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Tasks

Module for task-related functions and classes.

base_task

Base module for tasks.

BaseTask

Bases: ABC

Abstract base class for tasks in the promptolution library.

This class defines the interface that all concrete task implementations should follow.

Methods:

Name Description
evaluate

An abstract method that should be implemented by subclasses to evaluate prompts using a given predictor.
Source code in promptolution/tasks/base_task.py 
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class BaseTask(ABC):
    """Abstract base class for tasks in the promptolution library.

    This class defines the interface that all concrete task implementations should follow.

    Methods:
        evaluate: An abstract method that should be implemented by subclasses
                  to evaluate prompts using a given predictor.
    """

    def __init__(self, config: "ExperimentConfig" = None):
        """Initialize the BaseTask."""
        if config is not None:
            config.apply_to(self)

    @abstractmethod
    def evaluate(self, prompts: List[str], predictor, system_prompts: List[str] = None) -> np.ndarray:
        """Abstract method to evaluate prompts using a given predictor.

        Args:
            prompts (List[str]): List of prompts to evaluate.
            predictor: The predictor to use for evaluation.
            system_prompts (List[str]): List of system prompts to evaluate.

        Returns:
            np.ndarray: Array of evaluation scores for each prompt.

        Raises:
            NotImplementedError: If not implemented by a subclass.
        """
        raise NotImplementedError

__init__(config=None)

Initialize the BaseTask.

Source code in promptolution/tasks/base_task.py 
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def __init__(self, config: "ExperimentConfig" = None):
    """Initialize the BaseTask."""
    if config is not None:
        config.apply_to(self)

evaluate(prompts, predictor, system_prompts=None) abstractmethod

Abstract method to evaluate prompts using a given predictor.

Parameters:

Name Type Description Default
prompts List[str]

List of prompts to evaluate.

 required
predictor

The predictor to use for evaluation.

 required
system_prompts List[str]

List of system prompts to evaluate.

 None

Returns:

Type Description
ndarray

np.ndarray: Array of evaluation scores for each prompt.

Raises:

Type Description
NotImplementedError

If not implemented by a subclass.
Source code in promptolution/tasks/base_task.py 
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@abstractmethod
def evaluate(self, prompts: List[str], predictor, system_prompts: List[str] = None) -> np.ndarray:
    """Abstract method to evaluate prompts using a given predictor.

    Args:
        prompts (List[str]): List of prompts to evaluate.
        predictor: The predictor to use for evaluation.
        system_prompts (List[str]): List of system prompts to evaluate.

    Returns:
        np.ndarray: Array of evaluation scores for each prompt.

    Raises:
        NotImplementedError: If not implemented by a subclass.
    """
    raise NotImplementedError

classification_tasks

Module for classification tasks.

ClassificationTask

Bases: BaseTask

A class representing a classification task in the promptolution library.

This class handles the loading and management of classification datasets, as well as the evaluation of predictors on these datasets.

Source code in promptolution/tasks/classification_tasks.py 
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class ClassificationTask(BaseTask):
    """A class representing a classification task in the promptolution library.

    This class handles the loading and management of classification datasets,
    as well as the evaluation of predictors on these datasets.
    """

    def __init__(
        self,
        df: pd.DataFrame,
        description: str = None,
        x_column: str = "x",
        y_column: str = "y",
        n_subsamples: int = 30,
        eval_strategy: Literal["full", "subsample", "sequential_block", "random_block"] = "full",
        seed: int = 42,
        metric: Callable = accuracy_score,
        config: "ExperimentConfig" = None,
    ):
        """Initialize the ClassificationTask from a pandas DataFrame.

