1. 离散值特征的分类问题
from collections import Counter,defaultdict
import numpy as np
class Node:
def __init__(self,feat=-1,val=None,res=None,left=None,right=None):
self.feat=feat
self.val=val
self.res=res
self.left=left
self.right=right
def __repr__(self):
if self.res is not None:
return str(self.res)
return '['+repr(self.left)+repr(self.right)+']'
class CART:
def __init__(self,epsilon=1e-3,min_sample=1):
self.epsilon=epsilon
self.min_sample=min_sample
self.tree=None
def getGini(self,y_data):
counter=Counter(y_data)
length=y_data.shape[0]
return 1-sum([(v/length)**2 for v in counter.values() ])
def getFeatGini(self,*setn):
num=sum([seti.shape[0] for seti in setn])
return sum([
(seti.shape[0]/num)*self.getGini(seti) for seti in setn
])
def bestSplit(self,splits_set,x_data,y_data):
'''
返回所有切分点的基尼指数，以字典形式存储。
键为split，是一个元组，第一个元素为最优切分特征，
第二个为该特征对应的最优切分值
'''
pre_gini=self.getGini(y_data)
subdata_inds=defaultdict(list) # 切分点以及相应的样本点的索引
for split in splits_set:
for ind,sample in enumerate(x_data):
if sample[split[0]]==split[1]:
subdata_inds[split].append(ind)
min_gini=1 # 将最小基尼指数初始化为一个最大值
best_split=None
best_set=None
length=y_data.shape[0]
for split,data_ind in subdata_inds.items():
set1=y_data[data_ind]
set2_inds=list(
set(range(length))-set(data_ind)
)
set2=y_data[set2_inds]
if set1.shape[0]<1 or set2.shape[0]<1:
continue
now_gini=self.getFeatGini(set1,set2)
if now_gini<min_gini:
min_gini=now_gini
best_split=split
best_set=(data_ind,set2_inds)
if abs(pre_gini-min_gini)<self.epsilon:
best_split=None
return best_split,best_set,min_gini
def buildTree(self,splits_set,x_data,y_data):
if y_data.shape[0]<self.min_sample:
return Node(res=Counter(y_data).most_common(1)[0][0])
best_split, best_set, min_gini=self.bestSplit(splits_set,x_data,y_data)
if best_split is None:
return Node(res=Counter(y_data).most_common(1)[0][0])
splits_set.remove(best_split)
nodes=[0]*2
for i in range(2):
nodes[i]=self.buildTree(splits_set,x_data[best_set[i]],y_data[best_set[i]])
return Node(feat=best_split[0],val=best_split[1],left=nodes[0],right=nodes[1])
def fit(self,x_data,y_data):
splits_set=[]
for feat in range(x_data.shape[1]):
unique_vals=np.unique(x_data[:,feat])
if unique_vals.shape[0]<2:
continue
elif unique_vals.shape[0]==2:
splits_set.append((feat,unique_vals[0]))
else:
for val in unique_vals:
splits_set.append((feat,val))
self.tree=self.buildTree(splits_set,x_data,y_data)
def travel(self, feat, tree):
if tree.res is not None:
return tree.res
else:
if feat[tree.feat]==tree.val:
branch=tree.left
else:
branch=tree.right
return self.travel(feat, branch)
def predict(self,x):
return np.array([
self.travel(feat,self.tree) for feat in x
])
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from main import CART
x=load_iris().data
y=load_iris().target
x_train, x_test, y_train, y_test=train_test_split(x,y,test_size=0.2,random_state=10)
model=CART()
model.fit(x_train,y_train)
y_pred=model.predict(x_test)
print(y_pred)
print(y_test)
from collections import Counter,defaultdict
import numpy as np
class Node:
def __init__(self,feat=-1,val=None,res=None,left=None,right=None):
self.feat=feat
self.val=val
self.res=res
self.left=left
self.right=right
def __repr__(self):
if self.res is not None:
return str(self.res)
return '['+repr(self.left)+repr(self.right)+']'
class CART:
def __init__(self,epsilon=1e-3,min_sample=1):
self.epsilon=epsilon
self.min_sample=min_sample
self.tree=None
def getGini(self,y_data):
counter=Counter(y_data)
length=y_data.shape[0]
return 1-sum([(v/length)**2 for v in counter.values() ])
def getFeatGini(self,*setn):
num=sum([seti.shape[0] for seti in setn])
return sum([
(seti.shape[0]/num)*self.getGini(seti) for seti in setn
])
def bestSplit(self,splits_set,x_data,y_data):
'''
返回所有切分点的基尼指数，以字典形式存储。
键为split，是一个元组，第一个元素为最优切分特征，
第二个为该特征对应的最优切分值
'''
pre_gini=self.getGini(y_data)
subdata_inds=defaultdict(list) # 切分点以及相应的样本点的索引
for split in splits_set:
feat,s,e=split
for ind,sample in enumerate(x_data):
if s<=sample[feat]<e:
subdata_inds[split].append(ind)
min_gini=1 # 将最小基尼指数初始化为一个最大值
best_split=None
best_set=None
length=y_data.shape[0]
for split,data_ind in subdata_inds.items():
set1=y_data[data_ind]
set2_inds=list(
set(range(length))-set(data_ind)
)
set2=y_data[set2_inds]
if set1.shape[0]<1 or set2.shape[0]<1:
continue
now_gini=self.getFeatGini(set1,set2)
if now_gini<min_gini:
min_gini=now_gini
best_split=split
best_set=(data_ind,set2_inds)
if abs(pre_gini-min_gini)<self.epsilon:
best_split=None
return best_split,best_set,min_gini
def buildTree(self,splits_set,x_data,y_data):
if y_data.shape[0]<self.min_sample:
return Node(res=Counter(y_data).most_common(1)[0][0])
best_split, best_set, min_gini=self.bestSplit(splits_set,x_data,y_data)
if best_split is None:
return Node(res=Counter(y_data).most_common(1)[0][0])
feat,s,e=best_split
splits_set.remove(best_split)
nodes=[0]*2
for i in range(2):
nodes[i]=self.buildTree(splits_set,x_data[best_set[i]],y_data[best_set[i]])
return Node(feat=feat,val=(s,e),left=nodes[0],right=nodes[1])
def fit(self,x_data,y_data):
splits_set=[]
for feat in range(x_data.shape[1]):
unique_vals=np.unique(x_data[:,feat])
bins = np.concatenate([[-np.inf], unique_vals,[np.inf]])
for i in range(len(bins)-1):
s=bins[i]
e=bins[i+1]
splits_set.append((feat,s,e))
self.tree=self.buildTree(splits_set,x_data,y_data)
def travel(self, feat, tree):
if tree.res is not None:
return tree.res
else:
s,e=tree.val
if s<=feat[tree.feat]<e:
branch=tree.left
else:
branch=tree.right
return self.travel(feat, branch)
def predict(self,x):
return np.array([
self.travel(feat,self.tree) for feat in x
])
