sklearn.model_selection.KFold

K折交差検証:sklearn.model_selection.KFold(n_splits=3, shuffle=False, random_state=None)
構想:トレーニング/テストデータセットをn_に分割するsplits個の反発サブセット、毎回そのうちの1つのサブセットを検証セットとして使用し、残りのn_splits-1個をトレーニングセットとしてn_splits次訓練とテスト、n_を得るsplits個の結果
注意点:等分化できないデータセットの前のn_samples % n_splitsサブセット所有n_samples//n_splits+1サンプル、残りのサブセットはn_のみsamples//n_splitsサンプル
パラメータの説明:
n_splits:いくつかの等分化を表す
shuffle:区分ごとにトランプを洗うかどうか
①Falsesの場合、その効果はrandom_に等しいstateは整数に等しく、分割ごとの結果は同じです.
②Trueの場合、区分ごとに結果が異なります.
random_state:ランダムシード数
属性:
①get_n_splits(X=None,y=None,groups=None):パラメータn_を取得するsplitsの値
②split(X,y=None,groups=None):データセットをトレーニングセットとテストセットに分割し、インデックスジェネレータに戻る
均等に分けられない栗により,異なるパラメータ値を設定し,その結果を観察した.
①shuffle=Falseを設定し、2回実行し、2回結果が同じであることを発見

In [1]: from sklearn.model_selection import KFold
   ...: import numpy as np
   ...: X = np.arange(24).reshape(12,2)
   ...: y = np.random.choice([1,2],12,p=[0.4,0.6])
   ...: kf = KFold(n_splits=5,shuffle=False)
   ...: for train_index , test_index in kf.split(X):
   ...:     print('train_index:%s , test_index: %s ' %(train_index,test_index))
   ...:
   ...:
train_index:[ 3  4  5  6  7  8  9 10 11] , test_index: [0 1 2]
train_index:[ 0  1  2  6  7  8  9 10 11] , test_index: [3 4 5]
train_index:[ 0  1  2  3  4  5  8  9 10 11] , test_index: [6 7]
train_index:[ 0  1  2  3  4  5  6  7 10 11] , test_index: [8 9]
train_index:[0 1 2 3 4 5 6 7 8 9] , test_index: [10 11]

In [2]: from sklearn.model_selection import KFold
   ...: import numpy as np
   ...: X = np.arange(24).reshape(12,2)
   ...: y = np.random.choice([1,2],12,p=[0.4,0.6])
   ...: kf = KFold(n_splits=5,shuffle=False)
   ...: for train_index , test_index in kf.split(X):
   ...:     print('train_index:%s , test_index: %s ' %(train_index,test_index))
   ...:
   ...:
train_index:[ 3  4  5  6  7  8  9 10 11] , test_index: [0 1 2]
train_index:[ 0  1  2  6  7  8  9 10 11] , test_index: [3 4 5]
train_index:[ 0  1  2  3  4  5  8  9 10 11] , test_index: [6 7]
train_index:[ 0  1  2  3  4  5  6  7 10 11] , test_index: [8 9]
train_index:[0 1 2 3 4 5 6 7 8 9] , test_index: [10 11]

②shuffle=True設定時に2回運転し、2回運転の結果が異なることを発見

In [3]: from sklearn.model_selection import KFold
   ...: import numpy as np
   ...: X = np.arange(24).reshape(12,2)
   ...: y = np.random.choice([1,2],12,p=[0.4,0.6])
   ...: kf = KFold(n_splits=5,shuffle=True)
   ...: for train_index , test_index in kf.split(X):
   ...:     print('train_index:%s , test_index: %s ' %(train_index,test_index))
   ...:
   ...:
train_index:[ 0  1  2  4  5  6  7  8 10] , test_index: [ 3  9 11]
train_index:[ 0  1  2  3  4  5  9 10 11] , test_index: [6 7 8]
train_index:[ 2  3  4  5  6  7  8  9 10 11] , test_index: [0 1]
train_index:[ 0  1  3  4  5  6  7  8  9 11] , test_index: [ 2 10]
train_index:[ 0  1  2  3  6  7  8  9 10 11] , test_index: [4 5]

In [4]: from sklearn.model_selection import KFold
   ...: import numpy as np
   ...: X = np.arange(24).reshape(12,2)
   ...: y = np.random.choice([1,2],12,p=[0.4,0.6])
   ...: kf = KFold(n_splits=5,shuffle=True)
   ...: for train_index , test_index in kf.split(X):
   ...:     print('train_index:%s , test_index: %s ' %(train_index,test_index))
   ...:
   ...:
train_index:[ 0  1  2  3  4  5  7  8 11] , test_index: [ 6  9 10]
train_index:[ 2  3  4  5  6  8  9 10 11] , test_index: [0 1 7]
train_index:[ 0  1  3  5  6  7  8  9 10 11] , test_index: [2 4]
train_index:[ 0  1  2  3  4  6  7  9 10 11] , test_index: [5 8]
train_index:[ 0  1  2  4  5  6  7  8  9 10] , test_index: [ 3 11]

③shuffle=Trueとrandom_の設定state=整数で、実行するたびに結果が同じであることがわかります.

In [5]: from sklearn.model_selection import KFold
   ...: import numpy as np
   ...: X = np.arange(24).reshape(12,2)
   ...: y = np.random.choice([1,2],12,p=[0.4,0.6])
   ...: kf = KFold(n_splits=5,shuffle=True,random_state=0)
   ...: for train_index , test_index in kf.split(X):
   ...:     print('train_index:%s , test_index: %s ' %(train_index,test_index))
   ...:
   ...:
train_index:[ 0  1  2  3  5  7  8  9 10] , test_index: [ 4  6 11]
train_index:[ 0  1  3  4  5  6  7  9 11] , test_index: [ 2  8 10]
train_index:[ 0  2  3  4  5  6  8  9 10 11] , test_index: [1 7]
train_index:[ 0  1  2  4  5  6  7  8 10 11] , test_index: [3 9]
train_index:[ 1  2  3  4  6  7  8  9 10 11] , test_index: [0 5]

In [6]: from sklearn.model_selection import KFold
   ...: import numpy as np
   ...: X = np.arange(24).reshape(12,2)
   ...: y = np.random.choice([1,2],12,p=[0.4,0.6])
   ...: kf = KFold(n_splits=5,shuffle=True,random_state=0)
   ...: for train_index , test_index in kf.split(X):
   ...:     print('train_index:%s , test_index: %s ' %(train_index,test_index))
   ...:
   ...:
train_index:[ 0  1  2  3  5  7  8  9 10] , test_index: [ 4  6 11]
train_index:[ 0  1  3  4  5  6  7  9 11] , test_index: [ 2  8 10]
train_index:[ 0  2  3  4  5  6  8  9 10 11] , test_index: [1 7]
train_index:[ 0  1  2  4  5  6  7  8 10 11] , test_index: [3 9]
train_index:[ 1  2  3  4  6  7  8  9 10 11] , test_index: [0 5]

④n_splits属性値取得方式

In [8]: kf.split(X)
Out[8]: 

In [9]: kf.get_n_splits()
Out[9]: 5

In [10]: kf.n_splits
Out[10]: 5