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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Tuning a multi_match best_fields query
The following assumes familiarity with the first notebook "Query tuning".
The first query type we used was a multi_match cross_fields query. This searches for query terms individually across each of the three document fields. For example, given a query string "impact of the success of the manhattan project", we search for each of the query terms in each of the fields. So we could have "impact" matching the body only, while "manhattan" could match all three fields. Due to the nature of the queries which are all questions, this might not be the best query type to use.
In this step, we're going to try using the multi_match query of type best_fields, which is the default query type for multi_match. This variant will look across fields but will only return the field and score with the best matches. We will also experiment in this query with modifying a few parameters of the query that are sometimes hard to guess at. Specifically, we'll explore which field boosts to use for each of our three fields and also which tie_breaker parameter to use.
Step1:  Baseline evaluation
Step2:  That's pretty impressive for the baseline query. It beats our baseline cross_fields query but not quite the optimized one.
Query tuning
Let's try and optimize this best_fields query now. We'll put all the parameters into a single parameter space since there's only four. We'll use Bayesian optimization again to find the optimal parameters, with a fairly large number of iterations to make sure we test out a good portion of the parameter space.
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Python Code:
%load_ext autoreload
%autoreload 2
import importlib
import os
import sys
from elasticsearch import Elasticsearch
from skopt.plots import plot_objective
# project library
sys.path.insert(0, os.path.abspath('..'))
import qopt
importlib.reload(qopt)
from qopt.notebooks import evaluate_mrr100_dev, optimize_query_mrr100
from qopt.optimize import Config
# use a local Elasticsearch or Cloud instance (https://cloud.elastic.co/)
es = Elasticsearch('http://localhost:9200')
# set the parallelization parameter `max_concurrent_searches` for the Rank Evaluation API calls
max_concurrent_searches = 10
# max_concurrent_searches = 30
index = 'msmarco-document'
template_id = 'best_fields'
Explanation: Tuning a multi_match best_fields query
The following assumes familiarity with the first notebook "Query tuning".
The first query type we used was a multi_match cross_fields query. This searches for query terms individually across each of the three document fields. For example, given a query string "impact of the success of the manhattan project", we search for each of the query terms in each of the fields. So we could have "impact" matching the body only, while "manhattan" could match all three fields. Due to the nature of the queries which are all questions, this might not be the best query type to use.
In this step, we're going to try using the multi_match query of type best_fields, which is the default query type for multi_match. This variant will look across fields but will only return the field and score with the best matches. We will also experiment in this query with modifying a few parameters of the query that are sometimes hard to guess at. Specifically, we'll explore which field boosts to use for each of our three fields and also which tie_breaker parameter to use.
End of explanation
_ = evaluate_mrr100_dev(es, max_concurrent_searches, index, template_id,
    params={
        'tie_breaker': 0.0,
        'url|boost': 1.0,
        'title|boost': 1.0,
        'body|boost': 1.0,
    })
Explanation: Baseline evaluation
End of explanation
%%time
_, _, final_params_best_fields, metadata_best_fields = optimize_query_mrr100(es, max_concurrent_searches, index, template_id,
    config_space=Config.parse({
        'method': 'bayesian',
        'num_iterations': 100,
        'num_initial_points': 40,
        'space': {
            'tie_breaker': { 'low': 0.0, 'high': 1.0 },
            'url|boost': { 'low': 0.0, 'high': 10.0 },
            'title|boost': { 'low': 0.0, 'high': 10.0 },
            'body|boost': { 'low': 0.0, 'high': 10.0 },
        },
    }))
_ = plot_objective(metadata_best_fields, sample_source='result')
%%time
_ = evaluate_mrr100_dev(es, max_concurrent_searches, index, template_id, params=final_params_best_fields)
Explanation: That's pretty impressive for the baseline query. It beats our baseline cross_fields query but not quite the optimized one.
Query tuning
Let's try and optimize this best_fields query now. We'll put all the parameters into a single parameter space since there's only four. We'll use Bayesian optimization again to find the optimal parameters, with a fairly large number of iterations to make sure we test out a good portion of the parameter space.
End of explanation
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