Python下的机器学习工具sklearn--数据预处理
1.数据标准化(Standardization or Mean Removal and Variance Scaling)
进行标准化缩放的数据均值为0,具有单位方差。
from sklearn import preprocessing X = [[1., -1., 2.], [2., 0., 0.], [0., 1., -1.]] X_scaled = preprocessing.scale(X) print X_scaled #[[ 0. -1.22474487 1.33630621] # [ 1.22474487 0. -0.26726124] # [-1.22474487 1.22474487 -1.06904497]] print X_scaled.mean(axis = 0) print X_scaled.std(axis = 0) #[ 0. 0. 0.] #[ 1. 1. 1.]
同样我们也可以通过preprocessing模块提供的Scaler(StandardScaler 0.15以后版本)工具类来实现这个功能:
scaler = preprocessing.StandardScaler().fit(X) print scaler #StandardScaler(copy=True, with_mean=True, with_std=True) print scaler.mean_ #[ 1. 0. 0.33333333] print scaler.scale_#之前版本scaler.std_ #[ 0.81649658 0.81649658 1.24721913] print scaler.transform(X) #[[ 0. -1.22474487 1.33630621] # [ 1.22474487 0. -0.26726124] # [-1.22474487 1.22474487 -1.06904497]]
注:上述代码与下面代码等价
scaler = preprocessing.StandardScaler().fit_transform(X) print scaler #[[ 0. -1.22474487 1.33630621] # [ 1.22474487 0. -0.26726124] # [-1.22474487 1.22474487 -1.06904497]] print scaler.mean(axis = 0) #[ 0. 0. 0.] print scaler.std(axis = 0) #[ 1. 1. 1.]
2.数据规范化(Normalization)
把数据集中的每个样本所有数值缩放到(-1,1)之间。
X = [[1., -1., 2.], [2., 0., 0.], [0., 1., -1.]] X_normalized = preprocessing.normalize(X) print X_normalized #[[ 0.40824829 -0.40824829 0.81649658] # [ 1. 0. 0. ] # [ 0. 0.70710678 -0.70710678]]
等价于:
normalizer = preprocessing.Normalizer().fit(X) print normalizer #Normalizer(copy=True, norm='l2') print normalizer.transform(X) #[[ 0.40824829 -0.40824829 0.81649658] # [ 1. 0. 0. ] # [ 0. 0.70710678 -0.70710678]]
注:上述代码与下面代码等价
normalizer = preprocessing.Normalizer().fit_transform(X) print normalizer #[[ 0.40824829 -0.40824829 0.81649658] # [ 1. 0. 0. ] # [ 0. 0.70710678 -0.70710678]]
3.二进制化(Binarization)
将数值型数据转化为布尔型的二值数据,可以设置一个阈值(threshold)。
X = [[1., -1., 2.], [2., 0., 0.], [0., 1., -1.]] binarizer = preprocessing.Binarizer().fit(X) # 默认阈值为0.0 print binarizer #Binarizer(copy=True, threshold=0.0) print binarizer.transform(X) #[[ 1. 0. 1.] # [ 1. 0. 0.] # [ 0. 1. 0.]] binarizer = preprocessing.Binarizer(threshold=1.1) # 设定阈值为1.1 print binarizer.transform(X) #[[ 0. 0. 1.] # [ 1. 0. 0.] # [ 0. 0. 0.]]
4.标签预处理(Label preprocessing)
4.1)标签二值化(Label binarization)
LabelBinarizer通常用于通过一个多类标签(label)列表,创建一个label指示器矩阵.
lb = preprocessing.LabelBinarizer() print lb.fit([1, 2, 6, 4, 2]) #LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False) print lb.classes_ #[1 2 4 6] print lb.transform([1, 6]) #[[1 0 0 0] # [0 0 0 1]]
4.2)标签编码(Label encoding)
le = preprocessing.LabelEncoder() print le.fit([1, 2, 2, 6]) #LabelEncoder() print le.classes_ #[1 2 6] print le.transform([1, 1, 2, 6]) #[0 0 1 2] print le.inverse_transform([0, 0, 1, 2]) #[1 1 2 6]
也可以用于非数值类型的标签到数值类型标签的转化:
le = preprocessing.LabelEncoder() print le.fit(["paris", "paris", "tokyo", "amsterdam"]) #LabelEncoder() print list(le.classes_) #['amsterdam', 'paris', 'tokyo'] print le.transform(["tokyo", "tokyo", "paris"]) #[2 2 1] print list(le.inverse_transform([2, 2, 1])) #['tokyo', 'tokyo', 'paris']