inspect_viz.view.beta

Note

View functions are currently in beta and are exported from the inspect_ai.view.beta module. The beta module will be preserved after final release so that code written against it now will continue to work after the beta.

Scores

scores_by_task

Bar plot for comparing eval scores.

Summarize eval scores using a bar plot. By default, scores (y) are plotted by “task_display_name” (fx) and “model_display_name” (x). By default, confidence intervals are also plotted (disable this with y_ci=False).

Source

def scores_by_task(
    data: Data,
    model_name: str = "model_display_name",
    task_name: str = "task_display_name",
    score_value: str = "score_headline_value",
    score_stderr: str = "score_headline_stderr",
    score_label: str | None | NotGiven = NOT_GIVEN,
    ci: bool | float = 0.95,
    title: str | Title | None = None,
    marks: Marks | None = None,
    width: float | Param | None = None,
    height: float | Param | None = None,
    **attributes: Unpack[PlotAttributes],
) -> Component

data Data: Evals data table. This is typically created using a data frame read with the inspect evals_df() function.
model_name str: Name of field for the model name (defaults to “model_display_name”)
task_name str: Name of field for the task name (defaults to “task_display_name”)
score_value str: Name of field for the score value (defaults to “score_headline_value”).
score_stderr str: Name of field for stderr (defaults to “score_headline_metric”).
score_label str | None | NotGiven: Score axis label (pass None for no label).
ci bool | float: Confidence interval (e.g. 0.80, 0.90, 0.95, etc.). Defaults to 0.95.
title str | Title | None: Title for plot (str or mark created with the title() function).
marks Marks | None: Additional marks to include in the plot.
width float | Param | None: The outer width of the plot in pixels, including margins. Defaults to 700.
height float | Param | None: The outer height of the plot in pixels, including margins. The default is width / 1.618 (the golden ratio)
**attributes Unpack[PlotAttributes]: Additional PlotAttributes. By default, the margin_bottom are is set to 10 pixels and x_ticks is set to [].

scores_by_factor

Summarize eval scores with a factor of variation (e.g ‘No hint’ vs. ‘Hint’).

Source

def scores_by_factor(
    data: Data,
    factor: str,
    factor_labels: tuple[str, str],
    score_value: str = "score_headline_value",
    score_stderr: str = "score_headline_stderr",
    score_label: str = "Score",
    model: str = "model",
    model_label: str = "Model",
    ci: bool | float = 0.95,
    color: str | tuple[str, str] = "#3266ae",
    title: str | Mark | None = None,
    marks: Marks | None = None,
    width: float | Param | None = None,
    height: float | Param | None = None,
    **attributes: Unpack[PlotAttributes],
) -> Component

data Data: Evals data table. This is typically created using a data frame read with the inspect evals_df() function.
factor str: Field with factor of variation (should be of type boolean).
factor_labels tuple[str, str]: Tuple of labels for factor of variation. False value should be first, e.g. ("No hint", "Hint").
score_value str: Name of field for x (scoring) axis (defaults to “score_headline_value”).
score_stderr str: Name of field for scoring stderr (defaults to “score_headline_stderr”).
score_label str: Label for x-axis (defaults to “Score”).
model str: Name of field for y axis (defaults to “model”).
model_label str: Lable for y axis (defaults to “Model”).
ci bool | float: Confidence interval (e.g. 0.80, 0.90, 0.95, etc.). Defaults to 0.95.)
color str | tuple[str, str]: Hex color value (or tuple of two values). If one value is provided the second is computed by lightening the main color.
title str | Mark | None: Title for plot (str or mark created with the title() function).
marks Marks | None: Additional marks to include in the plot.
width float | Param | None: The outer width of the plot in pixels, including margins. Defaults to 700.
height float | Param | None: The outer height of the plot in pixels, including margins. Default to 65 pixels for each item on the “y” axis.
**attributes Unpack[PlotAttributes]: Additional `PlotAttributes

scores_timeline

Eval scores by model, organization, and release date.

