是否可以在 Neuraxle 或 sklearn 中将多个管道组合成单个估计器以创建多输出分类器并一次性适应

Question

我想创建多输出分类器。但是，我的问题是每个输出的正标签分布差异很大，例如对于输出 1，有 2% 的正标签，对于输出 2，有 20% 的正标签。因此，我想将每个输出的数据采样和模型拟合分离到多个流（多个子流水线）中，每个子流水线分别执行过采样，并且过采样和分类器的超参数也分别进行优化。

例如，假设我有

from sklearn.linear_model import LogisticRegression
from imblearn.over_sampling import SMOTE
from imblearn.pipeline import Pipeline

X = # some input features array here
y = np.array([[0,1],
              [0,1],
              [0,0],
              [1,0],
              [0,0]]) # unbalance label distribution

y_1 = y[:, 0]
y_2 = y[:, 1]


param_grid_shared = {'oversampler__sampling_strategy': [0.2, 0.4, 0.5], 'logit__C': [1, 0.1, 0.01]}

pipeline_output_1 = Pipeline([('oversampler', SMOTE()), ('logit', LogisticRegression())])
grid_1 = GridSearchCV(pipeline_output_1, param_grid_shared)
grid_1.fit(X, y_1)

pipeline_output_2 = Pipeline([('oversampler', SMOTE()), ('logit', LogisticRegression())])
grid_2 = GridSearchCV(pipeline_output_2, param_grid_shared)
grid_2.fit(X, y_2)

我想把它们结合起来创造类似

的东西

multi_pipe = Pipeline([(Something to separate X and y into multiple streams)
                       ((pipe_1, pipeline_output_1),
                       (pipe_2, pipeline_output_2)), # 2 pipeline optimized separately
                       (Evaluate and select hyperparameters for each pipeline separately)
                       (Something to combine output from pipeline 1 and pipeline 2)
                      ])

在 Neuraxle 或 Sklearn 中

MultiOutputClassifier 肯定不适合这种情况，我现在不太确定在哪里寻找解决方案。

Answer 1

我用以下想法创建了一个 issue：

pipe_1_with_oversampler_1 = Pipeline([
    Oversampler1().assert_has_services(DataRepository), Pipeline1()])
pipe_2_with_oversampler_2 = Pipeline([
    Oversampler2().assert_has_services(DataRepository), Pipeline2()])

multi_pipe = Pipeline([
    DataPreprocessingStep(),
    # Evaluate and select hyperparameters for each pipeline separately, but within one run, using `multi_pipe.fit(...)`: 
    FeatureUnion([
        AutoML(pipe_1_with_oversampler_1, **automl_args_1),
        AutoML(pipe_2_with_oversampler_2, **automl_args_2)
    ]),
    # And then combine output from pipeline 1 and pipeline 2 using feature union. 
    # Can do preprocessing and postprocessing as well.
    PostprocessingStep(),
])

为此，AutoML 对象可以重构为一个常规步骤，因此可以代替一个步骤使用。

是否可以在 Neuraxle 或 sklearn 中将多个管道组合成单个估计器以创建多输出分类器并一次性适应

Is it possible to combine multiple pipeline into single estimator in Neuraxle or sklearn to create multi-output classifer and fit in one go

python

machine-learning

scikit-learn

neuraxle