"""
===============================
Plot classification probability
===============================

This example illustrates the use of
:class:`xlearn.inspection.DecisionBoundaryDisplay` to plot the predicted class
probabilities of various classifiers in a 2D feature space, mostly for didactic
purposes.

The first three columns shows the predicted probability for varying values of
the two features. Round markers represent the test data that was predicted to
belong to that class.

In the last column, all three classes are represented on each plot; the class
with the highest predicted probability at each point is plotted. The round
markers show the test data and are colored by their true label.
"""

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

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib import cm

from xlearn import datasets
from xlearn.ensemble import HistGradientBoostingClassifier
from xlearn.gaussian_process import GaussianProcessClassifier
from xlearn.gaussian_process.kernels import RBF
from xlearn.inspection import DecisionBoundaryDisplay
from xlearn.kernel_approximation import Nystroem
from xlearn.linear_model import LogisticRegression
from xlearn.metrics import accuracy_score, log_loss, roc_auc_score
from xlearn.model_selection import train_test_split
from xlearn.pipeline import make_pipeline
from xlearn.preprocessing import (
    KBinsDiscretizer,
    PolynomialFeatures,
    SplineTransformer,
)

# %%
# Data: 2D projection of the iris dataset
# ---------------------------------------
iris = datasets.load_iris()
X = iris.data[:, 0:2]  # we only take the first two features for visualization
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.5, random_state=42
)


# %%
# Probabilistic classifiers
# -------------------------
#
# We will plot the decision boundaries of several classifiers that have a
# `predict_proba` method. This will allow us to visualize the uncertainty of
# the classifier in regions where it is not certain of its prediction.

classifiers = {
    "Logistic regression\n(C=0.01)": LogisticRegression(C=0.1),
    "Logistic regression\n(C=1)": LogisticRegression(C=100),
    "Gaussian Process": GaussianProcessClassifier(kernel=1.0 * RBF([1.0, 1.0])),
    "Logistic regression\n(RBF features)": make_pipeline(
        Nystroem(kernel="rbf", gamma=5e-1, n_components=50, random_state=1),
        LogisticRegression(C=10),
    ),
    "Gradient Boosting": HistGradientBoostingClassifier(),
    "Logistic regression\n(binned features)": make_pipeline(
        KBinsDiscretizer(n_bins=5, quantile_method="averaged_inverted_cdf"),
        PolynomialFeatures(interaction_only=True),
        LogisticRegression(C=10),
    ),
    "Logistic regression\n(spline features)": make_pipeline(
        SplineTransformer(n_knots=5),
        PolynomialFeatures(interaction_only=True),
        LogisticRegression(C=10),
    ),
}

# %%
# Plotting the decision boundaries
# --------------------------------
#
# For each classifier, we plot the per-class probabilities on the first three
# columns and the probabilities of the most likely class on the last column.

n_classifiers = len(classifiers)
scatter_kwargs = {
    "s": 25,
    "marker": "o",
    "linewidths": 0.8,
    "edgecolor": "k",
    "alpha": 0.7,
}
y_unique = np.unique(y)

# Ensure legend not cut off
mpl.rcParams["savefig.bbox"] = "tight"
fig, axes = plt.subplots(
    nrows=n_classifiers,
    ncols=len(iris.target_names) + 1,
    figsize=(4 * 2.2, n_classifiers * 2.2),
)
evaluation_results = []
levels = 100
for classifier_idx, (name, classifier) in enumerate(classifiers.items()):
    y_pred = classifier.fit(X_train, y_train).predict(X_test)
    y_pred_proba = classifier.predict_proba(X_test)
    accuracy_test = accuracy_score(y_test, y_pred)
    roc_auc_test = roc_auc_score(y_test, y_pred_proba, multi_class="ovr")
    log_loss_test = log_loss(y_test, y_pred_proba)
    evaluation_results.append(
        {
            "name": name.replace("\n", " "),
            "accuracy": accuracy_test,
            "roc_auc": roc_auc_test,
            "log_loss": log_loss_test,
        }
    )
    for label in y_unique:
        # plot the probability estimate provided by the classifier
        disp = DecisionBoundaryDisplay.from_estimator(
            classifier,
            X_train,
            response_method="predict_proba",
            class_of_interest=label,
            ax=axes[classifier_idx, label],
            vmin=0,
            vmax=1,
            cmap="Blues",
            levels=levels,
        )
        axes[classifier_idx, label].set_title(f"Class {label}")
        # plot data predicted to belong to given class
        mask_y_pred = y_pred == label
        axes[classifier_idx, label].scatter(
            X_test[mask_y_pred, 0], X_test[mask_y_pred, 1], c="w", **scatter_kwargs
        )

        axes[classifier_idx, label].set(xticks=(), yticks=())
    # add column that shows all classes by plotting class with max 'predict_proba'
    max_class_disp = DecisionBoundaryDisplay.from_estimator(
        classifier,
        X_train,
        response_method="predict_proba",
        class_of_interest=None,
        ax=axes[classifier_idx, len(y_unique)],
        vmin=0,
        vmax=1,
        levels=levels,
    )
    for label in y_unique:
        mask_label = y_test == label
        axes[classifier_idx, 3].scatter(
            X_test[mask_label, 0],
            X_test[mask_label, 1],
            c=max_class_disp.multiclass_colors_[[label], :],
            **scatter_kwargs,
        )

    axes[classifier_idx, 3].set(xticks=(), yticks=())
    axes[classifier_idx, 3].set_title("Max class")
    axes[classifier_idx, 0].set_ylabel(name)

# colorbar for single class plots
ax_single = fig.add_axes([0.15, 0.01, 0.5, 0.02])
plt.title("Probability")
_ = plt.colorbar(
    cm.ScalarMappable(norm=None, cmap=disp.surface_.cmap),
    cax=ax_single,
    orientation="horizontal",
)

# colorbars for max probability class column
max_class_cmaps = [s.cmap for s in max_class_disp.surface_]

for label in y_unique:
    ax_max = fig.add_axes([0.73, (0.06 - (label * 0.04)), 0.16, 0.015])
    plt.title(f"Probability class {label}", fontsize=10)
    _ = plt.colorbar(
        cm.ScalarMappable(norm=None, cmap=max_class_cmaps[label]),
        cax=ax_max,
        orientation="horizontal",
    )
    if label in (0, 1):
        ax_max.set(xticks=(), yticks=())


# %%
# Quantitative evaluation
# -----------------------
pd.DataFrame(evaluation_results).round(2)


# %%
# Analysis
# --------
#
# The two logistic regression models fitted on the original features display
# linear decision boundaries as expected. For this particular problem, this
# does not seem to be detrimental as both models are competitive with the
# non-linear models when quantitatively evaluated on the test set. We can
# observe that the amount of regularization influences the model confidence:
# lighter colors for the strongly regularized model with a lower value of `C`.
# Regularization also impacts the orientation of decision boundary leading to
# slightly different ROC AUC.
#
# The log-loss on the other hand evaluates both sharpness and calibration and
# as a result strongly favors the weakly regularized logistic-regression model,
# probably because the strongly regularized model is under-confident. This
# could be confirmed by looking at the calibration curve using
# :class:`xlearn.calibration.CalibrationDisplay`.
#
# The logistic regression model with RBF features has a "blobby" decision
# boundary that is non-linear in the original feature space and is quite
# similar to the decision boundary of the Gaussian process classifier which is
# configured to use an RBF kernel.
#
# The logistic regression model fitted on binned features with interactions has
# a decision boundary that is non-linear in the original feature space and is
# quite similar to the decision boundary of the gradient boosting classifier:
# both models favor axis-aligned decisions when extrapolating to unseen region
# of the feature space.
#
# The logistic regression model fitted on spline features with interactions
# has a similar axis-aligned extrapolation behavior but a smoother decision
# boundary in the dense region of the feature space than the two previous
# models.
#
# To conclude, it is interesting to observe that feature engineering for
# logistic regression models can be used to mimic some of the inductive bias of
# various non-linear models. However, for this particular dataset, using the
# raw features is enough to train a competitive model. This would not
# necessarily the case for other datasets.
