From f52e8ad96613b8f4031b1a6ffa4278d09c1bef5c Mon Sep 17 00:00:00 2001
From: Nikolai <osterei33@gmx.de>
Date: Tue, 14 Aug 2018 11:50:44 +0200
Subject: [PATCH] starting to develop yield_batch function for RNN
---
toolkit.py | 43 ++++++++++++++++++++++++++++++++++---------
1 file changed, 34 insertions(+), 9 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index befd03e..c6ff50e 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -1228,14 +1228,31 @@ class ClassificationProjectRNN(ClassificationProject):
"""
def __init__(self,
- recurrent_branches=None,
+ recurrent_fields=None,
mask_value=-999,
**kwargs):
- self.recurrent_branches = recurrent_branches
- if self.recurrent_branches is None:
- self.recurrent_branches = []
+ """
+ recurrent_fields example:
+ [["jet1Pt", "jet1Eta", "jet1Phi"],
+ ["jet2Pt", "jet2Eta", "jet2Phi"],
+ ["jet3Pt", "jet3Eta", "jet3Phi"]],
+ [["lep1Pt", "lep1Eta", "lep1Phi", "lep1flav"],
+ ["lep2Pt", "lep2Eta", "lep2Phi", "lep2flav"]],
+ """
+ self.recurrent_fields = recurrent_fields
+ if self.recurrent_fields is None:
+ self.recurrent_fields = []
+ for i, recurrent_field in enumerate(self.recurrent_fields):
+ self.recurrent_fields[i] = np.array(recurrent_field)
+ if self.recurrent_fields[i].dtype == np.object:
+ raise ValueError(
+ "Invalid entry for recurrent fields: {} - "
+ "please ensure that the length for all elements in the list is equal"
+ .format(recurrent_field)
+ )
self.mask_value = mask_value
super(ClassificationProjectRNN, self).__init__()
+ self.flat_fields = [field for field in self.fields if not field in self.recurrent_fields]
@property
@@ -1244,12 +1261,20 @@ class ClassificationProjectRNN(ClassificationProject):
def yield_batch(self):
+ x_train, y_train, w_train = self.training_data
while True:
- permutation = np.random.permutation
- x_train, y_train, w_train = self.training_data
- n_training = len(x_train)
- for batch_start in range(0, n_training, self.batch_size):
- pass
+ shuffled_idx = np.random.permutation(len(x_train))
+ for start in range(0, len(shuffled_idx), int(self.batch_size)):
+ x_batch = x_train[shuffled_idx[start:start+int(self.batch_size)]]
+ x_flat = x_batch[:,self.flat_fields]
+ x_input = []
+ x_input.append(x_flat)
+ for recurrent_field in self.recurrent_fields:
+ x_recurrent = x_batch[:,recurrent_field.reshape(-1)].reshape(-1, *recurrent_field.shape)
+ x_input.append(x_recurrent)
+ yield (x_input,
+ y_train[shuffled_idx[start:start+int(self.batch_size)]],
+ w_train[shuffled_idx[start:start+int(self.batch_size)]]*self.balanced_class_weight[class_label])
# # shuffle the entries for this class label
# rn_state = np.random.get_state()
# x_train[y_train==class_label] = np.random.permutation(x_train[y_train==class_label])
--
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