8000 DOC: use notebook-style for plot_feature_agglomeration_vs_univariate_selection.py by MarieSacksick · Pull Request #22796 · scikit-learn/scikit-learn · GitHub
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DOC: use notebook-style for plot_feature_agglomeration_vs_univariate_selection.py #22796

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Mar 12, 2022
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DOC: use notebook-style for plot_feature_agglomeration_vs_univariate_selection.py #22796

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20 changes: 14 additions & 6 deletions examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause

# %%
import shutil
import tempfile

Expand All @@ -33,15 +34,16 @@
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold

# #############################################################################
# Generate data
# %%
# Set parameters
n_samples = 200
size = 40 # image size
roi_size = 15
snr = 5.0
np.random.seed(0)
mask = np.ones([size, size], dtype=bool)

# %%
# Generate data
coef = np.zeros((size, size))
coef[0:roi_size, 0:roi_size] = -1.0
coef[-roi_size:, -roi_size:] = 1.0
Expand All @@ -53,17 +55,21 @@
X /= X.std(axis=0)

y = np.dot(X, coef.ravel())

# %%
# add noise
noise = np.random.randn(y.shape[0])
noise_coef = (linalg.norm(y, 2) / np.exp(snr / 20.0)) / linalg.norm(noise, 2)
y += noise_coef * noise # add noise
y += noise_coef * noise

# #############################################################################
# %%
# Compute the coefs of a Bayesian Ridge with GridSearch
cv = KFold(2) # cross-validation generator for model selection
ridge = BayesianRidge()
cachedir = tempfile.mkdtemp()
mem = Memory(location=cachedir, verbose=1)

# %%
# Ward agglomeration followed by BayesianRidge
connectivity = grid_to_graph(n_x=size, n_y=size)
ward = FeatureAgglomeration(n_clusters=10, connectivity=connectivity, memory=mem)
Expand All @@ -75,6 +81,7 @@
coef_ = clf.best_estimator_.steps[0][1].inverse_transform(coef_)
coef_agglomeration_ = coef_.reshape(size, size)

# %%
# Anova univariate feature selection followed by BayesianRidge
f_regression = mem.cache(feature_selection.f_regression) # caching function
anova = feature_selection.SelectPercentile(f_regression)
Expand All @@ -86,7 +93,7 @@
coef_ = clf.best_estimator_.steps[0][1].inverse_transform(coef_.reshape(1, -1))
coef_selection_ = coef_.reshape(size, size)

# #############################################################################
# %%
# Inverse the transformation to plot the results on an image
plt.close("all")
plt.figure(figsize=(7.3, 2.7))
Expand All @@ -102,5 +109,6 @@
plt.subplots_adjust(0.04, 0.0, 0.98, 0.94, 0.16, 0.26)
plt.show()

# %%
# Attempt to remove the temporary cachedir, but don't worry if it fails
shutil.rmtree(cachedir, ignore_errors=True)
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