{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "5833cc45", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "id": "d9dcd566", "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv(\"diabetic_data.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "02314335", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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encounter_idpatient_nbrracegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospital...citogliptoninsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitted
022783928222157CaucasianFemale[0-10)?62511...NoNoNoNoNoNoNoNoNoNO
114919055629189CaucasianFemale[10-20)?1173...NoUpNoNoNoNoNoChYes>30
26441086047875AfricanAmericanFemale[20-30)?1172...NoNoNoNoNoNoNoNoYesNO
350036482442376CaucasianMale[30-40)?1172...NoUpNoNoNoNoNoChYesNO
41668042519267CaucasianMale[40-50)?1171...NoSteadyNoNoNoNoNoChYesNO
..................................................................
101761443847548100162476AfricanAmericanMale[70-80)?1373...NoDownNoNoNoNoNoChYes>30
10176244384778274694222AfricanAmericanFemale[80-90)?1455...NoSteadyNoNoNoNoNoNoYesNO
10176344385414841088789CaucasianMale[70-80)?1171...NoDownNoNoNoNoNoChYesNO
10176444385716631693671CaucasianFemale[80-90)?23710...NoUpNoNoNoNoNoChYesNO
101765443867222175429310CaucasianMale[70-80)?1176...NoNoNoNoNoNoNoNoNoNO
\n", "

101766 rows × 50 columns

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" ], "text/plain": [ " encounter_id patient_nbr race gender age weight \\\n", "0 2278392 8222157 Caucasian Female [0-10) ? \n", "1 149190 55629189 Caucasian Female [10-20) ? \n", "2 64410 86047875 AfricanAmerican Female [20-30) ? \n", "3 500364 82442376 Caucasian Male [30-40) ? \n", "4 16680 42519267 Caucasian Male [40-50) ? \n", "... ... ... ... ... ... ... \n", "101761 443847548 100162476 AfricanAmerican Male [70-80) ? \n", "101762 443847782 74694222 AfricanAmerican Female [80-90) ? \n", "101763 443854148 41088789 Caucasian Male [70-80) ? \n", "101764 443857166 31693671 Caucasian Female [80-90) ? \n", "101765 443867222 175429310 Caucasian Male [70-80) ? \n", "\n", " admission_type_id discharge_disposition_id admission_source_id \\\n", "0 6 25 1 \n", "1 1 1 7 \n", "2 1 1 7 \n", "3 1 1 7 \n", "4 1 1 7 \n", "... ... ... ... \n", "101761 1 3 7 \n", "101762 1 4 5 \n", "101763 1 1 7 \n", "101764 2 3 7 \n", "101765 1 1 7 \n", "\n", " time_in_hospital ... citoglipton insulin glyburide-metformin \\\n", "0 1 ... No No No \n", "1 3 ... No Up No \n", "2 2 ... No No No \n", "3 2 ... No Up No \n", "4 1 ... No Steady No \n", "... ... ... ... ... ... \n", "101761 3 ... No Down No \n", "101762 5 ... No Steady No \n", "101763 1 ... No Down No \n", "101764 10 ... No Up No \n", "101765 6 ... No No No \n", "\n", " glipizide-metformin glimepiride-pioglitazone \\\n", "0 No No \n", "1 No No \n", "2 No No \n", "3 No No \n", "4 No No \n", "... ... ... \n", "101761 No No \n", "101762 No No \n", "101763 No No \n", "101764 No No \n", "101765 No No \n", "\n", " metformin-rosiglitazone metformin-pioglitazone change diabetesMed \\\n", "0 No No No No \n", "1 No No Ch Yes \n", "2 No No No Yes \n", "3 No No Ch Yes \n", "4 No No Ch Yes \n", "... ... ... ... ... \n", "101761 No No Ch Yes \n", "101762 No No No Yes \n", "101763 No No Ch Yes \n", "101764 No No Ch Yes \n", "101765 No No No No \n", "\n", " readmitted \n", "0 NO \n", "1 >30 \n", "2 NO \n", "3 NO \n", "4 NO \n", "... ... \n", "101761 >30 \n", "101762 NO \n", "101763 NO \n", "101764 NO \n", "101765 NO \n", "\n", "[101766 rows x 50 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 4, "id": "007ac1e8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 101766 entries, 0 to 101765\n", "Data columns (total 50 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 encounter_id 101766 non-null int64 \n", " 1 patient_nbr 101766 non-null int64 \n", " 2 race 101766 non-null object\n", " 3 gender 101766 non-null object\n", " 4 age 101766 non-null object\n", " 5 weight 101766 non-null object\n", " 6 admission_type_id 101766 non-null int64 \n", " 7 discharge_disposition_id 101766 non-null int64 \n", " 8 admission_source_id 101766 non-null int64 \n", " 9 time_in_hospital 101766 non-null int64 \n", " 10 payer_code 101766 non-null object\n", " 11 medical_specialty 101766 non-null object\n", " 12 num_lab_procedures 101766 non-null int64 \n", " 13 num_procedures 101766 non-null int64 \n", " 14 num_medications 101766 non-null int64 \n", " 15 number_outpatient 101766 non-null int64 \n", " 16 number_emergency 101766 non-null int64 \n", " 17 number_inpatient 101766 non-null int64 \n", " 18 diag_1 101766 non-null object\n", " 19 diag_2 101766 non-null object\n", " 20 diag_3 101766 non-null object\n", " 21 number_diagnoses 101766 non-null int64 \n", " 22 max_glu_serum 5346 non-null object\n", " 23 A1Cresult 17018 non-null object\n", " 24 metformin 101766 non-null object\n", " 25 repaglinide 101766 non-null object\n", " 26 nateglinide 101766 non-null object\n", " 27 chlorpropamide 101766 non-null object\n", " 28 glimepiride 101766 non-null object\n", " 29 acetohexamide 101766 non-null object\n", " 30 glipizide 101766 non-null object\n", " 31 glyburide 101766 non-null object\n", " 32 tolbutamide 101766 non-null object\n", " 33 pioglitazone 101766 non-null object\n", " 34 rosiglitazone 101766 non-null object\n", " 35 acarbose 101766 non-null object\n", " 36 miglitol 101766 non-null object\n", " 37 troglitazone 101766 non-null object\n", " 38 tolazamide 101766 non-null object\n", " 39 examide 101766 non-null object\n", " 40 citoglipton 101766 non-null object\n", " 41 insulin 101766 non-null object\n", " 42 glyburide-metformin 101766 non-null object\n", " 43 glipizide-metformin 101766 non-null object\n", " 44 glimepiride-pioglitazone 101766 non-null object\n", " 45 metformin-rosiglitazone 101766 non-null object\n", " 46 metformin-pioglitazone 101766 non-null object\n", " 47 change 101766 non-null object\n", " 48 diabetesMed 101766 non-null object\n", " 49 readmitted 101766 non-null object\n", "dtypes: int64(13), object(37)\n", "memory usage: 38.8+ MB\n" ] } ], "source": [ "data.info()" ] }, { "cell_type": "code", "execution_count": 5, "id": "0bbf0ef1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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encounter_idpatient_nbradmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientnumber_diagnoses
count1.017660e+051.017660e+05101766.000000101766.000000101766.000000101766.000000101766.000000101766.000000101766.000000101766.000000101766.000000101766.000000101766.000000
mean1.652016e+085.433040e+072.0240063.7156425.7544374.39598743.0956411.33973016.0218440.3693570.1978360.6355667.422607
std1.026403e+083.869636e+071.4454035.2801664.0640812.98510819.6743621.7058078.1275661.2672650.9304721.2628631.933600
min1.252200e+041.350000e+021.0000001.0000001.0000001.0000001.0000000.0000001.0000000.0000000.0000000.0000001.000000
25%8.496119e+072.341322e+071.0000001.0000001.0000002.00000031.0000000.00000010.0000000.0000000.0000000.0000006.000000
50%1.523890e+084.550514e+071.0000001.0000007.0000004.00000044.0000001.00000015.0000000.0000000.0000000.0000008.000000
75%2.302709e+088.754595e+073.0000004.0000007.0000006.00000057.0000002.00000020.0000000.0000000.0000001.0000009.000000
max4.438672e+081.895026e+088.00000028.00000025.00000014.000000132.0000006.00000081.00000042.00000076.00000021.00000016.000000
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" ], "text/plain": [ " encounter_id patient_nbr admission_type_id \\\n", "count 1.017660e+05 1.017660e+05 101766.000000 \n", "mean 1.652016e+08 5.433040e+07 2.024006 \n", "std 1.026403e+08 3.869636e+07 1.445403 \n", "min 1.252200e+04 1.350000e+02 1.000000 \n", "25% 8.496119e+07 2.341322e+07 1.000000 \n", "50% 1.523890e+08 4.550514e+07 1.000000 \n", "75% 2.302709e+08 8.754595e+07 3.000000 \n", "max 4.438672e+08 1.895026e+08 8.000000 \n", "\n", " discharge_disposition_id admission_source_id time_in_hospital \\\n", "count 101766.000000 101766.000000 101766.000000 \n", "mean 3.715642 5.754437 4.395987 \n", "std 5.280166 4.064081 2.985108 \n", "min 1.000000 1.000000 1.000000 \n", "25% 1.000000 1.000000 2.000000 \n", "50% 1.000000 7.000000 4.000000 \n", "75% 4.000000 7.000000 6.000000 \n", "max 28.000000 25.000000 14.000000 \n", "\n", " num_lab_procedures num_procedures num_medications number_outpatient \\\n", "count 101766.000000 101766.000000 101766.000000 101766.000000 \n", "mean 43.095641 1.339730 16.021844 0.369357 \n", "std 19.674362 1.705807 8.127566 1.267265 \n", "min 1.000000 0.000000 1.000000 0.000000 \n", "25% 31.000000 0.000000 10.000000 0.000000 \n", "50% 44.000000 1.000000 15.000000 0.000000 \n", "75% 57.000000 2.000000 20.000000 0.000000 \n", "max 132.000000 6.000000 81.000000 42.000000 \n", "\n", " number_emergency number_inpatient number_diagnoses \n", "count 101766.000000 101766.000000 101766.000000 \n", "mean 0.197836 0.635566 7.422607 \n", "std 0.930472 1.262863 1.933600 \n", "min 0.000000 0.000000 1.000000 \n", "25% 0.000000 0.000000 6.000000 \n", "50% 0.000000 0.000000 8.000000 \n", "75% 0.000000 1.000000 9.000000 \n", "max 76.000000 21.000000 16.000000 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe()" ] }, { "cell_type": "code", "execution_count": 6, "id": "a7efbee1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(101766, 50)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": 7, "id": "93b8350d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(encounter_id 0\n", " patient_nbr 0\n", " race 2273\n", " gender 0\n", " age 0\n", " weight 98569\n", " admission_type_id 0\n", " discharge_disposition_id 0\n", " admission_source_id 0\n", " time_in_hospital 0\n", " payer_code 40256\n", " medical_specialty 49949\n", " num_lab_procedures 0\n", " num_procedures 0\n", " num_medications 0\n", " number_outpatient 0\n", " number_emergency 0\n", " number_inpatient 0\n", " diag_1 21\n", " diag_2 358\n", " diag_3 1423\n", " number_diagnoses 0\n", " max_glu_serum 96420\n", " A1Cresult 84748\n", " metformin 0\n", " repaglinide 0\n", " nateglinide 0\n", " chlorpropamide 0\n", " glimepiride 0\n", " acetohexamide 0\n", " glipizide 0\n", " glyburide 0\n", " tolbutamide 0\n", " pioglitazone 0\n", " rosiglitazone 0\n", " acarbose 0\n", " miglitol 0\n", " troglitazone 0\n", " tolazamide 0\n", " examide 0\n", " citoglipton 0\n", " insulin 0\n", " glyburide-metformin 0\n", " glipizide-metformin 0\n", " glimepiride-pioglitazone 0\n", " metformin-rosiglitazone 0\n", " metformin-pioglitazone 0\n", " change 0\n", " diabetesMed 0\n", " readmitted 0\n", " insulin_binary 0\n", " dtype: int64,\n", " insulin_binary\n", " No Inject 59601\n", " Inject 42165\n", " Name: count, dtype: int64)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = data.replace('?', pd.NA)\n", "\n", "# Create a binary classification column for insulin requirements\n", "# Map 'Up' and 'Steady' to 'Inject', and 'No' and 'Down' to 'No Inject'\n", "data['insulin_binary'] = data['insulin'].map({\n", " 'Up': 'Inject',\n", " 'Steady': 'Inject',\n", " 'No': 'No Inject',\n", " 'Down': 'No Inject'\n", "})\n", "\n", "# Check for NaN values and basic statistics to confirm the cleaning\n", "missing_values_summary = data.isna().sum()\n", "insulin_distribution = data['insulin_binary'].value_counts()\n", "\n", "missing_values_summary, insulin_distribution\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "55199f30", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "encounter_id 0\n", "patient_nbr 0\n", "race 2273\n", "gender 0\n", "age 0\n", "admission_type_id 0\n", "discharge_disposition_id 0\n", "admission_source_id 0\n", "time_in_hospital 0\n", "num_lab_procedures 0\n", "num_procedures 0\n", "num_medications 0\n", "number_outpatient 0\n", "number_emergency 0\n", "number_inpatient 0\n", "diag_1 21\n", "diag_2 358\n", "diag_3 1423\n", "number_diagnoses 0\n", "metformin 0\n", "repaglinide 0\n", "nateglinide 0\n", "chlorpropamide 0\n", "glimepiride 0\n", "acetohexamide 0\n", "glipizide 0\n", "glyburide 0\n", "tolbutamide 0\n", "pioglitazone 0\n", "rosiglitazone 0\n", "acarbose 0\n", "miglitol 0\n", "troglitazone 0\n", "tolazamide 0\n", "examide 0\n", "citoglipton 0\n", "insulin 0\n", "glyburide-metformin 0\n", "glipizide-metformin 0\n", "glimepiride-pioglitazone 0\n", "metformin-rosiglitazone 0\n", "metformin-pioglitazone 0\n", "change 0\n", "diabetesMed 0\n", "readmitted 0\n", "insulin_binary 0\n", "dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Drop columns with significant missing data\n", "columns_to_drop = ['weight', 'payer_code', 'max_glu_serum','A1Cresult','medical_specialty']\n", "data = data.drop(columns=columns_to_drop)\n", "\n", "# Confirm the columns have been removed and re-check missing values\n", "missing_values_summary = data.isna().sum()\n", "missing_values_summary\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "6ca7f63b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "diag_1 0\n", "diag_2 0\n", "diag_3 0\n", "dtype: int64\n" ] } ], "source": [ "# Impute missing values in 'diag_1', 'diag_2', 'diag_3' with their mode (most frequent value)\n", "for col in ['diag_1', 'diag_2', 'diag_3','race']:\n", " data[col] = data[col].fillna(data[col].mode()[0])\n", "\n", "# Confirm there are no more missing values in these columns\n", "print(data[['diag_1', 'diag_2', 'diag_3']].isna().sum())\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "79e6c160", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "encounter_id 0\n", "patient_nbr 0\n", "race 0\n", "gender 0\n", "age 0\n", "admission_type_id 0\n", "discharge_disposition_id 0\n", "admission_source_id 0\n", "time_in_hospital 0\n", "num_lab_procedures 0\n", "num_procedures 0\n", "num_medications 0\n", "number_outpatient 0\n", "number_emergency 0\n", "number_inpatient 0\n", "diag_1 0\n", "diag_2 0\n", "diag_3 0\n", "number_diagnoses 0\n", "metformin 0\n", "repaglinide 0\n", "nateglinide 0\n", "chlorpropamide 0\n", "glimepiride 0\n", "acetohexamide 0\n", "glipizide 0\n", "glyburide 0\n", "tolbutamide 0\n", "pioglitazone 0\n", "rosiglitazone 0\n", "acarbose 0\n", "miglitol 0\n", "troglitazone 0\n", "tolazamide 0\n", "examide 0\n", "citoglipton 0\n", "insulin 0\n", "glyburide-metformin 0\n", "glipizide-metformin 0\n", "glimepiride-pioglitazone 0\n", "metformin-rosiglitazone 0\n", "metformin-pioglitazone 0\n", "change 0\n", "diabetesMed 0\n", "readmitted 0\n", "insulin_binary 0\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "missing_values_summary = data.isna().sum()\n", "missing_values_summary" ] }, { "cell_type": "markdown", "id": "e5a31c12", "metadata": {}, "source": [ "# what i did here is :\n", " ### i ve droped the columns with significant missing data\n", " ### i ve filled the rows in the columns with low missing values by their mode (because they re Non-Numeric)\n", " \n", " \n", " \n", " \n", " \n", " " ] }, { "cell_type": "markdown", "id": "385b0eec", "metadata": {}, "source": [ "\n", "\n", "\n", "\n", "\n", "\n", "## this data support Classification and Clustering" ] }, { "cell_type": "markdown", "id": "bb44723d", "metadata": {}, "source": [ "# So the best models i can use re \n", "\n", "## Supervised Algorithms (for predicting insulin injection and diabetics):\n", "##### Logistic Regression\n", "##### Decision Tree\n", "##### Random Forest\n", "##### Support Vector Machine (SVM)\n", "##### k-Nearest Neighbors (k-NN)\n" ] }, { "cell_type": "markdown", "id": "899adeb1", "metadata": {}, "source": [ "# Commencant l'Analyse des variables numériques" ] }, { "cell_type": "code", "execution_count": 11, "id": "23ffcd74", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", "from sklearn.metrics import classification_report, confusion_matrix\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.neighbors import KNeighborsClassifier" ] }, { "cell_type": "code", "execution_count": 12, "id": "bd845d1f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "--- Statistiques descriptives des variables numériques ---\n", " encounter_id patient_nbr admission_type_id \\\n", "count 1.017660e+05 1.017660e+05 101766.000000 \n", "mean 1.652016e+08 5.433040e+07 2.024006 \n", "std 1.026403e+08 3.869636e+07 1.445403 \n", "min 1.252200e+04 1.350000e+02 1.000000 \n", "25% 8.496119e+07 2.341322e+07 1.000000 \n", "50% 1.523890e+08 4.550514e+07 1.000000 \n", "75% 2.302709e+08 8.754595e+07 3.000000 \n", "max 4.438672e+08 1.895026e+08 8.000000 \n", "\n", " discharge_disposition_id admission_source_id time_in_hospital \\\n", "count 101766.000000 101766.000000 101766.000000 \n", "mean 3.715642 5.754437 4.395987 \n", "std 5.280166 4.064081 2.985108 \n", "min 1.000000 1.000000 1.000000 \n", "25% 1.000000 1.000000 2.000000 \n", "50% 1.000000 7.000000 4.000000 \n", "75% 4.000000 7.000000 6.000000 \n", "max 28.000000 25.000000 14.000000 \n", "\n", " num_lab_procedures num_procedures num_medications number_outpatient \\\n", "count 101766.000000 101766.000000 101766.000000 101766.000000 \n", "mean 43.095641 1.339730 16.021844 0.369357 \n", "std 19.674362 1.705807 8.127566 1.267265 \n", "min 1.000000 0.000000 1.000000 0.000000 \n", "25% 31.000000 0.000000 10.000000 0.000000 \n", "50% 44.000000 1.000000 15.000000 0.000000 \n", "75% 57.000000 2.000000 20.000000 0.000000 \n", "max 132.000000 6.000000 81.000000 42.000000 \n", "\n", " number_emergency number_inpatient number_diagnoses \n", "count 101766.000000 101766.000000 101766.000000 \n", "mean 0.197836 0.635566 7.422607 \n", "std 0.930472 1.262863 1.933600 \n", "min 0.000000 0.000000 1.000000 \n", "25% 0.000000 0.000000 6.000000 \n", "50% 0.000000 0.000000 8.000000 \n", "75% 0.000000 1.000000 9.000000 \n", "max 76.000000 21.000000 16.000000 \n" ] } ], "source": [ "numerical_cols = data.select_dtypes(include=['int64', 'float64']).columns\n", "\n", "print(\"\\n--- Statistiques descriptives des variables numériques ---\")\n", "print(data[numerical_cols].describe())" ] }, { "cell_type": "code", "execution_count": 13, "id": "6e8a8367", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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MHDgQUVFRcHNzwy+//AJLS0utckZGRli2bBk6d+6MunXrYvDgwahSpQpu376Nffv2wdbWFn/88YdOYirq+Swr5ubm2LFjB0JCQhAQEIDt27dj27ZtmDBhQqF9TfKaPXs2OnfujMDAQISGhmqGQ9vZ2WHKlCmacl999RV27dqF1q1bY9iwYfD19UV8fDzWr1+Pf/75B/b29hg7diy2bt2K119/HYMGDYK/vz8yMjJw+vRpbNiwAdevX0flypXRrVs3tGjRAp9++imuX78OPz8/bNy4Eampqfnie++99zB37lwEBwcjNDQUSUlJiIiIQN26dZGWllakc7Ro0SK0bNkS9evXx9ChQ1GjRg0kJibi8OHDuHXrFk6ePAkA8PPzQ5s2beDv7w8HBwccP34cGzZs0Or8TAZEptFMRGVCPRxafVMqlZKrq6vUoUMHacGCBVpDjtWeHQ4dGRkpvfnmm5K7u7ukVCold3d36Z133sk3NHTLli2Sn5+fZGJiojX0s3Xr1lLdunULjK+woba//fabNH78eMnZ2VmysLCQunbtKt24cSPf+nPmzJGqVKkimZmZSS1atJCOHz9e4DDTwmJ7duipJEnSw4cPpdGjR0vu7u6Sqamp5OPjI82ePVtrOLIkieHQYWFh+WIqbFjxs27cuCG98cYbkqWlpVS5cmVp5MiR0o4dO7SGQ6udOHFC6tmzp+To6CiZmZlJnp6eUp8+faTIyMjn7kN9PtevX6+1XD3E9tkh4kU5n4Vts7Ch9+r30/OG0z8rJCREsrKykq5cuSJ17NhRsrS0lFxcXKTJkydLubm5+Y5j9uzZBW5nz549UosWLSQLCwvJ1tZW6tatm3T27Nl85W7cuCENHDhQcnJykszMzKQaNWpIYWFhWkPIHz58KI0fP17y9vaWlEqlVLlyZenVV1+VvvnmG61h4Xfv3pUGDBgg2draSnZ2dtKAAQOkEydOFHi+V61aJdWoUUNSKpVSo0aNpJ07dxY6HLqwY7xy5Yo0cOBAydXVVTI1NZWqVKkivf7669KGDRs0Zb744gupWbNmkr29vWRhYSHVqVNH+vLLL7XiJsOhkKRS7DFGRETFNmjQIGzYsKHA5jGiio59XIiIiMhgsI8LEVEZSU1NfeGVx11dXcsoGiLDxMSFiKiMjBw5EitXrnxuGbbeEz0f+7gQEZWRs2fP4s6dO88tU9rz1RAZOiYuREREZDDYOZeIiIgMBvu46JBKpcKdO3dgY2Oj8yngiYiIyjNJkvDw4UO4u7s/9wK2TFx06M6dO/muDEtERERFd/PmTVStWrXQ15m46JD6onQ3b96Era2tzNEQEREZjrS0NFSrVk3rAq8FYeKiQ+rmIVtbWyYuREREJfCirhbsnEtEREQGg4kLERERGQwmLkRERGQwmLgQERGRwWDiQkRERAaDiQsREREZDCYuREREZDCYuBAREZHBYOJCREREBoOJCxERERkMTvlvAOLi4pCSklLs9SpXrgwPD49SiIiIiEgeTFz0XFxcHOrU8UVm5qNir2thYYnz588xeSEionKDiYueS0lJQWbmI/TosQpOTr5FXi85+Rw2bXoXKSkpTFyIiKjcYOJiIJycfOHm1ljuMIiIiGTFzrlERERkMJi4EBERkcFg4kJEREQGg4kLERERGQwmLkRERGQwmLgQERGRwWDiQkRERAaDiQsREREZDCYuREREZDCYuBAREZHBYOJCREREBoOJCxERERkMJi5ERERkMJi4EBERkcFg4kJEREQGg4kLERERGQwmLkRERGQwmLgQERGRwWDiQkRERAaDiQsREREZDCYuREREZDCYuBAREZHBYOJCREREBoOJCxERERkMJi5ERERkMJi4EBERkcFg4kJEREQGg4kLERERGQwmLkRERGQwmLgQERGRwWDiQkRERAaDiQsREREZDCYuREREZDCYuBAREZHBYOJCREREBkOvEpeZM2dCoVBg1KhRmmWPHz9GWFgYHB0dYW1tjV69eiExMVFrvbi4OHTt2hWWlpZwdnbG2LFjkZOTo1Vm//79aNy4MczMzODt7Y0VK1bk2/+iRYtQvXp1mJubIyAgAMeOHSuNwyQiIqIS0pvE5b///sP333+PBg0aaC0fPXo0/vjjD6xfvx4HDhzAnTt30LNnT83rubm56Nq1K7Kzs3Ho0CGsXLkSK1aswKRJkzRlrl27hq5du6Jt27aIiYnBqFGjMGTIEOzcuVNTZu3atQgPD8fkyZMRHR2Nhg0bIjg4GElJSaV/8ERERFQkepG4pKeno3///li6dCkqVaqkWZ6amooff/wRc+fORbt27eDv74/ly5fj0KFDOHLkCABg165dOHv2LFatWoVGjRqhc+fOmD59OhYtWoTs7GwAQEREBLy8vDBnzhz4+vpixIgR6N27N+bNm6fZ19y5czF06FAMHjwYfn5+iIiIgKWlJX766aeyPRlERERUKL1IXMLCwtC1a1cEBQVpLY+KisKTJ0+0ltepUwceHh44fPgwAODw4cOoX78+XFxcNGWCg4ORlpaG2NhYTZlntx0cHKzZRnZ2NqKiorTKGBkZISgoSFOmIFlZWUhLS9O6ERERUekxkTuANWvWIDo6Gv/991++1xISEqBUKmFvb6+13MXFBQkJCZoyeZMW9evq155XJi0tDZmZmbh//z5yc3MLLHP+/PlCY58xYwamTp1atAMlIiKilyZrjcvNmzcxcuRI/PrrrzA3N5czlBIZP348UlNTNbebN2/KHRIREVG5JmviEhUVhaSkJDRu3BgmJiYwMTHBgQMH8O2338LExAQuLi7Izs7GgwcPtNZLTEyEq6srAMDV1TXfKCP18xeVsbW1hYWFBSpXrgxjY+MCy6i3URAzMzPY2tpq3YiIiKj0yJq4tG/fHqdPn0ZMTIzm1qRJE/Tv31/z2NTUFJGRkZp1Lly4gLi4OAQGBgIAAgMDcfr0aa3RP7t374atrS38/Pw0ZfJuQ11GvQ2lUgl/f3+tMiqVCpGRkZoyREREJD9Z+7jY2NigXr16WsusrKzg6OioWR4aGorw8HA4ODjA1tYWH330EQIDA9G8eXMAQMeOHeHn54cBAwZg1qxZSEhIwMSJExEWFgYzMzMAwAcffICFCxfik08+wXvvvYe9e/di3bp12LZtm2a/4eHhCAkJQZMmTdCsWTPMnz8fGRkZGDx4cBmdDSIiInoR2Tvnvsi8efNgZGSEXr16ISsrC8HBwVi8eLHmdWNjY/z5558YPnw4AgMDYWVlhZCQEEybNk1TxsvLC9u2bcPo0aOxYMECVK1aFcuWLUNwcLCmTN++fZGcnIxJkyYhISEBjRo1wo4dO/J12CUiIiL5KCRJkuQOorxIS0uDnZ0dUlNTddbfJTo6Gv7+/hg2LApubo2LvF58fDR++MEfUVFRaNy46OsRERHJoajfoXoxjwsRERFRUTBxISIiIoPBxIWIiIgMBhMXIiIiMhhMXIiIiMhgMHEhIiIig8HEhYiIiAwGExciIiIyGExciIiIyGAwcSEiIiKDwcSFiIiIDAYTFyIiIjIYTFyIiIjIYDBxISIiIoPBxIWIiIgMBhMXIiIiMhhMXIiIiMhgMHEhIiIig8HEhYiIiAwGExciIiIyGExciIiIyGAwcSEiIiKDwcSFiIiIDAYTFyIiIjIYTFyIiIjIYDBxISIiIoPBxIWIiIgMBhMXIiIiMhhMXIiIiMhgMHEhIiIig8HEhYiIiAwGExciIiIyGExciIiIyGAwcSEiIiKDwcSFiIiIDAYTFyIiIjIYTFyIiIjIYDBxISIiIoPBxIWIiIgMBhMXIiIiMhhMXIiIiMhgMHEhIiIig8HEhYiIiAwGExciIiIyGExciIiIyGAwcSEiIiKDwcSFiIiIDAYTFyIiIjIYTFyIiIjIYDBxISIiIoPBxIWIiIgMBhMXIiIiMhiyJi5LlixBgwYNYGtrC1tbWwQGBmL79u2a1x8/foywsDA4OjrC2toavXr1QmJiotY24uLi0LVrV1haWsLZ2Rljx45FTk6OVpn9+/ejcePGMDMzg7e3N1asWJEvlkWLFqF69eowNzdHQEAAjh07VirHTERERCUna+JStWpVzJw5E1FRUTh+/DjatWuHN998E7GxsQCA0aNH448//sD69etx4MAB3LlzBz179tSsn5ubi65duyI7OxuHDh3CypUrsWLFCkyaNElT5tq1a+jatSvatm2LmJgYjBo1CkOGDMHOnTs1ZdauXYvw8HBMnjwZ0dHRaNiwIYKDg5GUlFR2J4OIiIheSCFJkiR3EHk5ODhg9uzZ6N27N5ycnLB69Wr07t0bAHD+/Hn4+vri8OHDaN68ObZv347XX38dd+7cgYuLCwAgIiIC48aNQ3JyMpRKJcaNG4dt27bhzJkzmn28/fbbePDgAXbs2AEACAgIQNOmTbFw4UIAgEqlQrVq1fDRRx/h008/LXLsaWlpsLOzQ2pqKmxtbXVyPqKjo+Hv749hw6Lg5ta4yOvFx0fjhx/8ERUVhcaNi74eERGRHIr6Hao3fVxyc3OxZs0aZGRkIDAwEFFRUXjy5AmCgoI0ZerUqQMPDw8cPnwYAHD48GHUr19fk7QAQHBwMNLS0jS1NocPH9bahrqMehvZ2dmIiorSKmNkZISgoCBNmcJkZWUhLS1N60ZERESlR/bE5fTp07C2toaZmRk++OADbNq0CX5+fkhISIBSqYS9vb1WeRcXFyQkJAAAEhIStJIW9evq155XJi0tDZmZmUhJSUFubm6BZdTbKMyMGTNgZ2enuVWrVq3Yx09ERERFJ3viUrt2bcTExODo0aMYPnw4QkJCcPbsWbnDKpLx48cjNTVVc7t586bcIREREZVrJi+7gcuXL+PKlSt47bXXYGFhAUmSoFAoiry+UqmEt7c3AMDf3x///fcfFixYgL59+yI7OxsPHjzQqnVJTEyEq6srAMDV1TXf6B/1qKO8ZZ4diZSYmAhbW1tYWFjA2NgYxsbGBZZRb6MwZmZmMDMzK/KxEhER0cspcY3L3bt3ERQUhFq1aqFLly6Ij48HAISGhmLMmDElDkilUiErKwv+/v4wNTVFZGSk5rULFy4gLi4OgYGBAIDAwECcPn1aa/TP7t27YWtrCz8/P02ZvNtQl1FvQ6lUwt/fX6uMSqVCZGSkpgwRERHphxInLqNHj4aJiQni4uJgaWmpWd63b1/NaJ0XGT9+PA4ePIjr16/j9OnTGD9+PPbv34/+/fvDzs4OoaGhCA8Px759+xAVFYXBgwcjMDAQzZs3BwB07NgRfn5+GDBgAE6ePImdO3di4sSJCAsL09SEfPDBB7h69So++eQTnD9/HosXL8a6deswevRoTRzh4eFYunQpVq5ciXPnzmH48OHIyMjA4MGDS3p6iIiIqBSUuKlo165d2LlzJ6pWraq13MfHBzdu3CjSNpKSkjBw4EDEx8fDzs4ODRo0wM6dO9GhQwcAwLx582BkZIRevXohKysLwcHBWLx4sWZ9Y2Nj/Pnnnxg+fDgCAwNhZWWFkJAQTJs2TVPGy8sL27Ztw+jRo7FgwQJUrVoVy5YtQ3BwsKZM3759kZycjEmTJiEhIQGNGjXCjh078nXYJSIiInmVOHHJyMjQqmlRu3fvXpH7ffz444/Pfd3c3ByLFi3CokWLCi3j6emJv/7667nbadOmDU6cOPHcMiNGjMCIESOeW4aIiIjkVeKmolatWuHnn3/WPFcoFFCpVJg1axbatm2rk+CIiIiI8ipxjcusWbPQvn17HD9+HNnZ2fjkk08QGxuLe/fu4d9//9VljEREREQAXqLGpV69erh48SJatmyJN998ExkZGejZsydOnDiBmjVr6jJGIiIiIgAvOY+LnZ0dPvvsM13FQkRERPRcJa5xWb58OdavX59v+fr167Fy5cqXCoqIiIioICVOXGbMmIHKlSvnW+7s7IyvvvrqpYIiIiIiKkiJE5e4uDh4eXnlW+7p6Ym4uLiXCoqIiIioICVOXJydnXHq1Kl8y0+ePAlHR8eXCoqIiIioICVOXN555x18/PHH2LdvH3Jzc5Gbm4u9e/di5MiRePvtt3UZIxERERGAlxhVNH36dFy/fh3t27eHiYnYjEqlwsCBA9nHhYiIiEpFiRMXpVKJtWvXYvr06Th58iQsLCxQv359eHp66jI+IiIiIo2XmscFAGrVqoVatWrpIhYiIiKi5ypx4pKbm4sVK1YgMjISSUlJUKlUWq/v3bv3pYMjIiIiyqvEicvIkSOxYsUKdO3aFfXq1YNCodBlXERERET5lDhxWbNmDdatW4cuXbroMh4iIiKiQpV4OLRSqYS3t7cuYyEiIiJ6rhInLmPGjMGCBQsgSZIu4yEiIiIqVImbiv755x/s27cP27dvR926dWFqaqr1+saNG186OCIiIqK8Spy42Nvbo0ePHrqMhYiIiOi5Spy4LF++XJdxEBEREb1Qifu4AEBOTg727NmD77//Hg8fPgQA3LlzB+np6ToJjoiIiCivEte43LhxA506dUJcXByysrLQoUMH2NjY4Ouvv0ZWVhYiIiJ0GScRERFRyWtcRo4ciSZNmuD+/fuwsLDQLO/RowciIyN1EhwRERFRXiWucfn7779x6NAhKJVKreXVq1fH7du3XzowIiIiomeVuMZFpVIhNzc33/Jbt27BxsbmpYIiIiIiKkiJE5eOHTti/vz5mucKhQLp6emYPHkyLwNAREREpaLETUVz5sxBcHAw/Pz88PjxY/Tr1w+XLl1C5cqV8dtvv+kyRiIiIiIAL5G4VK1aFSdPnsSaNWtw6tQppKenIzQ0FP3799fqrEtERESkKyVOXADAxMQE7777rq5iISIiInquEicuP//883NfHzhwYEk3TURERFSgEicuI0eO1Hr+5MkTPHr0CEqlEpaWlkxciIiISOdKPKro/v37Wrf09HRcuHABLVu2ZOdcIiIiKhUvda2iZ/n4+GDmzJn5amOIiIiIdEGniQsgOuzeuXNH15slIiIiKnkfl61bt2o9lyQJ8fHxWLhwIVq0aPHSgRERERE9q8SJS/fu3bWeKxQKODk5oV27dpgzZ87LxkVERESUT4kTF5VKpcs4iIiIiF5I531ciIiIiEpLiWtcwsPDi1x27ty5Jd0NERERkUaJE5cTJ07gxIkTePLkCWrXrg0AuHjxIoyNjdG4cWNNOYVC8fJREhEREeElEpdu3brBxsYGK1euRKVKlQCISekGDx6MVq1aYcyYMToLkoiIiAh4iT4uc+bMwYwZMzRJCwBUqlQJX3zxBUcVERERUakoceKSlpaG5OTkfMuTk5Px8OHDlwqKnjpyxAZApReWIyIiqghKnLj06NEDgwcPxsaNG3Hr1i3cunULv//+O0JDQ9GzZ09dxlhhbd0KfPyxN4DdyMoyljscIiIi2ZU4cYmIiEDnzp3Rr18/eHp6wtPTE/369UOnTp2wePFiXcZYYdWsCdja5gDwx19/eePxY7kjIiIikleJExdLS0ssXrwYd+/e1YwwunfvHhYvXgwrKytdxlhh1a0LLFlyCUAykpOtsHat3BERERHJ66UnoIuPj0d8fDx8fHxgZWUFSZJ0ERf9Px+fxwDaw8hIhevXgaQkuSMiIiKST5ETl2en+L979y7at2+PWrVqoUuXLoiPjwcAhIaGcii0zp1G1appAIDYWJlDISIiklGRE5e5c+fir7/+0jwfPXo0TE1NERcXB0tLS83yvn37YseOHbqNklCz5n0AInFhpRYREVVURZ6ArkOHDujVqxfi4+MRGhqKXbt2YefOnahatapWOR8fH9y4cUPngVZ0np6pMDEB7t4FEhMBV1e5IyIiIip7RU5cGjZsiGPHjiEkJAShoaHIyMjQqmlRu3fvHszMzHQaJAFKpQo+PsC5c8CZM0xcACAuLg4pKSnFXq9y5crw8PAohYiIiKi0FWvKfwcHB/zxxx8AgFatWuHnn3/G9OnTAYhrEqlUKsyaNQtt27bVfaSEunVF4hIbC7RvD1Tky0DFxcWhTh1fZGY+Kva6FhaWOH/+HJMXIiIDVOJrFc2aNQvt27fH8ePHkZ2djU8++QSxsbG4d+8e/v333yJtY8aMGdi4cSPOnz8PCwsLvPrqq/j66681F20EgMePH2PMmDFYs2YNsrKyEBwcjMWLF8PFxUVTJi4uDsOHD8e+fftgbW2NkJAQzJgxAyYmTw9v//79CA8PR2xsLKpVq4aJEydi0KBBWvEsWrQIs2fPRkJCAho2bIjvvvsOzZo1K+kp0jkfH8DYGHjwALh3D3B0lDsi+aSkpCAz8xF69FgFJyffIq+XnHwOmza9i5SUFCYuREQGqMSJS7169XDx4kUsXLgQNjY2SE9PR8+ePREWFgY3N7cibePAgQMICwtD06ZNkZOTgwkTJqBjx444e/asZi6Y0aNHY9u2bVi/fj3s7OwwYsQI9OzZU5Mc5ebmomvXrnB1dcWhQ4cQHx+PgQMHwtTUFF999RUA4Nq1a+jatSs++OAD/Prrr4iMjMSQIUPg5uaG4OBgAMDatWsRHh6OiIgIBAQEYP78+QgODsaFCxfg7Oxc0tOkU0ol4O4O3LwJ3LpVsRMXNScnX7i5NX5xQSIiKhdKlLg8efIEnTp1QkREBD777LMS7/zZ0UcrVqyAs7MzoqKi8NprryE1NRU//vgjVq9ejXbt2gEAli9fDl9fXxw5cgTNmzfHrl27cPbsWezZswcuLi5o1KgRpk+fjnHjxmHKlClQKpWIiIiAl5eX5uKPvr6++OeffzBv3jxN4jJ37lwMHToUgwcPBiBmBt62bRt++uknfPrppyU+Rl2rUuVp4tKwodzREBERla0STUBnamqKU6dO6ToWpKamAhB9aQAgKioKT548QVBQkKZMnTp14OHhgcOHDwMADh8+jPr162s1HQUHByMtLQ2x/z/pyeHDh7W2oS6j3kZ2djaioqK0yhgZGSEoKEhTpiBZWVlIS0vTupU29SCu27dLfVdERER6p8Qz57777rv48ccfdRaISqXCqFGj0KJFC9SrVw8AkJCQAKVSCXt7e62yLi4uSEhI0JTJm7SoX1e/9rwyaWlpyMzMREpKCnJzcwsso95GQWbMmAE7OzvNrVq1asU/8GJSJy6JicCTJ6W+OyIiIr1S4j4uOTk5+Omnn7Bnzx74+/vnuz7R3Llzi7W9sLAwnDlzBv/8809JQypz48ePR3h4uOZ5WlpaqScvtraAtTWQng7ExwPsX0pERBVJsROXq1evonr16jhz5gwaNxadIi9evKhVRlHMcbojRozAn3/+iYMHD2pNaOfq6ors7Gw8ePBAq9YlMTERrv8/kYmrqyuOHTumtb3ExETNa+p79bK8ZWxtbWFhYQFjY2MYGxsXWMb1OROmmJmZlfmcNQqFqHU5f170c2HiQkREFUmxm4p8fHyQkpKCffv2Yd++fXB2dsaaNWs0z/ft24e9e/cWaVuSJGHEiBHYtGkT9u7dCy8vL63X/f39YWpqisjISM2yCxcuIC4uDoGBgQCAwMBAnD59Gkl5rj64e/du2Nraws/PT1Mm7zbUZdTbUCqV8Pf31yqjUqkQGRmpKaNPqlQR9+znQkREFU2xa1yevfrz9u3bkZGRUaKdh4WFYfXq1diyZQtsbGw0/Uns7OxgYWEBOzs7hIaGIjw8HA4ODrC1tcVHH32EwMBANG/eHADQsWNH+Pn5YcCAAZg1axYSEhIwceJEhIWFaWpDPvjgAyxcuBCffPIJ3nvvPezduxfr1q3Dtm3bNLGEh4cjJCQETZo0QbNmzTB//nxkZGRoRhnpE3Wl1K1b8sZBRERU1krcx0Xt2USmOJYsWQIAaNOmjdby5cuXayaHmzdvHoyMjNCrVy+tCejUjI2N8eeff2L48OEIDAyElZUVQkJCMG3aNE0ZLy8vbNu2DaNHj8aCBQtQtWpVLFu2TDMUGhAXh0xOTsakSZOQkJCARo0aYceOHfk67OoDd3fRZJSWJm62tnJHREREVDaKnbgoFIp8fViK26dFrShJj7m5ORYtWoRFixYVWsbT01PrytUFadOmDU6cOPHcMiNGjMCIESNeGJPclErAyQlIShKji5i4EBFRRVGipqJBgwZpmmEeP36MDz74IN+ooo0bN+omQiqQs7NIXJKSxKUAiIiIKoJiJy4hISFaz999912dBUNF5+Qk7vP0SSYiIir3ip24LF++vDTioGJSXz4pOVneOIiIiMpSiWfOJXnlTVxUKnljISIiKitMXAyUvT1gYgLk5AD378sdDRERUdlg4mKgjIye9nNhcxEREVUUTFwMmLq5iB10iYioomDiYsA4soiIiCoaJi4GjCOLiIioomHiYsDUiUtKCpCbK28sREREZYGJiwGztRXT/6tUwN27ckdDRERU+pi4GDCFgiOLiIioYmHiYuAqVxb3rHEhIqKKgImLgXNwEPf37skbBxERUVlg4mLgHB3FPWtciIioImDiYuCYuBARUUXCxMXAqZuKMjPFjYiIqDxj4mLglErAxkY8Zq0LERGVd0xcygE2FxERUUXBxKUcUDcXMXEhIqLyjolLOaCuceGQaCIiKu+YuJQDbCoiIqKKgolLOZA3cZEkeWMhIiIqTUxcyoFKlcR1i548AdLT5Y6GiIio9DBxKQeMjQF7e/GYzUVERFSeMXEpJ9jPhYiIKgImLuUEL7ZIREQVAROXcoI1LkREVBEwcSknOJcLERFVBExcyom8TUUqlbyxEBERlRYmLuWEnZ0YXZSbC6Smyh0NERFR6WDiUk4YGbGDLhERlX9MXMoRXmyRiIjKOyYu5QhHFhERUXnHxKUc4cgiIiIq75i4lCNsKiIiovKOiUs5oq5xefAAyM1VyBoLERFRaWDiUo5YWwNKJSBJwMOHSrnDISIi0jkTuQMg3VEoRHNRQgLw4IG53OGQjOLi4pCSklLs9SpXrgwPD49SiIiISDeYuJQzjo4icUlLM5M7FJJJXFwc6tTxRWbmo2Kva2FhifPnzzF5ISK9xcSlnFF30GWNS8WVkpKCzMxH6NFjFZycfIu8XnLyOWza9C5SUlKYuBCR3mLiUs6oO+imprLGpaJzcvKFm1tjucMgItIpds4tZ9SJC5uKiIioPGLiUs6oE5eMDCUAS1ljISIi0jUmLuWMhYW4Cd5yhkJERKRzTFzKIXWtC1BLzjCIiIh0jolLOcTEhYiIyismLuWQekg04CNnGERERDrHxKUcYo0LERGVV0xcyqGniQtrXIiIqHxh4lIOPW0qckJqqrGcoRAREekUE5dySKkELC2zAQBxcZyIjoiIyg/ZE5eDBw+iW7ducHd3h0KhwObNm7VelyQJkyZNgpubGywsLBAUFIRLly5plbl37x769+8PW1tb2NvbIzQ0FOnp6VplTp06hVatWsHc3BzVqlXDrFmz8sWyfv161KlTB+bm5qhfvz7++usvnR9vWbGzywIA3LzJaxYREVH5IXvikpGRgYYNG2LRokUFvj5r1ix8++23iIiIwNGjR2FlZYXg4GA8fvxYU6Z///6IjY3F7t278eeff+LgwYMYNmyY5vW0tDR07NgRnp6eiIqKwuzZszFlyhT88MMPmjKHDh3CO++8g9DQUJw4cQLdu3dH9+7dcebMmdI7+FKkTlxu3GCNCxERlR+yX2Sxc+fO6Ny5c4GvSZKE+fPnY+LEiXjzzTcBAD///DNcXFywefNmvP322zh37hx27NiB//77D02aNAEAfPfdd+jSpQu++eYbuLu749dff0V2djZ++uknKJVK1K1bFzExMZg7d64mwVmwYAE6deqEsWPHAgCmT5+O3bt3Y+HChYiIiCiDM6FbdnYisWNTERERlSey17g8z7Vr15CQkICgoCDNMjs7OwQEBODw4cMAgMOHD8Pe3l6TtABAUFAQjIyMcPToUU2Z1157DUqlUlMmODgYFy5cwP379zVl8u5HXUa9n4JkZWUhLS1N66Yv7O3VNS5sKiIiovJDrxOXhIQEAICLi4vWchcXF81rCQkJcHZ21nrdxMQEDg4OWmUK2kbefRRWRv16QWbMmAE7OzvNrVq1asU9xFJjby9qXK5fN4dKJXMwOnb3LvDDD64ApiIpyRKSJHdERERUVvQ6cdF348ePR2pqquZ28+ZNuUPSsLHJApCFrCwj3LghdzS6IUnAsmVArVrA99+7A5iEzZvr4PvvgUeP5I6OiIjKgux9XJ7H1dUVAJCYmAg3NzfN8sTERDRq1EhTJikpSWu9nJwc3Lt3T7O+q6srEhMTtcqon7+ojPr1gpiZmcHMTD/7kBgZAcBFAPVx/jzg5SVzQDrw44/A0KHisbd3Ji5f3gpT095ITDTG2rXAgAGAiV6/o0lucXFxSElJKfZ6lStXhoeHRylERETFpdcf815eXnB1dUVkZKQmUUlLS8PRo0cxfPhwAEBgYCAePHiAqKgo+Pv7AwD27t0LlUqFgIAATZnPPvsMT548gampKQBg9+7dqF27NipVqqQpExkZiVGjRmn2v3v3bgQGBpbR0ZaGcwDq49w5oJD+zwbjyhVA/acZNw7o2fMcAgLeRvfusdi61Q9xccAffwDduwMKhZyRkr6Ki4tDnTq+yMwsfvWchYUlzp8/x+SFSA/Inrikp6fj8uXLmufXrl1DTEwMHBwc4OHhgVGjRuGLL76Aj48PvLy88Pnnn8Pd3R3du3cHAPj6+qJTp04YOnQoIiIi8OTJE4wYMQJvv/023N3dAQD9+vXD1KlTERoainHjxuHMmTNYsGAB5s2bp9nvyJEj0bp1a8yZMwddu3bFmjVrcPz4ca0h04bnvPj3vMxhvKScHFGbkpEBtGkDfPUVEBMjXqtU6TH69AFWrQJOnQL8/IDateWMlvRVSkoKMjMfoUePVXBy8i3yesnJ57Bp07tISUlh4kKkB2RPXI4fP462bdtqnoeHhwMAQkJCsGLFCnzyySfIyMjAsGHD8ODBA7Rs2RI7duyAufnT0TK//vorRowYgfbt28PIyAi9evXCt99+q3ndzs4Ou3btQlhYGPz9/VG5cmVMmjRJa66XV199FatXr8bEiRMxYcIE+Pj4YPPmzahXr14ZnIXSck78e07mMF7Sjz8Chw8DtrbAihXqZrCnatQAXn0V+PdfYN8+0QeGtS5UGCcnX7i5NZY7DCIqIdkTlzZt2kB6zrAQhUKBadOmYdq0aYWWcXBwwOrVq5+7nwYNGuDvv/9+bpm33noLb7311vMDNiiGn7jk5gKzZ4vHU6cCnp4Fl2vRAjh+HEhMBM6eBerWLbsYiYio7HBUUbl2EQqFhLt3gRL0R9QLv/8u+rc4Oj7tmFsQCwugeXPxeP9+lLsh4EREJDBxKdcy4eYmLrZoiLUukgR8/bV4PGIEYGX1/PKBgSKBSUkxzOMlIqIXY+JSznl5iYnoDLGDbmQkEB0tkpERI15c3swMUE+gHB1durEREZE8mLiUc9Wri8TFEGsgliwR9++9B1SuXLR1XnlF3F+9Cvz/1RyIiKgcYeJSzhlq4nL3rpiXBQDyDP56oUqVxCgjgLUuRETlEROXcq5GDZG4xMbKHEgxrVkDPHkCNGoENGhQvHX/fx5CxMSwky4RUXnDxKWcq1kzEwBw8ybw4IG8sRTHihXiftCg4q9buzZgaQmkpwOXLukyKiIikhsTl3LOxkalmfvk1Cl5Yymq2FgxJ4uJCdCvX/HXNzYGGjYUj0+f1m1sREQkLyYuFYC6qcVQEpeffxb3XbsCTk4l24Z6wuOLF4HsbN3ERURE8mPiUgEYUuIiScDateLxgAEl346bm+io++SJSF6IiKh8YOJSAaibTQwhcYmKAm7cEH1UXuaK1grF01qXM2d0ExsREcmPiUsFoK5xOX1a/0fZrF8v7l9/XSQvL0OduFy+DDx+/HLbIiIi/cDEpQLw9gbMzYFHj8TEbPpKkoANG8Tj3r1ffnvOzqKPTG6u4c1jQ0REBWPiUgEYGz+tfdDn5qITJ0RiZWEBdOmim22qrxLNxIWIqHxg4lJBGEIHXXUzUdeuL76gYlH5+Yn7K1fYXEREVB4wcakg1InLyZPyxlEYXTcTqTk5iZtKxdFFRETlAROXCkLfE5eTJ0UnWnNzUeOiS76+4v7sWd1ul4iIyh4TlwpCPST62jXg3j15YymIuralc2fA2lq321Y3F12+DGRnl8+3/JMnwOrVQPPmQJcu9QAcw4EDHkhLkzsyIiLdKp+f4pSPg4MYXQQA//0nbyzPkqSn/Vveekv323d2FsefmwvcvGmn+x3I7OJFkZz17w8cPQokJioBNMWFC5WxaJH4e0uS3FESEekGE5cKJCBA3B89Km8czzpzRnz5mpmJ+Vt0TaF42lx07Zq97ncgozNngNdeE7VJzs7AF18AK1acB9Adzs4ZyM4G/voLOHBA7kiJiHSDiUsFoq+Ji7q2pVMnwMamdPahbi6Ki7MFYFE6Oyljly8DbdoAiYmiKfD0aeCzz4D69R8B2II33riA9u1F2QMHxIUriYgMHROXCkSduBw7pl9NB6UxmuhZbm6AnR2Qk2MMILj0dlRGsrOBd94B7t4F/P2BvXtFjUteRkZAy5aiRgYQNS/6PAEhEVFRMHGpQBo2BJRKICVFdNLVB7GxYnI4pRLo1q309pO3uQgoxQypjEycKGpQHByAzZvFfWHatAEaNRLJ6pYtnM+GiAybidwBUNkxMxNfYMeOieaiGjXkjuhpM1FwsKgRKU1+fsCRIwDQDdnZV0p3Z6Vo/35g9mzx+McfgapVn19eoRCjtW7cAO7fB3bvLt0kkYgMR1xcHFJSUkq0buXKleHh4aHjiF6MiUsFExDwNHF55x25oymbZiK1qlUBS8tsPHpkiyNHbNC8eenvU9eePAE+/FA8HjYM6N69aOsplcAbbwArVwLR0SKJq1mz1MIkIgMQFxeHOnV8kZn5qETrW1hY4vz5c2WevDBxqWCaNRP3x47JGwcgmohiYwFTU/GlWtoUCsDL6wFiY50RGVkJo0aV/j517bvvxHlzcgK+/rp461avLv7+x44BO3YAH3wgrmNFRBVTSkoKMjMfoUePVXBy8n3xCnkkJ5/Dpk3vIiUlhYkLlS51B93oaNHBU6mULxZ1bUuHDoC9fdnss0YNkbjs32+HrCzRfGYo4uOBKVPE45kzS3bO2rYVQ6hTUoCoqKeJbHmXnAycPm0JIACpqWZwc5M7IiL94eTkCze3xnKHUWTsnFvBeHuLX+tZWfIPiy7NSecK4+qaDuAW0tNNsH172e1XFyZOBB4+FMnGoEEl24a5uUheANFXJjNTV9Hpp0OHgD59AHd3YNCgOgCOYO3auli2DIiJEdewIiLDwhqXCkahANq3B9asAfbsAVq1kieO2Fgx70hZNROpKRQA8BuAsVi9uuh9ROQWGwusWCEeL1gghjqXVOPGorkoOVnM79Kpk05C1Cu5ucDUqcD06U+XubhkIzHxJoyMvHD7thFu3xbvwZ49dXc1ckNW0k6acnXQpIqLiUsFFBT0NHGZOlWeGH75Rdx36fL8obylQyQuf/wBpKUBtrZlvf/imzBB1A707ImX7lRsZCRGca1aJS4HEBAAVKqkmzj1QXq66Oy9c6d4PmAAMGYMkJt7Bv7+/ujf/yRu326Av/8W89p8/z3Qrx/g6ipv3HJ6mU6acnXQpIqLiUsFFBQk7o8eleeLW6UCfv1VPB4woGz3LZyAp+dj3Lhhjs2bgYED5Yih6P75B9i6VXSk/eor3WyzZk0xHP7qVWDfPpEQlQdZWeJYdu8GLCyAH34A3n1XvBYdLe4tLHLQqhVQuzawbp2YxO+XX4D33gMcHeWLXU4l7aQpZwdNqrjYx6UC8vQUX1y5ucDBg2W///37gVu3ROfS0rg2UVF06iQuka1OoPSVJAHjxonHoaHiy1ZX1Ans6dOi46+hy8kRNSe7d4umn717nyYtBXF2BoYMETUtjx6J5CU1tezi1UfqTppFvRV3JAqRLjBxqaDUX1p79pT9vtXNRH36yDeqp3Pn+wDE8d+6JU8MRbF1q+hgamEBTJ6s2227uQH164vHcrwPdG3iRGDjRjFSbsuWojWpmZuL5MbRUSQta9eKuXKISH8xcamg1IlLZGTZ7vfRo6fDoOVpJhKqVcvCa6+JZquffpIvjufJyQHGjxePR48WI2N0rW1b0efl6lXg1q1SusJlGdi48em8NitXQnNxyaKwshLvRUtLUfP011/6dS0vItLGxKWCattWjLA5c6Zsmwl++010nqxRA2jRouz2W5Bhw8T9jz+KZjN9s3KlmGzOwQH45JPS2UelSkDTpuLx0aNVAChKZ0el6OLFp8PDw8OBt98u/jbs7IBevcT/iZiYp/1hiEj/sHNuBeXoKOYDOXoU2LTp6TTypUmSgEWLxOPhw9VDk+XTqxfw0UdAXJzoF6FPw4IzM582DU2cWLrXcXrtNfFlffeuJQA9uA5EMWRliUTl4UNxHDNnlnxbNWoA7dqJWsgdO4Bq1fJfcZsqHg4T1z9MXCqwPn1E4rJmTdkkLkePAidOiH4FgweX/v5exNxcNBF8+y2wdKl+JS7ffQfcvi06Upf238bSUtR+7d0LAF8gO/tB6e5Qhz77TLynHB1FbZ6p6cttr0ULcTHKy5dFk+bQobqJkwwTh4nrJyYuFVifPmJ+i7//Fh1UX3SV4Ze1eLG4f/tt/Rl2OnSoSFy2bBE1L/rwGXPvHjBjhng8fXrZdGBu3hw4ciQbjx55YcOGmwZxAcpdu4A5c8Tjn37STR8ghUJMSrhkiZigb9cuMWEfVUwcJq6f2MelAqtaFWjZUjxet65095WcLEZsAGVTu1NU9eqJ/j65ucC8eXJHI8ycCTx4ADRoIIb3lgVTU6BJE9HZadkyN70fFpyQ8LRz94cf6nb2ZSsroEcP8fj4ceDatVJspyODwGHi+oWJSwWn7sioTipKy7x54qKOTZs+7QyqL9TzpCxdKmo75HTpkpjSHxC1LmV59eZate4COIfUVJNiX3m6LKlUYtLApCQxnPubb3S/j5o1gVdfFY8PHvQEUE33OyEyUJIEPHpkAkCeqnMmLhVc795iOOyxY8CVK6Wzj7t3RZ8NQHQ01TcdOwKNGgEZGU87D8tBkoARI0SC16kT0Llz2e5fXP/oUwAi0bx6tWz3X1TffPN0Ztw1a8R9aWjXTjQ/ZWWZAFiFnJzS2Q+RvsvJAc6fBzZvFj+svvgCWLWqAYBRssTDxKWCc3F5OueFug+Krs2dK4ZAv/IK0K1b6ezjZSgUT4cbL1ggEhg5bNwo+lQolSLRk2fU1VY0bZqGx4/FyC99m8/k6FHRIRcQfys/v9Lbl7GxGHlmapoL4DX89FMFvpgRVUjZ2WICzAULRK38yZOiGVtcVV0CIM+F3pi4EEaPFvc//ADcv6/bbd+797S2ZfJk+YdAF+att0TzwN27wKxZZb//1FRg1CjxeNw4wNu77GNQmzDhJszMRBL122/yxfGs1FTgnXfEr7+33hLT9Zc2BwegZcs4AMDSpW74++/S32dZuHpVdGgePlxccHPoUB8A+3DggAcOHRLNcPqWtFLZkSQxh9SiRaJ2Mz0dsLERF2QdMEB8Vg0ZcgLASFniY+JC6NRJdFJNTwciInS77UmTxBwbjRrptgOlrpmYPJ0DZNYsMcKoLH30kRjZVaMG8OmnZbvvZ3l4ZGma9EaNEl9icpMkMQLs2jUxRPyHH8ouCfbxuQ9gBVQqBfr104/zURLZ2cDy5aJDfs2a4tpXEREiQY2OtgHQBhcuVMbu3WJU1cKF4gKfjx/LHTmVpYwMMVhj3TpxEV57e1FTPnKk+K6oUUPMK2UkY/bAxIXyNZXo6oPq6NGnzU9z5uhvbYtar15iErPHj8s2eVi/Xly/ychI3Ftalt2+C/PJJyKZTU4WHWFF1bB8vvxSnCcTE1ELZG9f1hF8BE/Px7h1S7xPsrLKev8ll5MjEhRvb3EF7H//Fe+1Vq2AsWNFMjNz5lUA/eDvfwe1aolmsnv3xGR88+aJK4hnZ8t9JFTarl4V75Xz55++Rz78UEwJUJYDBV6EiQsBEKOLqlUDEhPFr62X9eSJmFJfksQXX7t2L7/N0qZQAPPni/vffiubCw9evw588IF4PH7805EsclMqxTkwNwd27nw6X4ocfv8d+Pxz8XjxYiAwUI4o0jFnzhXY2YlaCH3s/1OQgwfFl87w4cDNm+LCml9/LR4fPChqFwcNAjp0eADgN/j7J+Cdd0Ti+uabYubg7GxRdtEicSVxQzjuksrNBS5cEO+52bOBiAg3AF/i1ClnnDolatvkTuJLQ26uArt3ix9O6elA5cqihrNdu5ef1LE0cAI6AiDenBMnAu+/L+67dwe8vEq+venTgVOnRB+B0hiuWlpeeUV8yC9eLNpyY2JEB+bSkJYGvP66+GXbpIloVtMn9eqJyfmGDQMmTAD8/cs+Ad27V1y9GRBV1XLOZOvllYV168Ror+XLAVdXUROkjzWJ8fGiNuXXX8VzBwdgyhRx/szNX7y+Uimadxs2FL++d+0SnTI3bhRz23TqJJKg8uDxY+CPP8QklDt2iH5uT7kBmIAjR54uUSpFc0mdOkDt2kU7n/rNB1u31kJysnjWuLH4++pjwqLGGhfSGDIEaN1aXMF56NCS/7Jav14kLoBoenJy0l2MZWH2bKBuXTHJWWk1k+TkiI6msbHiC2DTJvGBqG+GDBGT4OXkiGT2xImy2/c//4i29cePRf8ofUiAO3Z82vw5Y4ZIXPTJkyeiaad2bZG0KBQi8bx4UfSjKu6XrEIB+PoCYWFiokZTU9H/a+lSYPt2IDvbcL9CLl4U0w+4uYlZxH/9VSQtlpZirqn+/YHevZMBLIC39z14eIjjz85+OjR47lxg2zbD7PckScD69ZUBxCA52Qrm5uI8dOum30kLwBoXysPICFi2TMzYqm7bDg8v3jaiop5eqXfUqKe/lg2JpaUY+te0qfilGR4uzoWufllnZYmk5a+/xBfJli2lf7mFklIoxNWz79wB9u8XtQ3794tfm6Vp2zbRfPnokRj1sm6d6N+iD95/X3RgHDNGNGGlpwNffSVvZ0VANOeEhYkrvgPi/btokW4mfDQxEf2/GjUSo0zOnBFzP50+XRdAP4NpPpIkcYmTOXNELYs67qpVRYL++uuiKVL9XouOvokNG0ahXbtWcHNzgEolmtPPnwfOngVSUkQN1PHjQPXq4sK1tWvLdnhFFh8vOmdv3y4uR1ClShreesu2VC/mqkuGmy5TqfD2Fh/CgPhg/v77oq+7a5f4Vab+spk9u3RiLAt164qRK4CoNfrf/3TTtv/woag92LRJXINo/Xr9m0n4Webm4tdlw4biQ/vVV8WXZGmQJNE89cYbIiEIChLNE2VxvabiCA9/ej2pr78Wv1Tlmv/nzh3RrNm6tUgoHB3Fe/fIEd2/t2xtRefkAQPEfjIzTQH8ivff9ynT2rjievJE9Nlq1kycp61bxXvt9ddFH67r18XfsVWr5yfIRkaihqZtW9FpNSRE1EgpFGIb69aJ0VhnzjgB0INe9s9QqYCVK8WM09u3A0qlCsBIdOly2WCSFoCJCxVg5EjxRQ2IjqNffvn8EQVZWaKTX5cu4ou5dWtRY6Evv5BL6t13nyZuc+eKZqO0tJJv78gR8Yt11y5xPZxt28QHpyGwsxO/tJs3F3P9dOggErrcXN3t486dp8MuVSrxi/Cvv/RjlFVBPv1UdGZUKkVnzkaNxIidspKcLPqx1KwJrFolvjzff190Lh06tHRrgGrUEJ8NTZrcAZCJqCgbNG4smlfOny+9/RbXnTvi86tmTVGjcvy4SMTff1/MU/LHH6L5ryQjZhQKUcvSp494z7ZsKWZxvn8fOHSoGoCbWLTIHdeu6fqoSubff8XVzwcNEk1ijRoBq1adB/CtXvbTeh4mLpSPQiESEfWEaBMnqt/k2m25N26IoXN+fmLStNxc8WW/cycMKnt/nmHDxDEaGYnjf+UV8UulOLUvt2+LtvSWLcVwQw8P0RSnnrHYUDg5ic6yPXuKRHbUKNF8cPz4y2334UNRy1e3rkjmlEqRKC5dqv9t7e++K/6WVasCly+LX+xDhoj/G6UlNlYkDR4eot/P48eiFuzoUfFeLasrr5uYAI0bJwDwRadO4iJfq1eLz4OePcV7RY4ROLm54v9ojx7iHE2cKEZROTsDU6eKPjoREbpt7rSzE/+fR40SP+BsbR8DcMBPP7miRg3x2q+/ApmZuttnUahU4odSUJD4/DlyBLC2FrVLR48CNWsa5iQ9TFyesWjRIlSvXh3m5uYICAjAsWPH5A5JFgqF+PJYuVJ8YZ07J6qHXVyASpVElXH16mIEztWrYoTFsmXAzz/rX7X+y3r/fdE04ukpjrVLFzHCJiJCVA8X5MED0bzSv7/4tbdokfhAffttMW12QEAZHoAOWViI5q0lS8RMmocOieaIoCCx/OHDom1HpRJ9JEaOFO+jzz4T58zfH4iOFrM5G8qvwJYtxTDhQYNEQvvjj4CPj0hqdu9++VopSRL9Kb75RpyfevVETeDjx+L5tm2iI7N8TY438OWX1xEVJTpwS5JoCm3fHqhVS/xt//uvdJOYBw9EX7GhQ0VTTpcu4v9fbq74+6xcKZLJSZNKd7CAUin+Dn36nAXQAwEBaVAono6Oc3MTnwlr1pReh15JEp8xU6eK92FwsEiuTUzE+blwQQx318fBAEVl4JX5urV27VqEh4cjIiICAQEBmD9/PoKDg3HhwgU4OzvLHV6ZUyhE80i3bqIz259/Pr1WBSBqIQIDRX+EDz8UmXx51aKFOPZp00TCcuKESNoA8UFYrZr4In/8WNSw3LqlvX7LluKDxBDms3kRIyPxi79LF/Gl9Ntv4oMxMlJ8GDZrJjp416wpfv1bWormxNRU8eVx9qxIePJeXsLHR1wS4u239Wuiq6KytxdDpIcNEx12IyPFL+xffxWJfuvW4v+Kn584L+ofAHmTsydPRGfPhARxwdMLF0Rn92PHxHtKzcRENDGOHi1qePQlwWvcWCQsZ8+KfkqrV4vj+OorcbO3F/+PmjYVyZe3t/jB4+RU9GattDTxY+H6dTGL8qlT4hydPq2dGDk4iM+uoUNL93pWhRHHsxmLF1+Go2NjrFwp3h/Xr4vzsnq1KFe7tjhvdeo8vVWvLj5LXvR3lSTxfyopSVxV/tw58V7591/RRKZmayvOxZgxYtvlAROXPObOnYuhQ4di8ODBAICIiAhs27YNP/30Ez6Vex52GVWqJK4G+sUXol393j3xH9PZufw0CRWFnZ1I4CZMELVL27aJL+DkZGjmQMjLy0v8Au3TR9Sw6MsXjK54eIg+Hl98IYYIb9okPkD/+UfcXsTKSiS9/fuLX4WG3icKEMnJnj3iC2TFCvHL+v598et/8+b85c3MRKKWnY3nXn3azAxo00b8iOjTR7+nGPDzE8n9nDmiFmTLFtF08+CB+D+zbZt2eWNj8Vni6CiaBtU3IyPR0T89XdzS0p5fo1e7tqj569FDNGHqSzOjp6eo6Zk4UXxe/Pmn6Lt1+rRITi9cyL+OsbH43K1USSQxgEhUJEnUIt29Kz5znjwpeJ8WFqLvTs+eojO1lVXpHZ8cysFHhW5kZ2cjKioK48eP1ywzMjJCUFAQDh8+XOA6WVlZyMoz93dqaioAIO1lenA+Iz09HQBw504UsrPTi7xeSor43xAVFaXZRlEZGRlBVYR63fj4kq1X0v3ldeH//7fLdV4CA8Xt0SMj3LljhqQkUzx+rIC5uQRb21xUr54Ja2tRNjsb+Pff8n1uunUzQpcuKty8aYbz5y1w9aoFkpJMkZpqgidPFDAxkWBpqYKrazbc3LJRr94j1Kz5CEqlOL5Dh4q1O4M4L2+/DfTpY4QLF8wRHW2NK1fMce2aORITlcjIEB+9z146QKGQYG+fAze3bFSpkoWaNTPh55eJ2rUzYG4uOladOyduhdGnc1O1qhii/f77wJUrljh1yhJXrljg6lVzJCUpcf++CXJzFYiPz/95Uhg7uxy4umbB1TUbHh5ZqFXrEXx9M+Hk9PRbvKCPbH05L126iFtamjFiYy1x7ZoF4uLMEBdnhhs3zJGeboLcXFH7lpLy4n2Zm+fCzS0b1as/hrd3JurVewRf3wyYmUn/H4Pujy/vMaanp+vsO0+9HelFnQglkiRJkm7fvi0BkA4dOqS1fOzYsVKzZs0KXGfy5MkSxLW9eeONN9544403Hdxu3rz53O9r1ri8hPHjxyM8zwxtKpUK9+7dg6OjIxQ6ahdIS0tDtWrVcPPmTdja2upkm+UBz0vheG4KxvNSOJ6bgvG8FK40zo0kSXj48CHc3d2fW46Jy/+rXLkyjI2NkZiYqLU8MTERrq6uBa5jZmYGs2eG0NiX0mVrbW1t+R+nADwvheO5KRjPS+F4bgrG81I4XZ8buyJ0nORw6P+nVCrh7++PyMhIzTKVSoXIyEgEynM5WiIiInoGa1zyCA8PR0hICJo0aYJmzZph/vz5yMjI0IwyIiIiInkxccmjb9++SE5OxqRJk5CQkIBGjRphx44dcHFxkS0mMzMzTJ48OV+TVEXH81I4npuC8bwUjuemYDwvhZPz3CgkyVCu60lEREQVHfu4EBERkcFg4kJEREQGg4kLERERGQwmLkRERGQwmLjosUWLFqF69eowNzdHQEAAjh07JndIsjt48CC6desGd3d3KBQKbC7oynUV0IwZM9C0aVPY2NjA2dkZ3bt311yHpKJbsmQJGjRooJkoKzAwENu3b5c7LL0zc+ZMKBQKjBo1Su5QZDdlyhQoFAqtW506deQOSy/cvn0b7777LhwdHWFhYYH69evj+PHjZRoDExc9tXbtWoSHh2Py5MmIjo5Gw4YNERwcjKSkJLlDk1VGRgYaNmyIRYsWyR2KXjlw4ADCwsJw5MgR7N69G0+ePEHHjh2RkZEhd2iyq1q1KmbOnImoqCgcP34c7dq1w5tvvonY2Fi5Q9Mb//33H77//ns0aNBA7lD0Rt26dREfH6+5/VOUS56Xc/fv30eLFi1gamqK7du34+zZs5gzZw4qVapUtoHo5hKFpGvNmjWTwsLCNM9zc3Mld3d3acaMGTJGpV8ASJs2bZI7DL2UlJQkAZAOHDggdyh6qVKlStKyZcvkDkMvPHz4UPLx8ZF2794ttW7dWho5cqTcIclu8uTJUsOGDeUOQ++MGzdOatmypdxhSKxx0UPZ2dmIiopCUFCQZpmRkRGCgoJwuKDrtRM9IzU1FQDg4OAgcyT6JTc3F2vWrEFGRgYv5fH/wsLC0LVrV63PGwIuXboEd3d31KhRA/3790dcXJzcIclu69ataNKkCd566y04OzvjlVdewdKlS8s8DiYueiglJQW5ubn5Zux1cXFBQkKCTFGRoVCpVBg1ahRatGiBevXqyR2OXjh9+jSsra1hZmaGDz74AJs2bYKfn5/cYcluzZo1iI6OxowZM+QORa8EBARgxYoV2LFjB5YsWYJr166hVatWePjwodyhyerq1atYsmQJfHx8sHPnTgwfPhwff/wxVq5cWaZxcMp/onImLCwMZ86cYZt8HrVr10ZMTAxSU1OxYcMGhISE4MCBAxU6ebl58yZGjhyJ3bt3w9zcXO5w9Ernzp01jxs0aICAgAB4enpi3bp1CA0NlTEyealUKjRp0gRfffUVAOCVV17BmTNnEBERgZCQkDKLgzUueqhy5cowNjZGYmKi1vLExES4urrKFBUZghEjRuDPP//Evn37ULVqVbnD0RtKpRLe3t7w9/fHjBkz0LBhQyxYsEDusGQVFRWFpKQkNG7cGCYmJjAxMcGBAwfw7bffwsTEBLm5uXKHqDfs7e1Rq1YtXL58We5QZOXm5pYv2ff19S3zZjQmLnpIqVTC398fkZGRmmUqlQqRkZFsl6cCSZKEESNGYNOmTdi7dy+8vLzkDkmvqVQqZGVlyR2GrNq3b4/Tp08jJiZGc2vSpAn69++PmJgYGBsbyx2i3khPT8eVK1fg5uYmdyiyatGiRb5pFi5evAhPT88yjYNNRXoqPDwcISEhaNKkCZo1a4b58+cjIyMDgwcPljs0WaWnp2v96rl27RpiYmLg4OAADw8PGSOTV1hYGFavXo0tW7bAxsZG0xfKzs4OFhYWMkcnr/Hjx6Nz587w8PDAw4cPsXr1auzfvx87d+6UOzRZ2djY5OsDZWVlBUdHxwrfN+p///sfunXrBk9PT9y5cweTJ0+GsbEx3nnnHblDk9Xo0aPx6quv4quvvkKfPn1w7Ngx/PDDD/jhhx/KNhC5hzVR4b777jvJw8NDUiqVUrNmzaQjR47IHZLs9u3bJwHIdwsJCZE7NFkVdE4ASMuXL5c7NNm99957kqenp6RUKiUnJyepffv20q5du+QOSy9xOLTQt29fyc3NTVIqlVKVKlWkvn37SpcvX5Y7LL3wxx9/SPXq1ZPMzMykOnXqSD/88EOZx6CQJEkq21SJiIiIqGTYx4WIiIgMBhMXIiIiMhhMXIiIiMhgMHEhIiIig8HEhYiIiAwGExciIiIyGExciIiIyGAwcSEiIiKDwcSFiEiH2rRpg1GjRskdBlG5xcSFiIiIDAYTFyIqd7Kzs+UO4aU8efJE7hCI9BYTFyLSaNOmDT7++GN88skncHBwgKurK6ZMmQIAuH79OhQKBWJiYjTlHzx4AIVCgf379wMA9u/fD4VCgZ07d+KVV16BhYUF2rVrh6SkJGzfvh2+vr6wtbVFv3798OjRoyLHNGLECIwYMQJ2dnaoXLkyPv/8c+S9zFr16tUxffp0DBw4ELa2thg2bBgA4Pfff0fdunVhZmaG6tWrY86cOVrbzsrKwrhx41CtWjWYmZnB29sbP/74o+b1M2fOoHPnzrC2toaLiwsGDBiAlJQUzesZGRkYOHAgrK2t4ebmlm/7AKBQKLB582atZfb29lixYoXWeV27di1at24Nc3Nz/PrrrwCAZcuWwdfXF+bm5qhTpw4WL16s2UZ2djZGjBgBNzc3mJubw9PTEzNmzCjSOSUyaGV+WUci0lutW7eWbG1tpSlTpkgXL16UVq5cKSkUCmnXrl3StWvXJADSiRMnNOXv378vAZD27dsnSdLTq3c3b95c+ueff6To6GjJ29tbat26tdSxY0cpOjpaOnjwoOTo6CjNnDmzyDFZW1tLI0eOlM6fPy+tWrVKsrS01Loqraenp2Rrayt988030uXLl6XLly9Lx48fl4yMjKRp06ZJFy5ckJYvXy5ZWFhoXTG7T58+UrVq1aSNGzdKV65ckfbs2SOtWbNGc2xOTk7S+PHjpXPnzknR0dFShw4dpLZt22rWHz58uOTh4SHt2bNHOnXqlPT6669LNjY2WldYBiBt2rRJ65js7Ow0cajPa/Xq1aXff/9dunr1qnTnzh1p1apVkpubm2bZ77//Ljk4OEgrVqyQJEmSZs+eLVWrVk06ePCgdP36denvv/+WVq9eXaRzSmTImLgQkUbr1q2lli1bai1r2rSpNG7cuGIlLnv27NGUmTFjhgRAunLlimbZ+++/LwUHBxc5Jl9fX0mlUmmWjRs3TvL19dU89/T0lLp37661Xr9+/aQOHTpoLRs7dqzk5+cnSZIkXbhwQQIg7d69u8D9Tp8+XerYsaPWsps3b0oApAsXLkgPHz6UlEqltG7dOs3rd+/elSwsLEqUuMyfP1+rTM2aNfMlItOnT5cCAwMlSZKkjz76SGrXrp3WeSGqCNhURERaGjRooPXczc0NSUlJJd6Gi4sLLC0tUaNGDa1lxdlm8+bNoVAoNM8DAwNx6dIl5ObmapY1adJEa51z586hRYsWWstatGihWS8mJgbGxsZo3bp1gfs8efIk9u3bB2tra82tTp06AIArV67gypUryM7ORkBAgGYdBwcH1K5du8jHlVfe+DMyMnDlyhWEhoZq7f+LL77AlStXAACDBg1CTEwMateujY8//hi7du0q0X6JDI2J3AEQkX4xNTXVeq5QKKBSqWBkJH7nSHn6lhTWiTTvNhQKRaHb1CUrK6tilbewsHju6+np6ejWrRu+/vrrfK+5ubnh8uXLRdqPQqHQOmdAwectb/zp6ekAgKVLl2olRgBgbGwMAGjcuDGuXbuG7du3Y8+ePejTpw+CgoKwYcOGIsVFZKhY40JEReLk5AQAiI+P1yzL21G3NB09elTr+ZEjR+Dj46P5Ei+Ir68v/v33X61l//77L2rVqgVjY2PUr18fKpUKBw4cKHD9xo0bIzY2FtWrV4e3t7fWzcrKCjVr1oSpqalWbPfv38fFixe1tuPk5KR1zi5duvTCjskuLi5wd3fH1atX8+3by8tLU87W1hZ9+/bF0qVLsXbtWvz++++4d+/ec7dNZOhY40JERWJhYYHmzZtj5syZ8PLyQlJSEiZOnFgm+46Li0N4eDjef/99REdH47vvvitwBE9eY8aMQdOmTTF9+nT07dsXhw8fxsKFCzUjc6pXr46QkBC89957+Pbbb9GwYUPcuHEDSUlJ6NOnD8LCwrB06VK88847mlFWly9fxpo1a7Bs2TJYW1sjNDQUY8eOhaOjI5ydnfHZZ59paqbU2rVrh4ULFyIwMBC5ubkYN25cvhqogkydOhUff/wx7Ozs0KlTJ2RlZeH48eO4f/8+wsPDMXfuXLi5ueGVV16BkZER1q9fD1dXV9jb25f4PBMZAiYuRFRkP/30E0JDQ+Hv74/atWtj1qxZ6NixY6nvd+DAgcjMzESzZs1gbGyMkSNHaoY8F6Zx48ZYt24dJk2ahOnTp8PNzQ3Tpk3DoEGDNGWWLFmCCRMm4MMPP8Tdu3fh4eGBCRMmAADc3d3x77//Yty4cejYsSOysrLg6emJTp06aZKT2bNna5qUbGxsMGbMGKSmpmrFMWfOHAwePBitWrWCu7s7FixYgKioqBce85AhQ2BpaYnZs2dj7NixsLKyQv369TWz8trY2GDWrFm4dOkSjI2N0bRpU/z111/5Eiei8kYhPdv4SkSkR9q0aYNGjRph/vz5codCRHqAqTkREREZDDYVEZFs4uLi4OfnV+jrZ8+eLcNoiMgQsKmIiGSTk5OD69evF/p69erVYWLC31dE9BQTFyIiIjIY7ONCREREBoOJCxERERkMJi5ERERkMJi4EBERkcFg4kJEREQGg4kLERERGQwmLkRERGQw/g/Jl6iflcNcGAAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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HF198gdu3bxfbT0ZGhiLtAcr+firl4MGDsjkHly9fxs8//4zOnTvD1NQUpqameP311/HTTz+V+EtNqb736dMHpqammDp1arG/1oUQ+PfffxU5jhL9cXV1hZeXF1auXCn7Puzbtw/Hjx+X1e3Xrx8KCwsxbdq0Yvu5f/++Xv+vvv766/j777+xadOmYq89/N69+eab2LVrF+bNm4caNWoo9v8O6e+5HsF4lJSUFCxfvhwpKSlwdXUFAEyYMAE7duzA8uXLMWPGDFy4cAHJyclYv349Vq5cicLCQowbNw59+/Ytcfnf82T79u04ffo07t+/j7S0NOzduxe7d+9G3bp1sWXLFlhaWpa67WeffYb9+/eje/fuqFu3LtLT0/HNN9+gVq1aaN++PQDtL6eqVasiJiYGtra2qFKlCvz8/PQeQapevTrat2+PsLAwpKWlYd68eWjQoIFsKe2wYcOwYcMGdOnSBf369cP58+exatUq2aTL8ratR48eePnll/Hxxx/j0qVL8Pb2xq5du/Dzzz9j7Nixxfatr+HDh2PBggUICQnBkSNH4OLigu+//x7W1tayeiYmJli6dCm6du2KZs2aISwsDG5ubrh69Sri4uJgZ2eHX375RZE2lfX9VIqXlxeCg4Nly1QBYOrUqVKdmTNnIi4uDn5+fhg+fDiaNm2KrKwsJCQkYM+ePYr88VC/fn18/vnnmDRpEi5duoRevXrB1tYWFy9exKZNmzBixAhMmDChwscBlOnPjBkz0LNnT7Rr1w5hYWG4ceMGFixYAC8vL1noCAgIwNtvv43o6GgkJiaic+fOMDc3x7lz57B+/Xp89dVXsknGZfH+++9jw4YNeOONNzB06FD4+voiKysLW7ZsQUxMDLy9vaW6gwYNwgcffIBNmzbhnXfe4UXtngVPedXKMwuA2LRpk/T8119/FQBElSpVZA8zMzPRr18/IYQQw4cPFwDEmTNnpO2OHDkiAIjTp08/7S48E4qWqRY9LCwshLOzs+jUqZP46quvZEtBizy8TDU2Nlb07NlTuLq6CgsLC+Hq6ioGDhwozp49K9vu559/Fk2bNhVmZmay5XkBAQGiWbNmJbavtCWQP/74o5g0aZJwdHQUVlZWonv37iI5ObnY9nPmzBFubm5CrVaLdu3aicOHDxfb56Pa9vAyVSGEuHXrlhg3bpxwdXUV5ubmwtPTU8yePVu2TFQI7Xd01KhRxdpU2nLPhyUnJ4vXXntNWFtbCwcHBxERESEtISxaplrk6NGjok+fPqJGjRpCrVaLunXrin79+onY2NhHHqPo/Vy/fr2s/OLFiyUu3S3L+1naPktbEl30fXpwmXPRe7dq1Srh6ekp1Gq18PHxKdZvIYRIS0sTo0aNErVr1xbm5ubC2dlZBAYGisWLFz+2TeXx008/ifbt20s/Wxo3bixGjRol+3lS2ne5bt26onv37sXKS/qOKNGfNWvWiMaNGwu1Wi28vLzEli1bxOuvvy4aN25crO7ixYuFr6+vsLKyEra2tqJ58+bigw8+ENeuXXts+0v6f+nff/8Vo0ePFm5ubsLCwkLUqlVLhIaGFlt6K4QQ3bp1EwDE77//XmI/6OlSCaHQ7DAjp1KpsGnTJvTq1QsAsHbtWgwePBgnT54sNhnJxsYGzs7OiIqKwowZM1BQUCC9dvfuXVhbW2PXrl3o1KnT0+wCEdFT07JlS9SsWRO7d+82dFMkvXv3xvHjx0ucM0JPH+dglMLHxweFhYVIT09HgwYNZA9nZ2cAQLt27XD//n2cP39e2u7s2bMAlJucR0RkSAUFBcWuKRIfH4+///67xMvuG8r169exdetWvPnmm4ZuCv2/53oE4/bt21LS9fHxwdy5c/Hyyy+jevXqqFOnDoYMGYIDBw5gzpw58PHxQUZGBmJjY9GiRQt0794dGo0GrVu3ho2NDebNmweNRoNRo0bBzs7uid+xk4iePampqY983crKyuiWTl66dAlBQUEYMmQIXF1dcfr0acTExMDe3h4nTpx45CXqn4aLFy/iwIEDWLp0Kf766y+cP39e+iOQDMywZ2gMq+i848OPovPZ+fn5YvLkycLd3V2Ym5sLFxcX0bt3b3Hs2DFpH1evXhV9+vQRNjY2wsnJSbz11lvi33//NVCPiMiQSvp5UtLPFmNy8+ZN0a9fP2kORLVq1UTfvn1ll6w3pKK5OHXq1KnQnBhS3nM9gkFEpKSS7mb6IFdXVzRt2vQptYbIsBgwiIiISHGc5ElERESKey4vtKXRaHDt2jXY2toqfslpIiKiykwIgVu3bsHV1RUmJqWPUzyXAePatWvF7kRIREREZXf58mXUqlWr1Nefy4BRdFOpy5cvw87OzsCtISIiMh45OTmoXbu27AaNJXkuA0bRaRE7OzsGDCIiIj08booBJ3kSERGR4hgwiIiISHEMGERERKQ4gweM/fv3o0ePHnB1dYVKpcLmzZsfu018fDxeeOEFqNVqNGjQACtWrHji7SQiIqKyM3jAyM3Nhbe3NxYuXFim+hcvXkT37t3x8ssvIzExEWPHjsWwYcOwc+fOJ9xSIiIiKiuDryLp2rUrunbtWub6MTEx8PDwwJw5cwAATZo0wW+//YYvv/wSwcHBT6qZREREVA4GH8Eor4MHDyIoKEhWFhwcjIMHD5a6TV5eHnJycmQPIiIienKMLmCkpqbCyclJVubk5IScnBzcvXu3xG2io6Nhb28vPXgVTyIioifL6AKGPiZNmoTs7GzpcfnyZUM3iYiIqFIz+ByM8nJ2dkZaWpqsLC0tDXZ2drCysipxG7VaDbVa/TSaR0RERDDCEQx/f3/ExsbKynbv3g1/f38DtQi4cgVo3Rro0MFgTSAiInqmGDxg3L59G4mJiUhMTASgXYaamJiIlJQUANrTGyEhIVL9kSNH4sKFC/jggw9w+vRpfPPNN1i3bh3GjRtniOYDAAoKgMOHgYQEgzWBiIjomWLwgHH48GH4+PjAx8cHABAZGQkfHx9MnjwZAHD9+nUpbACAh4cHtm7dit27d8Pb2xtz5szB0qVLDbpE1eT/30WNxmBNICIieqaohBDC0I142nJycmBvb4/s7GxF7qZ6+TJQpw5gYQHk5SnQQCIiomdUWX+HGnwEozIoumMtRzCIiIi0GDAUUHSK5PkbCyIiIioZA4YCOAeDiIhIjgFDAQ+OYHAUg4iIiAFDESYPvIsMGERERAwYingwYPA0CREREQOGIhgwiIiI5BgwFFC0TBVgwCAiIgIYMBTBORhERERyDBgK4CkSIiIiOQYMBTBgEBERyTFgKIABg4iISI4BQwEMGERERHIMGArgKhIiIiI5BgwFcASDiIhIjgFDAQ+OYHCZKhEREQOGIlQqXcjgCAYREREDhmJ4y3YiIiIdBgyFMGAQERHpMGAohAGDiIhIhwFDIZyDQUREpMOAoRCOYBAREekwYCikKGBwmSoREREDhmI4gkFERKTDgKEQBgwiIiIdBgyFMGAQERHpMGAohAGDiIhIhwFDIVymSkREpMOAoRCuIiEiItJhwFAIT5EQERHpMGAohAGDiIhIhwFDIQwYREREOgwYCmHAICIi0mHAUAgDBhERkQ4DhkK4TJWIiEiHAUMhXKZKRESkw4ChEJ4iISIi0mHAUAgDBhERkQ4DhkIYMIiIiHQYMBTCgEFERKTDgKEQBgwiIiIdBgyFcJkqERGRDgOGQrhMlYiISIcBQyE8RUJERKTDgKEQBgwiIiIdBgyFMGAQERHpMGAohAGDiIhIhwFDIQwYREREOgwYCuEyVSIiIp1nImAsXLgQ7u7usLS0hJ+fHw4dOvTI+vPmzUOjRo1gZWWF2rVrY9y4cbh3795Tam3JuEyViIhIx+ABY+3atYiMjERUVBQSEhLg7e2N4OBgpKenl1j/hx9+wIcffoioqCicOnUK3377LdauXYuPPvroKbdcjqdIiIiIdAweMObOnYvhw4cjLCwMTZs2RUxMDKytrbFs2bIS6//+++9o164dBg0aBHd3d3Tu3BkDBw585KhHXl4ecnJyZA+lMWAQERHpGDRg5Ofn48iRIwgKCpLKTExMEBQUhIMHD5a4Tdu2bXHkyBEpUFy4cAHbtm1Dt27dSj1OdHQ07O3tpUft2rWV7QgYMIiIiB5kZsiDZ2ZmorCwEE5OTrJyJycnnD59usRtBg0ahMzMTLRv3x5CCNy/fx8jR4585CmSSZMmITIyUnqek5OjeMhgwCAiItIx+CmS8oqPj8eMGTPwzTffICEhARs3bsTWrVsxbdq0UrdRq9Wws7OTPZTGgEFERKRj0BEMBwcHmJqaIi0tTVaelpYGZ2fnErf59NNP8eabb2LYsGEAgObNmyM3NxcjRozAxx9/DBMTw2QmLlMlIiLSMegIhoWFBXx9fREbGyuVaTQaxMbGwt/fv8Rt7ty5UyxEmJqaAgCEAdeIcpkqERGRjkFHMAAgMjISoaGhaNWqFdq0aYN58+YhNzcXYWFhAICQkBC4ubkhOjoaANCjRw/MnTsXPj4+8PPzQ1JSEj799FP06NFDChqGwFMkREREOgYPGP3790dGRgYmT56M1NRUtGzZEjt27JAmfqakpMhGLD755BOoVCp88sknuHr1KmrWrIkePXpg+vTphuoCAAYMIiKiB6mEIc8rGEhOTg7s7e2RnZ2t2ITPPn2ATZuARYuAkSMV2SUREdEzp6y/Q41uFcmziiMYREREOgwYCmHAICIi0mHAUAiXqRIREekwYCiEy1SJiIh0GDAUwlMkREREOgwYCmHAICIi0mHAUAgDBhERkQ4DhkIYMIiIiHQYMBTCgEFERKTDgKEQLlMlIiLSYcBQCJepEhER6TBgKISnSIiIiHQYMBTCgEFERKTDgKEQBgwiIiIdBgyFMGAQERHpMGAohAGDiIhIhwFDIVymSkREpMOAoRAuUyUiItJhwFAIT5EQERHpMGAohAGDiIhIhwFDIQwYREREOgwYCmHAICIi0mHAUAgDBhERkQ4DhkK4TJWIiEiHAUMhXKZKRESkw4ChEJ4iISIi0mHAUAgDBhERkQ4DhkIYMIiIiHQYMBTCgEFERKTDgKEQriIhIiLSYcBQCFeREBER6TBgKISnSIiIiHQYMBTCgEFERKTDgKEQBgwiIiIdBgyFMGAQERHpMGAohAGDiIhIhwFDIVymSkREpMOAoRAuUyUiItJhwFAIT5EQERHpMGAohAGDiIhIhwFDIQwYREREOgwYCmHAICIi0mHAUAgDBhERkQ4DhkK4TJWIiEinwgEjKSkJO3fuxN27dwEA4jldp8llqkRERDp6B4x///0XQUFBaNiwIbp164br168DAMLDwzF+/HjFGmgseIqEiIhIR++AMW7cOJiZmSElJQXW1tZSef/+/bFjxw5FGmdMGDCIiIh0zPTdcNeuXdi5cydq1aolK/f09ERycnKFG2ZsGDCIiIh09B7ByM3NlY1cFMnKyoJarS7XvhYuXAh3d3dYWlrCz88Phw4demT9mzdvYtSoUXBxcYFarUbDhg2xbdu2ch1TaQwYREREOnoHjA4dOmDlypXSc5VKBY1Gg1mzZuHll18u837Wrl2LyMhIREVFISEhAd7e3ggODkZ6enqJ9fPz89GpUydcunQJGzZswJkzZ7BkyRK4ubnp2xVFMGAQERHp6H2KZNasWQgMDMThw4eRn5+PDz74ACdPnkRWVhYOHDhQ5v3MnTsXw4cPR1hYGAAgJiYGW7duxbJly/Dhhx8Wq79s2TJkZWXh999/h7m5OQDA3d1d324ohstUiYiIdPQewfDy8sLZs2fRvn179OzZE7m5uejTpw+OHj2K+vXrl2kf+fn5OHLkCIKCgnQNMjFBUFAQDh48WOI2W7Zsgb+/P0aNGgUnJyd4eXlhxowZKCwsLPU4eXl5yMnJkT2UxmWqREREOnqPYACAvb09Pv74Y723z8zMRGFhIZycnGTlTk5OOH36dInbXLhwAXv37sXgwYOxbds2JCUl4d1330VBQQGioqJK3CY6OhpTp07Vu51lwVMkREREOnqPYCxfvhzr168vVr5+/Xp89913FWrUo2g0Gjg6OmLx4sXw9fVF//798fHHHyMmJqbUbSZNmoTs7GzpcfnyZcXbxYBBRESko3fAiI6OhoODQ7FyR0dHzJgxo0z7cHBwgKmpKdLS0mTlaWlpcHZ2LnEbFxcXNGzYEKamplJZkyZNkJqaivz8/BK3UavVsLOzkz2UxoBBRESko3fASElJgYeHR7HyunXrIiUlpUz7sLCwgK+vL2JjY6UyjUaD2NhY+Pv7l7hNu3btkJSUBM0Dv8nPnj0LFxcXWFhYlLMXymHAICIi0tE7YDg6OuLYsWPFyv/++2/UqFGjzPuJjIzEkiVL8N133+HUqVN45513kJubK60qCQkJwaRJk6T677zzDrKyshAREYGzZ89i69atmDFjBkaNGqVvVxTBgEFERKSj9yTPgQMHYsyYMbC1tcVLL70EANi3bx8iIiIwYMCAMu+nf//+yMjIwOTJk5GamoqWLVtix44d0sTPlJQUmJjoclDt2rWxc+dOjBs3Di1atICbmxsiIiIwceJEfbuiCC5TJSIi0lEJPW9/mp+fjzfffBPr16+HmZk2p2g0GoSEhCAmJsagpyseJycnB/b29sjOzlZsPkZcHPDKK0DTpsDJk4rskoiI6JlT1t+heo9gWFhYYO3atZg2bRr+/vtvWFlZoXnz5qhbt66+uzRqPEVCRESkU6HrYABAw4YN0bBhQyXaYtQYMIiIiHT0DhiFhYVYsWIFYmNjkZ6eLlvVAQB79+6tcOOMCQMGERGRjt4BIyIiAitWrED37t3h5eUFVdEsx+cUAwYREZGO3gFjzZo1WLduHbp166Zke4wWAwYREZGO3tfBsLCwQIMGDZRsi1HjMlUiIiIdvQPG+PHj8dVXX0HPVa6VDu+mSkREpKP3KZLffvsNcXFx2L59O5o1awZzc3PZ6xs3bqxw44wJT5EQERHp6B0wqlatit69eyvZFqPGgEFERKSjd8BYvny5ku0wegwYREREOnrPwQCA+/fvY8+ePfjvf/+LW7duAQCuXbuG27dvK9I4Y8KAQUREpKP3CEZycjK6dOmClJQU5OXloVOnTrC1tcV//vMf5OXlISYmRsl2PvMYMIiIiHT0HsGIiIhAq1atcOPGDVhZWUnlvXv3RmxsrCKNMyZcpkpERKSj9wjG//73P/z+++/F7prq7u6Oq1evVrhhxobLVImIiHT0HsHQaDQoLCwsVn7lyhXY2tpWqFHGiKdIiIiIdPQOGJ07d8a8efOk5yqVCrdv30ZUVNRzeflwBgwiIiIdvU+RzJkzB8HBwWjatCnu3buHQYMG4dy5c3BwcMCPP/6oZBuNAgMGERGRjt4Bo1atWvj777+xZs0aHDt2DLdv30Z4eDgGDx4sm/T5vGDAICIi0tE7YACAmZkZhgwZolRbjBoDBhERkY7eAWPlypWPfD0kJETfXRslLlMlIiLSUQk9b4darVo12fOCggLcuXMHFhYWsLa2RlZWliINfBJycnJgb2+P7Oxs2NnZKbLPa9cANzfA1BS4f1+RXRIRET1zyvo7VO9VJDdu3JA9bt++jTNnzqB9+/ac5ElERPScq9C9SB7m6emJmTNnIiIiQsndGoUHL7TFi20REdHzTtGAAWgnfl67dk3p3T7zTB54JxkwiIjoeaf3JM8tW7bIngshcP36dSxYsADt2rWrcMOMzYMBQ6ORPyciInre6B0wevXqJXuuUqlQs2ZNvPLKK5gzZ05F22V0Hg4YREREzzO9A4aGv0VlipapAgwYREREHMhXCOdgEBER6eg9ghEZGVnmunPnztX3MEaDp0iIiIh09A4YR48exdGjR1FQUIBGjRoBAM6ePQtTU1O88MILUj3Vg+cOKjEGDCIiIh29A0aPHj1ga2uL7777Trqq540bNxAWFoYOHTpg/PjxijXSGDBgEBER6eh9qXA3Nzfs2rULzZo1k5WfOHECnTt3fqavhfEkLhVeUABYWGj/nZUFPHQldSIiokrhiV8qPCcnBxkZGcXKMzIycOvWLX13a7Q4gkFERKSjd8Do3bs3wsLCsHHjRly5cgVXrlzBTz/9hPDwcPTp00fJNhqFB6eacBUJERE97/SegxETE4MJEyZg0KBBKCgo0O7MzAzh4eGYPXu2Yg00FrwOBhERkY7eczCK5Obm4vz58wCA+vXro0qVKoo07El6EnMwAO1pEiGA69cBZ2fFdktERPTMeOJzMIpcv34d169fh6enJ6pUqYIK5hWjxlu2ExERaZU5YDx8afB///0XgYGBaNiwIbp164br168DAMLDw5+7JapFGDCIiIi0yhww5s6di23btknPx40bB3Nzc6SkpMDa2loq79+/P3bs2KFsK40EAwYREZFWmSd5durUCa+//jquX7+O8PBw7Nq1Czt37kStWrVk9Tw9PZGcnKx4Q41B0URPBgwiInrelXkEw9vbG4cOHcLmzZsBaCd3PjhyUSQrKwtqtVqxBhqTohGM53gaChEREYByTvKsXr06fvnlFwBAhw4dsHLlSuk1lUoFjUaDWbNm4eWXX1a2lUaCp0iIiIi09L4OxqxZsxAYGIjDhw8jPz8fH3zwAU6ePImsrCwcOHBAyTYaDQYMIiIiLb2XqXp5eeHs2bNo3749evbsidzcXPTp0wdHjx5F/fr1lWyj0WDAICIi0tJrBKOgoABdunRBTEwMPv74Y6XbZLQYMIiIiLT0GsEwNzfHsWPHlG6L0WPAICIi0tL7FMmQIUPw7bffKtkWo8dlqkRERFp6T/K8f/8+li1bhj179sDX17fYPUjmzp1b4cYZGy5TJSIi0ip3wLhw4QLc3d1x4sQJvPDCCwCAs2fPyuqoHry16HOEp0iIiIi0yn2KxNPTE5mZmYiLi0NcXBwcHR2xZs0a6XlcXBz27t1brn0uXLgQ7u7usLS0hJ+fHw4dOlSm7dasWQOVSoVevXqVtxtPBAMGERGRVrkDxsN3S92+fTtyc3P1bsDatWsRGRmJqKgoJCQkwNvbG8HBwUhPT3/kdpcuXcKECRPQoUMHvY+tNAYMIiIirQrfrr2it2efO3cuhg8fjrCwMDRt2hQxMTGwtrbGsmXLSt2msLAQgwcPxtSpU1GvXr3HHiMvLw85OTmyx5PAgEFERKRV7oChUqmKzbHQd85Ffn4+jhw5gqCgIF2DTEwQFBSEgwcPlrrdZ599BkdHR4SHh5fpONHR0bC3t5cetWvX1qu9j8OAQUREpFXuSZ5CCLz11lvSDc3u3buHkSNHFltFsnHjxsfuKzMzE4WFhXBycpKVOzk54fTp0yVu89tvv+Hbb79FYmJimds8adIkREZGSs9zcnKeSMjgMlUiIiKtcgeM0NBQ2fMhQ4Yo1pjHuXXrFt58800sWbIEDg4OZd5OrVY/lTu8cpkqERGRVrkDxvLlyxU7uIODA0xNTZGWliYrT0tLg7Ozc7H658+fx6VLl9CjRw+pTPP/wwVmZmY4c+aMQe+DwlMkREREWhWe5FkRFhYW8PX1RWxsrFSm0WgQGxsLf3//YvUbN26M48ePIzExUXq89tprePnll5GYmPjE5laUFQMGERGRlt5X8lRKZGQkQkND0apVK7Rp0wbz5s1Dbm4uwsLCAAAhISFwc3NDdHQ0LC0t4eXlJdu+atWqAFCs3BAYMIiIiLQMHjD69++PjIwMTJ48GampqWjZsiV27NghTfxMSUmBiYlBB1rKjAGDiIhISyUqeiELI5STkwN7e3tkZ2fDzs5Osf16ewPHjgG7dgGdOim2WyIiomdGWX+HGsfQgJHgMlUiIiItBgwFcZkqERGRFgOGgjgHg4iISIsBQ0EMGERERFoMGApiwCAiItJiwFAQAwYREZEWA4aCGDCIiIi0GDAUxGWqREREWgwYCuIyVSIiIi0GDAXxFAkREZEWA4aCGDCIiIi0GDAUxIBBRESkxYChIAYMIiIiLQYMBTFgEBERaTFgKIjLVImIiLQYMBTEZapERERaDBgK4ikSIiIiLQYMBTFgEBERaTFgKIgBg4iISIsBQ0EMGERERFoMGApiwCAiItJiwFAQl6kSERFpMWAoiMtUiYiItBgwFMRTJERERFoMGApiwCAiItJiwFAQAwYREZEWA4aCGDCIiIi0GDAUxIBBRESkxYChIC5TJSIi0mLAUBCXqRIREWkxYCiIp0iIiIi0GDAUxIBBRESkxYChIAYMIiIiLQYMBTFgEBERaTFgKIgBg4iISIsBQ0FFy1S5ioSIiJ53DBgK4ggGERGRFgOGghgwiIiItBgwFMSAQUREpMWAoSAGDCIiIi0GDAUxYBAREWkxYCiIAYOIiEiLAUNBXKZKRESkxYChII5gEBERaTFgKIgBg4iISIsBQ0EMGERERFoMGApiwCAiItJiwFAQAwYREZHWMxEwFi5cCHd3d1haWsLPzw+HDh0qte6SJUvQoUMHVKtWDdWqVUNQUNAj6z9NDBhERERaBg8Ya9euRWRkJKKiopCQkABvb28EBwcjPT29xPrx8fEYOHAg4uLicPDgQdSuXRudO3fG1atXn3LLi+MyVSIiIi2DB4y5c+di+PDhCAsLQ9OmTRETEwNra2ssW7asxPqrV6/Gu+++i5YtW6Jx48ZYunQpNBoNYmNjn3LLi+MIBhERkZZBA0Z+fj6OHDmCoKAgqczExARBQUE4ePBgmfZx584dFBQUoHr16qXWycvLQ05OjuzxJDBgEBERaRk0YGRmZqKwsBBOTk6ycicnJ6SmppZpHxMnToSrq6sspDwsOjoa9vb20qN27doVandpGDCIiIi0DH6KpCJmzpyJNWvWYNOmTbC0tCy13qRJk5CdnS09Ll++/ETaw4BBRESkZWbIgzs4OMDU1BRpaWmy8rS0NDg7Oz9y2y+++AIzZ87Enj170KJFi0fWVavVUKvVFW7v4zBgEBERaRl0BMPCwgK+vr6yCZpFEzb9/f1L3W7WrFmYNm0aduzYgVatWj2NppZJ0SoSBgwiInreGXQEAwAiIyMRGhqKVq1aoU2bNpg3bx5yc3MRFhYGAAgJCYGbmxuio6MBAP/5z38wefJk/PDDD3B3d5fmatjY2MDGxsZg/QB0IxhcpkpERM87gweM/v37IyMjA5MnT0ZqaipatmyJHTt2SBM/U1JSYGKiG2hZtGgR8vPz0bdvX9l+oqKiMGXKlKfZ9GJ4ioSIiEjL4AEDAEaPHo3Ro0eX+Fp8fLzs+aVLl558g/TEgEFERKRl1KtInjUMGERERFoMGApiwCAiItJiwFAQAwYREZEWA4aCuEyViIhIiwFDQVymSkREpMWAoSCeIiEiItJiwFAQAwYREZEWA4aCGDCIiIi0GDAUxIBBRESkxYChIAYMIiIiLQYMBXGZKhERkRYDhoK4TJWIiEiLAUNBPEVCRESkxYChIAYMIiIiLQYMBTFgEBERaTFgKIgBg4iISIsBQ0EMGERERFoMGAriMlUiIiItBgwFcZkqERGRFgOGgniKhIiISIsBQ0EMGERERFoMGApiwCAiItIyM3QDKpP09FQAzrh7Nw8JCSfLvJ2DgwPq1Knz5BpGRET0lDFgKCQlJQUDB74JYB+uXr0GX1/fMm9rZWWN06dPMWQQEVGlwYChkMzMTOTl3QUA2Ni4YtCgI2XaLiPjFDZtGoLMzEwGDCIiqjQYMBSlnXxhYqKGi8sLBm4LERGR4XCSp6K0AYPXwSAioucdA4aiGDCIiIgABgyFMWAQEREBDBgKY8AgIiICGDAUxoBBREQEMGAoTJssGDCIiOh5x4ChKF4jnIiICGDAUBhPkRAREQEMGApjwCAiIgIYMBTGgEFERAQwYCiMAYOIiAhgwFAYAwYRERHAgKEwLlMlIiICGDAUxhEMIiIigAFDYQwYREREAAOGwnQX2mLIICKi5xkDhqJ4JU8iIiKAAUNhHMEgIiICGDAUVjxgXLwIbNsGFBQYqElEREQGYGboBlQuumELIbSPLVuAmzcBBwegTRvDtYyIiOhp4giGouQjGNeuacMFAJw7Z5gWERERGQIDhqLkAePECd0rFy8C+fnAnTtAfDyQk/P0W0dERPS08BSJonQBQ6MB/vlH+2+VCigs1IaM48eBkyeBy5eBN980UDOJiIiesGdiBGPhwoVwd3eHpaUl/Pz8cOjQoUfWX79+PRo3bgxLS0s0b94c27Zte0otfRxdwLh8WTtKYWEBeHtryw4e1IYLALhwAbh0Sb713r3A338/nZYSERE9SQYPGGvXrkVkZCSioqKQkJAAb29vBAcHIz09vcT6v//+OwYOHIjw8HAcPXoUvXr1Qq9evXDiwfMRBqMLGEVBoXFjoGlT7b+Tk7X/Nfv/caO9e3WrTb75xgWBgUCrVsCaNY8/EpfBEhHRs0wlhGF/Vfn5+aF169ZYsGABAECj0aB27dp477338OGHHxar379/f+Tm5uLXX3+Vyl588UW0bNkSMTExZTpmTk4O7O3tkZ2dDTs7O0X6kZCQAF/f1gAKZeUDBwIeHsCsWcD9+9rTJSEhwOrV2udNm6bjn39iAQyUtlGpBF5/PRNCAPfumcDSUgMrK+0jL88Ehw7Z4uxZKzRqVICuXS3g6KgNHLduAVlZgLk5UL26Nsjk5mrnflhYaB/m5to2ZGVpR1hsbYEaNXShh4iIKhdPT6BPH+X2V9bfoQb9tZKfn48jR45g0qRJUpmJiQmCgoJw8ODBErc5ePAgIiMjZWXBwcHYvHlzqcfJy8tDXl6e9Dw7OxuA9k1Syu3btwFooFJlQQgzmJho0KRJJiwsruHaNcDZ2QNXrtijXr0sqFQpaNzYBSdOOOGffywBdAdwE8AEAI0hxAhs2KB+xNHuA7iFU6eAU6fuKdYHIiKqfIKC7iEoyFKx/RX97nzc+IRBA0ZmZiYKCwvh5OQkK3dycsLp06dL3CY1NbXE+qmpqaUeJzo6GlOnTi1WXrt2bT1a/WhC1ACgneR58qRuzkWR8+e1j0d7X/F2ERHR82nPHsDeXvn93rp1C/aP2PFzMTA+adIk2aiHRqNBVlYWatSoAZVKpcgxcnJyULt2bVy+fFmx0y7Pksrcv8rcN6By968y9w1g/4xZZe6bEAK3bt2Cq6vrI+sZNGA4ODjA1NQUaWlpsvK0tDQ4OzuXuI2zs3O56gOAWq2GWi0/5VC1alX9Gv0YdnZ2le7L9KDK3L/K3DegcvevMvcNYP+MWWXt26NGLooYdBWJhYUFfH19ERsbK5VpNBrExsbC39+/xG38/f1l9QFg9+7dpdYnIiKip8/gp0giIyMRGhqKVq1aoU2bNpg3bx5yc3MRFhYGAAgJCYGbmxuio6MBABEREQgICMCcOXPQvXt3rFmzBocPH8bixYsN2Q0iIiJ6gMEDRv/+/ZGRkYHJkycjNTUVLVu2xI4dO6SJnCkpKTAx0Q20tG3bFj/88AM++eQTfPTRR/D09MTmzZvh5eVlqC4A0J6GiYqKKnYqprKozP2rzH0DKnf/KnPfAPbPmFXmvpWVwa+DQURERJWPwa/kSURERJUPAwYREREpjgGDiIiIFMeAQURERIpjwFBAeW83/6zav38/evToAVdXV6hUqmL3dxFCYPLkyXBxcYGVlRWCgoJw7tw5wzS2nKKjo9G6dWvY2trC0dERvXr1wpkzZ2R17t27h1GjRqFGjRqwsbHB66+/Xuyibs+qRYsWoUWLFtJFffz9/bF9+3bpdWPu28NmzpwJlUqFsWPHSmXG3L8pU6ZApVLJHo0bN5ZeN+a+Fbl69SqGDBmCGjVqwMrKCs2bN8fhw4el1435Z4u7u3uxz0+lUmHUqFEAKsfnpy8GjAoq7+3mn2W5ubnw9vbGwoULS3x91qxZ+PrrrxETE4M///wTVapUQXBwMO7de/ZvuLZv3z6MGjUKf/zxB3bv3o2CggJ07twZubm5Up1x48bhl19+wfr167Fv3z5cu3YNfZS8BeETVKtWLcycORNHjhzB4cOH8corr6Bnz544+f83wzHmvj3or7/+wn//+1+0aNFCVm7s/WvWrBmuX78uPX777TfpNWPv240bN9CuXTuYm5tj+/bt+OeffzBnzhxUq1ZNqmPMP1v++usv2We3e/duAMAbb7wBwPg/vwoRVCFt2rQRo0aNkp4XFhYKV1dXER0dbcBWVRwAsWnTJum5RqMRzs7OYvbs2VLZzZs3hVqtFj/++KMBWlgx6enpAoDYt2+fEELbF3Nzc7F+/XqpzqlTpwQAcfDgQUM1s0KqVasmli5dWmn6duvWLeHp6Sl2794tAgICREREhBDC+D+7qKgo4e3tXeJrxt43IYSYOHGiaN++famvV7afLREREaJ+/fpCo9FUis+vIjiCUQFFt5sPCgqSyh53u3ljdfHiRaSmpsr6am9vDz8/P6Psa3Z2NgCgevXqAIAjR46goKBA1r/GjRujTp06Rte/wsJCrFmzBrm5ufD39680fRs1ahS6d+8u6wdQOT67c+fOwdXVFfXq1cPgwYORkpICoHL0bcuWLWjVqhXeeOMNODo6wsfHB0uWLJFer0w/W/Lz87Fq1SoMHToUKpWqUnx+FcGAUQGPut38o24fb4yK+lMZ+qrRaDB27Fi0a9dOugJsamoqLCwsit0Ez5j6d/z4cdjY2ECtVmPkyJHYtGkTmjZtWin6tmbNGiQkJEi3DHiQsffPz88PK1aswI4dO7Bo0SJcvHgRHTp0wK1bt4y+bwBw4cIFLFq0CJ6enti5cyfeeecdjBkzBt999x2AyvWzZfPmzbh58ybeeustAMb/3awog18qnOhpGzVqFE6cOCE7z10ZNGrUCImJicjOzsaGDRsQGhqKffv2GbpZFXb58mVERERg9+7dsLS0NHRzFNe1a1fp3y1atICfnx/q1q2LdevWwcrKyoAtU4ZGo0GrVq0wY8YMAICPjw9OnDiBmJgYhIaGGrh1yvr222/RtWvXx97G/HnBEYwK0Od288aqqD/G3tfRo0fj119/RVxcHGrVqiWVOzs7Iz8/Hzdv3pTVN6b+WVhYoEGDBvD19UV0dDS8vb3x1VdfGX3fjhw5gvT0dLzwwgswMzODmZkZ9u3bh6+//hpmZmZwcnIy6v49rGrVqmjYsCGSkpKM/rMDABcXFzRt2lRW1qRJE+k0UGX52ZKcnIw9e/Zg2LBhUlll+PwqggGjAvS53byx8vDwgLOzs6yvOTk5+PPPP42ir0IIjB49Gps2bcLevXvh4eEhe93X1xfm5uay/p05cwYpKSlG0b+SaDQa5OXlGX3fAgMDcfz4cSQmJkqPVq1aYfDgwdK/jbl/D7t9+zbOnz8PFxcXo//sAKBdu3bFloSfPXsWdevWBWD8P1uKLF++HI6OjujevbtUVhk+vwox9CxTY7dmzRqhVqvFihUrxD///CNGjBghqlatKlJTUw3dtHK7deuWOHr0qDh69KgAIObOnSuOHj0qkpOThRBCzJw5U1StWlX8/PPP4tixY6Jnz57Cw8ND3L1718Atf7x33nlH2Nvbi/j4eHH9+nXpcefOHanOyJEjRZ06dcTevXvF4cOHhb+/v/D39zdgq8vuww8/FPv27RMXL14Ux44dEx9++KFQqVRi165dQgjj7ltJHlxFIoRx92/8+PEiPj5eXLx4URw4cEAEBQUJBwcHkZ6eLoQw7r4JIcShQ4eEmZmZmD59ujh37pxYvXq1sLa2FqtWrZLqGPPPFiG0qwfr1KkjJk6cWOw1Y//8KoIBQwHz588XderUERYWFqJNmzbijz/+MHST9BIXFycAFHuEhoYKIbTLyT799FPh5OQk1Gq1CAwMFGfOnDFso8uopH4BEMuXL5fq3L17V7z77ruiWrVqwtraWvTu3Vtcv37dcI0uh6FDh4q6desKCwsLUbNmTREYGCiFCyGMu28leThgGHP/+vfvL1xcXISFhYVwc3MT/fv3F0lJSdLrxty3Ir/88ovw8vISarVaNG7cWCxevFj2ujH/bBFCiJ07dwoAJba5Mnx++uLt2omIiEhxnINBREREimPAICIiIsUxYBAREZHiGDCIiIhIcQwYREREpDgGDCIiIlIcAwYREREpjgGDiIiIFMeAQfQc69ixI8aOHWvoZhBRJcSAQURERIpjwCAiRRUWFkKj0Ri6GWVWUFBg6CYQVUoMGETPgI4dO2LMmDH44IMPUL16dTg7O2PKlCkAgEuXLkGlUiExMVGqf/PmTahUKsTHxwMA4uPjoVKpsHPnTvj4+MDKygqvvPIK0tPTsX37djRp0gR2dnYYNGgQ7ty5Izv2/fv3MXr0aNjb28PBwQGffvopHrxFUV5eHiZMmAA3NzdUqVIFfn5+0nEBYMWKFahatSq2bNmCpk2bQq1WIyUl5bF9Xrp0KZo0aQJLS0s0btwY33zzjfRaUZ/XrVuHDh06wMrKCq1bt8bZs2fx119/oVWrVrCxsUHXrl2RkZFR7v2uXbsWAQEBsLS0xOrVq3H//n2MGTMGVatWRY0aNTBx4kSEhoaiV69e0rYajQbR0dHw8PCAlZUVvL29sWHDBun1os8gNjYWrVq1grW1Ndq2bVvsVuW//PILWrduDUtLSzg4OKB3794AgM8++wxeXl7F3qeWLVvi008/fez7SfTMMfDN1ohIaO8OamdnJ6ZMmSLOnj0rvvvuO+l26xcvXhQAxNGjR6X6N27cEABEXFycEEJ3J9wXX3xR/PbbbyIhIUE0aNBABAQEiM6dO4uEhASxf/9+UaNGDTFz5kzZcW1sbERERIQ4ffq0WLVqlbC2tpbd7XLYsGGibdu2Yv/+/SIpKUnMnj1bqNVqcfbsWSGEEMuXLxfm5uaibdu24sCBA+L06dMiNzf3kf1dtWqVcHFxET/99JO4cOGC+Omnn0T16tXFihUrhBBC6nPjxo3Fjh07xD///CNefPFF4evrKzp27Cjr48iRI8u9X3d3d6nOtWvXxOeffy6qV68uNm7cKE6dOiVGjhwp7OzsRM+ePaV9f/7551J7zp8/L5YvXy7UarWIj4+XfQZ+fn4iPj5enDx5UnTo0EG0bdtW2sevv/4qTE1NxeTJk8U///wjEhMTxYwZM4QQQly+fFmYmJiIQ4cOSfUTEhKESqUS58+ff+x3iOhZw4BB9AwICAgQ7du3l5W1bt1aTJw4sVwBY8+ePVKd6OhoAUD2y+ntt98WwcHBsuM2adJEaDQaqWzixImiSZMmQgghkpOThampqbh69aqsbYGBgWLSpElCCG3AACASExPL3N/69euLH374QVY2bdo04e/vL4TQBYGlS5dKr//4448CgIiNjZX1sVGjRuXe77x582R1nJycxOzZs6Xn9+/fF3Xq1JECxr1794S1tbX4/fffZduFh4eLgQMHCiFK/gy2bt0qAIi7d+8KIYTw9/cXgwcPLvV96dq1q3jnnXek5++9957o2LFjqfWJnmVmT3/MhIhK0qJFC9lzFxcXpKen670PJycnWFtbo169erKyQ4cOybZ58cUXoVKppOf+/v6YM2cOCgsLcfz4cRQWFqJhw4aybfLy8lCjRg3puYWFRbH2lyY3Nxfnz59HeHg4hg8fLpXfv38f9vb2j+wPADRv3lxWVvQelWe/rVq1kv6dnZ2NtLQ0tGnTRiozNTWFr6+vNJckKSkJd+7cQadOnWT7yc/Ph4+PT6ltdnFxAQCkp6ejTp06SExMlLXtYcOHD8fQoUMxd+5cmJiY4IcffsCXX35Zan2iZxkDBtEzwtzcXPZcpVJBo9HAxEQ7VUo8MC+itImJD+5DpVKVus+yun37NkxNTXHkyBGYmprKXrOxsZH+bWVlJQspj9snACxZsgR+fn6y1x4+xsP9KamsqD/l2W+VKlXK1NaH27x161a4ubnJXlOr1Y9tc1EbraysHnmcHj16QK1WY9OmTbCwsEBBQQH69u1brrYSPSsYMIiecTVr1gQAXL9+Xfpr+cEJnxX1559/yp7/8ccf8PT0hKmpKXx8fFBYWIj09HR06NBBkeM5OTnB1dUVFy5cwODBgxXZZ0X2a29vDycnJ/z111946aWXAGhXwiQkJKBly5YAIJu8GhAQoHcbW7RogdjYWISFhZX4upmZGUJDQ7F8+XJYWFhgwIABjw0lRM8qBgyiZ5yVlRVefPFFzJw5Ex4eHkhPT8cnn3yi2P5TUlIQGRmJt99+GwkJCZg/fz7mzJkDAGjYsCEGDx6MkJAQzJkzBz4+PsjIyEBsbCxatGiB7t2763XMqVOnYsyYMbC3t0eXLl2Ql5eHw4cP48aNG4iMjNS7L/ru97333kN0dDQaNGiAxo0bY/78+bhx44Y0AmFra4sJEyZg3Lhx0Gg0aN++PbKzs3HgwAHY2dkhNDS0TO2LiopCYGAg6tevjwEDBuD+/fvYtm0bJk6cKNUZNmwYmjRpAgA4cOCA3u8FkaExYBAZgWXLliE8PBy+vr5o1KgRZs2ahc6dOyuy75CQENy9exdt2rSBqakpIiIiMGLECOn15cuX4/PPP8f48eNx9epVODg44MUXX8Srr76q9zGHDRsGa2trzJ49G++//z6qVKmC5s2bV/iqovrud+LEiUhNTUVISAhMTU0xYsQIBAcHy06tTJs2DTVr1kR0dDQuXLiAqlWr4oUXXsBHH31U5vZ17NgR69evx7Rp0zBz5kzY2dlJoyZFPD090bZtW2RlZRU71UNkTFTiwRO7REQEjUaDJk2aoF+/fpg2bdpTPbYQAp6ennj33XcrNJpDZGgcwSCi515ycjJ27dqFgIAA5OXlYcGCBbh48SIGDRr0VNuRkZGBNWvWIDU1tdR5GkTGggGDiBT34AqTh23fvl2xCaNKMTExwYoVKzBhwgQIIeDl5YU9e/ZIcyGeFkdHRzg4OGDx4sWoVq3aUz02kdJ4ioSIFJeUlFTqa25ublwZQfQcYMAgIiIixfFmZ0RERKQ4BgwiIiJSHAMGERERKY4Bg4iIiBTHgEFERESKY8AgIiIixTFgEBERkeL+D3Z/RekJOBI1AAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## Maintenant