From dc5e3d0df10762a141decebd2cc61a559e4dd5df Mon Sep 17 00:00:00 2001 From: ajoubrel-ensae Date: Thu, 14 Mar 2024 22:34:36 +0000 Subject: [PATCH 01/15] correction, renommer, remplir NaN pour tickets et mail --- 0_2_Dataset_construction.py | 48 +++---------------------- 0_KPI_functions.py | 70 +++++++++++++++---------------------- 2 files changed, 33 insertions(+), 85 deletions(-) diff --git a/0_2_Dataset_construction.py b/0_2_Dataset_construction.py index 9d246cd..6881072 100644 --- a/0_2_Dataset_construction.py +++ b/0_2_Dataset_construction.py @@ -22,52 +22,12 @@ exec(open('0_KPI_functions.py').read()) warnings.filterwarnings('ignore') -def display_covering_time(df, company, datecover): - """ - This function draws the time coverage of each company - """ - min_date = df['purchase_date'].min().strftime("%Y-%m-%d") - max_date = df['purchase_date'].max().strftime("%Y-%m-%d") - datecover[company] = [datetime.strptime(min_date, "%Y-%m-%d") + timedelta(days=x) for x in range((datetime.strptime(max_date, "%Y-%m-%d") - datetime.strptime(min_date, "%Y-%m-%d")).days)] - print(f'Couverture Company {company} : {min_date} - {max_date}') - return datecover - - -def compute_time_intersection(datecover): - """ - This function returns the time coverage for all companies - """ - timestamps_sets = [set(timestamps) for timestamps in datecover.values()] - intersection = set.intersection(*timestamps_sets) - intersection_list = list(intersection) - formated_dates = [dt.strftime("%Y-%m-%d") for dt in intersection_list] - return sorted(formated_dates) - - -def df_coverage_modelization(sport, coverage_features = 0.7): - """ - This function returns start_date, end_of_features and final dates - that help to construct train and test datasets - """ - datecover = {} - for company in sport: - df_products_purchased_reduced = display_databases(company, file_name = "products_purchased_reduced", - datetime_col = ['purchase_date']) - datecover = display_covering_time(df_products_purchased_reduced, company, datecover) - #print(datecover.keys()) - dt_coverage = compute_time_intersection(datecover) - start_date = dt_coverage[0] - end_of_features = dt_coverage[int(0.7 * len(dt_coverage))] - final_date = dt_coverage[-1] - return start_date, end_of_features, final_date - - def dataset_construction(min_date, end_features_date, max_date, directory_path): # Import customerplus - df_customerplus_clean_0 = display_databases(directory_path, file_name = "customerplus_cleaned") - df_campaigns_information = display_databases(directory_path, file_name = "campaigns_information", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at']) - df_products_purchased_reduced = display_databases(directory_path, file_name = "products_purchased_reduced", datetime_col = ['purchase_date']) + df_customerplus_clean_0 = display_input_databases(directory_path, file_name = "customerplus_cleaned") + df_campaigns_information = display_input_databases(directory_path, file_name = "campaigns_information", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at']) + df_products_purchased_reduced = display_input_databases(directory_path, file_name = "products_purchased_reduced", datetime_col = ['purchase_date']) # if directory_path == "101": # df_products_purchased_reduced_1 = display_databases(directory_path, file_name = "products_purchased_reduced_1", datetime_col = ['purchase_date']) @@ -90,7 +50,7 @@ def dataset_construction(min_date, end_features_date, max_date, directory_path): # Fusion de l'ensemble et creation des KPI # KPI sur les campagnes publicitaires - df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information) + df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information, max_date = end_features_date) # KPI sur le comportement d'achat df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_features) diff --git a/0_KPI_functions.py b/0_KPI_functions.py index f991ced..26b6814 100644 --- a/0_KPI_functions.py +++ b/0_KPI_functions.py @@ -3,7 +3,7 @@ def custom_date_parser(date_string): return pd.to_datetime(date_string, utc = True, format = 'ISO8601') -def display_databases(directory_path, file_name, datetime_col = None): +def display_input_databases(directory_path, file_name, datetime_col = None): """ This function returns the file from s3 storage """ @@ -13,17 +13,19 @@ def display_databases(directory_path, file_name, datetime_col = None): df = pd.read_csv(file_in, sep=",", parse_dates = datetime_col, date_parser=custom_date_parser) return df -def campaigns_kpi_function(campaigns_information = None): +def campaigns_kpi_function(campaigns_information = None, max_date = None): # Nombre de campagnes de mails nb_campaigns = campaigns_information[['customer_id', 'campaign_name']].groupby('customer_id').count().reset_index() nb_campaigns.rename(columns = {'campaign_name' : 'nb_campaigns'}, inplace = True) # Temps d'ouverture moyen (en minutes) - campaigns_information['time_to_open'] = pd.to_datetime(campaigns_information['opened_at'], utc = True, format = 'ISO8601') - pd.to_datetime(campaigns_information['delivered_at'], utc = True, format = 'ISO8601') + campaigns_information['time_to_open'] = (pd.to_datetime(campaigns_information['opened_at'], utc = True, format = 'ISO8601') - pd.to_datetime(campaigns_information['delivered_at'], utc = True, format = 'ISO8601')) / np.timedelta64(1, 'h') + campaigns_information['time_to_open'] = campaigns_information['time_to_open'].fillna((pd.to_datetime(campaigns_information['delivered_at'], utc = True, format = 'ISO8601') - pd.to_datetime(max_date, utc = True, format = 'ISO8601')) / np.timedelta64(1, 'h')) + time_to_open = campaigns_information[['customer_id', 'time_to_open']].groupby('customer_id').mean().reset_index() - # Nombre de mail ouvert + # Nombre de mail ouvert opened_campaign = campaigns_information[['customer_id', 'campaign_name', 'opened_at']] opened_campaign.dropna(subset=['opened_at'], inplace=True) opened_campaign = opened_campaign[['customer_id', 'campaign_name']].groupby('customer_id').count().reset_index() @@ -33,8 +35,11 @@ def campaigns_kpi_function(campaigns_information = None): campaigns_reduced = pd.merge(nb_campaigns, opened_campaign, on = 'customer_id', how = 'left') campaigns_reduced = pd.merge(campaigns_reduced, time_to_open, on = 'customer_id', how = 'left') + # Taux de mails ouvert + campaigns_reduced['taux_ouverture_mail'] = campaigns_reduced['nb_campaigns_opened'] / campaigns_reduced['nb_campaigns'] + # Fill NaN values - campaigns_reduced[['nb_campaigns', 'nb_campaigns_opened']] = campaigns_reduced[['nb_campaigns', 'nb_campaigns_opened']].fillna(0) + campaigns_reduced[['nb_campaigns', 'nb_campaigns_opened', 'taux_ouverture_mail']] = campaigns_reduced[['nb_campaigns', 'nb_campaigns_opened', 'taux_ouverture_mail']].fillna(0) # Remplir les NaT : time_to_open (??) return campaigns_reduced @@ -49,34 +54,21 @@ def tickets_kpi_function(tickets_information = None): tickets_information_copy['vente_internet'] = tickets_information_copy['supplier_name'].fillna('').str.contains('|'.join(liste_mots), case=False).astype(int) # Proportion de vente en ligne - prop_vente_internet = tickets_information_copy[tickets_information_copy['vente_internet'] == 1].groupby(['customer_id'])['ticket_id'].count().reset_index() - prop_vente_internet.rename(columns = {'ticket_id' : 'nb_tickets_internet'}, inplace = True) + prop_vente_internet = tickets_information_copy[tickets_information_copy['vente_internet'] == 1].groupby(['customer_id'])['purchase_id'].nunique().reset_index() + prop_vente_internet.rename(columns = {'purchase_id' : 'nb_purchases_internet'}, inplace = True) - # Average amount - # avg_amount = (tickets_information_copy.groupby(["event_type_id", 'name_event_types']) - # .agg({"amount" : "mean"}).reset_index() - # .rename(columns = {'amount' : 'avg_amount'})) - - + # Mixte KPI comportement achat tickets_kpi = (tickets_information_copy[['customer_id', 'purchase_id' ,'ticket_id','supplier_name', 'purchase_date', 'amount', 'vente_internet']] .groupby(['customer_id']) - .agg({'ticket_id': 'count', - 'purchase_id' : 'nunique', - 'amount' : 'sum', - 'supplier_name': 'nunique', - 'vente_internet' : 'max', - 'purchase_date' : ['min', 'max']}) - .reset_index() - ) - - tickets_kpi.columns = tickets_kpi.columns.map('_'.join) - - tickets_kpi.rename(columns = {'ticket_id_count' : 'nb_tickets', - 'purchase_id_nunique' : 'nb_purchases', - 'amount_sum' : 'total_amount', - 'supplier_name_nunique' : 'nb_suppliers', - 'customer_id_' : 'customer_id'}, inplace = True) - + .agg(nb_tickets=('ticket_id', 'nunique'), + nb_purchases=('purchase_id', 'nunique'), + total_amount=('amount', 'sum'), + nb_suppliers=('supplier_name', 'nunique'), + achat_internet=('vente_internet', 'max'), + purchase_date_min=('purchase_date', 'min'), + purchase_date_max=('purchase_date', 'max')) + .reset_index()) + tickets_kpi['time_between_purchase'] = tickets_kpi['purchase_date_max'] - tickets_kpi['purchase_date_min'] tickets_kpi['time_between_purchase'] = tickets_kpi['time_between_purchase'] / np.timedelta64(1, 'D') # En nombre de jours @@ -85,27 +77,23 @@ def tickets_kpi_function(tickets_information = None): tickets_kpi['purchase_date_max'] = (max_date - tickets_kpi['purchase_date_max']) / np.timedelta64(1, 'D') tickets_kpi['purchase_date_min'] = (max_date - tickets_kpi['purchase_date_min']) / np.timedelta64(1, 'D') - + # Proportion de ticket internet tickets_kpi = tickets_kpi.merge(prop_vente_internet, on = ['customer_id'], how = 'left') - tickets_kpi['nb_tickets_internet'] = tickets_kpi['nb_tickets_internet'].fillna(0) - - # tickets_kpi = tickets_kpi.merge(avg_amount, how='left', on= 'event_type_id') - - #Taux de ticket payé par internet selon les compagnies - - #tickets_kpi["Taux_ticket_internet"] = tickets_kpi["nb_tickets_internet"]*100 / tickets_kpi["nb_tickets"] - #tickets_kpi['Taux_ticket_internet'] = tickets_kpi['Taux_ticket_internet'].fillna(0) + tickets_kpi['nb_purchases_internet'] = tickets_kpi['nb_purchases_internet'].fillna(0) + tickets_kpi['prop_purchases_internet'] = tickets_kpi['nb_purchases_internet'] / tickets_kpi['nb_purchases'] return tickets_kpi def customerplus_kpi_function(customerplus_clean = None): - # KPI sur les données socio-demographique - ## Le genre + # KPI sur les données socio-demographique + + # Le genre customerplus_clean["gender_label"] = customerplus_clean["gender"].map({ 0: 'female', 1: 'male', 2: 'other' }) + gender_dummies = pd.get_dummies(customerplus_clean["gender_label"], prefix='gender').astype(int) customerplus_clean = pd.concat([customerplus_clean, gender_dummies], axis=1) From 3670299a0b71065fc99c819b34b77b266e872fc5 Mon Sep 17 00:00:00 2001 From: ajoubrel-ensae Date: Thu, 14 Mar 2024 22:35:25 +0000 Subject: [PATCH 02/15] Ajout brouillon --- Exploration_billet_AJ.ipynb | 59 +++++++++++++++++++++++++++++++++++++ 1 file changed, 59 insertions(+) diff --git a/Exploration_billet_AJ.ipynb b/Exploration_billet_AJ.ipynb index f1c2e31..b92df45 100644 --- a/Exploration_billet_AJ.ipynb +++ b/Exploration_billet_AJ.ipynb @@ -524,6 +524,65 @@ "export_in_temporary(target_agg, 'Target_kpi_concatenate')" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb6f06e6-78de-4b8d-a103-8366eff0493a", + "metadata": {}, + "outputs": [], + "source": [ + "v" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5e864b1-adad-4267-b956-3f7ef371d677", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def display_covering_time(df, company, datecover):\n", + " \"\"\"\n", + " This function draws the time coverage of each company\n", + " \"\"\"\n", + " min_date = df['purchase_date'].min().strftime(\"%Y-%m-%d\")\n", + " max_date = df['purchase_date'].max().strftime(\"%Y-%m-%d\")\n", + " datecover[company] = [datetime.strptime(min_date, \"%Y-%m-%d\") + timedelta(days=x) for x in range((datetime.strptime(max_date, \"%Y-%m-%d\") - datetime.strptime(min_date, \"%Y-%m-%d\")).days)]\n", + " print(f'Couverture Company {company} : {min_date} - {max_date}')\n", + " return datecover\n", + "\n", + "\n", + "def compute_time_intersection(datecover):\n", + " \"\"\"\n", + " This function returns the time coverage for all companies\n", + " \"\"\"\n", + " timestamps_sets = [set(timestamps) for timestamps in datecover.values()]\n", + " intersection = set.intersection(*timestamps_sets)\n", + " intersection_list = list(intersection)\n", + " formated_dates = [dt.strftime(\"%Y-%m-%d\") for dt in intersection_list]\n", + " return sorted(formated_dates)\n", + "\n", + "\n", + "def df_coverage_modelization(sport, coverage_features = 0.7):\n", + " \"\"\"\n", + " This function returns start_date, end_of_features and final dates\n", + " that help to construct train and test datasets\n", + " \"\"\"\n", + " datecover = {}\n", + " for company in sport:\n", + " df_products_purchased_reduced = display_input_databases(company, file_name = \"products_purchased_reduced\",\n", + " datetime_col = ['purchase_date'])\n", + " datecover = display_covering_time(df_products_purchased_reduced, company, datecover)\n", + " #print(datecover.keys())\n", + " dt_coverage = compute_time_intersection(datecover)\n", + " start_date = dt_coverage[0]\n", + " end_of_features = dt_coverage[int(0.7 * len(dt_coverage))]\n", + " final_date = dt_coverage[-1]\n", + " return start_date, end_of_features, final_date\n", + " " + ] + }, { "cell_type": "markdown", "id": "2435097a-95a5-43e1-84d0-7f6b701441ba", From 83a3c039eca6b9297d92b0bbbd9e794b05b1de1f Mon Sep 17 00:00:00 2001 From: tpique-ensae Date: Sat, 16 Mar 2024 09:42:41 +0000 Subject: [PATCH 03/15] baseline logit - exploratory study of variables --- .../2_bis_logit_baseline_statsmodels.ipynb | 2772 +++++++---------- 1 file changed, 1076 insertions(+), 1696 deletions(-) diff --git a/Spectacle/2_bis_logit_baseline_statsmodels.ipynb b/Spectacle/2_bis_logit_baseline_statsmodels.ipynb index 515f8cb..93776c0 100644 --- a/Spectacle/2_bis_logit_baseline_statsmodels.ipynb +++ b/Spectacle/2_bis_logit_baseline_statsmodels.ipynb @@ -8,6 +8,14 @@ "# Baseline logit on spectacle companies with statmodels" ] }, + { + "cell_type": "markdown", + "id": "eae443dc-6c28-401a-a30e-e02f5f4da2df", + "metadata": {}, + "source": [ + "## Importation des packages et des données" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -209,6 +217,15 @@ "print(\"Shape test : \", X_test.shape)" ] }, + { + "cell_type": "markdown", + "id": "29206597-bce8-41e0-9b68-9b9a2843787a", + "metadata": {}, + "source": [ + "## optionnel : calcul des poids\n", + "On pourrait utiliser les poids pour gérer le déséquilibre de classe, mais dans une optique exploratoire, c'est pas indispensable et ça a pas été utilisé ici !" + ] + }, { "cell_type": "code", "execution_count": 10, @@ -279,13 +296,373 @@ } ], "source": [ + "# verif\n", "print(2 * weights * class_counts[y_train['y_has_purchased'].values.astype(int)])\n", "print(len(y_train['y_has_purchased']))" ] }, + { + "cell_type": "markdown", + "id": "bd1f7d9d-1aff-49e4-81ca-038f732b1595", + "metadata": {}, + "source": [ + "## définition des variables X et y" + ] + }, { "cell_type": "code", - 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" + ], + "text/plain": [ + " nb_tickets nb_purchases total_amount nb_suppliers \\\n", + "0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "... ... ... ... ... \n", + "354360 0.0 0.0 0.0 0.0 \n", + "354361 0.0 0.0 0.0 0.0 \n", + "354362 2.0 2.0 50.0 1.0 \n", + "354363 1.0 1.0 55.0 1.0 \n", + "354364 0.0 0.0 0.0 0.0 \n", + "\n", + " vente_internet_max purchase_date_min purchase_date_max \\\n", + "0 0.0 550.000000 550.000000 \n", + "1 0.0 550.000000 550.000000 \n", + "2 0.0 550.000000 550.000000 \n", + "3 0.0 550.000000 550.000000 \n", + "4 0.0 550.000000 550.000000 \n", + "... ... ... ... \n", + "354360 0.0 550.000000 550.000000 \n", + "354361 0.0 550.000000 550.000000 \n", + "354362 0.0 91.030556 91.020139 \n", + "354363 0.0 52.284028 52.284028 \n", + "354364 0.0 550.000000 550.000000 \n", + "\n", + " time_between_purchase nb_tickets_internet fidelity is_email_true \\\n", + "0 -1.000000 0.0 1 True \n", + "1 -1.000000 0.0 0 True \n", + "2 -1.000000 0.0 1 True \n", + "3 -1.000000 0.0 0 True \n", + "4 -1.000000 0.0 0 True \n", + "... ... ... ... ... \n", + "354360 -1.000000 0.0 0 True \n", + "354361 -1.000000 0.0 0 True \n", + "354362 0.010417 0.0 4 True \n", + "354363 0.000000 0.0 1 True \n", + "354364 -1.000000 0.0 0 True \n", + "\n", + " opt_in gender_female gender_male gender_other nb_campaigns \\\n", + "0 True 1 0 0 13.0 \n", + "1 True 0 0 1 10.0 \n", + "2 True 0 1 0 14.0 \n", + "3 False 0 0 1 9.0 \n", + "4 False 0 0 1 4.0 \n", + "... ... ... ... ... ... \n", + "354360 False 0 0 1 7.0 \n", + "354361 True 0 1 0 11.0 \n", + "354362 False 1 0 0 6.0 \n", + "354363 True 0 1 0 3.0 \n", + "354364 False 0 1 0 7.0 \n", + "\n", + " nb_campaigns_opened \n", + "0 4.0 \n", + "1 9.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "354360 0.0 \n", + "354361 2.0 \n", + "354362 6.0 \n", + "354363 0.0 \n", + "354364 0.0 \n", + "\n", + "[354365 rows x 17 columns]" + ] + }, + "execution_count": 258, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# visu de X_train\n", + "X_train" + ] + }, + { + "cell_type": "code", + "execution_count": 259, "id": "648fb542-0186-493d-b274-be2c26a11967", "metadata": {}, "outputs": [], @@ -300,7 +677,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 260, "id": "978b9ebc-aa97-41d7-a48f-d1f79c1ed482", "metadata": {}, "outputs": [ @@ -639,7 +1016,7 @@ "[354365 rows x 17 columns]" ] }, - "execution_count": 125, + "execution_count": 260, "metadata": {}, "output_type": "execute_result" } @@ -1125,385 +1502,414 @@ ] }, { - "cell_type": "code", - "execution_count": 126, - "id": "2475f2fe-3d1f-4845-9ede-0416dac83271", + "cell_type": "markdown", + "id": "a022e8c3-93e7-4530-85a4-da8812d82737", "metadata": {}, - "outputs": [], "source": [ - "# Colonnes à standardiser\n", + "## Prétraitement des données + modèle\n", "\n", - "\"\"\"\n", - "var_num = ['nb_tickets', 'nb_purchases', \"total_amount\", \"nb_suppliers\", \"vente_internet_max\",\n", - " \"purchase_date_min\", \"purchase_date_max\", \"time_between_purchase\", \"nb_tickets_internet\",\n", - " \"fidelity\", \"nb_campaigns\", \"nb_campaigns_opened\"]\n", - " \"\"\"\n", - "\n", - "numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n", - " 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n", - " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", - "\n", - "# Standardisation des colonnes sélectionnées\n", - "scaler = StandardScaler()\n", - "X[var_num] = scaler.fit_transform(X[var_num])\n", - "X[numeric_features] = scaler.fit_transform(X[numeric_features])\n", - "\n" + "- variables à retirer : fidelity (valeurs trop grandes dont l'exp -> +inf, autre problème : st basé sur des infos qu'on a pas sur la période étudiée mais slt sur période d'évaluation), time between purchase (revoir sa construction), gender_other (colinéarité avec les autres var de genre)\n", + "- ajouter un intercept\n", + "- pas besoin de standardiser pour le moment, mais à faire quand on passera au modèle LASSO " ] }, { "cell_type": "code", - "execution_count": 128, - "id": "1763bad4-36b5-4ebb-9702-b77ba19fb30e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", + "
" + ], + "text/plain": [ + " const nb_tickets nb_purchases total_amount nb_suppliers \\\n", + "0 1.0 0 0 0 0 \n", + "1 1.0 0 0 0 0 \n", + "2 1.0 0 0 0 0 \n", + "3 1.0 0 0 0 0 \n", + "4 1.0 0 0 0 0 \n", + "... ... ... ... ... ... \n", + "354360 1.0 0 0 0 0 \n", + "354361 1.0 0 0 0 0 \n", + "354362 1.0 2 2 50 1 \n", + "354363 1.0 1 1 55 1 \n", + "354364 1.0 0 0 0 0 \n", + "\n", + " vente_internet_max purchase_date_min purchase_date_max \\\n", + "0 0 550 550 \n", + "1 0 550 550 \n", + "2 0 550 550 \n", + "3 0 550 550 \n", + "4 0 550 550 \n", + "... ... ... ... \n", + "354360 0 550 550 \n", + "354361 0 550 550 \n", + "354362 0 91 91 \n", + "354363 0 52 52 \n", + "354364 0 550 550 \n", + "\n", + " nb_tickets_internet is_email_true opt_in gender_female \\\n", + "0 0 1 1 1 \n", + "1 0 1 1 0 \n", + "2 0 1 1 0 \n", + "3 0 1 0 0 \n", + "4 0 1 0 0 \n", + "... ... ... ... ... \n", + "354360 0 1 0 0 \n", + "354361 0 1 1 0 \n", + "354362 0 1 0 1 \n", + "354363 0 1 1 0 \n", + "354364 0 1 0 0 \n", + "\n", + " gender_male nb_campaigns nb_campaigns_opened \n", + "0 0 13 4 \n", + "1 0 10 9 \n", + "2 1 14 0 \n", + "3 0 9 0 \n", + "4 0 4 0 \n", + "... ... ... ... \n", + "354360 0 7 0 \n", + "354361 1 11 2 \n", + "354362 0 6 6 \n", + "354363 1 3 0 \n", + "354364 1 7 0 \n", + "\n", + "[354365 rows x 15 columns]" + ] + }, + "execution_count": 266, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 0. on retire les variables citées ci-dessus et on ajoute l'intercept\n", + "\n", + "X = sm.add_constant(X.drop([\"fidelity\", \"time_between_purchase\", \"gender_other\"], axis=1))\n", + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "id": "0e968aa1-fbec-47db-b570-4730ef7eebf2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.234602\n", + " Iterations 8\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y No. Observations: 354365\n", + "Model: Logit Df Residuals: 354350\n", + "Method: MLE Df Model: 14\n", + "Date: Fri, 15 Mar 2024 Pseudo R-squ.: 0.2112\n", + "Time: 10:07:29 Log-Likelihood: -83135.\n", + "converged: True LL-Null: -1.0540e+05\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "=======================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "---------------------------------------------------------------------------------------\n", + "const -1.9633 0.093 -21.101 0.000 -2.146 -1.781\n", + "nb_tickets -0.0003 0.000 -2.191 0.028 -0.001 -2.85e-05\n", + "nb_purchases -0.0037 0.001 -3.609 0.000 -0.006 -0.002\n", + "total_amount 6.267e-05 1.63e-05 3.841 0.000 3.07e-05 9.46e-05\n", + "nb_suppliers 0.3368 0.019 17.662 0.000 0.299 0.374\n", + "vente_internet_max -1.9874 0.024 -82.965 0.000 -2.034 -1.940\n", + "purchase_date_min 0.0031 7.77e-05 39.936 0.000 0.003 0.003\n", + "purchase_date_max -0.0072 8.08e-05 -89.592 0.000 -0.007 -0.007\n", + "nb_tickets_internet 0.0938 0.004 22.652 0.000 0.086 0.102\n", + "is_email_true 0.8651 0.088 9.797 0.000 0.692 1.038\n", + "opt_in -1.9976 0.019 -107.305 0.000 -2.034 -1.961\n", + "gender_female 0.7032 0.024 29.395 0.000 0.656 0.750\n", + "gender_male 0.8071 0.024 33.201 0.000 0.759 0.855\n", + "nb_campaigns 0.0287 0.001 30.633 0.000 0.027 0.031\n", + "nb_campaigns_opened 0.0486 0.002 28.245 0.000 0.045 0.052\n", + "=======================================================================================\n" + ] + } + ], + "source": [ + "# 1. Premier modèle de régression logistique sans standardisation (permet une interprétation des coeffs)\n", + "\n", + "model_logit = sm.Logit(y, X)\n", + "\n", + "# Ajustement du modèle aux données\n", + "result = model_logit.fit()\n", + "\n", + "# Affichage des résultats - toutes les var sont significatives avec des p-valeurs de 0, et de 0.28 pour nbre tickets\n", + "print(result.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "id": "2475f2fe-3d1f-4845-9ede-0416dac83271", + "metadata": {}, + "outputs": [], + "source": [ + "# 2. Modèle logit avec données standardisées\n", + "\n", + "# Colonnes à standardiser\n", + "\n", + "\n", + "var_num = ['nb_tickets', 'nb_purchases', \"total_amount\", \"nb_suppliers\", \"vente_internet_max\",\n", + " \"purchase_date_min\", \"purchase_date_max\", \"nb_tickets_internet\",\n", + " \"nb_campaigns\", \"nb_campaigns_opened\"]\n", + "\n", + "# Standardisation des colonnes sélectionnées\n", + "scaler = StandardScaler()\n", + "X[var_num] = scaler.fit_transform(X[var_num])" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "id": "696fcc04-e5df-45dc-a1b9-57c30d4d671d", "metadata": {}, "outputs": [ { @@ -1535,9 +1941,7 @@ " vente_internet_max\n", " purchase_date_min\n", " purchase_date_max\n", - " time_between_purchase\n", " nb_tickets_internet\n", - " fidelity\n", " is_email_true\n", " opt_in\n", " gender_female\n", @@ -1557,9 +1961,7 @@ " -0.599511\n", " 0.755994\n", " 0.783940\n", - " -0.192978\n", " -0.264693\n", - " -0.004316\n", " 1\n", " 1\n", " 1\n", @@ -1577,9 +1979,7 @@ " -0.599511\n", " 0.755994\n", " 0.783940\n", - " -0.192978\n", " -0.264693\n", - " -0.029071\n", " 1\n", " 1\n", " 0\n", @@ -1597,9 +1997,7 @@ " -0.599511\n", " 0.755994\n", " 0.783940\n", - " -0.192978\n", " -0.264693\n", - " -0.004316\n", " 1\n", " 1\n", " 0\n", @@ -1617,9 +2015,7 @@ " -0.599511\n", " 0.755994\n", " 0.783940\n", - " -0.192978\n", " -0.264693\n", - " -0.029071\n", " 1\n", " 0\n", " 0\n", @@ -1637,9 +2033,7 @@ " -0.599511\n", " 0.755994\n", " 0.783940\n", - " -0.192978\n", " -0.264693\n", - " -0.029071\n", " 1\n", " 0\n", " 0\n", @@ -1664,8 +2058,6 @@ " ...\n", " ...\n", " ...\n", - " ...\n", - " ...\n", " \n", " \n", " 354360\n", @@ -1677,9 +2069,7 @@ " -0.599511\n", " 0.755994\n", " 0.783940\n", - " -0.192978\n", " -0.264693\n", - " -0.029071\n", " 1\n", " 0\n", " 0\n", @@ -1697,9 +2087,7 @@ " -0.599511\n", " 0.755994\n", " 0.783940\n", - " -0.192978\n", " -0.264693\n", - " -0.029071\n", " 1\n", " 1\n", " 0\n", @@ -1717,9 +2105,7 @@ " -0.599511\n", " -1.665887\n", " -1.557073\n", - " -0.175269\n", " -0.264693\n", - " 0.069949\n", " 1\n", " 0\n", " 1\n", @@ -1737,9 +2123,7 @@ " -0.599511\n", " -1.871668\n", " -1.755983\n", - " -0.175269\n", " -0.264693\n", - " -0.004316\n", " 1\n", " 1\n", " 0\n", @@ -1757,9 +2141,7 @@ " -0.599511\n", " 0.755994\n", " 0.783940\n", - " -0.192978\n", " -0.264693\n", - " -0.029071\n", " 1\n", " 0\n", " 0\n", @@ -1769,7 +2151,7 @@ " \n", " \n", "\n", - "

354365 rows × 17 columns

\n", + "

354365 rows × 15 columns

\n", "" ], "text/plain": [ @@ -1799,36 +2181,36 @@ "354363 -0.599511 -1.871668 -1.755983 \n", "354364 -0.599511 0.755994 0.783940 \n", "\n", - " time_between_purchase nb_tickets_internet fidelity is_email_true \\\n", - "0 -0.192978 -0.264693 -0.004316 1 \n", - "1 -0.192978 -0.264693 -0.029071 1 \n", - "2 -0.192978 -0.264693 -0.004316 1 \n", - "3 -0.192978 -0.264693 -0.029071 1 \n", - "4 -0.192978 -0.264693 -0.029071 1 \n", - "... ... ... ... ... \n", - "354360 -0.192978 -0.264693 -0.029071 1 \n", - "354361 -0.192978 -0.264693 -0.029071 1 \n", - "354362 -0.175269 -0.264693 0.069949 1 \n", - "354363 -0.175269 -0.264693 -0.004316 1 \n", - "354364 -0.192978 -0.264693 -0.029071 1 \n", + " nb_tickets_internet is_email_true opt_in gender_female \\\n", + "0 -0.264693 1 1 1 \n", + "1 -0.264693 1 1 0 \n", + "2 -0.264693 1 1 0 \n", + "3 -0.264693 1 0 0 \n", + "4 -0.264693 1 0 0 \n", + "... ... ... ... ... \n", + "354360 -0.264693 1 0 0 \n", + "354361 -0.264693 1 1 0 \n", + "354362 -0.264693 1 0 1 \n", + "354363 -0.264693 1 1 0 \n", + "354364 -0.264693 1 0 0 \n", "\n", - " opt_in gender_female gender_male nb_campaigns nb_campaigns_opened \n", - "0 1 1 0 0.607945 0.522567 \n", - "1 1 0 0 0.306155 1.701843 \n", - "2 1 0 1 0.708542 -0.420854 \n", - "3 0 0 0 0.205558 -0.420854 \n", - "4 0 0 0 -0.297426 -0.420854 \n", - "... ... ... ... ... ... \n", - "354360 0 0 0 0.004365 -0.420854 \n", - "354361 1 0 1 0.406752 0.050856 \n", - "354362 0 1 0 -0.096232 0.994277 \n", - "354363 1 0 1 -0.398023 -0.420854 \n", - "354364 0 0 1 0.004365 -0.420854 \n", + " gender_male nb_campaigns nb_campaigns_opened \n", + "0 0 0.607945 0.522567 \n", + "1 0 0.306155 1.701843 \n", + "2 1 0.708542 -0.420854 \n", + "3 0 0.205558 -0.420854 \n", + "4 0 -0.297426 -0.420854 \n", + "... ... ... ... \n", + "354360 0 0.004365 -0.420854 \n", + "354361 1 0.406752 0.050856 \n", + "354362 0 -0.096232 0.994277 \n", + "354363 1 -0.398023 -0.420854 \n", + "354364 1 0.004365 -0.420854 \n", "\n", - "[354365 rows x 17 columns]" + "[354365 rows x 15 columns]" ] }, - "execution_count": 122, + "execution_count": 269, "metadata": {}, "output_type": "execute_result" } @@ -1839,78 +2221,53 @@ }, { "cell_type": "code", - "execution_count": 133, - "id": "0e968aa1-fbec-47db-b570-4730ef7eebf2", + "execution_count": 289, + "id": "54421677-640f-4f37-9a0d-d9a2cc3572b0", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mamba/lib/python3.11/site-packages/statsmodels/discrete/discrete_model.py:2385: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1+np.exp(-X))\n", - "/opt/mamba/lib/python3.11/site-packages/statsmodels/discrete/discrete_model.py:2443: RuntimeWarning: divide by zero encountered in log\n", - " return np.sum(np.log(self.cdf(q * linpred)))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: Maximum number of iterations has been exceeded.\n", - " Current function value: inf\n", - " Iterations: 35\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mamba/lib/python3.11/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", - " warnings.warn(\"Maximum Likelihood optimization failed to \"\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.234602\n", + " Iterations 8\n", " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 354365\n", - "Model: Logit Df Residuals: 354349\n", - "Method: MLE Df Model: 15\n", - "Date: Thu, 14 Mar 2024 Pseudo R-squ.: -inf\n", - "Time: 10:47:16 Log-Likelihood: -inf\n", - "converged: False LL-Null: -1.0540e+05\n", - "Covariance Type: nonrobust LLR p-value: 1.000\n", - "=========================================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "-----------------------------------------------------------------------------------------\n", - "nb_tickets 4.9213 0.267 18.448 0.000 4.398 5.444\n", - "nb_purchases -7.9446 0.140 -56.905 0.000 -8.218 -7.671\n", - "total_amount 0.3039 0.061 4.945 0.000 0.183 0.424\n", - "nb_suppliers 0.1067 0.008 13.678 0.000 0.091 0.122\n", - "vente_internet_max -0.2784 0.008 -34.612 0.000 -0.294 -0.263\n", - "purchase_date_min -41.9693 2.640 -15.895 0.000 -47.144 -36.794\n", - "purchase_date_max 43.2793 2.734 15.829 0.000 37.920 48.638\n", - "time_between_purchase 12.7237 0.789 16.132 0.000 11.178 14.270\n", - "nb_tickets_internet -0.0212 0.014 -1.510 0.131 -0.049 0.006\n", - "fidelity 22.0749 0.222 99.561 0.000 21.640 22.509\n", - "is_email_true 0.0225 0.004 6.145 0.000 0.015 0.030\n", - "opt_in -0.1245 0.004 -30.646 0.000 -0.133 -0.117\n", - "gender_female 0.0018 nan nan nan nan nan\n", - "gender_male 0.0118 nan nan nan nan nan\n", - "gender_other -0.0182 nan nan nan nan nan\n", - "nb_campaigns -0.0049 0.005 -0.961 0.336 -0.015 0.005\n", - "nb_campaigns_opened 0.0867 0.005 18.211 0.000 0.077 0.096\n", - "=========================================================================================\n" + "Model: Logit Df Residuals: 354350\n", + "Method: MLE Df Model: 14\n", + "Date: Fri, 15 Mar 2024 Pseudo R-squ.: 0.2112\n", + "Time: 10:26:14 Log-Likelihood: -83135.\n", + "converged: True LL-Null: -1.0540e+05\n", + "Covariance Type: nonrobust LLR p-value: 0.000\n", + "=======================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "---------------------------------------------------------------------------------------\n", + "const -3.6025 0.091 -39.755 0.000 -3.780 -3.425\n", + "nb_tickets -0.0230 0.010 -2.191 0.028 -0.044 -0.002\n", + "nb_purchases -0.0519 0.014 -3.609 0.000 -0.080 -0.024\n", + "total_amount 0.0799 0.021 3.841 0.000 0.039 0.121\n", + "nb_suppliers 0.1694 0.010 17.662 0.000 0.151 0.188\n", + "vente_internet_max -0.8764 0.011 -82.965 0.000 -0.897 -0.856\n", + "purchase_date_min 0.5881 0.015 39.936 0.000 0.559 0.617\n", + "purchase_date_max -1.4197 0.016 -89.592 0.000 -1.451 -1.389\n", + "nb_tickets_internet 0.2895 0.013 22.652 0.000 0.264 0.315\n", + "is_email_true 0.8651 0.088 9.797 0.000 0.692 1.038\n", + "opt_in -1.9976 0.019 -107.305 0.000 -2.034 -1.961\n", + "gender_female 0.7032 0.024 29.395 0.000 0.656 0.750\n", + "gender_male 0.8071 0.024 33.201 0.000 0.759 0.855\n", + "nb_campaigns 0.2850 0.009 30.633 0.000 0.267 0.303\n", + "nb_campaigns_opened 0.2061 0.007 28.245 0.000 0.192 0.220\n", + "=======================================================================================\n" ] } ], "source": [ - "# Création du modèle de régression logistique avec poids équilibrés\n", - "# model_logit = sm.Logit(y, X, weights=weights)\n", + "# 2. modele avec var standardisées (permet de mieux jauger l'importance réelle de chaque variable)\n", + "\n", "model_logit = sm.Logit(y, X)\n", + "# model_logit = sm.Logit(y, X)\n", "\n", "# Ajustement du modèle aux données\n", "result = model_logit.fit()\n", @@ -1919,1330 +2276,353 @@ "print(result.summary())" ] }, + { + "cell_type": "markdown", + "id": "36c5e770-72b3-4482-ad61-45b511a11f06", + "metadata": {}, + "source": [ + "## graphique LASSO - quelles variables sont impotantes dans le modèle ? " + ] + }, { "cell_type": "code", - "execution_count": 130, - "id": "d1660ef9-438f-4427-ac2d-aa8179614e40", + "execution_count": 313, + "id": "af208fdf-b4c2-4acd-b29e-c5b67bec3a4d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results for solver lbfgs\n", + "intercept : -3.617357317895187\n", + "coefficients : [[-0.03114285 -0.06607353 0.10099873 0.16977395 -0.87625108 0.58870838\n", + " -1.42022841 0.28837776 0.87461022 -2.00037064 0.70874574 0.8136523\n", + " 0.2850802 0.20640785]]\n", + "\n", + "\n", + "results for solver newton-cg\n", + "intercept : -3.5774790840156467\n", + "coefficients : [[-0.0224498 -0.05092757 0.07842438 0.16941048 -0.87645255 0.58801191\n", + " -1.41953483 0.28961165 0.84037075 -1.99757163 0.70302619 0.8068438\n", + " 0.2849652 0.20613618]]\n", + "\n", + "\n", + "results for solver newton-cholesky\n", + "intercept : -3.602198310216717\n", + "coefficients : [[-0.02297134 -0.05187501 0.07986323 0.1693883 -0.87639043 0.58815512\n", + " -1.41963236 0.28949836 0.86505556 -1.99695897 0.70307973 0.80688729\n", + " 0.2849131 0.20610117]]\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mamba/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results for solver sag\n", + "intercept : -1.251116606796448\n", + "coefficients : [[-0.02952178 -0.05691972 0.08940743 0.18616406 -0.85908081 0.46577384\n", + " -1.26014292 0.32512459 -1.00339802 -1.84528471 0.15832219 0.24753693\n", + " 0.26318328 0.21288782]]\n", + "\n", + "\n", + "results for solver saga\n", + "intercept : -1.112341737293756\n", + "coefficients : [[-0.03349226 -0.02298918 0.09611619 0.23784438 -0.80928967 0.28520739\n", + " -1.01029862 0.30172469 -0.99503611 -1.53140972 -0.04449765 0.02363137\n", + " 0.20352875 0.22580284]]\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mamba/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# difference entre les solveurs (les resultats de statsmodel s'approchent de newtown cholesky)\n", + "\n", + "for solver in [\"lbfgs\", \"newton-cg\", \"newton-cholesky\", \"sag\", \"saga\"] :\n", + " modele_logit = LogisticRegression(penalty=None, solver=solver)\n", + " modele_logit.fit(X.drop(\"const\", axis=1), y)\n", + " print(f\"results for solver {solver}\")\n", + " print(f\"intercept : {modele_logit.intercept_[0]}\")\n", + " print(f\"coefficients : {modele_logit.coef_}\")\n", + " print(\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "e65ab8d9-54e5-4092-ad75-ac1909cb1f60", + "metadata": {}, + "source": [ + "on passe au graphique\n" + ] + }, + { + "cell_type": "code", + "execution_count": 449, + "id": "f0006351-9b43-449e-81a7-b4510dd55366", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1.07107945, -0.93363755])" + "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])" ] }, - "execution_count": 130, + "execution_count": 449, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "X[\"gender_female\"].unique()" + "# il faut environ alpha = 25k pour annuler tous les coeffs\n", + "# on utilise pas de balance pour les classes pour le moment car les résultats de statsmodels n equilibrent \n", + "# pas les classes - on utilisera cette option pr la validation croisee\n", + "\n", + "modele_logit = LogisticRegression(penalty=\"l1\", C=1/25000, # class_weight=\"balanced\", \n", + " solver=\"liblinear\" )\n", + "modele_logit.fit(X.drop(\"const\", axis=1),y)\n", + "modele_logit.coef_" ] }, { "cell_type": "code", - "execution_count": 131, - "id": "2079bae6-bce3-4de7-bf49-180177c31a55", + "execution_count": 370, + "id": "24083a2f-e520-4229-a510-09e352b25cbd", "metadata": {}, "outputs": [], "source": [ - "numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n", - " 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n", - " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", - "\n", - "numeric_transformer = Pipeline(steps=[\n", - " #(\"imputer\", SimpleImputer(strategy=\"mean\")), \n", - " (\"scaler\", StandardScaler()) \n", - "])\n", - "\n", - "categorical_features = ['opt_in'] \n", - "\n", - "# Transformer for the categorical features\n", - "categorical_transformer = Pipeline(steps=[\n", - " #(\"imputer\", SimpleImputer(strategy=\"most_frequent\")), # Impute missing values with the most frequent\n", - " (\"onehot\", OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n", - "])\n", - "\n", - "preproc = ColumnTransformer(\n", - " transformers=[\n", - " (\"num\", numeric_transformer, numeric_features),\n", - " (\"cat\", categorical_transformer, categorical_features)\n", - " ]\n", - ")" + "params = np.logspace(-5, 5, 11, 10)" ] }, { "cell_type": "code", - 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"0 1 1 0 0 13 \n", - "1 1 0 0 1 10 \n", - "2 1 0 1 0 14 \n", - "3 0 0 0 1 9 \n", - "4 0 0 0 1 4 \n", - "... ... ... ... ... ... \n", - "354360 0 0 0 1 7 \n", - "354361 1 0 1 0 11 \n", - "354362 0 1 0 0 6 \n", - "354363 1 0 1 0 3 \n", - "354364 0 0 1 0 7 \n", - "\n", - " nb_campaigns_opened \n", - "0 4 \n", - "1 9 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "... ... \n", - "354360 0 \n", - "354361 2 \n", - "354362 6 \n", - "354363 0 \n", - "354364 0 \n", - "\n", - "[354365 rows x 17 columns]" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "X" + "results=[]\n", + "for param in params :\n", + " modele_logit = LogisticRegression(penalty=\"l1\", C=param, # class_weight=\"balanced\", \n", + " solver=\"liblinear\" )\n", + " modele_logit.fit(X.drop(\"const\", axis=1),y)\n", + " results.append(modele_logit.coef_)" ] }, { "cell_type": "code", - "execution_count": 73, - "id": "f15b0d69-8470-4a36-bd25-9536a36c4756", + "execution_count": 383, + "id": "ceaec969-e72e-4520-afaf-7bcf5dad8365", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(354365,)" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "weights.shape" + "results.reverse()" ] }, { "cell_type": "code", - "execution_count": 74, - "id": "e97e26f6-b854-41e3-bbdf-318065b03254", + "execution_count": 384, + "id": "5b7c8d26-d1f8-441f-ab1d-89845e3e1ea3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(354365, 17)" + "[array([[-0.02299412, -0.05192013, 0.0799274 , 0.16931227, -0.87633381,\n", + " 0.58813399, -1.41967385, 0.28951886, 0.85509191, -1.99754475,\n", + " 0.70287087, 0.80669243, 0.28498239, 0.2061286 ]]),\n", + " array([[-0.02299201, -0.05191491, 0.07992075, 0.16931139, -0.87634243,\n", + " 0.58813708, -1.41968623, 0.28952223, 0.85577021, -1.99756453,\n", + " 0.70288563, 0.80669012, 0.28498258, 0.20612949]]),\n", + " array([[-0.02299764, -0.05192605, 0.07993569, 0.16930528, -0.87632586,\n", + " 0.58811345, -1.41964512, 0.28952983, 0.85374762, -1.99754811,\n", + " 0.70282334, 0.80664228, 0.28498228, 0.20613025]]),\n", + " array([[-0.02298949, -0.05191449, 0.07991828, 0.16931317, -0.87634417,\n", + " 0.58812319, -1.4196808 , 0.2895181 , 0.85546622, -1.99754003,\n", + " 0.70302758, 0.80684757, 0.28498265, 0.20613162]]),\n", + " array([[-0.02296458, -0.05187503, 0.07985942, 0.16928133, -0.87628414,\n", + " 0.5880753 , -1.41959837, 0.28951824, 0.85207105, -1.99743532,\n", + " 0.70275613, 0.80657079, 0.28497271, 0.20612744]]),\n", + " array([[-0.02266765, -0.05140588, 0.07913905, 0.16914597, -0.8759943 ,\n", + " 0.58782322, -1.41931263, 0.28941107, 0.84058764, -1.99706383,\n", + " 0.70135753, 0.805146 , 0.2849354 , 0.20613043]]),\n", + " array([[-0.01986108, -0.04710671, 0.07249967, 0.16755623, -0.8727931 ,\n", + " 0.58521605, -1.41621509, 0.28835319, 0.7063547 , -1.99262169,\n", + " 0.68764121, 0.79104559, 0.28452484, 0.20613349]]),\n", + " array([[ 0. , -0.02274081, 0.03249772, 0.15656967, -0.84560728,\n", + " 0.5601391 , -1.38630664, 0.27683263, 0. , -1.95240872,\n", + " 0.55820164, 0.65806397, 0.27970382, 0.20620792]]),\n", + " array([[ 0.00000000e+00, 0.00000000e+00, 1.55329481e-03,\n", + " 1.30027639e-01, -6.87367967e-01, 3.13022684e-01,\n", + " -1.08971896e+00, 1.74908692e-01, 0.00000000e+00,\n", + " -1.67160475e+00, 0.00000000e+00, 0.00000000e+00,\n", + " 2.21231437e-01, 2.08973175e-01]]),\n", + " array([[ 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , -0.2624159 , 0. , -0.01813001, -0.22665172,\n", + " 0. , 0. , 0. , 0.01487092]]),\n", + " array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])]" ] }, - "execution_count": 74, + "execution_count": 384, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "X.shape" + "results" ] }, { "cell_type": "code", - "execution_count": 75, - "id": "49621874-1e8c-4cb5-84a9-a5c9715f3b06", + "execution_count": 392, + "id": "9f6e6532-c593-4f3a-a718-5f4593749eb4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(354365,)" + "array([1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02,\n", + " 1.e+03, 1.e+04, 1.e+05])" ] }, - "execution_count": 75, + "execution_count": 392, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "y.shape" + "# le paramètre C est l'inverse de alpha. On préfère donc afficher les valeurs de alpha qui sont plus parlantes\n", + "# un alpha grand correspond à une plus grande pénalité \n", + "# et on utilise flip pour inverser le vecteur, et classer les alphas par ordre croissant\n", + "# par souci de coherence et de lisibilité, on inverse donc aussi l'ordre des resultats\n", + "\n", + "alphas_sorted = np.flip(1/params)\n", + "alphas_sorted" ] }, { "cell_type": "code", - "execution_count": 76, - "id": "8072cd81-d63f-430e-b0b2-c0589cf18871", + "execution_count": 447, + "id": "1de056b5-e37c-4272-9acb-a197bdb5ea3b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "nb_tickets 0\n", - "nb_purchases 0\n", - "total_amount 0\n", - "nb_suppliers 0\n", - "vente_internet_max 0\n", - "purchase_date_min 0\n", - "purchase_date_max 0\n", - "time_between_purchase 0\n", - "nb_tickets_internet 0\n", - "fidelity 0\n", - "is_email_true 0\n", - "opt_in 0\n", - "gender_female 0\n", - "gender_male 0\n", - "gender_other 0\n", - "nb_campaigns 0\n", - "nb_campaigns_opened 0\n", - "dtype: int64" + "Index(['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers',\n", + " 'vente_internet_max', 'purchase_date_min', 'purchase_date_max',\n", + " 'nb_tickets_internet', 'is_email_true', 'opt_in', 'gender_female',\n", + " 'gender_male', 'nb_campaigns', 'nb_campaigns_opened'],\n", + " dtype='object')" ] }, - "execution_count": 76, + "execution_count": 447, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "X_train.isna().sum()" + "X_colnames = X.drop(\"const\", axis=1).columns\n", + "X_colnames" ] }, { "cell_type": "code", - "execution_count": 80, - "id": "6f07a66f-5a46-4409-b0b6-ff5e212296f0", + "execution_count": 448, + "id": "4436abe2-ac0f-480d-aa12-491c059f906a", "metadata": {}, "outputs": [ { "data": { + "image/png": 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354365 rows × 17 columns

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", "text/plain": [ - " nb_tickets nb_purchases total_amount nb_suppliers \\\n", - "0 0.0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 0.0 \n", - "... ... ... ... ... \n", - "354360 0.0 0.0 0.0 0.0 \n", - "354361 0.0 0.0 0.0 0.0 \n", - "354362 2.0 2.0 50.0 1.0 \n", - "354363 1.0 1.0 55.0 1.0 \n", - "354364 0.0 0.0 0.0 0.0 \n", - "\n", - " vente_internet_max purchase_date_min purchase_date_max \\\n", - "0 0.0 550.000000 550.000000 \n", - "1 0.0 550.000000 550.000000 \n", - "2 0.0 550.000000 550.000000 \n", - "3 0.0 550.000000 550.000000 \n", - "4 0.0 550.000000 550.000000 \n", - "... ... ... ... \n", - "354360 0.0 550.000000 550.000000 \n", - "354361 0.0 550.000000 550.000000 \n", - "354362 0.0 91.030556 91.020139 \n", - "354363 0.0 52.284028 52.284028 \n", - "354364 0.0 550.000000 550.000000 \n", - "\n", - " time_between_purchase nb_tickets_internet fidelity is_email_true \\\n", - "0 -1.000000 0.0 1 True \n", - "1 -1.000000 0.0 0 True \n", - "2 -1.000000 0.0 1 True \n", - "3 -1.000000 0.0 0 True \n", - "4 -1.000000 0.0 0 True \n", - "... ... ... ... ... \n", - "354360 -1.000000 0.0 0 True \n", - "354361 -1.000000 0.0 0 True \n", - "354362 0.010417 0.0 4 True \n", - "354363 0.000000 0.0 1 True \n", - "354364 -1.000000 0.0 0 True \n", - "\n", - " opt_in gender_female gender_male gender_other nb_campaigns \\\n", - "0 True 1 0 0 13.0 \n", - "1 True 0 0 1 10.0 \n", - "2 True 0 1 0 14.0 \n", - "3 False 0 0 1 9.0 \n", - "4 False 0 0 1 4.0 \n", - "... ... ... ... ... ... \n", - "354360 False 0 0 1 7.0 \n", - "354361 True 0 1 0 11.0 \n", - "354362 False 1 0 0 6.0 \n", - "354363 True 0 1 0 3.0 \n", - "354364 False 0 1 0 7.0 \n", - "\n", - " nb_campaigns_opened \n", - "0 4.0 \n", - "1 9.0 \n", - "2 0.0 \n", - "3 0.0 \n", - "4 0.0 \n", - "... ... \n", - "354360 0.0 \n", - "354361 2.0 \n", - "354362 6.0 \n", - "354363 0.0 \n", - "354364 0.0 \n", - "\n", - "[354365 rows x 17 columns]" + "
