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import itertools |
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from abc import abstractmethod |
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from random import Random |
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from typing import Dict, List |
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from .artifact import Artifact |
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from .operator import InstanceOperatorWithMultiStreamAccess, MultiStreamOperator |
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from .random_utils import new_random_generator |
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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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rename_split, |
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slice_streams, |
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) |
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from .stream import MultiStream |
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class Splitter(MultiStreamOperator): |
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pass |
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class RenameSplits(Splitter): |
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mapper: Dict[str, str] |
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def process(self, multi_stream: MultiStream) -> MultiStream: |
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generators = rename_split(multi_stream, self.mapper) |
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return MultiStream(generators) |
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class SplitRandomMix(Splitter): |
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"""Splits a multistream into new streams (splits), whose names, source input stream, and amount of instances, are specified by arg 'mix'. |
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The keys of arg 'mix', are the names of the new streams, the values are of the form: 'name-of-source-stream[percentage-of-source-stream]' |
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Each input instance, of any input stream, is selected exactly once for inclusion in any of the output streams. |
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Examples: |
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When processing a multistream made of two streams whose names are 'train' and 'test', by |
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SplitRandomMix(mix = { "train": "train[99%]", "validation": "train[1%]", "test": "test" }) |
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the output is a multistream, whose three streams are named 'train', 'validation', and 'test'. |
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Output stream 'train' is made of randomly selected 99% of the instances of input stream 'train', |
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output stream 'validation' is made of the remaining 1% instances of input 'train', and output stream 'test' is made |
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of the whole of input stream 'test'. |
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When processing the above input multistream by |
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SplitRandomMix(mix = { "train": "train[50%]+test[0.1]", "validation": "train[50%]+test[0.2]", "test": "test[0.7]" }) |
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the output is a multistream, whose three streams are named 'train', 'validation', and 'test'. |
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Output stream 'train' is made of randomly selected 50% of the instances of input stream 'train' + randomly selected |
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0.1 (i.e., 10%) of the instances of input stream 'test'. |
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Output stream 'validation' is made of the remaining 50% instances of input 'train'+ randomly selected 0.2 (i.e., |
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20%) of the original instances of input 'test', that were not selected for output 'train', |
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and output stream 'test' is made of the remaining instances of input 'test'. |
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""" |
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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) |
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class SeparateSplit(Splitter): |
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"""Separates a split (e.g. train) into several splits (e.g. train1, train2). |
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sizes must indicate the size of every split except the last. If no size is give for the last split, |
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it includes all the examples not allocated to any split. |
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""" |
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from_split: str |
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to_split_names: List[str] |
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to_split_sizes: List[int] |
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remove_targets_from_source_split: bool = True |
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def verify(self): |
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assert ( |
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len(self.to_split_names) == len(self.to_split_sizes) |
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or len(self.to_split_names) == len(self.to_split_sizes) + 1 |
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), 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}" |
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return super().verify() |
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def process(self, multi_stream: MultiStream) -> MultiStream: |
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mapping = { |
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key: {key: [(None, None)]} |
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for key in multi_stream.keys() |
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if not self.remove_targets_from_source_split or key != self.from_split |
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} |
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so_far = 0 |
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for name, size in itertools.zip_longest( |
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self.to_split_names, self.to_split_sizes |
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): |
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if self.remove_targets_from_source_split or name != self.from_split: |
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mapping[name] = {self.from_split: [(so_far, size)]} |
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if size: |
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so_far += size |
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generators = slice_streams(multi_stream, mapping) |
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return MultiStream.from_generators(generators) |
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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) |
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class Sampler(Artifact): |
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sample_size: int = None |
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random_generator: Random = new_random_generator(sub_seed="Sampler") |
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def prepare(self): |
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super().prepare() |
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self.set_size(self.sample_size) |
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def set_size(self, size): |
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if isinstance(size, str): |
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assert ( |
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size.isdigit() |
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), f"sample_size must be a natural number, got {self.sample_size}" |
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size = int(size) |
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self.sample_size = size |
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def init_new_random_generator(self): |
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self.random_generator = new_random_generator( |
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sub_seed="init_new_random_generator" |
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) |
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@abstractmethod |
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def sample( |
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self, instances_pool: List[Dict[str, object]] |
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) -> List[Dict[str, object]]: |
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pass |
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def filter_source_by_instance( |
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self, instances_pool: List[Dict[str, object]], instance: Dict[str, object] |
