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"pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [metadata] lock-version = "2.0" -python-versions = "^3.9" -content-hash = "ae2e5e776997e3e9eb45b0a4eb7c30bc4b807b8eb0006efb214fd1dd73d51550" +python-versions = ">=3.9,<3.12" +content-hash = "d735cee93a7f5de8ad7fdea12a7f48f627b87cb93f1aa8285132e8eb246a556e" diff --git a/tests/conftest.py b/tests/conftest.py index e08ae25..33f22db 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,13 +1,16 @@ +import os +import shutil import pytest import yaml import numpy as np -import os +from deepbench.astro_object import StarObject from data import H5Data from data.simulator import Simulator from models import SBIModel -from utils.register import register_simulator from utils.config import get_item +from utils.register import register_simulator + class MockSimulator(Simulator): def generate_context(self, n_samples: int) -> np.ndarray: @@ -39,14 +42,45 @@ def simulate(self, theta: np.ndarray, context_samples: np.ndarray) -> np.ndarray y[:, i] = m * context_samples + b + epsilon[:, i] return y.T +class Mock2DSimulator(Simulator): + def generate_context(self, n_samples: int) -> np.ndarray: + return np.linspace(0, 28, n_samples) + + def simulate(self, theta, context_samples: np.ndarray): + generated_stars = [] + + if len(theta.shape) == 1: + theta = [theta] + + for sample_index, t in enumerate(theta): + star = StarObject( + image_dimensions = (28,28), + noise_level = 0.3, + radius = t[0].item(), + amplitude = t[1].item() + ) + generated_stars.append( + star.create_object( + context_samples[sample_index].item(), context_samples[sample_index].item() + ) + ) + return np.array(generated_stars) + @pytest.fixture(autouse=True) def setUp(): register_simulator("MockSimulator", MockSimulator) + register_simulator("Mock2DSimulator", Mock2DSimulator) yield + simulator_config_path = get_item("common", "sim_location", raise_exception=False) sim_paths = f"{simulator_config_path.strip('/')}/simulators.json" os.remove(sim_paths) + out_dir = get_item("common", "out_dir", raise_exception=False) + os.makedirs("resources/test_results/", exist_ok=True) + shutil.copytree(out_dir, "resources/test_results/", dirs_exist_ok=True) + shutil.rmtree(out_dir) + @pytest.fixture def model_path(): return "resources/savedmodels/sbi/sbi_linear_from_data.pkl" @@ -56,6 +90,9 @@ def model_path(): def data_path(): return "resources/saveddata/data_validation.h5" +@pytest.fixture +def result_output(): + return "./temp_results/" @pytest.fixture def simulator_name(): @@ -71,15 +108,18 @@ def mock_model(model_path): def mock_data(data_path, simulator_name): return H5Data(data_path, simulator_name) +@pytest.fixture +def mock_2d_data(data_path): + return H5Data(data_path, "Mock2DSimulator", simulation_dimensions=2) @pytest.fixture -def config_factory(): +def config_factory(result_output): def factory( - out_dir=None, model_path=None, model_engine=None, data_path=None, data_engine=None, + plot_2d=False, simulator=None, plot_settings=None, metrics_settings=None, @@ -97,8 +137,7 @@ def factory( } # Single settings - if out_dir is not None: - config["common"]["out_dir"] = out_dir + config["common"]["out_dir"] = result_output if model_path is not None: config["model"]["model_path"] = model_path if model_engine is not None: @@ -109,6 +148,8 @@ def factory( config["data"]["data_engine"] = data_engine if simulator is not None: config["data"]["simulator"] = simulator + if plot_2d: + config["data"]["simulator_dimensions"] = 2 # Dict settings if plot_settings is not None: From 2f5e5a634195ecbaee531b662d5c79f7fe63b1be Mon Sep 17 00:00:00 2001 From: voetberg Date: Fri, 14 Jun 2024 16:22:24 -0500 Subject: [PATCH 2/3] Set data up to use 2d output data optionally #72 --- src/data/data.py | 11 ++++++++--- src/data/h5_data.py | 13 ++++++++++--- src/data/pickle_data.py | 13 ++++++++++--- src/utils/defaults.py | 4 +++- src/utils/register.py | 6 ++++-- 5 files changed, 35 insertions(+), 12 deletions(-) diff --git a/src/data/data.py b/src/data/data.py index 38a9f9e..0dcdef1 100644 --- a/src/data/data.py +++ b/src/data/data.py @@ -1,6 +1,4 @@ -import importlib.util -import sys -import os +from typing import Optional import numpy as np from utils.config import get_item @@ -14,6 +12,7 @@ def __init__( simulator_kwargs: dict = None, prior: str = None, prior_kwargs: dict = None, + simulation_dimensions:Optional[int] = None, ): self.rng = np.random.default_rng( get_item("common", "random_seed", raise_exception=False) @@ -22,6 +21,12 @@ def __init__( self.simulator = load_simulator(simulator_name, simulator_kwargs) self.prior_dist = self.load_prior(prior, prior_kwargs) self.n_dims = self.get_theta_true().shape[1] + self.simulator_dimensions = simulation_dimensions if simulation_dimensions is not None else get_item("data", "simulator_dimensions", raise_exception=False) + + def get_simulator_output_shape(self): + context_shape = self.true_context().shape + sim_out = self.simulator(theta=self.get_theta_true()[0:1, :], n_samples=context_shape[-1]) + return sim_out.shape def _load(self, path: str): raise NotImplementedError diff --git a/src/data/h5_data.py b/src/data/h5_data.py index 56eb23e..8d6dd17 100644 --- a/src/data/h5_data.py +++ b/src/data/h5_data.py @@ -1,4 +1,4 @@ -from typing import Any, Callable +from typing import Any, Callable, Optional import h5py import numpy as np import torch @@ -8,8 +8,15 @@ class H5Data(Data): - def __init__(self, path: str, simulator: Callable): - super().__init__(path, simulator) + def __init__(self, + path: str, + simulator: Callable, + simulator_kwargs: dict = None, + prior: str = None, + prior_kwargs: dict = None, + simulation_dimensions:Optional[int] = None, + ): + super().__init__(path, simulator, simulator_kwargs, prior, prior_kwargs, simulation_dimensions) def _load(self, path): assert path.split(".")[-1] == "h5", "File extension must be h5" diff --git a/src/data/pickle_data.py b/src/data/pickle_data.py index 2dc9238..ea2fa9d 100644 --- a/src/data/pickle_data.py +++ b/src/data/pickle_data.py @@ -1,11 +1,18 @@ import pickle -from typing import Any, Callable +from typing import Any, Callable, Optional from data.data import Data class PickleData(Data): - def __init__(self, path: str, simulator: Callable): - super().__init__(path, simulator) + def __init__(self, + path: str, + simulator: Callable, + simulator_kwargs: dict = None, + prior: str = None, + prior_kwargs: dict = None, + simulation_dimensions:Optional[int] = None, + ): + super().__init__(path, simulator, simulator_kwargs, prior, prior_kwargs, simulation_dimensions) def _load(self, path: str): assert path.split(".")[-1] == "pkl", "File extension must be 'pkl'" diff --git a/src/utils/defaults.py b/src/utils/defaults.py index 3956bb2..8ef784f 100644 --- a/src/utils/defaults.py +++ b/src/utils/defaults.py @@ -11,6 +11,7 @@ "prior": "normal", "prior_kwargs": None, "simulator_kwargs": None, + "simulator_dimensions": 1, }, "plots_common": { "axis_spines": False, @@ -29,7 +30,8 @@ "TARP": { "coverage_sigma": 3 # How many sigma to show coverage over }, - "LC2ST": {} + "LC2ST": {}, + "PPC":{} }, "metrics_common": { "use_progress_bar": False, diff --git a/src/utils/register.py