From 887f16102a4ebabf7ab3d747d976718f9c14710b Mon Sep 17 00:00:00 2001
From: Nikolai <osterei33@gmx.de>
Date: Tue, 28 Aug 2018 09:25:46 +0200
Subject: [PATCH] adding generate_actmax.py script
---
scripts/generate_actmax.py | 98 ++++++++++++++++++++++++++++++++++++++
1 file changed, 98 insertions(+)
create mode 100755 scripts/generate_actmax.py
diff --git a/scripts/generate_actmax.py b/scripts/generate_actmax.py
new file mode 100755
index 0000000..21db4e4
--- /dev/null
+++ b/scripts/generate_actmax.py
@@ -0,0 +1,98 @@
+#!/usr/bin/env python
+
+import sys, argparse, re, random
+
+parser = argparse.ArgumentParser(description="generate events that maximise the activation for a given neuron")
+parser.add_argument("input_project")
+parser.add_argument("output_file")
+parser.add_argument("-n", "--nevents", default=100000, type=int)
+parser.add_argument("-j", "--mask-jets", action="store_true",
+ help="mask variables called jet*Pt/Eta/Phi and generate a random uniform distribution of the number of jets (only nescessary for non-recurrent NN)")
+args = parser.parse_args()
+
+import logging
+logging.basicConfig()
+logging.getLogger().setLevel(logging.INFO)
+
+import h5py
+import numpy as np
+
+from KerasROOTClassification.utils import (
+ weighted_quantile,
+ get_max_activation_events,
+ create_random_event,
+ get_ranges
+)
+from KerasROOTClassification import load_from_dir
+import meme
+
+meme.setOptions(deactivated=True)
+
+input_project = args.input_project
+output_file = args.output_file
+
+c = load_from_dir(input_project)
+
+ranges, mask_probs = get_ranges(c.transform(c.x_train), [0.01, 0.99], c.w_train_tot, mask_value=c.mask_value, max_evts=10000)
+
+def mask_uniform(x, mask_value, recurrent_field_idx):
+ """
+ Mask recurrent fields with a random (uniform) number of objects. Works in place.
+ """
+ for rec_idx in recurrent_field_idx:
+ for evt in x:
+ masked = False
+ nobj = int(random.random()*(rec_idx.shape[1]+1))
+ for obj_number, line_idx in enumerate(rec_idx.reshape(*rec_idx.shape[1:])):
+ if obj_number == nobj:
+ masked=True
+ if masked:
+ evt[line_idx] = mask_value
+
+def get_input_flat(x):
+ return x[0].reshape(-1, len(c.fields))
+
+
+if args.mask_jets:
+ jet_fields = {}
+ for field_name in c.fields:
+ if any(field_name.startswith("jet") and field_name.endswith(suffix) for suffix in ["Pt", "Eta", "Phi"]):
+ jet_number = re.findall("[0-9]+", field_name)[0]
+ if not jet_number in jet_fields:
+ jet_fields[jet_number] = []
+ jet_fields[jet_number].append(c.fields.index(field_name))
+ jet_fields = [np.array([[v for k, v in sorted(jet_fields.items(), key=lambda l:l[0])]])]
+
+
+def input_transform(x):
+ x = np.array(x)
+ if hasattr(c, "mask_uniform"):
+ c.mask_uniform(x)
+ return c.get_input_list(x)
+ elif args.mask_jets:
+ mask_uniform(x, c.mask_value, jet_fields)
+ return x
+
+
+opt_kwargs = dict()
+if hasattr(c, "mask_uniform"):
+ opt_kwargs["input_transform"] = input_transform
+ opt_kwargs["input_inverse_transform"] = c.get_input_flat
+if args.mask_jets:
+ opt_kwargs["input_transform"] = input_transform
+ opt_kwargs["input_inverse_transform"] = get_input_flat
+
+
+evts = get_max_activation_events(
+ c.model, ranges,
+ ntries=args.nevents,
+ layer=len(c.model.layers)-1,
+ neuron=0,
+ maxit=10,
+ seed=45,
+ threshold=0,
+ **opt_kwargs
+)
+
+with h5py.File(output_file, "w") as f:
+ f.create_dataset("actmax", data=evts[1])
--
GitLab