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# 导入必要的处理包
from pandas import read_csv
from pandas.plotting import scatter_matrix
from matplotlib import pyplot
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
# 导入数据
filename = './data/iris.data.csv'
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = read_csv(filename, names=names) # 这个数据集没有头部,手动指定即可
print(dataset.head())
sepal-length sepal-width petal-length petal-width class
0 5.1 3.5 1.4 0.2 Iris-setosa
1 4.9 3.0 1.4 0.2 Iris-setosa
2 4.7 3.2 1.3 0.2 Iris-setosa
3 4.6 3.1 1.5 0.2 Iris-setosa
4 5.0 3.6 1.4 0.2 Iris-setosa
现在开始对数据进行审查,加深对数据的了解。
牵涉到如下几个维度:
- 数据的维度
- 数据自身
- 所有的数据特征
- 数据的分布情况
print(dataset.shape)
(150, 5)
# 查看数据自身
print(dataset.head(10))
sepal-length sepal-width petal-length petal-width class
0 5.1 3.5 1.4 0.2 Iris-setosa
1 4.9 3.0 1.4 0.2 Iris-setosa
2 4.7 3.2 1.3 0.2 Iris-setosa
3 4.6 3.1 1.5 0.2 Iris-setosa
4 5.0 3.6 1.4 0.2 Iris-setosa
5 5.4 3.9 1.7 0.4 Iris-setosa
6 4.6 3.4 1.4 0.3 Iris-setosa
7 5.0 3.4 1.5 0.2 Iris-setosa
8 4.4 2.9 1.4 0.2 Iris-setosa
9 4.9 3.1 1.5 0.1 Iris-setosa
# 统计数据描述数据
print(dataset.describe())
sepal-length sepal-width petal-length petal-width
count 150.000000 150.000000 150.000000 150.000000
mean 5.843333 3.054000 3.758667 1.198667
std 0.828066 0.433594 1.764420 0.763161
min 4.300000 2.000000 1.000000 0.100000
25% 5.100000 2.800000 1.600000 0.300000
50% 5.800000 3.000000 4.350000 1.300000
75% 6.400000 3.300000 5.100000 1.800000
max 7.900000 4.400000 6.900000 2.500000
print(dataset.groupby('class').size())
class
Iris-setosa 50
Iris-versicolor 50
Iris-virginica 50
dtype: int64
可以看出数据的分布很均匀,如果分布不均匀,则会影响到模型的准确度。 如果不均匀,则需要对数据进行处理,使得数据达到相对均匀的状态。方法有:
- 扩大数据样本
- 数据的重新采样
- 生成人工样本
- 异常检测,变化检测
数据可视化
图表分成两大类:
- 单变量图表:理解每个特征属性
- 多变量图表:理解不同特征属性之间的关系
dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
pyplot.show()
dataset.hist()
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x115f1f748>,
<matplotlib.axes._subplots.AxesSubplot object at 0x1161c5400>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x1165a1080>,
<matplotlib.axes._subplots.AxesSubplot object at 0x1165dcbe0>]],
dtype=object)
# 多变量图表
scatter_matrix(dataset) # 这个工具很好用,单变量的直方图 + 变量间的散点分布图
pyplot.show()
算法评估
使用不同的算法来创建模型,并评估它们的准确度。主要有如下几个步骤:
- 分离出评估数据集
- 10折交叉评估验证算法模型
- 生成6个不同的模型来预测新数据
- 选择最优模型
# 分离数据集
array = dataset.values
X = array[:,0:4] # 输入特征,0-1-2-3
Y = array[:, 4]
validation_size = 0.2
seed = 7 # 随机数种子
X_train, X_validation, Y_train, Y_validation = train_test_split(X,Y, test_size=validation_size, random_state=seed)
X_train.shape
(120, 4)
Y_train.shape
(120,)
使用6种模型
线性算法:
- LR,线性回归
- LDA,线性判别分析
非线性算法:
- KNN,k近邻
- CART,分类与回归树
- NB,贝叶斯分类器
- SVM,支持向量机
models = {}
models['LR'] = LogisticRegression()
models['LDA'] = LinearDiscriminantAnalysis()
models['KNN'] = KNeighborsClassifier()
models['CART'] = DecisionTreeClassifier()
models['NB'] = GaussianNB()
models['SVM'] = SVC()
# 算法评估
results = []
for key in models:
kfold = KFold(n_splits=10, random_state=seed)
cv_results = cross_val_score(models[key], X_train, Y_train, cv=kfold, scoring='accuracy')
results.append(cv_results)
print('%s: %f (%f)' % (key, cv_results.mean(), cv_results.std()))
LR: 0.966667 (0.040825)
LDA: 0.975000 (0.038188)
KNN: 0.983333 (0.033333)
CART: 0.975000 (0.038188)
NB: 0.975000 (0.053359)
SVM: 0.991667 (0.025000)
# 绘图比较
fig = pyplot.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
pyplot.boxplot(results)
ax.set_xticklabels(models.keys())
pyplot.show()
# 使用评估数据集评估算法
svm = SVC()
svm.fit(X=X_train, y=Y_train)
predictions = svm.predict(X_validation)
print(accuracy_score(Y_validation, predictions))
print(confusion_matrix(Y_validation, predictions))
print(classification_report(Y_validation, predictions))
0.9333333333333333
[[ 7 0 0]
[ 0 10 2]
[ 0 0 11]]
precision recall f1-score support
Iris-setosa 1.00 1.00 1.00 7
Iris-versicolor 1.00 0.83 0.91 12
Iris-virginica 0.85 1.00 0.92 11
avg / total 0.94 0.93 0.93 30
END.
参考:
《机器学习Python实践》-- 魏贞原