Improve packaging, logging, style (#838)

* Update logger and add quiet mode

* Improve packaging and some cleanup

machine_learning_hep/__main__.py

@@ -0,0 +1,18 @@
+#############################################################################
+##  © Copyright CERN 2023. All rights not expressly granted are reserved.  ##
+##                                                                         ##
+## 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  ##
+## Free Software Foundation, either version 3 of the License, or (at your  ##
+## option) any later version. This program is distributed in the hope that ##
+##  it will be useful, but WITHOUT ANY WARRANTY; without even the implied  ##
+##     warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.    ##
+##           See the GNU General Public License for more details.          ##
+##    You should have received a copy of the GNU General Public License    ##
+##   along with this program. if not, see <https://www.gnu.org/licenses/>. ##
+#############################################################################
+
+import sys
+from machine_learning_hep.steer_analysis import main
+
+sys.exit(main())

machine_learning_hep/logger.py

@@ -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  ##
@@ -55,8 +55,9 @@ class MLLoggerFormatter(logging.Formatter):
     reset = '\x1b[0m'
 
     # Define default format string
-    def __init__(self, fmt='%(levelname)s in %(pathname)s:%(lineno)d:\n ↳ %(message)s',
+    def __init__(self, fmt=None,
                  datefmt=None, style='%', color=False):
+        fmt = fmt or '%(levelname)s in %(pathname)s:%(lineno)d:\n ↳ %(message)s'
         logging.Formatter.__init__(self, fmt, datefmt, style)
         self.color = color
 
@@ -86,7 +87,7 @@ def format(self, record):
         return logging.Formatter.format(self, cached_record)
 
 
-def configure_logger(debug, logfile=None):
+def configure_logger(debug, logfile=None, quiet=False):
     """
     Basic configuration adding a custom formatted StreamHandler and turning on
     debug info if requested.
@@ -95,14 +96,11 @@ def configure_logger(debug, logfile=None):
     if logger.hasHandlers():
         return
 
-    # Turn on debug info only on request
-    if debug:
-        logger.setLevel(logging.DEBUG)
-    else:
-        logger.setLevel(logging.INFO)
+    logger.setLevel(logging.DEBUG if debug else logging.INFO)
 
     sh = logging.StreamHandler()
-    formatter = MLLoggerFormatter(color=lambda : getattr(sh.stream, 'isatty', None))
+    formatter = MLLoggerFormatter(color=lambda : getattr(sh.stream, 'isatty', None),
+                                  fmt = '%(levelname)s ➞ %(message)s' if quiet else None)
 
     sh.setFormatter(formatter)
     logger.addHandler(sh)

machine_learning_hep/steer_analysis.py

@@ -24,9 +24,6 @@
 import yaml
 from pkg_resources import resource_stream
 
-# To set batch mode immediately
-from ROOT import gROOT # pylint: disable=import-error, no-name-in-module
-
 from machine_learning_hep.multiprocesser import MultiProcesser
 from machine_learning_hep.processer import Processer
 from machine_learning_hep.processerdhadrons import ProcesserDhadrons
@@ -55,9 +52,6 @@
 def do_entire_analysis(data_config: dict, data_param: dict, data_param_overwrite: dict, # pylint: disable=too-many-locals, too-many-statements, too-many-branches
                        data_model: dict, run_param: dict, clean: bool):
 
-    # Disable any graphical stuff. No TCanvases opened and shown by default
-    gROOT.SetBatch(True)
-
     logger = get_logger()
     logger.info("Do analysis chain")
 
@@ -491,6 +485,7 @@ def main():
 
     parser = argparse.ArgumentParser()
     parser.add_argument("--debug", action="store_true", help="activate debug log level")
+    parser.add_argument("--quiet", '-q', action="store_true", help="quiet logging")
     parser.add_argument("--log-file", dest="log_file", help="file to print the log to")
     parser.add_argument("--run-config", "-r", dest="run_config",
                         help="the run configuration to be used")
@@ -509,7 +504,7 @@ def main():
 
     args = parser.parse_args()
 
-    configure_logger(args.debug, args.log_file)
+    configure_logger(args.debug, args.log_file, args.quiet)
 
     # Extract which database and run config to be used
     pkg_data = "machine_learning_hep.data"
@@ -526,3 +521,6 @@ def main():
     # Run the chain
     do_entire_analysis(run_config, db_analysis, db_analysis_overwrite, db_ml_models, db_run_list,
                        args.clean)
+
+if __name__ == '__main__':
+    main()

machine_learning_hep/templates_xgboost.py

@@ -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  ##
@@ -25,7 +25,7 @@
 
 def xgboost_classifier(model_config): # pylint: disable=W0613
     return XGBClassifier(verbosity=1,
-                         n_gpus=0,
+                         # n_gpus=0,
                          **model_config)
 
 
@@ -55,7 +55,8 @@ def yield_model_(self, model_config, space):
 
     def save_model_(self, model, out_dir):
         out_filename = join(out_dir, "xgboost_classifier.sav")
-        pickle.dump(model, open(out_filename, 'wb'), protocol=4)
+        with open(out_filename, 'wb') as outfile:
+            pickle.dump(model, outfile, protocol=4)
         out_filename = join(out_dir, "xgboost_classifier.model")
         model.save_model(out_filename)