splitters.py

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)