Adding intersectional bias mitigation to AIF360

aif360/algorithms/__init__.py

@@ -1 +1,2 @@
 from aif360.algorithms.transformer import Transformer, addmetadata
+from aif360.algorithms.intersectional_fairness import IntersectionalFairness

aif360/algorithms/isf_helpers/isf_analysis/intersectional_bias.py

@@ -0,0 +1,68 @@
+# -*- coding: utf-8 -*-
+# SPDX-License-Identifier: Apache-2.0
+#
+# Copyright 2023 Fujitsu Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.gridspec import GridSpec
+import matplotlib.colors as mcolors
+import seaborn as sns
+
+from aif360.algorithms.isf_helpers.isf_utils.common import create_multi_group_label
+
+
+def plot_intersectionalbias_compare(df_bef, df_aft, vmax=0.8, vmin=0.2, center=0,
+                                    title={"right": "before", "left": "after"},
+                                    filename=None):
+    """
+    Compare drawing of intersectional bias in heat map
+
+    Parameters
+    ----------
+    ds_bef : StructuredDataset
+        Dataset containing two sensitive attributes (left figure)
+    ds_aft : StructuredDataset
+        Dataset containing two sensitive attributes (right figure)
+    filename : str, optional
+        File name(png)
+        e.g. "./result/pict.png"
+    metric : str
+        Fairness metric name
+        ["DisparateImpact"]
+    title : dictonary, optional
+        Graph title (right figure, left figure)
+    """
+
+    gs = GridSpec(1, 2)
+    ss1 = gs.new_subplotspec((0, 0))
+    ss2 = gs.new_subplotspec((0, 1))
+
+    ax1 = plt.subplot(ss1)
+    ax2 = plt.subplot(ss2)
+
+    max_val = df_bef.values.max()
+    cmap = mcolors.LinearSegmentedColormap.from_list("red_to_green", ["red", "green"])
+    norm = mcolors.Normalize(vmin=vmin, vmax=vmax, clip=True)
+
+    ax1.set_title(title['right'])
+    sns.heatmap(df_bef, ax=ax1, vmax=max_val, vmin=vmin, center=center, annot=True, cmap=cmap, norm=norm)
+
+    ax2.set_title(title['left'])
+    sns.heatmap(df_aft, ax=ax2, vmax=max_val, vmin=vmin, center=center, annot=True, cmap=cmap, norm=norm)
+
+    if filename is not None:
+        plt.savefig(filename, format="png", dpi=300)
+    plt.show()

aif360/algorithms/isf_helpers/isf_utils/checks.py

@@ -0,0 +1,60 @@
+# Copyright (c) 2017 Niels Bantilan
+# This software is the same as the original software licensed
+# under the MIT License.
+#
+# https://github.com/cosmicBboy/themis-ml/blob/master/LICENSE
+#
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+# Copyright 2023 Fujitsu Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Utility functions for doing checks."""
+
+
+def check_binary(x):
+    """
+    Binary check.
+
+    Parameters
+    ----------
+    x : numpy.ndarray
+        Target
+
+    Returns
+    -------
+    x : numpy.ndarray
+        ValueError if not binary.
+    """
+    if not is_binary(x):
+        raise ValueError("%s must be a binary variable" % x)
+    return x
+
+
+def is_binary(x):
+    """
+    Check if numpy multidimensional array consists of {0,1}
+
+    Parameters
+    ----------
+    x : numpy.ndarray
+        Target
+
+    Returns
+    -------
+    result : boolean
+        Check result
+    """
+    return set(x.ravel()).issubset({0, 1})