├── src ├── model │ ├── __init__.py │ ├── classifier.py │ └── fairClassifier.py ├── analysis │ ├── __init__.py │ ├── metrics.py │ └── visualize.py ├── preprocess │ ├── __init__.py │ └── preprocess.py └── notebooks │ └── main.py ├── requirements.txt ├── images └── architecture.png ├── configs └── constant.py ├── .gitignore ├── README.md └── LICENSE /src/model/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /src/analysis/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /src/preprocess/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | matplotlib 3 | pandas==0.23.3 4 | scikit-learn 5 | keras 6 | seaborn -------------------------------------------------------------------------------- /images/architecture.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/suraz09/FairClassifier/HEAD/images/architecture.png -------------------------------------------------------------------------------- /configs/constant.py: -------------------------------------------------------------------------------- 1 | #Defing the constant used in the project. 2 | # Units of the Dense layer 3 | UNIT = 32 4 | 5 | OUTPUT_UNIT = 1 6 | 7 | # Rate of the dropout 8 | DROP_RATE = 0.2 9 | 10 | # CONSTANT for P-Rule 11 | THRESHOLD = 0.5 12 | 13 | -------------------------------------------------------------------------------- /src/analysis/metrics.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import sys, os 3 | sys.path.append(os.path.join(os.path.dirname(os.path.dirname(sys.path[0])), 'configs')) 4 | 5 | import constant 6 | 7 | """ 8 | Class to determine the fairness metrics used to assess the classifier 9 | """ 10 | class FairMetrics: 11 | def __init__(self): 12 | pass 13 | 14 | """ 15 | Calculates the P% rule of predicted values of given sensitive attribute 16 | :param y_pred, Prediction values of the classifier 17 | :param z_values, the list of sensitive attributes of the dataset 18 | :param threshold, threshold for the classifier to decide fairness (0.5) for binary classification. 19 | """ 20 | def p_rule(selfs, y_pred, z_values, threshold = constant.THRESHOLD): 21 | y_z_1 = y_pred[z_values == 1] > threshold if threshold else y_pred[z_values == 1] 22 | y_z_0 = y_pred[z_values == 0] > threshold if threshold else y_pred[z_values == 0] 23 | odds = y_z_1.mean() / y_z_0.mean() 24 | return np.min([odds, 1 / odds]) * 100 25 | 26 | -------------------------------------------------------------------------------- /src/model/classifier.py: -------------------------------------------------------------------------------- 1 | from keras.layers import Input, Dense, Dropout 2 | from keras.models import Model 3 | import sys, os 4 | 5 | sys.path.append(os.path.join(os.path.dirname(os.path.dirname(sys.path[0])), 'configs' )) 6 | import constant 7 | 8 | """ 9 | Classifier class contains method to create NN classifier 10 | """ 11 | class Classifier: 12 | 13 | def __init__(self): 14 | pass 15 | 16 | """ 17 | Create a NN classifier given the shape number of features as input tensor. 18 | Returns the created model 19 | """ 20 | def create_nn_classifier(self,n_features): 21 | inputs = Input(shape=(n_features,)) 22 | dense1 = Dense(constant.UNIT, activation='relu')(inputs) 23 | dropout1 = Dropout(constant.DROP_RATE)(dense1) 24 | dense2 = Dense(constant.UNIT, activation='relu')(dropout1) 25 | dropout2 = Dropout(constant.DROP_RATE)(dense2) 26 | dense3 = Dense(constant.UNIT, activation="relu")(dropout2) 27 | dropout3 = Dropout(constant.DROP_RATE)(dense3) 28 | outputs = Dense(constant.OUTPUT_UNIT, activation='sigmoid')(dropout3) 29 | model = Model(inputs=[inputs], outputs=[outputs]) 30 | model.compile(loss='binary_crossentropy', optimizer='adam') 31 | 32 | return model 33 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | .idea/ 10 | .streamlit/ 11 | # Distribution / packaging 12 | .Python 13 | build/ 14 | develop-eggs/ 15 | dist/ 16 | downloads/ 17 | eggs/ 18 | .eggs/ 19 | lib/ 20 | lib64/ 21 | parts/ 22 | sdist/ 23 | var/ 24 | wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | .hypothesis/ 50 | .pytest_cache/ 51 | 52 | # Translations 53 | *.mo 54 | *.pot 55 | 56 | # Django stuff: 57 | *.log 58 | local_settings.py 59 | db.sqlite3 60 | 61 | # Flask stuff: 62 | instance/ 63 | .webassets-cache 64 | 65 | # Scrapy stuff: 66 | .scrapy 67 | 68 | # Sphinx documentation 69 | docs/_build/ 70 | 71 | # PyBuilder 72 | target/ 73 | 74 | # Jupyter Notebook 75 | .ipynb_checkpoints 76 | 77 | # pyenv 78 | .python-version 79 | 80 | # celery beat schedule file 81 | celerybeat-schedule 82 | 83 | # SageMath parsed files 84 | *.sage.py 85 | 86 | # Environments 87 | .env 88 | .venv 89 | env/ 90 | venv/ 91 | ENV/ 92 | env.bak/ 93 | venv.bak/ 94 | 95 | # Spyder project settings 96 | .spyderproject 97 | .spyproject 98 | 99 | # Rope project settings 100 | .ropeproject 101 | 102 | # mkdocs documentation 103 | /site 104 | 105 | # mypy 106 | .mypy_cache/ 107 | 108 | -------------------------------------------------------------------------------- /src/analysis/visualize.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import seaborn as sns 3 | 4 | sns.set(style="white", palette="muted", color_codes=True, context="talk") 5 | create_gif = False 6 | 7 | """ 8 | Show the results in the graph plot. 9 | Plots the distribution of sensitive attribute's predicted values 10 | :param y, the predicted values by the model 11 | :param Z, Identified sensitive attribute 12 | :param iteration, current training iteration values to be displayed on the graph 13 | :param val_metrics, Accuracy value to be displayed on the graph 14 | :param p_rule, the value computed from P-rule metrics to be displayed on the graph 15 | """ 16 | 17 | def plot_distributions(y, Z, iteration=None, val_metrics=None, p_rules=None, fname=None): 18 | fig, axes = plt.subplots(1, 1, figsize=(10, 4), sharey=True) 19 | legend = {'race': ['African-American', 'Others']} 20 | for idx, attr in enumerate(Z.columns): 21 | for attr_val in [0, 1]: 22 | ax = sns.distplot(y[Z[attr] == attr_val], hist=False, 23 | kde_kws={'shade': True, }, 24 | label='{}'.format(legend[attr][attr_val])) 25 | ax.set_xlim(0, 1) 26 | ax.set_ylim(0, 7) 27 | ax.set_yticks([]) 28 | ax.set_title("sensitive attibute: {}".format(attr)) 29 | if idx == 0: 30 | ax.set_ylabel('prediction distribution') 31 | ax.set_xlabel(r'$P({{risk>High}}|z_{{{}}})$'.format(attr)) 32 | if iteration: 33 | fig.text(1.0, 0.9, f"Training iteration #{iteration}", fontsize='16') 34 | if val_metrics is not None: 35 | fig.text(1.0, 0.65, '\n'.join(["Prediction performance:", 36 | f"- ROC AUC: {val_metrics['ROC AUC']:.2f}", 37 | f"- Accuracy: {val_metrics['Accuracy']:.1f}"]), 38 | fontsize='16') 39 | if p_rules is not None: 40 | fig.text(1.0, 0.4, '\n'.join(["Satisfied p%-rules:"] + 41 | [f"- {attr}: {p_rules[attr]:.0f}%-rule" 42 | for attr in p_rules.keys()]), 43 | fontsize='16') 44 | fig.tight_layout() 45 | if fname is not None: 46 | plt.savefig(fname, bbox_inches='tight') 47 | # st.pyplot() 48 | return fig 49 | 50 | 51 | """ 52 | Plots the result of accuracy vs fairness tradeoff in a scatter plot 53 | :param X, List of fairness satisfied using P% rule 54 | :param Y, List of accuracy 55 | :param x_lab, x-label of the figure 56 | :param y_lab, y-label of the figure 57 | """ 58 | def plotScatter(X,Y, x_lab, y_lab): 59 | plt.scatter(X, Y) 60 | plt.xlabel(x_lab) 61 | plt.ylabel(y_lab) 62 | plt.show() -------------------------------------------------------------------------------- /src/preprocess/preprocess.