我有一个 scikit 学习管道来缩放数字特征和编码分类特征。在我尝试实现 RandomUnderSampler 之前,它运行良好。来自 imblearn。我的目标是实现欠采样步骤,因为我的数据集非常不平衡 1:1000。
我确保使用 imblearn 的 Pipeline 方法而不是 sklearn。下面是我试过的代码。
代码数据在没有欠采样器方法的情况下工作(使用 sklearn 管道)。
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from imblearn.pipeline import make_pipeline as make_pipeline_imb
from imblearn.pipeline import Pipeline as Pipeline_imb
from sklearn.base import BaseEstimator, TransformerMixin
class TypeSelector(BaseEstimator, TransformerMixin):
def __init__(self, dtype):
self.dtype = dtype
def fit(self, X, y=None):
return self
def transform(self, X):
assert isinstance(X, pd.DataFrame)
return X.select_dtypes(include=[self.dtype])
transformer = Pipeline([
# Union numeric, categoricals and boolean
('features', FeatureUnion(n_jobs=1, transformer_list=[
# Select bolean features
('boolean', Pipeline([
('selector', TypeSelector('bool')),
])),
# Select and scale numericals
('numericals', Pipeline([
('selector', TypeSelector(np.number)),
('scaler', StandardScaler()),
])),
# Select and encode categoricals
('categoricals', Pipeline([
('selector', TypeSelector('category')),
('encoder', OneHotEncoder(handle_unknown='ignore')),
]))
])),
])
pipe = Pipeline([('prep', transformer),
('clf', RandomForestClassifier(n_estimators=500, class_weight='balanced'))
])
使用欠采样器方法不起作用的代码(使用 imblearn 管道)。
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from imblearn.pipeline import make_pipeline as make_pipeline_imb
from imblearn.pipeline import Pipeline as Pipeline_imb
from sklearn.base import BaseEstimator, TransformerMixin
class TypeSelector(BaseEstimator, TransformerMixin):
def __init__(self, dtype):
self.dtype = dtype
def fit(self, X, y=None):
return self
def transform(self, X):
assert isinstance(X, pd.DataFrame)
return X.select_dtypes(include=[self.dtype])
transformer = Pipeline_imb([
# Union numeric, categoricals and boolean
('features', FeatureUnion(n_jobs=1, transformer_list=[
# Select bolean features
('boolean', Pipeline_imb([
('selector', TypeSelector('bool')),
])),
# Select and scale numericals
('numericals', Pipeline_imb([
('selector', TypeSelector(np.number)),
('scaler', StandardScaler()),
])),
# Select and encode categoricals
('categoricals', Pipeline_imb([
('selector', TypeSelector('category')),
('encoder', OneHotEncoder(handle_unknown='ignore')),
]))
])),
])
pipe = Pipeline_imb([
('sampler', RandomUnderSampler(0.1)),
('prep', transformer),
('clf', RandomForestClassifier(n_estimators=500, class_weight='balanced'))
])
这是我得到的错误:
/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py in __init__(self, steps, memory, verbose)
133 def __init__(self, steps, memory=None, verbose=False):
134 self.steps = steps
--> 135 self._validate_steps()
136 self.memory = memory
137 self.verbose = verbose
/usr/local/lib/python3.6/dist-packages/imblearn/pipeline.py in _validate_steps(self)
144 if isinstance(t, pipeline.Pipeline):
145 raise TypeError(
--> 146 "All intermediate steps of the chain should not be"
147 " Pipelines")
148
TypeError: All intermediate steps of the chain should not be Pipelines
最佳答案
如果您在文件 imblearn/pipeline.py
中探索 imblean 的代码here , 在函数下 _validate_steps
, 他们会检查 transformers
中的每一项是否存在作为 scikit 管道实例的转换器 ( isinstance(t, pipeline.Pipeline)
)。
从您的代码中,transformers
是
RandomUnderSampler
transformer
和类(class)
Pipeline_imb
使用 Pipeline_imb
时继承了 scikit 的流水线在您的代码中是多余的。话虽如此,我会像下面这样调整你的代码
transformer = FeatureUnion(n_jobs=1, transformer_list=[
# Select bolean features
('selector1', TypeSelector('bool'),
# Select and scale numericals
('selector2', TypeSelector(np.number)),
('scaler', StandardScaler()),
# Select and encode categoricals
('selector3', TypeSelector('category')),
('encoder', OneHotEncoder(handle_unknown='ignore'))
])
pipe = Pipeline_imb([
('sampler', RandomUnderSampler(0.1)),
('prep', transformer),
('clf', RandomForestClassifier(n_estimators=500, class_weight='balanced'))
])
关于python - 如何在 scikit 学习管道中实现 RandomUnderSampler?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/57193413/