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| '''
朴素贝叶斯模型
'''
import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
from collections import defaultdict
from sklearn.cross_validation import train_test_split
def load_data():
'''
加载鸢尾花数据
'''
data = load_iris()
return data['data'],data['target']
class NBClassifier(object):
def __init__(self):
self.y = []
self.x = []
self.py = defaultdict(float)
self.pxy = defaultdict(dict)
self.n = 5
def prob(self,element,arr):
'''
计算元素在列表中出现的频率
'''
prob = 0.0
for a in arr:
if element == a:
prob += 1/len(arr)
if prob == 0.0:
prob = 0.001
return prob
def get_set(self,x,y):
self.y = list(set(y))
for i in range(x.shape[1]):
self.x.append(list(set(x[:,i])))
def fit(self,x,y):
'''
训练模型
'''
x = self.preprocess(x)
self.get_set(x,y)
for yi in self.y:
self.py[yi] = self.prob(yi,y)
for yi in self.y:
for i in range(x.shape[1]):
sample = x[y==yi,i]
pxy = [self.prob(xi,sample) for xi in self.x[i]]
self.pxy[yi][i] = pxy
print("train score",self.score(x,y))
def predict_one(self,x):
'''
预测单个样本
'''
max_prob = 0.0
max_yi = self.y[0]
for yi in self.y:
prob_y = self.py[yi]
for i in range(len(x)):
prob_x_y = self.pxy[yi][i][self.x[i].index(x[i])]
prob_y *= prob_x_y
if prob_y > max_prob:
max_prob = prob_y
max_yi = yi
return max_yi
def predict(self,samples):
'''
预测函数
'''
samples = self.preprocess(samples)
y_list = []
for m in range(samples.shape[0]):
yi = self.predict_one(samples[m,:])
y_list.append(yi)
return np.array(y_list)
def preprocess(self,x):
'''
因为不同特征的数值集大小相差巨大，造成部分概率矩阵变得稀疏，需要进行数据分割
'''
for i in range(x.shape[1]):
x[:,i] = self.step(x[:,i],self.n)
return x
def step(self,arr,n):
'''
分为n阶
'''
ma = max(arr)
mi = min(arr)
for i in range(len(arr)):
for j in range(n):
a = mi + (ma-mi)*(j/n)
b = mi + (ma-mi)*((j+1)/n)
if arr[i] >= a and arr[i] <= b:
arr[i] = j+1
break
return arr
def score(self,x,y):
y_test = self.predict(x)
score = 0.0
for i in range(len(y)):
if y_test[i] == y[i]:
score += 1/len(y)
return score
if __name__ == "__main__":
x,y = load_data()
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.5,random_state = 100)
clf = NBClassifier()
clf.fit(x_train,y_train)
score = clf.score(x_test,y_test)
print('test score',score)
|