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from sklearn.model_selection import train_test_split 3 | from sklearn.preprocessing import StandardScaler 4 | 5 | """ 6 | DataProcessor class contains the methods to load, preprocess and split the data into train and test set. 7 | """ 8 | class DataProcessor: 9 | 10 | def __init__(self, file): 11 | self.path = file 12 | 13 | """ 14 | Pulling the data in raw format found here: 15 | DataProcessing done as: 16 | 1. Identifying the sensitive attribute of the data 17 | 2. Dropping the sensitive attribute from the dataFrame 18 | """ 19 | 20 | def loadData(self, sensitive_attribute, attribute, predictionValue, prediction_column): 21 | input_data = pd.read_csv(self.path) 22 | df = pd.DataFrame(input_data) 23 | """ 24 | Perform the same preprocessing as the original analysis by Pro-Publica 25 | https://github.com/propublica/compas-analysis/blob/master/Compas%20Analysis.ipynb 26 | """ 27 | df = df[(df.days_b_screening_arrest <= 30) 28 | & (df.days_b_screening_arrest >= -30) 29 | & (df.is_recid != -1) 30 | & (df.c_charge_degree != 'O') 31 | & (df.score_text != 'N/A')] 32 | sensitive_attribs = [sensitive_attribute] 33 | Z = self.split_columns(df, sensitive_attribs, sensitive_attribute, attribute) 34 | y = (df[prediction_column] == predictionValue).astype(int) 35 | X = (df.drop(columns=['race', 'score_text']).fillna('Unknown').pipe(pd.get_dummies, drop_first=True)) 36 | 37 | return X, y, Z 38 | 39 | """ 40 | Split the sensitive attribute column so that this is not part of training set 41 | """ 42 | def split_columns(self, df, sensitive_attribs, sensitive_attribute, attribute): 43 | Z = (df.loc[:, sensitive_attribs].assign( 44 | new_column=lambda df: (df[sensitive_attribute] == attribute).astype(int))) 45 | Z.drop(columns=[sensitive_attribute], inplace=True) 46 | Z.rename(columns={'new_column': sensitive_attribute}, inplace=True) 47 | return Z 48 | 49 | 50 | """ 51 | Split the data into train and test set. 52 | """ 53 | def split_data(self, X, y, Z): 54 | X_train, X_test, y_train, y_test, Z_train, Z_test = train_test_split(X, y, Z, test_size=0.3, stratify=y) 55 | # standardize the data 56 | scaler = StandardScaler().fit(X_train) 57 | scale_df = lambda df, scaler: pd.DataFrame(scaler.transform(df), columns=df.columns, index=df.index) 58 | X_train = X_train.pipe(scale_df, scaler) 59 | X_test = X_test.pipe(scale_df, scaler) 60 | 61 | return X_train, X_test, y_train, y_test, Z_train, Z_test 62 | 63 | 64 | 65 | 66 | -------------------------------------------------------------------------------- /src/notebooks/main.py: -------------------------------------------------------------------------------- 1 | import sys, os 2 | import pandas as pd 3 | from sklearn.metrics import accuracy_score, roc_auc_score 4 | 5 | sys.path.append(os.path.join(os.path.dirname(sys.path[0]), 'preprocess')) 6 | sys.path.append(os.path.join(os.path.dirname(sys.path[0]), 'model')) 7 | sys.path.append(os.path.join(os.path.dirname(sys.path[0]), 'analysis')) 8 | 9 | from preprocess import DataProcessor 10 | from classifier import Classifier 11 | from fairClassifier import FairClassifier 12 | from metrics import FairMetrics 13 | 14 | create_gif = False 15 | 16 | 17 | # Method to parse arguments from command line, also set the threhold to be 70 in this case. 18 | def