def rating_metrics_pyspark(test, predictions):
rating_eval = SparkRatingEvaluation(test, predictions, **COL_DICT)
return {
"RMSE": rating_eval.rmse(),
"MAE": rating_eval.mae(),
"R2": rating_eval.exp_var(),
"Explained Variance": rating_eval.rsquared()
}
def rating_metrics_pyspark(test, predictions):
rating_eval = SparkRatingEvaluation(test, predictions, **COL_DICT)
return {
"RMSE": rating_eval.rmse(),
"MAE": rating_eval.mae(),
"R2": rating_eval.exp_var(),
"Explained Variance": rating_eval.rsquared(),
}
def test_spark_exp_var(spark_data, target_metrics):
df_true, df_pred = spark_data
evaluator1 = SparkRatingEvaluation(df_true, df_true, col_prediction="rating")
assert evaluator1.exp_var() == pytest.approx(1.0, TOL)
evaluator2 = SparkRatingEvaluation(df_true, df_pred)
assert evaluator2.exp_var() == target_metrics["exp_var"]
def test_spark_mae(spark_data, target_metrics):
df_true, df_pred = spark_data
evaluator1 = SparkRatingEvaluation(df_true, df_true, col_prediction="rating")
assert evaluator1.mae() == 0
evaluator2 = SparkRatingEvaluation(df_true, df_pred)
assert evaluator2.mae() == target_metrics["mae"]
def test_init_spark_rating_eval(spark_data):
df_true, df_pred = spark_data
evaluator = SparkRatingEvaluation(df_true, df_pred)
assert evaluator is not None