" ] }, - "execution_count": 134, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "X_train" + "# hide right part of the graphic\n", + "# some coefficients are still strictly positive even for alpha =10k, which makes the graphic quite confusing\n", + "# alternative syntax\n", + "\n", + "endpoint = 9\n", + "\n", + "fig, ax = plt.subplots(figsize=[12,8], dpi=110)\n", + "\n", + "for i in range(len(X_colnames)) :\n", + " var_name = X_colnames[i]\n", + " ax.plot(alphas_sorted[:endpoint], [results[p][0][i] for p in range(len(results[:endpoint]))], label=var_name)\n", + " \n", + "ax.set(xlabel=\"alpha\",\n", + " ylabel=\"valeur du coefficient\",\n", + " title = \"Evolution de la valeur des coefficents du logit LASSO en fonction du paramètre de pénalité alpha\")\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c3c9bb8c-5d8b-47a6-b0b5-273217ff2664", + "metadata": {}, + "source": [ + "A retenir : \\\n", + "D'après le premier tableau de résultats, toutes les variables sont significatives au seuil de 5%, et à l'exception de nb tickets, elles sont même significatives à 0.1%. \\\n", + "Le graphique ci-dessus confirme que opt in, purchase date max, ventes internet max sont très importantes dans le modèle (on l'avait déjà remarqué car les valeurs des coefficients étaient élevées). \\\n", + "Au contraire, des variables qui avaient un fort coefficient comme is email true (0.87) se trouvent finalement fortement pénalisées et tombent plus vite à 0 que les autres. " ] } ], From 14423b1d34614e5b158d257c4b63e824d47f212e Mon Sep 17 00:00:00 2001 From: tpique-ensae Date: Sat, 16 Mar 2024 10:43:11 +0000 Subject: [PATCH 04/15] init full modelization --- .../3_full_modelization_sport.ipynb | 219 ++++++++++++++++++ 1 file changed, 219 insertions(+) create mode 100644 Sport/Modelization/3_full_modelization_sport.ipynb diff --git a/Sport/Modelization/3_full_modelization_sport.ipynb b/Sport/Modelization/3_full_modelization_sport.ipynb new file mode 100644 index 0000000..d4193c1 --- /dev/null +++ b/Sport/Modelization/3_full_modelization_sport.ipynb @@ -0,0 +1,219 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ff8cc602-e733-4a31-bf46-a31087511fe0", + "metadata": {}, + "source": [ + "# Predict sales - sports companies" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "035dacc7-85db-47c9-b350-5ce54a2b5cdd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6666666666666666\n", + "0.5\n", + "0.6666666666666666 0.5714285714285715\n" + ] + } + ], + "source": [ + "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n", + "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n", + "\n", + "y_test, y_pred = [1,1,0], [1,0,0]\n", + "print(accuracy_score(y_test, y_pred))\n", + "print(recall_score(y_test, y_pred))\n", + "print(f1_score(y_test, y_pred), 2 * accuracy_score(y_test, y_pred) * recall_score(y_test, y_pred)/(accuracy_score(y_test, y_pred) + recall_score(y_test, y_pred)))" + ] + }, + { + "cell_type": "markdown", + "id": "415e466a-1a71-4150-bff7-2f8904766df4", + "metadata": {}, + "source": [ + "## Importations" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b5aaf421-850a-4a86-8e99-2c1f0723bd6c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import s3fs\n", + "import re\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n", + "from sklearn.utils import class_weight\n", + "from sklearn.neighbors import KNeighborsClassifier\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.model_selection import GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n", + "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n", + "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", + "\n", + "import pickle\n", + "import warnings" + ] + }, + { + "cell_type": "markdown", + "id": "c2f44070-451e-4109-9a08-3b80011d610f", + "metadata": {}, + "source": [ + "## Load data " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b5f8135f-b6e7-4d6d-b8e1-da185b944aff", + "metadata": {}, + "outputs": [], + "source": [ + "# Create filesystem object\n", + "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", + "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "2668a243-4ff8-40c6-9de2-5c9c07bcf714", + "metadata": {}, + "outputs": [], + "source": [ + "def load_train_test():\n", + " BUCKET = \"projet-bdc2324-team1/Generalization/sport\"\n", + " File_path_train = BUCKET + \"/Train_set.csv\"\n", + " File_path_test = BUCKET + \"/Test_set.csv\"\n", + " \n", + " with fs.open( File_path_train, mode=\"rb\") as file_in:\n", + " dataset_train = pd.read_csv(file_in, sep=\",\")\n", + " # dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n", + "\n", + " with fs.open(File_path_test, mode=\"rb\") as file_in:\n", + " dataset_test = pd.read_csv(file_in, sep=\",\")\n", + " # dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n", + " \n", + " return dataset_train, dataset_test" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "13eba3e1-3ea5-435b-8b05-6d7d5744cbe2", + "metadata": {}, + "outputs": [ + { + "ename": "PermissionError", + "evalue": "Forbidden", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mClientError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:529\u001b[0m, in \u001b[0;36mS3FileSystem.info\u001b[0;34m(self, path, version_id, refresh)\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 529\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_s3\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43ms3\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhead_object\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mBucket\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbucket\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 530\u001b[0m \u001b[43m \u001b[49m\u001b[43mKey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mversion_id_kw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mversion_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreq_kw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 531\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m 532\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m: out[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 533\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mKey\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([bucket, key]),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVersionId\u001b[39m\u001b[38;5;124m'\u001b[39m: out\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVersionId\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 541\u001b[0m }\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:200\u001b[0m, in \u001b[0;36mS3FileSystem._call_s3\u001b[0;34m(self, method, *akwarglist, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m additional_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_s3_method_kwargs(method, \u001b[38;5;241m*\u001b[39makwarglist,\n\u001b[1;32m 199\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43madditional_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:553\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:1009\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m 1008\u001b[0m error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m-> 1009\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[0;31mClientError\u001b[0m: An error occurred (403) when calling the HeadObject operation: Forbidden", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mPermissionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dataset_train, dataset_test \u001b[38;5;241m=\u001b[39m \u001b[43mload_train_test\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m dataset_train\u001b[38;5;241m.\u001b[39misna()\u001b[38;5;241m.\u001b[39msum()\n", + "Cell \u001b[0;32mIn[18], line 6\u001b[0m, in \u001b[0;36mload_train_test\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m File_path_train \u001b[38;5;241m=\u001b[39m BUCKET \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/Train_set.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4\u001b[0m File_path_test \u001b[38;5;241m=\u001b[39m BUCKET \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/Test_set.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[43mFile_path_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m file_in:\n\u001b[1;32m 7\u001b[0m dataset_train \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(file_in, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\u001b[39;00m\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1295\u001b[0m, in \u001b[0;36mAbstractFileSystem.open\u001b[0;34m(self, path, mode, block_size, cache_options, compression, **kwargs)\u001b[0m\n\u001b[1;32m 1293\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1294\u001b[0m ac \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mautocommit\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_intrans)\n\u001b[0;32m-> 1295\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1296\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1297\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1298\u001b[0m \u001b[43m \u001b[49m\u001b[43mblock_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mblock_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1299\u001b[0m \u001b[43m \u001b[49m\u001b[43mautocommit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mac\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1300\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1301\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1302\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1304\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfsspec\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompression\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compr\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:375\u001b[0m, in \u001b[0;36mS3FileSystem._open\u001b[0;34m(self, path, mode, block_size, acl, version_id, fill_cache, cache_type, autocommit, requester_pays, **kwargs)\u001b[0m\n\u001b[1;32m 372\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 373\u001b[0m cache_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_cache_type\n\u001b[0;32m--> 375\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mS3File\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43macl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43macl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 376\u001b[0m \u001b[43m \u001b[49m\u001b[43mversion_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mversion_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 377\u001b[0m \u001b[43m \u001b[49m\u001b[43ms3_additional_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 378\u001b[0m \u001b[43m \u001b[49m\u001b[43mautocommit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautocommit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrequester_pays\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequester_pays\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:1096\u001b[0m, in \u001b[0;36mS3File.__init__\u001b[0;34m(self, s3, path, mode, block_size, acl, version_id, fill_cache, s3_additional_kwargs, autocommit, cache_type, requester_pays)\u001b[0m\n\u001b[1;32m 1094\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ms3_additional_kwargs \u001b[38;5;241m=\u001b[39m s3_additional_kwargs \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[1;32m 1095\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreq_kw \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRequestPayer\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrequester\u001b[39m\u001b[38;5;124m'\u001b[39m} \u001b[38;5;28;01mif\u001b[39;00m requester_pays \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[0;32m-> 1096\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43ms3\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautocommit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautocommit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1097\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1098\u001b[0m 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refresh)\u001b[0m\n\u001b[1;32m 546\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m(S3FileSystem, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39minfo(path)\n\u001b[1;32m 547\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 548\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ee\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ParamValidationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 550\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to head path \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (path, e))\n", + "\u001b[0;31mPermissionError\u001b[0m: Forbidden" + ] + } + ], + "source": [ + "dataset_train, dataset_test = load_train_test()\n", + "dataset_train.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e46622e7-0fc1-43f8-a7e7-34a5e90068b2", + "metadata": {}, + "outputs": [], + "source": [ + "def features_target_split(dataset_train, dataset_test):\n", + " features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n", + " 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n", + " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", + " X_train = dataset_train[features_l]\n", + " y_train = dataset_train[['y_has_purchased']]\n", + "\n", + " X_test = dataset_test[features_l]\n", + " y_test = dataset_test[['y_has_purchased']]\n", + " return X_train, X_test, y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cec4f386-e643-4bd8-b8cd-8917d2c1b3d0", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)\n", + "print(\"Shape train : \", X_train.shape)\n", + "print(\"Shape test : \", X_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5978909b-eaad-4442-9bee-c98d603f0e80", + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 53f32000b53bc41c6ce87515eda4376cb1ce693a Mon Sep 17 00:00:00 2001 From: ajoubrel-ensae Date: Sat, 16 Mar 2024 14:47:46 +0000 Subject: [PATCH 05/15] Changement sur customerplus --- 0_Cleaning_and_merge_functions.py | 2 +- 0_KPI_functions.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/0_Cleaning_and_merge_functions.py b/0_Cleaning_and_merge_functions.py index 64ec7d4..35262d0 100644 --- a/0_Cleaning_and_merge_functions.py +++ b/0_Cleaning_and_merge_functions.py @@ -74,7 +74,7 @@ def preprocessing_customerplus(directory_path): cleaning_date(customerplus_copy, 'last_visiting_date') # Selection des variables - customerplus_copy.drop(['lastname', 'firstname', 'birthdate', 'profession', 'language', 'age', 'email', 'civility', 'note', 'extra', 'reference', 'extra_field', 'need_reload', 'preferred_category', 'preferred_supplier', 'preferred_formula', 'zipcode', 'last_visiting_date'], axis = 1, inplace=True) + customerplus_copy.drop(['lastname', 'firstname', 'birthdate', 'language', 'email', 'civility', 'note', 'extra', 'reference', 'extra_field', 'need_reload', 'preferred_category', 'preferred_supplier', 'preferred_formula', 'mcp_contact_id' 'last_visiting_date', 'deleted_at'], axis = 1, inplace=True) customerplus_copy.rename(columns = {'id' : 'customer_id'}, inplace = True) return customerplus_copy diff --git a/0_KPI_functions.py b/0_KPI_functions.py index 26b6814..a8828ce 100644 --- a/0_KPI_functions.py +++ b/0_KPI_functions.py @@ -97,6 +97,7 @@ def customerplus_kpi_function(customerplus_clean = None): gender_dummies = pd.get_dummies(customerplus_clean["gender_label"], prefix='gender').astype(int) customerplus_clean = pd.concat([customerplus_clean, gender_dummies], axis=1) + customerplus_clean['opt_in'] = np.multiply(customersplus['opt_in'], 1) ## Indicatrice si individue vit en France customerplus_clean["country_fr"] = customerplus_clean["country"].apply(lambda x : int(x=="fr") if pd.notna(x) else np.nan) From 4c7bdf712b97856ba2963e3ed3ebfa1e4a32c55f Mon Sep 17 00:00:00 2001 From: ajoubrel-ensae Date: Sat, 16 Mar 2024 15:01:52 +0000 Subject: [PATCH 06/15] Correction + fait tourner customerplus --- 0_1_Input_cleaning.py | 2 +- 0_Cleaning_and_merge_functions.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/0_1_Input_cleaning.py b/0_1_Input_cleaning.py index a7fc903..f5f4115 100644 --- a/0_1_Input_cleaning.py +++ b/0_1_Input_cleaning.py @@ -30,7 +30,7 @@ def export_dataset(df, output_name): df.to_csv(file_out, index = False) ## 1 - Cleaning of the datasets -for tenant_id in ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "101"]: +for tenant_id in ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14"]:#, "101" # Timer start = time.time() diff --git a/0_Cleaning_and_merge_functions.py b/0_Cleaning_and_merge_functions.py index 35262d0..3bf4618 100644 --- a/0_Cleaning_and_merge_functions.py +++ b/0_Cleaning_and_merge_functions.py @@ -74,7 +74,7 @@ def preprocessing_customerplus(directory_path): cleaning_date(customerplus_copy, 'last_visiting_date') # Selection des variables - customerplus_copy.drop(['lastname', 'firstname', 'birthdate', 'language', 'email', 'civility', 'note', 'extra', 'reference', 'extra_field', 'need_reload', 'preferred_category', 'preferred_supplier', 'preferred_formula', 'mcp_contact_id' 'last_visiting_date', 'deleted_at'], axis = 1, inplace=True) + customerplus_copy.drop(['lastname', 'firstname', 'birthdate', 'language', 'email', 'civility', 'note', 'extra', 'reference', 'extra_field', 'need_reload', 'preferred_category', 'preferred_supplier', 'preferred_formula', 'mcp_contact_id', 'last_visiting_date', 'deleted_at'], axis = 1, inplace=True) customerplus_copy.rename(columns = {'id' : 'customer_id'}, inplace = True) return customerplus_copy From 746f764973b9b1b81645580a883677f077548595 Mon Sep 17 00:00:00 2001 From: tpique-ensae Date: Sat, 16 Mar 2024 15:16:34 +0000 Subject: [PATCH 07/15] finished : logit pipeline + visu (sports) --- .../3_logit_cross_val_sport.ipynb | 4399 +++++++++++++++++ 1 file changed, 4399 insertions(+) create mode 100644 Sport/Modelization/3_logit_cross_val_sport.ipynb diff --git a/Sport/Modelization/3_logit_cross_val_sport.ipynb b/Sport/Modelization/3_logit_cross_val_sport.ipynb new file mode 100644 index 0000000..1e7627b --- /dev/null +++ b/Sport/Modelization/3_logit_cross_val_sport.ipynb @@ -0,0 +1,4399 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ff8cc602-e733-4a31-bf46-a31087511fe0", + "metadata": {}, + "source": [ + "# Predict sales - sports companies" + ] + }, + { + "cell_type": "markdown", + "id": "415e466a-1a71-4150-bff7-2f8904766df4", + "metadata": {}, + "source": [ + "## Importations" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b5aaf421-850a-4a86-8e99-2c1f0723bd6c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import s3fs\n", + "import re\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n", + "from sklearn.utils import class_weight\n", + "from sklearn.neighbors import KNeighborsClassifier\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.model_selection import GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n", + "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n", + "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", + "\n", + "import pickle\n", + "import warnings" + ] + }, + { + "cell_type": "markdown", + "id": "c2f44070-451e-4109-9a08-3b80011d610f", + "metadata": {}, + "source": [ + "## Load data " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b5f8135f-b6e7-4d6d-b8e1-da185b944aff", + "metadata": {}, + "outputs": [], + "source": [ + "# Create filesystem object\n", + "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", + "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2668a243-4ff8-40c6-9de2-5c9c07bcf714", + "metadata": {}, + "outputs": [], + "source": [ + "def load_train_test():\n", + " BUCKET = \"projet-bdc2324-team1/Generalization/sport\"\n", + " File_path_train = BUCKET + \"/Train_set.csv\"\n", + " File_path_test = BUCKET + \"/Test_set.csv\"\n", + " \n", + " with fs.open( File_path_train, mode=\"rb\") as file_in:\n", + " dataset_train = pd.read_csv(file_in, sep=\",\")\n", + " # dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n", + "\n", + " with fs.open(File_path_test, mode=\"rb\") as file_in:\n", + " dataset_test = pd.read_csv(file_in, sep=\",\")\n", + " # dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n", + " \n", + " return dataset_train, dataset_test" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "13eba3e1-3ea5-435b-8b05-6d7d5744cbe2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_462/2459610029.py:7: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " dataset_train = pd.read_csv(file_in, sep=\",\")\n" + ] + }, + { + "data": { + "text/plain": [ + "customer_id 0\n", + "nb_tickets 0\n", + "nb_purchases 0\n", + "total_amount 0\n", + "nb_suppliers 0\n", + "vente_internet_max 0\n", + "purchase_date_min 0\n", + "purchase_date_max 0\n", + "time_between_purchase 0\n", + "nb_tickets_internet 0\n", + "street_id 0\n", + "structure_id 222825\n", + "mcp_contact_id 70874\n", + "fidelity 0\n", + "tenant_id 0\n", + "is_partner 0\n", + "deleted_at 224213\n", + "gender 0\n", + "is_email_true 0\n", + "opt_in 0\n", + "last_buying_date 66139\n", + "max_price 66139\n", + "ticket_sum 0\n", + "average_price 66023\n", + "average_purchase_delay 66139\n", + "average_price_basket 66139\n", + "average_ticket_basket 66139\n", + "total_price 116\n", + "purchase_count 0\n", + "first_buying_date 66139\n", + "country 23159\n", + "gender_label 0\n", + "gender_female 0\n", + "gender_male 0\n", + "gender_other 0\n", + "country_fr 23159\n", + "nb_campaigns 0\n", + "nb_campaigns_opened 0\n", + "time_to_open 123159\n", + "y_has_purchased 0\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset_train, dataset_test = load_train_test()\n", + "dataset_train.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e46622e7-0fc1-43f8-a7e7-34a5e90068b2", + "metadata": {}, + "outputs": [], + "source": [ + "def features_target_split(dataset_train, dataset_test):\n", + " \"\"\"\n", + " features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n", + " 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n", + " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", + " \"\"\"\n", + "\n", + " # we suppress fidelity, time between purchase, and gender other (colinearity issue)\n", + " features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', \n", + " 'purchase_date_min', 'purchase_date_max', 'nb_tickets_internet', 'is_email_true', \n", + " 'opt_in', 'gender_female', 'gender_male', 'nb_campaigns', 'nb_campaigns_opened']\n", + " \n", + " X_train = dataset_train[features_l]\n", + " y_train = dataset_train[['y_has_purchased']]\n", + "\n", + " X_test = dataset_test[features_l]\n", + " y_test = dataset_test[['y_has_purchased']]\n", + " return X_train, X_test, y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "cec4f386-e643-4bd8-b8cd-8917d2c1b3d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape train : (224213, 14)\n", + "Shape test : (96096, 14)\n" + ] + } + ], + "source": [ + "X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)\n", + "print(\"Shape train : \", X_train.shape)\n", + "print(\"Shape test : \", X_test.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "c9e8edbd-7ff6-42f9-a8eb-10d27ca19c8a", + "metadata": {}, + "source": [ + "## Prepare preprocessing and Hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "639b432a-c39c-4bf8-8ee2-e136d156e0dd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0.0: 0.5837086520288036, 1.0: 3.486549107420539}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compute Weights\n", + "weights = class_weight.compute_class_weight(class_weight = 'balanced', classes = np.unique(y_train['y_has_purchased']),\n", + " y = y_train['y_has_purchased'])\n", + "\n", + "weight_dict = {np.unique(y_train['y_has_purchased'])[i]: weights[i] for i in range(len(np.unique(y_train['y_has_purchased'])))}\n", + "weight_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "34644a00-85a5-41c9-98df-41178cb3ac69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " nb_tickets nb_purchases total_amount nb_suppliers \\\n", + "0 2.0 1.0 60.00 1.0 \n", + "1 8.0 3.0 140.00 1.0 \n", + "2 2.0 1.0 50.00 1.0 \n", + "3 3.0 1.0 90.00 1.0 \n", + "4 2.0 1.0 78.00 1.0 \n", + "... ... ... ... ... \n", + "224208 0.0 0.0 0.00 0.0 \n", + "224209 1.0 1.0 20.00 1.0 \n", + "224210 0.0 0.0 0.00 0.0 \n", + "224211 1.0 1.0 97.11 1.0 \n", + "224212 0.0 0.0 0.00 0.0 \n", + "\n", + " vente_internet_max purchase_date_min purchase_date_max \\\n", + "0 0.0 355.268981 355.268981 \n", + "1 0.0 373.540289 219.262269 \n", + "2 0.0 5.202442 5.202442 \n", + "3 0.0 5.178958 5.178958 \n", + "4 0.0 5.174039 5.174039 \n", + "... ... ... ... \n", + "224208 0.0 550.000000 550.000000 \n", + "224209 1.0 392.501030 392.501030 \n", + "224210 0.0 550.000000 550.000000 \n", + "224211 1.0 172.334074 172.334074 \n", + "224212 0.0 550.000000 550.000000 \n", + "\n", + " nb_tickets_internet is_email_true opt_in gender_female \\\n", + "0 0.0 True False 0 \n", + "1 0.0 True False 0 \n", + "2 0.0 True False 0 \n", + "3 0.0 True False 0 \n", + "4 0.0 True False 1 \n", + "... ... ... ... ... \n", + "224208 0.0 True False 0 \n", + "224209 1.0 True False 0 \n", + "224210 0.0 True True 0 \n", + "224211 1.0 True False 0 \n", + "224212 0.0 True False 0 \n", + "\n", + " gender_male nb_campaigns nb_campaigns_opened \n", + "0 1 0.0 0.0 \n", + "1 1 0.0 0.0 \n", + "2 1 0.0 0.0 \n", + "3 1 0.0 0.0 \n", + "4 0 0.0 0.0 \n", + "... ... ... ... \n", + "224208 1 34.0 3.0 \n", + "224209 1 23.0 6.0 \n", + "224210 1 8.0 4.0 \n", + "224211 1 13.0 5.0 \n", + "224212 1 4.0 4.0 \n", + "\n", + "[224213 rows x 14 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "295676df-36ac-43d8-8b31-49ff08efd6e7", + "metadata": {}, + "outputs": [], + "source": [ + "# preprocess data \n", + "# numeric features - standardize\n", + "# categorical features - encode\n", + "# encoded features - do nothing\n", + "\n", + "numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', \n", + " 'purchase_date_min', 'purchase_date_max', 'nb_tickets_internet', 'nb_campaigns', \n", + " 'nb_campaigns_opened' # , 'gender_male', 'gender_female'\n", + " ]\n", + "\n", + "numeric_transformer = Pipeline(steps=[\n", + " #(\"imputer\", SimpleImputer(strategy=\"mean\")), \n", + " (\"scaler\", StandardScaler()) \n", + "])\n", + "\n", + "categorical_features = ['opt_in', 'is_email_true'] \n", + "\n", + "# Transformer for the categorical features\n", + "categorical_transformer = Pipeline(steps=[\n", + " #(\"imputer\", SimpleImputer(strategy=\"most_frequent\")), # Impute missing values with the most frequent\n", + " (\"onehot\", OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n", + "])\n", + "\n", + "preproc = ColumnTransformer(\n", + " transformers=[\n", + " (\"num\", numeric_transformer, numeric_features),\n", + " (\"cat\", categorical_transformer, categorical_features)\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "f46fb56e-c908-40b4-868f-9684d1ae01c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "nb_tickets 0\n", + "nb_purchases 0\n", + "total_amount 0\n", + "nb_suppliers 0\n", + "vente_internet_max 0\n", + "purchase_date_min 0\n", + "purchase_date_max 0\n", + "nb_tickets_internet 0\n", + "nb_campaigns 0\n", + "nb_campaigns_opened 0\n", + "dtype: int64" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train[numeric_features].isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "e729781b-4d65-42c5-bdc5-82b4d653aaf0", + "metadata": {}, + "outputs": [], + "source": [ + "# Set loss\n", + "balanced_scorer = make_scorer(balanced_accuracy_score)\n", + "recall_scorer = make_scorer(recall_score)\n", + "f1_scorer = make_scorer(f1_score)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a7ebbe6f-70ba-4276-be18-f10e7bfd7423", + "metadata": {}, + "outputs": [], + "source": [ + "def draw_confusion_matrix(y_test, y_pred):\n", + " conf_matrix = confusion_matrix(y_test, y_pred)\n", + " sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('Actual')\n", + " plt.title('Confusion Matrix')\n", + " plt.show()\n", + "\n", + "\n", + "def draw_roc_curve(X_test, y_test):\n", + " y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n", + "\n", + " # Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", + " fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", + " \n", + " # Calcul de l'aire sous la courbe ROC (AUC)\n", + " roc_auc = auc(fpr, tpr)\n", + " \n", + " plt.figure(figsize = (14, 8))\n", + " plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", + " plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", + " plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", + " plt.xlabel('Taux de faux positifs (FPR)')\n", + " plt.ylabel('Taux de vrais positifs (TPR)')\n", + " plt.title('Courbe ROC : modèle logistique')\n", + " plt.legend(loc=\"lower right\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "2334eb51-e6ea-4fd0-89ce-f54cd474d332", + "metadata": {}, + "outputs": [], + "source": [ + "def draw_features_importance(pipeline, model):\n", + " coefficients = pipeline.named_steps['logreg'].coef_[0]\n", + " feature_names = pipeline.named_steps['logreg'].feature_names_in_\n", + " \n", + " # Tracer l'importance des caractéristiques\n", + " plt.figure(figsize=(10, 6))\n", + " plt.barh(feature_names, coefficients, color='skyblue')\n", + " plt.xlabel('Importance des caractéristiques')\n", + " plt.ylabel('Caractéristiques')\n", + " plt.title('Importance des caractéristiques dans le modèle de régression logistique')\n", + " plt.grid(True)\n", + " plt.show()\n", + "\n", + "def draw_prob_distribution(X_test):\n", + " y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n", + " plt.figure(figsize=(8, 6))\n", + " plt.hist(y_pred_prob, bins=10, range=(0, 1), color='blue', alpha=0.7)\n", + " \n", + " plt.xlim(0, 1)\n", + " plt.ylim(0, None)\n", + " \n", + " plt.title('Histogramme des probabilités pour la classe 1')\n", + " plt.xlabel('Probabilité')\n", + " plt.ylabel('Fréquence')\n", + " plt.grid(True)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "83917b97-4d9b-4e3c-ba27-1e546ce885d3", + "metadata": {}, + "outputs": [], + "source": [ + "# Hyperparameter\n", + "\n", + "param_c = np.logspace(-10, 4, 15, base=2)\n", + "# param_penalty_type = ['l1', 'l2', 'elasticnet']\n", + "param_penalty_type = ['l1']\n", + "param_grid = {'logreg__C': param_c,\n", + " 'logreg__penalty': param_penalty_type} " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3ae25049-920c-4a6d-a59d-c26e3b45dec6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1024" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2 ** 10" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "ba4cde9f-a614-4a43-81b9-e16e78aa6c4c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('preprocessor',\n",
+       "                 ColumnTransformer(transformers=[('num',\n",
+       "                                                  Pipeline(steps=[('scaler',\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  ['nb_tickets', 'nb_purchases',\n",
+       "                                                   'total_amount',\n",
+       "                                                   'nb_suppliers',\n",
+       "                                                   'vente_internet_max',\n",
+       "                                                   'purchase_date_min',\n",
+       "                                                   'purchase_date_max',\n",
+       "                                                   'nb_tickets_internet',\n",
+       "                                                   'nb_campaigns',\n",
+       "                                                   'nb_campaigns_opened']),\n",
+       "                                                 ('cat',\n",
+       "                                                  Pipeline(steps=[('onehot',\n",
+       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
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+       "                                                  ['opt_in',\n",
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" + ], + "text/plain": [ + "Pipeline(steps=[('preprocessor',\n", + " ColumnTransformer(transformers=[('num',\n", + " Pipeline(steps=[('scaler',\n", + " StandardScaler())]),\n", + " ['nb_tickets', 'nb_purchases',\n", + " 'total_amount',\n", + " 'nb_suppliers',\n", + " 'vente_internet_max',\n", + " 'purchase_date_min',\n", + " 'purchase_date_max',\n", + " 'nb_tickets_internet',\n", + " 'nb_campaigns',\n", + " 'nb_campaigns_opened']),\n", + " ('cat',\n", + " Pipeline(steps=[('onehot',\n", + " OneHotEncoder(handle_unknown='ignore',\n", + " sparse_output=False))]),\n", + " ['opt_in',\n", + " 'is_email_true'])])),\n", + " ('logreg',\n", + " LogisticRegression(class_weight={0.0: 0.5837086520288036,\n", + " 1.0: 3.486549107420539},\n", + " max_iter=5000, solver='saga'))])" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Pipeline\n", + "pipeline = Pipeline(steps=[\n", + " ('preprocessor', preproc),\n", + " ('logreg', LogisticRegression(solver='saga', class_weight = weight_dict,\n", + " max_iter=5000)) \n", + "])\n", + "\n", + "pipeline.set_output(transform=\"pandas\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "1e4c1be5-176d-4222-9b3c-fe27225afe36", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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10000 rows × 14 columns

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" + ], + "text/plain": [ + " nb_tickets nb_purchases total_amount nb_suppliers \\\n", + "64466 11.0 4.0 281.4 1.0 \n", + "141327 0.0 0.0 0.0 0.0 \n", + "59999 2.0 1.0 0.0 1.0 \n", + "26882 0.0 0.0 0.0 0.0 \n", + "62952 11.0 3.0 325.0 1.0 \n", + "... ... ... ... ... \n", + "141318 0.0 0.0 0.0 0.0 \n", + "113838 3.0 2.0 15.0 1.0 \n", + "184926 0.0 0.0 0.0 0.0 \n", + "14617 1.0 1.0 20.0 1.0 \n", + "21685 4.0 1.0 88.0 1.0 \n", + "\n", + " vente_internet_max purchase_date_min purchase_date_max \\\n", + "64466 1.0 238.330591 30.285040 \n", + "141327 0.0 550.000000 550.000000 \n", + "59999 1.0 350.288926 350.288926 \n", + "26882 0.0 550.000000 550.000000 \n", + "62952 1.0 424.486781 237.282262 \n", + "... ... ... ... \n", + "141318 0.0 550.000000 550.000000 \n", + "113838 1.0 153.152945 90.277099 \n", + "184926 0.0 550.000000 550.000000 \n", + "14617 0.0 239.258970 239.258970 \n", + "21685 0.0 240.355162 240.355162 \n", + "\n", + " nb_tickets_internet is_email_true opt_in gender_female \\\n", + "64466 11.0 True False 1 \n", + "141327 0.0 True True 0 \n", + "59999 2.0 True False 1 \n", + "26882 0.0 True False 1 \n", + "62952 11.0 True False 0 \n", + "... ... ... ... ... \n", + "141318 0.0 True True 0 \n", + "113838 3.0 True True 0 \n", + "184926 0.0 True True 0 \n", + "14617 0.0 True True 0 \n", + "21685 0.0 True True 0 \n", + "\n", + " gender_male nb_campaigns nb_campaigns_opened \n", + "64466 0 0.0 0.0 \n", + "141327 0 10.0 0.0 \n", + "59999 0 0.0 0.0 \n", + "26882 0 4.0 1.0 \n", + "62952 0 0.0 0.0 \n", + "... ... ... ... \n", + "141318 0 16.0 1.0 \n", + "113838 1 31.0 14.0 \n", + "184926 1 18.0 0.0 \n", + "14617 1 0.0 0.0 \n", + "21685 1 0.0 0.0 \n", + "\n", + "[10000 rows x 14 columns]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reduce X_train to reduce the training time\n", + "\n", + "X_train_subsample = X_train.sample(n=10000)\n", + "y_train_subsample = y_train.loc[X_train_subsample.index]\n", + "X_train_subsample" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "2b09c2cd-fd5c-49b3-be66-cec6c5ec1351", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Pipeline(steps=[('preprocessor',\n",
+       "                 ColumnTransformer(transformers=[('num',\n",
+       "                                                  Pipeline(steps=[('scaler',\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  ['nb_tickets', 'nb_purchases',\n",
+       "                                                   'total_amount',\n",
+       "                                                   'nb_suppliers',\n",
+       "                                                   'vente_internet_max',\n",
+       "                                                   'purchase_date_min',\n",
+       "                                                   'purchase_date_max',\n",
+       "                                                   'nb_tickets_internetnb_campaigns',\n",
+       "                                                   'nb_campaigns_opened']),\n",
+       "                                                 ('cat',\n",
+       "                                                  Pipeline(steps=[('onehot',\n",
+       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
+       "                                                                                 sparse_output=False))]),\n",
+       "                                                  ['opt_in',\n",
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+       "                                    max_iter=5000, solver='saga'))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('preprocessor',\n", + " ColumnTransformer(transformers=[('num',\n", + " Pipeline(steps=[('scaler',\n", + " StandardScaler())]),\n", + " ['nb_tickets', 'nb_purchases',\n", + " 'total_amount',\n", + " 'nb_suppliers',\n", + " 'vente_internet_max',\n", + " 'purchase_date_min',\n", + " 'purchase_date_max',\n", + " 'nb_tickets_internetnb_campaigns',\n", + " 'nb_campaigns_opened']),\n", + " ('cat',\n", + " Pipeline(steps=[('onehot',\n", + " OneHotEncoder(handle_unknown='ignore',\n", + " sparse_output=False))]),\n", + " ['opt_in',\n", + " 'is_email_true'])])),\n", + " ('logreg',\n", + " LogisticRegression(class_weight={0.0: 0.5837086520288036,\n", + " 1.0: 3.486549107420539},\n", + " max_iter=5000, solver='saga'))])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "710ccccc-50c9-4aba-8cf1-11483dbbdd1c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", + " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", + " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", + " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", + " 'logreg__penalty': ['l1', 'l2', 'elasticnet']}" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "ab078cf8-0d4c-4b23-9f33-2483cf605b06", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "make_scorer(f1_score, response_method='predict')" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f1_scorer" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "8062169e-8305-42b0-aeff-8f714117da40", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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10000 rows × 14 columns

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" + ], + "text/plain": [ + " nb_tickets nb_purchases total_amount nb_suppliers \\\n", + "64466 11.0 4.0 281.4 1.0 \n", + "141327 0.0 0.0 0.0 0.0 \n", + "59999 2.0 1.0 0.0 1.0 \n", + "26882 0.0 0.0 0.0 0.0 \n", + "62952 11.0 3.0 325.0 1.0 \n", + "... ... ... ... ... \n", + "141318 0.0 0.0 0.0 0.0 \n", + "113838 3.0 2.0 15.0 1.0 \n", + "184926 0.0 0.0 0.0 0.0 \n", + "14617 1.0 1.0 20.0 1.0 \n", + "21685 4.0 1.0 88.0 1.0 \n", + "\n", + " vente_internet_max purchase_date_min purchase_date_max \\\n", + "64466 1.0 238.330591 30.285040 \n", + "141327 0.0 550.000000 550.000000 \n", + "59999 1.0 350.288926 350.288926 \n", + "26882 0.0 550.000000 550.000000 \n", + "62952 1.0 424.486781 237.282262 \n", + "... ... ... ... \n", + "141318 0.0 550.000000 550.000000 \n", + "113838 1.0 153.152945 90.277099 \n", + "184926 0.0 550.000000 550.000000 \n", + "14617 0.0 239.258970 239.258970 \n", + "21685 0.0 240.355162 240.355162 \n", + "\n", + " nb_tickets_internet is_email_true opt_in gender_female \\\n", + "64466 11.0 True False 1 \n", + "141327 0.0 True True 0 \n", + "59999 2.0 True False 1 \n", + "26882 0.0 True False 1 \n", + "62952 11.0 True False 0 \n", + "... ... ... ... ... \n", + "141318 0.0 True True 0 \n", + "113838 3.0 True True 0 \n", + "184926 0.0 True True 0 \n", + "14617 0.0 True True 0 \n", + "21685 0.0 True True 0 \n", + "\n", + " gender_male nb_campaigns nb_campaigns_opened \n", + "64466 0 0.0 0.0 \n", + "141327 0 10.0 0.0 \n", + "59999 0 0.0 0.0 \n", + "26882 0 4.0 1.0 \n", + "62952 0 0.0 0.0 \n", + "... ... ... ... \n", + "141318 0 16.0 1.0 \n", + "113838 1 31.0 14.0 \n", + "184926 1 18.0 0.0 \n", + "14617 1 0.0 0.0 \n", + "21685 1 0.0 0.0 \n", + "\n", + "[10000 rows x 14 columns]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train_subsample" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "0270013a-6523-4cf8-8de0-569c0d1c5db5", + "metadata": {}, + "outputs": [], + "source": [ + "warnings.filterwarnings('ignore')\n", + "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n", + "warnings.filterwarnings(\"ignore\", category=DataConversionWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "7a49d78a-5a9b-44a9-95cf-3fca1b3febfa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned hyperparameter: {'logreg__C': 0.03125, 'logreg__penalty': 'l1'}\n", + "Best classification accuracy in train is: 0.47785817197986385\n" + ] + } + ], + "source": [ + "# run the pipeline on the subsample\n", + "\n", + "logit_grid = GridSearchCV(pipeline, param_grid, cv=3, scoring = f1_scorer #, error_score=\"raise\"\n", + " )\n", + "logit_grid.fit(X_train_subsample, y_train_subsample)\n", + "\n", + "# print results\n", + "print('Returned hyperparameter: {}'.format(logit_grid.best_params_))\n", + "print('Best classification accuracy in train is: {}'.format(logit_grid.best_score_))\n", + "# print('Classification accuracy on test is: {}'.format(logit_grid.score(X_test, y_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "b1d5e71d-1078-4370-86e8-52b1ae378898", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", + " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", + " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", + " 4.000000e+00, 8.000000e+00, 1.600000e+01])" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param_c" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "cfe04739-fe9c-4802-9d34-885a8cfce0dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
GridSearchCV(cv=3,\n",
+       "             estimator=Pipeline(steps=[('preprocessor',\n",
+       "                                        ColumnTransformer(transformers=[('num',\n",
+       "                                                                         Pipeline(steps=[('scaler',\n",
+       "                                                                                          StandardScaler())]),\n",
+       "                                                                         ['nb_tickets',\n",
+       "                                                                          'nb_purchases',\n",
+       "                                                                          'total_amount',\n",
+       "                                                                          'nb_suppliers',\n",
+       "                                                                          'vente_internet_max',\n",
+       "                                                                          'purchase_date_min',\n",
+       "                                                                          'purchase_date_max',\n",
+       "                                                                          'nb_tickets_internet',\n",
+       "                                                                          'nb_campaigns',\n",
+       "                                                                          'nb_campaigns_opened']),\n",
+       "                                                                        ('cat',\n",
+       "                                                                         Pipeline(steps=[(...\n",
+       "                                                                         1.0: 3.486549107420539},\n",
+       "                                                           max_iter=5000,\n",
+       "                                                           solver='saga'))]),\n",
+       "             param_grid={'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n",
+       "       1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n",
+       "       2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n",
+       "       4.000000e+00, 8.000000e+00, 1.600000e+01]),\n",
+       "                         'logreg__penalty': ['l1']},\n",
+       "             scoring=make_scorer(f1_score, response_method='predict'))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GridSearchCV(cv=3,\n", + " estimator=Pipeline(steps=[('preprocessor',\n", + " ColumnTransformer(transformers=[('num',\n", + " Pipeline(steps=[('scaler',\n", + " StandardScaler())]),\n", + " ['nb_tickets',\n", + " 'nb_purchases',\n", + " 'total_amount',\n", + " 'nb_suppliers',\n", + " 'vente_internet_max',\n", + " 'purchase_date_min',\n", + " 'purchase_date_max',\n", + " 'nb_tickets_internet',\n", + " 'nb_campaigns',\n", + " 'nb_campaigns_opened']),\n", + " ('cat',\n", + " Pipeline(steps=[(...\n", + " 1.0: 3.486549107420539},\n", + " max_iter=5000,\n", + " solver='saga'))]),\n", + " param_grid={'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", + " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", + " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", + " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", + " 'logreg__penalty': ['l1']},\n", + " scoring=make_scorer(f1_score, response_method='predict'))" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logit_grid = GridSearchCV(pipeline, param_grid, cv=3, scoring = f1_scorer #, error_score=\"raise\"\n", + " )\n", + "logit_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "6debc66c-a56d-41fa-8ef8-ba388e0e14fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", + " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", + " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", + " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", + " 'logreg__penalty': ['l1']}" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "e394cc04-5d0b-4a64-9aa0-415dc8a3cbbc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned hyperparameter: {'logreg__C': 0.03125, 'logreg__penalty': 'l1'}\n", + "Best classification accuracy in train is: 0.42160313383818665\n", + "Classification accuracy on test is: 0.47078982841737305\n" + ] + } + ], + "source": [ + "# run the pipeline on the full sample\n", + "\n", + "logit_grid = GridSearchCV(pipeline, param_grid, cv=3, scoring = f1_scorer #, error_score=\"raise\"\n", + " )\n", + "logit_grid.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "8e6cf558-a4f4-4159-9835-364ee3bb1ed2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returned hyperparameter: {'logreg__C': 0.03125, 'logreg__penalty': 'l1'}\n", + "Best classification F1 score in train is: 0.42160313383818665\n", + "Classification F1 score on test is: 0.47078982841737305\n" + ] + } + ], + "source": [ + "# print results\n", + "print('Returned hyperparameter: {}'.format(logit_grid.best_params_))\n", + "print('Best classification F1 score in train is: {}'.format(logit_grid.best_score_))\n", + "print('Classification F1 score on test is: {}'.format(logit_grid.score(X_test, y_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "e2ff26cb-f137-4a23-9add-bdb61bebdf9c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
GridSearchCV(cv=3,\n",
+       "             estimator=Pipeline(steps=[('preprocessor',\n",
+       "                                        ColumnTransformer(transformers=[('num',\n",
+       "                                                                         Pipeline(steps=[('scaler',\n",
+       "                                                                                          StandardScaler())]),\n",
+       "                                                                         ['nb_tickets',\n",
+       "                                                                          'nb_purchases',\n",
+       "                                                                          'total_amount',\n",
+       "                                                                          'nb_suppliers',\n",
+       "                                                                          'vente_internet_max',\n",
+       "                                                                          'purchase_date_min',\n",
+       "                                                                          'purchase_date_max',\n",
+       "                                                                          'nb_tickets_internet',\n",
+       "                                                                          'nb_campaigns',\n",
+       "                                                                          'nb_campaigns_opened']),\n",
+       "                                                                        ('cat',\n",
+       "                                                                         Pipeline(steps=[(...\n",
+       "                                                                         1.0: 3.486549107420539},\n",
+       "                                                           max_iter=5000,\n",
+       "                                                           solver='saga'))]),\n",
+       "             param_grid={'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n",
+       "       1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n",