cli_parser(): 19 | sensitive_attribute = sys.argv[1] 20 | sensitive_value = sys.argv[2] 21 | predict_value = 'High' 22 | predict_column = 'score_text' 23 | 24 | # change the threshold if you want to consider less fairness 25 | threshold = 70 26 | return sensitive_attribute, sensitive_value, predict_value, predict_column, threshold 27 | 28 | 29 | 30 | try: 31 | # parse the attributes from command line 32 | sensitive_attribute, sensitive_value, predict_value, predict_column, threshold = cli_parser() 33 | 34 | # Load the data from the source 35 | data = DataProcessor(os.path.join(os.path.dirname(os.path.dirname(sys.path[0])), 'data', 'raw', 'recidivism.csv')) 36 | # function to pre-process the data 37 | X,y,Z = data.loadData(sensitive_attribute, sensitive_value, predict_value, predict_column) 38 | 39 | # ##split the data into train and test set 40 | X_train, X_test, y_train, y_test, Z_train, Z_test = data.split_data(X,y,Z) 41 | 42 | classifier = Classifier() 43 | # create a nn classifier 44 | clf = classifier.create_nn_classifier(n_features=X_train.shape[1]) 45 | # train on train set 46 | history = clf.fit(X_train.values, y_train.values, epochs=20, verbose=0) 47 | y_pred = pd.Series(clf.predict(X_test).ravel(), index=y_test.index) 48 | 49 | #Result of unfair classifier 50 | print(f"Accuracy: {100*accuracy_score(y_test, (y_pred>0.5)):.1f}%") 51 | 52 | # Display the result of fairness metric of unfair classifier. 53 | metrics = FairMetrics() 54 | print("The classifier satisfies the following %p-rules:") 55 | print("P-rule {0:.0f}".format(metrics.p_rule(y_pred, Z_test[sensitive_attribute]))) 56 | 57 | p_value = metrics.p_rule(y_pred, Z_test[sensitive_attribute]) 58 | 59 | if(p_value < threshold): 60 | # initialise FairClassifier 61 | clf = FairClassifier(n_features=X_train.shape[1], n_sensitive=Z_train.shape[1], lambdas=[130]) 62 | 63 | # pre-train both adverserial and classifier networks 64 | clf.pretrain(X_train, y_train, Z_train, verbose=0, epochs=5) 65 | 66 | #Get the result of fair classifier 67 | clf.fit(X_train, y_train, Z_train, validation_data=(X_test, y_test, Z_test), T_iter=160, save_figs=create_gif) 68 | print("Accuracy ", clf.accuracyArray[len(clf.accuracyArray) -1], "P-rule satisfied ",clf.pruleArray[len(clf.pruleArray)-1]) 69 | except Exception as ex: 70 | print(ex) 71 | 72 | #uncomment this line if you you want to visualize accuracy vs fairness tradeoff 73 | #fig = visualize.plotScatter(clf.pruleArray, clf.accuracyArray, x_lab="P-Rule", y_lab="Accuracy") 74 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # FairClassifier 2 | 3 | **Machine Learning** models are extensively used for the purpose of determining access to services such as eligibility for loans, insurance etc. 4 | 5 | With the growing impact and efficient results of Machine Learning and AI, transparency and fairness in the predictions of the models is of a major concern. 6 | Unintentional discrimination made by Machine Learning results affects the lives of people with certain sensitive characteristics. The sensitive characteristics might be sex, age or color. 7 | This flaw of unintentional discrimination caused by the source data has not been fully addressed though. 8 | 9 | 10 | **Fair Classifier** is an open source package that evaluates the fairness of a Neural Network. 11 | It is a command-line tool in which the user can check the fairness of the model against different sensitive attributes from the training dataset. 12 | 13 | ### Fairness metrics implemented 14 | > The fairness contraint P% rule is based on [this paper.](https://arxiv.org/pdf/1507.05259.pdf) 15 | 16 | ### Mitigation approach 17 | It is common to think that simply removing sensitive attribute from the training data might be able to solve the problem of biasness. 18 | However, results obtained by training our Neural Network shows that this is not the case. 19 | 20 | > The basic idea is to attach a neural network with an adversarial network to help mitigate the bias. For further reading please follow this [Link.](https://blog.godatadriven.com/fairness-in-ml) 21 | 22 | The detailed architecture is shown below. 23 | 24 | ## Architecture 25 | The overview of the architecture of the model is shown in this figure below: 26 | 27 | ![](images/architecture.png) 28 | 29 | The system of training two Neural Network might look similar to the one used for training [GANS.](https://arxiv.org/abs/1406.2661) 30 | However, it is not the case. 31 | 32 | First, the generative model used in GANs is replaced by a predictive model which 33 | instead of generating synthetic data gives actual predictions from the input data. 34 | Second, adversarial network doesn't distinguish real data from generated synthetic data in this case. 35 | However, it tries to predict the sensitive attribute values from the predicted labels from the earlier Neural Network. 36 | Both of these networks train simultaneously with an objective to optimize the prediction losses of prediction labels and sensitive attributes. 37 | 38 | 39 | 40 | 41 | ## Setup 42 | Follow these steps for installation. 43 | 44 | ### Manual Installation 45 | Clone this repository. 46 | 47 | To run the example script, install the additional libraries specified in requirements.txt file as above: 48 | 49 | Then change your directory to the Project directory. 50 | 51 | Currently, this package works only with 2 sensitive attribute of the dataset i.e race and sex. 52 | 53 | The below example is to check the fairness of the model for race attribute. 54 | 55 | Steps: 56 | 57 | `git clone git@github.com:suraz09/FairClassifier.git` 58 | 59 | `cd FairClassifier` 60 | 61 | `pip install -r requirements.txt` 62 | 63 | `python src/notebooks/main.py race African-American` 64 | 65 | 66 | ## Motivation 67 | This project is inspired by this [Blog.](https://blog.godatadriven.com/fairness-in-ml) 68 | 69 | 70 | ## Pre-Requisites 71 | `python` 72 | 73 | I have used `AWS` cloud services for faster training of the model. 74 | 75 | 76 | ### DataSet 77 | The [Dataset](https://raw.githubusercontent.com/propublica/compas-analysis/master/compas-scores-two-years.csv) used in this project 78 | was acquired, analyzed and released by [Propublica.](https://github.com/propublica/compas-analysis) It consists of ~12k records of criminals of Broward County. 79 | 80 | Using this dataset, the model predicts how likely a criminal defendant is to reoffend. 81 | Recidivism is defined as a new arrest within two years in the analysis of data by [Propublica.](https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm) 82 | Each defendant received a score for 'Risk of recidivism' also called as COMPAS score. 83 | The score for each defendant ranged from 1 to 10. 84 | To start with a binary classification problem, Scores 1 to 5 were re-labeled as 'Low' 85 | and 6-10 were re-labeled as 'High'. 86 | 87 | Some of the important attributes associated with each criminal defendants are: 88 | 89 | * Sex 90 | * Age 91 | * Race 92 | * Prior Arrest Count 93 | * Days arrested before assessment 94 | * Score Label 95 | 96 | -------------------------------------------------------------------------------- /src/model/fairClassifier.py: -------------------------------------------------------------------------------- 1 | from sklearn.metrics import accuracy_score, roc_auc_score 2 | from sklearn.utils.class_weight import compute_class_weight 3 | from keras.layers import Input, Dense, Dropout 4 | from keras.models import Model 5 | import pandas as pd 6 | import numpy as np 7 | import os, sys 8 | 9 | sys.path.append(os.path.join(os.path.dirname(sys.path[0]), 'analysis')) 10 | sys.path.append(os.path.join(os.path.dirname(os.path.dirname(sys.path[0])), 'configs' )) 11 | 12 | from metrics import FairMetrics 13 | import constant 14 | 15 | """ 16 | FairClassifier equipped with adversarial network 17 | """ 18 | class FairClassifier(object): 19 | 20 | def __init__(self, n_features, n_sensitive, lambdas): 21 | self.lambdas = lambdas 22 | 23 | clf_inputs = Input(shape=(n_features,)) 24 | adv_inputs = Input(shape=(1,)) 25 | self.metrics = FairMetrics() 26 | 27 | clf_net = self._create_clf_net(clf_inputs) 28 | adv_net = self._create_adv_net(adv_inputs, n_sensitive) 29 | self._trainable_clf_net = self._make_trainable(clf_net) 30 | self._trainable_adv_net = self._make_trainable(adv_net) 31 | self._clf = self._compile_clf(clf_net) 32 | self._clf_w_adv = self._compile_clf_w_adv(clf_inputs, clf_net, adv_net) 33 | self._adv = self._compile_adv(clf_inputs, clf_net, adv_net, n_sensitive) 34 | self._val_metrics = None 35 | self._fairness_metrics = None 36 | 37 | self.predict = self._clf.predict 38 | self.accuracyArray = [] 39 | self.pruleArray = [] 40 | 41 | """ 42 | Make the layers of the classifier trainable 43 | """ 44 | def _make_trainable(self, net): 45 | def make_trainable(flag): 46 | net.trainable = flag 47 | for layer in net.layers: 48 | layer.trainable = flag 49 | 50 | return make_trainable 51 | """ 52 | Create a 3 layer Neural Network 53 | """ 54 | def _create_clf_net(self, inputs): 55 | dense1 = Dense(constant.UNIT, activation='relu')(inputs) 56 | dropout1 = Dropout(constant.DROP_RATE)(dense1) 57 | dense2 = Dense(constant.UNIT, activation='relu')(dropout1) 58 | dropout2 = Dropout(constant.DROP_RATE)(dense2) 59 | dense3 = Dense(constant.UNIT, activation='relu')(dropout2) 60 | dropout3 = Dropout(constant.DROP_RATE)(dense3) 61 | outputs = Dense(constant.OUTPUT_UNIT, activation='sigmoid', name='y')(dropout3) 62 | return Model(inputs=[inputs], outputs=[outputs]) 63 | 64 | """ 65 | Create a 3 layer adversarial Neural Network 66 | """ 67 | def _create_adv_net(self, inputs, n_sensitive): 68 | dense1 = Dense(constant.UNIT, activation='relu')(inputs) 69 | dense2 = Dense(constant.UNIT, activation='relu')(dense1) 70 | dense3 = Dense(constant.UNIT, activation='relu')(dense2) 71 | outputs = [Dense(constant.OUTPUT_UNIT, activation='sigmoid')(dense3) for _ in range(n_sensitive)] 72 | return Model(inputs=[inputs], outputs=outputs) 73 | 74 | """ 75 | Compile the classifier Neural Network 76 | """ 77 | def _compile_clf(self, clf_net): 78 | clf = clf_net 79 | self._trainable_clf_net(True) 80 | clf.compile(loss='binary_crossentropy', optimizer='adam') 81 | return clf 82 | 83 | """ 84 | Compile classifier with adversarial network 85 | """ 86 | def _compile_clf_w_adv(self, inputs, clf_net, adv_net): 87 | clf_w_adv = Model(inputs=[inputs], outputs=[clf_net(inputs), adv_net(clf_net(inputs))]) 88 | self._trainable_clf_net(True) 89 | self._trainable_adv_net(False) 90 | loss_weights = [1.] + [-lambda_param for lambda_param in self.lambdas] 91 | clf_w_adv.compile(loss=['binary_crossentropy'] * (len(loss_weights)), 92 | loss_weights=loss_weights, 93 | optimizer='adam') 94 | return clf_w_adv 95 | 96 | """ 97 | Compile adversarial Network 98 | """ 99 | def _compile_adv(self, inputs, clf_net, adv_net, n_sensitive): 100 | adv = Model(inputs=[inputs], outputs=adv_net(clf_net(inputs))) 101 | self._trainable_clf_net(False) 102 | self._trainable_adv_net(True) 103 | adv.compile(loss=['binary_crossentropy'] * n_sensitive, optimizer='adam') 104 | return adv 105 | 106 | """ 107 | Compute class weights of the training set 108 | """ 109 | def _compute_class_weights(self, data_set): 110 | class_values = [0, 1] 111 | class_weights = [] 112 | if len(data_set.shape) == 1: 113 | balanced_weights = compute_class_weight('balanced', class_values, data_set) 114 | class_weights.append(dict(zip(class_values, balanced_weights))) 115 | else: 116 | n_attr = data_set.shape[1] 117 | for attr_idx in range(n_attr): 118 | balanced_weights = compute_class_weight('balanced', class_values, 119 | np.array(data_set)[:, attr_idx]) 120 | class_weights.append(dict(zip(class_values, balanced_weights))) 121 | return class_weights 122 | 123 | """ 124 | Compute class weight of the target set 125 | """ 126 | def _compute_target_class_weights(self, y): 127 | class_values = [0, 1] 128 | balanced_weights = compute_class_weight('balanced', class_values, y) 129 | class_weights = {'y': dict(zip(class_values, balanced_weights))} 130 | return class_weights 131 | 132 | """ 133 | Pretrain Classifier and adversarial network on initial data set 134 | """ 135 | def pretrain(self, x, y, z, epochs=10, verbose=0): 136 | self._trainable_clf_net(True) 137 | self._clf.fit(x.values, y.values, epochs=epochs, verbose=verbose) 138 | self._trainable_clf_net(False) 139 | self._trainable_adv_net(True) 140 | class_weight_adv = self._compute_class_weights(z) 141 | self._adv.fit(x.values, np.hsplit(z.values, z.shape[1]), class_weight=class_weight_adv, 142 | epochs=epochs, verbose=verbose) 143 | 144 | """ 145 | Train and test the accuracy and fairness of the model 146 | """ 147 | def fit(self, x, y, z, validation_data=None, T_iter=250, batch_size=128, 148 | save_figs=False): 149 | n_sensitive = z.shape[1] 150 | if validation_data is not None: 151 | x_val, y_val, z_val = validation_data 152 | 153 | class_weight_adv = self._compute_class_weights(z) 154 | class_weight_clf_w_adv = [{0: 1., 1: 1.}] + class_weight_adv 155 | self._val_metrics = pd.DataFrame() 156 | self._fairness_metrics = pd.DataFrame() 157 | for idx in range(T_iter): 158 | if validation_data is not None: 159 | y_pred = pd.Series(self._clf.predict(x_val).ravel(), index=y_val.index) 160 | self.accuracyArray.append(accuracy_score(y_val, (y_pred > 0.5)) * 100) 161 | # uncomment this line if you want to see the accuracy score 162 | # print("Accuracy ", accuracy_score(y_val, (y_pred > 0.5)) * 100) 163 | for sensitive_attr in z_val.columns: 164 | self.pruleArray.append(self.metrics.p_rule(y_pred, z_val[sensitive_attr])) 165 | # uncomment this line if you want to see the fairness metrics 166 | # print("P-rule ", self.metrics.p_rule(y_pred, z_val[sensitive_attr])) 167 | 168 | # train adverserial 169 | self._trainable_clf_net(False) 170 | self._trainable_adv_net(True) 171 | self._adv.fit(x.values, np.hsplit(z.values, z.shape[1]), batch_size=batch_size, 172 | class_weight=class_weight_adv, epochs=1, verbose=0) 173 | 174 | # train classifier 175 | self._trainable_clf_net(True) 176 | self._trainable_adv_net(False) 177 | indices = np.random.permutation(len(x))[:batch_size] 178 | self._clf_w_adv.train_on_batch(x.values[indices], 179 | [y.values[indices]] + np.hsplit(z.values[indices], n_sensitive), 180 | class_weight=class_weight_clf_w_adv) -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------