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Metrics

Evaluation modules.

MRR #

Bases: BaseRetrievalMetric

MRR (Mean Reciprocal Rank) metric with two calculation options.

  • The default method calculates the reciprocal rank of the first relevant retrieved document.
  • The more granular method sums the reciprocal ranks of all relevant retrieved documents and divides by the count of relevant documents.

Attributes:

Name Type Description
use_granular_mrr bool

Determines whether to use the granular method for calculation.
metric_name str

The name of the metric.
Source code in llama-index-core/llama_index/core/evaluation/retrieval/metrics.py 
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class MRR(BaseRetrievalMetric):
    """MRR (Mean Reciprocal Rank) metric with two calculation options.

    - The default method calculates the reciprocal rank of the first relevant retrieved document.
    - The more granular method sums the reciprocal ranks of all relevant retrieved documents and divides by the count of relevant documents.

    Attributes:
        use_granular_mrr (bool): Determines whether to use the granular method for calculation.
        metric_name (str): The name of the metric.
    """

    metric_name: str = "mrr"
    use_granular_mrr: bool = False

    def compute(
        self,
        query: Optional[str] = None,
        expected_ids: Optional[List[str]] = None,
        retrieved_ids: Optional[List[str]] = None,
        expected_texts: Optional[List[str]] = None,
        retrieved_texts: Optional[List[str]] = None,
    ) -> RetrievalMetricResult:
        """Compute MRR based on the provided inputs and selected method.

        Parameters:
            query (Optional[str]): The query string (not used in the current implementation).
            expected_ids (Optional[List[str]]): Expected document IDs.
            retrieved_ids (Optional[List[str]]): Retrieved document IDs.
            expected_texts (Optional[List[str]]): Expected texts (not used in the current implementation).
            retrieved_texts (Optional[List[str]]): Retrieved texts (not used in the current implementation).

        Raises:
            ValueError: If the necessary IDs are not provided.

        Returns:
            RetrievalMetricResult: The result with the computed MRR score.
        """
        # Checking for the required arguments
        if (
            retrieved_ids is None
            or expected_ids is None
            or not retrieved_ids
            or not expected_ids
        ):
            raise ValueError("Retrieved ids and expected ids must be provided")

        if self.use_granular_mrr:
            # Granular MRR calculation: All relevant retrieved docs have their reciprocal ranks summed and averaged
            expected_set = set(expected_ids)
            reciprocal_rank_sum = 0.0
            relevant_docs_count = 0
            for index, doc_id in enumerate(retrieved_ids):
                if doc_id in expected_set:
                    relevant_docs_count += 1
                    reciprocal_rank_sum += 1.0 / (index + 1)
            mrr_score = (
                reciprocal_rank_sum / relevant_docs_count
                if relevant_docs_count > 0
                else 0.0
            )
        else:
            # Default MRR calculation: Reciprocal rank of the first relevant document retrieved
            for i, id in enumerate(retrieved_ids):
                if id in expected_ids:
                    return RetrievalMetricResult(score=1.0 / (i + 1))
            mrr_score = 0.0

        return RetrievalMetricResult(score=mrr_score)

compute #

compute(query: Optional[str] = None, expected_ids: Optional[List[str]] = None, retrieved_ids: Optional[List[str]] = None, expected_texts: Optional[List[str]] = None, retrieved_texts: Optional[List[str]] = None) -> RetrievalMetricResult

Compute MRR based on the provided inputs and selected method.

Parameters:

Name Type Description Default
query Optional[str]

The query string (not used in the current implementation).

 None
expected_ids Optional[List[str]]

Expected document IDs.

 None
retrieved_ids Optional[List[str]]

Retrieved document IDs.

 None
expected_texts Optional[List[str]]

Expected texts (not used in the current implementation).

 None
retrieved_texts Optional[List[str]]

Retrieved texts (not used in the current implementation).

 None

Raises:

Type Description
ValueError

If the necessary IDs are not provided.

Returns:

Name Type Description
RetrievalMetricResult RetrievalMetricResult

The result with the computed MRR score.
Source code in llama-index-core/llama_index/core/evaluation/retrieval/metrics.py 
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def compute(
    self,
    query: Optional[str] = None,
    expected_ids: Optional[List[str]] = None,
    retrieved_ids: Optional[List[str]] = None,
    expected_texts: Optional[List[str]] = None,
    retrieved_texts: Optional[List[str]] = None,
) -> RetrievalMetricResult:
    """Compute MRR based on the provided inputs and selected method.

