ENH Impoving execution speed of plot_pca_vs_fa_model_selection.py by … #21671
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…dividing by 2 parameters n_samples, n_features, rank
ogrisel
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Nov 15, 2021
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LGTM, thanks for the speed improvement. The conclusions in the text still hold with this dataset.
In the text, I believe that the sentence "Under appropriate circumstances the low rank models are more likely than shrinkage models." is wrong. I should rather be written: "Under appropriate circumstances (choice of the number of components), the held-out data is more likely for low rank models than for shrinkage models.".
This is because the likelihood is a property of the data for a given model rather than a property of a model for given data. Maybe @agramfort or someone else can double check the suggested phrasing above.
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@ogrisel I agree with the phrasing you suggest |
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@ogrisel @agramfort Should I change this myself in this PR ? |
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yes go for it!
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…dividing by 2 parameters n_samples, n_features, rank
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@ogrisel @agramfort I applied your suggestions and the pipeline seems good. |
agramfort
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Nov 20, 2021
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Thx @Iglesys347 |
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scikit-learn#21671) * ENH Impoving execution speed of plot_pca_vs_fa_model_selection.py by dividing by 2 parameters n_samples, n_features, rank * Update docstring according to @ogrisel 's comment * ENH Impoving execution speed of plot_pca_vs_fa_model_selection.py by dividing by 2 parameters n_samples, n_features, rank * Update docstring according to @ogrisel 's comment
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scikit-learn#21671) * ENH Impoving execution speed of plot_pca_vs_fa_model_selection.py by dividing by 2 parameters n_samples, n_features, rank * Update docstring according to @ogrisel 's comment * ENH Impoving execution speed of plot_pca_vs_fa_model_selection.py by dividing by 2 parameters n_samples, n_features, rank * Update docstring according to @ogrisel 's comment
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scikit-learn#21671) * ENH Impoving execution speed of plot_pca_vs_fa_model_selection.py by dividing by 2 parameters n_samples, n_features, rank * Update docstring according to @ogrisel 's comment * ENH Impoving execution speed of plot_pca_vs_fa_model_selection.py by dividing by 2 parameters n_samples, n_features, rank * Update docstring according to @ogrisel 's comment
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scikit-learn#21671) * ENH Impoving execution speed of plot_pca_vs_fa_model_selection.py by dividing by 2 parameters n_samples, n_features, rank * Update docstring according to @ogrisel 's comment * ENH Impoving execution speed of plot_pca_vs_fa_model_selection.py by dividing by 2 parameters n_samples, n_features, rank * Update docstring according to @ogrisel 's comment
glemaitre
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#21671) * ENH Impoving execution speed of plot_pca_vs_fa_model_selection.py by dividing by 2 parameters n_samples, n_features, rank * Update docstring according to @ogrisel 's comment * ENH Impoving execution speed of plot_pca_vs_fa_model_selection.py by dividing by 2 parameters n_samples, n_features, rank * Update docstring according to @ogrisel 's comment
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…dividing by 2 parameters n_samples, n_features, rank
Reference Issues/PRs
References #21598
What does this implement/fix? Explain your changes.
Increase the execution speed of
scikit-learn/examples/decomposition/plot_pca_vs_fa_model_selection.pyby reducing some parameters.Time taken before modification (time taken measured with the
timeunix command):real 1m49,943s
user 4m6,328s
sys 2m52,386s
Time taken after modification:
real 0m14,672s
user 0m23,755s
sys 0m19,608s
Graphs before modification:
Graphs after modification:
Any other comments?