transform

commit 1ce979232de97cb635a26d4ec2a574464a567c61
parent 7cb97ecd8c457d0cc0712148b927620fc047558e
Author: Étienne Simon <esimon@esimon.eu>
Date:   Wed, 23 Apr 2014 11:56:17 +0200

Change config keys

Diffstat:
M
dataset.py
 | 
10
+---------
M
main.py
 | 
2
+-
M
model.py
 | 
27
+++++++++------------------

3 files changed, 11 insertions(+), 28 deletions(-)
diff --git a/dataset.py b/dataset.py
@@ -17,6 +17,7 @@ class Dataset(object):
         self.load_file(prefix, 'train')
         self.load_file(prefix, 'valid')
         self.load_file(prefix, 'test')
+        self.universe = scipy.sparse.eye(len(self.embeddings), format='csr', dtype=theano.config.floatX)
 
     def load_file(self, prefix, name):
         with open('{0}/{1}'.format(prefix, name), 'r') as file:
@@ -64,12 +65,3 @@ class Dataset(object):
         right = getattr(self, name+'_right')
         for i in xrange(N):
             yield (relation[i], left[i], right[i])
-
-    def universe_minibatch(self, batch_size):
-        N = len(self.embeddings)
-        entities = scipy.sparse.eye(N, format='csr', dtype=theano.config.floatX)
-        for i in xrange(N/batch_size):
-            yield entities[i*batch_size:(i+1)*batch_size]
-        last = (N/batch_size)*batch_size
-        if last != N:
-            yield entities[last:N]
diff --git a/main.py b/main.py
@@ -11,7 +11,7 @@ from relations import *
 
 if __name__ == '__main__':
     if len(sys.argv)<3:
-        print('Usage: {0} data parameters [model]'.format(sys.argv[0]), file=sys.stderr)
+        print('Usage: {0} data config [model]'.format(sys.argv[0]), file=sys.stderr)
         sys.exit(1)
     data = sys.argv[1]
     config_path = sys.argv[2]
diff --git a/model.py b/model.py
@@ -140,26 +140,17 @@ class Model(object):
 
     def error(self, name):
         """ Compute the mean rank and top 10 on a given data. """
-        batch_size = self.config['test batch size']
         count, mean, top10 = 0, 0, 0
         for (relation, left, right) in self.dataset.iterate(name):
             left_scores, right_scores = None, None
-            for entities in self.dataset.universe_minibatch(batch_size):
-                left_batch_result = self.left_scoring_function(relation, left, entities)
-                right_batch_result = self.right_scoring_function(relation, entities, right)
-                if left_scores is None:
-                    left_scores = numpy.array(left_batch_result, dtype=theano.config.floatX)
-                else:
-                    left_scores = numpy.concatenate((left_scores, left_batch_result), axis=1)
-                if right_scores is None:
-                    right_scores = numpy.array(right_batch_result, dtype=theano.config.floatX)
-                else:
-                    right_scores = numpy.concatenate((right_scores, right_batch_result), axis=1)
+            entities = self.dataset.universe
+            left_scores = self.left_scoring_function(relation, left, entities)
+            right_scores = self.right_scoring_function(relation, entities, right)
             left_rank = 1+numpy.asscalar(numpy.where(numpy.argsort(left_scores)==right.indices[0])[1]) # FIXME Ugly
             right_rank = 1+numpy.asscalar(numpy.where(numpy.argsort(right_scores)==left.indices[0])[1]) # FIXME Ugly
-            count = count + 2
-            mean = mean + left_rank + right_rank
-            top10 = top10 + (left_rank<=10) + (right_rank<=10)
+            count += 2
+            mean += left_rank + right_rank
+            top10 += (left_rank<=10) + (right_rank<=10)
         mean = float(mean) / count
         top10 = float(top10) / count
         return (mean, top10)
@@ -169,13 +160,13 @@ class Model(object):
         log('Validation epoch {:<5}'.format(epoch))
         (valid_mean, valid_top10) = self.error('valid')
         log(' valid mean: {0:<15} valid top10: {1:<15}'.format(valid_mean, valid_top10))
-        datalog(self.config['datalog filepath'], epoch, valid_mean, valid_top10)
+        datalog(self.config['datalog path']+'/'+self.config['model name'], epoch, valid_mean, valid_top10)
         if not hasattr(self, 'best_mean') or valid_mean < self.best_mean:
             self.best_mean = valid_mean
             log('(best so far')
             if self.config['save best model']:
                 log(', saving...')
-                self.save(self.config['best model save location'])
+                self.save('{0}/{1}.best'.format(self.config['best model save path'], self.config['model name']))
                 log(' done')
             log(')')
 
@@ -189,5 +180,5 @@ class Model(object):
         log('# Testing the model "{0}"'.format(self.tag))
         (mean, top10) = self.error('test')
         log(' mean: {0:<15} top10: {1:<15} (saving...'.format(mean, top10))
-        self.save(self.config['last model save location'])
+        self.save('{0}/{1}.last'.format(self.config['last model save path'], self.config['model name']))
         log(' done)\n')