def test_field_exceptions(self):
# test to make sure that the method that checks the field configs ids for each field name in the field_dict
# of the content analyzer works. It considers the three cases this can occur: when passing the field_dict
# with duplicate ids as argument for the content_analyzer, when setting the FieldConfig list with duplicates
# for a specific field_name, and when appending a FieldConfig to the list associated with a specific field_name
# but the config id is already in the list
config_1 = FieldConfig(SkLearnTfIdf(), NLTK(), id="test")
config_2 = FieldConfig(SkLearnTfIdf(), NLTK(), id="test")
config_list = [config_1, config_2]
field_dict = dict()
field_dict["test"] = config_list
with self.assertRaises(ValueError):
config = ItemAnalyzerConfig(JSONFile(movies_info_reduced), ["imdbID"], "movielens_test", field_dict)
ContentAnalyzer(config).fit()
with self.assertRaises(ValueError):
config = ItemAnalyzerConfig(JSONFile(movies_info_reduced), ["imdbID"], "movielens_test")
config.add_multiple_config("test", config_list)
ContentAnalyzer(config).fit()
with self.assertRaises(ValueError):
config = ItemAnalyzerConfig(JSONFile(movies_info_reduced), ["imdbID"], "movielens_test")
config.add_single_config("test", config_1)
config.add_single_config("test", config_2)
ContentAnalyzer(config).fit()
def test_fit_export_json(self):
movies_ca_config = ItemAnalyzerConfig(
source=JSONFile(movies_info_reduced),
id=['imdbID'],
output_directory=self.out_dir,
export_json=True
)
movies_ca_config.add_single_config('Plot', FieldConfig(OriginalData()))
movies_ca_config.add_single_config('Plot', FieldConfig(SkLearnTfIdf()))
movies_ca_config.add_single_config('imdbRating', FieldConfig())
ContentAnalyzer(movies_ca_config).fit()
self.assertTrue(os.path.isfile(os.path.join(self.out_dir, 'contents.json')))
processed_source = list(JSONFile(os.path.join(self.out_dir, 'contents.json')))
self.assertEqual(len(processed_source), 20)
for processed_content in processed_source:
self.assertIn('Plot#0', processed_content)
self.assertIn('Plot#1', processed_content)
self.assertIn('imdbRating#0', processed_content)
# def doCleanups(self) -> None:
# if os.path.isdir(self.out_dir):
# shutil.rmtree(self.out_dir)
def test_create_content(self):
file_path_content_analyzer = os.path.join(
THIS_DIR, "../../test/content_analyzer/movielens_test*")
entity_linking_pipeline = FieldRepresentationPipeline(
BabelPyEntityLinking())
plot_config = FieldConfig(None)
plot_config.append_pipeline(entity_linking_pipeline)
content_analyzer_config = ContentAnalyzerConfig(
'ITEM', JSONFile(file_path), ["imdbID"], "movielens_test")
content_analyzer_config.append_field_config("Plot", plot_config)
content_analyzer = ContentAnalyzer(content_analyzer_config)
content_analyzer.fit()
"""
def test_valid_id(self):
valid_object = FieldConfig(id='test')
self.assertIsNotNone(valid_object)
valid_object = FieldConfig(id='test_valid')
self.assertIsNotNone(valid_object)
valid_object = FieldConfig(id='test-valid')
self.assertIsNotNone(valid_object)
valid_object = FieldConfig(id='test1-valid2')
self.assertIsNotNone(valid_object)
valid_object = FieldConfig(id='1_2-3_')
self.assertIsNotNone(valid_object)
def test_invalid_id(self):
with self.assertRaises(ValueError):
FieldConfig(id='.in.vali.d')
with self.assertRaises(ValueError):
FieldConfig(id='#in#vali#d')
with self.assertRaises(ValueError):
FieldConfig(id=' ')
with self.assertRaises(ValueError):
FieldConfig(id='is invalid')
with self.assertRaises(ValueError):
FieldConfig(id='is/inva/lid')
def test_decode_field_data_embedding(self):
file_path_test_decode = os.path.join(
THIS_DIR, "../../datasets/test_decode/movies_title_embedding.json")
test_dir = os.path.join(THIS_DIR, "../../datasets/test_decode/")
movies_ca_config = ContentAnalyzerConfig(
content_type='Item',
source=JSONFile(file_path_test_decode),
id_field_name_list=['imdbID'],
output_directory=test_dir + 'movies_embedding_')
movies_ca_config.append_field_config(
field_name='Title',
field_config=FieldConfig(pipelines_list=[
FieldRepresentationPipeline(content_technique=None),
]))
ContentAnalyzer(config=movies_ca_config).fit()
for name in os.listdir(test_dir):
if os.path.isdir(os.path.join(test_dir, name)) \
and 'movies_embedding_' in str(name):
