Diagnostics plots

Import packages.

import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_diabetes

from generalized_additive_models import GAM, Categorical, Spline

/home/docs/checkouts/readthedocs.org/user_builds/generalized-additive-models/envs/latest/lib/python3.11/site-packages/generalized_additive_models/__init__.py:116: UserWarning:
Thank you for using generalized-additive-models, version 0.4.3.
Until version 1.0.0 is released, the package and API should be considered unstable.
You are welcome to use the package. Report bugs and join the discussion on GitHub:
https://github.com/tommyod/generalized-additive-models
  warnings.warn(message)

Load data.

data = load_diabetes(as_frame=True)
df = data.data
y = data.target

# Code the "sex" variable as strings
df = df.assign(sex=lambda df: np.where(df.sex < 0, "Male", "Female"))

df.sample(5)

	age	sex	bmi	bp	s1	s2	s3	s4	s5	s6
326	0.045341	Female	-0.008362	-0.033213	-0.007073	0.001191	-0.039719	0.034309	0.029935	0.027917
386	0.019913	Male	-0.040696	-0.015999	-0.008449	-0.017598	0.052322	-0.039493	-0.030748	0.003064
68	0.038076	Female	-0.029918	-0.040099	-0.033216	-0.024174	-0.010266	-0.002592	-0.012909	0.003064
363	-0.049105	Female	-0.024529	0.000079	-0.046975	-0.028245	-0.065491	0.028405	0.019196	0.011349
85	0.045341	Male	0.071397	0.001215	-0.009825	-0.001001	0.015505	-0.039493	-0.041176	-0.071494

terms = Spline("age") + Spline("bmi") + Spline("bp") + Categorical("sex")
gam = GAM(terms).fit(df, y)

Diagnostics with sklearn

from sklearn.metrics import PredictionErrorDisplay

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))

ax1.set_title("Actual vs. predicted")
PredictionErrorDisplay.from_estimator(gam, df, y, ax=ax1, kind="actual_vs_predicted")
ax1.grid(True, ls="--", alpha=0.2)

ax2.set_title("Residuals vs. predicted")
PredictionErrorDisplay.from_estimator(gam, df, y, ax=ax2, kind="residual_vs_predicted")
ax2.grid(True, ls="--", alpha=0.2)

fig.tight_layout()
plt.show()

GAM diagnostics plots

from generalized_additive_models.inspection import QQDisplay


fig, ax = plt.subplots(1, 1, figsize=(4, 3))

display = QQDisplay.from_estimator(
    gam, df, y, residuals="deviance", method="normal", ax=ax
)

ax.grid(True)
fig.tight_layout()
plt.show()