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 twodimensional 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, iflabels
contains only two classes. If None, the highest value inlabels
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]