文章目录
- 特征选择
- API
- 数据计算
降维实际上就是降低特征的个数，最终的结果就是特征和特征之间不相关。
降维：降维是指在某些限定条件下，降低随机变量（特征）个数，得到一组“不相关”主变量的过程
降维的两种方式
1.特征选择
2.主成分分析:可以理解为特征的提取
特征选择
- Filter（过滤式）：主要探究特征本身特点、特征与特征和目标值之间关联。
方差选择法：低方差特征过滤.例如鸟类是否可以飞作为特征值是不合适的，此时的方差为0
相关系数：目的是去除冗余，确定特征与特征之间的相关性 - Embedded（嵌入式）：算法自动选择特征（特征与目标值之间的关联）
决策树：信息熵、信息增益
正则化：L1、L2
深度学习：卷积等
API
模块
sklearn.feature_selection
特征选择_过滤式
API
sklearn.feature_selection.VarianceThreshold( threshold = 0.0 )
删除所有低方差特征
返回值:
X:numpy array格式的数据 [n_samples, n_features]
返回：训练集差异低于 threshold的特征将被删除。默认值是保留所有非零方差特征，即删除所有样本中具有相同值的特征。
数据计算
#删除低方差特征Demo
from sklearn.datasets import load_iris
from sklearn.feature_selection import VarianceThreshold
import pandas as pd
def variance_demo():
iris = load_iris()
data = pd.DataFrame(iris.data, columns = iris.feature_names)
data_new = data.iloc[:, :4].values
print("data_new:\n", data_new)
transfer = VarianceThreshold(threshold = 0.5)
data_variance_value = transfer.fit_transform(data_new)
print("data_variance_value:\n", data_variance_value)
return None
if __name__ == '__main__':
variance_demo()
输出结果：
data_new:
[[5.1 3.5 1.4 0.2]
[4.9 3. 1.4 0.2]
[4.7 3.2 1.3 0.2]
[4.6 3.1 1.5 0.2]
[5. 3.6 1.4 0.2]
[5.4 3.9 1.7 0.4]
[4.6 3.4 1.4 0.3]
[5. 3.4 1.5 0.2]
[4.4 2.9 1.4 0.2]
[4.9 3.1 1.5 0.1]
[5.4 3.7 1.5 0.2]
[4.8 3.4 1.6 0.2]
[4.8 3. 1.4 0.1]
[4.3 3. 1.1 0.1]
[5.8 4. 1.2 0.2]
[5.7 4.4 1.5 0.4]
[5.4 3.9 1.3 0.4]
[5.1 3.5 1.4 0.3]
[5.7 3.8 1.7 0.3]
[5.1 3.8 1.5 0.3]
[5.4 3.4 1.7 0.2]
[5.1 3.7 1.5 0.4]
[4.6 3.6 1. 0.2]
[5.1 3.3 1.7 0.5]
[4.8 3.4 1.9 0.2]
[5. 3. 1.6 0.2]
[5. 3.4 1.6 0.4]
[5.2 3.5 1.5 0.2]
[5.2 3.4 1.4 0.2]
[4.7 3.2 1.6 0.2]
[4.8 3.1 1.6 0.2]
[5.4 3.4 1.5 0.4]
[5.2 4.1 1.5 0.1]
[5.5 4.2 1.4 0.2]
[4.9 3.1 1.5 0.1]
[5. 3.2 1.2 0.2]
[5.5 3.5 1.3 0.2]
[4.9 3.1 1.5 0.1]
[4.4 3. 1.3 0.2]
[5.1 3.4 1.5 0.2]
[5. 3.5 1.3 0.3]
[4.5 2.3 1.3 0.3]
[4.4 3.2 1.3 0.2]
[5. 3.5 1.6 0.6]
[5.1 3.8 1.9 0.4]
[4.8 3. 1.4 0.3]
[5.1 3.8 1.6 0.2]
[4.6 3.2 1.4 0.2]
[5.3 3.7 1.5 0.2]
[5. 3.3 1.4 0.2]
[7. 3.2 4.7 1.4]
[6.4 3.2 4.5 1.5]
[6.9 3.1 4.9 1.5]
[5.5 2.3 4. 1.3]
[6.5 2.8 4.6 1.5]
[5.7 2.8 4.5 1.3]
[6.3 3.3 4.7 1.6]
[4.9 2.4 3.3 1. ]
[6.6 2.9 4.6 1.3]
[5.2 2.7 3.9 1.4]
[5. 2. 3.5 1. ]
[5.9 3. 4.2 1.5]
[6. 2.2 4. 1. ]
[6.1 2.9 4.7 1.4]
[5.6 2.9 3.6 1.3]
[6.7 3.1 4.4 1.4]
[5.6 3. 4.5 1.5]
[5.8 2.7 4.1 1. ]
[6.2 2.2 4.5 1.5]
[5.6 2.5 3.9 1.1]
[5.9 3.2 4.8 1.8]
[6.1 2.8 4. 1.3]
[6.3 2.5 4.9 1.5]
