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9727829cc6
* rename database to documentstore * move document, label, multilabel to haystack/schema.py * rename documentstore -> document_store * split indexing modules -> file_converter + preprocessor * fix order of imports * Update tutorial notebooks * fix torch version in tutorial 4
111 lines
5.4 KiB
Python
111 lines
5.4 KiB
Python
from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
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from haystack.preprocessor.utils import fetch_archive_from_http
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from haystack.retriever.sparse import ElasticsearchRetriever
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from haystack.retriever.dense import DensePassageRetriever
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from haystack.reader.farm import FARMReader
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from haystack.finder import Finder
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from farm.utils import initialize_device_settings
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import logging
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import subprocess
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import time
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logger = logging.getLogger(__name__)
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##############################################
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# Settings
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##############################################
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LAUNCH_ELASTICSEARCH = True
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eval_retriever_only = True
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eval_reader_only = False
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eval_both = False
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# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
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doc_index = "tutorial5_docs"
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label_index = "tutorial5_labels"
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##############################################
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# Code
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##############################################
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device, n_gpu = initialize_device_settings(use_cuda=True)
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# Start an Elasticsearch server
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# You can start Elasticsearch on your local machine instance using Docker. If Docker is not readily available in
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# your environment (eg., in Colab notebooks), then you can manually download and execute Elasticsearch from source.
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if LAUNCH_ELASTICSEARCH:
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logging.info("Starting Elasticsearch ...")
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status = subprocess.run(
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['docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2'], shell=True
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)
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if status.returncode:
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raise Exception("Failed to launch Elasticsearch. If you want to connect to an existing Elasticsearch instance"
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"then set LAUNCH_ELASTICSEARCH in the script to False.")
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time.sleep(30)
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# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents
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doc_dir = "../data/nq"
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s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
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fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
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# Connect to Elasticsearch
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document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document",
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create_index=False, embedding_field="emb",
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embedding_dim=768, excluded_meta_data=["emb"])
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# Add evaluation data to Elasticsearch document store
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# We first delete the custom tutorial indices to not have duplicate elements
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document_store.delete_all_documents(index=doc_index)
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document_store.delete_all_documents(index=label_index)
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document_store.add_eval_data(filename="../data/nq/nq_dev_subset_v2.json", doc_index=doc_index, label_index=label_index)
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# Initialize Retriever
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retriever = ElasticsearchRetriever(document_store=document_store)
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# Alternative: Evaluate DensePassageRetriever
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# Note, that DPR works best when you index short passages < 512 tokens as only those tokens will be used for the embedding.
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# Here, for nq_dev_subset_v2.json we have avg. num of tokens = 5220(!).
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# DPR still outperforms Elastic's BM25 by a small margin here.
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# retriever = DensePassageRetriever(document_store=document_store,
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# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
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# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
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# use_gpu=True,
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# embed_title=True,
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# remove_sep_tok_from_untitled_passages=True)
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# document_store.update_embeddings(retriever, index=doc_index)
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# Initialize Reader
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reader = FARMReader("deepset/roberta-base-squad2", top_k_per_candidate=4)
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# Initialize Finder which sticks together Reader and Retriever
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finder = Finder(reader, retriever)
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## Evaluate Retriever on its own
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if eval_retriever_only:
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retriever_eval_results = retriever.eval(top_k=10, label_index=label_index, doc_index=doc_index)
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## Retriever Recall is the proportion of questions for which the correct document containing the answer is
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## among the correct documents
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print("Retriever Recall:", retriever_eval_results["recall"])
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## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
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print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
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# Evaluate Reader on its own
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if eval_reader_only:
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reader_eval_results = reader.eval(document_store=document_store, device=device, label_index=label_index, doc_index=doc_index)
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# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
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#reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json", device=device)
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## Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer
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print("Reader Top-N-Accuracy:", reader_eval_results["top_n_accuracy"])
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## Reader Exact Match is the proportion of questions where the predicted answer is exactly the same as the correct answer
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print("Reader Exact Match:", reader_eval_results["EM"])
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## Reader F1-Score is the average overlap between the predicted answers and the correct answers
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print("Reader F1-Score:", reader_eval_results["f1"])
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# Evaluate combination of Reader and Retriever through Finder
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if eval_both:
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finder_eval_results = finder.eval(top_k_retriever=1, top_k_reader=10, label_index=label_index, doc_index=doc_index)
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finder.print_eval_results(finder_eval_results)
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