如何在超空间(spark)中运行createindex函数根据https://github.com/microsoft/hyperspace/discussions/285，这是databricks运行时的一个已知问题。如果您使用开源spark，它应该可以工作。与databricks团队寻求解决方案。
In the provided code section, we load a cleaned and feature-engineered dataset from the lakehouse using Delta format, split it into training and testing sets with an 80-20 ratio, and prepare the data for machine learning. This preparation involves importing the VectorAssembler from PySpark ML to...
fromdelta.tablesimport*frompyspark.sql.functionsimport*# Create a deltaTable objectdeltaTable = DeltaTable.forPath(spark, delta_table_path)# Update the table (reduce price of accessories by 10%)deltaTable.update( condition ="Category == 'Accessories'", set = {"Price":"Price * 0.9"}) ...
These datasets are now ready for use in building and evaluating machine learning models. Python Copy # Import the necessary library for feature vectorization from pyspark.ml.feature import VectorAssembler # Load the cleaned and feature-engineered dataset from the lakehouse df_final = spark.read....
In the provided code section, we load a cleaned and feature-engineered dataset from the lakehouse using Delta format, split it into training and testing sets with an 80-20 ratio, and prepare the data for machine learning. This preparation involves importing the VectorAssembler from PySpark ML to...
In the provided code section, we load a cleaned and feature-engineered dataset from the lakehouse using Delta format, split it into training and testing sets with an 80-20 ratio, and prepare the data for machine learning. This preparation involves importing the VectorAssembler from PySpark ML to...
# Import the necessary library for feature vectorization from pyspark.ml.feature import VectorAssembler # Load the cleaned and feature-engineered dataset from the lakehouse df_final = spark.read.format("delta").load("Tables/churn_data_clean") # Train-Test Separation train_raw, test_raw = df_fi...
In the provided code section, we load a cleaned and feature-engineered dataset from the lakehouse using Delta format, split it into training and testing sets with an 80-20 ratio, and prepare the data for machine learning. This preparation involves importing the VectorAssembler from PySpark ML to...
In the provided code section, we load a cleaned and feature-engineered dataset from the lakehouse using Delta format, split it into training and testing sets with an 80-20 ratio, and prepare the data for machine learning. This preparation involves importing the VectorAssembler from PySpark ML to...
fromdelta.tablesimport*frompyspark.sql.functionsimport*# Create a deltaTable objectdeltaTable = DeltaTable.forPath(spark, delta_table_path)# Update the table (reduce price of accessories by 10%)deltaTable.update( condition ="Category == 'Accessories'", set = {"Price":"Price * 0.9"}) ...