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@@ -6,3 +6,4 @@ optuna | |
| asyncpg | ||
| psycopg2 | ||
| psycopg2-binary | ||
| plotly | ||
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Learn more about bidirectional Unicode characters
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| """ | ||
| This script analyzes the distribution of the number of contracts and claims per cell. | ||
| """ | ||
|
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| from swafi.config import Config | ||
| from swafi.damages_mobiliar import DamagesMobiliar | ||
| from swafi.damages_gvz import DamagesGvz | ||
| import pandas as pd | ||
| import matplotlib.pyplot as plt | ||
|
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| config = Config(output_dir='analysis_damage_distribution') | ||
| output_dir = config.output_dir | ||
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| PICKLES_DIR = config.get('PICKLES_DIR') | ||
| DATASET = 'mobiliar' # 'mobiliar' or 'gvz' | ||
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| if DATASET == 'mobiliar': | ||
| EXPOSURE_CATEGORIES = ['external'] | ||
| CLAIM_CATEGORIES = ['external', 'pluvial'] | ||
| elif DATASET == 'gvz': | ||
| EXPOSURE_CATEGORIES = ['all_buildings'] | ||
| CLAIM_CATEGORIES = ['likely_pluvial'] | ||
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| def main(): | ||
| if DATASET == 'mobiliar': | ||
| damages = DamagesMobiliar(dir_exposure=config.get('DIR_EXPOSURE_MOBILIAR'), | ||
| dir_claims=config.get('DIR_CLAIMS_MOBILIAR'), | ||
| year_start=config.get('YEAR_START_MOBILIAR'), | ||
| year_end=config.get('YEAR_END_MOBILIAR')) | ||
| elif DATASET == 'gvz': | ||
| damages = DamagesGvz(dir_exposure=config.get('DIR_EXPOSURE_GVZ'), | ||
| dir_claims=config.get('DIR_CLAIMS_GVZ'), | ||
| year_start=config.get('YEAR_START_GVZ'), | ||
| year_end=config.get('YEAR_END_GVZ')) | ||
| else: | ||
| raise ValueError(f'Dataset {DATASET} not recognized.') | ||
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| # Format the date of the claims | ||
| df_claims = damages.claims | ||
| df_claims['date_claim'] = pd.to_datetime( | ||
| df_claims['date_claim'], errors='coerce') | ||
|
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| # Compute the monthly sum of claims for each month per category | ||
| df_claims_month = df_claims.copy() | ||
| df_claims_month['month'] = df_claims_month['date_claim'].dt.month | ||
| df_claims_month = df_claims_month.drop( | ||
| columns=['date_claim', 'mask_index', 'cid', 'x', 'y']) | ||
| df_claims_month_sum = df_claims_month.groupby('month').sum() | ||
|
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| # Plot the monthly distribution of the total # of claims for different categories | ||
| for category in damages.claim_categories: | ||
| sum_claims = df_claims_month_sum[category].sum() | ||
| plt.figure(figsize=(8, 4)) | ||
| plt.title(f'Monthly distribution of the claims for category {category} ' | ||
| f'(total: {sum_claims})') | ||
| plt.xlabel('Month') | ||
| plt.ylabel('Percentage of claims [%]') | ||
| nb_annual_claims = df_claims_month_sum[category] / sum_claims | ||
| plt.bar(df_claims_month_sum.index, 100 * nb_annual_claims) | ||
| plt.xticks(range(1, 13)) | ||
| plt.tight_layout() | ||
| plt.savefig(output_dir / f'monthly_distribution_tot_claims_{category}.png') | ||
| plt.savefig(output_dir / f'monthly_distribution_tot_claims_{category}.pdf') | ||
| plt.close() | ||
|
|
||
| # For the whole domain, aggregate by date (sum) | ||
| df_claims_date = df_claims.copy() | ||
| df_claims_date = df_claims_date.drop( | ||
| columns=['mask_index', 'cid', 'x', 'y']) | ||
| df_claims_date = df_claims_date.groupby('date_claim').sum() | ||
| df_claims_date['date_claim'] = pd.to_datetime(df_claims_date.index, errors='coerce') | ||
| df_claims_date['month'] = df_claims_date['date_claim'].dt.month | ||
|
|
||
| # Plot the monthly distribution of the mean # of claims for different categories | ||
| for category in damages.claim_categories: | ||
| df_claims_date_cat = df_claims_date.copy() | ||
| df_claims_date_cat = df_claims_date_cat[df_claims_date_cat[category] > 0] | ||
| df_claims_date_cat = df_claims_date_cat.groupby('month').mean() | ||
| plt.figure(figsize=(8, 4)) | ||
| plt.title(f'Monthly distribution of the mean # of claims / event for category {category}') | ||
| plt.xlabel('Month') | ||
| plt.ylabel('Mean number of claims') | ||
| plt.bar(df_claims_date_cat.index, df_claims_date_cat[category]) | ||
| plt.xticks(range(1, 13)) | ||
| plt.tight_layout() | ||
| plt.savefig(output_dir / f'monthly_distribution_mean_claims_{category}.png') | ||
| plt.savefig(output_dir / f'monthly_distribution_mean_claims_{category}.pdf') | ||
| plt.close() | ||
|
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||
| # Select the categories of interest | ||
