yujunjun/haystack
1
0
Fork 0
mirror of https://github.com/deepset-ai/haystack.git synced 2025-07-25 09:50:14 +00:00
Code Issues Packages Projects Releases Wiki Activity
haystack/tutorials/Tutorial5_Evaluation.py
Julian Risch 3c81103db7
Remove logging config from Haystack (#2848) 
* move logging config from haystack lib to application

* Update Documentation & Code Style

* config logging before importing haystack

* Update Documentation & Code Style

* add logging config to all tutorials

* Update Documentation & Code Style

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2022-07-25 17:57:30 +02:00

473 lines
27 KiB
Python
Raw Blame History

import logging
# We configure how logging messages should be displayed and which log level should be used before importing Haystack.
# Example log message:
# INFO - haystack.utils.preprocessing - Converting data/tutorial1/218_Olenna_Tyrell.txt
# Default log level in basicConfig is WARNING so the explicit parameter is not necessary but can be changed easily:
logging.basicConfig(format="%(levelname)s - %(name)s - %(message)s", level=logging.WARNING)
logging.getLogger("haystack").setLevel(logging.INFO)
import tempfile
from pathlib import Path
from haystack.document_stores import ElasticsearchDocumentStore, InMemoryDocumentStore
from haystack.pipelines import Pipeline, ExtractiveQAPipeline, DocumentSearchPipeline
from haystack.nodes import (
BM25Retriever,
DensePassageRetriever,
EmbeddingRetriever,
FARMReader,
PreProcessor,
TextConverter,
)
from haystack.utils import fetch_archive_from_http, launch_es
from haystack.schema import Answer, Document, EvaluationResult, Label, MultiLabel, Span
def tutorial5_evaluation():
# make sure these indices do not collide with existing ones, the indices will be wiped clean before data is inserted
doc_index = "tutorial5_docs"
label_index = "tutorial5_labels"
##############################################
# Code
##############################################
launch_es()
# Download evaluation data, which is a subset of Natural Questions development set containing 50 documents with one question per document and multiple annotated answers
doc_dir = "data/tutorial5"
s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/nq_dev_subset_v2.json.zip"
fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
# Connect to Elasticsearch
document_store = ElasticsearchDocumentStore(
host="localhost",
username="",
password="",
index=doc_index,
label_index=label_index,
embedding_field="emb",
embedding_dim=768,
excluded_meta_data=["emb"],
)
# Add evaluation data to Elasticsearch document store
# We first delete the custom tutorial indices to not have duplicate elements
# and also split our documents into shorter passages using the PreProcessor
preprocessor = PreProcessor(
split_by="word",
split_length=200,
split_overlap=0,
split_respect_sentence_boundary=False,
clean_empty_lines=False,
clean_whitespace=False,
)
document_store.delete_documents(index=doc_index)
document_store.delete_documents(index=label_index)
# The add_eval_data() method converts the given dataset in json format into Haystack document and label objects.
# Those objects are then indexed in their respective document and label index in the document store.
# The method can be used with any dataset in SQuAD format.
document_store.add_eval_data(
filename="data/tutorial5/nq_dev_subset_v2.json",
doc_index=doc_index,
label_index=label_index,
preprocessor=preprocessor,
)
# Initialize Retriever
retriever = BM25Retriever(document_store=document_store)
# Alternative: Evaluate dense retrievers (EmbeddingRetriever or DensePassageRetriever)
# The EmbeddingRetriever uses a single transformer based encoder model for query and document.
# In contrast, DensePassageRetriever uses two separate encoders for both.
# Please make sure the "embedding_dim" parameter in the DocumentStore above matches the output dimension of your models!
# Please also take care that the PreProcessor splits your files into chunks that can be completely converted with
# the max_seq_len limitations of Transformers
# The SentenceTransformer model "sentence-transformers/multi-qa-mpnet-base-dot-v1" generally works well with the EmbeddingRetriever on any kind of English text.
