{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "QZJSs4w9BCK-" }, "source": [ "# TD 1: Fairness notion examples\n", "\n", "In this first TD we are going to manipulate some data and see the behaviour of the different fairness metrics" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "qRJyTtCtBCLA" }, "source": [ "Environnement python3.10" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 41006, "status": "ok", "timestamp": 1707201882799, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "Ozn0aZUVBKKg", "outputId": "beda3d58-c106-4b6f-d632-b414af965070" }, "outputs": [], "source": [ "!python --version\n", "!pip install --upgrade pip\n", "!pip install numpy==1.25\n", "!pip install fairlearn==0.9.0\n", "!pip install plotly==5.24.1\n", "!pip install nbformat==5.10.4\n", "!pip install ipykernel==6.29.5" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "Oam0vn35n7jK" }, "source": [ "# !!! Attention !!!, après avoir executé la cellule ci-dessus, il faudra redémarrer la session (onglet \"Execution\") afin de charger l'environnement installé" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "MCKXApFuBCLC" }, "source": [ "\n", "## Objectives\n", "\n", "\n", " 1. Study the data, the distribution of each feature and its relation to the target.\n", "\n", " 2. Highlight some bias present in the data\n", "\n", " 3. Learn a basic machine learning model using logistic regression\n", "\n", " 4. Compute the confusion matrix and different fairness metrics\n", "\n", "## Dataset: Diabetes 130-Hospitals\n", "\n", "\n", "https://fairlearn.org/main/api_reference/generated/fairlearn.datasets.fetch_diabetes_hospital.html\n", "\n", "Ce dataset contient 101,766 lignes chacunes concernant un patient hospitalisé pour du diabètes sur une durée allant de 1 à 14 jours. Les données ont été récoltées sur 10 ans et 130 hopitaux différents. Chaque donnée possède 25 caractéristiques concernant des informations médicales, mais aussi demographiques, enfin la colonne 'readmitted' indique si le patient a été réadmis, et si oui s'il l'a été dans les 30jours ou après. Cette colonne est binarisée en deux autres 'readmit_30_days' (True si réadmis dans les 30 jours, False sinon) et 'readmitted' ( True si réadmis, False sinon).\n", "\n", "Nous utiliserons en label/vérité, la colonne 'readmit_30_days'.\n", "\n", "Nous allons simplifier en ne considérant qu'un sous-ensemble de 14 des caractéristiques fournies:\n", "age, gender, race, time_in_hospital, num_lab_procedures, num_procedures, num_medications, number_diagnoses, max_glu_serum, A1Cresult, insulin, had_emergency, had_inpatient_days, had_outpatient_days\n", "\n", "\n", "\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "v6YE9bSd3qgG" }, "source": [ "## Download and simplify the dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 19, "status": "ok", "timestamp": 1707201882800, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "XbEeXIwwKLqa", "outputId": "d2c14362-19a1-40df-de7c-de63275d5c5d" }, "outputs": [ { "data": { "text/plain": [ "('1.25.0', '0.9.0')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import fairlearn\n", "np.__version__, fairlearn.__version__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "executionInfo": { "elapsed": 19762, "status": "ok", "timestamp": 1707201902546, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "n3HDPkgdBCLD" }, "outputs": [], "source": [ "from fairlearn.datasets import fetch_diabetes_hospital\n", "dataset = fetch_diabetes_hospital()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 669 }, "executionInfo": { "elapsed": 11, "status": "ok", "timestamp": 1707201902547, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "eS5ap_RGBCLG", "outputId": "c51a8989-1135-4177-8c61-1196c30e428f" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_1912488/3640427316.py:20: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df.had_emergency = df.had_emergency.replace({\"True\":1, \"False\":0})\n", "/tmp/ipykernel_1912488/3640427316.py:20: FutureWarning: The behavior of Series.replace (and DataFrame.replace) with CategoricalDtype is deprecated. In a future version, replace will only be used for cases that preserve the categories. To change the categories, use ser.cat.rename_categories instead.