Skip to content

Goodness of fit fix #473

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
plakrisenko merged 6 commits into from
Mar 18, 2026
Merged

Goodness of fit fix #473

Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
39a487f
fix mean of residuals in plot_goodness_of_fit
plakrisenko Feb 4, 2026
7757a1d
add line break
plakrisenko Feb 4, 2026
29d2da0
Merge branch 'main' into goodness_of_fit_fix
plakrisenko Mar 18, 2026
80b83c2
ensure that normalized residuals are used
plakrisenko Mar 18, 2026
5513cd4
add possibility to choose between normalized and unnormalized errors
plakrisenko Mar 18, 2026
c71f742
ruff
plakrisenko Mar 18, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
34 changes: 26 additions & 8 deletions petab/v1/visualize/plot_residuals.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -136,6 +136,7 @@ def plot_goodness_of_fit(
size: tuple = (10, 7),
color=None,
ax: plt.Axes | None = None,
normalized_error: bool = True,
) -> matplotlib.axes.Axes:
"""
Plot goodness of fit.
Expand All @@ -154,6 +155,10 @@ def plot_goodness_of_fit(
`matplotlib.pyplot.scatter`.
ax:
Axis object.
normalized_error:
Type of error to display.
If True, mean of squared normalized residuals is shown,
otherwise mean of squared residuals.

Returns
-------
Expand All @@ -168,12 +173,26 @@ def plot_goodness_of_fit(
"are needed for goodness_of_fit"
)

residual_df = calculate_residuals(
measurement_dfs=petab_problem.measurement_df,
simulation_dfs=simulations_df,
observable_dfs=petab_problem.observable_df,
parameter_dfs=petab_problem.parameter_df,
)[0]
if normalized_error:
residual_df = calculate_residuals(
measurement_dfs=petab_problem.measurement_df,
simulation_dfs=simulations_df,
observable_dfs=petab_problem.observable_df,
parameter_dfs=petab_problem.parameter_df,
normalize=True,
)[0]
error_name = "mean of squared\nnormalized residuals"
else:
residual_df = calculate_residuals(
measurement_dfs=petab_problem.measurement_df,
simulation_dfs=simulations_df,
observable_dfs=petab_problem.observable_df,
parameter_dfs=petab_problem.parameter_df,
normalize=False,
)[0]
error_name = "mean of squared residuals"
error = np.mean(np.power(residual_df["residual"], 2))

slope, intercept, r_value, p_value, std_err = stats.linregress(
simulations_df["simulation"],
petab_problem.measurement_df["measurement"],
Expand All @@ -199,15 +218,14 @@ def plot_goodness_of_fit(
ax.plot(x, x, linestyle="--", color="gray")
ax.plot(x, intercept + slope * x, "r", label="fitted line")

mse = np.mean(np.abs(residual_df["residual"]))
ax.text(
0.1,
0.70,
f"$R^2$: {r_value**2:.2f}\n"
f"slope: {slope:.2f}\n"
f"intercept: {intercept:.2f}\n"
f"p-value: {p_value:.2e}\n"
f"mean squared error: {mse:.2e}\n",
f"{error_name}: {error:.2e}\n",
transform=ax.transAxes,
)

Expand Down
Loading