autologiccreateworld.py

# 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


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
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,
        }