Created
December 19, 2018 03:55
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| from pyspark.sql.functions import lit | |
| from pyspark.ml.evaluation import MulticlassClassificationEvaluator | |
| predictionsTrainingDF = preprocessedStageTraining3.withColumn("baseline_predicton", lit(0.0)) | |
| predictionsTestDF = preprocessedStageTest3.withColumn("baseline_predicton", lit(0.0)) | |
| def display_train_and_test_f1_score(name, | |
| predictionsTrainingDF, | |
| predictionsTestDF, | |
| predictionsColumn="prediction", | |
| display_train=True): | |
| evaluator = MulticlassClassificationEvaluator( | |
| labelCol="Churn", | |
| predictionCol = predictionsColumn, | |
| metricName='f1' | |
| ) | |
| if display_train: | |
| print("{} Training F-score: {}".format(name, evaluator.evaluate(predictionsTrainingDF))) | |
| print("{} Test F-score: {}".format(name, evaluator.evaluate(predictionsTestDF))) | |
| display_train_and_test_f1_score("Baseline", predictionsTrainingDF, predictionsTestDF, "baseline_predicton") |
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