Question

我目前正在阅读“使用Scikit-Learn＆amp; TensorFlow进行动手机器学习”。当我尝试重新创建Transformation Pipelines代码时出错。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

找不到

DataFrameSelector，需要导入。它不是sklearn的一部分，但sklearn-features中提供了相同名称的内容：

from sklearn_features.transformers import DataFrameSelector

（DOCS）

Answer 2

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

这应该有效。

Answer 3

如果您通过Sklearn和Tensorflow关注机器学习之手，在接下来的页面上，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

可能有效。

Answer 5

您似乎正在从事《California Housing Price Predictions》一书中的一个项目 Hands-On Machine Learning with Scikit-learn and TensorFlow。

错误

<块引用>

NameError: name '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 中还有另一个类 DataFrameMapper 也有类似的目标。您可以从以下链接找到有关此类的详细信息：
DataFrameMapper

名称'DataFrameSelector'未定义

代码：

错误：

5 个答案: