SpuriousCount#

from tmlt.tune import SpuriousCount

class tmlt.tune.SpuriousCount(join_columns, *, name=None, description=None, baseline=None, output=None, grouping_columns=None)#

Bases: JoinedOutputMetric

Computes the number of values in the DP output but not in the baseline output.

This metric returns the number of values of join_columns that appear in the DP output but not in the baseline output (such values are called spurious).

If grouping_columns is defined, then the DP output and the baseline output are both grouped by these columns, the spurious count is calculated separately for each group, and the metric returns a DataFrame. Otherwise, the metric returns a single number.

In each group (or globally if grouping_column is None), each combination of values of join_columns must appear in at most one row of the DP output and the baseline output. Otherwise, the metric returns an error.

Example

>>> dp_df = spark.createDataFrame(
...     pd.DataFrame(
...         {
...             "A": ["a1", "a2", "a3", "c"],
...             "X": [50, 110, 100, 50]
...         }
...     )
... )
>>> dp_outputs = {"O": dp_df}
>>> baseline_df = spark.createDataFrame(
...     pd.DataFrame(
...         {
...             "A": ["a1", "a2", "a3", "b"],
...             "X": [100, 100, 100, 50]
...         }
...     )
... )
>>> baseline_outputs = {"default": {"O": baseline_df}}

>>> metric = SpuriousCount(
...     join_columns=["A"]
... )
>>> metric.join_columns
['A']
>>> metric(dp_outputs, baseline_outputs).value
1

count_spurious_rows(joined_output, result_column_name)#

Computes spurious count given DP and baseline outputs.

Tumult Platform

SpuriousCount#