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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# This is a module to compute the Valid Efficiency Score (VES) of a model's predictions for text-to-SQL tasks as
# proposed in "Can LLM Already Serve as a Database Interface?
# A Big Bench for Large-Scale Database Grounded Text-to-SQLs" (Li et al., 2023)
import evaluate
import datasets
from time import time
import numpy as np
from math import sqrt
_CITATION = """\
@article{li2023can,
title={Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls},
author={Li, Jinyang and Hui, Binyuan and Qu, Ge and Li, Binhua and Yang, Jiaxi and Li, Bowen and Wang, Bailin and Qin, Bowen and Cao, Rongyu and Geng, Ruiying and others},
journal={arXiv preprint arXiv:2305.03111},
year={2023}
}
"""
_DESCRIPTION = """\
This module computes the Valid Efficiency Score (VES) of a model's predictions for text-to-SQL tasks.
"""
_KWARGS_DESCRIPTION = """
Calculates how good the predictions are given some ground truth sql queries, using the Valid Efficiency Score (VES).
Args:
predictions: list of predictions to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
execute: function that takes a sql query and returns a result.
The result should be a list of tuples, each tuple containing the values of a row.
filter_func: function that takes a string and returns a boolean.
If True, the string is kept, otherwise it is dropped.
num_executions: number of times to execute each sql query to get the execution time.
Returns:
ves: Valid Efficiency Score of the predictions compared to the references.
Examples:
>>> my_new_module = evaluate.load("valid_efficiency_score")
>>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
>>> print(results)
{'ves': 1.0}
"""
# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class ValidEfficiencyScore(evaluate.Metric):
"""Valid Efficiency Score (VES) metric for text-to-SQL tasks."""
def _info(self):
return evaluate.MetricInfo(
# This is the description that will appear on the modules page.
module_type="metric",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features=datasets.Features({
'predictions': datasets.Value('string'),
'references': datasets.Value('string'),
}),
# Homepage of the module for documentation
homepage="http://module.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
reference_urls=[]
)
def _download_and_prepare(self, dl_manager):
"""Optional: download external resources useful to compute the scores"""
# TODO: Download external resources if needed
pass
def _compute(self, predictions, references, execute, filter_func=None, num_executions=100):
"""Returns the valid efficiency score of the predictions compared to the references."""
# TODO: Compute the different scores of the module
if len(predictions) != len(references):
raise ValueError("Predictions and references must have the same number of elements.")
# Run filter_func on predictions and references if needed
filtered_predictions = []
filtered_references = []
passing_reference_only = 0
if filter_func is not None:
for prediction, reference in zip(predictions, references):
# Only keep if both prediction and reference pass the filter
if filter_func(prediction) and filter_func(reference):
filtered_predictions.append(prediction)
filtered_references.append(reference)
# If only the reference passes the filter, count it
elif filter_func(reference):
passing_reference_only += 1
# Execute ground truth sql queries to get the time it takes to execute them
reference_times = np.zeros(num_executions)
for i in range(num_executions):
start_time = time()
[execute(i) for i in filtered_references]
end_time = time()
reference_times[i] = end_time - start_time
# Execute predicted sql queries to get the time it takes to execute them
prediction_times = np.zeros(num_executions)
for i in range(num_executions):
start_time = time()
[execute(i) for i in filtered_predictions]
end_time = time()
prediction_times[i] = end_time - start_time
# Get mean, std and 3 sigma interval
reference_mean = np.mean(reference_times)
reference_std = np.std(reference_times)
lower_bound = reference_mean - 3 * reference_std
upper_bound = reference_mean + 3 * reference_std
# Drop outliers
filtered_reference_times = reference_times[(reference_times >= lower_bound) & (reference_times <= upper_bound)]
# Get mean, std and 3 sigma interval
prediction_mean = np.mean(prediction_times)
prediction_std = np.std(prediction_times)
lower_bound = prediction_mean - 3 * prediction_std
upper_bound = prediction_mean + 3 * prediction_std
# Drop outliers
filtered_prediction_times = prediction_times[
(prediction_times >= lower_bound) & (prediction_times <= upper_bound)]
# Get new means as e_scores
reference_mean = np.mean(filtered_reference_times)
prediction_mean = np.mean(filtered_prediction_times)
r_value = sqrt(reference_mean / prediction_mean)
# Run filter_func on predictions and references if needed
filtered_predictions = []
filtered_references = []
divider = 0
if filter_func is not None:
for prediction, reference in zip(predictions, references):
# Only keep if both prediction and reference pass the filter
pred_bool = filter_func(prediction)
ref_bool = filter_func(reference)
if pred_bool and ref_bool:
filtered_predictions.append(prediction)
filtered_references.append(reference)
divider += 1
# If only the reference passes the filter, count it
elif pred_bool != ref_bool:
divider += 1
else:
filtered_predictions = predictions
filtered_references = references
divider = len(predictions)
accuracy = sum(
execute(i) == execute(j) for i, j in zip(filtered_predictions, filtered_references)) / divider
return {
"ves": accuracy * r_value,
}
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