Upload splitters.py with huggingface_hub
Browse files- splitters.py +51 -0
splitters.py
CHANGED
@@ -102,6 +102,57 @@ class RandomSampler(Sampler):
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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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return random.sample(instances_pool, self.sample_size)
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class DiverseLabelsSampler(Sampler):
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choices: str = "choices"
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def prepare(self):
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super().prepare()
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self.labels = None
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def examplar_repr(self, examplar):
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assert (
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"inputs" in examplar and self.choices in examplar["inputs"]
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), f"DiverseLabelsSampler assumes each examplar has {self.choices} field in it input"
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examplar_outputs = next(iter(examplar["outputs"].values()))
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return str([choice for choice in examplar["inputs"][self.choices] if choice in examplar_outputs])
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def divide_by_repr(self, examplars_pool):
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labels = dict()
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for examplar in examplars_pool:
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label_repr = self.examplar_repr(examplar)
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if label_repr not in labels:
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labels[label_repr] = []
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labels[label_repr].append(examplar)
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return labels
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def sample(self, instances_pool: List[Dict[str, object]]) -> List[Dict[str, object]]:
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if self.labels is None:
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self.labels = self.divide_by_repr(instances_pool)
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all_labels = list(self.labels.keys())
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random.shuffle(all_labels)
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from collections import Counter
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total_allocated = 0
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allocations = Counter()
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while total_allocated < self.sample_size:
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for label in all_labels:
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if total_allocated < self.sample_size:
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if len(self.labels[label]) - allocations[label] > 0:
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allocations[label] += 1
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total_allocated += 1
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else:
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break
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result = []
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for label, allocation in allocations.items():
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sample = random.sample(self.labels[label], allocation)
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result.extend(sample)
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random.shuffle(result)
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return result
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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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