Spaces:
Runtime error
Runtime error
# 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 metric module is designed to solve the task of creating a world model and corresponding keys, | |
given a formula and whether that formula needs to be satiesfied. Please read my M.Sc. thesis for details.""" | |
import evaluate | |
import datasets | |
from Parser import parse_LLM_output | |
from nltk.sem.logic import * | |
import nltk | |
from nltk.sem.logic import LogicParser | |
from nltk.sem.evaluate import Valuation, Model | |
# TODO: Add BibTeX citation | |
_CITATION = """\ | |
@InProceedings{huggingface:module, | |
title = {Teaching LLMs predicate Logic (M.Sc. Thesis)}, | |
authors={Simon Döbele}, | |
year={upcoming} | |
} | |
""" | |
# TODO: Add description of the module here | |
_DESCRIPTION = """\ | |
This metric module is designed to solve the task of creating a world model and corresponding keys, | |
given a formula and whether that formula needs to be satiesfied. Please read my M.Sc. thesis for details. | |
In summary, my compute function behaves like it usually does, | |
but in order to be able to compare references and predictions, I need to | |
pass the predictions to a parser. Hence, why I rewrote just the compute function. | |
""" | |
# TODO: Add description of the arguments of the module here | |
_KWARGS_DESCRIPTION = """ | |
Calculates how good are predictions given some references, using certain scores | |
Args: | |
predictions: list of predictions to score. Each predictions | |
should be a string with tokens separated by spaces. | |
references: list of references, one for each prediction. Each | |
reference should be a string with tokens separated by spaces. | |
formulas: list of strings, needed for the parser to decide | |
whether the overall construction is valid (satisfied or unsatisfied). | |
Returns: | |
accuracy: description of the first score, | |
another_score: description of the second score, | |
Examples: | |
Examples should be written in doctest format, and should illustrate how | |
to use the function. | |
>>> my_new_module = evaluate.load("my_new_module") | |
>>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1]) | |
>>> print(results) | |
{'accuracy': 1.0} | |
""" | |
# TODO: Define external resources urls if needed (once thesis is published) | |
# BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt" | |
# def convert_valuation_back(valuation): | |
# # this is necessary, as jsonl could not serialize sets, but nltk expects sets for predicates. | |
# return [(v[0], set(v[1])) if v[0].isupper() else v for v in valuation] | |
# def eval_task1(dataset): | |
# results = [] | |
# for generated_output, target, valuation in zip(dataset["Predictions"], dataset["Target-sat"], dataset["Valuation"]): | |
# try: | |
# generated_formula = Expression.fromstring(generated_output) | |
# #print("Parsed output:" + generated_formula) | |
# valuation = convert_valuation_back(valuation) | |
# #print("Valuation:" + valuation) | |
# val = Valuation(valuation) | |
# dom = val.domain | |
# m = nltk.sem.evaluate.Model(dom, val) | |
# g = nltk.sem.Assignment(dom) | |
# sat = m.evaluate(generated_formula, g) | |
# if sat == True: | |
# prediction = "satisfied" | |
# elif sat == False: | |
# prediction = "unsatisfied" | |
# except: | |
# prediction = "undefined" | |
# #print("Output:" + prediction + "-----Target:" + target) | |
# results.append(prediction==target) | |
# accuracy = sum(results)/len(results) | |
# return accuracy | |
class AutoLogicCreateWorld(evaluate.Metric): | |
"""NOTE: only the compute function changes, compared to a normal evaluateMetric.compute() | |
as well as the datasets.Value (we are working with strings).""" | |
def _info(self): | |
# TODO: Specifies the evaluate.EvaluationModuleInfo object | |
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'), | |
'formulas': datasets.Value('string') | |
}), | |
# Homepage of the module for documentation | |
homepage="http://module.homepage", # TODO: change, once thesis is published | |
# Additional links to the codebase or references | |
codebase_urls=["http://github.com/path/to/codebase/of/new_module"], # TODO: change, once thesis is published | |
reference_urls=["http://path.to.reference.url/new_module"] # TODO: change, once thesis is published | |
) | |
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, formulas): | |
"""Returns the accuracy, given the parsed output.""" | |
results = [] | |
for generated_output, target, formula in zip(predictions, references, formulas): | |
v = parse_LLM_output(generated_output) | |
#print("Output:" + generated_output) | |
#print("Parsed output:") | |
#print(len(v)) | |
val = Valuation(v) | |
dom = val.domain | |
m = nltk.sem.evaluate.Model(dom, val) | |
g = nltk.sem.Assignment(dom) | |
sat = m.evaluate(formula, g) | |
if len(v) == 0: | |
prediction = "undefined" | |
elif sat == True: | |
prediction = "satisfied" | |
elif sat == False: | |
prediction = "unsatisfied" | |
else: | |
prediction = "undefined" | |
#print("Output:" + prediction + "-----Target:" + target) | |
results.append(prediction==target) | |
accuracy = sum(results)/len(results) | |
return { | |
"accuracy": accuracy, | |
} |