from typing import Tuple, Optional, List
import pandas as pd
from ._grouped_metric_result import GroupedMetricResult
[docs]class MaxSensitivityResult(GroupedMetricResult):
"""Represents results from running the Max-Sensitivity metric.
"""
def __init__(self, method_names: List[str], num_samples: int):
"""
Parameters
----------
method_names : List[str]
Names of attribution methods tested by Max-Sensitivity.
num_samples : int
Number of samples on which Max-Sensitivity was run.
"""
levels = {"method": method_names}
level_order = ["method"]
shape = [num_samples]
super().__init__(method_names, shape, levels, level_order)
@classmethod
def _load(cls, path: str, format="hdf5") -> "MaxSensitivityResult":
tree = cls._load_tree(path, format)
res = MaxSensitivityResult(tree.levels["method"], tree.shape[0])
res.tree = tree
return res
[docs] def get_df(
self, methods: Optional[List[str]] = None
) -> Tuple[pd.DataFrame, bool]:
"""Retrieves a dataframe from the result. The dataframe contains a row
for each method and a column for each sample. Each value is the
Max-Sensitivity for the given method on the given sample.
Parameters
----------
methods : Optional[List[str]], optional
the methods to include. If None, includes all methods.
Defaults to None.
Returns
-------
Tuple[pd.DataFrame, bool]
Dataframe containing results,
and boolean indicating if higher is better.
"""
methods = methods if methods is not None else self.method_names
df_dict = {}
for method in methods:
df_dict[method] = self.tree.get(method=method)
return pd.DataFrame.from_dict(df_dict), False