with lzma.open(os.path.join(test_dir, name, 'tt0113497.xz'),
'r') as file:
content = pickle.load(file)
self.assertIsInstance(
content.get_field("Title").get_representation('0'),
EmbeddingField)
self.assertIsInstance(
content.get_field("Title").get_representation(
'0').value, np.ndarray)
break
def test_create_content_embedding(self):
movies_ca_config = ItemAnalyzerConfig(
source=JSONFile(movies_info_reduced),
id=['imdbID'],
output_directory="movielens_test_embedding",
)
movies_ca_config.add_multiple_config(
field_name='Title',
config_list=[FieldConfig(
WordEmbeddingTechnique(Gensim('glove-twitter-25')),
NLTK(lemmatization=True, stopwords_removal=True))])
content_analyzer = ContentAnalyzer(movies_ca_config)
content_analyzer.fit()
for name in os.listdir(THIS_DIR):
if os.path.isdir(os.path.join(THIS_DIR, name)) \
and 'movielens_test_embedding' in str(name):
with lzma.open(os.path.join(THIS_DIR, name, 'tt0113497.xz'), 'r') as file:
content = pickle.load(file)
self.assertIsInstance(content.get_field("Title")[0], EmbeddingField)
self.assertIsInstance(content.get_field("Title")[0].value, np.ndarray)
break
def test_create_content_tfidf(self):
movies_ca_config = ItemAnalyzerConfig(
source=JSONFile(movies_info_reduced),
id='imdbID',
output_directory="movielens_test_tfidf",
)
movies_ca_config.add_multiple_config(
field_name='Title', config_list=[FieldConfig(SkLearnTfIdf())])
content_analyzer = ContentAnalyzer(movies_ca_config)
content_analyzer.fit()
for name in os.listdir(THIS_DIR):
if os.path.isdir(os.path.join(THIS_DIR, name)) \
and 'movielens_test_tfidf' in str(name):
with lzma.open(os.path.join(THIS_DIR, name, 'tt0113497.xz'),
'r') as file:
content = pickle.load(file)
self.assertIsInstance(
content.get_field("Title")[0], FeaturesBagField)
self.assertIsInstance(
content.get_field("Title")[0].value, dict)
break
def test_create_content(self):
filepath = '../../datasets/movies_info_reduced.json'
try:
with open(filepath):
pass
except FileNotFoundError:
filepath = 'datasets/movies_info_reduced.json'
entity_linking_pipeline = FieldRepresentationPipeline(
BabelPyEntityLinking())
plot_config = FieldConfig(None)
plot_config.append_pipeline(entity_linking_pipeline)
content_analyzer_config = ContentAnalyzerConfig(
'ITEM', JSONFile(filepath), ["imdbID"], "movielens_test")
content_analyzer_config.append_field_config("Plot", plot_config)
content_analyzer = ContentAnalyzer(content_analyzer_config)
content_analyzer.fit()
def test_create_contents_in_index(self):
output_dir = os.path.join(THIS_DIR, "movielens_test_original_index")
movies_ca_config = ItemAnalyzerConfig(
source=JSONFile(movies_info_reduced),
id=['imdbID'],
output_directory=output_dir,
)
movies_ca_config.add_multiple_config(
field_name='Title',
config_list=[
FieldConfig(OriginalData(),
NLTK(lemmatization=True, stopwords_removal=True),
SearchIndex(os.path.join(output_dir, "index")),
"test_search"),
FieldConfig(SkLearnTfIdf(), NLTK(),
KeywordIndex(os.path.join(output_dir, "index1")),
"test_keyword"),
FieldConfig(OriginalData(), NLTK(),
SearchIndex(os.path.join(output_dir, "index")))
])
content_analyzer = ContentAnalyzer(movies_ca_config)
content_analyzer.fit()
for name in os.listdir(THIS_DIR):
if os.path.isdir(os.path.join(THIS_DIR, name)) \
and 'movielens_test_original_index' in str(name):
with lzma.open(os.path.join(THIS_DIR, name, 'tt0113497.xz'),
'r') as file:
content = pickle.load(file)
self.assertIsInstance(
content.get_field("Title")[0], IndexField)
self.assertIsInstance(
content.get_field("Title")[0].value, str)
self.assertIsInstance(
content.get_field("Title")[1], IndexField)
self.assertIsInstance(
content.get_field("Title")[1].value, str)
break
def test_create_content_search_index(self):
movies_ca_config = ContentAnalyzerConfig(
content_type='Item',
source=JSONFile(file_path),
id_field_name_list=['imdbID'],
output_directory='movielens_test')
movies_ca_config.append_field_config(
field_name='Title',
field_config=FieldConfig(pipelines_list=[
FieldRepresentationPipeline(content_technique=SearchIndexing())
]))
content_analyzer = ContentAnalyzer(movies_ca_config)
content_analyzer.fit()
def test_create_content(self):
plot_config = FieldConfig(BabelPyEntityLinking())
exogenous_config = ExogenousConfig(DBPediaMappingTechnique('Film', 'EN', 'Title'))
