# Local Dev Sandbox for the solver # Useful for testing some concepts and functionality # and offers a much faster feedback loop than the usual end-to-end process in Local Dev # # How to use: # 1. run `compose exec meazure-solver bash` # 2. run `python` # 3. import this file in the python repl by `from services.solver_sandbox import SolverSandbox` # 4. run any of the methds below e.g. `SolverSandbox.yas_elastic()` import logging from pulp import LpProblem, LpVariable, LpInteger, LpMinimize, LpMaximize, LpAffineExpression, LpConstraint, LpStatus, lpSum from services.loft_service import LoftService from lib.application_configs import ApplicationConfigs class SolverSandbox: def loft_service(body = {}): if ApplicationConfigs.local_dev_env: body = {'Records': [{'eventVersion': '2.1', 'eventSource': 'aws:s3', 'awsRegion': 'us-east-1', 'eventTime': '2022-03-17T13:51:22.708Z', 'eventName': 'ObjectCreated:Put', 'userIdentity': {'principalId': 'AIDAJDPLRKLG7UEXAMPLE'}, 'requestParameters': {'sourceIPAddress': '127.0.0.1'}, 'responseElements': {'x-amz-request-id': '25ecd478', 'x-amz-id-2': 'eftixk72aD6Ap51TnqcoF8eFidJG9Z/2'}, 's3': {'s3SchemaVersion': '1.0', 'configurationId': 'testConfigRule', 'bucket': {'name': 'measure-local-solver-ingest', 'ownerIdentity': {'principalId': 'A3NL1KOZZKExample'}, 'arn': 'arn:aws:s3:::measure-local-solver-ingest'}, 'object': {'key': '40f23de0-8827-013a-a353-0242ac120010_solver_run.tar.gz', 'size': 491, 'eTag': '"2b423d91e80d931302192e781b6bd47c"', 'versionId': None, 'sequencer': '0055AED6DCD90281E5'}}}]} # CPNRE item bank with metadata and cases # body = {'Records': [{'eventVersion': '2.1', 'eventSource': 'aws:s3', 'awsRegion': 'us-east-1', 'eventTime': '2022-03-23T18:49:42.979Z', 'eventName': 'ObjectCreated:Put', 'userIdentity': {'principalId': 'AIDAJDPLRKLG7UEXAMPLE'}, 'requestParameters': {'sourceIPAddress': '127.0.0.1'}, 'responseElements': {'x-amz-request-id': 'c4efd257', 'x-amz-id-2': 'eftixk72aD6Ap51TnqcoF8eFidJG9Z/2'}, 's3': {'s3SchemaVersion': '1.0', 'configurationId': 'testConfigRule', 'bucket': {'name': 'measure-local-solver-ingest', 'ownerIdentity': {'principalId': 'A3NL1KOZZKExample'}, 'arn': 'arn:aws:s3:::measure-local-solver-ingest'}, 'object': {'key': 'e8f38480-8d07-013a-5ee6-0242ac120010_solver_run.tar.gz', 'size': 12716, 'eTag': '"94189c36aef04dde3babb462442c3af3"', 'versionId': None, 'sequencer': '0055AED6DCD90281E5'}}}]} # LOFT item bank with metadata and cases body = {'Records': [{'eventVersion': '2.1', 'eventSource': 'aws:s3', 'awsRegion': 'us-east-1', 'eventTime': '2022-03-22T19:36:53.568Z', 'eventName': 'ObjectCreated:Put', 'userIdentity': {'principalId': 'AIDAJDPLRKLG7UEXAMPLE'}, 'requestParameters': {'sourceIPAddress': '127.0.0.1'}, 'responseElements': {'x-amz-request-id': '61f320d0', 'x-amz-id-2': 'eftixk72aD6Ap51TnqcoF8eFidJG9Z/2'}, 's3': {'s3SchemaVersion': '1.0', 'configurationId': 'testConfigRule', 'bucket': {'name': 'measure-local-solver-ingest', 'ownerIdentity': {'principalId': 'A3NL1KOZZKExample'}, 'arn': 'arn:aws:s3:::measure-local-solver-ingest'}, 'object': {'key': '5971f500-8c45-013a-5d13-0242ac120010_solver_run.tar.gz', 'size': 619, 'eTag': '"a3cbba098e9f6a445cba6014e47ccaf9"', 'versionId': None, 'sequencer': '0055AED6DCD90281E5'}}}]} # Latest CPNRE Item Bank with metadata and cases body = {'Records': [{'eventVersion': '2.1', 'eventSource': 'aws:s3', 'awsRegion': 'us-east-1', 'eventTime': '2022-03-24T15:47:54.652Z', 'eventName': 'ObjectCreated:Put', 'userIdentity': {'principalId': 'AIDAJDPLRKLG7UEXAMPLE'}, 'requestParameters': {'sourceIPAddress': '127.0.0.1'}, 'responseElements': {'x-amz-request-id': '1969b1ed', 