鸢尾花(iris)数据集分析
Iris 鸢尾花数据集是一个经典数据集，在统计学习和机器学习领域都经常被用作示例。数据集内包含 3 类共 150 条记录，每类各 50 个数据，每条记录都有 4 项特征：花萼长度、花萼宽度、花瓣长度、花瓣宽度，可以通过这4个特征预测鸢尾花卉属于（iris-setosa, iris-versicolour, iris-virginica）中的哪一品种。
据说在现实中，这三种花的基本判别依据其实是种子（因为花瓣非常容易枯萎）。
0 准备数据
下面对 iris 进行探索性分析，首先导入相关包和数据集：
# 导入相关包
import numpy as np
import pandas as pd
from pandas import plotting
%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(\'F:\pydata\dataset\kaggle\iris.csv\', usecols=[1, 2, 3, 4, 5])
查看数据集信息：
iris.info()
<class \'pandas.core.frame.DataFrame\'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
SepalLengthCm 150 non-null float64
SepalWidthCm 150 non-null float64
PetalLengthCm 150 non-null float64
PetalWidthCm 150 non-null float64
Species 150 non-null object
dtypes: float64(4), object(1)
memory usage: 5.9+ KB
查看数据集的头 5 条记录：
iris.head()
1 探索性分析
先查看数据集各特征列的摘要统计信息：
iris.describe()
通过Violinplot 和 Pointplot，分别从数据分布和斜率，观察各特征与品种之间的关系：
# 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。
Andrews Curves 是一种通过将每个观察映射到函数来可视化多维数据的方法。
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)
从热图可看出，花萼的宽度和长度不相关，而花瓣的宽度和长度则高度相关。
2 机器学习
接下来，通过机器学习，以花萼和花瓣的尺寸为根据，预测其品种。
在进行机器学习之前，将数据集拆分为训练和测试数据集。首先，使用标签编码将 3 种鸢尾花的品种名称转换为分类值（0, 1, 2）。
# 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)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2]
接着，将数据集以 7: 3 的比例，拆分为训练数据和测试数据：
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)
(105, 4) (105,) (45, 4) (45,)
检查不同模型的准确性：
# 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)))
The accuracy of the SVM is: 1.0
# 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)))
The accuracy of the Logistic Regression is: 0.9555555555555556
# 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)))
The accuracy of the Decision Tree is: 0.9555555555555556
# 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)))
The accuracy of the KNN is: 1.0
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)))
The accuracy of the SVM using Petals is: 0.9777777777777777
The accuracy of the SVM using Sepal is: 0.8
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)))
The accuracy of the Logistic Regression using Petals is: 0.6888888888888889
The accuracy of the Logistic Regression using Sepals is: 0.6444444444444445
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)))
The accuracy of the Decision Tree using Petals is: 0.9555555555555556
The accuracy of the Decision Tree using Sepals is: 0.6666666666666666
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)))
The accuracy of the KNN using Petals is: 0.9777777777777777
The accuracy of the KNN using Sepals is: 0.7333333333333333
从中不难看出，使用花瓣的尺寸来训练数据较花萼更准确。正如在探索性分析的热图中所看到的那样，花萼的宽度和长度之间的相关性非常低，而花瓣的宽度和长度之间的相关性非常高。
