From d5c69ff4ebf38244c7b5d67a5c09a517e09c43f3 Mon Sep 17 00:00:00 2001
From: Vincent Tjeng <vincent.tjeng@gmail.com>
Date: Sun, 8 Nov 2020 10:05:20 -0800
Subject: [PATCH] Remove unused singleprocess/train.py file.

---
 poker_ai/ai/singleprocess/train.py | 127 -----------------------------
 1 file changed, 127 deletions(-)
 delete mode 100644 poker_ai/ai/singleprocess/train.py

diff --git a/poker_ai/ai/singleprocess/train.py b/poker_ai/ai/singleprocess/train.py
deleted file mode 100644
index 4bc147ca..00000000
--- a/poker_ai/ai/singleprocess/train.py
+++ /dev/null
@@ -1,127 +0,0 @@
-"""
-"""
-from __future__ import annotations
-
-import logging
-import random
-from pathlib import Path
-from typing import Dict
-
-import click
-import joblib
-import yaml
-from tqdm import tqdm, trange
-
-from poker_ai.ai.agent import Agent
-from poker_ai.ai import ai
-from poker_ai import utils
-from poker_ai.games.short_deck.state import new_game, ShortDeckPokerState
-
-
-def print_strategy(strategy: Dict[str, Dict[str, int]]):
-    """
-    Print strategy.
-
-    ...
-
-    Parameters
-    ----------
-    strategy : Dict[str, Dict[str, int]]
-        The preflop strategy for our agent.
-    """
-    for info_set, action_to_probabilities in sorted(strategy.items()):
-        norm = sum(list(action_to_probabilities.values()))
-        tqdm.write(f"{info_set}")
-        for action, probability in action_to_probabilities.items():
-            tqdm.write(f"  - {action}: {probability / norm:.2f}")
-
-
-def simple_search(
-    config: Dict[str, int],
-    save_path: Path,
-    lut_path: Union[str, Path],
-    pickle_dir: bool,
-    strategy_interval: int,
-    n_iterations: int,
-    lcfr_threshold: int,
-    discount_interval: int,
-    prune_threshold: int,
-    c: int,
-    n_players: int,
-    dump_iteration: int,
-    update_threshold: int,
-):
-    """
-    Train agent.
-
-    ...
-
-    Parameters
-    ----------
-    config : Dict[str, int],
-        Configurations for the simple search.
-    save_path : str
-        Path to save to.
-    strategy_interval : int
-        Iteration at which to update strategy.
-    n_iterations : int
-        Number of iterations.
-    lcfr_threshold : int
-        Iteration at which to begin linear CFR.
-    discount_interval : int
-        Iteration at which to discount strategy and regret.
-    prune_threshold : int
-        Iteration at which to begin pruning.
-    c : int
-        Floor for regret at which we do not search a node.
-    n_players : int
-        Number of players.
-    dump_iteration : int
-        Iteration at which we begin serialization.
-    update_threshold : int
-        Iteration at which we begin updating strategy.
-    """
-    utils.random.seed(42)
-    agent = Agent(use_manager=False)
-    card_info_lut = {}
-    for t in trange(1, n_iterations + 1, desc="train iter"):
-        if t == 2:
-            logging.disable(logging.DEBUG)
-        for i in range(n_players):  # fixed position i
-            # Create a new state.
-            state: ShortDeckPokerState = new_game(
-                n_players,
-                card_info_lut,
-                lut_path=lut_path,
-                pickle_dir=pickle_dir
-            )
-            card_info_lut = state.card_info_lut
-            if t > update_threshold and t % strategy_interval == 0:
-                ai.update_strategy(agent=agent, state=state, i=i, t=t)
-            if t > prune_threshold:
-                if random.uniform(0, 1) < 0.05:
-                    ai.cfr(agent=agent, state=state, i=i, t=t)
-                else:
-                    ai.cfrp(agent=agent, state=state, i=i, t=t, c=c)
-            else:
-                ai.cfr(agent=agent, state=state, i=i, t=t)
-        if t < lcfr_threshold & t % discount_interval == 0:
-            d = (t / discount_interval) / ((t / discount_interval) + 1)
-            for I in agent.regret.keys():
-                for a in agent.regret[I].keys():
-                    agent.regret[I][a] *= d
-                    agent.strategy[I][a] *= d
-        if (t > update_threshold) & (t % dump_iteration == 0):
-            # dump the current strategy (sigma) throughout training and then
-            # take an average. This allows for estimation of expected value in
-            # leaf nodes later on using modified versions of the blueprint
-            # strategy.
-            ai.serialise(
-                agent=agent, save_path=save_path, t=t, server_state=config,
-            )
-
-    print_strategy(agent.strategy)
-
-
-if __name__ == "__main__":
-    train()