[6.1 2.8 4.7 1.2]
[6.4 2.9 4.3 1.3]
[6.6 3. 4.4 1.4]
[6.8 2.8 4.8 1.4]
[6.7 3. 5. 1.7]
[6. 2.9 4.5 1.5]
[5.7 2.6 3.5 1. ]
[5.5 2.4 3.8 1.1]
[5.5 2.4 3.7 1. ]
[5.8 2.7 3.9 1.2]
[6. 2.7 5.1 1.6]
[5.4 3. 4.5 1.5]
[6. 3.4 4.5 1.6]
[6.7 3.1 4.7 1.5]
[6.3 2.3 4.4 1.3]
[5.6 3. 4.1 1.3]
[5.5 2.5 4. 1.3]
[5.5 2.6 4.4 1.2]
[6.1 3. 4.6 1.4]
[5.8 2.6 4. 1.2]
[5. 2.3 3.3 1. ]
[5.6 2.7 4.2 1.3]
[5.7 3. 4.2 1.2]
[5.7 2.9 4.2 1.3]
[6.2 2.9 4.3 1.3]
[5.1 2.5 3. 1.1]
[5.7 2.8 4.1 1.3]
[6.3 3.3 6. 2.5]
[5.8 2.7 5.1 1.9]
[7.1 3. 5.9 2.1]
[6.3 2.9 5.6 1.8]
[6.5 3. 5.8 2.2]
[7.6 3. 6.6 2.1]
[4.9 2.5 4.5 1.7]
[7.3 2.9 6.3 1.8]
[6.7 2.5 5.8 1.8]
[7.2 3.6 6.1 2.5]
[6.5 3.2 5.1 2. ]
[6.4 2.7 5.3 1.9]
[6.8 3. 5.5 2.1]
[5.7 2.5 5. 2. ]
[5.8 2.8 5.1 2.4]
[6.4 3.2 5.3 2.3]
[6.5 3. 5.5 1.8]
[7.7 3.8 6.7 2.2]
[7.7 2.6 6.9 2.3]
[6. 2.2 5. 1.5]
[6.9 3.2 5.7 2.3]
[5.6 2.8 4.9 2. ]
[7.7 2.8 6.7 2. ]
[6.3 2.7 4.9 1.8]
[6.7 3.3 5.7 2.1]
[7.2 3.2 6. 1.8]
[6.2 2.8 4.8 1.8]
[6.1 3. 4.9 1.8]
[6.4 2.8 5.6 2.1]
[7.2 3. 5.8 1.6]
[7.4 2.8 6.1 1.9]
[7.9 3.8 6.4 2. ]
[6.4 2.8 5.6 2.2]
[6.3 2.8 5.1 1.5]
[6.1 2.6 5.6 1.4]
[7.7 3. 6.1 2.3]
[6.3 3.4 5.6 2.4]
[6.4 3.1 5.5 1.8]
[6. 3. 4.8 1.8]
[6.9 3.1 5.4 2.1]
[6.7 3.1 5.6 2.4]
[6.9 3.1 5.1 2.3]
[5.8 2.7 5.1 1.9]
[6.8 3.2 5.9 2.3]
[6.7 3.3 5.7 2.5]
[6.7 3. 5.2 2.3]
[6.3 2.5 5. 1.9]
[6.5 3. 5.2 2. ]
[6.2 3.4 5.4 2.3]
[5.9 3. 5.1 1.8]]
data_variance_value:
[[5.1 1.4 0.2]
[4.9 1.4 0.2]
[4.7 1.3 0.2]
[4.6 1.5 0.2]
[5. 1.4 0.2]
[5.4 1.7 0.4]
[4.6 1.4 0.3]
[5. 1.5 0.2]
[4.4 1.4 0.2]
[4.9 1.5 0.1]
[5.4 1.5 0.2]
[4.8 1.6 0.2]
[4.8 1.4 0.1]
[4.3 1.1 0.1]
[5.8 1.2 0.2]
[5.7 1.5 0.4]
[5.4 1.3 0.4]
[5.1 1.4 0.3]
[5.7 1.7 0.3]
[5.1 1.5 0.3]
[5.4 1.7 0.2]
[5.1 1.5 0.4]
[4.6 1. 0.2]
[5.1 1.7 0.5]
[4.8 1.9 0.2]
[5. 1.6 0.2]
[5. 1.6 0.4]
[5.2 1.5 0.2]
[5.2 1.4 0.2]
[4.7 1.6 0.2]
[4.8 1.6 0.2]
[5.4 1.5 0.4]
[5.2 1.5 0.1]
[5.5 1.4 0.2]
[4.9 1.5 0.1]
[5. 1.2 0.2]
[5.5 1.3 0.2]
[4.9 1.5 0.1]
[4.4 1.3 0.2]
[5.1 1.5 0.2]
[5. 1.3 0.3]
[4.5 1.3 0.3]
[4.4 1.3 0.2]
[5. 1.6 0.6]
[5.1 1.9 0.4]
[4.8 1.4 0.3]
[5.1 1.6 0.2]
[4.6 1.4 0.2]
[5.3 1.5 0.2]
[5. 1.4 0.2]
[7. 4.7 1.4]
[6.4 4.5 1.5]
[6.9 4.9 1.5]
[5.5 4. 1.3]
[6.5 4.6 1.5]
[5.7 4.5 1.3]
[6.3 4.7 1.6]
[4.9 3.3 1. ]
[6.6 4.6 1.3]
[5.2 3.9 1.4]
[5. 3.5 1. ]
[5.9 4.2 1.5]
[6. 4. 1. ]
[6.1 4.7 1.4]
[5.6 3.6 1.3]
[6.7 4.4 1.4]
[5.6 4.5 1.5]
[5.8 4.1 1. ]
[6.2 4.5 1.5]
[5.6 3.9 1.1]
[5.9 4.8 1.8]
[6.1 4. 1.3]
