Param tuning code integration: pca chosen

[ntalluri]

No description provided.

[ntalluri]

How to do PCA on Binary Data?

[ntalluri]

https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html (data is not in a gaussian distribution; does not make sense to use standard scalar)

[ntalluri]

https://stackoverflow.com/questions/40795141/pca-for-categorical-features
https://stats.stackexchange.com/questions/159705/would-pca-work-for-boolean-binary-data-types

Based on a bunch of different forums, people suggest not using PCA

[ntalluri]

It might be best to keep these features are one hot encoded values (which they already are).

[ntalluri]

@agitter Do this PR Last

Will need to merge with updated master after #193, #207 is merged, and #208. (hopefully this will remove the repeated files through out the PRs)

• there will be merge conflicts with the Snakefile, evaluation.py and the test suite.

Included in this PR:

1. update to evaluation.py that updates the code to include precision_and_recall and pca chosen pathway (precision_and_recall is also used in Param tuning code integration:no param tuning #208)
2. a new test suite evaluate for pca chosen pathway
3. updates to Snakemake file that will run evaluation per dataset and per algortihm-dataset pair for pca chosen pathway
4. update to ml code for PCA to add centroids
5. still need to figure out how to rescale the binary data
6. update to ml test suite for expected pca coordinates and ml test code

[ntalluri]

for the create palette function, update the code to have a sorted list of unique column headers

unique_column_names = list(sorted(set(column_names)))
custom_palette = sns.color_palette(palette = "tab20c", n_colors = len(unique_column_names))
label_color_map = {label: color for label, color in zip(unique_column_names, custom_palette, strict=True)}
return label_color_map