"""
Demo for using and defining callback functions
==============================================

    .. versionadded:: 1.3.0
"""

import argparse
import os
import tempfile
from typing import Dict

import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

import xgboost as xgb


class Plotting(xgb.callback.TrainingCallback):
    """Plot evaluation result during training.  Only for demonstration purpose as it's
    quite slow to draw using matplotlib.

    """

    def __init__(self, rounds: int) -> None:
        self.fig = plt.figure()
        self.ax = self.fig.add_subplot(111)
        self.rounds = rounds
        self.lines: Dict[str, plt.Line2D] = {}
        self.fig.show()
        self.x = np.linspace(0, self.rounds, self.rounds)
        plt.ion()

    def _get_key(self, data: str, metric: str) -> str:
        return f"{data}-{metric}"

    def after_iteration(
        self, model: xgb.Booster, epoch: int, evals_log: Dict[str, dict]
    ) -> bool:
        """Update the plot."""
        if not self.lines:
            for data, metric in evals_log.items():
                for metric_name, log in metric.items():
                    key = self._get_key(data, metric_name)
                    expanded = log + [0] * (self.rounds - len(log))
                    (self.lines[key],) = self.ax.plot(self.x, expanded, label=key)
                    self.ax.legend()
        else:
            # https://pythonspot.com/matplotlib-update-plot/
            for data, metric in evals_log.items():
                for metric_name, log in metric.items():
                    key = self._get_key(data, metric_name)
                    expanded = log + [0] * (self.rounds - len(log))
                    self.lines[key].set_ydata(expanded)
            self.fig.canvas.draw()
        # False to indicate training should not stop.
        return False


def custom_callback() -> None:
    """Demo for defining a custom callback function that plots evaluation result during
    training."""
    X, y = load_breast_cancer(return_X_y=True)
    X_train, X_valid, y_train, y_valid = train_test_split(X, y, random_state=0)

    D_train = xgb.DMatrix(X_train, y_train)
    D_valid = xgb.DMatrix(X_valid, y_valid)

    num_boost_round = 100
    plotting = Plotting(num_boost_round)

    # Pass it to the `callbacks` parameter as a list.
    xgb.train(
        {
            "objective": "binary:logistic",
            "eval_metric": ["error", "rmse"],
            "tree_method": "hist",
            "device": "cuda",
        },
        D_train,
        evals=[(D_train, "Train"), (D_valid, "Valid")],
        num_boost_round=num_boost_round,
        callbacks=[plotting],
    )


def check_point_callback() -> None:
    """Demo for using the checkpoint callback. Custom logic for handling output is
    usually required and users are encouraged to define their own callback for
    checkpointing operations. The builtin one can be used as a starting point.

    """
    # Only for demo, set a larger value (like 100) in practice as checkpointing is quite
    # slow.
    rounds = 2

    def check(as_pickle: bool) -> None:
        for i in range(0, 10, rounds):
            if i == 0:
                continue
            if as_pickle:
                path = os.path.join(tmpdir, "model_" + str(i) + ".pkl")
            else:
                path = os.path.join(
                    tmpdir,
                    f"model_{i}.{xgb.callback.TrainingCheckPoint.default_format}",
                )
            assert os.path.exists(path)

    X, y = load_breast_cancer(return_X_y=True)
    m = xgb.DMatrix(X, y)
    # Check point to a temporary directory for demo
    with tempfile.TemporaryDirectory() as tmpdir:
        # Use callback class from xgboost.callback
        # Feel free to subclass/customize it to suit your need.
        check_point = xgb.callback.TrainingCheckPoint(
            directory=tmpdir, interval=rounds, name="model"
        )
        xgb.train(
            {"objective": "binary:logistic"},
            m,
            num_boost_round=10,
            verbose_eval=False,
            callbacks=[check_point],
        )
        check(False)

        # This version of checkpoint saves everything including parameters and
        # model.  See: doc/tutorials/saving_model.rst
        check_point = xgb.callback.TrainingCheckPoint(
            directory=tmpdir, interval=rounds, as_pickle=True, name="model"
        )
        xgb.train(
            {"objective": "binary:logistic"},
            m,
            num_boost_round=10,
            verbose_eval=False,
            callbacks=[check_point],
        )
        check(True)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--plot", default=1, type=int)
    args = parser.parse_args()

    check_point_callback()

    if args.plot:
        custom_callback()

"""
Demo for using and defining callback functions
==============================================

    .. versionadded:: 1.3.0
"""
import argparse
import os
import tempfile
from typing import Dict

import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

import xgboost as xgb


class Plotting(xgb.callback.TrainingCallback):
    """Plot evaluation result during training.  Only for demonstration purpose as it's
    quite slow to draw using matplotlib.

