make output to /tmp folder

tests/data/benchmarks_results/text_classification/args.json

@@ -1 +1 @@
-{"current_datetime": "2018-05-04 02:29:28.460915", "batchsize": 64, "epoch": 5, "gpu": -1, "layer": 1, "dropout": 0, "model": "bow", "char_based": false, "out": "./tests/data/benchmarks_results/text_classification/", "unit": 4, "dataset": "./tests/data/benchmarks/text_classification/", "vocab_path": "./tests/data/benchmarks_results/text_classification/vocab.json", "model_path": "./tests/data/benchmarks_results/text_classification/best_model.npz", "n_class": 2, "datetime": "2018-05-04 02:29:28.460915"}
+{"current_datetime": "2018-05-04 11:39:50.824318", "batchsize": 64, "epoch": 5, "gpu": -1, "layer": 1, "dropout": 0, "model": "bow", "char_based": false, "out": "./tests/data/benchmarks_results/text_classification/", "unit": 4, "dataset": "./tests/data/benchmarks/text_classification/", "vocab_path": "./tests/data/benchmarks_results/text_classification/vocab.json", "model_path": "./tests/data/benchmarks_results/text_classification/best_model.npz", "n_class": 2, "datetime": "2018-05-04 11:39:50.824318"}

tests/data/benchmarks_results/text_classification/log

@@ -1,38 +1,38 @@
 [
     {
-        "main/loss": 0.6962488293647766,
-        "main/accuracy": 0.4375,
-        "validation/main/loss": 0.6995988488197327,
-        "validation/main/accuracy": 0.46666666865348816,
+        "main/loss": 0.7636308670043945,
+        "main/accuracy": 0.46875,
+        "validation/main/loss": 0.7174736261367798,
+        "validation/main/accuracy": 0.5333333611488342,
         "epoch": 1,
         "iteration": 1,
-        "elapsed_time": 0.0065841870091389865
+        "elapsed_time": 0.00541385097312741
     },
     {
-        "main/loss": 0.6889790296554565,
-        "main/accuracy": 0.53125,
-        "validation/main/loss": 0.6992061138153076,
-        "validation/main/accuracy": 0.46666666865348816,
+        "main/loss": 0.7468587160110474,
+        "main/accuracy": 0.484375,
+        "validation/main/loss": 0.716312050819397,
+        "validation/main/accuracy": 0.5333333611488342,
         "epoch": 2,
         "iteration": 2,
-        "elapsed_time": 0.01622019399655983
+        "elapsed_time": 0.012854741973569617
     },
     {
-        "main/loss": 0.6849422454833984,
-        "main/accuracy": 0.5909090638160706,
-        "validation/main/loss": 0.6990599632263184,
-        "validation/main/accuracy": 0.46666666865348816,
+        "main/loss": 0.7709426283836365,
+        "main/accuracy": 0.4545454680919647,
+        "validation/main/loss": 0.7152009010314941,
+        "validation/main/accuracy": 0.5333333611488342,
         "epoch": 3,
         "iteration": 3,
-        "elapsed_time": 0.022680485009914264
+        "elapsed_time": 0.020706564973806962
     },
     {
-        "main/loss": 0.6885284185409546,
-        "main/accuracy": 0.5581395626068115,
-        "validation/main/loss": 0.6989443302154541,
-        "validation/main/accuracy": 0.46666666865348816,
+        "main/loss": 0.7399059534072876,
+        "main/accuracy": 0.5348837375640869,
+        "validation/main/loss": 0.7141532897949219,
+        "validation/main/accuracy": 0.5333333611488342,
         "epoch": 4,
         "iteration": 4,
-        "elapsed_time": 0.02903877801145427
+        "elapsed_time": 0.026553130999673158
     }
 ]

tests/test_benchmarks.py

@@ -67,15 +67,15 @@ def test_text_classification(self):
 
         tc = Text_classification(model='cnn')
         result = tc.get_result(embs, path_text_classification_dataset,
-                               "./tests/data/benchmarks_results/text_classification/")
+                               "/tmp/tests/data/benchmarks_results/text_classification/")
         print(result)
         tc = Text_classification(model='rnn')
         result = tc.get_result(embs, path_text_classification_dataset,
-                               "./tests/data/benchmarks_results/text_classification/")
+                               "/tmp/tests/data/benchmarks_results/text_classification/")
         print(result)
         tc = Text_classification(model='bow')
         result = tc.get_result(embs, path_text_classification_dataset,
-                               "./tests/data/benchmarks_results/text_classification/")
+                               "/tmp/tests/data/benchmarks_results/text_classification/")
         print(result)
 
