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use RandomizedSearchCV and estmator is RandomForestClassifier have bug?

See original GitHub issue

run code is about:


import numpy as np

from time import time
from scipy.stats import randint as sp_randint

# from sklearn.model_selection import GridSearchCV
# from sklearn.model_selection import RandomizedSearchCV
from dask_searchcv import GridSearchCV
from dask_searchcv import RandomizedSearchCV
from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier

# get some data
digits = load_digits()
X, y =,

# build a classifier
clf = RandomForestClassifier(n_estimators=20)

# Utility function to report best scores
def report(results, n_top=3):
    for i in range(1, n_top + 1):
        candidates = np.flatnonzero(results['rank_test_score'] == i)
        for candidate in candidates:
            print("Model with rank: {0}".format(i))
            print("Mean validation score: {0:.3f} (std: {1:.3f})".format(
            print("Parameters: {0}".format(results['params'][candidate]))

# specify parameters and distributions to sample from
param_dist = {"max_depth": [3, None],
              "max_features": sp_randint(1, 11),
              "min_samples_split": sp_randint(2, 11),
              "min_samples_leaf": sp_randint(1, 11),
              "bootstrap": [True, False],
              "criterion": ["gini", "entropy"]}

# run randomized search
n_iter_search = 20
random_search = RandomizedSearchCV(clf, param_distributions=param_dist,

start = time(), y)
print("RandomizedSearchCV took %.2f seconds for %d candidates"
      " parameter settings." % ((time() - start), n_iter_search))

# use a full grid over all parameters
param_grid = {"max_depth": [3, None],
              "max_features": [1, 3, 10],
              "min_samples_split": [2, 3, 10],
              "min_samples_leaf": [1, 3, 10],
              "bootstrap": [True, False],
              "criterion": ["gini", "entropy"]}

# run grid search
grid_search = GridSearchCV(clf, param_grid=param_grid)
start = time(), y)

print("GridSearchCV took %.2f seconds for %d candidate parameter settings."
      % (time() - start, len(grid_search.cv_results_['params'])))

the error is :

Traceback (most recent call last):
  File "/root/anaconda3/lib/python3.6/site-packages/IPython/core/", line 2862, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-139-ecec65d20380>", line 50, in <module>, y)
  File "/root/anaconda3/lib/python3.6/site-packages/dask_searchcv/", line 867, in fit
    out = scheduler(dsk, keys, num_workers=n_jobs)
  File "/root/anaconda3/lib/python3.6/site-packages/dask/", line 75, in get
    pack_exception=pack_exception, **kwargs)
  File "/root/anaconda3/lib/python3.6/site-packages/dask/", line 521, in get_async
    raise_exception(exc, tb)
  File "/root/anaconda3/lib/python3.6/site-packages/dask/", line 60, in reraise
    raise exc
File "/root/anaconda3/lib/python3.6/site-packages/dask/", line 290, in execute_task
    result = _execute_task(task, data)
  File "/root/anaconda3/lib/python3.6/site-packages/dask/", line 271, in _execute_task
    return func(*args2)
  File "/root/anaconda3/lib/python3.6/site-packages/dask_searchcv/", line 280, in fit_and_score
    fields, params, fit_params)
  File "/root/anaconda3/lib/python3.6/site-packages/dask_searchcv/", line 216, in fit, y, **fit_params)
  File "/root/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/", line 316, in fit
  File "/root/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/", line 125, in _make_estimator
    estimator = clone(self.base_estimator_)
  File "/root/anaconda3/lib/python3.6/site-packages/sklearn/", line 60, in clone
    new_object_params = estimator.get_params(deep=False)
  File "/root/anaconda3/lib/python3.6/site-packages/sklearn/", line 241, in get_params
IndexError: pop from empty list

Issue Analytics

  • State:closed
  • Created 5 years ago
  • Comments:10 (10 by maintainers)

github_iconTop GitHub Comments

TomAugspurgercommented, Jul 3, 2018

Probably not. I think that 0.20 is reasonably close, and scikit-learn doesn’t really have enough development resources to maintain backport branches.

wtbarnescommented, Jul 3, 2018

Ok I can confirm that the above example with GridSearchCV works if I use scikit-learn, i.e. the master branch. Is it worth creating an issue in scikit-learn to ask for a backport of this fix to 0.19?

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