initial agent based economic model [do not merge]

src/disease_model/models/compartment_model.py

@@ -228,11 +228,10 @@ def predict_with_params(
             population_data, past_health_data, params)
 
         prediction = integrate.solve_ivp(
-            self.differential_equations,
+            fun=lambda t, y: self.differential_equations(t, y, tuple(self.parameter_config.flatten(params))),
             t_span=(0, forecast_length),
             t_eval=np.arange(1, forecast_length + 1),
-            y0=initial_state,
-            args=self.parameter_config.flatten(params))
+            y0=initial_state)
 
         return self.format_output(prediction.y)
 

src/disease_model/models/seir.py

@@ -75,7 +75,7 @@ def differential_equations(
         # pylint: disable=invalid-name,too-many-locals
         s, e, i, r, _ = compartments
         population = s + e + i + r
-        parameters = self.parameter_config.parse(args)
+        parameters = self.parameter_config.parse(args[0])
         beta = parameters['beta']
         gamma = parameters['gamma']
         sigma = parameters['sigma']

src/economic_model/data/sector_mapping.csv

@@ -1,25 +1,25 @@
-lockdown_sector,sector
-agriculture,
-chemical,Manufacturing
-commerce,"Trade, Hotels, Transport, Communication and Services Related to Broadcasting"
-construction,Construction
-education,Other services
-fin_prof_services,"Financial, Real Estate and Professional Services"
-food_consumables,
-healthcare,Other services
-hospitality_tourism,"Trade, Hotels, Transport, Communication and Services Related to Broadcasting"
-manufacturing,Manufacturing
-mining,Mining and quarrying
-media,Other services
-energy,"Electricity, gas, water supply & other utility services"
-telecom,"Transport, storage, communication & services related to broadcasting"
-public_admin,"Public Administration Defence, and Other Services"
-supply_chain_ship,"Transport, storage, communication & services related to broadcasting"
-forest_husb_fish,
-textiles,"Trade, Hotels, Transport, Communication and Services Related to Broadcasting"
-transportation ,"Transport, storage, communication & services related to broadcasting"
-utilities,"Electricity, gas, water supply & other utility services"
-open_border,"Transport, storage, communication & services related to broadcasting"
-air_travel,"Transport, storage, communication & services related to broadcasting"
-roal_rail_travel,"Transport, storage, communication & services related to broadcasting"
-public_transport,"Transport, storage, communication & services related to broadcasting"
+lockdown_sector,sector,id
+agriculture,"TTL_01T03: Agriculture, forestry and fishing",0
+chemical,TTL_20T21: Chemicals and pharmaceutical products,9
+commerce,TTL_69T82: Other business sector services,30
+construction,TTL_41T43: Construction,21
+education,TTL_85: Education,32
+fin_prof_services,TTL_64T66: Financial and insurance activities,28
+food_consumables,"TTL_10T12: Food products, beverages and tobacco",4
+healthcare,TTL_86T88: Human health and social work,33
+hospitality_tourism,"TTL_90T96: Arts, entertainment, recreation and other service activities",34
+manufacturing,TTL_24: Manufacture of basic metals,12
+mining,TTL_05T06: Mining and extraction of energy producing products,1
+media,"TTL_58T60: Publishing, audiovisual and broadcasting activities",25
+energy,TTL_05T06: Mining and extraction of energy producing products,1
+telecom,TTL_61: Telecommunications,26
+public_admin,TTL_84: Public administration and defence,31
+supply_chain_ship,TTL_49T53: Transportation and storage,23
+forest_husb_fish,"TTL_01T03: Agriculture, forestry and fishing",0
+textiles,"TTL_13T15: Textiles, wearing apparel, leather and related products",5
+transportation ,TTL_49T53: Transportation and storage,23
+utilities,"TTL_35T39: Electricity, gas, water supply, sewerage, waste and remediation services",20
+open_border,TTL_49T53: Transportation and storage,23
+air_travel,TTL_49T53: Transportation and storage,23
+roal_rail_travel,TTL_49T53: Transportation and storage,23
+public_transport,TTL_49T53: Transportation and storage,23

src/economic_model/models/basic_lockdown_model.py

@@ -3,9 +3,12 @@
 """
 from typing import Dict
 import attr
+import os
 import numpy as np
+import pandas as pd
 from help_project.src.economic_model.utils import gva_data
 from help_project.src.exitstrategies import lockdown_policy as lockdown
+from help_project.src.economic_model.models.economy_simulation import simulate_economy
 