Source

def scores_timeline(
    data: Data,
    task_name: str = "task_display_name",
    model_name: str = "model_display_name",
    model_organization: str = "model_organization_name",
    model_release_date: str = "model_release_date",
    score_name: str = "score_headline_name",
    score_value: str = "score_headline_value",
    score_stderr: str = "score_headline_stderr",
    organizations: list[str] | None = None,
    filters: bool | list[Literal["task", "organization"]] = True,
    ci: float | bool = 0.95,
    time_label: str = "Release Date",
    score_label: str = "Score",
    eval_label: str = "Eval",
    title: str | Title | None = None,
    marks: Marks | None = None,
    width: float | Param | None = None,
    height: float | Param | None = None,
    regression: bool = False,
    legend: Legend | NotGiven | None = NOT_GIVEN,
    **attributes: Unpack[PlotAttributes],
) -> Component

data Data: Data read using evals_df() and amended with model metadata using the model_info() prepare operation (see Data Preparation for details).
task_name str: Column for task name (defaults to “task_display_name”).
model_name str: Column for model name (defaults to “model_display_name”).
model_organization str: Column for model organization (defaults to “model_organization_name”).
model_release_date str: Column for model release date (defaults to “model_release_date”).
score_name str: Column for scorer name (defaults to “score_headline_name”).
score_value str: Column for score value (defaults to “score_headline_value”).
score_stderr str: Column for score stderr (defaults to “score_headline_stderr”)
organizations list[str] | None: List of organizations to include (in order of desired presentation).
filters bool | list[Literal['task', 'organization']]: Provide UI to filter plot by task and organization(s).
ci float | bool: Confidence interval (defaults to 0.95, pass False for no confidence intervals)
time_label str: Label for time (x-axis).
score_label str: Label for score (y-axis).
eval_label str: Label for eval select input.
title str | Title | None: Title for plot (str or mark created with the title() function).
marks Marks | None: Additional marks to include in the plot.
width float | Param | None: The outer width of the plot in pixels, including margins. Defaults to 700.
height float | Param | None: The outer height of the plot in pixels, including margins. The default is width / 1.618 (the golden ratio)
regression bool: If True, adds a regression line to the plot (uses the confidence interval passed using ci). Defaults to False.
legend Legend | NotGiven | None: Legend to use for the plot (defaults to None, which uses the default legend).
**attributes Unpack[PlotAttributes]: Additional PlotAttributes. By default, the x_domain is set to “fixed”, the y_domain is set to [0,1.0], color_label is set to “Organizations”, and color_domain is set to organizations.

scores_by_model

Bar plot for comparing the scores of different models on a single evaluation.

Summarize eval scores using a bar plot. By default, scores (y) are plotted by “model_display_name” (y). By default, confidence intervals are also plotted (disable this with y_ci=False).

Source

def scores_by_model(
    data: Data,
    *,
    model_name: str = "model_display_name",
    score_value: str = "score_headline_value",
    score_stderr: str = "score_headline_stderr",
    ci: float = 0.95,
    sort: Literal["asc", "desc"] | None = None,
    score_label: str | None | NotGiven = None,
    model_label: str | None | NotGiven = None,
    color: str | None = None,
    title: str | Title | None = None,
    marks: Marks | None = None,
    width: float | None = None,
    height: float | None = None,
    **attributes: Unpack[PlotAttributes],
) -> Component

