inspect_ai.scorer

Scorers

match

Scorer which matches text or a number.

Source

@scorer(metrics=[accuracy(), stderr()])
def match(
    location: Literal["begin", "end", "any", "exact"] = "end",
    *,
    ignore_case: bool = True,
    numeric: bool = False,
) -> Scorer

location Literal['begin', 'end', 'any', 'exact']

Location to match at. “any” matches anywhere in the output; “exact” requires the output be exactly equal to the target (module whitespace, etc.)

ignore_case bool

Do case insensitive comparison.

numeric bool

Is this a numeric match? (in this case different punctuation removal rules are used and numbers are normalized before comparison).

includes

Check whether the specified text is included in the model output.

Source

@scorer(metrics=[accuracy(), stderr()])
def includes(ignore_case: bool = True) -> Scorer

ignore_case bool

Use a case insensitive comparison.

pattern

Scorer which extracts the model answer using a regex.

Note that at least one regex group is required to match against the target.

The regex can have a single capture group or multiple groups. In the case of multiple groups, the scorer can be configured to match either one or all of the extracted groups

Source

@scorer(metrics=[accuracy(), stderr()])
def pattern(pattern: str, ignore_case: bool = True, match_all: bool = False) -> Scorer

pattern str

Regular expression for extracting the answer from model output.

ignore_case bool

Ignore case when comparing the extract answer to the targets. (Default: True)

match_all bool

With multiple captures, do all captured values need to match the target? (Default: False)

answer

Scorer for model output that preceded answers with ANSWER:.

Some solvers including multiple_choice solicit answers from the model prefaced with “ANSWER:”. This scorer extracts answers of this form for comparison with the target.

Note that you must specify a type for the answer scorer.

Source

@scorer(metrics=[accuracy(), stderr()])
def answer(pattern: Literal["letter", "word", "line"]) -> Scorer

pattern Literal['letter', 'word', 'line']

Type of answer to extract. “letter” is used with multiple choice and extracts a single letter; “word” will extract the next word (often used for yes/no answers); “line” will take the rest of the line (used for more more complex answers that may have embedded spaces). Note that when using “line” your prompt should instruct the model to answer with a separate line at the end.

choice

Scorer for multiple choice answers, required by the multiple_choice solver.

This assumes that the model was called using a template ordered with letters corresponding to the answers, so something like:

What is the capital of France?

A) Paris
B) Berlin
C) London

The target for the dataset will then have a letter corresponding to the correct answer, e.g. the Target would be "A" for the above question. If multiple choices are correct, the Target can be an array of these letters.

Source

@scorer(metrics=[accuracy(), stderr()])
def choice() -> Scorer

f1

Scorer which produces an F1 score

Computes the F1 score for the answer (which balances recall precision by taking the harmonic mean between recall and precision).

Source

@scorer(metrics=[mean(), stderr()])
def f1(
    answer_fn: Callable[[str], str] | None = None, stop_words: list[str] | None = None
) -> Scorer

answer_fn Callable[[str], str] | None

Custom function to extract the answer from the completion (defaults to using the completion).

stop_words list[str] | None

Stop words to include in answer tokenization.

exact

Scorer which produces an exact match score

Normalizes the text of the answer and target(s) and performs an exact matching comparison of the text. This scorer will return CORRECT when the answer is an exact match to one or more targets.

Source

@scorer(metrics=[mean(), stderr()])
def exact() -> Scorer

model_graded_qa

Score a question/answer task using a model.

Source

@scorer(metrics=[accuracy(), stderr()])
def model_graded_qa(
    template: str | None = None,
    instructions: str | None = None,
    grade_pattern: str | None = None,
    include_history: bool | Callable[[TaskState], str] = False,
    partial_credit: bool = False,
    model: list[str | Model] | str | Model | None = None,
) -> Scorer

template str | None

Template for grading prompt. This template has four variables: - question, criterion, answer, and instructions (which is fed from the instructions parameter). Variables from sample metadata are also available in the template.

instructions str | None

Grading instructions. This should include a prompt for the model to answer (e.g. with with chain of thought reasoning) in a way that matches the specified grade_pattern, for example, the default grade_pattern looks for one of GRADE: C, GRADE: P, or GRADE: I.

grade_pattern str | None

Regex to extract the grade from the model response. Defaults to looking for e.g. GRADE: C The regex should have a single capture group that extracts exactly the letter C, P, I.

include_history bool | Callable[[TaskState], str]

Whether to include the full chat history in the presented question. Defaults to False, which presents only the original sample input. Optionally provide a function to customise how the chat history is presented.

partial_credit bool

Whether to allow for “partial” credit for answers (by default assigned a score of 0.5). Defaults to False. Note that this parameter is only used with the default instructions (as custom instructions provide their own prompts for grades).

model list[str | Model] | str | Model | None

Model or Models to use for grading. If multiple models are passed, a majority vote of their grade will be returned. By default the model being evaluated is used.

model_graded_fact

Score a question/answer task with a fact response using a model.

