iris_anly0.py

# -*- coding: utf-8 -*-
"""
Created on Fri May 31 10:55:52 2019

@author: Administrator
"""

# 导入相关包
import numpy as np
import pandas as pd
from pandas import plotting
from sklearn import datasets

#%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn')

import seaborn as sns
sns.set_style("whitegrid")

from sklearn.linear_model import LogisticRegression 
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.neighbors import KNeighborsClassifier
from sklearn import svm
from sklearn import metrics 
from sklearn.tree import DecisionTreeClassifier

# 导入数据集
iris = pd.read_csv(r'C:\Users\Administrator\.spyder-py3\iris.csv', usecols=[0, 1, 2, 3, 4])

iris.info()

iris.head()

print(iris.describe())

# 设置颜色主题
antV =['#1890FF', '#2FC25B', '#FACC14', '#223273', '#8543E0', '#13C2C2', '#3436c7', '#F04864']
        
# 绘制  Violinplot
f, axes = plt.subplots(2, 2, figsize=(8, 8), sharex=True)
sns.despine(left=True)

sns.violinplot(x='Species', y='SepalLengthCm', data=iris, palette=antV, ax=axes[(0, 0)])
sns.violinplot(x='Species', y='SepalWidthCm', data=iris, palette=antV, ax=axes[(0, 1)])
sns.violinplot(x='Species', y='PetalLengthCm', data=iris, palette=antV, ax=axes[(1, 0)])
sns.violinplot(x='Species', y='PetalWidthCm', data=iris, palette=antV, ax=axes[(1, 1)])

plt.show()

# 绘制  pointplot
f, axes = plt.subplots(2, 2, figsize=(8, 8), sharex=True)
sns.despine(left=True)

sns.pointplot(x='Species', y='SepalLengthCm', data=iris, color=antV[0], ax=axes[0, 0])
sns.pointplot(x='Species', y='SepalWidthCm', data=iris, color=antV[0], ax=axes[0, 1])
sns.pointplot(x='Species', y='PetalLengthCm', data=iris, color=antV[0], ax=axes[1, 0])
sns.pointplot(x='Species', y='PetalWidthCm', data=iris, color=antV[0], ax=axes[1, 1])

plt.show()

#生成各特征之间关系的矩阵图

g = sns.pairplot(data=iris, palette=antV, hue= 'Species')

#使用 Andrews Curves 将每个多变量观测值转换为曲线并表示傅立叶级数的系数，这对于检测时间序列数据中的异常值很有用。
plt.subplots(figsize = (10,8))
plotting.andrews_curves(iris, 'Species', colormap='cool')

plt.show()

#下面分别基于花萼和花瓣做线性回归的可视化：

g = sns.lmplot(data=iris, x='SepalWidthCm', y='SepalLengthCm', palette=antV, hue='Species')
g = sns.lmplot(data=iris, x='PetalWidthCm', y='PetalLengthCm', palette=antV, hue='Species')

#最后，通过热图找出数据集中不同特征之间的相关性，高正值或负值表明特征具有高度相关性：
fig=plt.gcf()
fig.set_size_inches(12, 8)
fig=sns.heatmap(iris.corr(), annot=True, cmap='GnBu', linewidths=1, linecolor='k', square=True, mask=False, vmin=-1, vmax=1, cbar_kws={"orientation": "vertical"}, cbar=True)

# 载入特征和标签集
X = iris[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]
y = iris['Species']

# 对标签集进行编码
encoder = LabelEncoder()
y = encoder.fit_transform(y)
print(y)

train_X, test_X, train_y, test_y = train_test_split(X, y, test_size = 0.3, random_state = 101)
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)

# Support Vector Machine
model = svm.SVC()
model.fit(train_X, train_y)
prediction = model.predict(test_X)
print('The accuracy of the SVM is: {0}'.format(metrics.accuracy_score(prediction,test_y)))

# Logistic Regression
model = LogisticRegression()
model.fit(train_X, train_y)
prediction = model.predict(test_X)
print('The accuracy of the Logistic Regression is: {0}'.format(metrics.accuracy_score(prediction,test_y)))

# Decision Tree
model=DecisionTreeClassifier()
model.fit(train_X, train_y)
prediction = model.predict(test_X)
print('The accuracy of the Decision Tree is: {0}'.format(metrics.accuracy_score(prediction,test_y)))

# K-Nearest Neighbours
model=KNeighborsClassifier(n_neighbors=3)
model.fit(train_X, train_y)
prediction = model.predict(test_X)
print('The accuracy of the KNN is: {0}'.format(metrics.accuracy_score(prediction,test_y)))


#上面使用了数据集的所有特征，下面将分别使用花瓣和花萼的尺寸：
petal = iris[['PetalLengthCm', 'PetalWidthCm', 'Species']]
train_p,test_p=train_test_split(petal,test_size=0.3,random_state=0) 
train_x_p=train_p[['PetalWidthCm','PetalLengthCm']]
train_y_p=train_p.Species
test_x_p=test_p[['PetalWidthCm','PetalLengthCm']]
test_y_p=test_p.Species

sepal = iris[['SepalLengthCm', 'SepalWidthCm', 'Species']]
train_s,test_s=train_test_split(sepal,test_size=0.3,random_state=0)
train_x_s=train_s[['SepalWidthCm','SepalLengthCm']]
train_y_s=train_s.Species
test_x_s=test_s[['SepalWidthCm','SepalLengthCm']]
test_y_s=test_s.Species

model=svm.SVC()

model.fit(train_x_p,train_y_p) 
prediction=model.predict(test_x_p) 
print('The accuracy of the SVM using Petals is: {0}'.format(metrics.accuracy_score(prediction,test_y_p)))

model.fit(train_x_s,train_y_s) 
prediction=model.predict(test_x_s) 
print('The accuracy of the SVM using Sepal is: {0}'.format(metrics.accuracy_score(prediction,test_y_s)))

model = LogisticRegression()

model.fit(train_x_p, train_y_p) 
prediction = model.predict(test_x_p) 
print('The accuracy of the Logistic Regression using Petals is: {0}'.format(metrics.accuracy_score(prediction,test_y_p)))

model.fit(train_x_s, train_y_s) 
prediction = model.predict(test_x_s) 
print('The accuracy of the Logistic Regression using Sepals is: {0}'.format(metrics.accuracy_score(prediction,test_y_s)))

model=DecisionTreeClassifier()

model.fit(train_x_p, train_y_p) 
prediction = model.predict(test_x_p) 
print('The accuracy of the Decision Tree using Petals is: {0}'.format(metrics.accuracy_score(prediction,test_y_p)))

model.fit(train_x_s, train_y_s) 
prediction = model.predict(test_x_s) 
print('The accuracy of the Decision Tree using Sepals is: {0}'.format(metrics.accuracy_score(prediction,test_y_s)))

model=KNeighborsClassifier(n_neighbors=3) 

model.fit(train_x_p, train_y_p) 
prediction = model.predict(test_x_p) 
print('The accuracy of the KNN using Petals is: {0}'.format(metrics.accuracy_score(prediction,test_y_p)))

model.fit(train_x_s, train_y_s) 
prediction = model.predict(test_x_s) 
print('The accuracy of the KNN using Sepals is: {0}'.format(metrics.accuracy_score(prediction,test_y_s)))