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,
max_iter=40)
qplot(ep_evo,
x="iter",
y=["mse", "sign_mse", "v"],
y_markers=["x", ".", "-"],
column="id",
y_legend=True)
plot_compare(scenario.x_true["x"], scenario.x_pred["x"])
# %%
# MSE curve
# See data/door_mse_curves.py for the code
rename = {
"alpha": r"$\alpha$",
"output_width": "K",
"a0": r"$a_0$",
"v": "MSE",
"n_iter": "iterations",
"x_id=": "",
"p_pos": r"$p_+$",
# 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
plot_compare(scenario.x_true["x"], scenario.x_pred["x"])
# %%
# Compare EP vs SE
# ----------------
# See `data/perceptron_ep_vs_se.py` for the corresponding script.
rename = {
"alpha": r"$\alpha$",
"n_iter": "iterations",
"p_pos": r"$p_+$",
"source=": ""
}
"""
Universality (noiseless CS)
===========================
"""
import pandas as pd
from matplotlib import rcParams
rcParams['axes.unicode_minus'] = False
from tramp.experiments import qplot
# %%
# Model
# -----
# We consider for the sensing matrix $F$ a random features matrix
# $F = \tfrac{1}{\sqrt{N}}f(WX)$ where $f$ = abs, relu, sgn or tanh.
# See `data/cs_universality.py` for the corresponding script.
rename = {
"alpha": r"$\alpha$", "prior_rho": r"$\rho$",
"source=": "", "n_iter": "iterations"
}
univ = pd.read_csv("data/cs_universality.csv")
qplot(
univ.query("source=='SE'"),
x="alpha", y="v", linestyle="f", column="prior_rho",
rename=rename, usetex=True, font_size=16
)
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)
qplot(ep_evo,
x="iter",
y=["phase_mse", "v"],
y_markers=["x", "-"],
y_legend=True)
ep_evo = scenario.ep_convergence(metrics=["mse", "phase_mse"],
max_iter=70,
damping=0.3)
qplot(ep_evo,
x="iter",
y=["mse", "phase_mse", "v"],
y_markers=[".", "x", "-"],
y_legend=True)
plot_compare_complex(scenario.x_true["x"], scenario.x_pred["x"])
# %%
# Compare EP vs SE
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,
x="iter",
y=["mse", "sign_mse", "v"],
y_markers=["x", ".", "-"],
column="id",
y_legend=True)
plot_compare(scenario.x_true["x"], scenario.x_pred["x"])
# %%
# MSE curve
# See data/sgn_retrieval_mse_curves.py for the code
rename = {
"alpha": r"$\alpha$",
"prior_rho": r"$\rho$",
"prior_mean": r"$\mu$",
"a0=0.1": "uninformed",
"a0=1000.0": "informed",
"v": "MSE",
"n_iter": "iterations",
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
)
plot_compare(scenario.x_true["x"], scenario.x_pred["x"])
# %%
# MSE curve
# See data/relu_mse_curves.py for the code
rename = {
"alpha": r"$\alpha$", "v": "MSE", "n_iter": "iter", "a0": r"$a_0$",
"prior_rho": r"$\rho$", "x_id=": "", "n_iter": "iterations"
}
mse_curves = pd.read_csv("data/relu_mse_curves.csv")
qplot(
mse_curves.query("x_id =='x'"), x="alpha", y="v", color="prior_rho",
rename=rename, usetex=True, font_size=14
ensemble_type="gaussian",
prior_type="gauss_bernoulli",
output_type="gaussian",
output_var=1e-11,
prior_rho=0.5)
for factor in teacher.factors:
print(factor)
scenario = BayesOptimalScenario(teacher, x_ids=["x"])
scenario.setup()
scenario.student.plot()
# %%
# EP dyanmics
ep_evo = scenario.ep_convergence(metrics=["mse"], max_iter=10)
plot_compare(scenario.x_true["x"], scenario.x_pred["x"])
qplot(ep_evo, x="iter", y=["v", "mse"], y_markers=["-", "."], y_legend=True)
# %%
# Compare EP vs SE
rename = {
"alpha": r"$\alpha$",
"prior_rho": r"$\rho$",
"source=": "",
"n_iter": "iterations"
}
ep_vs_se = pd.read_csv("data/compressed_sensing_ep_vs_se.csv")
qplot(ep_vs_se,
x="alpha",
y="v",
marker="source",
column="prior_rho",