Source

@scorer(metrics=[accuracy(), stderr()])
def model_graded_fact(
    template: str | None = None,
    instructions: str | None = None,
    grade_pattern: str | None = None,
    include_history: bool | Callable[[TaskState], str] = False,
    partial_credit: bool = False,
    model: list[str | Model] | str | Model | None = None,
) -> Scorer

template str | None

Template for grading prompt. This template uses four variables: question, criterion, answer, and instructions (which is fed from the instructions parameter). Variables from sample metadata are also available in the template.

instructions str | None

Grading instructions. This should include a prompt for the model to answer (e.g. with with chain of thought reasoning) in a way that matches the specified grade_pattern, for example, the default grade_pattern looks for one of GRADE: C, GRADE: P, or GRADE: I).

grade_pattern str | None

Regex to extract the grade from the model response. Defaults to looking for e.g. GRADE: C The regex should have a single capture group that extracts exactly the letter C, P, or I.

include_history bool | Callable[[TaskState], str]

Whether to include the full chat history in the presented question. Defaults to False, which presents only the original sample input. Optionally provide a function to customise how the chat history is presented.

partial_credit bool

Whether to allow for “partial” credit for answers (by default assigned a score of 0.5). Defaults to False. Note that this parameter is only used with the default instructions (as custom instructions provide their own prompts for grades).

model list[str | Model] | str | Model | None

Model or Models to use for grading. If multiple models are passed, a majority vote of their grade will be returned. By default the model being evaluated is used.

multi_scorer

Returns a Scorer that runs multiple Scorers in parallel and aggregates their results into a single Score using the provided reducer function.

Source

def multi_scorer(scorers: list[Scorer], reducer: str | ScoreReducer) -> Scorer

scorers list[Scorer]

a list of Scorers.

reducer str | ScoreReducer

a function which takes in a list of Scores and returns a single Score.

Metrics

accuracy

Compute proportion of total answers which are correct.

Source

@metric
def accuracy(to_float: ValueToFloat = value_to_float()) -> Metric

to_float ValueToFloat

Function for mapping Value to float for computing metrics. The default value_to_float() maps CORRECT (“C”) to 1.0, INCORRECT (“I”) to 0, PARTIAL (“P”) to 0.5, and NOANSWER (“N”) to 0, casts numeric values to float directly, and prints a warning and returns 0 if the Value is a complex object (list or dict).

mean

Compute mean of all scores.

Source

@metric
def mean() -> Metric

std

Calculates the sample standard deviation of a list of scores.

Source

@metric
def std(to_float: ValueToFloat = value_to_float()) -> Metric

to_float ValueToFloat

Function for mapping Value to float for computing metrics. The default value_to_float() maps CORRECT (“C”) to 1.0, INCORRECT (“I”) to 0, PARTIAL (“P”) to 0.5, and NOANSWER (“N”) to 0, casts numeric values to float directly, and prints a warning and returns 0 if the Value is a complex object (list or dict).

stderr

Standard error of the mean using Central Limit Theorem.

Source

@metric
def stderr(
    to_float: ValueToFloat = value_to_float(), cluster: str | None = None
) -> Metric

to_float ValueToFloat

Function for mapping Value to float for computing metrics. The default value_to_float() maps CORRECT (“C”) to 1.0, INCORRECT (“I”) to 0, PARTIAL (“P”) to 0.5, and NOANSWER (“N”) to 0, casts numeric values to float directly, and prints a warning and returns 0 if the Value is a complex object (list or dict).

cluster str | None

The key from the Sample metadata corresponding to a cluster identifier for computing clustered standard errors.

bootstrap_stderr

Standard error of the mean using bootstrap.

Source

@metric
def bootstrap_stderr(
    num_samples: int = 1000, to_float: ValueToFloat = value_to_float()
) -> Metric

num_samples int

Number of bootstrap samples to take.

to_float ValueToFloat

Function for mapping Value to float for computing metrics. The default value_to_float() maps CORRECT (“C”) to 1.0, INCORRECT (“I”) to 0, PARTIAL (“P”) to 0.5, and NOANSWER (“N”) to 0, casts numeric values to float directly, and prints a warning and returns 0 if the Value is a complex object (list or dict).

Reducers

at_least

Score correct if there are at least k score values greater than or equal to the value.

Source

@score_reducer
def at_least(
    k: int, value: float = 1.0, value_to_float: ValueToFloat = value_to_float()
) -> ScoreReducer

k int

Number of score values that must exceed value.

value float

Score value threshold.

value_to_float ValueToFloat

Function to convert score values to float.

pass_at

Probability of at least 1 correct sample given k epochs (https://arxiv.org/pdf/2107.03374).

Source

@score_reducer
def pass_at(
    k: int, value: float = 1.0, value_to_float: ValueToFloat = value_to_float()
) -> ScoreReducer

k int

Epochs to compute probability for.

value float

Score value threshold.

value_to_float ValueToFloat

Function to convert score values to float.

max_score

Take the maximum value from a list of scores.

Source

@score_reducer(name="max")
def max_score(value_to_float: ValueToFloat = value_to_float()) -> ScoreReducer

value_to_float ValueToFloat

Function to convert the value to a float

mean_score

Take the mean of a list of scores.

