pydantic-sweep pydantic_sweep is a library to programmatically, safely and flexibly define complex parameter sweeps over pydantic models in Python. Highlights: - Specify parameter sweeps in Python - Flexibility: specify complex parameter combinations by chaining simple functional operations - Safety checks for parameter combinations (get meaningful errors early) - pydantic field validation For example, the following code will instantiate models with (x=5, sub=Sub1(s=1) and (x=6, sub=Sub1(s=2) and try each of those with seed values of seed=43 and seed=44, leading to four different configurations: ```python import pydantic_sweep as ps class Sub1(ps.BaseModel): s: int = 5 class Sub2(ps.BaseModel): y: str = "hi" class Model(ps.BaseModel): seed: int = 42 x: int = 5 sub: Sub1 | Sub2 We want to test across two seeds configs = ps.config_product( ps.field("seed", [43, 44]), # And two specific Sub1 and x combinations ps.config_zip( ps.field("x", [ps.DefaultValue, 6]), ps.field("sub.s", [1, 2]), ) ) This includes safety checks that Sub1 / Sub2 are uniquely specified models = ps.initialize(Model, configs) The code above is equivalent to models_manual = [ Model(seed=43, sub=Sub1(s=1)), Model(seed=43, x=6, sub=Sub1(s=2)), Model(seed=44, sub=Sub1(s=1)), Model(seed=44, x=6, sub=Sub1(s=2)), ] assert models == models_manual We can also check that we didn't accidentally duplicate a setting ps.check_unique(models) ``` While in this toy example, manually specifying the combinations may still be viable, the library allows infinitely combining different configs and sub-models, making it a powerful tool for large-scale experiment definition. To learn mode about the capabilities of the library please visit the documentation page. Installation You can install the library by checking out the repo and running bash pip install 'pydantic-sweep' License The main code-base is licensed under MPL-2.0 a weak copy-left license that allows commercial use. See the license file for the exact clauses and this FAQ for a high-level description. An exception from this are the documentation in the docs and example folders as well as this README file, which are licensed under the 0BSD: a highly permissive license that does not require attribution. That way, you are free to copy & paste example code into your use-case. See here for a high-level description.
Latest version: 0.3.3 Released: 2025-02-23