diff --git a/README.md b/README.md index f17f8d4..a568e92 100644 --- a/README.md +++ b/README.md @@ -16,9 +16,12 @@ To install Dial from source, git clone this repository and run the following fro `pip install .` -Alternatively, both intersect-sdk and Dial may be installed with the following: +By default, **only scikit-learn is available as a backend**. We also support GPax and Sable as backends. To install these: -`pip install -e .` +- for GPax: `pip install ".[gpax]"` +- for Sable: `pip install ".[sable]"` + +Use commas to add multiple backends: `pip install ".[gpax,sable]"` ## Installing (developers) diff --git a/pyproject.toml b/pyproject.toml index b36ae29..42f3188 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,12 +20,9 @@ classifiers = ["Programming Language :: Python :: 3"] dependencies = [ "intersect_sdk>=0.9.3,<0.10.0", "numpy", - "scikit-learn>=1.4.0,<2.0.0", # TODO consider making an optional dependency group + "scikit-learn>=1.4.0,<2.0.0", # TODO consider making an optional dependency group "scipy>=1.12.0,<2.0.0", - "gpax>=0.1.8", # TODO consider making an optional dependency group - "numpyro<0.20.1", # depend on older numpyro for gpax (numpyro.contrib.module: random_haiku_module) - "pymongo>=4.12.1", # TODO - this is only needed for dial_service, dial_dataclass can use a simple fixture for ObjectID representation which allows it to skip this dependency - "sable @ git+https://code.ornl.gov/sable/sable.git", # TODO this references the internal repo, transition to public version + "pymongo>=4.12.1", # TODO - this is only needed for dial_service, dial_dataclass can use a simple fixture for ObjectID representation which allows it to skip this dependency ] [project.urls] @@ -36,6 +33,11 @@ Issues = "https://github.com/INTERSECT-DIAL/dial/issues" [project.optional-dependencies] docs = ["sphinx>=5.3.0", "furo>=2023.3.27"] +gpax = [ + "gpax>=0.1.8", + "numpyro<0.20.1", # depend on older numpyro for gpax (numpyro.contrib.module: random_haiku_module) +] +sable = ["sable @ git+https://code.ornl.gov/sable/sable.git"] [dependency-groups] dev = [ diff --git a/scripts/1d_sable_client.py b/scripts/1d_sable_client.py index 727365b..1e3bed0 100644 --- a/scripts/1d_sable_client.py +++ b/scripts/1d_sable_client.py @@ -89,8 +89,6 @@ def __init__(self, service_destination: str): ] self.meshgrids = np.meshgrid(*self.grid_points, indexing='ij') self.x_grid = np.hstack([mg.reshape(-1, 1) for mg in self.meshgrids]) - # Mirror the demo's RNG sequence before generating the noisy initial observations. - truth_model(self.x_grid[:, 0], 0.0, self.rng) self.y_raw, _ = truth_model(self.x_raw[:, 0], self.noise_level, self.rng) self.dataset_x = self.x_raw.reshape(-1, 1).tolist() @@ -100,16 +98,16 @@ def __init__(self, service_destination: str): self.test_points = self.x_test.reshape(-1, 1).tolist() self.kernel = 'rbf' - self.kernel_args = {'x_range': [0.0, 1.0], 'sigma_range': [2.5e-3, 0.5], 'gamma': 0.1} + self.kernel_args = { + 'sigma_range': [2.5e-3, 0.5], + 'gamma': 0.1, + } self.backend = 'sable' self.backend_args = { - 'n_features': 5000, - 'alpha': 0.05, - 'p': 1.25, - 'n_iter_irls': 100, + 'prior_std': 0.8, } - self.strategy = 'upper_confidence_bound' - self.strategy_args = {'exploit': 0.0, 'explore': 1.0} + self.strategy = 'uncertainty' + self.strategy_args = {} self.niter = 0 self.max_iter = 30 self.at_grids = True @@ -246,13 +244,6 @@ def handle_next_points(self, payload): self.dataset_x.append(self.x_next) self.dataset_y.append(y_scalar) - # In this example we are running pure exploration, no optimization: - # optpos = np.argmax(self.dataset_y) - # y_opt = self.dataset_y[optpos] - # optimal_coords = self.dataset_x[optpos] - # coord_str = ', '.join([f'{coord:.2f}' for coord in optimal_coords]) - # print(f'Optimal simulated datapoint at ({coord_str}), y={y_opt:.3f}\n') - def graph(self): plt.clf() diff --git a/scripts/1d_sinusoidal_growth_client.py b/scripts/1d_sinusoidal_growth_client.py index 756f4e8..dd5d6d2 100644 --- a/scripts/1d_sinusoidal_growth_client.py +++ b/scripts/1d_sinusoidal_growth_client.py @@ -76,13 +76,18 @@ def __init__(self, service_destination: str): # Assume that there is some small noise in the measurements to stabilize the fit self.statistics_y = Normal(loc='y', scale=1e-6) - self.backend = 'sklearn' + # Set the prior standard deviation of the surrogate model + self.prior_std = 1.0 + + self.backend = 'sklearn' # 'sklearn' or 'sable' if self.backend == 'sklearn': # configure kernel_hyperparameters self.kernel = 'matern' # 'rbf' or 'matern' + # set kernel hyperparameters + prior_variance = self.prior_std**2 self.kernel_args = { 'length_scale': 0.1, - 'constant_value': 1.0, + 'constant_value': prior_variance, } self.optimize_lengthscale = False if self.optimize_lengthscale: @@ -95,19 +100,18 @@ def __init__(self, service_destination: str): elif self.backend == 'sable': self.kernel = 'rbf' self.kernel_args = { - 'x_range': [-2.0, 2.0], 'sigma_range': [1.0e-3, 1.0], - 'gamma': 0.1, } + # increase the prior standard deviation to avoid overconfidence + self.prior_std *= 5.0 self.backend_args = { - 'n_features': 10000, - 'alpha': 0.0005, - 'p': 1.25, - 'n_iter_irls': 20, + 'prior_std': self.prior_std, } - self.strategy = 'upper_confidence_bound' - self.strategy_args = {'exploit': 0.4, 'explore': 1} + strat = ('upper_confidence_bound', {'exploit': 0.4, 'explore': 1}) + # strat = ('expected_improvement', {}) + self.strategy, self.strategy_args = strat + self.niter = 0 self.max_iter = 20 self.at_grids = True @@ -177,7 +181,6 @@ def callback_message(self, operation: str, **kwargs) -> IntersectClientCallback: kernel_args=self.kernel_args, backend=self.backend, backend_args=self.backend_args, - preprocess_standardize=True, y_is_good=True, ) @@ -265,7 +268,7 @@ def graph(self): self.mean_grid + 2 * self.stddev_grid, self.mean_grid - 2 * self.stddev_grid, alpha=0.5, - label='Confidence Interval', + label='$2\\sigma$ Confidence Interval', ) axs[0].scatter( np.array(self.dataset_x)[:-1, 0], diff --git a/src/dial_dataclass/dial_dataclass.py b/src/dial_dataclass/dial_dataclass.py index 8f45ebc..8f3ef6c 100644 --- a/src/dial_dataclass/dial_dataclass.py +++ b/src/dial_dataclass/dial_dataclass.py @@ -304,9 +304,13 @@ class DialWorkflowDatasetUpdate(BaseModel): description='The next collection of X values you want to append to your overall data', min_length=1, ) - """the next collection of X values you want to append""" - next_y: float = Field(description='The next Y value you want to append to your overall data') - """the next Y value you want to append""" + next_y: Annotated[ + float | list[float], + Field( + description=('The next Y value you want to append to your overall data'), + ), + ] + kernel_args: dict[str, float | int | bool | str | list[float] | tuple] | None = Field( default=None ) @@ -326,7 +330,7 @@ class DialWorkflowDatasetUpdates(BaseModel): workflow_id: ValidatedObjectId next_x_list: list[list[float]] = Field(min_length=1) - next_y_list: list[float] = Field(min_length=1) + next_y_list: list[float | list[float]] = Field(min_length=1) kernel_args: dict[str, float | int | bool | str | list[float] | tuple] | None = None backend_args: dict[str, float | int | bool | str | list[float] | tuple] | None = None extra_args: dict[str, float | int | bool | str | list[float] | tuple] | None = None diff --git a/src/dial_service/backends/sable_backend.py b/src/dial_service/backends/sable_backend.py index ba637b5..5762919 100644 --- a/src/dial_service/backends/sable_backend.py +++ b/src/dial_service/backends/sable_backend.py @@ -20,12 +20,19 @@ def _get_model_kwargs(data) -> dict: backend_args = {} if data.backend_args is None else data.backend_args - return { - 'n_features': backend_args.get('n_features', 10000), - 'alpha': backend_args.get('alpha', 0.3), - 'p': backend_args.get('p', 1.25), - 