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) -> List[Dict[str, object]]: |
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if "inputs" not in instance: |
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raise ValueError(f"'inputs' field is missing from '{instance}'.") |
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return list(filter(lambda x: x["inputs"] != instance["inputs"], instances_pool)) |
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class RandomSampler(Sampler): |
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def sample( |
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self, instances_pool: List[Dict[str, object]] |
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) -> List[Dict[str, object]]: |
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instances_pool = list(instances_pool) |
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return self.random_generator.sample(instances_pool, self.sample_size) |
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class DiverseLabelsSampler(Sampler): |
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"""Selects a balanced sample of instances based on an output field. |
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(used for selecting demonstrations in-context learning) |
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The field must contain list of values e.g ['dog'], ['cat'], ['dog','cat','cow']. |
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The balancing is done such that each value or combination of values |
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appears as equals as possible in the samples. |
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The `choices` param is required and determines which values should be considered. |
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Example: |
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If choices is ['dog,'cat'] , then the following combinations will be considered. |
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[''] |
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['cat'] |
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['dog'] |
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['dog','cat'] |
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If the instance contains a value not in the 'choice' param, it is ignored. For example, |
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if choices is ['dog,'cat'] and the instance field is ['dog','cat','cow'], then 'cow' is ignored |
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then the instance is considered as ['dog','cat']. |
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Args: |
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sample_size - number of samples to extract |
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choices - name of input field that contains the list of values to balance on |
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labels - name of output field with labels that must be balanced |
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""" |
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choices: str = "choices" |
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labels: str = "labels" |
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include_empty_label: bool = True |
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def prepare(self): |
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super().prepare() |
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self.labels_cache = None |
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def exemplar_repr(self, exemplar): |
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if "inputs" not in exemplar: |
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raise ValueError(f"'inputs' field is missing from '{exemplar}'.") |
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inputs = exemplar["inputs"] |
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if self.choices not in inputs: |
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raise ValueError(f"'{self.choices}' field is missing from '{inputs}'.") |
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choices = inputs[self.choices] |
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if not isinstance(choices, list): |
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raise ValueError( |
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f"Unexpected input choices value '{choices}'. Expected a list." |
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) |
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if "outputs" not in exemplar: |
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raise ValueError(f"'outputs' field is missing from '{exemplar}'.") |
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outputs = exemplar["outputs"] |
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if self.labels not in outputs: |
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raise ValueError(f"'{self.labels}' field is missing from '{outputs}'.") |
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exemplar_outputs = exemplar["outputs"][self.labels] |
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if not isinstance(exemplar_outputs, list): |
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raise ValueError( |
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f"Unexpected exemplar_outputs value '{exemplar_outputs}'. Expected a list." |
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) |
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return str([choice for choice in choices if choice in exemplar_outputs]) |
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def divide_by_repr(self, exemplars_pool): |
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labels = {} |
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for exemplar in exemplars_pool: |
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label_repr = self.exemplar_repr(exemplar) |
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if label_repr == "[]" and not self.include_empty_label: |
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continue |
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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(exemplar) |
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return labels |
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def sample( |
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self, instances_pool: List[Dict[str, object]] |
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) -> List[Dict[str, object]]: |
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if self.labels_cache is None: |
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self.labels_cache = self.divide_by_repr(instances_pool) |
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all_labels = list(self.labels_cache.keys()) |
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self.random_generator.shuffle(all_labels) |
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from collections import Counter |
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if self.sample_size > len(instances_pool): |
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raise ValueError( |
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f"Request sample size {self.sample_size} is greater than number of instances {len(instances_pool)}" |
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) |
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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_cache[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 = self.random_generator.sample(self.labels_cache[label], allocation) |
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result.extend(sample) |
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self.random_generator.shuffle(result) |
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return result |
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class SpreadSplit(InstanceOperatorWithMultiStreamAccess): |
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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.local_cache = None |
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self.sampler.prepare() |
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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( |
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self, instance: Dict[str, object], multi_stream: MultiStream |
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) -> Dict[str, object]: |
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try: |
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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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source_stream = self.sampler.filter_source_by_instance( |
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source_stream, instance |
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) |
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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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except Exception as e: |
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raise Exception( |
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f"Unable to fetch instances from '{self.source_stream}' to '{self.target_field}'" |
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) from e |
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