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Jan's MRI analysis #12

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7a638f2
fix `ValueError: y_true and y_pred contain different number of classes`
valosekj Jan 21, 2025
0977831
use number instead of str
valosekj Jan 22, 2025
47ef561
Merge branch 'master' into jv/experimental-fix_different_num_of_class…
valosekj Jan 22, 2025
3f276b6
Merge branch 'jv/experimental-fix_different_num_of_classes_between_y_…
valosekj Jan 22, 2025
9f8cae4
Merge branch 'master' into jv/experimental-fix_different_num_of_class…
valosekj Jan 28, 2025
6921a64
Merge branch 'master' into jv/experimental-fix_different_num_of_class…
valosekj Feb 12, 2025
e2f501a
Set default LogisticRegression solver to saga to make it working for RFE
valosekj Feb 12, 2025
e46d306
Merge branch 'master' into jv/experimental-fix_different_num_of_class…
valosekj Feb 13, 2025
b5cc4d1
Add a script to inspect and plot output db
valosekj Feb 15, 2025
f681fad
Add `y_true_collector` and `y_pred_proba_collector` metrics
valosekj Feb 20, 2025
707fe6b
Move getting the best replicate into a function
valosekj Feb 20, 2025
c5c61a3
Remove empty lines
valosekj Feb 20, 2025
a2f5934
Return `best_models_dict` by `get_best_replicate`
valosekj Feb 21, 2025
d69a385
Plot ROC
valosekj Feb 21, 2025
adab105
Keep only the models we are interested in
valosekj Feb 21, 2025
e58a68f
Improve figure titles
valosekj Feb 21, 2025
c14c7aa
Plot the mean ± std ROC curve for each model across replicates.
valosekj Feb 21, 2025
c55c2f4
Unify titles across figures
valosekj Feb 21, 2025
0b3ad3d
include metric (e.g., 'balanced_accuracy (test)' or 'balanced_accurac…
valosekj Feb 24, 2025
917f4ab
Add max_iter parameter to log_reg configuration
valosekj Apr 2, 2025
928df4a
Add proper argparse
valosekj Apr 10, 2025
bedb126
Get unique targets dynamically and iterate over them
valosekj Apr 10, 2025
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2 changes: 1 addition & 1 deletion data/hooks/feature_selection.py
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Expand Up @@ -203,7 +203,7 @@ def from_config(cls, config: dict, logger: Logger = Logger.root) -> Self:
def tune(self, trial: Trial):
self.prop_tuner.tune(trial)
# Generate the new backing model based on this setup
new_lor = LogisticRegression()
new_lor = LogisticRegression(solver='saga')
self.backing_rfe = RFE(estimator=new_lor, n_features_to_select=self.prop_tuner.value)

def tunable_params(self) -> list[TunableParam]:
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