"""
Quantile Regression
===================

The script is inspired by this awesome example in sklearn:
https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html

"""
import argparse
from typing import Dict

import numpy as np
from sklearn.model_selection import train_test_split

import xgboost as xgb


def f(x: np.ndarray) -> np.ndarray:
    """The function to predict."""
    return x * np.sin(x)


def quantile_loss(args: argparse.Namespace) -> None:
    """Train a quantile regression model."""
    rng = np.random.RandomState(1994)
    # Generate a synthetic dataset for demo, the generate process is from the sklearn
    # example.
    X = np.atleast_2d(rng.uniform(0, 10.0, size=1000)).T
    expected_y = f(X).ravel()

    sigma = 0.5 + X.ravel() / 10.0
    noise = rng.lognormal(sigma=sigma) - np.exp(sigma**2.0 / 2.0)
    y = expected_y + noise

    # Train on 0.05 and 0.95 quantiles. The model is similar to multi-class and
    # multi-target models.
    alpha = np.array([0.05, 0.5, 0.95])
    evals_result: Dict[str, Dict] = {}

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)
    # We will be using the `hist` tree method, quantile DMatrix can be used to preserve
    # memory.
    # Do not use the `exact` tree method for quantile regression, otherwise the
    # performance might drop.
    Xy = xgb.QuantileDMatrix(X, y)
    # use Xy as a reference
    Xy_test = xgb.QuantileDMatrix(X_test, y_test, ref=Xy)

    booster = xgb.train(
        {
            # Use the quantile objective function.
            "objective": "reg:quantileerror",
            "tree_method": "hist",
            "quantile_alpha": alpha,
            # Let's try not to overfit.
            "learning_rate": 0.04,
            "max_depth": 5,
        },
        Xy,
        num_boost_round=32,
        early_stopping_rounds=2,
        # The evaluation result is a weighted average across multiple quantiles.
        evals=[(Xy, "Train"), (Xy_test, "Test")],
        evals_result=evals_result,
    )
    xx = np.atleast_2d(np.linspace(0, 10, 1000)).T
    scores = booster.inplace_predict(xx)
    # dim 1 is the quantiles
    assert scores.shape[0] == xx.shape[0]
    assert scores.shape[1] == alpha.shape[0]

    y_lower = scores[:, 0]  # alpha=0.05
    y_med = scores[:, 1]  # alpha=0.5, median
    y_upper = scores[:, 2]  # alpha=0.95

    # Train a mse model for comparison
    booster = xgb.train(
        {
            "objective": "reg:squarederror",
            "tree_method": "hist",
            # Let's try not to overfit.
            "learning_rate": 0.04,
            "max_depth": 5,
        },
        Xy,
        num_boost_round=32,
        early_stopping_rounds=2,
        evals=[(Xy, "Train"), (Xy_test, "Test")],
        evals_result=evals_result,
    )
    xx = np.atleast_2d(np.linspace(0, 10, 1000)).T
    y_pred = booster.inplace_predict(xx)

    if args.plot:
        from matplotlib import pyplot as plt

        fig = plt.figure(figsize=(10, 10))
        plt.plot(xx, f(xx), "g:", linewidth=3, label=r"$f(x) = x\,\sin(x)$")
        plt.plot(X_test, y_test, "b.", markersize=10, label="Test observations")
        plt.plot(xx, y_med, "r-", label="Predicted median")
        plt.plot(xx, y_pred, "m-", label="Predicted mean")
        plt.plot(xx, y_upper, "k-")
        plt.plot(xx, y_lower, "k-")
        plt.fill_between(
            xx.ravel(), y_lower, y_upper, alpha=0.4, label="Predicted 90% interval"
        )
        plt.xlabel("$x$")
        plt.ylabel("$f(x)$")
        plt.ylim(-10, 25)
        plt.legend(loc="upper left")
        plt.show()


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--plot",
        action="store_true",
        help="Specify it to enable plotting the outputs.",
    )
    args = parser.parse_args()
    quantile_loss(args)