'n_iter_irls': backend_args.get('n_iter_irls', 100), + # set some default args + model_kwargs = { + # default to a low number of features in general + 'n_features': backend_args.get('n_features', 5000), + # set the prior standard deviation to a default value of 1. + # this works well for data where np.std(data.Y_train) ~ 1 (after output normalization) + 'prior_std': 1.0, + # default to moderate sparsity (p = 1.2) instead of full sparsity (p = 1.) for now + 'p': backend_args.get('p', 1.2), } + # update and add any user-supplied args + model_kwargs.update(backend_args) + return model_kwargs def _get_observation_errors(data, n_observations: int) -> np.ndarray: diff --git a/src/dial_service/serverside_data.py b/src/dial_service/serverside_data.py index 0602da2..19ca396 100644 --- a/src/dial_service/serverside_data.py +++ b/src/dial_service/serverside_data.py @@ -18,8 +18,8 @@ def __init__(self, data: DialWorkflowCreationParamsService): self.dim_y = data.dim_y self.labels_x = data.labels_x self.labels_y = data.labels_y - self.dataset_x = np.array(data.dataset_x) - self.dataset_y = np.array(data.dataset_y).reshape((-1, self.dim_y)) + self.dataset_x = np.array(data.dataset_x, float).reshape((-1, self.dim_x)) + self.dataset_y = np.array(data.dataset_y, float).reshape((-1, self.dim_y)) self.statistics_y = data.statistics_y # it seems like there should be a smarter way to do this, but stuff involving loops doesn't work with static autocompleters: self.bounds = data.bounds diff --git a/src/dial_service/utilities/strategies.py b/src/dial_service/utilities/strategies.py index 51d155a..592d796 100644 --- a/src/dial_service/utilities/strategies.py +++ b/src/dial_service/utilities/strategies.py @@ -190,13 +190,29 @@ def batch_sampling_acl(backend_module: AbstractBackend, model, data: ServersideI - ΔT(t) = max(current_batch_t_max, t) - current_batch_t_max (parallel reactor cost) """ + if data.dim_x > 1: + msg = f'strategy batch_sampling_acl supports only one input dimension, but {data.dim_x=}' + raise ValueError(msg) + x_grid = create_measurement_grid(data) x_grid = np.array(x_grid) data.set_x_predict(x_grid) - mean, sd_dev = backend_module.predict(model, data) - x_train = data.X_raw - _params = data.strategy_args + _, sd_dev = backend_module.predict(model, data) + x_train = data.dataset_x # get raw x data without scaling + + # set some default parameters + _params = { + 'lambda_time': 0.0, + 'lambda_near_train': 1.0, + 'lambda_near_batch': 1.0, + 'lambda_batchT': 0.0, + 'radius_train_factor': 0.1, + 'radius_batch_factor': 0.1, + 'eps': 1.0e-3, + } + # update with provided values + _params.update(data.strategy_args or {}) batch_size = data.points lambda_time = _params['lambda_time'] # penalty on large t diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/benchmarks/__init__.py b/tests/benchmarks/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/benchmarks/test_rosenbrock.py b/tests/benchmarks/test_rosenbrock.py index 690b556..3543dc9 100644 --- a/tests/benchmarks/test_rosenbrock.py +++ b/tests/benchmarks/test_rosenbrock.py @@ -12,6 +12,7 @@ # from pytest_benchmark.fixture import BenchmarkFixture from dial_dataclass import ( DialInputSingleOtherStrategy, + Normal, ) from dial_service import ( core as dial_core, @@ -89,15 +90,12 @@ def run_simulation( client_state = DialWorkflowCreationParamsService( dataset_x=dataset_x, dataset_y=dataset_y, + statistics_y=Normal(loc='y', scale=NOISE_LEVEL), bounds=INITIAL_BOUNDS, kernel='rbf', kernel_args={ 'length_scale': LENGTH_SCALE, - 'length_scale_bounds': 'fixed', - 'noise_level': NOISE_LEVEL, - 'noise_level_bounds': 'fixed', 'constant_value': CONSTANT_VALUE, - 'constant_value_bounds': 'fixed', }, y_is_good=False, # we wish to minimize y (the error) backend='sklearn', # "sklearn" or "gpax" @@ -252,6 +250,23 @@ def test_benchmark_rosenbrock_accuracy( ) +def _run_single(task: tuple) -> tuple: + """Worker: run one accuracy benchmark and return (strategy_name, iterations, target, guess, history).""" + import json + + strategy, strategy_args, run_index, dataset_x = task + strategy_name = f'{strategy}' + (f' {json.dumps(strategy_args)}' if strategy_args else '') + iterations, target, guess, history = accuracy_benchmark(strategy, strategy_args, dataset_x) + logger.info( + ' [%s] Run %d: iterations=%d, target=%.4f', + strategy_name, + run_index + 1, + iterations, + target, + ) + return (strategy_name, strategy, strategy_args, iterations, target, guess, history) + + if __name__ == '__main__': """Generate HTML benchmark report with plots comparing different strategies.""" import argparse @@ -303,31 +318,21 @@ def positive_int_type(arg): 'Running benchmarks with %d iterations per strategy using multiprocessing...', NUM_RUNS ) - def _run_single(task: tuple) -> tuple: - """Worker: run one accuracy benchmark and return (strategy_name, iterations, target, guess, history).""" - strategy, strategy_args, run_index, dataset_x = task - strategy_name = f'{strategy}' + (f' {json.dumps(strategy_args)}' if strategy_args else '') - iterations, target, guess, history = accuracy_benchmark(strategy, strategy_args, dataset_x) - logger.info( - ' [%s] Run %d: iterations=%d, target=%.4f', - strategy_name, - run_index + 1, - iterations, - target, - ) - return (strategy_name, strategy, strategy_args, iterations, target, guess, history) - # Use the same dataset for each parameter we have datasets = [generate_initial_dataset() for _ in range(NUM_RUNS)] tasks = [ - (strategy, strategy_args, run_index, dataset) + (strategy, strategy_args, run_index, dataset.copy()) for strategy, strategy_args in parameters for run_index, dataset in enumerate(datasets) ] + # run in parallel with Pool() as pool: run_outputs = pool.map(_run_single, tasks) + # run in serial + # run_outputs = [_run_single(task) for task in tasks] + # Aggregate results preserving strategy order strategy_order = [ f'{strategy}' + (f' {json.dumps(strategy_args)}' if strategy_args else '') diff --git a/tests/benchmarks/test_strainmap.py b/tests/benchmarks/test_strainmap.py index 2bca51c..3dd9725 100644 --- a/tests/benchmarks/test_strainmap.py +++ b/tests/benchmarks/test_strainmap.py @@ -1,840 +1,890 @@ -""" -Benchmark which is meant to test core DIAL functionality (without the INTERSECT or MONGO pieces) for the Strain-Mapping benchmark problem. -""" - -import logging -import sys -from dataclasses import dataclass -from pathlib import Path - -import numpy as np -import pandas as pd -import pytest -from scipy.interpolate import LinearNDInterpolator - -# from pytest_benchmark.fixture import BenchmarkFixture -from dial_dataclass import ( - DialInputPredictions, - DialInputSingleOtherStrategy, -) -from dial_service import ( - core as dial_core, -) -from dial_service.serverside_data import ( - ServersideInputBase, - ServersideInputPrediction, - ServersideInputSingle, -) -from dial_service.service_specific_dataclasses import DialWorkflowCreationParamsService - -logger = logging.getLogger(__name__) - -MOCK_WORKFLOW_ID = '6984e6a6ef6e6290dabced91' -"""fake ObjectID for testing purposes, we do not actually interact with a DB in these tests.""" - - -############### -low_f_data_name = ( - Path(__file__).parents[1] / 'fixtures' / 'adaptive_strain_manufacturing_low_fidelity.csv' -) -high_f_data_name = ( - Path(__file__).parents[1] / 'fixtures' / 'adaptive_strain_manufacturing_high_fidelity.csv' -) - - -def normalize_data(in_data): - m_data = (in_data.max() + in_data.min()) * 0.5 - d_data = (in_data.max() - in_data.min()) * 0.5 - return (in_data - m_data) / d_data - - -wall_st_index = 0 -wall_st_end = 676 -x_col_index = 0 # z follow next -e_col_index = 2 # e11, e22 and e33 -nrows = 26 # Rows in sim strain map of the wall -ncols = 26 # Cols in sim strain map of the wall -data = pd.read_csv(low_f_data_name) - -Y, Z, E11, E22, E33, R11, R22, R33 = np.array( - [ - data.values[:, x_col_index], - data.values[:, x_col_index + 1], - data.values[:, e_col_index], - data.values[:, e_col_index + 1], - data.values[:, e_col_index + 2], - data.values[:, e_col_index + 3], - data.values[:, e_col_index + 4], - data.values[:, e_col_index + 5], - ] -) - -x1 = Y.astype(np.float32).reshape(nrows, ncols) -x2 = Z.astype(np.float32).reshape(nrows, ncols) - -x1_norm = normalize_data(x1) -x2_norm = normalize_data(x2) - -Sim_e11 = np.array(E11).astype(np.float32).reshape(nrows, ncols) -Real_e11 = np.array(R11).astype(np.float32).reshape(nrows, ncols) - -Sim_e22 = np.array(E22).astype(np.float32).reshape(nrows, ncols) -Real_e22 = np.array(R22).astype(np.float32).reshape(nrows, ncols) - -Sim_e33 = np.array(E33).astype(np.float32).reshape(nrows, ncols) -Real_e33 = np.array(R33).astype(np.float32).reshape(nrows, ncols) - -# Normalize data -Sim_e11_norm = normalize_data(Sim_e11) -Real_e11_norm = normalize_data(Real_e11) - -Sim_e22_norm = normalize_data(Sim_e22) -Real_e22_norm = normalize_data(Real_e22) - -Sim_e33_norm = normalize_data(Sim_e33) -Real_e33_norm = normalize_data(Real_e33) - - -data = pd.read_csv(high_f_data_name) - -Y, Z, E11, E22, E33, R11, R22, R33 = np.array( - [ - data.values[:, x_col_index], - data.values[:, x_col_index + 1], - data.values[:, e_col_index], - data.values[:, e_col_index + 1], - data.values[:, e_col_index + 2], - data.values[:, e_col_index + 3], - data.values[:, e_col_index + 4], - data.values[:, e_col_index + 5], - ] -) - -Sim_hi_e11 = np.array(E11).astype(np.float32).reshape(nrows, ncols) -Sim_hi_e22 = np.array(E22).astype(np.float32).reshape(nrows, ncols) -Sim_hi_e33 = np.array(E33).astype(np.float32).reshape(nrows, ncols) - -# Transpose the high-fidelity simulation data -# TODO, figure out why this is transposed -Sim_hi_e11 = Sim_hi_e11.T -Sim_hi_e22 = Sim_hi_e22.T -Sim_hi_e33 = Sim_hi_e33.T - -# Normalize data -Sim_hi_e11_norm = normalize_data(Sim_hi_e11) -Sim_hi_e22_norm = normalize_data(Sim_hi_e22) -Sim_hi_e33_norm = normalize_data(Sim_hi_e33) - -############### - -## Select data for ground truth - -# default inputs -INITIAL_BOUNDS = [[-1.0, 1.0], [-1.0, 1.0]] -NUM_DIMS = len(INITIAL_BOUNDS) - -MESHGRID_SIZE = nrows -INITIAL_MESHGRIDS = (x1_norm, x2_norm) -INITIAL_POINTS_TO_PREDICT = np.hstack([mg.reshape(-1, 1) for mg in INITIAL_MESHGRIDS]) - -INITIAL_PREDICTIONS = Real_e33_norm.reshape(-1, 1) - -############### - -NOISE_LEVEL = 1.0e-2 - -truth_interp = LinearNDInterpolator(INITIAL_POINTS_TO_PREDICT, INITIAL_PREDICTIONS) - - -@dataclass -class StrainMap: - """Strain Map function.""" - - noise_level: float - - truth_interp: LinearNDInterpolator - """Strain interpolator that is used as 'ground_truth'""" - - def strain_map(self, x) -> float: - """ - Represents a measured strain as a function of two simulation parameters. - """ - x = np.asarray(x).reshape((-1, 2)) - x1, x2 = x[:, 0], x[:, 1] - - y_true = truth_interp(np.asarray(x1), np.asarray(x2)) - - # print("Evaluated truth model", np.hstack((x, y_true))) - - y_noise = y_true + self.noise_level * np.random.normal(size=y_true.shape) - return y_noise - - -# build a strain map from the selected truth values -truth_strain_map = StrainMap(truth_interp=truth_interp, noise_level=NOISE_LEVEL) - -############### - -# test parameters -INITIAL_NUM_POINTS = 9 -MAX_ITERATIONS = 150 # only allow a maximum of this many iterations in tests - -INITIAL_DATASET_X = np.random.uniform(-1.0, 1.0, size=(INITIAL_NUM_POINTS, NUM_DIMS)).tolist() - -TARGET_RMSE = 0.2 - - -def run_simulation( - dataset_x: list[list[float]], dataset_y: list[float], strategy: str, strategy_args: object -) -> tuple[np.ndarray, np.ndarray]: - # important "Hyper-parameters" - length_scale = 0.2 - noise_level = NOISE_LEVEL - constant_value = 1.0 - - # modify a local copy of strategy_args - strategy_args = strategy_args.copy() - backend = strategy_args.pop('backend', 'sklearn') - - if backend == 'sable': - # train model with new data - kernel_args = { - 'x_range': [-1.0, 1.0], - 'sigma_range': [1e-2, 1.0], - 'gamma': 0.1, - } - backend_args = { - 'n_features': 5000, - 'alpha': constant_value * 0.05, - 'p': 1.25, - 'n_iter_irls': 100, - 'noise_level': noise_level, - } - - client_state = DialWorkflowCreationParamsService( - dataset_x=dataset_x, - dataset_y=dataset_y, - bounds=INITIAL_BOUNDS, - kernel='rbf', - kernel_args=kernel_args, - y_is_good=False, - backend=backend, - backend_args=backend_args, - seed=-1, - dim_x=2, - ) - else: - # train model with new data - client_state = DialWorkflowCreationParamsService( - dataset_x=dataset_x, - dataset_y=dataset_y, - bounds=INITIAL_BOUNDS, - kernel='rbf', - kernel_args={ - 'length_scale': length_scale, - 'length_scale_bounds': 'fixed', - 'noise_level': noise_level, - 'noise_level_bounds': 'fixed', - 'constant_value': constant_value, - 'constant_value_bounds': 'fixed', - }, - y_is_good=False, # we wish to minimize y (the error) - backend=backend, # "sklearn" or "gpax" - seed=-1, # Use seed = -1 for random results - dim_x=2, - ) - - data = ServersideInputBase(client_state) - model = dial_core.train_model(data) - - # use get_surrogate_values to predict mean and standard deviation on the inital points grid - data = ServersideInputPrediction( - client_state, - DialInputPredictions( - workflow_id=MOCK_WORKFLOW_ID, - points_to_predict=INITIAL_POINTS_TO_PREDICT, - ), - ) - surrogate_mean, surrogate_std, _ = dial_core.get_surrogate_values(data, model) - mean_grid = np.array(surrogate_mean).reshape((-1, 1)) - - # subtract the true values and save mean absolute error and standard deviation - err_grid = np.abs(mean_grid - INITIAL_PREDICTIONS).reshape((MESHGRID_SIZE,) * NUM_DIMS) - std_grid = np.array(surrogate_std).reshape((MESHGRID_SIZE,) * NUM_DIMS) - - # get_next_point - data = ServersideInputSingle( - client_state, - DialInputSingleOtherStrategy( - workflow_id=MOCK_WORKFLOW_ID, - bounds=INITIAL_BOUNDS, - y_is_good=False, # we wish to minimize y (the error) - seed=-1, # Use seed = -1 for random results - strategy=strategy, - strategy_args=strategy_args, - ), - ) - next_point = dial_core.get_next_point(data, model) - - dataset_x.append(next_point) - - # compute at next point - next_point_y = truth_strain_map.strain_map(next_point).reshape(-1).tolist() - dataset_y.append(next_point_y[0]) - - return err_grid, std_grid - - -def graph(err_grid, dataset_x, strategy): - try: - import matplotlib as mpl - import matplotlib.pyplot as plt - - mpl.use('Agg') # Use non-interactive backend - except ImportError: - logger.error( # noqa: TRY400 - 'Error: matplotlib is required for generating plots. Install it with: pip install matplotlib' - ) - return - - plt.clf() - data = np.array(err_grid) - plt.contourf( - INITIAL_MESHGRIDS[0], - INITIAL_MESHGRIDS[1], - data, - levels=np.logspace(-2, 0, 10), - norm='log', - extend='both', - ) - cbar = plt.colorbar() - cbar.set_ticks(np.logspace(-2, 0, 7)) - cbar.set_label('RMSE') - plt.xlabel('Simulation Parameter #1') - plt.ylabel('Simulation Parameter #2') - # add black dots for data points and a red marker for the recommendation: - X_train = np.array(dataset_x) - plt.scatter(X_train[:, 0], X_train[:, 1], color='black', marker='o') - plt.scatter(1.0, 1.0, s=300, color='None', edgecolors='black', marker='o') - - plt.savefig(f'graph_{strategy}.png') - - -def accuracy_benchmark( - strategy: str, strategy_args: object, max_iterations=MAX_ITERATIONS -) -> tuple[int, float]: - """ - returns: - - number of iterations taken to reach an acceptably accurate target - - target value achieved - """ - - iterations = 0 - total_rmse = float('inf') - - initial_dataset_x = np.random.uniform(-1.0, 1.0, size=(INITIAL_NUM_POINTS, NUM_DIMS)).tolist() - - dataset_x = initial_dataset_x - dataset_y = truth_strain_map.strain_map(dataset_x).reshape(-1).tolist() - - # run simulations until we reach an acceptable target range - while