| --- |
| license: mit |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - matplotlib |
| - seaborn |
| - data-visualization |
| - rl-environment |
| - verifiers |
| - code |
| size_categories: |
| - n<1K |
| --- |
| |
| # matplotlib-tasks-v1 |
|
|
| Task dataset for a Matplotlib/Seaborn RL / eval environment, in the shape used by the |
| [Prime Intellect Environments Hub](https://app.primeintellect.ai/dashboard/environments). |
|
|
| 27 plotting tasks over a fixed set of small datasets. The model builds the figure, then reads |
| the answer back off the axes; the grade is an exact row comparison against a reference. |
| Deterministic — no LLM judge, no external API, no network, **and no pixels**. |
|
|
| | Category | Tasks | Covers | |
| |---|---|---| |
| | line_plots | 6 | ydata/xdata readback, multiple lines, legend texts, linestyle and marker, plotting a derived series | |
| | axes_config | 6 | explicit limits, title and axis labels, explicit ticks, subplot grids, log scale, limits not filtering data | |
| | bar_charts | 5 | heights, widths, `barh`, stacked bars via `bottom`, explicit tick labels | |
| | statistical | 5 | histogram counts and edges, custom bin edges, scatter offsets, boxplot median and whiskers | |
| | seaborn | 5 | `lineplot` passthrough, `barplot` group means, `barplot` with `estimator="sum"`, `scatterplot` collections, `histplot` patch heights | |
| |
| ## ⚠ Why this dataset does not compare images |
| |
| Image comparison is the obvious way to grade a plot and the wrong one: it fails on font |
| hinting, DPI, backend and antialiasing long before it tests whether the model plotted the right |
| thing. Here the model builds the figure and the grade reads values back off the artists. |
| |
| That still leaves a determinism trap, and it was **measured** on matplotlib 3.11.1, not assumed: |
| |
| ``` |
| ln.get_color() -> (0.1215…, 0.4666…, 0.7058…) from the STYLE CYCLE — version-dependent |
| ax.get_xlim() -> (-0.2, 4.2) after autoscale depends on margin defaults |
| ln.get_ydata() -> exactly what was passed in stable |
| hist counts -> computed from the data stable |
| ax.get_xlim() -> after ax.set_xlim(0, 10) stable — the task set it |
| ``` |
| |
| **Every task therefore grades only:** user-supplied data, computed values, and properties the |
| task itself sets explicitly. Never a default colour, never an autoscaled limit, never an |
| automatic tick location. |
| |
| ## Fields |
| |
| | Field | Description | |
| |---|---| |
| | `task_id` | stable id, e.g. `mpl-017` | |
| | `category` | one of the five above | |
| | `prompt` | the natural-language instruction | |
| | `data_description` | the preloaded variables, as shown to the model | |
| | `data` | those variables as JSON, so the task is self-contained | |
| | `expected_output` | `{"rows": [[...]]}` — the reference result | |
| |
| ## Verification |
| |
| Every task is independently checked: it runs, is deterministic **across two freshly built |
| figures**, returns a non-empty list of JSON-safe primitives — which catches an Artist or numpy |
| scalar leaking out instead of a plain value — contains no NaN/inf, and survives the |
| serialisation round-trip exactly. All 27 pass on matplotlib 3.11.1 / seaborn 0.13.2 with the |
| Agg backend. |
| |
| Builder and verifier: |
| [`build_tasks.py`](https://github.com/eltociear/my-molt-agent/blob/main/environments/matplotlib_env/build_tasks.py). |
| |