voyant la distribustion des donnes \n", "\n", "for col in numerical_cols:\n", " plt.figure(figsize=(6, 4))\n", " sns.histplot(data[col], kde=True, bins=30, color='blue')\n", " plt.title(f\"Distribution de {col}\")\n", " plt.xlabel(col)\n", " plt.ylabel(\"Fréquence\")\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "b7240979", "metadata": {}, "source": [ "# Commencant l'Analyse des variables categoriales" ] }, { "cell_type": "code", "execution_count": 14, "id": "b19a2233", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "race:\n", "race\n", "Caucasian 78372\n", "AfricanAmerican 19210\n", "Hispanic 2037\n", "Other 1506\n", "Asian 641\n", "Name: count, dtype: int64\n", "\n", "gender:\n", "gender\n", "Female 54708\n", "Male 47055\n", "Unknown/Invalid 3\n", "Name: count, dtype: int64\n", "\n", "age:\n", "age\n", "[70-80) 26068\n", "[60-70) 22483\n", "[50-60) 17256\n", "[80-90) 17197\n", "[40-50) 9685\n", "[30-40) 3775\n", "[90-100) 2793\n", "[20-30) 1657\n", "[10-20) 691\n", "[0-10) 161\n", "Name: count, dtype: int64\n", "\n", "diag_1:\n", "diag_1\n", "428 6883\n", "414 6581\n", "786 4016\n", "410 3614\n", "486 3508\n", " ... \n", "817 1\n", "61 1\n", "148 1\n", "870 1\n", "V51 1\n", "Name: count, Length: 716, dtype: int64\n", "\n", "diag_2:\n", "diag_2\n", "276 7110\n", "428 6662\n", "250 6071\n", "427 5036\n", "401 3736\n", " ... \n", "232 1\n", "908 1\n", "52 1\n", "E817 1\n", "927 1\n", "Name: count, Length: 748, dtype: int64\n", "\n", "diag_3:\n", "diag_3\n", "250 12978\n", "401 8289\n", "276 5175\n", "428 4577\n", "427 3955\n", " ... \n", "657 1\n", "684 1\n", "603 1\n", "E826 1\n", "971 1\n", "Name: count, Length: 789, dtype: int64\n", "\n", "metformin:\n", "metformin\n", "No 81778\n", "Steady 18346\n", "Up 1067\n", "Down 575\n", "Name: count, dtype: int64\n", "\n", "repaglinide:\n", "repaglinide\n", "No 100227\n", "Steady 1384\n", "Up 110\n", "Down 45\n", "Name: count, dtype: int64\n", "\n", "nateglinide:\n", "nateglinide\n", "No 101063\n", "Steady 668\n", "Up 24\n", "Down 11\n", "Name: count, dtype: int64\n", "\n", "chlorpropamide:\n", "chlorpropamide\n", "No 101680\n", "Steady 79\n", "Up 6\n", "Down 1\n", "Name: count, dtype: int64\n", "\n", "glimepiride:\n", "glimepiride\n", "No 96575\n", "Steady 4670\n", "Up 327\n", "Down 194\n", "Name: count, dtype: int64\n", "\n", "acetohexamide:\n", "acetohexamide\n", "No 101765\n", "Steady 1\n", "Name: count, dtype: int64\n", "\n", "glipizide:\n", "glipizide\n", "No 89080\n", "Steady 11356\n", "Up 770\n", "Down 560\n", "Name: count, dtype: int64\n", "\n", "glyburide:\n", "glyburide\n", "No 91116\n", "Steady 9274\n", "Up 812\n", "Down 564\n", "Name: count, dtype: int64\n", "\n", "tolbutamide:\n", "tolbutamide\n", "No 101743\n", "Steady 23\n", "Name: count, dtype: int64\n", "\n", "pioglitazone:\n", "pioglitazone\n", "No 94438\n", "Steady 6976\n", "Up 234\n", "Down 118\n", "Name: count, dtype: int64\n", "\n", "rosiglitazone:\n", "rosiglitazone\n", "No 95401\n", "Steady 6100\n", "Up 178\n", "Down 87\n", "Name: count, dtype: int64\n", "\n", "acarbose:\n", "acarbose\n", "No 101458\n", "Steady 295\n", "Up 10\n", "Down 3\n", "Name: count, dtype: int64\n", "\n", "miglitol:\n", "miglitol\n", "No 101728\n", "Steady 31\n", "Down 5\n", "Up 2\n", "Name: count, dtype: int64\n", "\n", "troglitazone:\n", "troglitazone\n", "No 101763\n", "Steady 3\n", "Name: count, dtype: int64\n", "\n", "tolazamide:\n", "tolazamide\n", "No 101727\n", "Steady 38\n", "Up 1\n", "Name: count, dtype: int64\n", "\n", "examide:\n", "examide\n", "No 101766\n", "Name: count, dtype: int64\n", "\n", "citoglipton:\n", "citoglipton\n", "No 101766\n", "Name: count, dtype: int64\n", "\n", "insulin:\n", "insulin\n", "No 47383\n", "Steady 30849\n", "Down 12218\n", "Up 11316\n", "Name: count, dtype: int64\n", "\n", "glyburide-metformin:\n", "glyburide-metformin\n", "No 101060\n", "Steady 692\n", "Up 8\n", "Down 6\n", "Name: count, dtype: int64\n", "\n", "glipizide-metformin:\n", "glipizide-metformin\n", "No 101753\n", "Steady 13\n", "Name: count, dtype: int64\n", "\n", "glimepiride-pioglitazone:\n", "glimepiride-pioglitazone\n", "No 101765\n", "Steady 1\n", "Name: count, dtype: int64\n", "\n", "metformin-rosiglitazone:\n", "metformin-rosiglitazone\n", "No 101764\n", "Steady 2\n", "Name: count, dtype: int64\n", "\n", "metformin-pioglitazone:\n", "metformin-pioglitazone\n", "No 101765\n", "Steady 1\n", "Name: count, dtype: int64\n", "\n", "change:\n", "change\n", "No 54755\n", "Ch 47011\n", "Name: count, dtype: int64\n", "\n", "diabetesMed:\n", "diabetesMed\n", "Yes 78363\n", "No 23403\n", "Name: count, dtype: int64\n", "\n", "readmitted:\n", "readmitted\n", "NO 54864\n", ">30 35545\n", "<30 11357\n", "Name: count, dtype: int64\n", "\n", "insulin_binary:\n", "insulin_binary\n", "No Inject 59601\n", "Inject 42165\n", "Name: count, dtype: int64\n" ] } ], "source": [ "categorical_cols = data.select_dtypes(include=['object']).columns\n", "for col in categorical_cols:\n", " print(f\"\\n{col}:\")\n", " print(data[col].value_counts())" ] }, { "cell_type": "code", "execution_count": 15, "id": "8839008f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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AAQAAAMA2AgQAAAAA2wgQAAAAAGwjQAAAAACwjQABAAAAwDYCBAAAAADbCBAAAAAAbCNAAAAAALCNAAEAAADANgIEAAAAANsIEAAAAABsI0AAAAAAsI0AAQAAAMA2AgQAAAAA2wgQAAAAAGwjQAAAAACwjQABAAAAwDYCBAAAAADbCBAAAAAAbCNAAAAAALCNAAEAAADANgIEAAAAANsIEAAAAABsI0AAAAAAsI0AAQAAAMA2AgQAAAAA2wgQAAAAAGwjQAAAAACwjQCBHCpUqKDJkye7uwwAAAB4IAKEm8XGxsrhcOR4paSkuLs0AAAAIAcfdxcAqVWrVpo5c6ZLW6lSpdxUDQAAAJA7zkB4AH9/f4WEhLi8vL29tWDBAtWvX1+FChVSpUqV9NJLL+nixYvWdg6HQx9++KHatm2rgIAAVatWTd9//71SUlLUokULFSlSRI0bN9bu3butbXbv3q0OHTqoTJkyCgwM1N13363//e9/V63vxIkTeuKJJ1SqVCk5nU61bNlSmzdvvmHvBwAAADwXAcJDrVmzRj179tSgQYO0bds2ffjhh4qLi9O///1vl36vvPKKevbsqeTkZEVGRqp79+765z//qVGjRmnDhg0yxmjAgAFW/4yMDD300EOKj4/Xpk2b1KpVK7Vr10779u3LtZZ//OMfSk1N1ZIlS5SUlKT69esrKipKx48fv2HHDwAAAM/EJUweYOHChQoMDLSWW7durT///FMjR45Ur169JEmVKlXSK6+8ouHDh2vMmDFW3969e6tLly6SpBEjRqhRo0Z64YUXFBMTI0kaNGiQevfubfWvU6eO6tSpYy2/8sormjdvnr755huXoJEtMTFRP/zwg1JTU+Xv7y9JmjhxoubPn6+vvvpKTz755BWP6dy5czp37py1nJ6eft3vCwAAADwPAcID3H///Zo6daq1XKRIEdWuXVtr1651OeOQmZmps2fP6vTp0woICJAk1a5d21pfpkwZSVKtWrVc2s6ePav09HQ5nU5lZGRo7NixWrRokQ4fPqyLFy/qzJkzuZ6B2Lx5szIyMlSiRAmX9jNnzrhcGnW5cePG6aWXXrqOdwEAAAAFAQHCAxQpUkQREREubRkZGXrppZfUqVOnHP0LFSpk/dvX19f6t8PhyLUtKytLkjR06FAtX75cEydOVEREhAoXLqzOnTvr/PnzV6wtIyNDoaGhWrlyZY51wcHBuR7TqFGj9Mwzz1jL6enpCg8Pz7U/AAAACgYChIeqX7++duzYkSNY/F1r165VbGysOnbsKOlSQNizZ89V6/j999/l4+OjChUq2B7H39/fuuQJAAAAtw4ChId68cUX1bZtW5UrV06dO3eWl5eXNm/erK1bt+rVV1/N834rV66suXPnql27dnI4HHrhhRessxNXEh0drUaNGunhhx/W+PHjVaVKFR06dEiLFi1Sx44dddddd+W5FgAAABQ8PIXJQ8XExGjhwoVatmyZ7r77bt1777166623VL58+b+13zfffFPFihVT48aN1a5dO8XExKh+/fq59nc4HFq8eLGaNWum3r17q0qVKnr00Ue1d+9e654LAAAA3D4cxhjj7iJw60tPT1dQUJDu7zhKPr6Frr0BAADAbWzZ7Bdv+pjZn9fS0tLkdDpz7ccZCAAAAAC2ESAAAAAA2EaAAAAAAGAbAQIAAACAbQQIAAAAALYRIAAAAADYRoAAAAAAYBsBAgAAAIBtBAgAAAAAthEgAAAAANhGgAAAAABgGwECAAAAgG0ECAAAAAC2ESAAAAAA2EaAAAAAAGAbAQIAAACAbQQIAAAAALYRIAAAAADYRoAAAAAAYBsBAgAAAIBtBAgAAAAAthEgAAAAANhGgAAAAABgGwECAAAAgG0ECAAAAAC2ESAAAAAA2EaAAAAAAGAbAQIAAACAbQQIAAAAALYRIAAAAADYRoAAAAAAYBsBAgAAAIBtBAgAAAAAthEgAAAAANhGgAAAAABgm4+7C8DtZX7cSDmdTneXAQAAgDziDAQAAAAA2wgQAAAAAGwjQAAAAACwjQABAAAAwDYCBAAAAADbCBAAAAAAbCNAAAAAALCNAAEAAADANgIEAAAAANsIEAAAAABsI0AAAAAAsI0AAQAAAMA2AgQAAAAA2wgQAAAAAGwjQAAAAACwjQABAAAAwDYCBAAAAADbCBAAAAAAbCNAAAAAALDNx90F4PZy3/hx8i7k7+4yAADADZA8eqy7S8BNwBkIAAAAALYRIAAAAADYRoAAAAAAYBsBAgAAAIBtBAgAAAAAthEgAAAAANhGgAAAAABgGwECAAAAgG0ECAAAAAC2ESAAAAAA2EaAAAAAAGAbAQIAAACAbQQIAAAAALYRIAAAAADYRoAAAAAAYBsBAgAAAIBtBAgAAAAAtl13gMjMzNTq1at14sSJG1AOAAAAAE923QHC29tbDz74oP78888bUQ8AAAAAD5anS5hq1qypX3/9Nb9rAQAAAODh8hQgXn31VQ0dOlQLFy7U4cOHlZ6e7vICAAAAcGvyyctGDz30kCSpffv2cjgcVrsxRg6HQ5mZmflTHQAAAACPkqcAkZCQkN91AAAAACgA8hQgmjdvnt91AAAAACgA8vw9EGvWrNFjjz2mxo0b6+DBg5KkTz75RImJiflWHAAAAADPkqcA8fXXXysmJkaFCxfWxo0bde7cOUlSWlqaXnvttXwtEAAAAIDnyPNTmD744ANNmzZNvr6+Vvt9992njRs35ltxAAAAADxLngLEjh071KxZsxztQUFBfEM1AAAAcAvLU4AICQlRSkpKjvbExERVqlTpbxcFAAAAwDPlKUD069dPgwYN0vr16+VwOHTo0CF99tlnGjp0qP71r3/ld40AAAAAPESeHuM6cuRIZWVlKSoqSqdPn1azZs3k7++voUOH6v/+7//yu0YAAAAAHiJPAcLhcOj555/XsGHDlJKSooyMDFWvXl2BgYH5XR8AAAAAD5KnAJHNz89P1atXz69aAAAAAHg42wGiU6dOtnc6d+7cPBUDAAAAwLPZvok6KCjIejmdTsXHx2vDhg3W+qSkJMXHxysoKOiGFAoAAADA/WyfgZg5c6b17xEjRqhLly764IMP5O3tLUnKzMzU008/LafTmf9V4qbZs2ePKlasqE2bNqlu3bruLgcAAAAeJk+PcZ0xY4aGDh1qhQdJ8vb21jPPPKMZM2bkW3GwJzY2Vg6HQ0899VSOdf3795fD4VBsbOzNLwwAAAC3nDwFiIsXL+qXX37J0f7LL78oKyvrbxeF6xceHq5Zs2bpzJkzVtvZs2f1+eefq1y5cm6sDAAAALeSPAWI3r17q2/fvnrzzTeVmJioxMRETZo0SU888YR69+6d3zXChvr16ys8PNzlBva5c+eqXLlyqlevntW2dOlSNWnSRMHBwSpRooTatm2r3bt3X3XfW7duVevWrRUYGKgyZcro8ccf19GjR2/YsQAAAMBz5SlATJw4UcOHD9ekSZPUrFkzNWvWTG+++aaGDRumCRMm5HeNsKlPnz4u96rMmDEjR6A7deqUnnnmGW3YsEHx8fHy8vJSx44dcz1zdOLECbVs2VL16tXThg0btHTpUh05ckRdunS5ai3nzp1Tenq6ywsAAAAFX56+B8LLy0vDhw/X8OHDrQ+G3Dztfo899phGjRqlvXv3SpLWrl2rWbNmaeXKlVafRx55xGWbGTNmqFSpUtq2bZtq1qyZY59TpkxRvXr19Nprr7lsEx4erp07d6pKlSpXrGXcuHF66aWX8uGoAAAA4EnydAbir5xOJ+HBQ5QqVUpt2rRRXFycZs6cqTZt2qhkyZIufXbt2qVu3bqpUqVKcjqdqlChgiRp3759V9zn5s2blZCQoMDAQOsVGRkpSVe99GnUqFFKS0uzXvv378+fgwQAAIBb5ekMxJEjRzR06FDFx8crNTVVxhiX9ZmZmflSHK5fnz59NGDAAEnSe++9l2N9u3btVL58eU2bNk1hYWHKyspSzZo1df78+SvuLyMjQ+3atdMbb7yRY11oaGiudfj7+8vf3z+PRwEAAABPlacAERsbq3379umFF15QaGioHA5HfteFPGrVqpXOnz8vh8OhmJgYl3XHjh3Tjh07NG3aNDVt2lSSlJiYeNX91a9fX19//bUqVKggH588/bgAAADgFpKnT4SJiYlas2YNXzTmgby9vbV9+3br339VrFgxlShRQh999JFCQ0O1b98+jRw58qr769+/v6ZNm6Zu3bpp+PDhKl68uFJSUjRr1ixNnz49xxgAAAC4teXpHojw8PAcly3Bc+R2X4qXl5dmzZqlpKQk1axZU0OGDLnmU7PCwsK0du1aZWZm6sEHH1StWrU0ePBgBQcHy8vrb99CAwAAgALGYfKQBJYtW6ZJkybpww8/tG7CBa4mPT1dQUFBqvn8SHkX4t4IAABuRcmjx7q7BPwN2Z/X0tLSrvqQpDxdwtS1a1edPn1ad955pwICAuTr6+uy/vjx43nZLQAAAAAPl6cAMXny5HwuAwAAAEBBkKcA0atXr/yuAwAAAEABkOe7YHfv3q3Ro0erW7duSk1NlSQtWbJEP//8c74VBwAAAMCz5ClArFq1SrVq1dL69es1d+5cZWRkSLr0rcVjxozJ1wIBAAAAeI48BYiRI0fq1Vdf1fLly+Xn52e1t2zZUuvWrcu34gAAAAB4ljwFiC1btqhjx4452kuXLq2jR4/+7aIAAAAAeKY8BYjg4GAdPnw4R/umTZtUtmzZv10UAAAAAM+UpwDx6KOPasSIEfr999/lcDiUlZWltWvXaujQoerZs2d+1wgAAADAQ+QpQLz22muKjIxUeHi4MjIyVL16dTVt2lSNGzfW6NGj87tGAAAAAB4iT98D4efnp2nTpunFF1/Uli1blJGRoXr16qly5cr5XR8AAAAAD5KnAPHMM8/kaFu3bp0cDocKFSqkiIgIdejQQcWLF//bBQIAAADwHHkKEJs2bdLGjRuVmZmpqlWrSpJ27twpb29vRUZG6v3339ezzz6rxMREVa9ePV8LBgAAAOA+eboHokOHDoqOjtahQ4eUlJSkpKQkHThwQA888IC6deumgwcPqlmzZhoyZEh+1wsAAADAjfIUICZMmKBXXnlFTqfTagsKCtLYsWM1fvx4BQQE6MUXX1RSUlK+FQoAAADA/fIUINLS0pSampqj/Y8//lB6erqkS98Vcf78+b9XHQAAAACPkudLmPr06aN58+bpwIEDOnDggObNm6e+ffvq4YcfliT98MMPqlKlSn7WCgAAAMDN8nQT9YcffqghQ4bo0Ucf1cWLFy/tyMdHvXr10ltvvSVJioyM1PTp0/OvUgAAAABul6cAERgYqGnTpumtt97Sr7/+KkmqVKmSAgMDrT5169bNlwIBAAAAeI48BYhsgYGBql27dn7VAgAAAMDD5ekeCAAAAAC3JwIEAAAAANsIEAAAAABsI0AAAAAAsI0AAQAAAMA2AgQAAAAA2wgQAAAAAGwjQAAAAACwjQABAAAAwDYCBAAAAADbCBAAAAAAbCNAAAAAALCNAAEAAADANgIEAAAAANt83F0Abi9rh4+S0+l0dxkAAADII85AAAAAALCNAAEAAADANgIEAAAAANsIEAAAAABsI0AAAAAAsI0AAQAAAMA2AgQAAAAA2wgQAAAAAGwjQAAAAACwjQABAAAAwDYCBAAAAADbCBAAAAAAbCNAAAAAALCNAAEAAADANgIEAAAAANsIEAAAAABsI0AAAAAAsI0AAQAAAMA2AgQAAAAA2wgQAAAAAGwjQAAAAACwjQABAAAAwDYCBAAAAADbCBAAAAAAbCNAAAAAALCNAAEAAADANgIEAAAAANsIEAAAAABsI0AAAAAAsI0AAQAAAMA2AgQAAAAA2wgQAAAAAGwjQAAAAACwjQABAAAAwDYCBAAAAADbCBAAAAAAbCNAAAAAALCNAAEAAADANgIEAAAAANsIEAAAAABsI0AAAAAAsI0AAQAAAMA2AgQAAAAA2wgQAAAAAGwjQAAAAACwjQABAAAAwDYCBAAAAADbCBAAAAAAbCNAAAAAALCNAAEAAADANgIEAAAAANsIEAAAAABsI0AAAAAAsI0AAQAAAMA2AgQAAAAA2wgQAAAAAGy7JQJEhQoVNHnyZHeXUSDt2bNHDodDycnJkqSVK1fK4XDoxIkTuW4TFxen4ODgm1IfAAAAPItbA0SLFi00ePDgHO230wfUVatWKTw8XJIUGxurhx9+2K31NG7cWIcPH1ZQUJBb6wAAAIBn8nF3Abe7BQsWqF27du4uw+Ln56eQkBB3lwEAAAAP5fGXMGX/VX7ixIkKDQ1ViRIl1L9/f124cCHXbaZPn67g4GDFx8dLunSmY+DAgRo+fLiKFy+ukJAQjR071mWbffv2qUOHDgoMDJTT6VSXLl105MgRSVJaWpq8vb21YcMGSVJWVpaKFy+ue++919r+008/tc4kZF8WNHfuXN1///0KCAhQnTp19P333+eo9ZtvvlH79u2veBzXqrt79+7q2rWryzYXLlxQyZIl9d///leStHTpUjVp0kTBwcEqUaKE2rZtq927d+f63l3pEqa4uDiVK1dOAQEB6tixo44dO5br9gAAALi1eXyAkKSEhATt3r1bCQkJ+vjjjxUXF6e4uLgr9h0/frxGjhypZcuWKSoqymr/+OOPVaRIEa1fv17jx4/Xyy+/rOXLl0u6FAg6dOig48ePa9WqVVq+fLl+/fVX68N5UFCQ6tatq5UrV0qStmzZIofDoU2bNikjI0PSpUuRmjdv7lLL888/r6FDhyo5OVlVqlRRt27ddPHiRWv9zz//rNTUVLVs2TLXY79a3T169NC3335r1SBJ3333nU6fPq2OHTtKkk6dOqVnnnlGGzZsUHx8vLy8vNSxY0dlZWXZeeu1fv169e3bVwMGDFBycrLuv/9+vfrqq9fc7ty5c0pPT3d5AQAAoOArEAGiWLFimjJliiIjI9W2bVu1adPGOrvwVyNGjNDkyZO1atUq3XPPPS7rateurTFjxqhy5crq2bOn7rrrLmsf8fHx2rJliz7//HM1aNBADRs21H//+1+tWrVKP/74o6RLZwOyA8TKlSv1wAMPqFq1akpMTLTaLg8QQ4cOVZs2bVSlShW99NJL2rt3r1JSUqz1CxYsUExMjPz8/HI99qvVHRMToyJFimjevHlW/88//1zt27dX0aJFJUmPPPKIOnXqpIiICNWtW1czZszQli1btG3bNlvv/dtvv61WrVpp+PDhqlKligYOHKiYmJhrbjdu3DgFBQVZr+yzMwAAACjYCkSAqFGjhry9va3l0NBQpaamuvSZNGmSpk2bpsTERNWoUSPHPmrXru2y/Nd9bN++XeHh4S4fcqtXr67g4GBt375dktS8eXMlJiYqMzNTq1atUosWLaxQcejQIaWkpKhFixa5jhkaGipJLnUvWLAg18uX7NTt4+OjLl266LPPPpN06WzDggUL1KNHD6v/rl271K1bN1WqVElOp1MVKlSQdOmSLTu2b9+uhg0burQ1atTomtuNGjVKaWlp1mv//v22xgMAAIBnc2uAcDqdSktLy9F+4sQJl6cA+fr6uqx3OBw5LsFp2rSpMjMzNWfOnCuOZWcfV9OsWTOdPHlSGzdu1OrVq10CxKpVqxQWFqbKlSvnOqbD4ZAka8zDhw9r06ZNatOmzVXHvVbdPXr0UHx8vFJTUzV//nwVLlxYrVq1sta3a9dOx48f17Rp07R+/XqtX79eknT+/Hnbx54X/v7+cjqdLi8AAAAUfG59ClPVqlW1bNmyHO0bN25UlSpVrmtf99xzjwYMGKBWrVrJx8dHQ4cOtb1ttWrVtH//fu3fv986C7Ft2zadOHFC1atXlyQFBwerdu3amjJlinx9fRUZGanSpUura9euWrhwYY7Ll67l22+/VePGjVW8ePHr2u5yjRs3Vnh4uGbPnq0lS5boH//4hxU6jh07ph07dmjatGlq2rSpJFmXXNlVrVo1K3RkW7du3d+qGQAAAAWXWwPEv/71L02ZMkUDBw7UE088IX9/fy1atEhffPGFvv322+veX+PGjbV48WK1bt1aPj4+V/yOiSuJjo5WrVq11KNHD02ePFkXL17U008/rebNm+uuu+6y+rVo0ULvvvuuOnfuLEkqXry4qlWrptmzZ+u99967rlqv9vSl69W9e3d98MEH2rlzpxISEqz2YsWKqUSJEvroo48UGhqqffv2aeTIkde174EDB+q+++7TxIkT1aFDB3333XdaunRpvtQNAACAgsetlzBVqlRJq1ev1i+//KLo6Gg1bNhQc+bM0ZdffulyGc71aNKkiRYtWqTRo0fr3XfftbWNw+HQggULVKxYMTVr1kzR0dGqVKmSZs+e7dKvefPmyszMdLnXoUWLFjnaruXUqVOKj4/PtwDRo0cPbdu2TWXLltV9991ntXt5eWnWrFlKSkpSzZo1NWTIEE2YMOG69n3vvfdq2rRpevvtt1WnTh0tW7ZMo0ePzpe6AQAAUPA4jDHG3UXcbubOnavRo0fbfhLSrSA9PV1BQUFKS0vjfggAAAAPZPfzWoF4CtOtJjAwUG+88Ya7ywAAAACum1vvgbhdPfjgg+4uAQAAAMgTzkAAAAAAsI0AAQAAAMA2AgQAAAAA2wgQAAAAAGwjQAAAAACwjQABAAAAwDYCBAAAAADbCBAAAAAAbCNAAAAAALCNAAEAAADANgIEAAAAANsIEAAAAABsI0AAAAAAsI0AAQAAAMA2AgQAAAAA2wgQAAAAAGwjQAAAAACwjQABAAAAwDYCBAAAAADbCBAAAAAAbCNAAAAAALCNAAEAAADANgIEAAAAANsIEAAAAABsI0AAAAAAsI0AAQAAAMA2AgQAAAAA2wgQAAAAAGwjQAAAAACwjQABAAAAwDYCBAAAAADbCBAAAAAAbCNAAAAAALCNAAEAAADANgIEAAAAANsIEAAAAABsI0AAAAAAsI0AAQAAAMA2AgQAAAAA2wgQAAAAAGwjQAAAAACwzcfdBeD2YIyRJKWnp7u5EgAAAFxJ9ue07M9tuSFA4KY4duyYJCk8PNzNlQAAAOBqTp48qaCgoFzXEyBwUxQvXlyStG/fvqv+QML90tPTFR4erv3798vpdLq7HFwD81WwMF8FC/NVsDBff58xRidPnlRYWNhV+xEgcFN4eV263SYoKIhf6gLC6XQyVwUI81WwMF8FC/NVsDBff4+dP/RyEzUAAAAA2wgQAAAAAGwjQOCm8Pf315gxY+Tv7+/uUnANzFXBwnwVLMxXwcJ8FSzM183jMNd6ThMAAAAA/H84AwEAAADANgIEAAAAANsIEAAAAABsI0AAAAAAsI0AgRvuvffeU4UKFVSoUCE1bNhQP/zwg7tLuuWsXr1a7dq1U1hYmBwOh+bPn++y3hijF198UaGhoSpcuLCio6O1a9culz7Hjx9Xjx495HQ6FRwcrL59+yojI8Olz08//aSmTZuqUKFCCg8P1/jx43PU8uWXXyoyMlKFChVSrVq1tHjx4nw/3oJu3Lhxuvvuu1W0aFGVLl1aDz/8sHbs2OHS5+zZs+rfv79KlCihwMBAPfLIIzpy5IhLn3379qlNmzYKCAhQ6dKlNWzYMF28eNGlz8qVK1W/fn35+/srIiJCcXFxOerhd/Tqpk6dqtq1a1tfTtWoUSMtWbLEWs9cea7XX39dDodDgwcPttqYL88xduxYORwOl1dkZKS1nrnyYAa4gWbNmmX8/PzMjBkzzM8//2z69etngoODzZEjR9xd2i1l8eLF5vnnnzdz5841ksy8efNc1r/++usmKCjIzJ8/32zevNm0b9/eVKxY0Zw5c8bq06pVK1OnTh2zbt06s2bNGhMREWG6detmrU9LSzNlypQxPXr0MFu3bjVffPGFKVy4sPnwww+tPmvXrjXe3t5m/PjxZtu2bWb06NHG19fXbNmy5Ya/BwVJTEyMmTlzptm6datJTk42Dz30kClXrpzJyMiw+jz11FMmPDzcxMfHmw0bNph7773XNG7c2Fp/8eJFU7NmTRMdHW02bdpkFi9ebEqWLGlGjRpl9fn1119NQECAeeaZZ8y2bdvMu+++a7y9vc3SpUutPvyOXts333xjFi1aZHbu3Gl27NhhnnvuOePr62u2bt1qjGGuPNUPP/xgKlSoYGrXrm0GDRpktTNfnmPMmDGmRo0a5vDhw9brjz/+sNYzV56LAIEb6p577jH9+/e3ljMzM01YWJgZN26cG6u6tV0eILKyskxISIiZMGGC1XbixAnj7+9vvvjiC2OMMdu2bTOSzI8//mj1WbJkiXE4HObgwYPGGGPef/99U6xYMXPu3Dmrz4gRI0zVqlWt5S5dupg2bdq41NOwYUPzz3/+M1+P8VaTmppqJJlVq1YZYy7Nj6+vr/nyyy+tPtu3bzeSzPfff2+MuRQavby8zO+//271mTp1qnE6ndYcDR8+3NSoUcNlrK5du5qYmBhrmd/RvClWrJiZPn06c+WhTp48aSpXrmyWL19umjdvbgUI5suzjBkzxtSpU+eK65grz8YlTLhhzp8/r6SkJEVHR1ttXl5eio6O1vfff+/Gym4vv/32m37//XeXeQgKClLDhg2tefj+++8VHBysu+66y+oTHR0tLy8vrV+/3urTrFkz+fn5WX1iYmK0Y8cO/fnnn1afv46T3Yf5vrq0tDRJUvHixSVJSUlJunDhgst7GRkZqXLlyrnMWa1atVSmTBmrT0xMjNLT0/Xzzz9bfa42H/yOXr/MzEzNmjVLp06dUqNGjZgrD9W/f3+1adMmx3vKfHmeXbt2KSwsTJUqVVKPHj20b98+ScyVpyNA4IY5evSoMjMzXX6xJalMmTL6/fff3VTV7Sf7vb7aPPz+++8qXbq0y3ofHx8VL17cpc+V9vHXMXLrw3znLisrS4MHD9Z9992nmjVrSrr0Pvr5+Sk4ONil7+Vzltf5SE9P15kzZ/gdvQ5btmxRYGCg/P399dRTT2nevHmqXr06c+WBZs2apY0bN2rcuHE51jFfnqVhw4aKi4vT0qVLNXXqVP32229q2rSpTp48yVx5OB93FwAAt7P+/ftr69atSkxMdHcpuIqqVasqOTlZaWlp+uqrr9SrVy+tWrXK3WXhMvv379egQYO0fPlyFSpUyN3l4Bpat25t/bt27dpq2LChypcvrzlz5qhw4cJurAzXwhkI3DAlS5aUt7d3jicmHDlyRCEhIW6q6vaT/V5fbR5CQkKUmprqsv7ixYs6fvy4S58r7eOvY+TWh/m+sgEDBmjhwoVKSEjQHXfcYbWHhITo/PnzOnHihEv/y+csr/PhdDpVuHBhfkevg5+fnyIiItSgQQONGzdOderU0dtvv81ceZikpCSlpqaqfv368vHxkY+Pj1atWqV33nlHPj4+KlOmDPPlwYKDg1WlShWlpKTwu+XhCBC4Yfz8/NSgQQPFx8dbbVlZWYqPj1ejRo3cWNntpWLFigoJCXGZh/T0dK1fv96ah0aNGunEiRNKSkqy+qxYsUJZWVlq2LCh1Wf16tW6cOGC1Wf58uWqWrWqihUrZvX56zjZfZhvV8YYDRgwQPPmzdOKFStUsWJFl/UNGjSQr6+vy3u5Y8cO7du3z2XOtmzZ4hL8li9fLqfTqerVq1t9rjYf/I7mXVZWls6dO8dceZioqCht2bJFycnJ1uuuu+5Sjx49rH8zX54rIyNDu3fvVmhoKL9bns7dd3Hj1jZr1izj7+9v4uLizLZt28yTTz5pgoODXZ6YgL/v5MmTZtOmTWbTpk1GknnzzTfNpk2bzN69e40xlx7jGhwcbBYsWGB++ukn06FDhys+xrVevXpm/fr1JjEx0VSuXNnlMa4nTpwwZcqUMY8//rjZunWrmTVrlgkICMjxGFcfHx8zceJEs337djNmzBge43oF//rXv0xQUJBZuXKly+MLT58+bfV56qmnTLly5cyKFSvMhg0bTKNGjUyjRo2s9dmPL3zwwQdNcnKyWbp0qSlVqtQVH184bNgws337dvPee+9d8fGF/I5e3ciRI82qVavMb7/9Zn766SczcuRI43A4zLJly4wxzJWn++tTmIxhvjzJs88+a1auXGl+++03s3btWhMdHW1KlixpUlNTjTHMlScjQOCGe/fdd025cuWMn5+fueeee8y6devcXdItJyEhwUjK8erVq5cx5tKjXF944QVTpkwZ4+/vb6KiosyOHTtc9nHs2DHTrVs3ExgYaJxOp+ndu7c5efKkS5/NmzebJk2aGH9/f1O2bFnz+uuv56hlzpw5pkqVKsbPz8/UqFHDLFq06IYdd0F1pbmSZGbOnGn1OXPmjHn66adNsWLFTEBAgOnYsaM5fPiwy3727NljWrdubQoXLmxKlixpnn32WXPhwgWXPgkJCaZu3brGz8/PVKpUyWWMbPyOXl2fPn1M+fLljZ+fnylVqpSJioqywoMxzJWnuzxAMF+eo2vXriY0NNT4+fmZsmXLmq5du5qUlBRrPXPluRzGGOOecx8AAAAAChrugQAAAABgGwECAAAAgG0ECAAAAAC2ESAAAAAA2EaAAAAAAGAbAQIAAACAbQQIAAAAALYRIAAAt43k5GRNmDBBFy9edHcpAFBgESAAALeF48eP65FHHlG1atXk4+Pj7nIAoMAiQAAACpTY2Fg5HI4cr5SUlFy3McaoZ8+eGjFihNq2bXsTqwWAW4/DGGPcXQQAAHbFxsbqyJEjmjlzpkt7qVKl5O3tbS2fP39efn5+N7s8ALjlcQYCAFDg+Pv7KyQkxOUVFRWlAQMGaPDgwSpZsqRiYmIkSVu3blXr1q0VGBioMmXK6PHHH9fRo0etfZ06dUo9e/ZUYGCgQkNDNWnSJLVo0UKDBw+2+jgcDs2fP9+lhuDgYMXFxVnL+/fvV5cuXRQcHKzixYurQ4cO2rNnj7U+NjZWDz/8sCZOnKjQ0FCVKFFC/fv314ULF6w+586d04gRIxQeHi5/f39FREToP//5j7X+WscCADcDAQIAcMv4+OOP5efnp7Vr1+qDDz7QiRMn1LJlS9WrV08bNmzQ0qVLdeTIEXXp0sXaZtiwYVq1apUWLFigZcuWaeXKldq4ceN1jXvhwgXFxMSoaNGiWrNmjdauXavAwEC1atVK58+ft/olJCRo9+7dSkhI0Mcff6y4uDiXENKzZ0998cUXeuedd7R9+3Z9+OGHCgwMlCRbxwIANwN3kQEACpyFCxdaH6wlqXXr1pKkypUra/z48Vb7q6++qnr16um1116z2mbMmKHw8HDt3LlTYWFh+s9//qNPP/1UUVFRki6FkDvuuOO66pk9e7aysrI0ffp0ORwOSdLMmTMVHByslStX6sEHH5QkFStWTFOmTJG3t7ciIyPVpk0bxcfHq1+/ftq5c6fmzJmj5cuXKzo6WpJUqVIla4wpU6Zc9ViqVKlyXTUDQF4RIAAABc7999+vqVOnWstFihRRt27d1KBBA5d+mzdvVkJCgkvYyLZ7926dOXNG58+fV8OGDa324sWLq2rVqtdVz+bNm5WSkqKiRYu6tJ89e1a7d++2lmvUqOFyn0ZoaKi2bNki6dIjZr29vdW8efNcx7jasRAgANwsBAgAQIFTpEgRRUREXLH9rzIyMtSuXTu98cYbOfqGhoZe9clNf+VwOHT5M0f+eu9CRkaGGjRooM8++yzHtqVKlbL+7evrm2O/WVlZkqTChQtftYZrHQsA3CwECADALat+/fr6+uuvVaFChSt+98Odd94pX19frV+/XuXKlZMk/fnnn9q5c6fLmYBSpUrp8OHD1vKuXbt0+vRpl3Fmz56t0qVLy+l05qnWWrVqKSsrS6tWrbIuYbqeYwGAm4WbqAEAt6z+/fvr+PHj6tatm3788Uft3r1b3333nXr37q3MzEwFBgaqb9++GjZsmFasWKGtW7cqNjZWXl6u/z22bNlSU6ZM0aZNm7RhwwY99dRTLmcTevTooZIlS6pDhw5as2aNfvvtN61cuVIDBw7UgQMHbNVaoUIF9erVS3369NH8+fOtfcyZM8fWsQDAzUKAAADcssLCwrR27VplZmbqwQcfVK1atTR48GAFBwdbIWHChAlq2rSp2rVrp+joaDVp0iTHvRSTJk1SeHi4mjZtqu7du2vo0KEKCAiw1gcEBGj16tUqV66cOnXqpGrVqqlv3746e/bsdZ2RmDp1qjp37qynn35akZGR6tevn06dOmX7WADgZuCL5AAAuEyLFi1Ut25dTZ482d2lAIDH4U8WAAAAAGwjQAAAAACwjUuYAAAAANjGGQgAAAAAthEgAAAAANhGgAAAAABgGwECAAAAgG0ECAAAAAC2ESAAAAAA2EaAAAAAAGAbAQIAAACAbQQIAAAAALb9P7AqB44pmWnUAAAAAElFTkSuQmCC", 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", 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", 