- WoodworkTableAccessor.mutual_information(num_bins=10, nrows=None, include_index=False, callback=None, extra_stats=False, min_shared=25, random_seed=0, max_nunique=6000)#
Calculates mutual information between all pairs of columns in the DataFrame that support mutual information. Call woodwork.utils.get_valid_mi_types to see which Logical Types support mutual information.
num_bins (int) – Determines number of bins to use for converting numeric features into categorical. Defaults to 10.
nrows (int) – The number of rows to sample for when determining mutual info. If specified, samples the desired number of rows from the data. Defaults to using all rows.
include_index (bool) – If True, the column specified as the index will be included as long as its LogicalType is valid for mutual information calculations. If False, the index column will not have mutual information calculated for it. Defaults to False.
callback (callable, optional) – Function to be called with incremental updates. Has the following parameters: - update (int): change in progress since last call - progress (int): the progress so far in the calculations - total (int): the total number of calculations to do - unit (str): unit of measurement for progress/total - time_elapsed (float): total time in seconds elapsed since start of call
extra_stats (bool) – If True, additional column “shared_rows” recording the number of shared non-null rows for a column pair will be included with the dataframe. Defaults to False.
min_shared (int) – The number of shared non-null rows needed to calculate. Less rows than this will be considered too sparse to measure accurately and will return a NaN value. Must be non-negative. Defaults to 25.
random_seed (int) – Seed for the random number generator. Defaults to 0.
max_nunique (int) – The total maximum number of unique values for all large categorical columns (> 800 unique values). Categorical columns will be dropped until this number is met or until there is only one large categorical column. Defaults to 6000.
A DataFrame containing mutual information with columns column_1, column_2, and mutual_info that is sorted in decending order by mutual info. Mutual information values are between 0 (no mutual information) and 1 (perfect dependency).
- Return type: