commit eee5f1e3d07f49043ce001b70af86095158a5bc6
parent 744de49cdcd00d5bead21197cc31bf226cdb03c0
Author: Étienne Simon <esimon@esimon.eu>
Date: Wed, 16 Apr 2014 17:30:41 +0200
Genericise and fix some bugs
Diffstat:
4 files changed, 27 insertions(+), 21 deletions(-)
diff --git a/dataset.py b/dataset.py
@@ -73,3 +73,4 @@ class Dataset(object):
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]
+ yield entities[(N/batch_size)*batch_size:]
diff --git a/main.py b/main.py
@@ -1,23 +1,26 @@
#!/usr/bin/env python2
+import sys
+import json
+
from dataset import *
from model import *
from relations.translations import *
if __name__ == '__main__':
- hyperparameters = dict()
- hyperparameters['similarity'] = L1_norm
- hyperparameters['rng'] = numpy.random
- hyperparameters['dimension'] = 20
- hyperparameters['margin'] = 1.
- hyperparameters['relation_learning_rate'] = 1
- hyperparameters['embeddings_learning_rate'] = 0.1
- hyperparameters['train_batch_size'] = 100
- hyperparameters['test_batch_size'] = 500
- hyperparameters['validation_frequency'] = 500
- hyperparameters['number_epoch'] = 10000
+ if len(sys.argv)<3:
+ print >>sys.stderr, 'Usage: {0} data parameters'.format(sys.argv[0])
+ sys.exit(1)
+ data = sys.argv[1]
+ config = sys.argv[2]
+
+ with open(config, 'r') as config_file:
+ hyperparameters = json.load(config_file)
+ for k, v in hyperparameters.iteritems():
+ if isinstance(v, basestring):
+ hyperparameters[k] = eval(v)
- data = Dataset('data/dummy')
- model = Model.initialise(Translations, data, hyperparameters, 'dummy')
+ data = Dataset(data)
+ model = Model.initialise(Translations, data, hyperparameters, 'TransE')
model.train()
model.test()
diff --git a/model.py b/model.py
@@ -1,7 +1,5 @@
#!/usr/bin/env python2
-import time
-
import sys
import numpy
import scipy
@@ -122,12 +120,15 @@ class Model(object):
for (relation, left, right) in self.dataset.iterate(name, batch_size):
scores = None
for entities in self.dataset.universe_minibatch(batch_size):
+ if left.shape != entities.shape:
+ left = left[0:entities.shape[0]]
+ relation = relation[0:entities.shape[0]]
batch_result = self.scoring_function(relation, left, entities)
scores = numpy.array(batch_result, dtype=theano.config.floatX) if scores is None else numpy.concatenate((scores, batch_result), axis=1)
- rank = 1+numpy.where(numpy.argsort(scores)==right.indices[0])[1] # FIXME ugly
- mean += rank
- count += 1
- top10 += (rank<=10)
+ rank = numpy.asscalar(numpy.where(numpy.argsort(scores)==right.indices[0])[1]) # FIXME Ugly
+ mean = mean + rank
+ count = count + 1
+ top10 = top10 + (rank<=10)
mean = float(mean) / count
top10 = float(top10) / count
return (mean, top10)
@@ -136,8 +137,9 @@ class Model(object):
""" Validate the model. """
print >>sys.stderr, 'Validation epoch {:<5}'.format(epoch),
(valid_mean, valid_top10) = self.error('valid')
+ print >>sys.stderr, 'valid mean: {0:<15} valid top10: {1:<15}'.format(valid_mean, valid_top10)
(train_mean, train_top10) = self.error('train')
- print >>sys.stderr, 'valid mean: {0:<15} valid top10: {1:<15} train mean: {0:<15} train top10: {1:<15}'.format(valid_mean, valid_top10, train_mean, train_top10)
+ print >>sys.stderr, 'train mean: {0:<15} train top10: {1:<15}'.format(train_mean, train_top10)
def test(self):
""" Test the model. """
diff --git a/utils/build Bordes FB15k.py b/utils/build Bordes FB15k.py
@@ -57,7 +57,7 @@ def compile_dataset(path):
print >>sys.stderr, 'Writting entities...',
e2i, i2e, r2i, i2r = {}, {}, {}, {}
- with open(path+'/entities', 'w') as file:
+ with open(path+'/embeddings', 'w') as file:
i=0
for entity in entities:
e2i[entity]=i
