"""
Collection of examples for using xgboost.spark estimator interface
==================================================================

@author: Weichen Xu
"""
import sklearn.datasets
from pyspark.ml.evaluation import MulticlassClassificationEvaluator, RegressionEvaluator
from pyspark.ml.linalg import Vectors
from pyspark.sql import SparkSession
from pyspark.sql.functions import rand
from sklearn.model_selection import train_test_split
from xgboost.spark import SparkXGBClassifier, SparkXGBRegressor

spark = SparkSession.builder.master("local[*]").getOrCreate()


def create_spark_df(X, y):
    return spark.createDataFrame(
        spark.sparkContext.parallelize(
            [(Vectors.dense(features), float(label)) for features, label in zip(X, y)]
        ),
        ["features", "label"],
    )


# load diabetes dataset (regression dataset)
diabetes_X, diabetes_y = sklearn.datasets.load_diabetes(return_X_y=True)
diabetes_X_train, diabetes_X_test, diabetes_y_train, diabetes_y_test = train_test_split(
    diabetes_X, diabetes_y, test_size=0.3, shuffle=True
)

diabetes_train_spark_df = create_spark_df(diabetes_X_train, diabetes_y_train)
diabetes_test_spark_df = create_spark_df(diabetes_X_test, diabetes_y_test)

# train xgboost regressor model
xgb_regressor = SparkXGBRegressor(max_depth=5)
xgb_regressor_model = xgb_regressor.fit(diabetes_train_spark_df)

transformed_diabetes_test_spark_df = xgb_regressor_model.transform(
    diabetes_test_spark_df
)
regressor_evaluator = RegressionEvaluator(metricName="rmse")
print(
    f"regressor rmse={regressor_evaluator.evaluate(transformed_diabetes_test_spark_df)}"
)

diabetes_train_spark_df2 = diabetes_train_spark_df.withColumn(
    "validationIndicatorCol", rand(1) > 0.7
)

# train xgboost regressor model with validation dataset
xgb_regressor2 = SparkXGBRegressor(
    max_depth=5, validation_indicator_col="validationIndicatorCol"
)
xgb_regressor_model2 = xgb_regressor2.fit(diabetes_train_spark_df2)
transformed_diabetes_test_spark_df2 = xgb_regressor_model2.transform(
    diabetes_test_spark_df
)
print(
    f"regressor2 rmse={regressor_evaluator.evaluate(transformed_diabetes_test_spark_df2)}"
)


# load iris dataset (classification dataset)
iris_X, iris_y = sklearn.datasets.load_iris(return_X_y=True)
iris_X_train, iris_X_test, iris_y_train, iris_y_test = train_test_split(
    iris_X, iris_y, test_size=0.3, shuffle=True
)

iris_train_spark_df = create_spark_df(iris_X_train, iris_y_train)
iris_test_spark_df = create_spark_df(iris_X_test, iris_y_test)

# train xgboost classifier model
xgb_classifier = SparkXGBClassifier(max_depth=5)
xgb_classifier_model = xgb_classifier.fit(iris_train_spark_df)

transformed_iris_test_spark_df = xgb_classifier_model.transform(iris_test_spark_df)
classifier_evaluator = MulticlassClassificationEvaluator(metricName="f1")
print(f"classifier f1={classifier_evaluator.evaluate(transformed_iris_test_spark_df)}")

iris_train_spark_df2 = iris_train_spark_df.withColumn(
    "validationIndicatorCol", rand(1) > 0.7
)

# train xgboost classifier model with validation dataset
xgb_classifier2 = SparkXGBClassifier(
    max_depth=5, validation_indicator_col="validationIndicatorCol"
)
xgb_classifier_model2 = xgb_classifier2.fit(iris_train_spark_df2)
transformed_iris_test_spark_df2 = xgb_classifier_model2.transform(iris_test_spark_df)
print(
    f"classifier2 f1={classifier_evaluator.evaluate(transformed_iris_test_spark_df2)}"
)

spark.stop()
