GirdSearchCV for multioutput RandomForest Regressor

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I have created a multioutput RandomForestRegressor using the sklearn.ensemble.RandomForestRegressor. I now want to perform a GridSearchCV to find good hyperparameters and output the r^2 scores for each individual target feature. The code is use looks as follows:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
param_grid = {
    'model__bootstrap': [True],
    'model__max_depth': [8,10,12],
    'model__max_features': [3,4,5],
    'model__min_samples_leaf': [3,4,5],
    'model__min_samples_split': [3, 5, 7],
    'model__n_estimators': [100, 200, 300]
}
model = RandomForestRegressor()
pipe = Pipeline(steps=[
    ('scaler', StandardScaler()),
    ('model', model)])

scorer = make_scorer(r2_score, multioutput='raw_values')
search = GridSearchCV(pipe, param_grid, scoring=scorer)
search.fit(X_train, y_train)
print(f'Best parameter score {ship_type} {target}: {search.best_score_}')

When running this code I get the following error

  File "run_xgb_rf_regressor.py", line 75, in <module>
    model, X = run_regression(ship_types[2], targets)
  File "run_xgb_rf_regressor.py", line 50, in run_regression
    search.fit(X_train, y_train)
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/utils/validation.py", line 63, in inner_f
    return f(*args, **kwargs)
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py", line 841, in fit
    self._run_search(evaluate_candidates)
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py", line 1296, in _run_search
    evaluate_candidates(ParameterGrid(self.param_grid))
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py", line 795, in evaluate_candidates
    out = parallel(delayed(_fit_and_score)(clone(base_estimator),
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/parallel.py", line 1043, in __call__
    if self.dispatch_one_batch(iterator):
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/parallel.py", line 861, in dispatch_one_batch
    self._dispatch(tasks)
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/parallel.py", line 779, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/_parallel_backends.py", line 572, in __init__
    self.results = batch()
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/parallel.py", line 262, in __call__
    return [func(*args, **kwargs)
  File "/home/lucas/.local/lib/python3.8/site-packages/joblib/parallel.py", line 262, in <listcomp>
    return [func(*args, **kwargs)
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/utils/fixes.py", line 222, in __call__
    return self.function(*args, **kwargs)
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 625, in _fit_and_score
    test_scores = _score(estimator, X_test, y_test, scorer, error_score)
  File "/home/lucas/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 721, in _score
    raise ValueError(error_msg % (scores, type(scores), scorer))
ValueError: scoring must return a number, got [0.57359176 0.54407165 0.40313057 0.32515033 0.346224   0.39513717
 0.34375699] (<class 'numpy.ndarray'>) instead. (scorer=make_scorer(r2_score, multioutput=raw_values))

Clearly the error suggests that I can only use a single numeric value, which in my case would be the average r^2 score over all target features. Does anybody know how I can use GridSearchCV so that I can output the individual r^2 scores?

Many thanks in advance.

gridsearchcv scikit-learn regression random-forest python
1 Answers

I think I would use the following option for scoring parameter (from the docs):

a callable returning a dictionary where the keys are the metric names and the values are the metric scores;

So something like

def my_scorer(estimator, X, y):
    preds = estimator.predict(X)
    scores = r2_score(y, preds, multioutput='raw_values')
    return {f'r2_y{i}': score for i, score in enumerate(scores)}

Note though in the docs that refit will need to be set more carefully with multimetric searches. Maybe deciding the "best" parameters should be done by some average, in which case you can add another entry to the custom scorer.

Other useful parts of the User Guide:
https://scikit-learn.org/stable/modules/grid_search.html#multimetric-grid-search
https://scikit-learn.org/stable/modules/model_evaluation.html#implementing-your-own-scoring-object

user_104958930
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