\n", " df.had_emergency = df.had_emergency.replace({\"True\":1, \"False\":0})\n", "/tmp/ipykernel_1912488/3640427316.py:21: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df.had_inpatient_days = df.had_inpatient_days.replace({\"True\":1, \"False\":0})\n", "/tmp/ipykernel_1912488/3640427316.py:21: FutureWarning: The behavior of Series.replace (and DataFrame.replace) with CategoricalDtype is deprecated. In a future version, replace will only be used for cases that preserve the categories. To change the categories, use ser.cat.rename_categories instead.\n", " df.had_inpatient_days = df.had_inpatient_days.replace({\"True\":1, \"False\":0})\n", "/tmp/ipykernel_1912488/3640427316.py:22: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df.had_outpatient_days = df.had_outpatient_days.replace({\"True\":1, \"False\":0})\n", "/tmp/ipykernel_1912488/3640427316.py:22: FutureWarning: The behavior of Series.replace (and DataFrame.replace) with CategoricalDtype is deprecated. In a future version, replace will only be used for cases that preserve the categories. To change the categories, use ser.cat.rename_categories instead.\n", " df.had_outpatient_days = df.had_outpatient_days.replace({\"True\":1, \"False\":0})\n" ] }, { "data": { "text/html": [ "
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agegenderracetime_in_hospitalnum_lab_proceduresnum_proceduresnum_medicationsnumber_diagnosesmax_glu_serumA1Cresultinsulinhad_emergencyhad_inpatient_dayshad_outpatient_daysreadmit_30_days
0'30 years or younger'FemaleCaucasian141011NoneNoneNo0000
1'30 years or younger'FemaleCaucasian3590189NoneNoneUp0000
2'30 years or younger'FemaleAfricanAmerican2115136NoneNoneNo0110
3'30-60 years'MaleCaucasian2441167NoneNoneUp0000
4'30-60 years'MaleCaucasian151085NoneNoneSteady0000
................................................
101761'Over 60 years'MaleAfricanAmerican3510169None>8Down0000
101762'Over 60 years'FemaleAfricanAmerican5333189NoneNoneSteady0100
101763'Over 60 years'MaleCaucasian1530913NoneNoneDown0010
101764'Over 60 years'FemaleCaucasian10452219NoneNoneUp0100
101765'Over 60 years'MaleCaucasian613339NoneNoneNo0000
\n", "

101766 rows × 15 columns

\n", "
" ], "text/plain": [ " age gender race time_in_hospital \\\n", "0 '30 years or younger' Female Caucasian 1 \n", "1 '30 years or younger' Female Caucasian 3 \n", "2 '30 years or younger' Female AfricanAmerican 2 \n", "3 '30-60 years' Male Caucasian 2 \n", "4 '30-60 years' Male Caucasian 1 \n", "... ... ... ... ... \n", "101761 'Over 60 years' Male AfricanAmerican 3 \n", "101762 'Over 60 years' Female AfricanAmerican 5 \n", "101763 'Over 60 years' Male Caucasian 1 \n", "101764 'Over 60 years' Female Caucasian 10 \n", "101765 'Over 60 years' Male Caucasian 6 \n", "\n", " num_lab_procedures num_procedures num_medications number_diagnoses \\\n", "0 41 0 1 1 \n", "1 59 0 18 9 \n", "2 11 5 13 6 \n", "3 44 1 16 7 \n", "4 51 0 8 5 \n", "... ... ... ... ... \n", "101761 51 0 16 9 \n", "101762 33 3 18 9 \n", "101763 53 0 9 13 \n", "101764 45 2 21 9 \n", "101765 13 3 3 9 \n", "\n", " max_glu_serum A1Cresult insulin had_emergency had_inpatient_days \\\n", "0 None None No 0 0 \n", "1 None None Up 0 0 \n", "2 None None No 0 1 \n", "3 None None Up 0 0 \n", "4 None None Steady 0 0 \n", "... ... ... ... ... ... \n", "101761 None >8 Down 0 0 \n", "101762 None None Steady 0 1 \n", "101763 None None Down 0 0 \n", "101764 None None Up 0 1 \n", "101765 None None No 0 0 \n", "\n", " had_outpatient_days readmit_30_days \n", "0 0 0 \n", "1 0 0 \n", "2 1 0 \n", "3 0 0 \n", "4 0 0 \n", "... ... ... \n", "101761 0 0 \n", "101762 0 0 \n", "101763 1 0 \n", "101764 0 0 \n", "101765 0 0 \n", "\n", "[101766 rows x 15 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selection = [\n", " \"age\",\n", " \"gender\",\n", " \"race\",\n", " \"time_in_hospital\",\n", " \"num_lab_procedures\",\n", " \"num_procedures\",\n", " \"num_medications\",\n", " \"number_diagnoses\",\n", " \"max_glu_serum\",\n", " \"A1Cresult\",\n", " \"insulin\",\n", " \"had_emergency\",\n", " \"had_inpatient_days\",\n", " \"had_outpatient_days\"]\n", "df = dataset.data[selection].copy(deep=True)\n", "label = 'readmit_30_days'\n", "df[label] = dataset.target\n", "# We transform boolean into integer, False=>0, True=>1\n", "df.had_emergency = df.had_emergency.replace({\"True\":1, \"False\":0})\n", "df.had_inpatient_days = df.had_inpatient_days.replace({\"True\":1, \"False\":0})\n", "df.had_outpatient_days = df.had_outpatient_days.replace({\"True\":1, \"False\":0})\n", "df" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "qySVWo6f7FkU" }, "source": [ "## Part 1: Data Analysis" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "_qjpeC-OQFDA" }, "source": [ "### Question1 : Count the number of positive and negative label" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 358, "status": "ok", "timestamp": 1707201902898, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "_sW3FdpK8H42", "outputId": "c1206160-6523-48ca-fe5a-f8983d839c6e" }, "outputs": [ { "data": { "text/plain": [ "readmit_30_days\n", "0 90409\n", "1 11357\n", "Name: count, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.readmit_30_days.value_counts()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "eLYrjnOlBCLH" }, "source": [ "Now we look at the different features.\n", "First the numerical features\n", "\n", "### Question2: Display the distribution of the numerical features and compute their correlation with the target" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "executionInfo": { "elapsed": 20, "status": "ok", "timestamp": 1707201902898, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "td0AM-NLaPWp" }, "outputs": [], "source": [ "def Compute_correlation(cola, colb):\n", " return np.corrcoef(df[cola].values, df[colb].values)[0][1]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 540, "status": "ok", "timestamp": 1704810401190, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "AGkdoHuf-sC-", "outputId": "441029bf-45e5-4a94-adde-914a9873949c" }, "outputs": [ { "data": { "text/plain": [ "age object\n", "gender object\n", "race object\n", "time_in_hospital int64\n", "num_lab_procedures int64\n", "num_procedures int64\n", "num_medications int64\n", "number_diagnoses int64\n", "max_glu_serum object\n", "A1Cresult object\n", "insulin object\n", "had_emergency category\n", "had_inpatient_days category\n", "had_outpatient_days category\n", "readmit_30_days int64\n", "dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 10, "status": "ok", "timestamp": 1707201902899, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "M3alTJYv_sOa", "outputId": "b5a82876-f611-4634-b387-ddedf5a7d50a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "age False\n", "gender False\n", "race False\n", "time_in_hospital False\n", "num_lab_procedures False\n", "num_procedures False\n", "num_medications False\n", "number_diagnoses False\n", "max_glu_serum False\n", "A1Cresult False\n", "insulin False\n", "had_emergency False\n", "had_inpatient_days False\n", "had_outpatient_days False\n", "readmit_30_days False\n" ] } ], "source": [ "# Check that there is not NaN values\n", "for column in df.columns:\n", " print(column, df[column].isnull().values.any())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 9, "status": "ok", "timestamp": 1707201902899, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "oydvAfT38ivu", "outputId": "6e08fad5-d5b1-46a0-bf21-5968908ad5e9" }, "outputs": [ { "data": { "text/plain": [ "['time_in_hospital',\n", " 'num_lab_procedures',\n", " 'num_procedures',\n", " 'num_medications',\n", " 'number_diagnoses',\n", " 'readmit_30_days']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numerical_features = list(df.select_dtypes(include=np.number).columns)\n", "numerical_features" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 6, "status": "ok", "timestamp": 1707201902899, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "nFZx9u1Ma1tS", "outputId": "180c051d-767a-4ccf-bd15-b6cce62413a9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time_in_hospital 0.044199418457446835\n", "num_lab_procedures 0.02036360256754239\n", "num_procedures -0.012226934251745272\n", "num_medications 0.038431822165995666\n", "number_diagnoses 0.04952421190568096\n", "readmit_30_days 0.9999999999999998\n" ] } ], "source": [ "for num_feature in numerical_features:\n", " print(num_feature, Compute_correlation(label, num_feature))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "1fwQvgwOBtHe" }, "source": [ "Then we consider the categorical features.\n", "\n", "### Question3: Display histogram of categorical distribution by label for each categorical features." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 5, "status": "ok", "timestamp": 1707201903143, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "nQKGi3V-DWuY", "outputId": "8015e155-defa-4b9d-bc3b-3d74c0a6a422" }, "outputs": [ { "data": { "text/plain": [ "['race',\n", " 'had_emergency',\n", " 'had_inpatient_days',\n", " 'age',\n", " 'max_glu_serum',\n", " 'A1Cresult',\n", " 'insulin',\n", " 'had_outpatient_days',\n", " 'gender']" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "categorical_features = list(set(df.columns)-set(numerical_features))\n", "categorical_features" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "executionInfo": { "elapsed": 536, "status": "ok", "timestamp": 1707201903677, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "rPkvob6lB82i" }, "outputs": [], "source": [ "import plotly.express as px\n", "\n", "def Display_categorical_hist(cat_feature, target):\n", " fig = px.histogram(df, x=cat_feature, color=target)\n", " fig.show()\n", "\n", "def Display_categorical_hist_percent(cat_feature, target):\n", " df_summarized = df.groupby([target,cat_feature]).agg(\"count\").reset_index()\n", " df_summarized[f\"percent of {cat_feature}\"] = df_summarized[[cat_feature,\"time_in_hospital\"]].apply(\n", " lambda x: 100*x[1]/df_summarized[df_summarized[cat_feature]==x[0]][\"time_in_hospital\"].sum(), axis=1\n", " )\n", " df_summarized[label] = df_summarized[label].astype(str)\n", " fig = px.bar(df_summarized, x=f\"{cat_feature}\", y=f\"percent of {cat_feature}\", color=target)\n", " fig.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_1912488/3388801361.py:10: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", " lambda x: 100*x[1]/df_summarized[df_summarized[cat_feature]==x[0]][\"time_in_hospital\"].sum(), axis=1\n" ] }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "readmit_30_days=0
race=%{x}
percent of race=%{y}", "legendgroup": "0", "marker": { "color": "#636efa", "pattern": { "shape": "" } }, "name": "0", "offsetgroup": "0", "orientation": "v", "showlegend": true, "textposition": "auto", "type": "bar", "x": [ "AfricanAmerican", "Asian", "Caucasian", "Hispanic", "Other", "Unknown" ], "xaxis": "x", "y": [ 88.78188443519001, 89.85959438377535, 88.70944427653451, 89.59253804614629, 90.3718459495352, 91.72899252089749 ], "yaxis": "y" }, { "alignmentgroup": "True", "hovertemplate": "readmit_30_days=1
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for cat_feature in categorical_features:\n", " Display_categorical_hist(cat_feature, label)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "NTiqlSctEbX_" }, "source": [ "### Question4: What are the bias highlighted by the data analysis ?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'analyse des correlations montre que les variables numériques sont faiblement corrélées linéairement à la réadmission.\n", "De même l'analyse des variables catégorielles ne montrent pas de différences signicatives en réadmission pour chacune de leur valeur.\n", "Il n'y a pas de bias mis en valeur ici." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "EVXBYtmeGUQ8" }, "source": [ "## Part 2: Learn a Decision Tree and study the fairness of its output" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "DGdBpyVHG3pA" }, "source": [ "### Question5: Utiliser la technique du \"one hot encoding\" pour transformer chaque colonne categorielle à N catégories en N colonnes binaires" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "executionInfo": { "elapsed": 8, "status": "ok", "timestamp": 1707201910956, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "L7VTsufGGjQL" }, "outputs": [], "source": [ "import pandas as pd\n", "one_hot_data = pd.get_dummies(df[categorical_features])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 444 }, "executionInfo": { "elapsed": 459, "status": "ok", "timestamp": 1707201911408, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "OUYj1UwIG20g", "outputId": "4e9a67b2-624f-4cf5-d3c5-481ed2228ece" }, "outputs": [ { "data": { "text/html": [ "