iterations < max_iterations: - try: - err_grid, std_grid = run_simulation(dataset_x, dataset_y, strategy, strategy_args) - except Exception as e: - logger.exception('Error during simulation') - raise AssertionError from e - - # guess has no meaning here, take the last acquired datapoint - guess = dataset_x[-1] - - # compute the RMSE on the grid and the average - mse = err_grid**2 + std_grid**2 - total_rmse = np.sqrt(np.mean(mse)) - print(iterations, total_rmse) - - graph(np.sqrt(mse), dataset_x, f'{strategy}_{strategy_args.get("backend", "sklearn")}') - - if total_rmse <= TARGET_RMSE: - break - iterations += 1 - - return iterations, total_rmse, guess - - -TEST_PARAMS = ( - ('strategy', 'strategy_args', 'max_iterations'), - [ - ('uncertainty', {}, 2), - ('uncertainty', {'backend': 'sable'}, 2), - ('random', {}, 2), - # use low number of iterations for unit test - ], -) - - -@pytest.mark.parametrize(*TEST_PARAMS) -def test_benchmark_strainmap_accuracy( - # benchmark: BenchmarkFixture, - strategy: str, - strategy_args: dict, - max_iterations: int, -) -> None: - # NUM_RUNS = 20 - # for _ in range(NUM_RUNS): - # iterations, target = benchmark( - # partial(accuracy_benchmark, strategy, strategy_args) - # ) - iterations, target, guess = accuracy_benchmark(strategy, strategy_args, max_iterations) - print( - 'Iterations for', - strategy, - ': ', - iterations, - ' best guess:', - guess, - ' with target value:', - target, - ) - print( - 'Maximum early terminus value', - TARGET_RMSE, - ' with ', - max_iterations, - ' maximum iterations.', - ) - print( - 'Accuracy benchmark for strategy:', - strategy, - 'reached' if iterations <= max_iterations else 'not reached', - ) - - -if __name__ == '__main__': - """Generate HTML benchmark report with plots comparing different strategies.""" - import argparse - import datetime - import json - from pathlib import Path - - logger = logging.getLogger(f'{__name__}_runner') - - try: - import matplotlib as mpl - import matplotlib.pyplot as plt - - mpl.use('Agg') # Use non-interactive backend - except ImportError: - logger.error( # noqa: TRY400 - 'Error: matplotlib is required for generating plots. Install it with: pip install matplotlib' - ) - sys.exit(1) - - def positive_int_type(arg): - try: - val = int(arg) - except ValueError as e: - msg = 'Must be an integer' - raise argparse.ArgumentTypeError(msg) from e - if val < 1: - msg = 'Argument must be a positive number' - raise argparse.ArgumentTypeError(msg) - return val - - parser = argparse.ArgumentParser(description='Generate the Strainmap HTML benchmark pages.') - parser.add_argument( - '--num-runs', - '-n', - type=positive_int_type, - default=3, - help='Number of runs for each strategy.', - ) - args = parser.parse_args() - - strategies = TEST_PARAMS[1] - - # Run multiple iterations for statistical analysis - NUM_RUNS = args.num_runs - logger.info('Running benchmarks with %d iterations per strategy...', NUM_RUNS) - - results = {} - for strategy, strategy_args in strategies: - strategy_name = f'{strategy}' + (f' {json.dumps(strategy_args)}' if strategy_args else '') - logger.info('\nBenchmarking: %s', strategy_name) - - iterations_list = [] - targets_list = [] - guesses_list = [] - - for run in range(NUM_RUNS): - iterations, target, guess = accuracy_benchmark(strategy, strategy_args) - iterations_list.append(iterations) - targets_list.append(target) - guesses_list.append(guess) - logger.info( - ' Run %d/%d: iterations=%d, target=%.4f', run + 1, NUM_RUNS, iterations, target - ) - - results[strategy_name] = { - 'strategy': strategy, - 'strategy_args': strategy_args, - 'iterations': iterations_list, - 'targets': targets_list, - 'guesses': guesses_list, - 'avg_iterations': np.mean(iterations_list), - 'std_iterations': np.std(iterations_list), - 'avg_target': np.mean(targets_list), - 'std_target': np.std(targets_list), - 'success_rate': sum(1 for t in targets_list if t <= TARGET_RMSE) / NUM_RUNS * 100, - } - - # Generate plots - output_dir = Path('reports/benchmarks') - output_dir.mkdir(parents=True, exist_ok=True) - - fig, axes = plt.subplots(2, 2, figsize=(14, 10)) - fig.suptitle('Strainmap Optimization Benchmark Comparison', fontsize=16, fontweight='bold') - - # Plot 1: Average iterations to convergence - ax1 = axes[0, 0] - strategy_names = list(results.keys()) - avg_iterations = [results[s]['avg_iterations'] for s in strategy_names] - std_iterations = [results[s]['std_iterations'] for s in strategy_names] - bars1 = ax1.bar( - range(len(strategy_names)), avg_iterations, yerr=std_iterations, capsize=5, alpha=0.7 - ) - ax1.set_xlabel('Strategy') - ax1.set_ylabel('Average Iterations') - ax1.set_title('Iterations to Convergence (Lower is Better)') - ax1.set_xticks(range(len(strategy_names))) - ax1.set_xticklabels( - [s.replace(' ', '\n') for s in strategy_names], rotation=0, ha='center', fontsize=8 - ) - ax1.grid(axis='y', alpha=0.3) - - # Add value labels on bars - for _i, (bar, val, std) in enumerate(zip(bars1, avg_iterations, std_iterations, strict=False)): - height = bar.get_height() - ax1.text( - bar.get_x() + bar.get_width() / 2.0, - height, - f'{val:.1f}±{std:.1f}', - ha='center', - va='bottom', - fontsize=9, - ) - - # Plot 2: Average target value achieved - ax2 = axes[0, 1] - avg_targets = [results[s]['avg_target'] for s in strategy_names] - std_targets = [results[s]['std_target'] for s in strategy_names] - bars2 = ax2.bar( - range(len(strategy_names)), - avg_targets, - yerr=std_targets, - capsize=5, - alpha=0.7, - color='orange', - ) - ax2.set_xlabel('Strategy') - ax2.set_ylabel('Average Target Value') - ax2.set_title('Final Target Value (Lower is Better)') - ax2.set_xticks(range(len(strategy_names))) - ax2.set_xticklabels( - [s.replace(' ', '\n') for s in strategy_names], rotation=0, ha='center', fontsize=8 - ) - ax2.axhline(y=TARGET_RMSE, color='r', linestyle='--', label=f'Target Threshold ({TARGET_RMSE})') - ax2.legend() - ax2.grid(axis='y', alpha=0.3) - - # Add value labels on bars - for _i, (bar, val, std) in enumerate(zip(bars2, avg_targets, std_targets, strict=False)): - height = bar.get_height() - ax2.text( - bar.get_x() + bar.get_width() / 2.0, - height, - f'{val:.2f}±{std:.2f}', - ha='center', - va='bottom', - fontsize=9, - ) - - # Plot 3: Success rate - ax3 = axes[1, 0] - success_rates = [results[s]['success_rate'] for s in strategy_names] - bars3 = ax3.bar(range(len(strategy_names)), success_rates, alpha=0.7, color='green') - ax3.set_xlabel('Strategy') - ax3.set_ylabel('Success Rate (%)') - ax3.set_title(f'Success Rate (Target ≤ {TARGET_RMSE})') - ax3.set_xticks(range(len(strategy_names))) - ax3.set_xticklabels( - [s.replace(' ', '\n') for s in strategy_names], rotation=0, ha='center', fontsize=8 - ) - ax3.set_ylim([0, 105]) - ax3.grid(axis='y', alpha=0.3) - - # Add value labels on bars - for bar, val in zip(bars3, success_rates, strict=False): - height = bar.get_height() - ax3.text( - bar.get_x() + bar.get_width() / 2.0, - height, - f'{val:.1f}%', - ha='center', - va='bottom', - fontsize=9, - ) - - # Plot 4: Box plot of iterations distribution - ax4 = axes[1, 1] - iterations_data = [results[s]['iterations'] for s in strategy_names] - bp = ax4.boxplot( - iterations_data, labels=[s.replace(' ', '\n') for s in strategy_names], patch_artist=True - ) - for patch in bp['boxes']: - patch.set_facecolor('lightblue') - ax4.set_xlabel('Strategy') - ax4.set_ylabel('Iterations') - ax4.set_title('Iterations Distribution') - ax4.set_xticklabels([s.replace(' ', '\n') for s in strategy_names], fontsize=8) - ax4.grid(axis='y', alpha=0.3) - - plt.tight_layout() - plot_path = output_dir / 'strainmap_benchmark.png' - plt.savefig(plot_path, dpi=150, bbox_inches='tight') - logger.info('✓ Plot saved to %s', plot_path) - plt.close() - - # Generate HTML report - html_content = f""" - - - - - Strainmap Optimization Benchmark Report - - - -

