Documentation of arguments

toolkit.py

@@ -42,6 +42,60 @@ import ROOT
 
 class KerasROOTClassification(object):
 
+    """Simple framework to load data from ROOT TTrees and train Keras
+    neural networks for classification according to some global settings.
+
+    See the `Keras documentation <https://keras.io>` for further information
+
+    All needed data that is created is stored in a project dir and can
+    be used again later without the need to be recreated.
+
+    :param name: Name of the project - this will also be the name of
+                 the project directory in the output dir. If no further arguments
+                 are given, this argument is interpreted as a directory name, from
+                 which a previously created project should be initialised
+
+    :param signal_trees: list of tuples (filename, treename) for the data that should be used as signal
+
+    :param bkg_trees: list of tuples (filename, treename) for the data that should be used as background
+
+    :param branches: list of branch names or expressions to be used as input values for training
+
+    :param weight_expr: expression to weight the events in the loss function
+
+    :param identifiers: list of branches or expressions that uniquely
+                        identify events. This is used to store the list of training
+                        events, such that they can be marked later on, for example when
+                        creating friend trees with output score
+
+    :param selection: selection expression that events have to fulfill to be considered for training
+
+    :param layers: number of layers in the neural network
+
+    :param nodes: number of nodes in each layer
+
+    :param batch_size: size of the training batches
+
+    :param validation_split: split off this fraction of training events for loss evaluation
+
+    :param activation_function: activation function in the hidden layers
+
+    :param out_dir: base directory in which the project directories should be stored
+
+    :param scaler_type: sklearn scaler class name to transform the data before training (options: "StandardScaler", "RobustScaler")
+
+    :param step_signal: step size when selecting signal training events (e.g. 2 means take every second event)
+
+    :param step_bkg: step size when selecting background training events (e.g. 2 means take every second event)
+
+    :param optimizer: name of optimizer class in keras.optimizers
+
+    :param optimizer_opts: dictionary of options for the optimizer
+
+    :param earlystopping_opts: options for the keras EarlyStopping callback
+
+    """
+
 
     # Datasets that are stored to (and dynamically loaded from) hdf5
     dataset_names = ["x_train", "x_test", "y_train", "y_test", "w_train", "w_test", "scores_train", "scores_test"]