content_analyzer_config = ItemAnalyzerConfig(JSONFile(movies_info_reduced), ["imdbID"], "movielens_test")
content_analyzer_config.add_single_config("Title", plot_config)
content_analyzer_config.add_single_exogenous(exogenous_config)
content_analyzer = ContentAnalyzer(content_analyzer_config)
content_analyzer.fit()
for name in os.listdir(THIS_DIR):
if os.path.isdir(os.path.join(THIS_DIR, name)) \
and 'movielens_test' in str(name):
with lzma.open(os.path.join(THIS_DIR, name, 'tt0113497.xz'), 'r') as file:
content = pickle.load(file)
self.assertIsInstance(content.get_field("Title")[0], FeaturesBagField)
self.assertIsInstance(content.get_field("Title")[0].value, dict)
break
def test_decode_field_data_embedding(self):
movies_ca_config = ItemAnalyzerConfig(
source=JSONFile(decode_embedding),
id=['imdbID'],
output_directory=decode_path + 'movies_embedding_'
)
movies_ca_config.add_multiple_config(
field_name='Title',
config_list=[FieldConfig()]
)
ContentAnalyzer(config=movies_ca_config).fit()
for name in os.listdir(decode_path):
if os.path.isdir(os.path.join(decode_path, name)) \
and 'movies_embedding_' in str(name):
with lzma.open(os.path.join(decode_path, name, 'tt0113497.xz'), 'r') as file:
content = pickle.load(file)
self.assertIsInstance(content.get_field("Title")[0], EmbeddingField)
self.assertIsInstance(content.get_field("Title")[0].value, np.ndarray)
break
def test_create_content_embedding(self):
movies_ca_config = ContentAnalyzerConfig(
content_type='Item',
source=JSONFile(file_path),
id_field_name_list=['imdbID'],
output_directory="movielens_test",
)
movies_ca_config.append_field_config(
field_name='Title',
field_config=FieldConfig(pipelines_list=[
FieldRepresentationPipeline(
preprocessor_list=[
NLTK(lemmatization=True, stopwords_removal=True)
],
content_technique=EmbeddingTechnique(
combining_technique=Centroid(),
embedding_source=GensimDownloader(
name='glove-twitter-25'),
granularity='doc'))
]))
content_analyzer = ContentAnalyzer(movies_ca_config)
content_analyzer.fit()
users_filename = '../../../datasets/examples/users_70.dat'
users_output_dir = '../../../contents/examples/ex_2/users_'
movies_ca_config = ContentAnalyzerConfig(
content_type='Item',
source=JSONFile(movies_filename),
id_field_name_list=['imdbID'],
output_directory=movies_output_dir,
)
movies_ca_config.append_field_config(
field_name='Title',
field_config=FieldConfig(pipelines_list=[
FieldRepresentationPipeline(content_technique=LuceneTfIdf())
]))
movies_ca_config.append_field_config(
field_name='Year',
field_config=FieldConfig(pipelines_list=[
FieldRepresentationPipeline(content_technique=LuceneTfIdf())
]))
movies_ca_config.append_field_config(
field_name='Genre',
field_config=FieldConfig(pipelines_list=[
FieldRepresentationPipeline(preprocessor_list=[
NLTK(lemmatization=True, stopwords_removal=True)
],
content_technique=LuceneTfIdf())
users_filename = '../../../datasets/examples/users_70.dat'
users_output_dir = '../../../contents/examples/ex_1/users_'
movies_ca_config = ContentAnalyzerConfig(
content_type='Item',
source=JSONFile(movies_filename),
id_field_name_list=['imdbID'],
output_directory=movies_output_dir,
)
movies_ca_config.append_field_config(
field_name='Title',
field_config=FieldConfig(pipelines_list=[
FieldRepresentationPipeline(content_technique=LuceneTfIdf())
]))
movies_ca_config.append_field_config(
field_name='Year',
field_config=FieldConfig(pipelines_list=[
FieldRepresentationPipeline(content_technique=LuceneTfIdf())
]))
movies_ca_config.append_field_config(
field_name='Genre',
field_config=FieldConfig(pipelines_list=[
FieldRepresentationPipeline(preprocessor_list=[
NLTK(lemmatization=True, stopwords_removal=True)
],
content_technique=LuceneTfIdf())
source = JSONFile(users_filename),
id = '0',
output_dir = users_output_dir,
)
ItemAnalyzerConfig(
source = JSONFile(items_filename),
id = 'imdbID',
output_dir = items_output_dir,
)
movies_ca_config.append_field_config(
field_name='Title',
field_config=FieldConfig(
pipelines_list=[FieldRepresentationPipeline(
content_technique=LuceneTfIdf())]
)
)
movies_ca_config.append_field_config(
field_name='Year',
field_config=FieldConfig(
pipelines_list=[FieldRepresentationPipeline(
content_technique=LuceneTfIdf())]
)
)
movies_ca_config.append_field_config(
field_name='Genre',