Add pipeline

pipeline.py

@@ -0,0 +1,57 @@
+from data_processing import read_in, shuffle_split_scale
+
+def main(class_files_dict):
+	'''
+	Args:
+	-----
+		class_files_dict: dictionary that links the names of the different classes
+		                  in the classification problem to the paths of the ROOT files
+		                  associated with each class; for example:
+
+		                  {
+		                  	"ttbar" :
+		                  	[
+		                  		"/path/to/file1.root",
+		                  		"/path/to/file2.root",
+		                  	],
+		                  	"qcd" :
+		                  	[
+		                  		"/path/to/file3.root",
+		                  		"/path/to/file4.root",
+		                  	],
+		                  	...
+		                  }
+
+	'''
+	# -- transform ROOT files into standard ML format (ndarrays) 
+	X_jets, X_photons, X_muons, y, w = read_in(class_files_dict)
+
+	# -- shuffle, split samples into train and test set, scale features
+	X_jets_train, \
+	X_jets_test, \
+	X_photons_train, \
+	X_photons_test, \
+	X_muons_train, \
+	X_muons_test, \
+	y_train, \
+	y_test, \
+	w_train, \
+	w_test = shuffle_split_scale(X_jets, X_photons, X_muons, y, w)
+
+	# -- plot distributions
+	# -- train
+	# -- test
+	# -- plot performance
+
+if __name__ == '__main__':
+
+ 	import sys
+ 	import argparse
+
+	# -- read in arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument('config', help="JSON file that specifies classes and corresponding ROOT files' paths", required=True)
+    args = parser.parse_args()
+
+    # -- pass arguments to main
+    sys.exit(main(args.config))