    Parameters:
        query (Optional[str]): The query string (not used in the current implementation).
        expected_ids (Optional[List[str]]): Expected document IDs.
        retrieved_ids (Optional[List[str]]): Retrieved document IDs.
        expected_texts (Optional[List[str]]): Expected texts (not used in the current implementation).
        retrieved_texts (Optional[List[str]]): Retrieved texts (not used in the current implementation).

    Raises:
        ValueError: If the necessary IDs are not provided.

    Returns:
        RetrievalMetricResult: The result with the computed MRR score.
    """
    # Checking for the required arguments
    if (
        retrieved_ids is None
        or expected_ids is None
        or not retrieved_ids
        or not expected_ids
    ):
        raise ValueError("Retrieved ids and expected ids must be provided")

    if self.use_granular_mrr:
        # Granular MRR calculation: All relevant retrieved docs have their reciprocal ranks summed and averaged
        expected_set = set(expected_ids)
        reciprocal_rank_sum = 0.0
        relevant_docs_count = 0
        for index, doc_id in enumerate(retrieved_ids):
            if doc_id in expected_set:
                relevant_docs_count += 1
                reciprocal_rank_sum += 1.0 / (index + 1)
        mrr_score = (
            reciprocal_rank_sum / relevant_docs_count
            if relevant_docs_count > 0
            else 0.0
        )
    else:
        # Default MRR calculation: Reciprocal rank of the first relevant document retrieved
        for i, id in enumerate(retrieved_ids):
            if id in expected_ids:
                return RetrievalMetricResult(score=1.0 / (i + 1))
        mrr_score = 0.0

    return RetrievalMetricResult(score=mrr_score)

HitRate #

Bases: BaseRetrievalMetric

Hit rate metric: Compute hit rate with two calculation options.

  • The default method checks for a single match between any of the retrieved docs and expected docs.
  • The more granular method checks for all potential matches between retrieved docs and expected docs.

Attributes:

Name Type Description
use_granular_hit_rate bool

Determines whether to use the granular method for calculation.
metric_name str

The name of the metric.
Source code in llama-index-core/llama_index/core/evaluation/retrieval/metrics.py 
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class HitRate(BaseRetrievalMetric):
    """Hit rate metric: Compute hit rate with two calculation options.

    - The default method checks for a single match between any of the retrieved docs and expected docs.
    - The more granular method checks for all potential matches between retrieved docs and expected docs.

    Attributes:
        use_granular_hit_rate (bool): Determines whether to use the granular method for calculation.
        metric_name (str): The name of the metric.
    """

    metric_name: str = "hit_rate"
    use_granular_hit_rate: bool = False

    def compute(
        self,
        query: Optional[str] = None,
        expected_ids: Optional[List[str]] = None,
        retrieved_ids: Optional[List[str]] = None,
        expected_texts: Optional[List[str]] = None,
        retrieved_texts: Optional[List[str]] = None,
    ) -> RetrievalMetricResult:
        """Compute metric based on the provided inputs.

        Parameters:
            query (Optional[str]): The query string (not used in the current implementation).
            expected_ids (Optional[List[str]]): Expected document IDs.
            retrieved_ids (Optional[List[str]]): Retrieved document IDs.
            expected_texts (Optional[List[str]]): Expected texts (not used in the current implementation).
            retrieved_texts (Optional[List[str]]): Retrieved texts (not used in the current implementation).

        Raises:
            ValueError: If the necessary IDs are not provided.

        Returns:
            RetrievalMetricResult: The result with the computed hit rate score.
        """
        # Checking for the required arguments
        if (
            retrieved_ids is None
            or expected_ids is None
            or not retrieved_ids
            or not expected_ids
        ):
            raise ValueError("Retrieved ids and expected ids must be provided")

        if self.use_granular_hit_rate:
            # Granular HitRate calculation: Calculate all hits and divide by the number of expected docs
            expected_set = set(expected_ids)
            hits = sum(1 for doc_id in retrieved_ids if doc_id in expected_set)
            score = hits / len(expected_ids) if expected_ids else 0.0
        else:
            # Default HitRate calculation: Check if there is a single hit
            is_hit = any(id in expected_ids for id in retrieved_ids)
            score = 1.0 if is_hit else 0.0

        return RetrievalMetricResult(score=score)

compute #

compute(query: Optional[str] = None, expected_ids: Optional[List[str]] = None, retrieved_ids: Optional[List[str]] = None, expected_texts: Optional[List[str]] = None, retrieved_texts: Optional[List[str]] = None) -> RetrievalMetricResult

Compute metric based on the provided inputs.