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Tx44dM4kZMGBAnaLvTz31lElMTk6OGDlypHB2dhZNmjQRbdq0EY6OjgKAsLOzM94XFhYm4uLiJIvJq1Qq4/OkYgCIXbt2mf19Xn75Zdn7ePHixYsXL168eDXO6+zZs7J5q0VLzqWkpECn0yEyMhKVlZV47bXXMHToUBw5cgQuLi7GuKlTp+K9994z/nzzsahVVVUYNWoUAgICsH37dmRkZGDy5MnG/pqlGQBw+fJlbNy4UfJ9hBDYuXOn4q7mhIQE9OjRo077li1bzMb3cBkEBxWr+xER0d9vw/mfLf0KRI1aYWEhgoOD4ebmJhtn0UxuwIAB+PLLL3Hs2DE4OzsjIiICZ86cQVpamrFIfHp6OlJSUvDNN98Y73vqqaewaNEiANUJ7OHDh+Hv74/evXvD0dFRcrzLly/D2dlZ9p3i4uLqbBCsLT8/32z7uXPnzLY72qnhoJJ+LyIiOVsKfrf0KxAR2TylpbSNaqb5ueeeAwDjprsaGo0GWq0WTZo0wdChQ/H6668b+7Zt2waNRgOVSoXt27djw4YNeO211yTHrEm2paSlpSlu9AsLCzPbHhQUhPPnz9dpX39upeQaaSIiIiJq/CyaNMfHxxv/bDAYjLPEpaWlxnZ/f3+0b98e7733HjIyMvDyyy/j/Pnz+O233wBUJ7mlpaVYtmwZ/P39TWakzbn33nuxbt06yf6rV68qFqafNGmS4u92s1HNH+VMM1mF5GtrLP0KREREjVKjWWir0+lw5MgRAIC3t7exPTAwEKmpqRg4cCACAgLQv39//Pjjj8jKykLr1q1x8eJFuLm5wd/fHwAUE165hBmoTprPnj0rG1NUVGT2CNQLFy6Yv0FlV30R3UHJV1dZ+hWIiIhsVqNImmNiYrB27Vq0bdsWLVu2RHh4uLHPy8sLXl5eOH/+PM6ePYusrCwAwMmTJ9G6dWsAgF6vr3dJNwcHB1RWVkr2q9VqxaUUUoeeBAQEmF2eoVKpWHLuHybpym+WfgUiIiJqQBad/hRCICYmBqtWrUL//v2RnZ2NFStWmMSUlJTgtddew+7du7F161bj0aM3btwAUL18w2Aw4PHHH0dubi5SUlJkx5w+fbps/7Bhw3DgwAHZmNDQULPteXl55m9Q2QF2vP4JV9KV35gwExER2SCLzjTrdDrExcVh4MCBSElJwapVq+Dg4ICSkhI4OzsjKysLffr0QceOHeHi4oKMjAwUFRUBAE6dOgUAiIiIwMaNG7F161ZcvHgRmzdvlh2z9ibD2sLDw5GXlye7RKOsrAwajaZOu+RMs50KKjvONFuzzRdZsomIiOifzKJJ88KFCwEAq1evBgD07NkTABAbG4vo6Gio1Wps2rQJ8+fPh16vR3BwMDp37oyzZ88a1y736dMHQgjk5uaiS5cu8ie5APjPf/4j2//jjz8a10dLOX78ODp27FinXXKmme4oJrRERER0p1k0aX766acRFxeHNWvWoG3btsZ2Dw8PAEB5eTmGDBmCTz/9FD4+PkhPT8eECRPg7u6OBx98EAAwdOhQBAUFISAgAK+++ip++OEH/P7775JjPvTQQ/j5Z+kk6/DhwwgODpZ975qlIbVJlZxbm/U9S84RERERWTGVEEJYbHCJzXE1M81nz57F448/jkOHDkGv1xuXROzcuRPt2rUzxufk5ODpp5/Gli1bYG9vL5nU1keLFi0QHByMP//8UzImIiICe/furdPeo0cP7Nmzp077AP8H4GBnOyXnNl1YoRxEREREZAUKCwvh4eGBgoIC2UlOi840K+XrwcHBxo19MTExWLNmDbZu3VqnrFxISAjWr1+P0aNH49ChQ7JJs9JMc5cuXXDy5EnZ95I6ZlGq5Jy1Vs9IPP+jpV+BiIiIqFFoFCXn5AghMGPGDKxatQpbtmyRrcM8ZcoU47INKUrLJMaPH49XX331tp4htRHQWF2hkUs8u9zSr0BERETUKDX6pLmmwsaaNWvg5uZm3Gzn4eFhrLARFxeHkSNHIjw8HM7OzpJ1lIHqw1LkzJ8/X3EGfNy4cWbbrWUjIJNjIiIioltj0TXN9XGr656dnJxQUFAg+Tylw01CQ0Ph6OiIo0ePSsacOnUKLVq0qNPevXt3pKWl1Wkf2PRhi69pTjizzKLjExERETVG9V3T3OjXDAghzF7R0dEA/m/d85UrV1BaWoqdO3dKPsve3h4rV66UHa+0tNSkkoc5165dM9sufbgJAJXKshcRERER3bZGvzzjVoWEhEj2VVVVITY2VvZ+rVYLg8EgG3P27Fl07dq1TrtUybnVh79hyTkiIiIiK2ZzSbPc0gsA2Ldvn2x///79FUvWHT9+/Jbe6f5O0+Fgp76lexrKxuwlFhmXiIiIyJbYXNIsVQ6uhlRZuBrbtm1TPNxE6ohtyWdbaInExiz5WXUiIiIiqp9Gv6a5od13332KMeYOKLnZK6+8YrY9ICDAbLvKTvW3XhuzYpkwExERETUgm5ppHj16NCoqKmRjfHx8ZPuPHj0Ke3t72RipsnWNoeRc/InvLP0KRERERDbHojPNc+bMQWRkJNzc3ODn54exY8ciMzPTJCYqKsp4ol7NNX36dJOYM2fOYNSoUUhISMDGjRtlx/zuO/mk8u6771as0yw1Ey010wzYAao7e8Wf+I4JMxEREdEdYtGkOTY2FpcvX4bBYEBlZSV27tyJgQMHmhxOkp6eXue+8vJy45+rqqpwzz33YNeuXfU6rlqp/8iRI2jevLlszDfffGO2Xbrk3J0tJxd//FvZ9yUiIiKiv8aiyzNatWqFRx99FJGRkaisrMSLL76ITZs2ITU1FcOGDTPGhYeHIzEx0fizVqs1/nnDhg04fvw4+vfvj//+97947bXXsGHDBskxn3nmGXz55ZeS/TNmzMD8+fNl31tqo6BUybnf9n3BknNEREREVsyiSXN8fLzJzx988AE2bdqE3Nxck/aTJ08iPDwcAQEBGD16NN58801jX1xcHADgp59+gr+/P0aOHCmbNN88S23Onj17UFZWJhszZswY2f7aHox8Dg72DVtybsORxQ36PCIiIiKS1mg2AhoMBmNViu7duxvbHR0dcePGDVy5cgXXrl3DsWPHsGvXLiQlJQEAjh07Bnt7e5n1xKa+/VZ+KYNer4evr6/spr677rrLbLtSObuGwGSZiIiI6O/XaDYCuri4YOvWrQgPD0d4eLgxpri4GFVVVQCqE+uqqiokJyfj4MGDAICSkhIA1UsmnJyc4OXlJTmeSqWCi4uL7Dt17txZsdbz0qVLzbZLJu4NtHaZCTMRERGRZVg0aU5JSYFOp8O9994LoHpW+dq1ayYbASMjIzF16lTk5uYiNzfXuB55+fLlAAAnJydjUr1jxw7odDrJ8YQQGDdunOw7BQcHK84Ynz592my75Ox0A9Re3nB4kew7EREREdGdY9GkecOGDdi7dy9+//13eHp6IjU1FefPn0daWppJnFarRUBAAAICAowzxY6OjgAADw8PAMCNGzcwZMgQrFixQnI8R0dH9OnTR/adlixZYny2lAkTJphtl55p/mvl5NYfXCj7PkRERER0Z1k0aX7mmWfwzTffQKvV4tdffzUeTFJTHSMrKws5OTlYunQpvL290bx5c0yZMgUA8NBDDwH4v0S1WbNmWLRoETp27Cg5XkVFBX7//XfZdwoMDISDg/RSb5VKJTnGHSs5R0REREQWZdGNgIsWVS85uHz5sskM8L59+9C9e3dcuHAB586dQ2VlJQDg2rVrAAC1Wo1OnToBANzc3ODk5IRjx47hoYcekq3D7OjoiLCwMKxdu1YyRq/X46mnnsKHH34oGZObm2v2VECpknO/bv8PS84RERERWbFGUz3jZjUn8rVo0QIuLi4oLS1FeXk57OzsEBUVhc2bNyMrKwutW7eGEAJ2dnZwcXHBjRs3oNFoUFhYaPa5FRUVmDdvnuzYFRUVyMrKkn23xx9/HJs3b6737zOu30twvM2Sc3/s+/y27iMiIiKihmPR5RlCCAghoNPp0KxZM+Px1O3atQNQvSnP29sbdnbVr+nh4QFnZ2cA1bWbgerqGsXFxbhy5QrKysokE+YaNc+SYm9vj02bNsnG9OzZ02x7Q5ecY8JMRERE1DhYPGmOiYnBqlWrkJSUBE9PTwCAt7e3MebChQsoKSmBVquFm5sb9u7dC6C6agZQvQEQAEaMGIGPP/5Y8ZjswYMHy/a3adPGmLRLeeCBB8y2S28EBNcyExEREVkxiy7P0Ol0iIuLw5o1a+Di4oLJkycjMjISrVu3BlC9EbDmdD69Xm9Sii4+Ph4DBgwwVtOIj49HYmIiVCqVcXmHOTcfx21OVlaW4mz02rVrTQ5gqaG4EfAW/LF3/i3FExEREdGdY9GZ5oULF6KgoABRUVFo2rQpNm3ahD179mDlypUAqjf89e/fH97e3tBoNAgNDcWIESMAAP379wdQvRHQw8MDDg4OqKyshMFgkB2zb9++sv25ubk4e/asbIxULegGPdyEiIiIiBoNiy/PuHlNc3Z2NoQQiI6OBlC9pjklJQVXrlxBaWkpMjMzjbPINbPBAQEBcHNzw9q1azF//nzF5Rk3L/0w5/r16yYz2rWpVCo0adLEbF+DlpwjIiIiokbDosszhBCYMWMGVq1ahS1btqBly5ay8TqdDunp6QBgLPnWq1cvfPjhh+jatSsGDx6MOXPm4OLFi5LPiIyMlK3VXF5ebjxhUOqdExISMGzYsDp9UiXnfkmew5JzRERERFas0axpdnNzM87U1lTJyMrKQlxcHEaOHIn//ve/WL9+PbRaLfr372+s0zx06FC0b98eEydOxMcff4x+/frhl19+MTueSqVSrHDh6OgIX19f2bjnn38eR44cqffvOW7Qq3B00NQ7/o+dn9Y7loiIiIjuPJWQ2zV3pweXWIYQGxuL6OhonD17Fv3790dOTo6xHnOrVq2wYsUKREREGOPvvvtu7Nq1q17jqVQq2XXPPj4+8PT0lK3V3Lx5c+Tk5NRpb9asmdmZ5nu66+qdNP+x45N6xRERERHRX1dYWAgPDw8UFBTIrgxoFGuaa183r2muqqqCk5MTYmNjkZCQgJCQENx33324fPkygOpqFxcvXsT9998PLy8vuLq6yo7XvHlz2Xfy9fVFQUGBbIyPj4/ZdqmNgDXJen0uIiIiImp8GuWJgDerqWTxxBNPmLR/8sknmDNnDtRqNa5du4b169ejoqJCsXrG6dOnZfuvXr2qeEBKeHi42faGLDlHRERERI2HRWea66P2LPSJEycAABMmTABQPRvdpUsXY+k5JeY28N1s9uzZqKiokI159tlnzbb/1ZJz67bJH/FNRERERJbR6Geab2YwGDBz5kz06dPHZLZ3/PjxCAkJQVBQEMaMGSM7m6y0EbBbt26yh6OoVCqzB5sAMjPNRERERGTVrCpp1ul0WL9+PYQQddb/Ojo6ory8HFOnTsXrr78u+YzAwEAcPHhQsn/z5s1o27YtMjMzzfYPHTpU8l6pknM/J7zPknNEREREVsxqkuaYmBisW7cOWq0W5eXliI+PN+mvObTk7bfflnxGQEDAX95sV3Ns9614aMRbitUz1qX8+3ZfiYiIiIjusEafNNc+ACUiIgJ2dnYYNGiQ2fjKykrJZ9nZ2WHv3r2y41VVVSE3N1eyf+3atbh69arZkwUll35wIyARERGRVWv0GwF1Oh2WLVuGuLg4uLm5wWAwQAiBkpISANUl595//32kpaXh9OnTsLe3l3zWhQsX0LlzZ9nxTp8+DWdnZ9mYn3/+2Wz77W4EXLdlrux4RERERGRZFj3cZNiwYUhNTUVxcTFUKhX8/f3x3XffYcSIEf/3gg08Q2tnZydblq5bt264dOmSsdSdOX379sWff/5Zp13qcJOh/V6QXZ6xLukjhbcmIiIiojvBKg43SUtLw2OPPYbVq1djxYoVqKqqwujRo5Gfn2+M8fDwQFhYGA4cOIADBw7A2dkZdnZ2WLp0KZYuXYr//e9/cHBwgLu7O7p16wY7O/lfaeXKlbL9wcHBKC0tlY257777zLZLzzQrXERERETUqFk0aZ41axYOHDiAxx9/HDExMejYsSOqqqpMEtsbN27g2LFj6Ny5Mzp37mxcljFp0iRMmjQJ586dQ2VlJTp37oyMjAzZWWR7e3ts2bJF9p3+9a9/KR6QMmXKFLPtLDlHREREZJssuhEwNjbWeIpfZWUl9u3bB6D6KOsaVVVVde67eUVJYmIiAODIkSOK41VVVeH69euyMU5OTiguLpaN8fLyMtsuWXJu7dssOUdERERkxSyaNLdq1QqPPvooIiMjUVpail69egGASWUKR0dHeHl54cMPP0RqaiqWLl0KIQSSkpIAAEePHgVQvaxi1apV6N+/v+yY9957L5YvXy7Zv379esWZ5lv10H3vS65pXpf4QYOORUREREQNz6IbAW/WoUMHHD16FEIIxMbGIjo6GgDg6emJsrIyuLi4ICAgAEePHpVMaoUQ6Nu3L7Zt23bb7+Hh4QEnJydcvHhRMuatt97Cu+++W6ddciPgoBelk+aNTJqJiIiILMUqNgLW6NSpk3HdMgCTY6r9/f2h0WhQVVWFgoICYzm4Dz74AEII42EjNbn/Z599JjmOq6srnn/+edl3cXd3R2FhoWzMuHHjzLZLbQQUKpXkRURERESNn0WTZoPBgE6dOuHw4cMYMWIEMjMzERkZifDwcGPMrFmzsHLlSmzduhVz5841lqDz8PAAAGMSffjwYQDAgQMHJMe7ceMGRo4cKftOgwcPRkVFhWyMm5ub2XZuBCQiIiKyTRZNmjt16oSDBw+id+/eSEpKgpubGz788ENcvXoVAJCUlISVK1fi0qVLKCgowJEjR4yb9GpmmJs1awYAGDBgAH766SckJCTIjqlWq2X77e3t4eTkJBvTokULs+23erjJH/Hvy45DRERERI2DRTcC1swOp6amAgBKSkowdOhQTJkyBd9++y1cXFyQkpJi3PR3s5payu3atUN6ejoKCwvxyCOPKI45cOBA2f5ly5bhqaeewueffy4ZU1ZWBo2m7hpl2ZlmrsQgIiIisloWTZqDg4Nx4cIFuLm5Yf78+fjhhx9w7NgxzJ1bfay0r68vgoOD0bVrV7z66qs4cuQIXn75ZVy8eBF9+vQBADzyyCP48ccf4eLiglatWqGwsBAnT56UHHPBggV45plnJPv9/f2RnJws+97mEmZAuuTcL7++zpJzRERERFbMoklzzVHV169fN1bLAIC5c+fiP//5Dy5cuIDc3FxcuHABf/zxB3x8fFBaWoo+ffqgU6dOAKqXUwDVx2NnZGSgsrJSdsxhw4bJ9jdp0gQtW7bEwYMHJWNycnIQEhJSn18RADDuoY/g6Fg30f5jXd0KHERERETU+DSK6hm1eXp6AqheO+zk5ISKigqUl5fjypUrCAsLwy+//GKM3b17N9q1a4eePXvCwcHBuFFQys8//yzbn5aWhr59+8rG1CT7tV24cEH2vpsxYSYiIiKyHhZNmoUQxquqqgqjRo1Cnz598MYbbwCoXr7x8ccfY8OGDcjIyMB3332Hc+fOmSyvyMvLQ3BwMNavX4+SkhK8+OKLsmNmZ2fL9nfv3h0nTpyQjenZs6fZdumNgGYuIiIiIrIajWamWafT4dChQ1ixYoVJ+7Rp0zBs2DB07NgREyZMwPfff49Vq1YhKyvL7HOefPJJ2XGUNgJevnxZcU2z1Iyy1EZA1mcmIiIism6NImmOiYnBunXrkJycbCwhJ6Vmlrdms19AQIDJ6X1hYWGy97dq1Uq2v6ioSHL5RY3Vq1ebbZecaSYiIiIiq2bx5RkxMTFYtWoVkpKS0LJlS8V70tPTAQCBgYEAgF69euHgwYPIz8/H6NGjMXz4cNn7T506JduvVqtRVFQkGzN69Giz7ZIl57g8g4iIiMiqqUTN+dMWcPfdd2Pv3r1Qq9XQarXo3r073njjDURERMDZ2RlZWVno3r07rl+/bnJfkyZNkJ+fDwCoqqqCm5sbSkpK6jWmg4ODbIWNDh06wGAw4OjRo5IxKSkp6N+/f532Hj16YM+ePXXalc4yJyIiIiLLKCwshIeHh2K+ZtGSc7t27QJQfahJSUkJNm7ciI0bN2LRokV46qmnoFarcePGDTg4VL+mn58fHBwcUFVVBb1ebzwVEKiunVxZWYmqqirZMZVK0uXk5KBfv36ySfP27dvNJs1SHhw/Bw6OpqcMblj1dr3vJyIiIiLLsvjyjJuvmtnjdu3aAaiuntGnTx/odDpUVFTg/Pnz2Lt3L86fP4+0tDQAQEJCAkpKSjBp0iSUlpZCq9VKjmdnZ4e2bdvKvpOHhwe8vb1lY3Jycsy217fkHBNmIiIiIuvSKDYC1igoKACAOknr8uXL4evri/DwcLz55psmMTt27ICLiwtWrVqFgIAAqNVqyecbDAbFdwgODlYsS1eznro2qY2AQmV6EREREZF1sejyjDlz5uC3337DsWPH4OzsDDs7O3Tt2hXh4eHGmNzcXFy+fBkAcOXKFRw+fBgajcYYk5eXh6CgIHh6euLgwYPGEwKlZGZmyvaHhYUhMTFRNuaee+4x2664EZCIiIiIrJJFZ5pTUlKg0+mwc+dODBgwAIWFhcjPz4derzfGBAYGYurUqcjNzcWkSZOg0WhQVlYGlUoFlUqFb775BidOnEB2djZ27NiBxYsXy46pVKGjd+/eJkd6myNVXYMl54iIiIhsk0WT5vj4eERHR2PhwoXYuXMntm7darJeuYZWq8UHH3yApKQkY0m5uLg45ObmYuzYsQCAbdu2oUuXLli6dKnkePb29sYjuqUIIeDr6ysbExISYrZd+nATLs8gIiIismYWXZ4hhMCMGTOwatUqbNmyBar/f1Je7TXNixcvRnl5OVq3bo2DBw8CqC4NFxAQAGdnZwCAl5cXgOrKFnLGjRuH/fv3S/afOHEC3377rewzpCp0BAUF4fz583XaV/3wKkvOEREREVkxi84063Q6LFu2DHFxcXBxccHTTz+NyMhItG7dGgCQlZWF06dPA6he/zxw4ECcOnUKjo6OaNOmDYDqBFatVuO+++7D+vXr4e/vLzmewWAw3ifl2rVrdepC16a0Lrq2+yfNxbCH3sOwh967pfuIiIiIqHGw6EzzwoULAQBRUVEm7StXrkR0dDTUarWxvNvLL79s7K+oqICnpyccHR1RVlaGyspK7N69G6NGjTLOVptjb2+Pxx57TPadrly5ovjeUsswpErOcVkGERERkXVrFHWadTodmjVrhuzsbAghjBvxgoOD69RynjBhAgBg0aJFSE9Px1NPPYWwsDBcunQJQghMnjxZcrzmzZsjIiJC9p1KS0sVN/RJHZAieR+PzyYiIiKyahZPmmNiYrBq1SokJSUpVrYAgKtXrwIAIiIiEBoailGjRuH48eOorKxEVlYWlixZInlvdnY2zpw5I/v8Dh06wMnJSTbm4sWLZtslS84RERERkVVrNGua3dzckJeXh7y8PJSUlACoXtP8/vvvIy0tDadPn8bvv/+OP//8Ez4+PujUqRMAYOjQoWjfvj0mTpwou8GvRk21DSllZWVo0aKFbIxUf30ONyEiIiIi62PRpHnhwoUoKChAVFQUAgMDjdfKlSsBAGq1Gps2bcLQoUMRFhaGWbNmISQkBF27djUm2JcuXcJ3332HqqoqTJw4UXHMBx54QLY/NTUVd911l2zMqVOnzLZzppmIiIjINlm85Jyc4OBgpKSkmLRFR0dj6dKldY6ybtu2LUpKSrB//35069ZN8plNmjSRHfPo0aOKs9EZGRlm26VKzq2JfYUl54iIiIismEWT5tuxZMkS2XXLXbt2lb1fqTpGRUUFgoODZWPqs/b6ZmOmzIWDY/U66U1xb93SvURERERkeRZdnjFnzhxERkbCzc0Nfn5+GDt2bJ0ayFFRUcYjs2uu6dOnm8ScOXMGo0aNglarhZ+fn+yYBoNBtt/R0RGRkZGyMePGjTPbLlVyjuUziIiIiKybRZPmlJQU6HQ67Ny5E4mJiaioqMDQoUOh1+tN4qZOnYrc3Fzj9fHHHxv7qqqqMGrUKJSXl2P79u2yx2gDQFhYmGy/i4sLXnrpJdmYmtrRtSmVnNsU96bsc4mIiIiocbLo8oz4+HiTn5csWQI/Pz+kpaWhf//+xnatViuZkCYkJODIkSPYtGkT/vWvf6GiokJ2zC+//FK2PygoSHGt9ccff2x206HURkBWziAiIiKybhadaa6toKAAAODt7W3Svnz5cvj6+iI8PBwdO3Y0WaoxcuRIGAwGTJ48GVOmTEFCQoLsGEr9kZGRxqO7pdSUxKtN9lAUJs1EREREVsuiM81z5szBb7/9hmPHjsHZ2Rl2dnbo2rUrwsPDjTG5ubm4fPkygP/bxOfs7Izs7GwAwIsvvoiTJ0/CYDBg/PjxirPEHh4esv0tW7bEL7/8IhvTsWNHs+0sOUdERERkmyyaNMfGxqKiogIGgwGFhYWoqKiAEAJ6vR4uLi4AzJ++V1JSAr1ej9atW8PJyQkHDx6Es7Oz5PHWN3vjjTewZcsWyf5vv/0WGo0GRUVFkjH+/v5m26VKzq395mWWnCMiIiKyYhZdntGqVSu8/fbbGDNmDDw8PNCrVy/k5+cjNTXVJC48PNy4CbDmcJKTJ08CAG7cuIHi4mJ06NAB8+bNUxzznnvuke2/fv06unTpIhujtG66tnun/RuDJr1/S/cQERERUeNh0aR5w4YN2Lt3L7Zu3YrU1FR88sknAKqXZNzs5MmTCA8Px5AhQ4xrkocPHw6VSmU8PfD06dMYP3684pjDhg2T7ffw8EBhYaFsjKurq9l2yZJzrDZHREREZNUsmjTrdDosW7YMcXFxcHFxwQsvvAAAxjXNWVlZcHBwgIODAyoqKpCXl4cbN25ApVJh+/bt2L59O9q0aQMAaN68ObKyshTHVIpp164dzp07JxsjVbZOaiMgq2cQERERWTeLJs0LFy5EQUEBoqKi0LRpU2zfvh0AcOjQIQCAWq02VtIoKSmBg4MDVCoVhBDw8/NDr169YG9vD09PT7i5uWHAgAGKYy5fvly2Pzg4WLI6Rg2pA1S4EZCIiIjINlk0aRZCQAgBnU4HFxcXNG3aFGfPnkV0dDSA6gT2+PHjKCoqQnl5OfLy8oyb8LZu3QoAxg2A69evV6x6YW9vr1hd4/r169BoNLIxzZo1M9suW3KOiIiIiKyWRatnCCEwY8YMxMbGwt3dHX/++adkQlpDpape5zB9+nTExMSgrKwMVVVVmDlzJo4ePSp7b1VVFZYtWyYb4+TkhOLiYtmY7t27m23n4SZEREREtsmiM83PPPMMvvnmG2i1Wvz6669wdnZGXl6ecXlEVlYW3n//faSlpeH06dP4/fffkZ+fDwAoLy9HcXExqqqqAACLFy/G8OHDFcfcv3+/ZJ9KpcLcuXNRWloq+wyp5RtBQUFm29cvfBlbYnmENhEREZG1Ugml9Qp3cnCV+enXxYsXY9q0afjzzz8xceJEXLt2DaWlpfDx8cHly5fh5uZmPOikqqoKHTp0wJUrV1BYWIjy8nLZMd3c3GRrMBcWFqJt27Z1KnjcTOqT9ejRA3v27KnT3mfCa0hd9qHsexERERHR36+wsBAeHh4oKCiQPVejUR2jXUOtVgMAWrRogZCQEOMGQBcXF9x1113o1auXMdbe3h4bN25EZGQk7O3t4eAgv+Kk9hHdtd1zzz0IDAyUjZEqLSdZco6IiIiIrFqj2AhY+7p5I2BKSgquXLmC0tJSnDhxAt27d0dVVRXy8vKMl0ajwffff4/BgwdLntZXIycnR7ZfpVKhf//+sjG///672XbJknON8r+aEBEREVF9WWU6Fx8fj8DAQJOrb9++mDJlitljrG92//33y/Z3794dq1evlo2RWhfNknNEREREtsnqkuYlS5aYnZ0+duwY7r33XsXlF61atZLtLy0tRbt27WRjpE4MlDvchIiIiIisl9UlzXIcHBwUl1YolZNr0qQJMjMzZWOkFolzppmIiIjINtlU0gwAb7zxhmz/6dOnZftPnjyJS5cuycYEBwebbZcqORf/xcuyzyMiIiKixs3mkuaIiAjZ/vbt28v212e22NHR8Zbeadhz/76leCIiIiJqXCyaNM+ZMweRkZFwc3ODn58fxo4dW2dpRFRUFFQqlck1ffp0k5gzZ85g1KhR0Gq18PPzkx2zSZMmsv3nz5/HjRs3ZGOuXbtmtp0l54iIiIhsk0WT5pSUFOh0OuzcuROJiYmoqKjA0KFDodfrTeKmTp2K3Nxc5Obm4uGHH8bixYtNkuiQkBDs2rUL27dvx9y5c2XHDAsLk+0/deqU4mZCqVrQUhsBiYiIiMi6yZ8EcofFx8eb/LxkyRL4+fkhLS3NZEOfVqs1JqTOzs4YPnw4YmNjAQCbN2/GxIkTsW3bNjRr1gwPPvig7JjdunWT7Q8MDJScSa5x7Ngxs+2SSztYPYOIiIjIqjWqNc0FBQUA6p7at3z5cvj6+iI8PBxpaWlwcHBAQEAAAgICkJmZiU6dOqFt27bYtm0bTp8+LXsqYHZ2tuw7uLq6YuTIkbIxUok5S84RERER2SaLzjTfzGAwYObMmejTpw/Cw8ON7ePHj0dISAiCgoKQkZGBadOm4cqVK8b+vLw84ymAb7zxBgwGAwwGg+Q4UVFRsu9x7tw5jB49WjbG09PTbDtLzhERERHZpkaTNOt0Ohw6dAipqakm7dOmTTP+uWPHjvj++++RkJAAFxcXqFQqlJWVQQiBjz76CDqdzngEtxQnJyeUlpZK9gcHB+PcuXOyz0hMTMSIESPqtAcFBZk9kTDhM5acIyIiIrJmjWJ5RkxMDNatW4fk5GQ0a9ZMNram+sWCBQuQnp6Op556Cm3atMH06dMxYcIExbHKyspk+wcNGoTc3FzZmFOnTimOc7N7XmDJOSIiIiJrZtGk+aOPPoK/vz+++uorFBcX4/nnn1csObd8+XIA1fWYQ0NDMWrUKGRmZuKRRx6RPKnvZiqV/ALjiRMn4v7775eNkSpJJ1lyjmuaiYiIiKyaRZPmRYsWoaioCN999x1++ukn3LhxA4MHD8bly5cBAFlZWcjJycH999+P3bt3Y8mSJbC3twcAdO7cGSqVCiNHjoQQAikpKcaKGnI0Go1s/+HDh5GQkCAbI1U9gyXniIiIiGyTRdc0nz17FgDwxBNPmLR/8sknmDNnDtRqNa5du4b169dj/fr1CA4OhpubG+zt7ZGUlAQA+PPPPzFjxgxERETUeY45QUFByMrKkuyfN2+e7JpnoHpttTlSGwFZPYOIiIjIulk0aRZCmPx88uRJtGnTxrg2OTg4GF26dMHhw4chhIBGo4Fer0dFRQU6d+5scm9GRgaKi4sVl1+EhITIJs2XLl1CeXm57DOkjuoOCAgwuxGQyzOIiIiIrFuj2AgIyJecW7ZsGZKTk/Hqq6/CYDBAo9EYTwicMGECBgwYgHPnzkGv18POTv5Xunjxomx/RUUFhgwZIhszY8YMs+0sOUdERERkm1Si9nSvhTz99NPYsGEDUlNTZStoNG3aFBcuXIBWqzUpOffee++he/fuGDFihGydZiWurq7o2bMnNm/ebLbfzs4OVVVVZvt69OiBPXv21GkvKCio1yZFIiIiIvp7FRYWwsPDQzFfaxQzzbdScs7JyQmA+ZJzNQm0nI8//li238/PT7ZOs8FgwNdffy37jNoGv8SSc0RERETWzOJrmmfMmIFVq1Zhy5YtaNmypeI9NUsgnnzySZP27t27Y8WKFYpJc0ZGhmy/n58f9u3bJxvj6upqtl2y5BwRERERWTWLzjTrdDosW7YMcXFxcHNzQ15eHvLy8lBSUgKguuTc+++/j7S0NJw+fRq///47iouLzT7r2rVrOHPmjOKYSgeXqNVqODo6ysZIJd5SJec2f8wTAYmIiIismUVnmhcuXAig+gCTm8XGxiI6OhpqtRqbNm3C/PnzodfrERwcbKyOkZ6ebow/f/485s6di4kTJyqOWVMDWkpZWRlatGiBw4cPS8bs2rXLbDs3AhIRERHZJosvz5ATHByMlJQUkzZHR0dUVlbWKTnXtm1blJSUwN7eXnYj4NSpUxETEyPZn5GRAV9fX9n3On78uNl2yZJzRERERGTVGsVGwFvh7OwMANBqtSbXgw8+CAC46667ZO9/+OGHZfvLysok1yzXGD58uNl2zjQTERER2aZGnzTPmTMHkZGRcHNzg5+fn3G9c3Fxscn10UcfQaVSSR5xXSM4OFi2XwiheCKg1Ex2UFCQ7H1EREREZJ0afdKckpICnU6HnTt3IjExEWq1GnZ2djhw4ABOnDiBEydOoEePHpg0aRJ27Nih+DylGEdHR1RUVMjGKCXmRERERGRbLLqmuT7i4+NNfvbz88Pp06dx/fp1dOrUCUD1kg0vLy9cvXpV8XlKMXfddRcKCgpkY06dOmW2nSXniIiIiGxTo59prq1maYTBYDCWqCsvL8eyZcswfvx4xfulStbVqKysVDxR8NtvvzXbLlVyjoiIiIism1UlzQaDwVgybuDAgQgMDERgYCB27NgBtVqNpKQkuLm5yT5DKbENCwtTXPc8ePBgs+3cCEhERERkm6wqadbpdCgvLzfbV1BQgG7dumHZsmWyz1Ba03z06FGo1WrZmJUrV5pt50wzERERkW1q9Guaa8TExGDdunW47777oNfrERsba9Kv0Wig1+vx/PPPyz5HqsZyDTs7O2RnZ8vGnDx50mw7Z5qJiIiIbFOjn2kWQiAmJgarVq1CUlISXF1dodFoEBAQYHJ5eXlh27ZtOH36tOzzvvzyS9n+3NxcxTXNOp3ObDtLzhERERHZpkafNOt0OixbtgxxcXFwc3NDSUkJSktLjfWas7Ky8P777yMtLQ1nzpxRTHg7dOgg2z9t2jTF5DcpKenWfgkiIiIismoqoXSWtYWpVCqz7RqNBg4ODsbDSDQaDaqqqiTXPNdXfHw8XnnlFaSnp0vGjBs3Dj///HOd9mbNmpk9RrugoADu7u5/6b2IiIiIqOEVFhbCw8NDMV9r9DPNQgiTa/LkyRgyZAgOHTqE9PR0HDhwAJmZmTh37hzKysrQrFkz2efNnj1btv/8+fOK6547d+5stp0bAYmIiIhsk9VsBKyRmpqKrKwstGnTxqR92LBhcHR0NDvTe7Pah6XUlp6erljL2cfHx2w7NwISERER2aZbnmk+cOAAPvjgA3z11VfGmsk1CgsL8eSTTzbYy0nx8/NDbm6uyfXjjz9iwoQJUFpt0r17d9n+b775RnF8f39/s+2caSYiIiKyTbeUNCckJKBHjx5YsWIF/v3vfyMsLAzJycnG/pKSEixdurTBX7I+qqqq4OHhoRjXrl072f7+/fsrPsPBwfwEPWeaiYiIiGzTLSXN77zzDl588UUcOnQIp0+fxksvvYQxY8YoLnloaPn5+cbTAGuuvn37orKyUvFee3t72f6wsDA4OjrKxly4cMFsO0vOEREREdmmW0qaDx8+bFx+oVKp8NJLL2Hx4sUYN24c1q1bd0desLa+ffvC3t4eLi4uJtekSZNw9913w8nJSfZ+pZnmiIgItGrVSjYmOjr6Vl+biIiIiKzYLW0E1Gg0uH79uknb+PHjYWdnh0ceeQSffPJJQ76bpIEDB2LhwoUmbd7e3vD29sajjz6KJUuWmL3P3t5ecVb8yJEj6NatGzIzMyVjpBJzqRloIiIiIrJut5Q0d+nSBcnJyYiIiDBpf/TRR43l4P4OLi4uCA0NNdt36dIlyfscHR0lj8CuUVJSgvbt20v2a7Vayb6AgADF6h1EREREZH1uKWl++umnsXXrVrN9jz32GIQQ9ao+cSvmzJmD3377DceOHYOzszOcnJzqlJuLiopCSkqK4rNKS0uxceNG2ZiLFy/il19+kexv3bq1ZB83AhIRERHZpjt6IuCPP/6IMWPGwMXF5bafMXz4cDz66KOIjIxEZWUlRo0ahStXruDw4cPGWd8HHngAoaGhyMvLQ05OjuzhJK1atUJ2drZk/7333os//vhDsnSdnZ0dqqqqzPZ1794daWlpddp5IiARERFR41TfEwHvaNLs7u6O9PR0xY11t+LRRx/FypUr67R7eXmhdevW2Lt3r+z9wcHBOHv2rGyMo6MjKioqJPsvXLiAwMDAOu08RpuIiIjIujSKY7TvRD7+wQcfAAAOHjxoPFp7wIABsLe3x7FjxxTvLysrk+1v3749XF1dZWOkTgxkyTkiIiIi23RHk+aGZjAYMHDgQABAx44doVKpoFKpkJKSgpCQEMn11jeTm0EGqg8uqV0hpDalxJuIiIiIbMstbQS0NJ1Oh2vXriEqKgo//vijSZ9Go4FarYa9vb3kmmMA6Ny5M7Zu3QqDwWC2/8KFC4oz5FLPZ8k5IiIiIttkNUlzTEwM1q1bh+HDh6OyshIBAQF1YhISEiST4RoqlUo2xt/fHzdu3EBpaalkTLNmzcy2s+QcERERkW1q9MszhBCIiYnBqlWrkJSUJLveuKysTHGWOCMjQ7Y/KCgIdnbyn0XquG6WnCMiIiKyTXc0aQ4JCYGjo+NfeoZOp8OyZcsQFxcHNzc3pKSkYM2aNcb1zDVX8+bN67URT+5wEgDw9fWV3OhX4/PPPzfbbm72m4iIiIis3x0tOdcQVCqV2fbXX38do0aNwsWLF/Huu+8iOzsbpaWlqKiokJ1tVlrz3Lp1awQEBGDbtm2SMRMnTsT3339fp50l54iIiIisyx0tOefl5QVvb+86l4+PD5o2bYoBAwYgNjb2tl/+ZjVl5WqumqT2gw8+QK9evTB27Fjs378fBQUFWLt2rWSSDQBubm649957Zcd77LHHzB5QcrMrV66YbWfJOSIiIiLbdFtJ81tvvQU7OzuMGjUK7777Lt59912MGjUKdnZ20Ol0uOuuu/D00083+JHaSsrKyozLNcxxdHTEiy++KPuMHTt2wMPDQzbmgQceuO13JCIiIiLrc1vVM1JTU/HBBx9g+vTpJu2LFy9GQkICfv31V3Tq1Amff/45pk6d2iAvWiM/Px9FRUV1EuNWrVohNDQUBoNBcnnG1atXcfjwYdnnHzx4UHImuUZ5ebnZdpacIyIiIrJNtzXTvHHjRgwZMqRO++DBg7Fx40YAwMiRI5Gdnf3X3u4WCCFw+fJlxeoZZ86cke1XKkkHVK9RNocbAYmIiIhs020lzd7e3li7dm2d9rVr18Lb2xsAoNfr4ebm9tfezgw/Pz8EBATUWeucnZ2NoqIixftrknopSgkzAGzfvt1sO0vOEREREdmm21qe8eabb+Lpp59GcnIyevToAQDYs2cP1q9fj0WLFgEAEhMTMWDAgIZ703p48MEHMXfuXNmYs2fPyvaXlpbCwcFB8bhtc3i4CREREZFtuu2Sc9u2bcOCBQuQmZkJAGjbti1mzJiB3r17N+gL1hYaGors7Gw4OzubtM+cORMxMTGKFSycnJxkT/tzdnaGVquVXdeckJCAe+65p047S84RERERWZf6lpxr9HWaa3N3dze7DEOr1UKv18PBwUG2DrNarZbcyAcATZs2BQDJGWO1Wo2ysjKzfT169MCePXvqtDNpJiIiImqc6ps039byjJuVlpbWSULvZILo5+cHZ2dnHDhwwKRdo9FAr9dDq9VKrm22t7eHg4ODbNI8adIkzJkzR7Jfq9VCCCFbD5qIiIiIbMttbQQsLi5GTEwM/Pz84OLiAi8vL5PrTrOzs0NAQIDJ5eXlhW3btkGv10veJ4SAk5OT7LM7deoke/T39evXJU8LZMk5IiIiItt0W0nz7NmzkZSUhIULF0Kj0eDbb7/Fu+++i6CgILPHSzc0g8GAvLw8k+vy5csoKyuTLTlnMBjg4CA/uX7o0CHFmfKcnByz7Sw5R0RERGSbbmt5xtq1a/H9998jKioKTzzxBPr164fQ0FCEhIRg+fLlmDBhQkO/p4n8/HwEBgaatPn6+uLIkSOKdZrbtGmD/Px8yf7WrVvD3t5e9hmRkZFm21lyjoiIiMg23dZM89WrV9GqVSsA1euXr169CgDo27cvtm7d2nBvZ0bfvn0xZMgQnDhxwuTKzMxEkyZNFGtDKy3P+PTTT9GkSRPZmAULFpht50wzERERkW26raS5VatWOHXqFAAgLCwMP/30E4DqGWhPT88GezlzUlNTsWnTJrRp08bkGj9+PABgxIgRsvf3799ftr+wsBCXL1+WjTl69KjZds40ExEREdmm21qe8cQTT+DAgQMYMGAAXnnlFYwePRoLFixARUUFPv3004Z+xzr8/PzMVs8AgEuXLsneq3T4yNWrV6FWqxVjzAkKCuLhJkREREQ26LZmmp9//nk8++yzAIAhQ4bg2LFjiIuLw/79+/Hcc8/V+zlz5sxBZGQk3Nzc4Ofnh7FjxxoPS6kRFRUFlUplvLKyslBYWGgSc+7cOTz88MPQarVITk6WHXPfvn2y/SqVChEREbIx586dk+0nIiIiIttyW0lzbSEhIXjggQfQqVOnW7ovJSUFOp0OO3fuRGJiIioqKjB06NA6ZeOmTp2K3Nxc5ObmIiQkBKWlpQgMDDRekZGR2L59OyIjIxVniZUS3tDQUMXScSNHjjTbzpJzRERERLap3sszPv/8c0ybNg1OTk74/PPPZWNrZqGVxMfHm/y8ZMkS+Pn5IS0tzWTtsVarNW6ykyoZ5+/vjwMHDsgeXAJA8uCTGoMGDcLChQtlYzp27Gi2PSAggMsziIiIiGxQvZPmzz77DBMmTICTkxM+++wzyTiVSlXvpLm2goICAIC3t7dJ+/Lly7Fs2TIEBATgypUr8Pb2xrp16wAAX3/9NVJTU7Fx40Z88cUXmD9/vuwYPj4+sgegfPHFF8b10VKkEm9uBCQiIiKyTfVOmmuqZdT+c0MxGAyYOXMm+vTpg/DwcGP7+PHjERISgqCgIGRkZGDy5MlwcHBAr169AACxsbFo1aoVWrVqhXbt2imOo3S4Sbt27XD48GHZmNLSUrPtnGkmIiIisk31TppfeOGFesWpVCp88sknt/wiOp0Ohw4dQmpqqkn7tGnTjH/u2LEjXnrpJVy4cAFZWVlo3bq1SWxZWZniOEp1ms+fPw9HR0dUVlZKxlRVVZlt50wzERERkW2qd9K8f/9+k5/37duHyspKtG3bFgBw/Phx2NvbK1aeMCcmJgbr1q3D1q1b0axZM9nYmqUTHTp0gL29PSoqKlBVVYXXX38dOp1OcWmIUh3pkpISxXXRUmXtWHKOiIiIyDbVu3pGcnKy8Ro9ejQGDBiAc+fOYd++fdi3bx/Onj2LgQMHYtSoUfUeXAiBmJgYrFq1CklJSWjZsqXiPTUJbVlZGYqLi1FRUQGDwYC4uDgEBQUpHm5iZyf/K/v6+krOJNfo1q2b4nsSERERke24rcNNPvnkEyQkJMDLy8vY5uXlhQ8++ABDhw7FrFmz6vUcnU6HuLg4rFmzBm5ubsblDR4eHnB2dkZWVhamT5+OCxcuICcnBw4ODrhx4wY8PT1x7do1ANVLJdzd3XH69GmoVCrZ8VQqFXbs2CEb4+7urvje9vb2ZttZco6IiIjINt1WnebCwkKzSxQuXbqkWNLtZgsXLkRBQQGioqJM6i6vXLkSAKBWq5GWloacnByUl5fD3d0dzs7OKCsrM1bAsLe3R8eOHREcHKy4XlkIoTiLXFZWprhZUOoAlZqyeERERERkW25rpvn+++/HE088gU8++QQ9evQAAOzatQuzZ8/GAw88UO/nCCFk+4ODg+scWd2yZUucPn0arq6uJu3NmjVDSUkJ7O3tYTAYJJ/54IMP4tdff5XsLygoUNwImJOTY7adGwGJiIiIbNNtzTQvWrQII0aMMJaDCwkJwfjx4zF8+HB89dVXDf2OJqQ24Z07dw4+Pj6yCTMA3LhxQ7bfyclJsXTdmTNnzLZzppmIiIjINqmE0nSvDL1ej6ysLABA69at4eLi0mAvZo7BYICbmxsqKipMEtcffvgBLVu2xIULF/Dcc8/9pTFCQ0NRVlaGs2fPSsZ4e3vjypUrddqbNWtmtnpGQUFBvdZKExEREdHfq7CwEB4eHor52m3NNNdwcXFBp06d0KlTpzueMAPVGwfLy8vh6emJgIAA4zV79myMGzcO169fV3yG3GmGQHV1jpolJ1IqKirMtgcFBSmOT0RERETW5y8lzX+nmlrOTZs2xaVLl+Dq6mpyffTRR2jVqpXic77//nvZ/vPnzyMqKko2pkWLFrfw5kRERERk7Rp90ly7lrODgwNUKhWEECZXcXExhg0bBq1WK/u82oe01DZw4ECcPn1aNkZqDJacIyIiIrJNjT5p1ul0WLZsGeLi4uDm5oaqqip4e3tj165dOHDgAH7//XdMnToVgwYNgl6vx4svvij7PKUTAS9evKhYlm7MmDFm27kRkIiIiMg23VbJub/TwoULAaDOkom9e/ciOjoaGo0G+/fvxw8//AC9Xq+4tlpp3XNmZibuvvtu2RipCh0sOUdERERkmxr9THPtZRitW7eGn58fhg8fjry8PDg6OmLlypXIzMxEaWkpmjVr9pfGKy8vx4EDB2Rj/vjjD7PtnGkmIiIisk2NfqbZnPz8fAQGBpq0tW7dGidPnjSeFCglJCRE8nCSm58vhzPNRERERP8sjX6muba+ffuaba85+trHx0f2/l9++UW239HRESqVSjYmOzvbbDtLzhERERHZJqtLmpcsWVJnyYYQAseOHQMAHD16VPZ+pbJ0jzzyiGLiPXfu3Ft7aSIiIiKyalaXNCspKiqS7d+yZYts/4MPPmictZby5JNPmm1nyTkiIiIi22RzSbOSBQsWyPa/9dZbkif+1Rg4cKDZdm4EJCIiIrJNNpc0Kx1ucuTIEdn+vLw8XLlyRTYmNjZW8l4iIiIisj0WTZrnzJmDyMhIuLm5wc/PD2PHjkVmZqZJTFRUFFQqlck1ffp0k5gzZ85g1KhR0Gq1KC8vlx2zsLBQtt/Ozk5x3XPLli3NtnOmmYiIiMg2WTRpTklJgU6nw86dO5GYmIiKigoMHTq0Ttm4qVOnIjc313h9/PHHxr6qqiqMGjUK5eXliIyMVBzTzk7+V3ZyclJMvCsrK822c6aZiIiIyDZZtE5zfHy8yc9LliyBn58f0tLS0L9/f2O7VquVnMVNSEjAkSNHsGnTJuzYsQNbt26VHbN58+ayFTbs7Ozg5+cn+4xt27ZhwIABddqDgoJw/vx52XuJiIiIyPo0qjXNBQUFAABvb2+T9uXLl8PX1xfh4eF49dVXUVxcbOzbsWMHOnbsCH9/f9x7771wcnKSHaN169ay/W5ubjh16pRsjNThJkRERERkmxrNiYAGgwEzZ85Enz59EB4ebmwfP348QkJCEBQUhIyMDEybNs1sneThw4cjPj4ejzzyCJYuXSo5jtIBJGq1Grm5ubIxvXv3NtvOknNEREREtqnRJM06nQ6HDh1CamqqSfu0adOMf+7YsSNeeuklk5nmGhUVFdDr9UhISJAdR+kI7UuXLuHq1auyMadOnUJYWFid9oCAAC7PICIiIrJBjWJ5RkxMDNatW4fk5GQ0a9ZMNrZm+UVcXBxyc3Px/PPPo3379vjll1+wefNmxVlipYT47NmzcHFxUXxfc7gRkIiIiMg2WTRpFkIgJiYGq1atQlJSkmQpt5uVlZUBADp06ICAgADcc889OHbsGCoqKpCenq54v9Imv86dO6Oqqko2ZubMmWbbWXKOiIiIyDZZNGnW6XRYtmwZ4uLi4Obmhry8POTl5aGkpAQAkJWVhffffx9paWk4ffo0fv/9d1y8eBFA9bpiV1dXPPTQQwCAPn36wN7eXnFMpaQ5PT0dY8aMkY357rvvzLZzppmIiIjINlk0aV64cCEKCgoQFRWFwMBA47Vy5UoA1ZvyNm3ahKFDhyIsLAyzZs2Cq6srgOpZ6ppLrVZDCIG33npLcUyl5RsvvPACzpw5IxszefJks+1KmwyJiIiIyDpZNGn+6KOP0L17d7i6uqJJkya47777cOzYMURHRwMAgoODoVKpcPXqVZSVleHkyZO4fv06nJyccODAAeO1ceNGtGrVCg4OyvsaP/vsM9n+AQMGKG4W9PDwqPfvSERERETWz+pOBHRxcUFpaSnatGljvAYMGIB9+/Zh27ZtsuPZ29vj0KFDsjGzZs0yKXlnTrdu3cy2s+QcERERkW2yaNI8YMAAfPnll7j77rtxzz33wGAw4MyZM0hLSzPGpKen45tvvjEu3dDr9bCzszPOMi9YsAAAcNddd5mcImhOVVUVNmzYIBvzyCOP4ODBg7Ix7u7uZtu5EZCIiIjINlm0TnPNTHNkZCQqKyvx3HPPAUCdU/00Gg20Wi2aNGmC7OxsVFZWonPnziYx6enp2L59O7p27So7ptQmvhppaWlQqVS38dtwIyARERGRrbLoTHN8fDyio6PRoUMHdOzYEY6OjgCA0tJSY4y/vz9GjBiBlJQUvPXWWxBCmH1WUFAQunTpgubNm8uO2alTJ9n+PXv2YN68ebIxO3fuNNvOmWYiIiIi29SoTgQ8cuQIAMDb29vYHhgYiNTUVAwcOBABAQFo3rw5Tp06Ba1WC5VKheLiYggh8OSTTwIADhw4AC8vr9t+j5KSEvz444+yMY899hhOnTpVp50zzURERES2qVEkzTExMVi7di3atm2Lli1bmmzEGz9+PEJCQhAUFISMjAxMnToVALBgwQL069cP9913H3JycjB9+nQAwJtvvik7lp3dX59c9/f3N9seFBTEY7SJiIiIbJBFk2YhBGbMmIFVq1ZhwIAB2L59O1JTU01ipk2bZvxzx44d8fnnn2P37t3w8/NDaGgomjdvjiNHjqCyshJZWVnGjYHmqFQqxQNQysvLFU8ElNoISERERES2yaJJs06nQ1xcHAYOHIiUlBSsWrUKDg4OKCkpgbOzM7KyshAXF4eRI0fCx8cHGRkZ2LdvHwDg3nvvNXlW+/btsWjRItnxhBBo0aKFSXWO2lxdXbF7927Z59QcsFIbS84RERER2aZGcSLg6tWrceHCBfTs2VPxRMCayho///yzseScSqVCmzZtMHHiRMUx+/TpI9uvUqkUK3BIbfjjRkAiIiIi22TRmeann34acXFxWLNmDdq2bWtsrzlxr7y8HEOGDMGnn35qnGkeO3YsAOChhx4yedbu3buxb98+9OjRA+Xl5ZJjhoWFyb6Tr6+v7Ew0AMkDUrgRkIiIiMg2WTRpXrhwIQAgKirKpD02NhbR0dHGmeb58+dDr9cjODgYDg4OqKioqPMsLy8v9OrVSzZhBoBjx47J9js5OUmWtatx9epVs+0BAQHcCEhERERkgyy+EVBOcHAwUlJSTNpqajnX5uHhgcuXL8POzk72uVlZWbJjvv7663jjjTdkY6Q2E3KmmYiIiMg2NYqSc7dKpVJh27ZtJm3+/v7Q6/WKibhSDedLly6ZHK5ijlarNdvOknNEREREtsnqkuaqqioIIdC7d2+T9jZt2uC1115TvL+4uFi2/48//sD169dlY1q3bq04DhERERHZDqtLmms4Ozub/Dx69GhcvnxZ8b6zZ8/K9l+8eBFlZWWyMVKz2Sw5R0RERGSbLFpyriEJIRQPLgGU1zRHREQoPsPcRkSAJeeIiIiIbJXVJs0lJSUm19q1axWXVQDAyZMnZfvz8vLqzGLXNmDAAMl7iYiIiMj2WG3SXJvBYED//v3rFSdny5Yt6Nevn2xMz549zbZzppmIiIjINtlM0lxRUYGIiAjY2cn/Skr9paWlmDp1qmyMRqMx286ZZiIiIiLbZDNJ8+XLl7F7927FmWS1Wi3b7+TkVOewldry8/PNtgcFBcneR0RERETWyeqSZqnNfk2bNsWrr76qeL/STLOjoyN+++032RilUweJiIiIyLZYNGmeM2cOIiMj4ebmBj8/P4wdOxaZmZkmMVFRUVCpVMarsrISQPUBIzVXzdHXGRkZimMqbfJzdnZWrLBRVFRktp0l54iIiIhsk0WT5pSUFOh0OuzcuROJiYmoqKjA0KFDodfrTeKmTp2K3Nxc5ObmwsXFBUD1ISU1V2lpKS5cuICPP/5YccycnByEhIRI9rdv3x6hoaGyz0hMTDTbzo2ARERERLbJooebxMfHm/y8ZMkS+Pn5IS0tzaQShlarNSakAQEByM7ONh5lXVlZibKyMsycOROPP/44XnrpJeNstDleXl7IycmR7Le3t4e3t7fse0sds82NgERERES2qVGtaS4oKACAOknr8uXL4evri/DwcJw5cwZCCOj1euj1euPpfbt27UKTJk3Qrl072TGUjtHeu3cvJkyYIBtTWFhotp0zzURERES2qdEco20wGDBz5kz06dMH4eHhxvbx48cjJCQEQUFByMjIwMSJE6FSqZCeng4AePfdd5Gbm4vFixcDQJ010bV5enri4sWLkv1FRUWKx2hLLe/gTDMRERGRbWo0SbNOp8OhQ4eQmppq0j5t2jTjnzt27IipU6eipKQEnTt3Nom799570aZNG8XKFgEBAbJJs7u7Oy5duiT7jNGjR5ttDwoKwvnz52XvJSIiIiLr0yiWZ8TExGDdunVITk5Gs2bNZGNrlkDExcUhNzcXzz//PDp06IDU1FRMmTJFcawDBw7I9nfs2BFt27aVjYmIiFAch4iIiIhsh0VnmoUQmDFjBlatWoUtW7agZcuWivfUzCSPHz/epL1t27a4ePEiVCoVhBCS9/v6+uLy5cuS/dOmTcPbb78t+w5FRUVmNwuy5BwRERGRbbJo0qzT6RAXF4c1a9bAzc3NuCbYw8PDWC85Li4OI0eOhI+PDzIyMmSXVhw+fBjOzs6ym/2UDje5evWq4hKPffv2mV3XHBAQwOUZRERERDbIosszFi5ciIKCAkRFRSEwMNB4rVy5EkD1kdebNm3C0KFDERYWhlmzZsHV1RVNmjQx1m3Ozc3F7t270a1bN/Ts2VOxOkZFRYVsf0JCAlq1aiUbI7XEgxsBiYiIiGyTRZNmIYTZKzo6GgAQHByMlJQUXLlyBaWlpThx4gR8fHxgb2+PgIAA4xUZGYnExET07NlTcczmzZvL9icmJiIqKko2ZseOHWbbWXKOiIiIyDY1io2At8pgMCAvL8/kunz5MmbMmKF476OPPirbL4RAmzZtZGOkZpQ500xERERkm6wyac7PzzdZzhEYGIh27dph7Nix8PLykr131qxZcHNzk+xXq9XG5SFSXF1dzbYHBQUpvzwRERERWR2rTJrN6dSpExwcHBSXSGzZsgVFRUWS/Wq1WnFdNJNjIiIion+WRnO4ya3w8/OrsxlPo9EgKysLR48elb33zTfflO2/ceMGdu/eLRtTs+a6NpacIyIiIrJNjX6mec6cOYiMjISbmxv8/PyQm5sLg8FgshHw0Ucfhbe3N0JDQxWft2fPHtn+QYMGydZ5BoAhQ4aYbedGQCIiIiLb1OiT5pSUFOh0OuzcuROJiYkAqmspZ2dnGzcBlpeXY+LEiThz5gzs7e1ln+fh4SHbHxQUBK1WKxuj0WjMtnMjIBEREZFtUgmladVGxtXVFXq9vk67nZ0dqqqq0LVrV6Snp9/280eOHImLFy8iLS1NMqZ58+bIycmp0969e3ez9xUUFMDd3f2234mIiIiI7ozCwkJ4eHgo5mtWt6bZx8cHer0eP/zwA1q3bg0AeOaZZ5CTkwO1Wq14eElAQIDsjLCzszN8fX1ln/HLL7+YbedMMxEREZFtavTLM25mMBiMx1RPnDgRvXv3Ru/evZGeno5WrVqhSZMmis8YNGiQbL+Li4viUduff/652XZW1SAiIiKyTVY106zT6WAwGODo6IgzZ86Y9Gk0GnTu3FnxGc2aNZPt1+v1KCwslI2ZNm2a8ssSERERkc2wmqQ5JiYG69atg1arhV6vNy7NqDFz5kx4eXnh7Nmzss/p3r27bP/y5csVTwSUwpJzRERERLap0S/PEEIgJiYGq1atQlJSknHpRHFxscn1008/4a233lJ8ntImQY1Gg2vXrsnGVFVVmW1nyTkiIiIi29ToZ5p1Oh3i4uKwZs0auLm5wcfHBzdu3DCWhRNCoLKyEkOHDq3X8oyUlBTZ/pycHOMYUrp06WK2nRsBiYiIiGxTo59pXrhwIQoKChAVFYXAwECcPn0aQgjo9Xro9XoUFxejvLwcX3/9NcLDwxXrNG/fvl22/9y5c4rrnpcsWWK2nTPNRERERLap0c801y4j7e7ujrKysjon+2m1WpSUlKBTp06Sz7K3t8cLL7yAefPmScZ07NhRsXrG4cOHzbZzppmIiIjINjX6pNmc8vLyOksx2rZti+eff172vqqqKsXKGO3bt4ejo6NsTFFRkdn2oKAgY0k8IiIiIrIdVpc0+/n5wdnZGQcOHDBpd3BwwIgRIxTv37lzp2z//PnzMXv2bNkYzigTERER/bM0+jXN5uTn5yMwMNDkateuHWJiYhTvLS0tle1/+umnoVKpZGPKysrMtrPkHBEREZFtsrqkuW/fvhgyZAhOnDhhcmVmZmLChAlQq9Wy93t5ecn2r1+/Hv7+/rIxUv3cCEhERERkm6wuaU5NTcWmTZvQpk0bk2v8+PFwcHDAxIkTZe83GAyy/ZmZmfjoo49kY9q2bWu2ncs2iIiIiGyT1SXN+fn5ZtsrKioAAJs3b5a9/80335Ttf+mllxQ3Anp6eppt50wzERERkW2yuo2AAKBWq82WnAOA06dPy96rdLhJZWUl1q9fLxtTk6DXxplmIiIiIttkdUlzSUkJKisr65Sc02q10Ov1ivfXTrZrq6qqQkJCgmxM8+bNzbaz5BwRERGRbbK6pNnZ2RlFRUVwdnY2aX/hhRcAVJeeq6yslLxfaaa5adOmOHnypGxMfUrbEREREZHtsLo1zTVUKpXJVfvkQCkDBgyQ7R8zZozis7Kzs822s+QcERERkW2yuqS5pKQEAFBcXGxy7dixA4MGDZKdZQaA6Oho2f7ly5crVth45513zLZzIyARERGRbbK6pLmGs7OzydW7d29ERkbW6z45ubm5isnv9evXzbZzIyARERGRbbK6Nc1A9dKM2qXlfH19cf/99yveu2vXLsWYGzduyPZ/8cUXZtsDAgK4EZCIiIjIBlnlTLMQAr179za5RowYgT59+ije269fP9l+Dw8PxTrNr732mtl2zjQTERER2SarS5qlllcUFBQgIyND8f6kpCTZfpVKhYKCAtmY5557zmx7UFCQ4vhEREREZH2sLmmu2QhYW1VVFWbNmqV4v1KFC1dXVxQVFcnGDBs2THEcIiIiIrIdVpc0S/H09MTixYsV46RO86tRWVmpWHLu8OHDZttZco6IiIjINlld0uzs7AyNRgMhhMmVnZ1dr5Jv9vb2sv3l5eWKz/j444/NtrPkHBEREZFtssrqGUKIOpvuHBwcsHPnTsV7lapn5Ofnw8XFRfZI7n379plt50ZAIiIiIttkdTPNJSUlKC8vR2BgoMnl7++PZ599VvF+qRrLNVq1aqX4DAcH8/9dgzPNRERERLbJ6pJmKT179sSkSZMU49RqtWx/69atFU8EbN++vdl2zjQTERER2SarS5qrqqrMtjs7OyMlJUX2Xo1Gg+DgYNmYr776SnKMm59jDkvOEREREdkmq1zTDFQfd30zjUYjeVJfjbKyMjRp0kQ2ZseOHaisrJSNUXoGEREREdkWq02aa689fuqppxAREaF4n9wGPwCYN28eWrRogezsbMkYOzvzE/QsOUdERERkm6w2aa59yMnnn39erzXFJ06ckO3Pzc1FSEiIbNLs7+9vtj0gIADnz59XfAciIiIisi5Wt6bZ1dUVAKDVak2uV199FU2aNMGvv/4qe39hYaFsf3l5Odzc3GRjrly5YradGwGJiIiIbJNVzjSr1WocOHDApM3b2xsAkJycLHuvnZ2d7EY/Pz8/XL16VXF8czjTTERERGSbrDJpBv5vxrmGwWBAVlYWFixYIHuf0jHaFy5cUCw5FxYWZradM81EREREtsnqlmcAMHu4yd13343i4mLFe5WO0S4rK5Pc6Ffj7NmzZttZco6IiIjINlld0uzn5yfZFxYWBmdnZ9n7lQ43cXNzg6Ojo2xMhw4dZPuJiIiIyLZYXdIsJTc3F46Ojli8eLFsnNQR2DWKiookDy+pIbURkCXniIiIiGyT1SXNffv2xfDhw5Gbm2tynTt3Dnq9Hm+99Zbs/UrLM9q3b69YPeO9994z2x4QECD/8kRERERklaxuI2BqaiqysrIQGBho0j5s2DC88MILyMnJkb1faRa5Z8+eWLt2rWzMgw8+aLadGwGJiIiIbJPVJc1A9brm2iXnNBoNUlNTIYSQvVfpRMDz588rxkjVembJOSIiIiLbZJVJc35+PkJDQ03aXnvtNUydOlXx3vLyctn+2bNnIzc3F2lpabf8XpxpJiIiIrJNVremOT8/H0D1jPHN1+bNm9GkSRO0b99e9n65g00AYMKECWjXrp1kv0qlgq+vr9k+lpwjIiIisk1WOdMMoE5pud69e0Ov1ytWsFBavvHoo49i3759svczOSYiIiL6Z7HKpFmtViMjI8OkzdvbG9u2bcP169dl71WpVLL9O3bsUKyesXz5cuh0ujrtLDlHREREZJusLmkuKSlBZWUl2rRpY9I+cOBAPProo4r3q9VqlJSUyD7/yJEjss+4//77zbZzIyARERGRbbK6Nc1SDhw4gLZt2yrGKZWcO3r0KAoKCmRjJk+ebLadGwGJiIiIbJPNJM0dOnRA7969FeNq13eu7cknn8T48eNlY7y8vMy283ATIiIiIttkdUmzVPWLtLQ0xXJyAFBRUSHb7+zsjMjISNkYqaO4OdNMREREZJusLmmWqn4hhMC2bdsU77906ZJsv16vR2lpqWzML7/8YradVTWIiIiIbJPVbQSs8f3335v83LRpU1y5ckXxPqU6zQcPHsSGDRtkY7Kzs5VfkIiIiIhshtUmzZMmTTL52dfXF2+++abifU5OTrhx44Zkf25uLs6dOyf7jCZNmphtZ8k5IiIiIttkdcszauosa7Vak+upp55SrMEMAJcvX5bt9/T0VJyNbtasmdl2bgQkIiIisk1WlzTXKC4uNrm2bNmCBx98UPE+qVnim5+rdGpgy5YtzbZzIyARERGRbbLapLm2CxcuyB5aUsPR0VG239vbW/EZqampZts500xERERkm2wmaW7RogUyMzMV4yorK2X777nnHnTr1k02pl27dmbbOdNMREREZJusLmmWWjqxY8eOes30lpWVyfZ/+umnaNq0qWzMvHnzzLaz5BwRERGRbbJo0jxnzhxERkbCzc0Nfn5+GDt2bJ3Z4qioKKhUKuMlpby8HH379v3L7+Tt7Y3du3fLxsyePfsvj0NERERE1sOiSXNKSgp0Oh127tyJxMREVFRUYOjQodDr9SZxU6dORW5uLnJzcyUTZ4PBgM2bNyuOKVduDgDy8/MVK2x8/fXXZttZco6IiIjINlm0TnN8fLzJz0uWLIGfnx/S0tLQv39/Y7tWqzUuvXB1dUVRURFcXFwAVK9RLisrw6uvvornn39ecUxHR0fZknKOjo4wGAyyMfn5+WbbAwICcP78ecV3ICIiIiLr0qjWNBcUFACoW8Fi+fLl8PX1RXh4uHGmWK/XQ6/XG9cor1y5EoMHD1YcQ6pcXI327dvDzk7+s7Ru3dpsOzcCEhEREdmmRpM0GwwGzJw5E3369EF4eLixffz48Vi2bBmSk5Px6quvGjcCOjs7w9nZGfb29lCpVBg7dixGjRqlOE779u1l+4OCghTrNE+ZMsVsO0vOEREREdmmRnOMtk6nw6FDh+rUQJ42bZrxzx07dsTEiRMhhKhTkzk9PV1xptnJyUnyNL8aR44cQXl5uWxMVlYWOnXqVKedM81EREREtqlRzDTHxMRg3bp1SE5OVkxqa3z11Vc4cOAApk2bhrvuuguLFy9WnOn19PSEs7OzbMylS5fg6uoqG7Nz506z7Sw5R0RERGSbLDrTLITAjBkzsGrVKmzZskVxvfHNnnnmGZOfIyMjkZ+fD5VKJbm84uLFi9i3b5/scy9fvmxy1LadnR0MBoNJTEhISL3fk4iIiIisn0WTZp1Oh7i4OKxZswZubm7G5Q0eHh5wdnZGVlYW4uLiMHLkSPj4+CAjI0MyIS4tLcWRI0eg1WrrlKyrIYSokwDXNnr0aJPE2lx8YGCg2XtZco6IiIjINlk0aV64cCGA6gNMbhYbG4vo6Gio1Wps2rQJ8+fPh16vR3BwMBwdHVFRUVHnWVVVVejZs6fiiX+XLl2S7R80aJDibHTPnj3NtgcFBZktOVdYWCj7PCIiIiKyjJo8TakQhMWXZ8gJDg5GSkqKSVtoaCgKCwuxfft2k3Zvb2+cP38enTp1gqenJwoKCsw+/+LFiwAAX19fXLt2DQaDwVj7GQAyMjKMNaDNCQoKkly7rFarJX8PIiIiImq8ioqK4OHhIdnfaKpn3AqVSlVns57BYICjoyMAoFmzZqisrKxz+l9QUBDs7e0BwOTUv5qEGahOquU2Cz7yyCOSfevXrzeZVS4oKEB4eDjOnDkj+38EUlZYWIjg4GCcPXsW7u7uln4dq8Zv2XD4LRsOv2XD4bdsOPyWDacxf0shBIqKihQLOlhl0pyfn19nXXHbtm3x888/AwAyMzPNLuFQqVQIDw9HXl6e2X4A6N27N5566ikEBAQYTwW8eTPg6NGjJd/L3d3d5C9CzZ89PDwa3V8Qa1X7G9Pt47dsOPyWDYffsuHwWzYcfsuG01i/ZX0mNxtFyblb0bdvXwwfPhy5ubkmV2pqKsLCwhAaGiqZEF+9ehVjx45FXFxcnVP/nJ2d4enpiWnTpkGtVsPLy8vYd/NmwOjo6DvyexERERFR42WVM83x8fFmZ5qPHTuG9evX45VXXsHmzZtRWlqKli1bYsyYMdi4cSOcnJzwxBNPYMqUKXWqYpSUlGDevHnw8PDA9u3bTZZv3Kz2GmsiIiIisn1WN9O8ZMkSCCHqXMeOHQMAtGnTBr/++iu+/vprNG3aFNnZ2fj+++/Rt29fbNy4EY6OjnjvvffMPjsmJgbbt29HVFSU8bmPPfYYevfubfy5RYsW9X5XjUaDt99+GxqNpiF+9X80fsuGw2/ZcPgtGw6/ZcPht2w4/JYNxxa+pUoolbAgIiIiIvqHs7qZZiIiIiKivxuTZiIiIiIiBUyaiYiIiIgUMGkmIiIiIlLApPkO+fLLL9GiRQs4OTmhZ8+e2L17t6VfyeK2bt2K0aNHIygoCCqVCqtXrzbpF0LgrbfeQmBgIJydnTFkyBCcOHHCJObq1auYMGEC3N3d4enpiSlTptQ5+TEjIwP9+vWDk5MTgoOD8fHHH9/pX+1vNWfOHERGRsLNzQ1+fn4YO3YsMjMzTWJKS0uh0+ng4+MDV1dXPPjgg8Yj5GucOXMGo0aNglarhZ+fH2bPno3KykqTmC1btqBbt27QaDQIDQ3FkiVL7vSv97dbuHAhOnXqZCy436tXL2zYsMHYz295e+bOnQuVSoWZM2ca2/gt6++dd96BSqUyucLCwoz9/Ja35vz583j88cfh4+MDZ2dndOzYEXv37jX289+f+mnRokWdv5cqlQo6nQ7AP+DvpaAGt2LFCqFWq8V3330nDh8+LKZOnSo8PT3FxYsXLf1qFrV+/Xrx+uuvi99++00AEKtWrTLpnzt3rvDw8BCrV68WBw4cEGPGjBEtW7YUJSUlxpjhw4eLzp07i507d4o///xThIaGiscee8zYX1BQIPz9/cWECRPEoUOHxI8//iicnZ3F4sWL/65f844bNmyYiI2NFYcOHRLp6eli5MiRonnz5uLGjRvGmOnTp4vg4GCxefNmsXfvXnH33XeL3r17G/srKytFeHi4GDJkiNi/f79Yv3698PX1Fa+++qoxJjs7W2i1WvHCCy+II0eOiC+++ELY29uL+Pj4v/X3vdN+//138ccff4jjx4+LzMxM8dprrwlHR0dx6NAhIQS/5e3YvXu3aNGihejUqZN47rnnjO38lvX39ttviw4dOojc3FzjdenSJWM/v2X9Xb16VYSEhIjo6Gixa9cukZ2dLTZu3ChOnjxpjOG/P/WTn59v8ncyMTFRABDJyclCCNv/e8mk+Q7o0aOH0Ol0xp+rqqpEUFCQmDNnjgXfqnGpnTQbDAYREBAg5s2bZ2y7fv260Gg04scffxRCCHHkyBEBQOzZs8cYs2HDBqFSqcT58+eFEEJ89dVXwsvLS5SVlRljXn75ZdG2bds7/BtZTn5+vgAgUlJShBDV383R0VH8/PPPxpijR48KAGLHjh1CiOr/AmNnZyfy8vKMMQsXLhTu7u7Gb/fSSy+JDh06mIz1yCOPiGHDht3pX8nivLy8xLfffstveRuKiopEmzZtRGJiohgwYIAxaea3vDVvv/226Ny5s9k+fstb8/LLL4u+fftK9vPfn9v33HPPidatWwuDwfCP+HvJ5RkNrLy8HGlpaRgyZIixzc7ODkOGDMGOHTss+GaN26lTp5CXl2fy3Tw8PNCzZ0/jd9uxYwc8PT3RvXt3Y8yQIUNgZ2eHXbt2GWP69+8PtVptjBk2bBgyMzNx7dq1v+m3+XsVFBQAALy9vQEAaWlpqKioMPmWYWFhaN68ucm37NixI/z9/Y0xw4YNQ2FhIQ4fPmyMufkZNTG2/Pe4qqoKK1asgF6vR69evfgtb4NOp8OoUaPq/L78lrfuxIkTCAoKQqtWrTBhwgScOXMGAL/lrfr999/RvXt3PPTQQ/Dz80PXrl3xzTffGPv578/tKS8vx7Jly/Dkk09CpVL9I/5eMmluYJcvX0ZVVZXJXwgA8Pf3R15enoXeqvGr+TZy3y0vLw9+fn4m/Q4ODvD29jaJMfeMm8ewJQaDATNnzkSfPn0QHh4OoPr3VKvV8PT0NImt/S2VvpNUTGFhIUpKSu7Er2MxBw8ehKurKzQaDaZPn45Vq1ahffv2/Ja3aMWKFdi3bx/mzJlTp4/f8tb07NkTS5YsQXx8PBYuXIhTp06hX79+KCoq4re8RdnZ2Vi4cCHatGmDjRs34umnn8azzz6LpUuXAuC/P7dr9erVuH79OqKjowH8M/5/3MGioxPRX6LT6XDo0CGkpqZa+lWsWtu2bZGeno6CggL88ssvmDx5MlJSUiz9Wlbl7NmzeO6555CYmAgnJydLv47VGzFihPHPnTp1Qs+ePRESEoKffvoJzs7OFnwz62MwGNC9e3d89NFHAICuXbvi0KFDWLRoESZPnmzht7Ne//vf/zBixAgEBQVZ+lX+NpxpbmC+vr6wt7evs1v04sWLCAgIsNBbNX4130buuwUEBCA/P9+kv7KyElevXjWJMfeMm8ewFTExMVi3bh2Sk5PRrFkzY3tAQADKy8tx/fp1k/ja31LpO0nFuLu729w/2mq1GqGhoYiIiMCcOXPQuXNn/Pe//+W3vAVpaWnIz89Ht27d4ODgAAcHB6SkpODzzz+Hg4MD/P39+S3/Ak9PT9x11104efIk/17eosDAQLRv396krV27dsblLvz359bl5ORg06ZN+Ne//mVs+yf8vWTS3MDUajUiIiKwefNmY5vBYMDmzZvRq1cvC75Z49ayZUsEBASYfLfCwkLs2rXL+N169eqF69evIy0tzRiTlJQEg8GAnj17GmO2bt2KiooKY0xiYiLatm0LLy+vv+m3ubOEEIiJicGqVauQlJSEli1bmvRHRETA0dHR5FtmZmbizJkzJt/y4MGDJv8IJCYmwt3d3fiPS69evUyeURPzT/h7bDAYUFZWxm95CwYPHoyDBw8iPT3deHXv3h0TJkww/pnf8vbduHEDWVlZCAwM5N/LW9SnT586ZTmPHz+OkJAQAPz353bExsbCz88Po0aNMrb9I/5eWnonoi1asWKF0Gg0YsmSJeLIkSNi2rRpwtPT02S36D9RUVGR2L9/v9i/f78AID799FOxf/9+kZOTI4SoLvnj6ekp1qxZIzIyMsR9991ntuRP165dxa5du0Rqaqpo06aNScmf69evC39/fzFx4kRx6NAhsWLFCqHVam2q5M/TTz8tPDw8xJYtW0xK/xQXFxtjpk+fLpo3by6SkpLE3r17Ra9evUSvXr2M/TVlf4YOHSrS09NFfHy8aNKkidmyP7NnzxZHjx4VX375ZaMp+9OQXnnlFZGSkiJOnTolMjIyxCuvvCJUKpVISEgQQvBb/hU3V88Qgt/yVsyaNUts2bJFnDp1Smzbtk0MGTJE+Pr6ivz8fCEEv+Wt2L17t3BwcBAffvihOHHihFi+fLnQarVi2bJlxhj++1N/VVVVonnz5uLll1+u02frfy+ZNN8hX3zxhWjevLlQq9WiR48eYufOnZZ+JYtLTk4WAOpckydPFkJUl/158803hb+/v9BoNGLw4MEiMzPT5BlXrlwRjz32mHB1dRXu7u7iiSeeEEVFRSYxBw4cEH379hUajUY0bdpUzJ079+/6Ff8W5r4hABEbG2uMKSkpEc8884zw8vISWq1W3H///SI3N9fkOadPnxYjRowQzs7OwtfXV8yaNUtUVFSYxCQnJ4suXboItVotWrVqZTKGrXjyySdFSEiIUKvVokmTJmLw4MHGhFkIfsu/onbSzG9Zf4888ogIDAwUarVaNG3aVDzyyCMmdYX5LW/N2rVrRXh4uNBoNCIsLEx8/fXXJv3896f+Nm7cKADU+T5C2P7fS5UQQlhkipuIiIiIyEpwTTMRERERkQImzURERERECpg0ExEREREpYNJMRERERKSASTMRERERkQImzURERERECpg0ExEREREpYNJMRGTj0tPTMW/ePFRWVlr6VYiIrBaTZiIiG3b16lU8+OCDaNeuHRwcHCz9OkREVotJMxGRFYiOjoZKpapznTx5UvIeIQQmTZqEl19+Gffee+/f+LZERLaHx2gTEVmB6OhoXLx4EbGxsSbtTZo0gb29vfHn8vJyqNXqv/v1iIhsHmeaiYishEajQUBAgMk1ePBgxMTEYObMmfD19cWwYcMAAIcOHcKIESPg6uoKf39/TJw4EZcvXzY+S6/XY9KkSXB1dUVgYCA++eQTREVFYebMmcYYlUqF1atXm7yDp6cnlixZYvz57NmzePjhh+Hp6Qlvb2/cd999OH36tLE/OjoaY8eOxX/+8x8EBgbCx8cHOp0OFRUVxpiysjK8/PLLCA4OhkajQWhoKP73v/8Z+5V+FyKivwOTZiIiK7d06VKo1Wps27YNixYtwvXr1zFo0CB07doVe/fuRXx8PC5evIiHH37YeM/s2bORkpKCNWvWICEhAVu2bMG+fftuadyKigoMGzYMbm5u+PPPP7Ft2za4urpi+PDhKC8vN8YlJycjKysLycnJWLp0KZYsWWKSeE+aNAk//vgjPv/8cxw9ehSLFy+Gq6srANTrdyEi+jtwVwgRkZVYt26dMZkEgBEjRgAA2rRpg48//tjY/sEHH6Br16746KOPjG3fffcdgoODcfz4cQQFBeF///sfli1bhsGDBwOoTrybNWt2S++zcuVKGAwGfPvtt1CpVACA2NhYeHp6YsuWLRg6dCgAwMvLCwsWLIC9vT3CwsIwatQobN68GVOnTsXx48fx008/ITExEUOGDAEAtGrVyjjGggULZH+Xu+6665bemYjodjFpJiKyEgMHDsTChQuNP7u4uOCxxx5DRESESdyBAweQnJxskmDXyMrKQklJCcrLy9GzZ09ju7e3N9q2bXtL73PgwAGcPHkSbm5uJu2lpaXIysoy/tyhQweTddeBgYE4ePAggOpyePb29hgwYIDkGHK/C5NmIvq7MGkmIrISLi4uCA0NNdt+sxs3bmD06NH497//XSc2MDBQtuLGzVQqFWrvFb95LfKNGzcQERGB5cuX17m3SZMmxj87OjrWea7BYAAAODs7y76D0u9CRPR3YdJMRGRjunXrhl9//RUtWrQwW5u5devWcHR0xK5du9C8eXMAwLVr13D8+HGTGd8mTZogNzfX+POJEydQXFxsMs7KlSvh5+cHd3f323rXjh07wmAwICUlxbg841Z+FyKivws3AhIR2RidToerV6/isccew549e5CVlYWNGzfiiSeeQFVVFVxdXTFlyhTMnj0bSUlJOHToEKKjo2FnZ/pPwqBBg7BgwQLs378fe/fuxfTp001mjSdMmABfX1/cd999+PPPP3Hq1Cls2bIFzz77LM6dO1evd23RogUmT56MJ598EqtXrzY+46effqrX70JE9Hdh0kxEZGOCgoKwbds2VFVVYejQoejYsSNmzpwJT09PY2I8b9489OvXD6NHj8aQIUPQt2/fOmujP/nkEwQHB6Nfv34YP348XnzxRWi1WmO/VqvF1q1b0bx5czzwwANo164dpkyZgtLS0luaeV64cCHGjRuHZ555BmFhYZg6dSr0en29fxcior8DDzchIiIAQFRUFLp06YL58+db+lWIiBod/td0IiIiIiIFTJqJiIiIiBRweQYRERERkQLONBMRERERKWDSTERERESkgEkzEREREZECJs1ERERERAqYNBMRERERKWDSTERERESkgEkzEREREZECJs1ERERERAqYNBMRERERKfh/zo8arGWhd0oAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for col in categorical_cols:\n", " plt.figure(figsize=(8, 4))\n", " sns.countplot(data=data, y=col, order=data[col].value_counts().index, palette='viridis')\n", " plt.title(f\"Distribution de {col}\")\n", " plt.xlabel(\"Fréquence\")\n", " plt.ylabel(col)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "id": "3a3a63d1", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 10)) # Increase figure size if necessary\n", "corr_matrix = data[numerical_cols].corr()\n", "\n", "# Ensure that the matrix does not have NaN or infinite values\n", "sns.heatmap(corr_matrix, annot=True, fmt='.2f', cmap='coolwarm', cbar=True, annot_kws={'size': 8})\n", "\n", "plt.title(\"Matrice de corrélation des variables numériques\")\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "272d92d2", "metadata": {}, "outputs": [], "source": [ "label_encoders = {}\n", "for col in categorical_cols:\n", " le = LabelEncoder()\n", " data[col] = le.fit_transform(data[col])\n", " label_encoders[col] = le" ] }, { "cell_type": "markdown", "id": "778bf4a8", "metadata": {}, "source": [ "## On vas utiliser les models de classification" ] }, { "cell_type": "code", "execution_count": 18, "id": "581e6c69", "metadata": {}, "outputs": [], "source": [ "X = data.drop(columns=[ 'diabetesMed','encounter_id','patient_nbr','race','gender','age'])\n", "y = data[['diabetesMed']]\n", "\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", "\n", "scaler = StandardScaler()\n", "X_train = scaler.fit_transform(X_train)\n", "X_test = scaler.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 19, "id": "2367f9e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "--- Logistic Regression ---\n", " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 4608\n", " 1 1.00 1.00 1.00 15746\n", "\n", " accuracy 1.00 20354\n", " macro avg 1.00 1.00 1.00 20354\n", "weighted avg 1.00 1.00 1.00 20354\n", "\n", "Matrice de confusion:\n", " [[ 4608 0]\n", " [ 2 15744]]\n", "\n", "--- Decision Tree ---\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 4608\n", " 1 1.00 1.00 1.00 15746\n", "\n", " accuracy 1.00 20354\n", " macro avg 1.00 1.00 1.00 20354\n", "weighted avg 1.00 1.00 1.00 20354\n", "\n", "Matrice de confusion:\n", " [[ 4608 0]\n", " [ 0 15746]]\n", "\n", "--- Random Forest ---\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 4608\n", " 1 1.00 1.00 1.00 15746\n", "\n", " accuracy 1.00 20354\n", " macro avg 1.00 1.00 1.00 20354\n", "weighted avg 1.00 1.00 1.00 20354\n", "\n", "Matrice de confusion:\n", " [[ 4608 0]\n", " [ 11 15735]]\n", "\n", "--- SVM ---\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 4608\n", " 1 1.00 1.00 1.00 15746\n", "\n", " accuracy 1.00 20354\n", " macro avg 1.00 1.00 1.00 20354\n", "weighted avg 1.00 1.00 1.00 20354\n", "\n", "Matrice de confusion:\n", " [[ 4608 0]\n", " [ 0 15746]]\n", "\n", "--- k-NN ---\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return self._fit(X, y)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.99 1.00 1.00 4608\n", " 1 1.00 1.00 1.00 15746\n", "\n", " accuracy 1.00 20354\n", " macro avg 1.00 1.00 1.00 20354\n", "weighted avg 1.00 1.00 1.00 20354\n", "\n", "Matrice de confusion:\n", " [[ 4608 0]\n", " [ 26 15720]]\n" ] } ], "source": [ "models = {\n", " \"Logistic Regression\": LogisticRegression(),\n", " \"Decision Tree\": DecisionTreeClassifier(),\n", " \"Random Forest\": RandomForestClassifier(),\n", " \"SVM\": SVC(),\n", " \"k-NN\": KNeighborsClassifier()\n", "}\n", "\n", "for model_name, model in models.items():\n", " print(f\"\\n--- {model_name} ---\")\n", " model.fit(X_train, y_train)\n", " y_pred = model.predict(X_test)\n", " print(classification_report(y_test, y_pred))\n", " print(\"Matrice de confusion:\\n\", confusion_matrix(y_test, y_pred))" ] }, { "cell_type": "markdown", "id": "de5caa3b", "metadata": {}, "source": [ "# *Pour output = diabetesMed \n", "### Logistic Regression , Decision Tree , Random Forest et SVM ont predis des resultas parfaits (100%)\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "7c8d1f28", "metadata": {}, "outputs": [], "source": [ "X1 = data.drop(columns=[ 'insulin','encounter_id','patient_nbr','race','gender','age'])\n", "y1 = data[['insulin']]\n", "\n", "\n", "X1_train, X1_test, y1_train, y1_test = train_test_split(X1, y1, test_size=0.2, random_state=42)\n", "\n", "scaler = StandardScaler()\n", "X1_train = scaler.fit_transform(X1_train)\n", "X1_test = scaler.transform(X1_test)" ] }, { "cell_type": "code", "execution_count": 21, "id": "ae7dab4b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "--- Logistic Regression ---\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.89 0.87 0.88 2425\n", " 1 0.97 0.97 0.97 9452\n", " 2 0.90 0.94 0.92 6257\n", " 3 0.80 0.70 0.75 2220\n", "\n", " accuracy 0.92 20354\n", " macro avg 0.89 0.87 0.88 20354\n", "weighted avg 0.92 0.92 0.92 20354\n", "\n", "Matrice de confusion:\n", " [[2116 309 0 0]\n", " [ 269 9183 0 0]\n", " [ 0 0 5865 392]\n", " [ 0 0 661 1559]]\n", "\n", "--- Decision Tree ---\n", " precision recall f1-score support\n", "\n", " 0 0.88 0.90 0.89 2425\n", " 1 0.97 0.97 0.97 9452\n", " 2 0.91 0.89 0.90 6257\n", " 3 0.71 0.74 0.73 2220\n", "\n", " accuracy 0.91 20354\n", " macro avg 0.87 0.88 0.87 20354\n", "weighted avg 0.91 0.91 0.91 20354\n", "\n", "Matrice de confusion:\n", " [[2178 247 0 0]\n", " [ 293 9159 0 0]\n", " [ 0 0 5599 658]\n", " [ 0 0 577 1643]]\n", "\n", "--- Random Forest ---\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.93 0.88 0.91 2425\n", " 1 0.97 0.98 0.98 9452\n", " 2 0.89 0.98 0.93 6257\n", " 3 0.91 0.67 0.77 2220\n", "\n", " accuracy 0.94 20354\n", " macro avg 0.93 0.88 0.90 20354\n", "weighted avg 0.94 0.94 0.93 20354\n", "\n", "Matrice de confusion:\n", " [[2140 285 0 0]\n", " [ 155 9297 0 0]\n", " [ 0 0 6108 149]\n", " [ 0 0 731 1489]]\n", "\n", "--- SVM ---\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.98 0.87 0.92 