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dreamcoder_dataset_loader.py
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dreamcoder_dataset_loader.py
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from Predictions.models import RulesPredictor
import glob
import pickle
from experiment_helper import filter_examples
from type_system import BOOL, INT, Arrow, List, Type
from typing import Any, Tuple
import typing
def load_tasks(folder: str = "list_dataset") -> typing.List[Tuple[str,Any]]:
# Load all tasks
tasks = []
for file in glob.glob(f"{folder}/*.pickle"):
with open(file, "rb") as fd:
(name, examples) = pickle.load(fd)
tasks.append((name, examples))
return tasks
def __get_type(el, fallback=None):
if isinstance(el, bool):
return BOOL
elif isinstance(el, int):
return INT
elif isinstance(el, list):
if len(el) > 0:
return List(__get_type(el[0]))
else:
return __get_type(fallback[0], fallback[1:])
elif isinstance(el, tuple):
assert el[-1] == None
return __get_type(el[0], el[1:-1])
assert False, f"Unknown type for:{el}"
def __get_type_request(examples):
input, output = examples[0]
return Arrow(__get_type(input[0], [i[0] for i, o in examples[1:]]), __get_type(output, [o for i, o in examples[1:]]))
def filter_tasks_for_model(tasks, model) -> typing.List[Tuple[str, Any]]:
filtered_tasks = []
for task in tasks:
name, examples = task
type_request: Type = __get_type_request(examples)
if isinstance(model, RulesPredictor) and type_request != Arrow(List(INT), List(INT)):
continue
examples = filter_examples(
examples, model.IOEncoder.nb_arguments_max, model.IOEncoder.size_max, model.IOEncoder.symbolToIndex)
if len(examples) == 0:
continue
filtered_tasks.append((name, examples))
return filtered_tasks