# eunoia.VennFit

Python · 0.6.0

[Source](<https://github.com/jolars/eunoia-py/blob/af16491d64a6d1f3b87b8645bd9697593daa0f02/python/eunoia/_models.py>)

Bases: `eunoia.EulerFit[S]`

<a id="constructor"></a>

```python
VennFit(
    shapes: tuple[S, ...],
    original_values: dict[str, float],
    fitted_values: dict[str, float],
    residuals: dict[str, float],
    region_error: dict[str, float],
    diag_error: float,
    stress: float,
    loss: float,
    container: Container | None = None,
    members: dict[str, list[str]] | None = None,
    plot_data: dict[str, typing.Any] = <factory>
)
```

Constructor inherited from [`EulerFit`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97>).

Result of laying out a (non-proportional) Venn diagram.

Shares [`EulerFit`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97>)'s structure and `plot()` method, but the diagram is *topological*: every set intersection is drawn regardless of its area, so the area-proportional error metrics are not meaningful and are left at zero. `fitted_values` holds the geometric area of each region; `original_values` is empty (a Venn layout has no requested areas).

- [`shapes`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97#shapes>) (inherited)

- [`original_values`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97#original_values>) (inherited)

- [`fitted_values`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97#fitted_values>) (inherited)

- [`residuals`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97#residuals>) (inherited)

- [`region_error`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97#region_error>) (inherited)

- [`diag_error`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97#diag_error>) (inherited)

- [`stress`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97#stress>) (inherited)

- [`loss`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97#loss>) (inherited)

- [`container`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97#container>) (inherited)

- [`members`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97#members>) (inherited)

- [`plot_data`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97#plot_data>) (inherited)

- [`plot`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97#plot>) (inherited)

- [`plot_plotly`](<368c6eef5f44d1af6db73a3b8b4a2c7058500ce15acc586ff7b67311c0627c97#plot_plotly>) (inherited)