        Args:
            df (pd.DataFrame): Input DataFrame containing the data
            description (str): Description of the task
            x_column (str, optional): Name of the column containing input texts. Defaults to "x".
            y_column (str, optional): Name of the column containing labels. Defaults to "y".
            n_subsamples (int, optional): Number of subsamples to use. No subsampling if None. Defaults to None.
            eval_strategy (str, optional): Subsampling strategy to use. Options:
                - "full": Uses the entire dataset for evaluation.
                - "evaluated": Uses only previously evaluated datapoints from the cache.
                - "subsample": Randomly selects n_subsamples datapoints without replacement.
                - "sequential_block": Uses a block of block_size consecutive datapoints, advancing through blocks sequentially.
                - "random_block": Randomly selects a block of block_size consecutive datapoints.
                Defaults to "full".
            seed (int, optional): Random seed for reproducibility. Defaults to 42.
            metric (Callable, optional): Metric to use for evaluation. Defaults to accuracy_score.
            config (ExperimentConfig, optional): Configuration for the task, overriding defaults.
        """
        self.description = description
        self.metric = metric

        self.x_column = x_column
        self.y_column = y_column
        self.eval_strategy = eval_strategy
        self.n_subsamples = n_subsamples
        super().__init__(config)

        self.xs = df[self.x_column].values
        self.ys = df[self.y_column].str.lower().values
        self.classes = np.unique(self.ys)

        self.block_idx = 0
        self.n_blocks = len(self.xs) // self.n_subsamples
        self.rng = np.random.default_rng(seed)

        self.eval_cache = {}  # (prompt, x, y): scores per datapoint
        self.seq_cache = {}  # (prompt, x, y): generating sequence per datapoint

    def subsample(
        self, eval_strategy: Literal["full", "subsample", "sequential_block", "random_block"] = None
    ) -> Tuple[np.ndarray, np.ndarray]:
        """Subsample the dataset based on the specified parameters.

        Args:
            strategy (str, optional): Subsampling strategy to use instead of self.subsample_strategy. Defaults to None.

        Returns:
            Tuple[np.ndarray, np.ndarray]: Subsampled input data and labels.
        """
        if eval_strategy is None:
            eval_strategy = self.eval_strategy

        if eval_strategy in ["full", "evaluated"]:
            return self.xs, self.ys

        elif eval_strategy == "subsample":
            indices = self.rng.choice(len(self.xs), self.n_subsamples, replace=False)
            return self.xs[indices], self.ys[indices]

        elif eval_strategy == "random_block":
            block_id = self.rng.integers(0, len(self.xs) // self.n_subsamples)
            indices = np.arange(block_id * self.n_subsamples, (block_id + 1) * self.n_subsamples)
            return self.xs[indices], self.ys[indices]

        elif eval_strategy == "sequential_block":
            indices = np.arange(self.block_idx * self.n_subsamples, (self.block_idx + 1) * self.n_subsamples)
            return self.xs[indices], self.ys[indices]

        else:
            raise ValueError(f"Unknown subsampling strategy: '{eval_strategy}")

    def _prepare_batch(
        self, prompts: List[str], xs: np.ndarray, ys: np.ndarray, eval_strategy: str
    ) -> List[Tuple[str, str, str]]:
        """Generates (prompt, x, y) keys that require prediction.

        If strategy is "evaluated", returns an empty list.
        Otherwise, returns keys not found in eval_cache.
        """
        if eval_strategy == "evaluated":
            return []

        keys_to_predict = []
        for prompt in prompts:
            for x, y in zip(xs, ys):
                cache_key = (prompt, x, y)
                if cache_key not in self.eval_cache:
                    keys_to_predict.append(cache_key)
        return keys_to_predict

    def _collect_results_from_cache(
        self,
        prompts: List[str],
        xs: np.ndarray,
        ys: np.ndarray,
        return_agg_scores: bool,
        return_seq: bool,
    ) -> Union[np.ndarray, Tuple[np.ndarray, Union[List[Any], np.ndarray]]]:
        """Collects all results for the current batch from the cache and formats them."""
        scores = []
        seqs = []

        for prompt in prompts:
            cache_keys = [(prompt, x, y) for x, y in zip(xs, ys)]
            scores += [[self.eval_cache.get(key, np.nan) for key in cache_keys]]
            seqs += [[self.seq_cache.get(key) for key in cache_keys]]
        if return_agg_scores:
            scores = [np.nanmean(s) for s in scores]
        scores = np.array(scores)
        seqs = np.array(seqs)

        return scores if not return_seq else (scores, seqs)

    def evaluate(
        self,
        prompts: Union[str, List[str]],
        predictor: "BasePredictor",
        system_prompts: List[str] = None,
        return_agg_scores: bool = True,
        return_seq: bool = False,
        eval_strategy: str = None,
    ) -> Union[np.ndarray, Tuple[np.ndarray, Union[List[Any], np.ndarray]]]:
        """Evaluate a set of prompts using a given predictor.