+       "       2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n",
+       "       4.000000e+00, 8.000000e+00, 1.600000e+01]),\n",
+       "                         'logreg__penalty': ['l1']},\n",
+       "             scoring=make_scorer(f1_score, response_method='predict'))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GridSearchCV(cv=3,\n", + " estimator=Pipeline(steps=[('preprocessor',\n", + " ColumnTransformer(transformers=[('num',\n", + " Pipeline(steps=[('scaler',\n", + " StandardScaler())]),\n", + " ['nb_tickets',\n", + " 'nb_purchases',\n", + " 'total_amount',\n", + " 'nb_suppliers',\n", + " 'vente_internet_max',\n", + " 'purchase_date_min',\n", + " 'purchase_date_max',\n", + " 'nb_tickets_internet',\n", + " 'nb_campaigns',\n", + " 'nb_campaigns_opened']),\n", + " ('cat',\n", + " Pipeline(steps=[(...\n", + " 1.0: 3.486549107420539},\n", + " max_iter=5000,\n", + " solver='saga'))]),\n", + " param_grid={'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", + " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", + " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", + " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", + " 'logreg__penalty': ['l1']},\n", + " scoring=make_scorer(f1_score, response_method='predict'))" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logit_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "5d553da2-5c2a-491a-b4d2-f31c30c201a6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'scoring': make_scorer(f1_score, response_method='predict'),\n", + " 'estimator': Pipeline(steps=[('preprocessor',\n", + " ColumnTransformer(transformers=[('num',\n", + " Pipeline(steps=[('scaler',\n", + " StandardScaler())]),\n", + " ['nb_tickets', 'nb_purchases',\n", + " 'total_amount',\n", + " 'nb_suppliers',\n", + " 'vente_internet_max',\n", + " 'purchase_date_min',\n", + " 'purchase_date_max',\n", + " 'nb_tickets_internet',\n", + " 'nb_campaigns',\n", + " 'nb_campaigns_opened']),\n", + " ('cat',\n", + " Pipeline(steps=[('onehot',\n", + " OneHotEncoder(handle_unknown='ignore',\n", + " sparse_output=False))]),\n", + " ['opt_in',\n", + " 'is_email_true'])])),\n", + " ('logreg',\n", + " LogisticRegression(class_weight={0.0: 0.5837086520288036,\n", + " 1.0: 3.486549107420539},\n", + " max_iter=5000, solver='saga'))]),\n", + " 'n_jobs': None,\n", + " 'refit': True,\n", + " 'cv': 3,\n", + " 'verbose': 0,\n", + " 'pre_dispatch': '2*n_jobs',\n", + " 'error_score': nan,\n", + " 'return_train_score': False,\n", + " 'param_grid': {'logreg__C': array([9.765625e-04, 1.953125e-03, 3.906250e-03, 7.812500e-03,\n", + " 1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n", + " 2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n", + " 4.000000e+00, 8.000000e+00, 1.600000e+01]),\n", + " 'logreg__penalty': ['l1']},\n", + " 'multimetric_': False,\n", + " 'best_index_': 5,\n", + " 'best_score_': 0.42160313383818665,\n", + " 'best_params_': {'logreg__C': 0.03125, 'logreg__penalty': 'l1'},\n", + " 'best_estimator_': Pipeline(steps=[('preprocessor',\n", + " ColumnTransformer(transformers=[('num',\n", + " Pipeline(steps=[('scaler',\n", + " StandardScaler())]),\n", + " ['nb_tickets', 'nb_purchases',\n", + " 'total_amount',\n", + " 'nb_suppliers',\n", + " 'vente_internet_max',\n", + " 'purchase_date_min',\n", + " 'purchase_date_max',\n", + " 'nb_tickets_internet',\n", + " 'nb_campaigns',\n", + " 'nb_campaigns_opened']),\n", + " ('cat',\n", + " Pipeline(steps=[('onehot',\n", + " OneHotEncoder(handle_unknown='ignore',\n", + " sparse_output=False))]),\n", + " ['opt_in',\n", + " 'is_email_true'])])),\n", + " ('logreg',\n", + " LogisticRegression(C=0.03125,\n", + " class_weight={0.0: 0.5837086520288036,\n", + " 1.0: 3.486549107420539},\n", + " max_iter=5000, penalty='l1',\n", + " solver='saga'))]),\n", + " 'refit_time_': 305.1356477737427,\n", + " 'feature_names_in_': array(['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers',\n", + " 'vente_internet_max', 'purchase_date_min', 'purchase_date_max',\n", + " 'nb_tickets_internet', 'is_email_true', 'opt_in', 'gender_female',\n", + " 'gender_male', 'nb_campaigns', 'nb_campaigns_opened'], dtype=object),\n", + " 'scorer_': make_scorer(f1_score, response_method='predict'),\n", + " 'cv_results_': {'mean_fit_time': array([ 11.07076669, 13.15744201, 27.35094929, 40.0343461 ,\n", + " 94.58210254, 140.45846391, 159.83818332, 162.80178094,\n", + " 163.94260454, 171.08749111, 169.26621262, 166.36741408,\n", + " 167.91208776, 173.06720233, 170.93666704]),\n", + " 'std_fit_time': array([ 0.09462032, 1.51362591, 6.70859141, 22.68643753, 28.72690872,\n", + " 70.8434823 , 85.23159321, 79.71538593, 82.70486235, 84.79706797,\n", + " 86.79005212, 84.67956107, 83.94889047, 89.68716252, 89.41361431]),\n", + " 'mean_score_time': array([0.11632609, 0.10857773, 0.18140252, 0.1291213 , 0.11651532,\n", + " 0.07535577, 0.12481014, 0.16039928, 0.15685773, 0.07996233,\n", + " 0.12988146, 0.10067987, 0.1194102 , 0.09737802, 0.09390028]),\n", + " 'std_score_time': array([0.02131792, 0.03620144, 0.05853886, 0.06555575, 0.03228018,\n", + " 0.01433186, 0.03501336, 0.05466042, 0.06882891, 0.01002881,\n", + " 0.00495894, 0.00905774, 0.04075337, 0.03269379, 0.01990173]),\n", + " 'param_logreg__C': masked_array(data=[0.0009765625, 0.001953125, 0.00390625, 0.0078125,\n", + " 0.015625, 0.03125, 0.0625, 0.125, 0.25, 0.5, 1.0, 2.0,\n", + " 4.0, 8.0, 16.0],\n", + " mask=[False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object),\n", + " 'param_logreg__penalty': masked_array(data=['l1', 'l1', 'l1', 'l1', 'l1', 'l1', 'l1', 'l1', 'l1',\n", + " 'l1', 'l1', 'l1', 'l1', 'l1', 'l1'],\n", + " mask=[False, False, False, False, False, False, False, False,\n", + " False, False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object),\n", + " 'params': [{'logreg__C': 0.0009765625, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 0.001953125, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 0.00390625, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 0.0078125, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 0.015625, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 0.03125, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 0.0625, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 0.125, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 0.25, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 0.5, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 1.0, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 2.0, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 4.0, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 8.0, 'logreg__penalty': 'l1'},\n", + " {'logreg__C': 16.0, 'logreg__penalty': 'l1'}],\n", + " 'split0_test_score': array([0.27289073, 0.2738913 , 0.27382853, 0.27409759, 0.27454764,\n", + " 0.27661894, 0.2766145 , 0.27584723, 0.27571682, 0.27576295,\n", + " 0.27580092, 0.27577943, 0.27581248, 0.27581909, 0.27581909]),\n", + " 'split1_test_score': array([0.4714244 , 0.47196015, 0.48362373, 0.48891733, 0.49066854,\n", + " 0.49091122, 0.49086284, 0.49065871, 0.49062783, 0.49049541,\n", + " 0.49048106, 0.49045238, 0.49043804, 0.49043804, 0.4904237 ]),\n", + " 'split2_test_score': array([0.50689906, 0.50092334, 0.4981377 , 0.49759178, 0.49725836,\n", + " 0.49727924, 0.49708801, 0.49738305, 0.49751781, 0.49738248,\n", + " 0.49738248, 0.49738248, 0.49738248, 0.49738248, 0.49738248]),\n", + " 'mean_test_score': array([0.4170714 , 0.4155916 , 0.41852999, 0.42020223, 0.42082484,\n", + " 0.42160313, 0.42152178, 0.42129633, 0.42128749, 0.42121361,\n", + " 0.42122149, 0.42120476, 0.421211 , 0.4212132 , 0.42120842]),\n", + " 'std_test_score': array([0.10297463, 0.1008925 , 0.10249081, 0.10337226, 0.10346859,\n", + " 0.10255226, 0.10249644, 0.10288467, 0.10297243, 0.10288758,\n", + " 0.10286646, 0.10287015, 0.10285136, 0.10284824, 0.10284503]),\n", + " 'rank_test_score': array([14, 15, 13, 12, 11, 1, 2, 3, 4, 6, 5, 10, 8, 7, 9],\n", + " dtype=int32)},\n", + " 'n_splits_': 3}" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logit_grid.__dict__" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "3573f34e-25d5-4afb-82cc-52323e2f63c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.34302143, 0. , 0.02675567, 0.45036527, -0.05004637,\n", + " 0.7663532 , -1.35216757, 0.17404712, 0.13679663, 0.10249737,\n", + " 0.40815146, -0.6311938 , 0.11194512, -0.33498749]])" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# coefficients trouvés pour le modèle optimal\n", + "logit_grid.best_estimator_.named_steps[\"logreg\"].coef_" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "0332a814-61fb-4b71-836a-e8ace70b1a44", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'preprocessor': ColumnTransformer(transformers=[('num',\n", + " Pipeline(steps=[('scaler', StandardScaler())]),\n", + " ['nb_tickets', 'nb_purchases', 'total_amount',\n", + " 'nb_suppliers', 'vente_internet_max',\n", + " 'purchase_date_min', 'purchase_date_max',\n", + " 'nb_tickets_internet', 'nb_campaigns',\n", + " 'nb_campaigns_opened']),\n", + " ('cat',\n", + " Pipeline(steps=[('onehot',\n", + " OneHotEncoder(handle_unknown='ignore',\n", + " sparse_output=False))]),\n", + " ['opt_in', 'is_email_true'])]),\n", + " 'logreg': LogisticRegression(C=0.03125,\n", + " class_weight={0.0: 0.5837086520288036,\n", + " 1.0: 3.486549107420539},\n", + " max_iter=5000, penalty='l1', solver='saga')}" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logit_grid.best_estimator_.named_steps" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "287615b9-e062-4b84-be61-26b9364b2cf4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.22304234])" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logit_grid.best_estimator_.named_steps[\"logreg\"].intercept_" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "4d50899d-cc0b-4a71-9406-f8b0a277c4a6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxnb_tickets_internetis_email_trueopt_ingender_femalegender_malenb_campaignsnb_campaigns_opened
02.01.060.001.00.0355.268981355.2689810.0TrueFalse010.00.0
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.............................................
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224213 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " nb_tickets nb_purchases total_amount nb_suppliers \\\n", + "0 2.0 1.0 60.00 1.0 \n", + "1 8.0 3.0 140.00 1.0 \n", + "2 2.0 1.0 50.00 1.0 \n", + "3 3.0 1.0 90.00 1.0 \n", + "4 2.0 1.0 78.00 1.0 \n", + "... ... ... ... ... \n", + "224208 0.0 0.0 0.00 0.0 \n", + "224209 1.0 1.0 20.00 1.0 \n", + "224210 0.0 0.0 0.00 0.0 \n", + "224211 1.0 1.0 97.11 1.0 \n", + "224212 0.0 0.0 0.00 0.0 \n", + "\n", + " vente_internet_max purchase_date_min purchase_date_max \\\n", + "0 0.0 355.268981 355.268981 \n", + "1 0.0 373.540289 219.262269 \n", + "2 0.0 5.202442 5.202442 \n", + "3 0.0 5.178958 5.178958 \n", + "4 0.0 5.174039 5.174039 \n", + "... ... ... ... \n", + "224208 0.0 550.000000 550.000000 \n", + "224209 1.0 392.501030 392.501030 \n", + "224210 0.0 550.000000 550.000000 \n", + "224211 1.0 172.334074 172.334074 \n", + "224212 0.0 550.000000 550.000000 \n", + "\n", + " nb_tickets_internet is_email_true opt_in gender_female \\\n", + "0 0.0 True False 0 \n", + "1 0.0 True False 0 \n", + "2 0.0 True False 0 \n", + "3 0.0 True False 0 \n", + "4 0.0 True False 1 \n", + "... ... ... ... ... \n", + "224208 0.0 True False 0 \n", + "224209 1.0 True False 0 \n", + "224210 0.0 True True 0 \n", + "224211 1.0 True False 0 \n", + "224212 0.0 True False 0 \n", + "\n", + " gender_male nb_campaigns nb_campaigns_opened \n", + "0 1 0.0 0.0 \n", + "1 1 0.0 0.0 \n", + "2 1 0.0 0.0 \n", + "3 1 0.0 0.0 \n", + "4 0 0.0 0.0 \n", + "... ... ... ... \n", + "224208 1 34.0 3.0 \n", + "224209 1 23.0 6.0 \n", + "224210 1 8.0 4.0 \n", + "224211 1 13.0 5.0 \n", + "224212 1 4.0 4.0 \n", + "\n", + "[224213 rows x 14 columns]" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# c'est la 2ème variable nb_purchases qui a été supprimée par le LASSO\n", + "X_train" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "e53b1f79-762d-4f1f-8505-91de1088af42", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32.0" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# best param : alpha = 32\n", + "1/logit_grid.best_params_[\"logreg__C\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "41bcaaf6-ab58-4004-a3c5-586d77e872d1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy Score: 0.7589597902097902\n", + "F1 Score: 0.47078982841737305\n", + "Recall Score: 0.7525931336742148\n" + ] + } + ], + "source": [ + "# print results for the best model\n", + "\n", + "y_pred = logit_grid.predict(X_test)\n", + "\n", + "# Calculate the F1 score\n", + "acc = accuracy_score(y_test, y_pred)\n", + "print(f\"Accuracy Score: {acc}\")\n", + "\n", + "f1 = f1_score(y_test, y_pred)\n", + "print(f\"F1 Score: {f1}\")\n", + "\n", + "recall = recall_score(y_test, y_pred)\n", + "print(f\"Recall Score: {recall}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "a454bb57-76eb-4a22-9950-0733d39e449f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# confusion matrix \n", + "\n", + "draw_confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "id": "25ec1701-ade5-4419-8b46-8a1bb109cf84", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ROC curve\n", + "\n", + "# Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", + "y_pred_prob = logit_grid.predict_proba(X_test)[:, 1]\n", + "\n", + "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", + "\n", + "# Calcul de l'aire sous la courbe ROC (AUC)\n", + "roc_auc = auc(fpr, tpr)\n", + "\n", + "plt.figure(figsize = (14, 8))\n", + "plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", + "plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", + "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", + "plt.xlabel('Taux de faux positifs (FPR)')\n", + "plt.ylabel('Taux de vrais positifs (TPR)')\n", + "plt.title('Courbe ROC : modèle logistique')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + } + ], + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From ab3b033f098c2bd4cc3a2a68d93824f05ece9dd0 Mon Sep 17 00:00:00 2001 From: tpique-ensae Date: Sat, 16 Mar 2024 15:18:21 +0000 Subject: [PATCH 08/15] delete former logit pipeline file --- .../3_full_modelization_sport.ipynb | 219 ------------------ 1 file changed, 219 deletions(-) delete mode 100644 Sport/Modelization/3_full_modelization_sport.ipynb diff --git a/Sport/Modelization/3_full_modelization_sport.ipynb b/Sport/Modelization/3_full_modelization_sport.ipynb deleted file mode 100644 index d4193c1..0000000 --- a/Sport/Modelization/3_full_modelization_sport.ipynb +++ /dev/null @@ -1,219 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ff8cc602-e733-4a31-bf46-a31087511fe0", - "metadata": {}, - "source": [ - "# Predict sales - sports companies" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "035dacc7-85db-47c9-b350-5ce54a2b5cdd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.6666666666666666\n", - "0.5\n", - "0.6666666666666666 0.5714285714285715\n" - ] - } - ], - "source": [ - "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n", - "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n", - "\n", - "y_test, y_pred = [1,1,0], [1,0,0]\n", - "print(accuracy_score(y_test, y_pred))\n", - "print(recall_score(y_test, y_pred))\n", - "print(f1_score(y_test, y_pred), 2 * accuracy_score(y_test, y_pred) * recall_score(y_test, y_pred)/(accuracy_score(y_test, y_pred) + recall_score(y_test, y_pred)))" - ] - }, - { - "cell_type": "markdown", - "id": "415e466a-1a71-4150-bff7-2f8904766df4", - "metadata": {}, - "source": [ - "## Importations" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b5aaf421-850a-4a86-8e99-2c1f0723bd6c", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import s3fs\n", - "import re\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n", - "from sklearn.utils import class_weight\n", - "from sklearn.neighbors import KNeighborsClassifier\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.model_selection import GridSearchCV\n", - "from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler\n", - "from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n", - "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", - "\n", - "import pickle\n", - "import warnings" - ] - }, - { - "cell_type": "markdown", - "id": "c2f44070-451e-4109-9a08-3b80011d610f", - "metadata": {}, - "source": [ - "## Load data " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "b5f8135f-b6e7-4d6d-b8e1-da185b944aff", - "metadata": {}, - "outputs": [], - "source": [ - "# Create filesystem object\n", - "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", - "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "2668a243-4ff8-40c6-9de2-5c9c07bcf714", - "metadata": {}, - "outputs": [], - "source": [ - "def load_train_test():\n", - " BUCKET = \"projet-bdc2324-team1/Generalization/sport\"\n", - " File_path_train = BUCKET + \"/Train_set.csv\"\n", - " File_path_test = BUCKET + \"/Test_set.csv\"\n", - " \n", - " with fs.open( File_path_train, mode=\"rb\") as file_in:\n", - " dataset_train = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\n", - "\n", - " with fs.open(File_path_test, mode=\"rb\") as file_in:\n", - " dataset_test = pd.read_csv(file_in, sep=\",\")\n", - " # dataset_test['y_has_purchased'] = dataset_test['y_has_purchased'].fillna(0)\n", - " \n", - " return dataset_train, dataset_test" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "13eba3e1-3ea5-435b-8b05-6d7d5744cbe2", - "metadata": {}, - "outputs": [ - { - "ename": "PermissionError", - "evalue": "Forbidden", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mClientError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:529\u001b[0m, in \u001b[0;36mS3FileSystem.info\u001b[0;34m(self, path, version_id, refresh)\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 529\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_s3\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43ms3\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhead_object\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mBucket\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbucket\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 530\u001b[0m \u001b[43m \u001b[49m\u001b[43mKey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mversion_id_kw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mversion_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreq_kw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 531\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m 532\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m: out[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 533\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mKey\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([bucket, key]),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVersionId\u001b[39m\u001b[38;5;124m'\u001b[39m: out\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVersionId\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 541\u001b[0m }\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:200\u001b[0m, in \u001b[0;36mS3FileSystem._call_s3\u001b[0;34m(self, method, *akwarglist, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m additional_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_s3_method_kwargs(method, \u001b[38;5;241m*\u001b[39makwarglist,\n\u001b[1;32m 199\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43madditional_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:553\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/botocore/client.py:1009\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m 1008\u001b[0m error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m-> 1009\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[0;31mClientError\u001b[0m: An error occurred (403) when calling the HeadObject operation: Forbidden", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mPermissionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dataset_train, dataset_test \u001b[38;5;241m=\u001b[39m \u001b[43mload_train_test\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m dataset_train\u001b[38;5;241m.\u001b[39misna()\u001b[38;5;241m.\u001b[39msum()\n", - "Cell \u001b[0;32mIn[18], line 6\u001b[0m, in \u001b[0;36mload_train_test\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m File_path_train \u001b[38;5;241m=\u001b[39m BUCKET \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/Train_set.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4\u001b[0m File_path_test \u001b[38;5;241m=\u001b[39m BUCKET \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/Test_set.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[43mFile_path_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m file_in:\n\u001b[1;32m 7\u001b[0m dataset_train \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(file_in, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# dataset_train['y_has_purchased'] = dataset_train['y_has_purchased'].fillna(0)\u001b[39;00m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1295\u001b[0m, in \u001b[0;36mAbstractFileSystem.open\u001b[0;34m(self, path, mode, block_size, cache_options, compression, **kwargs)\u001b[0m\n\u001b[1;32m 1293\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1294\u001b[0m ac \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mautocommit\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_intrans)\n\u001b[0;32m-> 1295\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1296\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1297\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1298\u001b[0m \u001b[43m \u001b[49m\u001b[43mblock_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mblock_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1299\u001b[0m \u001b[43m \u001b[49m\u001b[43mautocommit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mac\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1300\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1301\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1302\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1304\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfsspec\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompression\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compr\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:375\u001b[0m, in \u001b[0;36mS3FileSystem._open\u001b[0;34m(self, path, mode, block_size, acl, version_id, fill_cache, cache_type, autocommit, requester_pays, **kwargs)\u001b[0m\n\u001b[1;32m 372\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 373\u001b[0m cache_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_cache_type\n\u001b[0;32m--> 375\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mS3File\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43macl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43macl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 376\u001b[0m \u001b[43m \u001b[49m\u001b[43mversion_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mversion_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 377\u001b[0m \u001b[43m \u001b[49m\u001b[43ms3_additional_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 378\u001b[0m \u001b[43m \u001b[49m\u001b[43mautocommit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautocommit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrequester_pays\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequester_pays\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:1096\u001b[0m, in \u001b[0;36mS3File.__init__\u001b[0;34m(self, s3, path, mode, block_size, acl, version_id, fill_cache, s3_additional_kwargs, autocommit, cache_type, requester_pays)\u001b[0m\n\u001b[1;32m 1094\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ms3_additional_kwargs \u001b[38;5;241m=\u001b[39m s3_additional_kwargs \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[1;32m 1095\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreq_kw \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRequestPayer\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrequester\u001b[39m\u001b[38;5;124m'\u001b[39m} \u001b[38;5;28;01mif\u001b[39;00m requester_pays \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[0;32m-> 1096\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43ms3\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautocommit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautocommit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1097\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ms3 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfs \u001b[38;5;66;03m# compatibility\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwritable():\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1651\u001b[0m, in \u001b[0;36mAbstractBufferedFile.__init__\u001b[0;34m(self, fs, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs)\u001b[0m\n\u001b[1;32m 1649\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m=\u001b[39m size\n\u001b[1;32m 1650\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1651\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdetails\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1652\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache \u001b[38;5;241m=\u001b[39m caches[cache_type](\n\u001b[1;32m 1653\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocksize, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fetch_range, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msize, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcache_options\n\u001b[1;32m 1654\u001b[0m )\n\u001b[1;32m 1655\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/fsspec/spec.py:1664\u001b[0m, in \u001b[0;36mAbstractBufferedFile.details\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1661\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 1662\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdetails\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1663\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_details \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1664\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_details \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1665\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_details\n", - "File \u001b[0;32m/opt/mamba/lib/python3.11/site-packages/s3fs/core.py:548\u001b[0m, in \u001b[0;36mS3FileSystem.info\u001b[0;34m(self, path, version_id, refresh)\u001b[0m\n\u001b[1;32m 546\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m(S3FileSystem, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39minfo(path)\n\u001b[1;32m 547\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 548\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ee\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ParamValidationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 550\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to head path \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (path, e))\n", - "\u001b[0;31mPermissionError\u001b[0m: Forbidden" - ] - } - ], - "source": [ - "dataset_train, dataset_test = load_train_test()\n", - "dataset_train.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e46622e7-0fc1-43f8-a7e7-34a5e90068b2", - "metadata": {}, - "outputs": [], - "source": [ - "def features_target_split(dataset_train, dataset_test):\n", - " features_l = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n", - " 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n", - " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", - " X_train = dataset_train[features_l]\n", - " y_train = dataset_train[['y_has_purchased']]\n", - "\n", - " X_test = dataset_test[features_l]\n", - " y_test = dataset_test[['y_has_purchased']]\n", - " return X_train, X_test, y_train, y_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cec4f386-e643-4bd8-b8cd-8917d2c1b3d0", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = features_target_split(dataset_train, dataset_test)\n", - "print(\"Shape train : \", X_train.shape)\n", - "print(\"Shape test : \", X_test.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5978909b-eaad-4442-9bee-c98d603f0e80", - "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": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From cc30d7deb95c5c3aacd26f0b5a880dca6cf58397 Mon Sep 17 00:00:00 2001 From: ajoubrel-ensae Date: Sat, 16 Mar 2024 17:20:47 +0000 Subject: [PATCH 09/15] Ajout KPI sur customerplus --- 0_KPI_functions.py | 23 ++++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/0_KPI_functions.py b/0_KPI_functions.py index a8828ce..cdfd7e6 100644 --- a/0_KPI_functions.py +++ b/0_KPI_functions.py @@ -93,14 +93,31 @@ def customerplus_kpi_function(customerplus_clean = None): 1: 'male', 2: 'other' }) - gender_dummies = pd.get_dummies(customerplus_clean["gender_label"], prefix='gender').astype(int) customerplus_clean = pd.concat([customerplus_clean, gender_dummies], axis=1) - customerplus_clean['opt_in'] = np.multiply(customersplus['opt_in'], 1) - ## Indicatrice si individue vit en France + # Age + customerplus_clean['categorie_age_0_10'] = ((customerplus_clean['age'] >= 0) & (customerplus_clean['age'] < 10)).astype(int) + customerplus_clean['categorie_age_10_20'] = ((customerplus_clean['age'] >= 10) & (customerplus_clean['age'] < 20)).astype(int) + customerplus_clean['categorie_age_20_30'] = ((customerplus_clean['age'] >= 20) & (customerplus_clean['age'] < 30)).astype(int) + customerplus_clean['categorie_age_30_40'] = ((customerplus_clean['age'] >= 30) & (customerplus_clean['age'] < 40)).astype(int) + customerplus_clean['categorie_age_40_50'] = ((customerplus_clean['age'] >= 40) & (customerplus_clean['age'] < 50)).astype(int) + customerplus_clean['categorie_age_50_60'] = ((customerplus_clean['age'] >= 50) & (customerplus_clean['age'] < 60)).astype(int) + customerplus_clean['categorie_age_60_70'] = ((customerplus_clean['age'] >= 60) & (customerplus_clean['age'] < 70)).astype(int) + customerplus_clean['categorie_age_70_80'] = ((customerplus_clean['age'] >= 70) & (customerplus_clean['age'] < 80)).astype(int) + customerplus_clean['categorie_age_plus_80'] = (customerplus_clean['age'] >= 80).astype(int) + customerplus_clean['categorie_age_inconnue'] = customerplus_clean['age'].apply(lambda x: 1 if pd.isna(x) else 0) + + # Consentement au mailing + customerplus_clean['opt_in'] = customerplus_clean['opt_in'].astype(int) + + # Indicatrice si individue vit en France customerplus_clean["country_fr"] = customerplus_clean["country"].apply(lambda x : int(x=="fr") if pd.notna(x) else np.nan) + customerplus_clean['is_profession_known'] = customerplus_clean['profession'].notna().astype(int) + + customerplus_clean['is_zipcode_known'] = customerplus_clean['zipcode'].notna().astype(int) + # Dummy if the customer has a structure id (tags) # customerplus_clean['has_tags'] = customerplus_clean['structure_id'].apply(lambda x: 1 if not pd.isna(x) else 0) From 6eddec93bc74364567d9114299e8ae86033bec09 Mon Sep 17 00:00:00 2001 From: tpique-ensae Date: Sun, 17 Mar 2024 11:49:48 +0000 Subject: [PATCH 10/15] completed with random forest + naive bayes --- .../3_logit_cross_val_sport.ipynb | 3004 ++++++++++++++++- 1 file changed, 2832 insertions(+), 172 deletions(-) diff --git a/Sport/Modelization/3_logit_cross_val_sport.ipynb b/Sport/Modelization/3_logit_cross_val_sport.ipynb index 1e7627b..cf835b7 100644 --- a/Sport/Modelization/3_logit_cross_val_sport.ipynb +++ b/Sport/Modelization/3_logit_cross_val_sport.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "b5aaf421-850a-4a86-8e99-2c1f0723bd6c", "metadata": {}, "outputs": [], @@ -44,6 +44,7 @@ "import matplotlib.pyplot as plt\n", "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score\n", "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", + "from sklearn.naive_bayes import GaussianNB\n", "\n", "import pickle\n", "import warnings" @@ -59,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "b5f8135f-b6e7-4d6d-b8e1-da185b944aff", "metadata": {}, "outputs": [], @@ -71,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "2668a243-4ff8-40c6-9de2-5c9c07bcf714", "metadata": {}, "outputs": [], @@ -94,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "13eba3e1-3ea5-435b-8b05-6d7d5744cbe2", "metadata": {}, "outputs": [ @@ -102,7 +103,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_462/2459610029.py:7: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/tmp/ipykernel_1481/2459610029.py:7: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", " dataset_train = pd.read_csv(file_in, sep=\",\")\n" ] }, @@ -152,7 +153,7 @@ "dtype: int64" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "id": "e46622e7-0fc1-43f8-a7e7-34a5e90068b2", "metadata": {}, "outputs": [], @@ -191,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "id": "cec4f386-e643-4bd8-b8cd-8917d2c1b3d0", "metadata": {}, "outputs": [ @@ -215,12 +216,12 @@ "id": "c9e8edbd-7ff6-42f9-a8eb-10d27ca19c8a", "metadata": {}, "source": [ - "## Prepare preprocessing and Hyperparameters" + "## Logistic" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 9, "id": "639b432a-c39c-4bf8-8ee2-e136d156e0dd", "metadata": {}, "outputs": [ @@ -230,7 +231,7 @@ "{0.0: 0.5837086520288036, 1.0: 3.486549107420539}" ] }, - "execution_count": 20, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -246,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "id": "34644a00-85a5-41c9-98df-41178cb3ac69", "metadata": {}, "outputs": [ @@ -536,7 +537,7 @@ "[224213 rows x 14 columns]" ] }, - "execution_count": 21, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -616,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 19, "id": "e729781b-4d65-42c5-bdc5-82b4d653aaf0", "metadata": {}, "outputs": [], @@ -629,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 39, "id": "a7ebbe6f-70ba-4276-be18-f10e7bfd7423", "metadata": {}, "outputs": [], @@ -665,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 40, "id": "2334eb51-e6ea-4fd0-89ce-f54cd474d332", "metadata": {}, "outputs": [], @@ -1251,7 +1252,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 24, "id": "1e4c1be5-176d-4222-9b3c-fe27225afe36", "metadata": {}, "outputs": [ @@ -1294,58 +1295,75 @@ " \n", " \n", " \n", - 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"execution_count": 40, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1549,7 +1550,7 @@ "source": [ "# reduce X_train to reduce the training time\n", "\n", - "X_train_subsample = X_train.sample(n=10000)\n", + "X_train_subsample = X_train.sample(n=10000, random_state=42)\n", "y_train_subsample = y_train.loc[X_train_subsample.index]\n", "X_train_subsample" ] @@ -4373,6 +4374,2665 @@ "plt.legend(loc=\"lower right\")\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "id": "d2d5aca0-7e8b-4039-9bb2-ff5011c436a6", + "metadata": {}, + "source": [ + "## Random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "da8873e5-c4e7-4580-8567-70e411c029ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nb_ticketsnb_purchasestotal_amountnb_suppliersvente_internet_maxpurchase_date_minpurchase_date_maxnb_tickets_internetis_email_trueopt_ingender_femalegender_malenb_campaignsnb_campaigns_opened
430000.00.00.00.00.0550.000000550.0000000.0TrueTrue0114.012.0
1839230.00.00.00.00.0550.000000550.0000000.0TrueTrue0119.011.0
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1164870.00.00.00.00.0550.000000550.0000000.0TrueFalse105.00.0
.............................................
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10000 rows × 14 columns

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" + ], + "text/plain": [ + " nb_tickets nb_purchases total_amount nb_suppliers \\\n", + "43000 0.0 0.0 0.0 0.0 \n", + "183923 0.0 0.0 0.0 0.0 \n", + "97373 0.0 0.0 0.0 0.0 \n", + "66956 7.0 2.0 254.0 1.0 \n", + "116487 0.0 0.0 0.0 0.0 \n", + "... ... ... ... ... \n", + "83146 1.0 1.0 35.0 1.0 \n", + "223586 0.0 0.0 0.0 0.0 \n", + "56489 0.0 0.0 0.0 0.0 \n", + "141236 0.0 0.0 0.0 0.0 \n", + "6999 2.0 1.0 20.0 1.0 \n", + "\n", + " vente_internet_max purchase_date_min purchase_date_max \\\n", + "43000 0.0 550.000000 550.000000 \n", + "183923 0.0 550.000000 550.000000 \n", + "97373 0.0 550.000000 550.000000 \n", + "66956 1.0 378.343062 370.453947 \n", + "116487 0.0 550.000000 550.000000 \n", + "... ... ... ... \n", + "83146 1.0 37.474040 37.474040 \n", + "223586 0.0 550.000000 550.000000 \n", + "56489 0.0 550.000000 550.000000 \n", + "141236 0.0 550.000000 550.000000 \n", + "6999 0.0 171.446921 171.446921 \n", + "\n", + " nb_tickets_internet is_email_true opt_in gender_female \\\n", + "43000 0.0 True True 0 \n", + "183923 0.0 True True 0 \n", + "97373 0.0 True False 0 \n", + "66956 7.0 True False 0 \n", + "116487 0.0 True False 1 \n", + "... ... ... ... ... \n", + "83146 1.0 True False 0 \n", + "223586 0.0 True True 0 \n", + "56489 0.0 True True 0 \n", + "141236 0.0 True False 0 \n", + "6999 0.0 True True 1 \n", + "\n", + " gender_male nb_campaigns nb_campaigns_opened \n", + "43000 1 14.0 12.0 \n", + "183923 1 19.0 11.0 \n", + "97373 0 7.0 2.0 \n", + "66956 1 0.0 0.0 \n", + "116487 0 5.0 0.0 \n", + "... ... ... ... \n", + "83146 1 9.0 3.0 \n", + "223586 1 23.0 1.0 \n", + "56489 1 4.0 0.0 \n", + "141236 1 6.0 0.0 \n", + "6999 0 0.0 0.0 \n", + "\n", + "[10000 rows x 14 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train_subsample" + ] + }, + { + "cell_type": "markdown", + "id": "fcbb8bea-e9d3-4fd4-8b47-7e796c788a1f", + "metadata": {}, + "source": [ + "### Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "55e0c6d8-9e98-47be-9d5d-41e06505ceba", + "metadata": {}, + "outputs": [], + "source": [ + "# no need to standardize variables in a random forest\n", + "# we just encode categorical variables\n", + "\n", + "categorical_features = ['opt_in', 'is_email_true'] \n", + "\n", + "# Transformer for the categorical features\n", + "categorical_transformer = Pipeline(steps=[\n", + " #(\"imputer\", SimpleImputer(strategy=\"most_frequent\")), # Impute missing values with the most frequent\n", + " (\"onehot\", OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n", + "])\n", + "\n", + "preproc = ColumnTransformer(\n", + " transformers=[\n", + " (\"cat\", categorical_transformer, categorical_features)\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "27af28da-d2bb-4eff-b842-18cec9740c84", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
ColumnTransformer(transformers=[('cat',\n",
+       "                                 Pipeline(steps=[('onehot',\n",
+       "                                                  OneHotEncoder(handle_unknown='ignore',\n",
+       "                                                                sparse_output=False))]),\n",
+       "                                 ['opt_in', 'is_email_true'])])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "ColumnTransformer(transformers=[('cat',\n", + " Pipeline(steps=[('onehot',\n", + " OneHotEncoder(handle_unknown='ignore',\n", + " sparse_output=False))]),\n", + " ['opt_in', 'is_email_true'])])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preproc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cb46acb-647f-469d-b5e1-510bf1283196", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ce9acf4-3514-4056-a71a-c7654e25b9de", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "dfdd4601-4866-4102-b620-4f10648e7981", + "metadata": {}, + "source": [ + "### Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eeefae73-afe7-4441-a04c-bd6a04beedd2", + "metadata": {}, + "outputs": [], + "source": [ + "# Define models and parameters for GridSearch\n", + "model = {\n", + " 'model': RandomForestClassifier(),\n", + " 'params': {\n", + " 'randforest__n_estimators': [100, 150, 200, 250, 300],\n", + " 'randforest__max_depth': [None, 15, 20, 25, 30, 35, 40],\n", + " }\n", + " }\n", + "\n", + "# Test each model using GridSearchCV\n", + "pipe = Pipeline(steps=[('preprocessor', preproc), ('randforest', model['model'])])\n", + "clf = GridSearchCV(pipe, model['params'], cv=3)\n", + "clf.fit(X_train, y_train)\n", + "\n", + "print(f\"Model: {model['model']}\")\n", + "print(f\"Best parameters: {clf.best_params_}\")\n", + "print('Best classification accuracy in train is: {}'.format(clf.best_score_))\n", + "print('Classification accuracy on test is: {}'.format(clf.score(X_test, y_test)))\n", + "print(\"------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "2a88f13b-05bc-4a70-b08b-8b07c118cedc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('preprocessor',\n",
+       "                 ColumnTransformer(transformers=[('cat',\n",
+       "                                                  Pipeline(steps=[('onehot',\n",
+       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
+       "                                                                                 sparse_output=False))]),\n",
+       "                                                  ['opt_in',\n",
+       "                                                   'is_email_true'])])),\n",
+       "                ('random_forest',\n",
+       "                 RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n",
+       "                                                      1.0: 3.486549107420539}))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('preprocessor',\n", + " ColumnTransformer(transformers=[('cat',\n", + " Pipeline(steps=[('onehot',\n", + " OneHotEncoder(handle_unknown='ignore',\n", + " sparse_output=False))]),\n", + " ['opt_in',\n", + " 'is_email_true'])])),\n", + " ('random_forest',\n", + " RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n", + " 1.0: 3.486549107420539}))])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Pipeline - on joue sur : max_depth\n", + "\n", + "param_grid = {\"random_forest__max_depth\" : [None, 10, 20, 40, 50, 60]}\n", + "\n", + "pipeline = Pipeline(steps=[\n", + " ('preprocessor', preproc),\n", + " ('random_forest', RandomForestClassifier(bootstrap = False, class_weight = weight_dict,\n", + " )) \n", + "])\n", + "\n", + "pipeline.set_output(transform=\"pandas\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "494dca83-4d60-4e49-8689-7d7ac612bb83", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'estimator': DecisionTreeClassifier(),\n", + " 'n_estimators': 100,\n", + " 'estimator_params': ('criterion',\n", + " 'max_depth',\n", + " 'min_samples_split',\n", + " 'min_samples_leaf',\n", + " 'min_weight_fraction_leaf',\n", + " 'max_features',\n", + " 'max_leaf_nodes',\n", + " 'min_impurity_decrease',\n", + " 'random_state',\n", + " 'ccp_alpha',\n", + " 'monotonic_cst'),\n", + " 'bootstrap': True,\n", + " 'oob_score': False,\n", + " 'n_jobs': None,\n", + " 'random_state': None,\n", + " 'verbose': 0,\n", + " 'warm_start': False,\n", + " 'class_weight': None,\n", + " 'max_samples': None,\n", + " 'criterion': 'gini',\n", + " 'max_depth': None,\n", + " 'min_samples_split': 2,\n", + " 'min_samples_leaf': 1,\n", + " 'min_weight_fraction_leaf': 0.0,\n", + " 'max_features': 'sqrt',\n", + " 'max_leaf_nodes': None,\n", + " 'min_impurity_decrease': 0.0,\n", + " 'monotonic_cst': None,\n", + " 'ccp_alpha': 0.0}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "RandomForestClassifier().__dict__" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "ee7cbc1c-7c31-4111-82a3-995141e2f13f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
GridSearchCV(cv=3,\n",
+       "             estimator=Pipeline(steps=[('preprocessor',\n",
+       "                                        ColumnTransformer(transformers=[('cat',\n",
+       "                                                                         Pipeline(steps=[('onehot',\n",
+       "                                                                                          OneHotEncoder(handle_unknown='ignore',\n",
+       "                                                                                                        sparse_output=False))]),\n",
+       "                                                                         ['opt_in',\n",
+       "                                                                          'is_email_true'])])),\n",
+       "                                       ('random_forest',\n",
+       "                                        RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n",
+       "                                                                             1.0: 3.486549107420539}))]),\n",
+       "             param_grid={'random_forest__max_depth': [None, 10, 20, 40, 50,\n",
+       "                                                      60]},\n",
