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Sampling and Fewshot

FewShot

Bases: Sampler

Class to collect fewshot examples from the same or another dataset.

Source code in ragfit/processing/global_steps/sampling.py 
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class FewShot(Sampler):
    """
    Class to collect fewshot examples from the same or another dataset.
    """

    def __init__(self, k, output_key="fewshot", input_dataset=None, **kwargs):
        """
        Args:
            k (int): Number of examples to collect.
            output_key (str): output key to use for the collected examples.
            input_dataset (str): Name of the dataset to take the examples from. To use the same dataset, use None.
        """
        super().__init__(
            k=k,
            output_key=output_key,
            input_key=None,
            input_dataset=input_dataset,
            **kwargs,
        )

__init__(k, output_key='fewshot', input_dataset=None, **kwargs)

Parameters:

  • k (int) –

    Number of examples to collect.

  • output_key (str, default: 'fewshot' ) –

    output key to use for the collected examples.

  • input_dataset (str, default: None ) –

    Name of the dataset to take the examples from. To use the same dataset, use None.

Source code in ragfit/processing/global_steps/sampling.py 
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def __init__(self, k, output_key="fewshot", input_dataset=None, **kwargs):
    """
    Args:
        k (int): Number of examples to collect.
        output_key (str): output key to use for the collected examples.
        input_dataset (str): Name of the dataset to take the examples from. To use the same dataset, use None.
    """
    super().__init__(
        k=k,
        output_key=output_key,
        input_key=None,
        input_dataset=input_dataset,
        **kwargs,
    )

Sampler

Bases: GlobalStep

Class to augment a dataset with sampled examples from the same or another dataset.

Full examples can be collected, as well as an individual example keys like query, documents, etc.

The step can be used to collect negative documents, negative queries and collect fewshot examples. For fewshot examples, use the dedicated FewShot class.

Source code in ragfit/processing/global_steps/sampling.py 
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class Sampler(GlobalStep):
    """
    Class to augment a dataset with sampled examples from the same or another dataset.

    Full examples can be collected, as well as an individual example keys like `query`, `documents`, etc.

    The step can be used to collect negative documents, negative queries and collect fewshot examples.
    For fewshot examples, use the dedicated `FewShot` class.
    """

    def __init__(
        self, k, input_key=None, output_key="fewshot", input_dataset=None, **kwargs
    ):
        """
        Args:
            k (int): Number of examples to collect.
            input_key (str): a key to collect from the collected examples, or None to take entire example.
            output_key (str): output key to use for the examples.
            input_dataset (str): Name of the dataset to take the examples from. To use the same dataset, use None.
        """
        super().__init__(**kwargs)
        self.k = k
        self.input_key = input_key
        self.input_dataset = input_dataset
        self.output_key = output_key

    def process(self, dataset_name, datasets, **kwargs):
        input_dataset = datasets[self.input_dataset or dataset_name]

        def find_examples(item, idx):
            ids = []
            while len(ids) < self.k:
                rand_idx = random.randint(0, len(input_dataset) - 1)
                if self.input_dataset is None and rand_idx == idx:
                    continue
                if rand_idx in ids:
                    continue
                ids.append(rand_idx)
            examples = [
                (
                    input_dataset[id_]
                    if self.input_key is None
                    else input_dataset[id_][self.input_key]
                )
                for id_ in ids
            ]
            item[self.output_key] = examples if self.k > 1 else examples[0]
            return item

        datasets[dataset_name] = datasets[dataset_name].map(
            lambda item, index: find_examples(item, index),
            with_indices=True,
            load_from_cache_file=False,
        )

__init__(k, input_key=None, output_key='fewshot', input_dataset=None, **kwargs)

Parameters:

  • k (int) –

    Number of examples to collect.

  • input_key (str, default: None ) –

    a key to collect from the collected examples, or None to take entire example.

  • output_key (str, default: 'fewshot' ) –

    output key to use for the examples.

  • input_dataset (str, default: None ) –

    Name of the dataset to take the examples from. To use the same dataset, use None.

Source code in ragfit/processing/global_steps/sampling.py 
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def __init__(
    self, k, input_key=None, output_key="fewshot", input_dataset=None, **kwargs
):
    """
    Args:
        k (int): Number of examples to collect.
        input_key (str): a key to collect from the collected examples, or None to take entire example.
        output_key (str): output key to use for the examples.
        input_dataset (str): Name of the dataset to take the examples from. To use the same dataset, use None.
    """
    super().__init__(**kwargs)
    self.k = k
    self.input_key = input_key
    self.input_dataset = input_dataset
    self.output_key = output_key

ShuffleSelect

Bases: GlobalStep

Class to optionally shuffle and select a subset of the dataset.

Based on the shuffle and select methods of HF Dataset.

Source code in ragfit/processing/global_steps/sampling.py 
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class ShuffleSelect(GlobalStep):
    """
    Class to optionally shuffle and select a subset of the dataset.

    Based on the `shuffle` and `select` methods of HF Dataset.
    """

    def __init__(self, shuffle=None, limit=None, **kwargs):
        """
        Args:
            shuffle (int): Seed for shuffling the dataset.
            limit (int): Number of items to select from the dataset.
        """
        super().__init__(**kwargs)
        self.shuffle = shuffle
        self.limit = limit

    def process_all(self, dataset, datasets, **kwargs):
        if self.shuffle:
            dataset = dataset.shuffle(seed=self.shuffle)
        if self.limit:
            dataset = dataset.select(range(min(len(dataset), self.limit)))
        return dataset

__init__(shuffle=None, limit=None, **kwargs)

Parameters:

  • shuffle (int, default: None ) –

    Seed for shuffling the dataset.

  • limit (int, default: None ) –

    Number of items to select from the dataset.

Source code in ragfit/processing/global_steps/sampling.py 
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def __init__(self, shuffle=None, limit=None, **kwargs):
    """
    Args:
        shuffle (int): Seed for shuffling the dataset.
        limit (int): Number of items to select from the dataset.
    """
    super().__init__(**kwargs)
    self.shuffle = shuffle
    self.limit = limit