 
 class EconomicLockdownModel():
@@ -33,19 +36,32 @@ def _get_economic_vector_for_single_policy(self, lockdown_policy: lockdown.Lockd
                     sector_mappings.iloc[i]['lockdown_sector'])
 
         baseline_gva = gva.get_gvas()
-        adjusted_gva = {}
+        india_input_output = pd.read_csv(os.path.dirname(__file__) + '/../data/india_input.csv')
+        india_input_output.columns = india_input_output.iloc[0]
+        india_input_output = india_input_output.iloc[1:]
+        india_input_output = india_input_output.set_index('To: (sector in column)')
+        india_input_output = india_input_output.drop(
+            ['TXS_IMP_FNL: Taxes less subsidies on intermediate and final products (paid in foreign countries)',
+             'TXS_INT_FNL: Taxes less subsidies on intermediate and final products (paid in domestic agencies, includes duty on imported products)',
+             'TTL_INT_FNL: Total intermediate consumption at purchasers’ prices',
+             'TTL_97T98: Private households with employed persons'], axis=0)
+        india_input_output = india_input_output.drop(columns=india_input_output.columns[-8:])
+        india_input_output = india_input_output.drop(columns=[india_input_output.columns[-2]])
+        sectors = india_input_output.index.values[:-2]
+        labor_shocks = {}
+        for sector in sectors:
+            labor_shocks[sector]=1
         for key in mapping_dict:
-            if key not in baseline_gva:
+            if key not in sectors:
                 continue
             weights = []
             for sector in mapping_dict[key]:
                 if sector in lockdown_policy:
                     weights.append(lockdown_policy[sector])
             if len(weights) > 0:
-                adjusted_gva[key] = baseline_gva[key] * np.mean(weights)
-            else:
-                adjusted_gva[key] = baseline_gva[key]
-        return adjusted_gva
+                labor_shocks[key] = np.mean(weights)
+        adjusted_gvas = simulate_economy(labor_shocks)
+        return adjusted_gvas
 
     def get_economic_vector(
             self, lockdown_policy: lockdown.LockdownTimeSeries) -> Dict[str, float]:

