python - 自定义 Sklearn Transformer 单独工作，在管道中使用时抛出错误

Question

我有一个简单的 sklearn 类，我想将其用作 sklearn 管道的一部分。这个类只需要一个 pandas 数据框 X_DF 和一个分类列名，并调用 pd.get_dummies 返回数据框，其中的列变成了一个虚拟变量矩阵......

import pandas as pd
from sklearn.base import TransformerMixin, BaseEstimator

class dummy_var_encoder(TransformerMixin, BaseEstimator):
    '''Convert selected categorical column to (set of) dummy variables    
    '''


    def __init__(self, column_to_dummy='default_col_name'):
        self.column = column_to_dummy
        print self.column

    def fit(self, X_DF, y=None):
        return self 

    def transform(self, X_DF):
        ''' Update X_DF to have set of dummy-variables instead of orig column'''        

        # convert self-attribute to local var for ease of stepping through function
        column = self.column

        # add columns for new dummy vars, and drop original categorical column
        dummy_matrix = pd.get_dummies(X_DF[column], prefix=column)

        new_DF = pd.concat([X_DF[column], dummy_matrix], axis=1)

        return new_DF

现在单独使用这个转换器来适应/转换，我得到了预期的输出。对于一些玩具数据如下:

from sklearn import datasets
# Load toy data 
iris = datasets.load_iris()
X = pd.DataFrame(iris.data, columns = iris.feature_names)
y = pd.Series(iris.target, name='y')

# Create Arbitrary categorical features
X['category_1'] = pd.cut(X['sepal length (cm)'], 
                         bins=3, 
                         labels=['small', 'medium', 'large'])

X['category_2'] = pd.cut(X['sepal width (cm)'], 
                         bins=3, 
                         labels=['small', 'medium', 'large'])

...我的虚拟编码器产生正确的输出:

encoder = dummy_var_encoder(column_to_dummy = 'category_1')
encoder.fit(X)
encoder.transform(X).iloc[15:21,:]

category_1
   category_1  category_1_small  category_1_medium  category_1_large
15     medium                 0                  1                 0
16      small                 1                  0                 0
17      small                 1                  0                 0
18     medium                 0                  1                 0
19      small                 1                  0                 0
20      small                 1                  0                 0

但是，当我从如下定义的 sklearn 管道调用同一个转换器时:

from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import KFold, GridSearchCV

# Define Pipeline
clf = LogisticRegression(penalty='l1')
pipeline_steps = [('dummy_vars', dummy_var_encoder()),
                  ('clf', clf)
                  ]

pipeline = Pipeline(pipeline_steps)

# Define hyperparams try for dummy-encoder and classifier
# Fit 4 models - try dummying category_1 vs category_2, and using l1 vs l2 penalty in log-reg
param_grid = {'dummy_vars__column_to_dummy': ['category_1', 'category_2'],
              'clf__penalty': ['l1', 'l2']
                  }

# Define full model search process 
cv_model_search = GridSearchCV(pipeline, 
                               param_grid, 
                               scoring='accuracy', 
                               cv = KFold(),
                               refit=True,
                               verbose = 3) 

在我安装管道之前一切正常，此时我从虚拟编码器收到错误:

cv_model_search.fit(X,y=y)

In [101]: cv_model_search.fit(X,y=y) Fitting 3 folds for each of 4 candidates, totalling 12 fits

None None None None [CV] dummy_vars__column_to_dummy=category_1, clf__penalty=l1 .........