[6.3 4.9 1.5]
[6.1 4.7 1.2]
[6.4 4.3 1.3]
[6.6 4.4 1.4]
[6.8 4.8 1.4]
[6.7 5. 1.7]
[6. 4.5 1.5]
[5.7 3.5 1. ]
[5.5 3.8 1.1]
[5.5 3.7 1. ]
[5.8 3.9 1.2]
[6. 5.1 1.6]
[5.4 4.5 1.5]
[6. 4.5 1.6]
[6.7 4.7 1.5]
[6.3 4.4 1.3]
[5.6 4.1 1.3]
[5.5 4. 1.3]
[5.5 4.4 1.2]
[6.1 4.6 1.4]
[5.8 4. 1.2]
[5. 3.3 1. ]
[5.6 4.2 1.3]
[5.7 4.2 1.2]
[5.7 4.2 1.3]
[6.2 4.3 1.3]
[5.1 3. 1.1]
[5.7 4.1 1.3]
[6.3 6. 2.5]
[5.8 5.1 1.9]
[7.1 5.9 2.1]
[6.3 5.6 1.8]
[6.5 5.8 2.2]
[7.6 6.6 2.1]
[4.9 4.5 1.7]
[7.3 6.3 1.8]
[6.7 5.8 1.8]
[7.2 6.1 2.5]
[6.5 5.1 2. ]
[6.4 5.3 1.9]
[6.8 5.5 2.1]
[5.7 5. 2. ]
[5.8 5.1 2.4]
[6.4 5.3 2.3]
[6.5 5.5 1.8]
[7.7 6.7 2.2]
[7.7 6.9 2.3]
[6. 5. 1.5]
[6.9 5.7 2.3]
[5.6 4.9 2. ]
[7.7 6.7 2. ]
[6.3 4.9 1.8]
[6.7 5.7 2.1]
[7.2 6. 1.8]
[6.2 4.8 1.8]
[6.1 4.9 1.8]
[6.4 5.6 2.1]
[7.2 5.8 1.6]
[7.4 6.1 1.9]
[7.9 6.4 2. ]
[6.4 5.6 2.2]
[6.3 5.1 1.5]
[6.1 5.6 1.4]
[7.7 6.1 2.3]
[6.3 5.6 2.4]
[6.4 5.5 1.8]
[6. 4.8 1.8]
[6.9 5.4 2.1]
[6.7 5.6 2.4]
[6.9 5.1 2.3]
[5.8 5.1 1.9]
[6.8 5.9 2.3]
[6.7 5.7 2.5]
[6.7 5.2 2.3]
[6.3 5. 1.9]
[6.5 5.2 2. ]
[6.2 5.4 2.3]
[5.9 5.1 1.8]]
特征选择_过滤式_相关系数
皮尔森相关系数
反映变量之间相关关系密切程度的统计指标
数据计算
#过滤低方差特征 + 计算相关系数DEMO
#皮尔森相关系数，计算特征与目标变量之间的相关度
from scipy.stats import pearsonr
from sklearn.datasets import load_iris
from sklearn.feature_selection import VarianceThreshold
import pandas as pd
def variance_demo():
iris = load_iris()
data = pd.DataFrame(iris.data, columns = ['sepal length', 'sepal width', 'petal length', 'petal width'])
data_new = data.iloc[:, :4].values
print("data_new:\n", data_new)
transfer = VarianceThreshold(threshold = 0.5)
data_variance_value = transfer.fit_transform(data_new)
print("data_variance_value:\n", data_variance_value)
#计算两个变量之间的相关系数
r1 = pearsonr(data['sepal length'], data['petal length'])
print("sepal length与petal length的相关系数：\n", r1)
r2 = pearsonr(data['petal length'], data['petal width'])
print("petal length与petal width的相关系数：\n", r2)
import matplotlib.pyplot as plt
plt.scatter(data['petal length'], data['petal width'])
plt.show()
return None
if __name__ == '__main__':
variance_demo()
输出结果：
data_new:
[[5.1 3.5 1.4 0.2]
[4.9 3. 1.4 0.2]
[4.7 3.2 1.3 0.2]
[4.6 3.1 1.5 0.2]
[5. 3.6 1.4 0.2]
[5.4 3.9 1.7 0.4]
[4.6 3.4 1.4 0.3]
[5. 3.4 1.5 0.2]
[4.4 2.9 1.4 0.2]
[4.9 3.1 1.5 0.1]
[5.4 3.7 1.5 0.2]
[4.8 3.4 1.6 0.2]
[4.8 3. 1.4 0.1]
[4.3 3. 1.1 0.1]
[5.8 4. 1.2 0.2]
[5.7 4.4 1.5 0.4]
[5.4 3.9 1.3 0.4]