Parameters:

Name Type Description Default
query Optional[str]

The query string (not used in the current implementation).

 None
expected_ids Optional[List[str]]

Expected document IDs.

 None
retrieved_ids Optional[List[str]]

Retrieved document IDs.

 None
expected_texts Optional[List[str]]

Expected texts (not used in the current implementation).

 None
retrieved_texts Optional[List[str]]

Retrieved texts (not used in the current implementation).

 None

Raises:

Type Description
ValueError

If the necessary IDs are not provided.

Returns:

Name Type Description
RetrievalMetricResult RetrievalMetricResult

The result with the computed hit rate score.
Source code in llama-index-core/llama_index/core/evaluation/retrieval/metrics.py 
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def compute(
    self,
    query: Optional[str] = None,
    expected_ids: Optional[List[str]] = None,
    retrieved_ids: Optional[List[str]] = None,
    expected_texts: Optional[List[str]] = None,
    retrieved_texts: Optional[List[str]] = None,
) -> RetrievalMetricResult:
    """Compute metric based on the provided inputs.

    Parameters:
        query (Optional[str]): The query string (not used in the current implementation).
        expected_ids (Optional[List[str]]): Expected document IDs.
        retrieved_ids (Optional[List[str]]): Retrieved document IDs.
        expected_texts (Optional[List[str]]): Expected texts (not used in the current implementation).
        retrieved_texts (Optional[List[str]]): Retrieved texts (not used in the current implementation).

    Raises:
        ValueError: If the necessary IDs are not provided.

    Returns:
        RetrievalMetricResult: The result with the computed hit rate score.
    """
    # Checking for the required arguments
    if (
        retrieved_ids is None
        or expected_ids is None
        or not retrieved_ids
        or not expected_ids
    ):
        raise ValueError("Retrieved ids and expected ids must be provided")

    if self.use_granular_hit_rate:
        # Granular HitRate calculation: Calculate all hits and divide by the number of expected docs
        expected_set = set(expected_ids)
        hits = sum(1 for doc_id in retrieved_ids if doc_id in expected_set)
        score = hits / len(expected_ids) if expected_ids else 0.0
    else:
        # Default HitRate calculation: Check if there is a single hit
        is_hit = any(id in expected_ids for id in retrieved_ids)
        score = 1.0 if is_hit else 0.0

    return RetrievalMetricResult(score=score)

RetrievalMetricResult #

Bases: BaseModel

Metric result.

Attributes:

Name Type Description
score float

Score for the metric
metadata Dict[str, Any]

Metadata for the metric result
Source code in llama-index-core/llama_index/core/evaluation/retrieval/metrics_base.py 
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class RetrievalMetricResult(BaseModel):
    """Metric result.

    Attributes:
        score (float): Score for the metric
        metadata (Dict[str, Any]): Metadata for the metric result

    """

    score: float = Field(..., description="Score for the metric")
    metadata: Dict[str, Any] = Field(
        default_factory=dict, description="Metadata for the metric result"
    )

    def __str__(self) -> str:
        """String representation."""
        return f"Score: {self.score}\nMetadata: {self.metadata}"

    def __float__(self) -> float:
        """Float representation."""
        return self.score

resolve_metrics #

resolve_metrics(metrics: List[str]) -> List[Type[BaseRetrievalMetric]]

Resolve metrics from list of metric names.

Source code in llama-index-core/llama_index/core/evaluation/retrieval/metrics.py 
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def resolve_metrics(metrics: List[str]) -> List[Type[BaseRetrievalMetric]]:
    """Resolve metrics from list of metric names."""
    for metric in metrics:
        if metric not in METRIC_REGISTRY:
            raise ValueError(f"Invalid metric name: {metric}")

    return [METRIC_REGISTRY[metric] for metric in metrics]