Columns

class getml.pipeline.Columns(pipeline, targets, peripheral, data=None)

Container which holds a pipeline’s columns. Columns can be accessed by name, index or with a numpy array. The container supports slicing and is sort- and filterable.

Further, the container holds global methods to request columns’ importances and apply a column selection to data frames provided to the pipeline.

Note:

The container is an iterable. So, in addition to filter() you can also use python list comprehensions for filtering.

Example:

all_my_columns = my_pipeline.columns

first_column = my_pipeline.columns[0]

all_but_last_10_columns = my_pipeline.columns[:-10]

important_columns = [column for column in my_pipeline.columns if column.importance > 0.1]

names, importances = my_pipeline.columns.importances()

# Drops all categorical and numerical columns that are not
# in the top 20%.
new_container = my_pipeline.columns.select(
    container,
    share_selected_columns=0.2,
)

Methods

filter(conditional)	Filters the columns container.
importances([target_num, sort])	Returns the data for the column importances.
select(container[, share_selected_columns])	Returns a new data container with all insufficiently important columns dropped.
sort([by, key, descending])	Sorts the Columns container.
to_pandas()	Returns all information related to the columns in a pandas data frame.

Attributes

names

Holds the names of a Pipeline’s columns.