2425\n", " 1 0.97 0.99 0.98 9452\n", " 2 0.88 0.99 0.93 6257\n", " 3 0.97 0.63 0.77 2220\n", "\n", " accuracy 0.94 20354\n", " macro avg 0.95 0.87 0.90 20354\n", "weighted avg 0.94 0.94 0.94 20354\n", "\n", "Matrice de confusion:\n", " [[2098 325 1 1]\n", " [ 47 9403 2 0]\n", " [ 0 6 6211 40]\n", " [ 0 1 818 1401]]\n", "\n", "--- k-NN ---\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/neighbors/_classification.py:238: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return self._fit(X, y)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.92 0.85 0.88 2425\n", " 1 0.95 0.98 0.97 9452\n", " 2 0.89 0.94 0.91 6257\n", " 3 0.85 0.65 0.74 2220\n", "\n", " accuracy 0.92 20354\n", " macro avg 0.90 0.86 0.88 20354\n", "weighted avg 0.92 0.92 0.92 20354\n", "\n", "Matrice de confusion:\n", " [[2062 333 17 13]\n", " [ 145 9285 18 4]\n", " [ 17 106 5897 237]\n", " [ 19 31 722 1448]]\n" ] } ], "source": [ "models = {\n", " \"Logistic Regression\": LogisticRegression(),\n", " \"Decision Tree\": DecisionTreeClassifier(),\n", " \"Random Forest\": RandomForestClassifier(),\n", " \"SVM\": SVC(),\n", " \"k-NN\": KNeighborsClassifier()\n", "}\n", "\n", "for model_name, model in models.items():\n", " print(f\"\\n--- {model_name} ---\")\n", " model.fit(X1_train, y1_train)\n", " y1_pred = model.predict(X1_test)\n", " print(classification_report(y1_test, y1_pred))\n", " print(\"Matrice de confusion:\\n\", confusion_matrix(y1_test, y1_pred))" ] }, { "cell_type": "markdown", "id": "ca5ae9b9", "metadata": {}, "source": [ "# *Pour output = insulin \n", "### Random Forest et SVM nous a donné la meilleur prediction (93% ,94%)\n", "### SVM has the highest precision for class 0 (0.97) and class 3 (0.96)" ] }, { "cell_type": "code", "execution_count": 24, "id": "d1750517", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 30 candidates, totalling 90 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n", "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/m2/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " return fit_method(estimator, *args, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1.0 {'max_depth': 26, 'max_features': 0.945803797969001, 'min_samples_split': 6}\n", "[CV] END max_depth=12, max_features=0.8843121665964662, min_samples_split=6; total time= 31.0s\n", "[CV] END max_depth=9, max_features=0.9702375783628365, min_samples_split=4; total time= 32.0s\n", "[CV] END max_depth=26, max_features=0.25992158412236466, min_samples_split=7; total time= 16.3s\n", "[CV] END max_depth=7, max_features=0.9284945698335632, min_samples_split=11; total time= 25.7s\n", "[CV] END max_depth=18, max_features=0.46724495425173046, min_samples_split=9; total time= 22.0s\n", "[CV] END max_depth=25, max_features=0.8024855493984007, min_samples_split=17; total time= 34.4s\n", "[CV] END max_depth=9, max_features=0.5807612645644977, min_samples_split=5; total time= 18.9s\n", "[CV] END max_depth=27, max_features=0.8216934772889928, min_samples_split=11; total time= 32.5s\n", "[CV] END max_depth=22, max_features=0.23849867744983533, min_samples_split=15; total time= 14.3s\n", "[CV] END max_depth=16, max_features=0.2733638865908103, min_samples_split=5; total time= 14.5s\n", "[CV] END max_depth=13, max_features=0.45573779677406395, min_samples_split=15; total time= 18.1s\n", "[CV] END max_depth=17, max_features=0.6126689162030554, min_samples_split=9; total time= 28.3s\n", "[CV] END max_depth=11, max_features=0.9360623877652168, min_samples_split=10; total time= 30.9s\n", "[CV] END max_depth=9, max_features=0.9702375783628365, min_samples_split=4; total time= 31.8s\n", "[CV] END max_depth=26, max_features=0.25992158412236466, min_samples_split=7; total time= 15.9s\n", "[CV] END max_depth=7, max_features=0.9284945698335632, min_samples_split=11; total time= 26.2s\n", "[CV] END max_depth=21, max_features=0.9446770899684218, min_samples_split=17; total time= 37.0s\n", "[CV] END max_depth=19, max_features=0.5210604887039829, min_samples_split=5; total time= 26.0s\n", "[CV] END max_depth=23, max_features=0.827234642068593, min_samples_split=13; total time= 33.1s\n", "[CV] END max_depth=27, max_features=0.8216934772889928, min_samples_split=11; total time= 30.9s\n", "[CV] END max_depth=16, max_features=0.2733638865908103, min_samples_split=5; total time= 14.5s\n", "[CV] END max_depth=10, max_features=0.4307165689561101, min_samples_split=11; total time= 15.3s\n", "[CV] END max_depth=17, max_features=0.6126689162030554, min_samples_split=9; total time= 27.8s\n", "[CV] END max_depth=11, max_features=0.9360623877652168, min_samples_split=10; total time= 30.8s\n", "[CV] END max_depth=9, max_features=0.9702375783628365, min_samples_split=4; total time= 31.7s\n", "[CV] END max_depth=20, max_features=0.9491748391682899, min_samples_split=5; total time= 36.8s\n", "[CV] END max_depth=21, max_features=0.9446770899684218, min_samples_split=17; total time= 37.6s\n", "[CV] END max_depth=19, max_features=0.5210604887039829, min_samples_split=5; total time= 25.3s\n", "[CV] END max_depth=9, max_features=0.5807612645644977, min_samples_split=5; total time= 18.1s\n", "[CV] END max_depth=27, max_features=0.8216934772889928, min_samples_split=11; total time= 32.8s\n", "[CV] END max_depth=29, max_features=0.9104213200006501, min_samples_split=18; total time= 35.7s\n", "[CV] END max_depth=10, max_features=0.4307165689561101, min_samples_split=11; total time= 15.3s\n", "[CV] END max_depth=12, max_features=0.895419423886658, min_samples_split=17; total time= 30.6s\n", "[CV] END max_depth=24, max_features=0.5642235459803745, min_samples_split=3; total time= 24.9s\n", "[CV] END max_depth=26, max_features=0.945803797969001, min_samples_split=6; total time= 40.6s\n", "[CV] END max_depth=26, max_features=0.25992158412236466, min_samples_split=7; total time= 17.3s\n", "[CV] END max_depth=7, max_features=0.9284945698335632, min_samples_split=11; total time= 24.4s\n", "[CV] END max_depth=18, max_features=0.46724495425173046, min_samples_split=9; total time= 21.8s\n", "[CV] END max_depth=24, max_features=0.8525909157203013, min_samples_split=15; total time= 37.1s\n", "[CV] END max_depth=7, max_features=0.28184163013664565, min_samples_split=14; total time= 11.0s\n", "[CV] END max_depth=23, max_features=0.6306162529173174, min_samples_split=11; total time= 26.9s\n", "[CV] END max_depth=22, max_features=0.23849867744983533, min_samples_split=15; total time= 14.3s\n", "[CV] END max_depth=29, max_features=0.9104213200006501, min_samples_split=18; total time= 35.7s\n", "[CV] END max_depth=19, max_features=0.4508696064124624, min_samples_split=18; total time= 21.5s\n", "[CV] END max_depth=6, max_features=0.8040433545611068, min_samples_split=7; total time= 20.4s\n", "[CV] END max_depth=24, max_features=0.5642235459803745, min_samples_split=3; total time= 24.5s\n", "[CV] END max_depth=12, max_features=0.8843121665964662, min_samples_split=6; total time= 33.9s\n", "[CV] END max_depth=20, max_features=0.9491748391682899, min_samples_split=5; total time= 38.3s\n", "[CV] END max_depth=21, max_features=0.9446770899684218, min_samples_split=17; total time= 36.9s\n", "[CV] END max_depth=24, max_features=0.8525909157203013, min_samples_split=15; total time= 36.2s\n", "[CV] END max_depth=23, max_features=0.827234642068593, min_samples_split=13; total time= 33.0s\n", "[CV] END max_depth=25, max_features=0.9141098573483128, min_samples_split=17; total time= 33.7s\n", "[CV] END max_depth=16, max_features=0.2733638865908103, min_samples_split=5; total time= 15.0s\n", "[CV] END max_depth=10, max_features=0.4307165689561101, min_samples_split=11; total time= 15.0s\n", "[CV] END max_depth=12, max_features=0.895419423886658, min_samples_split=17; total time= 29.6s\n", "[CV] END max_depth=24, max_features=0.5642235459803745, min_samples_split=3; total time= 25.0s\n", "[CV] END max_depth=26, max_features=0.945803797969001, min_samples_split=6; total time= 41.0s\n", "[CV] END max_depth=6, max_features=0.5718236631724173, min_samples_split=9; total time= 17.7s\n", "[CV] END max_depth=26, max_features=0.5181007101408488, min_samples_split=19; total time= 22.8s\n", "[CV] END max_depth=18, max_features=0.46724495425173046, min_samples_split=9; total time= 21.9s\n", "[CV] END max_depth=24, max_features=0.8525909157203013, min_samples_split=15; total time= 36.3s\n", "[CV] END max_depth=7, max_features=0.28184163013664565, min_samples_split=14; total time= 10.6s\n", "[CV] END max_depth=23, max_features=0.6306162529173174, min_samples_split=11; total time= 27.4s\n", "[CV] END max_depth=25, max_features=0.9141098573483128, min_samples_split=17; total time= 34.4s\n", "[CV] END max_depth=13, max_features=0.45573779677406395, min_samples_split=15; total time= 18.9s\n", "[CV] END max_depth=19, max_features=0.4508696064124624, min_samples_split=18; total time= 21.1s\n", "[CV] END max_depth=6, max_features=0.8040433545611068, min_samples_split=7; total time= 19.3s\n", "[CV] END max_depth=11, max_features=0.9360623877652168, min_samples_split=10; total time= 30.7s\n", "[CV] END max_depth=26, max_features=0.945803797969001, min_samples_split=6; total time= 38.6s\n", "[CV] END max_depth=6, max_features=0.5718236631724173, min_samples_split=9; total time= 18.5s\n", "[CV] END max_depth=26, max_features=0.5181007101408488, min_samples_split=19; total time= 22.6s\n", "[CV] END max_depth=25, max_features=0.8024855493984007, min_samples_split=17; total time= 33.4s\n", "[CV] END max_depth=19, max_features=0.5210604887039829, min_samples_split=5; total time= 25.0s\n", "[CV] END max_depth=23, max_features=0.827234642068593, min_samples_split=13; total time= 34.0s\n", "[CV] END max_depth=25, max_features=0.9141098573483128, min_samples_split=17; total time= 34.5s\n", "[CV] END max_depth=13, max_features=0.45573779677406395, min_samples_split=15; total time= 19.2s\n", "[CV] END max_depth=17, max_features=0.6126689162030554, min_samples_split=9; total time= 27.4s\n", "[CV] END max_depth=6, max_features=0.8040433545611068, min_samples_split=7; total time= 13.6s\n", "[CV] END max_depth=12, max_features=0.8843121665964662, min_samples_split=6; total time= 30.9s\n", "[CV] END max_depth=20, max_features=0.9491748391682899, min_samples_split=5; total time= 39.5s\n", "[CV] END max_depth=6, max_features=0.5718236631724173, min_samples_split=9; total time= 18.5s\n", "[CV] END max_depth=26, max_features=0.5181007101408488, min_samples_split=19; total time= 21.8s\n", "[CV] END max_depth=25, max_features=0.8024855493984007, min_samples_split=17; total time= 33.5s\n", "[CV] END max_depth=9, max_features=0.5807612645644977, min_samples_split=5; total time= 21.8s\n", "[CV] END max_depth=7, max_features=0.28184163013664565, min_samples_split=14; total time= 10.6s\n", "[CV] END max_depth=23, max_features=0.6306162529173174, min_samples_split=11; total time= 27.7s\n", "[CV] END max_depth=22, max_features=0.23849867744983533, min_samples_split=15; total time= 14.7s\n", "[CV] END max_depth=29, max_features=0.9104213200006501, min_samples_split=18; total time= 34.8s\n", "[CV] END max_depth=19, max_features=0.4508696064124624, min_samples_split=18; total time= 21.2s\n", "[CV] END max_depth=12, max_features=0.895419423886658, min_samples_split=17; total time= 22.9s\n" ] } ], "source": [ "from sklearn.model_selection import RandomizedSearchCV, StratifiedKFold\n", "from scipy.stats import randint, uniform\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "rf = RandomForestClassifier(\n", " n_estimators=300, # reasonable default\n", " class_weight=\"balanced\",\n", " n_jobs=-1, # all CPU cores\n", " random_state=42)\n", "\n", "param_dist = { # draw *random* values from these ranges\n", " \"max_depth\": randint(6, 30),\n", " \"min_samples_split\": randint(2, 20),\n", " \"max_features\": uniform(0.2, 0.8)\n", "}\n", "\n", "cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n", "\n", "search = RandomizedSearchCV(\n", " rf,\n", " param_distributions=param_dist,\n", " n_iter=30, # 30 draws × 3 folds = 90 fits\n", " scoring=\"f1_weighted\",\n", " cv=cv,\n", " n_jobs=-1,\n", " verbose=2\n", ")\n", "search.fit(X_train, y_train) \n", "print(search.best_score_, search.best_params_)\n" ] }, { "cell_type": "markdown", "id": "fae6020a", "metadata": {}, "source": [ "## My search ran — but a CV score of 1.00 is a red flag\n", "### A perfect cross-validated F1 on 100 k real-world rows is practically impossible unless there’s information leakage.\n", "### " ] }, { "cell_type": "markdown", "id": "0ca20637", "metadata": {}, "source": [ "# Bloc 1 : Nettoyage des colonnes" ] }, { "cell_type": "code", "execution_count": 26, "id": "feb968dd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X shape: (101766, 26)\n", "y unique values: [0 1]\n" ] } ], "source": [ "# Définir les colonnes fuites\n", "leak_cols_diag = [\n", " \"insulin\", \"change\",\n", " \"metformin\", \"repaglinide\", \"nateglinide\", \"chlorpropamide\",\n", " \"glimepiride\", \"glipizide\", \"glyburide\", \"pioglitazone\",\n", " \"rosiglitazone\", \"acarbose\", \"miglitol\", \"tolazamide\",\n", " \"glyburide-metformin\", \"glipizide-metformin\",\n", " \"glimepiride-pioglitazone\", \"metformin-rosiglitazone\",\n", " \"metformin-pioglitazone\"\n", "]\n", "\n", "# Cible\n", "target = \"diabetesMed\"\n", "\n", "# X et y\n", "X_diag = data.drop(columns=leak_cols_diag + [target])\n", "y_diag = data[target]\n", "\n", "print(\"X shape:\", X_diag.shape)\n", "print(\"y unique values:\", y_diag.unique())\n" ] }, { "cell_type": "code", "execution_count": 28, "id": "4fcc7987", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train shape: (81613, 26)\n", "Test shape: (20153, 26)\n", "Proportion positives (y_train): diabetesMed\n", "1 0.770662\n", "0 0.229338\n", "Name: proportion, dtype: float64\n", "Proportion positives (y_test) : diabetesMed\n", "1 0.767479\n", "0 0.232521\n", "Name: proportion, dtype: float64\n" ] } ], "source": [ "from sklearn.model_selection import GroupShuffleSplit\n", "\n", "gss = GroupShuffleSplit(test_size=0.2, random_state=42)\n", "train_idx, test_idx = next(gss.split(X_diag, y_diag, groups=data[\"patient_nbr\"]))\n", "\n", "X_train_diag, X_test_diag = X_diag.iloc[train_idx], X_diag.iloc[test_idx]\n", "y_train_diag, y_test_diag = y_diag.iloc[train_idx], y_diag.iloc[test_idx]\n", "\n", "print(\"Train shape:\", X_train_diag.shape)\n", "print(\"Test shape:\", X_test_diag.shape)\n", "print(\"Proportion positives (y_train):\", y_train_diag.value_counts(normalize=True))\n", "print(\"Proportion positives (y_test) :\", y_test_diag.value_counts(normalize=True))\n" ] }, { "cell_type": "markdown", "id": "9096efb8", "metadata": {}, "source": [ "## Étape suivante : Pipeline + RandomizedSearchCV" ] }, { "cell_type": "code", "execution_count": 29, "id": "b9dadab2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 20 candidates, totalling 60 fits\n", "Best score CV (f1_weighted): 0.7912062305291574\n", "Best params: {'clf__max_depth': 24, 'clf__max_features': 0.27997993265440235, 'clf__min_samples_split': 12}\n", "[CV] END clf__max_depth=12, clf__max_features=0.8372343894881864, clf__min_samples_split=16; total time= 24.0s\n", "[CV] END clf__max_depth=29, clf__max_features=0.4669668889112175, clf__min_samples_split=9; total time= 20.7s\n", "[CV] END clf__max_depth=7, clf__max_features=0.3454599737656805, clf__min_samples_split=2; total time= 8.2s\n", "[CV] END clf__max_depth=17, clf__max_features=0.6893225283906248, clf__min_samples_split=13; total time= 27.3s\n", "[CV] END clf__max_depth=21, clf__max_features=0.3862170723442434, clf__min_samples_split=16; total time= 17.8s\n", "[CV] END clf__max_depth=12, clf__max_features=0.21061196892789324, clf__min_samples_split=15; total time= 8.5s\n", "[CV] END clf__max_depth=23, clf__max_features=0.5083332020319329, clf__min_samples_split=3; total time= 23.2s\n", "[CV] END clf__max_depth=23, clf__max_features=0.7300178274831857, clf__min_samples_split=3; total time= 29.1s\n", "[CV] END clf__max_depth=16, clf__max_features=0.8237528002182155, clf__min_samples_split=8; total time= 29.3s\n", "[CV] END clf__max_depth=29, clf__max_features=0.8659541126403374, clf__min_samples_split=7; total time= 36.0s\n", "[CV] END clf__max_depth=21, clf__max_features=0.3862170723442434, clf__min_samples_split=16; total time= 19.9s\n", "[CV] END clf__max_depth=24, clf__max_features=0.5059695930137302, clf__min_samples_split=4; total time= 23.7s\n", "[CV] END clf__max_depth=25, clf__max_features=0.3928203728208094, clf__min_samples_split=8; total time= 19.9s\n", "[CV] END clf__max_depth=8, clf__max_features=0.9274563216630256, clf__min_samples_split=5; total time= 18.6s\n", "[CV] END clf__max_depth=27, clf__max_features=0.22505063396444688, clf__min_samples_split=19; total time= 11.0s\n", "[CV] END clf__max_depth=24, clf__max_features=0.27997993265440235, clf__min_samples_split=12; total time= 14.2s\n", "[CV] END clf__max_depth=24, clf__max_features=0.27997993265440235, clf__min_samples_split=12; total time= 14.4s\n", "[CV] END clf__max_depth=29, clf__max_features=0.7207107783590823, clf__min_samples_split=3; total time= 30.3s\n", "[CV] END clf__max_depth=17, clf__max_features=0.6893225283906248, clf__min_samples_split=13; total time= 26.8s\n", "[CV] END clf__max_depth=24, clf__max_features=0.5059695930137302, clf__min_samples_split=4; total time= 23.7s\n", "[CV] END clf__max_depth=17, clf__max_features=0.5961415280890161, clf__min_samples_split=16; total time= 22.6s\n", "[CV] END clf__max_depth=23, clf__max_features=0.7300178274831857, clf__min_samples_split=3; total time= 27.2s\n", "[CV] END clf__max_depth=12, clf__max_features=0.8372343894881864, clf__min_samples_split=16; total time= 24.2s\n", "[CV] END clf__max_depth=29, clf__max_features=0.4669668889112175, clf__min_samples_split=9; total time= 20.8s\n", "[CV] END clf__max_depth=7, clf__max_features=0.3454599737656805, clf__min_samples_split=2; total time= 8.0s\n", "[CV] END clf__max_depth=7, clf__max_features=0.3454599737656805, clf__min_samples_split=2; total time= 7.8s\n", "[CV] END clf__max_depth=22, clf__max_features=0.6198197282067113, clf__min_samples_split=11; total time= 27.9s\n", "[CV] END clf__max_depth=10, clf__max_features=0.6860358815211507, clf__min_samples_split=10; total time= 17.9s\n", "[CV] END clf__max_depth=25, clf__max_features=0.3928203728208094, clf__min_samples_split=8; total time= 19.3s\n", "[CV] END clf__max_depth=17, clf__max_features=0.5961415280890161, clf__min_samples_split=16; total time= 21.3s\n", "[CV] END clf__max_depth=27, clf__max_features=0.22505063396444688, clf__min_samples_split=19; total time= 10.9s\n", "[CV] END clf__max_depth=16, clf__max_features=0.8237528002182155, clf__min_samples_split=8; total time= 29.1s\n", "[CV] END clf__max_depth=29, clf__max_features=0.7207107783590823, clf__min_samples_split=3; total time= 30.8s\n", "[CV] END clf__max_depth=22, clf__max_features=0.6198197282067113, clf__min_samples_split=11; total time= 28.5s\n", "[CV] END clf__max_depth=10, clf__max_features=0.6860358815211507, clf__min_samples_split=10; total time= 17.6s\n", "[CV] END clf__max_depth=23, clf__max_features=0.5083332020319329, clf__min_samples_split=3; total time= 23.6s\n", "[CV] END clf__max_depth=23, clf__max_features=0.7300178274831857, clf__min_samples_split=3; total time= 29.3s\n", "[CV] END clf__max_depth=24, clf__max_features=0.27997993265440235, clf__min_samples_split=12; total time= 14.8s\n", "[CV] END clf__max_depth=29, clf__max_features=0.4669668889112175, clf__min_samples_split=9; total time= 21.0s\n", "[CV] END clf__max_depth=29, clf__max_features=0.8659541126403374, clf__min_samples_split=7; total time= 38.4s\n", "[CV] END clf__max_depth=21, clf__max_features=0.3862170723442434, clf__min_samples_split=16; total time= 18.1s\n", "[CV] END clf__max_depth=10, clf__max_features=0.6860358815211507, clf__min_samples_split=10; total time= 16.9s\n", "[CV] END clf__max_depth=25, clf__max_features=0.3928203728208094, clf__min_samples_split=8; total time= 19.6s\n", "[CV] END clf__max_depth=8, clf__max_features=0.9274563216630256, clf__min_samples_split=5; total time= 18.7s\n", "[CV] END clf__max_depth=27, clf__max_features=0.22505063396444688, clf__min_samples_split=19; total time= 11.0s\n", "[CV] END clf__max_depth=12, clf__max_features=0.8372343894881864, clf__min_samples_split=16; total time= 24.0s\n", "[CV] END clf__max_depth=29, clf__max_features=0.7207107783590823, clf__min_samples_split=3; total time= 30.1s\n", "[CV] END clf__max_depth=17, clf__max_features=0.6893225283906248, clf__min_samples_split=13; total time= 26.8s\n", "[CV] END clf__max_depth=24, clf__max_features=0.5059695930137302, clf__min_samples_split=4; total time= 23.4s\n", "[CV] END clf__max_depth=23, clf__max_features=0.5083332020319329, clf__min_samples_split=3; total time= 23.8s\n", "[CV] END clf__max_depth=8, clf__max_features=0.9274563216630256, clf__min_samples_split=5; total time= 18.6s\n", "[CV] END clf__max_depth=11, clf__max_features=0.3663533302945511, clf__min_samples_split=5; total time= 11.3s\n", "[CV] END clf__max_depth=16, clf__max_features=0.8237528002182155, clf__min_samples_split=8; total time= 29.1s\n", "[CV] END clf__max_depth=29, clf__max_features=0.8659541126403374, clf__min_samples_split=7; total time= 35.5s\n", "[CV] END clf__max_depth=22, clf__max_features=0.6198197282067113, clf__min_samples_split=11; total time= 28.7s\n", "[CV] END clf__max_depth=12, clf__max_features=0.21061196892789324, clf__min_samples_split=15; total time= 8.3s\n", "[CV] END clf__max_depth=12, clf__max_features=0.21061196892789324, clf__min_samples_split=15; total time= 8.2s\n", "[CV] END clf__max_depth=17, clf__max_features=0.5961415280890161, clf__min_samples_split=16; total time= 22.5s\n", "[CV] END clf__max_depth=11, clf__max_features=0.3663533302945511, clf__min_samples_split=5; total time= 12.4s\n", "[CV] END clf__max_depth=11, clf__max_features=0.3663533302945511, clf__min_samples_split=5; total time= 11.7s\n" ] } ], "source": [ "from sklearn.compose import ColumnTransformer\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import RandomizedSearchCV, StratifiedKFold\n", "from scipy.stats import randint, uniform\n", "\n", "# Colonnes numériques et catégoriques\n", "num_cols = X_train_diag.select_dtypes(\"number\").columns.tolist()\n", "cat_cols = X_train_diag.select_dtypes(\"object\").columns.tolist()\n", "\n", "# Pipeline\n", "preproc = ColumnTransformer([\n", " (\"num\", SimpleImputer(strategy=\"median\"), num_cols),\n", " (\"cat\", Pipeline([\n", " (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n", " (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\", min_frequency=50))\n", " ]), cat_cols)\n", "])\n", "\n", "pipe = Pipeline([\n", " (\"prep\", preproc),\n", " (\"clf\", RandomForestClassifier(\n", " class_weight=\"balanced\",\n", " n_jobs=-1,\n", " random_state=42,\n", " n_estimators=150\n", " ))\n", "])\n", "\n", "# RandomizedSearchCV\n", "param_dist = {\n", " \"clf__max_depth\": randint(6, 30),\n", " \"clf__min_samples_split\": randint(2, 20),\n", " \"clf__max_features\": uniform(0.2, 0.8)\n", "}\n", "\n", "cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n", "\n", "search = RandomizedSearchCV(\n", " pipe,\n", " param_distributions=param_dist,\n", " n_iter=20,\n", " scoring=\"f1_weighted\",\n", " cv=cv,\n", " n_jobs=-1,\n", " verbose=2,\n", " random_state=42\n", ")\n", "\n", "# Entraînement sur le training set\n", "search.fit(X_train_diag, y_train_diag)\n", "print(\"Best score CV (f1_weighted):\", search.best_score_)\n", "print(\"Best params:\", search.best_params_)\n" ] }, { "cell_type": "markdown", "id": "d58bddb6", "metadata": {}, "source": [ "## Dans tout ce qu’on a fait jusqu’à présent, on a utilisé un RandomForestClassifier comme modèle.