Permutation Importance fails if dataset is large enough #15810
Description
Description
When using permutation_importance with a large enough pandas DataFrame and n_jobs > 0, joblib switches to read-only memmap mode, which proceeds to raise, as permutation_importance tries to assign to the DataFrame.
The error does not occur when passing a similarly sized Numpy array.
In previous, similar implementations, we fixed the bug by setting max_nbytes to None in the Parallel init, though I don't know what the broader consequences of that are.
Steps/Code to Reproduce
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.inspection import permutation_importance
data = load_iris()
# Make 150,000 samples
df = pd.DataFrame(data=data.data.repeat(1000, axis=0))
y = data.target.repeat(1000)
clf = RandomForestClassifier()
clf.fit(df, y)
r = permutation_importance(clf, df, y, n_jobs=-1)Expected Results
We expect no exception to be raised
Actual Results
_RemoteTraceback Traceback (most recent call last)
_RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py", line 418, in _process_worker
r = call_item()
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py", line 272, in __call__
return self.fn(*self.args, **self.kwargs)
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/joblib/_parallel_backends.py", line 600, in __call__
return self.func(*args, **kwargs)
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/joblib/parallel.py", line 256, in __call__
for func, args, kwargs in self.items]
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/joblib/parallel.py", line 256, in <listcomp>
for func, args, kwargs in self.items]
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/sklearn/inspection/_permutation_importance.py", line 37, in _calculate_permutation_scores
_safe_column_setting(X, col_idx, temp)
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/sklearn/inspection/_permutation_importance.py", line 15, in _safe_column_setting
X.iloc[:, col_idx] = values
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/pandas/core/indexing.py", line 205, in __setitem__
self._setitem_with_indexer(indexer, value)
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/pandas/core/indexing.py", line 576, in _setitem_with_indexer
self.obj[item_labels[indexer[info_axis]]] = value
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/pandas/core/frame.py", line 3487, in __setitem__
self._set_item(key, value)
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/pandas/core/frame.py", line 3565, in _set_item
NDFrame._set_item(self, key, value)
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/pandas/core/generic.py", line 3381, in _set_item
self._data.set(key, value)
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 1090, in set
blk.set(blk_locs, value_getitem(val_locs))
File "/home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 380, in set
self.values[locs] = values
ValueError: assignment destination is read-only
"""
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
<ipython-input-2-22d9b46d8800> in <module>
13 clf.fit(df, y)
14
---> 15 r = permutation_importance(clf, df, y, n_jobs=-1)
~/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/sklearn/inspection/_permutation_importance.py in permutation_importance(estimator, X, y, scoring, n_repeats, n_jobs, random_state)
119 scores = Parallel(n_jobs=n_jobs)(delayed(_calculate_permutation_scores)(
120 estimator, X, y, col_idx, random_state, n_repeats, scorer
--> 121 ) for col_idx in range(X.shape[1]))
122
123 importances = baseline_score - np.array(scores)
~/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/joblib/parallel.py in __call__(self, iterable)
1014
1015 with self._backend.retrieval_context():
-> 1016 self.retrieve()
1017 # Make sure that we get a last message telling us we are done
1018 elapsed_time = time.time() - self._start_time
~/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/joblib/parallel.py in retrieve(self)
906 try:
907 if getattr(self._backend, 'supports_timeout', False):
--> 908 self._output.extend(job.get(timeout=self.timeout))
909 else:
910 self._output.extend(job.get())
~/.pyenv/versions/3.7.4/envs/ml_tooling_env/lib/python3.7/site-packages/joblib/_parallel_backends.py in wrap_future_result(future, timeout)
552 AsyncResults.get from multiprocessing."""
553 try:
--> 554 return future.result(timeout=timeout)
555 except LokyTimeoutError:
556 raise TimeoutError()
~/.pyenv/versions/3.7.4/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
433 raise CancelledError()
434 elif self._state == FINISHED:
--> 435 return self.__get_result()
436 else:
437 raise TimeoutError()
~/.pyenv/versions/3.7.4/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
ValueError: assignment destination is read-onlyVersions
System:
python: 3.7.4 (default, Oct 14 2019, 12:42:45) [GCC 7.4.0]
executable: /home/anders/.pyenv/versions/3.7.4/envs/ml_tooling_env/bin/python3.7
machine: Linux-5.0.0-37-generic-x86_64-with-debian-buster-sid
Python dependencies:
pip: 19.3.1
setuptools: 40.8.0
sklearn: 0.22
numpy: 1.17.4
scipy: 1.3.3
Cython: None
pandas: 0.25.3
matplotlib: 3.1.2
joblib: 0.14.0
Built with OpenMP: True