From dca187ae2b5e49ed2bb498955c5f3871d3c6acf5 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <nikolai.hartmann@gmx.de>
Date: Thu, 1 Nov 2018 14:40:54 +0100
Subject: [PATCH] fixing evaluate_train_test (includes transformation now)
---
toolkit.py | 51 ++++++++++++++++++++++++++-------------------------
1 file changed, 26 insertions(+), 25 deletions(-)
diff --git a/toolkit.py b/toolkit.py
index 5a6aa8a..524a66f 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -990,6 +990,32 @@ class ClassificationProject(object):
self._dump_to_hdf5("scores_train")
+ def evaluate_train_test(self, do_train=True, do_test=True, batch_size=10000, mode=None):
+ "Calculate scores for training and test sample"
+
+ if mode is not None:
+ self._write_info("scores_mode", mode)
+
+ def eval_score(data_name):
+ logger.info("Create/Update scores for {} sample".format(data_name))
+ n_events = len(getattr(self, "x_"+data_name))
+ setattr(self, "scores_"+data_name, np.empty(n_events))
+ for start in range(0, n_events, batch_size):
+ stop = start+batch_size
+ getattr(self, "scores_"+data_name)[start:stop] = (
+ self.predict(
+ self.get_input_list(self.transform(getattr(self, "x_"+data_name)[start:stop])),
+ mode=mode
+ ).reshape(-1)
+ )
+ self._dump_to_hdf5("scores_"+data_name)
+
+ if do_test:
+ eval_score("test")
+ if do_train:
+ eval_score("train")
+
+
def predict(self, x, mode=None):
"""
Calculate the scores for a (transformed) array of input values.
@@ -1845,31 +1871,6 @@ class ClassificationProjectRNN(ClassificationProject):
return x_flat
- def evaluate_train_test(self, do_train=True, do_test=True, batch_size=10000, mode=None):
-
- if mode is not None:
- self._write_info("scores_mode", mode)
-
- def eval_score(data_name):
- logger.info("Create/Update scores for {} sample".format(data_name))
- n_events = len(getattr(self, "x_"+data_name))
- setattr(self, "scores_"+data_name, np.empty(n_events))
- for start in range(0, n_events, batch_size):
- stop = start+batch_size
- getattr(self, "scores_"+data_name)[start:stop] = (
- self.predict(
- self.get_input_list(getattr(self, "x_"+data_name)[start:stop]),
- mode=mode
- ).reshape(-1)
- )
- self._dump_to_hdf5("scores_"+data_name)
-
- if do_test:
- eval_score("test")
- if do_train:
- eval_score("train")
-
-
def evaluate(self, x_eval, mode=None):
logger.debug("Evaluate score for {}".format(x_eval))
x_eval = self.transform(x_eval)
--
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