# For more information and suggestions on different models check out the documentation at: https://www.sbert.net/docs/pretrained_models.html
# from haystack.retriever import EmbeddingRetriever, DensePassageRetriever
# retriever = EmbeddingRetriever(document_store=document_store,
# embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1")
# retriever = DensePassageRetriever(document_store=document_store,
# query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
# passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
# use_gpu=True,
# max_seq_len_passage=256,
# embed_title=True)
# document_store.update_embeddings(retriever, index=doc_index)
# Initialize Reader
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", top_k=4, return_no_answer=True)
# Define a pipeline consisting of the initialized retriever and reader
# Here we evaluate retriever and reader in an integrated (a.k.a. open domain) fashion on the full corpus of documents
# i.e. a document is considered
# correctly retrieved if it contains the gold answer string within it. The reader is evaluated based purely on the
# predicted answer string, regardless of which document this came from and the position of the extracted span.
# The generation of predictions is separated from the calculation of metrics.
# This allows you to run the computation-heavy model predictions only once and then iterate flexibly on the metrics or reports you want to generate.
pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever)
# The evaluation also works with any other pipeline.
# For example you could use a DocumentSearchPipeline as an alternative:
# pipeline = DocumentSearchPipeline(retriever=retriever)
# We can load evaluation labels from the document store
# We are also opting to filter out no_answer samples
eval_labels = document_store.get_all_labels_aggregated(drop_negative_labels=True, drop_no_answers=True)
## Alternative: Define queries and labels directly
# eval_labels = [
# MultiLabel(
# labels=[
# Label(
# query="who is written in the book of life",
# answer=Answer(
# answer="every person who is destined for Heaven or the World to Come",
# offsets_in_context=[Span(374, 434)]
# ),
# document=Document(
# id='1b090aec7dbd1af6739c4c80f8995877-0',
# content_type="text",
# content='Book of Life - wikipedia Book of Life Jump to: navigation, search This article is
# about the book mentioned in Christian and Jewish religious teachings...'
# ),
# is_correct_answer=True,
# is_correct_document=True,
# origin="gold-label"
# )
# ]
# )
# ]
# Similar to pipeline.run() we can execute pipeline.eval()
eval_result = pipeline.eval(labels=eval_labels, params={"Retriever": {"top_k": 5}})
# The EvaluationResult contains a pandas dataframe for each pipeline node.
# That's why there are two dataframes in the EvaluationResult of an ExtractiveQAPipeline.
retriever_result = eval_result["Retriever"]
retriever_result.head()
reader_result = eval_result["Reader"]
reader_result.head()
# We can filter for all documents retrieved for a given query
query = "who is written in the book of life"
retriever_book_of_life = retriever_result[retriever_result["query"] == query]
# We can also filter for all answers predicted for a given query
reader_book_of_life = reader_result[reader_result["query"] == "who is written in the book of life"]
# Save the evaluation result so that we can reload it later
# and calculate evaluation metrics without running the pipeline again.
eval_result.save("../")
## Calculating Evaluation Metrics
# Load an EvaluationResult to quickly calculate standard evaluation metrics for all predictions,
# such as F1-score of each individual prediction of the Reader node or recall of the retriever.
# To learn more about the metrics, see [Evaluation Metrics](https://haystack.deepset.ai/guides/evaluation#metrics-retrieval)
saved_eval_result = EvaluationResult.load("../")
metrics = saved_eval_result.calculate_metrics()
print(f'Retriever - Recall (single relevant document): {metrics["Retriever"]["recall_single_hit"]}')
print(f'Retriever - Recall (multiple relevant documents): {metrics["Retriever"]["recall_multi_hit"]}')
print(f'Retriever - Mean Reciprocal Rank: {metrics["Retriever"]["mrr"]}')
print(f'Retriever - Precision: {metrics["Retriever"]["precision"]}')
print(f'Retriever - Mean Average Precision: {metrics["Retriever"]["map"]}')
print(f'Reader - F1-Score: {metrics["Reader"]["f1"]}')
print(f'Reader - Exact Match: {metrics["Reader"]["exact_match"]}')
## Generating an Evaluation Report
# A summary of the evaluation results can be printed to get a quick overview.
# It includes some aggregated metrics and also shows a few wrongly predicted examples.
pipeline.print_eval_report(saved_eval_result)
## Advanced Evaluation Metrics
# Semantic Answer Similarity (SAS) is an advanced evaluation metric can be calculated in Haystack.
# This metric takes into account whether the meaning of a predicted answer is similar to the annotated gold answer
# rather than just doing string comparison. To this end SAS relies on pre-trained models.
# For English, we recommend "cross-encoder/stsb-roberta-large", whereas for German we recommend "deepset/gbert-large-sts".
