Question

我有一个熊猫数据框，在特定列中有一些NaN值：

1291   NaN
1841   NaN
2049   NaN
Name: some column, dtype: float64

为了解决这个问题，我制作了以下管道：

from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline

scaler = StandardScaler(with_mean = True)
imputer = SimpleImputer(strategy = 'median')
logistic = LogisticRegression()

pipe = Pipeline([('imputer', imputer),
                 ('scaler', scaler), 
                 ('logistic', logistic)])

现在，当我将此管道传递到RandomizedSearchCV时，出现以下错误：

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

实际上比这要长得多-如果需要，我可以在编辑中发布整个错误。无论如何，我很确定此列是唯一包含NaN的列。此外，如果我从SimpleImputer切换到管道中的Imputer（现已弃用）RandomizedSearchCV，则该管道在我的SimpleImputer中可以正常工作。我检查了文档，但似乎Imputer的行为（几乎）与Imputer完全相同。行为上有什么区别？

>如何在不使用不推荐使用的

class user{
  /**
   * @return array
   */
  public function showwinners(){
    $query = "SELECT points, memberid, uname FROM user";
    $all_answers = array();
    if( $query_run = mysql_query( $query ) ){
      if( mysql_num_rows( $query_run ) == NULL ){
         return 0;
      }
      while( $query_row = mysql_fetch_assoc( $query_run ) ){
        $one = $query_row['points'];
        $two = $query_row['memberid'];
        $three = $query_row['uname'];
        if( $one >=1 ){
          $first = $one;
          $second = $three;
          array_push( $all_answers, ['name'=>$second,'points'=>$first] );
        }
      }
    }

    return $all_answers;
  }
}

的情况下，在管道中使用imputer？

Answer 1

我遇到了同样的问题，但这已经解决了：

imputer = SimpleImputer(strategy = 'median', fill_value = 0)

Answer 2

make_pipeline中的SimpleImputer

preprocess_pipeline = make_pipeline(   
    FeatureUnion(transformer_list=[
        ('Handle numeric columns', make_pipeline(
            ColumnSelector(columns=['Amount']),
            SimpleImputer(strategy='constant', fill_value=0),
            StandardScaler()
        )),
        ('Handle categorical data', make_pipeline(
            ColumnSelector(columns=['Type', 'Name', 'Changes']),
            SimpleImputer(strategy='constant', missing_values=' ', fill_value='missing_value'),
            OneHotEncoder(sparse=False)
        ))
    ])
)

管道中的SimpleImputer

('features', FeatureUnion ([
     ('Cat Columns', Pipeline([
          ('Category Extractor', TypeSelector(np.number)),
                 ('Impute Zero', SimpleImputer(strategy="constant", fill_value=0))
                                    ])),
('Numerics', Pipeline([
      ('Numeric Extractor', TypeSelector("category")),
          ('Impute Missing', SimpleImputer(strategy="constant", fill_value='missing'))
          ]))        
     ]))

Sklearn的SimpleImputer无法在管道中运行？

2 个答案: