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| from matplotlib import pyplot as plt | ||
| from sim_transfer.sims.simulators import SinusoidsSim | ||
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| import os | ||
| import pickle | ||
| import jax | ||
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| PLOT_POST_SAMPLES = True | ||
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| PLOTS_1D_DIR = os.path.dirname(os.path.abspath(__file__)) | ||
| PLOT_DICT_DIR = os.path.join(PLOTS_1D_DIR, 'plot_dicts') | ||
| PLOT_DICT_PATHS = [ | ||
| ('BNN_SVGD', 'SinusoidsSim_BNN_SVGD_2.pkl'), | ||
| ('BNN_FSVGD', 'SinusoidsSim_BNN_FSVGD_2.pkl'), | ||
| ('BNN_FSVGD_SimPrior_gp', 'SinusoidsSim_BNN_FSVGD_SimPrior_gp_2.pkl'), | ||
| ('BNN_FSVGD_SimPrior_nu-method', 'SinusoidsSim_BNN_FSVGD_SimPrior_nu-method_2.pkl'), | ||
| ('BNN_FSVGD_SimPrior_kde', 'SinusoidsSim_BNN_FSVGD_SimPrior_kde_2.pkl'), | ||
| ] | ||
| PLOT_DICT_PATHS = map(lambda x: (x[0], os.path.join(PLOT_DICT_DIR, x[1])), PLOT_DICT_PATHS) | ||
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| PLOT_MODELS = ['BNN_SVGD', 'BNN_FSVGD', 'BNN_FSVGD_SimPrior_gp'] | ||
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| # draw the plot | ||
| fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(3 * 4, 6)) | ||
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| sim = SinusoidsSim(output_size=1) | ||
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| for i, (model, load_path) in enumerate(PLOT_DICT_PATHS): | ||
| with open(load_path, 'rb') as f: | ||
| plot_dict = pickle.load(f) | ||
| print(f'Plot dict loaded from {load_path}') | ||
| plot_data = plot_dict['plot_data'] | ||
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| if i == 0: | ||
| """ plot samples from the simulation env """ | ||
| f_sim = sim.sample_function_vals(plot_data['x_plot'], num_samples=10, rng_key=jax.random.PRNGKey(234234)) | ||
| for j in range(f_sim.shape[0]): | ||
| axes[0][0].plot(plot_data['x_plot'], f_sim[j]) | ||
| axes[0][0].set_title('sampled functions from sim prior') | ||
| axes[0][0].set_ylim((-14, 14)) | ||
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| ax = axes[(i+1)//3][(i+1)%3] | ||
| if PLOT_POST_SAMPLES: | ||
| for k, y in enumerate(plot_data['y_post_samples']): | ||
| ax.plot(plot_data['x_plot'], y[:, i], linewidth=0.2, color='tab:green', alpha=0.5, | ||
| label='BNN particles' if k == 0 else None) | ||
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| ax.scatter(plot_data['x_train'].flatten(), plot_data['y_train'][:, i], 100, label='train points', marker='x', | ||
| linewidths=2.5, color='tab:blue') | ||
| ax.plot(plot_data['x_plot'], plot_data['true_fun'], label='true fun') | ||
| ax.plot(plot_data['x_plot'].flatten(), plot_data['pred_mean'][:, i], label='pred mean') | ||
| ax.fill_between(plot_data['x_plot'].flatten(), plot_data['pred_mean'][:, i] - 2 * plot_data['pred_std'][:, i], | ||
| plot_data['pred_mean'][:, i] + 2 * plot_data['pred_std'][:, i], alpha=0.2, | ||
| label='95 % CI', color='tab:orange') | ||
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| if i == 4: | ||
| ax.legend() | ||
| ax.set_title(model) | ||
| ax.set_ylim((-14, 14)) | ||
| fig.tight_layout() | ||
| fig.show() | ||
| fig.savefig(os.path.join(PLOTS_1D_DIR, '1d_visualization.pdf')) |
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experiments/1d_visualization/1d_visualization_run_training.py
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| Original file line number | Diff line number | Diff line change |
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| from sim_transfer.sims.simulators import SinusoidsSim, QuadraticSim, LinearSim, ShiftedSinusoidsSim | ||
| from sim_transfer.models import BNN_FSVGD_SimPrior, BNN_SVGD, BNN_FSVGD | ||
| from matplotlib import pyplot as plt | ||
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| import pickle | ||
| import os | ||
| import jax | ||
| import jax.numpy as jnp | ||
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| # determine plot_dict_dir | ||
| plot_dict_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'plot_dicts') | ||
| os.makedirs(plot_dict_dir, exist_ok=True) | ||
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| def _key_iter(data_seed: int = 24359): | ||
| key = jax.random.PRNGKey(data_seed) | ||
| while True: | ||
| key, new_key = jax.random.split(key) | ||
| yield new_key | ||
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| def main(sim_type: str = 'SinusoidsSim', model: str = 'BNN_FSVGD_SimPrior_gp', num_train_points: int = 1, | ||
| plot_post_samples: bool = True, fun_seed: int = 24359): | ||
| key_iter = _key_iter() | ||
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| if sim_type == 'QuadraticSim': | ||
| sim = QuadraticSim() | ||
| elif sim_type == 'LinearSim': | ||
| sim = LinearSim() | ||
| elif sim_type == 'SinusoidsSim': | ||
| sim = SinusoidsSim(output_size=1) | ||
| else: | ||
| raise NotImplementedError | ||
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| x_plot = jnp.linspace(sim.domain.l, sim.domain.u, 100).reshape(-1, 1) | ||