b/src/utils/register.py index 5fcc8ff..60fdcfe 100644 --- a/src/utils/register.py +++ b/src/utils/register.py @@ -18,12 +18,14 @@ def register_simulator(simulator_name, simulator): if not os.path.exists(sim_paths): open(sim_paths, 'a').close() - with open(sim_paths, "w+") as f: + with open(sim_paths, "r+") as f: try: existing_sims = json.load(f) except json.decoder.JSONDecodeError: existing_sims = {} - + + existing_sims[simulator_name] = simulator_location + with open(sim_paths, "w") as f: existing_sims[simulator_name] = simulator_location json.dump(existing_sims, f) From c05ebdca020e1b5f957416b5128ee9b2e3b8a941 Mon Sep 17 00:00:00 2001 From: voetberg Date: Fri, 14 Jun 2024 16:22:57 -0500 Subject: [PATCH 3/3] Update existing plots and metrics to work with 2d #72 --- src/metrics/local_two_sample.py | 68 ++++++++--- src/plots/predictive_posterior_check.py | 145 +++++++++++++++++------- tests/test_plots.py | 35 ++++-- 3 files changed, 178 insertions(+), 70 deletions(-) diff --git a/src/metrics/local_two_sample.py b/src/metrics/local_two_sample.py index ce7c16a..8a563fb 100644 --- a/src/metrics/local_two_sample.py +++ b/src/metrics/local_two_sample.py @@ -37,29 +37,43 @@ def _collect_data_params(self): # P is the prior and x_P is generated via the simulator from the parameters P. self.p = self.data.sample_prior(self.number_simulations) self.q = np.zeros_like(self.p) - context_size = self.data.true_context().shape[-1] - self.outcome_given_p = np.zeros( - (self.number_simulations, context_size) + remove_first_dim = False - ) + if self.data.simulator_dimensions == 1: + self.outcome_given_p = np.zeros((self.number_simulations, context_size)) + elif self.data.simulator_dimensions == 2: + sim_out_shape = self.data.get_simulator_output_shape() + if len(sim_out_shape) != 2: + # TODO Debug log with a warning + sim_out_shape = (sim_out_shape[1], sim_out_shape[2]) + remove_first_dim = True + + sim_out_shape = np.product(sim_out_shape) + self.outcome_given_p = np.zeros((self.number_simulations, sim_out_shape)) + else: + raise NotImplementedError("LC2ST only implemented for 1 or two dimensions.") + self.outcome_given_q = np.zeros_like(self.outcome_given_p) - self.evaluation_context = np.zeros_like(self.outcome_given_p) + self.evaluation_context = np.zeros((self.number_simulations, context_size)) for index, p in enumerate(self.p): context = self.data.simulator.generate_context(context_size) - self.outcome_given_p[index] = self.data.simulator.simulate(p, context) - # Q is the approximate posterior amortized in x q = self.model.sample_posterior(1, context).ravel() self.q[index] = q - self.outcome_given_q[index] = self.data.simulator.simulate(q, context) + self.evaluation_context[index] = context + + p_outcome = self.data.simulator.simulate(p, context) + q_outcome = self.data.simulator.simulate(q, context) + + if remove_first_dim: + p_outcome = p_outcome[0] + q_outcome = q_outcome[0] + + self.outcome_given_p[index] = p_outcome.ravel() + self.outcome_given_q[index] = q_outcome.ravel() # Q is the approximate posterior amortized in x + - self.evaluation_context = np.array( - [ - self.data.simulator.generate_context(context_size) - for _ in range(self.num_simulations) - ] - ) def train_linear_classifier( self, p, q, x_p, x_q, classifier: str, classifier_kwargs: dict = {} @@ -127,9 +141,21 @@ def _cross_eval_score( cv_splits = kf.split(p) # train classifiers over cv-folds probabilities = [] - self.evaluation_data = np.zeros( - (n_cross_folds, len(next(cv_splits)[1]), self.evaluation_context.shape[-1]) - ) + + remove_first_dim = False + if self.data.simulator_dimensions == 1: + self.evaluation_data = np.zeros((n_cross_folds, len(next(cv_splits)[1]), self.evaluation_context.shape[-1])) + + elif self.data.simulator_dimensions == 2: + sim_out_shape = self.data.get_simulator_output_shape() + if len(sim_out_shape) != 2: + # TODO Debug log with a warning + sim_out_shape = (sim_out_shape[1], sim_out_shape[2]) + remove_first_dim = True + + sim_out_shape = np.product(sim_out_shape) + self.evaluation_data = np.zeros((n_cross_folds, len(next(cv_splits)[1]), sim_out_shape)) + self.prior_evaluation = np.zeros_like(p) kf = KFold(n_splits=n_cross_folds, shuffle=True, random_state=42) @@ -142,10 +168,16 @@ def _cross_eval_score( p_train, q_train, x_p_train, x_q_train, classifier, classifier_kwargs ) p_evaluate = p[val_index] + for index, p_validation in enumerate(p_evaluate): - self.evaluation_data[cross_trial][index] = self.data.simulator.simulate( + sim_output = self.data.simulator.simulate( p_validation, self.evaluation_context[val_index][index] ) + + if remove_first_dim: + sim_output = sim_output[0] + self.evaluation_data[cross_trial][index] = sim_output.ravel() + self.prior_evaluation[index] = p_validation probabilities.append( self._eval_model( diff --git a/src/plots/predictive_posterior_check.py b/src/plots/predictive_posterior_check.py index c1fc88b..9f7c06e 100644 --- a/src/plots/predictive_posterior_check.py +++ b/src/plots/predictive_posterior_check.py @@ -27,9 +27,41 @@ def __init__( def _plot_name(self): return "predictive_posterior_check.png" - def get_posterior(self, n_simulator_draws): + def get_posterior_2d(self, n_simulator_draws): context_shape = self.data.true_context().shape - self.posterior_predictive_samples = np.zeros((n_simulator_draws, self.samples_per_inference,context_shape[-1])) + sim_out_shape = self.data.get_simulator_output_shape() + remove_first_dim = False + if len(sim_out_shape) != 2: + # TODO Debug log with a warning + sim_out_shape = (sim_out_shape[1], sim_out_shape[2]) + remove_first_dim = True + + self.posterior_predictive_samples = np.zeros((n_simulator_draws, *sim_out_shape)) + self.posterior_true_samples = np.zeros_like(self.posterior_predictive_samples) + + random_context_indices = self.data.rng.integers(0, context_shape[0], n_simulator_draws) + for index, sample in enumerate(random_context_indices): + context_sample = self.data.true_context()[sample, :] + posterior_sample = self.model.sample_posterior(1, context_sample) + + # get the posterior samples for that context + sim_out_posterior = self.data.simulator.simulate( + theta=posterior_sample, context_samples = context_sample + ) + sim_out_true = self.data.simulator.simulate( + theta=self.data.get_theta_true()[sample, :], context_samples=context_sample + ) + if remove_first_dim: + sim_out_posterior = sim_out_posterior[0] + sim_out_true = sim_out_true[0] + + self.posterior_predictive_samples[index] = sim_out_posterior + self.posterior_true_samples[index] = sim_out_true + + + def get_posterior_1d(self, n_simulator_draws): + context_shape = self.data.true_context().shape + self.posterior_predictive_samples = np.zeros((n_simulator_draws, self.samples_per_inference, context_shape[-1])) self.posterior_true_samples = np.zeros_like(self.posterior_predictive_samples) self.context = np.zeros((n_simulator_draws, context_shape[-1])) @@ -48,20 +80,82 @@ def get_posterior(self, n_simulator_draws): theta=self.data.get_theta_true()[sample, :], context_samples=context_sample ) + def _plot_1d(self, + subplots: np.ndarray, + subplot_index: int, + n_coverage_sigma: Optional[int] = 3, + theta_true_marker: Optional[str] = '^' + ): + + dimension_y_simulation = self.posterior_predictive_samples[subplot_index] + y_simulation_mean = np.mean(dimension_y_simulation, axis=0).ravel() + y_simulation_std = np.std(dimension_y_simulation, axis=0).ravel() + + for sigma, color in zip(range(n_coverage_sigma), self.colors): + subplots[0, subplot_index].fill_between( + self.context[subplot_index].ravel(), + y_simulation_mean - sigma * y_simulation_std, + y_simulation_mean + sigma * y_simulation_std, + color=color, + alpha=0.6, + label=rf"Pred. with {sigma} $\sigma$", + ) + + subplots[0, subplot_index].plot( + self.context[subplot_index], + y_simulation_mean - self.true_sigma, + color="black", + linestyle="dashdot", + label="True Input Error" + ) + subplots[0, subplot_index].plot( + self.context[subplot_index], + y_simulation_mean + self.true_sigma, + color="black", + linestyle="dashdot", + ) + + true_y = np.mean(self.posterior_true_samples[subplot_index, :, :], axis=0).ravel() + subplots[1, subplot_index].scatter( + self.context[subplot_index], + true_y, + marker=theta_true_marker, + label='Theta True' + ) + + def _plot_2d(self, subplots, subplot_index, include_axis_ticks): + subplots[1, subplot_index].imshow(self.posterior_predictive_samples[subplot_index]) + subplots[0, subplot_index].imshow(self.posterior_true_samples[subplot_index]) + + if not include_axis_ticks: + subplots[1, subplot_index].set_xticks([]) + subplots[1, subplot_index].set_yticks([]) + + subplots[0, subplot_index].set_xticks([]) + subplots[0, subplot_index].set_yticks([]) + def _plot( self, n_coverage_sigma: Optional[int] = 3, true_sigma: Optional[float] = None, theta_true_marker: Optional[str] = '^', n_unique_plots: Optional[int] = 3, + include_axis_ticks: bool = False, title:str="Predictive Posterior", y_label:str="Simulation Output", x_label:str="X"): + if self.data.simulator_dimensions == 1: + self.get_posterior_1d(n_unique_plots) + self.true_sigma = true_sigma if true_sigma is not None else self.data.get_sigma_true() + self.colors = get_hex_colors(n_coverage_sigma, self.colorway) - self.get_posterior(n_unique_plots) - true_sigma = true_sigma if true_sigma is not None else self.data.get_sigma_true() + elif self.data.simulator_dimensions == 2: + self.get_posterior_2d(n_unique_plots) + else: + raise NotImplementedError("Posterior Checks only implemented for 1 or two dimensions.") + figure, subplots = plt.subplots( 2, n_unique_plots, @@ -69,49 +163,14 @@ def _plot( sharex=False, sharey=True ) - colors = get_hex_colors(n_coverage_sigma, self.colorway) for plot_index in range(n_unique_plots): + if self.data.simulator_dimensions == 1: + self._plot_1d(subplots, plot_index, n_coverage_sigma, theta_true_marker) - dimension_y_simulation = self.posterior_predictive_samples[plot_index] - - y_simulation_mean = np.mean(dimension_y_simulation, axis=0).ravel() - y_simulation_std = np.std(dimension_y_simulation, axis=0).ravel() - - for sigma, color in zip(range(n_coverage_sigma), colors): - subplots[0, plot_index].fill_between( - self.context[plot_index].ravel(), - y_simulation_mean - sigma * y_simulation_std, - y_simulation_mean + sigma * y_simulation_std, - color=color, - alpha=0.6, - label=rf"Pred. with {sigma} $\sigma$", - ) - - subplots[0, plot_index].plot( - self.context[plot_index], - y_simulation_mean - true_sigma, - color="black", - linestyle="dashdot", - label="True Input Error" - ) - subplots[0, plot_index].plot( - self.context[plot_index], - y_simulation_mean + true_sigma, - color="black", - linestyle="dashdot", - ) - - true_y = np.mean(self.posterior_true_samples[plot_index, :, :], axis=0).ravel() - subplots[1, plot_index].scatter( - self.context[plot_index], - true_y, - marker=theta_true_marker, - label='Theta