def benchmark_reader():
reader_results = []
doc_store = get_document_store("elasticsearch")
docs, labels = eval_data_from_file(data_dir / filename)
index_to_doc_store(doc_store, docs, None, labels)
for reader_name in reader_models:
for reader_type in reader_types:
try:
reader = get_reader(reader_name, reader_type)
results = reader.eval(document_store=doc_store,
doc_index=doc_index,
label_index=label_index,
device="cuda")
# print(results)
results["passages_per_second"] = n_passages / results[
"reader_time"]
results["reader"] = reader_name
results["error"] = ""
reader_results.append(results)
except Exception as e:
results = {
'EM': 0.,
'f1': 0.,
'top_n_accuracy': 0.,
'top_n': 0,
'reader_time': 0.,
"passages_per_second": 0.,
"seconds_per_query": 0.,
'reader': reader_name,
"error": e
}
reader_results.append(results)
reader_df = pd.DataFrame.from_records(reader_results)
reader_df.to_csv("reader_results.csv")
def prepare_data(data_dir, filename_gold, filename_negative, n_docs=None, n_queries=None, add_precomputed=False):
"""
filename_gold points to a squad format file.
filename_negative points to a csv file where the first column is doc_id and second is document text.
If add_precomputed is True, this fn will look in the embeddings files for precomputed embeddings to add to each Document
"""
gold_docs, labels = eval_data_from_file(data_dir / filename_gold)
# Reduce number of docs
gold_docs = gold_docs[:n_docs]
# Remove labels whose gold docs have been removed
doc_ids = [x.id for x in gold_docs]
labels = [x for x in labels if x.document_id in doc_ids]
# Filter labels down to n_queries
selected_queries = list(set(f"{x.document_id} | {x.question}" for x in labels))
selected_queries = selected_queries[:n_queries]
labels = [x for x in labels if f"{x.document_id} | {x.question}" in selected_queries]
n_neg_docs = max(0, n_docs - len(gold_docs))
neg_docs = prepare_negative_passages(data_dir, filename_negative, n_neg_docs)
docs = gold_docs + neg_docs
if add_precomputed:
docs = add_precomputed_embeddings(data_dir / embeddings_dir, embeddings_filenames, docs)
return docs, labels
def benchmark_reader(ci=False,
update_json=False,
save_markdown=False,
**kwargs):
if ci:
reader_models = reader_models_ci
max_docs = 100
# heuristic to estimate num of passages for the reduced num of docs
n_passages = n_total_passages * (max_docs / n_total_docs)
else:
reader_models = reader_models_full
max_docs = None
n_passages = n_total_passages
reader_results = []
doc_store = get_document_store("elasticsearch")
# download squad data
_download_extract_downstream_data(input_file=data_dir / filename)
docs, labels = eval_data_from_file(data_dir / filename, max_docs)
index_to_doc_store(doc_store, docs, None, labels)
for reader_name in reader_models:
for reader_type in reader_types:
logger.info(
f"##### Start reader run - model:{reader_name}, type: {reader_type} ##### "
)
try:
reader = get_reader(reader_name, reader_type)
results = reader.eval(document_store=doc_store,
doc_index=doc_index,
label_index=label_index,
device="cuda")
# results = reader.eval_on_file(data_dir, filename, device="cuda")
print(results)
results["passages_per_second"] = n_passages / results[
"reader_time"]
results["reader"] = reader_name
results["error"] = ""
reader_results.append(results)
except Exception as e:
results = {
'EM': 0.,
'f1': 0.,
'top_n_accuracy': 0.,
'top_n': 0,
'reader_time': 0.,
"passages_per_second": 0.,
"seconds_per_query": 0.,
'reader': reader_name,
"error": e
}
reader_results.append(results)
reader_df = pd.DataFrame.from_records(reader_results)
reader_df.to_csv(results_file)
if save_markdown:
md_file = results_file.replace(".csv", ".md")
with open(md_file, "w") as f:
f.write(str(reader_df.to_markdown()))
if update_json:
populate_reader_json()
def add_eval_data(self, filename: str, doc_index: str = "eval_document", label_index: str = "label"):
"""
Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
:param filename: Name of the file containing evaluation data
:type filename: str
:param doc_index: Elasticsearch index where evaluation documents should be stored
:type doc_index: str
:param label_index: Elasticsearch index where labeled questions should be stored
:type label_index: str
"""
docs, labels = eval_data_from_file(filename)
self.write_documents(docs, index=doc_index)
self.write_labels(labels, index=label_index)
