Minor cosmetic changes in calculate_fisher_information_full_detector()
@@ -250,7 +250,7 @@ class FisherInformation:
return_error=None,
ensemble_vote_expectation_weight=None,
batch_size=100000,
- test_split=None,
+ test_split=0.5,
):
"""
Calculates the full Fisher information in realistic detector-level observations, estimated with neural networks.
@@ -286,6 +286,10 @@ class FisherInformation:
batch_size : int, optional
Batch size. Default value: 100000.
+ test_split : float or None, optional
+ If unweighted_x_sample_file is None, this determines the fraction of weighted events used for evaluation.
+ If None, all events are used (this will probably include events used during training!). Default value: 0.5.
+
Returns
-------
fisher_information : ndarray or list of ndarray
@@ -346,7 +350,7 @@ class FisherInformation:
fisher_info_kin = None
covariance = None
- n_batches = np.ceil(self.n_samples / batch_size)
+ n_batches = int(np.ceil(self.n_samples / batch_size))
for i_batch, (observations, weights_benchmarks) in enumerate(
madminer_event_loader(self.madminer_filename, batch_size=batch_size, start=start_event)
