Pixel importances with a parallel forest of trees

This example shows the use of forests of trees to evaluate the impurity-based importance of the pixels in an image classification task (faces). The hotter the pixel, the more important.

The code below also illustrates how the construction and the computation of the predictions can be parallelized within multiple jobs.

Traceback (most recent call last):
  File "/build/scikit-learn-btOVnh/scikit-learn-0.23.2/examples/ensemble/plot_forest_importances_faces.py", line 25, in <module>
    data = fetch_olivetti_faces()
  File "/build/scikit-learn-btOVnh/scikit-learn-0.23.2/.pybuild/cpython3_3.9/build/sklearn/utils/validation.py", line 72, in inner_f
    return f(**kwargs)
  File "/build/scikit-learn-btOVnh/scikit-learn-0.23.2/.pybuild/cpython3_3.9/build/sklearn/datasets/_olivetti_faces.py", line 111, in fetch_olivetti_faces
    mat_path = _fetch_remote(FACES, dirname=data_home)
  File "/build/scikit-learn-btOVnh/scikit-learn-0.23.2/.pybuild/cpython3_3.9/build/sklearn/datasets/_base.py", line 1181, in _fetch_remote
    urlretrieve(remote.url, file_path)
  File "/usr/lib/python3.9/urllib/request.py", line 239, in urlretrieve
    with contextlib.closing(urlopen(url, data)) as fp:
  File "/usr/lib/python3.9/urllib/request.py", line 214, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib/python3.9/urllib/request.py", line 517, in open
    response = self._open(req, data)
  File "/usr/lib/python3.9/urllib/request.py", line 534, in _open
    result = self._call_chain(self.handle_open, protocol, protocol +
  File "/usr/lib/python3.9/urllib/request.py", line 494, in _call_chain
    result = func(*args)
  File "/usr/lib/python3.9/urllib/request.py", line 1389, in https_open
    return self.do_open(http.client.HTTPSConnection, req,
  File "/usr/lib/python3.9/urllib/request.py", line 1349, in do_open
    raise URLError(err)
urllib.error.URLError: <urlopen error [Errno -2] Name or service not known>

print(__doc__)

from time import time
import matplotlib.pyplot as plt

from sklearn.datasets import fetch_olivetti_faces
from sklearn.ensemble import ExtraTreesClassifier

# Number of cores to use to perform parallel fitting of the forest model
n_jobs = 1

# Load the faces dataset
data = fetch_olivetti_faces()
X, y = data.data, data.target

mask = y < 5  # Limit to 5 classes
X = X[mask]
y = y[mask]

# Build a forest and compute the pixel importances
print("Fitting ExtraTreesClassifier on faces data with %d cores..." % n_jobs)
t0 = time()
forest = ExtraTreesClassifier(n_estimators=1000,
                              max_features=128,
                              n_jobs=n_jobs,
                              random_state=0)

forest.fit(X, y)
print("done in %0.3fs" % (time() - t0))
importances = forest.feature_importances_
importances = importances.reshape(data.images[0].shape)

# Plot pixel importances
plt.matshow(importances, cmap=plt.cm.hot)
plt.title("Pixel importances with forests of trees")
plt.show()

Total running time of the script: ( 0 minutes 0.003 seconds)

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