📊 Strainmap Optimization Benchmark Report

- -
-

Generated: {datetime.datetime.now(tz=datetime.timezone.utc).astimezone().strftime('%Y-%m-%d %H:%M:%S')}

-

Number of Runs per Strategy: {NUM_RUNS}

-

Maximum Iterations: {MAX_ITERATIONS}

-

Target Threshold: {TARGET_RMSE}

-

Initial Bounds: {INITIAL_BOUNDS}

-

Initial Points: {INITIAL_NUM_POINTS}

-
- -
-

🎯 Test Objective

-

This benchmark evaluates different acquisition strategies for Bayesian optimization on a strain mapping experiment dataset. - The goal is to minimize the RMSE (error) within {MAX_ITERATIONS} iterations, starting from {INITIAL_NUM_POINTS} initial points.

-
- -

📈 Benchmark Results

- -
-

Performance Comparison

- Benchmark comparison plots -
- -

📋 Detailed Statistics

- - - - - - - - - - - - - - -""" - - # Find best performers - best_iterations_idx = min(range(len(strategy_names)), key=lambda i: avg_iterations[i]) - best_target_idx = min(range(len(strategy_names)), key=lambda i: avg_targets[i]) - best_success_idx = max(range(len(strategy_names)), key=lambda i: success_rates[i]) - - for i, strategy_name in enumerate(strategy_names): - result = results[strategy_name] - row_class = '' - if i in (best_iterations_idx, best_target_idx, best_success_idx): - row_class = ' class="best"' - - args_str = json.dumps(result['strategy_args']) if result['strategy_args'] else 'None' - - html_content += f""" - - - - - - - - -""" - - html_content += """ -
StrategyStrategy ArgsAvg IterationsStd IterationsAvg Target ValueStd Target ValueSuccess Rate
{result['strategy']}{args_str}{result['avg_iterations']:.2f}{result['std_iterations']:.2f}{result['avg_target']:.4f}{result['std_target']:.4f}{result['success_rate']:.1f}%
- -

🏆 Key Findings

-
-""" - - # Add findings - best_strategy = strategy_names[best_iterations_idx] - html_content += f"""

Fastest Convergence: {best_strategy} with {avg_iterations[best_iterations_idx]:.2f} ± {std_iterations[best_iterations_idx]:.2f} iterations on average

-""" - - best_accuracy_strategy = strategy_names[best_target_idx] - html_content += f"""

Best Accuracy: {best_accuracy_strategy} with average target value {avg_targets[best_target_idx]:.4f} ± {std_targets[best_target_idx]:.4f}

-""" - - best_reliability_strategy = strategy_names[best_success_idx] - html_content += f"""

Most Reliable: {best_reliability_strategy} with {success_rates[best_success_idx]:.1f}% success rate