# A good multilingual model is "sentence-transformers/paraphrase-multilingual-mpnet-base-v2".
# More info on this metric can be found in our [paper](https://arxiv.org/abs/2108.06130)
# or in our [blog post](https://www.deepset.ai/blog/semantic-answer-similarity-to-evaluate-qa).
advanced_eval_result = pipeline.eval(
labels=eval_labels,
params={"Retriever": {"top_k": 5}},
sas_model_name_or_path="cross-encoder/stsb-roberta-large",
)
metrics = advanced_eval_result.calculate_metrics()
print(metrics["Reader"]["sas"])
## Isolated Evaluation Mode
# The isolated node evaluation uses labels as input to the Reader node instead of the output of the preceeding retriever node.
# Thereby, we can additionally calculate the upper bounds of the evaluation metrics of the Reader.
# Note that even with isolated evaluation enabled, integrated evaluation will still be running.
eval_result_with_upper_bounds = pipeline.eval(
labels=eval_labels, params={"Retriever": {"top_k": 5}}, add_isolated_node_eval=True
)
pipeline.print_eval_report(eval_result_with_upper_bounds)
# ## Advanced Label Scopes
# Answers are considered correct if the predicted answer matches the gold answer in the labels.
# Documents are considered correct if the predicted document ID matches the gold document ID in the labels.
# Sometimes, these simple definitions of "correctness" are not sufficient.
# There are cases where you want to further specify the "scope" within which an answer or a document is considered correct.
# For this reason, `EvaluationResult.calculate_metrics()` offers the parameters `answer_scope` and `document_scope`.
#
# Say you want to ensure that an answer is only considered correct if it stems from a specific context of surrounding words.
# This is especially useful if your answer is very short, like a date (for example, "2011") or a place ("Berlin").
# Such short answer might easily appear in multiple completely different contexts.
# Some of those contexts might perfectly fit the actual question and answer it.
# Some others might not: they don't relate to the question at all but still contain the answer string.
# In that case, you might want to ensure that only answers that stem from the correct context are considered correct.
# To do that, specify `answer_scope="context"` in `calculate_metrics()`.
#
# `answer_scope` takes the following values:
# - `any` (default): Any matching answer is considered correct.
# - `context`: The answer is only considered correct if its context matches as well. It uses fuzzy matching (see `context_matching` parameters of `pipeline.eval()`).
# - `document_id`: The answer is only considered correct if its document ID matches as well. You can specify a custom document ID through the `custom_document_id_field` parameter of `pipeline.eval()`.
# - `document_id_and_context`: The answer is only considered correct if its document ID and its context match as well.
#
# In Question Answering, to enforce that the retrieved document is considered correct whenever the answer is correct, set `document_scope` to `answer` or `document_id_or_answer`.
#
# `document_scope` takes the following values:
# - `document_id`: Specifies that the document ID must match. You can specify a custom document ID through the `custom_document_id_field` parameter of `pipeline.eval()`.
# - `context`: Specifies that the content of the document must match. It uses fuzzy matching (see the `context_matching` parameters of `pipeline.eval()`).
