mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-07-29 11:50:34 +00:00
363be65a78
* Simplify ODES init * Add arguments to ES init and create script * Rename similarity_fn_name and add util fn * Create OpenSearchDocumentStore * Specify params of Open Search HNSW * Add better argument handling * Update opensearch index mapping * Edit opensearch default port * Fix HNSW mapping * Force small HNSW params * Implement auto start and stopping of document store services * Fix starting and stopping of ds service * Restore HNSW params * Add opensearch query benchmarks * Add write wait time * Revert wait time * Add timeout * Update benchmarks * Update benchmarks * Update benchmarks json * Update documentation * Update documentation * Fix similarity name * Improve argument passing * Improve stopping and starting of service
106 lines
3.1 KiB
Python
106 lines
3.1 KiB
Python
import json
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import pandas as pd
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from pprint import pprint
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def reader(reader_csv="reader_results.csv"):
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model_rename_map = {
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'deepset/roberta-base-squad2': "RoBERTa",
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'deepset/minilm-uncased-squad2': "MiniLM",
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'deepset/bert-base-cased-squad2': "BERT base",
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'deepset/bert-large-uncased-whole-word-masking-squad2': "BERT large",
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'deepset/xlm-roberta-large-squad2': "XLM-RoBERTa",
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}
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column_name_map = {
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"f1": "F1",
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"passages_per_second": "Speed",
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"reader": "Model"
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}
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df = pd.read_csv(reader_csv)
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df = df[["f1", "passages_per_second", "reader"]]
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df["reader"] = df["reader"].map(model_rename_map)
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df = df[list(column_name_map)]
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df = df.rename(columns=column_name_map)
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ret = [dict(row) for i, row in df.iterrows()]
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print("Reader overview")
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print(json.dumps(ret, indent=4))
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return ret
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def retriever(index_csv="retriever_index_results.csv", query_csv="retriever_query_results.csv"):
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column_name_map = {
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"model": "model",
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"n_docs": "n_docs",
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"docs_per_second": "index_speed",
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"queries_per_second": "query_speed",
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"map": "map"
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}
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name_cleaning = {
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"dpr": "DPR",
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"elastic": "BM25",
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"elasticsearch": "Elasticsearch",
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"faiss": "FAISS",
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"faiss_flat": "FAISS (flat)",
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"faiss_hnsw": "FAISS (HNSW)",
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"milvus_flat": "Milvus (flat)",
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"milvus_hnsw": "Milvus (HNSW)",
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"sentence_transformers": "Sentence Transformers",
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"opensearch_flat": "OpenSearch (flat)",
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"opensearch_hnsw": "OpenSearch (HNSW)"
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}
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index = pd.read_csv(index_csv)
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query = pd.read_csv(query_csv)
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df = pd.merge(index, query,
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how="right",
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left_on=["retriever", "doc_store", "n_docs"],
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right_on=["retriever", "doc_store", "n_docs"])
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df["retriever"] = df["retriever"].map(name_cleaning)
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df["doc_store"] = df["doc_store"].map(name_cleaning)
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df["model"] = df["retriever"] + " / " + df["doc_store"]
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df = df[list(column_name_map)]
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df = df.rename(columns=column_name_map)
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print("Retriever overview")
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retriever_overview_data = retriever_overview(df)
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print(json.dumps(retriever_overview_data, indent=4))
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print("Retriever MAP")
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retriever_map_data = retriever_map(df)
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print(json.dumps(retriever_map_data, indent=4))
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print("Retriever Speed")
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retriever_speed_data = retriever_speed(df)
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print(json.dumps(retriever_speed_data, indent=4))
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return retriever_overview_data, retriever_map_data, retriever_speed_data
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def retriever_map(df):
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columns = ["model", "n_docs", "map"]
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df = df[columns]
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ret = df.to_dict(orient="records")
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return ret
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def retriever_speed(df):
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columns = ["model", "n_docs", "query_speed"]
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df = df[columns]
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ret = df.to_dict(orient="records")
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return ret
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def retriever_overview(df, chosen_n_docs=100_000):
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df = df[df["n_docs"] == chosen_n_docs]
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ret = [dict(row) for i, row in df.iterrows()]
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return ret
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if __name__ == "__main__":
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reader()
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retriever() |