astoeckl

from collections import Counter
import operator

c=Counter(x)
sortlist = c.most_common(150)
topwords = list(map(operator.itemgetter(0), sortlist))
topcounts= list(map(operator.itemgetter(1), sortlist))

maxcount = sortlist[0][1]
mincount = sortlist[149][1]

astoeckl

x = df.explode()
len(set(x))

astoeckl

df=data[0].str.split()
df.head()

astoeckl

import pandas as pd

data = pd.read_csv('2016-06.pgn', header = None)
print(data.shape)
data.head()

astoeckl

import chess.svg

san ='''d4 d5 c4 c6 e3 a6 Nf3 e5 cxd5 e4 Ne5 cxd5 Qa4+ Bd7 Nxd7 Nxd7 Nc3 Nf6 Qb3 Be7 Nxd5 Qa5+ Nc3 O-O Be2 b5 O-O Rad8 
Bd2 Qc7 Rac1 Qd6 Qc2 Qe6 Nb1 Bd6 a3 Nb6 Qc6 Nfd5 Ba5 Rc8 Qb7 Qh6 h3 Nc4 Bxc4 bxc4 Qxd5 Rfd8 Qxe4 Rd7 Bc3 Re7 Qf3 Re6 Nd2 
Rf6 Qg4 Re8 Ne4 Rg6 Qd7 Rf8 Nxd6 Rxd6 Qc7 Rg6 Qh2 Re8 d5 f6 d6 Rd8 Rfd1'''

partie = san.split()

board = chess.Board()

astoeckl

from sklearn.ensemble import RandomForestClassifier

plot = decision_boundaries(X,y,RandomForestClassifier, max_depth= 5, n_estimators = 50)
plot.show()

astoeckl

from sklearn.tree import DecisionTreeClassifier

plot = decision_boundaries(X,y,DecisionTreeClassifier, max_depth= 4)
plot.show()

astoeckl

from sklearn.neural_network import MLPClassifier

plot = decision_boundaries(X,y,MLPClassifier,hidden_layer_sizes=(25,25,25),
      solver='lbfgs',alpha=1e-5,max_iter=3000)
plot.show()

astoeckl

plot = decision_boundaries(X,y,SVC, kernel='poly', degree=10)
plot.show()

astoeckl

plot = decision_boundaries(X,y,SVC, kernel='poly', degree=4)
plot.show()