def sk_learn(data="oldGames.arff", min_split=300, min_leaf=15):
folds = 10
mat = Arff(data, label_count=1)
counts = [] ## this is so you know how many types for each column
for i in range(mat.data.shape[1]):
counts += [mat.unique_value_count(i)]
# np.random.seed(35)
np.random.shuffle(mat.data)
splits = np.array_split(mat.data, folds)
Acc = 0
# min_split = 300
# print("Minsplit: {}".format(min_split))
for f in range(folds):
# print("Fold {}:".format(f))
train = np.array([])
for other in range(folds):
if train.size == 0 and other != f:
train = splits[other].copy()
elif other != f:
train = np.concatenate((train, splits[other]))
data = train[:, 0:-1]
labels = train[:, -1].reshape(-1, 1)
clf = tree.DecisionTreeClassifier(
) #min_samples_split=min_split, min_samples_leaf=min_leaf
clf = clf.fit(data, labels)
pred = clf.predict(data)
new_acc = score(pred, labels)
# print("\tTrain Acc {}".format(new_acc))
data2 = splits[f][:, 0:-1]
labels2 = splits[f][:, -1].reshape(-1, 1)
pred = clf.predict(data2)
new_acc = score(pred, labels2)
# print("\tTest Acc {}".format(new_acc))
Acc += new_acc
Acc = Acc / folds
print("Accuracy = [{:.4f}]".format(Acc))
classes = [
"Overwhelmingly_Positive", "Very_Positive", "Positive",
"Mostly_Positive", "Mixed", "Mostly_Negative", "Negative",
"Very_Negative", "Overwhelmingly_Negative"
]
dot_data = tree.export_graphviz(clf,
out_file=None,
feature_names=mat.get_attr_names()[:-1],
class_names=classes,
filled=True,
rounded=True) # max_depth=6,
graph = graphviz.Source(dot_data)
graph.render("old_games")
return Acc
"""
size = X.shape[0]
score = 0
predicted = self.predict(X)
for i in range(size):
if predicted[i] == y[i]:
score += 1
#print("score is ", score/size)
return score/size
if __name__ == "__main__":
mat = Arff("car.arff",label_count=1)
counts = [] ## this is so you know how many types for each column
for i in range(mat.data.shape[1]):
counts += [mat.unique_value_count(i)]
#print(counts)
data = mat.data[:,0:-1]
labels = mat.data[:,-1].reshape(-1,1)
#removing nans
median = np.median(data)
roundedMedian = np.round_(median, 0)
data[np.isnan(data)]= roundedMedian
DTClass = DTClassifier(counts)
DTClass.fit(data,labels, counts)
acc = DTClass.score(data, labels)
print("acc ", acc)
clf = DecisionTreeClassifier(random_state=0)
