Example PiCyberPreAnalysis

PiCyberPreAnalysis/LICENSE

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+BSD 2-Clause License
+
+Copyright (c) 2017, Oasis Loss Modelling Framework
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

PiCyberPreAnalysis/README.md

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+<img src="https://oasislmf.org/packages/oasis_theme_package/themes/oasis_theme/assets/src/oasis-lmf-colour.png" alt="Oasis LMF logo" width="250"/>
+
+# PiCyber Pre-Analysis
+
+A copy of the [PiCyber](../PiCyber) model with an exposure pre-analysis hook, to check that pre-analysis works for account only portfolios (no location file), as used by cyber and liability models.
+
+The hook, `src/exposure_modification/exposure_pre_analysis_revenue.py`, multiplies each account's `AnnualRevenue` by `revenue_scale_factor` (set in `tests/test_1/exposure_pre_analysis.json`). The lookup maps `AnnualRevenue` into revenue bands, so the scaled accounts can be assigned a different area peril and vulnerability.
+
+After a run, the modified account file is in `runs/losses-<UTC timestamp>/input/account.csv`.
+
+## Running the model
+
+```
+cd tests/test_1
+oasislmf model run --config oasislmf.json
+```

PiCyberPreAnalysis/keys_data/AnnualRevenueMap.csv

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+0
+100000
+1000000
+10000000
+50000000
+100000000

PiCyberPreAnalysis/keys_data/area_peril_dict.csv

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+area_peril_id,revenuecategory,countrycode,peril_id
+1,1,GB,CSB
+2,2,GB,CSB
+3,3,GB,CSB
+4,4,GB,CSB
+5,5,GB,CSB
+6,6,GB,CSB

PiCyberPreAnalysis/keys_data/lookup_config.json

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+{
+    "model": {
+        "supplier_id": "OasisLMF",
+        "model_id": "PiCyberPreAnalysis",
+        "model_version": "0.0.1"
+    },
+    "builtin_lookup_type": "new_lookup",
+    "keys_data_path": "./",
+    "step_definition": {
+        "coverage_type": {
+            "type": "prepare",
+            "parameters": {
+                "coverage_type": {
+                    "default": 10
+                }
+            }
+
+        },
+        "annual_revenue_map": {
+            "type": "interval_to_index",
+            "columns": [
+                "annualrevenue"
+            ],
+            "parameters": {
+                "sorted_array": "%%KEYS_DATA_PATH%%/AnnualRevenueMap.csv",
+                "value_column_name": "annualrevenue",
+                "index_column_name": "revenuecategory"
+            }
+        },
+        "area_peril": {
+            "type": "merge",
+            "columns": [
+                "revenuecategory",
+                "countrycode"
+            ],
+            "parameters": {
+                "file_path": "%%KEYS_DATA_PATH%%/area_peril_dict.csv",
+                "id_columns": ["area_peril_id"],
+                "usecols": [
+                    "revenuecategory",
+                    "countrycode",
+                    "area_peril_id",
+                    "peril_id"
+                ]
+
+            }
+        },
+        "vulnerability": {
+            "type": "merge",
+            "columns": [
+                "revenuecategory",
+                "countrycode"
+            ],
+            "parameters": {
+                "file_path": "%%KEYS_DATA_PATH%%/vulnerability_dict.csv",
+                "id_columns": ["vulnerability_id"],
+                "usecols": [
+                    "revenuecategory",
+                    "countrycode",
+                    "vulnerability_id",
+                    "peril_id"
+                ]
+
+            }
+        }
+    },
+    "strategy": [
+        "coverage_type",
+        "annual_revenue_map",
+        "area_peril",
+        "vulnerability"
+    ]
+}

PiCyberPreAnalysis/keys_data/vulnerability_dict.csv

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+vulnerability_id,revenuecategory,countrycode,peril_id
+1,1,GB,CSB
+2,2,GB,CSB
+3,3,GB,CSB
+4,4,GB,CSB
+5,5,GB,CSB
+6,6,GB,CSB

PiCyberPreAnalysis/model_data/damage_bin_dict.csv

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+bin_index,bin_from,bin_to,interpolation,damage_type
+1,0,0.5,0.25,0
+2,0.5,1,0.75,0

PiCyberPreAnalysis/model_data/events.csv

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+event_id
+1
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11
+12

PiCyberPreAnalysis/model_data/footprint.csv

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+event_id,areaperil_id,intensity_bin_id,probability
+1,1,1,1
+1,2,2,1
+1,3,1,1
+1,4,2,1
+1,5,1,1
+1,6,2,1
+2,1,1,1
+2,2,2,1
+2,3,1,1
+2,4,2,1
+2,5,1,1
+2,6,2,1
+3,1,1,1
+3,2,2,1
+3,3,1,1
+3,4,2,1
+3,5,1,1
+3,6,2,1
+4,1,1,1
+4,2,2,1
+4,3,1,1
+4,4,2,1
+4,5,1,1
+4,6,2,1
+5,1,1,1
+5,2,2,1
+5,3,1,1
+5,4,2,1
+5,5,1,1
+5,6,2,1
+6,1,1,1
+6,2,2,1
+6,3,1,1
+6,4,2,1
+6,5,1,1
+6,6,2,1
+7,1,1,1
+7,2,2,1
+7,3,1,1
+7,4,2,1
+7,5,1,1
+7,6,2,1
+8,1,1,1
+8,2,2,1
+8,3,1,1
+8,4,2,1
+8,5,1,1
+8,6,2,1
+9,1,1,1
+9,2,2,1
+9,3,1,1
+9,4,2,1
+9,5,1,1
+9,6,2,1
+10,1,1,1
+10,2,2,1
+10,3,1,1
+10,4,2,1
+10,5,1,1
+10,6,2,1
+11,1,1,1
+11,2,2,1
+11,3,1,1
+11,4,2,1
+11,5,1,1
+11,6,2,1
+12,1,1,1
+12,2,2,1
+12,3,1,1
+12,4,2,1
+12,5,1,1
+12,6,2,1

