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from dataclasses import field |
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from typing import Dict, List, Optional |
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from .artifact import Artifact |
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from .generator_utils import ReusableGenerator |
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from .operator import InstanceOperatorWithGlobalAccess, MultiStreamOperator |
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from .stream import MultiStream |
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class Splitter(MultiStreamOperator): |
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pass |
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import random |
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from .split_utils import ( |
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parse_random_mix_string, |
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parse_slices_string, |
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random_mix_streams, |
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slice_streams, |
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) |
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class SplitRandomMix(Splitter): |
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mix: Dict[str, str] |
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def process(self, multi_stream: MultiStream) -> MultiStream: |
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mapping = {k: parse_random_mix_string(v) for k, v in self.mix.items()} |
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generators = random_mix_streams(multi_stream, mapping) |
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return MultiStream.from_generators(generators, streaming=True) |
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class SliceSplit(Splitter): |
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slices: Dict[str, str] |
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def process(self, multi_stream: MultiStream) -> MultiStream: |
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mapping = {k: parse_slices_string(v) for k, v in self.slices.items()} |
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generators = slice_streams(multi_stream, mapping) |
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return MultiStream.from_generators(generators, streaming=True) |
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class Sampler(Artifact): |
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sample_size: int |
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class RandomSampler(Sampler): |
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def sample(self, instances_pool: List[Dict[str, object]]) -> List[Dict[str, object]]: |
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instances_pool = list(instances_pool) |
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return random.sample(instances_pool, self.sample_size) |
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class SpreadSplit(InstanceOperatorWithGlobalAccess): |
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source_stream: str = None |
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target_field: str = None |
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sampler: Sampler = None |
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def prepare(self): |
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self.accessible_streams = [self.source_stream] |
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self.cache_accessible_streams = True |
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self.local_cache = None |
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def verify(self): |
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assert self.source_stream is not None, "Source stream must be specified" |
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assert self.target_field is not None, "Target field must be specified" |
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assert self.sampler is not None, "Sampler must be specified" |
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return super().verify() |
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def process(self, instance: Dict[str, object], multi_stream: MultiStream) -> Dict[str, object]: |
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if self.local_cache is None: |
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self.local_cache = list(multi_stream[self.source_stream]) |
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source_stream = self.local_cache |
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sampled_instances = self.sampler.sample(source_stream) |
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instance[self.target_field] = sampled_instances |
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return instance |
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if __name__ == "__main__": |
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import random |
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random.seed(0) |
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splitter = SplitRandomMix( |
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mix={ |
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"train": "train[90%]+validation[50%]", |
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"validation": "train[10%]+validation[50%]", |
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"test": "test", |
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} |
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) |
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def generator(name, size): |
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for i in range(size): |
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yield {"text": f"{name}_{i}"} |
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stream = MultiStream.from_generators( |
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{ |
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"train": ReusableGenerator(generator, gen_kwargs={"name": "train", "size": 10}), |
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"validation": ReusableGenerator(generator, gen_kwargs={"name": "validation", "size": 10}), |
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"test": ReusableGenerator(generator, gen_kwargs={"name": "test", "size": 10}), |
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} |
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) |
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ds = splitter(stream) |
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for key, value in ds.items(): |
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print(key) |
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for item in value: |
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print(item) |
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splitter = SliceSplit( |
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slices={ |
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"train": "train[:2]+train[2:4]", |
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"validation": "train[4:6]", |
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"test": "train[6:]+test", |
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} |
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) |
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ds = splitter(stream) |
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for key, value in ds.items(): |
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print(key) |
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for item in value: |
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print(item) |
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