"""
Example of using callbacks with Dask
====================================
"""
import numpy as np
from dask.distributed import Client, LocalCluster
from dask_ml.datasets import make_regression
from dask_ml.model_selection import train_test_split

import xgboost as xgb
import xgboost.dask as dxgb
from xgboost.dask import DaskDMatrix


def probability_for_going_backward(epoch):
    return 0.999 / (1.0 + 0.05 * np.log(1.0 + epoch))


# All callback functions must inherit from TrainingCallback
class CustomEarlyStopping(xgb.callback.TrainingCallback):
    """A custom early stopping class where early stopping is determined stochastically.
    In the beginning, allow the metric to become worse with a probability of 0.999.
    As boosting progresses, the probability should be adjusted downward"""

    def __init__(self, *, validation_set, target_metric, maximize, seed):
        self.validation_set = validation_set
        self.target_metric = target_metric
        self.maximize = maximize
        self.seed = seed
        self.rng = np.random.default_rng(seed=seed)
        if maximize:
            self.better = lambda x, y: x > y
        else:
            self.better = lambda x, y: x < y

    def after_iteration(self, model, epoch, evals_log):
        metric_history = evals_log[self.validation_set][self.target_metric]
        if len(metric_history) < 2 or self.better(
            metric_history[-1], metric_history[-2]
        ):
            return False  # continue training
        p = probability_for_going_backward(epoch)
        go_backward = self.rng.choice(2, size=(1,), replace=True, p=[1 - p, p]).astype(
            np.bool
        )[0]
        print(
            "The validation metric went into the wrong direction. "
            + f"Stopping training with probability {1 - p}..."
        )
        if go_backward:
            return False  # continue training
        else:
            return True  # stop training


def main(client):
    m = 100000
    n = 100
    X, y = make_regression(n_samples=m, n_features=n, chunks=200, random_state=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    dtrain = DaskDMatrix(client, X_train, y_train)
    dtest = DaskDMatrix(client, X_test, y_test)

    output = dxgb.train(
        client,
        {
            "verbosity": 1,
            "tree_method": "hist",
            "objective": "reg:squarederror",
            "eval_metric": "rmse",
            "max_depth": 6,
            "learning_rate": 1.0,
        },
        dtrain,
        num_boost_round=1000,
        evals=[(dtrain, "train"), (dtest, "test")],
        callbacks=[
            CustomEarlyStopping(
                validation_set="test", target_metric="rmse", maximize=False, seed=0
            )
        ],
    )


if __name__ == "__main__":
    # or use other clusters for scaling
    with LocalCluster(n_workers=4, threads_per_worker=1) as cluster:
        with Client(cluster) as client:
            main(client)

"""
Example of using callbacks with Dask
====================================
"""
import numpy as np
from dask.distributed import Client, LocalCluster
from dask_ml.datasets import make_regression
from dask_ml.model_selection import train_test_split

import xgboost as xgb
from xgboost.dask import DaskDMatrix


def probability_for_going_backward(epoch):
    return 0.999 / (1.0 + 0.05 * np.log(1.0 + epoch))


# All callback functions must inherit from TrainingCallback
class CustomEarlyStopping(xgb.callback.TrainingCallback):
    """A custom early stopping class where early stopping is determined stochastically.
    In the beginning, allow the metric to become worse with a probability of 0.999.
    As boosting progresses, the probability should be adjusted downward"""

    def __init__(self, *, validation_set, target_metric, maximize, seed):
        self.validation_set = validation_set
        self.target_metric = target_metric
        self.maximize = maximize
        self.seed = seed
        self.rng = np.random.default_rng(seed=seed)
        if maximize:
            self.better = lambda x, y: x > y
        else:
            self.better = lambda x, y: x < y

    def after_iteration(self, model, epoch, evals_log):
        metric_history = evals_log[self.validation_set][self.target_metric]
        if len(metric_history) < 2 or self.better(
            metric_history[-1], metric_history[-2]
        ):
            return False  # continue training
        p = probability_for_going_backward(epoch)
        go_backward = self.rng.choice(2, size=(1,), replace=True, p=[1 - p, p]).astype(
            np.bool
        )[0]
        print(
            "The validation metric went into the wrong direction. "
            + f"Stopping training with probability {1 - p}..."
        )
        if go_backward:
            return False  # continue training
        else:
            return True  # stop training


def main(client):
    m = 100000
    n = 100
    X, y = make_regression(n_samples=m, n_features=n, chunks=200, random_state=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    dtrain = DaskDMatrix(client, X_train, y_train)
    dtest = DaskDMatrix(client, X_test, y_test)

    output = xgb.dask.train(
        client,
        {
            "verbosity": 1,
            "tree_method": "hist",
            "objective": "reg:squarederror",
            "eval_metric": "rmse",
            "max_depth": 6,
            "learning_rate": 1.0,
        },
        dtrain,
        num_boost_round=1000,
        evals=[(dtrain, "train"), (dtest, "test")],
        callbacks=[
            CustomEarlyStopping(
                validation_set="test", target_metric="rmse", maximize=False, seed=0
            )
        ],
    )


if __name__ == "__main__":
    # or use other clusters for scaling
    with LocalCluster(n_workers=4, threads_per_worker=1) as cluster:
        with Client(cluster) as client:
            main(client)
