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/stable/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
84	0.001751	Male	-0.039618	-0.100934	-0.029088	-0.030124	0.044958	-0.050195	-0.068332	-0.129483
138	0.034443	Female	0.111276	0.076958	-0.031840	-0.033881	-0.021311	-0.002592	0.028020	0.073480
175	0.067136	Male	-0.038540	-0.026328	-0.031840	-0.026366	0.008142	-0.039493	-0.027129	0.003064
241	0.030811	Female	-0.008362	0.004658	0.014942	0.027496	0.008142	-0.008127	-0.029526	0.056912
22	-0.085430	Male	-0.004050	-0.009113	-0.002945	0.007767	0.022869	-0.039493	-0.061176	-0.013504

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()