# - `document_id_and_context`: A Boolean operation specifying that both `'document_id' AND 'context'` must match.
# - `document_id_or_context`: A Boolean operation specifying that either `'document_id' OR 'context'` must match.
# - `answer`: Specifies that the document contents must include the answer. The selected `answer_scope` is enforced.
# - `document_id_or_answer` (default): A Boolean operation specifying that either `'document_id' OR 'answer'` must match.
metrics = saved_eval_result.calculate_metrics(answer_scope="context")
print(f'Retriever - Recall (single relevant document): {metrics["Retriever"]["recall_single_hit"]}')
print(f'Retriever - Recall (multiple relevant documents): {metrics["Retriever"]["recall_multi_hit"]}')
print(f'Retriever - Mean Reciprocal Rank: {metrics["Retriever"]["mrr"]}')
print(f'Retriever - Precision: {metrics["Retriever"]["precision"]}')
print(f'Retriever - Mean Average Precision: {metrics["Retriever"]["map"]}')
print(f'Reader - F1-Score: {metrics["Reader"]["f1"]}')
print(f'Reader - Exact Match: {metrics["Reader"]["exact_match"]}')
document_store.get_all_documents()[0]
# Let's try Document Retrieval on a file level (it's sufficient if the correct file identified by its name (for example, 'Book of Life') was retrieved).
eval_result_custom_doc_id = pipeline.eval(
labels=eval_labels, params={"Retriever": {"top_k": 5}}, custom_document_id_field="name"
)
metrics = eval_result_custom_doc_id.calculate_metrics(document_scope="document_id")
print(f'Retriever - Recall (single relevant document): {metrics["Retriever"]["recall_single_hit"]}')
print(f'Retriever - Recall (multiple relevant documents): {metrics["Retriever"]["recall_multi_hit"]}')
print(f'Retriever - Mean Reciprocal Rank: {metrics["Retriever"]["mrr"]}')
print(f'Retriever - Precision: {metrics["Retriever"]["precision"]}')
print(f'Retriever - Mean Average Precision: {metrics["Retriever"]["map"]}')
# Let's enforce the context again:
metrics = eval_result_custom_doc_id.calculate_metrics(document_scope="document_id_and_context")
print(f'Retriever - Recall (single relevant document): {metrics["Retriever"]["recall_single_hit"]}')
print(f'Retriever - Recall (multiple relevant documents): {metrics["Retriever"]["recall_multi_hit"]}')
print(f'Retriever - Mean Reciprocal Rank: {metrics["Retriever"]["mrr"]}')
print(f'Retriever - Precision: {metrics["Retriever"]["precision"]}')
print(f'Retriever - Mean Average Precision: {metrics["Retriever"]["map"]}')
# ## Storing results in MLflow
# Storing evaluation results in CSVs is fine but not enough if you want to compare and track multiple evaluation runs. MLflow is a handy tool when it comes to tracking experiments. So we decided to use it to track all of `Pipeline.eval()` with reproducability of your experiments in mind.
# ### Host your own MLflow or use deepset's public MLflow
# If you don't want to use deepset's public MLflow instance under https://public-mlflow.deepset.ai, you can easily host it yourself.
# !pip install mlflow
# !mlflow server --serve-artifacts
# ### Preprocessing the dataset
# Preprocessing the dataset works a bit differently than before. Instead of directly generating documents (and labels) out of a SQuAD file, we first save them to disk. This is necessary to experiment with different indexing pipelines.
document_store = InMemoryDocumentStore()
label_preprocessor = PreProcessor(
split_length=200,
split_overlap=0,
split_respect_sentence_boundary=False,
clean_empty_lines=False,
clean_whitespace=False,
)
# The add_eval_data() method converts the given dataset in json format into Haystack document and label objects.
# Those objects are then indexed in their respective document and label index in the document store.
# The method can be used with any dataset in SQuAD format.
# We only use it to get the evaluation set labels and the corpus files.
document_store.add_eval_data(
filename="data/tutorial5/nq_dev_subset_v2.json",
doc_index=document_store.index,
label_index=document_store.label_index,
preprocessor=label_preprocessor,
)
# the evaluation set to evaluate the pipelines on
evaluation_set_labels = document_store.get_all_labels_aggregated(drop_negative_labels=True, drop_no_answers=True)
# Pipelines need files as input to be able to test different preprocessors.
# Even though this looks a bit cumbersome to write the documents back to files we gain a lot of evaluation potential and reproducibility.
