python資料預處理

scikit-learn 提供的binarizer能夠將資料二元化

from  sklearn.preprocessing import binarizer

x = [[1,2,3,4,5],
[5,4,3,2,1],
[3,3,3,3,3],
[1,1,1,1,1]]
print("before transform:",x)
binarizer=binarizer(threshold=2.5)
print("after trandform :" , binarizer.trandform(x))

before transform: [[1, 2, 3, 4, 5], [5, 4, 3, 2, 1], [3, 3, 3, 3, 3], [1, 1, 1, 1, 1]]

after trandform : [[001
11][1110
0] [111
11][0000
0]]

from  sklearn.preprocessing import onehotencoder

x = [[1,2,3,4,5],
[5,4,3,2,1],
[3,3,3,3,3],
[1,1,1,1,1]]
print("before transform:",x)
encoder=onehotencoder(sparse=false)
encoder.fit(x)
print("active_features_:",encoder.active_features_)
print("feature_indices_",encoder.feature_indices_)
print("n_values_",encoder.n_values)
print("after transform:",encoder.transform([[1,2,3,4,5]]))

before transform: [[1, 2, 3, 4, 5], [5, 4, 3, 2, 1], [3, 3, 3, 3, 3], [1, 1, 1, 1, 1]]

active_features_: [ 135
78910
1214
1617
1819
2123
25]feature_indices_ [ 0611
1520
26]n_values_ auto
after transform: [[1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1.]]

from  sklearn.preprocessing import minmaxscaler

x = [[1,2,3,4,5],
[5,4,3,2,1],
[3,3,3,3,3],
[1,1,1,1,1]]
print("before transform:",x)
scaler=minmaxscaler(feature_range=(0,2))
scaler.fit(x)
print("min_is:", scaler.min_)
print("scale is",scaler.scale_)
print("data_max_ is",scaler.data_max_)
print("data_min_ is",scaler.data_min_)
print("data_range_ is",scaler.data_range_)
print("after transform  is",scaler.transform(x))

before transform: [[1, 2, 3, 4, 5], [5, 4, 3, 2, 1], [3, 3, 3, 3, 3], [1, 1, 1, 1, 1]]

min_is: [-0.5        -0.66666667 -1.         -0.66666667 -0.5       ]
scale is [0.5
0.66666667
1.0.66666667
0.5       ]
data_max_ is [5.
4.3.
4.5.]
data_min_ is [1.
1.1.
1.1.]
data_range_ is [4.
3.2.
3.4.]
after transform  is [[0.         0.66666667 2.         2.         2.        ]
[2.         2.         2.         0.66666667 0.        ]
[1.         1.33333333 2.         1.33333333 1.        ]
[0.         0.         0.         0.         0.        ]]

maxabsscaler

sklearn.preprocessing.maxabsscaler(copy=true)

standardscaler(z-score)

正則化

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