inspect_ai.analysis

Evals

evals_df

Read a dataframe containing evals.

def evals_df(
    logs: LogPaths = list_eval_logs(),
    columns: Sequence[Column] = EvalColumns,
    strict: bool = True,
    quiet: bool | None = None,
) -> "pd.DataFrame" | tuple["pd.DataFrame", Sequence[ColumnError]]

logs LogPaths: One or more paths to log files or log directories. Defaults to the contents of the currently active log directory (e.g. ./logs or INSPECT_LOG_DIR).
columns Sequence[Column]: Specification for what columns to read from log files.
strict bool: Raise import errors immediately. Defaults to True. If False then a tuple of DataFrame and errors is returned.
quiet bool | None: If True, do not show any output or progress. Defaults to False for terminal environments, and True for notebooks.

EvalColumn

Column which maps to EvalLog.

Source

class EvalColumn(Column)

EvalColumns

Default columns to import for evals_df().

Source

EvalColumns: list[Column] = (
    EvalInfo
    + EvalTask
    + EvalModel
    + EvalDataset
    + EvalConfiguration
    + EvalResults
    + EvalScores
)

EvalInfo

Eval basic information columns.

Source

EvalInfo: list[Column] = [
    EvalColumn("run_id", path="eval.run_id", required=True),
    EvalColumn("task_id", path="eval.task_id", required=True),
    *EvalLogPath,
    EvalColumn("created", path="eval.created", type=datetime, required=True),
    EvalColumn("tags", path="eval.tags", default="", value=list_as_str),
    EvalColumn("git_origin", path="eval.revision.origin"),
    EvalColumn("git_commit", path="eval.revision.commit"),
    EvalColumn("packages", path="eval.packages"),
    EvalColumn("metadata", path="eval.metadata"),
]

EvalTask

Eval task configuration columns.

Source

EvalTask: list[Column] = [
    EvalColumn("task_name", path="eval.task", required=True, value=remove_namespace),
    EvalColumn("task_display_name", path=eval_log_task_display_name),
    EvalColumn("task_version", path="eval.task_version", required=True),
    EvalColumn("task_file", path="eval.task_file"),
    EvalColumn("task_attribs", path="eval.task_attribs"),
    EvalColumn("task_arg_*", path="eval.task_args"),
    EvalColumn("solver", path="eval.solver"),
    EvalColumn("solver_args", path="eval.solver_args"),
    EvalColumn("sandbox_type", path="eval.sandbox.type"),
    EvalColumn("sandbox_config", path="eval.sandbox.config"),
]

EvalModel

Eval model columns.

Source

EvalModel: list[Column] = [
    EvalColumn("model", path="eval.model", required=True),
    EvalColumn("model_base_url", path="eval.model_base_url"),
    EvalColumn("model_args", path="eval.model_base_url"),
    EvalColumn("model_generate_config", path="eval.model_generate_config"),
    EvalColumn("model_roles", path="eval.model_roles"),
]

EvalConfiguration

Eval configuration columns.

Source

EvalConfiguration: list[Column] = [
    EvalColumn("epochs", path="eval.config.epochs"),
    EvalColumn("epochs_reducer", path="eval.config.epochs_reducer"),
    EvalColumn("approval", path="eval.config.approval"),
    EvalColumn("message_limit", path="eval.config.message_limit"),
    EvalColumn("token_limit", path="eval.config.token_limit"),
    EvalColumn("time_limit", path="eval.config.time_limit"),
    EvalColumn("working_limit", path="eval.config.working_limit"),
]

EvalResults

Eval results columns.

Source

EvalResults: list[Column] = [
    EvalColumn("status", path="status", required=True),
    EvalColumn("error_message", path="error.message"),
    EvalColumn("error_traceback", path="error.traceback"),
    EvalColumn("total_samples", path="results.total_samples"),
    EvalColumn("completed_samples", path="results.completed_samples"),
    EvalColumn("score_headline_name", path="results.scores[0].scorer"),
    EvalColumn("score_headline_metric", path="results.scores[0].metrics.*.name"),
    EvalColumn("score_headline_value", path="results.scores[0].metrics.*.value"),
    EvalColumn("score_headline_stderr", path=eval_log_headline_stderr),
]

EvalScores

Eval scores (one score/metric per-columns).

Source

EvalScores: list[Column] = [
    EvalColumn("score_*_*", path=eval_log_scores_dict),
]

Samples

samples_df

Read a dataframe containing samples from a set of evals.