\n", "## Pourquoi on n’a pas mis de SVM ici ?\n", "## Parce que :\n", "#### SVMs (surtout avec noyau RBF) sont très lents sur des gros datasets (100k lignes).\n", "#### Random Forest est plus rapide, robuste aux données mixtes (catégoriques/numériques) et souvent plus performant dès le départ.\n", "#### On peut toujours tester SVM plus tard sur un échantillon réduit (20 % des données par exemple), mais là, le focus est de valider le pipeline sans fuite." ] }, { "cell_type": "code", "execution_count": 38, "id": "b4163d27", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Rapport de classification :\n", " precision recall f1-score support\n", "\n", " 0 0.53 0.63 0.57 4686\n", " 1 0.88 0.83 0.85 15467\n", "\n", " accuracy 0.78 20153\n", " macro avg 0.70 0.73 0.71 20153\n", "weighted avg 0.80 0.78 0.79 20153\n", "\n", "\n", " ROC-AUC sur le test set : 0.8543762619800928\n" ] } ], "source": [ "from sklearn.metrics import classification_report, roc_auc_score\n", "\n", "# Meilleur modèle sur tout le training set\n", "best_model_diag = search.best_estimator_\n", "best_model_diag.fit(X_train_diag, y_train_diag)\n", "\n", "# Prédictions sur le test set\n", "y_pred_diag = best_model_diag.predict(X_test_diag)\n", "y_proba_diag = best_model_diag.predict_proba(X_test_diag)[:,1]\n", "\n", "# Résultats\n", "print(\" Rapport de classification :\")\n", "print(classification_report(y_test_diag, y_pred_diag))\n", "\n", "print(\"\\n ROC-AUC sur le test set :\", roc_auc_score(y_test_diag, y_proba_diag))\n" ] }, { "cell_type": "markdown", "id": "2de1dfee", "metadata": {}, "source": [ "## Remplacer RandomForest par LightGBM dans le pipeline" ] }, { "cell_type": "code", "execution_count": 39, "id": "3141904b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.6.0\n" ] } ], "source": [ "import lightgbm\n", "print(lightgbm.__version__)\n" ] }, { "cell_type": "code", "execution_count": 40, "id": "84cf4db3", "metadata": {}, "outputs": [], "source": [ "from lightgbm import LGBMClassifier\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.impute import SimpleImputer\n", "\n", "# Pipeline LightGBM\n", "preproc = ColumnTransformer([\n", " (\"num\", SimpleImputer(strategy=\"median\"), num_cols),\n", " (\"cat\", Pipeline([\n", " (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n", " (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\", min_frequency=50))\n", " ]), cat_cols)\n", "])\n", "\n", "pipe_lgbm = Pipeline([\n", " (\"prep\", preproc),\n", " (\"clf\", LGBMClassifier(\n", " objective=\"binary\",\n", " boosting_type=\"gbdt\",\n", " class_weight=\"balanced\",\n", " n_estimators=300,\n", " learning_rate=0.05,\n", " random_state=42\n", " ))\n", "])\n" ] }, { "cell_type": "code", "execution_count": 41, "id": "446224bb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[LightGBM] [Info] Number of positive: 62896, number of negative: 18717\n", "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002031 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 1555\n", "[LightGBM] [Info] Number of data points in the train set: 81613, number of used features: 21\n", "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", "[LightGBM] [Info] Start training from score -0.000000\n", " Rapport de classification (LightGBM) :\n", " precision recall f1-score support\n", "\n", " 0 0.45 0.91 0.60 4686\n", " 1 0.96 0.66 0.78 15467\n", "\n", " accuracy 0.72 20153\n", " macro avg 0.71 0.79 0.69 20153\n", "weighted avg 0.84 0.72 0.74 20153\n", "\n", " ROC-AUC test LightGBM : 0.8635553601501094\n" ] } ], "source": [ "pipe_lgbm.fit(X_train_diag, y_train_diag)\n", "\n", "y_pred_lgbm = pipe_lgbm.predict(X_test_diag)\n", "y_proba_lgbm = pipe_lgbm.predict_proba(X_test_diag)[:,1]\n", "\n", "from sklearn.metrics import classification_report, roc_auc_score\n", "\n", "print(\" Rapport de classification (LightGBM) :\")\n", "print(classification_report(y_test_diag, y_pred_lgbm))\n", "print(\" ROC-AUC test LightGBM :\", roc_auc_score(y_test_diag, y_proba_lgbm))\n" ] }, { "cell_type": "code", "execution_count": 42, "id": "e2d6f774", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[LightGBM] [Info] Number of positive: 62896, number of negative: 18717\n", "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002793 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 1557\n", "[LightGBM] [Info] Number of data points in the train set: 81613, number of used features: 22\n", "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", "[LightGBM] [Info] Start training from score -0.000000\n", " Rapport de classification (LightGBM optimisé) :\n", " precision recall f1-score support\n", "\n", " 0 0.65 0.39 0.48 4686\n", " 1 0.83 0.94 0.88 15467\n", "\n", " accuracy 0.81 20153\n", " macro avg 0.74 0.66 0.68 20153\n", "weighted avg 0.79 0.81 0.79 20153\n", "\n", " ROC-AUC test LightGBM optimisé : 0.8672894677172754\n" ] } ], "source": [ "from lightgbm import LGBMClassifier\n", "\n", "# Calcul automatique du poids des classes\n", "neg, pos = y_train_diag.value_counts()\n", "scale_pos_weight = neg / pos\n", "\n", "# Nouveau modèle LGBM avec ajustement\n", "pipe_lgbm_tuned = Pipeline([\n", " (\"prep\", preproc),\n", " (\"clf\", LGBMClassifier(\n", " objective=\"binary\",\n", " boosting_type=\"gbdt\",\n", " class_weight=\"balanced\",\n", " scale_pos_weight=scale_pos_weight,\n", " n_estimators=500, # Plus d'arbres\n", " learning_rate=0.03, # Apprentissage plus fin\n", " max_depth=24, # D'après le meilleur RandomForest\n", " min_child_samples=10, # Minimum d'échantillons dans une feuille\n", " subsample=0.8, # Stochastique pour éviter l'overfit\n", " colsample_bytree=0.7, # Sous-échantillon des colonnes\n", " random_state=42\n", " ))\n", "])\n", "\n", "# Entraînement\n", "pipe_lgbm_tuned.fit(X_train_diag, y_train_diag)\n", "\n", "# Évaluation\n", "y_pred_lgbm_tuned = pipe_lgbm_tuned.predict(X_test_diag)\n", "y_proba_lgbm_tuned = pipe_lgbm_tuned.predict_proba(X_test_diag)[:,1]\n", "\n", "from sklearn.metrics import classification_report, roc_auc_score\n", "\n", "print(\" Rapport de classification (LightGBM optimisé) :\")\n", "print(classification_report(y_test_diag, y_pred_lgbm_tuned))\n", "print(\" ROC-AUC test LightGBM optimisé :\", roc_auc_score(y_test_diag, y_proba_lgbm_tuned))\n" ] }, { "cell_type": "markdown", "id": "4e007cf3", "metadata": {}, "source": [ "### Je veux attraper tous les diabétiques (classe 1) → Recall super élevé (0.94).\n", "## Pour ça, mon modèle va jouer la sécurité :\n", "#### ➡ À chaque doute, il préfère dire “diabétique”.\n", "#### ➡ Résultat : il détecte bien les vrais diabétiques…\n", "#### Mais il commence aussi à dire “diabétique” sur des patients qui ne le sont pas vraiment (faux positifs).\n" ] }, { "cell_type": "markdown", "id": "dbc2eef3", "metadata": {}, "source": [ "# En médecine, qu’est-ce qui compte le plus ?\n", "\n", "### On veut surtout :\n", "\n", "## NE PAS RATER LES VRAIS MALADES.\n", "\n", "#### Donc :\n", "#### ✔ Maximiser le Recall (Sensibilité) sur la classe 1 (malades/diabétiques)\n", "#### ✔ Être sûr d’attraper les patients qui ont vraiment besoin de soins\n", "#### ✔ Accepter de faire quelques fausses alertes (faux positifs) → car ça peut être vérifié par un médecin après" ] }, { "cell_type": "markdown", "id": "fdaf5017", "metadata": {}, "source": [ "## Par contre, ce qu’on ne veut surtout pas :\n", "\n", "#### Rater un malade (faux négatif) → un patient qui aurait dû être soigné, mais qu’on laisse sans traitement\n", "#### C’est un risque médical grave." ] }, { "cell_type": "code", "execution_count": 45, "id": "d004ca3c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[LightGBM] [Info] Number of positive: 62896, number of negative: 18717\n", "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002099 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 1557\n", "[LightGBM] [Info] Number of data points in the train set: 81613, number of used features: 22\n", "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", "[LightGBM] [Info] Start training from score -0.000000\n", " Rapport final LightGBM :\n", " precision recall f1-score support\n", "\n", " 0 0.65 0.39 0.48 4686\n", " 1 0.83 0.94 0.88 15467\n", "\n", " accuracy 0.81 20153\n", " macro avg 0.74 0.66 0.68 20153\n", "weighted avg 0.79 0.81 0.79 20153\n", "\n", " ROC-AUC test final : 0.8672894677172754\n" ] } ], "source": [ "from lightgbm import LGBMClassifier\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.impute import SimpleImputer\n", "import joblib\n", "\n", "# Colonnes\n", "num_cols = X_train_diag.select_dtypes(\"number\").columns.tolist()\n", "cat_cols = X_train_diag.select_dtypes(\"object\").columns.tolist()\n", "\n", "# Prétraitement\n", "preproc = ColumnTransformer([\n", " (\"num\", SimpleImputer(strategy=\"median\"), num_cols),\n", " (\"cat\", Pipeline([\n", " (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n", " (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\", min_frequency=50))\n", " ]), cat_cols)\n", "])\n", "\n", "# LightGBM optimisé\n", "neg, pos = y_train_diag.value_counts()\n", "scale_pos_weight = neg / pos\n", "\n", "final_model_diag = Pipeline([\n", " (\"prep\", preproc),\n", " (\"clf\", LGBMClassifier(\n", " objective=\"binary\",\n", " boosting_type=\"gbdt\",\n", " class_weight=\"balanced\",\n", " scale_pos_weight=scale_pos_weight,\n", " n_estimators=500,\n", " learning_rate=0.03,\n", " max_depth=24,\n", " min_child_samples=10,\n", " subsample=0.8,\n", " colsample_bytree=0.7,\n", " random_state=42\n", " ))\n", "])\n", "\n", "# Entraînement final sur TOUT le dataset\n", "final_model_diag.fit(X_train_diag, y_train_diag)\n", "\n", "# Évaluation sur le test\n", "from sklearn.metrics import classification_report, roc_auc_score\n", "\n", "y_pred_final = final_model_diag.predict(X_test_diag)\n", "y_proba_final = final_model_diag.predict_proba(X_test_diag)[:,1]\n", "\n", "print(\" Rapport final LightGBM :\")\n", "print(classification_report(y_test_diag, y_pred_final))\n", "print(\" ROC-AUC test final :\", roc_auc_score(y_test_diag, y_proba_final))\n" ] }, { "cell_type": "code", "execution_count": 46, "id": "c16c0bbc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Modèle final sauvegardé sous : diabetes_model_final.joblib\n" ] } ], "source": [ "# Sauvegarde\n", "joblib.dump(final_model_diag, \"diabetes_model_final.joblib\")\n", "print(\" Modèle final sauvegardé sous : diabetes_model_final.joblib\")" ] }, { "cell_type": "markdown", "id": "2c7989bc", "metadata": {}, "source": [ "### Nous avons entraîné un modèle LightGBM optimisé pour maximiser le recall sur les diabétiques (0.94) tout en conservant une précision élevée (0.83).\n", "### Ce choix est crucial en santé, car il permet de détecter le plus de patients à risque, quitte à avoir quelques fausses alertes que le médecin pourra vérifier ensuite.\n" ] }, { "cell_type": "markdown", "id": "33349abc", "metadata": {}, "source": [ "### On préfère un modèle qui a un meilleur rappel sur les diabétiques, même si ça baisse un peu l’accuracy.\n", "### En santé, le but n’est pas d’avoir un gros chiffre d’accuracy, mais de ne pas rater les vrais malades." ] }, { "cell_type": "markdown", "id": "cfa5f1f9", "metadata": {}, "source": [ "# Maintenant, on passe à la prédiction de l’insuline" ] }, { "cell_type": "markdown", "id": "6b2c00c3", "metadata": {}, "source": [ "## Étape 1 : Préparation des données" ] }, { "cell_type": "code", "execution_count": 48, "id": "13cf24c0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X shape: (101766, 45)\n", "y unique values: [1 3 2 0]\n" ] } ], "source": [ "# Colonnes à supprimer (fuites connues pour insulin)\n", "leak_cols_insulin = [\n", " \"insulin\" # La cible qu'on veut prédire\n", " # Pas besoin de supprimer les colonnes médicaments (on veut prédire une médication)\n", " # Mais si tu veux, on peut en discuter après\n", "]\n", "\n", "# Nouvelle cible\n", "target_insulin = \"insulin\"\n", "\n", "# X et y\n", "X_insulin = data.drop(columns=leak_cols_insulin)\n", "y_insulin = data[target_insulin]\n", "\n", "print(\"X shape:\", X_insulin.shape)\n", "print(\"y unique values:\", y_insulin.unique())\n" ] }, { "cell_type": "code", "execution_count": 49, "id": "ec8316d8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "insulin\n", "1 47383\n", "2 30849\n", "0 12218\n", "3 11316\n", "Name: count, dtype: int64\n" ] } ], "source": [ "print(data[\"insulin\"].value_counts())\n" ] }, { "cell_type": "code", "execution_count": 50, "id": "a3b27290", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 3 2 0]\n" ] } ], "source": [ "print(data['insulin'].unique())\n" ] }, { "cell_type": "code", "execution_count": 51, "id": "3fa1b56b", "metadata": {}, "outputs": [], "source": [ "data['insulin_binary'] = data['insulin'].map({\n", " 0: 'No Inject', # 0 = Pas d'insuline\n", " 1: 'Inject', # 1 = Down = insuline (ajustement à la baisse)\n", " 2: 'Inject', # 2 = Steady = insuline\n", " 3: 'Inject' # 3 = Up = insuline\n", "})\n" ] }, { "cell_type": "code", "execution_count": 52, "id": "1edb1cba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "insulin_binary\n", "Inject 89548\n", "No Inject 12218\n", "Name: count, dtype: int64\n" ] } ], "source": [ "print(data['insulin_binary'].value_counts())\n" ] }, { "cell_type": "code", "execution_count": 53, "id": "f27af00b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "insulin_binary_num\n", "1 89548\n", "0 12218\n", "Name: count, dtype: int64\n" ] } ], "source": [ "data['insulin_binary_num'] = data['insulin'].apply(lambda x: 0 if x == 0 else 1)\n", "\n", "print(data['insulin_binary_num'].value_counts())\n" ] }, { "cell_type": "code", "execution_count": 54, "id": "ca9d7bf7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X shape: (101766, 44)\n", "y shape: (101766,)\n" ] } ], "source": [ "# Définir X et y\n", "X_insulin = data.drop(columns=['insulin', 'insulin_binary', 'insulin_binary_num'])\n", "y_insulin = data['insulin_binary_num']\n", "\n", "# Vérifie les shapes\n", "print(\"X shape:\", X_insulin.shape)\n", "print(\"y shape:\", y_insulin.shape)\n" ] }, { "cell_type": "code", "execution_count": 55, "id": "da2f2752", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train shape: (81613, 44)\n", "Test shape: (20153, 44)\n", "Proportion Inject (y_train): insulin_binary_num\n", "1 0.879333\n", "0 0.120667\n", "Name: proportion, dtype: float64\n" ] } ], "source": [ "from sklearn.model_selection import GroupShuffleSplit\n", "\n", "gss = GroupShuffleSplit(test_size=0.2, random_state=42)\n", "train_idx, test_idx = next(gss.split(X_insulin, y_insulin, groups=data[\"patient_nbr\"]))\n", "\n", "X_train_insulin, X_test_insulin = X_insulin.iloc[train_idx], X_insulin.iloc[test_idx]\n", "y_train_insulin, y_test_insulin = y_insulin.iloc[train_idx], y_insulin.iloc[test_idx]\n", "\n", "print(\"Train shape:\", X_train_insulin.shape)\n", "print(\"Test shape:\", X_test_insulin.shape)\n", "print(\"Proportion Inject (y_train):\", y_train_insulin.value_counts(normalize=True))\n" ] }, { "cell_type": "code", "execution_count": 56, "id": "e7eaf8e8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[LightGBM] [Info] Number of positive: 71765, number of negative: 9848\n", "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003725 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 1611\n", "[LightGBM] [Info] Number of data points in the train set: 81613, number of used features: 37\n", "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n", "[LightGBM] [Info] Start training from score -0.000000\n", "🔎 Rapport de classification (insulin) :\n", " precision recall f1-score support\n", "\n", " 0 0.59 0.41 0.48 2370\n", " 1 0.92 0.96 0.94 17783\n", "\n", " accuracy 0.90 20153\n", " macro avg 0.76 0.69 0.71 20153\n", "weighted avg 0.88 0.90 0.89 20153\n", "\n", "🎯 ROC-AUC (insulin) : 0.9204857386433873\n" ] } ], "source": [ "from lightgbm import LGBMClassifier\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.metrics import classification_report, roc_auc_score\n", "import joblib\n", "\n", "# 1️⃣ Colonnes numériques vs catégoriques\n", "num_cols_ins = X_train_insulin.select_dtypes(\"number\").columns.tolist()\n", "cat_cols_ins = X_train_insulin.select_dtypes(\"object\").columns.tolist()\n", "\n", "# 2️⃣ Prétraitement\n", "preproc_ins = ColumnTransformer([\n", " (\"num\", SimpleImputer(strategy=\"median\"), num_cols_ins),\n", " (\"cat\", Pipeline([\n", " (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n", " (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\", min_frequency=50))\n", " ]), cat_cols_ins)\n", "])\n", "\n", "# 3️⃣ Calcul du poids de classe\n", "neg_ins, pos_ins = y_train_insulin.value_counts()\n", "scale_pos_weight_ins = neg_ins / pos_ins\n", "\n", "# 4️⃣ Pipeline LightGBM\n", "pipe_ins = Pipeline([\n", " (\"prep\", preproc_ins),\n", " (\"clf\", LGBMClassifier(\n", " objective=\"binary\",\n", " boosting_type=\"gbdt\",\n", " class_weight=\"balanced\",\n", " scale_pos_weight=scale_pos_weight_ins,\n", " n_estimators=500,\n", " learning_rate=0.03,\n", " max_depth=24,\n", " min_child_samples=10,\n", " subsample=0.8,\n", " colsample_bytree=0.7,\n", " random_state=42\n", " ))\n", "])\n", "\n", "# 5️⃣ Entraînement\n", "pipe_ins.fit(X_train_insulin, y_train_insulin)\n", "\n", "# 6️⃣ Évaluation\n", "y_pred_ins = pipe_ins.predict(X_test_insulin)\n", "y_proba_ins = pipe_ins.predict_proba(X_test_insulin)[:,1]\n", "\n", "print(\"🔎 Rapport de classification (insulin) :\")\n", "print(classification_report(y_test_insulin, y_pred_ins))\n", "print(\"🎯 ROC-AUC (insulin) :\", roc_auc_score(y_test_insulin, y_proba_ins))\n", "\n" ] }, { "cell_type": "code", "execution_count": 57, "id": "336b3678", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ Modèle insuline sauvegardé sous : insulin_model_final.joblib\n" ] } ], "source": [ "# 7️⃣ Sauvegarde du modèle\n", "joblib.dump(pipe_ins, \"insulin_model_final.joblib\")\n", "print(\"✅ Modèle insuline sauvegardé sous : insulin_model_final.joblib\")\n" ] }, { "cell_type": "markdown", "id": "eb5b1d4f", "metadata": {}, "source": [ "\"Le déséquilibre important dans le dataset (88 % de patients nécessitant une injection d’insuline) limite les performances sur la classe minoritaire (“No Inject”).\n", "Cependant, dans un contexte médical, la priorité reste de ne pas rater les patients qui ont besoin d’insuline. C’est pourquoi le modèle est optimisé pour un rappel élevé sur la classe “Inject”, quitte à sacrifier un peu la précision sur la classe “No Inject”.\"" ] }, { "cell_type": "code", "execution_count": null, "id": "0b1ad89b", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }