mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-09-01 20:33:48 +00:00
337376c81d
* Add batch update of embeddings in document stores * Resolve merge conflict * Remove document ordering dependency in tests * Adjust index buffer size for tests * Adjust ES Scroll Slice * Use generator for document store pagination * Add pagination for InMemoryDocumentStore * Fix missing index parameter in FAISS update_embeddings() * Fix FAISS update_embeddings() * Update FAISS tests * Update eval tests * Revert code formatting change * Fix document count in FAISS update embeddings * Fix vector_ids reset in SQLDocumentStore * Update doctrings * Update docstring
127 lines
3.9 KiB
Python
127 lines
3.9 KiB
Python
import json
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from collections import defaultdict
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from itertools import islice
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import logging
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import pprint
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import pandas as pd
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from typing import Dict, Any, List
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from haystack.document_store.sql import DocumentORM
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logger = logging.getLogger(__name__)
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def print_answers(results: dict, details: str = "all"):
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answers = results["answers"]
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pp = pprint.PrettyPrinter(indent=4)
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if details != "all":
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if details == "minimal":
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keys_to_keep = set(["answer", "context"])
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elif details == "medium":
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keys_to_keep = set(["answer", "context", "score"])
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else:
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keys_to_keep = answers.keys()
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# filter the results
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filtered_answers = []
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for ans in answers:
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filtered_answers.append({k: ans[k] for k in keys_to_keep})
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pp.pprint(filtered_answers)
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else:
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pp.pprint(results)
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def export_answers_to_csv(agg_results: list, output_file):
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"""
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Exports answers coming from finder.get_answers() to a CSV file
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:param agg_results: list of predictions coming from finder.get_answers()
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:param output_file: filename of output file
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:return: None
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"""
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if isinstance(agg_results, dict):
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agg_results = [agg_results]
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assert "query" in agg_results[0], f"Wrong format used for {agg_results[0]}"
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assert "answers" in agg_results[0], f"Wrong format used for {agg_results[0]}"
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data = {} # type: Dict[str, List[Any]]
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data["query"] = []
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data["prediction"] = []
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data["prediction_rank"] = []
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data["prediction_context"] = []
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for res in agg_results:
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for i in range(len(res["answers"])):
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temp = res["answers"][i]
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data["query"].append(res["query"])
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data["prediction"].append(temp["answer"])
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data["prediction_rank"].append(i + 1)
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data["prediction_context"].append(temp["context"])
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df = pd.DataFrame(data)
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df.to_csv(output_file, index=False)
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def convert_labels_to_squad(labels_file: str):
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"""
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Convert the export from the labeling UI to SQuAD format for training.
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:param labels_file: path for export file from the labeling tool
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:return:
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"""
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with open(labels_file) as label_file:
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labels = json.load(label_file)
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labels_grouped_by_documents = defaultdict(list)
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for label in labels:
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labels_grouped_by_documents[label["document_id"]].append(label)
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labels_in_squad_format = {"data": []} # type: Dict[str, Any]
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for document_id, labels in labels_grouped_by_documents.items():
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qas = []
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for label in labels:
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doc = DocumentORM.query.get(label["document_id"])
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assert (
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doc.text[label["start_offset"] : label["end_offset"]]
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== label["selected_text"]
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)
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qas.append(
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{
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"question": label["question"],
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"id": label["id"],
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"question_id": label["question_id"],
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"answers": [
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{
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"text": label["selected_text"],
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"answer_start": label["start_offset"],
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"labeller_id": label["labeler_id"],
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}
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],
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"is_impossible": False,
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}
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)
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squad_format_label = {
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"paragraphs": [
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{"qas": qas, "context": doc.text, "document_id": document_id}
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]
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}
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labels_in_squad_format["data"].append(squad_format_label)
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with open("labels_in_squad_format.json", "w+") as outfile:
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json.dump(labels_in_squad_format, outfile)
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def get_batches_from_generator(iterable, n):
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"""
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Batch elements of an iterable into fixed-length chunks or blocks.
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"""
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it = iter(iterable)
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x = tuple(islice(it, n))
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while x:
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yield x
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x = tuple(islice(it, n)) |