贝叶斯
贝叶斯公式
后验概率 = 先验概率 * 似然估计
from sklearn.model_selection import GridSearchCV
from sklearn.naive_bayes import BernoulliNB
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import MultinomialNB
from sklearn.naive_bayes import ComplementNB
from sklearn.feature_selection import mutual_info_classif
from imblearn.over_sampling import SMOTE
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.metrics import brier_score_loss as BS
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from imblearn.over_sampling import RandomOverSampler
from sklearn.preprocessing import KBinsDiscretizer
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# 导入自己写的工具类
from my_tools import *
# 忽略warning
import warnings
warnings.filterwarnings("ignore")
加载数据
jibing_res = pd.read_excel("./jibing_feature_res_final.xlsx")
jibing = pd.read_excel("./jibing_feature_final.xlsx")
clf = GaussianNB()
Xtrain,Xtest,Ytrain,Ytest = train_test_split(jibing,jibing_res,test_size=0.3)
clf.fit(Xtrain, Ytrain)
y_pre = clf.predict(Xtest)
metrics_ = res_metrics(Ytest,y_pre,"贝叶斯")
#######################贝叶斯########################
+--------------------+--------+-------------------+
| precision | recall | f1 |
+--------------------+--------+-------------------+
| 0.8585909417685119 | 0.9375 | 0.896312084415292 |
+--------------------+--------+-------------------+
按照高斯贝叶斯的原理，将样本标准化
并且解决样本不均衡的问题
col = jibing.columns.tolist()
col = col[10:59]
col.append("年龄")
smote = SMOTE(sampling_strategy=1, random_state=42)
Xtrain,Xtest,Ytrain,Ytest = train_test_split(jibing,jibing_res,test_size=0.3,random_state=42)
Xtrain, Ytrain = smote.fit_resample(Xtrain,Ytrain)
归一化
jibing = guiyihua(jibing)
标准化
jibing = biaozhunhua(jibing)
Xtrain,Xtest,Ytrain,Ytest = train_test_split(jibing,jibing_res,test_size=0.3)
jibing.head()
| 左右 | 是否外伤 | 症状持续时间 | 明显夜间痛 | 年龄 | 高血压 | 高血脂 | 2型糖尿病 | 吸烟与否 | 饮酒与否 | ... | 腺苷脱氨酶ADA | 果糖胺 | 肌酸激酶 | α-L-盐藻糖苷酶 | 乳酸 | 淀粉酶 | 同型半胱氨酸 | 铁 | 总铁结合力 | 血型 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 3 | 0 | 0.402864 | 1 | 0 | 0 | 0 | -0.448892 | ... | -0.396787 | -0.160764 | -0.176406 | -1.241122 | 0.269307 | -0.755958 | -0.420427 | -0.880622 | -1.226099 | 3 |
| 1 | 1 | 1 | 2 | 0 | 0.180258 | 1 | 0 | 0 | 0 | -0.448892 | ... | -0.396787 | -0.079732 | -0.098498 | -0.773740 | -0.390723 | 0.608493 | -0.538745 | -0.132586 | 0.088761 | 0 |
| 2 | 1 | 0 | 4 | 1 | -0.339156 | 0 | 0 | 0 | 0 | -0.448892 | ... | 1.055008 | -0.035743 | -0.095811 | -0.072667 | 0.269307 | 0.949606 | -0.420427 | -1.742489 | -0.360483 | 0 |
| 3 | 1 | 0 | 3 | 0 | 0.031854 | 0 | 0 | 0 | 0 | -0.448892 | ... | 1.345367 | -0.077417 | -0.058200 | -1.241122 | -0.390723 | 0.096824 | -0.521842 | -0.311464 | -0.185168 | 0 |
| 4 | 0 | 1 | 3 | 0 | 0.106056 | 0 | 0 | 0 | 0 | -0.448892 | ... | 0.474290 | -0.095938 | -0.149541 | -1.007431 | 0.005295 | 3.678509 | -0.724673 | -0.734267 | -0.963127 | 0 |
5 rows × 60 columns
高斯贝叶斯的 f1-score 为0.902
clf = GaussianNB()
clf = clf.fit(Xtrain,Ytrain)
y_pre = clf.predict(Xtest)
metrics_ = res_metrics(Ytest,y_pre,"高斯朴素贝叶斯")
#####################高斯朴素贝叶斯######################
+--------------------+---------+--------------------+
| precision | recall | f1 |
+--------------------+---------+--------------------+
| 0.8452734465080802 | 0.96875 | 0.9028093356576747 |
+--------------------+---------+--------------------+
多项式贝叶斯
由于多项式贝叶斯中样本不能为负数，所以要重新加载数据
只进行归一化
jibing_res = pd.read_excel("./jibing_feature_res_final.xlsx")
jibing = pd.read_excel("./jibing_feature_final.xlsx")
