Decision tree practice based on penguin data set

#下载需要用到的数据集
!wget https://tianchi-media.oss-cn-beijing.aliyuncs.com/DSW/6tree/penguins_raw.csv

insert image description here

Step1: Function library import

##  基础函数库
import numpy as np 
import pandas as pd

## 绘图函数库
import matplotlib.pyplot as plt
import seaborn as sns

Data description:
insert image description here

Step2: Data reading/loading

## 我们利用Pandas自带的read_csv函数读取并转化为DataFrame格式

data = pd.read_csv('./penguins_raw.csv')

## 为了方便我们仅选取四个简单的特征，有兴趣的同学可以研究下其他特征的含义以及使用方法
data = data[['Species','Culmen Length (mm)','Culmen Depth (mm)',
            'Flipper Length (mm)','Body Mass (g)']]

Step3: Simple viewing of data information

## 利用.info()查看数据的整体信息
data.info()

insert image description here

## 进行简单的数据查看，我们可以利用 .head() 头部.tail()尾部
data.head()

insert image description here
Here we find that there are missing values in the data, and we can use the corresponding method to complete their values

data = data.fillna(-1)

## 其对应的类别标签为'Adelie Penguin', 'Gentoo penguin', 'Chinstrap penguin'三种不同企鹅的类别。
data['Species'].unique()

insert image description here
View the statistics of the data label

## 利用value_counts函数查看每个类别数量
pd.Series(data['Species']).value_counts()

insert image description here

## 对于特征进行一些统计描述
data.describe()

insert image description here

Step4: Visual description

## 特征与标签组合的散点可视化
sns.pairplot(data=data, diag_kind='hist', hue= 'Species')
plt.show()

insert image description here

## 将分类变量转化成数字变量，方便后续计算
def trans(x):
    if x == data['Species'].unique()[0]:
        return 0
    if x == data['Species'].unique()[1]:
        return 1
    if x == data['Species'].unique()[2]:
        return 2

data['Species'] = data['Species'].apply(trans)

Box plot display:

for col in data.columns:
    if col != 'Species':
        sns.boxplot(x='Species', y=col, saturation=0.5, palette='pastel', data=data)
        plt.title(col)
        plt.show()

insert image description here

Step5: Use the decision tree model to train and predict on the binary classification

## 为了正确评估模型性能，将数据划分为训练集和测试集，并在训练集上训练模型，在测试集上验证模型性能。
from sklearn.model_selection import train_test_split

## 选择其类别为0和1的样本 （不包括类别为2的样本）
data_target_part = data[data['Species'].isin([0,1])][['Species']]
data_features_part = data[data['Species'].isin([0,1])][['Culmen Length (mm)','Culmen Depth (mm)',
            'Flipper Length (mm)','Body Mass (g)']]

## 测试集大小为20%， 80%/20%分
x_train, x_test, y_train, y_test = train_test_split(data_features_part, data_target_part, test_size = 0.2, random_state = 2020)

## 从sklearn中导入决策树模型
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
## 定义 决策树模型 
clf = DecisionTreeClassifier(criterion='entropy')
# 在训练集上训练决策树模型
clf.fit(x_train, y_train)

## 在训练集和测试集上分布利用训练好的模型进行预测
train_predict = clf.predict(x_train)
test_predict = clf.predict(x_test)
from sklearn import metrics

## 利用accuracy（准确度）【预测正确的样本数目占总预测样本数目的比例】评估模型效果
print('The accuracy of the Logistic Regression is:',metrics.accuracy_score(y_train,train_predict))
print('The accuracy of the Logistic Regression is:',metrics.accuracy_score(y_test,test_predict))

## 查看混淆矩阵 (预测值和真实值的各类情况统计矩阵)
confusion_matrix_result = metrics.confusion_matrix(test_predict,y_test)
print('The confusion matrix result:\n',confusion_matrix_result)

# 利用热力图对于结果进行可视化
plt.figure(figsize=(8, 6))
sns.heatmap(confusion_matrix_result, annot=True, cmap='Blues')
plt.xlabel('Predicted labels')
plt.ylabel('True labels')
plt.show()

insert image description here

Step6: Use the decision tree model to train and predict on three classifications (multi-classification)

## 测试集大小为20%， 80%/20%分
x_train, x_test, y_train, y_test = train_test_split(data[['Culmen Length (mm)','Culmen Depth (mm)',
            'Flipper Length (mm)','Body Mass (g)']], data[['Species']], test_size = 0.2, random_state = 2020)
## 定义 决策树模型 
clf = DecisionTreeClassifier()
# 在训练集上训练决策树模型
clf.fit(x_train, y_train)

## 在训练集和测试集上分布利用训练好的模型进行预测
train_predict = clf.predict(x_train)
test_predict = clf.predict(x_test)

## 由于决策树模型是概率预测模型（前文介绍的 p = p(y=1|x,\theta)）,所有我们可以利用 predict_proba 函数预测其概率
train_predict_proba = clf.predict_proba(x_train)
test_predict_proba = clf.predict_proba(x_test)

print('The test predict Probability of each class:\n',test_predict_proba)
## 其中第一列代表预测为0类的概率，第二列代表预测为1类的概率，第三列代表预测为2类的概率。

## 利用accuracy（准确度）【预测正确的样本数目占总预测样本数目的比例】评估模型效果
print('The accuracy of the Logistic Regression is:',metrics.accuracy_score(y_train,train_predict))
print('The accuracy of the Logistic Regression is:',metrics.accuracy_score(y_test,test_predict))

## 查看混淆矩阵
confusion_matrix_result = metrics.confusion_matrix(test_predict,y_test)
print('The confusion matrix result:\n',confusion_matrix_result)

# 利用热力图对于结果进行可视化
plt.figure(figsize=(8, 6))
sns.heatmap(confusion_matrix_result, annot=True, cmap='Blues')
plt.xlabel('Predicted labels')
plt.ylabel('True labels')
plt.show()

insert image description here