Plot Residuals

This example demonstrates plotting errors / residuals.

Code has been adapted from the plotly example

import logging

import plotly
from sklearn import datasets
from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline

from elphick.sklearn_viz.residuals import Errors

logging.basicConfig(level=logging.DEBUG,
                    format='%(asctime)s %(levelname)s %(module)s - %(funcName)s: %(message)s',
                    datefmt='%Y-%m-%dT%H:%M:%S%z')

Data generation and model fitting

We mimic a simple model fit per the sklearn example.

# Load the diabetes dataset
diabetes = datasets.load_diabetes(as_frame=True)
X, y = diabetes.data, diabetes.target
y.name = "progression"

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.1, random_state=13
)

mdl = make_pipeline(Lasso())
mdl.set_output(transform="pandas")
mdl.fit(X_train, y_train)

Pipeline(steps=[('lasso', Lasso())])

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Demonstrate the plot

obj_res: Errors = Errors(mdl=mdl, x_test=X_test, y_test=y_test)
fig = obj_res.plot()
# noinspection PyTypeChecker
plotly.io.show(fig)

Add marginal histograms

fig = obj_res.plot(marginal=True)
fig