Source

def samples_df(
    logs: LogPaths = list_eval_logs(),
    columns: Sequence[Column] = SampleSummary,
    full: bool = False,
    strict: bool = True,
    parallel: bool | int = False,
    quiet: bool | None = None,
) -> "pd.DataFrame" | tuple["pd.DataFrame", list[ColumnError]]

logs LogPaths: One or more paths to log files or log directories. Defaults to the contents of the currently active log directory (e.g. ./logs or INSPECT_LOG_DIR).
columns Sequence[Column]: Specification for what columns to read from log files.
full bool: Read full sample metadata. This will be much slower, but will include the unfiltered values of sample metadata rather than the abbrevivated metadata from sample summaries (which includes only scalar values and limits string values to 1k).
strict bool: Raise import errors immediately. Defaults to True. If False then a tuple of DataFrame and errors is returned.
parallel bool | int: If True, use ProcessPoolExecutor to read logs in parallel (with workers based on mp.cpu_count(), capped at 8). If int, read in parallel with the specified number of workers. If False (the default) do not read in parallel.
quiet bool | None: If True, do not show any output or progress. Defaults to False for terminal environments, and True for notebooks.

SampleColumn

Column which maps to EvalSample or EvalSampleSummary.

Source

class SampleColumn(Column)

SampleSummary

Sample summary columns.

Source

SampleSummary: list[Column] = [
    SampleColumn("id", path="id", required=True, type=str),
    SampleColumn("epoch", path="epoch", required=True),
    SampleColumn("input", path=sample_input_as_str, required=True),
    SampleColumn("target", path="target", required=True, value=list_as_str),
    SampleColumn("metadata_*", path="metadata"),
    SampleColumn("score_*", path="scores", value=score_values),
    SampleColumn("model_usage", path="model_usage"),
    SampleColumn("total_time", path="total_time"),
    SampleColumn("working_time", path="total_time"),
    SampleColumn("error", path="error", default=""),
    SampleColumn("limit", path="limit"),
    SampleColumn("retries", path="retries"),
]

SampleMessages

Sample messages as a string.

Source

SampleMessages: list[Column] = [
    SampleColumn("messages", path=sample_messages_as_str, required=True, full=True)
]

Messages

messages_df

Read a dataframe containing messages from a set of evals.

Source

def messages_df(
    logs: LogPaths = list_eval_logs(),
    columns: Sequence[Column] = MessageColumns,
    filter: MessageFilter | None = None,
    strict: bool = True,
    parallel: bool | int = False,
    quiet: bool | None = None,
) -> "pd.DataFrame" | tuple["pd.DataFrame", list[ColumnError]]

logs LogPaths: One or more paths to log files or log directories. Defaults to the contents of the currently active log directory (e.g. ./logs or INSPECT_LOG_DIR).
columns Sequence[Column]: Specification for what columns to read from log files.
filter MessageFilter | None: Callable that filters messages
strict bool: Raise import errors immediately. Defaults to True. If False then a tuple of DataFrame and errors is returned.
parallel bool | int: If True, use ProcessPoolExecutor to read logs in parallel (with workers based on mp.cpu_count(), capped at 8). If int, read in parallel with the specified number of workers. If False (the default) do not read in parallel.
quiet bool | None: If True, do not show any output or progress. Defaults to False for terminal environments, and True for notebooks.

MessageFilter

Filter for messages_df() rows.

Source

MessageFilter: TypeAlias = Callable[[ChatMessage], bool]

MessageColumn

Column which maps to ChatMessage.

Source

class MessageColumn(Column)

MessageContent

Message content columns.

Source

MessageContent: list[Column] = [
    MessageColumn("message_id", path="id"),
    MessageColumn("role", path="role", required=True),
    MessageColumn("source", path="source"),
    MessageColumn("content", path=message_text),
]

MessageToolCalls

Message tool call columns.

Source

MessageToolCalls: list[Column] = [
    MessageColumn("tool_calls", path=message_tool_calls),
    MessageColumn("tool_call_id", path="tool_call_id"),
    MessageColumn("tool_call_function", path="function"),
    MessageColumn("tool_call_error", path="error.message"),
]

MessageColumns

Chat message columns.

Source

MessageColumns: list[Column] = MessageContent + MessageToolCalls

Events

events_df

Read a dataframe containing events from a set of evals.