+       "             scoring=make_scorer(f1_score, response_method='predict'))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GridSearchCV(cv=3,\n", + " estimator=Pipeline(steps=[('preprocessor',\n", + " ColumnTransformer(transformers=[('cat',\n", + " Pipeline(steps=[('onehot',\n", + " OneHotEncoder(handle_unknown='ignore',\n", + " sparse_output=False))]),\n", + " ['opt_in',\n", + " 'is_email_true'])])),\n", + " ('random_forest',\n", + " RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n", + " 1.0: 3.486549107420539}))]),\n", + " param_grid={'random_forest__max_depth': [None, 10, 20, 40, 50,\n", + " 60]},\n", + " scoring=make_scorer(f1_score, response_method='predict'))" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pipeline on the subsample\n", + "\n", + "random_forest_grid = GridSearchCV(pipeline, param_grid, cv=3, scoring = f1_scorer #, error_score=\"raise\"\n", + " )\n", + "\n", + "random_forest_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "3f149137-6313-4b4e-99d6-b3af7f296ad7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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", + "/opt/mamba/lib/python3.11/site-packages/sklearn/base.py:1351: 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": [ + "Returned hyperparameter: {'random_forest__max_depth': None}\n", + "Best classification F1 score in train is: 0.33107422141513826\n", + "Classification F1 score on test is: 0.31752789604029275\n" + ] + } + ], + "source": [ + "# run the pipeline on the full sample\n", + "\n", + "random_forest_grid.fit(X_train, y_train)\n", + "\n", + "# print results\n", + "print('Returned hyperparameter: {}'.format(random_forest_grid.best_params_))\n", + "print('Best classification F1 score in train is: {}'.format(random_forest_grid.best_score_))\n", + "print('Classification F1 score on test is: {}'.format(random_forest_grid.score(X_test, y_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "cd79f942-abd0-48c9-aa0d-0d22673abeec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'scoring': make_scorer(f1_score, response_method='predict'),\n", + " 'estimator': Pipeline(steps=[('preprocessor',\n", + " ColumnTransformer(transformers=[('cat',\n", + " Pipeline(steps=[('onehot',\n", + " OneHotEncoder(handle_unknown='ignore',\n", + " sparse_output=False))]),\n", + " ['opt_in',\n", + " 'is_email_true'])])),\n", + " ('random_forest',\n", + " RandomForestClassifier(bootstrap=False,\n", + " class_weight={0.0: 0.5837086520288036,\n", + " 1.0: 3.486549107420539}))]),\n", + " 'n_jobs': None,\n", + " 'refit': True,\n", + " 'cv': 3,\n", + " 'verbose': 0,\n", + " 'pre_dispatch': '2*n_jobs',\n", + " 'error_score': nan,\n", + " 'return_train_score': False,\n", + " 'param_grid': {'random_forest__max_depth': [None, 10, 20, 40, 50, 60]},\n", + " 'multimetric_': False,\n", + " 'best_index_': 0,\n", + " 'best_score_': 0.33107422141513826,\n", + " 'best_params_': {'random_forest__max_depth': None},\n", + " 'best_estimator_': Pipeline(steps=[('preprocessor',\n", + " ColumnTransformer(transformers=[('cat',\n", + " Pipeline(steps=[('onehot',\n", + " OneHotEncoder(handle_unknown='ignore',\n", + " sparse_output=False))]),\n", + " ['opt_in',\n", + " 'is_email_true'])])),\n", + " ('random_forest',\n", + " RandomForestClassifier(bootstrap=False,\n", + " class_weight={0.0: 0.5837086520288036,\n", + " 1.0: 3.486549107420539}))]),\n", + " 'refit_time_': 2.2247676849365234,\n", + " 'feature_names_in_': array(['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers',\n", + " 'vente_internet_max', 'purchase_date_min', 'purchase_date_max',\n", + " 'nb_tickets_internet', 'is_email_true', 'opt_in', 'gender_female',\n", + " 'gender_male', 'nb_campaigns', 'nb_campaigns_opened'], dtype=object),\n", + " 'scorer_': make_scorer(f1_score, response_method='predict'),\n", + " 'cv_results_': {'mean_fit_time': array([1.64734515, 1.4220806 , 1.43256299, 1.68632547, 1.4271005 ,\n", + " 1.42404906]),\n", + " 'std_fit_time': array([0.32811727, 0.01915 , 0.02151065, 0.2729267 , 0.02447776,\n", + " 0.02384922]),\n", + " 'mean_score_time': array([0.14065607, 0.13571024, 0.13531415, 0.17512798, 0.13398822,\n", + " 0.13499872]),\n", + " 'std_score_time': array([0.00759402, 0.00653712, 0.00743453, 0.04901062, 0.00848726,\n", + " 0.00789539]),\n", + " 'param_random_forest__max_depth': masked_array(data=[None, 10, 20, 40, 50, 60],\n", + " mask=[False, False, False, False, False, False],\n", + " fill_value='?',\n", + " dtype=object),\n", + " 'params': [{'random_forest__max_depth': None},\n", + " {'random_forest__max_depth': 10},\n", + " {'random_forest__max_depth': 20},\n", + " {'random_forest__max_depth': 40},\n", + " {'random_forest__max_depth': 50},\n", + " {'random_forest__max_depth': 60}],\n", + " 'split0_test_score': array([0.19168873, 0.19168873, 0.19168873, 0.19168873, 0.19168873,\n", + " 0.19168873]),\n", + " 'split1_test_score': array([0.34428494, 0.34428494, 0.34428494, 0.34428494, 0.34428494,\n", + " 0.34428494]),\n", + " 'split2_test_score': array([0.45724899, 0.45724899, 0.45724899, 0.45724899, 0.45724899,\n", + " 0.45724899]),\n", + " 'mean_test_score': array([0.33107422, 0.33107422, 0.33107422, 0.33107422, 0.33107422,\n", + " 0.33107422]),\n", + " 'std_test_score': array([0.10881622, 0.10881622, 0.10881622, 0.10881622, 0.10881622,\n", + " 0.10881622]),\n", + " 'rank_test_score': array([1, 1, 1, 1, 1, 1], dtype=int32)},\n", + " 'n_splits_': 3}" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_forest_grid.__dict__" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "1806fe6d-cf98-459d-b05a-eb95972281dc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy Score: 0.48955211455211456\n", + "F1 Score: 0.31752789604029275\n", + "Recall Score: 0.8335281227173119\n" + ] + } + ], + "source": [ + "# print results for the best model\n", + "\n", + "y_pred = random_forest_grid.predict(X_test)\n", + "\n", + "# Calculate the F1 score\n", + "acc = accuracy_score(y_test, y_pred)\n", + "print(f\"Accuracy Score: {acc}\")\n", + "\n", + "f1 = f1_score(y_test, y_pred)\n", + "print(f\"F1 Score: {f1}\")\n", + "\n", + "recall = recall_score(y_test, y_pred)\n", + "print(f\"Recall Score: {recall}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "1a6a8e07-bd93-496b-986e-d219c03b82c5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# confusion matrix \n", + "\n", + "draw_confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "1e1b3e42-1075-4a4a-bf44-3dadde3dbed1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ROC curve\n", + "\n", + "# Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", + "y_pred_prob = random_forest_grid.predict_proba(X_test)[:, 1]\n", + "\n", + "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", + "\n", + "# Calcul de l'aire sous la courbe ROC (AUC)\n", + "roc_auc = auc(fpr, tpr)\n", + "\n", + "plt.figure(figsize = (14, 8))\n", + "plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", + "plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", + "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", + "plt.xlabel('Taux de faux positifs (FPR)')\n", + "plt.ylabel('Taux de vrais positifs (TPR)')\n", + "plt.title('Courbe ROC : random forest')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "854f6242-813f-400a-be43-7414a859b355", + "metadata": {}, + "source": [ + "## Naive Bayes " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b083d10d-8510-4a07-974b-e0c324175d7f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mamba/lib/python3.11/site-packages/sklearn/utils/validation.py:1229: 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" + ] + }, + { + "data": { + "text/html": [ + "
GaussianNB()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GaussianNB()" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "clf = GaussianNB()\n", + "clf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a5459639-be3d-4292-89d2-061f276dc9a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy Score: 0.8780906593406593\n", + "F1 Score: 0.3673381217259815\n", + "Recall Score: 0.24842951059167276\n" + ] + } + ], + "source": [ + "# print results for the best model\n", + "\n", + "y_pred = clf.predict(X_test)\n", + "\n", + "# Calculate the F1 score\n", + "acc = accuracy_score(y_test, y_pred)\n", + "print(f\"Accuracy Score: {acc}\")\n", + "\n", + "f1 = f1_score(y_test, y_pred)\n", + "print(f\"F1 Score: {f1}\")\n", + "\n", + "recall = recall_score(y_test, y_pred)\n", + "print(f\"Recall Score: {recall}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e962eeed-4099-407b-a619-a34a539a404a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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yMvj9998Bc3W8HTt28MILLwBw7733Wt7PBx98QIMGDWjatCnLly9n4sSJ53z+m266idGjR7Nw4UIeffRRj8ZhQWu/7bbbCA0NpUOHDlSqVImEhAQmTJhAVFQUl156aYH+HkVERHyarVNai4iIyAX766+/jKFDhxrVqlUzgoKCjPDwcKNFixbG448/bhw6dCh/v7y8POP555836tWrZwQGBhoxMTHGTTfdZOzZs8fyfG6323jhhReMWrVqGSEhIUbr1q2N+fPnn3OVsWnTpnnUdHKVsblz5xo333yzUbZsWSM0NNTo3bu3sWXLFo/9f/nlF6NPnz5GdHS0ERgYaMTHxxt9+vQ563Of7uQqYxMnTjzr/YMGDTICAgKMrVu3WsZnz55t9O7d2yhfvnz+6918883nXTXrl19+MQYOHGhUqlTJCAwMNCIjI4127doZEydONFJSUs5b58m/qyeeeOK8+xmGuTJX48aNPcaHDh1qVK9e3TL23HPPGTVq1DCCg4ONhg0bGu+8847xxBNPGGd+a3euleOSk5ON0NBQAzDeeeeds9Zz+PBh45577jFq1qxpBAYGGtHR0UarVq2MsWPHGqmpqX/7XjhtdTE/Pz+jcuXKRq9evYwFCxZY9j169KgxfPhwIzY21ggLCzM6duxo/Prrrx7/7053yy23GAEBAcbevXsvuPYPP/zQ6NatmxEXF2cEBQUZlStXNq6//npj9erV531vIiIipYXLMM5yDrSIiIiIiBfKzs6mRo0adOzYkS+++MLuckRERHyWLhkTEREREa93+PBhNm3axPvvv8/Bgwd55JFH7C5JRETEp6khJCIiIiJeb9asWQwbNoxKlSrx+uuvW5aaFxERkcLTJWMiIiIiIiIiIg6jZedFRERERERERBxGDSEREREREREREYdRQ0hERERERERExGEcN6m02+1m//79lClTBpfLZXc5IiIiIiIiIiJFwjAMjh8/TuXKlfHzO/85QI5rCO3fv5+qVavaXYaIiIiIiIiISLHYs2cPVapUOe8+jmsIlSlTBjD/ciIjI22u5uLMmTOHq666yu4yRLyGMiFipUyIeFIuRKyUCRErX89ESkoKVatWze99nI/jlp1PSUkhKiqK5ORkn28I5ebmEhDguJ6eyDkpEyJWyoSIJ+VCxEqZELHy9UwUpuehSaV92LRp0+wuQcSrKBMiVsqEiCflQsRKmRCxclIm1BASEREREREREXEYNYR8WKNGjewuQcSrKBMiVsqEiCflQsRKmRCxclIm1BDyYVFRUXaXIOJVlAkRK2VCxJNyIWKlTIhYOSkTagj5sCVLlthdgohXUSZErJQJEU/KhYiVMiFi5aRMqCEkIiIiIiIiIuIwWnbehx05coTy5cvbXYaI11AmRKyUCRFPyoWIlTIhYuXrmdCy8w6xfv16u0sQ8SrKhIiVMiHiSbkQsVImRKyclAk1hHzY3r177S5BxKsoEyJWyoSIJ+VCxEqZELFyUibUEPJhYWFhdpcg4lWUCRErZULEk3IhYqVMiFg5KROaQ0hEREREREREpBTQHEIOMWXKFLtLEPEqyoSIlTIh4km5ELFSJkSsnJQJNYRERERERERERBxGDSEfVq9ePbtLEPEqyoSIlTIh4km5ELFSJkSsnJQJNYR8WGxsrN0liHgVZULESpkQ8aRciFgpEyJWTsqEGkI+bNGiRXaXIOJVlAkRK2VCxJNyIWKlTIhYOSkTagiJiIiIiIiIiDiMlp33YQcPHiQuLs7uMkS8hjIhYqVMiHhSLkSslAkRK1/PhJadd4ht27bZXYKIV1EmRKyUCRFPyoWIlTIhYuWkTKgh5MN27dpldwkiXkWZELFSJkQ8KRciVsqEiJWTMqGGkA8LCgqyuwQRr6JMiFgpEyKelAsRK2VCxMpJmbB1DqGFCxcyceJEli9fzoEDB5gxYwb9+vU772N++eUX7rvvPtatW0flypV56KGHuPPOOwv8mqVpDiERERERERERkZN8Zg6htLQ0mjVrxmuvvVag/Xfs2EHv3r3p1KkTK1euZMyYMdxzzz189dVXxVypd5o2bZrdJYh4FWVCxEqZEPGkXIhYKRMiVk7KRICdL96rVy969epV4P3ffPNNqlWrxssvvwxAw4YNWbZsGZMmTWLAgAHFVKX3ys3NtbsEEa+iTIhYKRMinpQLEStlQkqT3Dw32Xlu8twGbjfkut3kGaduu92QZxjkuU99ZeXmkZGdR3aem+ycPFYmQt/sPEKD/O1+O8XO1oZQYS1ZsoSePXtaxq688kreffddcnJyCAwM9HhMVlYWWVlZ+dspKSnFXmdJqVWrlt0liHgVZULESpkQ8aRciFgpEwJgGAaGAcaJ224DDMwx835z221ATq7ZdDEMcBtG/mMMAzJz8jh8PAv3mfcBnPacJ1/LbZx8jdNf39wvJ89NVo6bXLeB+7QmTnp2Hst2HSU1M4fsPDfp2WZDJy0rl5TMC2twlktPZtSizwjOzeaz3v9hZFoWVYLCLv4v1sv5VEMoISGBuLg4y1hcXBy5ubkkJiZSqVIlj8dMmDCBcePGeYxPmzaNsLAw+vfvz08//URycjKxsbG0adOG7777DoCWLVvidrv566+/ALj22mtZtGgRR44cITo6ms6dOzNz5kwAmjZtSmBgIMuXLwegT58+LFu2jIMHDxIZGUnPnj358ssvAWjcuDERERH88ccfgNnUWrt2Lfv27SM8PJy+ffsydepUAOrXr09MTAy//fYbAFdccQWbN29m9+7duN1u2rZty9SpU3G73dSuXZv4+HgWLlwIQNeuXdm9ezfbt28nICCAQYMG8dVXX5GdnU316tWpXbs28+fPB6Bjx44cOnSIzZs3AzBkyBC+/vpr0tPTqVKlCo0aNWLu3LkAtGvXjuTkZNavXw/AoEGDmDNnDsePH6dixYq0bNmS2bNnA3DppZeSmZnJmjVrALjuuutYsGABR48eJSYmhnbt2vHtt98C0KJFCwBWrlwJwNVXX82SJUtITEykXLlydO3alRkzZgBwySWXEBISwtKlSwHo3bs3K1asICEhgTJlynDVVVfln+rXqFEjoqKiWLJkCQA9e/Zk/fr17N27l7CwMK699lqmTJkCQL169YiNjWXRokUAXH755Wzbto1du3YRFBTEgAEDmDZtGrm5udSqVYtq1aqxYMECADp37sy+ffvYtm0bfn5+DB48mOnTp5OVlUW1atWoV68eP/74IwAdOnQgMTGRTZs2ATB48GC+++470tLSiI+Pp0mTJvzwww8AtG3bltTUVNatWwfAwIEDmTt3LikpKcTFxdG6dWtmzZoFQKtWrcjJyWH16tUA9OvXj4ULF5KUlET58uXp2LEjX3/9NQDNmzfHz8+PFStWANC3b1/+/PNPDh06RFRUFN27d2f69OkANGnShLCwMP7880/APLtv1apV7N+/n4iICHr37s0XX3wBQIMGDYiOjmbx4sUA9OjRg40bN7Jnzx5CQ0Pp168fn3/+OYZhULduXSpWrMivv/4KQLdu3di5cyc7duwgMDCQgQMH8uWXX5KTk0PNmjWpUaMGP//8MwCdOnUiISGBLVu24HK5uOGGG9i2bRvbt2+natWqNGjQgHnz5gHQvn17kpKS2LhxIwDXX389s2fPJjU1lcqVK9OsWTO+//57ANq0aUN6ejpr164F8OnPiODgYPr376/PCJz7GREeHk5KSoo+I058RsycOZOMjAx9Rjj8MyIzM5Pw8HB9Ruj7CH1GYH5GBAUF5f8fPv0zIigomH7XXce7n07jeI5BpcpVKF+hAitW/oXbgKbNmrN1TwKJhw7i7+9P+/btWLLkd3Jzc6lQoQKxcXH5fw8NGjQgOTmFAwf2Y5z4P7xs6TKysrKILl+eKlXiWbXK/H9Xt1490tPS2btvb/7/4VWrVpORmUHZqLLUqFGDv1atAqBWzZpk5+SwZ8/e/P/DGzduIC0tnYgyEdSpXYeVJ/5+q1WrBsCu3bvN/8PNmrN121ZSj6cSFh5G/foN8v8/x8fHExgYyM6dOzEwv6/YvWs3ySnJBIeE0KRxY5YtX05GrougyBjcfgEcOnQI48Rjjx49Rnp6Gv7+AVSrVo3t27cDUCYykpDQUA4dPIgBVKpYiZTjx0lLS8XPz59q1aqxc9cuDLebgJAIknL8OHY8nVwDgoKCyc3NIzfPbHgEB4eQmZWFYRi4/Pzx8/MjJycXA/D398dtGLjdZtPGz9+fvLw8symDC5fLZd4HGLgo7Vwn3mWgvx9mV8rA3wWhIcG4M1K5cel33LFoKmUy03C7XHzdtR+HDx4kYdsRn/yMSE9PL/jfjZ2TSp/O5XL97aTS9erVY9iwYYwePTp/7LfffqNjx44cOHCAihUrejzmbGcIVa1atVRMKj1lyhSGDBlidxkiXkOZELFSJkQ8KRfiZHlug40JKfyy+TBpWbmkZeWxduNm4qpUNy+ZyXWTnesmMS2LHYlpeMdPiuKtAvxcuFzmz/IuwM9lbkeFBhIVal69c/I+c78T+5h3nBo/7bEuzIGTDZyQQD/8/Vwnvvzwd4Gfn4s6sRHUiokgOMCP0CB/wk58lQ0LIizIHz/Xice4XPj5naXpZRjwzTfwwAOwdas51rw5vPQSUxISfPo4UZhJpX3qDKGKFSuSkJBgGTt06BABAQGUL1/+rI8JDg4mODi4JMoTEREREREpVjl5brYcTGXNvmOs3ptMYmoWhgG5bvMSm9w8g1y3m5wTf+bmGWSfGD+QnEFO3pldniBIOvC3rxtfNhQ/P/A/+YO2nws/l4uElExqxoTj7zJ/6Had9rO3C8vG2W6ec3/LuOv895/Oddod536dwu3POV43MiSQCmWCiQj2tzzP2Wo+tf33+5ysq2ZMGFGhQQQH+OU3S042VvJvn9jXcptTzZeTz2tpzODCz8WJxovnY/1ODJxt/OTrBwf48Pw6u3fDsGFw4ixX4uLgmWfgllvA3x9OnDHnBD7VEDr9tOCT5s6dS+vWrc86f1Bp17lzZ7tLEPEqyoSIlTIh4km5EF+y/1gGa/clcyw9h7X7k1m9N5n1B1LIznVf1PM2rhzJpTWiCQ/2JzcznUox5QgN8ic4wJ+gAPOsjAYVIwkL8ifQ34/wYJ/6sVHk/MqWhTVrIDgY7r8fHnkEypTJv9tJxwlbk52amsrWk6dnYS4r/9dffxEdHU21atUYPXo0+/bt46OPPgLgzjvv5LXXXuO+++7jtttuY8mSJbz77rv517w6zb59+4iPj7e7DBGvoUyIWCkTIp6UC/EWJyfuzXW7zQlxs/NYty+Zj3/fxbH0HI6kZrE/OfOsjy0THECT+CguqRJF1eiwE5fXuAjw8yPA30Wgvx+B/idunzYWHOBH7QoRltWT/vzzT9q0qVlC71rEBpmZ8PnnMHSoeXpTZCR89hnUrQvVq3vs7qTjhK0NoWXLltGtW7f87fvuuw+AoUOH8sEHH3DgwAF2n5j0C6BmzZrMnj2bUaNG8b///Y/KlSvzyiuvOHLJeYBt27bRpk0bu8sQ8RrKhIiVMiHiSbkoXY6kZvHHjiR2JKaRnJFDZk6eZTWk01cvOrmaUf4qRqetgOQ2YPPB48REBBMc4GdZ/ejkPpw+5rFa0ql9l+86Sv24MuYS1waW5a1TMs0a3YWYm6dqdCg1yodTP64Ml1SJommVslSPDjv7vCgXQJmQUsswYNo0ePhh2LkTwsLg+uvN+6644pwPc1ImbG0Ide3alfPNaf3BBx94jHXp0iV/9nen8/Pzs7sEEa+iTIhYKRMinpQL3+V2G+xPzmBHYhordh1jyp+7SUg5+xk0F+54kTzLpoOFf56gAD/Cg/xpUa0c17euSvmIIGpXiCA6PKhIajoXZUJKpaVLYdQoOLHKJvHxEFSwLDkpE16zylhJKcyM2yIiIiIiUrKOZ+aw+WAqOxLT2JGYyvbDaSdup5F1lrlzGlQsQ5P4KCJDAokI9geXOWHu6ZPlnjyb5vQJda2T9JqT5h5Jy6Jy2VAC/f3OOkFv/iTAZ06yy8kJgc3trFw3FSKCT0y+zInVkVwEBrgoFxaUvwKS34kVk0ID/U8siS0iF2XvXhgzBj7+2NwOC4OHHjJXEwsPt7e2ElJqVxkTq+nTp9O/f3+7yxDxGsqEiJUyIeJJubBfbp6blMxc0rJy2Xcsg+2H09h2OJXth1PZnpjG7qT0cy53Hujvonr5cGpXCOfSGtF0bxhHzRhn/JBXXJQJKVWuvx6WLDFv33wzPPssVKlSqKdwUibUEPJhWVlZdpcg4lWUCRErZULEk3JRcjJz8tiTlM6OxDR2HkljR2I6Ww4eZ82+5LOe6XO62DLB1ImNoFaFcGrGRFArJpxaFcKJLxtKgM6kKVLKhPg0txvy8uDkquNPPw2PPw6TJ8Oll17QUzopE2oI+bBq1arZXYKIV1EmRKyUCRFPykXhGIbB0XRzIuT07LwTq2HlkpFtbqdn5574M4+M7FyS0rPZmWg2gfYnZ5zzTB+A4AA/4iJDqFUhnFoxZvOnVoVwaleIILZMMC5X0UyaLOenTIjPWrwY7r0Xrr0Wxo41xy6/HLp1g4v4/HBSJtQQ8mH16tWzuwQRr6JMiFgpEyKelItzc7sN1uxLZndSOlsPpXLoeBa/bU1kd1L6BT9nRHAANWLCqFE+nJox5lezqmWpUT4c/yJaJUsujjIhPmfXLnPlsKlTze29e805goKDze2LbCY7KRNqCPmwH3/8kSFDhthdhojXUCZErJQJEU/KhVVqVi4rdh1l7voEflh3kMPHz36pRICfi7Agf8KCAggL9jdvB552OyiAsCB/IkMCqVY+jJox4dQoH05MRJDO9PFyyoT4jOPHYcIEeOklyMoyGz/Dh8P48aeaQUXASZlQQ0hERERExAFSs3LZmZjGkm1HWLD5ECt2HSMjJ8+yT5ngABpUKkON8uFUigqhVoUIejaOIyxIPzaIiI1+/BFuugkOHjS3u3UzG0PNm9talq/TJ7sP69Chg90liHgVZULESpkQ8VRac5Gb52ZXUjoHkzM5lpFD8mlficezOJCcyaKtiWd9bIUywVzRMJYrG1ekfe0YggI0abOTlNZMSClTowYkJUGdOjBpElxzzUVfGnYuTsqEGkI+LDEx0VETXon8HWVCxEqZEPHkK7nIzXOTnJHDsYwcjqVncyw9h2PpOSSmZrE7KZ3dSemkZuWSnes+sZpXBtl551+566TO9SrQtV4FOtaNIa5MCJGhAbqsy8F8JRPiMFu3wty5MHKkuV2nDsybB+3aQVBQsb60kzKhhpAP27RpEy1btrS7DBGvoUyIWCkTIp7szEWe2yAzJ4+ktGwOpmRyMCXL/PN4JodO3k7J5NDxLI5n5hb6+YMD/KgWHUbZsECiQgOJDDX/jAoNJCYimAYVy9Cqejk1f8RCxwrxKseOmUvHv/IK5ObCZZfByf+fXbqUSAlOyoQaQiIiIiJSKhmGwbH0HA4dzyInz02u22BjSgDfrNpPbp6b3DyDHLebPLdBTp5BnttNTp5B7snbbuPEfea+uW7DfJz7jNv5fxrknniOk4/LO7FvenYuB1POPmHz+ZQJCaBcWFB+kyc6PIiq5cKoVj6MqNBAggL8CPb3I75cKFXLheGnlbtExBfl5sLbb8MTT0Diictbr7oKIiLsrauUcxmGYdhdRElKSUkhKiqK5ORkIiMj7S7norjdbvz8dI23yEnKhIiVMiFOkp3rZueRNBKSM/lxw0H+2nOM3UnpHEvPsbs0D0H+flSMCiEuMpjYyBDiypi34yJDiI0MJrZMCNHhQUSGBBDgrwxL8dKxQmz3ww9w332wfr253bAhvPgi9OplSzm+nonC9Dx0hpAP++6777jmmmvsLkPEaygTIlbKhJRWa/clM3/jIZLSstl7NIPkjGzW7kvxWDHrpLJhgYQE+OPv5yI3K51alWII8HcR6O9HgJ+LAH8XAX5+J/50EXBy3M+PQH8X/ifGAv1c+Pu7CDxjX38/F4Enn+Pk40/ef+I5Avz9iC8bSkRwACGBfrpsS7yGjhViq7Q0uPlmOHwYypeHcePg9tshMNC2kpyUCTWEfFhaWprdJYh4FWVCxEqZkNJkT1I6M1buY+rSPew7lnHWfcKC/IkOD6JJ5SiuaBRHw0plqF0hgpBA//x9pkyZwpAhfUuqbBGvp2OFlLhjxyAqylwlLDwcJkyAdevgscegXDm7q3NUJtQQ8mHx8fF2lyDiVZQJEStlQnyV222w71gGmxKO8+3q/fyy+bDHpV/dG8RSr2IZKkWFUDYsiLqxEdSLK4P/38yho1yIWCkTUmKys+H11+Gpp+Ctt2DQIHN8+HB76zqDkzKhhpAPa9Kkid0liHgVZULESpkQX5KVm8cf25OYsy6BH9YmcCQt23K/nwtaVCvHoFZV6NEojvIRwRf0OsqFiJUyIcXOMOC77+D++2HLFnPsk09ONYS8jJMy4bszJQk//PCD3SWIeBVlQsRKmRBvl5KZw7z1B3ls5lpaP/0j/3zvTz77YzdH0rIJ9HfRoGIZrmlWmZcHN+evJ3ry1b/ac0ObahfcDALlQuRMyoQUq9WroUcPuOYasxkUGwvvvAPTp9td2Tk5KRM6Q0hERERESkRWbh4rdh3jt62J/LYtkdV7k8lzn1rwtnx4ED0bV6TPJZVoWyuaQK2wJSLiu557DsaOBbcbgoLMlcRGjwYfX+27NFFDyIe1bdvW7hJEvIoyIWKlTIg3cLsNFm87whfL9jB3fQKZOW7L/TXKh9GhTgyd6lbgioaxxb7MunIhYqVMSLFp1cpsBg0aBM8/DzVr2l1RgTgpE2oI+bDU1FS7SxDxKsqEiJUyIXbIzXOz+WAqGw6ksP5ACnPWJlhWBYuJCKZDnfJ0qB1D+zrlqVIurETrUy5ErJQJKRKGAV9+CcnJMGKEOdajB6xZAz42J4+TMqGGkA9bt24dTZs2tbsMEa+hTIhYKRNS3AzD4EhaNtsPp7EnKZ1lu5KYszaBo2esCFYmJIBrm1dmYKuqNKsShct1/pXAipNyIWKlTMhFW7YMRo2CRYsgIgL69oWKFc37fKwZBM7KhBpCIiIiIvK3jmfmsHZfCmv3JbPhQArbEtPYcTiVlMxcj33LhATQsFIkjSpF0qp6OXo0iiMk0N+GqkVEpNjs2wdjxsBHH5nboaHmPEFlythblxSYyzAM4+93Kz1SUlKIiooiOTmZSB+fzConJ4fAwEC7yxDxGsqEiJUyIRcqNSuXdfuSWXPya28y2xPTzrqvywXxZUOpXj6MOhUi6Nm4Im1rRhf7XEAXSrkQsVImpNDS02HSJHNeoPR0c+ymm+DZZ6FqVXtrKwK+nonC9Dx0hpAPmzt3Ln369LG7DBGvoUyIWCkTUhBpWbms259yovFzjDX7zObP2X5lGF82lCbxkTSuHEWd2AhqVQinRvlwnzr7R7kQsVImpND27oXx4yE3F9q1g5dfhjZt7K6qyDgpE2oI+bCUlBS7SxDxKsqEiJUyIWAu9b50x1F2HknjWHo2SWk5HEvP5mh6NnuOZrDtcOpZmz+Vo0JoEh9F0ypRNImP4pL4KMpHBJf8GyhiyoWIlTIhBbJjx6lVwurVg6efhho14PrrzVNFSxEnZUINIR8WFxdndwkiXkWZELFSJpzF7TbYcSSNNXuTWbDpEKv2JpOYmsXxs8zxc6aKkSFcUsVs+pz8M6YUNH/ORrkQsVIm5Lx27YJHHoEvvjAnj27Rwhx/+GF76ypGTsqEGkI+rHXr1naXIOJVlAkRK2Wi9DIMg71HM1i9N5nV+46xek8ya/clczzr7M2f8uFBtKxejuiwIMqGBxIdFkS5sCAqRAbTuHIksWVCSvgd2Ee5ELFSJuSsUlPhuefgxRchM9M8C+jnn081hEoxJ2VCDSEfNmvWLIYMGWJ3GSJeQ5kQsVImSo+E5ExW7z12ogFkzvVz5tLuAMEBfjSuHEmLauW4vEEscZEhxEQEERkSiJ9f6Tql/0IpFyJWyoRYuN3w4Yfm6mEJCeZYly4webIjmkHgrEyoISQiIiLiRY6kZrF6XzKr9ySzZp/ZBDp0PMtjv0B/Fw0rRXLJiXl+mlYpS93YCK9d3UtERHxAr14wd655u3ZtmDgR+vUrdfMEiUkNIR/WqlUru0sQ8SrKhIiVMuF9DMMgO89NZrab9JxcMrLzSM/OY/+xDLYcSuXHDQdZufuYx+P8/VzUjY3Ib/w0rRJF/YplCA7wndW9vIVyIWKlTIjFtdfC77/DY4/B3XdDcOmcT+58nJQJNYR8WE6O56niIk6mTIhYKRNFKzMnj+2H00jPzmX74TSOpmeTnp1HZk4eGTlmYycjJ4+M7BNfOaf+PH2/PPdZlvQ6jcsFtWLCaValLJdUMc/+aVQpitAgNX+KgnIhYqVMOFhysrlaWIcO5llAALffDoMGQYUKtpZmJydlQg0hH7Z69WoaN25sdxkiXkOZELFSJi6eYRis2H2UL5fv47vV+wu0YldBBfq7CAn0JzTQn4iQABpWiqR19XL0uaQSsZHOmeS5pCkXIlbKhAPl5sL//R88/jgcPgzTp0Pv3hAUBAEBjm4GgbMyoYaQiIiICJCT5+bbVfvZdSSd45m5HEnLYtnOo+w7lpG/T1RoIFGhgZQNC6R2hQjCgvwJCzKbOiFB/oQF+hMa5E9oUAChgeZ9J5s+YUEn7zO3AzXXj4iIlLR582DUKFi3ztxu0MBcSSww0N66xBYuwzDOf95yKZOSkkJUVBTJyclERkbaXc5FycjIIDQ01O4yRLyGMiFipUwUTJ7b4KcNBxk/az17kjI87g8L8ueqJhUZ2KoKl9Usr9W6fJxyIWKlTDjEli1w333w3XfmdnQ0jBsHd9yhZtAZfD0Thel56AwhH7Zw4UKuvPJKu8sQ8RrKhIiVMnFuhmGwOymdX7ck8t6iHWxPTAPM5s/lDWKpUi6MyNAAqpYL44qGcZq/pxRRLkSslAmH2LHDbAYFBMBdd5mXi5UrZ3dVXslJmVBDyIclJSXZXYKIV1EmRKyUiVMMw2BHYhp/7Ejij+1H+H17Egkpmfn3lw0L5LoW8QxrX5Nq5cNsrFSKm3IhYqVMlFI5ObB6NZxcMatnT3MC6UGDoF49e2vzck7KhBpCPqx8+fJ2lyDiVZQJESunZyI1K5fth1OZu+4gM1bus8wFBOakzs2qlKVn4zj+0bY6EcH6tsgJnJ4LkTMpE6WMYcCsWfDAA7B/v3mpWFyced/YsfbW5iOclAnNIeTD0tPTCQvTbzFFTlImRKyclol9xzJYuiOJP3cmsXRHElsOpVruD/L3o3m1slxWqzyX1YymRbVyuhTMgZyWC5G/o0yUImvXmvMEzZtnbleoAF9+CZ0721uXj/H1TGgOIYf4+uuvGTJkiN1liHgNZULEqrRnYtvhVH7ffoSlO5JYesZqYCdFhwfRrEoUA1pV4YqGcYQEqgHkdKU9FyKFpUyUAocOmXMCvfMOuN3m8vH33gtjxkBUlN3V+RwnZUINIREREfEZJ1cEe3X+VtbsS7bc5+/noknlSC6tEc2lNaNpXb0c5SOCbapURESkBKSmQqNGcOSIuT1wIDz/PNSqZW9d4hPUEPJhzZs3t7sEEa+iTIhYlZZMZObk8euWRH7bmsj3aw9wMCULMOcAalW9HG1qlqdNjWhaVCtLuOYBkr9RWnIhUlSUCR8XEQE33giLFsHkybo8rAg4KRP6rsmH+fn52V2CiFdRJkSsfD0Tc9cl8P3aBGas3GcZjwoNZEibatzasQaxZUJsqk58la/nQqSoKRM+ZsUKc8Lol16Ck42L556D4GDQv2WRcFIm1BDyYStWrKB+/fp2lyHiNZQJEStfzERaVi6Ltibyye+7+HVLouW+IW2q0q52DFc0jCUsSN/CyIXxxVyIFCdlwkccOGCuEvbBB+ZKYmPGwOzZ5n2hobaWVto4KRP6bkpERERsdyw9mzd/2c6bv2zLH/NzwXUtqtCzcRxd61cgOEATQouIiMNkZJhnA02YAGlp5tiNN5rbIhdJy877sOPHj1OmTBm7yxDxGsqEiJW3ZyI7182PGw4ydeke/tyRREZOXv591zavzN2X16FOrPfWL77J23MhUtKUCS82Y4a5Wtju3eb2ZZfByy9D27Z2VlXq+XomCtPzcM7FcaXQn3/+aXcJIl5FmRCx8sZMGIbBmr3JPDhtFS2emsvIT1fwy+bDZOTk0aBiGV6/sSVbn+nFf29ooWaQFAtvzIWInZQJL7Zvn9kMqloVPvsMFi9WM6gEOCkTumTMhx06dMjuEkS8ijIhYuUtmUhMzWL2mgP87+etHD6ehfu0c5PLhQXSp2klejSqSMc6Mfj7uewrVBzBW3Ih4i2UCS+yZw/s33+q6XPHHeZ8QSNGaJ6gEuSkTKgh5MOioqLsLkHEqygTIlZ2ZiIzJ48l244wY+U+Zq05QJ7beoX6FQ3juL1zLVpXL4efmkBSgnSsELFSJrxAaiq88AJMnAjx8bBunblqWGAg3H233dU5jpMyoTmEfFhWVhbBwcF2lyHiNZQJEauSzsTBlEye/34jf+05xq6kdEsTqFGlSPq3jKdj3RhqlA8nJFATRIs9dKwQsVImbOR2w0cfmSuGHThgjnXuDFOmQOXK9tbmYL6eCc0h5BDTp0+3uwQRr6JMiFiVVCbW7E3mxbmbaPvsT0xfuY/tiWnkuQ1iywRzY9tqfP3vDsz+TydGdKpFg4qRagaJrXSsELFSJmzy66/Qpg0MG2Y2g2rVgq++ggUL1AyymZMyoUvGREREpNDcboNVe4/xf4t2MGv1gfzxsCB/XhjYlCaVo6hSLpQAf/3uSURExGLlSvNMIIDISHj0UbjnHvMyMZESpIaQD2vSpIndJYh4FWVCxKq4MrHrSBqjp69h8bYj+WPNqkRxTfN4bri0KuHB+vZCvJeOFSJWykQJcbvB78QvSVq0gL59oUoVGDcOYmPtrU0snJQJfcfmw8LCwuwuQcSrKBMiVkWZCcMwWLL9CKOm/sXBlKz88U51Y7jpsupc2bhikb2WSHHSsULESpkoZnl58O67MGkSLFp0qvkzcyb46xJqb+SkTOg8bh/2559/2l2CiFdRJkSsiiITB5IzuO7136j36Pf8450/8ptBrauX47u7O/Lx8LZqBolP0bFCxEqZKEY//WSeDXTHHbBlC7z66qn71AzyWk7KhM4QEhEREQ/p2bmMnr6Gr//abxm/qnFFbu9Si5bVytlUmYiIiJfbvBkeeAC+/dbcLlcOnngCRo60ty6RM2jZeR927NgxypYta3cZIl5DmRCxupBMJGfk8Mvmw4ydsYbjmbkA1K4Qzv0969O9YSzBAfqNpvg2HStErJSJImQY8PDDMHky5OZCQIDZBHriCYiOtrs6KSBfz4SWnXeIVatW2V2CiFdRJkSsCpOJxdsSGf7BUpqNm8s9U1bmN4OGtKnG7P90ovclldQMklJBxwoRK2WiCLlckJlpNoP69IE1a+C//1UzyMc4KRO6ZMyH7d+//+93EnEQZULE6u8ycTwzh0//2M3MlfvYmHDcct+1zStzW6daNImPKs4SRUqcjhUiVsrERTAMmD0batSAxo3NsSeeMFcQ69nT1tLkwjkpE2oI+bCIiAi7SxDxKsqEiNW5MrHrSBrvLdrBh0t2WcYvbxDL8I41aV+7PC6XqyRKFClxOlaIWCkTF2jtWrj/fpg7F7p3h3nzzDOEypdXM8jHOSkTmkPIh+Xl5eGv2elF8ikTIlanZ8IwDL5dfYB7pqz02O8/3esyqHUVqpRzzjKr4lw6VohYKROFdPiweRbQW2+B2w2BgfCf/8Czz5q3xef5eiY0h5BDfPHFF3aXIOJVlAkRqy+++AK32+DlHzdTc/Rsj2bQB8MuZduzvRnVo56aQeIYOlaIWCkTBZSVBZMmQd268MYbZjOof3/YsAEmTlQzqBRxUiZ0yZiIiEgpdCQ1i3kJwYweM9vjvtdvbEmvJhV1WZiIiEhBffwxPPigebtFC3MlsS5d7K1J5CKpIeTDGjRoYHcJIl5FmRCBz/7YzZgZa05sBeePx5cN5at/tadiVIg9hYl4CR0rRKyUifPIyIDQUPP20KHw2Wdw883wz3+CD19SJOfnpEyoIeTDorV8oYiFMiFO9ueOJB6YtordSemW8YGtqjC2d0PKhQfZVJmId9GxQsRKmTiLAwfg0Ufht99g9WoICjIvCZs/3+7KpAQ4KROaQ8iHLV682O4SRLyKMiFOtPXQcbq/uIDr31qS3wxqXrUs0+5sx4RmKUwa1EzNIJHT6FghYqVMnCYjw5wcum5deO892LTJXEVMHMVJmdAZQiIiIj4oPTuXt37Zzn9/2pI/VqVcKK8MaUHLauUA2LrErupERER8iGHA1Knw8MOwe7c51ratOU9Qu3b21iZSjLTsvA9LTEwkJibG7jJEvIYyIU7w+/YjfLtqP5/+sdsy/uCV9bmzS238/U5NFK1MiHhSLkSsHJ+JlBTo1QtOnhVSpQo89xwMGQJ+uqDGiXw9E1p23iE2btxodwkiXkWZkNJs4ebDXPvaIm54+3dLM6hZ1bJsevoq/t2tjqUZBMqEyNkoFyJWjs9EmTLmV1gYPPWUeZnYjTeqGeRgTsqELhnzYXv27LG7BBGvokxIabRg0yFueX+px/jzAy7huhZVCAo49zesyoSIJ+VCxMpxmUhLg5degjvvhAoVwOWCN94wJ46Oj7e7OvECTsqEGkI+LPTkEogiAigTUrokpWXz/PcbmbrM+k3Jy4Ob069Fwb5hVSZEPCkXIlaOyYTbDR9/DGPGwP795kpir79u3lezpr21iVdxTCbQHEJ2lyMiImJxKCWTkZ+uYNmuo/ljcZHBvH5jK1pVL2djZSIiIj5q0SIYNQqWLTO3a9aESZOgf3976xIpBppDyCE+//xzu0sQ8SrKhPiqX7cc5uEvV9Po8Tm0efan/GZQfNlQHunVgN9Hd7+gZpAyIeJJuRCxKtWZ2LEDrr8eOnUym0FlypgTRq9fr2aQnFOpzsQZdMmYD3PYyV0if0uZEF/01i/bmPC95+SFr9/Ykl5NKuJyuc7yqIJRJkQ8KRciVqU6E5Mnw7Rp5gTRw4fD+PEQF2d3VeLlSnUmzqCGkA+rW7eu3SWIeBVlQnxFUlo2j85cw/JdRzmYkpU/fmuHmlzdrBLNq5a9qEbQScqEiCflQsSqVGUiLw+OHoWTS4Y//jjs2QNPPgnNmtlamviOUpWJv6GGkA+rWLGi3SWIeBVlQrydYRiMnbmWz05bNh6gW/0KvHfLpUXSBDqdMiHiSbkQsSo1mZg/35wnKC4OfvjBXD0sJgZmzLC7MvExpSYTBaA5hHzYr7/+ancJIl5FmRBvdjAlkxve/t3SDLqiYRxf/asd7w9rU+TNIFAmRM5GuRCx8vlMbNkC114L3bvD6tWwdKl5VpDIBfL5TBSCzhASEREpZm/9so0X520mO9cNwI1tqzHumsYE+Ov3MiIiIhfk6FFzTqDXXoOcHPD3h5Ej4YknoHx5u6sT8QlqCPmwbt262V2CiFdRJsTbJKfn0HbCj2TmmI2gOrERPN63EZ3rVSiR11cmRDwpFyJWPpmJVavMM4KOHDG3e/c2l5Fv2NDeuqRU8MlMXCD9atKH7dy50+4SRLyKMiHexDAMmj01N78ZdFmtaGbf06nEmkGgTIicjXIhYuWTmWjYEKKjoVEjmDMHZs1SM0iKjE9m4gKpIeTDduzYYXcJIl5FmRBv8dOGg9QcPTt/+40bW/L57e0ICijZw64yIeJJuRCx8olMrF8Pd9wB2dnmdlCQOXH0qlVw5ZX21ialjk9koojokjEfFhgYaHcJIl5FmRA7GYbBvPUHuXfqX6Rn5+WP/7NddXpdUsmWmpQJEU/KhYiVV2ciMdFcMv7NN80l5Rs2hHvvNe+rWdPOyqQU8+pMFDGXYRiG3UWUpJSUFKKiokhOTiYyMtLuckRExMdl57pZtPUw937+FymZufnjnetVYETHmiV6iZiIiEipkJ1tThb91FOQnGyOXXcdvPAC1Kljb20iXq4wPQ9dMubDvvzyS7tLEPEqyoSUtP/9vJX6j33PrR8sy28GhQX58/4tl/LRrW1sbwYpEyKelAsRK6/KhGHA119D48Zw//1mM6h5c/j5Z5g+Xc0gKRFelYlipkvGfFhOTo7dJYh4FWVCSkp6di73TPmLHzcczB+LiQjirZtb06p6ORsrs1ImRDwpFyJWXpeJ116DrVshLg6eeQZuucVcUl6khHhdJoqRGkI+rKaumxWxUCakJMzfeJBbP1iWv92lXgXeu+VS/P1cNlZ1dsqEiCflQsTK9kwkJJiTREdHg8sFL70EU6bA6NFQpoy9tYkj2Z6JEqSGkA+rUaOG3SWIeBVlQorL0p1JfLxkF9+s2m8Zb1szmg+GXYrL5X3NIFAmRM5GuRCxsi0TmZkweTI8+ywMHWqeGQRwySXml4hNnHSc0BxCPuznn3+2uwQRr6JMSFGbveYAvf/7K4PeXGJpBkWFBvLFHe2Yekc7r20GgTIhcjbKhYhViWfCMOCLL8wVw8aMgdRUWLkScnP//rEiJcBJxwmdISQiInKGX7cc5rnvN7Juf4pl/Jb2NRjesSZVo8NsqkxERMSHLV0Ko0bBb7+Z2/Hx8Nxz8I9/gJ/OVRApaWoI+bBOnTrZXYKIV1Em5GKt2ZvMmBlrWLMv2TL+4a1t6OKDy8crEyKelAsRqxLLxEcfmZeGAYSFwcMPwwMPmLdFvIiTjhNqCPmwhIQEqlSpYncZIl5DmZAL4XYbvLlwGx8u3snBlCzLfeP7NeHmy6rbVNnFUyZEPCkXIlYllolevaBsWbjmGnPeoPj44n9NkQvgpOOEzsvzYVu2bLG7BBGvokxIQRmGwR/bj9D5hZ+pNWY2L8zZlN8MCgvy55b2Ndj2bG+fbgaBMiFyNsqFiFWxZMLthk8+geHDT41VqGAuJ//hh2oGiVdz0nFCZwj5MG+eyFTEDsqE/J2ktGzGTF/DnHUJHvfFRATz+e1tqRNbepa4VSZEPCkXIlZFnonFi+Hee835ggAGD4aePc3b5csX7WuJFAMnHSdchmEYdhdRklJSUoiKiiI5OZnIyEi7yxERkRKQm+fm4a/W8NWKvZbx/i3iaV0jmutaxBMa5G9TdSIiIqXArl3mvEBTp5rbERHmKmKjRkFIiL21iThIYXoeumTMh82cOdPuEkS8ijIhZ7Ni91HqjP3e0gyaOLApG566ipcGN+cfbauV2maQMiHiSbkQsbroTGRkwNixUL++2QxyuWDECNiyBUaPVjNIfI6TjhO6ZMyHZWRk2F2CiFdRJuR0R1KzeO+3Hfzv5235Y62rl+Odf7amXHiQjZWVHGVCxJNyIWJ10Znw94cvvoCsLOjWDV56CZo3L5LaROzgpOOEGkI+rGrVqnaXIOJVlAkBWLYzifunrWLXkfT8sZox4dzVrQ4DWjljxYiTlAkRT8qFiNUFZWLRImjbFgIDISgI3ngD0tLMFcQcNP+KlE5OOk6oIeTDGjRoYHcJIl5FmXC2xdsSeejL1ew9av2tzpjeDRjRsRZ+fs77BlWZEPGkXIhYFSoTW7fCgw/CzJnw3//CPfeY41dcUSy1idjBSccJzSHkw+bNm2d3CSJeRZlwnpw8N2/9so2ao2fxj3f+sDSDhrSpxqanr+L2zrUd2QwCZULkbJQLEasCZeLYMXjgAWjUyGwG+fvDoUPFXZqILZx0nNAZQiIi4lPcboPv1hzg9Z+3sjHhuMf9//fP1lzRKM6GykREREqZ3Fx45x14/HFITDTHrroKXnzRbA6JiE9TQ8iHtW/f3u4SRLyKMlG6bTucyvdrDjBp7maP+1pWK8vwjrXo07SSDZV5L2VCxJNyIWJ13kz8+9/w9tvm7YYNzQmjr7qqZAoTsYmTjhNqCPmwpKQkqlevbncZIl5DmSh9snLzmLM2gf98/tdZ7x/ZtTb3XlGPoABdAX02yoSIJ+VCxMojE4ZxamLof//bvETs8cfhjjsgQD8+SunnpOOE7d9Bv/7669SsWZOQkBBatWrFr7/+et79P/30U5o1a0ZYWBiVKlVi2LBhHDlypISq9S4bN260uwQRr6JMlA4bDqQw5O3fqfHILOo/OsejGXRXtzqsHXclO5/rw0NXNVAz6DyUCRFPyoWIVX4mjhyBu+825wo6qWlT2L3bbAypGSQO4aTjhK3fRU+dOpV7772XsWPHsnLlSjp16kSvXr3YvXv3WfdftGgR//znPxk+fDjr1q1j2rRpLF26lBEjRpRw5SIiUtTSsnIZ9+06ev33V5Zstzb6G1eO5IUBTdkxoTcPXFmfiGB9UyoiIlIU/HJzYfJkqFMHXnsNXnkF9uw5tUNwsH3FiUixchmGYdj14m3btqVly5a88cYb+WMNGzakX79+TJgwwWP/SZMm8cYbb7Bt27b8sVdffZUXXniBPad/aJ1HSkoKUVFRJCcnExkZefFvwkZ5eXn4+/vbXYaI11AmfNPCzYd58tt1bD+cZhnv3iCWEZ1q0ap6OZ0FdIGUCRFPyoXICYYB336L8cADuLZsMceaNjXnCere3d7aRGzk68eJwvQ8bPsOOzs7m+XLl9OzZ0/LeM+ePVm8ePFZH9O+fXv27t3L7NmzMQyDgwcP8uWXX9KnT59zvk5WVhYpKSmWr9Ji9uzZdpcg4lWUCd9hGAYvzt3E1a8u4p/v/WlpBtWLi2DZo1fw7i2X0q52eTWDLoIyIeJJuRABduyAHj3g2mvNZlBsrDl59IoVagaJ4znpOGHbOfeJiYnk5eURF2ddGjguLo6EhISzPqZ9+/Z8+umnDB48mMzMTHJzc7nmmmt49dVXz/k6EyZMYNy4cR7j06ZNIywsjP79+/PTTz+RnJxMbGwsbdq04bvvvgOgZcuWuN1u/vrrLwCuvfZaFi1axJEjR4iOjqZz587MnDkTgKZNmxIYGMjy5csB6NOnD8uWLePgwYNERkbSs2dPvvzySwAaN25MREQEf/zxBwBXXnkla9euZd++fYSHh9O3b1+mTp0KQP369YmJieG3334D4IorrmDz5s3s3r2bAwcOAOald263m9q1axMfH8/ChQsB6Nq1K7t372b79u0EBAQwaNAgvvrqK7Kzs6levTq1a9dm/vz5AHTs2JFDhw6xebO5es+QIUP4+uuvSU9Pp0qVKjRq1Ii5c+cC0K5dO5KTk1m/fj0AgwYNYs6cORw/fpyKFSvSsmXL/BBdeumlZGZmsmbNGgCuu+46FixYwNGjR4mJiaFdu3Z8++23ALRo0QKAlStXAnD11VezZMkSEhMTKVeuHF27dmXGjBkAXHLJJYSEhLB06VIAevfuzYoVK0hISKBMmTJcddVVTJs2DYBGjRoRFRXFkiVLALPpuH79evbu3UtYWBjXXnstU6ZMAaBevXrExsayaNEiAC6//HK2bdvGrl27CAoKYsCAAUybNo3c3Fxq1apFtWrVWLBgAQCdO3dm3759bNu2DT8/PwYPHsz06dPJysqiWrVq1KtXjx9//BGADh06kJiYyKZNmwAYPHgw3333HWlpacTHx9OkSRN++OEHwDyTLjU1lXXr1gEwcOBA5s6dS0pKCnFxcbRu3ZpZs2YB0KpVK3Jycli9ejUA/fr1Y+HChSQlJVG+fHk6duzI119/DUDz5s3x8/NjxYoVAPTt25c///yTQ4cOERUVRffu3Zk+fToATZo0ISwsjD///BOAXr16sWrVKvbv309ERAS9e/fmiy++AKBBgwZER0fnN3Z79OjBxo0b2bNnD6GhofTr14/PP/8cwzCoW7cuFStWzJ87rFu3buzcuZMdO3YQGBjIwIED+fLLL8nJyaFmzZrUqFGDn3/+GYBOnTqRkJDAli1bcLlc3HDDDWzdupUpU6ZQtWpVGjRowLx58wDzsyMpKSn/euDrr7+e2bNnk5qaSuXKlWnWrBnff/89AG3atCE9PZ21a9cC+PRnRHBwMP379/eaz4jDx46zOrM8K5IC2JWUwen8MLiyUhbj/tmTFX/8xrxvp+szogg+IxITE0lJSdFnxInPiJkzZ5KRkaHPCC/9jCip7yP27duXP3+l0z8j9H2Ecz8jZv7wA30XLyYgMJC1V1zBpgEDyA0L44qkJMd/RoC+j3D6Z8S+ffto0aKFz35GpKenU1C2XTK2f/9+4uPjWbx4Me3atcsff+aZZ/j444/POpHT+vXrueKKKxg1ahRXXnklBw4c4MEHH+TSSy/l3XffPevrZGVlkZWVlb+dkpJC1apVS8UlY7/88gtdunSxuwwRr6FMeKcNB1IYO2MNK3Yf87gvvmwob93ciibxUSVfmAMoEyKelAtxpKwsc7WwwYNPjX37LTRpwi+7dysTIqfx9eNEYS4Zs+0MoZiYGPz9/T3OBjp06JDHWUMnTZgwgQ4dOvDggw8CZhcsPDycTp068fTTT1OpUiWPxwQHBxNcSidCa9asmd0liHgVZcK7TPlzN+O+XUdmjtsyfnWzyjSoWIYejeKoF1fGpuqcQZkQ8aRciKMYBnz1FTz0kHmZWFQUXHWVed/VVwPQrFw5GwsU8T5OOk7YNjFDUFAQrVq1yj/N6qR58+bRvn37sz4mPT0dPz9ryScne7JxbmzbnDztTERMyoT98twGE3/YSI1HZjF6+hpLM+i/NzRnx4TevDqkBf/uVkfNoBKgTIh4Ui7EMZYvhy5dYNAgsxlUuTLk5HjspkyIWDkpE7au23vfffdx880307p1a9q1a8fbb7/N7t27ufPOOwEYPXo0+/bt46OPPgLM6zxvu+023njjjfxLxu69917atGlD5cqV7XwrIiKO5nYbPPHNOj7+fZdlPCzIn1duaEH3hrG4XC6bqhMREXGQ/fthzBj46CPzDKHQUHjwQfMsofBwu6sTES9ia0No8ODBHDlyhKeeeooDBw7QpEkTZs+eTfXq1QE4cOAAu3fvzt//lltu4fjx47z22mvcf//9lC1blssvv5znn3/errdgqzZt2thdgohXUSZK3oHkDKav2MekuZs4/UTN+nFlePH6ZpobyGbKhIgn5UJKNcOAXr3gxKS/3HQTPPssVK16zocoEyJWTsqErQ0hgJEjRzJy5Miz3vfBBx94jN19993cfffdxVyVbyjM7OEiTqBMlAy32+C933bw9KwNHvfFlw3l27s7Eh0eZENlciZlQsSTciGljtttNoL8/cHlgiefhIkT4eWXoQA/2CoTIlZOyoRtcwjJxTu5VJ2ImJSJ4nc0LZtm4+Z6NIMGtarC6id78tsjl6sZ5EWUCRFPyoWUKkuWQLt28Oabp8b69YPffitQMwiUCZEzOSkTtp8hJCIivmHVnmMMfHMxOXmnrg0b1qEGj/dtpPmBREREStKuXfDII/D55+b2wYNwxx0QEGCeJSQiUgAuw2HLc6WkpBAVFUVycjKRkZF2l3NRsrKyCA4OtrsMEa+hTBQ9wzD4Yd1B7vxkuWX8peub0b9lFZuqkoJSJkQ8KRfi044fh+eeg5degsxMs/kzbBg8/TRUqnRBT6lMiFj5eiYK0/PQJWM+7KeffrK7BBGvokwUnd1H0qnxyCxajJ/n0Qz68b7Oagb5CGVCxJNyIT5rzhyoV8+cJDozE7p2NZeWf/fdC24GgTIhciYnZUKXjPmw5ORku0sQ8SrKxMXbdyyDzi/8TJ7bPHn0WHoOAJ3rVeA/3evQqnq0neVJISkTIp6UC/FZFSual4bVrg2TJsG11xbJ5WHKhIiVkzKhhpAPi42NtbsEEa+iTFycz/7YzZgZayxj17WI57G+jTRRtI9SJkQ8KRfiM7Ztg0WLYOhQc7t5c/j+e/PMoCK8nEWZELFyUiY0h5APO378OGXKlLG7DBGvoUxcmJ2JaQz/cCnbDqflj427pjE3XVYdfz9NTOnLlAkRT8qFeL3kZHNOoFdeMZeUX7sW6tcvtpdTJkSsfD0TmkPIIb777ju7SxDxKspE4fyy+TAD3lhM10kLLM2gH+/rwtD2NdQMKgWUCRFPyoV4rdxcc/n4unXNS8Kys+Hyy8GveH9kUyZErJyUCV0yJiLiMLNWH+Dfn63wGB/arjpPXN0YPzWCREREStbcuXDffbBunbldv765klivXlpGXkSKjRpCPqxly5Z2lyDiVZSJc3O7DV6dv5XJP272uO/lwc3pfUklggJ00mhpo0yIeFIuxOscOwYDB5pLykdHw5NPwp13QmBgiby8MiFi5aRMqCHkw9xut90liHgVZcJTnttg3vqDHkvHA7wwsCnXt65qQ1VSUpQJEU/KhXiF48fh5BwlZcvCE0/Anj3w+ONmU6gEKRMiVk7KhH4d7MP++usvu0sQ8SrKxClZuXmMnbGG2mNmW5pBwQF+fPWvduyY0FvNIAdQJkQ8KRdiq5wcc7Lo6tXNy8ROuv9+ePnlEm8GgTIhciYnZUJnCImIlCKHjmcyZvpaftxw0DJeOSqET0a0pVaFCJsqExERcTDDgFmz4IEHYNMmc+zdd6FnT3vrEhFH07LzPiw9PZ2wsDC7yxDxGk7OxLr9yfR5ZZHHeJP4SN75Z2sqRYXaUJXYzcmZEDkX5UJK3Nq15oTR8+aZ2xUqmMvKDx8O/v721oYyIXImX8+Elp13iEWLPH/4E3Eyp2XCMAz+++MWao+Z7dEMuq1TTVY/2ZPv7u6kZpCDOS0TIgWhXEiJGj8emjUzm0FBQfDQQ7BlC9x+u1c0g0CZEDmTkzKhS8Z82JEjR+wuQcSrOCUTmTl5vDZ/K6/9vNUyHuTvx9PXNdHcQJLPKZkQKQzlQkpUo0bgdpuriD3/PNSqZXdFHpQJESsnZUINIR8WbcOkcyLezAmZmLp0Nw9/tcYyFhbkz+s3tqRr/VibqhJv5YRMiBSWciHFxjBgxgzIzoYbbjDH+veH5cvBi5exViZErJyUCc0h5MMyMjIIDdWlICInleZMHEvPZuh7f7Jqb3L+WK2YcF4Z0oIm8VE2ViberDRnQuRCKRdSLFasgFGjYOFCKF8etm41l5P3AcqEiJWvZ0JzCDnEzJkz7S5BxKuUxkxk5uRx7+craf7UvPxmUHzZULY804v5D3RVM0jOqzRmQuRiKRdSpPbvh2HDoHVrsxkUEgL/+hcEBtpdWYEpEyJWTsqELhkTEfEyhmHw04ZDvPPrdv7YkWS578mrGzG0fQ1cLpdN1YmIiAgZGfDii/Dcc5CWZo794x8wYQJUq2ZvbSIiBaSGkA9r2rSp3SWIeBVfz0Se2+CLZXv4749bSEjJtNzXsU4MT13bmFoVImyqTnyRr2dCpDgoF1IkNm2Cxx835w267DKYPNn80wcpEyJWTsqEGkI+LNCHTkUVKQm+nImDKZnc/O4fbD6Ymj9WLTqMcdc0pmPdGAL9dYWvFJ4vZ0KkuCgXcsH27oUqVczbzZvDmDHQuLE5gbQPn7mrTIhYOSkT+gnDhy1fvtzuEkS8iq9m4qcNB2n77E/5zaBL4qP44o52LHyoG90axKoZJBfMVzMhUpyUCym0PXvgxhvNJeM3bz41/vTTMGSITzeDQJkQOZOTMqEzhEREbLLl4HH6/e830rLzAAgK8OODYZfSvnaMzZWJiIgIqanwwgswcSJkZpqNn3nzoF49uysTESkSWnbeh6WkpPj8exApSr6SieOZObQcP4+cPOvH77xRnakbV8amqqQ08pVMiJQk5UL+ltsNH38Mo0fDgQPmWOfO5jxBLVvaW1sxUCZErHw9E1p23iGWLVtmdwkiXsUXMrFg0yEueXKupRn0v3+0ZOdzfdQMkiLnC5kQKWnKhZyXYUD37nDLLWYzqGZN+OorWLCgVDaDQJkQOZOTMqFLxnzYwYMH7S5BxKt4cybW7E1m0FuLycxx54/d36Med3eva2NVUtp5cyZE7KJcyHm5XNCzJyxfDo89BvfcA8HBdldVrJQJESsnZUINIR/my6exiRQHb8xEbp6b1xds46V5pyahbFa1LC8OakqdWJ0RJMXLGzMhYjflQixSUuCZZ8wmUPfu5tioUTB8OMTG2ltbCVEmRKyclAnNIeTDcnJyHLUknsjf8bZM7ExMo+ukBfnb/n4uXhvSgl6XVLKvKHEUb8uEiDdQLgSAvDx491149FE4fBiaNIG//gJ/f7srK3HKhIiVr2dCcwg5xJdffml3CSJexZsyceh4pqUZ1LNRHGue7KlmkJQob8qEiLdQLoQff4QWLeCOO8xmUL16MGEC+DnzRyNlQsTKSZnQJWMiIkUoKzePt37ZbrlE7KNb29C5XgUbqxIRERE2b4YHHoBvvzW3y5WDJ56AkSPBh88GEBG5UGoI+bDGjRvbXYKIV7E7E9sPp3LP5ytZuy8lf+zj4W3oVFfNILGH3ZkQ8UbKhYOtWmU2gwICzCbQE09AdLTdVdlOmRCxclIm1BDyYREREXaXIOJV7MzEf3/cwqvzt5DrNgjwczGwVRXu61mP2DIhttUkouOEiCflwkFycmDTJnN+IICBA+GRR2DoUGjQwN7avIgyIWLlpEw480LZUuKPP/6wuwQRr2JHJhZtSeS6139j8o+byXUbVI0O5fPbL+O5AU3VDBLb6Tgh4km5cADDgNmzoWlT6NYNkpPNcZfLnCtIzSALZULEykmZ0BlCIiIX4M8dSTw6cw2bD6bmj4UE+rHggW74+7lsrExERMTB1q2D++6DuXPN7ZgYWL8e2rWzty4RES+kZed9WFJSEtG67lkkX0ll4qW5m3hl/tb87eZVyzKqRz26aOJo8TI6Toh4Ui5KqcOHzTmB3noL3G5zkuh774WxYyEqyu7qvJoyIWLl65nQsvMOsXbtWrtLEPEqxZ2JrNw8/vP5yvxmUHCAHx8Pb8PMf3dQM0i8ko4TIp6Ui1IoKQnq14c33jCbQf37w4YN8MILagYVgDIhYuWkTOiSMR+2b98+u0sQ8SrFmYnnvt/Im79ss4wtGd2d6PCgYntNkYul44SIJ+WiFIqOhmuvNVcRe+kl6NrV7op8ijIhYuWkTKgh5MPCw8PtLkHEqxRHJt5ZuJ23Fm4nMTUrf+yW9jV48hrnLEcpvkvHCRFPykUpsHIlPPwwvP461Kljjr3yCoSFgb+/vbX5IGVCxMpJmdAcQj7M7Xbj56er/kROKqpMHE3L5od1Cfz3py0cSM7MH68TG8GMke0pExJ40a8hUhJ0nBDxpFz4sAMHzDmBPvjAXEls0CD44gu7q/J5yoSIla9nQnMIOcTUqVPtLkHEq1xMJgzDYMqfu2nw2Pe0GD+PR6avsTSDZt/TiR/v66JmkPgUHSdEPCkXPigjA555BurWhfffN5tBN9wAEyfaXVmpoEyIWDkpE7pkTEQcb+uhVK546ReP8YaVIhnWvgaDWlfB5dJS8iIiIiVu+nQYNQp27za327aFyZO1jLyISBFQQ8iH1a9f3+4SRLxKYTNhGAYPfbmaacv35o/FlglmfL8mXNm4YlGXJ1LidJwQ8aRc+JgNG8xmUJUq8NxzMGQI+PClHN5ImRCxclIm1BDyYTExMXaXIOJVCpOJ1XuPcc1rv1nGXh3SgqubVS7qskRso+OEiCflwsvt3WsuI9+0qbl9330QEgL/+pc5abQUOWVCxMpJmVB73Yf99ttvf7+TiIP8XSYMw+CnDQfp+Px8SzPo6maVWf1kTzWDpNTRcULEk3LhpdLS4IknoF49uPlmyMszx0ND4f771QwqRsqEiJWTMqEzhETEEXLz3HSdtIC9RzPyx0ID/Xnx+mb0vqSSjZWJiIg4mNsNn3wCo0fD/v3mWGQkHDkCsbH21iYiUspp2XkfdvjwYSpUqGB3GSJe41yZWLYziYFvLsnfDg30Z2yfhtx0WfWSLE+kxOk4IeJJufAiixaZE0YvW2Zu16hhrhw2YABoMYcSo0yIWPl6JgrT89AZQj5s8+bNPv0fVaSonS0T//l8JV//tT9/+6Gr6jOya52SLk3EFjpOiHhSLrzEb79Bp07m7TJlYOxY+M9/zPmCpEQpEyJWTsqE5hDyYbtPLr8pIoA1E7l5bh7+cnV+MyjQ38V3d3dUM0gcRccJEU/KhY1OvzChfXuzIXT77bBlCzz8sJpBNlEmRKyclAmdIeTDgoOD7S5BxKuczIRhGNz5yXJ+3HAIgJ6N4njzplb4+en0c3EWHSdEPCkXNsjLg/feg1dfNS8Ti4w0Lwn76ScIDLS7OsdTJkSsnJQJzSEkIqXKH9uPMOLDZRzPygXgqWsb8892NewtSkRExKnmzzfnCVq92tyeMAEeecTemkRESrHC9Dx0yZgPmzp1qt0liHiNnDw3LR//lsFv/57fDHrwyvpqBomj6Tgh4km5KCFbtkC/ftC9u9kMKlsWJk+G++6zuzI5gzIhYuWkTOiSMR/mdrvtLkHEK6zac4xr//cbp/e4Z93TkcaVo+wrSsQL6Dgh4km5KGaGAQ8+CK+8Ajk54O8PI0fCE09A+fJ2VydnoUyIWDkpE2oI+bDatWvbXYKI7eZvPMitH5jL1fq74NKa0XwyvC0B/joBUkTHCRFPykUxc7ng8GGzGdS7N0yaBA0b2l2VnIcyIWLlpEyoIeTD4uPj7S5BxFbLdyVx+0fL87f/b3A9ujWva2NFIt5FxwkRT8pFMZgzB+rVg1q1zO1nn4V//AOuvNLeuqRAlAkRKydlQr9C92ELFy60uwQRW+S5DR6YtooBbywh121QOSqEjeOvImHDMrtLE/EqOk6IeFIuitD69dCrl/n14IOnxuPj1QzyIcqEiJWTMqEzhETEp2xKOM6gNxeTkmlOHB0a6M/Xd3UkJNDf5spEREQcIjERnnwS3nzTXFI+MBBq1gS3G/z0+2YREV+hhpAP69q1q90liJSoV37awkvzNudvX1Yrms9GXIafnwtQJkTOpEyIeFIuLkJ2Nrz2Gjz1FCQnm2P9+sHEiVCnjq2lyYVTJkSsnJQJtfB92O7du+0uQaREfL/mAM3GzbU0g767uyOf394uvxkEyoTImZQJEU/KxUV4/XW4/36zGdS8OcyfDzNmqBnk45QJESsnZUINIR+2fft2u0sQKVbZuW6e+34j//p0BckZOQA0rRLFlmd60STec0l5ZULESpkQ8aRcFFJ29qnbt98Ol14K//d/sGwZdOtmX11SZJQJESsnZUKXjPmwgAD980npdfh4Fl0m/kx6dh4AVaNDeeum1jSqHHnOxygTIlbKhIgn5aKADh6ERx+FlSvhjz/A3x/CwszbLtffP158hjIhYuWkTLgMwzDsLqIkpaSkEBUVRXJyMpGR5/7BUkTsM33FXh75ag3ZeW4ARl1Rj7sur4O/n74BFRERKVaZmfDyy+bS8cePm2M//gjdu9taloiIFExheh66ZMyHffXVV3aXIFKk9iSlM+jNxdz3xSqy89yUDQvkxUHN+M8VdQvUDFImRKyUCRFPysU5GAZMmwYNG8Lo0WYzqE0b+O03NYNKOWVCxMpJmXDOuVClUPbp13SL+LhJP2zitZ+35m+HBfnz/X86USkqtMDPoUyIWCkTIp6Ui7M4csRcLWzRInM7Ph6eew7+8Q8tI+8AyoSIlZMyoYaQD6tevbrdJYhctCOpWTwzewPTV+zLH7urWx0euLJ+oZ9LmRCxUiZEPCkXZxEdDbm55hxBDz0EDzwA4eF2VyUlRJkQsXJSJtQQ8mG1a9e2uwSRi7InKZ1OL/ycv10xMoSFD3UjKODCfhupTIhYKRMinpQLID0dXnkFRo6EyEhzkuj33oMyZaBKFburkxKmTIhYOSkTOgfUh82fP9/uEkQu2APTVlmaQSM61mTRwxfeDAJlQuRMyoSIJ0fnwu2GTz6BevXMeYImTDh1X8OGagY5lKMzIXIWTsqEzhASkRKV5za4d+pffLtqPwCB/i7eurkVlzeIs7kyERGRUmzxYrj3Xli61NyuXh0uvdTWkkRExF5qCPmwjh072l2CSKHkuQ1u+r8/WLL9CADhQf4sf6wHIYH+RfL8yoSIlTIh4slxudi1Cx5+GKZONbcjImDsWLM5FBJia2niHRyXCZG/4aRMFOrajE2bNvHkk0/SvXt3ateuTaVKlWjatClDhw7ls88+Iysrq7jqlLM4dOiQ3SWIFModHy/Pbwbd0r4G6566qsiaQaBMiJxJmRDx5LhcPPmk2QxyuWDECNiyBR55RM0gyee4TIj8DSdlokANoZUrV9KjRw+aNWvGwoULufTSS7n33nsZP348N910E4ZhMHbsWCpXrszzzz+vxlAJ2bx5s90liBTYozPX8OOGg4A5X9ATVzcq8tdQJkSslAkRT6U+F3l5kJx8anv8eOjdG1asgHfegYoV7atNvFKpz4RIITkpEwW6ZKxfv348+OCDTJ06lejo6HPut2TJEiZPnsyLL77ImDFjiqxIEfFt//frdj75fTcAd19eh/t7Fn5JeREREfkbCxbAqFFQpw5Mm2aOVakCs2bZWpaIiHgnl2EYxt/tlJ2dTVBQUIGftLD7l6SUlBSioqJITk4mMjLS7nJESr1JP2zitZ+3AtCpbgwfD29rc0UiIiKlzNat8OCDMHOmuV22LGzcCHFasEFExGkK0/Mo0CVjBW3u7Nu3r1D7y8X5+uuv7S5B5LzmrD3AG79sA6BNjWjev6V4VzNRJkSslAkRT6UqF8eOwQMPQKNGZjPI3x/+/W9zniA1g6SASlUmRIqAkzJRJKuMJSQk8Mwzz/B///d/ZGRkFMVTSgGkp6fbXYJIvkMpmazam8z+YxmkZecyb/1BVu4+BkCT+Eim3nEZLperWGtQJkSslAkRT6UmF0uXmnMDJSaa21ddBS++aDaHRAqh1GRCpIg4KRMFbggdO3aMf//738ydO5fAwEAeeeQR7rrrLp588kkmTZpE48aNee+994qzVjlDlSpV7C5BhP/7dTuT520mLTvvrPfXKB/Gl3e2L/ZmECgTImdSJkQ8lZpcNGoEwcHQsKHZCOrVy+6KxEeVmkyIFBEnZaLADaExY8awcOFChg4dypw5cxg1ahRz5swhMzOT77//ni5duhRnnXIWjfQbILGR220wftZ63v9tp2X80hrlqFoujMjQQC6rVZ6ejeLw8yv+ZhAoEyJnUiZEPPlsLjZsgLfegpdeAj8/CA+Hn36CWrUgMNDu6sSH+WwmRIqJkzJRoDmEAGbNmsX777/PpEmT+OabbzAMg3r16jF//nw1g2wyd+5cu0sQBzIMg//7dTu1xszObwbVjyvDB8MuZfuzvZl2Z3teGtycJ69pzFVNKpZYMwiUCZEzKRMinnwuF0eOwN13wyWXwH//Cx99dOq++vXVDJKL5nOZEClmTspEgc8Q2r9/f36nrFatWoSEhDBixIhiK0xEvM/eo+nc+sFSNh9MzR/7R9tqPNOvSYlcEiYiIuIY2dnw+uswbpw5eTTANddA+/a2liUiIqVHgRtCbrebwNN+A+Hv7094eHixFCUF065dO7tLEIdIz87l1flbeWPBtvwxfz8X39zVgcaVo2yszEqZELFSJkQ8eX0uDAO++w7uv99cLQygaVOYPBkuv9ze2qRU8vpMiJQwJ2WiwA0hwzC45ZZbCA4OBiAzM5M777zToyk0ffr0oq1Qzik5OdnuEsQhhr73J0t3Hs3fvvvyOtzfs76NFZ2dMiFipUyIePKJXDzzjNkMio01bw8bZi4pL1IMfCITIiXISZko8BxCQ4cOJTY2lqioKKKiorjpppuoXLly/vbJLyk569evt7sEKeXy3AYPTFuV3wwa1qEGW5/p5ZXNIFAmRM6kTIh48spcHDwIqScux3a54OWX4ZFHzKbQiBFqBkmx8spMiNjISZko8BlC77//fnHWISJeJjfPzT/f+5PF244A8J/udRnVo57NVYmIiJQimZnmRNHPPAP33ANPP22OX3aZ+SUiIlKMXIZhGAXdedeuXcydO5ecnBy6du3qk8uxpaSkEBUVRXJyMpGRkXaXc1Fyc3MJCChwT0+kUO76bAXfrT4AwG2dajK2j/fnXZkQsVImRDx5RS4MA776Ch56CHbsMMc6doRffjGXlBcpQV6RCREv4uuZKEzPo8BHnIULF9K4cWPuuOMO7rrrLpo3b86UKVMuuli5cHPmzLG7BCmFMnPyuOX9P/ObQUPaVPOJZhAoEyJnUiZEPNmei+XLoUsXGDTIbAZVrgwffqhmkNjG9kyIeBknZaLAR53HHnuMbt26sXfvXo4cOcKtt97KQw89VJy1yd84fvy43SVIKWMY5pxBCzYdBswl5Sf0v8TmqgpOmRCxUiZEPNmai7ffhtat4ddfITQUHn8cNm+Gf/5TzSCxjY4VIlZOykSBz4Nas2YNCxcupHLlygC8+OKLvPPOOxw9epRy5coVW4FybhUrVrS7BCkFth5K5cW5m9idlM66/Sn540PbVWfctU1srKzwlAkRK2VCxJOtuejVC8LCoH9/ePZZqFrVvlpETtCxQsTKSZkocEPo2LFjxMbG5m+Hh4cTFhbGsWPH1BCyScuWLe0uQXxYRnYe933xF9+vTfC4b0ibqj7XDAJlQuRMyoSIpxLLhdsNU6bAsmUwebI5VrUqbNsGDvphQ7yfjhUiVk7KRKHOTV2/fj2rV6/O/zIMgw0bNljGpOTMnj3b7hLEh42aam0GDWxVhTn3dmLDU1cxoX9TGyu7cMqEiJUyIeKpRHKxZAm0awc33WQuIb948an71AwSL6NjhYiVkzJRqKmzu3fvzpmLkvXt2xeXy4VhGLhcLvLy8oq0QBEpeit2H2XOOrMZNHlwM65rUcXmikREREqBXbvgkUfg88/N7YgIGDMGWrSwty4REZGzKHBDaMfJJTHFa1x66aV2lyA+KDMnj/6vm7+pbFGtbKlqBikTIlbKhIinYslFero5J9CLL0JmJrhcMGwYPP00VKpU9K8nUoR0rBCxclImCtwQ+vDDD3nggQcICwsrznqkEDIzM+0uQXzQ7R8vz7/98FUNbKyk6CkTIlbKhIinYsmFYcD775vNoK5d4aWXdFaQ+AwdK0SsnJSJAs8hNG7cOFJTU4uzFimkNWvW2F2C+BC322Dct+tYuNlcUv7Z6y7hslrlba6qaCkTIlbKhIinIsvFH3+YE0cDhIfD//4HM2bA/PlqBolP0bFCxMpJmShwQ+jMuYNExHckZ+RwxUu/8P5vOwG4o0st/tG2mr1FiYiI+KJt22DAALjsMvjkk1Pj/fqZXy6XXZWJiIgUissoYKfHz8+PgwcPUqFCheKuqVilpKQQFRVFcnIykZGRdpdzUTIzMwkJCbG7DPEBA99YzLJdRwF48Mr6jOxaG1cp/IZVmRCxUiZEPF1wLpKTzTmBXnkFsrPBzw8eewyefLLIaxQpSTpWiFj5eiYK0/Mo1LLz3bt3p2XLluf9kpKzYMECu0sQH/Dtqv35zaDX/tGCf3erUyqbQaBMiJxJmRDxVOhc5ObCm29C3bowaZLZDOrZE1avVjNISgUdK0SsnJSJQi07f+WVVxIREVFctUghHT161O4SxMu9t2gHT323HoDaFcLp27SyzRUVL2VCxEqZEPFU6Fzceit8/LF5u0EDcyWxXr10aZiUGjpWiFg5KROFagg9+OCDxMbGFlctUkgxMTF2lyBe7Pk5G3ljwTYAyoQEMH1kB5srKn7KhIiVMiHiqdC5uOMOmD3bPBvojjsgMLBY6hKxi44VIlZOykSB5xDy9/fnwIEDPt8QKk1zCKWmpuqMLTmrl+Zt5pWftgAQXzaU+Q90ITjA3+aqip8yIWKlTIh4Om8ukpJg3DgoXx4ef/zUeFqauZKYSCmkY4WIla9noljmENIqY97n22+/tbsE8TJut8HT363PbwYB/HS/M5pBoEyInEmZEPF01lzk5JiTRdepY/45YQIcPnzqfjWDpBTTsULEykmZKPAlYzt27HDUqVMiviYzJ4+2z/5EckYOAD0axfHGjS0J8C/U3PEiIiLOYRgwaxY88ABs2mSOXXIJTJ4MPr6yroiIyN8p0E+Kzz33HBUqVMDP7+93/+OPP5g1a1aBC3j99depWbMmISEhtGrVil9//fW8+2dlZTF27FiqV69OcHAwtWvX5r333ivw65UmLVq0sLsE8RK5eW4aPDYnvxnUqW4Mb9/cynHNIGVCxEqZEPGUn4tt2+DKK+Hqq81mUIUK8NZbsHIldO9ub5EiJUjHChErJ2WiQGcIrV+/nmrVqjFo0CCuueYaWrduTYUTvzXJzc1l/fr1LFq0iE8++YQDBw7w0UcfFejFp06dyr333svrr79Ohw4deOutt+jVq1f+653N9ddfz8GDB3n33XepU6cOhw4dIjc3t4BvV6T0ScnMYeh7f+Zv33N5He7rWd/GikRERHxAQAD8+isEBcG998KYMRAVZXdVIiIiJaZApw989NFHzJ8/H7fbzY033kjFihUJCgqiTJkyBAcH06JFC9577z1uueUWNm7cSKdOnQr04i+99BLDhw9nxIgRNGzYkJdffpmqVavyxhtvnHX/OXPm8MsvvzB79myuuOIKatSoQZs2bWjfvn3B33EpsnLlSrtLEJslZ+TQ95VFrNx9DIB7r6jr6GaQMiFipUyInCYrC7755lQuqleHDz6ADRvg+efVDBLH0rFCxMpJmSjwHEJNmzblrbfe4s0332T16tXs3LmTjIwMYmJiaN68eaHnF8rOzmb58uU88sgjlvGePXuyePHisz7mm2++oXXr1rzwwgt8/PHHhIeHc8011zB+/HhCQ0PP+pisrCyysrLyt1NSUgpVp4i3Ss/Opdm4ufnb469tzM3tathXkIiIiDcyDJg+HR56CLZvJ+bJJ0/dN3iwbWWJiIjYrcANoZNcLhfNmjWjWbNmF/XCiYmJ5OXlERcXZxmPi4sjISHhrI/Zvn07ixYtIiQkhBkzZpCYmMjIkSNJSko65zxCEyZMYNy4cR7j06ZNIywsjP79+/PTTz+RnJxMbGwsbdq04bvvvgOgZcuWuN1u/vrrLwCuvfZaFi1axJEjR4iOjqZz587MnDkTMBtmgYGBLF++HIA+ffqwbNkyDh48SGRkJD179uTLL78EoHHjxkRERPDHH38AcOWVV7J27Vr27dtHeHg4ffv2ZerUqQDUr1+fmJgYfvvtNwCuuOIKNm/ezO7du/H3N1eOmjp1Km63m9q1axMfH8/ChQsB6Nq1K7t372b79u0EBAQwaNAgvvrqK7Kzs6levTq1a9dm/vz5AHTs2JFDhw6xefNmAIYMGcLXX39Neno6VapUoVGjRsydazYf2rVrR3JyMuvXrwdg0KBBzJkzh+PHj1OxYkVatmzJ7NmzAbj00kvJzMxkzZo1AFx33XUsWLCAo0ePEhMTQ7t27fJncT95rebJjuzVV1/NkiVLSExMpFy5cnTt2pUZM2YAcMkllxASEsLSpUsB6N27NytWrCAhIYEyZcpw1VVXMW3aNAAaNWpEVFQUS5YsAcym4/r169m7dy9hYWFce+21TJkyBYB69eoRGxvLokWLALj88svZtm0bu3btIigoiAEDBjBt2jRyc3OpVasW1apVY8GCBQB07tyZffv2sW3bNvz8/Bg8eDDTp08nKyuLatWqUa9ePX788UcAOnToQGJiIptOTGA5ePBgvvvuO9LS0oiPj6dJkyb88MMPALRt25bU1FTWrVsHwMCBA5k7dy6v/JUDBJr/LypmErBzCZvLZ5OTk8Pq1asB6NevHwsXLiQpKYny5cvTsWNHvv76awCaN2+On58fK1asAKBv3778+eefHDp0iKioKLp378706dMBaNKkCWFhYfz5p3lpWq9evVi1ahX79+8nIiKC3r1788UXXwDQoEEDoqOj8xu7PXr0YOPGjezZs4fQ0FD69evH559/jmEY1K1bl4oVK+bPHdatWzd27tzJjh07CAwMZODAgXz55Zfk5ORQs2ZNatSowc8//wxAp06dSEhIYMuWLbhcLm644QYCAgKYMmUKVatWpUGDBsybNw+A9u3bk5SUxMaNGwHz0tPZs2eTmppK5cqVadasGd9//z0Abdq0IT09nbVr1wL49GdEcHAw/fv312cEzvyMSElJITo6mpSUlPy5/Vq1auXoz4iZM2eSkZGhzwgHfUas/egjar36KrEn/m3Ty5bF7/hx1q5dq8+IlBTi4uJo3bq1PiMc/hnRokWL/P/DTvuM0PcR+ow422dEbm4ue/fu9dnPiPT0dArKZdi0nvz+/fuJj49n8eLFtGvXLn/8mWee4eOPP87/yzxdz549+fXXX0lISCDqxGm906dPZ+DAgaSlpZ31LKGznSFUtWpVkpOTiYyMLIZ3VnLmzZtHjx497C5DbDB16W4e/so88L15U0uualLJ5oq8gzIhYqVMiGPt3w9jx8KHH5pnCIWEwIMPwkMPMW/JEuVC5DQ6VohY+XomUlJSiIqKKlDPo9BnCBWVmJgY/P39Pc4GOnTokMdZQydVqlSJ+Pj4/GYQQMOGDTEMg71791K3bl2PxwQHBxMcHFy0xXuJxMREu0sQG+xMTOO5782G6bXNK6sZdBplQsRKmRBHcruhWzc4cSYCN94IEyZA1aqAciFyJmVCxMpJmbBtTeqgoCBatWqVf5rVSfPmzTvnJNEdOnRg//79pKam5o9t3rwZPz8/qlSpUqz1eqNy5crZXYKUsL/2HKPrpAUcTc8hKjSQR/s0srskr6JMiFgpE+IYhmE2ggD8/GD0aLjsMvj9d/jkk/xmECgXImdSJkSsnJQJ2y4ZA3Pum5tvvpk333yTdu3a8fbbb/POO++wbt06qlevzujRo9m3b1/+Mvapqak0bNiQyy67jHHjxpGYmMiIESPo0qUL77zzToFeszCnT3m7zMxMQkJC7C5DSsix9GyueOkXElOzCQrwY9bdHakbV8busryKMiFipUyII/z+O4waBSNHws03m2NuN7hc5tcZlAsRK2VCxMrXM1GYnsdFnyGUkpLCzJkz2bBhQ6EfO3jwYF5++WWeeuopmjdvzsKFC5k9ezbVq1cH4MCBA+zevTt//4iICObNm8exY8do3bo1N954I1dffTWvvPLKxb4Nn3Ry0jMp/fLcBr3++yuJqdkAfHuXmkFno0yIWCkTUqrt2WNeDtaundkUeuYZ61lCZ2kGgXIhciZlQsTKSZko9BxC119/PZ07d+auu+4iIyOD1q1bs3PnTgzD4PPPP2fAgAGFer6RI0cycuTIs973wQcfeIydPpu3iFO8+cs2DiRnAvBAz3rUr6hmkIiIOFRqKrzwAkycCJmZZuNn6FCzIeRn22wIIiIiPqfQR82FCxfSqVMnwOycGYbBsWPHeOWVV3j66aeLvEA5t0suucTuEqQEzFi5l4k/mEtGjuhYk7su95w8XUzKhIiVMiGlzqxZUK8ejB9vNoM6d4Zly+D996Fy5QI9hXIhYqVMiFg5KROFbgglJycTHR0NwJw5cxgwYABhYWH06dOHLVu2FHmBcm6+fF2jFMzHS3YyauoqAOIig3mkVwObK/JuyoSIlTIhpU5kJBw4ADVrwpdfwoIF0LJloZ5CuRCxUiZErJyUiUI3hKpWrcqSJUtIS0tjzpw59OzZE4CjR4866i/OGyxdutTuEqQYGYbBJ7+fmkNr3n1dCPDXqfDno0yIWCkT4vO2b4dp005td+oEM2bAhg0wYMA55wk6H+VCxEqZELFyUiYK/dPlvffey4033kiVKlWoXLkyXbt2BcxLyZx0apVIcftoyS42HTwOwPf/6URkSKDNFYmIiJSQlBR4+GFo2NCcH2jPnlP39esHwcG2lSYiIlJaFGjZ+ZSUFMtyZcuXL2f37t306NGDiIgIAGbNmkXZsmXp0KFD8VVbBErTsvPJyclERUXZXYYUA7fboPUzP5KUlk27WuWZcvtldpfkE5QJEStlQnxOXh68+y48+igcPmyO9egBb7wBtWsXyUsoFyJWyoSIla9nosiXnS9XrhyHDh0C4PLLL6d27dpcd911+c0ggD59+nh9M6i0WbFihd0lSDF5cd4mktLMJeZfGdLC5mp8hzIhYqVMiE/56Sdo0QLuuMNsBtWvD999Bz/8UGTNIFAuRM6kTIhYOSkTBWoIRUREcOTIEQAWLFhATk5OsRYlBZOQkGB3CVIM5m88yP9+3gZArZhwKpTRafEFpUyIWCkT4jMOHoQ+fWDNGihXDv77X/N2nz4XNE/Q+SgXIlbKhIiVkzIRUJCdrrjiCrp160bDhg0BuO666wgKCjrrvvPnzy+66uS8ypQpY3cJUsQyc/J4+rsNAMREBPPt3R1trsi3KBMiVsqEeLX0dAgLM2/HxZlzBh07Bk88ASdWtC0OyoWIlTIhYuWkTBRoDqGMjAw+/PBDtm3bxosvvshtt91G2MkD+BkmT55c5EUWpdI0h1Bubi4BAQXq6YkXMAyDLYdS+WP7ETYkHCfI34/1+1PYdjiVilEhZOe62XIoNX//7+7uSJN437121Q7KhIiVMiFeKScH3nwTxo0zLwm7rGTnyVMuRKyUCRErX89EYXoeBXqXoaGh3HnnnQAsW7aM559/nrJly150oXJxpk2bxpAhQ+wuQ/7G7DUHGPnp+a9DPXJivqCTXh7cXM2gC6BMiFgpE+JVDANmz4YHHoCNG82xN94o8YaQciFipUyIWDkpE4Vue/3888/FUYdIqbNufzL//nQFO4+ke9xXo3wYsZEhXFarPIZhEBcZQq0K4QQH+FG5bCiVokJtqFhERKSYrF0L998Pc+ea2zExMH48jBhhb10iIiIOVqCG0H333cf48eMJDw/nvvvuO+++L730UpEUJn+vUaNGdpcgZ2EYBgs2HWbYB0vzx6pFh3Ffj3r0aBRHeLDvnn7o7ZQJEStlQrzC44/DM8+A2w2BgfCf/8DYsWDT2ebKhYiVMiFi5aRMFOgn05UrV+avLLZixQpcRbzag1yYqChdUuSN+v3vN1btTc7ffrxvI27tWNPGipxDmRCxUibEK9SoYTaDrrsOXngB6tSxtRzlQsRKmRCxclImCtQQOv0ysQULFhRXLVJIS5YsoUaNGnaXIaeZPG+zpRn0432dqRPrnFnq7aZMiFgpE1LiDAO+/hr8/eHqq82xoUOhYUNo187e2k5QLkSslAkRKydlwq+wD7j11ls5fvy4x3haWhq33nprkRQl4msOpWTSZeLP/PenLQAMaVOVHRN6qxkkIiLO8ddfcPnl5plAI0eay8qD2RzykmaQiIiInFKgZedP5+/vz4EDB4iNjbWMJyYmUrFiRXJzc4u0wKJWmpadP3LkCOXLl7e7DMfbfSSd617/LX+lsMtqRfPJ8LYE+Be63yoXSZkQsVImpEQkJMCjj8J775lnCIWEmBNIjxkDYWF2V+dBuRCxUiZErHw9E4XpeRT4J9aUlBSSk5MxDIPjx4+TkpKS/3X06FFmz57t0SSS4rV+/Xq7S3C8jQkpXP7iAo6kZRPo7+K9W1rz+e3t1AyyiTIhYqVMSLHKyIBnn4W6deHdd81m0A03mEvKP/20VzaDQLkQOZMyIWLlpEwUeLmjsmXL4nK5cLlc1KtXz+N+l8vFuHHjirQ4Ob+9e/faXYKjGYbBXZ+tJNdtnmQ37c72NK9a1t6iHE6ZELFSJqRYLV9urhYG0KYNTJ4M7dvbW1MBKBciVsqEiJWTMlHghtDPP/+MYRhcfvnlfPXVV0RHR+ffFxQURPXq1alcuXKxFClnF+alv3lzArfbYOzMtWw9lArAtDvbqRnkBZQJEStlQorcwYMQF2fe7tgR7roLLrsMhgwBP984O1a5ELFSJkSsnJSJQs8htGvXLqpVq+azS8+XpjmExB7ZuW4GvLGYNfvM1cRu71yLMb0b2lyViIhIMdqzB0aPhpkzYdMmiI+3uyIRERE5iyKfQ2j16tW43W4AkpOTWbNmDatXrz7rl5ScKVOm2F2C4+S5Dfq/8Vt+M2hIm6o8fFUDm6uSk5QJEStlQi5aWho88QTUrw+ffmpuf/+93VVdFOVCxEqZELFyUiYKdMlY8+bNSUhIIDY2lubNm+NyuTjbiUUul4u8vLwiL1LEW3y5fA9r96UA8OCV9fl3tzo2VyQiIlIM3G74+GNzpbD9+82xjh3NeYJat7a3NhERESkSBWoI7dixgwoVKuTfFu9wtsm9pfgkZ+Twyk9bAejRKE7NIC+kTIhYKRNyQdxu6NIFFi0yt2vWhBdegAEDwEenDDidciFipUyIWDkpEwVqCFWvXv2st8VesbGxdpfgKPd/8Rf7jmXg7+fi8b6N7C5HzkKZELFSJuSC+PlBhw6wahU8+ijccw+EhNhdVZFRLkSslAkRKydlotDLQXz44YfMmjUrf/uhhx6ibNmytG/fnl27dhVpcXJ+i07+5k6KXU6emx83HALg4avqUzXaOTPP+xJlQsRKmZACSUkxJ4z+889TY2PHwpYt8NBDpaoZBMqFyJmUCRErJ2Wi0A2hZ599ltDQUACWLFnCa6+9xgsvvEBMTAyjRo0q8gJFvMEnv59qdg5tX8O+QkRERIpKXh688w7UrQvPPQf33gsn54gsU+bU8vIiIiJSKhXokrHT7dmzhzp1zLlTZs6cycCBA7n99tvp0KEDXbt2Ler65Dwuv/xyu0twhOSMHN78ZRsAl9YoR3CAv80VybkoEyJWyoSc0/z5MGoUnFwhtl49cwJpB1AuRKyUCRErJ2Wi0GcIRUREcOTIEQDmzp3LFVdcAUBISAgZGRlFW52c17Zt2+wuodTLznXT9tkfOZiSRZC/H6/9o6XdJcl5KBMiVsqEeNiyBfr1g+7dzWZQ2bLmymFr1kDfvqVi0ui/o1yIWCkTIlZOykShG0I9evRgxIgRjBgxgs2bN9OnTx8A1q1bR40aNYq6PjkPzdlU/EZN/YvMHDcAo3s3IC6ydM2jUNooEyJWyoR4+OUX+Ppr8PeHu++GrVvNS8WCguyurMQoFyJWyoSIlZMyUeiG0P/+9z/atWvH4cOH+eqrryhfvjwAy5cvZ8iQIUVeoJxbkIO+ebPD4eNZzFpzAIDLG8QyrENNmyuSv6NMiFgpE0JuLmzefGp72DCzEbRmDbzyCpz4Ps5JlAsRK2VCxMpJmXAZxsnZA50hJSWFqKgokpOTiYyMtLsc8VJut0GtMbMBqBgZwuJHLsfPr/SfRi8iIqXInDlw332QmgqbNsGJRUFERESk9CpMz6PQZwgBHDt2jBdffJERI0Zw22238dJLL5GcnHxBxcqFmzZtmt0llFrX/u+3/Nv3dK+rZpCPUCZErJQJh1q/Hnr1Mr82bID0dFi3zu6qvIZyIWKlTIhYOSkThW4ILVu2jNq1azN58mSSkpJITExk8uTJ1K5dmxUrVhRHjXIOubm5dpdQKm09lMqafacanP9oW83GaqQwlAkRK2XCYRIT4a67oGlT8+ygwEC4/35znqDWre2uzmsoFyJWyoSIlZMyUehl50eNGsU111zDO++8Q0CA+fDc3FxGjBjBvffey8KFC4u8SDm7WrVq2V1CqfTYzLUA+LlgyzO9ba5GCkOZELFSJhwkIQEaNoRjx8ztfv1g4kSoU8fOqrySciFipUyIWDkpE4VuCC1btszSDAIICAjgoYceorV++1SiqlXTmStF7dM/drFk+xEA3r65Nf66VMynKBMiVsqEg1SsCN26wY4d8NJL5m05K+VCxEqZELFyUiYKfclYZGQku3fv9hjfs2cPZcqUKZKipGAWLFhgdwmlimEYjJ1hnh10SXwUVzSKs7kiKSxlQsRKmSjFVq2Cvn1h375TY++9B8uWqRn0N5QLEStlQsTKSZkodENo8ODBDB8+nKlTp7Jnzx727t3L559/zogRI7TsvPi06StOfVP9WN9GNlYiIiJyDgkJcNtt0KIFzJoFjz9+6r6yZcHf37bSRERExLcU+pKxSZMm4XK5+Oc//5k/2VJgYCD/+te/eO6554q8QDm3zp07211CqZGencvDX60G4Jb2NWhTM9rmiuRCKBMiVspEKZKZCZMnw7PPmsvIAwweDI89Zm9dPki5ELFSJkSsnJSJQp8hFBQUxH//+1+OHj3KX3/9xcqVK0lKSmLy5MkEBwcXR41yDvtOP01cLkq3SQvIdRtEhwcxuncDu8uRC6RMiFgpE6XE9OnmhNFjxpjNoEsvhUWL4PPPoUYNu6vzOcqFiJUyIWLlpEwUuiF0UlhYGGXLliU6OpqwsLCirEkKaNu2bXaXUCqs3ZfMwZQsANrVKk9wgE6391XKhIiVMlFK/P477NwJ8fHw8cfmdocOdlfls5QLEStlQsTKSZkodEMoNzeXxx57jKioKGrUqEH16tWJiori0UcfJScnpzhqlHPw87vgfp6ckJSWza0fLM3ffu0fLWysRi6WMiFipUz4qL17YdOmU9tjx5qXim3aBDfdBPp3vSjKhYiVMiFi5aRMuAzDMArzgDvvvJMZM2bw1FNP0a5dOwCWLFnCk08+ybXXXsubb75ZLIUWlZSUFKKiokhOTiYyMtLucsRmIz5cxo8bDhIc4MevD3cjtkyI3SWJiIhTpafDxInw/PPQsiX8+iu4XHZXJSIiIj6kMD2PQre+pkyZwgcffMAdd9xB06ZNadq0KXfccQfvvfceU6ZMueCipfCmT59udwk+bd3+ZH7ccBCAiYOaqRlUCigTIlbKhI9wu+GTT6BePXjyScjIMMePHrW1rNJKuRCxUiZErJyUiUI3hEJCQqhxlgkMa9SoQVBQUFHUJAWUlZVldwk+7bX5WwGILxvKNc0q21yNFAVlQsRKmfABixfDZZfBzTfDvn1QvTpMnWqeHRStFS+Lg3IhYqVMiFg5KROFbgj9+9//Zvz48Za/pKysLJ555hnuuuuuIi1Ozq9atWp2l+CzFm1J5Pu1CQA8P6CpzdVIUVEmRKyUCS/344/m5NBLl0JEBEyYABs3wvXX61KxYqRciFgpEyJWTspEQGEfsHLlSn766SeqVKlCs2bNAFi1ahXZ2dl0796d/v375+/rpFOt7FCvXj27S/BJx9KzGfr+nwC0rl6OjnVjbK5IiooyIWKlTHghwzjV7OnWDZo3h9atYfx4qFjR1tKcQrkQsVImRKyclIlCnyFUtmxZBgwYQN++