| damages.select_categories_type(EXPOSURE_CATEGORIES, CLAIM_CATEGORIES) | ||
|
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||
| # Analyze the occurrences of damages to the structure and/or content | ||
| if DATASET == 'mobiliar': | ||
| df_mobi = damages.claims | ||
|
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| # Sum priv and sme | ||
| df_mobi['ext_struc_pluv'] = ( | ||
| df_mobi['priv_ext_struc_pluv'] + df_mobi['sme_ext_struc_pluv']) | ||
| df_mobi['ext_cont_pluv'] = ( | ||
| df_mobi['priv_ext_cont_pluv'] + df_mobi['sme_ext_cont_pluv']) | ||
| df_mobi['ext_both_pluv'] = ( | ||
| df_mobi[['ext_struc_pluv', 'ext_cont_pluv']].min(axis=1)) | ||
| df_mobi['ext_struc_only_pluv'] = ( | ||
| df_mobi['ext_struc_pluv'] - df_mobi['ext_both_pluv']) | ||
| df_mobi['ext_cont_only_pluv'] = ( | ||
| df_mobi['ext_cont_pluv'] - df_mobi['ext_both_pluv']) | ||
|
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| nb_both = df_mobi['ext_both_pluv'].sum() | ||
| nb_struc = df_mobi['ext_struc_only_pluv'].sum() | ||
| nb_cont = df_mobi['ext_cont_only_pluv'].sum() | ||
| nb_tot = nb_both + nb_struc + nb_cont | ||
| pc_both = 100 * nb_both / nb_tot | ||
| pc_struc = 100 * nb_struc / nb_tot | ||
| pc_cont = 100 * nb_cont / nb_tot | ||
|
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| print(f"Number of claims with both structure and content: {pc_both:.2f}%") | ||
| print(f"Number of claims with structure only: {pc_struc:.2f}%") | ||
| print(f"Number of claims with content only: {pc_cont:.2f}%") | ||
|
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||
| # Analyze the distribution of the number of contracts and claims per cell | ||
| df_contracts = damages.exposure | ||
| df_contracts = df_contracts[['mask_index', 'selection', 'cid']] | ||
|
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| # Average the number of annual contracts per location | ||
| df_contracts = df_contracts.groupby('mask_index').mean() | ||
|
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||
| # Plot the histogram of the number of contracts per cell | ||
| plt.figure() | ||
| plt.title('Histogram of the number of contracts per cell') | ||
| plt.xlabel('Number of contracts') | ||
| plt.ylabel('Number of cells') | ||
| plt.hist(df_contracts['selection'], bins=100) | ||
| plt.yscale('log') | ||
| plt.xlim(0, None) | ||
| plt.tight_layout() | ||
| plt.savefig(output_dir / 'histogram_contracts.png') | ||
| plt.savefig(output_dir / 'histogram_contracts.pdf') | ||
|
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| df_claims = damages.claims | ||
| df_claims = df_claims[['mask_index', 'selection']] | ||
|
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| # Sum the number of claims per location and divide by the number of years | ||
| df_claims = df_claims.groupby('mask_index').sum() | ||
| n_years = damages.year_end - damages.year_start + 1 | ||
| df_claims['selection'] = df_claims['selection'] / n_years | ||
|
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||
| # Plot the histogram of the number of claims per cell | ||
| plt.figure() | ||
| plt.title('Histogram of the number of annual claims per cell') | ||
| plt.xlabel('Number of claims') | ||
| plt.ylabel('Number of cells') | ||
| plt.hist(df_claims['selection'], bins=50) | ||
| plt.yscale('log') | ||
| plt.xlim(0, None) | ||
| plt.tight_layout() | ||
| plt.savefig(output_dir / 'histogram_claims.png') | ||
| plt.savefig(output_dir / 'histogram_claims.pdf') | ||
|
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| # Merge the contracts and claims dataframes on the index | ||
| df_merged = df_contracts.merge(df_claims, left_index=True, | ||
| right_index=True, how='left') | ||
|
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||
| # Rename the columns | ||
| df_merged.columns = ['contracts', 'cid', 'claims'] | ||
|
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| # Replace nan values with 0 | ||
| df_merged.fillna(0, inplace=True) | ||
|
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||
| # Plot the relationship between the number of contracts and the number of claims | ||
| plt.figure() | ||
| plt.title('Relationship between the number of contracts and the claims') | ||
| plt.xlabel('Number of contracts (mean per cell)') | ||
| plt.ylabel('Mean number of annual claims (sum per cell)') | ||
| plt.scatter(df_merged['contracts'], df_merged['claims'], | ||
| facecolors='none', edgecolors='k') | ||
| plt.xscale('log') | ||
| plt.yscale('log') | ||
| plt.tight_layout() | ||
| plt.savefig(output_dir / 'scatter_contracts_claims.png') | ||
| plt.savefig(output_dir / 'scatter_contracts_claims.pdf') | ||
|
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| if __name__ == '__main__': | ||
| main() |
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