def main():
Settings.new(
linear_solver_partial_time_limit=None,
non_linear_solver_partial_time_limit=None,
solver_total_time_limit=20000,
)
products = [i for i in range(11)]
ground_truth = MixedLogitModel(
products=products,
mus=[p + 1 for p in products],
sigmas=[1 if p % 2 == 0 else 10 for p in products])
offersets = OfferedProductsGenerator(products).generate(TRANSACTIONS)
transactions = TransactionGenerator(ground_truth).generate_for(offersets)
initial_solution = MixedLogitModel.simple_deterministic(products)
estimator_mx = MaximumLikelihoodEstimator()
estimation_mx = estimator_mx.estimate(initial_solution, transactions)
initial_solution = MultinomialLogitModel.simple_deterministic(products)
estimator_mnl = MaximumLikelihoodEstimator()
estimation_mnl = estimator_mnl.estimate(initial_solution, transactions)
plot(ground_truth, estimation_mx, estimator_mx, estimation_mnl,
estimator_mnl, transactions)
def main():
Settings.new(
linear_solver_partial_time_limit=None,
non_linear_solver_partial_time_limit=None,
solver_total_time_limit=20000,
)
nests = [
{'lambda': 0.8, 'products': [0, 1, 2]},
{'lambda': 0.8, 'products': [3, 4, 5]},
{'lambda': 0.8, 'products': [6, 7, 8]},
{'lambda': 0.8, 'products': [9, 10]},
]
products = [i for i in range(11)]
ground_truth = NestedLogitModel(products=products, etas=[0.1 * i for i in range(1, 12)], nests=nests)
offersets = OfferedProductsGenerator(products).generate(TRANSACTIONS)
transactions = TransactionGenerator(ground_truth).generate_for(offersets)
initial_solution = NestedLogitModel.simple_deterministic(products, nests)
estimator_nl = MaximumLikelihoodEstimator()
estimation_nl = estimator_nl.estimate(initial_solution, transactions)
initial_solution = MultinomialLogitModel.simple_deterministic(products)
estimator_mnl = MaximumLikelihoodEstimator()
estimation_mnl = estimator_mnl.estimate(initial_solution, transactions)
plot(ground_truth, estimator_mnl, estimation_mnl, estimator_nl, estimation_nl, transactions)
def main():
Settings.new(
linear_solver_partial_time_limit=300.0,
non_linear_solver_partial_time_limit=300.0,
solver_total_time_limit=1800.0,
)
ground_truth, transactions = read_synthetic_instance(
'instances/100_lists_300_periods_1_instance.json')
products = ground_truth.products
nests = [{
'products': [0],
'lambda': 0.8
}, {
'products': [i for i in range(1, len(products)) if i % 2 == 0],
'lambda': 0.8
}, {
'products': [i for i in range(1, len(products)) if i % 2 == 1],
'lambda': 0.8
}]
lists = [[i] + list(sorted(set(products) - {i}))
for i in range(len(products))]
models_to_run = [
(ExponomialModel.simple_deterministic(products),
MaximumLikelihoodEstimator()),
(MultinomialLogitModel.simple_deterministic(products),
MaximumLikelihoodEstimator()),
(RandomChoiceModel.simple_deterministic(products),
RandomChoiceModelMaximumLikelihoodEstimator()),
(LatentClassModel.simple_deterministic(products, 1),
LatentClassFrankWolfeEstimator()),
(MarkovChainModel.simple_deterministic(products),
MarkovChainExpectationMaximizationEstimator()),
(MarkovChainRank2Model.simple_deterministic(products),
MaximumLikelihoodEstimator()),
(MixedLogitModel.simple_deterministic(products),
MaximumLikelihoodEstimator()),
(NestedLogitModel.simple_deterministic(products, nests=nests),
MaximumLikelihoodEstimator()),
(RankedListModel.simple_deterministic(products, ranked_lists=lists),
RankedListExpectationMaximizationEstimator(MIPMarketExplorer())),
]
results = []
for initial_solution, estimator in models_to_run:
if hasattr(estimator, 'estimate_with_market_discovery'):
model = estimator.estimate_with_market_discovery(
initial_solution, transactions)
else:
model = estimator.estimate(initial_solution, transactions)
soft_rmse = model.soft_rmse_for(ground_truth)
results.append((model, estimator.profiler(), soft_rmse))
plot(results)
def look_for_new_mnl_model(self, model,
likelihood_loss_function_coefficients):
possible_mnl_model = MultinomialLogitModel.simple_deterministic(
model.products)
problem = NewMNLSubProblem(model, possible_mnl_model,
likelihood_loss_function_coefficients)
solution = NonLinearSolver.default().solve(problem, self.profiler())
possible_mnl_model.update_parameters_from_vector(solution)
return possible_mnl_model
def main():
Settings.new(
linear_solver_partial_time_limit=None,
non_linear_solver_partial_time_limit=None,
solver_total_time_limit=20000,
)
products = [i for i in range(11)]
ground_truth = MultinomialLogitModel.simple_random(products=products)
offersets = OfferedProductsGenerator(products).generate(TRANSACTIONS)
transactions = TransactionGenerator(ground_truth).generate_for(offersets)
initial_solution = ExponomialModel.simple_deterministic(products)
estimator_exp = MaximumLikelihoodEstimator()
estimation_exp = estimator_exp.estimate(initial_solution, transactions)
initial_solution = MultinomialLogitModel.simple_deterministic(products)
estimator_mnl = MaximumLikelihoodEstimator()
estimation_mnl = estimator_mnl.estimate(initial_solution, transactions)
plot(ground_truth, estimation_exp, estimator_exp, estimation_mnl, estimator_mnl, transactions)
'mkv2': {
'estimator':
MaximumLikelihoodEstimator(),
'model_class':
lambda products: MarkovChainRank2Model.simple_deterministic(
products),
'name':
'Markov Chain Rank 2',
'settings':
NORMAL_SETTINGS
},
'mnl': {
'estimator':
MaximumLikelihoodEstimator(),
'model_class':
lambda products: MultinomialLogitModel.simple_deterministic(
products),
'name':
'Multinomial Logit',
'settings':
NORMAL_SETTINGS
},
'nl': {
'estimator':
MaximumLikelihoodEstimator(),
'model_class':
lambda products: NestedLogitModel.
simple_deterministic_ordered_nests(products, [
1,
len(products) // 2,
len(products) - (len(products) // 2) - 1
]),