[5.1 3.5 1.4 0.3]
[5.7 3.8 1.7 0.3]
[5.1 3.8 1.5 0.3]
[5.4 3.4 1.7 0.2]
[5.1 3.7 1.5 0.4]
[4.6 3.6 1. 0.2]
[5.1 3.3 1.7 0.5]
[4.8 3.4 1.9 0.2]
[5. 3. 1.6 0.2]
[5. 3.4 1.6 0.4]
[5.2 3.5 1.5 0.2]
[5.2 3.4 1.4 0.2]
[4.7 3.2 1.6 0.2]
[4.8 3.1 1.6 0.2]
[5.4 3.4 1.5 0.4]
[5.2 4.1 1.5 0.1]
[5.5 4.2 1.4 0.2]
[4.9 3.1 1.5 0.1]
[5. 3.2 1.2 0.2]
[5.5 3.5 1.3 0.2]
[4.9 3.1 1.5 0.1]
[4.4 3. 1.3 0.2]
[5.1 3.4 1.5 0.2]
[5. 3.5 1.3 0.3]
[4.5 2.3 1.3 0.3]
[4.4 3.2 1.3 0.2]
[5. 3.5 1.6 0.6]
[5.1 3.8 1.9 0.4]
[4.8 3. 1.4 0.3]
[5.1 3.8 1.6 0.2]
[4.6 3.2 1.4 0.2]
[5.3 3.7 1.5 0.2]
[5. 3.3 1.4 0.2]
[7. 3.2 4.7 1.4]
[6.4 3.2 4.5 1.5]
[6.9 3.1 4.9 1.5]
[5.5 2.3 4. 1.3]
[6.5 2.8 4.6 1.5]
[5.7 2.8 4.5 1.3]
[6.3 3.3 4.7 1.6]
[4.9 2.4 3.3 1. ]
[6.6 2.9 4.6 1.3]
[5.2 2.7 3.9 1.4]
[5. 2. 3.5 1. ]
[5.9 3. 4.2 1.5]
[6. 2.2 4. 1. ]
[6.1 2.9 4.7 1.4]
[5.6 2.9 3.6 1.3]
[6.7 3.1 4.4 1.4]
[5.6 3. 4.5 1.5]
[5.8 2.7 4.1 1. ]
[6.2 2.2 4.5 1.5]
[5.6 2.5 3.9 1.1]
[5.9 3.2 4.8 1.8]
[6.1 2.8 4. 1.3]
[6.3 2.5 4.9 1.5]
[6.1 2.8 4.7 1.2]
[6.4 2.9 4.3 1.3]
[6.6 3. 4.4 1.4]
[6.8 2.8 4.8 1.4]
[6.7 3. 5. 1.7]
[6. 2.9 4.5 1.5]
[5.7 2.6 3.5 1. ]
[5.5 2.4 3.8 1.1]
[5.5 2.4 3.7 1. ]
[5.8 2.7 3.9 1.2]
[6. 2.7 5.1 1.6]
[5.4 3. 4.5 1.5]
[6. 3.4 4.5 1.6]
[6.7 3.1 4.7 1.5]
[6.3 2.3 4.4 1.3]
[5.6 3. 4.1 1.3]
[5.5 2.5 4. 1.3]
[5.5 2.6 4.4 1.2]
[6.1 3. 4.6 1.4]
[5.8 2.6 4. 1.2]
[5. 2.3 3.3 1. ]
[5.6 2.7 4.2 1.3]
[5.7 3. 4.2 1.2]
[5.7 2.9 4.2 1.3]
[6.2 2.9 4.3 1.3]
[5.1 2.5 3. 1.1]
[5.7 2.8 4.1 1.3]
[6.3 3.3 6. 2.5]
[5.8 2.7 5.1 1.9]
[7.1 3. 5.9 2.1]
[6.3 2.9 5.6 1.8]
[6.5 3. 5.8 2.2]
[7.6 3. 6.6 2.1]
[4.9 2.5 4.5 1.7]
[7.3 2.9 6.3 1.8]
[6.7 2.5 5.8 1.8]
[7.2 3.6 6.1 2.5]
[6.5 3.2 5.1 2. ]
[6.4 2.7 5.3 1.9]
[6.8 3. 5.5 2.1]
[5.7 2.5 5. 2. ]
[5.8 2.8 5.1 2.4]
[6.4 3.2 5.3 2.3]
[6.5 3. 5.5 1.8]
[7.7 3.8 6.7 2.2]
[7.7 2.6 6.9 2.3]
[6. 2.2 5. 1.5]
[6.9 3.2 5.7 2.3]
[5.6 2.8 4.9 2. ]
[7.7 2.8 6.7 2. ]
[6.3 2.7 4.9 1.8]
[6.7 3.3 5.7 2.1]
[7.2 3.2 6. 1.8]
[6.2 2.8 4.8 1.8]
[6.1 3. 4.9 1.8]
[6.4 2.8 5.6 2.1]
[7.2 3. 5.8 1.6]
[7.4 2.8 6.1 1.9]
[7.9 3.8 6.4 2. ]
[6.4 2.8 5.6 2.2]
[6.3 2.8 5.1 1.5]
[6.1 2.6 5.6 1.4]
[7.7 3. 6.1 2.3]
[6.3 3.4 5.6 2.4]
[6.4 3.1 5.5 1.8]
[6. 3. 4.8 1.8]
[6.9 3.1 5.4 2.1]
[6.7 3.1 5.6 2.4]
[6.9 3.1 5.1 2.3]
[5.8 2.7 5.1 1.9]
[6.8 3.2 5.9 2.3]
[6.7 3.3 5.7 2.5]
[6.7 3. 5.2 2.3]
[6.3 2.5 5. 1.9]
[6.5 3. 5.2 2. ]
[6.2 3.4 5.4 2.3]
[5.9 3. 5.1 1.8]]
data_variance_value:
[[5.1 1.4 0.2]
[4.9 1.4 0.2]
[4.7 1.3 0.2]
[4.6 1.5 0.2]
[5. 1.4 0.2]
[5.4 1.7 0.4]
[4.6 1.4 0.3]
[5. 1.5 0.2]
[4.4 1.4 0.2]
[4.9 1.5 0.1]
[5.4 1.5 0.2]
[4.8 1.6 0.2]
[4.8 1.4 0.1]
[4.3 1.1 0.1]
[5.8 1.2 0.2]
[5.7 1.5 0.4]
[5.4 1.3 0.4]
[5.1 1.4 0.3]
[5.7 1.7 0.3]
[5.1 1.5 0.3]
[5.4 1.7 0.2]
[5.1 1.5 0.4]
[4.6 1. 0.2]
[5.1 1.7 0.5]
[4.8 1.9 0.2]
[5. 1.6 0.2]
[5. 1.6 0.4]
[5.2 1.5 0.2]
[5.2 1.4 0.2]
[4.7 1.6 0.2]
[4.8 1.6 0.2]
[5.4 1.5 0.4]
[5.2 1.5 0.1]
[5.5 1.4 0.2]
[4.9 1.5 0.1]
[5. 1.2 0.2]
[5.5 1.3 0.2]
[4.9 1.5 0.1]
[4.4 1.3 0.2]
[5.1 1.5 0.2]
[5. 1.3 0.3]
[4.5 1.3 0.3]
[4.4 1.3 0.2]
[5. 1.6 0.6]
[5.1 1.9 0.4]
[4.8 1.4 0.3]
[5.1 1.6 0.2]
[4.6 1.4 0.2]
[5.3 1.5 0.2]
[5. 1.4 0.2]
[7. 4.7 1.4]
[6.4 4.5 1.5]
[6.9 4.9 1.5]
[5.5 4. 1.3]
[6.5 4.6 1.5]
[5.7 4.5 1.3]
[6.3 4.7 1.6]
[4.9 3.3 1. ]
[6.6 4.6 1.3]
[5.2 3.9 1.4]
[5. 3.5 1. ]
[5.9 4.2 1.5]
[6. 4. 1. ]
[6.1 4.7 1.4]
[5.6 3.6 1.3]
[6.7 4.4 1.4]
[5.6 4.5 1.5]
[5.8 4.1 1. ]
[6.2 4.5 1.5]
[5.6 3.9 1.1]
[5.9 4.8 1.8]
[6.1 4. 1.3]
[6.3 4.9 1.5]
[6.1 4.7 1.2]
[6.4 4.3 1.3]
[6.6 4.4 1.4]
[6.8 4.8 1.4]
[6.7 5. 1.7]
[6. 4.5 1.5]
[5.7 3.5 1. ]
[5.5 3.8 1.1]
[5.5 3.7 1. ]
[5.8 3.9 1.2]
[6. 5.1 1.6]
[5.4 4.5 1.5]
[6. 4.5 1.6]
[6.7 4.7 1.5]
[6.3 4.4 1.3]
[5.6 4.1 1.3]
[5.5 4. 1.3]
[5.5 4.4 1.2]
[6.1 4.6 1.4]
[5.8 4. 1.2]
[5. 3.3 1. ]
[5.6 4.2 1.3]
[5.7 4.2 1.2]
[5.7 4.2 1.3]
[6.2 4.3 1.3]
[5.1 3. 1.1]
[5.7 4.1 1.3]
[6.3 6. 2.5]
[5.8 5.1 1.9]
[7.1 5.9 2.1]
[6.3 5.6 1.8]
[6.5 5.8 2.2]
[7.6 6.6 2.1]
[4.9 4.5 1.7]
[7.3 6.3 1.8]
[6.7 5.8 1.8]
[7.2 6.1 2.5]
[6.5 5.1 2. ]
[6.4 5.3 1.9]
[6.8 5.5 2.1]
[5.7 5. 2. ]
[5.8 5.1 2.4]
[6.4 5.3 2.3]
[6.5 5.5 1.8]
[7.7 6.7 2.2]
[7.7 6.9 2.3]
[6. 5. 1.5]
[6.9 5.7 2.3]
[5.6 4.9 2. ]
[7.7 6.7 2. ]
[6.3 4.9 1.8]
[6.7 5.7 2.1]
[7.2 6. 1.8]
[6.2 4.8 1.8]
[6.1 4.9 1.8]
[6.4 5.6 2.1]
[7.2 5.8 1.6]
[7.4 6.1 1.9]
[7.9 6.4 2. ]
[6.4 5.6 2.2]
[6.3 5.1 1.5]
[6.1 5.6 1.4]
[7.7 6.1 2.3]
[6.3 5.6 2.4]
[6.4 5.5 1.8]
[6. 4.8 1.8]
[6.9 5.4 2.1]
[6.7 5.6 2.4]
[6.9 5.1 2.3]
[5.8 5.1 1.9]
[6.8 5.9 2.3]
[6.7 5.7 2.5]
[6.7 5.2 2.3]
[6.3 5. 1.9]
[6.5 5.2 2. ]
[6.2 5.4 2.3]
[5.9 5.1 1.8]]
sepal length与petal length的相关系数：
(0.8717541573048712, 1.0384540627941809e-47)
petal length与petal width的相关系数：
(0.9627570970509662, 5.776660988495158e-86)
主成分分析PCA
定义：高维数据转化为地位数据的过程，在此过程中可能会舍弃原有数据、创造新的变量
作用：是数据维数的压缩，尽可能降低原数据的维数（复杂度），损失少量信息
应用：回归分析或者据类分析当中
API
sklearn.decomposition.PCA(n_components = None)
