# %% Import packages
from bnn_mcmc_examples.examples.mlp.noisy_xor.setting1.constants import output_path
# %% Define optimizer-specific output directories
optimizer_output_path = output_path.joinpath('sgd')
optimizer_output_pilot_path = optimizer_output_path.joinpath('pilot_run')
optimizer_output_benchmark_path = optimizer_output_path.joinpath('benchmark_run')
# %% Import packages
from bnn_mcmc_examples.examples.mlp.noisy_xor.setting1.constants import output_path
from bnn_mcmc_examples.examples.mlp.noisy_xor.setting1.mcmc.constants import num_chains
# %% Define sampler-specific output directories
sampler_output_path = output_path.joinpath('prior')
sampler_output_run_paths = [
sampler_output_path.joinpath('run' +
str(i + 1).zfill(len(str(num_chains))))
for i in range(num_chains)
]
# %% Load packages
import pandas as pd
from bnn_mcmc_examples.examples.mlp.noisy_xor.setting1.constants import output_path
from bnn_mcmc_examples.stats import mc_efficiency
# %% Define sampler-specific output directories
sampler_output_paths = output_path.joinpath('metropolis_hastings')
sampler_output_paths = [
output_path.joinpath(name)
for name in ['metropolis_hastings', 'mala', 'smmala']
]
# %% Compute Monte Carlo efficiency
efficiency = mc_efficiency(sampler_output_paths, keys=['rhat', 'ess'])
# %% Save Monte Carlo efficiency
df = pd.DataFrame(data=efficiency)
df.to_csv(output_path.joinpath('mc_efficiency.csv'), index=False)
with open(output_path.joinpath('mc_efficiency.tex'), 'w') as file:
file.write(df.round({'rhat': 4}).to_latex(index=False))
# %% Load packages
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from kanga.plots import redblue_cmap
from bnn_mcmc_examples.examples.mlp.noisy_xor.setting1.constants import output_path
from bnn_mcmc_examples.examples.mlp.noisy_xor.setting1.mcmc.constants import pred_interval_x1, pred_interval_x2
# %% Load ground truth
pred_interval_y = np.loadtxt(output_path.joinpath('ground_truth.csv'),
delimiter=',',
skiprows=0)
# %% Plot heat map of ground truth
num_ticks = 8
xticks = np.linspace(0, len(pred_interval_x1) - 1, num=num_ticks, dtype=np.int)
xticklabels = [np.round(pred_interval_x1[idx], decimals=2) for idx in xticks]
yticks = np.linspace(0, len(pred_interval_x2) - 1, num=num_ticks, dtype=np.int)
yticklabels = [np.round(pred_interval_x2[idx], decimals=2) for idx in yticks]
ax = sns.heatmap(pred_interval_y,
cmap=redblue_cmap,
linewidths=0.01,
linecolor='white',
# %% Import packages
from bnn_mcmc_examples.examples.mlp.noisy_xor.setting1.constants import output_path
from bnn_mcmc_examples.examples.mlp.noisy_xor.setting1.mcmc.constants import num_chains
# %% Define sampler-specific output directories
sampler_output_path = output_path.joinpath('metropolis_hastings')
sampler_output_pilot_path = sampler_output_path.joinpath('pilot_run')
sampler_output_run_paths = [
sampler_output_path.joinpath('run' +
str(i + 1).zfill(len(str(num_chains))))
for i in range(num_chains)
]
# %% Import packages
from bnn_mcmc_examples.examples.mlp.noisy_xor.setting1.constants import output_path
from bnn_mcmc_examples.examples.mlp.noisy_xor.setting1.mcmc.constants import num_chains
# %% Define sampler-specific output directories
sampler_output_path = output_path.joinpath('hmc')
sampler_output_pilot_path = sampler_output_path.joinpath('pilot_run')
sampler_output_run_paths = [
sampler_output_path.joinpath('run' +
str(i + 1).zfill(len(str(num_chains))))
for i in range(num_chains)
]
# %% Import packages
from bnn_mcmc_examples.examples.mlp.noisy_xor.setting1.constants import output_path
from bnn_mcmc_examples.examples.mlp.noisy_xor.setting1.mcmc.constants import num_chains
# %% Define sampler-specific output directories
sampler_output_path = output_path.joinpath('power_posteriors')
sampler_output_pilot_path = sampler_output_path.joinpath('pilot_run')
sampler_output_run_paths = [
sampler_output_path.joinpath('run' +
str(i + 1).zfill(len(str(num_chains))))
for i in range(num_chains)
]