は、私は次のPythonコードを持っているのPython

Question

で一覧形式で出力

を取得：

from sklearn.datasets import load_iris 
iris = load_iris() 
from sklearn.linear_model import LogisticRegression 
logreg = LogisticRegression() 
from sklearn.model_selection import StratifiedKFold 
for n_splits in [2, 3, 5]:  
    skf = StratifiedKFold(n_splits=n_splits, shuffle=False, random_state=0) 
    X, y = iris.data, iris.target 
    for train_index, test_index in skf.split(X, y): 
     X_train, X_test = X[train_index], X[test_index] 
     y_train, y_test = y[train_index], y[test_index]  
     logreg_fit = LogisticRegression().fit(X_train, y_train) 
     logreg_scores = logreg_fit.score(X_test, y_test) 
     print('{}-fold STRATIFIED Cross-validation scores: {:.3f}'.format(
      n_splits, logreg_scores)) 

（Pythonの本と機械学習入門からと「http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold.split」）は、このの出力は次のようになります。

2-fold STRATIFIED Cross-validation scores: 0.947 
2-fold STRATIFIED Cross-validation scores: 0.947 
3-fold STRATIFIED Cross-validation scores: 0.961 
3-fold STRATIFIED Cross-validation scores: 0.922 
3-fold STRATIFIED Cross-validation scores: 0.958 
5-fold STRATIFIED Cross-validation scores: 1.000 
5-fold STRATIFIED Cross-validation scores: 0.967 
5-fold STRATIFIED Cross-validation scores: 0.933 
5-fold STRATIFIED Cross-validation scores: 0.900 
5-fold STRATIFIED Cross-validation scores: 1.000 

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私はこのように、すなわち、それぞれの折り目のため をリスト形式で出力を取得したいと思います：

2-fold STRATIFIED Cross-validation scores: [ 0.947 0.947] 
3-fold STRATIFIED Cross-validation scores: [ 0.961 0.922 0.958] 
5-fold STRATIFIED Cross-validation scores: [ 1.000 0.967 0.933 0.900 1.000] 

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私は本当にありがとうと思います。どうもありがとう。

Answer 1

これは基本的に結果をリストに保存してから、内側のループが完了すると印刷するのに役立ちます。

from sklearn.datasets import load_iris 
iris = load_iris() 
from sklearn.linear_model import LogisticRegression 
logreg = LogisticRegression() 
from sklearn.model_selection import StratifiedKFold 
for n_splits in [2, 3, 5]:  
    skf = StratifiedKFold(n_splits=n_splits, shuffle=False, random_state=0) 
    X, y = iris.data, iris.target 
    res = [] 
    for train_index, test_index in skf.split(X, y): 
     X_train, X_test = X[train_index], X[test_index] 
     y_train, y_test = y[train_index], y[test_index]  
     logreg_fit = LogisticRegression().fit(X_train, y_train) 
     logreg_scores = logreg_fit.score(X_test, y_test) 
     res.append(logreg_scores) 
    print('{}-fold STRATIFIED Cross-validation scores: {}'.format(
     n_splits, res)) 

Answer 2

あり形式のリストの中でそれを行う方法であってもよいが、ここで代替がある可能性があります

print('{}-fold STRATIFIED Cross-validation scores: {}'.format(
     n_splits, ["{:.3f"}.format(member) for member in logreg_scores])) 

これは、各メンバーが3DPの文字列である文字列の新しいリストを作成します。浮く。

あなたは上のラインにそれを移動することで読みやすくすることができます：これは単なるPythonの問題ではありません

formatted_logreg_scores = ["{:.3f"}.format(member) for member in logreg_scores] 
    print('{}-fold STRATIFIED Cross-validation scores: {}'.format(
      n_splits, formatted_logreg_scores))