Dataset : https://archive.ics.uci.edu/ml/datasets/Connect-4
This database contains all legal 8-ply positions in the game of connect-4 in which neither player has won yet, and in which the next move is not forced.
x is the first player; o the second.
The outcome class is the game theoretical value for the first player.
Attribute Information: (x=player x has taken, o=player o has taken, b=blank)
The board is numbered like:
6 . . . . . . .
5 . . . . . . .
4 . . . . . . .
3 . . . . . . .
2 . . . . . . .
1 . . . . . . .
a b c d e f g
1. a1: {x,o,b}
2. a2: {x,o,b}
3. a3: {x,o,b}
4. a4: {x,o,b}
5. a5: {x,o,b}
6. a6: {x,o,b}
7. b1: {x,o,b}
8. b2: {x,o,b}
9. b3: {x,o,b}
10. b4: {x,o,b}
11. b5: {x,o,b}
12. b6: {x,o,b}
13. c1: {x,o,b}
14. c2: {x,o,b}
15. c3: {x,o,b}
16. c4: {x,o,b}
17. c5: {x,o,b}
18. c6: {x,o,b}
19. d1: {x,o,b}
20. d2: {x,o,b}
21. d3: {x,o,b}
22. d4: {x,o,b}
23. d5: {x,o,b}
24. d6: {x,o,b}
25. e1: {x,o,b}
26. e2: {x,o,b}
27. e3: {x,o,b}
28. e4: {x,o,b}
29. e5: {x,o,b}
30. e6: {x,o,b}
31. f1: {x,o,b}
32. f2: {x,o,b}
33. f3: {x,o,b}
34. f4: {x,o,b}
35. f5: {x,o,b}
36. f6: {x,o,b}
37. g1: {x,o,b}
38. g2: {x,o,b}
39. g3: {x,o,b}
40. g4: {x,o,b}
41. g5: {x,o,b}
42. g6: {x,o,b}
43. Class: {win,loss,draw}
In [6]:
import os
from sklearn.tree import DecisionTreeClassifier, export_graphviz
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn import cross_validation, metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import BernoulliNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from time import time
from sklearn import preprocessing
from sklearn.pipeline import Pipeline
from sklearn.metrics import roc_auc_score , classification_report
from sklearn.grid_search import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.metrics import precision_score, recall_score, accuracy_score, classification_report
In [7]:
index=[]
board = ['a','b','c','d','e','f','g']
for i in board:
for j in range(6):
index.append(i + str(j+1))
In [8]:
column_names = index +['Class']
In [9]:
# read .csv from provided dataset
csv_filename="connect-4.data"
# df=pd.read_csv(csv_filename,index_col=0)
df=pd.read_csv(csv_filename,
names= column_names)
In [10]:
df.head()
Out[10]:
In [11]:
df['Class'].unique()
Out[11]:
In [12]:
#Convert animal labels to numbers
le = preprocessing.LabelEncoder()
for col in df.columns:
df[col] = le.fit_transform(df[col])
In [13]:
df.head()
Out[13]:
In [14]:
for col in df.columns:
df[col] = pd.get_dummies(df[col])
In [15]:
df.head()
Out[15]:
In [16]:
X = df[index]
y = df['Class']
In [17]:
X.head()
Out[17]:
In [18]:
# split dataset to 60% training and 40% testing
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.4, random_state=0)
In [22]:
print (X_train.shape, y_train.shape)
In [25]:
features = index
In [26]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import ExtraTreesClassifier
# Build a classification task using 3 informative features
# Build a forest and compute the feature importances
forest = ExtraTreesClassifier(n_estimators=250,
random_state=0)
forest.fit(X, y)
importances = forest.feature_importances_
std = np.std([tree.feature_importances_ for tree in forest.estimators_],
axis=0)
indices = np.argsort(importances)[::-1]
# Print the feature ranking
print("Feature ranking:")
for f in range(X.shape[1]):
print("%d. feature %d - %s (%f) " % (f + 1, indices[f], features[indices[f]], importances[indices[f]]))
# Plot the feature importances of the forest
plt.figure(num=None, figsize=(14, 10), dpi=80, facecolor='w', edgecolor='k')
plt.title("Feature importances")
plt.bar(range(X.shape[1]), importances[indices],
color="r", yerr=std[indices], align="center")
plt.xticks(range(X.shape[1]), indices)
plt.xlim([-1, X.shape[1]])
plt.show()
In [27]:
importances[indices[:5]]
Out[27]:
In [28]:
for f in range(5):
print("%d. feature %d - %s (%f)" % (f + 1, indices[f], features[indices[f]] ,importances[indices[f]]))
In [29]:
best_features = []
for i in indices[:5]:
best_features.append(features[i])
In [30]:
# Plot the top 5 feature importances of the forest
plt.figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')
plt.title("Feature importances")
plt.bar(range(5), importances[indices][:5],
color="r", yerr=std[indices][:5], align="center")
plt.xticks(range(5), best_features)
plt.xlim([-1, 5])
plt.show()
In [31]:
t0=time()
print ("DecisionTree")
dt = DecisionTreeClassifier(min_samples_split=20,random_state=99)
# dt = DecisionTreeClassifier(min_samples_split=20,max_depth=5,random_state=99)
clf_dt=dt.fit(X_train,y_train)
print ("Acurracy: ", clf_dt.score(X_test,y_test))