'x-amz-id-2': 'eftixk72aD6Ap51TnqcoF8eFidJG9Z/2'}, 's3': {'s3SchemaVersion': '1.0', 'configurationId': 'testConfigRule', 'bucket': {'name': 'measure-local-solver-ingest', 'ownerIdentity': {'principalId': 'A3NL1KOZZKExample'}, 'arn': 'arn:aws:s3:::measure-local-solver-ingest'}, 'object': {'key': 'ab40ca20-8db7-013a-a88f-0242ac120013_solver_run.tar.gz', 'size': 24111, 'eTag': '"718a1a17b5dd5219b8e179bfd1ddf1ca"', 'versionId': None, 'sequencer': '0055AED6DCD90281E5'}}}]} # Latest LOFT Item Bank with metadata and cases with target variance body = {'Records': [{'eventVersion': '2.1', 'eventSource': 'aws:s3', 'awsRegion': 'us-east-1', 'eventTime': '2022-03-25T18:03:18.829Z', 'eventName': 'ObjectCreated:Put', 'userIdentity': {'principalId': 'AIDAJDPLRKLG7UEXAMPLE'}, 'requestParameters': {'sourceIPAddress': '127.0.0.1'}, 'responseElements': {'x-amz-request-id': '204c718f', 'x-amz-id-2': 'eftixk72aD6Ap51TnqcoF8eFidJG9Z/2'}, 's3': {'s3SchemaVersion': '1.0', 'configurationId': 'testConfigRule', 'bucket': {'name': 'measure-local-solver-ingest', 'ownerIdentity': {'principalId': 'A3NL1KOZZKExample'}, 'arn': 'arn:aws:s3:::measure-local-solver-ingest'}, 'object': {'key': 'beb35dc0-8e93-013a-5807-0242ac120013_solver_run.tar.gz', 'size': 24112, 'eTag': '"a5a4aad0eb8c9d9af2aad9684437022a"', 'versionId': None, 'sequencer': '0055AED6DCD90281E5'}}}]} LoftService(body).process() def yosh_loop(): Items = [1,2,3,4,5] tif = { 1: 0.2, 2: 0.5, 3: 0.3, 4: 0.8, 5: 0.1 } iif = { 1: 0.09, 2: 0.2, 3: 0.113, 4: 0.3, 5: 0.1 } drift = 0.0 drift_limit = 0.2 iif_target = 0.5 tif_target = 0.9 item_vars = LpVariable.dicts("Item", Items, cat="Binary") while drift <= drift_limit: prob = LpProblem("tif_tcc_test", LpMinimize) prob += lpSum([(tif[i] + iif[i]) * item_vars[i] for i in Items]), "TifTccSum" prob += lpSum([item_vars[i] for i in Items]) == 3, "TotalItems" prob += lpSum([tif[i] * item_vars[i] for i in Items]) >= tif_target - (tif_target * drift), 'TifMin' prob += lpSum([tif[i] * item_vars[i] for i in Items]) <= tif_target + (tif_target * drift), 'TifMax' prob += lpSum([iif[i] * item_vars[i] for i in Items]) >= iif_target - (iif_target * drift), 'TccMin' prob += lpSum([iif[i] * item_vars[i] for i in Items]) <= iif_target + (iif_target * drift), 'TccMax' prob.solve() print(prob) if LpStatus[prob.status] == "Infeasible": print('attempt infeasible') for v in prob.variables(): print(v.name, "=", v.varValue) drift += 0.02 else: print(f"solution found with drift of {drift}!") for v in prob.variables(): print(v.name, "=", v.varValue) break def yas_elastic(tif_targets, tcc_targets): # [50, 55, 46], [60, 40, 50] Items = [1,2,3,4,5,6,7,8,9,10] iif = { 1: 5, 2: 5, 3: 5, 4: 10, 5: 10, 6: 10, 7: 15, 8: 20, 9: 20, 10: 20 } # --- irf = { 1: 5, 2: 5, 3: 5, 4: 10, 5: 10, 6: 10, 7: 15, 8: 20, 9: 20, 10: 20 } items = LpVariable.dicts('Item', Items, cat='Binary') drift = 0 max_drift = 25# 25% elasticity while drift <= max_drift: drift_percent = drift / 100 problem = LpProblem('TIF_TCC', LpMinimize) # objective function problem += lpSum([items[i] for i in Items]) # Constraint 1 problem += lpSum([items[i] for i in Items]) == 5, 'TotalItems' # Our own "Elastic Constraints" for tif_target in tif_targets: print(f"Calculating TIF target of {tif_target} with drift of {drift}%") problem += lpSum( [iif[i] * items[i] for i in Items] ) >= tif_target - (tif_target * drift_percent) problem += lpSum( [iif[i] * items[i] for i in Items] ) <= tif_target + (tif_target * drift_percent) for tcc_target in tcc_targets: print(f"Calculating TIF target of {tcc_target} with drift of {drift}%") problem += lpSum( [irf[i] * items[i] for