共计 7671 个字符，预计需要花费 20 分钟才能阅读完成。
Gaussian Naive Bayes (GNB) 是一种基于概率办法和高斯分布的机器学习的分类技术。奢侈贝叶斯假如每个参数（也称为特色或预测变量）具备预测输入变量的独立能力。所有参数的预测组合是最终预测，它返回因变量被分类到每个组中的概率，最初的分类被调配给概率较高的分组（类）。
什么是高斯分布？
高斯分布也称为正态分布，是形容自然界中间断随机变量的统计散布的统计模型。正态分布由其钟形曲线定义，正态分布中两个最重要的特色是均值 (μ) 和标准差 (σ)。平均值是散布的平均值，标准差是散布在平均值四周的“宽度”。
重要的是要晓得正态分布的变量 (X) 从 -∞ < X < +∞ 间断散布（连续变量），并且模型曲线下的总面积为 1。
多分类的高斯奢侈贝叶斯
导入必要的库：
from random import random
from random import randint
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import statistics
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import confusion_matrix
from mlxtend.plotting import plot_decision_regions当初创立一个预测变量呈正态分布的数据集。
#Creating values for FeNO with 3 classes:
FeNO_0 = np.random.normal(20, 19, 200)
FeNO_1 = np.random.normal(40, 20, 200)
FeNO_2 = np.random.normal(60, 20, 200)
#Creating values for FEV1 with 3 classes:
FEV1_0 = np.random.normal(4.65, 1, 200)
FEV1_1 = np.random.normal(3.75, 1.2, 200)
FEV1_2 = np.random.normal(2.85, 1.2, 200)
#Creating values for Broncho Dilation with 3 classes:
BD_0 = np.random.normal(150,49, 200)
BD_1 = np.random.normal(201,50, 200)
BD_2 = np.random.normal(251, 50, 200)
#Creating labels variable with three classes:(2)disease (1)possible disease (0)no disease:
not_asthma = np.zeros((200,), dtype=int)
poss_asthma = np.ones((200,), dtype=int)
asthma = np.full((200,), 2, dtype=int)
#Concatenate classes into one variable:
FeNO = np.concatenate([FeNO_0, FeNO_1, FeNO_2])
FEV1 = np.concatenate([FEV1_0, FEV1_1, FEV1_2])
BD = np.concatenate([BD_0, BD_1, BD_2])
dx = np.concatenate([not_asthma, poss_asthma, asthma])
#Create DataFrame:
df = pd.DataFrame()
#Add variables to DataFrame:
df['FeNO'] = FeNO.tolist()
df['FEV1'] = FEV1.tolist()
df['BD'] = BD.tolist()
df['dx'] = dx.tolist()
#Check database:
df
咱们的 df 有 600 行和 4 列。当初咱们能够通过可视化查看变量的散布：
fig, axs = plt.subplots(2, 3, figsize=(14, 7))
sns.kdeplot(df['FEV1'], shade=True, color="b", ax=axs[0, 0])
sns.kdeplot(df['FeNO'], shade=True, color="b", ax=axs[0, 1])
sns.kdeplot(df['BD'], shade=True, color="b", ax=axs[0, 2])
sns.distplot(a=df["FEV1"], hist=True, kde=True, rug=False, ax=axs[1, 0])
sns.distplot(a=df["FeNO"], hist=True, kde=True, rug=False, ax=axs[1, 1])
sns.distplot(a=df["BD"], hist=True, kde=True, rug=False, ax=axs[1, 2])
plt.show()
通过人肉的查看，数据仿佛靠近高斯分布。还能够应用 qq-plots 仔细检查：
from statsmodels.graphics.gofplots import qqplot
from matplotlib import pyplot
#q-q plot:
fig, axs = pyplot.subplots(1, 3, figsize=(15, 5))
qqplot(df['FEV1'], line='s', ax=axs[0])
qqplot(df['FeNO'], line='s', ax=axs[1])
qqplot(df['BD'], line='s', ax=axs[2])
pyplot.show()
尽管不是完满的正态分布，但曾经很靠近了。上面查看的数据集和变量之间的相关性：
#Exploring dataset:
sns.pairplot(df, kind="scatter", hue="dx")
plt.show()
能够应用框线图查看这三组的散布，看看哪些特色能够更好的辨别出类别
# plotting both distibutions on the same figure
fig, axs = plt.subplots(2, 3, figsize=(14, 7))