Source

def events_df(
    logs: LogPaths = list_eval_logs(),
    columns: Sequence[Column] = EventInfo,
    filter: EventFilter | None = None,
    strict: bool = True,
    parallel: bool | int = False,
    quiet: bool | None = None,
) -> "pd.DataFrame" | tuple["pd.DataFrame", list[ColumnError]]

logs LogPaths: One or more paths to log files or log directories. Defaults to the contents of the currently active log directory (e.g. ./logs or INSPECT_LOG_DIR).
columns Sequence[Column]: Specification for what columns to read from log files.
filter EventFilter | None: Callable that filters event types.
strict bool: Raise import errors immediately. Defaults to True. If False then a tuple of DataFrame and errors is returned.
parallel bool | int: If True, use ProcessPoolExecutor to read logs in parallel (with workers based on mp.cpu_count(), capped at 8). If int, read in parallel with the specified number of workers. If False (the default) do not read in parallel.
quiet bool | None: If True, do not show any output or progress. Defaults to False for terminal environments, and True for notebooks.

EventColumn

Column which maps to Event.

Source

class EventColumn(Column)

EventInfo

Event basic information columns.

Source

EventInfo: list[Column] = [
    EventColumn("event_id", path="uuid"),
    EventColumn("event", path="event"),
    EventColumn("span_id", path="span_id"),
]

EventTiming

Event timing columns.

Source

EventTiming: list[Column] = [
    EventColumn("timestamp", path="timestamp", type=datetime),
    EventColumn("completed", path="completed", type=datetime),
    EventColumn("working_start", path="working_start"),
    EventColumn("working_time", path="working_time"),
]

ModelEventColumns

Model event columns.

Source

ModelEventColumns: list[Column] = [
    EventColumn("model_event_model", path="model"),
    EventColumn("model_event_role", path="role"),
    EventColumn("model_event_input", path=model_event_input_as_str),
    EventColumn("model_event_tools", path="tools"),
    EventColumn("model_event_tool_choice", path=tool_choice_as_str),
    EventColumn("model_event_config", path="config"),
    EventColumn("model_event_usage", path="output.usage"),
    EventColumn("model_event_time", path="output.time"),
    EventColumn("model_event_completion", path=completion_as_str),
    EventColumn("model_event_retries", path="retries"),
    EventColumn("model_event_error", path="error"),
    EventColumn("model_event_cache", path="cache"),
    EventColumn("model_event_call", path="call"),
]

ToolEventColumns

Tool event columns.

Source

ToolEventColumns: list[Column] = [
    EventColumn("tool_event_function", path="function"),
    EventColumn("tool_event_arguments", path="arguments"),
    EventColumn("tool_event_view", path=tool_view_as_str),
    EventColumn("tool_event_result", path="result"),
    EventColumn("tool_event_truncated", path="truncated"),
    EventColumn("tool_event_error_type", path="error.type"),
    EventColumn("tool_event_error_message", path="error.message"),
]

Prepare

prepare

Prepare a data frame for analysis using one or more transform operations.

Source

def prepare(
    df: "pd.DataFrame", operation: Operation | Sequence[Operation]
) -> "pd.DataFrame"

df pd.DataFrame: Input data frame.
operation Operation | Sequence[Operation]: Operation or sequence of operations to apply.

log_viewer

Add a log viewer column to an eval data frame.

Tranform operation to add a log_viewer column to a data frame based on one more more url_mappings.

URL mappings define the relationship between log file paths (either fileystem or S3) and URLs where logs are published. The URL target should be the location where the output of the inspect view bundle command was published.

Source

def log_viewer(
    target: Literal["eval", "sample", "event", "message"],
    url_mappings: dict[str, str],
    log_column: str = "log",
    log_viewer_column: str = "log_viewer",
) -> Operation

target Literal['eval', 'sample', 'event', 'message']: Target for log viewer (“eval”, “sample”, “event”, or “message”).
url_mappings dict[str, str]: Map log file paths (either filesystem or S3) to URLs where logs are published.
log_column str: Column in the data frame containing log file path (defaults to “log”).
log_viewer_column str: Column to create with log viewer URL (defaults to “log_viewer”)

model_info

Amend data frame with model metadata.

Fields added (when available) include:

model_organization_name: Displayable model organization (e.g. OpenAI, Anthropic, etc.)
model_display_name: Displayable model name (e.g. Gemini Flash 2.5)
model_snapshot: A snapshot (version) string, if available (e.g. “latest” or “20240229”)
model_release_date: The model’s release date
model_knowledge_cutoff_date: The model’s knowledge cutoff date

Inspect includes built in support for many models (based upon the model string in the dataframe). If you are using models for which Inspect does not include model metadata, you may include your own model metadata via the model_info argument.

