08f049c9fc5fc0f3da5a830de72a583007cdec66,elfi/methods/methods.py,BayesianOptimization,prepare_new_batch,#BayesianOptimization#Any#,400

Before Change

        pending_params = self._get_pending_params()

        t = self.batches.total - self.n_initial_evidence
        if t >= self.n_initial_evidence:
            new_param = self.acquisition_method.acquire(1, pending_params, t)
        else:
            new_param = self.init_acquisition.acquire(1, pending_params, t)

        // Add the next evaluation location to the pool
        self.pool.add_batch(batch_index, dict(zip(self.parameters, new_param[0])))

    def extract_result(self):
        param, min_value = stochastic_optimization(self.target_model.predict_mean,

After Change

            self.state["last_update"] = self.target_model.n_evidence

    def prepare_new_batch(self, batch_index):
        if self.n_submitted_evidence < self.n_initial_evidence:
            return

        pending_params = self._get_pending_params()
        t = self.batches.total - int(self.n_initial_evidence/self.batch_size)
        new_param = self.acquisition_method.acquire(self.batch_size, pending_params, t)

Instances