i in Items] ) >= tcc_target - (tcc_target * drift_percent) problem += lpSum( [irf[i] * items[i] for i in Items] ) <= tcc_target + (tcc_target * drift_percent) problem.solve() if LpStatus[problem.status] == 'Infeasible': print(f"attempt infeasible for drift of {drift}") for v in problem.variables(): print(v.name, "=", v.varValue) # if drift == max_drift: breakpoint(); print(problem.objective.value()) print(problem.constraints) print(problem.objective) drift += 1 else: print(f"solution found with drift of {drift}!") for v in problem.variables(): print(v.name, "=", v.varValue); print(problem.objective.value()) print(problem.constraints) print(problem.objective) break # Implementation of the Whiskas Cat problem, with elastic constraints # https://www.coin-or.org/PuLP/CaseStudies/a_blending_problem.html # https://stackoverflow.com/questions/27278691/how-can-an-elastic-subproblem-in-pulp-be-used-as-a-constraint?noredirect=1&lq=1 def whiskas(): # Creates a list of the Ingredients Ingredients = ['CHICKEN', 'BEEF', 'MUTTON', 'RICE', 'WHEAT', 'GEL'] # A dictionary of the costs of each of the Ingredients is created costs = {'CHICKEN': 0.013, 'BEEF': 0.008, 'MUTTON': 0.010, 'RICE': 0.002, 'WHEAT': 0.005, 'GEL': 0.001} # A dictionary of the protein percent in each of the Ingredients is created proteinPercent = {'CHICKEN': 0.100, 'BEEF': 0.200, 'MUTTON': 0.150, 'RICE': 0.000, 'WHEAT': 0.040, 'GEL': 0.000} # A dictionary of the fat percent in each of the Ingredients is created fatPercent = {'CHICKEN': 0.080, 'BEEF': 0.100, 'MUTTON': 0.110, 'RICE': 0.010, 'WHEAT': 0.010, 'GEL': 0.000} # A dictionary of the fibre percent in each of the Ingredients is created fibrePercent = {'CHICKEN': 0.001, 'BEEF': 0.005, 'MUTTON': 0.003, 'RICE': 0.100, 'WHEAT': 0.150, 'GEL': 0.000} # A dictionary of the salt percent in each of the Ingredients is created saltPercent = {'CHICKEN': 0.002, 'BEEF': 0.005, 'MUTTON': 0.007, 'RICE': 0.002, 'WHEAT': 0.008, 'GEL': 0.000} logging.info('Running Test...') # create problem problem = LpProblem("The Whiskas Problem", LpMinimize) # A dictionary called 'ingredient_vars' is created to contain the referenced Variables ingredient_vars = LpVariable.dicts("Ingr", Ingredients, 0) # set objective problem += lpSum([costs[i]*ingredient_vars[i] for i in Ingredients]), "Total Cost of Ingredients per can" # The five constraints are added to 'prob' problem += lpSum([ingredient_vars[i] for i in Ingredients]) == 100, "PercentagesSum" problem += lpSum([proteinPercent[i] * ingredient_vars[i] for i in Ingredients]) >= 8.0, "ProteinRequirement" problem += lpSum([fatPercent[i] * ingredient_vars[i] for i in Ingredients]) >= 6.0, "FatRequirement" problem += lpSum([fibrePercent[i] * ingredient_vars[i] for i in Ingredients]) <= 2.0, "FibreRequirement" problem += lpSum([saltPercent[i] * ingredient_vars[i] for i in Ingredients]) <= 0.4, "SaltRequirement" # ELASTICIZE # c6_LHS_A = LpAffineExpression([ingredient_vars]) c6_LHS = LpAffineExpression([(ingredient_vars['GEL'],1), (ingredient_vars['BEEF'],1)]) c6= LpConstraint(e=c6_LHS, sense=-1, name='GelBeefTotal', rhs=30) c6_elastic = c6.makeElasticSubProblem(penalty = 100, proportionFreeBound = .10) problem.extend(c6_elastic) print(problem) # solve problem problem.solve() # The status of the solution is printed to the screen print("Status:", LpStatus[problem.status]) # Each of the variables is printed with it's resolved optimum value for v in problem.variables(): print(v.name, "=", v.varValue) # The optimised objective function value is printed to the screen print("Total Cost of Ingredients per can = ", problem.objective.value())