 

vecto/benchmarks/analogy/visualize.py

@@ -51,7 +51,7 @@ def run_results(path_embeds=["tests/data/embeddings/text/plain_with_file_header"
             analogy = method()
             results = analogy.run(embs, path_analogy_dataset)
             print(results)
-            save_json(results, os.path.join("tests/data/benchmarks_results/analogy/", datetime.datetime.now().isoformat()))
+            save_json(results, os.path.join("/tmp/tests/data/benchmarks_results/analogy/", datetime.datetime.now().isoformat()))
 
 
 

vecto/benchmarks/similarity/visualize.py

@@ -50,7 +50,7 @@ def run_results(path_embeds=["tests/data/embeddings/text/plain_with_file_header"
         similarity = Similarity()
         results = similarity.run(embs, path_analogy_dataset)
         print(results)
-        save_json(results, os.path.join("tests/data/benchmarks_results/similarity/", datetime.datetime.now().isoformat()))
+        save_json(results, os.path.join("/tmp/tests/data/benchmarks_results/similarity/", datetime.datetime.now().isoformat()))
 
 
 

vecto/benchmarks/text_classification/text_classification.py

@@ -49,19 +49,21 @@ def load_model(model_path, wv):
 
     return model, vocab, setup
 
+
 def predict(model, sentence):
     model, vocab, setup = model
     sentence = sentence.strip()
     text = nlp_utils.normalize_text(sentence)
     words = nlp_utils.split_text(text, char_based=setup['char_based'])
     xs = nlp_utils.transform_to_array([words], vocab, with_label=False)
-    xs = nlp_utils.convert_seq(xs, device=-1, with_label=False) # todo use GPU
+    xs = nlp_utils.convert_seq(xs, device=-1, with_label=False)  # todo use GPU
     with chainer.using_config('train', False), chainer.no_backprop_mode():
         prob = model.predict(xs, softmax=True)[0]
     answer = int(model.xp.argmax(prob))
     score = float(prob[answer])
     return answer, score
 
+
 def get_vectors(model, sentences):
     model, vocab, setup = model
     vectors = []
@@ -70,16 +72,14 @@ def get_vectors(model, sentences):
         text = nlp_utils.normalize_text(sentence)
         words = nlp_utils.split_text(text, char_based=setup['char_based'])
         xs = nlp_utils.transform_to_array([words], vocab, with_label=False)
-        xs = nlp_utils.convert_seq(xs, device=-1, with_label=False) # todo use GPU
+        xs = nlp_utils.convert_seq(xs, device=-1, with_label=False)  # todo use GPU
         with chainer.using_config('train', False), chainer.no_backprop_mode():
             vector = model.encoder(xs)
             vectors.append(vector.data[0])
     vectors = numpy.asarray(vectors)
     return vectors
 
 
-
-
 class Text_classification(Benchmark):
 
     def __init__(self, batchsize=64, epoch=5, gpu=-1, layer=1, dropout=0, model=['cnn', 'rnn', 'bow'][1],
@@ -97,21 +97,23 @@ def get_result(self, embs, path_dataset, path_output='/tmp/text_classification/'
         self.out = path_output
         self.unit = embs.matrix.shape[1]
 
+        if not os.path.isdir(path_output):
+            os.makedirs(path_output)
 
         # Load a dataset
         self.dataset = path_dataset
         if self.dataset == 'dbpedia':
             train, test, vocab = text_datasets.get_dbpedia(
-                char_based=self.char_based, vocab=embs.vocabulary.dic_words_ids,)
+                char_based=self.char_based, vocab=embs.vocabulary.dic_words_ids, )
         elif self.dataset.startswith('imdb.'):
             train, test, vocab = text_datasets.get_imdb(
                 fine_grained=self.dataset.endswith('.fine'),
-                char_based=self.char_based, vocab=embs.vocabulary.dic_words_ids,)
+                char_based=self.char_based, vocab=embs.vocabulary.dic_words_ids, )
         elif self.dataset in ['TREC', 'stsa.binary', 'stsa.fine',
                               'custrev', 'mpqa', 'rt-polarity', 'subj']:
             train, test, vocab = text_datasets.get_other_text_dataset(
-                self.dataset, char_based=self.char_based, vocab=embs.vocabulary.dic_words_ids,)
-        else: # finallly, if file is not downloadable, load from local path
+                self.dataset, char_based=self.char_based, vocab=embs.vocabulary.dic_words_ids, )
+        else:  # finallly, if file is not downloadable, load from local path
             train, test, vocab = text_datasets.get_dataset_from_path(path_dataset, vocab=embs.vocabulary.dic_words_ids,
                                                                      char_based=self.char_based)
 
@@ -194,4 +196,4 @@ def get_result(self, embs, path_dataset, path_output='/tmp/text_classification/'
         result['experiment_setup'] = experiment_setup
         result['log'] = load_json(os.path.join(self.out, 'log'))
         result['result'] = result['log'][-1]['validation/main/accuracy']
-        return result
+        return result