falatSpVq1alb9++9O/fn6ioKMuXFK8ff/zR7hJ80rAPlpLnNudSf/3GljZXI0VJmRCxUia8SF4e/N//Qbt2kJlpjvn7m0vIv/OOmkElSLkQsVImRKyclIlCnyH0/vvvF0cdIiXi67/2sXL3MQAe7dOQ2EhNJC0iIsXs559h1ChYtcrcfvttuOce83ZwsH11iYiIiKMV+gwh8R4dOnT4f/buOzyKem3j+De9EBI6odfQQTqC9CagCCICVrBjQ+wVBVGsgHI8IIrK0VcBkaaIFJUqSEfA0KR3hEAChPR5/xizMG6ALCSZ3cz9ua5c7M7O7j674d6Qh1+xuwSfcioxhScmbwDgxnqluL9VZXsLkhynTIhYKRM227EDevaE9u3NZlBUFIwcCQMH2l2ZoykXIlbKhIiVkzKhhpAPO378uN0l+JRB/zSDggP9efPmuvYWI7lCmRCxUiZskp4OTz8NtWvDrFnm1LBHHoG//oKnngLtymor5ULESpkQsXJSJtQQ8mHbtm2zuwSf8d68rSzZ/jcAY29vSFRYkM0VSW5QJkSslAmbBATAzp2QmgpdusDGjfDf/0IxbWLgDZQLEStlQsTKSZlQQ0jyvWMJSYxfvAuAnvVL07FWSZsrEhGRfGfePDh8+Pz199+HOXPgp5+gVi376hIRERG5CD/DMIyrfZBTp05RqFChHCgn9yUkJBAVFUV8fDyRkZF2l3NVMjIy8PdXT+9yXpm5if/7fR/hwQFsfK0zgQF6z/IrZULESpnIA1u2wDPPmM2fe+6Bzz+3uyK5DOVCxEqZELHy9Ux40vPw+FW+8847TJkyxXW9T58+FC1alDJlyvBH5u4Zkidmz55tdwleb/H2v/m/3/cBMLpvfTWD8jllQsRKmchFJ07A449D3bpmMygwEIoUgav/fzbJZcqFiJUyIWLlpEx4/Nvx+PHjKVeuHAALFixgwYIF/PTTT3Tt2pVnn302xwuUizt79qzdJXi9/p+vAiA4wJ/OmiqW7ykTIlbKRC5ISYEPPoCqVeGjj8wFpHv0gNhYc5qYn5/dFcplKBciVsqEiJWTMhHo6R0OHz7sagjNnj2bPn360LlzZypWrEizZs1yvEC5uDJlythdglf78OcdrsuTHrwWP/0jPd9TJkSslIlc8N578Mor5uV69WD0aHNbefEZyoWIlTIhYuWkTHg8Qqhw4cLs378fgLlz59KxY0cADMMgPT09Z6uTS6pTp47dJXitr1bsYfTP2wFoW704jSoUtrkiyQvKhIiVMpFD0tLOX370UXM7+U8+gXXr1AzyQcqFiJUyIWLlpEx43BDq1asXt99+O506deLEiRN07doVgA0bNlC1atUcL1Aubt68eXaX4JX2nUhkyKw/XdfH3NbAxmokLykTIlbKxFU6ehQefBA6dTq/NlChQrBpEzzwgLm9vPgc5ULESpkQsXJSJjyeMjZ69GgqVqzI/v37effdd4mIiADMqWSPPPJIjhco4qnhP8YCULRAMMueb09YsP7BLiIiHkhKgg8/hDffhNOnzWMrVkCLFuZlTUEWERGRfMDjhlBQUBDPPPOM2/HBgwfnRD3iAa3Z5G7a2gMsiD0KwHu31lMzyGGUCRErZcJDhgHffQfPPQd79pjHGjUy1wnKbAaJz1MuRKyUCRErJ2UiWw2h77//nq5duxIUFMT3339/yXNvuummHClMLu/MmTN2l+BVxi76i3fnbgOgdulI2tfQrmJOo0yIWCkTHjh6FHr3hmXLzOulS8Nbb8Gdd4K/xzPsxYspFyJWyoSIlZMyka2GUM+ePTly5AglSpSgZ8+eFz3Pz89PC0vnoT///JN69erZXYZXWLz9b1czCMxdxcR5lAkRK2XCA0WLwqlTEBYGzz5rjhIqUMDuqiQXKBciVsqEiJWTMpGthlBGRkaWl0W8xVNTNgBQKiqUZc+3J8Bf6zuIiMglJCbCxx/DI49AaCgEBsJXX5mNoXLl7K5OREREJNf5GUbmthnOkJCQQFRUFPHx8URGRtpdzlVJTU0lKCjI7jJs99XvexkyczMA/3dfM1rGFLO5IrGLMiFipUxkISMDJk2CF16AAwfg7bfh+eftrkrykHIhYqVMiFj5eiY86Xlc0aT4s2fPMmfOHD7++GPGjBlj+ZK8M3/+fLtLsF1GhsH4xTsBuL1ZeTWDHE6ZELFSJv4lc6ewO+80m0Hly0NMjN1VSR5TLkSslAkRKydlwuNdxtavX0+3bt1ITEzk7NmzFClShOPHjxMeHk6JEiUYNGhQbtQpWUhISLC7BNt9u2Y/B06eA+DpTtVsrkbspkyIWCkT/9i71xwRNHmyeT0iAl58EZ580lwzSBxFuRCxUiZErJyUCY9HCD355JN0796duLg4wsLC+P3339m7dy+NGjXi/fffz40a5SJKlnT2Llonz6bwwvRNADzargpFI0Jsrkjs5vRMiPybMvGPp582m0F+fnDvvbB9O7z0kppBDqVciFgpEyJWTsqEx2sIFSpUiJUrV1K9enUKFSrEihUrqFmzJitXrqR///5s3bo1t2rNEflpDaGEhASffw1Xo8/4FazaHQfAjje7EhSgbYGdzumZEPk3x2YiPR3OnTNHAoHZAHrsMXjnHWjQwN7axHaOzYXIRSgTIla+nolcXUMoKCgIPz9zB6eSJUuyb98+AKKiolyXJW/8+OOPdpdgm3fmbnU1gz6+s5GaQQI4OxMiWXFkJhYvhiZNYPDg88eqVYP589UMEsChuRC5BGVCxMpJmfB4DaEGDRqwZs0aqlWrRrt27Xj11Vc5fvw4X331FXXr1s2NGkUs0tIzGLfIXEi6ScXCdKkTbXNFIiJiu5074bnnYPp08/qePfDee1C4sK1liYiIiHgrj4dVjBgxglKlSgEwfPhwihYtysMPP8yxY8f45JNPcrxAubhGjRrZXYItJi7fA4C/H3w2oIm9xYhXcWomRC7GEZmIjzcbQbVqmc0gf394+GHYtk3NIMmSI3Ih4gFlQsTKSZnwaISQYRgUL16c2rVrA1C8eHHmzJmTK4XJ5aWmptpdQp47l5LOGz9uAaBy8QgiQ4Nsrki8iRMzIXIp+T4Ty5dDz57w99/m9c6dYdQo+OffKSJZyfe5EPGQMiFi5aRMeDRCyDAMYmJiOHDgQG7VIx7YuHGj3SXkuf/7fa/r8jcPNLOxEvFGTsyEyKXk+0zUrAkZGVCjBvz4I8ydq2aQXFa+z4WIh5QJESsnZcKjhpC/vz8xMTGcOHEit+oRuaRRC7YD8FDrypQoGGpzNSIikqe2boWXX4bMDVILF4aFC2HjRujWzdxWXkRERESyxeNt53/88Ufefvttxo0bR506dXKrrlyTn7adP3fuHGFhYXaXkWfW7zvJzWOXA7DixfaUinLOa5fscVomRC4n32QiLg6GDYOxYyEtzVwr6Oab7a5KfFS+yYVIDlEmRKx8PRO5uu38nXfeyapVq7jmmmsICwujSJEili/JO0uWLLG7hDw1YeluAFpUKapmkGTJaZkQuRyfz0RqKowZA1Wrmn+mpUH37poWJlfF53MhksOUCRErJ2XC423nR48ejZ+GZHuFuLg4u0vIM/GJqfy46TAAD7SqbHM14q2clAmR7PDZTBiGuSbQM8+Yu4UB1K1rLhjdsaO9tYnP89lciOQSZULEykmZ8LghNGDAgFwoQ65E0aJF7S4hzzw/7fzCXq1iitlYiXgzJ2VCJDt8NhMZGfDCC2YzqHhxeOMNuO8+CAiwuzLJB3w2FyK5RJkQsXJSJjyeMtauXTs+++wz4uPjc6Me8UDLli3tLiFPJKak8cvWowB0qxtNYIDHf23FIZySCZHs8qlM/P03JCWZlwMCYPRoeO452LEDHnxQzSDJMT6VC5E8oEyIWDkpEx7/Zl23bl1eeeUVoqOjueWWW5g5cyYpKSm5UZtcxqxZs+wuIU8M+Hw1qenm2uej+9a3txjxak7JhEh2+UQmkpPhvffOrxOUqVMneOcdiIqyrzbJl3wiFyJ5SJkQsXJSJjxuCI0ZM4aDBw8ya9YsChYsSP/+/YmOjubBBx9k8eLFuVGjONgf+0+xao85h7Nv43KEBOp/iEVE8gXDMHcLq1XLHAmUkABz557fUl5EREREctUVzb3x9/enc+fOTJw4kaNHjzJ+/HhWrVpF+/btc7o+uYT69evbXUKuMgyDZ7/7A4DgQH9G9Kprc0Xi7fJ7JkQ85bWZWLcO2rWDW26BXbugVCmYOBF+/hm0cYXkMq/NhYhNlAkRKydlwuNFpS905MgRJk+ezP/93/+xceNGmjRpklN1STb4++fvtXRmbTjE9qNnAJg/uDUB/volQS4tv2dCxFNemYmPPoJBg8yRQKGh8Oyz5gihiAi7KxOH8MpciNhImRCxclImPH6lCQkJfPHFF3Tq1Ily5coxbtw4unfvzvbt21m5cmVu1CgXsW7dOrtLyFXzY48AULdMFBWLFbC5GvEF+T0TIp7yykx07AiBgXD77eYuYq+/rmaQ5CmvzIWIjZQJESsnZcLjEUIlS5akcOHC9OnThxEjRmhUkOSKpNR0Fm/7G4AXu9WwuRoREbkihgGTJ8OWLWbjB6BGDfjrLyhf3t7aRERERBzOzzA8W71x/vz5dOzY0WeHUSUkJBAVFUV8fDyRkZF2l3NVTp8+TcGCBe0uI1d8+PMORv+8nZKRIfz2fHttNS/Zkp8zIXIlbM3E77/Dk0+af/r5wYYNUK+ePbWIXEA/K0SslAkRK1/PhCc9D49/y+7cubPPNoPym1WrVtldQq7YH5fI6J+3A/BAq8pqBkm25ddMiFwpWzKxfz/ccQc0b242gwoUMEcHxcTkfS0iWdDPChErZULEykmZuKpFpcVex44ds7uEHGcYBq3eXQhA4fAg7r2uks0ViS/Jj5kQuRp5momzZ+Gdd+C99yApyRwVNGAAvPEGlC6dd3WIXIZ+VohYKRMiVk7KhBpCPiwqKsruEnLc1DUHXJeH96yDv3YWEw/kx0yIXI08zURKCvz3v2YzqHVrGD0aGjbMu+cXySb9rBCxUiZErJyUCY/XEPJ1+WkNoeTkZEJCQuwuI8cYhkGjN34m7mwKAHvevsHmisTX5LdMiFytXM/E+vVQv745Ggjg668hLAxuvvn8MREvo58VIlbKhIiVr2ciV9cQulBSUtLV3F2u0vTp0+0uIUd9tmy3qxm06Jm29hYjPim/ZULkauVaJnbtgt69zRFA339//vgdd0CvXmoGiVfTzwoRK2VCxMpJmfC4IZSRkcHw4cMpU6YMERER7Nq1C4AhQ4bw2Wef5XiB4gwZGQYfLfwLgOjIUCoWK2BzRSIi4iYhAZ5/HmrWhGnTwN8fNm2yuyoRERERuQIeN4TeeOMNJk6cyLvvvktwcLDreN26dZkwYUKOFieXVqdOHbtLyDFfLN/DqcRUAKY8dK3N1Yivyk+ZEMkJOZaJ9HT45BOoWhXefddcL6hTJ3Mr+VdeyZnnEMkj+lkhYqVMiFg5KRMeN4S+/PJLPvnkE+644w4CAgJcx+vVq8fWrVtztDi5tPDwcLtLyBF/HTvN8NmxADzUpjIVimp0kFyZ/JIJkZySY5m47TZ46CH4+2+oVg1mz4Z586Bu3Zx5fJE8pJ8VIlbKhIiVkzLhcUPo4MGDVK1a1e14RkYGqampOVKUZM+qVavsLiFHvPHjFgD8/eC562vYXI34svySCZGckmOZuOceKFwYPvgANm+GG27QOkHis/SzQsRKmRCxclImPN52vnbt2ixdupQKFSpYjk+dOpUGDRrkWGHiDAtij7Jo298AfPtQcwK0zbyIiL3i4uD116FSJXjiCfNY166wZw/4+O6cIiIiInKexw2h1157jbvuuouDBw+SkZHB9OnT2bZtG19++SWzZ8/OjRrlIrp27Wp3CVfl2OkkHvhyDQA31itF44pFbK5IfJ2vZ0Ikp3mUidRU+PhjGDrUbApFRkL//lCokHm7mkGST+hnhYiVMiFi5aRMeDxlrHv37kyZMoU5c+bg5+fHq6++ypYtW/jhhx/o1KlTbtQoF/HHH3/YXcJV+WblPtfloTfVtrESyS98PRMiOS1bmTAMmDMH6tWDQYPMZlCdOvDdd+ebQSL5iH5WiFgpEyJWTsqExyOEAK6//nquv/76nK5FPHTo0CG7S7hi6RkGX63YC8DANlUoFhFic0WSH/hyJkRyw2UzsWMHPP64uUA0QLFiMHw43H8/BF7RPxFEvJ5+VohYKRMiVk7KhP6158MiIiLsLuGKTVt7gBNnUwAY2KayzdVIfuHLmRDJDZfNRFoa/PwzBAXB4MHw8ssQFZUntYnYRT8rRKyUCRErJ2XCzzAM43InFS5cGL9s7iYSFxd31UXlpoSEBKKiooiPjyfSx9dDSE9PJyAgwO4yPGYYBh1HLWbn32d5vH1Vnu5c3e6SJJ/w1UyI5Ba3TCQnw5IlcOEU7y++gNatoUqVvC9QxAb6WSFipUyIWPl6JjzpeWRrDaEPPviA0aNHM3r0aF555RXAnDY2dOhQhg4d6po+NmTIEI+LHTt2LJUqVSI0NJRGjRqxdOnSbN3vt99+IzAwkPr163v8nPnFt99+a3cJV2TR9r/Z+fdZAPq3qGhvMZKv+GomRHKLKxOGATNmQO3a0KULbNp0/qR77lEzSBxFPytErJQJESsnZSJbU8b69+/vunzLLbfw+uuv89hjj7mODRo0iI8++oiff/6ZJ598MttPPmXKFAYPHszYsWO57rrrGD9+PF27diU2Npby5ctf9H7x8fHcfffddOjQgaNHj2b7+cQ7/N8/awdFR4Zq7SARkdy2fj089RQsWmRej46GQ4egbl1byxIRERERe3m8y9i8efPo0qWL2/Hrr7+en3/+2aPHGjVqFPfddx/3338/NWvW5IMPPqBcuXKMGzfukvd76KGHuP3222nevLlHz5ff1KhRw+4SPLZy1wl+2XoMgLd66ZcRyVm+mAmRXHP4MJ2nTIFGjcxmUGiouUbQ9u2gjSHEwfSzQsRKmRCxclImPG4IFS1alBkzZrgdnzlzJkWLFs3246SkpLB27Vo6d+5sOd65c2eWL19+0ft98cUX7Ny5k9deey1bz5OcnExCQoLlK78oUqSI3SV4JDU9gzsmrASgYflCtKtRwuaKJL/xtUyI5Jq0NLj2WorOmmVOF+vXD7ZuhTfegIIF7a5OxFb6WSFipUyIWDkpEx7vMjZs2DDuu+8+Fi1a5Bqh8/vvvzN37lwmTJiQ7cc5fvw46enplCxZ0nK8ZMmSHDlyJMv77NixgxdeeIGlS5cSmM3tcN966y2GDRvmdnzq1KmEh4fTq1cvfvnlF+Lj4ylRogRNmzZl9uzZADRs2JCMjAw2bNgAQI8ePVi2bBknTpygSJEitG7dmpkzZwJQr149goKCWLt2LQA33HADa9as4ejRo0RGRtK5c2e+++47AGrXrk1ERAQrV5rNkeuvv57Nmzdz8OBBChQowI033siUKVMAqF69OsWKFeO3334DoGPHjmzfvp19+/Zx+PBhnnrqKaZMmUJGRgZVqlShTJkyLFmyBIC2bduyb98+du3aRWBgILfeeivTpk0jJSWFChUqUKVKFX799VcAWrZsybFjx9i+fTsAt912G7NmzSIxMZGyZctSq1Yt5s+fD0Dz5s2Jj48nNjYWgFtvvZW5c+dy+vRpoqOjadiwIXPmzAGgSZMmJCUlsWnTJjafCiQtIxyArgX3s2DBApo3b84PP/wAQIMGDQBYv349AN27d2fFihUcP36cwoUL07ZtW1czsm7duoSGhrJ69WoAunXrxrp16zhy5AgFCxakS5cuTJ06FYBatWoRFRXFihUrALPpGBsby4EDBwgPD6dHjx5MmjQJgGrVqlGiRAmWLVsGQPv27dm5cyd79+4lODiYW265halTp5KWlkblypUpX748i/6ZhtG6dWsOHjzIzp078ff3p2/fvkyfPp3k5GTKly9PtWrVXKPorrvuOo4fP862bdsA6Nu3L7Nnz+bs2bOUKVOGOnXqMO+fbaCbNWvGmTNn+PPPPwHo3bs38+fPJyEhgZIlS9K4cWN+/PFHABo1akRqaiobN24EoGfPnixZsoS4uDiKFi1Ky5YtmTVrFgD169fH39+fdevWAXDjjTeyatUqjh07RlRUFB06dGD69OkA1KlTh/DwcFatWmV+/7p25Y8//uDQoUNERETQrVs311zbGjVqUKRIEVdjt1OnTmzdupX9+/cTFhZGz549mTx5MoZhEBMTQ3R0tGvtsHbt2rFnzx52795NUFAQvXv35rvvviM1NZVKlSpRsWJFFi5cCECrVq04cuQIO3bswM/Pj379+jF9+nSio6MpV64cNWrUYMGCBQC0aNGCuLg4tm7dCkCfPn2YM2cOZ86coXTp0lxzzTX89NNPADRt2pTExEQ2b94M4NOfESEhIfTq1ctnPiMAbr75ZhYtWsTJkycpVqyYPiM8+Yz45+9s71tvZf78+ZRq04bSS5ZQcMIEvv/7b1i+nEbJyY7+jJg5cybnzp3TZ4TDPyMOHjxIly5dnPcZoX9H6DPiIp8RGzZsICwsDNBnhKP/HaHPCNdnxMGDB+nXr5/PfkYkJiaSXdnaZezfVq5cyZgxY9iyZQuGYVCrVi0GDRpEs2bNsv0Yhw4dokyZMixfvtwy9evNN9/kq6++cr2ZmdLT07n22mu57777GDhwIABDhw5l5syZrjcoK8nJySQnJ7uuJyQkUK5cuXyxy9ikSZO47bbb7C4j2+q8No8zyWk0q1SEKQ85e7qf5A5fy4RIjlm1Cp58Ep5/Hm66yTyWns6kyZO57Y477K1NxMvoZ4WIlTIhYuXrmfBkl7EragjlhJSUFMLDw5k6dSo333yz6/gTTzzBhg0bWLx4seX8U6dOUbhwYcv2bxkZGRiGQUBAAPPnz6d9+/aXfd78tO388ePHKVasmN1lZMuaPXH0/tjsmk97uAWNKhS2uSLJj3wpEyI54sABePFF+L//M683aABr14KfH6BMiGRFuRCxUiZErHw9Ezm+7XxuCA4OplGjRq5hVpkWLFhAixYt3M6PjIxk06ZNbNiwwfU1cOBA1zBHT0Yn5Rf/HkXlzT5duguACkXD1QySXONLmRC5KmfPwmuvQbVq55tBAwbA7NmuZhAoEyJZUS5ErJQJESsnZcLjNYRy0lNPPcVdd91F48aNad68OZ988gn79u1zTQl78cUXOXjwIF9++SX+/v7UqVPHcv8SJUoQGhrqdtwp9u/fb3cJ2XLw1Dnm/XkUgH5NyttcjeRnvpIJkavyww8wcKC5dTxAq1YwerS5m9i/KBMi7pQLEStlQsTKSZmwtSHUt29fTpw4weuvv87hw4epU6cOc+bMoUKFCgAcPnyYffv22VmiV8tc/M3bzVx/EIDiBUN4sHVlm6uR/MxXMiFyVfz9zWZQpUrw3nvQq5dlVNCFlAkRd8qFiJUyIWLlpEzYtoaQXfLTGkK+wDAMOo1ewl/HzvBC1xoMbFPF7pJERHzL7t0QGws33GBeNwyYPBluvhlCQ+2tTURERES8Sq6uIXT06NGL3pa5/ZzkjcmTJ9tdwmV9/8ch/jp2huAAf3o1KGN3OZLP+UImRLItIcFcMLpmTbjjDjh+3Dzu5we33ZatZpAyIeJOuRCxUiZErJyUCY8bQnXr1uX77793O/7+++87cmFnO3n74C7DMHh//jYAOtUqSYlI/U+25C5vz4RItqSnw6efQkwMvP02JCdD48Zw+rTHD6VMiLhTLkSslAkRKydlwuOG0PPPP0/fvn0ZOHAg586d4+DBg7Rv35733nuPKVOm5EaNchExMTF2l3BJw2dvYX/cOQD6NS1nczXiBN6eCZHL+vVXaNgQHnwQjh0zdxH7/ntYsMBcM8hDyoSIO+VCxEqZELFyUiY8XlT66aefpmPHjtx5553Uq1ePuLg4rr32WjZu3EjJkiVzo0a5iOjoaLtLuKTPf9sNQLkiYbSKKW5zNeIE3p4JkUvatw86dzZHCBUqZG4r/8gjEBx8xQ+pTIi4Uy5ErJQJESsnZcLjEUIAlStXpnbt2uzZs4eEhAT69OmjZpANli5dancJF7XpQLzr8of9GthYiTiJN2dCJEvJyecvly8Pjz0Gjz8Of/0FgwdfVTMIlAmRrCgXIlbKhIiVkzLhcUPot99+o169evz1119s3LiRcePG8fjjj9OnTx9OnjyZGzWKDxrz6w4ACoYE0rB8YZurERHxMmlp8N//QoUKsHnz+eOjR8OYMVC0qH21iYiIiIgjeNwQat++PX379mXFihXUrFmT+++/n/Xr13PgwAHq1q2bGzXKRbRr187uErJkGAYLYs3d6J7rWsPmasRJvDUTIhZz50K9euZooKNH4aOPzt/m55ejT6VMiLhTLkSslAkRKydlwuOG0Pz583n77bcJCgpyHatSpQrLli3joYceytHi5NL27NljdwlZWrHrhOty74ZlbaxEnMZbMyECQGwsdO1qfm3ZYo4CGjvW2hDKYcqEiDvlQsRKmRCxclImPG4ItWnTJusH8vdnyJAhV12QZN/u3bvtLiFLS3ccB8zpYmHBATZXI07irZkQ4eWXzVFBc+dCUBA8/bS5TtDDD0Ogx/s7ZJsyIeJOuRCxUiZErJyUCY//Ffr6669f8vZXX331iosRz1w4SstbpGcYjFu0E4AhN9ayuRpxGm/MhAgAxYqZu4f17AnvvQdVq+bJ0yoTIu6UCxErZULEykmZ8DMMw/DkDg0aWHeMSk1NZffu3QQGBlKlShXWrVuXowXmtISEBKKiooiPjycyMtLucvKduZuPMPD/1gKwbkgnihS4uh1yRER8jmHA999DwYLQvr15LCUFVq6EVq3srU1ERERE8jVPeh4eTxlbv3695Wvz5s0cPnyYDh068OSTT15x0eK57777zu4S3KzeEwdAnTKRagZJnvPGTIjD/PEHdOhgjgR65BFITTWPBwfb0gxSJkTcKRciVsqEiJWTMuFxQygrkZGRvP7661pDKI+lZv6i4SUyMgw+W2bOt+xcK9rmasSJvC0T4iBHjsADD0CDBrBwIYSEQK9e5vbyNlImRNwpFyJWyoSIlZMykWMrWZ46dYr4+PicejjJhkqVKtldgkXm6CCAO5qVt7EScSpvy4Q4QFISjB4NI0bAmTPmsb594e23oWJFW0sDZUIkK8qFiJUyIWLlpEx43BAaM2aM5bphGBw+fJivvvqKLl265FhhcnkVveCXjQv9uvUYANeUjaJoRIjN1YgTeVsmxAEWLYKXXjIvN21qNodatLC1pAspEyLulAsRK2VCxMpJmfB4ytjo0aMtX2PGjGHRokX079+fTz75JDdqlItYuHCh3SW4GIbBgtijAPRrqtFBYg9vyoTkY3HnR0Ny/fXQvz989RWsWOFVzSBQJkSyolyIWCkTIlZOyoTHI4R2796dG3WIj/v+j0PsOn6WAH8/utbR+kEikg8dPGiOBpo9G7Zvh6JFwc8PJk60uzIREREREY/lyKLSYo9WXrR98bAfYgHoVLMkhcK1u5jYw5syIflIYiIMGwbVqsGXX5ojhH780e6qskWZEHGnXIhYKRMiVk7KxBUtKr169WqmTp3Kvn37SElJsdw2ffr0HClMLu/IkSOULVvW7jJYuO0YcWfNvwf9W1S0txhxNG/JhOQTGRnwzTfwwgvm6CAwp4SNHm2uF+QDlAkRd8qFiJUyIWLlpEx4PEJo8uTJXHfddcTGxjJjxgxSU1OJjY3l119/JSoqKjdqlIvYsWOH3SUAMOLHLQD0qF+a5lWK2lyNOJm3ZELygdRUaNkS7rrLbAZVqABTpsCyZT7TDAJlQiQryoWIlTIhYuWkTHjcEBoxYgSjR49m9uzZBAcH8+GHH7Jlyxb69OlD+fJaTDgv+fn52V0CO/8+w45j5lbLdzevaG8x4njekAnJJ4KC4JprICLC3FJ+61bo08dcM8iHKBMi7pQLEStlQsTKSZnwMwzD8OQOBQoU4M8//6RixYoUK1aMhQsXUrduXbZs2UL79u05fPhwbtWaIxISEoiKiiI+Pp7IyEi7y/F593yxioXb/gZgz9s32FyNiMgVOn0a3noL7rwTatUyj504YY4UitZC+SIiIiLiGzzpeXg8QqhIkSKcPn0agDJlyrB582YATp06RWJi4hWUK1dq5syZtj5/WnqGqxl0z3UVba1FBOzPhPig9HT47DOIiTEbQk8/ff62okV9vhmkTIi4Uy5ErJQJESsnZSLbDaF7772X06dP06pVKxYsWABAnz59eOKJJ3jggQe47bbb6NChQ64VKu7OnTtn6/Nn7iwG8ESHGBsrETHZnQnxMQsXQuPGcP/9cPQoVK0KAweCZwNnvZoyIeJOuRCxUiZErJyUiWzvMva///2Pt99+m48++oikpCQAXnzxRYKCgli2bBm9evViyJAhuVaouCtXrpxtz306KZVJq/YBMKBFRW01L17BzkyID/nrL3j2Wcj835+oKHjtNXj0UQjOX59lyoSIO+VCxEqZELFyUiay3RDKXGqoSJEirmP+/v4899xzPPfcczlfmVxWjRo1bHvupTuOk5ZhEBrkzys31LStDpEL2ZkJ8SGzZpnNoIAAc0TQ0KFQrJjdVeUKZULEnXIhYqVMiFg5KRMerSHkpNW2fUHm1D1bnjv2KAA31itNYIDHS1GJ5Ao7MyFeLC0N9uw5f/3xx81pYhs3wkcf5dtmECgTIllRLkSslAkRKydlItsjhACqVat22aZQXFzcVRUk3i8xJY0Z6w8C0LlWSZurERG5hHnz4KmnICPDbAAFBZnTwj791O7KRERERERs5VFDaNiwYURFReVWLeKhFi1a2PK836zc57rcSQ0h8SJ2ZUK80JYt5o5hP/1kXi9SxDxWr569deUxZULEnXIhYqVMiFg5KRMeNYT69etHiRIlcqsW8VBcXBwVKlTI8+f94rc9ADzQqpKmEYpXsSsT4kVOnDDXBBo3ztxSPjDQnCI2ZAgULmx3dXlOmRBxp1yIWCkTIlZOykS2F3/RL/7eZ+vWrXn+nPtOJHLwlLkNX5/Gzll9XXyDHZkQL7Jnj7l1/Ecfmc2gm26CP/+EUaMc2QwCZUIkK8qFiJUyIWLlpEx4vMuYOFvfT1YA4OcHMSUL2lyNiMgFKlSAhg3h+HGzCdShg90ViYiIiIh4LT/DYZ2ehIQEoqKiiI+PJzIy0u5yrkp6ejoBAQF59nynElOo/7q54vqd15bnjZ518+y5RbIjrzMhNtu40ZweNmGCuUYQwN9/m5f19wBQJkSyolyIWCkTIla+nglPeh7aL9yHzZkzJ0+f76sVe12XX7+pTp4+t0h25HUmxCZHj8KDD0KDBjBjBrz++vnbihdXM+gCyoSIO+VCxEqZELFyUiY8WlRavMuZM2fy9PkWbDkKwE3XlMbfX2tKiffJ60xIHktKgg8/hDffhNOnzWO33gpPPGFvXV5MmRBxp1yIWCkTIlZOyoQaQj6sdOnSefZc8YmpbDwQD8Aj7ark2fOKeCIvMyF5bPp0eOYZ2L3bvN64MYweDS1b2luXl1MmRNwpFyJWyoSIlZMyoSljPuyaa67Js+f6afNhAMoUCqNGtG+vvST5V15mQvLYvHlmM6h0afjf/2DlSjWDskGZEHGnXIhYKRMiVk7KhBpCPuynn37Ks+f6ZesxAFLSM/LsOUU8lZeZkFx26JC5jXym11+HYcNg+3a4+27w14+v7FAmRNwpFyJWyoSIlZMyoX9Ry2WdS0nn950nABjQoqK9xYhI/paYaDZ/YmLg0UfPHy9ZEl59FQoUsK82EREREZF8RGsI+bCmTZvmyfN8sXw3p5PTKBYRzEOtK+fJc4pcibzKhOSCjAyYNAleeAEOHDCPnTxpLh5dsKC9tfkwZULEnXIhYqVMiFg5KRMaIeTDEhMTc/05MjIM3p27DYB7W1YiMEB/ZcR75UUmJBesWAHNm8Odd5rNoPLlYfJk+O03NYOukjIh4k65ELFSJkSsnJQJ/XbvwzZv3pzrz5G5dlBQgB+3NSmf688ncjXyIhOSw374AVq0gFWrICLC3FJ+61bo2xf8/OyuzucpEyLulAsRK2VCxMpJmdCUMbmkT5bsBODaykUpXCDY5mpEJN/p3BmqVoXWreGNN6BUKbsrEhERERFxBD/DMAy7i8hLCQkJREVFER8fT2Skb2+fnpycTEhISK4+R40hP5GUmsGIm+tyezONEBLvlheZkKuQkWFuGf/11zB3LgT+838SiYkQHm5vbfmUMiHiTrkQsVImRKx8PROe9Dw0ZcyH/fLLL7n6+McSkkhKNbeZv6Gu/tdevF9uZ0KuwpIl0KQJ3Hsv/PILfPnl+dvUDMo1yoSIO+VCxEqZELFyUibUEPJh8fHxufr4o3/eDkBokD9R4UG5+lwiOSG3MyFXYOdOuOUWaNMG1q2DqCh4/3244w67K3MEZULEnXIhYqVMiFg5KRNaQ8iHlShRIlcff8n24wA807l6rj6PSE7J7UyIB1JT4eWX4cMPISUF/P3hoYdg2DAoXtzu6hxDmRBxp1yIWCkTIlZOyoQaQj6sadOmufbY24+e5uCpcwD0aVIu155HJCflZibEQ4GBsHq12Qzq3BlGjoQ6deyuynGUCRF3yoWIlTIhYuWkTGjKmA+bPXt2rj32e/O2AXBN2SgiQzVdTHxDbmZCsmHBAoiLMy/7+cGYMfDjj+YC0moG2UKZEHGnXIhYKRMiVk7KhBpCkqWVu04AULpQmM2ViIjX27oVbrzRHAk0fPj543XrQrduZnNIRERERES8ihpCPqxhw4a58ri7j58lISkNgPtbVcqV5xDJDbmVCbmIuDh44gmz8fPjj+Y0sSCNKPQmyoSIO+VCxEqZELFyUia0hpAPy8jIyJXHnf/nEQCiwoJoVKFIrjyHSG7IrUzIv6SmwrhxMHQonDxpHuveHd57D6prEXpvokyIuFMuRKyUCRErJ2VCI4R82IYNG3LlcRfEHgXgrmsr5Mrji+SW3MqE/MvQoebIoJMnzdFBCxbA99+rGeSFlAkRd8qFiJUyIWLlpEyoISQWJ8+msGav+T/+N15TyuZqRMRrXPg/JYMGQdWqMH48rF8PHTvaV5eIiIiIiFwRP8MwDLuLyEsJCQlERUURHx9PZGSk3eVclcTERMLDw3P0Md+bt5X/LtxJ5WIF+PWZtjn62CK5LTcy4XjHjsGrr8KRIzBz5vnjGRngr/9T8HbKhIg75ULESpkQsfL1THjS89C/5n3YsmXLcvwxZ64/BED7GiVy/LFFcltuZMKxkpPNNYFiYsyRQLNmwYXDZ9UM8gnKhIg75ULESpkQsXJSJvQveh924sSJHH28s8lpHDx1DoBu9TRdTHxPTmfCkQwDpk2DWrXguecgIQEaNoTFi6F+fburEw8pEyLulAsRK2VCxMpJmdAuYz6sSJGc3QHs2zX7XZdrl/bt6XTiTDmdCcc5eBBuvx2WLDGvlyoFI0bA3XdrRJCPUiZE3CkXIlbKhIiVkzKhhpAPa926dY4+3sYD8QC0iilGSGBAjj62SF7I6Uw4TrFisH8/hIbCs8+aI4QiIuyuSq6CMiHiTrkQsVImRKyclAn9l68Pm3nhAq9XyTAMZqw/CMCAFhVz7HFF8lJOZsIRzp2Djz6CtDTzekgIfPMNbNsGr7+uZlA+oEyIuFMuRKyUCRErJ2VCI4QEgN93xbkuX1e1mI2ViEiuMwyYPBmef94cERQQAA8/bN527bX21iYiIiIiInlCDSEfVq9evRx7rP/8ugOAmBIRhAZpupj4ppzMRL71++/w5JPmnwDlykF0tL01Sa5RJkTcKRciVsqEiJWTMqEpYz4sKCgoxx5r00Fz/aAG5Qvl2GOK5LWczES+s38/3HEHNG9uNoMKFIA33jCnh918s93VSS5RJkTcKRciVsqEiJWTMqGGkA9bu3ZtjjxOfGIqZ5LNNUTuaFYhRx5TxA45lYl86YEHzPWB/Pzgnntgxw54+WUIC7O7MslFyoSIO+VCxEqZELFyUiY0ZUz4YeMhDAMqFSvANeUK2V2OiOSEjAxISTF3DANz+/jkZBg5Eho2tLc2ERERERGxnZ9hGIbdReSlhIQEoqKiiI+PJzIy0u5yrkpCQkKOvIa27y1kz4lEBnWI4alO1XKgMhF75FQmfN6SJeY6QW3bmg0gcSxlQsSdciFipUyIWPl6JjzpeWjKmA9bs2bNVT+GYRjsOZEIQNvqxa/68UTslBOZ8Gm7dkHv3tCmDaxbB19+CWfO2F2V2MjxmRDJgnIhYqVMiFg5KRNqCPmwo0ePXvVjrN9/ynW5bpmoq348ETvlRCZ8Unw8PPcc1KwJ06aBvz889BD8+SdERNhdndjIsZkQuQTlQsRKmRCxclImtIaQD8uJYWxbDie4LgcFqD8ovs2Xh3ZescWL4dZb4e+/zesdO8KoUVC3rr11iVdwZCZELkO5ELFSJkSsnJQJrSHkw1JTU696S7xBk9bz/R+H6NWgDKP61s+ZwkRskhOZ8DmHD0O1alC6tLle0A03mDuJieDQTIhchnIhYqVMiFj5eia0hpBDfPfdd1f9GPtPmusH1dF0MckHciITXm/7dnjrrfPXS5WCX3+FzZvhxhvVDBILR2RCxEPKhYiVMiFi5aRMqCHkcCfOpAAQHRVqcyUickknT5o7h9WuDS+9BD//fP62Jk3Ah/8XQ0RERERE8p7WEPJhtWvXvqr7J6aksS/OHCHUrFKRnChJxFZXmwmvlJoKH38MQ4dCXJx57IYboHx5W8sS35AvMyFylZQLEStlQsTKSZlQQ8iHRVzl7kHbjpx2XS5SIPhqyxGx3dVmwqsYBvz0Ezz9NGzdah6rU8dcMLpTJ3trE5+RrzIhkkOUCxErZULEykmZ0JQxH7Zy5cqruv/ynSdcl/207ojkA1ebCa+SmgqPPmo2g4oVg3HjYP16NYPEI/kqEyI5RLkQsVImRKyclAmNEHKwNXvM6SeNKxS2uRIRAeD4cShUCAIDITjY3DVsxQp4+WXzuIiIiIiISA7RtvM+LC4ujiJFrmztn9T0DGJe/gmAyQ9ey7WVi+ZkaSK2uJpM2Co5Gf7zHxg+HN59Fx56yO6KJJ/w2UyI5CLlQsRKmRCx8vVMaNt5h9i8efMV33fNnpMABAX4aYSQ5BtXkwlbGAbMmGHuHPbss5CQYF4XySE+lwmRPKBciFgpEyJWTsqEGkI+7ODBg1d8313HzwAQU6IggQH6ayD5w9VkIs+tXw/t2kGvXrBzJ0RHw+efw48/2l2Z5CM+lQmRPKJciFgpEyJWTsqE1hDyYQUKFLji+2YuKN2xVsmcKkfEdleTiTw1cqQ5IsgwIDTU3EnshRfAQTsaSN7wmUyI5CHlQsRKmRCxclImtIaQD8vIyMDf3/PRPYZh0OTNnzl+JoVv7m9Gi6rFcqE6kbx3pZnIc2vWQNOm0K8fvP02lC9vd0WST/lMJkTykHIhYqVMiFj5eia0hpBDTJky5Yruty8ukeNnUvD3gwbltX6Q5B9XmolcZRgwZQq8//75Y40bw44d8M03agZJrvLKTIjYTLkQsVImRKyclAlNGXOgpTuOA1CrdCRhwQE2VyOSj61aBU8+CcuXQ1AQ3HwzVKli3pb5p4iIiIiIiA00QsiHVa9e/Yrut3yn2RAqEKx+oOQvV5qJHHfgANx1FzRrZjaDwsNhyBAoVcruysRhvCYTIl5EuRCxUiZErJyUCXUEfFixYle29s/aveaW8zc3KJOT5YjY7kozkWPOnoX33oN334Vz58xj/fvDm29CGeVN8p7tmRDxQsqFiJUyIWLlpExohJAP++233zy+z5H4JI4mJAPQoopz/qKLM1xJJnJUfLy5VtC5c9CyJaxeDRMnqhkktrE9EyJeSLkQsVImRKyclAmNEHKYXcfPAFCiYAjli4bbXI1IPhAbC7VqmZdLlza3lC9aFG65Bfz87K1NRERERETkIjRCyId17NjR4/vEHkoAoHbpS28/J+KLriQTV2z3bujTB2rXhoULzx9/6CHo3VvNIPEKeZoJER+hXIhYKRMiVk7KhBpCPmz79u0e3+fzZbsBbTcv+dOVZMJjCQnw4otQsyZMnQr+/uZuYiJeKE8yIeJjlAsRK2VCxMpJmVBDyIft27fPo/NPnEnmUHwSADddUzo3ShKxlaeZ8Eh6OkyYADEx8PbbkJwMHTrA+vXw/PO597wiVyFXMyHio5QLEStlQsTKSZnQGkI+LCQkxKPzV+w64bpcsViBnC5HxHaeZsIjvXrB99+bl6tVMxePvvFGTQ0Tr5armRDxUcqFiJUyIWLlpEz4GYZh2F1EXkpISCAqKor4+HgiI521js4L0zYyefV++jUpx9u31LO7HBHfMnkyPPwwvPYaPPIIBAfbXZGIiIiIiIiFJz0PTRnzYVOmTPHo/MXb/wagY82SuVGOiO08zcRFnTwJTz0FX3xx/ljfvrBrFwwerGaQ+Iwcy4RIPqJciFgpEyJWTsqEpoz5sIyMjGyfG3c2hcP/rB/UpGKR3CpJxFaeZCJLaWkwfrw5CujECShRwtxJrEABc2pYYS3GLr7lqjMhkg8pFyJWyoSIlZMyoRFCPqxKlSrZPnfJP6ODwoICiAoPyq2SRGzlSSbczJ0L9erBY4+ZzaBateDLL81mkIiPuqpMiORTyoWIlTIhYuWkTKgh5MPKlCmT7XM37D8FQEiQvuWSf3mSCZft26FrV/NryxYoWhTGjoU//oDrr8/5IkXy0BVlQiSfUy5ErJQJESsnZULdAR+2ZMmSbJ87d/MRAO5pUSm3yhGxnSeZcImPN0cHBQXB00/DX3+Zi0cHakat+L4ryoRIPqdciFgpEyJWTsqEfuNxiCMJ5vpB1UpG2FyJiM1SUmDlSmjVyrzepAn85z/QpQtUrWpvbSIiIiIiInlEI4R8WNu2bbN13pF/FpMGaFRBi+JK/nXJTBgGzJoFtWtDx47mjmGZHntMzSDJl7L7c0LESZQLEStlQsTKSZlQQ8iH7du3L1vnTV9/AIDqJQtSIjI0N0sSsdVFM/HHH9ChA/TsaU4JK1wYdu/O09pE7JDdnxMiTqJciFgpEyJWTsqEGkI+bNeFIxwuYeHWYwC0q1EiN8sRsZ1bJo4ehQcegAYNYOFCCAmBl16CHTvMBpFIPpfdnxMiTqJciFgpEyJWTsqE7Q2hsWPHUqlSJUJDQ2nUqBFLly696LnTp0+nU6dOFC9enMjISJo3b868efPysFrvEpjNRW9X7zkJQJtqxXOzHBHbWTKRnAz168OECeZ0sb59YetWePNNKFjQthpF8lJ2f06IOIlyIWKlTIhYOSkTfoZhGHY9+ZQpU7jrrrsYO3Ys1113HePHj2fChAnExsZSvnx5t/MHDx5M6dKladeuHYUKFeKLL77g/fffZ+XKlTRo0CBbz5mQkEBUVBTx8fFERkbm9EvyOkfik7j2rV8A2DS0MwVDg2yuSCQXGQb4+Z2//vrrMHs2jB4N111nX10iIiIiIiJ5wJOeh60jhEaNGsV9993H/fffT82aNfnggw8oV64c48aNy/L8Dz74gOeee44mTZoQExPDiBEjiImJ4Ycffsjjyr3DtGnTLnvOzr/PuC6rGST52urVHK9VCxYtOn/shRfg99/VDBLHys7PCRGnUS5ErJQJESsnZcK2hlBKSgpr166lc+fOluOdO3dm+fLl2XqMjIwMTp8+TZEiRS56TnJyMgkJCZav/CIlJeWy5+yLSwSgeeWiuV2OiD0OHIC774amTSm2dSu88sr524KDwd/2mbEitsnOzwkRp1EuRKyUCRErJ2XCtslxx48fJz09nZIlS1qOlyxZkiNHjmTrMUaOHMnZs2fp06fPRc956623GDZsmNvxqVOnEh4eTq9evfjll1+Ij4+nRIkSNG3alNmzZwPQsGFDMjIy2LBhAwA9evRg2bJlnDhxgiJFitC6dWtmzpwJQL169QgKCmLt2rUA3HDDDaxZs4ajR48SGRlJ586d+e677wCoXbs2ERERrFy5EoDrr7+ezZs3c/DgQQoUKMCNN97IlClTAKhevTrFihXjt99+A6Bjx45s376dffv2uZpbU6ZMISMjgypVqlCmTBmWLFkCmNvlffrrFgCCz5rv6bRp00hJSaFChQpUqVKFX3/9FYCWLVty7Ngxtm/fDsBtt93GrFmzSExMpGzZstSqVYv58+cD0Lx5c+Lj44mNjQXg1ltvZe7cuZw+fZro6GgaNmzInDlzAGjSpAlJSUls2rQJgJtvvplFixZx8uRJihUrRvPmzV0jvDKn/a1fvx6A7t27s2LFCo4fP07hwoVp27YtM2bMAKBu3bqEhoayevVqALp168a6des4cuQIBQsWpEuXLkydOhWAWrVqERUVxYoVKwCz6RgbG8uBAwcIDw+nR48eTJo0CYBq1apRokQJli1bBkD79u3ZuXMne/fuJTg4mFtuuYWpU6eSlpZG5cqVKV++PIv+GZHSunVrDh48yM6dO/H396dv375Mnz6d5ORkypcvT7Vq1fj5558BuO666zh+/Djbtm0DoG/fvsyePZuzZ89SpkwZ6tSp41ofq1mzZpw5c4Y///wTgN69ezN//nwSEhIoWbIkjRs35scffwSgUaNGpKamsnHjRgB69uzJkiVLiIuLo2jRorRs2ZJZs2YBUL9+ffz9/Vm3bh0AN954I6tWreLYsWNERUXRoUMHpk+fDkCdOnUIDw9n1apVAHTt2pU//viDQ4cOERERQbdu3fj2228BqFGjBkWKFHE1djt16sTWrVvZv38/YWFh9OzZk8mTJ2MYBjExMURHR7vWDmvXrh179uxh9+7dBAUF0bt3b7777jtSU1OpVKkSFStWZOHCheb73agRjBxJyYkTCfznQ3v7tdcS268fxZYto0aNGixYsACAFi1aEBcXx9atWwHo06cPc+bM4cyZM5QuXZprrrmGn376CYCmTZuSmJjI5s2bAXz6MyIkJIRevXpd8jNi37597Nq1i8DAQG699VZ9RpC/PiNSU1NJSEhw5GdEq1atOHLkCDt27MDPz49+/foxc+ZMzp07R7ly5fQZ4eDPiLi4ODZv3qzPCIf/O0KfEec/IyIjI11/h/UZoX9H6DNiIXFxcRw4cMBnPyMSE81BIdlh2xpChw4dokyZMixfvpzmzZu7jr/55pt89dVXrjfzYiZNmsT999/PrFmz6Nix40XPS05OJjk52XU9ISGBcuXK5Ys1hI4ePerWUPu3ii+Y4f3o9gbcWK90XpQlkvtmzoTHHoODB83r110Ho0dztHz5y2ZCxEmy83NCxGmUCxErZULEytcz4RNrCBUrVoyAgAC30UDHjh277Js/ZcoU7rvvPr799ttLNoMAQkJCiIyMtHzlF5kd94s5l5LuulyvTKFcrkYkDyUmms2gChVgyhRYuhSaNLlsJkScRpkQcadciFgpEyJWTsqEbQ2h4OBgGjVq5BpmlWnBggW0aNHiovebNGkSAwYM4JtvvuGGG27I7TJ92k+bD7sulysSZmMlIldp7174Z4g3ALfdBp99Zm4j36ePdWcxERERERERuSzb1hACeOqpp7jrrrto3LgxzZs355NPPmHfvn0MHDgQgBdffJGDBw/y5ZdfAmYz6O677+bDDz/k2muvdY0uCgsLIyoqyrbXYZeWLVte8vZdf58FIDjQHz/9wiy+6PRpeOstGDUKChWCHTugYEGzAXTvvW6nXy4TIk6jTIi4Uy5ErJQJESsnZcLW7Xf69u3LBx98wOuvv079+vVZsmQJc+bMoUKFCgAcPnyYffv2uc4fP348aWlpPProo5QqVcr19cQTT9j1Emx17NixS96+9Yi56PTj7armRTkiOSc93RwBFBNjNoSSk6FWLTh58pJ3u1wmRJxGmRBxp1yIWCkTIlZOyoTt+zE/8sgj7Nmzh+TkZNauXUvr1q1dt02cONG1qjrAokWLMAzD7WvixIl5X7gXyFyl/2J2HzdHCNUrVygPqhHJIQsXQuPGcP/9cPQoVK1qLiL9yy9Qvvwl73q5TIg4jTIh4k65ELFSJkSsnJQJW6eMSe45lZjCzn+mjFUuVsDmakSyaft2aN/evBwVBa++au4mFhxsb10iIiIiIiL5jG3bztvFky3YfNn0dQd46ts/CA3yZ+vwrnaXI3JxqakQFHT++oABUKAADBsGxYrZVpaIiIiIiIiv8Ylt5+XqzZo166K3zdxwCIB+TS49xUbENmlpMHYsVKoEu3efP/7FF/Df/15RM+hSmRBxImVCxJ1yIWKlTIhYOSkTagj5sMTExIvediT+HAAxJSPyqhyR7Js3D665Bh59FA4ehDFjzt92FTviXSoTIk6kTIi4Uy5ErJQJESsnZUINIR9WtmzZLI+nZxj8dewMAI0qFM7LkkQubcsWuOEG6NIFYmOhaFH46CN4990cefiLZULEqZQJEXfKhYiVMiFi5aRMaFFpH1arVq0sj2/Yf4oMA8KCAqhaXCOExEu88AK8/765pXxgIDz+OAwZAoVzrml5sUyIOJUyIeJOuRCxUiZErJyUCY0Q8mHz58/P+vifRwCoUaoggQH6FouXCAszm0E9epijg0aNytFmEFw8EyJOpUyIuFMuRKyUCRErJ2VCI4TyoT8PJQBQsai2mxebGAbMng0lSkCzZuaxZ5+F1q2hXTt7axMRERERERGNEPJlzZs3z/L4sr+OA9AqRlt2iw02boROneCmm8xFozMyzOPh4bneDLpYJkScSpkQcadciFgpEyJWTsqEGkI+LD4+3u3YmeQ01+WOtUrmZTnidEePwoMPQoMG8MsvEBJiNoZSU/OshKwyIeJkyoSIO+VCxEqZELFyUibUEPJhsbGxbsf2HD/ruhwZGpSX5YhTJSXBO+9ATAx8+qk5IujWW80dxd56y2wM5ZGsMiHiZMqEiDvlQsRKmRCxclImtIZQPvP7rhMA1C9XyN5CxDm+/97cQQygcWMYPRpatrS3JhEREREREbkkP8MwDLuLyEsJCQlERUURHx9PZGSk3eVclbS0NAIDrT29+/+3mp+3HKN1teJ8eW9TmyqTfC8hATLzk5EBvXtDz55w553gb9/Aw6wyIeJkyoSIO+VCxEqZELHy9Ux40vPQlDEfNnfuXLdjq/ecBDRCSHLJoUMwYADUrAmnT5vH/P1h+nS4+25bm0GQdSZEnEyZEHGnXIhYKRMiVk7KhBpCPux05i/kF4g/Zy7ge22lInldjuRniYkwfLi5TtD//mc2hrzwgzKrTIg4mTIh4k65ELFSJkSsnJQJ3x0HJURHR1uun0tJd12uVdq3p8OJl8jIgEmTzDWCDhwwj7VoYa4T1NT7piT+OxMiTqdMiLhTLkSslAkRKydlQg0hH9awYUPL9b9PJ7suR4VphzG5SklJ0K4d/P67eb1CBXM3sT59wM/P3tou4t+ZEHE6ZULEnXIhYqVMiFg5KROaMubD5syZY7m+/2Si67Kfl/7CLj4kNBQqVYKICBgxwtxGvm9fr20GgXsmRJxOmRBxp1yIWCkTIlZOyoQaQvnItiPmXMeCoRr4JVfgzBkYMgT27Dl/bORI2LEDXnwRwsJsK01ERERERERyljoHPqxJkyaW66cSUwBoXrmoHeWIr8rIMBeKfuklOHLEbABNnmzeVqqUvbV56N+ZEHE6ZULEnXIhYqVMiFg5KRNqCPmwpKQky/X1+08BUKOUFpSWbFq8GJ58EtavN69XqWJOC/NR/86EiNMpEyLulAsRK2VCxMpJmdCUMR+2adMmy/W/jp0BICRQ31a5jJ074ZZboG1bsxkUFQXvvw9//gk332x3dVfs35kQcTplQsSdciFipUyIWDkpExohlI9kGAYAZQtrrRe5jC+/hOnTwd8fHnoIhg2D4sXtrkpERERERETyiJ9h/NNFcIiEhASioqKIj48nMtK3p1YlJSURGhoKQEaGQY0hc0lJz+DXp9tQuXiEzdWJV0lLg2PHoHRp8/qZMzBwILzwAtSpY29tOejCTIiIMiGSFeVCxEqZELHy9Ux40vPQ3CIftmjRItflo6eTSEnPIMDfj/JFwu0rSrzPggXQoAH06GEuIA3mVvL/93/5qhkE1kyIiDIhkhXlQsRKmRCxclIm1BDyYSdPnnRdzlw/qEKRcAID9G0VYOtWuPFG6NwZNm+GXbvMHcTysQszISLKhEhWlAsRK2VCxMpJmVDnwIcVK1bMdflUYioAxQuG2FWOeIu4OHjiCahbF378EQIDYfBg+OsvqF7d7upy1YWZEBFlQiQryoWIlTIhYuWkTGhRaR/WvHlz1+WEJLMhFBkWZFc54g22bYPmzSGzq929O7z3Xr5vBGW6MBMiokyIZEW5ELFSJkSsnJQJjRDyYT/88IPrcsK5NACi1BBytpgYqFLFXBtowQL4/nvHNIPAmgkRUSZEsqJciFgpEyJWTsqEGkL5RPy5f0YIhaoh5CibN8Ptt5u7hoG5jfysWbB+PXTsaG9tIiIiIiIi4rXUEPJhDRo0cF0+P2VMswAd4dgxc9v4a66BSZPg3XfP31a6tLlukANdmAkRUSZEsqJciFgpEyJWTsqEM39rzIcS/hkhpClj+VxyMowZA2+8AQkJ5rHevWHAAFvLEhEREREREd+iEUI+bP369a7LmjLmANOnQ61a8NxzZjOoYUNYvBimToXKle2uzitcmAkRUSZEsqJciFgpEyJWTsqEGkL5REKSuai0dhnLx6ZOhV27oFQpmDgRVq+G1q3trkpERERERER8kJ9hGIbdReSlhIQEoqKiiI+PJzIy0u5yrsqZM2eIiIgAoP37i9h1/CzfPtScppWK2FyZ5IjDh80/S5Uy/9y7Fz7/HJ59Fv75vovVhZkQEWVCJCvKhYiVMiFi5euZ8KTnoRFCPmzFihWuy64pY1pU2vedO2euERQTA08/ff54hQowbJiaQZdwYSZERJkQyYpyIWKlTIhYOSkT6h74sOPHjwNgGMb5Xca0hpDvMgyYPBmefx727zeP7dkDSUkQGmprab4iMxMiYlImRNwpFyJWyoSIlZMyoRFCPqxw4cIAJKVmkJpuzvzTLmM+6vffoUULuP12sxlUrhx8/TX89puaQR7IzISImJQJEXfKhYiVMiFi5aRMaA0hH5aUlERoaChH4pO49q1fCPD34683u+Ln52d3aeKJb7+Fvn3NywUKwAsvwFNPQXi4vXX5oMxMiIhJmRBxp1yIWCkTIla+ngmtIeQQM2bMALhguligmkG+qFs3KFMGBgyA7dvhlVfUDLpCmZkQEZMyIeJOuRCxUiZErJyUCa0hlA8k/LOgtKaL+YCMDPjyS5g5E6ZPB39/c5Ho2Fjw8RFrIiIiIiIi4js0QsiH1a1bF7hghJAaQt5tyRJo0gTuuQdmzYLvvjt/m5pBOSIzEyJiUiZE3CkXIlbKhIiVkzKhhpAPy5zX6NpyXjuMeaddu6B3b2jTBtatM5s/774LPXrYXVm+48tzfUVygzIh4k65ELFSJkSsnJQJNYR82OrVqwFIOJcGQGSYZgB6laQkcwv5mjVh2jRzetjAgbBjBzz7LISE2F1hvpOZCRExKRMi7pQLEStlQsTKSZlQByEf0BpCXio4GH7+GVJSoFMnGDkSHDT8UERERERERLyXGkI+rFu3boCmjHmVX3+FZs3M7eP9/WHsWDh+3NxJTDvA5brMTIiISZkQcadciFgpEyJWTsqEpoz5sHXr1gFaVNorbNsG3btDhw7w3nvnjzdrBjfcoGZQHsnMhIiYlAkRd8qFiJUyIWLlpEyoIeTDjhw5Aly4hpAaQnkuLg4GD4Y6dWD2bAgIMNcOEltkZkJETMqEiDvlQsRKmRCxclImNGXMhxUsWBC4cMqYvp15JjUVPv4Yhg41m0JgjgR6/32oUcPW0pwsMxMiYlImRNwpFyJWyoSIlZMy4WcYhmF3EXkpISGBqKgo4uPjiYyMtLucq5KWlkZgYCA3jFnKn4cS+OKeJrSrXsLuspzhySfhgw/My7Vrw6hR0LmzrSXJ+UyIiEmZEHGnXIhYKRMiVr6eCU96Hpoy5sOmTp0KnF9DSLuM5bILe6eDBkHZsjBuHGzYoGaQl8jMhIiYlAkRd8qFiJUyIWLlpEz4bttLXOITtctYrvr7b3j1VUhMhP/9zzxWqRLs3g0+3DkWERERERER59IIIR9Wq1YtMjIMTidnLiqt5kSOSk421wSqWtVcL+jLL2H79vO3qxnkdWrVqmV3CSJeRZkQcadciFgpEyJWTsqEGkI+LCoqijMpaa6ZTBohlEMMA2bMMNcGevZZSEiABg1g0SKoVs3u6uQSoqKi7C5BxKsoEyLulAsRK2VCxMpJmVBDyIetWLHCNV0sJNCf0KAAmyvKB/btg/btoVcv2LkToqPh889h9Wpo08bu6uQyVqxYYXcJIl5FmRBxp1yIWCkTIlZOyoTmvPi4zAWlI7WgdM4oXBi2bIHQUHj6aXjhBYiIsLsqERERERERkRylhpAP69y5M9tPmesHaYexK3TuHHz9Ndx7L/j7Q8GC8M03UKUKVKhgd3Xioc7a7U3EQpkQcadciFgpEyJWTsqEpoz5sNjYWOLPZe4wpt6eRwwDJk+GGjXggQfMy5nat1czyEfFxsbaXYKIV1EmRNwpFyJWyoSIlZMyoYaQDztw4ICmjF2JVaugZUu47TZzzaCyZaFAAburkhxw4MABu0sQ8SrKhIg75ULESpkQsXJSJtQQ8mHh4eEk/DNCSFPGsmH/frjzTmjWDJYvh/BweP112LYNevSwuzrJAeHh4XaXIOJVlAkRd8qFiJUyIWLlpEz4GUbmpuXOkJCQQFRUFPHx8URGRtpdzlUbNX8bY379i7uurcDwnnXsLse7tWoFy5aZl/v3hzffhDJl7K1JREREREREJId40vPQCCEfNmnSJBKSzEWlI8O0hpCbjAxISTl//Y03zKbQ6tUwcaKaQfnQpEmT7C5BxKsoEyLulAsRK2VCxMpJmVBDyMcluBaV1pQxi99+M6eGvf32+WNt2sDixdC4sX11iYiIiIiIiHgBNYR8WLVq1Vy7jGkNoX/s2QN9+5qLRq9ZA+PGQXLy+dv9/GwrTXJftWrV7C5BxKsoEyLulAsRK2VCxMpJmVBDyIeVKFFCu4xlSkiAF180t5H/9lvw94cHH4QNGyAkxO7qJI+UKFHC7hJEvIoyIeJOuRCxUiZErJyUCTWEfNiyZctIOPfPGkJOnjL2888QE2NOD0tOhg4dYP16GD8eSpa0uzrJQ8syFw0XEUCZEMmKciFipUyIWDkpE1qJ2MdpyhhQuTKcOmU2hUaOhBtv1NQwERERERERkUvQCCEf1r59+wumjDmot7djB4wZc/565crwyy+weTN0765mkIO1b9/e7hJEvIoyIeJOuRCxUiZErJyUCTWEfNi2HX+RmJIOOGTK2MmT8NRTULs2PPEErFp1/raWLSE42L7axCvs3LnT7hJEvIoyIeJOuRCxUiZErJyUCTWEfNi23ftdlwuG5uMRQqmp8NFH5pSw0aPN6127QqFCdlcmXmbv3r12lyDiVZQJEXfKhYiVMiFi5aRM5OMuQv6X7m+OiIkICSQwIJ/29n76CZ5+GrZsMa/XqgWjRsH119tbl3ilYI0SE7FQJkTcKRciVsqEiJWTMuFnGIZhdxF5KSEhgaioKOLj44mMjLS7nKvyx/5T9Pjvb5SOCmX5ix3sLifnJSZCpUpw7BgULQrDh8MDD0Cg+pgiIiIiIiIi/+ZJzyOfDitxhh/m/wJAZH7aYSwuDjJ7lOHh8M475gihv/6Chx9WM0guaerUqXaXIOJVlAkRd8qFiJUyIWLlpEyoIeTDzqZkAPmkIZSSYk4Fq1IFpkw5f3zAAHj/fa0XJNmSlpZmdwkiXkWZEHGnXIhYKRMiVk7KhBpCPiyicAnAx3cYMwyYNcvcOezpp+HUKfjmG7urEh9VuXJlu0sQ8SrKhIg75ULESpkQsXJSJtQQ8mFBBcz5gFG+OkLojz+gQwfo2dOcElayJEyYADNm2F2Z+Kjy5cvbXYKIV1EmRNwpFyJWyoSIlZMyoYaQD/tzx24AIsN8cF2dd96BBg1g4UIICYEXX4QdO+C++yAgwO7qxEctWrTI7hJEvIoyIeJOuRCxUiZErJyUCR/sJEimpHQ/wEenjDVrZk4X69sX3n4bKla0uyIRERERERERx1BDyIdFFCkBJ055/6LShgHffgsnT8LAgeaxtm3hzz+hVi1bS5P8pXXr1naXIOJVlAkRd8qFiJUyIWLlpEyoIeTDjsefBbx8DaHVq+HJJ+G338xt5G+6CUqXNm9TM0hy2MGDBylTpozdZYh4DWVCxJ1yIWJ1NZlIT08nNTU1hysSsdfBgwcpWrSo3WVcUnBwMP7+V78CkBpCPizuzDkgkMhQL/w2HjgAL70EX31lXg8Ph+eeg6goe+uSfG3nzp00bdrU7jJEvIYyIeJOuRCxupJMGIbBkSNHOHXqVO4UJWIjf39/du/ebXcZl+Tv70+lSpUIDg6+qsfxwk6CZNe5dLMj6FVTxhIT4b33zEWjz50zj911F4wYAWXL2lub5Hs50SUXyU+UCRF3yoWI1ZVkIrMZVKJECcLDw/Hz88uFykTscerUKQoVKmR3GReVkZHBoUOHOHz4MOXLl7+q/PkZhmHkYG1eLyEhgaioKOLj44mMjLS7nKvS+I0FHD+Twk9PtKJmKS95LTt3mlPBUlLguutg9Gho0sTuqkREREREJAekp6ezfft2SpQo4fXTakTyq/j4eA4dOkTVqlUJCrIOEPGk56H/IvFRhmFw8mwy4AUjhP766/zlKlXM0UDffgtLl6oZJHlq+vTpdpcg4lWUCRF3yoWIlaeZyFwzKDw8PDfKEbHdyZMn7S7hsjKniqWnp1/V46gh5KOSUjNINzK3nbdp5t+ePea28dWqwZo1548//TTceito6KjkseTkZLtLEPEqyoSIO+VCxOpKM6FpYpJfZWRk2F3CZeVU/tQQ8lEJSWZn3t8PIkLyuCF0+rS5YHSNGuZIIIAlS/K2BpEslC9f3u4SRLyKMiHiTrkQsVImRKxCQkLsLiHPqCHkoxLOmQ2hyLCgvOvOp6fDhAkQEwNvvQXJydCuHaxbB089lTc1iFxCtWrV7C5BxKsoEyLulAsRK2VCsvLZZ5/RuXNnu8uwhd0NoeTkZMqXL8/atWtz/bnUEPJR8ZkNodA8XD+oWzd44AE4ehSqVoWZM+GXX6B+/byrQeQSfv75Z7tLEPEqyoSIO+VCxMpJmRgwYAB+fn74+fkRGBhI+fLlefjhh7NcM2b58uV069aNwoULExoaSt26dRk5cmSWa7YsXLiQbt26UbRoUcLDw6lVqxZPP/00Bw8ezIuXleOSk5N59dVXGTJkiN2l5BrDMBg6dCilS5cmLCyMtm3b8ueffwLmoswX88EHH1C9enXCwsIoV64cTz75JElJSa7bx40bR7169YiMjCQyMpLmzZvz008/WR7j6NGjDBgwgNKlSxMeHk6XLl3YsWOH6/aQkBCeeeYZnn/++Rx+1e7UEPJRmVPGovJyQelbboGoKBg5Ev78E3r00DpBIiIiIiLiM7p06cLhw4fZs2cPEyZM4IcffuCRRx6xnDNjxgzatGlD2bJlWbhwIVu3buWJJ57gzTffpF+/fly4Uff48ePp2LEj0dHRTJs2jdjYWD7++GPi4+MZOXJknr2ulJSUHHusadOmERERQatWra7qcTIXIPdG7777LqNGjeKjjz5i9erVREdH06lTJ06fPn3R+3z99de88MILvPbaa2zZsoXPPvuMKVOm8OKLL7rOKVu2LG+//TZr1qxhzZo1tG/fnh49eriaTYZh0LNnT3bt2sWsWbNYv349FSpUoGPHjpw9e9b1OHfccQdLly5ly5Ytufcm/FOQo8THxxuAER8fb3cpV2XGugNGhednG7d/uiJ3nuDkScN4+mnD+O6788fS0gzj779z5/lEcsDevXvtLkHEqygTIu6UCxErTzNx7tw5IzY21jh37pzrWEZGhnE2OdWWr4yMjGzX3r9/f6NHjx6WY0899ZRRpEgR1/UzZ84YRYsWNXr16uV2/++//94AjMmTJxuGYRj79+83goODjcGDB2f5fCdPnrxoLSdPnjQeeOABo0SJEkZISIhRu3Zt44cffjAMwzBee+0145prrrGcP3r0aKNChQpur2XEiBFGqVKljAoVKhgvvPCC0axZM7fnqlu3rvHqq6+6rn/++edGjRo1jJCQEKN69erGf//7X8v53bt3N5555hnLsVWrVhkdO3Y0ihYtakRGRhqtW7c21q5dazkHMMaNG2fcdNNNRnh4uOs5v//+e6Nhw4ZGSEiIUalSJWPo0KFGamqq634jR4406tSpY4SHhxtly5Y1Hn74YeP06dMXfe+uVkZGhhEdHW28/fbbrmNJSUlGVFSU8fHHHxtJSUlZ3u/RRx812rdvbzn21FNPGS1btrzk8xUuXNiYMGGCYRiGsW3bNgMwNm/e7Lo9LS3NKFKkiPHpp59a7te2bVtjyJAhWT5mVjnM5EnPw6btqeRq5dqUsbQ0+PRTePVVOH4cKlSAG2+EkBAICIBixXL2+URy0PHjx7UwosgFlAkRd8qFiFVOZOJcajq1Xp2XQxV5Jvb16wkPvrJfa3ft2sXcuXMJCjr/O9X8+fM5ceIEzzzzjNv53bt3p1q1akyaNIm+ffsydepUUlJSeO6557J8/EKFCmV5PCMjg65du3L69Gn+7//+jypVqhAbG0tAQIBH9f/yyy9ERkayYMEC16ilt99+m507d1KlShUA/vzzTzZt2sR3330HwKeffsprr73GRx99RIMGDVi/fj0PPPAABQoUoH///gAsXbqUO+64w/Jcp0+fpn///owZMwaAkSNH0q1bN3bs2EHBggVd57322mu89dZbjB49moCAAObNm8edd97JmDFjaNWqFTt37uTBBx90nQvg7+/PmDFjqFixIrt37+aRRx7hueeeY+zYsRd97V27dmXp0qWXfH/OnDmT5fHdu3dz5MgRyxpJISEhtGnThuXLl3PnnXdmuY5Qy5Yt+b//+z9WrVpF06ZN2bVrF3PmzHG9b/+Wnp7O1KlTOXv2LM2bNwfO7+oXGhrqOi8gIIDg4GCWLVvG/fff7zretGnTy77Gq6WGkI/KXFQ6R6eMzZtnbhn/z3A2atY0p4c5aJV18W3btm2jYcOGdpch4jWUCRF3yoWIldMyMXv2bCIiIkhPT3et/TJq1CjX7du3bwegZs2aWd6/Ro0arnN27NhBZGQkpUqV8qiGn3/+mVWrVrFlyxbXot6VK1f2+LUUKFCACRMmEBwc7DpWr149vvnmG9f6P19//TVNmjRxPc/w4cMZOXIkvXr1AqBSpUrExsYyfvx4+vfvz6lTpzh16hSlS5e2PFf79u0t18ePH0/hwoVZvHgxN954o+v47bffzr333uu6ftddd/HCCy+4miaVK1dm+PDhPPfcc66G0ODBg13nV6pUieHDh/Pwww9fsiE0YcIEzp07l+336kJHjhwBoGTJkpbjJUuWZO/evSQlJVGgQAG3+/Xr14+///6bli1bYhgGaWlpPPzww7zwwguW8zZt2kTz5s1JSkoiIiKCGTNmUKtWLcD8+1OhQgVefPFFxo8fT4ECBRg1ahRHjhzh8OHDlscpU6YMe/bsuaLXmF1qCPmozDWEInOiIbR9Ozz5JMyZY14vWhSGDYMHH4SgPFyjSEREREREfE5YUACxr19v23N7ol27dowbN47ExEQmTJjA9u3befzxx93OMy5YJ+jfxzN3eb7wsic2bNhA2bJlr3qHt7p161qaQWCuPfP5558zZMgQDMNg0qRJrobL33//zf79+7nvvvt44IEHXPdJS0sjKioKwNVkuXAEC8CxY8d49dVX+fXXXzl69Cjp6ekkJiayb98+y3mNGze2XF+7di2rV6/mzTffdB3LbMYlJiYSHh7OwoULGTFiBLGxsSQkJJCWlkZSUhJnz57NsjEDZrPkav37e3e57+eiRYt48803GTt2LM2aNeOvv/7iiSeeoFSpUpYFuKtXr86GDRs4deoU06ZNo3///ixevJhatWoRFBTEtGnTuO+++yhSpAgBAQF07NiRrl27uj1fWFgYiYmJV/06L0UNIR91fspYDnwL9+83m0GBgfD44zBkCBQufPWPK5LH+vbta3cJIl5FmRBxp1yIWOVEJvz8/K542lZeK1CgAFWrVgVgzJgxtGvXjmHDhjF8+HAAV5Nmy5YttGjRwu3+W7dudY32qFatGvHx8Rw+fNijUUJhYWGXvN3f39+tIZXVAs1ZNUtuv/12XnjhBdatW8e5c+fYv38//fr1A8ypamBOG2vWrJnlfpnT1YoWLYqfn5/bzmsDBgzg77//5oMPPqBChQqEhITQvHlzt8Ws/11TRkYGw4YNc41IulBoaCh79+6lW7duDBw4kOHDh1OkSBGWLVvGfffdd8lFqa9mylh0dDRgjhS68Pt27NgxSpYsSZEiRbK835AhQ7jrrrtc07rq1q3L2bNnefDBB3n55Zfx9zf37AoODnb9HWvcuDGrV6/mww8/ZPz48QA0atSIDRs2EB8fT0pKCsWLF6dZs2ZuzbS4uDiKFy9+ydd4tbTLmI9KOJcGXOGUsZQUWLXq/PUOHWDECHOq2KhRagaJz5o9e7bdJYh4FWVCxJ1yIWLl9Ey89tprvP/++xw6dAiAzp07U6RIkSx3CPv+++/ZsWMHt912GwC9e/cmODiYd999N8vHPnXqVJbH69Wrx4EDB1xTz/6tePHiHDlyxNIU2rBhQ7ZeT9myZWndujVff/01X3/9NR07dnRNjSpZsiRlypRh165dVK1a1fJVqVIlwGxm1KpVi9jYWMvjLl26lEGDBtGtWzdq165NSEgIx48fv2w9DRs2ZNu2bW7PV7VqVfz9/VmzZg1paWmMHDmSa6+9lmrVqrm+F5cyYcIENmzYcMmvi6lUqRLR0dEsWLDAdSwlJYXFixfTokUL4uPjs7xfYmKiq+mTKSAgAMMwLjqiDMyRR5lrB10oKiqK4sWLs2PHDtasWUOPHj0st2/evJkGDRpc9HFzgm+0ccXNFU0ZMwz44Qd45hk4dAh27IDMjugFW+WJ+KoLt2oUEWVCJCvKhYiV0zPRtm1bateuzYgRI/joo48oUKAA48ePp1+/fjz44IM89thjREZG8ssvv/Dss8/Su3dv+vTpA0C5cuUYPXo0jz32GAkJCdx9991UrFiRAwcO8OWXXxIREZFlY6lNmza0bt2aW265hVGjRlG1alW2bt2Kn58fXbp0oW3btvz999+8++679O7dm7lz5/LTTz8RGRmZrdd0xx13MHToUFJSUhg9erTltqFDhzJo0CAiIyPp2rUrycnJrFmzhpMnT/LUU08BcP3117Ns2TLL2j5Vq1blq6++onHjxiQkJPDss89edqQTwKuvvsqNN95IuXLluPXWW/H392fjxo1s2rSJN954gypVqpCWlsZ//vMfunfvzm+//cbHH3982ce9miljfn5+DB48mBEjRhATE0NMTAwjRowgPDyc22+/3TXq6e6776ZMmTK89dZbgLmo+KhRo2jQoIFrytiQIUO46aabXCOsXnrpJbp27Uq5cuU4ffo0kydPZtGiRcydO9f1/FOnTqV48eKUL1+eTZs28cQTT9CzZ0/LItdgNuEyR67lmsvuQ5bP5Jdt57t9uMSo8Pxs49ctR7N3hz/+MIz27Q3DbAsZRokShrFoUe4WKZLHFi9ebHcJIl5FmRBxp1yIWHmaiUttd+3tstp23jAM4+uvvzaCg4ONffv2uY4tWbLE6NKlixEVFWUEBwcbtWrVMt5//30jLS3N7f4LFiwwrr/+eqNw4cJGaGioUaNGDeOZZ54xDh06dNFaTpw4Ydxzzz1G0aJFjdDQUKNOnTrG7NmzXbePGzfOKFeunFGgQAHj7rvvNt58880st53PysmTJ42QkBAjPDw8y+3bv/76a6N+/fpGcHCwUbhwYaN169bG9OnTXbdv2bLFCAsLM06dOuU6tm7dOqNx48ZGSEiIERMTY0ydOtWoUKGCMXr0aNc5gDFjxgy355s7d67RokULIywszIiMjDSaNm1qfPLJJ67bR40aZZQqVcoICwszrr/+euPLL780AOPkyZMXff+uVkZGhvHaa68Z0dHRRkhIiNG6dWtj06ZNhmEYRkJCgmEYhtGmTRujf//+rvukpqYaQ4cONapUqWKEhoYa5cqVMx555BFLnffee69RoUIFIzg42ChevLjRoUMHY/78+Zbn/vDDD42yZcsaQUFBRvny5Y1XXnnFSE5OtpyzfPlyo1ChQkZiYmKW9efUtvN+hnGJsU35UEJCAlFRUcTHx2e7w+qNWr37K/vjzjHt4eY0qpD1HEcAjh411wT67DPIyDB3DHvySXNEkA+/fpGsxMXFXXTOr4gTKRMi7pQLEStPM5GUlMTu3bupVKmS28LDkn/06dOHBg0a8KIDZ5KkpaURGGjvZKpbb72VBg0a8NJLL2V5+6Vy6EnPQ2sI+ahsrSF09izUrg2ffmo2g269FbZsgbfeUjNI8qV58+bZXYKIV1EmRNwpFyJWyoRk5b333iMiIsLuMmxxsTWE8kpycjLXXHMNTz75ZK4/l9YQ8kEZGcb5NYRCL9EQKlAA+veHJUtg9Gho2TKPKhQRERERERFfVaFCBR5//HG7y3CkkJAQXnnllTx5Lo0Q8kFnUtLInOhnWVR67Vpo2xbWrTt/7M03YeVKNYPEEf69faaI0ykTIu6UCxErZULEykkjo2xvCI0dO9Y1761Ro0YsXbr0kucvXryYRo0aERoaSuXKlbO1Anl+k3DOHB0U5A+hQQHmjmEDBkCTJrB4MVw4zzA0FPxt/zaL5IkzZ87YXYKIV1EmRNwpFyJWyoSIVXp6ut0l5BlbOwVTpkxh8ODBvPzyy6xfv55WrVrRtWtX9u3bl+X5u3fvplu3brRq1Yr169fz0ksvMWjQIKZNm5bHldvLtX5Q+jkYPhxiYuB//zP3D7vzTnPNIBEH+vPPP+0uQcSrKBMi7pQLEStlQsTq3LlzdpeQZ2xdQ2jUqFHcd9993H///QB88MEHzJs3j3HjxvHWW2+5nf/xxx9Tvnx5PvjgAwBq1qzJmjVreP/997nlllvysnRbxZ9L5frty3n9l/GQcMI82Lw5fPABNG1qa20iIiIiIiIi4v1sGyGUkpLC2rVr6dy5s+V4586dWb58eZb3WbFihdv5119/PWvWrCE1NTXL+yQnJ5OQkGD58nUJSakUP3uKkgknoHx5mDwZfvtNzSBxvN69e9tdgohXUSZE3CkXIlbKhIhV4cKF7S4hz9g2Quj48eOkp6dTsmRJy/GSJUty5MiRLO9z5MiRLM9PS0vj+PHjlCpVyu0+b731FsOGDXM7PnXqVMLDw+nVqxe//PIL8fHxlChRgqZNmzJ79mwAGjZsSEZGBhs2bACgR48eLFu2jBMnTlCkSBFat27NzJkzAahXrx5BQUGsXbsWgBtuuIE1a9Zw9OhRIiMj6dy5M9999x0AtWvXJiIigpUrVwJmU2vz5s0cPHiQAgUKcOONNzJlyhQAqlevTrFixfjtt98A6NixI3t2bGVF8w58HJzIwG/HMuX778mYPJkqVapQpkwZlixZAkDbtm3Zt28fu3btIjAwkFtvvZVp06aRkpJChQoVqFKlCr/++isALVu25NixY2zfvh2A2267jVmzZpGYmEjZsmWpVasW8+fPB6B58+bEx8cTGxsLwK233srcuXM5ffo00dHRNGzYkDlz5gDQpEkTkpKS2LRpEwA333wzixYt4uTJkxQrVozmzZvzww8/ANCgQQMA1q9fD0D37t1ZsWIFx48fp3DhwrRt25YZM2YAULduXUJDQ1m9ejUA3bp1Y926dRw5coSCBQvSpUsXpk6dCkCtWrWIiopixYoVgNl0jI2N5cCBA4SHh9OjRw8mTZoEQLVq1ShRogTLli0DoH379uzcuZO9e/cSHBzMLbfcwtSpU0lLS6Ny5cqUL1+eRYsWAdC6dWsOHjzIzp078ff3p2/fvkyfPp3k5GTKly9PtWrV+PnnnwG47rrrOH78ONu2bQOgb9++zJ49m7Nnz1KmTBnq1Knj2gK0WbNmnDlzxjWct3fv3syfP5+EhARKlixJ48aN+fHHHwFo1KgRqampbNy4EYCePXuyZMkS4uLiKFq0KC1btmTWrFkA1K9fH39/f9b9swj5jTfeyKpVqzh27BhRUVF06NCB6dOnA1CnTh3Cw8NZtWoVAF27duWPP/7g0KFDRERE0K1bN7799lsAatSoQZEiRVyN3U6dOrF161b2799PWFgYPXv2ZPLkyRiGQUxMDNHR0a61w9q1a8eePXvYvXs3QUFB9O7dm++++47U1FQqVapExYoVWbhwIQCtWrXiyJEj7NixAz8/P/r168f48eMpWrQo5cqVo0aNGixYsACAFi1aEBcXx9atWwHo06cPc+bM4cyZM5QuXZprrrmGn376CYCmTZuSmJjI5s2bAXzyM2L79u3s27ePkJAQevXqxZQpU8jIyNBnhAM/I06fPk2/fv30GfHPZ8TMmTM5d+6cPiMc/hlx9OhROnbsqM8I/TtCnxGYnxHbt2/H+GfHmux8RqxYsYKKFSuSkpJCWloaycnJ+Pn5UaRIEU6ePElGRgYhISGEhIS4/jO+YMGCpKamkpSUBEDRokU5deoU6enpBAcHExYW5trqOyIigvT0dNe0nSJFihAfH096ejpBQUGEh4e7zi1QoACGYZCYmAiYv8ifPn2atLQ0goKCKFCgAKdOnQIgPDwcwHVuoUKFOHv2LKmpqQQGBlKwYEFOnjzpOtfPz4+zZ88CEBUVRWJiIqmpqQQEBBAVFUVcXBwAYWFhBAQEuNZiioqK4ty5c6SkpBAQEEChQoU4ccKc1REaGkpQUBCnT58GIDIykuTkZJKTk/H396dw4cLExcVhGAYhISEEBwe7zr3wPbzc+x0REUFaWprr/b7wPbzc+124cGESEhJc7/eF7+Gl3u/AwEAiIiIs7/eF7+Gl3u+wsDD8/f0t7/eF7+Gl3u/IyEiSkpJISUlxew8v9X5nvocXvt+Z72FaWhqFCxe+6PsdHBxMaGio5f2+2N/Zf7/fBQoUICMjw/J+X+zv7L/f70KFCnHmzBnS0tLIyMjAMAxmz55Namqq5TMi8/zs8DMy05/HDh06RJkyZVi+fDnNmzd3HX/zzTf56quvXB+4F6pWrRr33HMPL774ouvYb7/9RsuWLTl8+DDR0dFu98n8hmdKSEigXLlyxMfHExkZmcOvKm9NmjSJ2267ze4yRLyGMiFipUyIuFMuRKw8zURSUhK7d+92bQwkkt+cOHGCokWL2l3GJV0qhwkJCURFRWWr52HblLFixYoREBDgNhro2LFjbqOAMkVHR2d5fmBg4EW/YSEhIURGRlq+8ouLvU8iTqVMiFgpEyLulAsRK2Ui71SsWNG1Hq4TtW3blsGDB7uue+v7ERQUZHcJeca2hlBwcDCNGjVyDcXMtGDBAlq0aJHlfZo3b+52/vz582ncuLGjvmmZGjdubHcJIl5FmRCxUiZE3CkXIlZOysSAAQPw8/PDz8+PwMBAypcvz8MPP+yaupRfDR061PW6/fz8iIqKolWrVixevNjWulavXs2DDz5oaw1ZKVCggN0l5Blbt51/6qmnmDBhAp9//jlbtmzhySefZN++fQwcOBCAF198kbvvvtt1/sCBA9m7dy9PPfUUW7Zs4fPPP+ezzz7jmWeesesl2CpzvreImJQJEStlQsSdciFi5bRMdOnShcOHD7Nnzx4mTJjADz/8wCOPPGJ3Wbmudu3aHD58mMOHD7NixQpiYmK48cYbXWvb2KF48eKutZ28SeYaPk5ga0Oob9++fPDBB7z++uvUr1+fJUuWMGfOHCpUqADA4cOH2bdvn+v8SpUqMWfOHBYtWkT9+vUZPnw4Y8aMcdSW8yIiIiIiIl7p7NmLf/2zwHG2zv1nwd3LnnsFQkJCiI6OpmzZsnTu3Jm+ffu6Fr0HSE9P57777qNSpUqEhYVRvXp1PvzwQ8tjDBgwgJ49e/L+++9TqlQpihYtyqOPPmrZ+frYsWN0796dsLAwKlWqxNdff+1Wy759++jRowcRERFERkbSp08fjh496rp96NCh1K9fn88//5zy5csTERHBww8/THp6Ou+++y7R0dGUKFGCN99887KvOzAwkOjoaKKjo6lVqxbDhg3jzJkzrs0AAEaNGkXdunUpUKAA5cqV45FHHnEt3gywd+9eunfvTuHChSlQoAC1a9d2bQIAEBsbS7du3YiIiKBkyZLcddddHD9+/KI1/XvKmJ+fHxMmTODmm28mPDycmJgYvv/+e8t9PH0OuTRbG0IAjzzyCHv27CE5OZm1a9fSunVr120TJ0507byQqU2bNqxbt47k5GR2797tGk3kRI0aNbK7BBGvokyIWCkTIu6UCxGrHM1ERMTFv/79n/glSlz83K5dredWrJj1eVdp165dzJ0717L8SEZGBmXLluXbb78lNjaWV199lZdeesm1E16mhQsXsnPnThYuXMj//vc/Jk6cyMSJE123DxgwgD179vDrr7/y3XffMXbsWI4dO+a63TAMevbsSVxcHIsXL2bBggXs3LmTvn37Wp5n586d/PTTT8ydO5dJkybx+eefc8MNN3DgwAEWL17MO++8wyuvvMLvv/+e7dednJzMxIkTKVSoENWrV3cd9/f3Z8yYMWzevJn//e9//Prrrzz33HOu2x999FGSk5NZsmQJmzZt4p133iHin+/D4cOHadOmDfXr12fNmjXMnTuXo0eP0qdPn2zXBTBs2DD69OnDxo0b6datG3fccYdrh7Gceo7LcdKUMdu2nZerd2EHWkSUCZF/UyZE3CkXIlZOy8Ts2bNd24Rnbss+atQo1+1BQUEMGzbMdb1SpUosX76cb7/91tJ4KFy4MB999BEBAQHUqFGDG264gV9++YUHHniA7du389NPP/H777/TrFkzAD777DNq1qzpuv/PP//Mxo0b2b17N+XKlQPgq6++onbt2qxevZomTZoAZoPq888/p2DBgtSqVYt27dqxbds25syZg7+/P9WrV+edd95h0aJFXHvttRd93Zs2bXI1bxITEylYsCBTpkyxbLp04YLPlSpVYvjw4Tz88MOMHTsWMEc03XLLLdStWxeAypUru84fN24cDRs2ZMSIEa5jn3/+OeXKlWP79u1Uq1btkt+XTAMGDHDtejdixAj+85//sGrVKrp06ZJjz3E5Nm3Ebgs1hHzYxo0bqV27tt1liHgNZULESpkQcadciFjlaCYumF7kJiDAev2C0TJu/P81kWXPnisu6d/atWvHuHHjSExMZMKECWzfvp3HH3/ccs7HH3/MhAkT2Lt3L+fOnSMlJYX69etbzqlduzYBF7ymUqVKsWnTJgC2bNlCYGCgZcHuGjVqUKhQIdf1LVu2UK5cOVczCKBWrVoUKlSILVu2uBpCFStWpGDBgq5zSpYsSUBAAP4XvEclS5a0jD7KSvXq1V3Tr06fPs2UKVO49dZbWbhwoavOhQsXMmLECGJjY0lISCAtLY2kpCTOnj1LgQIFGDRoEA8//DDz58+nY8eO3HLLLdSrVw+AtWvXsnDhQlfT6UI7d+7MdrMm8/HAHKlTsGBB12vLqee4nMTERMLCwnLksbyd7VPGREREREREJB8oUODiX6Gh2T/337+MX+y8KyqxAFWrVqVevXqMGTOG5ORky4igb7/9lieffJJ7772X+fPns2HDBu655x5SUlIsj/PvXa79/PzIyMgAzo8w8fPzu2gdhmFkefu/j2f1PJd67osJDg6matWqVK1alQYNGvD2229TpkwZ1xo+e/fupVu3btSpU4dp06axdu1a/vvf/wLnR5Hdf//97Nq1i7vuuotNmzbRuHFj/vOf/wDmSKbu3buzYcMGy9eOHTssy8JczqVeW049h5ynEUI+rGfPnnaXIOJVlAkRK2VCxJ1yIWLl9Ey89tprdO3alYcffpjSpUuzdOlSWrRoYdl5bOfOnR49Zs2aNUlLS2PNmjU0bdoUgG3btll2r6pVqxb79u1j//79rlFCsbGxxMfHW6aW5aaAgADO/bOA95o1a0hLS2PkyJGu0Uf/XjcJoFy5cgwcOJCBAwfy4osv8umnn/L444/TsGFDpk2bRsWKFQkMzJ02Q148B5jTAZ1CI4R82JIlS+wuQcSrKBMiVsqEiDvlQsTK6Zlo27YttWvXdq1LU7VqVdasWcO8efPYvn07Q4YMYfXq1R49ZvXq1enSpQsPPPAAK1euZO3atdx///2WaUgdO3akXr163HHHHaxbt45Vq1Zx991306ZNG8tUs5ySlpbGkSNHOHLkCDt27OCNN94gNjaWHj16AFClShXS0tL4z3/+w65du/jqq6/4+OOPLY8xePBg5s2bx+7du1m3bh2//vqrq3n16KOPEhcXx2233caqVavYtWsX8+fP59577yU9PT1HXkNePAeYU+qcQg0hH5a52rqImJQJEStlQsSdciFipUzAU089xaeffsr+/fsZOHAgvXr1om/fvjRr1owTJ05YRgtl1xdffEG5cuVo06YNvXr14sEHH6REiRKu2/38/Jg5cyaFCxemdevWdOzYkcqVKzNlypScfGkuf/75J6VKlaJUqVLUr1+fb7/9lnHjxnH33XcDUL9+fUaNGsU777xDnTp1+Prrr3nrrbcsj5Gens6jjz5KzZo16dKlC9WrV3ctOF26dGl+++030tPTuf7666lTpw5PPPEEUVFRlvWOrkZePAeYzTOn8DOctIQ2kJCQQFRUFPHx8ZYV1X3R/Pnz6dy5s91liHgNZULESpkQcadciFh5momkpCR2795NpUqVCP33ukAi+UB8fDxRUVF2l3FJl8qhJz0PjRDyYS1btrS7BBGvokyIWCkTIu6UCxErZULEKqtdzPIrNYR82KxZs+wuQcSrKBMiVsqEiDvlQsRKmRCxunDx7/xODSEREREREREREYdRQ8iH1a9f3+4SRLyKMiFipUyIuFMuRKyUCRGr8PBwu0vIM2oI+bCcXEldJD9QJkSslAkRd8qFiNWVZsJhexOJg/j5+dldwmXlVP70E9GHrVu3zu4SRLyKMiFipUyIuFMuRKw8zURQUBAAiYmJuVGOiO3Onj1rdwmXlZKSAkBAQMBVPU5gThQjIiIiIiIi+V9AQACFChXi2LFjgDm9xhdGVIhkV0pKCklJSXaXcVEZGRn8/fffhIeHExh4dS0dP8NhY/0SEhKIiooiPj6eyMhIu8u5KqdPn6ZgwYJ2lyHiNZQJEStlQsSdciFidSWZMAyDI0eOOGo3JnGOjIwMr59e7O/vT6VKlQgODna7zZOeh0YI+bBVq1bRoUMHu8sQ8RrKhIiVMiHiTrkQsbqSTPj5+VGqVClKlChBampqLlUmYo8VK1bQvHlzu8u4pODgMM4slQAAFlJJREFU4BxpWqkh5MMyh2mKiEmZELFSJkTcKRciVleTiYCAgKtew0TE2xw5coTQ0FC7y8gT3j0OSi4pKirK7hJEvIoyIWKlTIi4Uy5ErJQJESsnZUJrCPmw5ORkQkJC7C5DxGsoEyJWyoSIO+VCxEqZELHy9Ux40vPQCCEfNn36dLtLEPEqyoSIlTIh4k65ELFSJkSsnJQJx60hlDkgKiEhweZKrl5iYmK+eB0iOUWZELFSJkTcKRciVsqEiJWvZyKz9uxMBnPclLEDBw5Qrlw5u8sQEREREREREckV+/fvp2zZspc8x3ENoYyMDA4dOkTBggXx8/Ozu5wrlpCQQLly5di/f7/Pr4UkkhOUCRErZULEnXIhYqVMiFjlh0wYhsHp06cpXbr0Zbemd9yUMX9//8t2yXxJZGSkz/5FFckNyoSIlTIh4k65ELFSJkSsfD0T2d0pTYtKi4iIiIiIiIg4jBpCIiIiIiIiIiIOo4aQjwoJCeG1114jJCTE7lJEvIIyIWKlTIi4Uy5ErJQJESunZcJxi0qLiIiIiIiIiDidRgiJiIiIiIiIiDiMGkIiIiIiIiIiIg6jhpCIiIiIiIiIiMOoISQiIiIiIiIi4jBqCHmxsWPHUqlSJUJDQ2nUqBFLly695PmLFy+mUaNGhIaGUrlyZT7++OM8qlQkb3iSienTp9OpUyeKFy9OZGQkzZs3Z968eXlYrUju8/TnRKbffvuNwMBA6tevn7sFiuQxTzORnJzMyy+/TIUKFQgJCaFKlSp8/vnneVStSN7wNBdff/0111xzDeHh4ZQqVYp77rmHEydO5FG1IrlryZIldO/endKlS+Pn58fMmTMve5/8/Hu2GkJeasqUKQwePJiXX36Z9evX06pVK7p27cq+ffuyPH/37t1069aNVq1asX79el566SUGDRrEtGnT8rhykdzhaSaWLFlCp06dmDNnDmvXrqVdu3Z0796d9evX53HlIrnD00xkio+P5+6776ZDhw55VKlI3riSTPTp04dffvmFzz77jG3btjFp0iRq1KiRh1WL5C5Pc7Fs2TLuvvtu7rvvPv7880+mTp3K6tWruf/++/O4cpHccfbsWa655ho++uijbJ2f33/P1rbzXqpZs2Y0bNiQcePGuY7VrFmTnj178tZbb7md//zzz/P999+zZcsW17GBAwfyxx9/sGLFijypWSQ3eZqJrNSuXZu+ffvy6quv5laZInnmSjPRr18/YmJiCAgIYObMmWzYsCEPqhXJfZ5mYu7cufTr149du3ZRpEiRvCxVJM94mov333+fcePGsXPnTtex//znP7z77rvs378/T2oWySt+fn7MmDGDnj17XvSc/P57tkYIeaGUlBTWrl1L586dLcc7d+7M8uXLs7zPihUr3M6//vrrWbNmDampqblWq0heuJJM/FtGRganT5/WP/olX7jSTHzxxRfs3LmT1157LbdLFMlTV5KJ77//nsaNG/Puu+9SpkwZqlWrxjPPPMO5c+fyomSRXHcluWjRogUHDhxgzpw5GIbB0aNH+e6777jhhhvyomQRr5Pff88OtLsAcXf8+HHS09MpWbKk5XjJkiU5cuRIlvc5cuRIluenpaVx/PhxSpUqlWv1iuS2K8nEv40cOZKzZ8/Sp0+f3ChRJE9dSSZ27NjBCy+8wNKlSwkM1I9/yV+uJBO7du1i2bJlhIaGMmPGDI4fP84jjzxCXFyc1hGSfOFKctGiRQu+/vpr+vbtS1JSEmlpadx000385z//yYuSRbxOfv89WyOEvJifn5/lumEYbscud35Wx0V8laeZyDRp0iSGDh3KlClTKFGiRG6VJ5LnspuJ9PR0br/9doYNG0a1atXyqjyRPOfJz4mMjAz8/Pz4+uuvadq0Kd26dWPUqFFMnDhRo4QkX/EkF7GxsQwaNIhXX32VtWvXMnfuXHbv3s3AgQPzolQRr5Sff8/WfxF6oWLFihEQEODWuT927JhbdzJTdHR0lucHBgZStGjRXKtVJC9cSSYyTZkyhfvuu4+pU6fSsWPH3CxTJM94monTp0+zZs0a1q9fz2OPPQaYvwwbhkFgYCDz58+nffv2eVK7SG64kp8TpUqVokyZMkRFRbmO1axZE8MwOHDgADExMblas0huu5JcvPXWW1x33XU8++yzANSrV48CBQrQqlUr3njjDZ8fDSHiqfz+e7ZGCHmh4OBgGjVqxIIFCyzHFyxYQIsWLbK8T/Pmzd3Onz9/Po0bNyYoKCjXahXJC1eSCTBHBg0YMIBvvvlGc98lX/E0E5GRkWzatIkNGza4vgYOHEj16tXZsGEDzZo1y6vSRXLFlfycuO666zh06BBnzpxxHdu+fTv+/v6ULVs2V+sVyQtXkovExET8/a2/IgYEBADnR0WIOEm+/z3bEK80efJkIygoyPjss8+M2NhYY/DgwUaBAgWMPXv2GIZhGC+88IJx1113uc7ftWuXER4ebjz55JNGbGys8dlnnxlBQUHGd999Z9dLEMlRnmbim2++MQIDA43//ve/xuHDh11fp06dsusliOQoTzPxb6+99ppxzTXX5FG1IrnP00ycPn3aKFu2rNG7d2/jzz//NBYvXmzExMQY999/v10vQSTHeZqLL774wggMDDTGjh1r7Ny501i2bJnRuHFjo2nTpna9BJEcdfr0aWP9+vXG+vXrDcAYNWqUsX79emPv3r2GYTjv92w1hLzYf//7X6NChQpGcHCw0bBhQ2Px4sWu2/r372+0adPGcv6iRYuMBg0aGMHBwUbFihWNcePG5XHFIrnLk0y0adPGANy++vfvn/eFi+QST39OXEgNIcmPPM3Eli1bjI4dOxphYWFG2bJljaeeespITEzM46pFcpenuRgzZoxRq1YtIywszChVqpRxxx13GAcOHMjjqkVyx8KFCy/5O4LTfs/2MwyN/RMRERERERERcRKtISQiIiIiIiIi4jBqCImIiIiIiIiIOIwaQiIiIiIiIiIiDqOGkIiIiIiIiIiIw6ghJCIiIiIiIiLiMGoIiYiIiIiIiIg4jBpCIiIiIiIiIiIOo4aQiIiIiIiIiIjDqCEkIiIiXmvixIkUKlToqh9n6NChlCxZEj8/P2bOnHnVj+et9uzZg5+fHxs2bLjkeW3btmXw4MGu64mJidxyyy1ERkbi5+fHqVOnruj577rrLkaMGHFF970azzzzDIMGDcrz5xUREfFlagiJiIg4kJ+f3yW/BgwYYHeJOWbLli0MGzaM8ePHc/jwYbp27Wp3SbmmXLlyHD58mDp16gCwaNGiLBs806dPZ/jw4a7r//vf/1i6dCnLly/n8OHDREVFefzcGzdu5Mcff+Txxx93HWvbtm2Wf7/S0tLcbg8JCaFatWqMGDGC9PR0S/2ZX0WLFqV9+/b89ttvlud+7rnn+OKLL9i9e7fHdYuIiDiVGkIiIiIOdPjwYdfXBx98QGRkpOXYhx9+aHeJOWbnzp0A9OjRg+joaEJCQmyuKPcEBAQQHR1NYGDgJc8rUqQIBQsWdF3fuXMnNWvWpE6dOkRHR+Pn5+fxc3/00UfceuutlscFeOCBByx/tw4fPmypL/P2bdu2MWjQIF555RXef/99y2Ns27aNw4cPs2jRIooXL84NN9zAsWPHXLeXKFGCzp078/HHH3tct4iIiFOpISQiIuJA0dHRrq+oqCj8/Pxc14OCghg4cCBly5YlPDycunXrMmnSJMv9K1asyAcffGA5Vr9+fYYOHQqYIzuCg4NZunSp6/aRI0dSrFgxDh8+fNG6Jk6cSPny5QkPD+fmm2/mxIkTbuf88MMPNGrUiNDQUCpXrsywYcNcI07+bejQoXTv3h0Af39/V6Nj9erVdOrUiWLFihEVFUWbNm1Yt26d635ZTb06deoUfn5+LFq0CIDXX3+d0qVLW2q86aabaN26NRkZGVnWM2DAAHr27MmwYcMoUaIEkZGRPPTQQ6SkpLjOSU5OZtCgQZQoUYLQ0FBatmzJ6tWrXbefPHmSO+64g+LFixMWFkZMTAxffPGFW9179uyhXbt2ABQuXNgy8uvCKWNt27Zl5MiRLFmyBD8/P9q2bQvA2LFjiYmJITQ0lJIlS9K7d+8sXxNARkYGU6dO5aabbnK7LTw83PL3LTo6OsvbK1asyGOPPUaHDh3cpvWVKFGC6Oho6tatyyuvvEJ8fDwrV660nHPTTTe5/T0VERGRi1NDSERERCySkpJo1KgRs2fPZvPmzTz44IPcddddbr+AX0pmw+Guu+4iPj6eP/74g5dffplPP/2UUqVKZXmflStXcu+99/LII4+wYcMG2rVrxxtvvGE5Z968edx5550MGjSI2NhYxo8fz8SJE3nzzTezfMxnnnnG1SzJHJ0CcPr0afr378/SpUv5/fffiYmJoVu3bpw+fTrbr/Hll1+mYsWK3H///QB8/PHHLFmyhK+++gp//4v/E+uXX35hy5YtLFy4kEmTJjFjxgyGDRvmuv25555j2rRp/O9//2PdunVUrVqV66+/nri4OACGDBlCbGwsP/30E1u2bGHcuHEUK1bM7XnKlSvHtGnTgPMjbLIa+TV9+nQeeOABmjdvzuHDh5k+fTpr1qxh0KBBvP7662zbto25c+fSunXri76mjRs3curUKRo3bpy9N+8SwsLCSE1NzfK2xMRE1/czKCjIclvTpk3Zv38/e/fuveoaREREHMEQERERR/viiy+MqKioS57TrVs34+mnn3Zdr1ChgjF69GjLOddcc43x2muvua4nJycbDRo0MPr06WPUrl3buP/++y/5HLfddpvRpUsXy7G+fftaamvVqpUxYsQIyzlfffWVUapUqYs+7owZM4zL/ZMnLS3NKFiwoPHDDz8YhmEYu3fvNgBj/fr1rnNOnjxpAMbChQtdx3bu3GkULFjQeP75543w8HDj//7v/y75PP379zeKFClinD171nVs3LhxRkREhJGenm6cOXPGCAoKMr7++mvX7SkpKUbp0qWNd9991zAMw+jevbtxzz33ZPn4/6574cKFBmCcPHnScl6bNm2MJ554wnX9iSeeMNq0afP/7d1tSFPvGwfwry6ntR4MkqQHRrYSk9RWydaDhZaaFGmCvdDVSkwkMQusJPPhRbQkS0okSlLUUF9kBZNMQ0RDCstKpqMy0CyoDEux+bC2/V+Eo+N8/PX7F///vh/Yi3POfc5139sL8eK+rmM9vn37tmX+/PmW/v7+Sdcz6s6dOxaRSGQxm802cZycnCwSicT6OXHixLjzMJlMlvv371vEYrHl5MmTgvmP3uvg4GABYFm/fr1lZGREEKuvr88CwFJfXz+tORMREdm7yQvMiYiIyO6YTCZoNBpUVFTgw4cPGB4exvDwMCQSyYyeIxaLUVpaCh8fH0ilUpsSs7H0ej0iIiIE55RKJaqrq63Hz549Q3Nzs2BHkMlkwtDQEAwGA+bMmTOtuX3+/Bnp6emoq6vDp0+fYDKZYDAY8O7du+kvEICHhwcuXryI+Ph47N+/H9HR0VPe4+vrK5inUqnEwMAAuru70dfXB6PRiM2bN1uvOzk5wd/fH3q9HgCQkJCAyMhItLS0IDg4GOHh4di0adOM5j2VnTt3QiqVwsPDA6GhoQgNDUVERMSE3+/g4CCcnZ3H7T0UHR2NM2fOWI/HvjUuPz8fBQUF1rI5lUqFjIwMwZjGxkZIJBI8f/4cp06dQlFRkc0OodmzZwP4uYuIiIiIpsaEEBEREQnk5OTg8uXLyM3Nxdq1ayGRSJCcnCzoc+Po6AiLxSK4b7wyn6amJgBAb28vent7J00qjX3eeMxmM7KysrBv3z6bay4uLlPeP0qtVqOnpwe5ubmQSqVwdnaGUqm0rnG05OvXOU1UxtTQ0ACRSITOzk78+PFjyobOE3FwcLDGG5tYsVgs1nO7du1CV1cXqqqq8PDhQwQFBeHo0aM2jZh/x7x589DS0oL6+nrU1NQgPT0dmZmZaG5utknoAMCiRYtgMBgwMjICsVgsuLZgwQLIZLIJY40mjJydnbFkyRKIRCKbMStWrICrqytWr16NoaEhREREQKfTCRqEj5bUubm5/cNVExER2Rf2ECIiIiKBxsZG7N27FzExMfD19YWHhwfevHkjGOPm5iZoDt3f32/zyu+3b9/i+PHjuHHjBhQKBQ4cODBhs2UAWLNmDR4/fiw4N/ZYLpfj1atXkMlkNp/J+vaMt8akpCSEhYXB29sbzs7O+PLli2B9AARr/LXB9KiKigpUVlaivr4e3d3dgle5T+Tly5cYHBwUrHHu3LlYtmwZZDIZxGIxHj16ZL1uNBrx9OlTeHl5CeanVqtRWlqK3NxcXL9+fdxYo8mZ0de4z8SsWbOwY8cOZGdno7W1FZ2dnairqxt3rJ+fHwCgvb19xnFGE0bLly8fNxk0lkqlgtlsRn5+vuC8TqeDk5MTvL29ZzwHIiIie8SEEBEREQnIZDLU1taiqakJer0e8fHx+Pjxo2BMYGAgSkpK0NjYCJ1Oh4MHDwr+mTeZTFCpVAgODsahQ4dQWFgInU6HnJycCeMmJSWhuroa2dnZeP36NfLy8gTlYgCQnp6O4uJiZGZmoq2tDXq9HhUVFUhLS5vxGktKSqDX6/HkyRNER0dbS46An+VHCoUCGo0G7e3taGhosInx/v17JCQk4MKFC9iyZQuKiopw/vx5myTWWCMjI4iNjbU2hs7IyEBiYiIcHR0hkUiQkJCAlJQUVFdXo729HXFxcTAYDIiNjbV+B/fu3UNHRwfa2tqg1WoFyaJfSaVSODg4QKvVoqenBwMDA9P6frRaLa5cuYIXL16gq6sLxcXFMJvN8PT0HHe8m5sb5HK5IJH13+Lo6Ijk5GRoNBpBeVhjYyO2bt0q+B2JiIhoYkwIERERkcDZs2chl8sREhKC7du3w93dHeHh4YIxqampCAgIwO7duxEWFobw8HCsXLnSev3cuXPo7Oy07lxxd3dHQUEB0tLSxt1pAwAKhQIFBQW4evUq/Pz8UFNTY5OECQkJgVarRW1tLTZu3AiFQoFLly5BKpXOaI03b97E169fsW7dOqhUKutr3seOMRqN2LBhA44dOyZ445nFYoFarYa/vz8SExMB/Oy7k5iYiJiYmEkTL0FBQVi1ahUCAgIQFRWFPXv2IDMz03pdo9EgMjISKpUKcrkcHR0dePDgARYuXAjg566f1NRU+Pj4ICAgACKRCOXl5ePGWrp0KbKysnD69GksXrzYOtepuLq6orKyEoGBgfDy8sK1a9dQVlY26e6bI0eO4NatW9N6/u86fPgwjEYj8vLyrOfKysoQFxf3R+ITERH9P3CwTKdgn4iIiIh+m1qtxrdv33D37t2/PZV/3dDQEDw9PVFeXg6lUvlHY1dVVSElJQWtra3/uIcTERGRveEOISIiIiL6bS4uLiguLhb0YvpTvn//jsLCQiaDiIiIZoB/NYmIiIjoX7Ft27a/EjcqKuqvxCUiIvpfxpIxIiIiIiIiIiI7w5IxIiIiIiIiIiI7w4QQEREREREREZGdYUKIiIiIiIiIiMjOMCFERERERERERGRnmBAiIiIiIiIiIrIzTAgREREREREREdkZJoSIiIiIiIiIiOwME0JERERERERERHbmP7sfYCZHvzz+AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ROC