Source

def model_info(
    model_info: Dict[str, ModelInfo] | None = None,
) -> Operation

model_info Dict[str, ModelInfo] | None: Additional model info for models not supported directly by Inspect’s internal database.

task_info

Amend data frame with task display name.

Maps task names to task display names for plotting (e.g. “gpqa_diamond” -> “GPQA Diamond”)

If no mapping is provided for a task then name will come from the display_name attribute of the Task (or failing that from the registered name of the Task).

Source

def task_info(
    display_names: dict[str, str],
    task_name_column: str = "task_name",
    task_display_name_column: str = "task_display_name",
) -> Operation

display_names dict[str, str]: Mapping of task log names (e.g. “gpqa_diamond”) to task display names (e.g. “GPQA Diamond”).
task_name_column str: Column to draw the task name from (defaults to “task_name”).
task_display_name_column str: Column to populate with the task display name (defaults to “task_display_name”)

frontier

Add a frontier column to an eval data frame.

Tranform operation to add a frontier column to a data frame based using a task, release date, and score.

The frontier column will be True if the model was the top-scoring model on the task among all models available at the moment the model was released; otherwise it will be False.

Source

def frontier(
    task_column: str = "task_name",
    date_column: str = "model_release_date",
    score_column: str = "score_headline_value",
    frontier_column: str = "frontier",
) -> Operation

task_column str: The column in the data frame containing the task name (defaults to “task_name”).
date_column str: The column in the data frame containing the model release date (defaults to “model_release_date”).
score_column str: The column in the data frame containing the score (defaults to “score_headline_value”).
frontier_column str: The column to create with the frontier value (defaults to “frontier”).

score_to_float

Converts score columns to float values.

For each column specified, this operation will convert the values to floats using the provided value_to_float function. The column value will be replaced with the float value.

Source

def score_to_float(
    columns: str | Sequence[str], *, value_to_float: ValueToFloat = value_to_float()
) -> Operation

columns str | Sequence[str]: The name of the score column(s) to convert to float. This can be a single column name or a sequence of column names.
value_to_float ValueToFloat: Function to convert values to float. Defaults to the built-in value_to_float function.

Operation

Operation to transform a data frame for analysis.

Source

class Operation(Protocol):
    def __call__(self, df: "pd.DataFrame") -> "pd.DataFrame"

df pd.DataFrame: Input data frame.

ModelInfo

Model information and metadata

Source

class ModelInfo(BaseModel)

Attributes

organization str | None: Model organization (e.g. Anthropic, OpenAI).
model str | None: Model name (e.g. Gemini 2.5 Flash).
snapshot str | None: A snapshot (version) string, if available (e.g. “latest” or “20240229”)..
release_date date | None: The mode’s release date.

Columns

Column

Specification for importing a column into a dataframe.

Extract columns from an EvalLog path either using JSONPath expressions or a function that takes EvalLog and returns a value.

By default, columns are not required, pass required=True to make them required. Non-required columns are extracted as None, provide a default to yield an alternate value.

The type option serves as both a validation check and a directive to attempt to coerce the data into the specified type. Coercion from str to other types is done after interpreting the string using YAML (e.g. "true" -> True).

The value function provides an additional hook for transformation of the value read from the log before it is realized as a column (e.g. list to a comma-separated string).

The root option indicates which root eval log context the columns select from.

Source

class Column(abc.ABC)

Attributes

name str: Column name.
path JSONPath | None: Path to column in EvalLog
required bool: Is the column required? (error is raised if required columns aren’t found).
default JsonValue | None: Default value for column when it is read from the log as None.
type Type[ColumnType] | None: Column type (import will attempt to coerce to the specified type).

Methods

value

Convert extracted value into a column value (defaults to identity function).

Source

def value(self, x: JsonValue) -> JsonValue

x JsonValue: Value to convert.

ColumnType

Valid types for columns.

Values of list and dict are converted into column values as JSON str.

Source

ColumnType: TypeAlias = int | float | bool | str | date | time | datetime | None

ColumnError

Error which occurred parsing a column.

Source

@dataclass
class ColumnError

Attributes

column str

Target column name.

path str | None

Path to select column value.

error Exception

Underlying error.

log EvalLog

Eval log where the error occurred.

Use log.location to determine the path where the log was read from.