import argparse

import matplotlib as mpl
import xgboost as xgb
from sklearn import datasets
from sklearn.metrics import accuracy_score, log_loss
from sklearn.model_selection import train_test_split

import mlflow
import mlflow.xgboost

mpl.use("Agg")


def parse_args():
    parser = argparse.ArgumentParser(description="XGBoost example")
    parser.add_argument(
        "--learning-rate",
        type=float,
        default=0.3,
        help="learning rate to update step size at each boosting step (default: 0.3)",
    )
    parser.add_argument(
        "--colsample-bytree",
        type=float,
        default=1.0,
        help="subsample ratio of columns when constructing each tree (default: 1.0)",
    )
    parser.add_argument(
        "--subsample",
        type=float,
        default=1.0,
        help="subsample ratio of the training instances (default: 1.0)",
    )
    return parser.parse_args()


def main():
    # parse command-line arguments
    args = parse_args()

    # prepare train and test data
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # enable auto logging
    mlflow.xgboost.autolog()

    dtrain = xgb.DMatrix(X_train, label=y_train)
    dtest = xgb.DMatrix(X_test, label=y_test)

    with mlflow.start_run():
        # train model
        params = {
            "objective": "multi:softprob",
            "num_class": 3,
            "learning_rate": args.learning_rate,
            "eval_metric": "mlogloss",
            "colsample_bytree": args.colsample_bytree,
            "subsample": args.subsample,
            "seed": 42,
        }
        model = xgb.train(params, dtrain, evals=[(dtrain, "train")])

        # evaluate model
        y_proba = model.predict(dtest)
        y_pred = y_proba.argmax(axis=1)
        loss = log_loss(y_test, y_proba)
        acc = accuracy_score(y_test, y_pred)

        # log metrics
        mlflow.log_metrics({"log_loss": loss, "accuracy": acc})


if __name__ == "__main__":
    main()