frompyspark.sql.functionsimportcol,expr,when,udffromurllib.parseimporturlparse# Define a UDF (User Defined Function) to extract the domaindefextract_domain(url):ifurl.startswith('http'):returnurlparse(url).netlocreturnNone# Register the UDF with Sparkextract_domain_udf=udf(extract_domain)# Featur...
Python getPred = F.udf(lambdav: float(v[1]), FloatType())# Cache the resulting DataFrame for easier accesstest_pred_df = ( test_df.mlTransform(treatment_model) .withColumn("treatment_pred", getPred("probability")) .drop("rawPrediction","probability","prediction") .mlTransform(control_mod...
pythonclasscreate # Python 类的创建与应用 在Python中,类是面向对象编程(OOP)的核心构件。通过类,我们可以创建具有特定特征和行为的对象。这种编程范式使得代码更易于管理、重用和扩展。 ## 什么是类? 类是一种创建对象的蓝图或模板。通过类,我们可以定义对象的属性(特点)和方法(行为)。在Python中,使用`class`关...
HiveUDFDynamicLoadSuite.scala 5 changes: 2 additions & 3 deletions5python/pyspark/sql/tests/test_catalog.py Original file line numberDiff line numberDiff line change Expand Up@@ -128,9 +128,8 @@ def test_list_functions(self): withself.function("func1","some_db.func2"): ...
Python Kopija test_ranked_df = test_ranked_df.withColumn("group_uplift", F.col("treatment_cumsum") - F.col("control_cumsum")).cache() display(test_ranked_df.limit(20)) Now, plot the uplift curve for the test dataset prediction. You must convert the PySpark DataFrame to a Pandas ...
Python getPred = F.udf(lambdav: float(v[1]), FloatType())# Cache the resulting DataFrame for easier accesstest_pred_df = ( test_df.mlTransform(treatment_model) .withColumn("treatment_pred", getPred("probability")) .drop("rawPrediction","probability","prediction") .mlTransform(control_mod...
Python Копіювати test_ranked_df = test_ranked_df.withColumn("group_uplift", F.col("treatment_cumsum") - F.col("control_cumsum")).cache() display(test_ranked_df.limit(20)) Now, plot the uplift curve for the test dataset prediction. You must convert the PySpark DataFrame ...
Python getPred = F.udf(lambdav: float(v[1]), FloatType())# Cache the resulting DataFrame for easier accesstest_pred_df = ( test_df.mlTransform(treatment_model) .withColumn("treatment_pred", getPred("probability")) .drop("rawPrediction","probability","prediction") .mlTransform(control_mod...
Python Copy test_ranked_df = test_ranked_df.withColumn("group_uplift", F.col("treatment_cumsum") - F.col("control_cumsum")).cache() display(test_ranked_df.limit(20)) Now, plot the uplift curve for the test dataset prediction. You must convert the PySpark DataFrame to a Pandas ...
Python getPred = F.udf(lambdav: float(v[1]), FloatType())# Cache the resulting DataFrame for easier accesstest_pred_df = ( test_df.mlTransform(treatment_model) .withColumn("treatment_pred", getPred("probability")) .drop("rawPrediction","probability","prediction") .mlTransform(control_mod...