curve\n", + "\n", + "# Calcul des taux de faux positifs (FPR) et de vrais positifs (TPR)\n", + "y_pred_prob = clf.predict_proba(X_test)[:, 1]\n", + "\n", + "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob, pos_label=1)\n", + "\n", + "# Calcul de l'aire sous la courbe ROC (AUC)\n", + "roc_auc = auc(fpr, tpr)\n", + "\n", + "plt.figure(figsize = (14, 8))\n", + "plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", + "plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", + "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", + "plt.xlabel('Taux de faux positifs (FPR)')\n", + "plt.ylabel('Taux de vrais positifs (TPR)')\n", + "plt.title('Courbe ROC : naive Bayes')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ad1a0b57-e382-4ae3-90b6-1f790099711b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mamba/lib/python3.11/site-packages/numpy/core/fromnumeric.py:86: FutureWarning: The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)\n", + " return reduction(axis=axis, out=out, **passkwargs)\n" + ] + }, + { + "data": { + "image/png": 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FYmwkgZEEMJJIHv+76LWxUdFriUQCmYmR8j0JAMnj5Y0kEkgkRedHI8l/7cWvjSUSmEuNITMxhszECNZmJjAxlsBYIoHR4+09vU9LmQnqOFnBxtwUNmamsJAaw9TYCFITI5iZGsHMxBhGRvofEqNSFTc1atRAQkKCSltiYiJMTEyeOaKwTCaDTCYrj3h686ICr1hERAQ8PDxUijtfX1/Y2dkhIiJCo+LG39+/1PaAgIASr4ufNLpw4QJu3boFa2trlWVyc3MRFRX1zMytW7dW+WxP76N4u5s3b1a2CSGgUCgQHR2NBg0aqP25iKjiycgtQHa+/PHtkqL+HQD+e/34dovyts7j2yk5BXLk5MuRnS9Hdn4h8goVOB2VjKv30nAnOQviORdNJBKgQQ0b+HlWg6XMBE7WMnjYW8DW3PRxoSD5r1jAf4UDAGVRoSwuoLq8kQSQmhgpC5DiwkFZUDxe1thIAlNjjthSFpWquAkICMCePXtU2g4cOAB/f/9S+9tog7mpMcLnddfJttXZtzrq1KkDiUSCiIgIlceZnyaEKLUAerLdyMhI5TYTUPpou+r2ZQH+K7oUCgX8/PxUipBi1atXf2bmF1EoFHj//fcxceLEEu/VrFlT7ZxEVDHkFshxPSED/16Jx87Qe3iYkffilcrAwVKKAX7u6OrrjOpWMkhNjIquQhgbQWZqBDM1fwdTxaPX4iYzMxO3bt1Svo6OjkZYWBjs7e1Rs2ZNzJo1C/fu3cPGjRsBAOPGjcP//vc/TJ06FWPHjkVwcDDWrFmD33//XWcZJRKJWreG9Mne3h7du3fHTz/9hIkTJ5YoPFJTU2FnZwdfX1/ExsYiLi5OefUmPDwcaWlpyqsb1atXx9WrV1XWDwsLU7t4PHPmDIYPH67yunnz5gCAFi1aYOvWrXBycoKNjY1a2/P19S3x+PWZM2dUXrdo0QLXrl1D7dq11dpmWZmamkIu10//KyJDJYTAw8w8RCZk4ELMI5y9nYLzd1JKdFAtvj1TfEvlydsuRk+0PXkLxtzUGGamxjCXGsNKZlJ0+8XUCA1dbdC7iSs87DnVjqHS61k7JCQEHTt2VL4u7hszYsQIrF+/HvHx8YiNjVW+7+3tjb1792LKlCn46aef4OrqiqVLl1b5x8ABYPny5QgMDETLli0xb948NGnSBIWFhTh48CBWrFiBiIgIdOnSBU2aNMHQoUOxZMkSZYfi9u3bK28zderUCd999x02btyIgIAA/Prrr7h69aqyQHmRbdu2wd/fH6+++io2b96Mc+fOYc2aNQCAoUOH4rvvvkPfvn0xb948uLu7IzY2Fjt37sSMGTPg7u5eYnvjxo3DokWLMHXqVLz//vu4cOGCyng0APDxxx+jdevW+PDDDzF27FhYWloiIiJC2d9HW7y8vHD48GG0adMGMpkM1apV09q2iaoChULgUXY+YlKycT46BRdjH+HUrWRk5hWWWLaahSma16yG/s3d0Km+EyxlFfuPTKpY9Prd0qFDh+fednj6JAYA7du3x8WLF3WYqnLy9vbGxYsX8dVXX2HatGmIj49H9erV4efnhxUrVgD4bwThjz76CO3atVN5FLxY9+7d8dlnn2HmzJnIzc3F6NGjMXz4cFy5ckWtHHPnzsWWLVswfvx41KhRA5s3b4avry+Aokkljx8/jo8//hhvvPEGMjIy4Obmhs6dOz/zSk7NmjWxY8cOTJkyBcuXL0fLli3x9ddfqzze3qRJEwQFBWH27Nlo27YthBCoVasWBg0aVNbDWariIuuXX36Bm5sbHwUneoa0nAKcuZ2MU7eScCkuFRl5hUjJykdaTkGp/VwkEsDLwRINXKzRomY1dG7gDC8HC87VRmUmEep0ajAg6enpsLW1RVpaWokTam5uLqKjo+Ht7a3xEzZEAL+HqOrKK5TjwLUHCLrxEHsu3Ude4bOfOnK0kqGusxUCfBzQpo4jfF1s2L+FXuh55++n8TofERGpLT23ALHJ2UjNLkDkgwwkZuTibkoOgm48VLm95OVggZbe9mhT2xE1bMxgbWYKR2sp7MylkJrwCSDSLRY3REQEoKhPTG6hHMmZ+UjMyEVEfAbuPh687t6jbDzKLnjuI9TONjL0auyK9vWqo10dR95WIr1hcUNEZGByC+Q4cTMJF2Mf4WFGHhIz8pBbIIdcUTT6bqFcAbmiaByYvAIF8uUK5BcqSu3YWxpHKxnsLExR094CXg6WcLaRobG7LVp7O1SIAdyIWNwQERmAq/fSsC0kDtcTMnD+TgpeZlR/EyMJnKxlqONsDW9HS9SwNYOnvQWqWUrh6WABF1tz7QUn0gEWN6WoYn2sSYv4vUPlKSuvEHuvxGPP5Xgcv/FQ5T17Syk613eCl6MlbM1NUc1C+njE26Ih9U2MHg+Xb/rf8PlWMhNYSI1hbloxhtAnKisWN08oHqguOzsb5ub8y4Q0l52dDQA6GzGbCAAS0nKx7MhN7A69h6z8/waWbFvHEf2auaGxuy3qOFmxzwtVWSxunmBsbAw7OzskJiYCKBqXhb8cSB1CCGRnZyMxMRF2dnacNZy0Tq4QOB2VhK3n43Ao4gFyC4oeta5hY4ZuDZ3xdsuaaOCi3sjfRIaOxc1Timd8Li5wiDTBWcNJ2+6nFj1mveH0HVxPyFC2+7rYYFyHWni9sQtvIRE9hcXNUyQSCVxcXODk5FTqhJFEz2JqasorNvTSCuQKnIsuml/pQswjnLqVpOwcbG5qjJ6Na+Cd1p5o7mHHK8tEz8Di5hmMjY15oiKicpGYnouz0Sk4cfMhjlxPRFJmvsr7jd1s0aFedQwL8ISTNUe+JnoRFjdEROUoO78QwVHJuJWYifi0XITGpeJSXKrKMvaWUrT2sUdrHwe09LZH/RrsS0OkCRY3REQ6duNBBvZeicfeK/G48SCz1GXq17BGK297dKjvhMBaDpCZ8MoxUVmxuCEi0rJCuQKX7qbh78v3ceDaA9xLzVF5v7q1DC297eFqa4b6NWwQWNuBA+MRaRGLGyKiMlA8nr4gp0COzNxCZOQW4nZSJraF3MWFmEfIKfhv/BljIwna1HbE641d0L5edThZy9gZmEiHWNwQEb3A1Xtp2H7hLq4npONRVgEeZefjUXY+CuTPHpHa2swE7etWx+tNXNGmtgOszTiwI1F5YXFDRFSKhLRcLD1yEzcSMnAh9tEzZ8IGih7RtjE3gZ25FK187DGkVU3UcbKGMcefIdILFjdERE/ILZDj93Ox+PLvcJXJJ3s2qoGuvs5wtjGDjZkpHKyksLMwhZkJ52EiqmhY3BARAbgQk4LNZ2Nx4NoDZOYVAgDqOVtjbDsfNHG3RV1naz0nJCJ1sbghoirr5oMM7Aq9h78vxyM2JVvZ7mglw0edauOd1p68tURUCbG4IaIqRQiBbSF3sfJ4FG4/zFK2mxhJ0KepK/o2d8OrtR1Z1BBVYixuiKhKyMmXY9uFOKw/fUdZ1JgYSfBqHUf0b+6GDnWdYGvBJ5qIDAGLGyIyaEIIHI1MxIK913EzsWh0YDNTI3zQvjZGBHrCzkKq54REpG0sbojIYF29l4YfDt7A4euJAIBqFqb4sGNtvOnnzqKGyICxuCEigyJXCByKeIBtIXE4FFFU1BgbSTAy0AvjO9SCg5VMzwmJSNdY3BCRQXiUlY/jNx9ixbEoXE/IULa3reOIj3vURyM3Wz2mI6LyxOKGiCo1hUJg24U4fPFXuHI+JyuZCd70c8ebfu4saoiqIBY3RFRpxSZnY+KWUITFpQIAnG1k6NfcDe+19eHtJ6IqjMUNEVU66bkF2Hj6Dn46GoWcAjmMJMC49rUwsXMdmJka6zseEekZixsiqlTORadgytYw3EvNAQA0dLXB9281RQMXGz0nI6KKgsUNEVUKaTkF+Pl4FH45EY38QgUcrWT4sGMtDA/w4mjCRKSCxQ0RVWh5hXKsPXkHq45HITW7AEDRE1D/e7sFRxQmolKVubjJz89HdHQ0atWqBRMT1khEpH0nbj7E539eQ3RS0XQJPtUtMalzHfRq7AITYyM9pyOiikrjqiQ7OxsfffQRNmzYAAC4ceMGfHx8MHHiRLi6uuKTTz7RekgiqloepOfiq38i8Nel+wCKRhae0rUu3m5ZE6YsaojoBTT+LTFr1ixcunQJx44dg5mZmbK9S5cu2Lp1q1bDEVHVklsgx9d7I9B24VH8dek+jCTAsNaeODq9A4YHeLGwISK1aHzlZvfu3di6dStat24NieS/Tny+vr6IiorSajgiqjquJ6Tjw80XEfV4xu5GbjaY26cR/Dyr6TkZEVU2Ghc3Dx8+hJOTU4n2rKwslWKHiEgdT48wbGdhirl9GqJPU1f+TiGiMtH4Gu8rr7yCf/75R/m6+JfPL7/8goCAAO0lIyKDdyEmBZ0XB+HjHVeQUyBH/RrW+GdiW/Rt5sbChojKTOMrNwsWLECPHj0QHh6OwsJC/Pjjj7h27RqCg4MRFBSki4xEZGAUCoH1p+9g/j/hUAhAamKEjzrWxth2PhxhmIhemsZXbgIDA3Hq1ClkZ2ejVq1aOHDgAJydnREcHAw/Pz9dZCQiAyGEwPk7KXhrVTDm/V1U2HT1dcax6R3wEadOICItkQghhL5DlKf09HTY2toiLS0NNjYcrp2ovGTnF2LK1jDsv/YAAGAkAf6vly9GtfHiLSgieiFNzt9q3ZZKT09Xe+csGIjoafuuJuD/dl9BUmY+jI0k6NvMFRM61oZPdSt9RyMiA6RWcWNnZ/fCv6yEEJBIJJDL5VoJRkSG4dczMfj8z6tQCMDazATL3m6ODvVKPnFJRKQtahU3R48e1XUOIjIwQgh8s+86VgXdBgD0b+6Gr/s3hrmU/WqISLfUKm7at2+v6xxEZECEEPjp6C1lYTO1a1181Kk2+9YQUbko01jmhw4dwuDBg9GqVStl29q1a3Hy5EmtBSOiyikxPRcj153H9wduAAAmdqqNiZ3rsLAhonKjcXGzZcsW9O3bF7a2tggJCVG2Z2Zm4uuvv9ZqOCKqXNacjEb7744h6MZDSE2M8HGP+pjcpa6+YxFRFaN2cZOZmQmgaBC/VatWYdWqVXjyKfJ27drh4sWL2k9IRBVeoVyB7/dH4su/i6ZQaOhqgz0TXsUHHWrByIhXbIiofKnV5+by5csYMWIEQkNDcfPmTbRt2xYAVC4zW1tbIzU1VSchiahiEkJg6/k4/O/oLdx9lAMAeL+9Dz7pUZ+3oYhIb15Y3Pz777/44IMP8McffwAAXFxcEBUVBU9PT5UrN0FBQfDx8dFdUiKqUFKz8zFxSxiO33gIALCSmeDjHvUwLMBLv8GIqMp7YXGTnJyMQ4cOoXbt2gCA999/H5MnT8batWshkUgQGxuLQ4cOYebMmfjiiy90nZeI9EyhENh0JgbLjtxUDso3qXMdjGrjBWszU33HIyJ6cXHzzjvvqLyeOXMm0tLS0K5dOwgh4O3tDalUiunTp2PChAk6C0pE+pVfqMDmszHYcPoO7iRnAwBcbc3w09AWaF6zmp7TERH9p8xzS2VnZyM8PBwKhQK+vr6wsqocw6hzbikizSWm5+Kj30NxNjoFAGAtM8GUrnUxtHVNyEw4KB8R6Z7W55YqjYWFBfz9/cu6OhFVEhdiHmHi76G4l5oDc1NjTOlaB0NbecJSVuZfH0REOqXWb6c33nhD7Q3u3LmzzGGIqOLIyC3A4oM3sDE4BnKFgIe9OVYPfwX1aljrOxoR0XOpVdzY2toq/y2EwK5du2Bra6u8cnPhwgWkpqZqVAQRUcUkVwhsC4nDkkM3kZCeCwDoXN8Jiwc1g605OwwTUcWn1iB+69atU345Oztj4MCBiI6Oxs6dO7Fz507cvn0bgwcPhqOjo8YBli9fDm9vb5iZmcHPzw8nTpx47vKbN29G06ZNYWFhARcXF4waNQrJycka75eIVCkUArtC76LbD0H4ZOcVJKTnwr2aOdaPegWrR/izsCGiSkPjDsXVq1fHyZMnUa9ePZX2yMhIBAYGalRobN26FcOGDcPy5cvRpk0brFq1CqtXr0Z4eDhq1qxZYvmTJ0+iffv2+OGHH9C7d2/cu3cP48aNQ506dbBr1y619skOxUSqcgvk2B16DxuCYxARnw6gqMPwe+18MKatNyyk7FtDRPqn0w7FhYWFiIiIKFHcREREQKFQaLStxYsXY8yYMXj33XcBAEuWLMH+/fuxYsUKLFiwoMTyZ86cgZeXFyZOnAgA8Pb2xvvvv4+FCxdq+jGICMC56BR8uusKbiUWTa9ibmqsLGpsOGYNEVVSGhc3o0aNwujRo3Hr1i20bt0aQFHR8c0332DUqFFqbyc/Px8XLlzAJ598otLerVs3nD59utR1AgMDMXv2bOzduxc9e/ZEYmIitm/fjl69ej1zP3l5ecjLy1O+Tk9PVzsjkaG6l5qDr/4Jx94rCQAAmYkRxneojaGta8LRSqbndEREL0fj4ub7779HjRo18MMPPyA+Ph5A0ZQMM2fOxLRp09TeTlJSEuRyOZydnVXanZ2dkZCQUOo6gYGB2Lx5MwYNGoTc3FwUFhaiT58+WLZs2TP3s2DBAsydO1ftXESGLPx+OlYERWHf1XgUyIvuSPdsVAOf9/aFi625ntMREWlHmQfxA/67ClKWviv379+Hm5sbTp8+jYCAAGX7V199hU2bNuH69esl1gkPD0eXLl0wZcoUdO/eHfHx8ZgxYwZeeeUVrFmzptT9lHblxsPDg31uqErJLZBjyaGbWH3iNgoVRT/yDV1t8HX/xmjqYaffcEREaiiXQfwePnyIyMhISCQS1KtXT+MnpRwdHWFsbFziKk1iYmKJqznFFixYgDZt2mDGjBkAgCZNmsDS0hJt27bF/Pnz4eLiUmIdmUwGmYyX2anqOnHzIWbvuorYlKIpE16t7YgZ3euhibstZ+4mIoOk1qPgT8rKysLo0aPh4uKCdu3aoW3btnBxccGYMWOQnZ2t9nakUin8/Pxw8OBBlfaDBw8iMDCw1HWys7NhZKQa2di4aOj3l7gARWSQ4lKy8dHvoRi25hxiU7JR3VqGHwc3w6YxLdHUw46FDREZLI2Lm6lTpyIoKAh79uxBamoqUlNT8eeffyIoKEijPjfF21q9ejXWrl2LiIgITJkyBbGxsRg3bhwAYNasWRg+fLhy+d69e2Pnzp1YsWIFbt++jVOnTmHixIlo2bIlXF1dNf0oRAZr+4W76LToGPZcug8A6NvMFUemtUffZm4saojI4Gl8W2rHjh3Yvn07OnTooGx77bXXYG5ujoEDB2LFihVqb2vQoEFITk7GvHnzEB8fj0aNGmHv3r3w9PQEAMTHxyM2Nla5/MiRI5GRkYH//e9/mDZtGuzs7NCpUyd8++23mn4MIoOUnV+IFceisOzILQCAv2c1/N/rvmjGfjVEVIVo3KHYwsICFy5cQIMGDVTar127hpYtWyIrK0urAbWNg/iRIRJCYFvIXXz9bwRSswsAAAP93fHNG01gZMQrNURU+Wly/tb4tlRAQADmzJmD3NxcZVtOTg7mzp2r8tQTEZUPuUJg9u6rmLnjMlKzC+BoJcMPg5ri2wEsbIioatL4ttSPP/6IHj16wN3dHU2bNoVEIkFYWBjMzMywf/9+XWQkomeITMjAtG1huHovHRIJ8EH7WpjUpQ5kJsb6jkZEpDdlGucmJycHv/76K65fvw4hBHx9fTF06FCYm1f8QcB4W4oMQVZeIf539BZWHIsCAEiNjbDgjcYY4Oeu52RERLqh83FuzM3NMXbs2DKFI6KXc/pWEmbuuIy7j3IAAIG1HLDgjcbwdLDUczIiooqhTMXNvXv3cOrUKSQmJpaYLLN4Uksi0q7E9FzM/ycCfz1+vNvOwhRz+zRE32Zuek5GRFSxaFzcrFu3DuPGjYNUKoWDg4PKmBkSiYTFDZGWFcoV2BAcg0UHIpGdLwcAdGnghO/ebIpqllI9pyMiqng07nPj4eGBcePGYdasWSVGC64M2OeGKpMLMSmY93cELsWlAgBqO1lhfr9GaO3joN9gRETlTKd9brKzszF48OBKWdgQVSa/n4vFp7uuQAjA3NQYk7vUwZhXvWFizJ89IqLn0fi35JgxY7Bt2zZdZCEiFM0JNXP7JczaWVTYdGngjEPT2uP99rVY2BARqUHj21JyuRyvv/46cnJy0LhxY5iamqq8v3jxYq0G1DbelqKKSq4QWHcqGgv3RyK/sKij/shAL3z2ui+MORgfEVVxOr0t9fXXX2P//v2oV68eAJToUExEmiuUKzB5axj+vhwPoGhOqOnd67FvDRFRGWhc3CxevBhr167FyJEjdRCHqOq5k5SFmTsu41x0CgBg9msNMPpVb16tISIqI42LG5lMhjZt2ugiC1GVExyVjLEbQ5CZVwhLqTG+fbMJXm/iqu9YRESVmsa9EydNmoRly5bpIgtRlSGEwM/Ho/D2L2eQmVeIes7W+GdiWxY2RERaoPGVm3PnzuHIkSP4+++/0bBhwxIdinfu3Km1cESGSAiBuXvCsf70HQBA/+ZumN+vESxlZRownIiInqLxb1M7Ozu88cYbushCZPAK5ArM3XMNv56JBQB83KM+xrX3YWd8IiItKtP0C0Skuez8QozffBHHIh8CAGb2qIcPOtTScyoiIsPD6+BE5SAsLhWf7LiM6wkZkJoY4fu3mqJPU/avISLSBRY3RDp29Hoixm4MQaFCwNbcFKtH+OMVL3t9xyIiMlgsboh0aP2paMz/JwKFCoEO9apj4ZtN4GRtpu9YREQGjcUNkQ48ysrHxzsu40D4AwBAj4Y18OPbzSAzMdZzMiIiw8fihkiLMvMK8d2+6/j9XBzy5UXzQ03qXAcTO9fhiMNEROVE4+Jm4sSJqF27NiZOnKjS/r///Q+3bt3CkiVLtJWNqFKJTc7G2I0hiHyQAQCoX8MaX/VvBD9P9q8hIipPGo9QvGPHjlKnXwgMDMT27du1Eoqosgm68RB9fzqJyAcZsLeUYuPolvh3UlsWNkREeqDxlZvk5GTY2tqWaLexsUFSUpJWQhFVFgqFwBd7rmFjcAwAoJ6zNX4e7gdPB0s9JyMiqro0vnJTu3Zt7Nu3r0T7v//+Cx8fH62EIqoMCuQKTPj9orKwGdbaEzvHB7KwISLSM42v3EydOhUTJkzAw4cP0alTJwDA4cOHsWjRIva3oSqjUK7A+M0XcTD8AUyNJVj4ZhP0b+6u71hERIQyFDejR49GXl4evvrqK3z55ZcAAC8vL6xYsQLDhw/XekCiiia3QI7JW8JwMPwBpCZGWPlOC3Sq76zvWERE9JhECCHKuvLDhw9hbm4OKysrbWbSqfT0dNja2iItLQ02Njb6jkOVTEpWPt7dcB4XY1MhkQDL3m6O15twGgUiIl3T5PytcZ8bACgsLMShQ4ewc+dOFNdG9+/fR2ZmZlk2R1QpZOcX4p3VZ3ExNhXmpsZYM8KfhQ0RUQWk8W2pmJgY9OjRA7GxscjLy0PXrl1hbW2NhQsXIjc3FytXrtRFTiK9SsspwHsbQxAenw5rMxNsfS8Avq688kdEVBFpfOVm0qRJ8Pf3x6NHj2Bubq5s79+/Pw4fPqzVcEQVwf3UHLy18jTORqfAzNQIPw/zZ2FDRFSBaXzl5uTJkzh16hSkUqlKu6enJ+7du6e1YEQVwdnbyXhv0wWk5RTAwVKKVcP84M8ZvYmIKjSNixuFQgG5XF6i/e7du7C2ttZKKKKK4N8r8Zi0JQz5cgW8HS2xapgf6jrze5yIqKLT+LZU165dVcazkUgkyMzMxJw5c/Daa69pMxuR3vxxPg4f/R6KfLkCbes4Ys9Hr7KwISKqJDR+FPz+/fvo2LEjjI2NcfPmTfj7++PmzZtwdHTE8ePH4eTkpKusWsFHwel5hBD4Zt91rAq6DQDoXN8JPw1tATNTYz0nIyKq2jQ5f2t8W8rV1RVhYWH4/fffcfHiRSgUCowZMwZDhw5V6WBMVNnIFQLT/gjD7rD7AIAPO9bClC51YWJcphETiIhITzS+cpOdnQ0LCwtd5dE5Xrmh0hTKFZi4JRR7ryQAAP6vVwO825ZzpRERVRQ6HcTPyckJ77zzDvbv3w+FQlHmkEQVyZy/rmHvlQRIjY2weGBTFjZERJWYxsXNxo0bkZeXh/79+8PV1RWTJk3C+fPndZGNqFxsOReLzWdjAQDfvdUEb7TgBJhERJWZxsXNG2+8gW3btuHBgwdYsGABIiIiEBgYiLp162LevHm6yEikMyF3UvD5X9cAAJM610HfZm56TkRERC/rpSbOLBYeHo6hQ4fi8uXLpY6BU5Gwzw0BQGZeIc5FJ+PjHVfwMCMPHetVx5oRr8DISKLvaEREVAqdPi1VLDc3F3/99Rd+++037Nu3D05OTpg+fXpZN0dULnIL5Jjz5zXsDL2LAnlRXe/jaIllQ1qwsCEiMhAaFzcHDhzA5s2bsXv3bhgbG+PNN9/E/v370b59e13kI9KahLRcTNkahuDbyQAAe0spmnvY4Ys+DWElK3OdT0REFYzGv9H79euHXr16YcOGDejVqxdMTU11kYtIq85Fp2DyllDcT8uFuakxFr7ZBL2buuo7FhER6YDGxU1CQgL7qlClcupWEkasPYdChYB7NXOsHuGP+jX4PUxEZKg0Lm6eLGxycnJQUFDwzPeJ9O32w0x8+NtFFCoE2tetjiWDmqGapfTFKxIRUaWl8aPgWVlZmDBhApycnGBlZYVq1aqpfBFVFOm5BRi7MQSp2QVo4GKD5UNbsLAhIqoCNC5uZs6ciSNHjmD58uWQyWRYvXo15s6dC1dXV2zcuFEXGYk0FpeSjRFrzyHqYRZq2Jhh7Uh/WLLTMBFRlaDxb/s9e/Zg48aN6NChA0aPHo22bduidu3a8PT0xObNmzF06FBd5CRSS06+HHP3XMMfIXFQCMDc1Bg/D/eDiy0ndSUiqio0vnKTkpICb29vAEX9a1JSUgAAr776Ko4fP67ddEQamrvnGracLypsWnrZY80IfzRxt9N3LCIiKkcaFzc+Pj64c+cOAMDX1xd//PEHgKIrOnZ2dtrMRqSRS3Gp+CMkDgDwy3B//DEuAIG1HfWcioiIypvGxc2oUaNw6dIlAMCsWbOUfW+mTJmCGTNmaD0gkTrScwsw5Y8wKATQs1ENdPV11nckIiLSk5eeWyo2NhYhISGoVasWmjZtqq1cOsO5pQxPboEcff93CpEPMuBsI8O+Se34VBQRkYEpl7mlitWsWRM1a9Z82c0QldnyY1GIfJABGzMT/DLcn4UNEVEVp/FtKaKKZFfoXSw9fBMA8GW/Ruw8TERE+i9uli9fDm9vb5iZmcHPzw8nTpx47vJ5eXmYPXs2PD09IZPJUKtWLaxdu7ac0lJFci46BZ/suAIAeKd1TfThXFFERAQt3JZ6GVu3bsXkyZOxfPlytGnTBqtWrULPnj0RHh7+zFtdAwcOxIMHD7BmzRrUrl0biYmJKCwsLOfkpG8XYlIwat055BUq0KFedczr0wgSiUTfsYiIqAJ46Q7FL6NVq1Zo0aIFVqxYoWxr0KAB+vXrhwULFpRYft++fRg8eDBu374Ne3v7Mu2THYorv7iUbPRffgpJmfnw96yGTWNawVxqrO9YRESkQ5qcvzW+LXXx4kVcuXJF+frPP/9Ev3798OmnnyI/P1/t7eTn5+PChQvo1q2bSnu3bt1w+vTpUtf566+/4O/vj4ULF8LNzQ1169bF9OnTkZOT88z95OXlIT09XeWLKq/kzDyMWn8eSZn5qOtshY1jWrKwISIiFRoXN++//z5u3LgBALh9+zYGDx4MCwsLbNu2DTNnzlR7O0lJSZDL5XB2Vh2PxNnZGQkJCaWuc/v2bZw8eRJXr17Frl27sGTJEmzfvh0ffvjhM/ezYMEC2NraKr88PDzUzkgVixAC07Zdwq3ETDhaybBuVEtYSDlfFBERqdK4uLlx4waaNWsGANi2bRvatWuH3377DevXr8eOHTs0DvB0PwkhxDP7TigUCkgkEmzevBktW7bEa6+9hsWLF2P9+vXPvHoza9YspKWlKb/i4uI0zkgVw59h93Es8iGkxkZYP+oVuNlxvigiIipJ4z97hRBQKBQAgEOHDuH1118HAHh4eCApKUnt7Tg6OsLY2LjEVZrExMQSV3OKubi4wM3NDba2tsq2Bg0aQAiBu3fvok6dOiXWkclkkMlkaueiiuna/TR8vOMyAOCDDrXQyM32BWsQEVFVpfGVG39/f8yfPx+bNm1CUFAQevXqBQCIjo5+ZlFSGqlUCj8/Pxw8eFCl/eDBgwgMDCx1nTZt2uD+/fvIzMxUtt24cQNGRkZwd3fX9KNQJZFbIMeE30KRV6hA2zqO+LBjbX1HIiKiCkzj4mbJkiW4ePEiJkyYgNmzZ6N27aITzfbt259ZlDzL1KlTsXr1aqxduxYRERGYMmUKYmNjMW7cOABFt5SGDx+uXH7IkCFwcHDAqFGjEB4ejuPHj2PGjBkYPXo0zM15i8JQLdwXieikLFSzMMX3bzWF1ETvwzMREVEFpvFtqSZNmqg8LVXsu+++g7GxZk+tDBo0CMnJyZg3bx7i4+PRqFEj7N27F56engCA+Ph4xMbGKpe3srLCwYMH8dFHH8Hf3x8ODg4YOHAg5s+fr+nHoEriwLUErD0VDQD4dkATONuY6TkRERFVdGUa5yY1NRXbt29HVFQUZsyYAXt7e1y8eBHOzs5wc3PTRU6t4Tg3lUd+oQKdFx9DXEoORrXxwpzeDfUdiYiI9ESrE2c+ePBApS/N5cuX0blzZ9jZ2eHOnTsYO3Ys7O3tsWvXLsTExGDjxo0v/wmIAGw5H4u4lBxUt5ZhRvd6+o5DRESVxAs7L6xatQqffvqp8vXUqVMxatQo3Lx5E2Zm/90i6NmzJ44fP66blFTlxKVkY9GBovGUPuxQi+PZEBGR2l54xpg0aRJGjhyJESNGYMOGDTh//jxWrVpVYjk3N7dnDr5H9CIP0nNx9HoiLt9LQ2xyNi7GPkJ2vhyN3WwxtLWnvuMREVEl8sLixtbWFrt27cLixYsBAGZmZqVOYRAZGYnq1atrPyEZNCEElh6+hf8dvYkCuWr3L2uZCb5/qylMjfl0FBERqU/ta/1Tp04FAPTt2xfz5s3DH3/8AaBohOHY2Fh88sknGDBggG5SkkFSKAQ+3XUFW84XjRrt42iJHo1qwMvREh7VLODrYgNbC1M9pyQiospG46el0tPT8dprr+HatWvIyMiAq6srEhISEBAQgL1798LS0lJXWbWCT0tVDEII/HDwBpYeuQUAmNipNqZ0rfvMqTeIiKhq0+rTUk+zsbHByZMnceTIEVy8eBEKhQItWrRAly5dyhyYqp7FB29g2ePC5v96NcC7bX30nIiIiAxFmR9B6dSpEzp16qTNLFRFbDkXqyxsJnWugzGveus5ERERGRK1ipulS5fivffeg5mZGZYuXfrcZSdOnKiVYGSYbj/MxFf/RAAAxrWvhSld6+o5ERERGRq1+tx4e3sjJCQEDg4O8PZ+9l/ZEokEt2/f1mpAbWOfG/1JzszD68tOIj4tF43dbLFrfCBM+CQUERGpQet9bsLCwmBrawugaPZvorL48u9wxKflwtPBAquG+bGwISIinVDr7GJvb4/ExEQARX1tUlNTdZmJDNC56BTsDrsPAPjuzaZwteMs7kREpBtqFTdWVlZITk4GABw7dgwFBQU6DUWG5+fjUQCAN5q7oaW3vZ7TEBGRIVPrtlSXLl3QsWNHNGjQAADQv39/SKXSUpc9cuSI9tKRQThzOxmHIoqu/I3vWFvPaYiIyNCpVdz8+uuv2LBhA6KiohAUFISGDRvCwsJC19nIAKTnFmDCbxcBAAP93VHbyUrPiYiIyNCpVdyYm5tj3LhxAICQkBB8++23sLOz02UuMhA/HbmFpMx8eDlY4PPeDfUdh4iIqgCNB/E7evSoLnKQAboUl4rVJ4uervu4R31Yyco8ZiQREZHa1DrbTJ06FV9++SUsLS2VE2g+S/Hs4VS1KRQCn/95FXKFQM9GNdCjUQ19RyIioipCreImNDRU+YTUxYsXObkhvdCu0Hu4dDcNZqZG+Ly3L79niIio3KhV3Dx5K+rYsWO6ykIGIidfjh8O3QAAfNSpDlxsOaYNERGVH42HiB09ejQyMjJKtGdlZWH06NFaCUWV27rT0bj7KAcutmYYGeil7zhERFTFaFzcbNiwATk5OSXac3JysHHjRq2Eosor6mEmfjx0EwAwo3s9WLITMRERlTO1zzzp6ekQQkAIgYyMDJiZmSnfk8vl2Lt3L5ycnHQSkioHIQQ+3XkFeYUKBNZyQP/mbvqOREREVZDaxY2dnR0kEgkkEgnq1q1b4n2JRIK5c+dqNRxVLkcjE3E2OgVSYyN83b8xOxETEZFeqF3cHD16FEIIdOrUCTt27IC9/X/zA0mlUnh6esLV1VUnIaniyy2Q46t/IgAAQ1vXhJejpZ4TERFRVaV2cdO+fXsAQHR0NDw8PGBkpHF3HTJgy47cRNTDLDhYSjGpcx19xyEioipM496enp6eSE1Nxblz55CYmAiFQqHy/vDhw7UWjiqHmw8ysCroNgBgXt9GsLMofVJVIiKi8qBxcbNnzx4MHToUWVlZsLa2VulXIZFIWNxUMQVyBaZvv4xChUCn+k7o1cRF35GIiKiK0/je0rRp05Rj3aSmpuLRo0fKr5SUFF1kpArs93OxuBSXCiuZCb7s10jfcYiIiDQvbu7du4eJEyfCwsJCF3moEknLLlCOaTOzRz242XEkYiIi0j+Ni5vu3bsjJCREF1mokvnh0A0kZ+XD29ESg1+pqe84REREAMrQ56ZXr16YMWMGwsPD0bhxY5iamqq836dPH62Fo4rr+I2HWH/6DgBgTm9fSE349BwREVUMEiGE0GSF5z0CLpFIIJfLXzqULqWnp8PW1hZpaWmwsbHRd5xKKbdAjg7fHUNCei4GtHDHooFN9R2JiIgMnCbnb42v3Dz96DdVLUIIzN1zDQnpuXC2keGr/uxETEREFctL3UvIzc3VVg6qJLZfuIvfz8UBAOb0bggzU2M9JyIiIlKlcXEjl8vx5Zdfws3NDVZWVrh9u2jwts8++wxr1qzRekCqODLzCvH9gUgAwKTOdfBaY45pQ0REFY/Gxc1XX32F9evXY+HChZBK/xuJtnHjxli9erVWw1HF8uuZGDxIz4OHvTk+6FBL33GIiIhKpXFxs3HjRvz8888YOnQojI3/uyXRpEkTXL9+XavhqOJITM/FssNFY9p81LEOb0cREVGFVaZB/GrXrl2iXaFQoKCgQCuhqOKZvfsqsvLlaOpuizf93PUdh4iI6Jk0Lm4aNmyIEydOlGjftm0bmjdvrpVQVLFciHmEg+EPAABf9W8MIyPJC9YgIiLSH40fBZ8zZw6GDRuGe/fuQaFQYOfOnYiMjMTGjRvx999/6yIj6ZEQAt/vL+pE/EYLNzRys9VzIiIioufT+MpN7969sXXrVuzduxcSiQSff/45IiIisGfPHnTt2lUXGUmPzkWnIPh2MqQmRpjcua6+4xAREb2QxldugKL5pbp3767tLFQBrQyKAgAMaOGOmg6cLJWIiCo+TghEz3T7YSaORj6ERAK8185H33GIiIjUotaVm2rVqkEiUa8TaUpKyksFoopj8cEbAIBO9Zzg7Wip5zRERETqUau4WbJkifLfycnJmD9/Prp3746AgAAAQHBwMPbv34/PPvtMJyGp/IXFpeKfK/EAgCld2deGiIgqD41nBR8wYAA6duyICRMmqLT/73//w6FDh7B7925t5tM6zgr+YgqFQL/lp3D5bhr6NnPFj4P5iD8REemXJudvjfvc7N+/Hz169CjR3r17dxw6dEjTzVEFdDQyEZfvpsFCaoz/6+Wr7zhEREQa0bi4cXBwwK5du0q07969Gw4ODloJRfq1/vQdAMDbLWuiurVMv2GIiIg0pPGj4HPnzsWYMWNw7NgxZZ+bM2fOYN++fZw40wAE3XiIEzeTYGIkwchAL33HISIi0pjGxc3IkSPRoEEDLF26FDt37oQQAr6+vjh16hRatWqli4xUTgrkCnz+51UAwLAAT3jYc1wbIiKqfMo0iF+rVq2wefNmbWchPdsWchcxydmwszDFVD4hRURElZRaxU16erqyZ3J6evpzl+UTSJVTdn4hFh0omkNqQsfasDYz1XMiIiKislF7EL/4+Hg4OTnBzs6u1AH9hBCQSCSQy+VaD0m6t/jADSRn5cPD3hwj2NeGiIgqMbWKmyNHjsDe3h4AcPToUZ0GovIX9TAT6x4/ITW3T0OYGnNWDiIiqrzUKm7at29f6r/JMHz+51XIFQLt6lZHp/rO+o5DRET0UvT+J/ry5cvh7e0NMzMz+Pn54cSJE2qtd+rUKZiYmKBZs2a6DWjgzkWn4NStZJgaS/BFbw7YR0RElZ9ei5utW7di8uTJmD17NkJDQ9G2bVv07NkTsbGxz10vLS0Nw4cPR+fOncspqeH64fHkmANauMOnupWe0xAREb08vRY3ixcvxpgxY/Duu++iQYMGWLJkCTw8PLBixYrnrvf+++9jyJAhykEEqWyu3ktD8O1kGEmADzvW1nccIiIirdBbcZOfn48LFy6gW7duKu3dunXD6dOnn7neunXrEBUVhTlz5ug6okETQigf/e7RqAYH7CMiIoOh8SB+OTk5EELAwqLoZBgTE4Ndu3bB19e3RKHyPElJSZDL5XB2Vu3A6uzsjISEhFLXuXnzJj755BOcOHECJibqRc/Ly0NeXp7y9YvG6akqTtxMwtHIh5AaG3HAPiIiMigaX7np27cvNm7cCABITU1Fq1atsGjRIvTt2/eFt5NK8/SYOcXj5TxNLpdjyJAhmDt3LurWVf9kvGDBAtja2iq/PDw8NM5oaIQQWHEsCgDwdksP1Hay1nMiIiIi7dG4uLl48SLatm0LANi+fTucnZ0RExODjRs3YunSpWpvx9HREcbGxiWu0iQmJpa4mgMAGRkZCAkJwYQJE2BiYgITExPMmzcPly5dgomJCY4cOVLqfmbNmoW0tDTlV1xcnAaf1jCFxaUi+HYyTIwkeLetj77jEBERaZXGt6Wys7NhbV30l/6BAwfwxhtvwMjICK1bt0ZMTIza25FKpfDz88PBgwfRv39/ZfvBgwfRt2/fEsvb2NjgypUrKm3Lly/HkSNHsH37dnh7e5e6H5lMBplMpnauqmDZkVsAgD7NXNnXhoiIDI7GxU3t2rWxe/du9O/fH/v378eUKVMAFF1x0XReqalTp2LYsGHw9/dHQEAAfv75Z8TGxmLcuHEAiq663Lt3Dxs3boSRkREaNWqksr6TkxPMzMxKtNOz3X6YiaORiQCK5pAiIiIyNBoXN59//jmGDBmCKVOmoHPnzsrHsQ8cOIDmzZtrtK1BgwYhOTkZ8+bNQ3x8PBo1aoS9e/fC09MTABAfH//CMW9IM8uPRUEIoHN9J45rQ0REBkkihBCarpSQkID4+Hg0bdoURkZF3XbOnTsHW1tb1KtXT+shtSk9PR22trZIS0urcjOYX7ufhl5LTwIAdo4PRIua1fSciIiISD2anL817lA8evRoWFpaonnz5srCBgAaNmyIb7/9VvO0VG7WnIgGALzexIWFDRERGSyNi5sNGzYgJyenRHtOTo7yEXGqeBLTc/HnpfsAgLF8QoqIiAyY2n1u0tPTIYSAEAIZGRkwMzNTvieXy7F37144OTnpJCS9vO0X70KuEGhe0w5NPez0HYeIiEhn1C5u7OzsIJFIIJFISh1ETyKRYO7cuVoNR9pRKFdgy7mi8X0G+XMQQyIiMmxqFzdHjx6FEAKdOnXCjh07YG9vr3xPKpXC09MTrq6uOglJL+fEzSTEpmTDzsIUfZrx/4iIiAyb2sVN+/btAQDR0dHw8PBQ6UxMFdvms0WP0/dr5gYLqcZP/xMREVUqGp/pPD09kZqainPnziExMREKhULl/eHDh2stHL28hLRcHLn+AADwTmtPPachIiLSPY2Lmz179mDo0KHIysqCtbW1yiSXEomExU0Fs/70HSgE0NLLHrWdOGgfEREZPo3vLU2bNg2jR49GRkYGUlNT8ejRI+VXSkqKLjJSGSVl5mFT8B0AwOhXS597i4iIyNBoXNzcu3cPEydOhIUFJ1ys6NadikZWvhz1a1ijm2/JmdaJiIgMkcbFTffu3RESEqKLLKRF2fmF2BhcNEv75C51YGQkecEaREREhkHjPje9evXCjBkzEB4ejsaNG8PU1FTl/T59+mgtHJXdr2dikJFbCC8HC3TzraHvOEREROVG44kzn/cIuEQigVwuf+lQulQVJs5Mzy1AmwVHkJFXiAVvNMbbLWvqOxIREdFL0eT8rfGVm6cf/aaKZ+/leGTkFcLD3hwDOSIxERFVMS81El9ubq62cpCWCCGw/vQdAMCQlp4wZl8bIiKqYjQubuRyOb788ku4ubnBysoKt2/fBgB89tlnWLNmjdYDkmZORyXjekIGzE2N8XZLXrUhIqKqR+Pi5quvvsL69euxcOFCSKVSZXvjxo2xevVqrYYjzf1yoqjYHODnBjsL6QuWJiIiMjwaFzcbN27Ezz//jKFDh8LY2FjZ3qRJE1y/fl2r4UgzV+6m4VjkQxhJgNFtOGgfERFVTWUaxK927dol2hUKBQoKCrQSisrm1zNF49r0auIKn+qcaoGIiKomjYubhg0b4sSJEyXat23bhubNm2slFGkut0COf67EAwCGcYJMIiKqwjR+FHzOnDkYNmwY7t27B4VCgZ07dyIyMhIbN27E33//rYuMpIa/L8cjM68Qbnbm8Pespu84REREeqPxlZvevXtj69at2Lt3LyQSCT7//HNERERgz5496Nq1qy4y0gvIFQJLD98EALzT2pNTLRARUZWm8ZUboGh+qe7du2s7C5XRvqsJiE3JhrWZCYYF8JYUERFVbS81iB9VDL+fiwUAjAjwgpWsTPUqERGRwVDrTGhvb48bN27A0dER1apVg0Ty7NseKSkpWgtHL5aQlovTUUkAgLf83fWchoiISP/UKm5++OEHWFtbAwCWLFmiyzykoU1n7kAhgJZe9vB0sNR3HCIiIr1Tq7gZMWJEqf8m/SqUK7At5C4AYHgg+9oQEREBahY36enpam/wRdOQk/acuJWExIw82FmYoptvDX3HISIiqhDUKm7s7Oye288GKJqNWiKRQC6XayUYvdjak9EAgP7N3SA1Yd9wIiIiQM3i5ujRo7rOQRq6+ygbp24VdSQeEeCl3zBEREQViFrFTfv27XWdgzS0+WwsFAIIrOUAL0d2JCYiIiqm8b2MdevWYdu2bSXat23bhg0bNmglFD1fwZMdiTloHxERkQqNi5tvvvkGjo6OJdqdnJzw9ddfayUUPd/R64lIyizqSNyhnpO+4xAREVUoGhc3MTEx8Pb2LtHu6emJ2NhYrYSi5/sjJA4A8GYLd5iZGus5DRERUcWicXHj5OSEy5cvl2i/dOkSHBwctBKKni05Mw/HIh8CAAa+4qHnNERERBWPxsXN4MGDMXHiRBw9ehRyuRxyuRxHjhzBpEmTMHjwYF1kpCfsDruPQoVAYzdb1HW21nccIiKiCkfjWRbnz5+PmJgYdO7cGSYmRasrFAoMHz6cfW50TAiBLY8nyeRVGyIiotJpXNxIpVJs3boV8+fPR1hYGMzNzdG4cWN4evKpHV0Li0vFzcRMmJkaoU9TV33HISIiqpA0Lm6K1alTB3Xq1NFmFnqBf68mAAA6N3CGrbmpntMQERFVTByzvxL592o8AKB3Exc9JyEiIqq4WNxUEtFJWYhLyYGJkQRt61TXdxwiIqIKi8VNJXEo/AEAoJWPPSxlZb6bSEREZPBY3FQS+64V9bfp0sBZz0mIiIgqtjIVNydOnMA777yDgIAA3Lt3DwCwadMmnDx5UqvhqEh0UhYuxDyCsZEEPRuxvw0REdHzaFzc7NixA927d4e5uTlCQ0ORl5cHAMjIyOA4Nzqy59J9AECb2o6oYWum5zREREQVm8bFzfz587Fy5Ur88ssvMDX973HkwMBAXLx4UavhqGjgvt1hRVfHXudTUkRERC+kcXETGRmJdu3alWi3sbFBamqqNjLREyLiM3D7YRZkJkbo2aiGvuMQERFVeBoXNy4uLrh161aJ9pMnT8LHx0croeg/e68UjW3Trm51WJtx4D4iIqIX0bi4ef/99zFp0iScPXsWEokE9+/fx+bNmzF9+nSMHz9eFxmrLCEE/nrc34a3pIiIiNSj8YApM2fORFpaGjp27Ijc3Fy0a9cOMpkM06dPx4QJE3SRscq6ei8dsSnZMDc1RldfPgJORESkjjKNBvfVV19h9uzZCA8Ph0KhgK+vL6ysrLSdrcr7R3lLyhEWUg7cR0REpI4ynzEtLCzg7++vzSz0hAK5AtsvxAEA+jd303MaIiKiykOt4uaNN95Qe4M7d+4scxj6z8mbSUjKzIeDpRSdOSoxERGR2tTqUGxra6v8srGxweHDhxESEqJ8/8KFCzh8+DBsbW11FrSq2X7xLgCgd1NXmBpzlgwiIiJ1qXXlZt26dcp/f/zxxxg4cCBWrlwJY2NjAIBcLsf48eNhY2Ojm5RVTF6hHEGRDwEAfZu56jkNERFR5aLxJYG1a9di+vTpysIGAIyNjTF16lSsXbtWq+GqqpM3k5CZVwhnGxmautvpOw4REVGlonFxU1hYiIiIiBLtERERUCgUGgdYvnw5vL29YWZmBj8/P5w4ceKZy+7cuRNdu3ZF9erVYWNjg4CAAOzfv1/jfVZ0/14tmgG8m28NGBlJ9JyGiIioctH4aalRo0Zh9OjRuHXrFlq3bg0AOHPmDL755huMGjVKo21t3boVkydPxvLly9GmTRusWrUKPXv2RHh4OGrWrFli+ePHj6Nr1674+uuvYWdnh3Xr1qF37944e/YsmjdvrulHqZCy8wvx7+NHwHs35S0pIiIiTUmEEEKTFRQKBb7//nv8+OOPiI8vOgm7uLhg0qRJmDZtmsrtqhdp1aoVWrRogRUrVijbGjRogH79+mHBggVqbaNhw4YYNGgQPv/8c7WWT09Ph62tLdLS0ipkH6E/w+5h0pYweNibI2h6R165ISIigmbnb42v3BgZGWHmzJmYOXMm0tPTAaBMRUJ+fj4uXLiATz75RKW9W7duOH36tFrbUCgUyMjIgL29vcb7r6gOXHsAAOjT1JWFDRERURm81LC3L3PlIykpCXK5HM7OqmO4ODs7IyEhQa1tLFq0CFlZWRg4cOAzl8nLy0NeXp7ydXFBVhHlFypw/GbRU1Ic24aIiKhs9D6AikSienVCCFGirTS///47vvjiC2zduhVOTk7PXG7BggUq4/R4eHi8dGZdOXUrCRm5hXC04lNSREREZaW34sbR0RHGxsYlrtIkJiaWuJrztK1bt2LMmDH4448/0KVLl+cuO2vWLKSlpSm/4uLiXjq7rvx7tagP02uNa8CYt6SIiIjKRG/FjVQqhZ+fHw4ePKjSfvDgQQQGBj5zvd9//x0jR47Eb7/9hl69er1wPzKZDDY2NipfFVGBXIF9jx8B79nIRc9piIiIKi+Ni5uNGzeq9GEplp+fj40bN2q0ralTp2L16tVYu3YtIiIiMGXKFMTGxmLcuHEAiq66DB8+XLn877//juHDh2PRokVo3bo1EhISkJCQgLS0NE0/RoVzPjoF6bmFsLeUoqW34XSQJiIiKm8aFzejRo0qtZjIyMjQeJybQYMGYcmSJZg3bx6aNWuG48ePY+/evfD09AQAxMfHIzY2Vrn8qlWrUFhYiA8//BAuLi7Kr0mTJmn6MSqcP8PuAwC6NnDmLSkiIqKXoPE4N0ZGRnjw4AGqV6+u0n7p0iV07NgRKSkpWg2obRVxnJu8Qjn8vjyEzLxCbH2vNVr5OOg7EhERUYWik3FumjdvDolEAolEgs6dO8PE5L9V5XI5oqOj0aNHj7KnrsKCo5KRmVcIJ2sZXvHiLSkiIqKXoXZx069fPwBAWFgYunfvDisrK+V7UqkUXl5eGDBggNYDVgXFHYm7+Dpz4D4iIqKXpHZxM2fOHMjlcnh6eqJ79+5wceETPdqQVyjHvmtFxU2vxjymREREL0ujDsXGxsYYN24ccnNzdZWnyjl7OwWp2QWobi1DKz4lRURE9NI0flqqcePGuH37ti6yVElHIxMBAB3rVYeJsd4HjCYiIqr0ND6bfvXVV5g+fTr+/vtvxMfHIz09XeWL1CeEwKGIookyO9V/9hQSREREpD6NJ84sfiKqT58+KnNAFc8JJZfLtZfOwEU+yEBcSg5kJkZoV7f6i1cgIiKiF9K4uDl69KguclRJJ28mAQACajnAQvpSE7QTERHRYxqfUdu3b6+LHFXSyVuPixsO2kdERKQ1Zb5ckJ2djdjYWOTn56u0N2nS5KVDVQW5BXKcuZ0MAOhQj/1tiIiItEXj4ubhw4cYNWoU/v3331LfZ58b9YTGpiK3QAEnaxnqOlu9eAUiIiJSi8ZPS02ePBmPHj3CmTNnYG5ujn379mHDhg2oU6cO/vrrL11kNEgnbj4EUNTf5smO2URERPRyNL5yc+TIEfz555945ZVXYGRkBE9PT3Tt2hU2NjZYsGABevXqpYucBudYZFFx055PSREREWmVxldusrKy4ORU1EfE3t4eDx8WnaQbN26MixcvajedgXqQnovw+HRIJEDbOixuiIiItEnj4qZevXqIjIwEADRr1gyrVq3CvXv3sHLlSs43pabgqKKOxI1cbVHdWqbnNERERIZF49tSkydPxv379wEUTabZvXt3bN68GVKpFOvXr9d2PoN0NrqouGntw7mkiIiItE3j4mbo0KHKfzdv3hx37tzB9evXUbNmTTg6Omo1nKEKjU0FAPh5srghIiLSNrVvS2VnZ+PDDz+Em5sbnJycMGTIECQlJcHCwgItWrRgYaOmzLxC3HiQAQBoXtNOv2GIiIgMkNrFzZw5c7B+/Xr06tULgwcPxsGDB/HBBx/oMptBunw3FQoBuNmZw9nGTN9xiIiIDI7at6V27tyJNWvWYPDgwQCAd955B23atIFcLoexsbHOAhqaizGPAADNPOz0G4SIiMhAqX3lJi4uDm3btlW+btmyJUxMTJSdi0k95+4UFTctvdnfhoiISBfULm7kcjmkUqlKm4mJCQoLC7UeylDJFUJ55cbfq5qe0xARERkmtW9LCSEwcuRIyGT/jcuSm5uLcePGwdLSUtm2c+dO7SY0INcT0pGZVwgrmQnq17DRdxwiIiKDpHZxM2LEiBJt77zzjlbDGLqLjx8Bb17TDsZGnE+KiIhIF9QubtatW6fLHFVC8S2pFjV5S4qIiEhXNJ5+gcruXHQKAMDPk8UNERGRrrC4KSf3U3NwLzUHxkYSFjdEREQ6xOKmnFyMLbol1cDFGpYyjWe9ICIiIjWxuCknIY/Ht/FjfxsiIiKdYnFTTor727zCwfuIiIh0isVNOUjLKUBEQjoAoKUXixsiIiJdYnFTDkLupEAIwMvBAk6cLJOIiEinWNyUg5O3kgAAAbUc9JyEiIjI8LG4KQfBUckAgLZ1qus5CRERkeFjcaNjadkFiHyQAQB4hf1tiIiIdI7FjY5djH2k7G9T3Vr24hWIiIjopbC40bGQmKJHwP151YaIiKhcsLjRsfOPB+97xYuD9xEREZUHFjc6lFcoR1hcKgBeuSEiIiovLG506MrdNOQXKuBoJYOPo6W+4xAREVUJLG506Nydx1MueFWDRCLRcxoiIqKqgcWNDl0onizTk/1tiIiIyguLGx1RKAQuxBYVN+xvQ0REVH5Y3OjI7aQspGYXQGZiBF8XG33HISIiqjJY3OhIyOP+Nk097CA14WEmIiIqLzzr6kjx+DYteUuKiIioXLG40ZH/RiZmZ2IiIqLyxOJGBx6k5yImORtGEqAFn5QiIiIqVyxudOBiTNEtqXo1bGBjZqrnNERERFULixsdCIkpHt/GTr9BiIiIqiAWNzrwX3HDW1JERETljcWNluXky3HtXhoAwN+TT0oRERGVNxY3WnbpbioKFQLONjK4VzPXdxwiIqIqh8WNlp2PLn4E3J6TZRIREekBixstu1g8nxT72xAREekFixstUigELsamAmBnYiIiIn1hcaNFt5MykZZTADNTIzTgZJlERER6offiZvny5fD29oaZmRn8/Pxw4sSJ5y4fFBQEPz8/mJmZwcfHBytXriynpC9WfNWmibsdTI31fmiJiIiqJL2egbdu3YrJkydj9uzZCA0NRdu2bdGzZ0/ExsaWunx0dDRee+01tG3bFqGhofj0008xceJE7Nixo5yTly70cXHT3MNOrzmIiIiqMokQQuhr561atUKLFi2wYsUKZVuDBg3Qr18/LFiwoMTyH3/8Mf766y9EREQo28aNG4dLly4hODhYrX2mp6fD1tYWaWlpsLHR7q2jnj+eQER8Ola+0wI9GrloddtERERVmSbnb71ducnPz8eFCxfQrVs3lfZu3brh9OnTpa4THBxcYvnu3bsjJCQEBQUFpa6Tl5eH9PR0lS9dyM4vRGRC0babebAzMRERkb7orbhJSkqCXC6Hs7OzSruzszMSEhJKXSchIaHU5QsLC5GUlFTqOgsWLICtra3yy8PDQzsf4Cn3HuXAydoMLrZmqGFrppN9EBER0Yvpvdfr0wPdCSGeO/hdacuX1l5s1qxZSEtLU37FxcW9ZOLS1XG2xplPO2Pf5HY62T4RERGpx0RfO3Z0dISxsXGJqzSJiYklrs4Uq1GjRqnLm5iYwMHBodR1ZDIZZDKZdkKrwdbctNz2RURERCXp7cqNVCqFn58fDh48qNJ+8OBBBAYGlrpOQEBAieUPHDgAf39/mJqyqCAiIiI935aaOnUqVq9ejbVr1yIiIgJTpkxBbGwsxo0bB6DoltLw4cOVy48bNw4xMTGYOnUqIiIisHbtWqxZswbTp0/X10cgIiKiCkZvt6UAYNCgQUhOTsa8efMQHx+PRo0aYe/evfD09AQAxMfHq4x54+3tjb1792LKlCn46aef4OrqiqVLl2LAgAH6+ghERERUweh1nBt90OU4N0RERKQblWKcGyIiIiJdYHFDREREBoXFDRERERkUFjdERERkUFjcEBERkUFhcUNEREQGhcUNERERGRQWN0RERGRQWNwQERGRQdHr9Av6UDwgc3p6up6TEBERkbqKz9vqTKxQ5YqbjIwMAICHh4eekxAREZGmMjIyYGtr+9xlqtzcUgqFAvfv34e1tTUkEolWt52eng4PDw/ExcVx3iod4nEuHzzO5YPHufzwWJcPXR1nIQQyMjLg6uoKI6Pn96qpcldujIyM4O7urtN92NjY8AenHPA4lw8e5/LB41x+eKzLhy6O84uu2BRjh2IiIiIyKCxuiIiIyKCwuNEimUyGOXPmQCaT6TuKQeNxLh88zuWDx7n88FiXj4pwnKtch2IiIiIybLxyQ0RERAaFxQ0REREZFBY3REREZFBY3BAREZFBYXGjoeXLl8Pb2xtmZmbw8/PDiRMnnrt8UFAQ/Pz8YGZmBh8fH6xcubKcklZumhznnTt3omvXrqhevTpsbGwQEBCA/fv3l2PaykvT7+dip06dgomJCZo1a6bbgAZC0+Ocl5eH2bNnw9PTEzKZDLVq1cLatWvLKW3lpelx3rx5M5o2bQoLCwu4uLhg1KhRSE5OLqe0ldPx48fRu3dvuLq6QiKRYPfu3S9cRy/nQUFq27JlizA1NRW//PKLCA8PF5MmTRKWlpYiJiam1OVv374tLCwsxKRJk0R4eLj45ZdfhKmpqdi+fXs5J69cND3OkyZNEt9++604d+6cuHHjhpg1a5YwNTUVFy9eLOfklYumx7lYamqq8PHxEd26dRNNmzYtn7CVWFmOc58+fUSrVq3EwYMHRXR0tDh79qw4depUOaaufDQ9zidOnBBGRkbixx9/FLdv3xYnTpwQDRs2FP369Svn5JXL3r17xezZs8WOHTsEALFr167nLq+v8yCLGw20bNlSjBs3TqWtfv364pNPPil1+ZkzZ4r69eurtL3//vuidevWOstoCDQ9zqXx9fUVc+fO1XY0g1LW4zxo0CDxf//3f2LOnDksbtSg6XH+999/ha2trUhOTi6PeAZD0+P83XffCR8fH5W2pUuXCnd3d51lNDTqFDf6Og/ytpSa8vPzceHCBXTr1k2lvVu3bjh9+nSp6wQHB5dYvnv37ggJCUFBQYHOslZmZTnOT1MoFMjIyIC9vb0uIhqEsh7ndevWISoqCnPmzNF1RINQluP8119/wd/fHwsXLoSbmxvq1q2L6dOnIycnpzwiV0plOc6BgYG4e/cu9u7dCyEEHjx4gO3bt6NXr17lEbnK0Nd5sMpNnFlWSUlJkMvlcHZ2Vml3dnZGQkJCqeskJCSUunxhYSGSkpLg4uKis7yVVVmO89MWLVqErKwsDBw4UBcRDUJZjvPNmzfxySef4MSJEzAx4a8OdZTlON++fRsnT56EmZkZdu3ahaSkJIwfPx4pKSnsd/MMZTnOgYGB2Lx5MwYNGoTc3FwUFhaiT58+WLZsWXlErjL0dR7klRsNSSQSlddCiBJtL1q+tHZSpelxLvb777/jiy++wNatW+Hk5KSreAZD3eMsl8sxZMgQzJ07F3Xr1i2veAZDk+9nhUIBiUSCzZs3o2XLlnjttdewePFirF+/nldvXkCT4xweHo6JEyfi888/x4ULF7Bv3z5ER0dj3Lhx5RG1StHHeZB/fqnJ0dERxsbGJf4KSExMLFGVFqtRo0apy5uYmMDBwUFnWSuzshznYlu3bsWYMWOwbds2dOnSRZcxKz1Nj3NGRgZCQkIQGhqKCRMmACg6CQshYGJiggMHDqBTp07lkr0yKcv3s4uLC9zc3GBra6tsa9CgAYQQuHv3LurUqaPTzJVRWY7zggUL0KZNG8yYMQMA0KRJE1haWqJt27aYP38+r6xrib7Og7xyoyapVAo/Pz8cPHhQpf3gwYMIDAwsdZ2AgIASyx84cAD+/v4wNTXVWdbKrCzHGSi6YjNy5Ej89ttvvGeuBk2Ps42NDa5cuYKwsDDl17hx41CvXj2EhYWhVatW5RW9UinL93ObNm1w//59ZGZmKttu3LgBIyMjuLu76zRvZVWW45ydnQ0jI9VToLGxMYD/rizQy9PbeVCn3ZUNTPGjhmvWrBHh4eFi8uTJwtLSUty5c0cIIcQnn3wihg0bply++BG4KVOmiPDwcLFmzRo+Cq4GTY/zb7/9JkxMTMRPP/0k4uPjlV+pqan6+giVgqbH+Wl8Wko9mh7njIwM4e7uLt58801x7do1ERQUJOrUqSPeffddfX2ESkHT47xu3TphYmIili9fLqKiosTJkyeFv7+/aNmypb4+QqWQkZEhQkNDRWhoqAAgFi9eLEJDQ5WP3FeU8yCLGw399NNPwtPTU0ilUtGiRQsRFBSkfG/EiBGiffv2KssfO3ZMNG/eXEilUuHl5SVWrFhRzokrJ02Oc/v27QWAEl8jRowo/+CVjKbfz09icaM+TY9zRESE6NKlizA3Nxfu7u5i6tSpIjs7u5xTVz6aHuelS5cKX19fYW5uLlxcXMTQoUPF3bt3yzl15XL06NHn/r6tKOdBiRC8/kZERESGg31uiIiIyKCwuCEiIiKDwuKGiIiIDAqLGyIiIjIoLG6IiIjIoLC4ISIiIoPC4oaISAOpqamYO3cu4uPj9R3FIKSlpWHevHm4f/++vqOQAWFxQ6QjI0eORL9+/XS2/WPHjkEikSA1NRUAsH79etjZ2elsf2X1dM7SlGf2Dh06YPLkycrX2dnZGDBgAGxsbJQ5vby8sGTJklLXHzlyJHJycjj3UBlJJBLs3r1b+drW1hbW1tYYPHgwCgsLSyyv658jMkwsbsggjBw5EhKJBBKJBKampvDx8cH06dORlZX1Utv94osv0KxZM+2E1LFBgwbhxo0bWt2mOoXJiwQGBiI+Pl5lIsjy8KzsO3fuxJdffql8vWHDBpw4cQKnT59W5jx//jzee++9EttctGgRrKyssGDBAl3Hr1KmTJkCf39/fPrpp/qOQgaCs4KTwejRowfWrVuHgoICnDhxAu+++y6ysrKwYsUKjbclhIBcLtdBSt0xNzeHubm5vmOUIJVKUaNGDX3HULK3t1d5HRUVhQYNGqBRo0bKturVq5e67rRp03SarSpbvHixviOQAeGVGzIYMpkMNWrUgIeHB4YMGYKhQ4cqL3//+uuv8Pf3h7W1NWrUqIEhQ4YgMTFRuW7xX/n79++Hv78/ZDIZNm3ahLlz5+LSpUvKq0Lr168vdd9yuRxTp06FnZ0dHBwcMHPmzBIzCwshsHDhQvj4+MDc3BxNmzbF9u3bn/uZ8vLyMHPmTHh4eEAmk6FOnTpYs2ZNqcuWdmtnz5498PPzg5mZGXx8fDB37lyVS/8SiQSrV69G//79YWFhgTp16uCvv/4CANy5cwcdO3YEAFSrVg0SiQQjR44EAGzfvh2NGzeGubk5HBwc0KVLl2deJSvtCsr69etRs2ZNWFhYoH///khOTi6xnq6yP3lbqkOHDli0aBGOHz8OiUSCDh06AECJ21JpaWl477334OTkBBsbG3Tq1AmXLl1Svn/p0iV07NgR1tbWsLGxgZ+fH0JCQko9HkDRFcGaNWtCJpPB1dUVEydOVL6n7vfq4cOH4e/vDwsLCwQGBiIyMrLMeSQSCVatWoXXX38dFhYWaNCgAYKDg3Hr1i106NABlpaWCAgIQFRUlMp6K1asQK1atSCVSlGvXj1s2rRJ5f2bN2+iXbt2MDMzg6+vb4nZoQHg3r17GDRoEKpVqwZ7e3u8/vrruHXr1jOzluXniKognc9eRVQORowYIfr27avS9tFHHwkHBwchhBBr1qwRe/fuFVFRUSI4OFi0bt1a9OzZU7ls8WRwTZo0EQcOHBC3bt0Sd+/eFdOmTRMNGzZUzjT+rMkLv/32W2Frayu2b98uwsPDxZgxY4S1tbVKpk8//VTUr19f7Nu3T0RFRYl169YJmUwmjh079szPNXDgQOHh4SF27twpoqKixKFDh8SWLVtUMj969EgIUTTLsa2trXLdffv2CRsbG7F+/XoRFRUlDhw4ILy8vMQXX3yhXAaAcHd3F7/99pu4efOmmDhxorCyshLJycmisLBQ7NixQwAQkZGRypnW79+/L0xMTMTixYtFdHS0uHz5svjpp59ERkZGqZ/h6ZxnzpwREolELFiwQERGRooff/xR2NnZlUt2IYomWp00aZIQQojk5GQxduxYERAQIOLj40VycrIQQghPT0/xww8/CCGEUCgUok2bNqJ3797i/Pnz4saNG2LatGnCwcFBuXzDhg3FO++8IyIiIsSNGzfEH3/8IcLCwko9Htu2bRM2NjZi7969IiYmRpw9e1b8/PPPyvfV/V5t1aqVOHbsmLh27Zpo27atCAwMVC6jSZ7iY+nm5ia2bt0qIiMjRb9+/YSXl5fo1KmT2LdvnwgPDxetW7cWPXr0UK6zc+dOYWpqKn766ScRGRkpFi1aJIyNjcWRI0eEEELI5XLRqFEj0aFDBxEaGiqCgoJE8+bNBQCxa9cuIYQQWVlZok6dOuK9994TV65cEeHh4WLYsGHCx8dH5OTkCCFK/myX5eeIqh4WN2QQnv4FePbsWeHg4CAGDhxY6vLnzp0TAJQn5OITxu7du1WWU3fmaxcXF/HNN98oXxcUFAh3d3dlpszMTGFmZiZOnz6tst6YMWPE22+/Xeo2IyMjBQBx8ODBUt9/UXHTtm1b8fXXX6uss2nTJuHi4qJ8DUD83//9n/J1ZmamkEgk4t9//y11H0IIceHCBQFA3Llzp/SD8YKcb7/9tspJUgghBg0aVC7ZhVAtboQQYtKkSSVmMX6yuDl8+LCwsbERubm5KsvUqlVLrFq1SgghhLW1tVi/fv1zj0OxRYsWibp164r8/Hy1ln/W9+qhQ4eUy/zzzz8CgLIg0CSPECWPZXBwsAAg1qxZo2z7/fffhZmZmfJ1YGCgGDt2rMp23nrrLfHaa68JIYTYv3+/MDY2FnFxccr3//33X5XiZs2aNcLX11dlG/n5+cLS0lL8888/QgjVn+2y/BxR1cTbUmQw/v77b1hZWcHMzAwBAQFo164dli1bBgAIDQ1F37594enpCWtra+Xth9jYWJVt+Pv7a7zftLQ0xMfHIyAgQNlmYmKisq3w8HDk5uaia9eusLKyUn5t3LixxKX+YmFhYTA2Nkb79u01zgQAFy5cwLx581T2N3bsWMTHxyM7O1u5XJMmTZT/trS0hLW1tcptkKc1bdoUnTt3RuPGjfHWW2/hl19+waNHj9TOFRERoXKsAJR4ravsZXHhwgVkZmbCwcFBJU90dLTy/27q1Kl499130aVLF3zzzTfP/D8FgLfeegs5OTnw8fHB2LFjsWvXLpXbbep+rz752Yuf3Cr+7JrkKW17zs7OAIDGjRurtOXm5iI9PR1A0f9jmzZtVLbRpk0bREREKN+vWbMm3N3dle+X9v8cHh6uvO0rkUgglUqRlZWF27dvl8hYlp8jqprYoZgMRseOHbFixQqYmprC1dUVpqamAICsrCx069YN3bp1w6+//orq1asjNjYW3bt3R35+vso2LC0tdZJNoVAAAP755x+4ubmpvCeTyUpd52U7BysUCsydOxdvvPFGiffMzMyU/y4+TsUkEokyb2mMjY1x8OBBnD59GgcOHMCyZcswe/ZsnD17Ft7e3i/MJZ7qi1Se2ctCoVDAxcUFx44dK/FecR+nL774AkOGDME///yDf//9F3PmzMGWLVvQv3//Eut4eHggMjISBw8exKFDhzB+/Hh89913CAoKQn5+vtrfq09+dolEosyqaZ7nbe95+3iyrZgQQtlW2v/z08srFAq0a9cOQUFBz8z19PKAZj9HVDWxuCGDYWlpidq1a5dov379OpKSkvDNN9/Aw8MDAJ7bufJJUqn0hU9N2drawsXFBWfOnEG7du0AAIWFhbhw4QJatGgBAPD19YVMJkNsbKzaV2IaN24MhUKBoKAgdOnSRa11ntSiRQtERkaWekzUJZVKAaDEMZBIJGjTpg3atGmDzz//HJ6enti1axemTp36wm36+vrizJkzKm1Pv9Zldk21aNECCQkJMDExgZeX1zOXq1u3LurWrYspU6bg7bffxrp1655ZTJibm6NPnz7o06cPPvzwQ9SvXx9XrlyBEKLM36svk6csGjRogJMnT2L48OHKttOnT6NBgwYAiv6fY2Njcf/+fbi6ugIAgoODVbbRokUL/Pbbb0hJSSnxFFtpyvJzRFUTixsyeDVr1oRUKsWyZcswbtw4XL16VWWck+fx8vJCdHQ0wsLC4O7uDmtr61L/Qpw0aRK++eYb1KlTBw0aNMDixYtVng6ytrbG9OnTMWXKFCgUCrz66qtIT0/H6dOnYWVlhREjRpS67xEjRmD06NFYunQpmjZtipiYGCQmJmLgwIEvzP7555/j9ddfh4eHB9566y0YGRnh8uXLuHLlCubPn6/W5/f09IREIsHff/+N1157Debm5rh27RoOHz6Mbt26wcnJCWfPnsXDhw+VJ7UXmThxIgIDA7Fw4UL069cPBw4cwL59+8olu5WVlVrrPqlLly4ICAhAv3798O2336JevXq4f/8+9u7di379+qFhw4aYMWMG3nzzTXh7e+Pu3bs4f/48BgwYUOr21q9fD7lcjlatWsHCwgKbNm2Cubk5PD09oVAoyvy9WiwnJ0ejPGU1Y8YMDBw4EC1atEDnzp2xZ88e7Ny5E4cOHQJQdNzq1auH4cOHY9GiRUhPT8fs2bNVtjF06FB899136NOnD7766ivUrFkTMTEx2LJlCz799FPUrFlTZfmy/BxRFaXfLj9E2lHa01JP+u2334SXl5eQyWQiICBA/PXXXwKACA0NFUI8u/Npbm6uGDBggLCzsxMAxLp160rdfkFBgZg0aZKwsbERdnZ2YurUqWL48OEqmRQKhfjxxx9FvXr1hKmpqahevbro3r27CAoKembunJwcMWXKFOHi4iKkUqmoXbu2WLt2bamZn+5QLETRU0eBgYHC3Nxc2NjYiJYtW6o8mYMnOncWs7W1Vfmc8+bNEzVq1BASiUSMGDFChIeHi+7du4vq1asLmUwm6tatK5YtW/bMz1DasV2zZo1wd3cX5ubmonfv3uL7778vl+xCaN6hWAgh0tPTxUcffSRcXV2Fqamp8PDwEEOHDhWxsbEiLy9PDB48WHh4eAipVCpcXV3FhAkTlJ17n7Zr1y7RqlUrYWNjIywtLUXr1q1VOgeX5Xs1NDRUABDR0dEa5yntWEZHR6vs81n7Xb58ufDx8RGmpqaibt26YuPGjSrbjYyMFK+++qqQSqWibt26Yt++fSX2FR8fL4YPHy4cHR2FTCYTPj4+YuzYsSItLU0IUfJnuyw/R1T1SIRQ4wY4ERERUSXBp6WIiIjIoLC4ISIiIoPC4oaIiIgMCosbIiIiMigsboiIiMigsLghIiIig8LihoiIiAwKixsiIiIyKCxuiIiIyKCwuCEiIiKDwuKGiIiIDAqLGyIiIjIo/w9nz8s77zJ1nAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# utilisation d'une métrique plus adaptée aux modèles de marketing : courbe de lift\n", + "\n", + "# Tri des prédictions de probabilités et des vraies valeurs\n", + "sorted_indices = np.argsort(y_pred_prob)[::-1]\n", + "y_pred_prob_sorted = y_pred_prob[sorted_indices]\n", + "y_test_sorted = y_test.iloc[sorted_indices]\n", + "\n", + "# Calcul du gain cumulatif\n", + "cumulative_gain = np.cumsum(y_test_sorted) / np.sum(y_test_sorted)\n", + "\n", + "# Tracé de la courbe de lift\n", + "plt.plot(np.linspace(0, 1, len(cumulative_gain)), cumulative_gain, label='Courbe de lift')\n", + "plt.xlabel('Part de clients identifiés sans modèle ')\n", + "plt.ylabel('Part de clients identifiés avec modèle')\n", + "plt.title('Courbe de Lift')\n", + "plt.legend()\n", + "plt.show()" + ] } ], "metadata": { From 5408ce677b52dd13d9bd4b5c9438a5bd83ea8a05 Mon Sep 17 00:00:00 2001 From: arevelle-ensae Date: Mon, 18 Mar 2024 09:10:28 +0000 Subject: [PATCH 11/15] add calibration curve --- Sport/Modelization/2_Modelization_sport.ipynb | 30 +++++++++++++++++-- 1 file changed, 27 insertions(+), 3 deletions(-) diff --git a/Sport/Modelization/2_Modelization_sport.ipynb b/Sport/Modelization/2_Modelization_sport.ipynb index b3ff399..a531471 100644 --- a/Sport/Modelization/2_Modelization_sport.ipynb +++ b/Sport/Modelization/2_Modelization_sport.ipynb @@ -324,10 +324,24 @@ " plt.plot(fpr, tpr, label=\"ROC curve(area = %0.3f)\" % roc_auc)\n", " plt.plot([0, 1], [0, 1], color=\"red\",label=\"Random Baseline\", linestyle=\"--\")\n", " plt.grid(color='gray', linestyle='--', linewidth=0.5)\n", - " plt.xlabel('Taux de faux positifs (FPR)')\n", - " plt.ylabel('Taux de vrais positifs (TPR)')\n", - " plt.title('Courbe ROC : modèle logistique')\n", + " plt.xlabel(\"False Positive Rate\")\n", + " plt.ylabel(\"True Positive Rate\")\n", + " plt.title(\"ROC Curve\", size=18)\n", " plt.legend(loc=\"lower right\")\n", + " plt.show()\n", + "\n", + "\n", + "def draw_calibration_curve(X_test, y_test):\n", + " y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n", + " frac_pos, mean_pred = calibration_curve(y_test, y_probs_bs, n_bins=10)\n", + "\n", + " # Plot the calibration curve\n", + " plt.plot(mean_pred, frac_pos, 's-', label='Logistic Regression')\n", + " plt.plot([0, 1], [0, 1], 'k--', label='Perfectly calibrated')\n", + " plt.xlabel('Mean predicted value')\n", + " plt.ylabel('Fraction of positive predictions')\n", + " plt.title(\"Calibration Curve\")\n", + " plt.legend()\n", " plt.show()" ] }, @@ -1552,6 +1566,16 @@ "draw_prob_distribution(X_test)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7ee0972-79ac-481e-a370-d71b085a3c27", + "metadata": {}, + "outputs": [], + "source": [ + "draw_calibration_curve(X_test, y_test)" + ] + }, { "cell_type": "markdown", "id": "ae8e9bd3-0f6a-4f82-bb4c-470cbdc8d6bb", From 9a0ac320d0b424e9c40f28ba09e77040b1be88b9 Mon Sep 17 00:00:00 2001 From: arevelle-ensae Date: Mon, 18 Mar 2024 09:35:48 +0000 Subject: [PATCH 12/15] add benchmark random forest --- Sport/Modelization/2_Modelization_sport.ipynb | 740 ++++++++++++++++-- 1 file changed, 695 insertions(+), 45 deletions(-) diff --git a/Sport/Modelization/2_Modelization_sport.ipynb b/Sport/Modelization/2_Modelization_sport.ipynb index a531471..f653877 100644 --- a/Sport/Modelization/2_Modelization_sport.ipynb +++ b/Sport/Modelization/2_Modelization_sport.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 24, "id": "f271eb45-1470-4764-8c2e-31374efa1fe5", "metadata": {}, "outputs": [], @@ -27,6 +27,7 @@ "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.compose import ColumnTransformer\n", + "from sklearn.calibration import calibration_curve\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.model_selection import GridSearchCV\n", @@ -259,7 +260,7 @@ "outputs": [], "source": [ "numeric_features = ['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', \n", - " 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', #'is_partner',\n", + " 'time_between_purchase', 'nb_tickets_internet', 'is_email_true', 'opt_in', #'is_partner',\n", " 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']\n", "\n", "numeric_transformer = Pipeline(steps=[\n", @@ -297,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 26, "id": "4f9b2bbf-5f8a-4ac1-8e6c-51bd0dd8ac85", "metadata": {}, "outputs": [], @@ -333,7 +334,7 @@ "\n", "def draw_calibration_curve(X_test, y_test):\n", " y_pred_prob = pipeline.predict_proba(X_test)[:, 1]\n", - " frac_pos, mean_pred = calibration_curve(y_test, y_probs_bs, n_bins=10)\n", + " frac_pos, mean_pred = calibration_curve(y_test, y_pred_prob, n_bins=10)\n", "\n", " # Plot the calibration curve\n", " plt.plot(mean_pred, frac_pos, 's-', label='Logistic Regression')\n", @@ -347,21 +348,25 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 35, "id": "cf400c70-0192-42cc-9919-f61bae8382b0", "metadata": {}, "outputs": [], "source": [ - "def draw_features_importance(pipeline, model):\n", - " coefficients = pipeline.named_steps['logreg'].coef_[0]\n", - " feature_names = pipeline.named_steps['logreg'].feature_names_in_\n", + "def draw_features_importance(pipeline, model, randomF = False):\n", + " if randomF:\n", + " coefficients = pipeline.named_steps[model].feature_importances_\n", + " else: \n", + " coefficients = pipeline.named_steps[model].coef_[0]\n", + " \n", + " feature_names = pipeline.named_steps[model].feature_names_in_\n", " \n", " # Tracer l'importance des caractéristiques\n", " plt.figure(figsize=(10, 6))\n", " plt.barh(feature_names, coefficients, color='skyblue')\n", - " plt.xlabel('Importance des caractéristiques')\n", + " plt.xlabel(\"Features' Importance\")\n", " plt.ylabel('Caractéristiques')\n", - " plt.title('Importance des caractéristiques dans le modèle de régression logistique')\n", + " plt.title(\"Features' Importance\")\n", " plt.grid(True)\n", " plt.show()\n", "\n", @@ -818,8 +823,8 @@ " 'purchase_date_max',\n", " 'time_between_purchase',\n", " 'nb_tickets_internet',\n", - " 'fidelity', 'is_email_true',\n", - " 'opt_in', 'gender_female',\n", + " 'is_email_true', 'opt_in',\n", + " 'gender_female',\n", " 'gender_male',\n", " 'gender_other',\n", " 'nb_campaigns',\n", @@ -844,8 +849,8 @@ " 'purchase_date_max',\n", " 'time_between_purchase',\n", " 'nb_tickets_internet',\n", - " 'fidelity', 'is_email_true',\n", - " 'opt_in', 'gender_female',\n", + " 'is_email_true', 'opt_in',\n", + " 'gender_female',\n", " 'gender_male',\n", " 'gender_other',\n", " 'nb_campaigns',\n", @@ -864,15 +869,15 @@ " 'nb_suppliers', 'vente_internet_max',\n", " 'purchase_date_min', 'purchase_date_max',\n", " 'time_between_purchase',\n", - " 'nb_tickets_internet', 'fidelity',\n", - " 'is_email_true', 'opt_in', 'gender_female',\n", - " 'gender_male', 'gender_other', 'nb_campaigns',\n", + " 'nb_tickets_internet', 'is_email_true',\n", + " 'opt_in', 'gender_female', 'gender_male',\n", + " 'gender_other', 'nb_campaigns',\n", " 'nb_campaigns_opened']),\n", " ('cat',\n", " Pipeline(steps=[('onehot',\n", " OneHotEncoder(handle_unknown='ignore',\n", " sparse_output=False))]),\n", - " ['opt_in'])]) 
['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']
StandardScaler()
['opt_in']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
+       "                                 ['opt_in'])])
['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet', 'is_email_true', 'opt_in', 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']
StandardScaler()
['opt_in']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
        "                                 1.0: 3.486549107420539},\n",
        "                   max_iter=5000, solver='saga')
" ], @@ -889,8 +894,8 @@ " 'purchase_date_max',\n", " 'time_between_purchase',\n", " 'nb_tickets_internet',\n", - " 'fidelity', 'is_email_true',\n", - " 'opt_in', 'gender_female',\n", + " 'is_email_true', 'opt_in',\n", + " 'gender_female',\n", " 'gender_male',\n", " 'gender_other',\n", " 'nb_campaigns',\n", @@ -916,7 +921,7 @@ "pipeline = Pipeline(steps=[\n", " ('preprocessor', preproc),\n", " ('logreg', LogisticRegression(solver='saga', class_weight = weight_dict,\n", - " max_iter=5000)) \n", + " max_iter=5000, n_jobs=-1)) \n", "])\n", "\n", "pipeline.set_output(transform=\"pandas\")" @@ -1355,8 +1360,8 @@ " 'purchase_date_max',\n", " 'time_between_purchase',\n", " 'nb_tickets_internet',\n", - " 'fidelity', 'is_email_true',\n", - " 'opt_in', 'gender_female',\n", + " 'is_email_true', 'opt_in',\n", + " 'gender_female',\n", " 'gender_male',\n", " 'gender_other',\n", " 'nb_campaigns',\n", @@ -1381,8 +1386,8 @@ " 'purchase_date_max',\n", " 'time_between_purchase',\n", " 'nb_tickets_internet',\n", - " 'fidelity', 'is_email_true',\n", - " 'opt_in', 'gender_female',\n", + " 'is_email_true', 'opt_in',\n", + " 'gender_female',\n", " 'gender_male',\n", " 'gender_other',\n", " 'nb_campaigns',\n", @@ -1401,15 +1406,15 @@ " 'nb_suppliers', 'vente_internet_max',\n", " 'purchase_date_min', 'purchase_date_max',\n", " 'time_between_purchase',\n", - " 'nb_tickets_internet', 'fidelity',\n", - " 'is_email_true', 'opt_in', 'gender_female',\n", - " 'gender_male', 'gender_other', 'nb_campaigns',\n", + " 'nb_tickets_internet', 'is_email_true',\n", + " 'opt_in', 'gender_female', 'gender_male',\n", + " 'gender_other', 'nb_campaigns',\n", " 'nb_campaigns_opened']),\n", " ('cat',\n", " Pipeline(steps=[('onehot',\n", " OneHotEncoder(handle_unknown='ignore',\n", " sparse_output=False))]),\n", - " ['opt_in'])]) 
['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet', 'fidelity', 'is_email_true', 'opt_in', 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']
StandardScaler()
['opt_in']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
+       "                                 ['opt_in'])])
['nb_tickets', 'nb_purchases', 'total_amount', 'nb_suppliers', 'vente_internet_max', 'purchase_date_min', 'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet', 'is_email_true', 'opt_in', 'gender_female', 'gender_male', 'gender_other', 'nb_campaigns', 'nb_campaigns_opened']
StandardScaler()
['opt_in']
OneHotEncoder(handle_unknown='ignore', sparse_output=False)
LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
        "                                 1.0: 3.486549107420539},\n",
        "                   max_iter=5000, solver='saga')