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101766 rows × 35 columns

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" ], "text/plain": [ " time_in_hospital num_lab_procedures num_procedures num_medications \\\n", "0 1 41 0 1 \n", "1 3 59 0 18 \n", "2 2 11 5 13 \n", "3 2 44 1 16 \n", "4 1 51 0 8 \n", "... ... ... ... ... \n", "101761 3 51 0 16 \n", "101762 5 33 3 18 \n", "101763 1 53 0 9 \n", "101764 10 45 2 21 \n", "101765 6 13 3 3 \n", "\n", " number_diagnoses race_AfricanAmerican race_Asian race_Caucasian \\\n", "0 1 False False True \n", "1 9 False False True \n", "2 6 True False False \n", "3 7 False False True \n", "4 5 False False True \n", "... ... ... ... ... \n", "101761 9 True False False \n", "101762 9 True False False \n", "101763 13 False False True \n", "101764 9 False False True \n", "101765 9 False False True \n", "\n", " race_Hispanic race_Other ... A1Cresult_Norm insulin_Down \\\n", "0 False False ... False False \n", "1 False False ... False False \n", "2 False False ... False False \n", "3 False False ... False False \n", "4 False False ... False False \n", "... ... ... ... ... ... \n", "101761 False False ... False True \n", "101762 False False ... False False \n", "101763 False False ... False True \n", "101764 False False ... False False \n", "101765 False False ... False False \n", "\n", " insulin_No insulin_Steady insulin_Up had_outpatient_days_0 \\\n", "0 True False False True \n", "1 False False True True \n", "2 True False False False \n", "3 False False True True \n", "4 False True False True \n", "... ... ... ... ... \n", "101761 False False False True \n", "101762 False True False True \n", "101763 False False False False \n", "101764 False False True True \n", "101765 True False False True \n", "\n", " had_outpatient_days_1 gender_Female gender_Male \\\n", "0 False True False \n", "1 False True False \n", "2 True True False \n", "3 False False True \n", "4 False False True \n", "... ... ... ... \n", "101761 False False True \n", "101762 False True False \n", "101763 True False True \n", "101764 False True False \n", "101765 False False True \n", "\n", " gender_Unknown/Invalid \n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", "... ... \n", "101761 False \n", "101762 False \n", "101763 False \n", "101764 False \n", "101765 False \n", "\n", "[101766 rows x 35 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_numerical_features = [num_feature for num_feature in numerical_features if num_feature!=label]\n", "df_X = pd.concat([df[X_numerical_features], one_hot_data], axis = 1)\n", "df_X" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "w9qF0cjzOCIp" }, "source": [ "### Question6 : Split data into train and test sets\n", "\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "executionInfo": { "elapsed": 260, "status": "ok", "timestamp": 1707201911661, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "T8O2FYXIEqCp" }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(df_X, df[label], test_size=0.33, random_state=42)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "RLOBuscUORlg" }, "source": [ "### Question 7: Train a DecisionTreeClassifier (https://scikit-learn.org/stable/modules/tree.html#classification)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "executionInfo": { "elapsed": 1374, "status": "ok", "timestamp": 1707201913032, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "XWsdllKaOTao" }, "outputs": [], "source": [ "from sklearn import tree\n", "clf = tree.DecisionTreeClassifier(random_state=42)\n", "clf = clf.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "executionInfo": { "elapsed": 8, "status": "ok", "timestamp": 1707201913033, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "aTtPXhWnFJeJ" }, "outputs": [], "source": [ "preds = clf.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 7, "status": "ok", "timestamp": 1707201913033, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "HvxZij8CIukW", "outputId": "a4755238-afbe-4d45-b759-7ad17b0627f6" }, "outputs": [ { "data": { "text/plain": [ "0.7874519846350833" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.score(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "sffH4hl2IWh4" }, "outputs": [], "source": [ "# tree.plot_tree(clf) #visualise the tree, only if max_depth<=6" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "tl9tq0RbPLWG" }, "source": [ "### Question8: Compute the Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 332, "status": "ok", "timestamp": 1707201914639, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "qLBdxD7lIdWL", "outputId": "933855b0-f907-404b-887b-1aca5a99204e" }, "outputs": [ { "data": { "text/plain": [ "(25833, 3984, 3154, 612)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "tn, fp, fn, tp = confusion_matrix(y_test, preds).ravel()\n", "tn, fp, fn, tp" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "w20fbl9aP6GW" }, "source": [ "### Question 9: Compute base rate metrics for a sensitive binary attribute (gender, race etc)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "executionInfo": { "elapsed": 712, "status": "ok", "timestamp": 1707202886752, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "WVrXF37RgjuA" }, "outputs": [], "source": [ "def compute_stat(preds, sensitive, value = True):\n", " preds_F = preds[sensitive == value]\n", " n_F = len(preds_F)\n", " n_F_pos = len(preds_F[preds_F == 1])\n", " n_F_neg = len(preds_F[preds_F == 0])\n", " print(n_F, n_F_pos, n_F_neg)\n", " return n_F, n_F_pos, n_F_neg, preds_F\n", "\n", "\n", "def disparate_impact(preds, sensitive):\n", "\n", " n_F, n_F_pos, n_F_neg, preds_F = compute_stat(preds=preds, sensitive=sensitive)\n", " n_M, n_M_pos, n_M_neg, preds_M = compute_stat(preds=preds, sensitive=sensitive, value = False)\n", "\n", " DI = n_F_pos/n_F*n_M/n_M_pos\n", " p_DI = np.min([DI, 1/DI])\n", " DP = n_F_pos/n_F - n_M_pos/n_M\n", "\n", " ret = {}\n", " ret[\"disparate_impact\"] = DI\n", " ret[\"P_rule_disparate_impact\"] = p_DI\n", " ret[\"demography_parity\"] = DP\n", "\n", " return ret" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 349, "status": "ok", "timestamp": 1707202889613, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "22X6dQMhjKRF", "outputId": "a19d58cb-c8f4-40ad-d949-be188c01f5d8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18063 2458 15605\n", "15520 2138 13382\n" ] }, { "data": { "text/plain": [ "{'disparate_impact': 0.9878159007655721,\n", " 'P_rule_disparate_impact': 0.9878159007655721,\n", " 'demography_parity': -0.0016784538764952894}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "disparate_impact(preds, sensitive=X_test[\"gender_Female\"])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 320, "status": "ok", "timestamp": 1707202579852, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "bQSp3X9cM9R4", "outputId": "7a3ab50f-c7fb-48e2-b9ac-d9ef589a356a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18063 2052 16011\n", "15520 1714 13806\n" ] }, { "data": { "text/plain": [ "{'disparate_impact': 1.028651760844047,\n", " 'P_rule_disparate_impact': 0.9721462967986977,\n", " 'demography_parity': 0.003164247299400555}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "disparate_impact(y_test, sensitive=X_test[\"gender_Female\"])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 415, "status": "ok", "timestamp": 1707202896779, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "mMgxJnDvKmke", "outputId": "ac75a347-386b-4f77-b758-3e3bd5a62ace" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "25172 3330 21842\n", "8411 1266 7145\n" ] }, { "data": { "text/plain": [ "{'disparate_impact': 0.8789019696149261,\n", " 'P_rule_disparate_impact': 0.8789019696149261,\n", " 'demography_parity': -0.018227334023006025}" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "disparate_impact(preds, sensitive=X_test[\"race_Caucasian\"])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 599, "status": "ok", "timestamp": 1707202900980, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "SNTEAVXPLp5E", "outputId": "56bcf1b8-f06c-400c-8584-f83f5165e3e4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "25172 2868 22304\n", "8411 898 7513\n" ] }, { "data": { "text/plain": [ "{'disparate_impact': 1.0671678185929359,\n", " 'P_rule_disparate_impact': 0.9370597412865234,\n", " 'demography_parity': 0.00717116883800456}" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "disparate_impact(y_test,sensitive=X_test[\"race_Caucasian\"])" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "mDTWzh1OgjuE" }, "source": [ "### Question 10: Compute model perfomance for a sensitive binary attribute (gender, race etc)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "id": "RIv9JeSOgjuE" }, "outputs": [], "source": [ "def compute_cm(preds, y_test, sensitive, value = True):\n", " preds_F = preds[sensitive == value]\n", " y_test_F = y_test[sensitive == value]\n", " tn, fp, fn, tp = confusion_matrix(y_test_F, preds_F).ravel()\n", "\n", " return tn, fp, fn, tp\n", "\n", "\n", "def fairness_metric_performance(preds, y_test, sensitive):\n", " tn_F, fp_F, fn_F, tp_F = compute_cm(preds, y_test, sensitive=sensitive, value = True)\n", " tn_M, fp_M, fn_M, tp_M = compute_cm(preds, y_test, sensitive=sensitive, value = False)\n", "\n", " ret = {}\n", " ret[\"odds_pos\"] = tp_F/(tp_F+fn_F)/(tp_M/(tp_M+fn_M))\n", " ret[\"odds_neg\"] = tn_F/(tn_F+fp_F)/(tn_M/(tn_M+fp_M))\n", "\n", " return ret" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 35, "status": "ok", "timestamp": 1704810429489, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "muVaV8WWgjuF", "outputId": "afc60eb7-e9b5-409d-a04a-c86bdb2bd548" }, "outputs": [ { "data": { "text/plain": [ "{'odds_pos': 0.9839094573127165, 'odds_neg': 1.0019186376275675}" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fairness_metric_performance(preds=preds,y_test=y_test, sensitive=X_test[\"gender_Female\"])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'odds_pos': 0.8275054791791192, 'odds_neg': 1.0196202883754113}" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fairness_metric_performance(preds=preds,y_test=y_test, sensitive=X_test[\"race_Caucasian\"])" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "ibLZX7YagjuH" }, "source": [ "### Question 11: Compute model calibration according to a sensitive binary attribute (gender, race etc)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "i1co-oNngjuI" }, "outputs": [], "source": [ "\n", "def fairness_metric_calibration(preds, y_test, sensitive):\n", " tn_F, fp_F, fn_F, tp_F = compute_cm(preds, y_test, sensitive=sensitive, value = True)\n", " tn_M, fp_M, fn_M, tp_M = compute_cm(preds, y_test, sensitive=sensitive, value = False)\n", "\n", " ret = {}\n", " ret[\"PPR\"] = tp_F/(tp_F+fp_F)/(tp_M/(tp_M+fp_M))\n", " ret[\"FPR\"] = tn_F/(tn_F+fn_F)/(tn_M/(tn_M+fn_M))\n", " return ret" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 474, "status": "ok", "timestamp": 1704810429941, "user": { "displayName": "Alice H", "userId": "13901604971984976961" }, "user_tz": -60 }, "id": "NPAmWVU1gjuJ", "outputId": "dcfdc4eb-3a33-4586-b3f4-de313685da74" }, "outputs": [ { "data": { "text/plain": [ "{'PPR': 1.0245838267955025, 'FPR': 0.9964150909548858}" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fairness_metric_calibration(preds=preds, y_test=y_test, sensitive=X_test[\"gender_Female\"])" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'PPR': 1.0047619047619047, 'FPR': 0.9901880172082603}" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fairness_metric_calibration(preds=preds, y_test=y_test, sensitive=X_test[\"race_Caucasian\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "colab": { "provenance": [ { "file_id": "https://github.com/alheliou/Fairness/blob/main/TD1.ipynb", "timestamp": 1704809313812 } ] }, "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.4" } }, "nbformat": 4, "nbformat_minor": 0 }