MNT Accelerate_examples plot_digits_linkage.py #21737
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…for plot_digits_linkage.py
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| Various Agglomerative Clustering on a 2D embedding of digits | |||
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Please revert all the changes in the first docstring. They are not linked with the purpose of the PR and this is fine to have paragraph.
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Happy to merge once there are reverted
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| # Authors: Gael Varoquaux | ||
| # License: BSD 3 clause (C) INRIA 2014 | ||
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| from time import time |
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Let's not introduce perf_counter that is never used in the documentation and stick to time.
| clustering.fit(X_red) | ||
| print("%s :\t%.2fs" % (linkage, time() - t0)) | ||
| print("%s :\t%.2fs" % (linkage, perf_counter() - t0)) |
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However, here we can use an f-string and the same in the title in plot_clustering below.
| str(y[i]), | ||
| color=plt.cm.nipy_spectral(labels[i] / 10.0), | ||
| fontdict={"weight": "bold", "size": 9}, | ||
| for t in np.sort(np.unique(y)): |
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Instead of make the sorting we can load earlier the full Bunch:
digits = datasets.load_digits()
X, y = digits.data, digits.target
and then use the target_names that will be the array that you are expecting with the unique + sort.
| for t in np.sort(np.unique(y)): | |
| for digit in digits.target_names: |
| *X_red[y == t].T, | ||
| marker=f"${t}$", | ||
| s=50, | ||
| c=plt.cm.nipy_spectral(labels[y == t] / 10), |
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| c=plt.cm.nipy_spectral(labels[y == t] / 10), | |
| c=plt.cm.nipy_spectral(labels[y == digit] / 10), |
…g back perf_counter to time
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Closing since the other PR was opened before this one. Please check if there's an open PR before working on an example @siavrez |
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Sorry, didn't see that we had already merged that one. |
| An illustration of various linkage option for agglomerative clustering on | ||
| a 2D embedding of the digits dataset. | ||
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We only to revert these small changes here and we can merge this one.
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Thanks @siavrez |
Changing matplotlib.text with matplotlib.scatter and Markers created from TeX symbols improves runtime by almost 15 seconds.
Also changed time.time with time.perf_counter.
Reference Issues/PRs
#21598
What does this implement/fix? Explain your changes.
Changing matplotlib.text with matplotlib.scatter and Markers created from TeX symbols improves runtime by almost 15 seconds.
Also changed time.time with time.perf_counter.
Any other comments?