python 动手实现朴素贝叶斯

python 动手实现朴素贝叶斯

最近尝试不调用scikit-learn来实现一下朴素贝叶斯，发现还是不那么容易上手，我这里分享一下我的实现过程，也欢迎大家来批评指正哈

导入库和数据

import pandas as pdimport numpy as npfrom sklearn.preprocessing import LabelEncoder,OneHotEncoderfrom collections import defaultdictdata = pd.read_csv("A&E Synthetic Data Excerpt.csv",index_col = "Index")labelEncoder = LabelEncoder()labelEncoder.fit(data['AE_HRG'].astype('str'))data['AE_HRG_num'] = labelEncoder.fit_transform(data['AE_HRG'].astype('str'))data['AE_Arrive_Date'] = pd.to_datetime(data['AE_Arrive_Date'])data['year'] = data['AE_Arrive_Date'].dt.yeardata['month'] = data['AE_Arrive_Date'].dt.monthdata['day']=data['AE_Arrive_Date'].dt.daylabelEncoder1 = LabelEncoder()labelEncoder1.fit(data['Age_Band'].astype('str'))data['Age_Band_num'] = labelEncoder1.fit_transform(data['Age_Band'].astype('str'))labelEncoder2 = LabelEncoder()labelEncoder2.fit(data['AE_Arrive_HourOfDay'].astype('str'))data['AE_Arrive_HourOfDay_num'] = labelEncoder2.fit_transform(data['AE_Arrive_HourOfDay'].astype('str'))feature = data[['IMD_Decile_From_LSOA', 'Sex', 'AE_Time_Mins', 'AE_Num_Diagnoses', 'AE_Num_Investigations', 'AE_Num_Treatments', 'AE_Arrival_Mode', 'Provider_Patient_Distance_Miles', 'AE_HRG_num', 'year', 'month', 'day', 'Age_Band_num', 'AE_Arrive_HourOfDay_num']]label = data["Admitted_Flag"]

这里面的数据就是一些表格数据，数据不方便公开，但是就是那个意思了，我把日期进行了离散化，其他的字段也做响应的离散化，后面想象不离散化也可以，本来就是离散的哈哈，如果不是离散的，就离散化一下就行了。

朴素贝叶斯

class NaiveBayesScratch(): """朴素贝叶斯算法Scratch实现""" def __init__(self): # 存储先验概率 P(Y=ck) self._prior_prob = defaultdict(float) # 存储似然概率 P(X|Y=ck) self._likelihood = defaultdict(defaultdict) # 存储每个类别的样本在训练集中出现次数 self._ck_counter = defaultdict(float) # 存储每一个特征可能取值的个数 self._Sj = defaultdict(float) def fit(self, X, y): """ 模型训练，参数估计使用贝叶斯估计 X: 训练集，每一行表示一个样本，每一列表示一个特征或属性 y: 训练集标签 """ n_sample, n_feature = X.shape # 计算每个类别可能的取值以及每个类别样本个数 ck, num_ck = np.unique(y, return_counts=True) self._ck_counter = dict(zip(ck, num_ck)) for label, num_label in self._ck_counter.items(): # 计算先验概率，做了拉普拉斯平滑处理 self._prior_prob[label] = (num_label + 1) / (n_sample + ck.shape[0]) # 记录每个类别样本对应的索引 ck_idx = [] for label in ck: label_idx = np.squeeze(np.argwhere(y == label)) ck_idx.append(label_idx) # 遍历每个类别 for label, idx in zip(ck, ck_idx): xdata = X[idx] # 记录该类别所有特征对应的概率 label_likelihood = defaultdict(defaultdict) # 遍历每个特征 for i in range(n_feature): # 记录该特征每个取值对应的概率 feature_val_prob = defaultdict(float) # 获取该列特征可能的取值和每个取值出现的次数 feature_val, feature_cnt = np.unique(xdata[:, i], return_counts=True) self._Sj[i] = feature_val.shape[0] feature_counter = dict(zip(feature_val, feature_cnt)) for fea_val, cnt in feature_counter.items(): # 计算该列特征每个取值的概率，做了拉普拉斯平滑 feature_val_prob[fea_val] = (cnt + 1) / (self._ck_counter[label] + self._Sj[i]) label_likelihood[i] = feature_val_prob self._likelihood[label] = label_likelihood def predict(self, x): """ 输入样本，输出其类别，本质上是计算后验概率 **注意计算后验概率的时候对概率取对数**，概率连乘可能导致浮点数下溢，取对数将连乘转化为求和 """ # 保存分类到每个类别的后验概率 post_prob = defaultdict(float) # 遍历每个类别计算后验概率 for label, label_likelihood in self._likelihood.items(): prob = np.log(self._prior_prob[label]) # 遍历样本每一维特征 for i, fea_val in enumerate(x): feature_val_prob = label_likelihood[i] # 如果该特征值出现在训练集中则直接获取概率 if fea_val in feature_val_prob: prob += np.log(feature_val_prob[fea_val]) else: # 如果该特征没有出现在训练集中则采用拉普拉斯平滑计算概率 laplace_prob = 1 / (self._ck_counter[label] + self._Sj[i]) prob += np.log(laplace_prob) post_prob[label] = prob prob_list = list(post_prob.items()) prob_list.sort(key=lambda v: v[1], reverse=True) # 返回后验概率最大的类别作为预测类别 return prob_list[0][0]