t1=time()
print ("time elapsed: ", t1-t0)
In [32]:
tt0=time()
print ("cross result========")
scores = cross_validation.cross_val_score(dt, X, y, cv=5)
print (scores)
print (scores.mean())
tt1=time()
print ("time elapsed: ", tt1-tt0)
In [33]:
from sklearn.metrics import classification_report
pipeline = Pipeline([
('clf', DecisionTreeClassifier(criterion='entropy'))
])
parameters = {
'clf__max_depth': (5, 25 , 50),
'clf__min_samples_split': (2, 5, 10),
'clf__min_samples_leaf': (1, 2, 3)
}
grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1, scoring='f1')
grid_search.fit(X_train, y_train)
print ('Best score: %0.3f' % grid_search.best_score_)
print ('Best parameters set:')
best_parameters = grid_search.best_estimator_.get_params()
for param_name in sorted(parameters.keys()):
print ('\t%s: %r' % (param_name, best_parameters[param_name]))
predictions = grid_search.predict(X_test)
print (classification_report(y_test, predictions))
In [35]:
t0=time()
print ("DecisionTree")
dt1 = DecisionTreeClassifier(min_samples_split=2,max_depth=25,min_samples_leaf=1, random_state=99)
clf_dt1=dt1.fit(X_train,y_train)
print ("Acurracy: ", clf_dt1.score(X_test,y_test))
t1=time()
print ("time elapsed: ", t1-t0)
export_graphviz(clf_dt1,
out_file='tree.dot',
feature_names=features)
After we have installed GraphViz on our computer, we can convert the tree.dot file into a PNG file by executing the following command from the command line in the location where we saved the tree.dot file:
dot -Tpng tree.dot -o tree.png
In [36]:
t2=time()
print ("RandomForest")
rf = RandomForestClassifier(n_estimators=100,n_jobs=-1)
clf_rf = rf.fit(X_train,y_train)
print ("Acurracy: ", clf_rf.score(X_test,y_test))
t3=time()
print ("time elapsed: ", t3-t2)
In [37]:
tt2=time()
print ("cross result========")
scores = cross_validation.cross_val_score(rf, X, y, cv=5)
print (scores)
print (scores.mean())
tt3=time()
print ("time elapsed: ", tt3-tt2)
In [39]:
pipeline2 = Pipeline([
('clf', RandomForestClassifier(criterion='entropy'))
])
parameters = {
'clf__n_estimators': (5, 25, 50, 100),
'clf__max_depth': (5, 25 , 50),
'clf__min_samples_split': (2, 5, 10),
'clf__min_samples_leaf': (1, 2, 3)
}
grid_search = GridSearchCV(pipeline2, parameters, n_jobs=-1, verbose=1, scoring='accuracy', cv=3)
grid_search.fit(X_train, y_train)
print ('Best score: %0.3f' % grid_search.best_score_)
print ('Best parameters set:')
best_parameters = grid_search.best_estimator_.get_params()
for param_name in sorted(parameters.keys()):
print ('\t%s: %r' % (param_name, best_parameters[param_name]))
predictions = grid_search.predict(X_test)
print ('Accuracy:', accuracy_score(y_test, predictions))
print (classification_report(y_test, predictions))
In [40]:
t4=time()
print ("NaiveBayes")
nb = BernoulliNB()
clf_nb=nb.fit(X_train,y_train)
print ("Acurracy: ", clf_nb.score(X_test,y_test))
t5=time()
print ("time elapsed: ", t5-t4)
In [41]:
tt4=time()
print ("cross result========")
scores = cross_validation.cross_val_score(nb, X,y, cv=3)
print (scores)
print (scores.mean())
tt5=time()
print ("time elapsed: ", tt5-tt4)
In [42]:
t6=time()
print( "KNN")
# knn = KNeighborsClassifier(n_neighbors=3)
knn = KNeighborsClassifier(n_neighbors=3)
clf_knn=knn.fit(X_train, y_train)
print ("Acurracy: ", clf_knn.score(X_test,y_test) )
t7=time()
print ("time elapsed: ", t7-t6)
In [43]:
tt6=time()
print ("cross result========")
scores = cross_validation.cross_val_score(knn, X,y, cv=5)
print (scores)
print (scores.mean())
tt7=time()
print ("time elapsed: ", tt7-tt6)
In [44]:
from sklearn.svm import SVC
from sklearn.cross_validation import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn import grid_search
knn = KNeighborsClassifier()
parameters = {'n_neighbors':[1,10]}
grid = grid_search.GridSearchCV(knn, parameters, n_jobs=-1, verbose=1, scoring='accuracy')
grid.fit(X_train, y_train)
print ('Best score: %0.3f' % grid.best_score_)
print ('Best parameters set:')
best_parameters = grid.best_estimator_.get_params()
for param_name in sorted(parameters.keys()):
print ('\t%s: %r' % (param_name, best_parameters[param_name]))
predictions = grid.predict(X_test)
print (classification_report(y_test, predictions))
In [46]:
t7=time()
print ("SVM")
svc = SVC(kernel='rbf')
clf_svc=svc.fit(X_train, y_train)
print ("Acurracy: ", clf_svc.score(X_test,y_test) )
t8=time()
print ("time elapsed: ", t8-t7)
In [49]:
tt7=time()
print ("cross result========")
scores = cross_validation.cross_val_score(svc,X,y, cv=5)
print (scores)
print (scores.mean())
tt8=time()
print ("time elapsed: ", tt7-tt6)