docs = document_store.get_all_documents()
temp_dir = tempfile.TemporaryDirectory()
file_paths = []
for doc in docs:
file_name = doc.id + ".txt"
file_path = Path(temp_dir.name) / file_name
file_paths.append(file_path)
with open(file_path, "w") as f:
f.write(doc.content)
file_metas = [d.meta for d in docs]
# ### Run experiments
# In this experiment we evaluate extractive QA pipelines with two different retrievers on the evaluation set given the corpus:
# **ElasticsearchRetriever vs. EmbeddingRetriever**
# helper function to create query and index pipeline
def create_pipelines(document_store, preprocessor, retriever, reader):
query_pipeline = Pipeline()
query_pipeline.add_node(component=retriever, inputs=["Query"], name="Retriever")
query_pipeline.add_node(component=reader, inputs=["Retriever"], name="Reader")
index_pipeline = Pipeline()
index_pipeline.add_node(component=TextConverter(), inputs=["File"], name="TextConverter")
index_pipeline.add_node(component=preprocessor, inputs=["TextConverter"], name="Preprocessor")
index_pipeline.add_node(component=retriever, inputs=["Preprocessor"], name="Retriever")
index_pipeline.add_node(component=document_store, inputs=["Retriever"], name="DocumentStore")
return query_pipeline, index_pipeline
# Name of the experiment in MLflow
EXPERIMENT_NAME = "haystack-tutorial-5"
# #### Run using BM25Retriever
document_store = ElasticsearchDocumentStore(index="sparse_index", recreate_index=True)
preprocessor = PreProcessor(
split_length=200,
split_overlap=0,
split_respect_sentence_boundary=False,
clean_empty_lines=False,
clean_whitespace=False,
)
es_retriever = BM25Retriever(document_store=document_store)
reader = FARMReader("deepset/roberta-base-squad2", top_k=3, return_no_answer=True, batch_size=8)
query_pipeline, index_pipeline = create_pipelines(document_store, preprocessor, es_retriever, reader)
sparse_eval_result = Pipeline.execute_eval_run(
index_pipeline=index_pipeline,
query_pipeline=query_pipeline,
evaluation_set_labels=evaluation_set_labels,
corpus_file_paths=file_paths,
corpus_file_metas=file_metas,
experiment_name=EXPERIMENT_NAME,
experiment_run_name="sparse",
corpus_meta={"name": "nq_dev_subset_v2.json"},
evaluation_set_meta={"name": "nq_dev_subset_v2.json"},
pipeline_meta={"name": "sparse-pipeline"},
add_isolated_node_eval=True,
experiment_tracking_tool="mlflow",
experiment_tracking_uri="https://public-mlflow.deepset.ai",
reuse_index=True,
)
# #### Run using EmbeddingRetriever
document_store = ElasticsearchDocumentStore(index="dense_index", recreate_index=True)
emb_retriever = EmbeddingRetriever(
document_store=document_store,
model_format="sentence_transformers",
embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1",
batch_size=8,
)
query_pipeline, index_pipeline = create_pipelines(document_store, preprocessor, emb_retriever, reader)
dense_eval_result = Pipeline.execute_eval_run(
index_pipeline=index_pipeline,
query_pipeline=query_pipeline,
evaluation_set_labels=evaluation_set_labels,
corpus_file_paths=file_paths,
corpus_file_metas=file_metas,
experiment_name=EXPERIMENT_NAME,
experiment_run_name="embedding",
corpus_meta={"name": "nq_dev_subset_v2.json"},
evaluation_set_meta={"name": "nq_dev_subset_v2.json"},
pipeline_meta={"name": "embedding-pipeline"},
add_isolated_node_eval=True,
experiment_tracking_tool="mlflow",
experiment_tracking_uri="https://public-mlflow.deepset.ai",
reuse_index=True,
answer_scope="context",
)
# You can now open MLflow (e.g. https://public-mlflow.deepset.ai/ if you used the public one hosted by deepset) and look for the haystack-eval-experiment experiment.
# Try out mlflow's compare function and have fun...
#
# Note that on our public mlflow instance we are not able to log artifacts like the evaluation results or the piplines.yaml file.
## Evaluation of Individual Components
# Sometimes you might want to evaluate individual components,
# for example, if you don't have a pipeline but only a retriever or a reader with a model that you trained yourself.
# Evaluate Retriever on its own
# Here we evaluate only the retriever, based on whether the gold_label document is retrieved.
# Note that no_answer samples are omitted when evaluation is performed with this method
retriever_eval_results = retriever.eval(top_k=5, label_index=label_index, doc_index=doc_index)
## Retriever Recall is the proportion of questions for which the correct document containing the answer is
## among the correct documents
print("Retriever Recall:", retriever_eval_results["recall"])
## Retriever Mean Avg Precision rewards retrievers that give relevant documents a higher rank
print("Retriever Mean Avg Precision:", retriever_eval_results["map"])
# Just as a sanity check, we can compare the recall from `retriever.eval()` with the multi hit recall from `pipeline.eval(add_isolated_node_eval=True)`.
# These two recall metrics are only comparable since we chose to filter out no_answer samples when generating eval_labels and setting document_scope to `"document_id"`.
# Per default `calculate_metrics()` has document_scope set to `"document_id_or_answer"` which interprets documents as relevant if they either match the gold document ID or contain the answer.
metrics = eval_result_with_upper_bounds.calculate_metrics(document_scope="document_id")
print(metrics["Retriever"]["recall_multi_hit"])
# Evaluate Reader on its own
# Here we evaluate only the reader in a closed domain fashion i.e. the reader is given one query
# and its corresponding relevant document and metrics are calculated on whether the right position in this text is selected by
# the model as the answer span (i.e. SQuAD style)
reader_eval_results = reader.eval(document_store=document_store, label_index=label_index, doc_index=doc_index)
top_n = reader_eval_results["top_n"]
# Evaluation of Reader can also be done directly on a SQuAD-formatted file without passing the data to Elasticsearch
# reader_eval_results = reader.eval_on_file("../data/nq", "nq_dev_subset_v2.json")
# Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer including no_answers
print(f"Reader Top-{top_n}-Accuracy:", reader_eval_results["top_n_accuracy"])
# Reader Top-1-Exact Match is the proportion of questions where the first predicted answer is exactly the same as the correct answer including no_answers
print("Reader Top-1-Exact Match:", reader_eval_results["EM"])
# Reader Top-1-F1-Score is the average overlap between the first predicted answers and the correct answers including no_answers
print("Reader Top-1-F1-Score:", reader_eval_results["f1"])
# Reader Top-N-Accuracy is the proportion of predicted answers that match with their corresponding correct answer excluding no_answers
print(f"Reader Top-{top_n}-Accuracy (without no_answers):", reader_eval_results["top_n_accuracy_text_answer"])
# Reader Top-N-Exact Match is the proportion of questions where the predicted answer within the first n results is exactly the same as the correct answer excluding no_answers (no_answers are always present within top n).
print(f"Reader Top-{top_n}-Exact Match (without no_answers):", reader_eval_results["top_n_EM_text_answer"])
# Reader Top-N-F1-Score is the average overlap between the top n predicted answers and the correct answers excluding no_answers (no_answers are always present within top n).
print(f"Reader Top-{top_n}-F1-Score (without no_answers):", reader_eval_results["top_n_f1_text_answer"])
# Just as a sanity check, we can compare the top-n exact_match and f1 metrics from `reader.eval()` with the exact_match and f1 from `pipeline.eval(add_isolated_node_eval=True)`.
# These two approaches return the same values because pipeline.eval() calculates top-n metrics per default.
# Small discrepancies might occur due to string normalization in pipeline.eval()'s answer-to-label comparison.
# reader.eval() does not use string normalization.
metrics = eval_result_with_upper_bounds.calculate_metrics(eval_mode="isolated")
print(metrics["Reader"]["exact_match"])
print(metrics["Reader"]["f1"])
if __name__ == "__main__":
tutorial5_evaluation()
# This Haystack script was made with love by deepset in Berlin, Germany
# Haystack: https://github.com/deepset-ai/haystack
# deepset: https://deepset.ai/
Reference in New Issue View Git Blame Copy Permalink