训练和验证

features = np.array(feature)labels= np.array(label)xtrain, xtest, ytrain, ytest = train_test_split(features, labels, train_size=0.8, shuffle=True)model = NaiveBayesScratch()model.fit(xtrain, ytrain)n_test = xtest.shape[0]n_right = 0n_wrong=0for i in range(n_test): y_pred = model.predict(xtest[i]) if y_pred == ytest[i]: n_right += 1 else: n_wrong+=1print(n_right)print(n_wrong)print(n_right/n_test)

K-fold验证

from random import seedfrom random import randrange# Split a dataset into k foldsdef custom_k_fold(dataset, folds=3): dataset_split = list() dataset_copy = list(range(0,dataset.shape[0])) fold_size = len(dataset) // folds for i in range(folds): fold = list() while len(fold) < fold_size: index = randrange(len(dataset_copy)) fold.append(dataset_copy.pop(index)) dataset_split.append(fold) return dataset_splitfold_10=custom_k_fold(features,10) for i in range(10): # 选其中的N-1份作为训练集（training set），剩余的1份作为验证集(validation set) test_index=fold_10[i] x_train = feature.drop(feature.index[test_index]) y_train=label.drop(label.index[test_index]) print(x_train.shape) x_test = feature.iloc[test_index] y_test=label.iloc[test_index] x_train = np.array(x_train) y_train= np.array(y_train) model.fit(x_train, y_train) x_test = np.array(x_test) y_test= np.array(y_test) n_test = x_test.shape[0] n_right = 0 n_wrong=0 TP=0 FP=0 TN=0 FN=0 for i in range(n_test): y_pred = model.predict(x_test[i])# print(y_pred)# print(y_test) if(y_pred==1 and y_test[i]==1): TP+=1 elif(y_pred==1 and y_test[i]==0): FP+=1 elif(y_pred==0 and y_test[i]==0): TN+=1 elif(y_pred==0 and y_test[i]==1): FN+=1 precision=TP / (TP + FP) recall=TP / (TP + FN) F1_Score = 2*precision*recall/(precision+recall) print('precision:{}'.format(precision)) print('recall:{}'.format(precision)) print('f1_score:{}'.format(precision))

好久没这么密集的贴代码了，哈哈，说实话还是有点不适应，想了想，如果放在电脑里，或许我一辈子都不会看了，但是放在网上，或许能够帮助到别人。

参考文献

​​https://github.com/lookenwu/lihang/blob/master/naive_bayes.py​​

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