Scores by Factor

Overview

The scores_by_factor() function renders a bar plot for comparing eval scores by model and a boolean factor (e.g. non-reasoning vs. reasoning, no hint vs. hint, etc.).

from inspect_viz import Data
from inspect_viz.view.beta import scores_by_factor

evals = Data.from_file("evals-hint.parquet")
scores_by_factor(evals, "task_arg_hint", ("No hint", "Hint"))

Data Preparation

Above we read the data for the plot from a parquet file. This file was in turn created by:

Reading logs into a data frame with evals_df().
Using the prepare() function to add model_info() and log_viewer() columns to the data frame.

from inspect_ai.analysis import evals_df, log_viewer, model_info, prepare

df = evals_df("logs")
df = prepare(df, [
    model_info(),
    log_viewer("eval", {"logs": "https://samples.meridianlabs.ai/"})
])
df.to_parquet("evals-hint.parquet")

You can additionally use the task_info() operation to map lower-level task names to task display names (e.g. “gpqa_diamond” -> “GPQA Diamond”).

You should also ensure that your evals data frame has a boolean field corresponding to the factor you are splitting on (in the example above this is “task_arg_hint”).

Function Reference

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,
    legend: Legend | NotGiven | None = NOT_GIVEN,
    **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.
legend Legend | NotGiven | None: Options for the legend. Pass None to disable the legend.
**attributes Unpack[PlotAttributes]: Additional `PlotAttributes

Implementation

The Scores by Factor example demonstrates how this view was implemented using lower level plotting components.