Get SCM Predictions and LOIO Probabilities #29
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roshankern
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axiomcura
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axiomcura
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axiomcura
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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 <[email protected]> * eric suggestions --------- Co-authored-by: Erik Serrano <[email protected]> * 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 <[email protected]> * Update 2.train_model/README.md Co-authored-by: Gregory Way <[email protected]> * Update 2.train_model/README.md Co-authored-by: Gregory Way <[email protected]> * notebook run --------- Co-authored-by: Gregory Way <[email protected]> * 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 <[email protected]> * dave suggestions * reconvert notebook --------- Co-authored-by: Dave Bunten <[email protected]> * 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 <[email protected]> * Update utils/evaluate_utils.py Co-authored-by: Dave Bunten <[email protected]> * Update 3.evaluate_model/scripts/nbconverted/F1_scores.py Co-authored-by: Dave Bunten <[email protected]> * dave suggestions --------- Co-authored-by: Dave Bunten <[email protected]> * 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 <[email protected]> Co-authored-by: Gregory Way <[email protected]> Co-authored-by: Dave Bunten <[email protected]>
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In this PR, predictions and leave-one-image-out probabilities are derived for the single-class models.
For predictions, we simply load in the model for a particular phenotypic class, model type (final or shuffled baseline), and feature type (CP, DP, CP_and_DP) then derive model predictions for cells from both data subsets (train and test). Predictions are saved to
predictions/.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/.