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kasparov.py
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kasparov.py
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# Write-up: http://techn.ology.net/investigating-kasparov-chess-moves-with-scikit-learn/
import chess # https://python-chess.readthedocs.io/en/latest/
import chess.pgn
import re
import csv
import os
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import metrics
import matplotlib.pyplot as plt
def number_of_moves(node):
i = 0
while not node.is_end():
i = i + 1
next_node = node.variations[0]
node = next_node
return i
def get_nth_move(node, n):
i = 0
move = ''
while not node.is_end():
i = i + 1
next_node = node.variations[0]
if i == n:
move = node.board().san(next_node.move)
break
node = next_node
return move
def board_transform(b):
fen = b.fen().split(' ')[0].split('/')
values = {
'p' : 10,
'r' : 11,
'n' : 12,
'b' : 13,
'q' : 14,
'k' : 15,
'P' : 20,
'R' : 21,
'N' : 22,
'B' : 23,
'Q' : 24,
'K' : 25
}
transformed = []
for row in fen:
for r in list(row):
if (re.search('^\d+$', r)):
for i in range(1, int(r) + 1):
transformed.append('0')
else:
transformed.append(str(values.get(r)))
return transformed
def get_pgn(path):
file_list = []
for fn in os.listdir(path):
file_list.append(fn)
return file_list
def create_csv(threshold):
threshold = threshold * 2
# Write CSV
with open('kasparov.csv', 'wb') as csvfile:
fh = csv.writer(csvfile)
# Get the files to parse
path = 'pgn'
pgn_files = get_pgn(path)
for pgn_file in pgn_files:
# Read the game from the given PGN file
file_name = path + '/' + pgn_file
#print 'Reading: ' + file_name
with open(file_name) as pgn:
game = chess.pgn.read_game(pgn)
# Decide what player is Kasperov
if 'Kasparov' in game.headers['White']:
player = 1
else:
player = 0
# Get the number of game moves
moves = number_of_moves(game)
# Restrict to threshold
if (threshold > 0) and (moves >= threshold):
moves = threshold
# Get a new board
board = chess.Board()
# For each move...
for i in range(1, moves + 1):
move = get_nth_move(game, i)
# Write the CSV transformed board for the player move
if (player and (i % 2)) or (not(player) and not(i % 2)):
t = board_transform(board)
# Prepend the move number
t.insert(0, i)
# Add the subsequent move
t.append(move)
fh.writerow(t)
board.push_san(move) # Make the move
def train():
data = pd.read_csv('kasparov.csv', header=None)
X = data.loc[:, 0:list(data)[-2]]
y = data.loc[:, list(data)[-1]]
#print type(X), X.shape
#print type(y), y.shape
X_train, X_test, y_train, y_test = train_test_split(X, y)
#print X_train.shape, y_train.shape
#print X_test.shape, y_test.shape
return X_train, X_test, y_train, y_test
def decision_tree(X_train, X_test, y_train, y_test):
decisiontree = DecisionTreeClassifier()
model = decisiontree.fit(X_train, y_train)
y_pred = model.predict(X_test)
return metrics.accuracy_score(y_test, y_pred)
create_csv(0)
X_train, X_test, y_train, y_test = train()
def trial_and_error():
# K Nearest Neighbors
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
print(metrics.accuracy_score(y_test, y_pred)) # 0.12101669195751139 for all moves in game
# Better k?
k_range = range(1, 26)
scores = []
for k in k_range:
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
scores.append(metrics.accuracy_score(y_test, y_pred))
plt.plot(k_range, scores)
plt.xlabel('k')
plt.ylabel('Accuracy')
plt.show()
# Multinomial Naive Bayes
from sklearn.naive_bayes import MultinomialNB
nb = MultinomialNB()
nb.fit(X_train, y_train)
y_pred = nb.predict(X_test)
print(metrics.accuracy_score(y_test, y_pred)) # 0.07169954476479515 for all moves in game
# Support Vector Machine
from sklearn import svm
classifier = svm.SVC(gamma=0.001)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
print(metrics.accuracy_score(y_test, y_pred)) # 0.15440060698027314 for all moves in game
# Decision Tree
from sklearn.tree import DecisionTreeClassifier
accuracy = decision_tree(X_train, X_test, y_train, y_test)
print(accuracy) # 0.17071320182094082 for all moves in game
create_csv(3)
X_train, X_test, y_train, y_test = train()
accuracy = decision_tree(X_train, X_test, y_train, y_test)
print(accuracy)
create_csv(6)
X_train, X_test, y_train, y_test = train()
accuracy = decision_tree(X_train, X_test, y_train, y_test)
print(accuracy)
create_csv(12)
X_train, X_test, y_train, y_test = train()
accuracy = decision_tree(X_train, X_test, y_train, y_test)
print(accuracy)
k_range = range(1, 15)
scores = []
for k in k_range:
create_csv(k)
X_train, X_test, y_train, y_test = train()
accuracy = decision_tree(X_train, X_test, y_train, y_test)
print(accuracy)
scores.append(accuracy)
plt.plot(k_range, scores)
plt.xlabel('Moves')
plt.ylabel('Accuracy')
plt.show()