mmlspark.cyber.utils package¶
Submodules¶
mmlspark.cyber.utils.spark_utils module¶
-
class
mmlspark.cyber.utils.spark_utils.
DataFrameUtils
[source]¶ Bases:
object
Extension methods over Spark DataFrame
-
static
get_spark_session
(df: pyspark.sql.dataframe.DataFrame) → pyspark.sql.session.SparkSession[source]¶ get the associated Spark session
- Parameters
df (DataFrame) – the dataframe of which we want to get its Spark session
-
static
make_empty
(df: pyspark.sql.dataframe.DataFrame) → pyspark.sql.dataframe.DataFrame[source]¶ make an empty dataframe with the same schema
- Parameters
the dataframe whose schema we wish to use (df) –
an empty dataframe (Returns) –
------- –
-
static
zip_with_index
(df: pyspark.sql.dataframe.DataFrame, start_index: int = 0, col_name: str = 'rowId', partition_col: Union[List[str], str] = [], order_by_col: Union[List[str], str] = []) → pyspark.sql.dataframe.DataFrame[source]¶ add an index to the given dataframe
- Parameters
df (dataframe) – the dataframe to add the index to
start_index (int) – the value to start the count from
col_name (str) – the name of the index column which will be added as last column in the output data frame
partition_col (Union[List[str], str]) – optional column name or list of columns names that define a partitioning to assign indices independently to, e.g., assign sequential indices separately to each distinct tenant
order_by_col (Union[List[str], str]) – optional order by column name or list of columns that are used for sorting the data frame or partitions before indexing
-
static
Module contents¶
MicrosoftML is a library of Python classes to interface with the Microsoft scala APIs to utilize Apache Spark to create distibuted machine learning models.
MicrosoftML simplifies training and scoring classifiers and regressors, as well as facilitating the creation of models using the CNTK library, images, and text.