root_numpy.tmva.add_classification_events

root_numpy.tmva.add_classification_events(obj, events, labels, signal_label=None, weights=None, test=False)

Add classification events to a TMVA::Factory or TMVA::DataLoader from NumPy arrays.

Parameters:

obj : TMVA::Factory or TMVA::DataLoader

A TMVA::Factory or TMVA::DataLoader (TMVA’s interface as of ROOT 6.07/04) instance with variables already booked in exactly the same order as the columns in events.

events : numpy array of shape [n_events, n_variables]

A two-dimensional NumPy array containing the rows of events and columns of variables. The order of the columns must match the order in which you called AddVariable() for each variable.

labels : numpy array of shape [n_events]

The class labels (signal or background) corresponding to each event in events.

signal_label : float or int, optional (default=None)

The value in labels for signal events, if labels contains only two classes. If None, the highest value in labels is used.

weights : numpy array of shape [n_events], optional

Event weights.

test : bool, optional (default=False)

If True, then the events will be added as test events, otherwise they are added as training events by default.

Notes

• A TMVA::Factory or TMVA::DataLoader requires you to add both training and test events even if you don’t intend to call TestAllMethods().

• When using MethodCuts, the first event added must be a signal event, otherwise TMVA will fail with <FATAL> Interval : maximum lower than minimum. To place a signal event first:

  # Get index of first signal event
  first_signal = np.nonzero(labels == signal_label)[0][0]
  # Swap this with first event
  events[0], events[first_signal] = events[first_signal].copy(), events[0].copy()
  labels[0], labels[first_signal] = labels[first_signal], labels[0]
  weights[0], weights[first_signal] = weights[first_signal], weights[0]