|
import itertools |
|
from abc import abstractmethod |
|
from random import Random |
|
from typing import Dict, List |
|
|
|
from .artifact import Artifact |
|
from .operator import InstanceOperatorWithMultiStreamAccess, MultiStreamOperator |
|
from .random_utils import new_random_generator |
|
from .split_utils import ( |
|
parse_random_mix_string, |
|
parse_slices_string, |
|
random_mix_streams, |
|
rename_split, |
|
slice_streams, |
|
) |
|
from .stream import MultiStream |
|
|
|
|
|
class Splitter(MultiStreamOperator): |
|
pass |
|
|
|
|
|
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 |
|
random_generator: Random = new_random_generator(sub_seed="Sampler") |
|
|
|
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 |
|
|
|
def init_new_random_generator(self): |
|
self.random_generator = new_random_generator( |
|
sub_seed="init_new_random_generator" |
|
) |
|
|
|
@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 self.random_generator.sample(instances_pool, self.sample_size) |
|
|
|
|
|
class DiverseLabelsSampler(Sampler): |
|
"""Selects a balanced sample of instances based on an output field. |
|
|
|
(used for selecting demonstrations in-context learning) |
|
|
|
The field must contain list of values e.g ['dog'], ['cat'], ['dog','cat','cow']. |
|
The balancing is done such that each value or combination of values |
|
appears as equals as possible in the samples. |
|
|
|
The `choices` param is required and determines which values should be considered. |
|
|
|
Example: |
|
If choices is ['dog,'cat'] , then the following combinations will be considered. |
|
[''] |
|
['cat'] |
|
['dog'] |
|
['dog','cat'] |
|
|
|
If the instance contains a value not in the 'choice' param, it is ignored. For example, |
|
if choices is ['dog,'cat'] and the instance field is ['dog','cat','cow'], then 'cow' is ignored |
|
then the instance is considered as ['dog','cat']. |
|
|
|
Args: |
|
sample_size - number of samples to extract |
|
choices - name of input field that contains the list of values to balance on |
|
labels - name of output field with labels that must be balanced |
|
|
|
|
|
""" |
|
|
|
choices: str = "choices" |
|
labels: str = "labels" |
|
|
|
def prepare(self): |
|
super().prepare() |
|
self.labels_cache = None |
|
|
|
def examplar_repr(self, examplar): |
|
if "inputs" not in examplar: |
|
raise ValueError(f"'inputs' field is missing from '{examplar}'.") |
|
inputs = examplar["inputs"] |
|
if self.choices not in inputs: |
|
raise ValueError(f"'{self.choices}' field is missing from '{inputs}'.") |
|
choices = inputs[self.choices] |
|
if not isinstance(choices, list): |
|
raise ValueError( |
|
f"Unexpected input choices value '{choices}'. Expected a list." |
|
) |
|
|
|
if "outputs" not in examplar: |
|
raise ValueError(f"'outputs' field is missing from '{examplar}'.") |
|
outputs = examplar["outputs"] |
|
if self.labels not in outputs: |
|
raise ValueError(f"'{self.labels}' field is missing from '{outputs}'.") |
|
|
|
examplar_outputs = examplar["outputs"][self.labels] |
|
if not isinstance(examplar_outputs, list): |
|
raise ValueError( |
|
f"Unexpected examplar_outputs value '{examplar_outputs}'. Expected a list." |
|
) |
|
|
|
return str([choice for choice in choices if choice in examplar_outputs]) |
|
|
|
def divide_by_repr(self, examplars_pool): |
|
labels = {} |
|
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_cache is None: |
|
self.labels_cache = self.divide_by_repr(instances_pool) |
|
all_labels = list(self.labels_cache.keys()) |
|
self.random_generator.shuffle(all_labels) |
|
from collections import Counter |
|
|
|
if self.sample_size > len(instances_pool): |
|
raise ValueError( |
|
f"Request sample size {self.sample_size} is greater than number of instances {len(instances_pool)}" |
|
) |
|
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_cache[label]) - allocations[label] > 0: |
|
allocations[label] += 1 |
|
total_allocated += 1 |
|
else: |
|
break |
|
|
|
result = [] |
|
for label, allocation in allocations.items(): |
|
sample = self.random_generator.sample(self.labels_cache[label], allocation) |
|
result.extend(sample) |
|
|
|
self.random_generator.shuffle(result) |
|
return result |
|
|
|
|
|
class SpreadSplit(InstanceOperatorWithMultiStreamAccess): |
|
source_stream: str = None |
|
target_field: str = None |
|
sampler: Sampler = None |
|
|
|
def prepare(self): |
|
self.local_cache = None |
|
self.sampler.prepare() |
|
|
|
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]: |
|
try: |
|
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 |
|
except Exception as e: |
|
raise Exception( |
|
f"Unable to fetch instances from '{self.source_stream}' to '{self.target_field}'" |
|
) from e |
|
|