" ], @@ -1426,8 +1431,8 @@ " 'purchase_date_max',\n", " 'time_between_purchase',\n", " 'nb_tickets_internet',\n", - " 'fidelity', 'is_email_true',\n", - " 'opt_in', 'gender_female',\n", + " 'is_email_true', 'opt_in',\n", + " 'gender_female',\n", " 'gender_male',\n", " 'gender_other',\n", " 'nb_campaigns',\n", @@ -1462,9 +1467,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy Score: 0.7829358141858141\n", - "F1 Score: 0.5016842256145632\n", - "Recall Score: 0.7669831994156319\n" + "Accuracy Score: 0.764547952047952\n", + "F1 Score: 0.4741074748977315\n", + "Recall Score: 0.7449963476990504\n" ] } ], @@ -1490,7 +1495,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1505,13 +1510,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "id": "580b58d7-596f-4207-8c99-4365aba2bc9f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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Em3IhYqVMiFj5eyaK0/PQotJ+bNy4cXaXIOJTlAkRK2VCxJtyIWKlTIhYOSkTagiJiIiIiIiIiDiMGkJ+rFmzZnaXIOJTlAkRK2VCxJtyIWKlTIhYOSkTagj5sdjYWLtLEPEpyoSIlTIh4k25ELFSJkSsnJQJNYT82KJFi+wuQcSnKBMiVsqEiDflQsRKmRCxclIm1BASEREREREREXEYbTvvx1JSUkhISLC7DBGfoUyIWCkTIt6UCxErZULEyt8zoW3nHWLdunV2lyDiU5QJEStlQsSbciFipUyIWDkpE2oI+bE9e/bYXYKIT1EmRKyUCRFvyoWIlTIhYuWkTKgh5MciIiLsLkHEpygTIlbKhIg35ULESpkQsXJSJrSGkIiIiIiIiIhIOaA1hBxizJgxdpcg4lOUCRErZULEm3IhYqVMiFg5KRNqCImIiIiIiIiIOIwaQn6sUaNGdpcg4lOUCRErZULEm3IhYqVMiFg5KRNqCPmxypUr212CiE9RJkSslAkRb8qFiJUyIWLlpEyoIeTH5s+fb3cJIj5FmRCxUiZEvCkXIlbKhIiVkzKhhpCIiIiIiIiIiMNo23k/duDAAapUqWJ3GSI+Q5kQsVImRLwpFyJWyoSIlb9nQtvOO8TWrVvtLkHEpygTIlbKhIg35ULESpkQsXJSJtQQ8mM7d+60uwQRn6JMiFgpEyLelAsRK2VCxMpJmVBDyI+FhITYXYKIT1EmRKyUCRFvyoWIlTIhYuWkTNi6htDcuXN57bXXWLZsGfv27WPSpEn079//rM/57bffGDZsGGvXrqV69eo8+uijDBkypMjvWZ7WEBIREREREREROc5v1hDKzMykVatWvPvuu0V6/Pbt2+nbty8XX3wxK1as4Mknn+TBBx9kwoQJpVypbxo3bpzdJYj4FGVCxEqZEPGmXIhYKRMiVk7KRJCdb96nTx/69OlT5Md/8MEH1KpVi7feeguApk2bsnTpUl5//XWuu+66UqrSdxUUFNhdgohPUSZErJQJEW/KhYiVMiHF4XYb5BW6OX6dkYFx0u1jfxrGSbex3FnUxxsYJz3nxH3HH3nyaxy/6Onka58sr+N57on3O/HcY9+XYbA/NYes3Hz+TIYr8wqICLG1XVIm/Oo7XLRoEb1797aMXX755Xz66afk5+cTHBzs9Zzc3Fxyc3M9x2lpaaVeZ1mpV6+e3SWI+BRlQsRKmRDxplyIWJWnTBS6DY5k5bFubxrpOQW4T2o0wIlGAJaxk25ztsaC9+tYXu2UBsSZn1u0x3Ga98grcHuaMYZheJochgH5hW4OpecSFOg6qdFh4HafaMIYmA2dPUeyyS0o9Pq7AEjJzCMztwC3YT7WbRgUGubruA2DArdtK86UqgpZqQyd/y2hBXl80/dfDMnIIyLer9ol58SvvsP9+/dTpUoVy1iVKlUoKCggOTmZatWqeT1n9OjRjBo1ymt83LhxREREMGDAAH755RdSU1OpXLkyHTt2ZOrUqQC0bdsWt9vNn3/+CUC/fv2YP38+KSkpxMfH061bNyZPngxAy5YtCQ4OZtmyZQBceeWVLF26lAMHDhATE0Pv3r0ZP348AM2bNycqKoo//vgDMJtaa9asISkpicjISK666irGjh0LQOPGjalYsSILFiwA4LLLLmPTpk3s2rULt9tNp06dGDt2LG63m/r165OYmMjcuXMB6N69O7t27WLbtm0EBQUxcOBAJkyYQF5eHrVr16Z+/frMnj0bgK5du3Lw4EE2bdoEwODBg5kyZQpZWVnUqFGDZs2aMXPmTAA6d+5Mamoq69atA2DgwIH89NNPpKenU7VqVdq2bcv06dMB6NChAzk5OaxevRqAa6+9ljlz5nDkyBEqVqxI586d+eGHHwBo06YNACtWrADg6quvZtGiRSQnJ1OhQgW6d+/OpEmTALjgggsICwtjyZIlAPTt25fly5ezf/9+oqOjueKKKzxT/Zo1a0ZsbCyLFi0CoHfv3qxbt449e/YQERFBv379GDNmDACNGjWicuXKzJ8/H4BLL72UrVu3snPnTkJCQrjuuusYN24cBQUF1KtXj1q1ajFnzhwAunXrRlJSElu3biUgIIBBgwYxceJEcnNzqVWrFo0aNeLnn38GoEuXLiQnJ7Nx40YABg0axNSpU8nMzCQxMZEWLVowY8YMADp16kRGRgZr164F4Prrr2fmzJmkpaVRpUoV2rdvz7Rp0wBo164d+fn5rFq1CoD+/fszd+5cDh8+TEJCAl27dmXKlCkAtG7dmoCAAJYvXw7AVVddxeLFizl48CCxsbH07NmTiRMnAtCiRQsiIiJYvHgxYM7uW7lyJXv37iUqKoq+ffvy/fffA9CkSRPi4+NZuHAhAL169WLDhg3s3r2b8PBw+vfvz3fffYdhGDRs2JCqVasyb948AHr06MGOHTvYvn07wcHBXH/99YwfP578/Hzq1q1LnTp1+PXXXwG4+OKL2b9/P5s3b8blcnHjjTeydetWtm3bRs2aNWnSpAmzZs0C4KKLLuLw4cNs2LABgBtuuIHp06eTkZFB9erVadWqFT/++CMAHTt2JCsrizVr1gD49WdEaGgoAwYM0GcEzv2MiIyMJC0tTZ8Rxz4jJk+eTHZ2tj4jHP4ZkZOTQ2RkpD4j9HNEuf2MaNy0GUcLQ1ixchUFbhct23Vg67btJCcnExYWRoeOHZk7dx4GUCMxEXAx9YNxx96nFXv3JnEoOZmgoGA6derE/AULMAyDKlWqUiG+AuvXrcc4lo3k5BQOHDxAQEAgbdp34I8lyygoKKBCfDwVK1Ziw8ZNGEDdunVJT8/g4KFDGECrVq1Zu24tuXn5hETEEFMhnm07dpGe78IVHkNBQSGZ2dlgQFyFChxNTaWw0E1QUBCh4eGkp6djGBAWFk5GXiEZuQUUuMGNC/F/J/4rGrgAV0AAhtsNQECACxcu3Ib5XzsgIMBs0BkGuCAoMIiCwgLPfS6XC3eh2fwKCgrEcBu43W5cLswJJTlZ3LR0GvfN/46Y3EzcLheTuvcn5dABDmxL8cvPiKysrKL/Xdu5qPTJXC7XXy4q3ahRI+644w6eeOIJz9iCBQvo2rUr+/bto2rVql7POd0MoZo1a5aLRaXHjBnD4MGD7S5DxGcoEyJWyoSIN+VCyhvDMNiXmsOqPams2nOUMYt3cSQr3+6ybJUYF05ihXACXSdaC8dvnjSE61jrwXWaPpLr5Oee8hrWsbM/jtO8x+lf7zSPc1nvCwxwERoUgMsFAS7Xsftdx44h0OUiKiyIkMBAXC7zfVyuEzWaYy4qRARTKTrU+5vGfN1K0aGEBQcSGOAi8Nj7BAa4CAwwb0eEBHle+3h9p/4dHn8v6/dx4u/s1L8D1+n+I5Qmw4D/+z8YPhy2bDHHWreGN95gzP79fn2eKM6i0n41Q6hq1ars37/fMnbw4EGCgoJISEg47XNCQ0MJDT39/+wiIiIiIiK+wjAMcgvcFLgNCgsN8t1uCt0G+YXH/zQocLvZmZLF2r1prN5zlNVJqSRn5Hm9VuXoUIIDAwgJCjj2j3tz/Pg/3s1egouUlBQqJiSc1EA49o94TvpH/Un/oD8+dnKD5fhxaFAgQQEuAgJOjB1vXAQcawK4XC4CXHjGIkODCAkKICQwgAoRwVSNDSfg2H0nv47neRx//onvIyEqlKjQIEKDAggNDiA8OLDsGwziP3btgjvugGOzXKlSBV58EW6/HQID4disTyfwq4bQydOCj5s5cybt27c/7fpB5V23bt3sLkHEpygTIlbKhIg35ULsYhgGR7LyMQyD7PxCth7KZMWuI6zak0pWXgF7j+ZwNCuPtJziL/IcGOCicZVoWtaIpVXNOPq2qEZsRNH+fZSUlERiYmKx31PEb8XFwerVEBoKDz8Mjz8O0dGeu510nrC1IZSRkcGW49OzMLeV//PPP4mPj6dWrVo88cQTJCUl8dVXXwEwZMgQ3n33XYYNG8bdd9/NokWL+PTTTz3XbTuNPrxFrJQJEStlQsSbciHFVeg2Z+UYhrmortuAgkI3R7LyKSh0k19oUOg2OJyVx47kTA5n5nE4M4+M3AJ2Hc5i79FsjmTlkVvg5lwW6wg6dqlOcGDAsT9dhAYF0rpmHB3rxtOyRixNq8UQFhx4Tt+fMiHlXk4OfPcd3HabOTUtJga+/RYaNoTatb0e7qRM2NoQWrp0KT169PAcDxs2DIDbbruNL774gn379rFr1y7P/XXr1mX69OkMHTqU//73v1SvXp23337bkVvOA2zdupWOHTvaXYaIz1AmRKyUCRFvyoVvMIwTlz/lFxqkZOSSk+8mv9DN4aw883KpQjc7UrKIDA2k4Nhj8wrcbDqQQbW4MLNB4zYbNG7DwDBO3HYf24Upr8BNTkEhBYWGZ4ekgkKDjNyCY40eg4JCN5sPZlAxKsTT7HEbZiOo8NgW2yXN5YL6laKICQuia8NK1KsYSYXIEKrHhlE5JozQoABPI6i0L31SJqTcMgwYNw4eewx27ICICLjhBvO+yy4749OclAlbG0Ldu3c/7dZ/x33xxRdeY5dccolnNwOnCwgIsLsEEZ+iTIhYKRMi3pyYC7fbIDOvgPScAjJyC0jPyScn39rkMAzIyS8kNTuf1Ox80nLycbvNjbHzCw1y8gvJLSgkN99ssOQVmA2agmONm+ONleNNnoJj69+Y93s/ttAHt64+3To8ZxMaFEBkaJA5ayfARWCgi1rxEdRJiCQ+MoTosCDCQ4JoXj2GSlGhuFxQMSr0nGfylBYnZkIcYMkSGDoUju2ySWIihIQU6alOyoTP7DJWVoqz4raIiIiIiK/KPHZJUkZuAQfTctlzJIvMvEIKCt3sT8the3Ime49mk5yR55MNmNNJjAsnONDFjpQsWteMIzI0kM0HMuhUL4HgABdBgS7cBiRn5NK4SrRnoeEA14mFho/fDjg2wyYixNwtyZxxY868CQsOJDT4xCycoABz56bY8GACXC7PeGCAi5CgAEKDAo697vEFjiEkMEALF4v4mj174Mkn4euvzeOICHj0UXM3schIe2srI+V2lzGxmjhxIgMGDLC7DBGfoUyIWCkTIt7sykVegZvcgkKy8ws5mJbL3qPZ5q5RboPCY5dN7TmcRVpOAbkF5qVT+YXmrJq0nHxy8gs9M3XScwpIzc4nI7d4iw8HBbiIDgsiOiyYsOAATmz8bAoJCiAuIpiY8GBiwoIJDnQde14AYcHmTlVhwQGEBgUSHBhAUKC5nk1QQMCJP4MCjjVujt0fYH2cedtsxAQFnnieuTOVmit20LlCypUbboBFi8zbt9wCL70ENWoU6yWclAk1hPxYbm6u3SWI+BRlQsRKmRDxdq65yC0oZH9qDou3H2bLoQwwzEupth7K4EhWHi6Xi/wCt2edm+MzdQoKDdKL2bgpjtjwYOIigkmIDKFmfATRYUEEBQRQMSqEepWiqFkhgkrRocRFBBMapBkt4k3nCvFrbjcUFsLxXcdfeAGeeQbefBM6dDinl3RSJtQQ8mO1atWyuwQRn6JMiFgpEyLeipqLnSmZTF21j1nrDvDn7qMl9v4uF8SFB1MrPoLQoGOXMgWalyZVjg6lUnQoIYGBhAQdnz3jIiosmMiQQIICzUuXosOCqBARQlxEMLHhwWryyHnRuUL81sKF8NBD0K8fjBhhjl16KfToYX7YniMnZUINIT/WqFEju0sQ8SnKhIiVMiHi7Wy5OJqVxzd/7OK1GRvP+JiIEHPHqw51K9CwcjQVIkKICAmkelw4UWFBnsulokKDzIbOscujwkMCiQ4L0roz4nN0rhC/s3OnuXPY2LHm8Z495hpBoaHm8Xl+xjopE2oI+bGff/6ZwYMH212GiM9QJkSslAkRb6fmIiUjl5nrDjB99T4WbU2h4KTFlxPjwqlRIZzEuHAGtq9Jp7rxWudGyh2dK8RvpKfD6NHwxhuQm2s2fu68E55//kQzqAQ4KRNqCImIiIhIuZKTX8juw1kcysglM7eQjNx8MnILycwt4I99oWz8v7Uczcpj95FsVuw6wskbcDWpGk2zajH0a5PIJY0q2fdNiIjICT//DH/7Gxw4YB736GE2hlq3trUsf6eGkB/r0qWL3SWI+BRlQsRKmZDyLK/Aze4jWexIzmR7ciY7UjLZkZxlbrWemo1xxl3WQ+HgDsvIBYmx9LmgKn1aVKNuRWdsSyxynM4V4hfq1IHDh6FBA3j9dbjmmvO+NOxMnJQJNYT8WHJysqMWvBL5K8qEiJUyIeVFanY+25Mz2XYog5W7j7Js1xE27k8nv/CMXR+iQoOoGhtGZGgQ0aFBRIYGEhkaRHbaEerWqEZseDBVY8NoW6sCNeMjyvC7EfEtOleIT9qyBWbOhPvuM48bNIBZs6BzZwgJKdW3dlIm1BDyYxs3bqRt27Z2lyHiM5QJEStlQvxFZm4BGw+ksysli/TcAtKy8z0zf7YnZ5KSmXfa50WEBFInIZI6FSOO/RlJ3YqR1EmIpGJUyGkXbx4zZgyDr+hR2t+SiN/QuUJ8ytGj5tbxb78NBQVw4YVw/P/PSy4pkxKclAk1hERERESkTB3OzGPJjsMs2X6YxTsOs3ZvGoXuM8/2AagSE0rdipHUrRhF1ZgwOtaN58J68dqxS0SkPCgogI8+gpEjITnZHLviCoiKsreucs5lGGe+wro8SktLIzY2ltTUVGJiYuwu57y43W4CAgLsLkPEZygTIlbKhPiC5Ixclu08QtKRbLYcymDt3jRW7j7q9bhK0aHUrxRJbHgwkaFB1I6PpG6lSOpVNGf+RIWWzO8xlQsRK2VCbDdjBgwbBuvWmcdNm8K//w19+thSjr9nojg9D80Q8mNTp07lmmuusbsMEZ+hTIhYKRNih3V705iwfA8rdh1h04EMMnILzvjYwR1rcmG9BDrUiad6XHiZ1KdciFgpE2KrzEy45RY4dAgSEmDUKLjnHggOtq0kJ2VCDSE/lpmZaXcJIj5FmRCxUiakrCQdzeanNfv5YuF2dh/O9rq/UnQoIYEBXNO6OnUrRnJJo0pUiQmzoVLlQuRUyoSUuaNHITbW3CUsMhJGj4a1a+Hpp6FCBburc1Qm1BDyY4mJiXaXIOJTlAkRK2VCStv6fWm8MG0dC7akeMYCXBAaFMgjlzemVnwEHerGExtu3296T6VciFgpE1Jm8vLgvffguefgww9h4EBz/M477a3rFE7KhBpCfqxFixZ2lyDiU5QJEStlQkpaodtg4dZkFm5N4dcNB9mwPx0wm0Cta8ZxbZtELm9RlcrR9sz+KQrlQsRKmZBSZxgwdSo8/DBs3myO/e9/JxpCPsZJmfDflZKEGTNm2F2CiE9RJkSslAkpKTn5hfzn5830fvM3bvl0Me/P2cqG/ekEB7q4vHkVfh3enYn3deGWznV8uhkEyoXIqZQJKVWrVkGvXnDNNWYzqHJl+PhjmDjR7srOyEmZ0AwhERERETmtg2k5TPlzL2//spn0Y4tDhwUH0KdFNbo2qEjPppWJiwixuUoREfFJL78MI0aA2w0hIeZOYk88AX6+23d5ooaQH+vUqZPdJYj4FGVCxEqZkHOVlVfA//25l2emrCWv0O0Zv75dDZ65uhkxYb6zJlBxKRciVsqElJp27cxm0MCB8MorULeu3RUViZMyoYaQH8vIyLC7BBGfokyIWCkTUlw5+YWM+mEtY5fsxm2YY4lx4VzXNpEh3esTEeL/PzoqFyJWyoSUCMOA8eMhNRXuussc69ULVq8GP1uTx0mZ0BpCfmzt2rV2lyDiU5QJEStlQorjy4U76Pnv3xiz2GwGJcaF06NxJX57pDvDejcuF80gUC5ETqVMyHlbuhS6dYMbboChQ2H//hP3+VkzCJyVifJxZhcRERGRYssvdPPR3G28NmOjZfz9m9tyRYuquFwumyoTERGfl5QETz4JX31lHoeHm+sERUfbW5cUmcswDMPuIspSWloasbGxpKamEuPni1nl5+cTHOy/1/CLlDRlQsRKmZAzMQyDMYt3887szexLzfGM168Uyff3diYhKtTG6kqXciFipUxIsWVlweuvm+sCZWWZY3/7G7z0EtSsaW9tJcDfM1GcnocuGfNjM2fOtLsEEZ+iTIhYKRNyqkK3wbRV+2g+cgZPTlrNvtQcKkWH8s8eDZh8fxd+ebh7uW4GgXIhciplQoptzx54/nmzGdS5M/zxB3z9dbloBoGzMqFLxvxYWlqa3SWI+BRlQsRKmZDjdqVk8fy0daxNSmXvsRlBYcEB3Nq5Dg9d1rDcrA9UFMqFiJUyIUWyffuJXcIaNYIXXoA6dcx1g8rZ5cVOyoRzzv7lUJUqVewuQcSnKBMiVsqE7D2azcj/W8usdQc8Y9GhQVzUIIEn+zaldkKkjdXZQ7kQsVIm5Kx27oTHH4fvvzcXj27Txhx/7DF76ypFTsqEGkJ+rH379naXIOJTlAkRK2XCuQ6m5/Dx3G18uXAneYVuz/i/ejbkH93rExYcaGN19lIuRKyUCTmtjAx4+WX4978hJ8ecBfTrrycaQuWYkzKhNYT82LRp0+wuQcSnKBMiVsqEc7jdBmv3pvLZ/O1c8dZcOr74Cx/P205eoZsL68Uzfkhndrx8JUN7NXJ0MwiUC5FTKRNi4XbD559Dw4bw4otmM+iSS2DZMnMHMQdwUiY0Q0hERETEzxS6DdbtTeOP7Sn8vi2FxdsPk5ZTYHlMTFgQr17fisubV9H28SIiUjR9+sDxRZXr14fXXoP+/cvdOkFiUkPIj7Vr187uEkR8ijIhYqVMlC+7D2excGsyczcnM3fjIdJzrQ2gyJBA2tWJ58J68XSqm0C72hVsqtS3KRciVsqEWPTrB7//Dk8/DQ88AKHle+fJ03FSJtQQ8mP5+fl2lyDiU5QJEStlwr8dycxj4dYUFmxNZsGWZHamZFnujw4NokPdeDrVjadTvQRaVI8hKFCrAfwV5ULESplwsNRUc7ewLl3MWUAA99wDAwdCpUq2lmYnJ2VCDSE/tmrVKpo3b253GSI+Q5kQsVImfI9hGOQWuMnKKyQ7v5DsvAKy8gpJzylgw/509h3NJjkjl72pOSzdcRi3ceK5gQEuWteMo0v9BC5pXInWNSsQGKAp/MWlXIhYKRMOVFAAn3wCzzwDhw7BxInQty+EhEBQkKObQeCsTKghJCIiIlJMBYVuZq47wLZDGWTlFZoNnrxCsk5q8mTlFZKTX3jS/QVk5xdamjx/pXZCBD2bVKFLgwQ61o0nOiy49L4pEREp/2bNgqFDYe1a87hJE3MnsWCdX5zIZRhGMX4s8X9paWnExsaSmppKTEyM3eWcl+zsbMLDw+0uQ8RnKBMiVspEyXK7DbYeymDRthQ+mbedXYez/vpJZxESFEBESCDhwYGEhwRSMTKUVjVjqRQdSsWoUGpUiKBDnQpaELqEKRciVsqEQ2zebO4SNnWqeRwfD6NGwb33qhl0Cn/PRHF6Hpoh5Mfmzp3L5ZdfbncZIj5DmRCxUibOnWEY7DmSzao9qazcc5SVu4+yJimVzLxCz2PiI0O4rGllIkODiAgJJCIkyNPcOd7oiQgJOuXYvD88OFDr/dhEuRCxUiYcYvt2sxkUFAT//Kd5uVgFbT5wOk7KhBpCfuzw4cN2lyDiU5QJEStlougOpeeyas9RVu5JZdWeo6zak8rhzDyvx4UHB9IiMYY+LapxY8eaRIToRyl/o1yIWCkT5VR+PqxaBcd3zOrd21xAeuBAaNTI3tp8nJMyoZ9i/FhCQoLdJYj4FGVCxEqZOL20nHxWH5v5s2q32QDam5rj9bjgQBdNqsbQskYsrWrE0bJmLA0qRWlmj59TLkSslIlyxjBg2jQYPhz27jUvFatSxbxvxAh7a/MTTsqE1hDyY1lZWURERNhdhojPUCZErJSJE/alZjNxeRJT/kxi04EMr/tdLmhQKYqWNeJoVTOWljXiaFI1mrDgQBuqldKkXIhYKRPlyJo15jpBs2aZx5Uqwfjx0K2bvXX5GX/PhNYQcogpU6YwePBgu8sQ8RnKhIiVUzORmp3PmqRUVu1JZXXSUX7dcIjs/ELLY2rGh5vNnxpm86dFYixRofqxyAmcmguRM1EmyoGDB801gT7+GNxuc/v4hx6CJ5+E2Fi7q/M7TsqEfvIRERERv5WVV8DavWms3H2U1ceaQNuTM8/4+Fevb8llTasQHxlShlWKiIiUkowMaNYMUlLM4+uvh1degXr17K1L/IIaQn6sdevWdpcg4lOUCRGr8piJQ+m5zFp3gOW7jrB6TyqbD6bjPs3F78dnALVMPD4DKIboMG2rK+UzFyLnQ5nwc1FRcPPNMH8+vPmmLg8rAU7KhBpCfiwgQItaipxMmRCx8udM5BW4WbcvjS0HMzxfWw9lnHb2T9WYMFrWiKVljVguONYEqqAZQHIG/pwLkdKgTPiZ5cvNBaPfeAOONy5efhlCQ0H/LUuEkzKhhpAfW758OY0bN7a7DBGfoUyIWPl6JnLyC1m7N5UVu45yKD2X5Iw8DmfmsjMli21nuewL4JpW1bmmVXVa1oilckxYGVUs5YGv50KkrCkTfmLfPnOXsC++MHcSe/JJmD7dvC883NbSyhsnZUINIRERESkzWw5mMHlFEvM2H2LdvjTyC8++2emF9eJpUDmKBpWiaFA5mgaVo6gSE4rL5SqjikVERGyUnW3OBho9GjKP/bLk5pvNY5HzpG3n/Vh6ejrR0dF2lyHiM5QJEStfyUTS0Wx+WrOfySuSWJ2UarmvYlQIjatG06RqDPGRIVSMCiE+MpSKUSG0SIwlONA507albPhKLkR8hTLhwyZNMncL27XLPL7wQnjrLejUyc6qyj1/z4S2nXeIxYsX07NnT7vLEPEZyoSIlV2ZyC0oZMn2I8zZeJA5mw6x5WCG576gABeXNKrElS2r0aFOPDUqhGu2j5QpnStErJQJH5aUZDaDatY0dw678UbQObPUOSkTagj5sYMHD9pdgohPUSZErMoyE3uOZPHrhoP8tukQC7emkJVX6LkvwAXtalfg6lbVufKCaiREhZZZXSKn0rlCxEqZ8CG7d8PevSdmAN17r7le0F13aZ2gMuSkTKgh5MdiY2PtLkHEpygTIlalkQnDMNiRksXeo9nsPZrNloMZzFi7nx0pWZbHVYoO5ZJGlejeuBIXN6hEbIS2fBffoHOFiJUy4QMyMuDVV+G11yAxEdauNXcNCw6GBx6wuzrHcVImtIaQH8vNzSU0VL9lFTlOmRCxKslM7DmSxZQ/9zJh2Z7T7gB2fBZQ98aV6d64Es2qxehSMPFJOleIWCkTNnK74auvzB3D9u0zx7p1gzFjoHp1e2tzMH/PRHF6Hlqp0Y9NnDjR7hJEfIoyIWJ1vpk4kJbD17/v5IYPFtH1lV95bcZGTzOoYeUoLm5YkZs61eLtwW1YObI344ZcxP09GtC8eqyaQeKzdK4QsVImbDJvHnTsCHfcYTaD6tWDCRNgzhw1g2zmpEzokjEREREBzMvB/th+mMkrkli684hlMWiXCzrXS6B/60SuuKAqMWG6BExEROScrFhhzgQCiImBp56CBx80LxMTKUNqCPmxFi1a2F2CiE9RJkSs/ioThW6DWev289umQ6zcncq6fWlej6lXKZIrL6jGoA41qVEhorRKFSkzOleIWCkTZcTthoBjF+i0aQNXXQU1asCoUVC5sr21iYWTMqGGkB+LiNAP5iInUyZErE6XiZz8QpbuOMKynUd48+dNp33ejR1q0rNpFS5IjKVqbFhplylSpnSuELFSJkpZYSF8+im8/jrMn3+i+TN5MgQG2lqanJ6TMqGGkB9bvHgx9evXt7sMEZ+hTIhYnZyJNUmpTFqRxMTleziSle95TGx4MC1rxHJ586pUjAqlc70E7Qgm5ZrOFSJWykQp+uUXGDoUVq82j995B55/3rytZpDPclIm1BASEREpp/ZmB/DR3K28MWsTOfluz3iVmFAuSIzlypbV6NOiGmHB+qFURESkxGzaBMOHww8/mMcVKsDIkXDfffbWJXIKbTvvx44ePUpcXJzdZYj4DGVCBA6m5fDDqn0s2JLM7A0HLfc1qhLF8N6N6dm0CoEB2gVMnEnnChErZaIEGQY89hi8+SYUFEBQkNkEGjkS4uPtrk6KyN8zUZyeh2YI+bGVK1dyySWX2F2GiM9QJsTJVuw6wvtztjJz3QHLeO2ECC6sm0CHuvFc366GTdWJ+A6dK0SslIkS5HJBTo7ZDLrySnPdoCZN7K5KislJmVBDyI/t3bvX7hJEfIoyIU6TkVvAL+sP8On87azak+oZb1A5iosbViQueQ3/uuNKGysU8T06V4hYKRPnwTBg+nSoUweaNzfHRo40dxDr3dvW0uTcOSkTagj5saioKLtLEPEpyoQ4xcG0HB6bsIolO46QkVvgGb+8eRXu6VaPtrUq4HK5+OGHbTZWKeKbdK4QsVImztGaNfDwwzBzJvTsCbNmmTOEEhLUDPJzTsqE1hDyY4WFhQRqdXoRD2VCyru1e1N5b85Wftt4yNIIuveSevRpUY3WNeMsj1cmRLwpFyJWykQxHTpkzgL68ENwuyE4GP71L3jpJfO2+D1/z0Rxeh4BZVSTlILvv//e7hJEfIoyIeVRckYu3/yxk2vfW8CVb89n2qp9ZOQWUCs+gleuu4BtL/XliT5NvZpBoEyInI5yIWKlTBRRbq65JlDDhvD++2YzaMAAWL8eXntNzaByxEmZ0CVjIiIiPmZfajaLtx9mzsZDTFqRZLmvQeUonrmqGRfVTyAoUL/XERERKRNffw2PPGLebtPG3EnMIQsPS/mlhpAfa6IV60UslAnxd5m5Bbz18yY+nrfdMh4WHMDgjrW4tXMd6laMLPLrKRMi3pQLEStl4iyysyE83Lx9223w7bdwyy1w663gx5cUydk5KRNqCPmx+Ph4u0sQ8SnKhPij7LxCJizfw/tztrIvNRv3sZX9QgID+NuFtbm6VTXa1KpwTq+tTIh4Uy5ErJSJ09i3D556ChYsgFWrICTEvCRs9my7K5My4KRMqCHkxxYuXEjt2rXtLkPEZygT4k/cboPxy/bw9JQ15Ba4PeOJceEMbF+D+3s0IPg8LwlTJkS8KRciVsrESbKzzUvBXnoJMjPNsZkzzW3kxTGclAk1hERERMrYmqRUnpmyhuW7jnrGRvRtyiWNK9GoSrR9hYmIiDiRYcDYsfDYY7BrlznWqZPZHOrc2d7aREqRtp33Y8nJyVSsWNHuMkR8hjIhvi4jt4DXZ2zkq0U7cBsQERLIQ5c15PaL6hISVPILRCsTIt6UCxErx2ciLQ369IGFC83jGjXg5Zdh8GAI0OYNTuTvmdC28w6xYcMGu0sQ8SnKhPiqP3cf5aXp62n3/Cy+WGg2g7o1qsTsh7tzT7f6pdIMAmVC5HSUCxErx2ciOtr8ioiA556DjRvh5pvVDHIwJ2VCl4z5sd27d9tdgohPUSbE12zcn843f+zkq0U7PWOVokMZelkjbuxQk4AAV6m+vzIh4k25ELFyXCYyM+GNN2DIEKhUCVwueP99c+HoxES7qxMf4KRMqCHkx8KPb4EoIoAyIfYzDINFW1MYt2wPk1YkWe6rVzGSId3rc22bxPNeLLqolAkRb8qFiJVjMuF2w9dfw5NPwt695k5i771n3le3rr21iU9xTCbQGkJ2lyMiIuVAbkEhczYe4pN521iy44jlvi4NEhhySX0ubljJpupEREQcbv58GDoUli41j+vWhddfhwED7K1LpBRoDSGH+O677+wuQcSnKBNS1tbvS+PGjxbR+KmfuPfrZSzZcYSw4AAGd6zJK9ddwMyh3fjmrgttawYpEyLelAsRq3Kdie3b4YYb4OKLzWZQdLS5YPS6dWoGyRmV60ycQpeM+TGHTe4S+UvKhJSFg+k5vDB1PSv3HGVnSpblvqbVYvjktvYkxvnGVGNlQsSbciFiVa4z8eabMG6cuUD0nXfC889DlSp2VyU+rlxn4hRqCPmxhg0b2l2CiE9RJqS0GIbBgi0p3PrZH7hP+RnhkkaVGNi+Bn1aVCOwlBeJLi5lQsSbciFiVa4yUVgIR47A8S3Dn3kGdu+GZ5+FVq1sLU38R7nKxF9QQ8iPVa1a1e4SRHyKMiEl7XBmHqN+WMvs9QdJzy2w3HdHlzoMbFeTZtV9dz06ZULEm3IhYlVuMjF7trlOUJUqMGOGuXtYxYowaZLdlYmfKTeZKAKtIeTH5s2bZ3cJIj5FmZCSsiM5kzs+X0zb52cx5c+9nmZQj8aVeOaqZmx7qS8jr27u080gUCZETke5ELHy+0xs3gz9+kHPnrBqFSxZYs4KEjlHfp+JYtAMIRERESArr4BP521n3uZkFu84bLmvV7MqvHhtCypHh9lUnYiIiFgcOWKuCfTuu5CfD4GBcN99MHIkJCTYXZ2IX1BDyI/16NHD7hJEfIoyIcXldhtMWZnE279sYXtypuW+kKAA/nFJff7RvT5hwYE2VXh+lAkRb8qFiJVfZmLlSnNGUEqKedy3r7mNfNOm9tYl5YJfZuIcqSHkx3bs2OGo6xtF/ooyIUW1IzmTq9+Z77UuEMB1bWtwQ/sadKrn/79dVCZEvCkXIlZ+mYmmTSE+3lwv6I034PLL7a5IyhG/zMQ50hpCfmz79u12lyDiU5QJ+SuGYfDJvG10f32OpRnUsU48/7mxNVtf6su/b2hVLppBoEyInI5yIWLlF5lYtw7uvRfy8szjkBBz4eiVK9UMkhLnF5koIZoh5MeCg4PtLkHEpygTcia7D2fx31+38N0S6yKTr1x3Ade2qUFIUPn8/YgyIeJNuRCx8ulMJCebW8Z/8IG5pXzTpvDQQ+Z9devaWZmUYz6diRLmMgzDsLuIspSWlkZsbCypqanExPj27jAiInLuDMNg5Z5UFmxJ5rUZGy33ta0Vxxd/70hMmHNO+CIiIn4jL89cLPq55yA11Ry79lp49VVo0MDe2kR8XHF6HuXzV6IOMX78eLtLEPEpyoQAJGfk8vYvm6n7xHT6/3eBpRl0efMqzH2kBxPv6+KIZpAyIeJNuRCx8qlMGAZMmQLNm8PDD5vNoNat4ddfYeJENYOkTPhUJkqZLhnzY/n5+XaXIOJTlAlnyytwc+eXS5i3OdkyHhjg4ob2NRlyST1qJ0TaVJ09lAkRb8qFiJXPZeLdd2HLFnPB6BdfhNtvN7eUFykjPpeJUqSGkB+rq+tmRSyUCWfKyivg9Rmb+GyBdQHARy5vzN861SY2ovzPBDoTZULEm3IhYmV7JvbvNxeJjo8Hl8vcNWzMGHjiCYiOtrc2cSTbM1GG1BDyY3Xq1LG7BBGfokw4x7ZDGXz7xy7W709jwZYUy31tasXx3T0XEhqk3yYqEyLelAsRK9sykZMDb74JL70Et91mzgwCuOAC80vEJk46T2gNIT/266+/2l2CiE9RJsq/75fu5tJ/z+HSf//GJ/O3W5pBrWrE8tEt7Zh0Xxc1g45RJkS8KRciVmWeCcOA7783dwx78knIyIAVK6CgoGzrEDkDJ50nNENIRER8Wn6hmwnL9vD4xNVe93VtUJGhvRrStlYFXC6XDdWJiIhIkS1ZAkOHwoIF5nFiIrz8Mtx0EwRoroJIWVNDyI9dfPHFdpcg4lOUifJly8F03puzlYnLkyzjFaNC+OS2DrSuGWdPYX5EmRDxplyIWJVZJr76yrw0DCAiAh57DIYPN2+L+BAnnSfUEPJj+/fvp0aNGnaXIeIzlAn/ZhgGWw9l8tmC7czfnMyuw1mW+ytFh/LRLe1oU6uCTRX6H2VCxJtyIWJVZpno0wfi4uCaa8x1gxITS/89Rc6Bk84TmpfnxzZv3mx3CSI+RZnwT4Zh8MpPG6j7xHQue+M3vv1jl6UZdFOnWqx/7gqWjLhMzaBiUiZEvCkXIlalkgm3G/73P7jzzhNjlSqZ28l/+aWaQeLTnHSe0AwhP6b1MkSslAn/kpVXwOQVe3lykvfaQJ3rJfDApQ3oXD9B/13Pg/7uRLwpFyJWJZ6JhQvhoYfM9YIABg2C3r3N2wkJJfteIqXASecJl2EYht1FlKW0tDRiY2NJTU0lJibG7nJERBxn3uZDPD15DTtSrJeE1a8UyZR/diUqVL+rEBER8Ts7d5rrAo0dax5HRZm7iA0dCmFh9tYm4iDF6XnokjE/NnnyZLtLEPEpyoRv+3zBduo8Po1bPl3saQZVjArhxg41+eBvbfnl4e5qBpUwZULEm3IhYnXemcjOhhEjoHFjsxnkcsFdd8HmzfDEE2oGid9x0nlCP3n7sezsbLtLEPEpyoRv2pGcyY0f/c7+tBzP2AWJsQzqUJObO9Vy1LTcsqZMiHhTLkSszjsTgYHw/feQmws9esAbb0Dr1iVSm4gdnHSeUEPIj9WsWdPuEkR8ijLhW5KOZnPXl0tZvy/NM1Y5OpSPb21PK20ZXyaUCRFvyoWI1TllYv586NQJgoMhJATefx8yM80dxPSLHvFzTjpPqCHkx5o0aWJ3CSI+RZnwDftSs/nXd3+yePthy/i4IZ3pUCfepqqcSZkQ8aZciFgVKxNbtsAjj8DkyfCf/8CDD5rjl11WKrWJ2MFJ5wmtIeTHZs2aZXcJIj5FmbBXdl4hny/YTufRsy3NoKeubMr20X3VDLKBMiHiTbkQsSpSJo4eheHDoVkzsxkUGAgHD5Z2aSK2cNJ5QjOERETkvM1cu597vl7mOXa5YNhljfjnpQ20RpCIiIi/KiiAjz+GZ56B5GRz7Ior4N//NptDIuLX1BDyYxdddJHdJYj4FGWibGXkFvDMlDWsSUpl04EMz/g1rarzfP8WxIYH21idgDIhcjrKhYjVWTNx//3w0Ufm7aZNzQWjr7iibAoTsYmTzhO6ZMyPHT58+K8fJOIgykTZyCtw889vl9Ni5AwmLk/yNIPa1opjw/NX8PbgNmoG+QhlQsSbciFi5ZUJwzhx+/77oXJlePddWLVKzSBxBCedJ2xvCL333nvUrVuXsLAw2rVrx7x58876+G+++YZWrVoRERFBtWrVuOOOO0hJSSmjan3Lhg0b7C5BxKcoE6VrTVIq//puBY2e+pGpq/Z5xlvViOWjW9oxfshFhAUH2lihnEqZEPGmXIhYeTKRkgIPPGCuFXRcy5awa5fZGArSxSXiDE46T9ia6rFjx/LQQw/x3nvv0aVLFz788EP69OnDunXrqFWrltfj58+fz6233sqbb77J1VdfTVJSEkOGDOGuu+5i0qRJNnwHIiLl33eLd/H4xNVe4wPb1eDV61tqjSARERE/FlBQAG++Cc89Zy4eHRQEDz0Ex7feDg21szwRKUUuwzh5TmDZ6tSpE23btuX999/3jDVt2pT+/fszevRor8e//vrrvP/++2zdutUz9s477/Dqq6+ye/fuIr1nWloasbGxpKamEhMTc/7fhI0KCwsJDNRv40WOUyZKTk5+Id8t3sU3f+xi88EMy30P92rE/T0aEBCgRpCvUyZEvCkXIscYBvzwA8bw4bg2bzbHWrY01wnq2dPe2kRs5O/nieL0PGy7ZCwvL49ly5bRu3dvy3jv3r1ZuHDhaZ9z0UUXsWfPHqZPn45hGBw4cIDx48dz5ZVXnvF9cnNzSUtLs3yVF9OnT7e7BBGfokyUjPfnbKXJ0z/x7A/r2Hwwg6AAF9e3q8GCxy9lx8tX8kDPhmoG+QllQsSbciECbN8OvXpBv35mM6hyZXPx6OXL1QwSx3PSecK2S8aSk5MpLCykSpUqlvEqVaqwf//+0z7noosu4ptvvmHQoEHk5ORQUFDANddcwzvvvHPG9xk9ejSjRo3yGh83bhwREREMGDCAX375hdTUVCpXrkzHjh2ZOnUqAG3btsXtdvPnn38C0K9fP+bPn09KSgrx8fF069aNyZMnA9CyZUuCg4NZtszcdvnKK69k6dKlHDhwgJiYGHr37s348eMBaN68OVFRUfzxxx8AXH755axZs4akpCQiIyO56qqrGDt2LACNGzemYsWKLFiwAIDLLruMTZs2sWvXLvbtM9fwGDt2LG63m/r165OYmMjcuXMB6N69O7t27WLbtm0EBQUxcOBAJkyYQF5eHrVr16Z+/frMnj0bgK5du3Lw4EE2bdoEwODBg5kyZQpZWVnUqFGDZs2aMXPmTAA6d+5Mamoq69atA2DgwIH89NNPpKenU7VqVdq2besJUYcOHcjJyWH1avNyk2uvvZY5c+Zw5MgRKlasSOfOnfnhhx8AaNOmDQArVqwA4Oqrr2bRokUkJydToUIFunfv7rk08IILLiAsLIwlS5YA0LdvX5YvX87+/fuJjo7miiuuYNy4cQA0a9aM2NhYFi1aBJhNx3Xr1rFnzx4iIiLo168fY8aMAaBRo0ZUrlyZ+fPnA3DppZeydetWdu7cSUhICNdddx3jxo2joKCAevXqUatWLebMmQNAt27dSEpKYuvWrQQEBDBo0CAmTpxIbm4utWrVolGjRvz8888AdOnSheTkZDZu3AjAoEGDmDp1KpmZmSQmJtKiRQtmzJgBmDPpMjIyWLt2LQDXX389M2fOJC0tjSpVqtC+fXumTZsGQLt27cjPz2fVqlUA9O/fn7lz53L48GESEhLo2rUrU6ZMAaB169YEBASwfPlyAK666ioWL17MwYMHiY2NpWfPnkycOBGAFi1aEBERweLFiwHo06cPK1euZO/evURFRdG3b1++//57AJo0aUJ8fLynsdurVy82bNjA7t27CQ8Pp3///nz33XcYhkHDhg2pWrWqZ+2wHj16sGPHDrZv305wcDDXX38948ePJz8/n7p161KnTh1+/fVXAC6++GL279/P5s2bcblc3HjjjWzZsoUxY8ZQs2ZNmjRpwqxZswDzs+Pw4cOe64FvuOEGpk+fTkZGBtWrV6dVq1b8+OOPAHTs2JGsrCzWrFkD4NefEaGhoQwYMKBInxFHC4NZkFOT+VuObSl7TMf4PHpUyeWqCy9g/9Z1zNVnhF99RiQnJ5OWlqbPiGOfEZMnTyY7O1ufEefwGVGefo5ISkryrF/p9M8I/Rzh3M+IyTNmcNXChQQFB7PmssvYeN11FEREcNnhw47/jAD9HOH0z4ikpCTatGnjt58RWVlZFJVtl4zt3buXxMREFi5cSOfOnT3jL774Il9//fVpF3Jat24dl112GUOHDuXyyy9n3759PPLII3To0IFPP/30tO+Tm5tLbm6u5zgtLY2aNWuWi0vGfvvtNy655BK7yxDxGcpE8e1MyeTqd+aTllNgGb+4YUXevamtdgvzc8qEiDflQhwpNxcmT4ZBg06M/fADtGjBb7t2KRMiJ/H380RxLhmzbYZQxYoVCQwM9JoNdPDgQa9ZQ8eNHj2aLl268MgjjwBmFywyMpKLL76YF154gWrVqnk9JzQ0lNByuhBaq1at7C5BxKcoE0W392g2o35Yy4y1ByzjfS+oytDLGtGwSrRNlUlJUiZEvCkX4iiGARMmwKOPmpeJxcae2Dr+6qsBaFWhgo0FivgeJ50nbFtDKCQkhHbt2nmmWR03a9YsLrrootM+Jysri4AAa8nHF3uycW1s2xyfdiYiJmXir6Vm5TPqh7Vc9PJsSzPo9YGt2PHylbx3czs1g8oRZULEm3IhjrFsGVxyCQwcaDaDqleH/HyvhykTIlZOyoSt284PGzaMW265hfbt29O5c2c++ugjdu3axZAhQwB44oknSEpK4quvvgLM6zzvvvtu3n//fc8lYw899BAdO3akevXqdn4rIiI+ze02+HbxLp6avMYzFh0axD8vbcDfu9YlONC23w+IiIhISdq7F558Er76ypwhFB4OjzxizhKKjLS7OhHxIbY2hAYNGkRKSgrPPfcc+/bto0WLFkyfPp3atWsDsG/fPnbt2uV5/O233056ejrvvvsuDz/8MHFxcVx66aW88sordn0LturYsaPdJYj4FGXCm2EYLNqawk2f/GEZH9yxJi9dewEul3YLK8+UCRFvyoWUa4YBffrAsUV/+dvf4KWXoGbNMz5FmRCxclImbG0IAdx3333cd999p73viy++8Bp74IEHeOCBB0q5Kv9QnNXDRZxAmTih0G3w4HcrmLZqn2W8Vc04vrqjI7ERWizaCZQJEW/KhZQ7brfZCAoMBJcLnn0WXnsN3noLivAPW2VCxMpJmdA1An7s+FZ1ImJSJkzTVu2j/pPTvZpBX/69I1Pu76JmkIMoEyLelAspVxYtgs6d4YMPToz17w8LFhSpGQTKhMipnJQJ22cIiYjI+TMMg7d/2cKbP2+yjEeHBTH/sUu1fbyIiEh5snMnPP44fPedeXzgANx7LwQFmbOERESKwGU4bHuutLQ0YmNjSU1NJSYmxu5yzktubi6hoaF2lyHiM5yaiQVbkrn5lDWCWtWM48s7OhAXEWJTVeILnJoJkbNRLsSvpafDyy/DG29ATo7Z/LnjDnjhBahW7ZxeUpkQsfL3TBSn56FLxvzYL7/8YncJIj7FSZkwZwRtps7j0yzNoJY1YpkzvDtT7u+iZpA4KhMiRaVciN/66Sdo1MhcJDonB7p3N7eW//TTc24GgTIhcionZUKXjPmx1NRUu0sQ8SlOycTXi3bw6oyNpOcUWMYn39+F1jXj7ClKfJJTMiFSHMqF+K2qVc1Lw+rXh9dfh379SuTyMGVCxMpJmVBDyI9VrlzZ7hJEfEp5zsSqPUd5fMJq1u1Ls4y3qhnHywMuoGk1/74EVkpHec6EyLlSLsRvbN0K8+fDbbeZx61bw48/mjODSvByFmVCxMpJmdAaQn4sPT2d6Ohou8sQ8RnlMRMH0nJ44NsVLN5x2DLeu1kVHu/ThHqVomyqTPxBecyEyPlSLsTnpaaaawK9/ba5pfyaNdC4cam9nTIhYuXvmdAaQg4xdepUu0sQ8SnlLRMPjFlBp5d+8TSDKkWH8o/u9Vk8oicf3dpezSD5S+UtEyIlQbkQn1VQYG4f37CheUlYXh5ceikElO4/2ZQJESsnZUKXjImI+Jg1Salc9c58y9gL/Vvwtwtr21SRiIiIlKqZM2HYMFi71jxu3NjcSaxPH20jLyKlRg0hP9a2bVu7SxDxKeUhE5NXJPHQ2D89x6FBAax6tjehQYH2FSV+qzxkQqSkKRfic44eheuvN7eUj4+HZ5+FIUMgOLhM3l6ZELFyUibUEPJjbrfb7hJEfIo/Z2LpjsMMH7eSHSlZAFSMCuXNQa24uGElmysTf+bPmRApLcqF+IT0dDi+RklcHIwcCbt3wzPPmE2hMqRMiFg5KRNaQ8iP/fnnn3aXIOJT/DETWXkF3Pv1Uq7/YJGnGdS/dXVmD79EzSA5b/6YCZHSplyIrfLzzcWia9c2LxM77uGH4a23yrwZBMqEyKmclAnNEBIRscH8zcl8u3gn01fvt4y/PbgN17SqblNVIiIiUioMA6ZNg+HDYeNGc+zTT6F3b3vrEhFH07bzfiwrK4uIiAi7yxDxGf6Qiamr9vLitPXsS83xjMVFBDOofU3+eWkDosPKZr0AcQZ/yIRIWVMupMytWWMuGD1rlnlcqZK5rfydd0Kg/WsEKhMiVv6eCW077xDz58//6weJOIgvZ8IwDF6bsYF/frvC0wyKDgvio1vasfypXjzRt6maQVLifDkTInZRLqRMPf88tGplNoNCQuDRR2HzZrjnHp9oBoEyIXIqJ2VCl4z5sZSUFLtLEPEpvpiJnPxCXpy2nq9/32kZ/+auTnRpUNGmqsQpfDETInZTLqRMNWsGbre5i9grr0C9enZX5EWZELFyUibUEPJj8TYsOifiy3wtExOW7eHhcSstY9e1rcHrA1vicrlsqkqcxNcyIeILlAspNYYBkyZBXh7ceKM5NmAALFsGPryNtTIhYuWkTGgNIT+WnZ1NeHi43WWI+AxfysSSHYcZ+MEiz3HjKtGMuedC4iNDbKxKnMaXMiHiK5QLKRXLl8PQoTB3LiQkwJYt5nbyfkCZELHy90xoDSGHmDx5st0liPgUX8lETn6hpRn0+xM9mTG0m5pBUuZ8JRMivkS5kBK1dy/ccQe0b282g8LC4B//gGD/WRdQmRCxclImdMmYiEgJu/XTxZ7bfzzZkyoxYTZWIyIiIiUuOxv+/W94+WXIzDTHbroJRo+GWrXsrU1EpIjUEPJjLVu2tLsEEZ/iC5l4dPxKFu84DMAr112gZpDYyhcyIeJrlAspERs3wjPPmOsGXXghvPmm+acfUiZErJyUCTWE/FiwH01FFSkLdmdi2Ng/mbgiCYDLm1dhUAf9hlDsZXcmRHyRciHnbM8eqFHDvN26NTz5JDRvbi4g7cebRSgTIlZOyoTWEPJjy5Yts7sEEZ9iVybWJKXScMR0TzNoYLsafPC3drbUInIynSdEvCkXUmy7d8PNN5tbxm/adGL8hRdg8GC/bgaBMiFyKidlQjOERETO0YG0HK58ex7JGXmesds61+bZa5prW3kRERF/l5EBr74Kr70GOTlm42fWLGjUyO7KRERKhLad92NpaWl+/z2IlKSyykROfiGjfljHmMW7LOOvXteSGzrULPX3FykqnSdEvCkX8pfcbvj6a3jiCdi3zxzr1s1cJ6htW3trKwXKhIiVv2dC2847xNKlS+0uQcSnlHYmft+WQv//LqDJ0z9ZmkFvD27DjpevVDNIfI7OEyLelAs5K8OAnj3h9tvNZlDdujBhAsyZUy6bQaBMiJzKSZnQJWN+7MCBA3aXIOJTSisTKRm53PzJH2zYn24Z79mkMu//rR0hQeqti2/SeULEm3IhZ+VyQe/esGwZPP00PPgghIbaXVWpUiZErJyUCTWE/Jg/T2MTKQ0lnQnDMHhk/CrGL9vjGQtwwah+LbipYy0CA7ROkPg2nSdEvCkXYpGWBi++aDaBevY0x4YOhTvvhMqV7a2tjCgTIlZOyoTWEPJj+fn5jtoST+SvlGQmMnMLuOOLJSzeftgz1q91dd4a1FoLRovf0HlCxJtyIQAUFsKnn8JTT8GhQ9CiBfz5JwQG2l1ZmVMmRKz8PRNaQ8ghxo8fb3cJIj6lpDIxacUemo+c4WkGXVgvno0vXMF/bmyjZpD4FZ0nRLwpF8LPP0ObNnDvvWYzqFEjGD0aApz5TyNlQsTKSZnQJWMiIses2nOU8cv28NWinZ6xZ65qxt+71rWxKhERESkRmzbB8OHwww/mcYUKMHIk3Hcf+PFsABGRc6WGkB9r3ry53SWI+JRzyURWXgHDxq5k88F0th7K9IwfXyvolgtrl2SJImVK5wkRb8qFg61caTaDgoLMJtDIkRAfb3dVtlMmRKyclAk1hPxYVFSU3SWI+JSiZiI1K59n/m8Nczcd4khWvuW+sOAAnrumBQPb19DlYeL3dJ4Q8aZcOEh+PmzcaK4PBHD99fD443DbbdCkib21+RBlQsTKSZlw5oWy5cQff/xhdwkiPuWvMpFbUMjwcStp9dxMpvy519IMqlEhnLH3XMiG5/twQ4eaagZJuaDzhIg35cIBDAOmT4eWLaFHD0hNNcddLnOtIDWDLJQJESsnZUIzhESk3DMMg0/nb+eFaest45c0qsQDlzagWfUYIkL0cSgiIuL31q6FYcNg5kzzuGJFWLcOOne2ty4RER+kbef92OHDh4nXdc8iHqfLRE5+IX3fnse2k9YHuqdbPe7rXp+4iJCyLlGkTOk8IeJNuSinDh0y1wT68ENwu81Foh96CEaMgNhYu6vzacqEiJW/Z0LbzjvEmjVr7C5BxKecmom9R7O54NkZnmZQu9oV+OXhS3iyb1M1g8QRdJ4Q8aZclEOHD0PjxvD++2YzaMAAWL8eXn1VzaAiUCZErJyUCV0j4ceSkpLsLkHEpxzPhGEYvPzTBj78bZvnvju61GHk1c7ZMUAEdJ4QOR3lohyKj4d+/cxdxN54A7p3t7siv6JMiFg5KRNqCPmxyMhIu0sQ8SmRkZEcSs+lw4s/W8bfu7ktfS+oZlNVIvbReULEm3JRDqxYAY89Bu+9Bw0amGNvvw0RERAYaG9tfkiZELFyUia0hpAfc7vdBAToqj+R435Zv587v1zmOa4QEczCx3sSHqIfDsWZdJ4Q8aZc+LF9+8w1gb74wtxJbOBA+P57u6vye8qEiJW/Z0JrCDnE2LFj7S5BxCcs3JpMncenWZpBz/drzopneqsZJI6m84SIN+XCD2Vnw4svQsOG8PnnZjPoxhvhtdfsrqxcUCZErJyUCV0yJiJ+bdnOI9z08R+e46AAF3882ZOEqFAbqxIREZESMXEiDB0Ku3aZx506wZtvaht5EZESoIaQH2vcuLHdJYjYZn9qDgM/XMjuw9mesce7VGDI1RfZWJWIb9F5QsSbcuFn1q83m0E1asDLL8PgweDHl3L4ImVCxMpJmVBDyI9VrFjR7hJEypzbbfDWL5t5+5fNnrGo0CB+/NfFGBnJNlYm4nt0nhDxplz4uD17zG3kW7Y0j4cNg7Aw+Mc/zEWjpcQpEyJWTsqE2ut+bMGCBXaXIFKmdiRn0vGlny3NoLu61mX1s72pGR+hTIicQpkQ8aZc+KjMTBg5Eho1gltugcJCczw8HB5+WM2gUqRMiFg5KROaISQiPi85I5d/fbeCBVtSPGMta8Ty9Z2diA0PtrEyEREROS9uN/zvf/DEE7B3rzkWEwMpKVC5sr21iYiUc9p23o8dOnSISpUq2V2GSKnJzivkzZ838dHcbZbxj29tT69mVbwer0yIWCkTIt6UCx8yf765YPTSpeZxnTrmzmHXXQcul62lOYkyIWLl75koTs9DM4T82KZNm/z6f1SRs/ljWwqDPvrdMja4Y02G9WpMpejT7yCmTIhYKRMi3pQLH7FgAVx8sXk7OhpGjIB//ctcL0jKlDIhYuWkTGgNIT+26/j2myLlzKo9Ry3NoObVY1j+dC9GD2h5xmYQKBMip1ImRLwpFzY6+cKEiy4yG0L33AObN8Njj6kZZBNlQsTKSZnQDCE/Fhp65n8Yi/iro1l5XPOuuZBbeHAgM4d2o2Z80RaSVCZErJQJEW/KhQ0KC+Gzz+Cdd8zLxGJizEvCfvkFgrUWoN2UCRErJ2VCawiJiM/IyS/k5k/+YNnOIwDMe7RHkZtBIiIi4oNmzzbXCVq1yjwePRoef9zemkREyrHi9Dx0yZgfGzt2rN0liJSYn9cd4OJXf2XZziOEBAbw7V2dit0MUiZErJQJEW/KRRnZvBn694eePc1mUFwcvPkmDBtmd2VyCmVCxMpJmdAlY37M7XbbXYLIeTuYnsONH/7OtuRMAEKDAnjv5rZc1KBisV9LmRCxUiZEvCkXpcww4JFH4O23IT8fAgPhvvtg5EhISLC7OjkNZULEykmZUEPIj9WvX9/uEkTOWU5+IY+OX8X/rdzrGetUN553b2p71oWjz0aZELFSJkS8KRelzOWCQ4fMZlDfvvD669C0qd1VyVkoEyJWTsqEGkJ+LDEx0e4SRM7JmqRUrnpnvmVsWK9GPNiz4Xm9rjIhYqVMiHhTLkrBTz9Bo0ZQr555/NJLcNNNcPnl9tYlRaJMiFg5KRNaQ8iPzZ071+4SRIrtz91H6fffBZ7jzvUSWPF0r/NuBoEyIXIqZULEm3JRgtatgz59zK9HHjkxnpioZpAfUSZErJyUCc0QEpEykZNfyKgf1jFm8S4AYsKC+OLvHWlbq4LNlYmIiEixJCfDs8/CBx+YW8oHB0PduuB2Q4B+3ywi4i/UEPJj3bt3t7sEkSLZfCCdvm/PI7/QACA2PJjp/7qYxLjwEn0fZULESpkQ8aZcnIe8PHj3XXjuOUhNNcf694fXXoMGDWwtTc6dMiFi5aRMqIXvx3bt2mV3CSJ/6cPfttLrzbmeZlDPJpVZPKJniTeDQJkQOZUyIeJNuTgP770HDz9sNoNat4bZs2HSJDWD/JwyIWLlpEyoIeTHtm3bZncJImc16oe1jP5xg+f48zs68OntHQgNCiyV91MmRKyUCRFvykUx5eWduH3PPdChA3zyCSxdCj162FeXlBhlQsTKSZnQJWN+LChI//nENxmGwfBxq5iwfA8AdStGMuOhboQElW4PWpkQsVImRLwpF0V04AA89RSsWAF//AGBgRARYd52ueyuTkqQMiFi5aRMuAzDMOwuoiylpaURGxtLamoqMTExdpcjUu5sOZjObZ8tIeloNgC14iP47ZHuuPTDo4iIiO/LyYG33jK3jk9PN8d+/hl69rS1LBERKZri9Dx0yZgfmzBhgt0liFgs3XGYy96Y62kGXd+uBrMfvqTMmkHKhIiVMiHiTbk4A8OAceOgaVN44gmzGdSxIyxYoGZQOadMiFg5KRPOmQtVDuWdfE23iM1+WX+AO79cCpi7iD10WUPu6FK3TGtQJkSslAkRb8rFaaSkmLuFzZ9vHicmwssvw003aRt5B1AmRKyclAk1hPxY7dq17S5BBMMw+GLhDp6bug4wlxWY/fAlJESFlnktyoSIlTIh4k25OI34eCgoMNcIevRRGD4cIiPtrkrKiDIhYuWkTKgh5Mfq169vdwnicGuSUrn988UkZ5hd9Coxofzwz662NINAmRA5lTIh4k25ALKy4O234b77ICbG/G3OZ59BdDTUqGF3dVLGlAkRKydlQnNA/djs2bPtLkEcbOqqvVz1znxPM2hwx5r89kgPKseE2VaTMiFipUyIeHN0Ltxu+N//oFEjc52g0aNP3Ne0qZpBDuXoTIichpMyoRlCIlJsP67exz+/XeE5fvX6ltzQvqaNFYmIiMhZLVwIDz0ES5aYx7VrQ4cOtpYkIiL2UkPIj3Xt2tXuEsSB/vvrFl6bsRGADnUq8OXfOxIR4hsfJcqEiJUyIeLNcbnYuRMeewzGjjWPo6JgxAizORRm36xe8R2Oy4TIX3BSJnTJmB87ePCg3SWIw/y0Zr+nGQTw8a3tfaYZBMqEyKmUCRFvjsvFs8+azSCXC+66CzZvhscfVzNIPByXCZG/4KRMqCHkxzZt2mR3CeIgczYeZMj/lnmOlz11GXERITZW5E2ZELFSJkS8lftcFBZCauqJ4+efh759Yfly+PhjqFrVvtrEJ5X7TIgUk5MyoYaQiPylxdsPW9YM+vOZXrbtJCYiIiJnMGcOtG9vzgQ6rkYNmDYNWre2qyoREfFRLsMwDLuLKEtpaWnExsaSmppKTEyM3eWI+Lz352zllZ82ABAY4GLRE5dSOVrTzEVERHzGli3wyCMwebJ5HBcHGzZAlSp2ViUiIjYoTs9DM4T82JQpU+wuQcqxrLwCrn1vgacZVK9iJMuf6uXTzSBlQsRKmRDxVq5ycfQoDB8OzZqZzaDAQLj/fnOdIDWDpIjKVSZESoCTMuE7q8FKsWVlZdldgpRTR7PyaP3cLM/xFc2r8u5NbQgK9O0esjIhYqVMiHgrN7lYssRcGyg52Ty+4gr497/N5pBIMZSbTIiUECdlQg0hP1ajRg27S5ByaFdKFtd9sNBz/PcudXnmav/44VKZELFSJkS8lZtcNGsGoaHQtKnZCOrTx+6KxE+Vm0yIlBAnZUINIT/WTL8BkhJ2JDOP279YzKH0XCJCArm/RwPu79HA7rKKTJkQsVImRLz5bS7Wr4cPP4Q33oCAAIiMhF9+gXr1IDjY7urEj/ltJkRKiZMy4dvXf8hZzZw50+4SpBxZk5RKm+dnse1QJgBT7u/iV80gUCZETqVMiHjzu1ykpMADD8AFF8B//gNffXXivsaN1QyS8+Z3mRApZU7KhGYIiQgb96dz/UmXiX17VycaVom2sSIRERGHy8uD996DUaPMxaMBrrkGLrrI1rJERKT8UEPIj3Xu3NnuEqQcWLAlmb9/sYTcAjcRIYFMub+L3zaDlAkRK2VCxJvP58IwYOpUePhhc7cwgJYt4c034dJL7a1NyiWfz4RIGXNSJtQQ8mOpqal2lyB+buSUNXy5aCcA0aFBTLr/IhpU9s9mECgTIqdSJkS8+UUuXnzRbAZVrmzevuMOc0t5kVLgF5kQKUNOyoTWEPJj69ats7sE8WMvTV/vaQYBzH/sUr9uBoEyIXIqZULEm0/m4sAByMgwb7tc8NZb8PjjZlPorrvUDJJS5ZOZELGRkzKhhpCIw6Rk5HLR6F/4aO42AFrWiGXHy1cSG6FFKUVERMpUTg688go0bAgvv3xi/MILYfRoiImxrzYRESn3XIZhGHYXUZbS0tKIjY0lNTWVGD8/yRYUFBAUpKv+pOh+WLmXB8as8Bw3qRrN//2zKyFB5aM3rEyIWCkTIt58IheGARMmwKOPwvbt5ljXrvDbb+aW8iJlyCcyIeJD/D0Txel56Izjx3766Se7SxA/8se2FEszaHDHmvz0ULdy0wwCZULkVMqEiDfbc7FsGVxyCQwcaDaDqleHL79UM0hsY3smRHyMkzLhv20vIT093e4SxE+s35fGoI9+B8ylCX4edgn1K0XZXFXJUyZErJQJEW+25uKjj+Dee83b4eHwyCPmLKHISPtqEsfTuULEykmZUEPIj1WtWtXuEsQPfLVoB89MWes5/m14D2olRNhYUelRJkSslAkRb7bmok8fiIiAAQPgpZegZk37ahE5RucKESsnZUINIT/Wtm1bu0sQH/fOL5v596xNJ44Htym3zSBQJkROpUyIeCuzXLjdMGYMLF0Kb75pjtWsCVu3goP+sSG+T+cKESsnZUIXKvux6dOn212C+KhCt8FV78zzNIMCA1ysHXU5V7eqbnNlpUuZELFSJkS8lUkuFi2Czp3hb38zt5BfuPDEfWoGiY/RuULEykmZ0AwhkXJmwZZkbv7kD89xYlw4vw7vXq4WjxYREfFJO3fC44/Dd9+Zx1FR8OST0KaNvXWJiIichhpCfqxDhw52lyA+ZtnOw5Zm0IvXtuDmTrVtrKhsKRMiVsqEiLdSyUVWlrkm0L//DTk55g4Od9wBL7wA1aqV/PuJlCCdK0SsnJQJNYT8WE5Ojt0liA/5dP52np+6znP85d87ckmjSjZWVPaUCRErZULEW6nkwjDg88/NZlD37vDGG5oVJH5D5woRKydlQteQ+LHVq1fbXYL4iLu/WuppBlWJCeWPJ3s6rhkEyoTIqZQJEW8llos//jAXjgZz2/j//hcmTYLZs9UMEr+ic4WIlZMyoYaQiB9Ly8nnmnfnM2vdAQA61oln0eM9qRITZnNlIiIi5dTWrXDddXDhhfC//50Y79/f/HK57KpMRESkWFyGYRh2F1GW0tLSiI2NJTU1lZiYGLvLOS85OTmEhekf/k6VX+im4YgfPcdVYkL5/YmeuBz8g6gyIWKlTIh4O+dcpKaaawK9/Tbk5UFAADz9NDz7bInXKFKWdK4QsfL3TBSn56EZQn5szpw5dpcgNur7n3me28N7N3J8MwiUCZFTKRMi3oqdi4IC+OADaNgQXn/dbAb17g2rVqkZJOWCzhUiVk7KhBaV9mNHjhyxuwSxySfztrH5YAYAN3eqxT8vbWhzRb5BmRCxUiZEvBU7F3//O3z9tXm7SRNzJ7E+fXRpmJQbOleIWDkpE5oh5McqVqxodwlig1d+2sAL09YDEBoUwPP9Wthcke9QJkSslAkRb8XOxb33QkICvPOOOSuob181g6Rc0blCxMpJmdAaQn4sIyODqKgou8uQMjRx+R6Gfb/Sc7z+uSsIDwm0sSLfokyIWCkTIt7OmovDh2HUKLMB9MwzJ8YzM82dxETKIZ0rRKz8PRNaQ8ghfvjhB7tLkDJiGAYPfbfC0wzqUKcCm1/so2bQKZQJEStlQsTbaXORn28uFt2ggfnn6NFw6NCJ+9UMknJM5woRKydl4pwaQgUFBfz88898+OGHpKenA7B3714yMjJKtDgRgdTsfG786Hcm/7kXgCZVo/n6zk4EB6qfKyIicl4MA6ZOhQsugH/9C44cMW9PnQqVKtldnYiISKkq9r8od+7cyQUXXEC/fv24//77OXTstyevvvoqw4cPL3YB7733HnXr1iUsLIx27doxb968sz4+NzeXESNGULt2bUJDQ6lfvz6fffZZsd+3PGjTpo3dJUgpSzqaTYcXf+aP7YcB6HtBVX7818WEBWtm0OkoEyJWyoSIN08utm6Fyy+Hq6+GjRvNBtCHH8KKFdCzp71FipQhnStErJyUiWLvMvavf/2L9u3bs3LlShISEjzj1157LXfddVexXmvs2LE89NBDvPfee3Tp0oUPP/yQPn36sG7dOmrVqnXa59xwww0cOHCATz/9lAYNGnDw4EEKCgqK+22I+Lx5mw9xy6eLPcfv3dyWvhdUs7EiERGRciQoCObNg5AQeOghePJJiI21uyoREZEyU+yG0Pz581mwYAEhISGW8dq1a5OUlFSs13rjjTe48847PY2kt956ixkzZvD+++8zevRor8f/9NNP/Pbbb2zbto34+HgA6tSpU9xvodxYsWIFTZo0sbsMKQVr96ZamkET/tGZdrXjbazIPygTIlbKhMhJcnNhxgxWZGaauahdG774Ajp0gHr17K5OxDY6V4hYOSkTxb5kzO12U1hY6DW+Z88eoqOji/w6eXl5LFu2jN69e1vGe/fuzcKFC0/7nP/7v/+jffv2vPrqqyQmJtKoUSOGDx9Odnb2Gd8nNzeXtLQ0y5eIL8vILeDKt+cDEBMWxOIne6oZJCIicq4MAyZMgGbNoF8/Km7adOK+QYPUDBIREccq9gyhXr168dZbb/HRRx8B4HK5yMjIYOTIkfTt27fIr5OcnExhYSFVqlSxjFepUoX9+/ef9jnbtm1j/vz5hIWFMWnSJJKTk7nvvvs4fPjwGdcRGj16NKNGjfIaHzduHBEREQwYMIBffvmF1NRUKleuTMeOHZk6dSoAbdu2xe128+effwLQr18/5s+fT0pKCvHx8XTr1o3JkycD0LJlS4KDg1m2bBkAV155JUuXLuXAgQPExMTQu3dvxo8fD0Dz5s2Jiorijz/+AODyyy9nzZo1JCUlERkZyVVXXcXYsWMBaNy4MRUrVmTBggUAXHbZZWzatIldu3YRGGiuIzN27Fjcbjf169cnMTGRuXPnAtC9e3d27drFtm3bCAoKYuDAgUyYMIG8vDxq165N/fr1mT17NgBdu3bl4MGDbDr2Q9LgwYOZMmUKWVlZ1KhRg2bNmjFz5kwAOnfuTGpqKuvWrQNg4MCB/PTTT6Snp1O1alXatm3L9OnTAejQoQM5OTmsXr0aMC8tnDNnDkeOHKFixYp07tzZs4r78Ws1V6xYAcDVV1/NokWLSE5OpkKFCnTv3p1JkyYBcMEFFxAWFsaSJUsA6Nu3L8uXL2f//v1ER0dzxRVXMG7cOACaNWtGbGwsixYtAsym47p169izZw8RERH069ePMWPGANCoUSMqV67M/PlmQ+bSSy9l69at7Ny5k5CQEK677jrGjRtHQUEB9erVo1atWsyZMweAbt26kZSUxNatWwkICGDQoEFMnDiR3NxcatWqRaNGjfj5558B6NKlC8nJyWzcuBGAQYMGMXXqVA6nZfLxjjjP/6e31zpKRvJe9u/IYO3atQBcf/31zJw5k7S0NKpUqUL79u2ZNm0aAO3atSM/P59Vq1YB0L9/f+bOncvhw4dJSEiga9euTJkyBYDWrVsTEBDA8uXLAbjqqqtYvHgxBw8eJDY2lp49ezJx4kQAWrRoQUREBIsXm7OW+vTpw8qVK9m7dy9RUVH07duX77//HoAmTZoQHx/vaez26tWLDRs2sHv3bsLDw+nfvz/fffcdhmHQsGFDqlat6lk7rEePHuzYsYPt27cTHBzM9ddfz/jx48nPz6du3brUqVOHX3/9FYCLL76Y/fv3s3nzZlwuFzfeeCNBQUGMGTOGmjVr0qRJE2bNmgXARRddxOHDh9mwYQNgXno6ffp0MjIyqF69Oq1ateLHH38EoGPHjmRlZbFmzRoAv/6MCA0NZcCAAfqMoPx8RmRmZpKYmEiLFi2YMWMGAJ06dSIj4/SfEfHx8aSlpekz4thnxOTJk8nOztZnhIM+I9Z89RX13nmHysf+22bFxRGQns6aNWv0GaGfI/QZgfkZ0aZNG8//w077jNDPEfqMON1nREFBAXv27PHbz4isrCyKymUYhlHkR2PuJtajRw8CAwPZvHkz7du3Z/PmzVSsWJG5c+dSuXLlIr9OYmIiCxcupHPnzp7xF198ka+//trzl3my3r17M2/ePPbv30/ssWu8J06cyPXXX09mZibh4eFez8nNzSU3N9dznJaWRs2aNUlNTSUmJqY437rPmTVrFr169bK7DCkh6/am0fftE4uq39GlDiOvbm5jRf5HmRCxUibEsfbuhREj4MsvzRlCYWHwyCPw6KPMWrRIuRA5ic4VIlb+nom0tDRiY2OL1PMo9gyh6tWr8+eff/Ldd9+xbNky3G43d955JzfffPNpGzJnUrFiRQIDA71mAx08eNBr1tBx1apVIzEx0dMMAmjatCmGYbBnzx4aNmzo9ZzQ0FBCQ0OLXJc/SU5OtrsEKSHDx61k/LI9nuP/3tSWK1tqAeniUiZErJQJcSS3G3r0gOOXht18M4weDTVrAsqFyKmUCRErJ2Wi2A2huXPnctFFF3HHHXdwxx13eMYLCgqYO3cu3bp1K9LrhISE0K5dO2bNmsW1117rGZ81axb9+vU77XO6dOnCuHHjyMjIICoqCoBNmzYREBBAjRo1ivut+L0KFSrYXYKco9TsfMYt3U1aTgFT/kxiZ8qJaX3/u7MTXRtWtLE6/6VMiFgpE+IYhmF+BQSYX088YW4h/9Zb0KmT5aHKhYiVMiFi5aRMFPuSscDAQPbt2+d1aVhKSgqVK1c+7YLTZzJ27FhuueUWPvjgAzp37sxHH33Exx9/zNq1a6lduzZPPPEESUlJfPXVVwBkZGTQtGlTLrzwQkaNGkVycjJ33XUXl1xyCR9//HGR3rM406d8XU5ODmFhYXaXIcU0ffU+7vtmudd4Ylw48x7tQUCAy4aqygdlQsRKmRBH+P13GDoU7rsPbrnFHHO7weUyv06hXIhYKRMiVv6eieL0PIq9y5hhGLhOc3JNSUkhMjKyWK81aNAg3nrrLZ577jlat27N3LlzmT59OrVr1wZg37597Nq1y/P4qKgoZs2axdGjR2nfvj0333wzV199NW+//XZxv41y4fiiZ+If9hzJov9/F3iaQYEBLipGhXD7RXV48NIGzB5+iZpB50mZELFSJqRc273bvBysc2ezKfTii2YjCMxZQqf5eRWUC5FTKRMiVk7KRJEvGRswYABg7ip2++23W9blKSwsZNWqVVx00UXFLuC+++7jvvvuO+19X3zxhdfYyat5i/iLPUey6P3mXLLyTsygW/xkTxKiyuf6ViIiIqUmIwNefRVeew1ycszGz223mQ2hgGL/rlNERMSxitwQOr6Qs2EYREdHWxaQDgkJ4cILL+Tuu+8u+QrljC644AK7S5AiuOXTP5i32VyYLCo0iMf7NOH6djUICw60ubLyR5kQsVImpNyZNg3uvhv27TOPu3WDN9+Etm2L/BLKhYiVMiFi5aRMFLkh9PnnnwNQp04dhg8fXuzLw6Tk+fN1jU7x6fztnmYQwCe3tefCegk2VlS+KRMiVsqElDsxMWYzqG5dc4bQgAFnvDTsTJQLEStlQsTKSZko9rzakSNHqhnkI5YsWWJ3CXIWy3Ye4YVp6wC4IDGWTS/0UTOolCkTIlbKhPi9bdtg3LgTxxdfDJMmwfr1cN11xW4GgXIhciplQsTKSZko9rbzAOPHj+f7779n165d5OXlWe5bvtx79yQRp0k6ms09Xy3FMKBjnXjG3nvhaRdjFxERkdNISzPXBHrrLQgMhAsvhJo1zfv697ezMhERkXKj2DOE3n77be644w4qV67MihUr6NixIwkJCWzbto0+ffqURo1yBn379rW7BDmN//y8mW6v/kpKZh51EiL44JZ2agaVEWVCxEqZEL9TWAgffQQNGpgLR+flQdeu5p8lRLkQsVImRKyclIliN4Tee+89PvroI959911CQkJ49NFHmTVrFg8++CCpqamlUaOcgWZj+Z77v1nOmz9votBtUDEqhA9uaUd8ZIjdZTmGMiFipUyIX/nlF2jTBu69Fw4dgsaNYepUmDED6tcvsbdRLkSslAkRKydlotgNoV27dnm2lw8PDyc9PR2AW265hTFjxpRsdXJW+/fvt7sEOcm0VfuYttrc9aRuxUgWPH4pTarG2FyVsygTIlbKhPiNAwfgyith9WqoUAH+8x/z9pVXntM6QWejXIhYKRMiVk7KRLEbQlWrViUlJQWA2rVr8/vvvwOwfft2DMMo2erkrKKjo+0uQY5ZtzeN+781O8nRYUH8+K+LCQ3StvJlTZkQsVImxKdlZZ24XaUKPPYYPPggbNli/hkcXCpvq1yIWCkTIlZOyoTLKGYX56677qJmzZqMHDmSDz74gGHDhtGlSxeWLl3KgAED+PTTT0ur1hKRlpZGbGwsqampxMT49+yNgoICgoLOaV1wKUETlu3h4XErPceLn+xJ5RjnbFXoS5QJEStlQnxSfj588AGMGmVeEnbhhWX69sqFiJUyIWLl75koTs+j2DOEPvroI0aMGAHAkCFD+OKLL2jatCmjRo3i/fffP7eK5ZyMO3kbVrHF17/vtDSDpj7QVc0gGykTIlbKhPgUw4Bp06BlS3MGUEoK2PCzo3IhYqVMiFg5KRPFbnsFBAQQEHCij3TDDTdwww03AJCUlERiYmLJVSfiw37deJCnJ68BoEpMKJPv70K12HCbqxIREfFBa9bAww/DzJnmccWK8PzzcNdd9tYlIiLiYMWeIXQ6+/fv54EHHqBBgwYl8XJSRM2aNbO7BMdKzcrnjs+XABAaFMBP/+qmZpAPUCZErJQJ8QnPPAOtWpnNoOBgGD4cNm+GIUPAhin5yoWIlTIhYuWkTBS5IXT06FFuvvlmKlWqRPXq1Xn77bdxu90888wz1KtXj99//53PPvusNGuVU8TGxtpdgiMVFLq54j9zPce/PHwJFbS1vE9QJkSslAnxCXXqgNsN114L69bBa69BXJxt5SgXIlbKhIiVkzJR5IbQk08+ydy5c7ntttuIj49n6NChXHXVVcyfP58ff/yRJUuWMHjw4NKsVU6xaNEiu0twnC0HM7j037+xLzUHgNEDLqBGhQibq5LjlAkRK2VCypxhwOTJ8MMPJ8Zuuw0WLoSJE8EHZpMrFyJWyoSIlZMyUeR5utOmTePzzz/nsssu47777qNBgwY0atSIt956qxTLE/Edu1Ky6Pv2PPIK3AC8ct0FDOpQy+aqREREfMSff8LQoTBnDtSoAT17QkQEBAZC5852VyciIiKnKPK288HBwezcuZPq1asDEBERweLFi2nRokWpFljSytO28ykpKSQkJNhdhiOs3ZvKtf9dSF6h2Qz6+s6OXNywks1VyamUCRErZULKxP798NRT8Nln5gyhsDBzAeknnzQbQj5GuRCxUiZErPw9E6Wy7bzb7SY4ONhzHBgYSGRk5LlXKedt3bp1dpfgCIfSc7n98yWeZtCEf3RWM8hHKRMiVsqElKrsbHjpJWjYED791GwG3XgjbNgAL7zgk80gUC5ETqVMiFg5KRNFvmTMMAxuv/12QkNDAcjJyWHIkCFeTaGJEyeWbIVyRnv27LG7hHKv0G1w5dvzOJSeC8D4IZ1pVzve5qrkTJQJEStlQkrVsmUwYoR5u2NHePNNuOgie2sqAuVCxEqZELFyUiaK3BC67bbbLMd/+9vfSrwYKZ4IH/3NW3lhGAbP/bCWg+m5hAQG8NrAlrSvo2aQL1MmRKyUCSlxBw5AlSrm7a5d4Z//hAsvhMGDIaDIE89tpVyIWCkTIlZOykSR1xAqL8rTGkJSur5cuIOR/7cWgBevbcHNnWrbXJGIiIhNdu+GJ54wdxDbuBESE+2uSERERE6jVNYQEt8zZswYu0sot9bvS/M0g+7qWlfNID+hTIhYKRNy3jIzYeRIaNwYvvnGPP7xR7urOi/KhYiVMiFi5aRMFPmSMRGnyC908/D3KwFoWi2Gx/o0sbkiERGRMuZ2w9dfmzuF7d1rjnXtaq4T1L69vbWJiIhIiVBDyI81atTI7hLKpccnrGbdvjQCXPDmoFYEB2oinb9QJkSslAk5J243XHIJzJ9vHtetC6++CtddBy6XvbWVAOVCxEqZELFyUib0L10/VrlyZbtLKHfScvKZsNxcVf6aVtVpUlXrTPkTZULESpmQcxIQAF26QHQ0vPIKrFsH119fLppBoFyInEqZELFyUibUEPJj84//5k5KzCdzt3luvzawlY2VyLlQJkSslAkpkrQ0c8HoxYtPjI0YAZs3w6OPQliYfbWVAuVCxEqZELFyUibOqSH09ddf06VLF6pXr87OnTsBeOutt5gyZUqJFidSlnalZPH27C0A3H5RHV0qJiIi5VthIXz8MTRsCC+/DA89BMc3n42OPrG9vIiIiJRLxf4X7/vvv8+wYcPo27cvR48epbCwEIC4uDjeeuutkq5PzuLSSy+1u4Ryo6DQzbXvLQCgRoVwHtdC0n5JmRCxUibkjGbPhrZt4Z574OBBaNTIXEDaAZQLEStlQsTKSZkodkPonXfe4eOPP2bEiBEEBgZ6xtu3b8/q1atLtDg5u61bt9pdQrkxe8NBUjLzAPjs9g6EBQf+xTPEFykTIlbKhHjZvBn694eePWHVKoiLM3cOW70arrqq3KwTdDbKhYiVMiFi5aRMFLshtH37dtq0aeM1HhoaSmZmZokUJUVz/HI9OX8fHVs76No2iTSqEm1zNXKulAkRK2VCvPz2G0yZAoGB8MADsGWLealYSIjdlZUZ5ULESpkQsXJSJoq97XzdunX5888/qV27tmX8xx9/pFmzZiVWmPy1EAf98Faaflqzj6U7jxDggrsurmt3OXIelAkRK2VCKCiAbdvMS8IA7rjDnBn0j39A06b21mYT5ULESpkQsXJSJlyGcXz1wKL5/PPPefrpp/n3v//NnXfeySeffMLWrVsZPXo0n3zyCTfeeGNp1Voi0tLSiI2NJTU1lZgYbSnudAWFbto+P4u0nAJu7FCTl69raXdJIiIiJeOnn2DYMMjIgI0bITzc7opERESklBWn51HsS8buuOMORo4cyaOPPkpWVhY33XQTH3zwAf/5z398vhlU3owbN87uEvzex/O2k5ZTAMAjlze2uRo5X8qEiJUy4VDr1kGfPubX+vWQlQVr19pdlc9QLkSslAkRKydlotiXjAHcfffd3H333SQnJ+N2u6lcuXJJ1yVFUFBQYHcJfi0nv5BXftoAwAOXNiAhKtTmiuR8KRMiVsqEwyQnw7PPwgcfmFvKBwfDgw/CU0+Zi0cLoFyInEqZELFyUiaK3RAaNWoUf/vb36hfvz4VK1YsjZqkiOrVq2d3CX5t1A/rAAgOdDHkkvo2VyMlQZkQsVImHGT/fnNNoKNHzeP+/eG116BBAzur8knKhYiVMiFi5aRMFPuSsQkTJtCoUSMuvPBC3n33XQ4dOlQadUkR1KpVy+4S/NbRrDzGLN4FwN0X1yMy9Jwmy4mPUSZErJQJB6laFXr0gNatYfZsmDRJzaAzUC5ErJQJESsnZaLYDaFVq1axatUqLr30Ut544w0SExPp27cv3377LVlZWaVRo5zBnDlz7C7Bbz03dZ3nttYOKj+UCRErZaIcW7kSrroKkpJOjH32GSxdajaG5IyUCxErZULEykmZKHZDCKB58+a89NJLbNu2jV9//ZW6devy0EMPUbVq1ZKuT6TEzVy7n4nLzR+gB3esicvlsrkiERGRItq/H+6+G9q0gWnT4JlnTtwXFweBgbaVJiIiIv7lnBpCJ4uMjCQ8PJyQkBDy8/NLoiYpom7dutldgt8pKHTz8LiVAESHBfFC/wtsrkhKkjIhYqVMlCM5OTB6NDRsCJ98AoYBgwbB00/bXZnfUS5ErJQJESsnZeKcGkLbt2/nxRdfpFmzZrRv357ly5fz7LPPsn///pKuT84i6eRp4lIk//llM+nHtpmf92gPAgM0O6g8USZErJSJcmLiRHPB6CefhIwM6NAB5s+H776DOnXsrs7vKBciVsqEiJWTMlHshlDnzp1p0KAB48aN44477mDnzp3Mnj2bu+66i9jY2NKoUc5g69atdpfgVzJyC3hn9hYABrRJJC4ixOaKpKQpEyJWykQ58fvvsGMHJCbC11+bx1262F2V31IuRKyUCRErJ2Wi2Fsr9ejRg08++YTmzZuXRj1SDAEB533Fn6O8/ON6z+2RV+v/3/JImRCxUib81J49kJkJjY9tejBiBFSoAA8+CJGR9tZWDigXIlbKhIiVkzLhMgzDsLuIspSWlkZsbCypqanExMTYXY6UkZ0pmfR6cy55BW4euqwhD13WyO6SRERErLKy4LXX4JVXoG1bmDcPtPGBiIiIFENxeh5FmiE0bNgwnn/+eSIjIxk2bNhZH/vGG28UvVI5LxMnTmTAgAF2l+HzcvIL+cf/lpNX4KZSdCj/7NHA7pKklCgTIlbKhJ9wu+Hbb+Hxx63byB85AvHx9tVVTikXIlbKhIiVkzJRpIbQihUrPDuIrVixolQLkqLLzc21uwS/cP83y1m3L42gABff3NWJoEDnTAF0GmVCxEqZ8AMLF8JDD8GSJeZx7drw6qswcKBmB5US5ULESpkQsXJSJorUEPr1119Pe1vsVatWLbtL8Hk/rNzLLxsOAvDuTW1oVCXa5oqkNCkTIlbKhI/7+Wfo1cu8HRVlrhX00EMQFmZrWeWdciFipUyIWDkpE8WeKvH3v/+d9PR0r/HMzEz+/ve/l0hRUjSNGmkdnLM5lJ7Lw+NWAlArPoIrWlSzuSIpbcqEiJUy4YNOXrqxRw9o3Rruugs2bzYvGVMzqNQpFyJWyoSIlZMyUeyG0Jdffkl2drbXeHZ2Nl999VWJFCVF8/PPP9tdgs8pKHTz6k8buPGjRXR48WfyCtwAjLnnQpsrk7KgTIhYKRM+pLAQPvkEOneGnBxzLDDQ3EL+44+halV763MQ5ULESpkQsXJSJoq87XxaWhqGYWAYBunp6YSd9BuswsJCpk+fTuXKlUulSJGi2Lg/nWvenU/usSYQQHCgi7cGtSExLtzGykRExNF+/RWGDoWV5qxVPvrI3EIeIDTUvrpERETE0YrcEIqLi8PlcuFyuU47hcrlcjFq1KgSLU7OrkuXLnaXYIu8AjebDqRzKD2XP3cfJTU7H7dh8NWinZ7HdKhTgft6NKB59RgqR2v6vVM4NRMiZ6JM2GzzZnjkEZgyxTyOjYVnnoEhQ+yty+GUCxErZULEykmZKHJD6Ndff8UwDC699FImTJhA/EnboIaEhFC7dm2qV69eKkXK6SUnJztqwSvDMHjlp4188NvWsz5uwj860662tul1IqdlQuSvKBM2KSyERx+Fd96B/Hzz0rB774VRo6BiRburczzlQsRKmRCxclImitwQuuSSSwDYvn07tWrVwqWtUG23ceNG2rZta3cZZWLPkSweGLOCFbuOWsbb1Ipj9+FsOtSpQNNqMfRqVoWm1WLsKVJs56RMiBSFMmGTwEDYutVsBl1xBfz739Csmd1VyTHKhYiVMiFi5aRMFKkhtGrVKlq0aEFAQACpqamsXr36jI9t2bJliRUnAvDTmn0M+d9yz/FlTSvz0S3tCQhQU1JERHzEjBnQsiVUO7aj5euvm7OC+vSxty4RERGRM3AZxsn7n55eQEAA+/fvp3LlygQEBOByuTjd01wuF4WFhaVSaElJS0sjNjaW1NRUYmL8eyaJ2+0mIKDYG8X5lQNpOVz1znwOpecC0LRaDOOHdCYytMiT28RBnJAJkeJQJsrA+vUwfDhMnw533AGffWZ3RfIXlAsRK2VCxMrfM1GcnkeRvsvt27dTqVIlz+1t27axfft2r69t27adf/VSZFOnTrW7hFKz7VAG7V/4mU4v/eJpBv087BJ+/NfFagbJGZXnTIicC2WiFKWkwAMPwAUXmM2goCCIj4e//j2b2Ey5ELFSJkSsnJSJIv3Lunbt2qe9LfbKzMy0u4RSc9kbv+E+9jN1dGgQj17RmAaVo+wtSnxeec6EyLlQJkpBXh689565QPTRo+ZYv37w2mvQsKGtpUnRKBciVsqEiJWTMlHseVBffvkl06ZN8xw/+uijxMXFcdFFF7Fz586zPFNKWmJiot0llIo1SameZtA7g9uwetTl3NK5jq01iX8or5kQOVfKRCl47TUYOtRsBrVsCb/8ApMnqxnkR5QLEStlQsTKSZkodkPopZdeIjw8HIBFixbx7rvv8uqrr1KxYkWGDh1a4gXKmbVo0cLuEkrFbZ8tBqBJ1WiublXd5mrEn5TXTIicK2WihBQUnLh9//3QvDl89BEsXw6XXmpfXXJOlAsRK2VCxMpJmSh2Q2j37t00aNAAgMmTJ3P99ddzzz33MHr0aObNm1fiBcqZzZgxw+4SStyWg+mkZOYBcF+PBjZXI/6mPGZC5HwoE+fpwAG45x7o1evE2kBxcbB6Ndx9t7m9vPgd5ULESpkQsXJSJordEIqKiiIlJQWAmTNnctlllwEQFhZGdnZ2yVYnjnPnl0sBaJEYw9Utq9lcjYiIOFJODrzyinkZ2Mcfw5w5sGjRiftdLttKExERESkpxd6uqVevXtx11120adOGTZs2ceWVVwKwdu1a6tSpU9L1yVl06tTJ7hJK1Nq9qexMyQLg+X4tcOkHbimm8pYJkfOlTBSTYcD48fDoo7BjhznWrh28+SZcdJGtpUnJUS5ErJQJESsnZaLYM4T++9//0rlzZw4dOsSECRNISEgAYNmyZQwePLjEC5Qzy8jIsLuEElNQ6GbwR78DEBMWRJtaFWyuSPxRecqESElQJorhwAHo1g1uuMFsBlWvDl9+CYsXw8UX212dlCDlQsRKmRCxclImij1DKC4ujnfffddrfNSoUSVSkBTd2rVradmypd1llIgvF+0kLcdctHPy/V1srkb8VXnKhEhJUCaKISHB3DksPBweecScJRQZaXdVUgqUCxErZULEykmZKHZDCODo0aN8+umnrF+/HpfLRdOmTbnzzjuJjY0t6frEAZIzcnl+6joA+reuTr1KUTZXJCIi5V5WFnzwAdx3H4SFQVAQfP212RiqWdPu6kRERERKncswjm+bUTRLly7l8ssvJzw8nI4dO2IYBkuXLiU7O5uZM2fStm3b0qq1RKSlpREbG0tqaioxMTF2l3Ne8vPzCQ4OtruM8/bgmBX838q9AKwddTmRoefUpxQpN5kQKSnKxGm43TBmDDz+OOzZAy+/DI89ZndVUoaUCxErZULEyt8zUZyeR7HXEBo6dCjX/H979x0eRbm+cfybXknohBIglNCRJkgHRZogiBSVI+IBPYiKYjuWnwI2bCCWA3pERT1ILzakiHRQupTQpLeAtAQS0uf3x8LCuAESSHZ2M/fnunKxOzs78+yGe0Me3nnfO+9k3759zJw5k1mzZrF37166dOnCk08+eb01y3WYP3++1SXcsPlb453NoMdvraJmkNyQgpAJkbykTPzNqlWOyaH/8Q9HM6h8ecdKYmIryoWImTIhYmanTOT6t++1a9fy2Wef4e9/6an+/v4899xzNGrUKE+Lk6tLTEy0uoQbkpll8MTkjQAUDg3gidv0j3K5Md6eCZG8pkxcsH+/Y0TQ5MmO++Hh8MILMHSoY84gsRXlQsRMmRAxs1Mmcj1CKCIiggMHDrhsP3jwIIUKFcqToiRnSpUqZXUJN2TwxHWcT88EYNq/muLvl+u/jiIm3p4JkbymTFzw9NOOZpCPD/zzn7BzJ7z4oppBNqVciJgpEyJmdspErkcI9enThwEDBvDee+/RrFkzfHx8WL58Oc8++6yWnXczbx6RFXckkXlbjwGOS8WqllIzUW6cN2dCJD/YNhOZmXD+vGMkEMCbb0JiIrz9NtSvb21tYjnb5kLkCpQJETM7ZSLXQzLee+89evToQb9+/ahYsSIVKlSgf//+9OzZk7fffjs/apQr+Omnn6wu4bq9OGuz8/aT7WItrEQKEm/OhEh+sGUmliyBm2+Gy+c1jI2F+fPVDBLAprkQuQplQsTMTpnI9QihwMBAPvjgA0aOHMnu3bsxDIMqVaoQGhqaH/VJAbRm3yk2HjwDwC9PtcbP18fagkRExPvt3g3PPQczZzru79sH774LRYpYWpaIiIiIp8rxCKHk5GQeffRRypYtS8mSJRk4cCClS5embt26agZZpGHDhlaXcF3+s+hPAG6tXpIqJcMtrkYKEm/NhEh+sUUmEhIcjaCaNR3NIF9feOQR2LFDzSDJli1yIZILyoSImZ0ykeMRQsOGDWPChAn07duX4OBgJk2axCOPPMK0adPysz65ivT0dKtLyLV35m5n8Y6/ALSqmOQ5b8yESH4q8JlYuRK6d4e/HD9XaN8eRo+GWrUsLUs8W4HPhUguKRMiZnbKRI5HCM2cOZPPP/+c//73v3z44Yf89NNPzJ49m8zMzPysT65i06ZNVpeQK0cTzjN28W4AetQvy03Rha0tSAocb8uESH4r8JmoUQOysqB6dfjpJ5g7V80guaYCnwuRXFImRMzslIkcN4QOHjxIy5YtnfcbN26Mv78/R44cyZfCpGDZeewsTUf+6rw/7E79g11ERHJp+3Z46SUwDMf9IkVg0SLYtAk6d3YsKy8iIiIiOeJjGBf/VXV1fn5+xMfHU6JECee2QoUKsWnTJmJiYvKtwLyWmJhIZGQkCQkJREREWF3ODTl//jwhISFWl5Ejt763mD0nkgCYObgZDcprXgfJe96UCRF3KDCZOHUKRoyAsWMhI8MxV9Bdd1ldlXipApMLkTyiTIiYeXsmctPzyPEIIcMw6N+/Pz169HB+paSkMGjQINM2cZ+lS5daXUKOfPzrLmcz6KN766sZJPnGWzIh4i5en4n0dPjwQ6hSxfFnRgZ07arLwuSGeH0uRPKYMiFiZqdM5HhS6QceeMBl2z/+8Y88LUZy59SpU1aXcE0ZmVnMXH8YgJvKRdL1pjIWVyQFmTdkQsSdvDYThuGYE+iZZxyrhQHUqeOYMLpdO2trE6/ntbkQySfKhIiZnTKR44bQl19+mZ91yHUoVqyY1SVc00+bjzpHB314b32Lq5GCzhsyIeJOXpuJrCx4/nlHM6hECXj9dRgwAPz8rK5MCgCvzYVIPlEmRMzslIkczyFUUBSkOYSSk5MJDQ21uowrSkrN4PbRSziSkMK9jcszskcdq0uSAs7TMyHibl6Vib/+gkKFIDjYcX/BAvjlF3jxRYiMtLY2KVC8KhcibqBMiJh5eybyZQ4h8Tzfffed1SVc1WfL9nAkIYVCwf4837G61eWIDXh6JkTczSsykZoK7757aZ6gi26/Hd5+W80gyXNekQsRN1ImRMzslAk1hCRfGIbBp0v2APBwy0pEhgZYXJGIiHgUw3CsFlazJjz3HCQmwty5l5aUFxEREZF8pYaQF6tXr57VJVzR73tPcT49E4C7G5azuBqxC0/OhIgVPDYT69dD27Zw992wZw+ULg0TJjguEfPxsbo6KeA8NhciFlEmRMzslIkcTyotnsfX13P7ec9O/wOA7vXKUKZwiMXViF14ciZErOCRmfj4YxgyxDESKDgYnn3WMUIoPNzqysQmPDIXIhZSJkTM7JSJ63ql33zzDc2bN6dMmTLs378fgDFjxtjqWjtPsH79eqtLyNYvccc4eOo8AL1vjra4GrETT82EiFU8MhPt2oG/P9x3n2MVsVdfVTNI3MojcyFiIWVCxMxOmch1Q2jcuHE89dRTdO7cmTNnzpCZ6bgsqHDhwowZMyav6xMv9NWqfQA0q1yMZpWLW1uMiIhYxzBg0iR45ZVL26pXhz//hIkToXx562oTERERsblcLztfs2ZN3nzzTbp3706hQoX4448/qFSpElu2bKFNmzacOHEiv2rNEwVp2fmzZ89SqFAhq8sw2XciiTbvLQZgzpCW1Czj3e+xeBdPzISIlSzNxG+/wdChjj99fGDjRqhb15paRC6jnxUiZsqEiJm3ZyJfl53fu3cv9evXd9keFBREUlJSbg8nN2D16tVWl+Di40V/AhAR7K9mkLidJ2ZCxEqWZOLgQejbF5o2dTSDwsIcl4VVrer+WkSyoZ8VImbKhIiZnTKR64ZQTEwMGzdudNn+888/U7NmzbyoSXLo+PHjVpdgcvBUMtPXHQLg/T71rC1GbMnTMiFiNbdmIinJcWlYbCx8+61jVNCDD8LOnfB//wchWmBAPIN+VoiYKRMiZnbKRK5XGXv22Wd59NFHSUlJwTAMVq9ezaRJkxg5ciTjx4/PjxrlCiIjI60uwWT8sj0A3FQuklurl7S4GrEjT8uEiNXcmom0NPjPfyAlBVq1gvffhwYN3Hd+kRzSzwoRM2VCxMxOmcj1HEIAn332Ga+//joHDx4EoGzZsgwfPpwBAwbkeYF5rSDNIZSamkpQUJDVZQBgGAYxL8wB4KN769P1pjIWVyR25EmZEPEE+Z6JDRugXj3HaCBwTBQdEgJ33XVpm4iH0c8KETNlQsTM2zORr3MIATz00EPs37+f48ePEx8fz8GDB72iGVTQzJw50+oSnL5dfcB5+7YaGh0k1vCkTIh4gnzLxJ490LOnYwTQ999f2t63L/TooWaQeDT9rBAxUyZEzOyUietqCF1UvHhxSpbUL/8Cv+05BUC96MKEBub6SkQREfEGiYnw739DjRowYwb4+sLmzVZXJSIiIiLXIde/ucfExOBzlf/527Nnzw0VJDlXu3Ztq0sAICMziwVx8QA817GaxdWInXlKJkQ8RZ5lIjMTPv/cMTn0X385tt1+O4waBXXq5M05RNxEPytEzJQJETM7ZSLXDaEnn3zSdD89PZ0NGzYwd+5cnn322byqS3IgNDTU6hIAWBB3jJT0LAL9fGlQvojV5YiNeUomRDxFnmXi3nth2jTH7dhYGD0aOnfWpWHilfSzQsRMmRAxs1Mmct0QeuKJJ7Ld/p///Ie1a9fecEGSc6tXr6Zy5cpWl8Hny/cC0LlOFMEBfhZXI3bmKZkQ8RR5lokHH4RffoFhw2DwYAgIuPFjilhEPytEzJQJETM7ZeKG5hC6XKdOnZgxY0ZeHU68RML5dNbuPw1AWy01LyLi/U6dgiefhA8+uLStUyfYtw+eeELNIBEREZECIs9m/50+fTpFixbNq8NJDnTq1MnqEli68y/n7Tu11LxYzBMyIeJJcpWJ9HT45BMYPtzRFIqIgAcegMKFHY9fY9lSEW+hnxUiZsqEiJmdMpHrEUL169enQYMGzq/69etTunRpXnzxRV588cX8qFGu4I8//rC6BH7bcxKATrWjrjrZuIg7eEImRDxJjjJhGDBnDtStC0OGOJpBtWvD9OmXmkEiBYh+VoiYKRMiZnbKRK5HCHXv3t1039fXlxIlStCmTRuqV6+eV3VJDhw5csTqEpi7xbG6WKc6pS2uRMQzMiHiSa6ZiV274PHHYd48x/3ixeG112DgQPDPs0HEIh5FPytEzJQJETM7ZSJX/9rLyMigYsWKdOjQgaioqPyqSXIoPDzc0vOv+PMEJ5PS8PP1oVXV4pbWIgLWZ0LE01wzExkZjsmiAwIc8wa99BJERrqlNhGr6GeFiJkyIWJmp0z4GIZh5OYJoaGhbNu2jQoVKuRXTfkqMTGRyMhIEhISiPDy+RAyMzPx87NuVa+BX63ll23H6FK3NB/f18CyOkQusjoTIp7GJROpqbB0Kdx++6VtX34JrVqBTVbTENHPChEzZULEzNszkZueR67nEGrSpAkbNmy47uL+buzYscTExBAcHEzDhg1ZtmxZjp63YsUK/P39qVevXp7V4m2mTp1q2bnPpqTzy7ZjAAxqrV8ixDNYmQkRT+TMhGHArFlQqxZ07AibN1/a6cEH1QwSW9HPChEzZULEzE6ZyPUEAYMHD+bpp5/m0KFDNGzYkLCwMNPjdevWzfGxpkyZwpNPPsnYsWNp3rw5n376KZ06dSIuLo7y5ctf8XkJCQn069eP2267jWPHjuX2JUgemLXhMAC+PlCrjHePtBIRKdA2bICnnoLFix33o6LgyBGoU8fSskRERETEWjluCP3zn/9kzJgx9OnTB4AhQ4Y4H/Px8cEwDHx8fMjMzMzxyUePHs2AAQMYOHAgAGPGjGHevHmMGzeOkSNHXvF5//rXv7jvvvvw8/Nj9uzZOT5fQWPlJN7ztzoacX1uLq/VxcRjaGJ7kcscPUr7KVPg++8dI4SCg+Hpp+Hf/4ZChayuTsQy+lkhYqZMiJjZKRM5bgh99dVXvPXWW+zduzdPTpyWlsa6det4/vnnTdvbt2/PypUrr/i8L7/8kt27d/O///2P119//ZrnSU1NJTU11Xk/MTHx+ov2MEWLFrXkvMlpGSz/8wQAd9Uva0kNItmxKhMiHicjA265hWIHDjju33MPvPUWeOn8fyJ5ST8rRMyUCREzO2Uixw2hi3NP59Vk0idOnCAzM5NSpUqZtpcqVYr4+Phsn7Nr1y6ef/55li1bhn8Ol8MdOXIkI0aMcNk+bdo0QkND6dGjBwsXLiQhIYGSJUvSuHFjfvzxRwAaNGhAVlYWGzduBKBbt24sX76ckydPUrRoUVq1auUcoVS3bl0CAgJYt24dAHfccQdr167l2LFjRERE0L59e6ZPnw5ArVq1CA8P5/fffwegQ4cObNmyhcOHDxMWFkaXLl2YMmUKANWqVaN48eKsWLECgHbt2rFz504OHDjA0aNHeeqpp5gyZQpZWVlUrlyZsmXLsnTpUgDatGnDgQMH2LNnD/7+/vTq1YsZM2aQlpZGhQoVqFy5Mr/++isALVq04Pjx4+zcuROAe++9l++++47k5GTKlStHzZo1mT9/PgBZpWsBEO6fxZ+r5tEguhdz587l7NmzREVF0aBBA+bMmQPAzTffTEpKCpsvzFdx1113sXjxYk6fPk3x4sVp2rQpP/zwAwD169cHcM5R1bVrV1atWsWJEycoUqQIbdq0YdasWQDUqVOH4OBg1qxZA0Dnzp1Zv3498fHxFCpUiI4dOzJt2jQAatasSWRkJKtWrQIcTce4uDgOHTpEaGgo3bp1Y9KkSQDExsZSsmRJli9fDsCtt97K7t272b9/P4GBgdx9991MmzaNjIwMKlWqRPny5Vl84TKMVq1acfjwYXbv3o2vry99+vRh5syZpKamUr58eWJjY/nll18AaN68OSdOnGDHjh0A9OnThx9//JGkpCTKli1L7dq1mXdhGegmTZpw7tw5tm7dCkDPnj2ZP38+iYmJlCpVikaNGvHTTz8B0LBhQ9LT09m0aRMA3bt3Z+nSpZw6dYpixYrRokULvvvuOwDq1auHr68v69evB6BLly6sXr2a48ePExkZyW233cbMmTMBqF27NqGhoaxevRqATp068ccff3DkyBHCw8Pp3Lmz81rb6tWrU7RoUWdj9/bbb2f79u0cPHiQkJAQunfvzuTJkzEMg6pVqxIVFeWcO6xt27bs27ePvXv3EhAQQM+ePZk+fTrp6enExMRQsWJFFi1aBEDLli2Jj49n165d+Pj4cM899zBz5kyioqKIjo6mevXqLFiwAIBmzZpx6tQptm/fDkDv3r2ZM2cO586do0yZMtx00038/PPPADRu3Jjk5GS2bNkC4NWfEUFBQfTo0cPtnxFNmzYlISGBuLg4AHr10mcEuOEz4sLf2Z69ejF//nxKt25NmaVLKTR+PN//9ResXEnD1FRbf0bMnj2b8+fP6zPC5p8Rhw8fpmPHjvb7jNC/I/QZcYXPiI0bNxISEgLoM8LW/47QZ4TzM+Lw4cPcc889XvsZkZycTE7leJUxX19fjh07RokSJXJ88Ks5cuQIZcuWZeXKlTRt2tS5/Y033uCbb75xvpkXZWZmcssttzBgwAAGDRoEwPDhw5k9e7bzDcpOdiOEoqOjC8QqY5MmTeLee+91+3mfnfYH09YdolaZCH4a0tLt5xe5EqsyIWK51ath6FDH5WB33unYlpnJpMmTubdvX2trE/Ew+lkhYqZMiJh5eyZys8pYriaVjo2NveZ8MadOncrRsYoXL46fn5/LaKDjx4+7jBoCOHv2LGvXrmXDhg089thjAGRlZWEYBv7+/syfP59bb73V5XlBQUEEBQXlqCZvc/vlywa7SXpmFgsurC7Wq2E5t59f5GqsyISIpQ4dghdegP/9z3F/+HDo2hV8fMDPj9s7dLC0PBFPpJ8VImbKhIiZnTKRq4bQiBEjiIyMzJMTBwYG0rBhQxYsWMBdd93l3L5gwQK6devmsn9ERIRzKOBFY8eO5ddff2X69OnExMTkSV3eZPv27bRo0cKt51yz9xRnktPx9/Wh7y2ai0I8ixWZELFEUhK88w68+y6cP+/Y1r8/vPGGoxl0gTIh4kq5EDFTJkTM7JSJXDWE7rnnHkqWLJlnJ3/qqae4//77adSoEU2bNuW///0vBw4ccF4S9sILL3D48GG+/vprfH19qV27tun5JUuWJDg42GW7XRw8eNDt5/x40Z8AtKlWggA/X7efX+RqrMiEiNv98AMMGuRYOh6gZUt4/31o2NBlV2VCxJVyIWKmTIiY2SkTOW4I5cfS4n369OHkyZO8+uqrHD16lNq1azNnzhznxNVHjx7lwMUVUsTFxcnf3OV4Ygord58E4N7G5d16bpGccHcmRCzh6+toBsXEOEYI9ehhGhV0OWVCxJVyIWKmTIiY2SkTuZpUOj4+Pk9HCFkhNxMsidkHv+zi/V92UqVkOAuGtsqXJqGIiPzN3r0QFwd33OG4bxgweTLcdRcEB1tbm4iIiIh4lNz0PHJ8zU9WVpbXN4MKmsmTJ7v1fN9tPAxAw/JF1AwSj+TuTIjkq8REx4TRNWpA375w4oRju48P3HtvjppByoSIK+VCxEyZEDGzUyY0CYwXy+HgrjxxPi2TPSeSAOh6Uxm3nVckN9yZCZF8k5kJn30GVavCW29Baio0agRnz+b6UMqEiCvlQsRMmRAxs1MmcjWptHiWqlWruu1cc7ceBaBQsD+NY4q67bwiueHOTIjki19/haFDYdMmx/3YWHjvPejS5YrzBF2NMiHiSrkQMVMmRMzslAk1hLxYVFSU2861aPtfAPSoX5ZAfw0sE8/kzkyI5LkDB6B9e8cIocKFYdgwGDwYAgOv+5DKhIgr5ULETJkQMbNTJvSbvRdbtmyZW86TlWUwZ7NjhFC7mqXcck6R6+GuTIjkmdTUS7fLl4fHHoPHH4c//4Qnn7yhZhAoEyLZUS5EzJQJETM7ZUINIbmmuVvjycgyCA304+aKulxMROSGZWTAf/4DFSrAli2Xtr//Pnz4IRQrZl1tIiIiImILagh5sbZt27rlPNuPJgJQs3QEwQF+bjmnyPVwVyZEbsjcuVC3rmM00LFj8PHHlx7L4xUclQkRV8qFiJkyIWJmp0yoIeTF9u3b55bzrNh9EoAOtexzLaV4J3dlQuS6xMVBp06Or23bHKOAxo41N4TymDIh4kq5EDFTJkTM7JQJNYS82N69e/P9HKeT0li3/zSAVhcTj+eOTIhcl5decowKmjsXAgLg6acd8wQ98gj459/6DsqEiCvlQsRMmRAxs1MmtMqYFwsICMj3c3z0658A+Pn6ULdcZL6fT+RGuCMTIteleHHH6mHdu8O770KVKm45rTIh4kq5EDFTJkTM7JQJH8MwDKuLcKfExEQiIyNJSEggIiLC6nI83n2f/cbK3Se5qVwk3z3WwupyREQ8n2HA999DoUJw662ObWlp8Pvv0LKltbWJiIiISIGWm56HLhnzYtOnT8/X46dlZPHbHsf8QS93qZmv5xLJC/mdCZFr+uMPuO02x0igwYMhPd2xPTDQkmaQMiHiSrkQMVMmRMzslAk1hLxY+sVfNPLJL9uOkWVARLA/DcoXyddzieSF/M6EyBXFx8NDD0H9+rBoEQQFQY8ejuXlLaRMiLhSLkTMlAkRMztlQnMIebGYmJh8Pf73G48A0K5mKXx983YpZJH8kN+ZEHGRkgLvvw9vvgnnzjm29ekDb70FFStaWhooEyLZUS5EzJQJETM7ZUINIS9WMR9/2UhJz2TFnycAuOfm8vl2HpG8lJ+ZEMnW4sXw4ouO240bO5pDzZpZWtLllAkRV8qFiJkyIWJmp0zokjEvtmjRonw79uIdf3E2NYPCoQE0KF84384jkpfyMxMiTqdOXbrdoQM88AB88w2sWuVRzSBQJkSyo1yImCkTImZ2yoQaQpKtib/vB+CmcoXx99NfExERDh92NH+qVoWTjgn38fGBCRPgH/8AX31WioiIiIj30L9evVjLfFyxZs9fSQDcFF04384hktfyMxNiY8nJMGIExMbC1187Rgj99JPVVeWIMiHiSrkQMVMmRMzslAk1hLxYfHx8vhz38JnzHD5zHoABze0zoZZ4v/zKhNhUVhb873+ORtDw4Y7GULNm8Pvv0K+f1dXliDIh4kq5EDFTJkTM7JQJNYS82K5du/LluCt2OSaTrlQ8jMjQgHw5h0h+yK9MiA2lp0OLFnD//Y5LxSpUgClTYPlyx+TRXkKZEHGlXIiYKRMiZnbKhBpCXszHJ3+Wgt91/CwARr4cXST/5FcmxIYCAuCmmyA83LGk/Pbt0Lu3Y84gL6JMiLhSLkTMlAkRMztlwscwDFv93p+YmEhkZCQJCQlERERYXY5HenTien7afJS+Tcrzxl11rC5HRCT/nT0LI0c6JoeuWdOx7eRJx0ihqChraxMRERERyaHc9Dw0QsiLzZ49O1+Ou+nwGQBax5bIl+OL5Jf8yoQUYJmZ8PnnjpXDRo6Ep5++9FixYl7fDFImRFwpFyJmyoSImZ0y4W91AXL9zp8/n+fHzMwyOHjKcdwqJcPz/Pgi+Sk/MiEF2KJF8NRTsHGj436VKjBoEBiG110adiXKhIgr5ULETJkQMbNTJjRCyItFR0fn+TF/3X7cebtCsbA8P75IfsqPTEgB9OefcNddcOutjmZQZCSMHg1bt0K3bgWmGQTKhEh2lAsRM2VCxMxOmdAIIS9WvXr1PD/mrA2HAMfvQ36+BeeXIrGH/MiEFEDffQezZ4Ofn2NE0PDhULy41VXlC2VCxJVyIWKmTIiY2SkTGiHkxRYsWJDnx5yzOR6AZztUy/Nji+S3/MiEFAAZGbBv36X7jz8OAwfCpk3w8ccFthkEyoRIdpQLETNlQsTMTplQQ0icziSnOW93rOXdE6mKiAAwb55j+fhOnRwrhgEEBsJnn11aTUxERERExIbUEPJizZo1y9PjbY8/67xdqYQmlBbvk9eZEC+2bRt07gwdO0JcHBw/7thmM8qEiCvlQsRMmRAxs1Mm1BDyYqdOncrT4205nABAy6oF9/IJKdjyOhPihU6edFwSVqcO/Pwz+PvD0KGOiaTr1rW6OrdTJkRcKRciZsqEiJmdMqGGkBfbvn17nh7vhz+OAFA/unCeHlfEXfI6E+Jl9u1zLB3/8ceQmQl33ulYOWz0aChSxOrqLKFMiLhSLkTMlAkRMztlQquMidOJc445hMoVDbW4EhGR61ChAjRoACdOOJpAt91mdUUiIiIiIh7LxzAMw+oi3CkxMZHIyEgSEhKIiIiwupwbkpmZiZ+fX54c69DpZFq8vQiAP4a1JzIkIE+OK+JOeZkJ8QKbNjmWjB8/HooWdWz76y/Hbf09AJQJkewoFyJmyoSImbdnIjc9D10y5sXmzJmTZ8datfskANVKFVIzSLxWXmZCPNixY/Dww1C/PsyaBa++eumxEiXUDLqMMiHiSrkQMVMmRMzslAldMubFzp07l2fHurjCWIMKhfPsmCLulpeZEA+UkgIffABvvAFnL6yK2KsXPPGEtXV5MGVCxJVyIWKmTIiY2SkTagh5sTJlyuTZsRbtOA5AZS03L14sLzMhHmbmTHjmGdi713G/USN4/31o0cLaujycMiHiSrkQMVMmRMzslAldMubFbrrppjw71uHT5wEoWzgkz44p4m55mQnxMPPmOZpBZcrAV1/B77+rGZQDyoSIK+VCxEyZEDGzUybUEPJiP//8c54c5+CpZFIzsgBoXrV4nhxTxAp5lQnxAEeOOJaRv+jVV2HECNi5E/r1A1/9+MoJZULElXIhYqZMiJjZKRP6F7Ww72SS83ZEsCaUFhELJSc7mj9Vq8Kjj17aXqoUvPIKhIVZV5uIiIiISAGiOYS8WOPGjfPkOFsOJwJQv3zhPDmeiFXyKhNigawsmDQJnn8eDh1ybDt92jF5dKFC1tbmxZQJEVfKhYiZMiFiZqdMaISQF0tOTs6T48za4PjlK6aY/uddvFteZULcbNUqaNoU/vEPRzOofHmYPBlWrFAz6AYpEyKulAsRM2VCxMxOmVBDyItt2bIlT46z85hjWb1WsSXy5HgiVsmrTIgb/fADNGsGq1dDeLhjSfnt26FPH/Dxsbo6r6dMiLhSLkTMlAkRMztlQpeM2VzahcmkARpWKGJhJSJiS+3bQ5Uq0KoVvP46lC5tdUUiIiIiIrbgYxiGYXUR7pSYmEhkZCQJCQlERERYXc4NSU1NJSgo6IaO8cfBM3T7zwoA9o7sjI/+R168WF5kQvJRVpZjyfiJE2HuXPC/8H8SyckQGmptbQWUMiHiSrkQMVMmRMy8PRO56XnokjEvtnDhwhs+xter9jtvqxkk3i4vMiH5ZOlSuPlm+Oc/YeFC+PrrS4+pGZRvlAkRV8qFiJkyIWJmp0yoIeTFEhISbvgYS3YeB+DuBuVu+FgiVsuLTEge270b7r4bWreG9eshMhLeew/69rW6MltQJkRcKRciZsqEiJmdMqE5hLxYyZIlb/gYJ86lAdAkpugNH0vEanmRCckj6enw0kvwwQeQlga+vvCvf8GIEVBCE9i7izIh4kq5EDFTJkTM7JQJNYS8WOPGjW/o+Ykp6c7bt9awz196KbhuNBOSh/z9Yc0aRzOofXsYNQpq17a6KttRJkRcKRciZsqEiJmdMqFLxrzYjz/+eEPPP3Ay2Xm7eLj3TpolctGNZkJu0IIFcOqU47aPD3z4Ifz0k2MCaTWDLKFMiLhSLkTMlAkRMztlQg0hG/ttz0kAgvz110BEbsD27dCli2Mk0GuvXdpepw507uxoDomIiIiIiEdRJ8CLNWjQ4Iae/9sex//kN6tcLC/KEbHcjWZCcunUKXjiCUfj56efHJeJBQRYXZVcRpkQcaVciJgpEyJmdsqE5hDyYllZWTf0/P0nkwBoV7NUXpQjYrkbzYTkUHo6jBsHw4fD6dOObV27wrvvQrVqlpYmZsqEiCvlQsRMmRAxs1MmNELIi23cuPG6n2sYBruOnwOgUvHwPKpIxFo3kgnJheHDHSODTp92jA5asAC+/17NIA+kTIi4Ui5EzJQJETM7ZUINIZvaf9mE0vXLF7auEBHxDpf/T8mQIVClCnz6KWzYAO3aWVeXiIiIiIhcFx/DMAyri3CnxMREIiMjSUhIICIiwupybkhycjKhoaHX9dz//baf/5u9hULB/mwe3iGPKxOxxo1kQq7g+HF45RWIj4fZsy9tz8oCX/2fgqdTJkRcKRciZsqEiJm3ZyI3PQ/9a96LLV++/Lqf+/OWowD0a1ohr8oRsdyNZEL+JjXVMSdQ1aqOkUDffQeXD59VM8grKBMirpQLETNlQsTMTpnQv+i92MmTJ6/7uSv+dDy3RmnvHiUlcrkbyYRcYBgwYwbUrAnPPQeJidCgASxZAvXqWV2d5JIyIeJKuRAxUyZEzOyUCa0y5sWKFi16Xc+7/CrBisXC8qocEctdbybkgsOH4b77YOlSx/3SpeHNN6FfP40I8lLKhIgr5ULETJkQMbNTJtQQ8mKtWrW6ruftOZHkvF21lFYYk4LjejMhFxQvDgcPQnAwPPusY4RQuD4jvJkyIeJKuRAxUyZEzOyUCf2XrxebffkEr7nw0ybH/EElCwUR5O+XhxWJWOt6M2Fb58/Dxx9DRobjflAQfPst7NgBr76qZlABoEyIuFIuRMyUCREzO2VCI4RsaO3+0wDcXNE+Q+FE5DKGAZMnw7//7RgR5OcHjzzieOyWW6ytTURERERE3EINIS9Wt27d63rewVPJABQNC8zLckQsd72ZsJXffoOhQx1/AkRHQ1SUtTVJvlEmRFwpFyJmyoSImZ0yoUvGvFhAQMB1Pc/nwp/1yxfOs1pEPMH1ZsIWDh6Evn2haVNHMygsDF5/3XF52F13WV2d5BNlQsSVciFipkyImNkpE2oIebF169bl+jlJqRnOSaV1yZgUNNeTCdt46CHH/EA+PvDgg7BrF7z0EoSEWF2Z5CNlQsSVciFipkyImNkpE7pkzGZW7T7pvB1dNNTCSkQkX2VlQVqaY8UwcCwfn5oKo0ZBgwbW1iYiIiIiIpbzMQzDsLoId0pMTCQyMpKEhAQiIiKsLueGJCYm5vo1jF38J+/M3QHAvrfuyI+yRCxzPZkokJYudcwT1KaNowEktqVMiLhSLkTMlAkRM2/PRG56HrpkzIutXbs218/ZfvQsAPc1KZ/X5YhY7noyUaDs2QM9e0Lr1rB+PXz9NZw7Z3VVYiHbZ0IkG8qFiJkyIWJmp0yoIeTFjh07luvnfP/HEQBqRBXK63JELHc9mSgQEhLgueegRg2YMQN8feFf/4KtWyE83OrqxEK2zYTIVSgXImbKhIiZnTKhOYS8WG6HsZ1OSnPebl9Ly0xLwePNQzuv25Il0KsX/PWX4367djB6NNSpY21d4hFsmQmRa1AuRMyUCREzO2VCcwh5sfT09Fwtibfp0Bnu/HgFURHB/PbibflYmYg1cpuJAuHoUYiNhTJlHPMF3XGHYyUxEWyaCZFrUC5EzJQJETNvz4TmELKJ6dOn52r/LYcTAShRKCg/yhGxXG4z4ZV27oSRIy/dL10afv0VtmyBLl3UDBITW2RCJJeUCxEzZULEzE6ZUEPIRuITzgNgYKtBYSIFw+nTjpXDatWCF1+EX3659NjNN4MX/y+GiIiIiIi4n+YQ8mK1atXK1f5/HEoAoGvdMvlRjojlcpsJr5CeDp98AsOHw6lTjm133AHltVKgXFuBzITIDVIuRMyUCREzO2VCDSEvFp7L1YN2HnMsOV+7bGR+lCNiudxmwqMZBvz8Mzz9NGzf7thWu7Zjwujbb7e2NvEaBSoTInlEuRAxUyZEzOyUCV0y5sV+//33HO+bkJzO0YQUAGJLacl5KZhykwmPl54Ojz7qaAYVLw7jxsGGDWoGSa4UqEyI5BHlQsRMmRAxs1MmNELIJjYcPO28rUmlRTzUiRNQuDD4+0NgoGPVsFWr4KWXHNtFRERERETyiEYIebEOHTrkeN/Vex1zj3Srp/mDpODKTSY8SmoqvPceVK4Mn39+aXuPHvDuu2oGyXXz2kyI5CPlQsRMmRAxs1Mm1BDyYlu2bMnxvnM2HwV0uZgUbLnJhEcwDJg1y7Fy2LPPQmKi475IHvG6TIi4gXIhYqZMiJjZKRNqCHmxw4cP53jfrAsrzZcpHJxP1YhYLzeZsNyGDdC2rWMU0O7dEBUFX3wBP/1kdWVSgHhVJkTcRLkQMVMmRMzslAnNIeTFwsLCcrRfakYmh04nA3BLpWL5WZKIpXKaCcuNGuUYEWQYEBzsWEns+efBRisaiHt4TSZE3Ei5EDFTJkTM7JQJH8MwDKuLcKfExEQiIyNJSEggIiLC6nJuSFZWFr6+1x7kdeh0Mi3eXoSfrw9/vtEJHx8fN1Qn4n45zYTl1q6Fxo3hnnvgrbegfHmrK5ICymsyIeJGyoWImTIhYubtmchNz8N7X6UwZcqUHO33x8EEADKzDDWDpEDLaSbcyjBgyhTHpNEXNWoEu3bBt9+qGST5yiMzIWIx5ULETJkQMbNTJnTJmA2cS023ugQRe1q9GoYOhZUrISAA7rrLsZIYXPpTRERERETEAhoh5MWqVauWo/3+OpsKQI8GZfOzHBHL5TQT+e7QIbj/fmjSxNEMCg2Fl1+G0qWtrkxsxmMyIeJBlAsRM2VCxMxOmdAIIS9WvHjxHO138NR5AMoVCc3PckQsl9NM5JukJHj3XXjnHTjvyB0PPABvvAFl1ZAV97M8EyIeSLkQMVMmRMzslAmNEPJiK1asyNF++04mAVC5hH1mSxd7ymkm8k1CgmOuoPPnoUULWLMGJkxQM0gsY3kmRDyQciFipkyImNkpExohZAO/7z0FQNGwQIsrESmA4uKgZk3H7TJlHEvKFysGd98NmsRdREREREQ8lEYIebF27drlav9CwQH5VImIZ8htJm7I3r3QuzfUqgWLFl3a/q9/Qc+eagaJR3BrJkS8hHIhYqZMiJjZKRNqCHmxnTt3XnOfvSeSnLerlAzPz3JELJeTTNywxER44QWoUQOmTQNfX8dqYiIeyC2ZEPEyyoWImTIhYmanTKgh5MUOHDhwzX1W7z0JQPHwQMKDdIWgFGw5ycR1y8yE8eOhalV46y1ITYXbboMNG+Df/86/84rcgHzNhIiXUi5EzJQJETM7ZUIdAi8WFBR0zX02HjwDQFmtMCY2kJNMXLcePeD77x23Y2Mdk0d36aJLw8Sj5WsmRLyUciFipkyImNkpEz6GYRhWF+FOiYmJREZGkpCQQEREhNXl5LtGry/gxLk02lQrwYQHG1tdjoj3mjwZHnkEhg2DwYMhUJO0i4iIiIiIZ8lNz0OXjHmxKVOmXHOfk0lpALSoUjy/yxGxXE4ykSOnT8NTT8GXX17a1qcP7NkDTz6pZpB4jTzLhEgBolyImCkTImZ2yoQuGfNiWVlZ19zn4vivxjFF87kaEevlJBNXlZEBn37qGAV08iSULOlYSSwszHFpWJEieVOoiJvccCZECiDlQsRMmRAxs1MmNELIi1WuXPmqj6ekZzpvVygalt/liFjuWpm4qrlzoW5deOwxRzOoZk34+mtHM0jES91QJkQKKOVCxEyZEDGzUybUEPJiZcuWverjR86cd96OCNFgMCn4rpWJbO3cCZ06Ob62bYNixWDsWPjjD+jQIe+LFHGj68qESAGnXIiYKRMiZnbKhBpCXmzp0qVXfXzX8XMAVCoRho9WQhIbuFYmspWQ4BgdFBAATz8Nf/7pmDzaX01U8X7XlQmRAk65EDFTJkTM7JQJ/cZTgJ04lwpAarp9roEUuaa0NPj9d2jZ0nH/5pvho4+gY0eoUsXa2kRERERERNxEI4S8WJs2ba76+PJdJwBoW72EG6oRsd5VM2EY8N13UKsWtGvnWDHsosceUzNICqRr/ZwQsSPlQsRMmRAxs1Mm1BDyYgcOHLjq4xcnlQ4PCnBHOSKWu2Im/vgDbrsNund3XBJWpAjs3evW2kSscK2fEyJ2pFyImCkTImZ2yoQaQl5sz+UjHLKxPf4sAK1ii7ujHBHLuWTi2DF46CGoXx8WLYKgIHjxRdi1y9EgEingrvVzQsSOlAsRM2VCxMxOmbC8ITR27FhiYmIIDg6mYcOGLFu27Ir7zpw5k9tvv50SJUoQERFB06ZNmTdvnhur9Sz+V5n0du+JJI4mpABQPSrCXSWJWMqUidRUqFcPxo93XC7Wpw9s3w5vvAGFCllWo4g7Xe3nhIhdKRciZsqEiJmdMuFjGIZh1cmnTJnC/fffz9ixY2nevDmffvop48ePJy4ujvLly7vs/+STT1KmTBnatm1L4cKF+fLLL3nvvff4/fffqV+/fo7OmZiYSGRkJAkJCUREFNxGyY+bjvDYtxuILhrCsudutbocEfcwDLh8Rb1XX4Uff4T334fmza2rS0RERERExA1y0/OwdITQ6NGjGTBgAAMHDqRGjRqMGTOG6Ohoxo0bl+3+Y8aM4bnnnuPmm2+matWqvPnmm1StWpUffvjBzZV7hhkzZlzxsXX7TwNQt1xhN1UjYrE1azhRsyYsXnxp2/PPw2+/qRkktnW1nxMidqVciJgpEyJmdsqEZQ2htLQ01q1bR/v27U3b27dvz8qVK3N0jKysLM6ePUvRokWvuE9qaiqJiYmmr4IiLS3tio/5XRglcTYlw13liFjj0CHo1w8aN6b49u3wf/936bHAQPC1/MpYEctc7eeEiF0pFyJmyoSImZ0yYdnFcSdOnCAzM5NSpUqZtpcqVYr4+PgcHWPUqFEkJSXRu3fvK+4zcuRIRowY4bJ92rRphIaG0qNHDxYuXEhCQgIlS5akcePG/PjjjwA0aNCArKwsNm7cCEC3bt1Yvnw5J0+epGjRorRq1YrZs2cDULduXQICAli3bh0Ad9xxB2vXruXYsWNERETQvn17pk+fDkCtWrUIDw/n999/B6BDhw5s2bKFw4cPExYWRpcuXZgyZQoA1apVo3jx4qxYsQKAdu3asXPnTg4cOOBsbk2ZMoWsrCwqV65M2bJlWbp0KXN3hQF+RAckMWnSJPz9/enVqxczZswgLS2NChUqULlyZX799VcAWrRowfHjx9m5cycA9957L9999x3JycmUK1eOmjVrMn/+fACaNm1KQkICcXFxAPTq1Yu5c+dy9uxZoqKiaNCgAXPmzAHg5ptvJiUlhc2bNwNw1113sXjxYk6fPk3x4sVp2rSpc4TXxcv+NmzYAEDXrl1ZtWoVJ06coEiRIrRp04ZZs2YBUKdOHYKDg1mzZg0AnTt3Zv369cTHx1OoUCE6duzItGnTAKhZsyaRkZGsWrUKcDQd4+LiOHToEKGhoXTr1o1JkyYBEBsbS8mSJVm+fDkAt956K7t372b//v0EBgZy9913M23aNDIyMqhUqRLly5dn8YURKa1ateLw4cPs3r0bX19f+vTpw8yZM0lNTaV8+fLExsbyyy+/ANC8eXNOnDjBjh07AOjTpw8//vgjSUlJlC1bltq1azvnx2rSpAnnzp1j69atAPTs2ZP58+eTmJhIqVKlaNSoET/99BMADRs2JD09nU2bNgHQvXt3li5dyqlTpyhWrBgtWrTgu+++A6BevXr4+vqyfv16ALp06cLq1as5fvw4kZGR3HbbbcycOROA2rVrExoayurVqwHo1KkTf/zxB0eOHCE8PJzOnTszdepUAKpXr07RokWdjd3bb7+d7du3c/DgQUJCQujevTuTJ0/GMAyqVq1KVFSUc+6wtm3bsm/fPvbu3UtAQAA9e/Zk+vTppKenExMTQ8WKFVm0aJHj/W7YEEaNotSECfhf+NDeecstxN1zD8WXL6d69eosWLAAgGbNmnHq1Cm2b98OQO/evZkzZw7nzp2jTJky3HTTTfz8888ANG7cmOTkZLZs2QLg1Z8RQUFB9OjRI9vPCHAsqXngwAH27Nmjz4gC+hmRnp5OYmKiLT8jWrZsSXx8PLt27cLHx4d77rmH2bNnc/78eaKjo/UZYePPiFOnTrFlyxZ9Rtj83xH6jLj0GREREeH8O6zPCP07Qp8Rizh16hSHDh3y2s+I5ORkcsqyOYSOHDlC2bJlWblyJU2bNnVuf+ONN/jmm2+cb+aVTJo0iYEDB/Ldd9/Rrl27K+6XmppKamqq835iYiLR0dEFYg6hY8eOuTTULrppxHwSzqfzRf9G3Fo9+31EvNbs2fDYY3D4sON+8+bw/vscK1/+ipkQsaOr/ZwQsSvlQsRMmRAx8/ZMeMUcQsWLF8fPz89lNNDx48ev+eZPmTKFAQMGMHXq1Ks2gwCCgoKIiIgwfRUUFzvuf5eZZXA2JR2AKiW0mpIUQMnJjmZQhQowZQosWwY333zFTIjYlTIh4kq5EDFTJkTM7JQJyxpCgYGBNGzY0DnM6qIFCxbQrFmzKz5v0qRJ9O/fn2+//ZY77rgjv8v0StuOJpJlQEiAH+WKhFhdjsiN278fLgzxBuDee+Hzzx3LyPfubV5ZTERERERERK7JsjmEAJ566inuv/9+GjVqRNOmTfnvf//LgQMHGDRoEAAvvPAChw8f5uuvvwYczaB+/frxwQcfcMsttzhHF4WEhBAZGWnZ67BKixYtst2+cNtxAGJLhePrq1+UxYudPQsjR8Lo0VC4MOzaBYUKORpA//yny+5XyoSIXSkTIq6UCxEzZULEzE6ZsHT5nT59+jBmzBheffVV6tWrx9KlS5kzZw4VKlQA4OjRoxw4cMC5/6effkpGRgaPPvoopUuXdn498cQTVr0ESx0/fjzb7efTMx03NGpCvFVmpmMEUNWqjoZQairUrAmnT1/1aVfKhIhdKRMirpQLETNlQsTMTpmwfD3mwYMHs2/fPlJTU1m3bh2tWrVyPjZhwgTnrOoAixcvxjAMl68JEya4v3APcHGW/r9bvfckALdVL+nOckTyxqJF0KgRDBwIx45BlSqOSaQXLoTy5a/61CtlQsSulAkRV8qFiJkyIWJmp0xYesmY5I/MLMfCcZEhARZXIpJLO3fCrbc6bkdGwiuvOFYTCwy0ti4REREREZECxrJl562SmyXYvFXF538CYOq/mtI4pqjF1YhcQ3o6BFzWvOzfH8LCYMQIKF7csrJERERERES8jVcsOy837rvvvnPZlpyW4bwdWyrcneWI5E5GBowdCzExsHfvpe1ffgn/+c91NYOyy4SInSkTIq6UCxEzZULEzE6ZUEPIiyUnJ7ts2xF/1nlbl4yJx5o3D266CR59FA4fhg8/vPTYDUyGnl0mROxMmRBxpVyImCkTImZ2yoQaQl6sXLlyLtu2HE5w3vbRKmPiabZtgzvugI4dIS4OihWDjz+Gd97Jk8NnlwkRO1MmRFwpFyJmyoSImZ0yoUmlvVjNmjVdtq3a41hhrEvd0u4uR+Tqnn8e3nvPsaS8vz88/ji8/DIUKZJnp8guEyJ2pkyIuFIuRMyUCREzO2VCI4S82Pz581227Tx2DoAAP31rxcOEhDiaQd26OUYHjR6dp80gyD4TInamTIi4Ui5EzJQJETM7ZUIjhAqY0EA/ABpWyNtftEVyxTDgxx+hZElo0sSx7dlnoVUraNvW2tpEREREREREI4S8WdOmTV22HT59HoBaZa6+vJxIvtm0CW6/He680zFpdFaWY3toaL43g7LLhIidKRMirpQLETNlQsTMTplQQ8iLJSQkmO5nZhmcTEoDoEShICtKEjs7dgwefhjq14eFCyEoyNEYSk93Wwl/z4SI3SkTIq6UCxEzZULEzE6ZUEPIi8XFxZnuXxwdBFA6MsTd5YhdpaTA229D1arw2WeOEUG9ejlWFBs50tEYcpO/Z0LE7pQJEVfKhYiZMiFiZqdMaA6hAmT3iXPO236+WnJe3OT77x0riAE0agTvvw8tWlhbk4iIiIiIiFyVj2EYhtVFuFNiYiKRkZEkJCQQEeHd8+xkZGTg73+ppzd1zUGem7GJqIhgfnvxNgsrkwIvMREu5icrC3r2hO7d4R//AF/rBh7+PRMidqdMiLhSLkTMlAkRM2/PRG56HrpkzIvNnTvXdH/DwTMANKtSzIJqxBaOHIH+/aFGDTh71rHN1xdmzoR+/SxtBoFrJkTsTpkQcaVciJgpEyJmdsqEGkJe7OzFX8gvOH1hQunE8xlWlCMFWXIyvPaaY56gr75yNIY88IPy75kQsTtlQsSVciFipkyImNkpE947DkqIiooy3d9zYQ6h6lGFrChHCqKsLJg0yTFH0KFDjm3NmjnmCWrc2NrasvH3TIjYnTIh4kq5EDFTJkTM7JQJNYS8WIMGDUz30zMd00GVitCS85IHUlKgbVv47TfH/QoVHKuJ9e4NPp45afnfMyFid8qEiCvlQsRMmRAxs1MmdMmYF5szZ47p/t4TSQBUKakRQpIHgoMhJgbCw+HNNx3LyPfp47HNIHDNhIjdKRMirpQLETNlQsTMTplQQ6gAKh4eaHUJ4o3OnYOXX4Z9+y5tGzUKdu2CF16AkBDLShMREREREZG8pUvGvNjNN9/svH02Jd15u0xh/eIuuZCV5Zgo+sUXIT7e0QCaPNnxWOnS1taWS5dnQkSUCZHsKBciZsqEiJmdMqGGkBdLSUlx3t557NJM6GFB+rZKDi1ZAkOHwoYNjvuVKzsuC/NSl2dCRJQJkewoFyJmyoSImZ0yoUvGvNjmzZsv3T6UAECAn+fO7yIeZPduuPtuaNPG0QyKjIT33oOtW+Guu6yu7rpdngkRUSZEsqNciJgpEyJmdsqEhpIUEIdOnwegdtlIiysRr/D11zBzJvj6wr/+BSNGQIkSVlclIiIiIiIibuJjGIZhdRHulJiYSGRkJAkJCURERFhdzg1JSUkhODgYgKembmTm+sPcUac0/+lrn2XyJIcyMuD4cShTxnH/3DkYNAiefx5q17a2tjx0eSZERJkQyY5yIWKmTIiYeXsmctPz0CVjXmzx4sXO2wnJjkmlKxYPtaga8VgLFkD9+tCtm2MCaXAsJf+//xWoZhCYMyEiyoRIdpQLETNlQsTMTplQQ8iLnT592nl765FEABpWKGJVOeJptm+HLl2gfXvYsgX27HGsIFaAXZ4JEVEmRLKjXIiYKRMiZnbKhBpCXqx48eIAGIZBfKJjJnQtOS+cOgVPPAF16sBPP4G/Pzz5JPz5J1SrZnV1+epiJkTEQZkQcaVciJgpEyJmdsqEJpX2Yk2bNgUgMSXDuS26iC4Zs7UdO6BpU7jY1e7aFd59t8A3gi66mAkRcVAmRFwpFyJmyoSImZ0yoRFCXuyHH34AYEf8WQCKhAYQFqQen61VrQqVKzvmBlqwAL7/3jbNILiUCRFxUCZEXCkXImbKhIiZnTKhhlABsO2oY/6g5LRMiysRt9uyBe67z7FqGDiWkf/uO9iwAdq1s7Y2ERERERER8VhqCHmx+vXrA3D4zHkAYoqHWVmOuNPx445l42+6CSZNgnfeufRYmTKOeYNs6GImRMRBmRBxpVyImCkTImZ2yoQ9f2ssoKKLav6gAi81FT78EF5/HRIdI8Po2RP697e0LBEREREREfEuGiHkxTZs2ADA8QsrjDUoryXnC7SZM6FmTXjuOUczqEEDWLIEpk2DSpWsrs4jXMyEiDgoEyKulAsRM2VCxMxOmVBDqABYteckAMXCAi2uRPLVtGmwZw+ULg0TJsCaNdCqldVViYiIiIiIiBfSJWNerGvXrsBlk0n7WFiM5L2jRx1/li7t+POttyA2Fp59FsLDravLg13MhIg4KBMirpQLETNlQsTMTpnQCCEvtmrVKgB8fRydoPKaQ6hgOH/eMUdQ1arw9NOXtleoACNGqBl0FRczISIOyoSIK+VCxEyZEDGzUyY0QsiLnThxgvNpmSScTwegUgmtMubVDAMmT4Z//xsOHnRs27cPUlIgONjS0rzFiRMnrC5BxKMoEyKulAsRM2VCxMxOmdAIIS9WpEgRDp5Odt4vER5kYTVyQ377DZo1g/vuczSDoqNh4kRYsULNoFwoUkQTq4tcTpkQcaVciJgpEyJmdsqEj2EYhtVFuFNiYiKRkZEkJCQQERFhdTk3JCUlhd8PnOWBL1YTXTSEZc/danVJcj2mToU+fRy3w8Lg+efhqacgVJcA5lZKSgrBaqCJOCkTIq6UCxEzZULEzNszkZueh0YIebFZs2ax/2QSAKUjQyyuRq5b585Qtiz07w87d8L//Z+aQddp1qxZVpcg4lGUCRFXyoWImTIhYmanTGgOIS8X4Ofo6V1sDImHy8qCr7+G2bNh5kzw9XVMEh0XB14+Yk1ERERERES8h0YIebE6deqQku5Ycr5RxaIWVyPXtHQp3HwzPPggfPcdTJ9+6TE1g/JEnTp1rC5BxKMoEyKulAsRM2VCxMxOmVBDyIsFBweTlJrhuO3vZ3E1ckV79kDPntC6Naxf72j+vPMOdOtmdWUFjjdf6yuSH5QJEVfKhYiZMiFiZqdMqCHkxdasWcPmwwkARIYEWFyNuEhJcSwhX6MGzJjhuDxs0CDYtQuefRaCtCpcXluzZo3VJYh4FGVCxJVyIWKmTIiY2SkTmkPIy51PzwIgPFjfSo8TGAi//AJpaXD77TBqFNho+KGIiIiIiIh4Lo0Q8mKdO3cmJc0xh1CJQhpt4hF+/RWSLkzw7esLY8fCjz/CvHlqBrlB586drS5BxKMoEyKulAsRM2VCxMxOmVBDyIutX7+e7fGJAMQUC7O4GpvbsQO6doXbboN33720vUkTuOMO8PGxrjYbWb9+vdUliHgUZULElXIhYqZMiJjZKRNqCHmx+Ph4ElMck0oXDQu0uBqbOnUKnnwSatd2jATy83PMHSSWiI+Pt7oEEY+iTIi4Ui5EzJQJETM7ZUITz3ixsPBCztslI3TJmFulp8Mnn8Dw4Y6mEDhGAr33HlSvbmlpdlaoUKFr7yRiI8qEiCvlQsRMmRAxs1MmfAzDMKwuwp0SExOJjIwkISGBiIgIq8u5IccTkmk8chEAu97oRICfBny5zdChMGaM43atWjB6NLRvb2lJAhkZGfj7q88tcpEyIeJKuRAxUyZEzLw9E7npeaiD4MWmzvreeVvNIDe4vHc6ZAiUKwfjxsHGjWoGeYhp06ZZXYKIR1EmRFwpFyJmyoSImZ0y4b1tLyE5UxMVu8Vff8Err0ByMnz1lWNbTAzs3Qte3DkWERERERER+9KwEi9WrnwMAJEhARZXUkClpjrmBKpSxTFf0Ndfw86dlx5XM8jj1KxZ0+oSRDyKMiHiSrkQMVMmRMzslAk1hLxYQHAoAGUKh1hcSQFjGDBrlmNuoGefhcREqF8fFi+G2Firq5OriIyMtLoEEY+iTIi4Ui5EzJQJETM7ZUINIS+2Ycs2AMKD/CyupAA5cABuvRV69IDduyEqCr74Atasgdatra5OrmHVqlVWlyDiUZQJEVfKhYiZMiFiZqdM6JoXL3YsxdEI0iVjeahIEdi2DYKD4emn4fnnITzc6qpERERERERE8pRGCHmx2KqVATiVlGZxJV7s/HkYPx6yshz3CxWCb7+F7dvh9dfVDPIy7bXam4iJMiHiSrkQMVMmRMzslAk1hLzY77viAahd1j7XOOYZw4DJk6F6dXjoIcfti269FSpUsK42uW5xcXFWlyDiUZQJEVfKhYiZMiFiZqdM6JIxL+aXngwEkpyWaXUp3mX1ahg6FFaudNwvVw7CwqytSfLEoUOHrC5BxKMoEyKulAsRM2VCxMxOmdAIIS9m+Dr6edVKFbK4Ei9x8CD84x/QpImjGRQaCq++Cjt2QLduVlcneSA0NNTqEkQ8ijIh4kq5EDFTJkTM7JQJH8MwDKuLcKfExEQiIyNJSEggIiLC6nJuyMCv1vDLtuO81q0W9zetaHU5nq9lS1i+3HH7gQfgjTegbFlraxIRERERERHJI7npeWiEkBf788ARAIqEBVpciYfKyoK0yybcfv11R1NozRqYMEHNoAJo0qRJVpcg4lGUCRFXyoWImTIhYmanTKgh5MXSsnwACA/SVFAuVqxwXBr21luXtrVuDUuWQKNG1tUlIiIiIiIi4gHUEPJi8Sl+AESEBFhciQfZtw/69IEWLWDtWhg3DlJTLz3u42NZaZL/YmNjrS5BxKMoEyKulAsRM2VCxMxOmVBDyEsZhkHWhdmfiobqkjESE+GFFxzLyE+dCr6+8PDDsHEjBAVZXZ24ScmSJa0uQcSjKBMirpQLETNlQsTMTplQQ8hLpWZkOW8XC7d5Q+iXX6BqVcflYampcNttsGEDfPoplCpldXXiRssvThouIoAyIZId5ULETJkQMbNTJjT5jJdKOJ/uvB0WaPNvY6VKcOaMoyk0ahR06aJLw0RERERERESuQiOEvFR8Qorztq+vzZofu3bBhx9eul+pEixcCFu2QNeuagbZ2K233mp1CSIeRZkQcaVciJgpEyJmdsqEGkJe6mjCeatLcL/Tp+Gpp6BWLXjiCVi9+tJjLVpAoM0vnRN2795tdQkiHkWZEHGlXIiYKRMiZnbKhBpCXiopNROAsoVDLK7EDdLT4eOPHZeEvf++436nTlC4sNWViYfZv3+/1SWIeBRlQsSVciFipkyImNkpEzaffMZ7ZWQ5JpUuGVHAV9D6+Wd4+mnYts1xv2ZNGD0aOnSwti7xSIEaJSZiokyIuFIuRMyUCREzO2XCxzAMw+oi3CkxMZHIyEgSEhKIiIiwupzrNnr+Dj789U8614libN+GVpeTP5KTISYGjh+HYsXgtdfgoYfAX31MERERERERkb/LTc9Dl4x5KZ8LEyefSU6/xp5e5tQpuNijDA2Ft992jBD680945BE1g+Sqpk2bZnUJIh5FmRBxpVyImCkTImZ2yoQaQl7q4rCuEoUKyCVjaWmOS8EqV4YpUy5t798f3ntP8wVJjmRkZFhdgohHUSZEXCkXImbKhIiZnTKhhpCX2n38HAAlvb0hZBjw3XeOlcOefhrOnIFvv7W6KvFSlSpVsroEEY+iTIi4Ui5EzJQJETM7ZUINIS8VFuQHePklY3/8AbfdBt27Oy4JK1UKxo+HWbOsrky8VPny5a0uQcSjKBMirpQLETNlQsTMTplQQ8hLXZxmp4y3Ljv/9ttQvz4sWgRBQfDCC7BrFwwYAH5+VlcnXmrx4sVWlyDiUZQJEVfKhYiZMiFiZqdMaIZeL5WS4Vh2PiIkwOJKrlOTJo6uVp8+8NZbULGi1RWJiIiIiIiI2IYaQl5q48HTAAT6+VhcSQ4YBkydCqdPw6BBjm1t2sDWrVCzpqWlScHSqlUrq0sQ8SjKhIgr5ULETJkQMbNTJtQQ8lIxxcM5eOo8Z1M9fAb0NWtg6FBYscKxjPydd0KZMo7H1AySPHb48GHKli1rdRkiHkOZEHGlXIiY3UgmMjMzSU/34jlNRbJx+PBhihUrZnUZVxUYGIiv743PAKSGkJdKv3DJWLkioRZXcgWHDsGLL8I33zjuh4bCc89BZKS1dUmBtnv3bho3bmx1GSIeQ5kQcaVciJhdTyYMwyA+Pp4zZ87kT1EiFvL19WXv3r1Wl3FVvr6+xMTEEBgYeEPHUUPIS6VnOhpCgX4eNi94cjK8+65j0ujz5x3b7r8f3nwTypWztjYp8PKiSy5SkCgTIq6UCxGz68nExWZQyZIlCQ0NxcfHC6axEMmhM2fOULhwYavLuKKsrCyOHDnC0aNHKV++/A3lz8cwLq5XZQ+JiYlERkaSkJBARESE1eVct4rP/wTAF/0bcWv1UhZXc5ndux2XgqWlQfPm8P77cPPNVlclIiIiIiJ5IDMzk507d1KyZEmPv6xGpKBKSEjgyJEjVKlShYAA80JTuel56L9IvFTVkuEApF24dMxSf/556Xblyo7RQFOnwrJlagaJW82cOdPqEkQ8ijIh4kq5EDHLbSYuzhkUGuqhU1eI3KDTp09bXcI1XbxULDMz84aOo4aQl8q8MLCrSOiNXTN4Q/btcywbHxsLa9de2v7009CrF2joqLhZamqq1SWIeBRlQsSVciFidr2Z0GViUlBlZXnAoItryKv8qSHkpTKzHA0hfyvmEDp71jFhdPXqjpFAAEuXur8Okb8pX7681SWIeBRlQsSVciFipkyImAUFBVldgtuoIeSlMjIvNIR83diZz8yE8eOhalUYORJSU6FtW1i/Hp56yn11iFxBbGys1SWIeBRlQsSVciFipkxIdj7//HPat29vdRmWsLohlJqaSvny5Vm3bl2+n0sNIS+VcWEYm7+fGxtCnTvDQw/BsWNQpQrMng0LF0K9eu6rQeQqfvnlF6tLEPEoyoSIK+VCxMxOmejfvz8+Pj74+Pjg7+9P+fLleeSRR7KdM2blypV07tyZIkWKEBwcTJ06dRg1alS2c7YsWrSIzp07U6xYMUJDQ6lZsyZPP/00hw8fdsfLynOpqam88sorvPzyy1aXkm8Mw2D48OGUKVOGkJAQ2rRpw9atWwHHpMxXMmbMGKpVq0ZISAjR0dEMHTqUlJQU5+PDhw93/h27+BUVFWU6xsyZM+nQoQPFixfHx8eHjRs3mh4PCgrimWee4d///nfeveArUEPISx1LdFzr6+/OpVPvvhsiI2HUKNi6Fbp10zxBIiIiIiLiNTp27MjRo0fZt28f48eP54cffmDw4MGmfWbNmkXr1q0pV64cixYtYvv27TzxxBO88cYb3HPPPVy+UPenn35Ku3btiIqKYsaMGcTFxfHJJ5+QkJDAqFGj3Pa60tLS8uxYM2bMIDw8nJYtW97QcS5OQO6J3nnnHUaPHs3HH3/MmjVriIqK4vbbb+fs2bNXfM7EiRN5/vnnGTZsGNu2bePzzz9nypQpvPDCC6b9atWqxdGjR51fmzdvNj2elJRE8+bNeeutt654rr59+7Js2TK2bdt2Yy/0GtQQ8lJ+Fy4Vy7crxs6cgWeegRkzLm0bMMCxothTT0GghZNZi1xB8+bNrS5BxKMoEyKulAsRs7zIhGEYJKdlWPJ1eXMmJ4KCgoiKiqJcuXK0b9+ePn36MH/+fOfjSUlJPPTQQ9x5553897//pV69elSsWJGBAwfy1VdfMX36dKZemEf10KFDDBkyhCFDhvDFF1/Qpk0bKlasSKtWrRg/fjyvvPLKFes4c+YMDz/8MKVKlSI4OJjatWvz448/Ao5RJvX+dhXGmDFjqFixovN+//796d69OyNHjqRMmTLExsbywgsvcMstt7icq27dugwbNsx5/8svv6RGjRoEBwdTvXp1xo4da9p/8uTJ3HnnnaZta9as4fbbb6d48eJERkbSunVr1q9fb9rHx8eHTz75hG7duhEWFsbrr78OwA8//EDDhg0JDg6mUqVKjBgxgoyMDOfzRo8eTZ06dQgLCyM6OprBgwdz7ty5K753N8owDMaMGcNLL71Ejx49qF27Nl999RXJycl8++23hIeHZ/u8VatW0bx5c+677z4qVqxI+/btuffee1l7+QJLgL+/P1FRUc6vEiVKmB6///77eeWVV2jXrt0VayxWrBjNmjVj0qRJN/6Cr8I/X48u+cbPx4dMDMKC8vhbmJEBn30Gr7wCJ05AhQrQpQsEBYGfHxQvnrfnE8lDJ06c0MSIIpdRJkRcKRciZnmRifPpmdR8ZV4eVZQ7ca92IDTw+n4n2rNnD3PnziUgIMC5bf78+Zw8eZJnnnnGZf+uXbsSGxvLpEmT6NOnD9OmTSMtLY3nnnsu2+MXLlw42+1ZWVl06tSJs2fP8r///Y/KlSsTFxeHn59frupfuHAhERERLFiwwNkYe+utt9i9ezeVK1cGYOvWrWzevJnp06cD8NlnnzFs2DA+/vhj6tevz4YNG3jooYcICwvjgQceAGDZsmX07dvXdK6zZ8/ywAMP8OGHHwIwatQoOnfuzK5duyhUqJBzv2HDhjFy5Ejef/99/Pz8mDdvHv/4xz/48MMPadmyJbt37+bhhx927gvg6+vLhx9+SMWKFdm7dy+DBw/mueeec2lUXa5Tp04sW7bsqu/PlZpKe/fuJT4+3jRHUlBQEK1bt2blypX84x//yHYeoRYtWvC///2P1atX07hxY/bs2cOcOXOc79tFu3btokyZMgQFBdGkSRPefPNNKlWqdNVas9O4ceNrvsYbpYaQFzIMg7RMxxxCgf55OMhr3jzHkvEXrp2kRg3H5WE2mmVdvNuOHTto0KCB1WWIeAxlQsSVciFiZrdM/Pjjj4SHh5OZmemc+2X06NHOx3fu3AlAjRo1sn1+9erVnfvs2rWLiIgISpcunasafvnlF1avXs22bduck3pfT8MgLCyM8ePHE3jZ1Rt169bl22+/dc7/M3HiRG6++WbneV577TVGjRpFjx49AIiJiSEuLo5PP/2UBx54gDNnznDmzBnKlCljOtett95quv/pp59SpEgRlixZQpcuXZzb77vvPv75z386799///08//zzzqZJpUqVeO2113juueecDaEnn3zSuX9MTAyvvfYajzzyyFUbQuPHj+f8+fM5fq8uFx8fD0CpUqVM20uVKsX+/ftJSUkhLCzM5Xn33HMPf/31Fy1atMAwDDIyMnjkkUd4/vnnnfs0adKEr7/+mtjYWI4dO8brr79Os2bN2Lp1K8WKFctVnWXLlmXfvn25f4G5oIaQF0rNyHLeDsiLZed37oShQ2HOHMf9YsVgxAh4+GG4rFsuIiIiIiLydyEBfsS92sGyc+dG27ZtGTduHMnJyYwfP56dO3fy+OOPu+x3pUvRDMPA58I8qpffzo2NGzdSrly5G17hrU6dOqZmEDjmnvniiy94+eWXMQyDSZMmORsuf/31FwcPHmTAgAE89NBDzudkZGQQGRkJ4GyyBAcHm457/PhxXnnlFX799VeOHTtGZmYmycnJHDhwwLRfo0aNTPfXrVvHmjVreOONN5zbLjbjkpOTCQ0NZdGiRbz55pvExcWRmJhIRkYGKSkpJCUlZduYAUez5Eb9/Xt3re/n4sWLeeONNxg7dixNmjThzz//5IknnqB06dLOBlynTp2c+9epU4emTZtSuXJlvvrqK57K5crcISEhJCcn5+o5uaWGkBdKTb/UEArKixFCBw86mkH+/vD44/Dyy1CkyI0fV8TN+vTpY3UJIh5FmRBxpVyImOVFJnx8fK77si13CwsLo0qVKgB8+OGHtG3blhEjRvDaa68BOJs027Zto1mzZi7P3759OzVr1nTum5CQwNGjR3M1SigkJOSqj/v6+ro0pLKboDm7Zsl9993H888/z/r16zl//jwHDx7knnvuARyXqoHjsrEmTZqYnnfxcrVixYrh4+PjsvJa//79+euvvxgzZgwVKlQgKCiIpk2bukxm/feasrKyGDFihHNE0uWCg4PZv38/nTt3ZtCgQbz22msULVqU5cuXM2DAgKtOSn0jl4xdXPUrPj7e9H07fvw4pUqVomjRotk+7+WXX+b+++9n4MCBgKPhk5SUxMMPP8xLL72EbzYLPoWFhVGnTh127dp11Vqzc+rUKZf5h/KaJpX2QulZN9gQSkuD1asv3b/tNnjzTcelYqNHqxkkXuviRHwi4qBMiLhSLkTM7J6JYcOG8d5773HkyBEA2rdvT9GiRbNdIez7779n165d3HvvvQD07NmTwMBA3nnnnWyPfebMmWy3161bl0OHDjkvPfu7EiVKEB8fb2oK/X1p8ispV64crVq1YuLEiUycOJF27do5L40qVaoUZcuWZc+ePVSpUsX0FRMTA0BgYCA1a9YkLi7OdNxly5YxZMgQOnfuTK1atQgKCuLEiRPXrKdBgwbs2LHD5XxVqlTB19eXtWvXkpGRwahRo7jllluIjY11fi+uZvz48WzcuPGqX1cSExNDVFQUCxYscG5LS0tjyZIlNGvWjISEhGyfl5yc7NL08fPzwzCMK44oS01NZdu2bbm+rBBgy5Yt1K9fP9fPyw3vaOOKSUam4y+bL7kcomgY8MMPjtXDjhyBXbvg4l/Mvy2VJ+KNkpKSrC5BxKMoEyKulAsRM7tnok2bNtSqVYs333yTjz/+mLCwMD799FPuueceHn74YR577DEiIiJYuHAhzz77LD179qR3794AREdH8/777/PYY4+RmJhIv379qFixIocOHeLrr78mPDw828ZS69atadWqFXfffTejR4+mSpUqbN++HR8fHzp27EibNm3466+/eOedd+jZsydz587l559/JiIiIkevqW/fvgwfPpy0tDTef/9902PDhw9nyJAhRERE0KlTJ1JTU1m7di2nT592XtLUoUMHli9fbprbp0qVKnzzzTc0atSIxMREnn322WuOdAJ45ZVX6NKlC9HR0fTq1QtfX182bdrE5s2bef3116lcuTIZGRl89NFHdO3alRUrVvDJJ59c87g3csmYj48PTz75JG+++SZVq1alatWqvPnmm4SGhnLfffc5Rz3169ePsmXLMnLkSMAxqfjo0aOpX7++85Kxl19+mTvvvNM5wuqZZ56ha9eulC9fnuPHj/P666+TmJhomnj61KlTHDhwwNn42rFjB4BzVbKLli1b5hy5lm8Mm0lISDAAIyEhwepSrtuBk0lGhX//aFR54cecP+mPPwzj1lsNw9EWMoySJQ1j8eL8K1LEAkuWLLG6BBGPokyIuFIuRMxym4nz588bcXFxxvnz5/OpovzzwAMPGN26dXPZPnHiRCMwMNA4cOCAc9vSpUuNjh07GpGRkUZgYKBRs2ZN47333jMyMjJcnr9gwQKjQ4cORpEiRYzg4GCjevXqxjPPPGMcOXLkirWcPHnSePDBB41ixYoZwcHBRu3atY0ff7z0+924ceOM6OhoIywszOjXr5/xxhtvGBUqVLjmazEMwzh9+rQRFBRkhIaGGmfPns329darV88IDAw0ihQpYrRq1cqYOXOm8/Ft27YZISEhxpkzZ5zb1q9fbzRq1MgICgoyqlatakybNs2oUKGC8f777zv3AYxZs2a5nG/u3LlGs2bNjJCQECMiIsJo3Lix8d///tf5+OjRo43SpUsbISEhRocOHYyvv/7aAIzTp09f8f27UVlZWcawYcOMqKgoIygoyGjVqpWxefNmwzAMIzEx0TAMw2jdurXxwAMPOJ+Tnp5uDB8+3KhcubIRHBxsREdHG4MHDzbV2adPH6N06dJGQECAUaZMGaNHjx7G1q1bTef+8ssvDcDla9iwYc59Vq5caRQuXNhITk7Otv6r5TA3PQ8fw7jC2KYCKjExkcjISBISEnLcYfU0+04k0ea9xYQF+rH11Y5X3/nYMcecQJ9/DllZjhXDhg51jAjy0tcvciWnTp264jW/InakTIi4Ui5EzHKbiZSUFPbu3UtMTIzLxMNScPTu3Zv69evzgg2vJMnIyMDf39qLqXr16kX9+vV58cUXs338ajnMTc9Dcwh5oeS0TACyMjOuvmNSEtSqBZ995mgG9eoF27bByJFqBkmBNG/ePKtLEPEoyoSIK+VCxEyZkOy8++67hIeHW12GJa40h5C7pKamctNNNzF06NB8P5fmEPJCWRcGdZ3PvMb8QWFh8MADsHQpvP8+tGjhhupERERERETEm1WoUIHHH3/c6jJsKSgoiP/7v/9zy7k0QsgLpWY4VhkrXSjA/MC6ddCmDaxff2nbG2/A77+rGSS28PflM0XsTpkQcaVciJgpEyJmdhoZZXlDaOzYsc7r3ho2bMiyZcuuuv+SJUto2LAhwcHBVKpUKUczkBc0aRcaQv4+F6Z/OnIE+veHm2+GJUvg8usMg4PB1/Jvs4hbnDt3zuoSRDyKMiHiSrkQMVMmRMwyMzOtLsFtLO0UTJkyhSeffJKXXnqJDRs20LJlSzp16sSBAwey3X/v3r107tyZli1bsmHDBl588UWGDBnCjBkz3Fy5tdIyHQ0hv6REeO01qFoVvvrKsX7YP/7hmDNIxIa2bt1qdQkiHkWZEHGlXIiYKRMiZufPn7e6BLexdA6h0aNHM2DAAAYOHAjAmDFjmDdvHuPGjWPkyJEu+3/yySeUL1+eMWPGAFCjRg3Wrl3Le++9x9133+3O0i2VnpFFh50reXXhp5B40rGxaVMYMwYaN7a0NhERERERERHxfJaNEEpLS2PdunW0b9/etL19+/asXLky2+esWrXKZf8OHTqwdu1a0tPTs31OamoqiYmJpi9vl5GVRYmkM5RKPAnly8PkybBihZpBYns9e/a0ugQRj6JMiLhSLkTMlAkRsyJFilhdgttYNkLoxIkTZGZmUqpUKdP2UqVKER8fn+1z4uPjs90/IyODEydOULp0aZfnjBw5khEjRrhsnzZtGqGhofTo0YOFCxeSkJBAyZIlady4MT/++CMADRo0ICsri40bNwLQrVs3li9fzsmTJylatCitWrVi9uzZANStW5eAgADWrVsHwB133MHatWs5duwYERERtG/fnunTpwNQq1YtwsPD+f333wFHU2vLli0cPnyYsLAwunTpwpQpUwCoVq0axYsXZ8WKFQC0a9eOg7t3sKrpbXwSmMygqWOZ8v33ZE2eTOXKlSlbtixLly4FoE2bNhw4cIA9e/bg7+9Pr169mDFjBmlpaVSoUIHKlSvz66+/AtCiRQuOHz/Ozp07Abj33nv57rvvSE5Oply5ctSsWZP58+cD0LRpUxISEoiLiwOgV69ezJ07l7NnzxIVFUWDBg2YM2cOADfffDMpKSls3rwZgLvuuovFixdz+vRpihcvTtOmTfnhhx8AqF+/PgAbNmwAoGvXrqxatYoTJ05QpEgR2rRpw6xZswCoU6cOwcHBrFmzBoDOnTuzfv164uPjKVSoEB07dmTatGkA1KxZk8jISFatWgU4mo5xcXEcOnSI0NBQunXrxqRJkwCIjY2lZMmSLF++HIBbb72V3bt3s3//fgIDA7n77ruZNm0aGRkZVKpUifLly7N48WIAWrVqxeHDh9m9eze+vr706dOHmTNnkpqaSvny5YmNjeWXX34BoHnz5pw4cYIdO3YA0KdPH3788UeSkpIoW7YstWvXdi4B2qRJE86dO+ccztuzZ0/mz59PYmIipUqVolGjRvz0008ANGzYkPT0dDZt2gRA9+7dWbp0KadOnaJYsWK0aNGC7777DoB69erh6+vL+guTkHfp0oXVq1dz/PhxIiMjue2225g5cyYAtWvXJjQ0lNWrVwPQqVMn/vjjD44cOUJ4eDidO3dm6tSpAFSvXp2iRYs6G7u3334727dv5+DBg4SEhNC9e3cmT56MYRhUrVqVqKgo59xhbdu2Zd++fezdu5eAgAB69uzJ9OnTSU9PJyYmhooVK7Jo0SIAWrZsSXx8PLt27cLHx4d77rmHTz/9lGLFihEdHU316tVZsGABAM2aNePUqVNs374dgN69ezNnzhzOnTtHmTJluOmmm/j5558BaNy4McnJyWzZsgXAKz8jdu7cyYEDBwgKCqJHjx5MmTKFrKwsfUbY8DPi7Nmz3HPPPfqMuPAZMXv2bM6fP6/PCJt/Rhw7dox27drpM0L/jtBnBI7PiJ07d2JcWMU4J58Rq1atomLFiqSlpZGRkUFqaio+Pj4ULVqU06dPk5WVRVBQEEFBQc7/jC9UqBDp6emkpKQAUKxYMc6cOUNmZiaBgYGEhIQ4l/oODw8nMzPTedlO0aJFSUhIIDMzk4CAAEJDQ537hoWFYRgGycnJgOMX+bNnz5KRkUFAQABhYWGcOXMGgNDQUADnvoULFyYpKYn09HT8/f0pVKgQp0+fdu7r4+NDUlISAJGRkSQnJ5Oeno6fnx+RkZGcOnUKgJCQEPz8/JxzMUVGRnL+/HnS0tLw8/OjcOHCnDzpuKojODiYgIAAzp49C0BERASpqamkpqbi6+tLkSJFOHXqFIZhEBQURGBgoHPfy9/Da73f4eHhZGRkON/vy9/Da73fRYoUITEx0fl+X/4eXu399vf3Jzw83PR+X/4eXu39DgkJwdfX1/R+X/4eXu39joiIICUlhbS0NJf38Grv98X38PL3++J7mJGRQZEiRa74fgcGBhIcHGx6v6/0d/bv73dYWBhZWVmm9/tKf2f//n4XLlyYc+fOkZGRQVZWFoZh8OOPP5Kenm76jLi4f074GBfT72ZHjhyhbNmyrFy5kqZNmzq3v/HGG3zzzTfOD9zLxcbG8uCDD/LCCy84t61YsYIWLVpw9OhRoqKiXJ5z8Rt+UWJiItHR0SQkJBAREZHHr8q9Jk2axL333mt1GSIeQ5kQMVMmRFwpFyJmuc1ESkoKe/fudS4MJFLQnDx5kmLFilldxlVdLYeJiYlERkbmqOdh2SVjxYsXx8/Pz2U00PHjx11GAV0UFRWV7f7+/v5X/IYFBQURERFh+ioorvQ+idiVMiFipkyIuFIuRMyUCfepWLGicz5cO2rTpg1PPvmk876nvh8BAQFWl+A2ljWEAgMDadiwoXMo5kULFiygWbNm2T6nadOmLvvPnz+fRo0a2eqbdlGjRo2sLkHEoygTImbKhIgr5ULEzE6Z6N+/Pz4+Pvj4+ODv70/58uV55JFHnJcuFVTDhw93vm4fHx8iIyNp2bIlS5YssbSuNWvW8PDDD1taQ3bCwsKsLsFtLF12/qmnnmL8+PF88cUXbNu2jaFDh3LgwAEGDRoEwAsvvEC/fv2c+w8aNIj9+/fz1FNPsW3bNr744gs+//xznnnmGategqUuXu8tIg7KhIiZMiHiSrkQMbNbJjp27MjRo0fZt28f48eP54cffmDw4MFWl5XvatWqxdGjRzl69CirVq2iatWqdOnSxTm3jRVKlCjhnNvJk1ycw8cOLG0I9enThzFjxvDqq69Sr149li5dypw5c6hQoQIAR48e5cCBA879Y2JimDNnDosXL6ZevXq89tprfPjhh7Zacl5ERERERMQjJSVd+evCBMc52vfChLvX3Pc6BAUFERUVRbly5Wjfvj19+vRxTnoPkJmZyYABA4iJiSEkJIRq1arxwQcfmI7Rv39/unfvznvvvUfp0qUpVqwYjz76qGnl6+PHj9O1a1dCQkKIiYlh4sSJLrUcOHCAbt26ER4eTkREBL179+bYsWPOx4cPH069evX44osvKF++POHh4TzyyCNkZmbyzjvvEBUVRcmSJXnjjTeu+br9/f2JiooiKiqKmjVrMmLECM6dO+dcDABg9OjR1KlTh7CwMKKjoxk8eLBz8maA/fv307VrV4oUKUJYWBi1atVyLgIAEBcXR+fOnQkPD6dUqVLcf//9nDhx4oo1/f2SMR8fH8aPH89dd91FaGgoVatW5fvvvzc9J7fnkKuztCEEMHjwYPbt20dqairr1q2jVatWzscmTJjgXHnhotatW7N+/XpSU1PZu3evczSRHTVs2NDqEkQ8ijIhYqZMiLhSLkTM8jQT4eFX/vr7f+KXLHnlfTt1Mu9bsWL2+92gPXv2MHfuXNP0I1lZWZQrV46pU6cSFxfHK6+8wosvvuhcCe+iRYsWsXv3bhYtWsRXX33FhAkTmDBhgvPx/v37s2/fPn799VemT5/O2LFjOX78uPNxwzDo3r07p06dYsmSJSxYsIDdu3fTp08f03l2797Nzz//zNy5c5k0aRJffPEFd9xxB4cOHWLJkiW8/fbb/N///R+//fZbjl93amoqEyZMoHDhwlSrVs253dfXlw8//JAtW7bw1Vdf8euvv/Lcc885H3/00UdJTU1l6dKlbN68mbfffpvwC9+Ho0eP0rp1a+rVq8fatWuZO3cux44do3fv3jmuC2DEiBH07t2bTZs20blzZ/r27etcYSyvznEtdrpkzLJl5+XGXd6BFhFlQuTvlAkRV8qFiJndMvHjjz86lwm/uCz76NGjnY8HBAQwYsQI5/2YmBhWrlzJ1KlTTY2HIkWK8PHHH+Pn50f16tW54447WLhwIQ899BA7d+7k559/5rfffqNJkyYAfP7559SoUcP5/F9++YVNmzaxd+9eoqOjAfjmm2+oVasWa9as4eabbwYcDaovvviCQoUKUbNmTdq2bcuOHTuYM2cOvr6+VKtWjbfffpvFixdzyy23XPF1b9682dm8SU5OplChQkyZMsW06NLlEz7HxMTw2muv8cgjjzB27FjAMaLp7rvvpk6dOgBUqlTJuf+4ceNo0KABb775pnPbF198QXR0NDt37iQ2Nvaq35eL+vfv71z17s033+Sjjz5i9erVdOzYMc/OcS0WLcRuCTWEvNimTZuoVauW1WWIeAxlQsRMmRBxpVyImOVpJi67vMiFn5/5/mWjZVz4/u1Cln37rrukv2vbti3jxo0jOTmZ8ePHs3PnTh5//HHTPp988gnjx49n//79nD9/nrS0NOrVq2fap1atWvhd9ppKly7N5s2bAdi2bRv+/v6mCburV69O4cKFnfe3bdtGdHS0sxkEULNmTQoXLsy2bducDaGKFStSqFAh5z6lSpXCz88P38veo1KlSplGH2WnWrVqzsuvzp49y5QpU+jVqxeLFi1y1rlo0SLefPNN4uLiSExMJCMjg5SUFJKSkggLC2PIkCE88sgjzJ8/n3bt2nH33XdTt25dANatW8eiRYucTafL7d69O8fNmovHA8dInUKFCjlfW16d41qSk5MJCQnJk2N5OssvGRMREREREZECICzsyl/BwTnf9++/jF9pv+sqMYwqVapQt25dPvzwQ1JTU00jgqZOncrQoUP55z//yfz589m4cSMPPvggaWlppuP8fZVrHx8fsrKygEsjTHx8fK5Yh2EY2T7+9+3Znedq576SwMBAqlSpQpUqVahfvz5vvfUWZcuWdc7hs3//fjp37kzt2rWZMWMG69at4z//+Q9waRTZwIED2bNnD/fffz+bN2+mUaNGfPTRR4BjJFPXrl3ZuHGj6WvXrl2maWGu5WqvLa/OIZdohJAX6969u9UliHgUZULETJkQcaVciJjZPRPDhg2jU6dOPPLII5QpU4Zly5bRrFkz08pju3fvztUxa9SoQUZGBmvXrqVx48YA7Nixw7R6Vc2aNTlw4AAHDx50jhKKi4sjISHBdGlZfvLz8+P8hQm8165dS0ZGBqNGjXKOPvr7vEkA0dHRDBo0iEGDBvHCCy/w2Wef8fjjj9OgQQNmzJhBxYoV8ffPnzaDO84BjssB7UIjhLzY0qVLrS5BxKMoEyJmyoSIK+VCxMzumWjTpg21atVyzktTpUoV1q5dy7x589i5cycvv/wya9asydUxq1WrRseOHXnooYf4/fffWbduHQMHDjRdhtSuXTvq1q1L3759Wb9+PatXr6Zfv360bt3adKlZXsnIyCA+Pp74+Hh27drF66+/TlxcHN26dQOgcuXKZGRk8NFHH7Fnzx6++eYbPvnkE9MxnnzySebNm8fevXtZv349v/76q7N59eijj3Lq1CnuvfdeVq9ezZ49e5g/fz7//Oc/yczMzJPX4I5zgOOSOrtQQ8iLXZxtXUQclAkRM2VCxJVyIWKmTMBTTz3FZ599xsGDBxk0aBA9evSgT58+NGnShJMnT5pGC+XUl19+SXR0NK1bt6ZHjx48/PDDlCxZ0vm4j48Ps2fPpkiRIrRq1Yp27dpRqVIlpkyZkpcvzWnr1q2ULl2a0qVLU69ePaZOncq4cePo168fAPXq1WP06NG8/fbb1K5dm4kTJzJy5EjTMTIzM3n00UepUaMGHTt2pFq1as4Jp8uUKcOKFSvIzMykQ4cO1K5dmyeeeILIyEjTfEc3wh3nAEfzzC58DDtNoQ0kJiYSGRlJQkKCaUZ1bzR//nzat29vdRkiHkOZEDFTJkRcKRciZrnNREpKCnv37iUmJobgv88LJFIAJCQkEBkZaXUZV3W1HOam56ERQl6sRYsWVpcg4lGUCREzZULElXIhYqZMiJhlt4pZQaWGkBf77rvvrC5BxKMoEyJmyoSIK+VCxEyZEDG7fPLvgk4NIRERERERERERm1FDyIvVq1fP6hJEPIoyIWKmTIi4Ui5EzJQJEbPQ0FCrS3AbNYS8WF7OpC5SECgTImbKhIgr5ULE7HozYbO1icRGfHx8rC7hmvIqf/qJ6MXWr19vdQkiHkWZEDFTJkRcKRciZrnNREBAAADJycn5UY6I5ZKSkqwu4ZrS0tIA8PPzu6Hj+OdFMSIiIiIiIlLw+fn5UbhwYY4fPw44Lq/xhhEVIjmVlpZGSkqK1WVcUVZWFn/99RehoaH4+99YS8fHsNlYv8TERCIjI0lISCAiIsLqcm7I2bNnKVSokNVliHgMZULETJkQcaVciJhdTyYMwyA+Pt5WqzGJfWRlZXn85cW+vr7ExMQQGBjo8lhueh4aIeTFVq9ezW233WZ1GSIeQ5kQMVMmRFwpFyJm15MJHx8fSpcuTcmSJUlPT8+nykSssWrVKpo2bWp1GVcVGBiYJ00rNYS82MVhmiLioEyImCkTIq6UCxGzG8mEn5/fDc9hIuJp4uPjCQ4OtroMt/DscVByVZGRkVaXIOJRlAkRM2VCxJVyIWKmTIiY2SkTmkPIi6WmphIUFGR1GSIeQ5kQMVMmRFwpFyJmyoSImbdnIjc9D40Q8mIzZ860ugQRj6JMiJgpEyKulAsRM2VCxMxOmbDdHEIXB0QlJiZaXMmNS05OLhCvQySvKBMiZsqEiCvlQsRMmRAx8/ZMXKw9JxeD2e6SsUOHDhEdHW11GSIiIiIiIiIi+eLgwYOUK1fuqvvYriGUlZXFkSNHKFSoED4+PlaXc90SExOJjo7m4MGDXj8XkkheUCZEzJQJEVfKhYiZMiFiVhAyYRgGZ8+epUyZMtdcmt52l4z5+vpes0vmTSIiIrz2L6pIflAmRMyUCRFXyoWImTIhYubtmcjpSmmaVFpERERERERExGbUEBIRERERERERsRk1hLxUUFAQw4YNIygoyOpSRDyCMiFipkyIuFIuRMyUCREzu2XCdpNKi4iIiIiIiIjYnUYIiYiIiIiIiIjYjBpCIiIiIiIiIiI2o4aQiIiIiIiIiIjNqCEkIiIiIiIiImIzagh5sLFjxxITE0NwcDANGzZk2bJlV91/yZIlNGzYkODgYCpVqsQnn3zipkpF3CM3mZg5cya33347JUqUICIigqZNmzJv3jw3ViuS/3L7c+KiFStW4O/vT7169fK3QBE3y20mUlNTeemll6hQoQJBQUFUrlyZL774wk3VirhHbnMxceJEbrrpJkJDQyldujQPPvggJ0+edFO1Ivlr6dKldO3alTJlyuDj48Ps2bOv+ZyC/Hu2GkIeasqUKTz55JO89NJLbNiwgZYtW9KpUycOHDiQ7f579+6lc+fOtGzZkg0bNvDiiy8yZMgQZsyY4ebKRfJHbjOxdOlSbr/9dubMmcO6deto27YtXbt2ZcOGDW6uXCR/5DYTFyUkJNCvXz9uu+02N1Uq4h7Xk4nevXuzcOFCPv/8c3bs2MGkSZOoXr26G6sWyV+5zcXy5cvp168fAwYMYOvWrUybNo01a9YwcOBAN1cukj+SkpK46aab+Pjjj3O0f0H/PVvLznuoJk2a0KBBA8aNG+fcVqNGDbp3787IkSNd9v/3v//N999/z7Zt25zbBg0axB9//MGqVavcUrNIfsptJrJTq1Yt+vTpwyuvvJJfZYq4zfVm4p577qFq1ar4+fkxe/ZsNm7c6IZqRfJfbjMxd+5c7rnnHvbs2UPRokXdWaqI2+Q2F++99x7jxo1j9+7dzm0fffQR77zzDgcPHnRLzSLu4uPjw6xZs+jevfsV9ynov2drhJAHSktLY926dbRv3960vX379qxcuTLb56xatcpl/w4dOrB27VrS09PzrVYRd7ieTPxdVlYWZ8+e1T/6pUC43kx8+eWX7N69m2HDhuV3iSJudT2Z+P7772nUqBHvvPMOZcuWJTY2lmeeeYbz58+7o2SRfHc9uWjWrBmHDh1izpw5GIbBsWPHmD59OnfccYc7ShbxOAX992x/qwsQVydOnCAzM5NSpUqZtpcqVYr4+PhsnxMfH5/t/hkZGZw4cYLSpUvnW70i+e16MvF3o0aNIikpid69e+dHiSJudT2Z2LVrF88//zzLli3D318//qVguZ5M7Nmzh+XLlxMcHMysWbM4ceIEgwcP5tSpU5pHSAqE68lFs2bNmDhxIn369CElJYWMjAzuvPNOPvroI3eULOJxCvrv2Roh5MF8fHxM9w3DcNl2rf2z2y7irXKbiYsmTZrE8OHDmTJlCiVLlsyv8kTcLqeZyMzM5L777mPEiBHExsa6qzwRt8vNz4msrCx8fHyYOHEijRs3pnPnzowePZoJEyZolJAUKLnJRVxcHEOGDOGVV15h3bp1zJ07l7179zJo0CB3lCrikQry79n6L0IPVLx4cfz8/Fw698ePH3fpTl4UFRWV7f7+/v4UK1Ys32oVcYfrycRFU6ZMYcCAAUybNo127drlZ5kibpPbTJw9e5a1a9eyYcMGHnvsMcDxy7BhGPj7+zN//nxuvfVWt9Qukh+u5+dE6dKlKVu2LJGRkc5tNWrUwDAMDh06RNWqVfO1ZpH8dj25GDlyJM2bN+fZZ58FoG7duoSFhdGyZUtef/11rx8NIZJbBf33bI0Q8kCBgYE0bNiQBQsWmLYvWLCAZs2aZfucpk2buuw/f/58GjVqREBAQL7VKuIO15MJcIwM6t+/P99++62ufZcCJbeZiIiIYPPmzWzcuNH5NWjQIKpVq8bGjRtp0qSJu0oXyRfX83OiefPmHDlyhHPnzjm37dy5E19fX8qVK5ev9Yq4w/XkIjk5GV9f86+Ifn5+wKVRESJ2UuB/zzbEI02ePNkICAgwPv/8cyMuLs548sknjbCwMGPfvn2GYRjG888/b9x///3O/ffs2WOEhoYaQ4cONeLi4ozPP//cCAgIMKZPn27VSxDJU7nNxLfffmv4+/sb//nPf4yjR486v86cOWPVSxDJU7nNxN8NGzbMuOmmm9xUrUj+y20mzp49a5QrV87o2bOnsXXrVmPJkiVG1apVjYEDB1r1EkTyXG5z8eWXXxr+/v7G2LFjjd27dxvLly83GjVqZDRu3NiqlyCSp86ePWts2LDB2LBhgwEYo0ePNjZs2GDs37/fMAz7/Z6thpAH+89//mNUqFDBCAwMNBo0aGAsWbLE+dgDDzxgtG7d2rT/4sWLjfr16xuBgYFGxYoVjXHjxrm5YpH8lZtMtG7d2gBcvh544AH3Fy6ST3L7c+JyaghJQZTbTGzbts1o166dERISYpQrV8546qmnjOTkZDdXLZK/cpuLDz/80KhZs6YREhJilC5d2ujbt69x6NAhN1ctkj8WLVp01d8R7PZ7to9haOyfiIiIiIiIiIidaA4hERERERERERGbUUNIRERERERERMRmpIRa9QAACS9JREFU1BASEREREREREbEZNYRERERERERERGxGDSEREREREREREZtRQ0hERERERERExGbUEBIRERERERERsRk1hEREREREREREbEYNIREREfFoEyZMoHDhwlaXcd0qVqzImDFjrrrP8OHDqVevnlvqEREREQE1hERERMQN+vfvj4+Pj8vXn3/+aXVpTJgwwVRT6dKl6d27N3v37s2T469Zs4aHH37Yed/Hx4fZs2eb9nnmmWdYuHBhnpzvSv7+OkuVKkXXrl3ZunVrro/jzQ06ERERcVBDSERERNyiY8eOHD161PQVExNjdVkAREREcPToUY4cOcK3337Lxo0bufPOO8nMzLzhY5coUYLQ0NCr7hMeHk6xYsVu+FzXcvnr/Omnn0hKSuKOO+4gLS0t388tIiIinkUNIREREXGLoKAgoqKiTF9+fn6MHj2aOnXqEBYWRnR0NIMHD+bcuXNXPM4ff/xB27ZtKVSoEBERETRs2JC1a9c6H1+5ciWtWrUiJCSE6OhohgwZQlJS0lVr8/HxISoqitKlS9O2bVuGDRvGli1bnCOYxo0bR+XKlQkMDKRatWp88803pucPHz6c8uXLExQURJkyZRgyZIjzscsvGatYsSIAd911Fz4+Ps77l18yNm/ePIKDgzlz5ozpHEOGDKF169Z59jobNWrE0KFD2b9/Pzt27HDuc7Xvx+LFi3nwwQdJSEhwjjQaPnw4AGlpaTz33HOULVuWsLAwmjRpwuLFi69aj4iIiFhHDSERERGxlK+vLx9++CFbtmzhq6++4tdff+W555674v59+/alXLlyrFmzhnXr1vH8888TEBAAwObNm+nQoQM9evRg06ZNTJkyheXLl/PYY4/lqqaQkBAA0tPTmTVrFk888QRPP/00W7Zs4V//+hcPPvggixYtAmD69Om8//77fPrpp+zatYvZs2dTp06dbI+7Zs0aAL788kuOHj3qvH+5du3aUbhwYWbMmOHclpmZydSpU+nbt2+evc4zZ87w7bffAjjfP7j696NZs2aMGTPGOdLo6NGjPPPMMwA8+OCDrFixgsmTJ7Np0yZ69epFx44d2bVrV45rEhERETcyRERERPLZAw88YPj5+RlhYWHOr549e2a779SpU41ixYo573/55ZdGZGSk836hQoWMCRMmZPvc+++/33j44YdN25YtW2b4+voa58+fz/Y5fz/+wYMHjVtuucUoV66ckZqaajRr1sx46KGHTM/p1auX0blzZ8MwDGPUqFFGbGyskZaWlu3xK1SoYLz//vvO+4Axa9Ys0z7Dhg0zbrrpJuf9IUOGGLfeeqvz/rx584zAwEDj1KlTN/Q6ASMsLMwIDQ01AAMw7rzzzmz3v+ha3w/DMIw///zT8PHxMQ4fPmzafttttxkvvPDCVY8vIiIi1vC3th0lIiIidtG2bVvGjRvnvB8WFgbAokWLePPNN4mLiyMxMZGMjAxSUlJISkpy7nO5p556ioEDB/LNN9/Qrl07evXqReXKlQFYt24df/75JxMnTnTubxgGWVlZ7N27lxo1amRbW0JCAuHh4RiGQXJyMg0aNGDmzJkEBgaybds206TQAM2bN+eDDz4AoFevXowZM4ZKlSrRsWNHOnfuTNeuXfH3v/5/ZvXt25emTZty5MgRypQpw8SJE+ncuTNFihS5oddZqFAh1q9fT0ZGBkuWLOHdd9/lk08+Me2T2+8HwPr16zEMg9jYWNP21NRUt8yNJCIiIrmnhpCIiIi4RVhYGFWqVDFt279/P507d2bQoEG89tprFC1alOXLlzNgwADS09OzPc7w4cO57777+Omnn/j5558ZNmwYkydP5q677iIrK4t//etfpjl8LipfvvwVa7vYKPH19aVUqVIujQ8fHx/TfcMwnNuio6PZsWMHCxYs4JdffmHw4MG8++67LFmyxHQpVm40btyYypUrM3nyZB555BFmzZrFl19+6Xz8el+nr6+v83tQvXp14uPj6dOnD0uXLgWu7/txsR4/Pz/WrVuHn5+f6bHw8PBcvXYRERFxDzWERERExDJr164lIyODUaNG4evrmNpw6tSp13xebGwssbGxDB06lHvvvZcvv/ySu+66iwYNGrB161aXxtO1XN4o+bsaNWqwfPly+vXr59y2cuVK0yickJAQ7rzzTu68804effRRqlevzubNm2nQoIHL8QICAnK0etl9993HxIkTKVeuHL6+vtxxxx3Ox673df7d0KFDGT16NLNmzeKuu+7K0fcjMDDQpf769euTmZnJ8ePHadmy5Q3VJCIiIu6hSaVFRETEMpUrVyYjI4OPPvqIPXv28M0337hcwnS58+fP89hjj7F48WL279/PihUrWLNmjbM58+9//5tVq1bx6KOPsnHjRnbt2sX333/P448/ft01Pvvss0yYMIFPPvmEXbt2MXr0aGbOnOmcTHnChAl8/vnnbNmyxfkaQkJCqFChQrbHq1ixIgsXLiQ+Pp7Tp09f8bx9+/Zl/fr1vPHGG/Ts2ZPg4GDnY3n1OiMiIhg4cCDDhg3DMIwcfT8qVqzIuXPnWLhwISdOnCA5OZnY2Fj69u1Lv379mDlzJnv37mXNmjW8/fbbzJkzJ1c1iYiIiHuoISQiIiKWqVevHqNHj+btt9+mdu3aTJw4kZEjR15xfz8/P06ePEm/fv2IjY2ld+/edOrUiREjRgBQt25dlixZwq5du2jZsiX169fn5ZdfpnTp0tddY/fu3fnggw949913qVWrFp9++ilffvklbdq0AaBw4cJ89tlnNG/enLp167Jw4UJ++OGHK86dM2rUKBYsWEB0dDT169e/4nmrVq3KzTffzKZNm5yri12Ul6/ziSeeYNu2bUybNi1H349mzZoxaNAg+vTpQ4kSJXjnnXcAx8pp/fr14+mnn6ZatWrceeed/P7770RHR+e6JhEREcl/PoZhGFYXISIiIiIiIiIi7qMRQiIiIiIiIiIiNqOGkIiIiIiIiIiIzaghJCIiIiIiIiJiM2oIiYiIiIiIiIjYjBpCIiIiIiIiIiI2o4aQiIiIiIiIiIjNqCEkIiIiIiIiImIzagiJiIiIiIiIiNiMGkIiIiIiIiIiIjajhpCIiIiIiIiIiM2oISQiIiIiIiIiYjP/D5v3BVHc1bkeAAAAAElFTkSuQmCC", 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", 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", 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" ] @@ -1547,13 +1552,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "id": "f3782ec2-9f2c-4c23-9691-79413c4e04be", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1568,10 +1573,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "e7ee0972-79ac-481e-a370-d71b085a3c27", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "draw_calibration_curve(X_test, y_test)" ] @@ -1579,9 +1595,7 @@ { "cell_type": "markdown", "id": "ae8e9bd3-0f6a-4f82-bb4c-470cbdc8d6bb", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "metadata": {}, "source": [ "## Cross Validation" ] @@ -2145,6 +2159,642 @@ "plt.grid(True)\n", "plt.show()\n" ] + }, + { + "cell_type": "markdown", + "id": "98119520-17ae-4b15-afb2-3e2ba0ceaeb0", + "metadata": {}, + "source": [ + "### Random Forest" + ] + }, + { + "cell_type": "markdown", + "id": "59280d0d-b03e-445c-b9e8-689960275b7d", + "metadata": {}, + "source": [ + "#### Benchmark " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "d585a6b9-6943-45a3-b37b-4fb3c0164a0c", + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_rf = Pipeline(steps=[\n", + " ('preprocessor', preproc),\n", + " ('randomF', RandomForestClassifier(class_weight = weight_dict,\n", + " n_jobs=-1)) \n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "6f1aacc1-c251-43bd-8681-919ec5efbd87", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('preprocessor',\n",
+       "                 ColumnTransformer(transformers=[('num',\n",
+       "                                                  Pipeline(steps=[('scaler',\n",
+       "                                                                   StandardScaler())]),\n",
+       "                                                  ['nb_tickets', 'nb_purchases',\n",
+       "                                                   'total_amount',\n",
+       "                                                   'nb_suppliers',\n",
+       "                                                   'vente_internet_max',\n",
+       "                                                   'purchase_date_min',\n",
+       "                                                   'purchase_date_max',\n",
+       "                                                   'time_between_purchase',\n",
+       "                                                   'nb_tickets_internet',\n",
+       "                                                   'is_email_true', 'opt_in',\n",
+       "                                                   'gender_female',\n",
+       "                                                   'gender_male',\n",
+       "                                                   'gender_other',\n",
+       "                                                   'nb_campaigns',\n",
+       "                                                   'nb_campaigns_opened']),\n",
+       "                                                 ('cat',\n",
+       "                                                  Pipeline(steps=[('onehot',\n",
+       "                                                                   OneHotEncoder(handle_unknown='ignore',\n",
+       "                                                                                 sparse_output=False))]),\n",
+       "                                                  ['opt_in'])])),\n",
+       "                ('randomF',\n",
+       "                 RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n",
+       "                                                      1.0: 3.486549107420539},\n",
+       "                                        n_jobs=-1))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('preprocessor',\n", + " ColumnTransformer(transformers=[('num',\n", + " Pipeline(steps=[('scaler',\n", + " StandardScaler())]),\n", + " ['nb_tickets', 'nb_purchases',\n", + " 'total_amount',\n", + " 'nb_suppliers',\n", + " 'vente_internet_max',\n", + " 'purchase_date_min',\n", + " 'purchase_date_max',\n", + " 'time_between_purchase',\n", + " 'nb_tickets_internet',\n", + " 'is_email_true', 'opt_in',\n", + " 'gender_female',\n", + " 'gender_male',\n", + " 'gender_other',\n", + " 'nb_campaigns',\n", + " 'nb_campaigns_opened']),\n", + " ('cat',\n", + " Pipeline(steps=[('onehot',\n", + " OneHotEncoder(handle_unknown='ignore',\n", + " sparse_output=False))]),\n", + " ['opt_in'])])),\n", + " ('randomF',\n", + " RandomForestClassifier(class_weight={0.0: 0.5837086520288036,\n", + " 1.0: 3.486549107420539},\n", + " n_jobs=-1))])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline_rf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "ad83f5de-3e0d-40d0-bcb0-427530642d22", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy Score: 0.8915667665667666\n", + "F1 Score: 0.5773505313539385\n", + "Recall Score: 0.5198685171658145\n" + ] + } + ], + "source": [ + "y_pred = pipeline_rf.predict(X_test)\n", + "\n", + "# Calculate the F1 score\n", + "acc = accuracy_score(y_test, y_pred)\n", + "print(f\"Accuracy Score: {acc}\")\n", + "\n", + "f1 = f1_score(y_test, y_pred)\n", + "print(f\"F1 Score: {f1}\")\n", + "\n", + "recall = recall_score(y_test, y_pred)\n", + "print(f\"Recall Score: {recall}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "d48d7b80-1a30-47f4-a179-e7522d2a905a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_confusion_matrix(y_test, y_pred)\n", + "draw_roc_curve(X_test, y_test)\n", + "draw_features_importance(pipeline_rf, 'randomF', randomF =True)\n", + "draw_prob_distribution(X_test)" + ] } ], "metadata": { From b892ca79c7aa6546fe00a267ca7d924b11a2228f Mon Sep 17 00:00:00 2001 From: tpique-ensae Date: Mon, 18 Mar 2024 15:47:05 +0000 Subject: [PATCH 13/15] added segementation to the model --- .../3_logit_cross_val_sport.ipynb | 2663 ++++++++++++++--- 1 file changed, 2257 insertions(+), 406 deletions(-) diff --git a/Sport/Modelization/3_logit_cross_val_sport.ipynb b/Sport/Modelization/3_logit_cross_val_sport.ipynb index cf835b7..ef23062 100644 --- a/Sport/Modelization/3_logit_cross_val_sport.ipynb +++ b/Sport/Modelization/3_logit_cross_val_sport.ipynb @@ -221,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 96, "id": "639b432a-c39c-4bf8-8ee2-e136d156e0dd", "metadata": {}, "outputs": [ @@ -231,7 +231,7 @@ "{0.0: 0.5837086520288036, 1.0: 3.486549107420539}" ] }, - "execution_count": 9, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -247,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 97, "id": "34644a00-85a5-41c9-98df-41178cb3ac69", "metadata": {}, "outputs": [ @@ -537,7 +537,7 @@ "[224213 rows x 14 columns]" ] }, - "execution_count": 8, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -548,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 98, "id": "295676df-36ac-43d8-8b31-49ff08efd6e7", "metadata": {}, "outputs": [], @@ -586,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 99, "id": "f46fb56e-c908-40b4-868f-9684d1ae01c2", "metadata": {}, "outputs": [ @@ -606,7 +606,7 @@ "dtype: int64" ] }, - "execution_count": 80, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -617,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 100, "id": "e729781b-4d65-42c5-bdc5-82b4d653aaf0", "metadata": {}, "outputs": [], @@ -630,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 101, "id": "a7ebbe6f-70ba-4276-be18-f10e7bfd7423", "metadata": {}, "outputs": [], @@ -666,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 102, "id": "2334eb51-e6ea-4fd0-89ce-f54cd474d332", "metadata": {}, "outputs": [], @@ -701,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 103, "id": "83917b97-4d9b-4e3c-ba27-1e546ce885d3", "metadata": {}, "outputs": [], @@ -738,14 +738,14 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 104, "id": "ba4cde9f-a614-4a43-81b9-e16e78aa6c4c", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Pipeline(steps=[('preprocessor',\n",
+       "
Pipeline(steps=[('preprocessor',\n",
        "                 ColumnTransformer(transformers=[('num',\n",
        "                                                  Pipeline(steps=[('scaler',\n",
        "                                                                   StandardScaler())]),\n",
@@ -1171,7 +1171,7 @@
        "                ('logreg',\n",
        "                 LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
        "                                                  1.0: 3.486549107420539},\n",
-       "                                    max_iter=5000, solver='saga'))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
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