From 4f02d77d75bea898ee2c786c3de0692df502e00a Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Wed, 22 Aug 2018 11:21:00 +0200
Subject: [PATCH] adding functionality to mask values in random events
---
toolkit.py | 16 ++++++++++++++++
utils.py | 11 +++++++++--
2 files changed, 25 insertions(+), 2 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index c3040d8..95fdcbd 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -1710,6 +1710,22 @@ class ClassificationProjectRNN(ClassificationProject):
self.checkpoint_model()
+ def clean_mask(self, x):
+ """
+ Mask recurrent fields such that once a masked value occurs,
+ all values corresponding to the same and following objects are
+ masked as well. Works in place.
+ """
+ for recurrent_field_idx in self.recurrent_field_idx:
+ for evt in x:
+ masked = False
+ for line_idx in recurrent_field_idx.reshape(*recurrent_field_idx.shape[1:]):
+ if (evt[line_idx] == self.mask_value).any():
+ masked=True
+ if masked:
+ evt[line_idx] = self.mask_value
+
+
def get_input_list(self, x):
"Format the input starting from flat ntuple"
x_input = []
diff --git a/utils.py b/utils.py
index f3323c4..029f76e 100644
--- a/utils.py
+++ b/utils.py
@@ -33,22 +33,29 @@ def get_single_neuron_function(model, layer, neuron, input_transform=None):
return eval_single_neuron
-def create_random_event(ranges):
+def create_random_event(ranges, mask_probs=None, mask_value=None):
random_event = np.array([p[0]+(p[1]-p[0])*np.random.rand() for p in ranges])
random_event = random_event.reshape(-1, len(random_event))
+ # if given, mask values with a certain probability
+ if mask_probs is not None:
+ for var_index, mask_prob in enumerate(mask_probs):
+ random_event[:,var_index][np.random.rand(len(random_event)) < mask_prob] = mask_value
return random_event
def get_ranges(x, quantiles, weights, mask_value=None, filter_index=None):
"Get ranges for plotting or random event generation based on quantiles"
ranges = []
+ mask_probs = []
for var_index in range(x.shape[1]):
if (filter_index is not None) and (var_index != filter_index):
continue
x_var = x[:,var_index]
not_masked = np.where(x_var != mask_value)[0]
+ masked = np.where(x_var == mask_value)[0]
ranges.append(weighted_quantile(x_var[not_masked], quantiles, sample_weight=weights[not_masked]))
- return ranges
+ mask_probs.append(float(len(masked))/float(len(x_var)))
+ return ranges, mask_probs
def max_activation_wrt_input(gradient_function, random_event, threshold=None, maxthreshold=None, maxit=100, step=1, const_indices=[],
--
GitLab