    """

    def __init__(self, rounds: int) -> None:
        self.fig = plt.figure()
        self.ax = self.fig.add_subplot(111)
        self.rounds = rounds
        self.lines: Dict[str, plt.Line2D] = {}
        self.fig.show()
        self.x = np.linspace(0, self.rounds, self.rounds)
        plt.ion()

    def _get_key(self, data: str, metric: str) -> str:
        return f"{data}-{metric}"

    def after_iteration(
        self, model: xgb.Booster, epoch: int, evals_log: Dict[str, dict]
    ) -> bool:
        """Update the plot."""
        if not self.lines:
            for data, metric in evals_log.items():
                for metric_name, log in metric.items():
                    key = self._get_key(data, metric_name)
                    expanded = log + [0] * (self.rounds - len(log))
                    (self.lines[key],) = self.ax.plot(self.x, expanded, label=key)
                    self.ax.legend()
        else:
            # https://pythonspot.com/matplotlib-update-plot/
            for data, metric in evals_log.items():
                for metric_name, log in metric.items():
                    key = self._get_key(data, metric_name)
                    expanded = log + [0] * (self.rounds - len(log))
                    self.lines[key].set_ydata(expanded)
            self.fig.canvas.draw()
        # False to indicate training should not stop.
        return False


def custom_callback() -> None:
    """Demo for defining a custom callback function that plots evaluation result during
    training."""
    X, y = load_breast_cancer(return_X_y=True)
    X_train, X_valid, y_train, y_valid = train_test_split(X, y, random_state=0)

    D_train = xgb.DMatrix(X_train, y_train)
    D_valid = xgb.DMatrix(X_valid, y_valid)

    num_boost_round = 100
    plotting = Plotting(num_boost_round)

    # Pass it to the `callbacks` parameter as a list.
    xgb.train(
        {
            "objective": "binary:logistic",
            "eval_metric": ["error", "rmse"],
            "tree_method": "hist",
            "device": "cuda",
        },
        D_train,
        evals=[(D_train, "Train"), (D_valid, "Valid")],
        num_boost_round=num_boost_round,
        callbacks=[plotting],
    )


def check_point_callback() -> None:
    """Demo for using the checkpoint callback. Custom logic for handling output is
    usually required and users are encouraged to define their own callback for
    checkpointing operations. The builtin one can be used as a starting point.

    """
    # Only for demo, set a larger value (like 100) in practice as checkpointing is quite
    # slow.
    rounds = 2

    def check(as_pickle: bool) -> None:
        for i in range(0, 10, rounds):
            if i == 0:
                continue
            if as_pickle:
                path = os.path.join(tmpdir, "model_" + str(i) + ".pkl")
            else:
                path = os.path.join(
                    tmpdir,
                    f"model_{i}.{xgb.callback.TrainingCheckPoint.default_format}",
                )
            assert os.path.exists(path)

    X, y = load_breast_cancer(return_X_y=True)
    m = xgb.DMatrix(X, y)
    # Check point to a temporary directory for demo
    with tempfile.TemporaryDirectory() as tmpdir:
        # Use callback class from xgboost.callback
        # Feel free to subclass/customize it to suit your need.
        check_point = xgb.callback.TrainingCheckPoint(
            directory=tmpdir, interval=rounds, name="model"
        )
        xgb.train(
            {"objective": "binary:logistic"},
            m,
            num_boost_round=10,
            verbose_eval=False,
            callbacks=[check_point],
        )
        check(False)

        # This version of checkpoint saves everything including parameters and
        # model.  See: doc/tutorials/saving_model.rst
        check_point = xgb.callback.TrainingCheckPoint(
            directory=tmpdir, interval=rounds, as_pickle=True, name="model"
        )
        xgb.train(
            {"objective": "binary:logistic"},
            m,
            num_boost_round=10,
            verbose_eval=False,
            callbacks=[check_point],
        )
        check(True)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--plot", default=1, type=int)
    args = parser.parse_args()

    check_point_callback()

    if args.plot:
        custom_callback()
