名称“DataFrameSelector”未定义

Question

我目前正在阅读“使用 Scikit-Learn 和 TensorFlow 进行机器学习实践”。尝试重新创建转换管道代码时出现错误。我该如何解决这个问题？

代码：

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

num_pipeline = Pipeline([('imputer', Imputer(strategy = "median")),
                        ('attribs_adder', CombinedAttributesAdder()),
                        ('std_scaler', StandardScaler()),
                        ])

housing_num_tr = num_pipeline.fit_transform(housing_num)

from sklearn.pipeline import FeatureUnion

num_attribs = list(housing_num)
cat_attribs = ["ocean_proximity"]

num_pipeline = Pipeline([
                         ('selector', DataFrameSelector(num_attribs)),
                         ('imputer', Imputer(strategy = "median")),
                         ('attribs_adder', CombinedAttributesAdder()),
                         ('std_scaler', StandardScaler()),
                        ])

cat_pipeline = Pipeline([('selector', DataFrameSelector(cat_attribs)), 
                         ('label_binarizer', LabelBinarizer()),
                        ])

full_pipeline = FeatureUnion(transformer_list = [("num_pipeline", num_pipeline), 
                                                 ("cat_pipeline", cat_pipeline),
                                                ])

# And we can now run the whole pipeline simply:

housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared

错误：

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-350-3a4a39e5bc1c> in <module>()
     43 
     44 num_pipeline = Pipeline([
---> 45                          ('selector', DataFrameSelector(num_attribs)),
     46                          ('imputer', Imputer(strategy = "median")),
     47                          ('attribs_adder', CombinedAttributesAdder()),

NameError: name 'DataFrameSelector' is not defined

Answer 1

您应该在当前代码单元格之前插入一个单元格，然后键入以下代码

from sklearn.base import BaseEstimator, TransformerMixin

class DataFrameSelector(BaseEstimator, TransformerMixin):

def __init__(self, attribute_names):
    self.attribute_names = attribute_names
def fit(self, X, y=None):
    return self
def transform(self, X, y=None):
    return X[self.attribute_names].values   

通过这种方式，您的 DataFrameSelector 类将被预先定义

Answer 2

看起来您正在从事 California Housing Price Predictions 书中 Hands-On Machine Learning with Scikit-learn and TensorFlow 中的一个项目。

错误

NameError: 名称“DataFrameSelector”未定义

出现是因为 sklearn 中没有 DataFrameSelector 转换器。要克服此错误，您需要为此编写自己的自定义转换器。

在本书中，您可以在下一页找到DataFrameSelector 转换器代码，但我也会在下面复制此代码。

from sklearn.base import BaseEstimator, TransformerMixin

class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values

BaseEstimator 和 TransformerMixin 类用于继承 fit()、transform() 和 fit_transform() 方法。

现在，sklearn-pandas 中也提供了另一个类DataFrameMapper，具有类似的目标。 您可以通过以下链接找到有关该课程的详细信息：
DataFrameMapper

Answer 3

如果您正在使用 Sklearn 和 Tensorflow 关注机器学习之手， 就在下一页，一个定制的 Dataframe 生成器

from sklearn.pipeline import FeatureUnion
class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values

Answer 4

from sklearn.pipeline import FeatureUnion
class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values

它可能会起作用。

Answer 5

from sklearn.base import BaseEstimator, TransformerMixin

class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names=attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values

这应该可行。

Answer 6

DataFrameSelector 未找到，需要导入。它不是sklearn 的一部分，但在sklearn-features 中有同名的东西：

from sklearn_features.transformers import DataFrameSelector

(DOCS)