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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,5 +1,5 @@ | ||
| ############################################################################# | ||
| ## © Copyright CERN 2018. All rights not expressly granted are reserved. ## | ||
| ## © Copyright CERN 2023. All rights not expressly granted are reserved. ## | ||
| ## Author: [email protected] ## | ||
| ## This program is free software: you can redistribute it and/or modify it ## | ||
| ## under the terms of the GNU General Public License as published by the ## | ||
|
|
@@ -31,7 +31,7 @@ | |
| from machine_learning_hep.correlations import vardistplot, scatterplot, correlationmatrix | ||
| from machine_learning_hep.models import getclf_scikit, getclf_xgboost, getclf_keras | ||
| from machine_learning_hep.models import fit, savemodels, readmodels, test, apply, decisionboundaries | ||
| from machine_learning_hep.root import write_tree | ||
| # from machine_learning_hep.root import write_tree | ||
| from machine_learning_hep.mlperformance import cross_validation_mse, plot_cross_validation_mse | ||
| from machine_learning_hep.mlperformance import plot_learning_curves, precision_recall | ||
| from machine_learning_hep.mlperformance import roc_train_test, plot_overtraining | ||
|
|
@@ -54,10 +54,11 @@ def __init__(self, data_param, case, typean, model_config, binmin, | |
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| self.logger = get_logger() | ||
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| dirprefix = data_param["multi"]["data"].get("prefix_dir", "") | ||
| dirprefixdata = data_param["multi"]["data"].get("prefix_dir", "") | ||
| dirprefixmc = data_param["multi"]["mc"].get("prefix_dir", "") | ||
| dirprefix_ml = data_param["ml"].get("prefix_dir_ml", "") | ||
| dirmcml = dirprefix + data_param["multi"]["mc"]["pkl_skimmed_merge_for_ml_all"] | ||
| dirdataml = dirprefix + data_param["multi"]["data"]["pkl_skimmed_merge_for_ml_all"] | ||
| dirmcml = dirprefixmc + data_param["multi"]["mc"]["pkl_skimmed_merge_for_ml_all"] | ||
| dirdataml = dirprefixdata + data_param["multi"]["data"]["pkl_skimmed_merge_for_ml_all"] | ||
| self.v_bin = data_param["var_binning"] | ||
| #directory | ||
| self.dirmlout = dirprefix_ml + data_param["ml"]["mlout"] | ||
|
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@@ -75,6 +76,8 @@ def __init__(self, data_param, case, typean, model_config, binmin, | |
| print(f"rm -r {self.dirmlplot}") | ||
| self.logger.fatal("Please remove above directories as indicated above first and " \ | ||
| "run again") | ||
| if self.steps_done is None: | ||
| self.steps_done = [] | ||
|
|
||
| #ml file names | ||
| self.n_reco = data_param["files_names"]["namefile_reco"] | ||
|
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@@ -205,7 +208,7 @@ def __init__(self, data_param, case, typean, model_config, binmin, | |
| self.f_mltest_applied = f"{self.dirmlout}/testsample_{self.s_suffix}_mldecision.pkl" | ||
| self.df_mltest_applied = None | ||
|
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||
| print(training_var) | ||
| self.logger.info('training variables: %s', training_var) | ||
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| def create_suffix(self): | ||
| string_selection = createstringselection(self.v_bin, self.p_binmin, self.p_binmax) | ||
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@@ -320,16 +323,10 @@ def preparesample(self): | |
| self.step_done("preparemlsamples") | ||
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| def step_done(self, step): | ||
| if self.steps_done is None: | ||
| self.steps_done = [] | ||
|
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| step_name = f"{step}_{self.p_binmin}_{self.p_binmax}" | ||
| if step_name in self.steps_done: | ||
| print("\n\n") | ||
| self.logger.warning("Done ML step %s already. It's skipped now. Remove the step " \ | ||
| "from the list in the following file", step_name) | ||
| print(self.file_steps_done) | ||
| print("\n\n") | ||
| "from the list in %s", step_name, self.file_steps_done) | ||
| return True | ||
|
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||
| # Add this steps and update the corresponsing file | ||
|
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@@ -443,9 +440,9 @@ def do_test(self): | |
| self.logger.info("Testing") | ||
| self.df_mltest_applied = test(self.p_mltype, self.p_classname, self.p_trainedmod, | ||
| self.df_mltest, self.v_train, self.v_sig) | ||
| df_ml_test_to_root = self.dirmlout+"/testsample_%s_mldecision.root" % (self.s_suffix) | ||
| pickle.dump(self.df_mltest_applied, openfile(self.f_mltest_applied, "wb"), protocol=4) | ||
| write_tree(df_ml_test_to_root, self.n_treetest, self.df_mltest_applied) | ||
| # df_ml_test_to_root = self.dirmlout+"/testsample_%s_mldecision.root" % (self.s_suffix) | ||
| # write_tree(df_ml_test_to_root, self.n_treetest, self.df_mltest_applied) | ||
|
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| def do_apply(self): | ||
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