def run_index(project_id, directory, suffix, force, limit, threshold,
backend_param):
"""
Index a directory with documents, suggesting subjects for each document.
Write the results in TSV files with the given suffix.
"""
project = get_project(project_id)
backend_params = parse_backend_params(backend_param, project)
hit_filter = SuggestionFilter(project.subjects, limit, threshold)
for docfilename, dummy_subjectfn in annif.corpus.DocumentDirectory(
directory, require_subjects=False):
with open(docfilename, encoding='utf-8-sig') as docfile:
text = docfile.read()
subjectfilename = re.sub(r'\.txt$', suffix, docfilename)
if os.path.exists(subjectfilename) and not force:
click.echo("Not overwriting {} (use --force to override)".format(
subjectfilename))
continue
with open(subjectfilename, 'w', encoding='utf-8') as subjfile:
results = project.suggest(text, backend_params)
for hit in hit_filter(results).as_list(project.subjects):
line = "<{}>\t{}\t{}".format(
hit.uri, '\t'.join(filter(None,
(hit.label, hit.notation))),
hit.score)
click.echo(line, file=subjfile)
def test_hitfilter_vector_suggestion_results_with_deprecated_subjects(
subject_index):
subject_index.append('http://example.org/deprecated', None, None)
vector = np.ones(len(subject_index))
suggestions = VectorSuggestionResult(vector)
filtered_suggestions = SuggestionFilter(subject_index)(suggestions)
assert len(suggestions) == len(filtered_suggestions) \
+ len(subject_index.deprecated_ids())
deprecated = SubjectSuggestion(uri='http://example.org/deprecated',
label=None,
notation=None,
score=1.0)
assert deprecated in suggestions.as_list(subject_index)
assert deprecated not in filtered_suggestions.as_list(subject_index)
def test_hitfilter_zero_score(subject_index):
origsuggestions = ListSuggestionResult(
[SubjectSuggestion(uri='uri', label='label', score=0.0)],
subject_index)
suggestions = SuggestionFilter()(origsuggestions)
assert isinstance(suggestions, SuggestionResult)
assert len(suggestions) == 0
def generate_filter_batches(subjects):
filter_batches = collections.OrderedDict()
for limit in range(1, 16):
for threshold in [i * 0.05 for i in range(20)]:
hit_filter = SuggestionFilter(limit, threshold)
batch = annif.eval.EvaluationBatch(subjects)
filter_batches[(limit, threshold)] = (hit_filter, batch)
return filter_batches
def run_suggest(project_id, limit, threshold, backend_param):
"""
Suggest subjects for a single document from standard input.
"""
project = get_project(project_id)
text = sys.stdin.read()
backend_params = parse_backend_params(backend_param)
hit_filter = SuggestionFilter(limit, threshold)
hits = hit_filter(project.suggest(text, backend_params))
for hit in hits:
click.echo("<{}>\t{}\t{}".format(hit.uri, hit.label, hit.score))
def test_hitfilter_list_suggestion_results_with_deprecated_subjects(
subject_index):
subject_index.append('http://example.org/deprecated', None, None)
suggestions = ListSuggestionResult([
SubjectSuggestion(uri='http://www.yso.fi/onto/yso/p7141',
label='sinetit',
notation=None,
score=1.0),
SubjectSuggestion(uri='http://www.yso.fi/onto/yso/p6479',
label='viikingit',
notation=None,
score=0.5),
SubjectSuggestion(uri='http://example.org/deprecated',
label=None,
notation=None,
score=0.5)
])
filtered_suggestions = SuggestionFilter(subject_index)(suggestions)
assert isinstance(filtered_suggestions, SuggestionResult)
assert len(filtered_suggestions) == 2
assert filtered_suggestions.as_list(
subject_index)[0] == suggestions.as_list(subject_index)[0]
assert filtered_suggestions.as_list(
subject_index)[1] == suggestions.as_list(subject_index)[1]
def suggest(project_id, text, limit, threshold):
"""suggest subjects for the given text and return a dict with results
formatted according to Swagger spec"""
try:
project = annif.registry.get_project(
project_id, min_access=Access.hidden)
except ValueError:
return project_not_found_error(project_id)
try:
hit_filter = SuggestionFilter(project.subjects, limit, threshold)
result = project.suggest(text)
except AnnifException as err:
return server_error(err)
hits = hit_filter(result).as_list(project.subjects)
return {'results': [hit._asdict() for hit in hits]}
def run_eval(project_id, paths, limit, threshold, backend_param):
"""
Analyze documents and evaluate the result.
Compare the results of automated indexing against a gold standard. The
path may be either a TSV file with short documents or a directory with
documents in separate files.
"""
project = get_project(project_id)
backend_params = parse_backend_params(backend_param)
hit_filter = SuggestionFilter(limit=limit, threshold=threshold)
eval_batch = annif.eval.EvaluationBatch(project.subjects)
docs = open_documents(paths)
for doc in docs.documents:
results = project.suggest(doc.text, backend_params)
hits = hit_filter(results)
eval_batch.evaluate(hits,
annif.corpus.SubjectSet((doc.uris, doc.labels)))
template = "{0:<20}\t{1}"
for metric, score in eval_batch.results().items():
click.echo(template.format(metric + ":", score))
def test_hitfilter_threshold(subject_index):
origsuggestions = generate_suggestions(10, subject_index)
suggestions = SuggestionFilter(threshold=0.5)(origsuggestions)
assert isinstance(suggestions, SuggestionResult)
assert len(suggestions) == 2
def test_hitfilter_limit(subject_index):
origsuggestions = generate_suggestions(10, subject_index)
suggestions = SuggestionFilter(limit=5)(origsuggestions)
assert isinstance(suggestions, SuggestionResult)
assert len(suggestions) == 5