diff --git a/toolkit.py b/toolkit.py
index b31a15a273293af6c5cc516ea33abe400b0524e1..c941dd0decfe64feb0093be5178e0fb0761642e1 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -19,6 +19,7 @@ import math
import glob
import shutil
import gc
+import random
import logging
logger = logging.getLogger("KerasROOTClassification")
@@ -33,7 +34,7 @@ from sklearn.externals import joblib
from sklearn.metrics import roc_curve, auc
from sklearn.utils.extmath import stable_cumsum
from keras.models import Sequential, Model, model_from_json
-from keras.layers import Dense, Dropout, Input, Masking, GRU, concatenate
+from keras.layers import Dense, Dropout, Input, Masking, GRU, concatenate, SimpleRNN
from keras.callbacks import History, EarlyStopping, CSVLogger, ModelCheckpoint, TensorBoard
from keras.optimizers import SGD
import keras.optimizers
@@ -744,7 +745,7 @@ class ClassificationProject(object):
# plot model
with open(os.path.join(self.project_dir, "model.svg"), "wb") as of:
- of.write(model_to_dot(self._model).create("dot", format="svg"))
+ of.write(model_to_dot(self._model, show_shapes=True).create("dot", format="svg"))
@property
@@ -1626,6 +1627,7 @@ class ClassificationProjectRNN(ClassificationProject):
recurrent_field_names=None,
rnn_layer_nodes=32,
mask_value=-999,
+ recurrent_unit_type="GRU",
**kwargs):
"""
recurrent_field_names example:
@@ -1644,6 +1646,7 @@ class ClassificationProjectRNN(ClassificationProject):
self.recurrent_field_names = []
self.rnn_layer_nodes = rnn_layer_nodes
self.mask_value = mask_value
+ self.recurrent_unit_type = recurrent_unit_type
# convert to of indices
self.recurrent_field_idx = []
@@ -1684,7 +1687,13 @@ class ClassificationProjectRNN(ClassificationProject):
for field_idx in self.recurrent_field_idx:
chan_inp = Input(field_idx.shape[1:])
channel = Masking(mask_value=self.mask_value)(chan_inp)
- channel = GRU(self.rnn_layer_nodes)(channel)
+ if self.recurrent_unit_type == "GRU":
+ channel = GRU(self.rnn_layer_nodes)(channel)
+ elif self.recurrent_unit_type == "SimpleRNN":
+ channel = SimpleRNN(self.rnn_layer_nodes)(channel)
+ else:
+ raise NotImplementedError("{} not implemented".format(self.recurrent_unit_type))
+ logger.info("Added {} unit".format(self.recurrent_unit_type))
# TODO: configure dropout for recurrent layers
#channel = Dropout(0.3)(channel)
rnn_inputs.append(chan_inp)
@@ -1731,6 +1740,37 @@ 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 mask_uniform(self, x):
+ """
+ Mask recurrent fields with a random (uniform) number of objects. Works in place.
+ """
+ for recurrent_field_idx in self.recurrent_field_idx:
+ for evt in x:
+ masked = False
+ nobj = int(random.random()*(recurrent_field_idx.shape[1]+1))
+ for obj_number, line_idx in enumerate(recurrent_field_idx.reshape(*recurrent_field_idx.shape[1:])):
+ if obj_number == nobj:
+ 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 f3323c42a18772bd3b4b68004af752ef7610b157..48f58f60b909b912d8b47038fd732c1ce339a05c 100644
--- a/utils.py
+++ b/utils.py
@@ -33,22 +33,31 @@ 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:
+ if mask_value is None:
+ raise ValueError("Need to provide mask_value if random events should be masked")
+ 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=[],
@@ -115,7 +124,7 @@ def get_grad_function(model, layer, neuron):
],
ignoreKwargs=["input_transform", "input_inverse_transform"],
)
-def get_max_activation_events(model, ranges, ntries, layer, neuron, seed=42, **kwargs):
+def get_max_activation_events(model, ranges, ntries, layer, neuron, seed=42, mask_probs=None, mask_value=None, **kwargs):
gradient_function = get_grad_function(model, layer, neuron)
@@ -125,9 +134,15 @@ def get_max_activation_events(model, ranges, ntries, layer, neuron, seed=42, **k
for i in range(ntries):
if not (i%100):
logger.info(i)
- res = max_activation_wrt_input(gradient_function,
- create_random_event(ranges),
- **kwargs)
+ res = max_activation_wrt_input(
+ gradient_function,
+ create_random_event(
+ ranges,
+ mask_probs=mask_probs,
+ mask_value=mask_value
+ ),
+ **kwargs
+ )
if res is not None:
loss, event = res
loss = np.array([loss])
@@ -188,7 +203,6 @@ class WeightedRobustScaler(RobustScaler):
self.center_ = wqs[:,1]
self.scale_ = wqs[:,2]-wqs[:,0]
self.scale_ = _handle_zeros_in_scale(self.scale_, copy=False)
- print(self.scale_)
return self
@@ -202,6 +216,15 @@ class WeightedRobustScaler(RobustScaler):
return super(WeightedRobustScaler, self).transform(X)
+ def inverse_transform(self, X):
+ if np.isnan(X).any():
+ X *= self.scale_
+ X += self.center_
+ return X
+ else:
+ return super(WeightedRobustScaler, self).inverse_transform(X)
+
+
def poisson_asimov_significance(s, ds, b, db):
"see `<http://www.pp.rhul.ac.uk/~cowan/stat/medsig/medsigNote.pdf>`_)"
db = np.sqrt(db**2+ds**2)