        This method orchestrates subsampling, prediction, caching, and result collection.
        """
        prompts = [prompts] if isinstance(prompts, str) else prompts
        eval_strategy = eval_strategy or self.eval_strategy

        xs, ys = self.subsample(eval_strategy=eval_strategy)
        batches = self._prepare_batch(prompts, xs, ys, eval_strategy)
        prompts_to_evaluate, xs_to_evaluate, ys_to_evaluate = zip(*batches) if batches else ([], [], [])

        preds = predictor.predict(
            prompts=prompts_to_evaluate,
            xs=xs_to_evaluate,
            system_prompts=system_prompts,
            return_seq=return_seq,
        )

        if return_seq:
            preds, seqs = preds

        for i, cache_key in enumerate(batches):
            y_pred, y_true = preds[i], ys_to_evaluate[i]
            if return_seq:
                self.seq_cache[cache_key] = seqs[i]
            self.eval_cache[cache_key] = self.metric([y_pred], [y_true])

        return self._collect_results_from_cache(
            prompts,
            xs,
            ys,
            return_agg_scores,
            return_seq,
        )

    def pop_datapoints(self, n: int = None, frac: float = None) -> pd.DataFrame:
        """Pop a number of datapoints from the dataset.

        Args:
            n (int, optional): Number of datapoints to pop. Defaults to None.
            frac (float, optional): Fraction of datapoints to pop. Defaults to None.

        Returns:
            pd.DataFrame: DataFrame containing the popped datapoints.
        """
        assert n is None or frac is None, "Only one of n or frac can be specified."
        if n is not None:
            indices = self.rng.choice(len(self.xs), n, replace=False)
        elif frac is not None:
            indices = self.rng.choice(len(self.xs), int(len(self.xs) * frac), replace=False)
        else:
            raise ValueError("Either n or frac must be specified.")

        xs = self.xs[indices]
        ys = self.ys[indices]
        df = pd.DataFrame({self.x_column: xs, self.y_column: ys})

        self.xs = np.delete(self.xs, indices)
        self.ys = np.delete(self.ys, indices)

        self.n_blocks = len(self.xs) // self.n_subsamples
        self.block_idx = min(self.block_idx, self.n_blocks - 1)

        return df

    def increment_block_idx(self) -> None:
        """Increment the block index for subsampling.

        Raises:
            ValueError: If the eval_strategy does not contain "block".
        """
        if "block" not in self.eval_strategy:
            raise ValueError("Block increment is only valid for block subsampling.")
        self.block_idx += 1
        if self.block_idx >= self.n_blocks:
            self.block_idx = 0

    def reset_block_idx(self) -> None:
        """Reset the block index for subsampling.

        Raises:
            ValueError: If the eval_strategy does not contain "block".
        """
        if "block" not in self.eval_strategy:
            raise ValueError("Block reset is only valid for block subsampling.")
        self.block_idx = 0

__init__(df, description=None, x_column='x', y_column='y', n_subsamples=30, eval_strategy='full', seed=42, metric=accuracy_score, config=None)

Initialize the ClassificationTask from a pandas DataFrame.

Parameters:

Name Type Description Default
df DataFrame

Input DataFrame containing the data

 required
description str

Description of the task

 None
x_column str

Name of the column containing input texts. Defaults to "x".

 'x'
y_column str

Name of the column containing labels. Defaults to "y".

 'y'
n_subsamples int

Number of subsamples to use. No subsampling if None. Defaults to None.

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eval_strategy str

Subsampling strategy to use. Options: - "full": Uses the entire dataset for evaluation. - "evaluated": Uses only previously evaluated datapoints from the cache. - "subsample": Randomly selects n_subsamples datapoints without replacement. - "sequential_block": Uses a block of block_size consecutive datapoints, advancing through blocks sequentially. - "random_block": Randomly selects a block of block_size consecutive datapoints. Defaults to "full".

 'full'
seed int

Random seed for reproducibility. Defaults to 42.

 42
metric Callable

Metric to use for evaluation. Defaults to accuracy_score.

 accuracy_score
config ExperimentConfig

Configuration for the task, overriding defaults.