jibing = guiyihua(jibing)
col = jibing.columns.tolist()
col = col[10:59]
col.append("年龄")
smote = SMOTE(sampling_strategy=1, random_state=42)
Xtrain,Xtest,Ytrain,Ytest = train_test_split(jibing,jibing_res,test_size=0.3,random_state=42)
Xtrain, Ytrain = smote.fit_resample(Xtrain,Ytrain)
clf = MultinomialNB()
clf.fit(Xtrain, Ytrain)
y_pre = clf.predict(Xtest)
metrics_ = res_metrics(Ytest,y_pre,"多项式朴素贝叶斯")
#####################多项式朴素贝叶斯#####################
+--------------------+--------------------+--------------------+
| precision | recall | f1 |
+--------------------+--------------------+--------------------+
| 0.8068610589952052 | 0.5689655172413793 | 0.6673459107453557 |
+--------------------+--------------------+--------------------+
分箱
jibing_res = pd.read_excel("./jibing_feature_res_final.xlsx")
jibing = pd.read_excel("./jibing_feature_final.xlsx")
col = jibing.columns.tolist()
col = col[10:59]
col.append("年龄")
est = KBinsDiscretizer(n_bins=67, encode='ordinal', strategy="kmeans")
est.fit(jibing[col])
jibing[col] = est.transform(jibing[col])
clf = MultinomialNB()
sampler = RandomOverSampler(sampling_strategy=1, random_state=42)
jibing, jibing_res = sampler.fit_resample(jibing,jibing_res)
Xtrain,Xtest,Ytrain,Ytest = train_test_split(jibing,jibing_res,test_size=0.3)
clf.fit(Xtrain, Ytrain)
y_pre = clf.predict(Xtest)
metrics_ = res_metrics(Ytest,y_pre,"分箱的多项式朴素贝叶斯")
###################分箱的多项式朴素贝叶斯####################
+--------------------+------------------+--------------------+
| precision | recall | f1 |
+--------------------+------------------+--------------------+
| 0.6029950247501015 | 0.58679706601467 | 0.5947857849977222 |
+--------------------+------------------+--------------------+
多项式贝叶斯的 f1-score 为0.66
伯努利朴素贝叶斯
伯努利贝叶斯分类器中，指定各个特征是独立的，没有相互关联。
这里考虑到疾病的理化指标不可能是相互独立的，所以这种方法不适用。
ComplementNB 互补朴素贝叶斯
jibing_res = pd.read_excel("./jibing_feature_res_final.xlsx")
jibing = pd.read_excel("./jibing_feature_final.xlsx")
col = jibing.columns.tolist()
col = col[10:59]
col.append("年龄")
est = KBinsDiscretizer(n_bins=67, encode='ordinal', strategy="kmeans")
est.fit(jibing[col])
jibing[col] = est.transform(jibing[col])
smote = SMOTE(sampling_strategy=1, random_state=42)
Xtrain,Xtest,Ytrain,Ytest = train_test_split(jibing,jibing_res,test_size=0.3,random_state=42)
Xtrain, Ytrain = smote.fit_resample(Xtrain,Ytrain)
clf = ComplementNB()
param_grid = {'alpha': np.linspace(0,1,20), 'fit_prior': [True, False]}
grid_search = GridSearchCV(clf, param_grid=param_grid, cv=5)
grid_search.fit(Xtrain, Ytrain)
GridSearchCV(cv=5, estimator=ComplementNB(),
param_grid={'alpha': array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,
0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,
0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,
0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ]),
'fit_prior': [True, False]})
grid_search.best_params_
{'alpha': 0.0, 'fit_prior': True}
clf = ComplementNB(alpha=0,fit_prior=True)
clf.fit(Xtrain, Ytrain)
y_pre = clf.predict(Xtest)
metrics_ = res_metrics(Ytest,y_pre,"互补朴素贝叶斯")
#####################互补朴素贝叶斯######################
+-------------------+--------------------+--------------------+
| precision | recall | f1 |
+-------------------+--------------------+--------------------+
| 0.818403768342936 | 0.5172413793103449 | 0.6338693996893425 |
+-------------------+--------------------+--------------------+
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