Traceback (most recent call last):

File "", line 1, in cv_model_search.fit(X,y=y)

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/sklearn/model_selection/_search.py", line 638, in fit cv.split(X, y, groups)))

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 779, in call while self.dispatch_one_batch(iterator):

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 625, in dispatch_one_batch self._dispatch(tasks)

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 588, in _dispatch job = self._backend.apply_async(batch, callback=cb)

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 111, in apply_async result = ImmediateResult(func)

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 332, in init self.results = batch()

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 131, in call return [func(*args, **kwargs) for func, args, kwargs in self.items]

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/sklearn/model_selection/_validation.py", line 437, in _fit_and_score estimator.fit(X_train, y_train, **fit_params)

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/sklearn/pipeline.py", line 257, in fit Xt, fit_params = self._fit(X, y, **fit_params)

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/sklearn/pipeline.py", line 222, in _fit **fit_params_steps[name])

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/sklearn/externals/joblib/memory.py", line 362, in call return self.func(*args, **kwargs)

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/sklearn/pipeline.py", line 589, in _fit_transform_one res = transformer.fit_transform(X, y, **fit_params)

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/sklearn/base.py", line 521, in fit_transform return self.fit(X, y, **fit_params).transform(X)

File "", line 21, in transform dummy_matrix = pd.get_dummies(X_DF[column], prefix=column)

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/pandas/core/frame.py", line 1964, in getitem return self._getitem_column(key)

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/pandas/core/frame.py", line 1971, in _getitem_column return self._get_item_cache(key)

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/pandas/core/generic.py", line 1645, in _get_item_cache values = self._data.get(item)

File "/home/max/anaconda3/envs/remine/lib/python2.7/site-packages/pandas/core/internals.py", line 3599, in get raise ValueError("cannot label index with a null key")

ValueError: cannot label index with a null key

Answer 1

trace 告诉你到底出了什么问题。学习诊断跟踪确实非常宝贵，尤其是当您继承自您可能不完全了解的库时。

现在，我自己在 sklearn 中做了一些继承，我可以毫无疑问地告诉你 GridSearchCV 如果输入到你的 中的数据类型会给你带来一些麻烦fit 或 fit_transform 方法不是 NumPy 数组。正如 Vivek 在他的评论中提到的，传递给 fit 方法的 X 不再是 DataFrame。但让我们先看一下轨迹。

ValueError: cannot label index with a null key

虽然 Vivek 对 NumPy 数组的看法是正确的，但这里还有另一个问题。您得到的实际错误是您的 fit 方法中 column 的值为 None。如果您查看上面的 encoder 对象，您会看到 __repr__ 方法输出以下内容:

dummy_var_encoder(column_to_dummy=None)

当使用 Pipeline 时，这个参数会被初始化并传递给 GridSearchCV。这种行为也可以在交叉验证和搜索方法中看到，并且具有与输入参数不同名称的属性会导致此类问题。解决此问题将使您走上正确的道路。

这样修改 __init__ 方法将解决这个特定问题:

def __init__(self, column='default_col_name'):
    self.column = column
    print(self.column)

但是，一旦您完成此操作，Vivek 提到的问题就会浮出水面，您将不得不处理它。这是我以前遇到过的事情，尽管不是专门针对 DataFrames 的。我想出了一个解决方案 Use sklearn GridSearchCV on custom class whose fit method takes 3 arguments .基本上，我创建了一个实现 __getitem__ 方法的包装器，使数据的外观和行为方式能够通过 GridSearchCV 中使用的验证方法，管道，以及其他交叉验证方法。

编辑

我进行了这些更改，看起来您的问题来自验证方法 check_array .虽然使用 dtype=pd.DataFrame 调用此方法会起作用，但线性模型使用 dtype=np.float64 调用此方法会抛出错误。要解决这个问题，而不是将原始数据与你的虚拟数据连接起来，你可以只返回你的虚拟列并使用它们进行拟合。这是无论如何都应该做的事情，因为您不想在您尝试拟合的模型中同时包含虚拟列和原始数据。您也可以考虑使用 drop_first 选项，但我要跑题了。因此，像这样更改您的 fit 方法可以让整个过程按预期工作。

def transform(self, X_DF):
    ''' Update X_DF to have set of dummy-variables instead of orig column'''        

    # convert self-attribute to local var for ease of stepping through function
    column = self.column

    # add columns for new dummy vars, and drop original categorical column
    dummy_matrix = pd.get_dummies(X_DF[column], prefix=column)

    return dummy_matrix