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nathancday/hyperparameter_optimization.py

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Companion code for the OSC blogpost "Maximizing Search Relevance with Hyperparameter Optimization"
Raw
hyperparameter_optimization.py
#### SETUP ####
# 1. Download this GIST as a zip (download icon upper-right in web UI)
# 2. Unzip locally and navigate into new folder
# 3. Setup virtual Python environment with: `python3 -m venv venv`
# 4. Activate new virtual environment with: `source venv/bin/activate`
# 5. Install Python dependencies with: `pip install -r requirements.txt`
# 5. Run this file with: `python hyperparameter_optimization.py`
#### HELPERS ####
# Some helper code to hit ES and run a basic recall metric. For production use-cases you will
# likely want to multithread the evaluation process.
import csv
import json
import requests
import statistics
import urllib3
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
# The ES endpoint to hit
# Check out es-tmdb project for setting up a local tmdb index: https://github.com/o19s/es-tmdb
ES = 'http://localhost:9200/tmdb/_search'
# Read in queries and ratings data
with open('ratings.json') as src:
ratings = json.load(src)
with open('queries.json') as src:
queries = json.load(src)
# Evaluates recall for a given queryId and the associated hits from ES
# For this example: recall = (Number of relevant docs from ES query) / (Number of relevant documents we know about)
def eval_recall(qid, hits):
# Extract doc ID's from es response
hit_ids = [x['_id'] for x in hits]
# Get doc ID's from ratings with relevance grade > 0
relevant_ids = [k for k,v in ratings[qid].items() if int(v) > 0]
# Number of hits that were relevant from ES
search_count = len([x for x in hit_ids if x in relevant_ids])
# Number of relevant documents for query in ratings
ratings_count = min(len(relevant_ids), 10)
return (search_count / ratings_count) if ratings_count > 0 else 0
# Runs all rated queries for the given template
#
# In this example the minimizer will allocate different hyper-parameters
# and call this function expecting a metric back. We are using recall here but
# you should optimize for whatever makes sense for your search use-case.
def run_queries(es_template):
global queries
metrics = []
# Run each query and evaluate recall against ratings
for qid, query in queries.items():
es_template['query']['multi_match']['query'] = query
resp = requests.post(ES, json=es_template, verify=False).json()
# Metric is negated to work with the minimize process
metrics.append(-1 * eval_recall(qid, resp['hits']['hits']))
# Return aggregate mean
return statistics.mean(metrics)
def log_result(filename, result):
iters = []
for ix, x in enumerate(result.x_iters):
iters.append([result.func_vals[ix]] + x)
with open(f'{filename}.csv', 'w') as out:
writer = csv.writer(out)
writer.writerow(['eval', 'title_boost', 'overview_boost', 'tagline_boost', 'cast_boost'])
writer.writerows(iters)
print(f'[{filename}] Optimum recall: ', result.fun)
print(f'[{filename}] Optimum hyper parameters: ', result.x)
#### HYPERPARAMETER OPTIMIZATION ####
from skopt import dummy_minimize, gbrt_minimize, forest_minimize, gp_minimize
from skopt.space import Integer
from skopt.utils import use_named_args
# Dimensions to tune
# For this example we're using boost values, but you could tune any query parameters being sent into your search engine
space = [
Integer(1, 20, name='title_boost'),
Integer(1, 20, name='overview_boost'),
Integer(1, 20, name='tagline_boost'),
Integer(1, 20, name='cast_boost'),
]
# The objective function for our minimize operation
#
# This objective replaces the boosts in an ES query with the allocated hyper parameters from forest_optimize
# It will then call run_queries to optimize on the returned metric, which is recall in this example.
@use_named_args(space)
def objective(**params):
es_query = {
'_source': ['_id'],
'query': {
'multi_match': {
'query': 'placeholder',
'fields': [
f'title^{params["title_boost"]}',
f'overview^{params["overview_boost"]}',
f'tagline^{params["tagline_boost"]}',
f'cast^{params["cast_boost"]}',
]
}
},
'size': 10
}
return run_queries(es_query)
# The seed values for the dimensions
#
# You can replace the values here if you're working on optimizing an existing configuration
# or if you have a gut feeling about certain parameters.
seed = [1, 1, 1, 1]
min_args = {
'func': objective,
'x0': seed,
'dimensions': space,
'n_calls': 100,
'base_estimator': 'ET',
'random_state': 540,
'verbose': True
}
dummy_args = min_args.copy()
dummy_args.pop('base_estimator')
dummy_result = dummy_minimize(**dummy_args)
forest_result = forest_minimize(**min_args)
gbrt_result = gbrt_minimize(**min_args)
gp_result = gp_minimize(**min_args)
log_result('dummy', dummy_result)
log_result('forest', forest_result)
log_result('gbrt', gbrt_result)
log_result('gp', gp_result)
Raw
queries.json
{
"1": "rambo",
"2": "rocky",
"3": "war games",
"4": "crocodile dundee",
"5": "matrix",
"6": "contact",
"7": "space jam",
"8": "battlestar galactica",
"9": "her",
"10": "jobs",
"11": "social network",
"12": "rocky horror",
"13": "shawshank redemption",
"14": "french connection",
"15": "star wars new hope",
"16": "lego batman",
"17": "crouching tiger hidden dragon",
"18": "fight club",
"19": "die hard",
"20": "last temptation of christ",
"21": "tree of life",
"22": "murray saves christmas",
"23": "black swan",
"24": "emma",
"25": "mad max fury road",
"26": "kung fury",
"27": "mr smith goes to washington",
"28": "kill bill",
"29": "the godfather",
"30": "pulp fiction",
"31": "christmas vacation",
"32": "psycho",
"33": "life is beautiful",
"34": "dark knight",
"35": "the green mile",
"36": "forrest gump",
"37": "top gun",
"38": "how to train your dragon",
"39": "how to train your dragon 2",
"40": "star wars"
}
Raw
ratings.json
{
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},
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}
Raw
requirements.txt
certifi==2022.9.14
charset-normalizer==2.1.1
idna==3.4
joblib==1.2.0
numpy==1.21.6
pyaml==21.10.1
PyYAML==6.0
requests==2.28.1
scikit-learn==1.0.2
scikit-optimize==0.9.0
scipy==1.7.3
threadpoolctl==3.1.0
urllib3==1.26.12
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