def crossval(X,Y,size,k=10):
score = []
for i in range(0,k-1):
rem = range(size*i,size*i+size)
rem = set(rem)
m = X.shape[0]
left = set(range(0,m)) - rem
left = list(left)
train = np.take(X,left,axis=0)
tree = learn(train)
a,b = dt.test(tree,Y)
c = dt.accr(a,b)
score.append(c)
return score
def learningcurve(df,p=10,n=100):
m,z = df.shape
size = int(0.7*m/n)
sizes =[]
traina = []
testa = []
times = []
for i in range(1,n):
train,trial = dt.split(df,size*i,int(0.3*m))
s = time.clock()
tree = learn(train)
a,b = dt.test(tree, trial)
score = dt.accr(a,b)
c,d = dt.test(tree, train)
scoret = dt.accr(c,d)
e = time.clock()
sizes.append(size*i)
traina.append(scoret)
testa.append(score)
times.append(e-s)
print("Trial Times")
print(times)
return sizes,testa,traina
print(times)
return sizes,testa,traina
if __name__=="__main__":
print ("Boosted Decision Tree")
df1,df2 = dt.readin()
train1, trial1, size = dt.cross(df1)
score = crossval(train1,trial1,size)
print("Cross Validation for Collection 1")
print(score)
m,n = df1.shape
train1, trial1 = dt.split(df1,int(0.7*m),int(0.3*m))
s = time.clock()
tree1 = learn(train1)
a,b = dt.test(tree1, trial1)
score = dt.accr(a,b)
c,d = dt.test(tree1, train1)
scoret = dt.accr(c,d)
e = time.clock()
print("Testing Set Score for Collection 1")
print(score)
print("Training Set Score for Collection 1")
print(scoret)
print("Runtime")
print(e-s)
a,b,c = learningcurve(df1,50)
plt.plot(a,b)
plt.title("Boosting - Collection 1")
plt.xlabel("Training Sample Size")
plt.ylabel("Accuracy")