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| # """ plot samples from the simulation env """ | ||
| # f_sim = sim.sample_function_vals(x_plot, num_samples=10, rng_key=jax.random.PRNGKey(234234)) | ||
| # for i in range(f_sim.shape[0]): | ||
| # plt.plot(x_plot, f_sim[i]) | ||
| # plt.show() | ||
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| """ generate data """ | ||
| fun = sim.sample_function(rng_key=jax.random.PRNGKey(291)) # 764 | ||
| x_train = jax.random.uniform(key=next(key_iter), shape=(50,), | ||
| minval=sim.domain.l, maxval=sim.domain.u).reshape(-1, 1) | ||
| x_train = x_train[:num_train_points] | ||
| y_train = fun(x_train) | ||
| x_test = jnp.linspace(sim.domain.l, sim.domain.u, 100).reshape(-1, 1) | ||
| y_test = fun(x_test) | ||
|
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| """ fit the model """ | ||
| common_kwargs = { | ||
| 'input_size': 1, | ||
| 'output_size': 1, | ||
| 'rng_key': next(key_iter), | ||
| 'hidden_layer_sizes': [64, 64, 64], | ||
| 'data_batch_size': 4, | ||
| 'num_particles': 20, | ||
| 'likelihood_std': 0.05, | ||
| 'normalization_stats': sim.normalization_stats, | ||
| } | ||
| if model == 'BNN_SVGD': | ||
| bnn = BNN_SVGD(**common_kwargs, bandwidth_svgd=10., num_train_steps=2) | ||
| elif model == 'BNN_FSVGD': | ||
| bnn = BNN_FSVGD(**common_kwargs, domain=sim.domain, bandwidth_svgd=0.5, num_measurement_points=8) | ||
| elif model == 'BNN_FSVGD_SimPrior_gp': | ||
| bnn = BNN_FSVGD_SimPrior(**common_kwargs, domain=sim.domain, function_sim=sim, | ||
| num_train_steps=20000, num_f_samples=256, num_measurement_points=8, | ||
| bandwidth_svgd=1., score_estimator='gp') | ||
| elif model == 'BNN_FSVGD_SimPrior_kde': | ||
| bnn = BNN_FSVGD_SimPrior(**common_kwargs, domain=sim.domain, function_sim=sim, | ||
| num_train_steps=40000, num_f_samples=256, num_measurement_points=16, | ||
| bandwidth_svgd=1., score_estimator='kde') | ||
| elif model == 'BNN_FSVGD_SimPrior_nu-method': | ||
| bnn = BNN_FSVGD_SimPrior(**common_kwargs, domain=sim.domain, function_sim=sim, | ||
| num_train_steps=20000, num_f_samples=256, num_measurement_points=16, | ||
| bandwidth_svgd=1., score_estimator='nu-method', bandwidth_score_estim=1.0) | ||
|
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| else: | ||
| raise NotImplementedError | ||
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| bnn.fit(x_train, y_train, x_eval=x_test, y_eval=y_test) | ||
|
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| """ make predictions and save the plot """ | ||
| x_plot = jnp.linspace(sim.domain.l, sim.domain.u, 200).reshape((-1, 1)) | ||
|
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| # make predictions | ||
| pred_mean, pred_std = bnn.predict(x_plot) | ||
| y_post_samples = bnn.predict_post_samples(x_plot) | ||
|
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| # get true function value | ||
| true_fun = fun(x_plot) | ||
| typical_fun = sim._typical_f(x_plot) | ||
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| plot_dict = { | ||
| 'model': model, | ||
| 'plot_data': { | ||
| 'x_train': x_train, | ||
| 'y_train': y_train, | ||
| 'x_plot': x_plot, | ||
| 'true_fun': true_fun, | ||
| 'pred_mean': pred_mean, | ||
| 'pred_std': pred_std, | ||
| 'y_post_samples': y_post_samples, | ||
| } | ||
| } | ||
| dump_path = os.path.join(plot_dict_dir, f'{sim_type}_{model}_{num_train_points}.pkl') | ||
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| with open(dump_path, 'wb') as f: | ||
| pickle.dump(plot_dict, f) | ||
| print(f'Plot dict saved to {dump_path}') | ||
|
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| # draw the plot | ||
| fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(1 * 4, 4)) | ||
| if bnn.output_size == 1: | ||
| ax = [ax] | ||
| for i in range(1): | ||
| ax[i].scatter(x_train.flatten(), y_train[:, i], label='train points') | ||
| ax[i].plot(x_plot, fun(x_plot)[:, i], label='true fun') | ||
| ax[i].plot(x_plot, typical_fun, label='typical fun') | ||
| ax[i].plot(x_plot.flatten(), pred_mean[:, i], label='pred mean') | ||
| ax[i].fill_between(x_plot.flatten(), pred_mean[:, i] - 2 * pred_std[:, i], | ||
| pred_mean[:, i] + 2 * pred_std[:, i], alpha=0.3) | ||
|
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| if plot_post_samples: | ||
| y_post_samples = bnn.predict_post_samples(x_plot) | ||
| for y in y_post_samples: | ||
| ax[i].plot(x_plot, y[:, i], linewidth=0.2, color='green') | ||
|
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| ax[i].legend() | ||
| fig.suptitle(model) | ||
| fig.show() | ||
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| if __name__ == '__main__': | ||
| for num_train_points in [2]: #, 3, 5]: | ||
| for model in [ | ||
| 'BNN_SVGD', | ||
| 'BNN_FSVGD', | ||
| 'BNN_FSVGD_SimPrior_gp', | ||
| 'BNN_FSVGD_SimPrior_nu-method', | ||
| 'BNN_FSVGD_SimPrior_kde' | ||
| ]: | ||
| main(model=model, num_train_points=num_train_points) |
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