data Data: Evals data table. This is typically created using a data frame read with the inspect evals_df() function.
model_name str: Column containing the model name (defaults to “model_display_name”)
score_value str: Column containing the score value (defaults to “score_headline_value”).
score_stderr str: Column containing the score standard error (defaults to “score_headline_stderr”).
ci float: Confidence interval (e.g. 0.80, 0.90, 0.95, etc.). Defaults to 0.95.
sort Literal['asc', 'desc'] | None: Sort order for the bars (sorts using the ‘x’ value). Can be “asc” or “desc”. Defaults to “asc”.
score_label str | None | NotGiven: x-axis label (defaults to None).
model_label str | None | NotGiven: x-axis label (defaults to None).
color str | None: The color for the bars. Defaults to “#416AD0”. Pass any valid hex color value.
title str | Title | None: Title for plot (str or mark created with the title() function)
marks Marks | None: Additional marks to include in the plot.
width float | None: The outer width of the plot in pixels, including margins. Defaults to 700.
height float | None: The outer height of the plot in pixels, including margins. The default is width / 1.618 (the golden ratio)
**attributes Unpack[PlotAttributes]: Additional PlotAttributes. By default, the y_inset_top and margin_bottom are set to 10 pixels and x_ticks is set to [].

scores_heatmap

Creates a heatmap plot of success rate of eval data.

Source

def scores_heatmap(
    data: Data,
    task_name: str = "task_display_name",
    task_label: str | None | NotGiven = None,
    model_name: str = "model_display_name",
    model_label: str | None | NotGiven = None,
    score_value: str = "score_headline_value",
    cell: CellOptions | None = None,
    tip: bool = True,
    title: str | Title | None = None,
    marks: Marks | None = None,
    height: float | None = None,
    width: float | None = None,
    legend: Legend | bool | None = None,
    sort: Literal["ascending", "descending"] | SortOrder | None = "ascending",
    orientation: Literal["horizontal", "vertical"] = "horizontal",
    **attributes: Unpack[PlotAttributes],
) -> Component

data Data: Evals data table.
task_name str: Name of column to use for columns.
task_label str | None | NotGiven: x-axis label (defaults to None).
model_name str: Name of column to use for rows.
model_label str | None | NotGiven: y-axis label (defaults to None).
score_value str: Name of the column to use as values to determine cell color.
cell CellOptions | None: Options for the cell marks.
tip bool: Whether to show a tooltip with the value when hovering over a cell (defaults to True).
title str | Title | None: Title for plot (str or mark created with the title() function)
marks Marks | None: Additional marks to include in the plot.
height float | None: The outer height of the plot in pixels, including margins. The default is width / 1.618 (the golden ratio).
width float | None: The outer width of the plot in pixels, including margins. Defaults to 700.
legend Legend | bool | None: Options for the legend. Pass None to disable the legend.
sort Literal['ascending', 'descending'] | SortOrder | None: Sort order for the x and y axes. If ascending, the highest values will be sorted to the top right. If descending, the highest values will appear in the bottom left. If None, no sorting is applied. If a SortOrder is provided, it will be used to sort the x and y axes.
orientation Literal['horizontal', 'vertical']: The orientation of the heatmap. If “horizontal”, the tasks will be on the x-axis and models on the y-axis. If “vertical”, the tasks will be on the y-axis and models on the x-axis.
**attributes Unpack[PlotAttributes]: Additional `PlotAttributes

scores_by_limit

Visualizes success rate as a function of a resource limit (time, tokens).

Model success rate is plotted as a function of the time, tokens, or other resource limit.

Source

def scores_by_limit(
    data: Data,
    model: str = "model_display_name",
    success: str = "success_rate",
    stderr: str | None = "standard_error",
    facet: str | None = None,
    other_termination_rate: str | bool = False,
    limit: str | None = None,
    limit_label: str | NotGiven = NOT_GIVEN,
    scale: Literal["log", "linear", "auto"] = "auto",
    height: float | None = None,
    width: float | None = None,
    ci: float = 0.95,
    **attributes: Unpack[PlotAttributes],
) -> Component

data Data: A dataframe prepared using the prepare_limit_dataframe function.
model str: Name of field holding the model (defaults to “model_display_name”).
success str: Name of field containing the success rate (defaults to “success_rate”).
stderr str | None: Name of field containing the standard_error (defaults to “standard_error”).
facet str | None: Name of field to use for faceting (defaults to None).
other_termination_rate str | bool: Name of field containing the other termination rate (defaults to “other_termination_rate”).
limit str | None: Name of field for x axis (by default, will detect limit type using the columns present in the data frame).
limit_label str | NotGiven: The limit label (by default, will select limit label using the columns present in the data frame). Pass None for no label.
scale Literal['log', 'linear', 'auto']: The scale type for the limit access. If ‘auto’, will use log scale if the range is 2 or more orders of magnitude (defaults to ‘auto’).
height float | None: The outer height of the plot in pixels, including margins. The default is width / 1.618 (the golden ratio)
width float | None: The outer width of the plot in pixels, including margins. Defaults to 700.
ci float: Confidence interval (e.g. 0.80, 0.90, 0.95, etc.). Defaults to 0.95.
**attributes Unpack[PlotAttributes]: Additional PlotAttributes.

scores_by_limit_df

Prepares a dataframe for plotting success rate as a function of a resource limit (time, tokens).

Source

def scores_by_limit_df(
    df: pd.DataFrame,
    score: str,
    limit: Literal["total_tokens", "total_time", "working_time"] = "total_tokens",
    scale: Literal["log", "linear", "auto"] = "auto",
    steps: int = 100,
) -> pd.DataFrame

df pd.DataFrame: A dataframe containing sample summaries and eval information.
score str: Name of field containing the score (0 = fail, 1 = success).
limit Literal['total_tokens', 'total_time', 'working_time']: The resource limit to use (one of ‘total_tokens’, ‘total_time’, ‘working_time’). Defaults to ‘total_tokens’.
scale Literal['log', 'linear', 'auto']: The scale type for the limit access. If ‘auto’, will use log scale if the range is 2 or more orders of magnitude (defaults to ‘auto’).
steps int: The number of points to use when sampling the limit range (defaults to 100).

Tools

tool_calls

Heat map visualising tool calls over evaluation turns.

Source

def tool_calls(
    data: Data,
    x: str = "order",
    y: str = "id",
    tool: str = "tool_call_function",
    limit: str = "limit",
    tools: list[str] | None = None,
    x_label: str | None = "Message",
    y_label: str | None = "Sample",
    title: str | Title | None = None,
    marks: Marks | None = None,
    width: float | None = None,
    height: float | None = None,
    **attributes: Unpack[PlotAttributes],
) -> Component

data Data: Messages data table. This is typically created using a data frame read with the inspect messages_df() function.
x str: Name of field for x axis (defaults to “order”)
y str: Name of field for y axis (defaults to “id”).
tool str: Name of field with tool name (defaults to “tool_call_function”)
limit str: Name of field with sample limit (defaults to “limit”).
tools list[str] | None: Tools to include in plot (and order to include them). Defaults to all tools found in data.
x_label str | None: x-axis label (defaults to “Message”).
y_label str | None: y-axis label (defaults to “Sample”).
title str | Title | None: Title for plot (str or mark created with the title() function)
marks Marks | None: Additional marks to include in the plot.
width float | None: The outer width of the plot in pixels, including margins. Defaults to 700.
height float | None: The outer height of the plot in pixels, including margins. The default is width / 1.618 (the golden ratio)
**attributes Unpack[PlotAttributes]: Additional PlotAttributes. By default, the margin_top is set to 0, margin_left to 20, margin_right to 100, color_label is “Tool”, y_ticks is empty, and x_ticks and color_domain are calculated from data.

Types

CellOptions

Cell options for the heatmap.

Source

class CellOptions(TypedDict, total=False)

Attributes

inset float | None: Inset for the cell marks. Defaults to 1 pixel.
text str | None: Text color for the cell marks. Defaults to “white”. Set to None to disable text.