机器学习-贝叶斯数据集

 

#朴素贝叶斯定力

import numpy as np
import pandas as pd
import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline
matplotlib.rcParams['font.sans-serif'] = ['SimHei']
data = pd.read_csv('./010-data_multivar.csv',header=None)
#拆分数据
dataset_X,dataset_y = data.iloc[:,:-1],data.iloc[:,-1]
# print(dataset_X.head())
dataset_X = dataset_X.values
dataset_y = dataset_y.values
# print(dataset_y)
#将标签去重
classes = list(set(dataset_y))
print(classes)

#数据集可视化

def visual_2D_dataset(dataset_X,dataset_y):
    '''将二维数据集dataset_X和对应的类别dataset_y显示在散点图中'''
    assert dataset_X.shape[1]==2,'only support dataset with 2 features'
    plt.figure()
    classes=list(set(dataset_y)) 
    markers=['.',',','o','v','^','<','>','1','2','3','4','8'
             ,'s','p','*','h','H','+','x','D','d','|']
    colors=['b','c','g','k','m','w','r','y']
    for class_id in classes:
        one_class=np.array([feature for (feature,label) in 
                   zip(dataset_X,dataset_y) if label==class_id])
        plt.scatter(one_class[:,0],one_class[:,1],marker=np.random.choice(markers,1)[0],
                    c=np.random.choice(colors,1)[0],label='class_'+str(class_id))
    plt.legend()

visual_2D_dataset(dataset_X,dataset_y)

　　

# 将分类器绘制到图中

def plot_classifier(classifier, X, y):
    x_min, x_max = min(X[:, 0]) - 1.0, max(X[:, 0]) + 1.0 # 计算图中坐标的范围
    y_min, y_max = min(X[:, 1]) - 1.0, max(X[:, 1]) + 1.0
    step_size = 0.01 # 设置step size
    x_values, y_values = np.meshgrid(np.arange(x_min, x_max, step_size), np.arange(y_min, y_max, step_size))
    # 构建网格数据
    mesh_output = classifier.predict(np.c_[x_values.ravel(), y_values.ravel()])
    mesh_output = mesh_output.reshape(x_values.shape) 
    plt.figure()
    plt.pcolormesh(x_values, y_values, mesh_output, cmap=plt.cm.gray)
    plt.scatter(X[:, 0], X[:, 1], c=y, s=80, edgecolors='black', linewidth=1, cmap=plt.cm.Paired)
    # specify the boundaries of the figure
    plt.xlim(x_values.min(), x_values.max())
    plt.ylim(y_values.min(), y_values.max())

    # specify the ticks on the X and Y axes
    plt.xticks((np.arange(int(min(X[:, 0])-1), int(max(X[:, 0])+1), 1.0)))
    plt.yticks((np.arange(int(min(X[:, 1])-1), int(max(X[:, 1])+1), 1.0)))
    plt.show()
 
from sklearn.naive_bayes import GaussianNB
gaussian = GaussianNB()
gaussian.fit(dataset_X,dataset_y)
# 预测
y_pre = gaussian.predict(dataset_X)
# print(y_pre)
# 统计判断预测结果和真实值得匹配数量
correct_count = (dataset_y == y_pre).sum()
# print(correct_count)
# print(type(dataset_y))
# print(dataset_y == y_pre)
# 调用绘图函数
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
plot_classifier(gaussian,dataset_X,dataset_y)
plot_classifier(gaussian,dataset_X,y_pre)
# 预习分类模型评估指标

 

　　

#多项式朴素贝叶斯模型

from sklearn.naive_bayes import MultinomialNB
from sklearn.preprocessing import MinMaxScaler
#范围缩放
scalar = MinMaxScaler(feature_range=(10,20))
dataset_X = scalar.fit_transform(dataset_X)
# print(dataset_X)
# 要求所有特征必须是非负数,否则没法训练
mul_nb = MultinomialNB()
mul_nb.fit(dataset_X,dataset_y)

print(np.r_[dataset_X,dataset_y.reshape(-1,2)]) #row方向合并
print(np.c_[dataset_X,dataset_y])  #colume:列向合并
y_pre = mul_nb.predict(dataset_X) #预测
print(y_pre)
plot_classifier(mul_nb,dataset_X,dataset_y)

 

　　

#伯努利贝叶斯模型

from sklearn.naive_bayes import BernoulliNB
clf = BernoulliNB()
clf.fit(dataset_X,dataset_y)
y_pre=clf.predict(dataset_X)
print(y_pre)
plot_classifier(clf,dataset_X,dataset_y)