src/economic_model/models/economy_simulation.py

@@ -0,0 +1,138 @@
+from __future__ import division  # makes division work correctly
+import random
+from builtins import range
+from help_project.src.economic_model.models.firm import Firm
+from help_project.src.economic_model.models.household import Household
+from abcEconomics import Simulation
+import pandas as pd
+import numpy as np
+import sys,os
+""" 
+1. declares the timeline
+2. build one Household agent representing all the households and one Firm each for each sector
+3. For every labor_endowment an agent has gets one trade or usable labor per round. If it is not used at the end of the round it disaapears.
+4. Firms' and Households' possesions are monitored ot the points marked in timeline.
+"""
+
+# Disable
+def blockPrint():
+    sys.stdout = open(os.devnull, 'w')
+
+# Restore
+def enablePrint():
+    sys.stdout = sys.__stdout__
+
+# blockPrint()
+def main(parameters, agent_parameters, labor_alphas, shocks_to_labor_supply):
+    simulation = Simulation(processes=1)
+
+    firms = simulation.build_agents(
+        Firm, 'firm', num_sectors = len(agent_parameters), agent_parameters=agent_parameters)
+    households = simulation.build_agents(
+        Household, 'household', number=1, firms_info=agent_parameters, labor_alphas = labor_alphas[0], needed_labor = labor_alphas[1])
+
+
+    for rnd in range(parameters['rounds']):
+        if rnd>5:
+            # for i in range(number_sectors):
+                # enablePrint()
+                # print(firms[i].last_output)
+                # blockPrint()
+            households.apply_shocks(shocks_to_labor_supply)
+        simulation.advance_round(rnd)
+        households.create_labor()
+        households.sell_labor()
+        firms.buy_labor()
+        firms.production()
+        firms.panel_log(goods=['money', 'GOOD_sector1', 'GOOD_sector2'],
+                        variables=['last_output'])
+        firms.quotes()
+        households.buy_goods()
+        firms.buy_goods()
+        firms.sell_goods()
+        households.panel_log(goods=['money', 'GOOD_sector1', 'GOOD_sector2'],
+                           variables=['current_utility'])
+        households.consumption()
+        firms.adjust_target()
+        # firms.adjust_price()
+    simulation.finalize()
+    firms = pd.read_csv(simulation.path +'/panel_firm.csv')
+    shocks = []
+    for i in range(len(agent_parameters)):
+        temp_firms = firms.loc[firms['name']=='firm'+str(i)]
+        shocks.append(temp_firms['last_output'].iloc[-1]/temp_firms['last_output'].iloc[0])
+    return shocks
+
+def comma_convert(stri):
+    return float(stri.replace(",", ""))
+
+def series_convert(series):
+    return series.apply(comma_convert)
+
+def series_normalize(series):
+    return (series / np.sum(series.iloc[:-1]))
+
+
+def simulate_economy(shocks_to_labor_supply=None):
+    parameters = {'name': '2x2',
+                  'random_seed': None,
+                  'rounds': 500,}
+
+    # Cleaning input output table for india
+    india_input_output = pd.read_csv(os.path.dirname(__file__) + '/../data/india_input.csv')
+    india_input_output.columns = india_input_output.iloc[0]
+    india_input_output = india_input_output.iloc[1:]
+    india_input_output = india_input_output.set_index('To: (sector in column)')
+    india_input_output = india_input_output.apply(series_convert)
+    india_input_output.iloc[-2] = india_input_output.iloc[-2] + india_input_output.iloc[-4] + india_input_output.iloc[-5] +india_input_output.iloc[-6]
+    india_input_output = india_input_output.drop(['TXS_IMP_FNL: Taxes less subsidies on intermediate and final products (paid in foreign countries)' , 'TXS_INT_FNL: Taxes less subsidies on intermediate and final products (paid in domestic agencies, includes duty on imported products)', 'TTL_INT_FNL: Total intermediate consumption at purchasers’ prices', 'TTL_97T98: Private households with employed persons'], axis = 0)
+    india_input_output = india_input_output.drop(columns = india_input_output.columns[-8:])
+    india_input_output = india_input_output.drop(columns = [india_input_output.columns[-2]])
+
+    number_sectors = len(india_input_output)-2
+    ratio_df = india_input_output.iloc[:,:-1].apply(series_normalize)
+    labor_df = india_input_output.iloc[:-2,-1]/np.sum(india_input_output.iloc[:-2,-1])
+
+    agent_parameters = []
+    labor_dict = {}
+    labor_requirement = 0
+    for i in range(number_sectors):
+        temp_dict = {}
+        temp_dict['sector'] = 'sector'+str(i)
+        ratio_dict = {}
+        for j in range(number_sectors):
+            if ratio_df.iloc[j,i] > 0.00001:
+                ratio_dict['GOOD_sector'+str(j)] = ratio_df.iloc[j,i]
+        ratio_dict['labor'] = ratio_df.iloc[number_sectors,i]
+        temp_dict['ratio_dict'] = ratio_dict
+        agent_parameters.append(temp_dict)
+        labor_dict['GOOD_sector'+str(i)] = labor_df.iloc[i]
+
+    output_input_ratio = [0]*number_sectors
+    for i in range(number_sectors):
+        for key in agent_parameters[i]['ratio_dict'].keys():
+            if key=="labor":
+                labor_requirement+=agent_parameters[i]['ratio_dict'][key]
+                continue
+            output_input_ratio[int(key.split('r')[-1])] += agent_parameters[i]['ratio_dict'][key]
+
+    for key in labor_dict.keys():
+        output_input_ratio[int(key.split('r')[-1])] += labor_dict[key]
+    labor_alphas = [labor_dict, labor_requirement]
+    for i in range(number_sectors):
+        agent_parameters[i]['output_input_ratio']=output_input_ratio[i]
+    shocks_array=[]
+    for i in range(len(india_input_output)-2):
+        if india_input_output.index[i] in shocks_to_labor_supply.keys():
+            shocks_array.append(0.5 + 0.5*(shocks_to_labor_supply[india_input_output.index[i]]))
+        else:
+            shocks_array.append(1)
+    shocks_to_labor_supply = shocks_array
+    if shocks_to_labor_supply is None:
+        shocks_to_labor_supply = [random.uniform(0.5,1) for i in range(number_sectors)]
+    shocks =  main(parameters,agent_parameters,labor_alphas, shocks_to_labor_supply)
+    GVAs={}
+    for i in range(number_sectors):
+        GVAs[india_input_output.index[i]]=(shocks[i]*india_input_output.iloc[-1,i])
+    return GVAs
+