Get Leave One Image Out Probabilities #23
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Forgot to mention that it seems like running the entire notebook will take a very long time (~9hrs), so the entire notebook will be run and the last couple of lines deleted after this PR has been reviewed. That way, if there are any suggestions the entire notebook will not need to be rerun. |
d33bs
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Mar 29, 2023
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Nice work! I left a few comments and suggestions, please don't hesitate to let me know if you have any questions. Overall LGTM!
roshankern
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* Refactor Download Module (#18) * refactor module * remove training data file * Update 0.download_data/scripts/nbconverted/download_data.py Co-authored-by: Erik Serrano <31600622+axiomcura@users.noreply.github.com> * eric suggestions --------- Co-authored-by: Erik Serrano <31600622+axiomcura@users.noreply.github.com> * Refactor Split Data Module (#19) * refactor module * greg suggestions * Train module refactor (#20) * refactor format module * use straify function * rerun train module * black formatting * docs, nbconvert * nbconvert * rerun pipeline, rename model * fix typo * Update 2.train_model/README.md Co-authored-by: Gregory Way <gregory.way@gmail.com> * Update 2.train_model/README.md Co-authored-by: Gregory Way <gregory.way@gmail.com> * Update 2.train_model/README.md Co-authored-by: Gregory Way <gregory.way@gmail.com> * notebook run --------- Co-authored-by: Gregory Way <gregory.way@gmail.com> * Refactor evaluate module (#21) * refactor clas pr curves * refactor confusion matrix * refactor F1 scores * refactor model predictions * documentation * dave suggestions * erik suggestions, reconvert * Refactor interpret module (#22) * refactor interpret notebook * docs, reconvert script * greg suggestions * Get Leave One Image Out Probabilities (#23) * add LOIO notebook * LOIO notebook * update notebook * download and split data with cell UUIDs * move LOIO * finish LOIO * black formatting * rerun notebook * rerun notebook, dave suggestions * greg comment * Train single class models (#25) * move multiclass models * rename files, fix sh * single class models notebook * run notebook * binarize labels * train single class models * reconvert notebooks * update readme * rename sh file * remove models * eric readme suggestions * rerun notebook, eric suggestions * Add Single Class Model PR Curves (#26) * get SCM PR curves * shuffled baseline * retrain single class models with correct kernel * rerun pr curves notebook * remove nones * rerun multiclass model * rerun notebook * move file * docs, black formatting * format notebook * Update 3.evaluate_model/README.md Co-authored-by: Dave Bunten <ekgto445@gmail.com> * dave suggestions * reconvert notebook --------- Co-authored-by: Dave Bunten <ekgto445@gmail.com> * Add SCM confusion matrices and F1 scores (#27) * get SCM PR curves * shuffled baseline * retrain single class models with correct kernel * rerun pr curves notebook * remove nones * rerun multiclass model * rerun notebook * move file * create SCM confusion matrix * rerun notebook * add changes from last PR * rerun notebook * add SCM F1, update SCM confusion matrices * documentation * rerun notebook * Update utils/evaluate_utils.py Co-authored-by: Dave Bunten <ekgto445@gmail.com> * Update utils/evaluate_utils.py Co-authored-by: Dave Bunten <ekgto445@gmail.com> * Update 3.evaluate_model/scripts/nbconverted/F1_scores.py Co-authored-by: Dave Bunten <ekgto445@gmail.com> * dave suggestions --------- Co-authored-by: Dave Bunten <ekgto445@gmail.com> * Get SCM Predictions and LOIO Probabilities (#29) * get SCM LOIO probas * reconvert notebook * get model predictions * rerun LOIO * reconvert notebook * save and reconvert notebook * eric suggestions * Add SCM Interpretations (#30) * add scm coefficients * rerun interpret multi-class model * compare model coefficients * nbconvert * readme * make all correlations negative * rerun training * rerun evaluate * rerun interpret * docs * newline * rerun LOIO * Remove unused cp features (#31) * rerun download/split modules * rerun multicalss models * rerun single class model * rerun evaluate module * get LOIO probas * rerun interpret module * rerun download data * Adding CP features to ggplot visualization (#24) * set colors for model types * visualize precision recall with CP and DP+CP * add F1 score barchart visualization * minor tweak of f1 score print * ignore mac files * merge main and rerun viz * change color scheme for increased contrast * add f1 score of the top model, and rerun with updated colors * nrow = 3 in facet * change name of weighted f1 score * update single cell images module (#32) * Refactor validate module (#33) * update validate module * refactor validation * get correlations * convert notebook * update readme * formatting, documentation * reset index * vadd view notebook * docs, black formatting * ccc credit * show all correlations * add notebook * remove preview notebook * convert notebook * add differences heatmaps * preview correlation differences * add docs * black formatting --------- Co-authored-by: Erik Serrano <31600622+axiomcura@users.noreply.github.com> Co-authored-by: Gregory Way <gregory.way@gmail.com> Co-authored-by: Dave Bunten <ekgto445@gmail.com>
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In
get_LOIO_probabilities.ipynb, we use the optimal hyperparameters from each final logistic regression model (DP, CP, CP_and_DP) to fit and evaluate new models in a Leave One Image Out (LOIO) fashion.The LOIO evaluation procedure is as follows:
Metadata_DNAfield):Candl1_ratio, saved with model data in models/) on every cell that is not in the specific image.The probabilities are saved to
LOIO_probas/.