在 Mlens Pipeline 中使用 StandardScaler 作为预处理器会生成分类警告

Question

I am trying to scale my data within the crossvalidation folds of a MLENs Superlearner pipeline. When I use StandardScaler in the pipeline (as demonstrated below), I receive the following warning:

/miniconda3/envs/r_env/lib/python3.7/site-packages/mlens/parallel/_base_functions.py:226: MetricWarning: [pipeline-1.mlpclassifier.0.2] Could not score pipeline-1.mlpclassifier. Details: ValueError("Classification metrics can't handle a mix of binary and continuous-multioutput targets") (name, inst_name, exc), MetricWarning)

Of note, when I omit the StandardScaler() the warning disappears, but the data is not scaled.

breast_cancer_data = load_breast_cancer()

X = breast_cancer_data['data']
y = breast_cancer_data['target']

from sklearn.model_selection import train_test_split
X, X_val, y, y_val = train_test_split(X, y, test_size=.3, random_state=0)

from sklearn.base import BaseEstimator
class RFBasedFeatureSelector(BaseEstimator):
  
    def __init__(self, n_estimators):
        self.n_estimators = n_estimators
        self.selector = None

    def fit(self, X, y):
        clf = RandomForestClassifier(n_estimators=self.n_estimators, random_state = RANDOM_STATE, class_weight = 'balanced')
        clf = clf.fit(X, y)
        self.selector = SelectFromModel(clf, prefit=True, threshold = 0.001)

    def transform(self, X):
        if self.selector is None:
            raise AttributeError('The selector attribute has not been assigned. You cannot call transform before first calling fit or fit_transform.')
        return self.selector.transform(X)

    def fit_transform(self, X, y):
        self.fit(X, y)
        return self.transform(X)

N_FOLDS = 5
RF_ESTIMATORS = 1000
N_ESTIMATORS = 1000
RANDOM_STATE = 42

from mlens.metrics import make_scorer
from sklearn.metrics import roc_auc_score, balanced_accuracy_score
accuracy_scorer = make_scorer(balanced_accuracy_score, average='micro', greater_is_better=True)

from mlens.ensemble.super_learner import SuperLearner
from sklearn.linear_model import LogisticRegression
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import SelectFromModel


ensemble = SuperLearner(folds=N_FOLDS, shuffle=True, random_state=RANDOM_STATE, n_jobs=10, scorer=balanced_accuracy_score, backend="multiprocessing")

preprocessing1 = {'pipeline-1': [StandardScaler()]
                 }

preprocessing2 = {'pipeline-1': [RFBasedFeatureSelector(N_ESTIMATORS)]
                 }

estimators = {'pipeline-1': [RandomForestClassifier(RF_ESTIMATORS, random_state=RANDOM_STATE, class_weight='balanced'), 
                             MLPClassifier(hidden_layer_sizes=(10, 10, 10), activation='relu', solver='sgd',
                                           max_iter=5000)
                                         ]
                 }

ensemble.add(estimators, preprocessing2, preprocessing1)

ensemble.add_meta(LogisticRegression(solver='liblinear', class_weight = 'balanced'))

ensemble.fit(X,y)

yhat = ensemble.predict(X_val)
balanced_accuracy_score(y_val, yhat)```

>Error text: /miniconda3/envs/r_env/lib/python3.7/site-packages/mlens/parallel/_base_functions.py:226: MetricWarning: [pipeline-1.mlpclassifier.0.2] Could not score pipeline-1.mlpclassifier. Details:
ValueError("Classification metrics can't handle a mix of binary and continuous-multioutput targets")
  (name, inst_name, exc), MetricWarning)

Answer 1

You are currently passing your preprocessing steps as two separate arguments when calling the add method. You can instead combine them as follows:

preprocessing = {'pipeline-1': [RFBasedFeatureSelector(N_ESTIMATORS),StandardScaler()]}

Please refer to the documentation for the add method found here: https://mlens.readthedocs.io/en/0.1.x/source/mlens.ensemble.super_learner/