PiCyberPreAnalysis/model_data/model_to_bin.sh

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+#!/bin/bash
+
+
+# damage bin dict
+csvtobin damagebin -i damage_bin_dict.csv -o damage_bin_dict.bin
+
+# events
+csvtobin eve -i events.csv -o events.bin
+
+# footprint
+csvtobin footprint -z -m2 -n -i footprint.csv -o footprint.bin.z -x footprint.idx.z
+
+# occurrence
+csvtobin occurrence -P10 -i occurrence.csv -o occurrence.bin
+
+# return periods
+csvtobin returnperiods -i returnperiods.csv -o returnperiods.bin
+
+# vulnerability
+csvtobin vulnerability -d2 -N -i vulnerability.csv -o vulnerability.bin
+
+
+
+

PiCyberPreAnalysis/model_data/occurrence.csv

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+event_id,period_no,occ_year,occ_month,occ_day
+1,1,2026,1,1
+2,4,2026,2,2
+3,10,2026,3,3

PiCyberPreAnalysis/model_data/returnperiods.csv

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+return_period
+10
+5
+2
+1

PiCyberPreAnalysis/model_data/vulnerability.csv

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+vulnerability_id,intensity_bin_id,damage_bin_id,probability
+1,1,1,1
+1,2,2,1
+2,1,1,1
+2,2,2,1
+3,1,1,1
+3,2,2,1
+4,1,1,1
+4,2,2,1
+5,1,1,1
+5,2,2,1
+6,1,1,1
+6,2,2,1

PiCyberPreAnalysis/src/exposure_modification/exposure_pre_analysis_revenue.py

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+import logging
+
+
+class ExposurePreAnalysis:
+    """
+    Example of custom module called by oasislmf/computation/hooks/pre_analysis.py
+    for an account only (cyber) portfolio - there is no location file.
+
+    The account's AnnualRevenue is multiplied by "revenue_scale_factor" (from
+    exposure_pre_analysis.json), e.g. to bring revenues up to date. The lookup maps
+    AnnualRevenue into revenue bands, so the scaled accounts can move to a different
+    area peril / vulnerability.
+    """
+
+    def __init__(self, exposure_data, exposure_pre_analysis_setting, **kwargs):
+        self.exposure_data = exposure_data
+        self.exposure_pre_analysis_setting = exposure_pre_analysis_setting
+
+    def run(self):
+        scale_factor = self.exposure_pre_analysis_setting.get('revenue_scale_factor', 1)
+        account_df = self.exposure_data.account.dataframe
+
+        logging.info(f'Scaling AnnualRevenue by {scale_factor}')
+        account_df['AnnualRevenue'] = account_df['AnnualRevenue'] * scale_factor
+
+        self.exposure_data.account.dataframe = account_df

PiCyberPreAnalysis/tests/test_1/account.csv

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+PortNumber,AccNumber,AccCurrency,PolNumber,PolInceptionDate,PolExpiryDate,LOB,PolPerilsCovered,LayerParticipation,LayerLimit,LayerAttachment,OEDVersion,CountryCode,AreaCode,IndustryScheme1,IndustryCode1,IndustryVintage1,IndustryDescription,AnnualRevenue,AnnualRevenueCurrency,PolDed,DedCurrency,PolCoverage,InsuredName,URL
+PiCyber,1,GBP,P1,2026-01-01,2026-12-31,,CSB,1,10000000,0,5.0.0,GB,,NAICS,72111,2022,Laser Quest,50000,GBP,10000,GBP,CSB,A People,a.com
+PiCyber,2,GBP,P2,2026-01-01,2026-12-31,,CSB,1,10000000,0,5.0.0,GB,,NAICS,72111,2022,Laser Quest,1000000,GBP,10000,GBP,CSB,Bee Keepers,b.com
+PiCyber,3,GBP,P3,2026-01-01,2026-12-31,,CSB,1,10000000,0,5.0.0,GB,,NAICS,72111,2022,Laser Quest,10000000,GBP,10000,GBP,CSB,C Shanty,c.com
+PiCyber,4,GBP,P4,2026-01-01,2026-12-31,,CSB,1,10000000,0,5.0.0,GB,,NAICS,72111,2022,Laser Quest,50000000,GBP,10000,GBP,CSB,Big D,d.com

PiCyberPreAnalysis/tests/test_1/analysis_settings.json

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+{   
+    "version": "3", 
+    "model_name_id": "PiCyberPreAnalysis",
+    "model_supplier_id": "OasisLMF",
+    "gul_output": true,
+    "number_of_samples": 10,
+    "model_settings": {},
+    "gul_summaries": [
+        {
+           "id": 1,
+            "ord_output": {
+                "elt_moment": true,
+                "alt_period": true
+            }
+        }
+    ]
+}
+

PiCyberPreAnalysis/tests/test_1/exposure_pre_analysis.json

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+{
+    "revenue_scale_factor": 10
+}

PiCyberPreAnalysis/tests/test_1/oasislmf.json

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+{
+    "analysis_settings_json": "analysis_settings.json",
+    "lookup_config_json": "../../keys_data/lookup_config.json",
+    "lookup_data_dir": "../../keys_data/",
+    "model_data_dir": "../../model_data/",
+    "oed_accounts_csv": "./account.csv",
+    "exposure_pre_analysis_module": "../../src/exposure_modification/exposure_pre_analysis_revenue.py",
+    "exposure_pre_analysis_setting_json": "exposure_pre_analysis.json"
+}