64
account_emb_6      int64
account_emb_7    float64
account_emb_8      int64
account_emb_9      int64
dtype: object

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