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train-ml-bot.py
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train-ml-bot.py
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"""
Train a machine learning model for the classifier bot. We create a player, and watch it play games against itself.
Every observed state is converted to a feature vector and labeled with the eventual outcome
(-1.0: player 2 won, 1.0: player 1 won)
This is part of the second worksheet.
"""
from api import State, util
# This package contains various machine learning algorithms
import sys
import sklearn
import sklearn.linear_model
from sklearn.externals import joblib
from bots.rand import rand
# from bots.alphabeta import alphabeta
from bots.ml import ml
from bots.ml.ml import features
import matplotlib.pyplot as plt
# How many games to play
GAMES = 1000
# Number of planets in the field
NUM_PLANETS = 6
# Maximum number of turns to play
NUM_TURNS = 100
# Train for symmetric start states
SYM = True
# The player we'll observe
player = rand.Bot()
# player = alphabeta.Bot()
data = []
target = []
for g in range(GAMES):
state, id = State.generate(NUM_PLANETS, symmetric=SYM)
state_vectors = []
i = 0
while not state.finished() and i <= NUM_TURNS:
state_vectors.append(features(state))
move = player.get_move(state)
state = state.next(move)
i += 1
winner = state.winner()
for state_vector in state_vectors:
data.append(state_vector)
if winner == 1:
result = 'won'
elif winner == 2:
result = 'lost'
else:
result = 'draw'
target.append(result)
sys.stdout.write(".")
sys.stdout.flush()
if g % (GAMES/10) == 0:
print("")
print('game {} finished ({}%)'.format(g, (g/float(GAMES)*100)))
# Train a logistic regression model
learner = sklearn.linear_model.LogisticRegression()
model = learner.fit(data, target)
# Check for class imbalance
count = {}
for str in target:
if str not in count:
count[str] = 0
count[str] += 1
print('instances per class: {}'.format(count))
# Store the model in the ml directory
joblib.dump(model, './bots/ml/model.pkl')
print('Done')