leaderboard/irt/pyirt/three_param_logistic.py [101:183]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ) def guide_hierarchical(self, models, items, obs): loc_mu_b_param = pyro.param("loc_mu_b", torch.tensor(0.0, device=self.device)) scale_mu_b_param = pyro.param( "scale_mu_b", torch.tensor(1.0e2, device=self.device), constraint=constraints.positive, ) loc_mu_gamma_param = pyro.param("loc_mu_gamma", torch.tensor(0.0, device=self.device)) scale_mu_gamma_param = pyro.param( "scale_mu_gamma", torch.tensor(1.0e2, device=self.device), constraint=constraints.positive, ) loc_mu_theta_param = pyro.param("loc_mu_theta", torch.tensor(0.0, device=self.device)) scale_mu_theta_param = pyro.param( "scale_mu_theta", torch.tensor(1.0e2, device=self.device), constraint=constraints.positive, ) alpha_b_param = pyro.param( "alpha_b", torch.tensor(1.0, device=self.device), constraint=constraints.positive, ) beta_b_param = pyro.param( "beta_b", torch.tensor(1.0, device=self.device), constraint=constraints.positive, ) alpha_gamma_param = pyro.param( "alpha_gamma", torch.tensor(1.0, device=self.device), constraint=constraints.positive, ) beta_gamma_param = pyro.param( "beta_gamma", torch.tensor(1.0, device=self.device), constraint=constraints.positive, ) alpha_theta_param = pyro.param( "alpha_theta", torch.tensor(1.0, device=self.device), constraint=constraints.positive, ) beta_theta_param = pyro.param( "beta_theta", torch.tensor(1.0, device=self.device), constraint=constraints.positive, ) m_theta_param = pyro.param("loc_ability", torch.zeros(self.num_models, device=self.device)) s_theta_param = pyro.param( "scale_ability", torch.ones(self.num_models, device=self.device), constraint=constraints.positive, ) m_b_param = pyro.param("loc_diff", torch.zeros(self.num_items, device=self.device)) s_b_param = pyro.param( "scale_diff", torch.ones(self.num_items, device=self.device), constraint=constraints.positive, ) m_gamma_param = pyro.param("loc_disc", torch.zeros(self.num_items, device=self.device)) s_gamma_param = pyro.param( "scale_disc", torch.ones(self.num_items, device=self.device), constraint=constraints.positive, ) # lambda_param = pyro.param( # "lambda_lower", # torch.ones(self.num_items, device=self.device), # constraint=constraints.unit_interval, # ) # sample statements mu_b = pyro.sample("mu_b", dist.Normal(loc_mu_b_param, scale_mu_b_param)) u_b = pyro.sample("u_b", dist.Gamma(alpha_b_param, beta_b_param)) mu_gamma = pyro.sample("mu_gamma", dist.Normal(loc_mu_gamma_param, scale_mu_gamma_param)) u_gamma = pyro.sample("u_gamma", dist.Gamma(alpha_gamma_param, beta_gamma_param)) mu_theta = pyro.sample("mu_theta", dist.Normal(loc_mu_theta_param, scale_mu_theta_param)) u_theta = pyro.sample("u_theta", dist.Gamma(alpha_theta_param, beta_theta_param)) # with pyro.plate("lambdas", self.num_items, device=self.device): # pyro.sample("lambda", dist.Uniform(lambda_param, 1)) with pyro.plate("thetas", self.num_models, device=self.device): pyro.sample("theta", dist.Normal(m_theta_param, s_theta_param)) with pyro.plate("bs", self.num_items, device=self.device): pyro.sample("b", dist.Normal(m_b_param, s_b_param)) with pyro.plate("gammas", self.num_items, device=self.device): pyro.sample("gamma", dist.Normal(m_gamma_param, s_gamma_param)) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - leaderboard/irt/pyirt/two_param_logistic.py [90:166]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ) def guide_hierarchical(self, models, items, obs): loc_mu_b_param = pyro.param("loc_mu_b", torch.tensor(0.0, device=self.device)) scale_mu_b_param = pyro.param( "scale_mu_b", torch.tensor(1.0e2, device=self.device), constraint=constraints.positive, ) loc_mu_gamma_param = pyro.param("loc_mu_gamma", torch.tensor(0.0, device=self.device)) scale_mu_gamma_param = pyro.param( "scale_mu_gamma", torch.tensor(1.0e2, device=self.device), constraint=constraints.positive, ) loc_mu_theta_param = pyro.param("loc_mu_theta", torch.tensor(0.0, device=self.device)) scale_mu_theta_param = pyro.param( "scale_mu_theta", torch.tensor(1.0e2, device=self.device), constraint=constraints.positive, ) alpha_b_param = pyro.param( "alpha_b", torch.tensor(1.0, device=self.device), constraint=constraints.positive, ) beta_b_param = pyro.param( "beta_b", torch.tensor(1.0, device=self.device), constraint=constraints.positive, ) alpha_gamma_param = pyro.param( "alpha_gamma", torch.tensor(1.0, device=self.device), constraint=constraints.positive, ) beta_gamma_param = pyro.param( "beta_gamma", torch.tensor(1.0, device=self.device), constraint=constraints.positive, ) alpha_theta_param = pyro.param( "alpha_theta", torch.tensor(1.0, device=self.device), constraint=constraints.positive, ) beta_theta_param = pyro.param( "beta_theta", torch.tensor(1.0, device=self.device), constraint=constraints.positive, ) m_theta_param = pyro.param("loc_ability", torch.zeros(self.num_models, device=self.device)) s_theta_param = pyro.param( "scale_ability", torch.ones(self.num_models, device=self.device), constraint=constraints.positive, ) m_b_param = pyro.param("loc_diff", torch.zeros(self.num_items, device=self.device)) s_b_param = pyro.param( "scale_diff", torch.ones(self.num_items, device=self.device), constraint=constraints.positive, ) m_gamma_param = pyro.param("loc_disc", torch.zeros(self.num_items, device=self.device)) s_gamma_param = pyro.param( "scale_disc", torch.ones(self.num_items, device=self.device), constraint=constraints.positive, ) # sample statements mu_b = pyro.sample("mu_b", dist.Normal(loc_mu_b_param, scale_mu_b_param)) u_b = pyro.sample("u_b", dist.Gamma(alpha_b_param, beta_b_param)) mu_gamma = pyro.sample("mu_gamma", dist.Normal(loc_mu_gamma_param, scale_mu_gamma_param)) u_gamma = pyro.sample("u_gamma", dist.Gamma(alpha_gamma_param, beta_gamma_param)) mu_theta = pyro.sample("mu_theta", dist.Normal(loc_mu_theta_param, scale_mu_theta_param)) u_theta = pyro.sample("u_theta", dist.Gamma(alpha_theta_param, beta_theta_param)) with pyro.plate("thetas", self.num_models, device=self.device): pyro.sample("theta", dist.Normal(m_theta_param, s_theta_param)) with pyro.plate("bs", self.num_items, device=self.device): pyro.sample("b", dist.Normal(m_b_param, s_b_param)) with pyro.plate("gammas", self.num_items, device=self.device): pyro.sample("gamma", dist.Normal(m_gamma_param, s_gamma_param)) - 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