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Simplicity vs. Informativeness trade-off for quantifiers

This code accompanies XXX.

Acknowledgments: The code was initially developed by Wouter Posdijk for his MSc Logic thesis in Amsterdam (see the repo this is forked from). Qi C Guo also provided significant development time.

The code consists of the following parts:

Generation:

  • Expression generation from a grammar
  • Generating quantifiers with presupposition by combining expressions
  • Sampling expressions into languages
  • Generating optimal languages using an evolutionary algorithm

Measuring of expressions:

  • Simplicity
  • Informativeness
  • Monotonicity
  • Conservativity

Measuring of quantifiers with presupposition:

  • Simplicity
  • Informativeness

Measuring of languages:

  • Simplicity
  • Informativeness
  • Monotonicity
  • Conservativity
  • Naturalness
  • Optimality wrt a Pareto front

Requirements

Python >=3.5. Get the required packages by running pip install.

Or:

conda create --name siminf python=3.7
conda activate siminf
conda install --file requirements.txt -c conda-forge

Running the code

Almost all code requires three main parameters:

  • A path to the Experiment Setup (see the folder ExperimentSetups). This specifies the grammar, using operators from Operator.py
  • The model size
  • The maximum quantifier length

This will put the results of said code in results/[ExperimentSetupName]_length=[length]_size=[size]

Replicating the experimental results

export PYTHONPATH=$PYTHONPATH:./

generate individual quantifiers

python bin/individual_quantifiers/generate.py --setup experiment_setups/final.json

measure properties of them

python bin/individual_quantifiers/measure_expression_complexity.py --setup experiment_setups/final.json

python bin/individual_quantifiers/measure_expression_monotonicity.py --setup experiment_setups/final.json

python bin/individual_quantifiers/measure_expression_conservativity.py --setup experiment_setups/final.json

generate pseudo-natural quantifiers

python bin/individual_quantifiers/generate_natural_expressions.py --setup experiment_setups/final.json

run evolutionary algorithm to estimate pareto frontier

python bin/languages/generate_evolutionary.py --setup=experiment_setups/final.json --lang_size 10 --sample_size 2000 --generations 100 --max_mutations 3

generate languages with varying degrees of naturalness

python bin/languages/sample_indexset_degrees.py --setup experiment_setups/final.json --indices natural --sample 8000

generate "random" languages

python bin/languages/languages.py --setup experiment_setups/final.json --sample 2000

measure complexity and informativeness

python bin/languages/measure.py --setup experiment_setups/final.json --name natural_gradual

python bin/languages/measure.py --setup experiment_setups/final.json --name random

measure monotonicity and conservativity

python bin/languages/measure_monotonicity.py --setup experiment_setups/final.json --name natural_gradual

python bin/languages/measure_monotonicity.py --setup experiment_setups/final.json --name random

python bin/languages/measure_conservativity.py --setup experiment_setups/final.json --name natural_gradual

python bin/languages/measure_conservativity.py --setup experiment_setups/final.json --name random

analysis

python bin/languages/analysis/estimate_pareto.py --setup experiment_setups/final.json

python bin/languages/analysis/analyze.py --setup experiment_setups/final.json

TODOs

  • Experiment setups: move from json to yaml?
  • General cleaning:
  • Finalize siminf as actual package

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