def run_perceptron(N, alpha, p_pos): model = glm_generative(N=N, alpha=alpha, ensemble_type="gaussian", prior_type="binary", output_type="sgn", prior_p_pos=p_pos) scenario = BayesOptimalScenario(model, x_ids=["x"]) early = EarlyStopping() records = scenario.run_all(max_iter=200, callback=early) return records
def run_cs(N, alpha, ensemble_type, prior_rho): model = glm_generative( N=N, alpha=alpha, ensemble_type=ensemble_type, prior_type="gauss_bernoulli", output_type="gaussian", prior_rho=prior_rho, output_var=1e-11 ) scenario = BayesOptimalScenario(model, x_ids=["x"]) early = EarlyStopping() records = scenario.run_all( metrics=["mse"], max_iter=200, callback=early ) return records
def run_EP(alpha, rho, seed): model = glm_generative(N=2000, alpha=alpha, ensemble_type="gaussian", prior_type="gauss_bernoulli", output_type="abs", prior_rho=rho, prior_mean=0) scenario = BayesOptimalScenario(model, x_ids=["x"]) scenario.setup(seed) x_data = scenario.run_ep(max_iter=200, damping=0.2) x_pred = x_data["x"]["r"] mse = sign_symmetric_mse(x_pred, scenario.x_true["x"]) return dict(source="EP", v=mse)
def run_EP(alpha, rho, seed): model = glm_generative(N=2000, alpha=alpha, ensemble_type="gaussian", prior_type="gauss_bernoulli", output_type="gaussian", prior_rho=rho, output_var=1e-10) scenario = BayesOptimalScenario(model, x_ids=["x"]) scenario.setup(seed) x_data = scenario.run_ep(max_iter=200) x_pred = x_data["x"]["r"] mse = mean_squared_error(x_pred, scenario.x_true["x"]) return dict(source="EP", v=mse)
def run_phase_retrieval(N, alpha, prior_mean): model = glm_generative(N=N, alpha=alpha, ensemble_type="complex_gaussian", prior_type="gauss_bernouilli", output_type="modulus", prior_mean=prior_mean, prior_rho=0.5) scenario = BayesOptimalScenario(model, x_ids=["x"]) early = EarlyStopping(wait_increase=10) records = scenario.run_all(metrics=["mse", "phase_mse"], max_iter=200, damping=0.3, callback=early) return records
import pandas as pd from tramp.algos import EarlyStoppingEP from tramp.models import glm_generative from tramp.experiments import BayesOptimalScenario, qplot, plot_compare # %% # Model # ----- # We wish to infer the binary signal # $x \sim \mathrm{Bin}( . | p_+) \in \pm^N$ from # $y = \mathrm{sgn}(Fx) \in \pm^M$, where # $F \in \mathbb{R}^{M \times N}$ is a Gaussian random matrix. # You can build the perceptron directly, or use the `glm_generative` model builder. teacher = glm_generative(N=1000, alpha=1.7, ensemble_type="gaussian", prior_type="binary", output_type="sgn") scenario = BayesOptimalScenario(teacher, x_ids=["x"]) scenario.setup(seed=42) scenario.student.plot() # %% # EP dynamics ep_evo = scenario.ep_convergence(metrics=["mse"], max_iter=30, callback=EarlyStoppingEP()) qplot(ep_evo, x="iter", y=["mse", "v"], y_markers=[".", "-"], y_legend=True) # %% # Recovered signal
# Setup from tramp.algos import EarlyStoppingEP from tramp.models import glm_generative from tramp.experiments import BayesOptimalScenario, qplot, plot_compare import matplotlib.pyplot as plt import pandas as pd import numpy as np # %% # Model alpha = 1.6 N = 1000 teacher = glm_generative(N=N, alpha=alpha, ensemble_type="gaussian", prior_type="binary", output_type="door", output_width=1., prior_p_pos=0.51) for factor in teacher.factors: print(factor) scenario = BayesOptimalScenario(teacher, x_ids=["x", "z"]) scenario.setup(seed=42) scenario.student.plot() # %% # EP dyanmics ep_evo = scenario.ep_convergence(metrics=["mse", "sign_mse"], damping=0.1,
# Setup from tramp.algos import EarlyStoppingEP from tramp.experiments import BayesOptimalScenario, qplot, plot_compare_complex from tramp.models import glm_generative import matplotlib.pyplot as plt import pandas as pd import numpy as np # %% # Model np.random.seed(42) model = glm_generative(N=1000, alpha=2, ensemble_type="complex_gaussian", prior_type="gauss_bernouilli", output_type="modulus", prior_mean=0.01, prior_rho=0.5) scenario = BayesOptimalScenario(model, x_ids=["x"]) scenario.setup() scenario.student.plot() for factor in scenario.student.factors: print(factor.id, factor) # %% # EP dyanmics # Damping is needed # really bad without damping ep_evo = scenario.ep_convergence(metrics=["mse", "phase_mse"], max_iter=20)
from tramp.experiments import run_experiments, qplot, plot_compare from tramp.models import glm_generative from tramp.experiments import BayesOptimalScenario import numpy as np import pandas as pd import matplotlib.pyplot as plt import logging logging.basicConfig(level=logging.INFO) # %% # Model np.random.seed(42) teacher = glm_generative(N=1000, alpha=1.2, ensemble_type="gaussian", prior_type="gauss_bernoulli", output_type="abs", prior_rho=0.5, prior_mean=0.1) for factor in teacher.factors: print(factor) scenario = BayesOptimalScenario(teacher, x_ids=["x", "z"]) scenario.setup() scenario.student.plot() # %% # EP dyanmics ep_evo = scenario.ep_convergence(metrics=["mse", "sign_mse"], damping=0.1, max_iter=20) qplot(ep_evo,
from tramp.models import glm_generative from tramp.experiments import BayesOptimalScenario import numpy as np import pandas as pd import matplotlib.pyplot as plt import logging logging.basicConfig(level=logging.INFO) # %% # Model alpha = 2. N = 1000 teacher = glm_generative( N=N, alpha=alpha, ensemble_type="gaussian", prior_type="gauss_bernoulli", output_type="relu", prior_rho=0.5 ) for factor in teacher.factors: print(factor) scenario = BayesOptimalScenario(teacher, x_ids=["x", "z"]) scenario.setup(seed=42) scenario.student.plot() # %% # EP dyanmics ep_evo = scenario.ep_convergence(metrics=["mse"], max_iter=10) qplot( ep_evo, x="iter", y=["mse", "v"], y_markers=[".", "-"], column="id", y_legend=True )