将数据分解为较低维数空间
n_components:
小数：表示包留百分之多少的信息
整数：减少到多少特征
PCA.fit_transform(X)
X:numpy array格式的数据[n_samples, n_features]
返回：转换后指定维度的array
数据计算
from sklearn.decomposition import PCA
from sklearn.datasets import load_iris
import pandas as pd
def pca_demo():
iris = load_iris()
data = pd.DataFrame(iris.data, columns = iris.feature_names)
data_array = data.iloc[:, :4].values
print("data_array:\n", data_array)
transfer = PCA(n_components = 2)
#ransfer = PCA(n_components = 0.95)
data_pca_value = transfer.fit_transform(data_array)
print("data_pca_value:\n", data_pca_value)
return None
if __name__ == '__main__':
pca_demo()
输出结果：
data_array:
[[5.1 3.5 1.4 0.2]
[4.9 3. 1.4 0.2]
[4.7 3.2 1.3 0.2]
[4.6 3.1 1.5 0.2]
[5. 3.6 1.4 0.2]
[5.4 3.9 1.7 0.4]
[4.6 3.4 1.4 0.3]
[5. 3.4 1.5 0.2]
[4.4 2.9 1.4 0.2]
[4.9 3.1 1.5 0.1]
[5.4 3.7 1.5 0.2]
[4.8 3.4 1.6 0.2]
[4.8 3. 1.4 0.1]
[4.3 3. 1.1 0.1]
[5.8 4. 1.2 0.2]
[5.7 4.4 1.5 0.4]
[5.4 3.9 1.3 0.4]
[5.1 3.5 1.4 0.3]
[5.7 3.8 1.7 0.3]
[5.1 3.8 1.5 0.3]
[5.4 3.4 1.7 0.2]
[5.1 3.7 1.5 0.4]
[4.6 3.6 1. 0.2]
[5.1 3.3 1.7 0.5]
[4.8 3.4 1.9 0.2]
[5. 3. 1.6 0.2]
[5. 3.4 1.6 0.4]
[5.2 3.5 1.5 0.2]
[5.2 3.4 1.4 0.2]
[4.7 3.2 1.6 0.2]
[4.8 3.1 1.6 0.2]
[5.4 3.4 1.5 0.4]
[5.2 4.1 1.5 0.1]
[5.5 4.2 1.4 0.2]
[4.9 3.1 1.5 0.1]
[5. 3.2 1.2 0.2]
[5.5 3.5 1.3 0.2]
[4.9 3.1 1.5 0.1]
[4.4 3. 1.3 0.2]
[5.1 3.4 1.5 0.2]
[5. 3.5 1.3 0.3]
[4.5 2.3 1.3 0.3]
[4.4 3.2 1.3 0.2]
[5. 3.5 1.6 0.6]
[5.1 3.8 1.9 0.4]
[4.8 3. 1.4 0.3]
[5.1 3.8 1.6 0.2]
[4.6 3.2 1.4 0.2]
[5.3 3.7 1.5 0.2]
[5. 3.3 1.4 0.2]
[7. 3.2 4.7 1.4]
[6.4 3.2 4.5 1.5]
[6.9 3.1 4.9 1.5]
[5.5 2.3 4. 1.3]
[6.5 2.8 4.6 1.5]
[5.7 2.8 4.5 1.3]
[6.3 3.3 4.7 1.6]
[4.9 2.4 3.3 1. ]
[6.6 2.9 4.6 1.3]
[5.2 2.7 3.9 1.4]
[5. 2. 3.5 1. ]
[5.9 3. 4.2 1.5]
[6. 2.2 4. 1. ]
[6.1 2.9 4.7 1.4]
[5.6 2.9 3.6 1.3]
[6.7 3.1 4.4 1.4]
[5.6 3. 4.5 1.5]
[5.8 2.7 4.1 1. ]
[6.2 2.2 4.5 1.5]
[5.6 2.5 3.9 1.1]
[5.9 3.2 4.8 1.8]
[6.1 2.8 4. 1.3]
[6.3 2.5 4.9 1.5]
[6.1 2.8 4.7 1.2]
[6.4 2.9 4.3 1.3]
[6.6 3. 4.4 1.4]
[6.8 2.8 4.8 1.4]
[6.7 3. 5. 1.7]
[6. 2.9 4.5 1.5]
[5.7 2.6 3.5 1. ]
[5.5 2.4 3.8 1.1]
[5.5 2.4 3.7 1. ]
[5.8 2.7 3.9 1.2]
[6. 2.7 5.1 1.6]
[5.4 3. 4.5 1.5]
[6. 3.4 4.5 1.6]
[6.7 3.1 4.7 1.5]
[6.3 2.3 4.4 1.3]
[5.6 3. 4.1 1.3]
[5.5 2.5 4. 1.3]
[5.5 2.6 4.4 1.2]
[6.1 3. 4.6 1.4]
[5.8 2.6 4. 1.2]
[5. 2.3 3.3 1. ]
[5.6 2.7 4.2 1.3]
[5.7 3. 4.2 1.2]
[5.7 2.9 4.2 1.3]
[6.2 2.9 4.3 1.3]
