applies some minor refactoring on model configuration for model small
moves method evaluate from __main__.py to module evaluate.py
changes the names of model run folders to that model name is used, for easier retrieval
introduces new CLI option save_model to save each component of the model pipeline as pickle into model_comp{comp_id}.pickle
introduces CLI option predict_siret_list to specify a filepath containing a list of SIRETs. Dataset predictis then restricted to SIRETs in this list
introduces CLI option explain to specify whether the fields containing the explanations of model predictions should be computed. This option has a store_true behaviour, such that by default, explanations fields are not computed.
Moreover, the PR introduces tools to select model thresholds from predicted failure risk, allowing to build lists of selected companies using a few intuitive functions:
introduces methods to select thresholds from various criteria in evaluate.py
introduces notebook 03-select-thresholds.ipynb to leverage these new functions
introduces notebook 04-merge_models_into_list.ipynb to use the selected thresholds to produce a list
On the refactoring side, this PR:
smallevaluatefrom__main__.pyto moduleevaluate.pysave_modelto save each component of the model pipeline as pickle intomodel_comp{comp_id}.picklepredict_siret_listto specify a filepath containing a list of SIRETs. Datasetpredictis then restricted to SIRETs in this listexplainto specify whether the fields containing the explanations of model predictions should be computed. This option has a store_true behaviour, such that by default, explanations fields are not computed.Moreover, the PR introduces tools to select model thresholds from predicted failure risk, allowing to build lists of selected companies using a few intuitive functions:
evaluate.py03-select-thresholds.ipynbto leverage these new functions04-merge_models_into_list.ipynbto use the selected thresholds to produce a list