fig = sns.kdeplot(df['FEV1'], hue= df['dx'], shade=True, color="r", ax=axs[0, 0])
fig = sns.kdeplot(df['FeNO'], hue= df['dx'], shade=True, color="r", ax=axs[0, 1])
fig = sns.kdeplot(df['BD'], hue= df['dx'], shade=True, color="r", ax=axs[0, 2])
sns.boxplot(x=df["dx"], y=df["FEV1"], palette = 'magma', ax=axs[1, 0])
sns.boxplot(x=df["dx"], y=df["FeNO"], palette = 'magma',ax=axs[1, 1])
sns.boxplot(x=df["dx"], y=df["BD"], palette = 'magma',ax=axs[1, 2])
plt.show()
手写奢侈贝叶斯分类
手写代码并不是让咱们反复的制作轮子，而是通过本人编写代码对算法更好的了解。在进行贝叶斯分类之前，先要理解正态分布。
正态分布的数学公式定义了一个观测值呈现在某个群体中的概率：
咱们能够创立一个函数来计算这个概率:
def normal_dist(x , mean , sd):
prob_density = (1/sd*np.sqrt(2*np.pi)) * np.exp(-0.5*((x-mean)/sd)**2)
return prob_density晓得正态分布公式，就能够计算该样本在三个分组（分类）概率。首先，须要计算所有预测特色和组的均值和标准差：
#Group 0:
group_0 = df[df['dx'] == 0]print('Mean FEV1 group 0:', statistics.mean(group_0['FEV1']))
print('SD FEV1 group 0:', statistics.stdev(group_0['FEV1']))
print('Mean FeNO group 0:', statistics.mean(group_0['FeNO']))
print('SD FeNO group 0:', statistics.stdev(group_0['FeNO']))
print('Mean BD group 0:', statistics.mean(group_0['BD']))
print('SD BD group 0:', statistics.stdev(group_0['BD']))
#Group 1:
group_1 = df[df['dx'] == 1]
print('Mean FEV1 group 1:', statistics.mean(group_1['FEV1']))
print('SD FEV1 group 1:', statistics.stdev(group_1['FEV1']))
print('Mean FeNO group 1:', statistics.mean(group_1['FeNO']))
print('SD FeNO group 1:', statistics.stdev(group_1['FeNO']))
print('Mean BD group 1:', statistics.mean(group_1['BD']))
print('SD BD group 1:', statistics.stdev(group_1['BD']))
#Group 2:
group_2 = df[df['dx'] == 2]
print('Mean FEV1 group 2:', statistics.mean(group_2['FEV1']))
print('SD FEV1 group 2:', statistics.stdev(group_2['FEV1']))
print('Mean FeNO group 2:', statistics.mean(group_2['FeNO']))
print('SD FeNO group 2:', statistics.stdev(group_2['FeNO']))
print('Mean BD group 2:', statistics.mean(group_2['BD']))
print('SD BD group 2:', statistics.stdev(group_2['BD']))
当初，应用一个随机的样本进行测试：FEV1 = 2.75FeNO = 27BD = 125
#Probability for:
#FEV1 = 2.75
#FeNO = 27
#BD = 125
#We have the same number of observations, so the general probability is: 0.33
Prob_geral = round(0.333, 3)
#Prob FEV1:
Prob_FEV1_0 = round(normal_dist(2.75, 4.70, 1.08), 10)
print('Prob FEV1 0:', Prob_FEV1_0)
Prob_FEV1_1 = round(normal_dist(2.75, 3.70, 1.13), 10)
print('Prob FEV1 1:', Prob_FEV1_1)
Prob_FEV1_2 = round(normal_dist(2.75, 3.01, 1.22), 10)
print('Prob FEV1 2:', Prob_FEV1_2)
#Prob FeNO:
Prob_FeNO_0 = round(normal_dist(27, 19.71, 19.29), 10)
print('Prob FeNO 0:', Prob_FeNO_0)
Prob_FeNO_1 = round(normal_dist(27, 42.34, 19.85), 10)
print('Prob FeNO 1:', Prob_FeNO_1)
Prob_FeNO_2 = round(normal_dist(27, 61.78, 21.39), 10)
print('Prob FeNO 2:', Prob_FeNO_2)
#Prob BD:
Prob_BD_0 = round(normal_dist(125, 152.59, 50.33), 10)
print('Prob BD 0:', Prob_BD_0)
Prob_BD_1 = round(normal_dist(125, 199.14, 50.81), 10)
print('Prob BD 1:', Prob_BD_1)
Prob_BD_2 = round(normal_dist(125, 256.13, 47.04), 10)
print('Prob BD 2:', Prob_BD_2)
#Compute probability:
Prob_group_0 = Prob_geral*Prob_FEV1_0*Prob_FeNO_0*Prob_BD_0
print('Prob group 0:', Prob_group_0)
Prob_group_1 = Prob_geral*Prob_FEV1_1*Prob_FeNO_1*Prob_BD_1
print('Prob group 1:', Prob_group_1)
Prob_group_2 = Prob_geral*Prob_FEV1_2*Prob_FeNO_2*Prob_BD_2
print('Prob group 2:', Prob_group_2)
能够看到，这个样本具备属于第 2 组的概率最高。这就是奢侈贝叶斯手动计算的的流程，然而这种成熟的算法能够应用来自 Scikit-Learn 的更高效的实现。
Scikit-Learn 的分类器样例
Scikit-Learn 的 GaussianNB 为咱们提供了更加高效的办法，上面咱们应用 GaussianNB 进行残缺的分类实例。首先创立 X 和 y 变量，并执行训练和测试拆分：
#Creating X and y:
X = df.drop('dx', axis=1)
y = df['dx']
#Data split into train and test:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)在输出之前还须要应用 standardscaler 对数据进行标准化：
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)当初构建和评估模型：
#Build the model:
classifier = GaussianNB()
classifier.fit(X_train, y_train)
#Evaluate the model:
print("training set score: %f" % classifier.score(X_train, y_train))
print("test set score: %f" % classifier.score(X_test, y_test))上面应用混同矩阵来可视化后果：
# Predicting the Test set results
y_pred = classifier.predict(X_test)
#Confusion Matrix:
cm = confusion_matrix(y_test, y_pred)
print(cm)
通过混同矩阵能够看到，的模型最适宜预测类别 0，但类别 1 和 2 的错误率很高。为了查看这个问题，咱们应用变量构建决策边界图：
df.to_csv('data.csv', index = False)
data = pd.read_csv('data.csv')
def gaussian_nb_a(data):
x = data[['BD','FeNO',]].values
y = data['dx'].astype(int).values
Gauss_nb = GaussianNB()
Gauss_nb.fit(x,y)
print(Gauss_nb.score(x,y))
#Plot decision region:
plot_decision_regions(x,y, clf=Gauss_nb, legend=1)
#Adding axes annotations:
plt.xlabel('X_train')
plt.ylabel('y_train')
plt.title('Gaussian Naive Bayes')
plt.show()
def gaussian_nb_b(data):
x = data[['BD','FEV1',]].values
y = data['dx'].astype(int).values
Gauss_nb = GaussianNB()
Gauss_nb.fit(x,y)
print(Gauss_nb.score(x,y))
#Plot decision region:
plot_decision_regions(x,y, clf=Gauss_nb, legend=1)
#Adding axes annotations:
plt.xlabel('X_train')
plt.ylabel('y_train')
plt.title('Gaussian Naive Bayes')
plt.show()
def gaussian_nb_c(data):
x = data[['FEV1','FeNO',]].values
y = data['dx'].astype(int).values
Gauss_nb = GaussianNB()
Gauss_nb.fit(x,y)
print(Gauss_nb.score(x,y))
#Plot decision region:
plot_decision_regions(x,y, clf=Gauss_nb, legend=1)
#Adding axes annotations:
plt.xlabel('X_train')
plt.ylabel('y_train')
plt.title('Gaussian Naive Bayes')
plt.show()
gaussian_nb_a(data)
gaussian_nb_b(data)
gaussian_nb_c(data)
通过决策边界咱们能够察看到分类谬误的起因，从图中咱们看到，很多点都是落在决策边界之外的，如果是理论数据咱们须要剖析具体起因，然而因为是测试数据所以咱们也不须要更多的剖析。
https://www.overfit.cn/post/0457f85f2c184ff0864db5256654aef1
正文完