Source

@score_reducer(name="mean")
def mean_score(value_to_float: ValueToFloat = value_to_float()) -> ScoreReducer

value_to_float ValueToFloat

Function to convert the value to a float

median_score

Take the median value from a list of scores.

Source

@score_reducer(name="median")
def median_score(value_to_float: ValueToFloat = value_to_float()) -> ScoreReducer

value_to_float ValueToFloat

Function to convert the value to a float

mode_score

Take the mode from a list of scores.

Source

@score_reducer(name="mode")
def mode_score() -> ScoreReducer

Types

Scorer

Score model outputs.

Evaluate the passed outputs and targets and return a dictionary with scoring outcomes and context.

Source

class Scorer(Protocol):
    async def __call__(
        self,
        state: TaskState,
        target: Target,
    ) -> Score

state TaskState

Task state

target Target

Ideal target for the output.

Examples

@scorer
def custom_scorer() -> Scorer:
    async def score(state: TaskState, target: Target) -> Score:
        # Compare state / model output with target
        # to yield a score
        return Score(value=...)

    return score

Target

Target for scoring against the current TaskState.

Target is a sequence of one or more strings. Use the text property to access the value as a single string.

Source

class Target(Sequence[str])

Score

Score generated by a scorer.

Source

class Score(BaseModel)

Attributes

value Value

Score value.

answer str | None

Answer extracted from model output (optional)

explanation str | None

Explanation of score (optional).

metadata dict[str, Any] | None

Additional metadata related to the score

text str

Read the score as text.

Methods

as_str

Read the score as a string.

Source

def as_str(self) -> str

as_int

Read the score as an integer.

Source

def as_int(self) -> int

as_float

Read the score as a float.

Source

def as_float(self) -> float

as_bool

Read the score as a boolean.

Source

def as_bool(self) -> bool

as_list

Read the score as a list.

Source

def as_list(self) -> list[str | int | float | bool]

as_dict

Read the score as a dictionary.

Source

def as_dict(self) -> dict[str, str | int | float | bool | None]

Value

Value provided by a score.

Use the methods of Score to easily treat the Value as a simple scalar of various types.

Source

Value = Union[
    str | int | float | bool,
    list[str | int | float | bool],
    dict[str, str | int | float | bool | None],
]

ScoreReducer

Reduce a set of scores to a single score.

Source

class ScoreReducer(Protocol):
    def __call__(self, scores: list[Score]) -> Score

scores list[Score]

List of scores.

Metric

Metric protocol.

The Metric signature changed in release v0.3.64. Both the previous and new signatures are supported – you should use MetricProtocol for new code as the depreacated signature will eventually be removed.

Source

Metric = MetricProtocol | MetricDeprecated

MetricProtocol

Compute a metric on a list of scores.

Source

class MetricProtocol(Protocol):
    def __call__(self, scores: list[SampleScore]) -> Value

scores list[SampleScore]

List of scores.

Examples

@metric
def mean() -> Metric:
    def metric(scores: list[SampleScore]) -> Value:
        return np.mean([score.score.as_float() for score in scores]).item()
    return metric

SampleScore

Score for a Sample.

Source

class SampleScore(BaseModel)

Attributes

score Score

A score

sample_id str | int | None

A sample id

sample_metadata dict[str, Any] | None

Metadata from the sample

scorer str | None

Registry name of scorer that created this score.

Decorators

scorer

Decorator for registering scorers.

Source

def scorer(
    metrics: list[Metric | dict[str, list[Metric]]] | dict[str, list[Metric]],
    name: str | None = None,
    **metadata: Any,
) -> Callable[[Callable[P, Scorer]], Callable[P, Scorer]]

metrics list[Metric | dict[str, list[Metric]]] | dict[str, list[Metric]]

One or more metrics to calculate over the scores.

name str | None

Optional name for scorer. If the decorator has no name argument then the name of the underlying ScorerType object will be used to automatically assign a name.

**metadata Any

Additional values to serialize in metadata.

Examples

@scorer
def custom_scorer() -> Scorer:
    async def score(state: TaskState, target: Target) -> Score:
        # Compare state / model output with target
        # to yield a score
        return Score(value=...)

    return score

metric

Decorator for registering metrics.

Source

def metric(
    name: str | Callable[P, Metric],
) -> Callable[[Callable[P, Metric]], Callable[P, Metric]] | Callable[P, Metric]

name str | Callable[P, Metric]

Optional name for metric. If the decorator has no name argument then the name of the underlying MetricType will be used to automatically assign a name.

Examples

```python @metric def mean() -> Metric: def metric(scores: list[SampleScore]) -> Value: return np.mean([score.score.as_float() for score in scores]).item() return metric

score_reducer

Decorator for registering Score Reducers.

Source

def score_reducer(
    func: ScoreReducerType | None = None, *, name: str | None = None
) -> Callable[[ScoreReducerType], ScoreReducerType] | ScoreReducerType

func ScoreReducerType | None

Function returning ScoreReducer targeted by plain task decorator without attributes (e.g. @score_reducer)

name str | None

Optional name for reducer. If the decorator has no name argument then the name of the function will be used to automatically assign a name.