-""" - - html_content += """
- -

📊 Raw Data

-
- Click to expand raw results JSON - 
-"""
-
-    # Prepare JSON-serializable results
-    json_results = {}
-    for strategy_name, result in results.items():
-        json_results[strategy_name] = {
-            'strategy': result['strategy'],
-            'strategy_args': result['strategy_args'],
-            'iterations': result['iterations'],
-            'targets': result['targets'],
-            'guesses': [[float(x) for x in guess] for guess in result['guesses']],
-            'statistics': {
-                'avg_iterations': float(result['avg_iterations']),
-                'std_iterations': float(result['std_iterations']),
-                'avg_target': float(result['avg_target']),
-                'std_target': float(result['std_target']),
-                'success_rate': float(result['success_rate']),
-            },
-        }
-
-    html_content += json.dumps(json_results, indent=2)
-    html_content += """
-
-
- - - - -""" - - # Save HTML report - html_path = output_dir / 'strainmap_benchmark.html' - html_path.write_text(html_content) - logger.info('✓ HTML report saved to %s', html_path) - - # Save JSON data - json_path = output_dir / 'strainmap_benchmark.json' - json_path.write_text(json.dumps(json_results, indent=2)) - logger.info('✓ JSON data saved to %s', json_path) - - logger.info('\n%s', '=' * 60) - logger.info('📊 Benchmark Summary') - logger.info('%s', '=' * 60) - for strategy_name in strategy_names: - result = results[strategy_name] - logger.info('\n%s:', strategy_name) - logger.info( - ' Avg Iterations: %.2f ± %.2f', result['avg_iterations'], result['std_iterations'] - ) - logger.info(' Avg Target: %.4f ± %.4f', result['avg_target'], result['std_target']) - logger.info(' Success Rate: %.1f%%', result['success_rate']) - - logger.info('\n%s', '=' * 60) - logger.info('✓ Open %s in your browser to view the full report', html_path) - logger.info('%s', '=' * 60) - # assert iterations <= MAX_ITERATIONS +""" +Benchmark which is meant to test core DIAL functionality (without the INTERSECT or MONGO pieces) for the Strain-Mapping benchmark problem. +""" + +import logging +import sys +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest +from scipy.interpolate import LinearNDInterpolator + +# from pytest_benchmark.fixture import BenchmarkFixture +from dial_dataclass import ( + DialInputPredictions, + DialInputSingleOtherStrategy, + Normal, +) +from dial_service import ( + core as dial_core, +) +from dial_service.serverside_data import ( + ServersideInputBase, + ServersideInputPrediction, + ServersideInputSingle, +) +from dial_service.service_specific_dataclasses import ( + DialWorkflowCreationParamsService, +) + +from ..helpers import generate_pytest_parameters + +logger = logging.getLogger(__name__) + +MOCK_WORKFLOW_ID = '6984e6a6ef6e6290dabced91' +"""fake ObjectID for testing purposes, we do not actually interact with a DB in these tests.""" + + +############### +low_f_data_name = ( + Path(__file__).parents[1] / 'fixtures' / 'adaptive_strain_manufacturing_low_fidelity.csv' +) +high_f_data_name = ( + Path(__file__).parents[1] / 'fixtures' / 'adaptive_strain_manufacturing_high_fidelity.csv' +) + + +def read_and_prepare_data(): + # normalization helper + def normalize_data(in_data): + m_data = (in_data.max() + in_data.min()) * 0.5 + d_data = (in_data.max() - in_data.min()) * 0.5 + return (in_data - m_data) / d_data + + # define constants and read data + # wall_st_index = 0 + # wall_st_end = 676 + x_col_index = 0 # z follow next + e_col_index = 2 # e11, e22 and e33 + nrows = 26 # Rows in sim strain map of the wall + ncols = 26 # Cols in sim strain map of the wall + data = pd.read_csv(low_f_data_name) + + Y, Z, E11, E22, E33, R11, R22, R33 = np.array( + [ + data.values[:, x_col_index], + data.values[:, x_col_index + 1], + data.values[:, e_col_index], + data.values[:, e_col_index + 1], + data.values[:, e_col_index + 2], + data.values[:, e_col_index + 3], + data.values[:, e_col_index + 4], + data.values[:, e_col_index + 5], + ] + ) + + # Convert data + x1 = Y.astype(np.float32).reshape(nrows, ncols) + x2 = Z.astype(np.float32).reshape(nrows, ncols) + + # Sim_e11 = np.array(E11).astype(np.float32).reshape(nrows, ncols) + # Real_e11 = np.array(R11).astype(np.float32).reshape(nrows, ncols) + + # Sim_e22 = np.array(E22).astype(np.float32).reshape(nrows, ncols) + # Real_e22 = np.array(R22).astype(np.float32).reshape(nrows, ncols) + + # Sim_e33 = np.array(E33).astype(np.float32).reshape(nrows, ncols) + Real_e33 = np.array(R33).astype(np.float32).reshape(nrows, ncols) + + # Normalize data + x1_norm = normalize_data(x1) + x2_norm = normalize_data(x2) + + # Sim_e11_norm = normalize_data(Sim_e11) + # Real_e11_norm = normalize_data(Real_e11) + + # Sim_e22_norm = normalize_data(Sim_e22) + # Real_e22_norm = normalize_data(Real_e22) + + # Sim_e33_norm = normalize_data(Sim_e33) + Real_e33_norm = normalize_data(Real_e33) + + # read high fidelity data + # data = pd.read_csv(high_f_data_name) + + # Y, Z, E11, E22, E33, R11, R22, R33 = np.array( + # [ + # data.values[:, x_col_index], + # data.values[:, x_col_index + 1], + # data.values[:, e_col_index], + # data.values[:, e_col_index + 1], + # data.values[:, e_col_index + 2], + # data.values[:, e_col_index + 3], + # data.values[:, e_col_index + 4], + # data.values[:, e_col_index + 5], + # ] + # ) + + # Sim_hi_e11 = np.array(E11).astype(np.float32).reshape(nrows, ncols) + # Sim_hi_e22 = np.array(E22).astype(np.float32).reshape(nrows, ncols) + # Sim_hi_e33 = np.array(E33).astype(np.float32).reshape(nrows, ncols) + + # Transpose the high-fidelity simulation data + # TODO, figure out why this is transposed + # Sim_hi_e11 = Sim_hi_e11.T + # Sim_hi_e22 = Sim_hi_e22.T + # Sim_hi_e33 = Sim_hi_e33.T + + # Normalize data + # Sim_hi_e11_norm = normalize_data(Sim_hi_e11) + # Sim_hi_e22_norm = normalize_data(Sim_hi_e22) + # Sim_hi_e33_norm = normalize_data(Sim_hi_e33) + + # return data that will be used for the benchmark + return nrows, (x1_norm, x2_norm), Real_e33_norm + + +############### + +## Select data for ground truth + +ngrid, x_grids, y_grid = read_and_prepare_data() + +# default inputs +INITIAL_BOUNDS = [[-1.0, 1.0], [-1.0, 1.0]] +NUM_DIMS = len(INITIAL_BOUNDS) + +MESHGRID_SIZE = ngrid +INITIAL_MESHGRIDS = x_grids +INITIAL_POINTS_TO_PREDICT = np.hstack([mg.reshape(-1, 1) for mg in INITIAL_MESHGRIDS]) + +INITIAL_PREDICTIONS = y_grid.reshape(-1, 1) + +############### + +NOISE_LEVEL = 1.0e-2 + +truth_interp = LinearNDInterpolator(INITIAL_POINTS_TO_PREDICT, INITIAL_PREDICTIONS) + + +@dataclass +class StrainMap: + """Strain Map function.""" + + noise_level: float + + truth_interp: LinearNDInterpolator + """Strain interpolator that is used as 'ground_truth'""" + + def strain_map(self, x) -> float: + """ + Represents a measured strain as a function of two simulation parameters. + """ + x = np.asarray(x).reshape((-1, 2)) + x1, x2 = x[:, 0], x[:, 1] + + y_true = truth_interp(np.asarray(x1), np.asarray(x2)) + + # print("Evaluated truth model", np.hstack((x, y_true))) + + y_noise = y_true + self.noise_level * np.random.normal(size=y_true.shape) + return y_noise + + +# build a strain map from the selected truth values +truth_strain_map = StrainMap(truth_interp=truth_interp, noise_level=NOISE_LEVEL) + +############### + +# test parameters +INITIAL_NUM_POINTS = 25 +MAX_ITERATIONS = 150 # only allow a maximum of this many iterations in tests + +INITIAL_DATASET_X = np.random.uniform(-1.0, 1.0, size=(INITIAL_NUM_POINTS, NUM_DIMS)).tolist() + +TARGET_ERROR = 0.11 + + +def run_simulation( + dataset_x: list[list[float]], + dataset_y: list[float], + strategy: str, + strategy_args: object, +) -> tuple[np.ndarray, np.ndarray]: + # important "Hyper-parameters" + length_scale = 0.4 + prior_std = 1.0 + + # Assume that there is some small noise in the measurements to stabilize the fit + statistics_y = Normal(loc='y', scale=NOISE_LEVEL) + + # modify a local copy of strategy_args + strategy_args = strategy_args.copy() + backend = strategy_args.pop('backend', 'sklearn') + + if backend == 'sable': + # train model with new data + kernel = 'rbf' + kernel_args = { + 'sigma_range': [5e-2, 1.0], + 'gamma': 0.5, + } + backend_args = { + 'prior_std': prior_std, + } + + else: + # train model with new data + kernel = 'matern' + kernel_args = { + 'length_scale': length_scale, + 'constant_value': prior_std**2, + } + backend_args = {} + + client_state = DialWorkflowCreationParamsService( + dataset_x=dataset_x, + dataset_y=dataset_y, + statistics_y=statistics_y, + bounds=INITIAL_BOUNDS, + kernel=kernel, + kernel_args=kernel_args, + y_is_good=False, + backend=backend, + backend_args=backend_args, + seed=-1, + dim_x=2, + ) + + data = ServersideInputBase(client_state) + model = dial_core.train_model(data) + + # use get_surrogate_values to predict mean and standard deviation on the inital points grid + data = ServersideInputPrediction( + client_state, + DialInputPredictions( + workflow_id=MOCK_WORKFLOW_ID, + points_to_predict=INITIAL_POINTS_TO_PREDICT, + ), + ) + surrogate_mean, surrogate_std, _ = dial_core.get_surrogate_values(data, model) + mean_grid = np.array(surrogate_mean).reshape((-1, 1)) + + # subtract the true values and save mean absolute error and standard deviation + err_grid = np.abs(mean_grid - INITIAL_PREDICTIONS).reshape((MESHGRID_SIZE,) * NUM_DIMS) + std_grid = np.array(surrogate_std).reshape((MESHGRID_SIZE,) * NUM_DIMS) + + # get_next_point + data = ServersideInputSingle( + client_state, + DialInputSingleOtherStrategy( + workflow_id=MOCK_WORKFLOW_ID, + bounds=INITIAL_BOUNDS, + y_is_good=False, + seed=-1, + strategy=strategy, + strategy_args=strategy_args, + discrete_measurements=True, + discrete_measurement_grid_size=[MESHGRID_SIZE, MESHGRID_SIZE], + ), + ) + next_point = dial_core.get_next_point(data, model) + + dataset_x.append(next_point) + + # compute at next point + next_point_y = truth_strain_map.strain_map(next_point).reshape(-1).tolist() + dataset_y.append(next_point_y[0]) + + return err_grid, std_grid + + +def graph(err_grid, dataset_x, strategy): + try: + import matplotlib as mpl + import matplotlib.pyplot as plt + + mpl.use('Agg') # Use non-interactive backend + except ImportError: + logger.error( # noqa: TRY400 + 'Error: matplotlib is required for generating plots. Install it with: pip install matplotlib' + ) + return + + plt.clf() + data = np.array(err_grid) + plt.contourf( + INITIAL_MESHGRIDS[0], + INITIAL_MESHGRIDS[1], + data, + levels=np.logspace(-2, 0, 10), + norm='log', + extend='both', + ) + cbar = plt.colorbar() + cbar.set_ticks(np.logspace(-2, 0, 7)) + cbar.set_label('Overall error') + plt.xlabel('Simulation Parameter #1') + plt.ylabel('Simulation Parameter #2') + # add black dots for data points and a red marker for the recommendation: + X_train = np.array(dataset_x) + plt.scatter(X_train[:, 0], X_train[:, 1], color='black', marker='o') + plt.scatter(1.0, 1.0, s=300, color='None', edgecolors='black', marker='o') + + plt.savefig(f'graph_{strategy}.png') + + +def accuracy_benchmark( + strategy: str, strategy_args: object, max_iterations=MAX_ITERATIONS +) -> tuple[int, float, float]: + """ + returns: + - number of iterations taken to reach an acceptably accurate target + - target value achieved + """ + + iterations = 0 + + initial_dataset_x = np.random.uniform(-1.0, 1.0, size=(INITIAL_NUM_POINTS, NUM_DIMS)).tolist() + + dataset_x = initial_dataset_x + dataset_y = truth_strain_map.strain_map(dataset_x).reshape(-1).tolist() + + # run simulations until we reach an acceptable target range + while iterations < max_iterations: + try: + err_grid, std_grid = run_simulation(dataset_x, dataset_y, strategy, strategy_args) + except Exception as e: + logger.exception('Error during simulation') + raise AssertionError from e + + # guess has no meaning here, take the last acquired datapoint + guess = dataset_x[-1] + + # compute the RMSE and MAD on the grid and the average + # rmse = np.sqrt(err_grid**2 + std_grid**2) + mad = np.abs(err_grid) + # total_rmse = np.sqrt(np.mean(rmse**2)) + total_mad = np.sqrt(np.mean(mad**2)) + + # pick the MAD over the RMSE as an error criterion, it only measures the mean deviation: + # it is less sensitive to wrong std and hyperparameter calibration, but does not measure how well + # the "error bars" (std_grid) quantify the actual ty + error = total_mad + print(iterations, error) + + graph( + mad, + dataset_x, + f'{strategy}_{strategy_args.get("backend", "sklearn")}', + ) + + if error <= TARGET_ERROR: + break + iterations += 1 + + return iterations, error, guess + + +TEST_PARAMS = ( + ('backend', 'strategy', 'strategy_args', 'max_iterations'), + [ + ('sklearn', 'uncertainty', {}, 2), + ('sable', 'uncertainty', {}, 2), + ('sklearn', 'random', {}, 2), + # use low number of iterations for unit test + ], +) + + +@pytest.mark.parametrize(*generate_pytest_parameters(TEST_PARAMS, 0)) +def test_benchmark_strainmap_accuracy( + # benchmark: BenchmarkFixture, + backend: str, + strategy: str, + strategy_args: dict, + max_iterations: int, +) -> None: + # NUM_RUNS = 20 + # for _ in range(NUM_RUNS): + # iterations, target = benchmark( + # partial(accuracy_benchmark, strategy, strategy_args) + # ) + iterations, target, guess = accuracy_benchmark( + strategy, {'backend': backend, **strategy_args}, max_iterations + ) + print( + 'Iterations for', + strategy, + ': ', + iterations, + ' best guess:', + guess, + ' with target value:', + target, + ) + print( + 'Maximum early terminus value', + TARGET_ERROR, + ' with ', + max_iterations, + ' maximum iterations.', + ) + print( + 'Accuracy benchmark for strategy:', + strategy, + 'reached' if iterations <= max_iterations else 'not reached', + ) + + +if __name__ == '__main__': + """Generate HTML benchmark report with plots comparing different strategies.""" + import argparse + import datetime + import json + from pathlib import Path + + logger = logging.getLogger(f'{__name__}_runner') + + try: + import matplotlib as mpl + import matplotlib.pyplot as plt + + mpl.use('Agg') # Use non-interactive backend + except ImportError: + logger.error( # noqa: TRY400 + 'Error: matplotlib is required for generating plots. Install it with: pip install matplotlib' + ) + sys.exit(1) + + def positive_int_type(arg): + try: + val = int(arg) + except ValueError as e: + msg = 'Must be an integer' + raise argparse.ArgumentTypeError(msg) from e + if val < 1: + msg = 'Argument must be a positive number' + raise argparse.ArgumentTypeError(msg) + return val + + parser = argparse.ArgumentParser(description='Generate the Strainmap HTML benchmark pages.') + parser.add_argument( + '--num-runs', + '-n', + type=positive_int_type, + default=3, + help='Number of runs for each strategy.', + ) + args = parser.parse_args() + + strategies = TEST_PARAMS[1] + + # Run multiple iterations for statistical analysis + NUM_RUNS = args.num_runs + logger.info('Running benchmarks with %d iterations per strategy...', NUM_RUNS) + + results = {} + for strategy, strategy_args, _ in strategies: + strategy_name = f'{strategy}' + (f' {json.dumps(strategy_args)}' if strategy_args else '') + logger.info('\nBenchmarking: %s', strategy_name) + + iterations_list = [] + targets_list = [] + guesses_list = [] + + for run in range(NUM_RUNS): + iterations, target, guess = accuracy_benchmark(strategy, strategy_args) + iterations_list.append(iterations) + targets_list.append(target) + guesses_list.append(guess) + logger.info( + ' Run %d/%d: iterations=%d, target=%.4f', + run + 1, + NUM_RUNS, + iterations, + target, + ) + + results[strategy_name] = { + 'strategy': strategy, + 'strategy_args': strategy_args, + 'iterations': iterations_list, + 'targets': targets_list, + 'guesses': guesses_list, + 'avg_iterations': np.mean(iterations_list), + 'std_iterations': np.std(iterations_list), + 'avg_target': np.mean(targets_list), + 'std_target': np.std(targets_list), + 'success_rate': sum(1 for t in targets_list if t <= TARGET_ERROR) / NUM_RUNS * 100, + } + + # Generate plots + output_dir = Path('reports/benchmarks') + output_dir.mkdir(parents=True, exist_ok=True) + + fig, axes = plt.subplots(2, 2, figsize=(14, 10)) + fig.suptitle( + 'Strainmap Optimization Benchmark Comparison', + fontsize=16, + fontweight='bold', + ) + + # Plot 1: Average iterations to convergence + ax1 = axes[0, 0] + strategy_names = list(results.keys()) + avg_iterations = [results[s]['avg_iterations'] for s in strategy_names] + std_iterations = [results[s]['std_iterations'] for s in strategy_names] + bars1 = ax1.bar( + range(len(strategy_names)), + avg_iterations, + yerr=std_iterations, + capsize=5, + alpha=0.7, + ) + ax1.set_xlabel('Strategy') + ax1.set_ylabel('Average Iterations') + ax1.set_title('Iterations to Convergence (Lower is Better)') + ax1.set_xticks(range(len(strategy_names))) + ax1.set_xticklabels( + [s.replace(' ', '\n') for s in strategy_names], + rotation=0, + ha='center', + fontsize=8, + ) + ax1.grid(axis='y', alpha=0.3) + + # Add value labels on bars + for _i, (bar, val, std) in enumerate(zip(bars1, avg_iterations, std_iterations, strict=False)): + height = bar.get_height() + ax1.text( + bar.get_x() + bar.get_width() / 2.0, + height, + f'{val:.1f}±{std:.1f}', + ha='center', + va='bottom', + fontsize=9, + ) + + # Plot 2: Average target value achieved + ax2 = axes[0, 1] + avg_targets = [results[s]['avg_target'] for s in strategy_names] + std_targets = [results[s]['std_target'] for s in strategy_names] + bars2 = ax2.bar( + range(len(strategy_names)), + avg_targets, + yerr=std_targets, + capsize=5, + alpha=0.7, + color='orange', + ) + ax2.set_xlabel('Strategy') + ax2.set_ylabel('Average Target Value') + ax2.set_title('Final Target Value (Lower is Better)') + ax2.set_xticks(range(len(strategy_names))) + ax2.set_xticklabels( + [s.replace(' ', '\n') for s in strategy_names], + rotation=0, + ha='center', + fontsize=8, + ) + ax2.axhline( + y=TARGET_ERROR, + color='r', + linestyle='--', + label=f'Target Threshold ({TARGET_ERROR})', + ) + ax2.legend() + ax2.grid(axis='y', alpha=0.3) + + # Add value labels on bars + for _i, (bar, val, std) in enumerate(zip(bars2, avg_targets, std_targets, strict=False)): + height = bar.get_height() + ax2.text( + bar.get_x() + bar.get_width() / 2.0, + height, + f'{val:.2f}±{std:.2f}', + ha='center', + va='bottom', + fontsize=9, + ) + + # Plot 3: Success rate + ax3 = axes[1, 0] + success_rates = [results[s]['success_rate'] for s in strategy_names] + bars3 = ax3.bar(range(len(strategy_names)), success_rates, alpha=0.7, color='green') + ax3.set_xlabel('Strategy') + ax3.set_ylabel('Success Rate (%)') + ax3.set_title(f'Success Rate (Target ≤ {TARGET_ERROR})') + ax3.set_xticks(range(len(strategy_names))) + ax3.set_xticklabels( + [s.replace(' ', '\n') for s in strategy_names], + rotation=0, + ha='center', + fontsize=8, + ) + ax3.set_ylim([0, 105]) + ax3.grid(axis='y', alpha=0.3) + + # Add value labels on bars + for bar, val in zip(bars3, success_rates, strict=False): + height = bar.get_height() + ax3.text( + bar.get_x() + bar.get_width() / 2.0, + height, + f'{val:.1f}%', + ha='center', + va='bottom', + fontsize=9, + ) + + # Plot 4: Box plot of iterations distribution + ax4 = axes[1, 1] + iterations_data = [results[s]['iterations'] for s in strategy_names] + bp = ax4.boxplot(iterations_data, patch_artist=True) + for patch in bp['boxes']: + patch.set_facecolor('lightblue') + ax4.set_xlabel('Strategy') + ax4.set_ylabel('Iterations') + ax4.set_title('Iterations Distribution') + ax4.set_xticklabels([s.replace(' ', '\n') for s in strategy_names], fontsize=8) + ax4.grid(axis='y', alpha=0.3) + + plt.tight_layout() + plot_path = output_dir / 'strainmap_benchmark.png' + plt.savefig(plot_path, dpi=150, bbox_inches='tight') + logger.info('✓ Plot saved to %s', plot_path) + plt.close() + + # Generate HTML report + html_content = f""" + + + + + Strainmap Optimization Benchmark Report + + + +

