Summary

Currently the batch method fails with a validation error if any of the generated rows fail the schema validators. To allow use of the package in a testing environment, it would be useful to be able to generate a dataframe of any size using a rejection sampler method. This method should store the random seeds of successful builds in order to reproduce the same dataframe each time.

I have created a class that performs these actions included below. Given this is something I have needed for my project, it could be a useful feature for others wanting to use Polyfactory for testing. I built it based off the original pydantic factories package, but I imagine it would be pretty similar for the additional Factory options in Polyfactory.

Basic Example

import time
import json
import pandas as pd
from polyfactory.factories.pydantic_factory import ModelFactory

class RejectionSampler:
    """Function to create a synthetic dataset based off the pydantic schema,
    dropping rows that do not meet the validation set up in the schema.

    Parameters
    ----------

    factory (ModelFactory): pydantic factories ModelFactory created from pydantic schema
    size (int): Length of dataset to create
    """

    def __init__(self, factory: ModelFactory, size: int) -> None:

        self.factory = factory
        self.size = size
        self.used_seeds = []

    def setup_seeds(self):

        start = time.time()

        synthetic_data = pd.DataFrame()

        # start seed at 1, increase seed by 1 each pass/fail of factory.build() to ensure reproducibility
        seed_no = 1

        for _ in range(self.size):
            result = None
            while not result:
                try:
                    self.factory.seed_random(seed_no)
                    result = self.factory.build()
                    result_dict = json.loads(result.json())
                    synthetic_data = synthetic_data.append(
                        pd.DataFrame(result_dict, index=[0])
                    )
                    self.used_seeds += [seed_no]
                    seed_no += 1
                    result = True
                except ValidationError:
                    seed_no += 1

        end = time.time()

        print(f"finished, took {seed_no-1} attempts to generate {self.size} rows")
        print(f"took {end-start} seconds to setup seeds")

    def generate(self):

        start = time.time()

        synthetic_data = pd.DataFrame()

        for seed in self.used_seeds:
            self.factory.seed_random(seed)
            result = self.factory.build()
            result_dict = json.loads(result.json())
            synthetic_data = synthetic_data.append(pd.DataFrame(result_dict, index=[0]))

        end = time.time()

        print(f"took {end-start} seconds to generate new data")

        return synthetic_data

Drawbacks and Impact

No response

Unresolved questions

No response