"""
================================
Nearest Neighbors Classification
================================

This example shows how to use :class:`~xlearn.neighbors.KNeighborsClassifier`.
We train such a classifier on the iris dataset and observe the difference of the
decision boundary obtained with regards to the parameter `weights`.
"""

# Authors: The jax-sklearn developers
# SPDX-License-Identifier: BSD-3-Clause

# %%
# Load the data
# -------------
#
# In this example, we use the iris dataset. We split the data into a train and test
# dataset.
from xlearn.datasets import load_iris
from xlearn.model_selection import train_test_split

iris = load_iris(as_frame=True)
X = iris.data[["sepal length (cm)", "sepal width (cm)"]]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)

# %%
# K-nearest neighbors classifier
# ------------------------------
#
# We want to use a k-nearest neighbors classifier considering a neighborhood of 11 data
# points. Since our k-nearest neighbors model uses euclidean distance to find the
# nearest neighbors, it is therefore important to scale the data beforehand. Refer to
# the example entitled
# :ref:`sphx_glr_auto_examples_preprocessing_plot_scaling_importance.py` for more
# detailed information.
#
# Thus, we use a :class:`~xlearn.pipeline.Pipeline` to chain a scaler before to use
# our classifier.
from xlearn.neighbors import KNeighborsClassifier
from xlearn.pipeline import Pipeline
from xlearn.preprocessing import StandardScaler

clf = Pipeline(
    steps=[("scaler", StandardScaler()), ("knn", KNeighborsClassifier(n_neighbors=11))]
)

# %%
# Decision boundary
# -----------------
#
# Now, we fit two classifiers with different values of the parameter
# `weights`. We plot the decision boundary of each classifier as well as the original
# dataset to observe the difference.
import matplotlib.pyplot as plt

from xlearn.inspection import DecisionBoundaryDisplay

_, axs = plt.subplots(ncols=2, figsize=(12, 5))

for ax, weights in zip(axs, ("uniform", "distance")):
    clf.set_params(knn__weights=weights).fit(X_train, y_train)
    disp = DecisionBoundaryDisplay.from_estimator(
        clf,
        X_test,
        response_method="predict",
        plot_method="pcolormesh",
        xlabel=iris.feature_names[0],
        ylabel=iris.feature_names[1],
        shading="auto",
        alpha=0.5,
        ax=ax,
    )
    scatter = disp.ax_.scatter(X.iloc[:, 0], X.iloc[:, 1], c=y, edgecolors="k")
    disp.ax_.legend(
        scatter.legend_elements()[0],
        iris.target_names,
        loc="lower left",
        title="Classes",
    )
    _ = disp.ax_.set_title(
        f"3-Class classification\n(k={clf[-1].n_neighbors}, weights={weights!r})"
    )

plt.show()

# %%
# Conclusion
# ----------
#
# We observe that the parameter `weights` has an impact on the decision boundary. When
# `weights="unifom"` all nearest neighbors will have the same impact on the decision.
# Whereas when `weights="distance"` the weight given to each neighbor is proportional
# to the inverse of the distance from that neighbor to the query point.
#
# In some cases, taking the distance into account might improve the model.
