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Signed-off-by: Kalousios <[email protected]>
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‎aif360/algorithms/__init__.py‎

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from aif360.algorithms.transformer import Transformer, addmetadata
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from aif360.algorithms.intersectional_fairness import IntersectionalFairness

‎aif360/algorithms/intersectional_fairness.py‎

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‎aif360/algorithms/isf_helpers/inprocessing/adversarial_debiasing.py‎

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# SPDX-License-Identifier: Apache-2.0
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#
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# Copyright 2023 Fujitsu Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from aif360.algorithms.inprocessing.adversarial_debiasing import AdversarialDebiasing as AD
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import tensorflow as tf
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from aif360.algorithms.isf_helpers.inprocessing.inprocessing import InProcessing
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tf.compat.v1.disable_eager_execution()
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class AdversarialDebiasing(InProcessing):
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"""
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Debiasing intersectional bias with adversarial learning(AD) called by ISF.
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Parameters
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----------
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options : dictionary
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parameter of AdversarialDebiasing
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num_epochs: trials of model training
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batch_size:Batch size for model training
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Notes
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-----
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https://aif360.readthedocs.io/en/v0.2.3/_modules/aif360/algorithms/inprocessing/adversarial_debiasing.html
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"""
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def __init__(self, options):
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super().__init__()
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self.ds_train = None
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self.options = options
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def fit(self, ds_train):
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"""
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Save training dataset
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Attributes
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----------
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ds_train : Dataset
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Dataset for training
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"""
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self.ds_train = ds_train.copy(deepcopy=True)
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def predict(self, ds_test):
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"""
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Model learning with debias using the training dataset imported by fit(), and predict using that model
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Parameters
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----------
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ds_test : Dataset
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Dataset for prediction
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Returns
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-------
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ds_predict : numpy.ndarray
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Predicted label
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"""
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ikey = ds_test.protected_attribute_names[0]
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priv_g = [{ikey: ds_test.privileged_protected_attributes[0]}]
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upriv_g = [{ikey: ds_test.unprivileged_protected_attributes[0]}]
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sess = tf.compat.v1.Session()
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model = AD(
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privileged_groups=priv_g,
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unprivileged_groups=upriv_g,
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scope_name='debiased_classifier',
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debias=True,
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sess=sess)
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model.fit(self.ds_train)
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ds_predict = model.predict(ds_test)
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sess.close()
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tf.compat.v1.reset_default_graph()
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return ds_predict
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def bias_predict(self, ds_train):
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"""
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Model learning and prediction using AdversarialDebiasing of AIF360 without debias.
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Parameters
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----------
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ds_train : Dataset
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Dataset for training and prediction
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Returns
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-------
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ds_predict : numpy.ndarray
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Predicted label
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"""
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ikey = ds_train.protected_attribute_names[0]
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priv_g = [{ikey: ds_train.privileged_protected_attributes[0]}]
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upriv_g = [{ikey: ds_train.unprivileged_protected_attributes[0]}]
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sess = tf.compat.v1.Session()
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model = AD(
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privileged_groups=priv_g,
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unprivileged_groups=upriv_g,
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scope_name='plain_classifier',
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debias=False,
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sess=sess,
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num_epochs=self.options['num_epochs'],
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batch_size=self.options['batch_size'])
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model.fit(ds_train)
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ds_predict = model.predict(ds_train)
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sess.close()
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tf.compat.v1.reset_default_graph()
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return ds_predict

‎aif360/algorithms/isf_helpers/inprocessing/inprocessing.py‎

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# SPDX-License-Identifier: Apache-2.0
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#
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# Copyright 2023 Fujitsu Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from abc import ABCMeta
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from abc import abstractmethod
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class InProcessing(metaclass=ABCMeta):
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"""
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Abstract Base Class for all inprocessing techniques.
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"""
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def __init__(self):
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super().__init__()
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self.model = None
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@abstractmethod
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def fit(self, ds_train):
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"""
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Train a model on the input.
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Parameters
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----------
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ds_train : Dataset
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Training Dataset.
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"""
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pass
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@abstractmethod
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def predict(self, ds):
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"""
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Predict on the input.
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Parameters
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----------
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ds : Dataset
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Dataset to predict.
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"""
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pass

‎aif360/algorithms/isf_helpers/isf_analysis/intersectional_bias.py‎

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# -*- coding: utf-8 -*-
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# SPDX-License-Identifier: Apache-2.0
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#
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# Copyright 2023 Fujitsu Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pandas as pd
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import matplotlib.pyplot as plt
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from matplotlib.gridspec import GridSpec
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import seaborn as sns
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from aif360.algorithms.isf_helpers.isf_metrics.disparate_impact import DisparateImpact
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from aif360.algorithms.isf_helpers.isf_utils.common import create_multi_group_label
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def calc_intersectionalbias(dataset, metric="DisparateImpact"):
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"""
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Calculate intersectional bias(DisparateImpact) by more than one sensitive attributes
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Parameters
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----------
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dataset : StructuredDataset
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A dataset containing more than one sensitive attributes
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metric : str
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Fairness metric name
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["DisparateImpact"]
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Returns
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-------
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df_result : DataFrame
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Intersectional bias(DisparateImpact)
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"""
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df = dataset.convert_to_dataframe()[0]
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label_info = {dataset.label_names[0]: dataset.favorable_label}
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if metric == "DisparateImpact":
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fs = DisparateImpact()
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else:
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raise ValueError("metric name not in the list of allowed metrics")
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df_result = pd.DataFrame(columns=[metric])
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for multi_group_label in create_multi_group_label(dataset)[0]:
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protected_attr_info = multi_group_label[0]
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di = fs.bias_predict(df,
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protected_attr_info=protected_attr_info,
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label_info=label_info)
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name = ''
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for k, v in protected_attr_info.items():
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name += k + " = " + str(v) + ","
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df_result.loc[name[:-1]] = di
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return df_result
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def plot_intersectionalbias_compare(ds_bef, ds_aft, vmax=1, vmin=0, center=0,
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metric="DisparateImpact",
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title={"right": "before", "left": "after"},
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filename=None):
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"""
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Compare drawing of intersectional bias in heat map
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Parameters
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----------
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ds_bef : StructuredDataset
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Dataset containing two sensitive attributes (left figure)
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ds_aft : StructuredDataset
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Dataset containing two sensitive attributes (right figure)
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filename : str, optional
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File name(png)
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e.g. "./result/pict.png"
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metric : str
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Fairness metric name
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["DisparateImpact"]
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title : dictonary, optional
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Graph title (right figure, left figure)
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"""
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df_bef = calc_intersectionalbias_matrix(ds_bef, metric)
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df_aft = calc_intersectionalbias_matrix(ds_aft, metric)
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gs = GridSpec(1, 2)
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ss1 = gs.new_subplotspec((0, 0))
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ss2 = gs.new_subplotspec((0, 1))
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ax1 = plt.subplot(ss1)
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ax2 = plt.subplot(ss2)
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ax1.set_title(title['right'])
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sns.heatmap(df_bef, ax=ax1, vmax=vmax, vmin=vmin, center=center, annot=True, cmap='hot')
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ax2.set_title(title['left'])
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sns.heatmap(df_aft, ax=ax2, vmax=vmax, vmin=vmin, center=center, annot=True, cmap='hot')
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if filename is not None:
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plt.savefig(filename, format="png", dpi=300)
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plt.show()
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def calc_intersectionalbias_matrix(dataset, metric="DisparateImpact"):
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"""
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Comparison drawing of intersectional bias in heat map
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Parameters
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----------
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dataset : StructuredDataset
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Dataset containing two sensitive attributes
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metric : str
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Fairness metric name
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["DisparateImpact"]
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Returns
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-------
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df_result : DataFrame
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Intersectional bias(DisparateImpact)
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"""
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protect_attr = dataset.protected_attribute_names
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if len(protect_attr) != 2:
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raise ValueError("specify 2 sensitive attributes.")
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if metric == "DisparateImpact":
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fs = DisparateImpact()
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else:
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raise ValueError("metric name not in the list of allowed metrics")
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df = dataset.convert_to_dataframe()[0]
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label_info = {dataset.label_names[0]: dataset.favorable_label}
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protect_attr0_values = list(set(df[protect_attr[0]]))
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protect_attr1_values = list(set(df[protect_attr[1]]))
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df_result = pd.DataFrame(columns=protect_attr1_values)
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for val0 in protect_attr0_values:
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tmp_li = []
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col_list = []
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for val1 in protect_attr1_values:
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di = fs.bias_predict(df,
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protected_attr_info={protect_attr[0]: val0, protect_attr[1]: val1},
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label_info=label_info)
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tmp_li += [di]
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col_list += [protect_attr[1]+"="+str(val1)]
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df_result.loc[protect_attr[0]+"="+str(val0)] = tmp_li
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df_result = df_result.set_axis(col_list, axis=1)
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return df_result

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