import itertools from abc import abstractmethod from dataclasses import field from typing import Dict, List, Optional from .artifact import Artifact from .generator_utils import ReusableGenerator from .operator import InstanceOperatorWithGlobalAccess, MultiStreamOperator from .stream import MultiStream class Splitter(MultiStreamOperator): pass from .random_utils import random from .split_utils import ( parse_random_mix_string, parse_slices_string, random_mix_streams, rename_split, slice_streams, ) class RenameSplits(Splitter): mapper: Dict[str, str] def process(self, multi_stream: MultiStream) -> MultiStream: generators = rename_split(multi_stream, self.mapper) return MultiStream(generators) class SplitRandomMix(Splitter): mix: Dict[str, str] def process(self, multi_stream: MultiStream) -> MultiStream: mapping = {k: parse_random_mix_string(v) for k, v in self.mix.items()} generators = random_mix_streams(multi_stream, mapping) return MultiStream.from_generators(generators) class SeparateSplit(Splitter): """ Separates a split (e.g. train) into several splits (e.g. train1, train2) sizes must indicate the size of every split except the last. If no size is give for the last split, it includes all the examples not allocated to any split. """ from_split: str to_split_names: List[str] to_split_sizes: List[int] def verify(self): assert ( len(self.to_split_names) == len(self.to_split_sizes) or len(self.to_split_names) == len(self.to_split_sizes) + 1 ), f"Examples num should be specified to all or all but the last splits, instead given {len(self.to_split_names)} split names and {len(self.to_split_sizes)} split sizes. \n split names:{self.to_split_names} split sizes {self.to_split_sizes}" return super().verify() def process(self, multi_stream: MultiStream) -> MultiStream: mapping = {key: {key: [(None, None)]} for key in multi_stream.keys() if key != self.from_split} so_far = 0 for name, size in itertools.zip_longest(self.to_split_names, self.to_split_sizes): mapping[name] = {self.from_split: [(so_far, size)]} if size: so_far += size generators = slice_streams(multi_stream, mapping) return MultiStream.from_generators(generators) class SliceSplit(Splitter): slices: Dict[str, str] def process(self, multi_stream: MultiStream) -> MultiStream: mapping = {k: parse_slices_string(v) for k, v in self.slices.items()} generators = slice_streams(multi_stream, mapping) return MultiStream.from_generators(generators) class Sampler(Artifact): sample_size: int = None def prepare(self): super().prepare() self.set_size(self.sample_size) def set_size(self, size): if isinstance(size, str): assert size.isdigit(), f"sample_size must be a natural number, got {self.sample_size}" size = int(size) self.sample_size = size @abstractmethod def sample(self, instances_pool: List[Dict[str, object]]) -> List[Dict[str, object]]: pass class RandomSampler(Sampler): def sample(self, instances_pool: List[Dict[str, object]]) -> List[Dict[str, object]]: instances_pool = list(instances_pool) return random.sample(instances_pool, self.sample_size) class DiverseLabelsSampler(Sampler): choices: str = "choices" def prepare(self): super().prepare() self.labels = None def examplar_repr(self, examplar): assert ( "inputs" in examplar and self.choices in examplar["inputs"] ), f"DiverseLabelsSampler assumes each examplar has {self.choices} field in it input" examplar_outputs = next(iter(examplar["outputs"].values())) return str([choice for choice in examplar["inputs"][self.choices] if choice in examplar_outputs]) def divide_by_repr(self, examplars_pool): labels = dict() for examplar in examplars_pool: label_repr = self.examplar_repr(examplar) if label_repr not in labels: labels[label_repr] = [] labels[label_repr].append(examplar) return labels def sample(self, instances_pool: List[Dict[str, object]]) -> List[Dict[str, object]]: if self.labels is None: self.labels = self.divide_by_repr(instances_pool) all_labels = list(self.labels.keys()) random.shuffle(all_labels) from collections import Counter total_allocated = 0 allocations = Counter() while total_allocated < self.sample_size: for label in all_labels: if total_allocated < self.sample_size: if len(self.labels[label]) - allocations[label] > 0: allocations[label] += 1 total_allocated += 1 else: break result = [] for label, allocation in allocations.items(): sample = random.sample(self.labels[label], allocation) result.extend(sample) random.shuffle(result) return result class SpreadSplit(InstanceOperatorWithGlobalAccess): source_stream: str = None target_field: str = None sampler: Sampler = None def prepare(self): self.accessible_streams = [self.source_stream] self.cache_accessible_streams = True self.local_cache = None def verify(self): assert self.source_stream is not None, "Source stream must be specified" assert self.target_field is not None, "Target field must be specified" assert self.sampler is not None, "Sampler must be specified" return super().verify() def process(self, instance: Dict[str, object], multi_stream: MultiStream) -> Dict[str, object]: if self.local_cache is None: self.local_cache = list(multi_stream[self.source_stream]) source_stream = self.local_cache sampled_instances = self.sampler.sample(source_stream) instance[self.target_field] = sampled_instances return instance if __name__ == "__main__": # some tests import random random.seed(0) splitter = SplitRandomMix( mix={ "train": "train[90%]+validation[50%]", "validation": "train[10%]+validation[50%]", "test": "test", } ) def generator(name, size): for i in range(size): yield {"text": f"{name}_{i}"} stream = MultiStream.from_generators( { "train": ReusableGenerator(generator, gen_kwargs={"name": "train", "size": 10}), "validation": ReusableGenerator(generator, gen_kwargs={"name": "validation", "size": 10}), "test": ReusableGenerator(generator, gen_kwargs={"name": "test", "size": 10}), } ) ds = splitter(stream) for key, value in ds.items(): print(key) for item in value: print(item) splitter = SliceSplit( slices={ "train": "train[:2]+train[2:4]", "validation": "train[4:6]", "test": "train[6:]+test", } ) ds = splitter(stream) for key, value in ds.items(): print(key) for item in value: print(item)