def opinion_ids_to_names(opinion_ids: Iterable[str]) -> List[str]:
op_names = []
for op_id in opinion_ids:
if (op_model := Opinion.select().where(
Opinion.resource_id == op_id).first()) is not None:
op_names.append(op_model.cluster.case_name)
else:
op_names.append("Unknown")
def search_cases(query, max_cases=25):
search_text = CaseSearch.prepare_query(query)
return (Opinion.select(
Opinion,
fn.ts_headline(Cluster.case_display_name(),
fn.to_tsquery(search_text)).alias("headline"),
).join(Cluster).where(
ts_match(Cluster.searchable_case_name,
fn.to_tsquery(search_text))).order_by(
Cluster.citation_count.desc()).limit(max_cases))
def get_case_clusters():
case_resource_ids = [int(c) for c in request.args.getlist("cases")]
num_clusters = int(request.args.get("num_clusters") or 0) or None
if len(case_resource_ids) < 1:
return "You must provide at least one case ID.", HTTPStatus.UNPROCESSABLE_ENTITY
clusters = clustering.spectral_cluster(set(case_resource_ids),
num_clusters=num_clusters)
output_dict = {}
for cluster_name, opinion_ids in clusters.items():
opinion_models = Opinion.select().where(
Opinion.resource_id << opinion_ids)
output_dict[str(cluster_name)] = model_list_to_dicts(opinion_models)
return output_dict
def get_recommended_cases():
case_resource_ids = frozenset(map(int, request.args.getlist("cases")))
court_ids = frozenset(map(str, request.args.getlist("courts")))
max_cases = int(request.args.get("max_cases") or 10)
if len(case_resource_ids) < 1:
return "You must provide at least one case ID.", HTTPStatus.UNPROCESSABLE_ENTITY
recommendations = recommendation.recommendations(case_resource_ids,
max_cases,
courts=court_ids)
recommended_opinions = sorted(
Opinion.select().join(Cluster).where(
Opinion.resource_id << list(recommendations.keys())),
key=lambda op: recommendations[op.resource_id],
reverse=True,
)
return model_list_to_json(recommended_opinions)
def ingest_citation_data(citations_file):
# Since there's only ~65,000 opinions, it's feasible to just load all the IDs into memory to avoid making
# millions of DB queries.
opinion_set = {o.resource_id for o in Opinion.select()}
citation_records = []
with open(citations_file) as csv_file:
csv_reader = csv.reader(csv_file, delimiter=",")
for row in csv_reader:
try:
integer_row = [int(cell) for cell in row]
if integer_row[0] in opinion_set and integer_row[1] in opinion_set:
new_record = Citation(
citing_opinion=integer_row[0],
cited_opinion=integer_row[1],
depth=integer_row[2],
)
citation_records.append(new_record)
except Exception as e:
print(f"Failure on row {row}: {e}")
with db.atomic():
Citation.bulk_create(citation_records, batch_size=100)