 None
Source code in promptolution/tasks/classification_tasks.py 
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def __init__(
    self,
    df: pd.DataFrame,
    description: str = None,
    x_column: str = "x",
    y_column: str = "y",
    n_subsamples: int = 30,
    eval_strategy: Literal["full", "subsample", "sequential_block", "random_block"] = "full",
    seed: int = 42,
    metric: Callable = accuracy_score,
    config: "ExperimentConfig" = None,
):
    """Initialize the ClassificationTask from a pandas DataFrame.

    Args:
        df (pd.DataFrame): Input DataFrame containing the data
        description (str): Description of the task
        x_column (str, optional): Name of the column containing input texts. Defaults to "x".
        y_column (str, optional): Name of the column containing labels. Defaults to "y".
        n_subsamples (int, optional): Number of subsamples to use. No subsampling if None. Defaults to None.
        eval_strategy (str, optional): Subsampling strategy to use. Options:
            - "full": Uses the entire dataset for evaluation.
            - "evaluated": Uses only previously evaluated datapoints from the cache.
            - "subsample": Randomly selects n_subsamples datapoints without replacement.
            - "sequential_block": Uses a block of block_size consecutive datapoints, advancing through blocks sequentially.
            - "random_block": Randomly selects a block of block_size consecutive datapoints.
            Defaults to "full".
        seed (int, optional): Random seed for reproducibility. Defaults to 42.
        metric (Callable, optional): Metric to use for evaluation. Defaults to accuracy_score.
        config (ExperimentConfig, optional): Configuration for the task, overriding defaults.
    """
    self.description = description
    self.metric = metric

    self.x_column = x_column
    self.y_column = y_column
    self.eval_strategy = eval_strategy
    self.n_subsamples = n_subsamples
    super().__init__(config)

    self.xs = df[self.x_column].values
    self.ys = df[self.y_column].str.lower().values
    self.classes = np.unique(self.ys)

    self.block_idx = 0
    self.n_blocks = len(self.xs) // self.n_subsamples
    self.rng = np.random.default_rng(seed)

    self.eval_cache = {}  # (prompt, x, y): scores per datapoint
    self.seq_cache = {}  # (prompt, x, y): generating sequence per datapoint

evaluate(prompts, predictor, system_prompts=None, return_agg_scores=True, return_seq=False, eval_strategy=None)

Evaluate a set of prompts using a given predictor.

This method orchestrates subsampling, prediction, caching, and result collection.

Source code in promptolution/tasks/classification_tasks.py 
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def evaluate(
    self,
    prompts: Union[str, List[str]],
    predictor: "BasePredictor",
    system_prompts: List[str] = None,
    return_agg_scores: bool = True,
    return_seq: bool = False,
    eval_strategy: str = None,
) -> Union[np.ndarray, Tuple[np.ndarray, Union[List[Any], np.ndarray]]]:
    """Evaluate a set of prompts using a given predictor.

    This method orchestrates subsampling, prediction, caching, and result collection.
    """
    prompts = [prompts] if isinstance(prompts, str) else prompts
    eval_strategy = eval_strategy or self.eval_strategy

    xs, ys = self.subsample(eval_strategy=eval_strategy)
    batches = self._prepare_batch(prompts, xs, ys, eval_strategy)
    prompts_to_evaluate, xs_to_evaluate, ys_to_evaluate = zip(*batches) if batches else ([], [], [])

    preds = predictor.predict(
        prompts=prompts_to_evaluate,
        xs=xs_to_evaluate,
        system_prompts=system_prompts,
        return_seq=return_seq,
    )

    if return_seq:
        preds, seqs = preds

    for i, cache_key in enumerate(batches):
        y_pred, y_true = preds[i], ys_to_evaluate[i]
        if return_seq:
            self.seq_cache[cache_key] = seqs[i]
        self.eval_cache[cache_key] = self.metric([y_pred], [y_true])

    return self._collect_results_from_cache(
        prompts,
        xs,
        ys,
        return_agg_scores,
        return_seq,
    )

increment_block_idx()

Increment the block index for subsampling.