True' - ) + else: + self._plot_2d(subplots, plot_index, include_axis_ticks) - subplots[1, -1].legend() - subplots[0, -1].legend() subplots[1, 0].set_ylabel("True Parameters") subplots[0, 0].set_ylabel("Predicted Parameters") diff --git a/tests/test_plots.py b/tests/test_plots.py index f32e546..88b368a 100644 --- a/tests/test_plots.py +++ b/tests/test_plots.py @@ -17,14 +17,13 @@ @pytest.fixture def plot_config(config_factory): - out_dir = "./temp_results/" metrics_settings = { "use_progress_bar": False, "samples_per_inference": 10, "percentiles": [95], } - config = config_factory(out_dir=out_dir, metrics_settings=metrics_settings) - Config(config) + config = config_factory(metrics_settings=metrics_settings) + return config def test_all_defaults(plot_config, mock_model, mock_data): @@ -32,40 +31,58 @@ def test_all_defaults(plot_config, mock_model, mock_data): Ensures each metric has a default set of parameters and is included in the defaults list Ensures each test can initialize, regardless of the veracity of the output """ + Config(plot_config) for plot_name, plot_obj in Plots.items(): assert plot_name in Defaults["plots"] plot_obj(mock_model, mock_data, save=True, show=False) - def test_plot_cdf(plot_config, mock_model, mock_data): + Config(plot_config) plot = CDFRanks(mock_model, mock_data, save=True, show=False) plot(**get_item("plots", "CDFRanks", raise_exception=False)) assert os.path.exists(f"{plot.out_dir}/{plot.plot_name}") - def test_plot_ranks(plot_config, mock_model, mock_data): + Config(plot_config) plot = Ranks(mock_model, mock_data, save=True, show=False) plot(**get_item("plots", "Ranks", raise_exception=False)) assert os.path.exists(f"{plot.out_dir}/{plot.plot_name}") - def test_plot_coverage(plot_config, mock_model, mock_data): + Config(plot_config) plot = CoverageFraction(mock_model, mock_data, save=True, show=False) plot(**get_item("plots", "CoverageFraction", raise_exception=False)) assert os.path.exists(f"{plot.out_dir}/{plot.plot_name}") - def test_plot_tarp(plot_config, mock_model, mock_data): + Config(plot_config) plot = TARP(mock_model, mock_data, save=True, show=False) plot(**get_item("plots", "TARP", raise_exception=False)) assert os.path.exists(f"{plot.out_dir}/{plot.plot_name}") -def test_lc2st(plot_config, mock_model, mock_data): +def test_lc2st(plot_config, mock_model, mock_data, mock_2d_data, result_output): + Config(plot_config) plot = LocalTwoSampleTest(mock_model, mock_data, save=True, show=False) plot(**get_item("plots", "LC2ST", raise_exception=False)) assert os.path.exists(f"{plot.out_dir}/{plot.plot_name}") -def test_ppc(plot_config, mock_model, mock_data): + plot = LocalTwoSampleTest( + mock_model, mock_2d_data, save=True, show=False, + out_dir=f"{result_output.strip('/')}/mock_2d/") + assert type(plot.data.simulator).__name__ == "Mock2DSimulator" + plot(**get_item("plots", "LC2ST", raise_exception=False)) + assert os.path.exists(f"{plot.out_dir}/{plot.plot_name}") + +def test_ppc(plot_config, mock_model, mock_data, mock_2d_data, result_output): + Config(plot_config) plot = PPC(mock_model, mock_data, save=True, show=False) plot(**get_item("plots", "PPC", raise_exception=False)) + assert os.path.exists(f"{plot.out_dir}/{plot.plot_name}") + + plot = PPC( + mock_model, + mock_2d_data, save=True, show=False, + out_dir=f"{result_output.strip('/')}/mock_2d/") + assert type(plot.data.simulator).__name__ == "Mock2DSimulator" + plot(**get_item("plots", "PPC", raise_exception=False)) assert os.path.exists(f"{plot.out_dir}/{plot.plot_name}") \ No newline at end of file