[5.1 2.5 3. 1.1]
[5.7 2.8 4.1 1.3]
[6.3 3.3 6. 2.5]
[5.8 2.7 5.1 1.9]
[7.1 3. 5.9 2.1]
[6.3 2.9 5.6 1.8]
[6.5 3. 5.8 2.2]
[7.6 3. 6.6 2.1]
[4.9 2.5 4.5 1.7]
[7.3 2.9 6.3 1.8]
[6.7 2.5 5.8 1.8]
[7.2 3.6 6.1 2.5]
[6.5 3.2 5.1 2. ]
[6.4 2.7 5.3 1.9]
[6.8 3. 5.5 2.1]
[5.7 2.5 5. 2. ]
[5.8 2.8 5.1 2.4]
[6.4 3.2 5.3 2.3]
[6.5 3. 5.5 1.8]
[7.7 3.8 6.7 2.2]
[7.7 2.6 6.9 2.3]
[6. 2.2 5. 1.5]
[6.9 3.2 5.7 2.3]
[5.6 2.8 4.9 2. ]
[7.7 2.8 6.7 2. ]
[6.3 2.7 4.9 1.8]
[6.7 3.3 5.7 2.1]
[7.2 3.2 6. 1.8]
[6.2 2.8 4.8 1.8]
[6.1 3. 4.9 1.8]
[6.4 2.8 5.6 2.1]
[7.2 3. 5.8 1.6]
[7.4 2.8 6.1 1.9]
[7.9 3.8 6.4 2. ]
[6.4 2.8 5.6 2.2]
[6.3 2.8 5.1 1.5]
[6.1 2.6 5.6 1.4]
[7.7 3. 6.1 2.3]
[6.3 3.4 5.6 2.4]
[6.4 3.1 5.5 1.8]
[6. 3. 4.8 1.8]
[6.9 3.1 5.4 2.1]
[6.7 3.1 5.6 2.4]
[6.9 3.1 5.1 2.3]
[5.8 2.7 5.1 1.9]
[6.8 3.2 5.9 2.3]
[6.7 3.3 5.7 2.5]
[6.7 3. 5.2 2.3]
[6.3 2.5 5. 1.9]
[6.5 3. 5.2 2. ]
[6.2 3.4 5.4 2.3]
[5.9 3. 5.1 1.8]]
data_pca_value:
[[-2.68420713 0.32660731]
[-2.71539062 -0.16955685]
[-2.88981954 -0.13734561]
[-2.7464372 -0.31112432]
[-2.72859298 0.33392456]
[-2.27989736 0.74778271]
[-2.82089068 -0.08210451]
[-2.62648199 0.17040535]
[-2.88795857 -0.57079803]
[-2.67384469 -0.1066917 ]
[-2.50652679 0.65193501]
[-2.61314272 0.02152063]
[-2.78743398 -0.22774019]
[-3.22520045 -0.50327991]
[-2.64354322 1.1861949 ]
[-2.38386932 1.34475434]
[-2.6225262 0.81808967]
[-2.64832273 0.31913667]
[-2.19907796 0.87924409]
[-2.58734619 0.52047364]
[-2.3105317 0.39786782]
[-2.54323491 0.44003175]
[-3.21585769 0.14161557]
[-2.30312854 0.10552268]
[-2.35617109 -0.03120959]
[-2.50791723 -0.13905634]
[-2.469056 0.13788731]
[-2.56239095 0.37468456]
[-2.63982127 0.31929007]
[-2.63284791 -0.19007583]
[-2.58846205 -0.19739308]
[-2.41007734 0.41808001]
[-2.64763667 0.81998263]
[-2.59715948 1.10002193]
[-2.67384469 -0.1066917 ]
[-2.86699985 0.0771931 ]
[-2.62522846 0.60680001]
[-2.67384469 -0.1066917 ]
[-2.98184266 -0.48025005]
[-2.59032303 0.23605934]
[-2.77013891 0.27105942]
[-2.85221108 -0.93286537]
[-2.99829644 -0.33430757]
[-2.4055141 0.19591726]
[-2.20883295 0.44269603]
[-2.71566519 -0.24268148]
[-2.53757337 0.51036755]
[-2.8403213 -0.22057634]
[-2.54268576 0.58628103]
[-2.70391231 0.11501085]
[ 1.28479459 0.68543919]
[ 0.93241075 0.31919809]
[ 1.46406132 0.50418983]
[ 0.18096721 -0.82560394]
[ 1.08713449 0.07539039]
[ 0.64043675 -0.41732348]
[ 1.09522371 0.28389121]