Raises:

Type Description
ValueError

If the eval_strategy does not contain "block".
Source code in promptolution/tasks/classification_tasks.py 
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def increment_block_idx(self) -> None:
    """Increment the block index for subsampling.

    Raises:
        ValueError: If the eval_strategy does not contain "block".
    """
    if "block" not in self.eval_strategy:
        raise ValueError("Block increment is only valid for block subsampling.")
    self.block_idx += 1
    if self.block_idx >= self.n_blocks:
        self.block_idx = 0

pop_datapoints(n=None, frac=None)

Pop a number of datapoints from the dataset.

Parameters:

Name Type Description Default
n int

Number of datapoints to pop. Defaults to None.

 None
frac float

Fraction of datapoints to pop. Defaults to None.

 None

Returns:

Type Description
DataFrame

pd.DataFrame: DataFrame containing the popped datapoints.
Source code in promptolution/tasks/classification_tasks.py 
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def pop_datapoints(self, n: int = None, frac: float = None) -> pd.DataFrame:
    """Pop a number of datapoints from the dataset.

    Args:
        n (int, optional): Number of datapoints to pop. Defaults to None.
        frac (float, optional): Fraction of datapoints to pop. Defaults to None.

    Returns:
        pd.DataFrame: DataFrame containing the popped datapoints.
    """
    assert n is None or frac is None, "Only one of n or frac can be specified."
    if n is not None:
        indices = self.rng.choice(len(self.xs), n, replace=False)
    elif frac is not None:
        indices = self.rng.choice(len(self.xs), int(len(self.xs) * frac), replace=False)
    else:
        raise ValueError("Either n or frac must be specified.")

    xs = self.xs[indices]
    ys = self.ys[indices]
    df = pd.DataFrame({self.x_column: xs, self.y_column: ys})

    self.xs = np.delete(self.xs, indices)
    self.ys = np.delete(self.ys, indices)

    self.n_blocks = len(self.xs) // self.n_subsamples
    self.block_idx = min(self.block_idx, self.n_blocks - 1)

    return df

reset_block_idx()

Reset the block index for subsampling.

Raises:

Type Description
ValueError

If the eval_strategy does not contain "block".
Source code in promptolution/tasks/classification_tasks.py 
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def reset_block_idx(self) -> None:
    """Reset the block index for subsampling.

    Raises:
        ValueError: If the eval_strategy does not contain "block".
    """
    if "block" not in self.eval_strategy:
        raise ValueError("Block reset is only valid for block subsampling.")
    self.block_idx = 0

subsample(eval_strategy=None)

Subsample the dataset based on the specified parameters.

Parameters:

Name Type Description Default
strategy str

Subsampling strategy to use instead of self.subsample_strategy. Defaults to None.

 required

Returns:

Type Description
Tuple[ndarray, ndarray]

Tuple[np.ndarray, np.ndarray]: Subsampled input data and labels.
Source code in promptolution/tasks/classification_tasks.py 
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def subsample(
    self, eval_strategy: Literal["full", "subsample", "sequential_block", "random_block"] = None
) -> Tuple[np.ndarray, np.ndarray]:
    """Subsample the dataset based on the specified parameters.

    Args:
        strategy (str, optional): Subsampling strategy to use instead of self.subsample_strategy. Defaults to None.

    Returns:
        Tuple[np.ndarray, np.ndarray]: Subsampled input data and labels.
    """
    if eval_strategy is None:
        eval_strategy = self.eval_strategy

    if eval_strategy in ["full", "evaluated"]:
        return self.xs, self.ys

    elif eval_strategy == "subsample":
        indices = self.rng.choice(len(self.xs), self.n_subsamples, replace=False)
        return self.xs[indices], self.ys[indices]

    elif eval_strategy == "random_block":
        block_id = self.rng.integers(0, len(self.xs) // self.n_subsamples)
        indices = np.arange(block_id * self.n_subsamples, (block_id + 1) * self.n_subsamples)
        return self.xs[indices], self.ys[indices]

    elif eval_strategy == "sequential_block":
        indices = np.arange(self.block_idx * self.n_subsamples, (self.block_idx + 1) * self.n_subsamples)
        return self.xs[indices], self.ys[indices]

    else:
        raise ValueError(f"Unknown subsampling strategy: '{eval_strategy}")