[-0.75146714 -1.00110751]
[ 1.04329778 0.22895691]
[-0.01019007 -0.72057487]
[-0.5110862 -1.26249195]
[ 0.51109806 -0.10228411]
[ 0.26233576 -0.5478933 ]
[ 0.98404455 -0.12436042]
[-0.174864 -0.25181557]
[ 0.92757294 0.46823621]
[ 0.65959279 -0.35197629]
[ 0.23454059 -0.33192183]
[ 0.94236171 -0.54182226]
[ 0.0432464 -0.58148945]
[ 1.11624072 -0.08421401]
[ 0.35678657 -0.06682383]
[ 1.29646885 -0.32756152]
[ 0.92050265 -0.18239036]
[ 0.71400821 0.15037915]
[ 0.89964086 0.32961098]
[ 1.33104142 0.24466952]
[ 1.55739627 0.26739258]
[ 0.81245555 -0.16233157]
[-0.30733476 -0.36508661]
[-0.07034289 -0.70253793]
[-0.19188449 -0.67749054]
[ 0.13499495 -0.31170964]
[ 1.37873698 -0.42120514]
[ 0.58727485 -0.48328427]
[ 0.8072055 0.19505396]
[ 1.22042897 0.40803534]
[ 0.81286779 -0.370679 ]
[ 0.24519516 -0.26672804]
[ 0.16451343 -0.67966147]
[ 0.46303099 -0.66952655]
[ 0.89016045 -0.03381244]
[ 0.22887905 -0.40225762]
[-0.70708128 -1.00842476]
[ 0.35553304 -0.50321849]
[ 0.33112695 -0.21118014]
[ 0.37523823 -0.29162202]
[ 0.64169028 0.01907118]
[-0.90846333 -0.75156873]
[ 0.29780791 -0.34701652]
[ 2.53172698 -0.01184224]
[ 1.41407223 -0.57492506]
[ 2.61648461 0.34193529]
[ 1.97081495 -0.18112569]
[ 2.34975798 -0.04188255]
[ 3.39687992 0.54716805]
[ 0.51938325 -1.19135169]
[ 2.9320051 0.35237701]
[ 2.31967279 -0.24554817]
[ 2.91813423 0.78038063]
[ 1.66193495 0.2420384 ]
[ 1.80234045 -0.21615461]
[ 2.16537886 0.21528028]
[ 1.34459422 -0.77641543]
[ 1.5852673 -0.53930705]
[ 1.90474358 0.11881899]
[ 1.94924878 0.04073026]
[ 3.48876538 1.17154454]
[ 3.79468686 0.25326557]
[ 1.29832982 -0.76101394]
[ 2.42816726 0.37678197]
[ 1.19809737 -0.60557896]
[ 3.49926548 0.45677347]
[ 1.38766825 -0.20403099]
[ 2.27585365 0.33338653]
[ 2.61419383 0.55836695]
[ 1.25762518 -0.179137 ]
[ 1.29066965 -0.11642525]
[ 2.12285398 -0.21085488]
[ 2.3875644 0.46251925]
[ 2.84096093 0.37274259]
[ 3.2323429 1.37052404]
[ 2.15873837 -0.21832553]
[ 1.4431026 -0.14380129]
[ 1.77964011 -0.50146479]
[ 3.07652162 0.68576444]
[ 2.14498686 0.13890661]
[ 1.90486293 0.04804751]
[ 1.16885347 -0.1645025 ]
[ 2.10765373 0.37148225]
[ 2.31430339 0.18260885]
[ 1.92245088 0.40927118]
[ 1.41407223 -0.57492506]
[ 2.56332271 0.2759745 ]
[ 2.41939122 0.30350394]
[ 1.94401705 0.18741522]
[ 1.52566363 -0.37502085]
[ 1.76404594 0.07851919]
[ 1.90162908 0.11587675]
[ 1.38966613 -0.28288671]]