📊 Strainmap Optimization Benchmark Report

+ +
+

Generated: {datetime.datetime.now(tz=datetime.timezone.utc).astimezone().strftime('%Y-%m-%d %H:%M:%S')}

+

Number of Runs per Strategy: {NUM_RUNS}

+

Maximum Iterations: {MAX_ITERATIONS}

+

Target Threshold: {TARGET_ERROR}

+

Initial Bounds: {INITIAL_BOUNDS}

+

Initial Points: {INITIAL_NUM_POINTS}

+
+ +
+

🎯 Test Objective

+

This benchmark evaluates different acquisition strategies for Bayesian optimization on a strain mapping experiment dataset. + The goal is to minimize the total surrogate error (over all inputs) within {MAX_ITERATIONS} iterations, starting from {INITIAL_NUM_POINTS} initial points.

+
+ +

📈 Benchmark Results

+ +
+

Performance Comparison

+ Benchmark comparison plots +
+ +

📋 Detailed Statistics

+ + + + + + + + + + + + + + +""" + + # Find best performers + best_iterations_idx = min(range(len(strategy_names)), key=lambda i: avg_iterations[i]) + best_target_idx = min(range(len(strategy_names)), key=lambda i: avg_targets[i]) + best_success_idx = max(range(len(strategy_names)), key=lambda i: success_rates[i]) + + for i, strategy_name in enumerate(strategy_names): + result = results[strategy_name] + row_class = '' + if i in (best_iterations_idx, best_target_idx, best_success_idx): + row_class = ' class="best"' + + args_str = json.dumps(result['strategy_args']) if result['strategy_args'] else 'None' + + html_content += f""" + + + + + + + + +""" + + html_content += """ +
StrategyStrategy ArgsAvg IterationsStd IterationsAvg Target ValueStd Target ValueSuccess Rate
{result['strategy']}{args_str}{result['avg_iterations']:.2f}{result['std_iterations']:.2f}{result['avg_target']:.4f}{result['std_target']:.4f}{result['success_rate']:.1f}%
+ +

🏆 Key Findings

+
+""" + + # Add findings + best_strategy = strategy_names[best_iterations_idx] + html_content += f"""

Fastest Convergence: {best_strategy} with {avg_iterations[best_iterations_idx]:.2f} ± {std_iterations[best_iterations_idx]:.2f} iterations on average

+""" + + best_accuracy_strategy = strategy_names[best_target_idx] + html_content += f"""

Best Accuracy: {best_accuracy_strategy} with average target value {avg_targets[best_target_idx]:.4f} ± {std_targets[best_target_idx]:.4f}

+""" + + best_reliability_strategy = strategy_names[best_success_idx] + html_content += f"""

Most Reliable: {best_reliability_strategy} with {success_rates[best_success_idx]:.1f}% success rate

+""" + + html_content += """
+ +

📊 Raw Data

+
+ Click to expand raw results JSON + 
+"""
+
+    # Prepare JSON-serializable results
+    json_results = {}
+    for strategy_name, result in results.items():
+        json_results[strategy_name] = {
+            'strategy': result['strategy'],
+            'strategy_args': result['strategy_args'],
+            'iterations': result['iterations'],
+            'targets': result['targets'],
+            'guesses': [[float(x) for x in guess] for guess in result['guesses']],
+            'statistics': {
+                'avg_iterations': float(result['avg_iterations']),
+                'std_iterations': float(result['std_iterations']),
+                'avg_target': float(result['avg_target']),
+                'std_target': float(result['std_target']),
+                'success_rate': float(result['success_rate']),
+            },
+        }
+
+    html_content += json.dumps(json_results, indent=2)
+    html_content += """
+
+
+ + + + +""" + + # Save HTML report + html_path = output_dir / 'strainmap_benchmark.html' + html_path.write_text(html_content) + logger.info('✓ HTML report saved to %s', html_path) + + # Save JSON data + json_path = output_dir / 'strainmap_benchmark.json' + json_path.write_text(json.dumps(json_results, indent=2)) + logger.info('✓ JSON data saved to %s', json_path) + + logger.info('\n%s', '=' * 60) + logger.info('📊 Benchmark Summary') + logger.info('%s', '=' * 60) + for strategy_name in strategy_names: + result = results[strategy_name] + logger.info('\n%s:', strategy_name) + logger.info( + ' Avg Iterations: %.2f ± %.2f', + result['avg_iterations'], + result['std_iterations'], + ) + logger.info( + ' Avg Target: %.4f ± %.4f', + result['avg_target'], + result['std_target'], + ) + logger.info(' Success Rate: %.1f%%', result['success_rate']) + + logger.info('\n%s', '=' * 60) + logger.info('✓ Open %s in your browser to view the full report', html_path) + logger.info('%s', '=' * 60) + # assert iterations <= MAX_ITERATIONS diff --git a/tests/helpers.py b/tests/helpers.py new file mode 100644 index 0000000..930ea72 --- /dev/null +++ b/tests/helpers.py @@ -0,0 +1,30 @@ +from copy import deepcopy + +import pytest + +from dial_service.service_specific_dataclasses import AVAILABLE_DIAL_BACKENDS + + +def generate_pytest_parameters( + old_params: tuple[tuple[str], tuple[list[object]]], backend_idx: int +) -> tuple[tuple[str], tuple[list[object]]]: + """ + Generates test safeguards from generic parameters. + + This should only really be used if you simultaneously want to use generic parameters in contexts other than Pytest. + + Params: + - old_params = your normal pytest.mark.parametrize parameters (don't use pytest.param) + - backend_idx = the index of the "backend" parameter in your args list""" + new_params = deepcopy(old_params) + for i, test in enumerate(old_params[1]): + backend_name = test[backend_idx] + if backend_name != 'sklearn': + new_params[1][i] = pytest.param( + *test, + marks=pytest.mark.skipif( + backend_name not in AVAILABLE_DIAL_BACKENDS, + reason=f'{backend_name} not installed', + ), + ) + return new_params diff --git a/tests/unit/__init__.py b/tests/unit/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/unit/test_internals.py b/tests/unit/test_internals.py index a84d894..c2f2329 100644 --- a/tests/unit/test_internals.py +++ b/tests/unit/test_internals.py @@ -9,6 +9,7 @@ DialInputMultiple, DialInputPredictions, DialInputSingleOtherStrategy, + Normal, ) from dial_service import core from dial_service.serverside_data import ( @@ -17,6 +18,7 @@ ServersideInputSingle, ) from dial_service.service_specific_dataclasses import ( + AVAILABLE_DIAL_BACKENDS, DialWorkflowCreationParamsService, ) @@ -253,11 +255,35 @@ def single_3D(backend, strategy, strategy_args, discrete_measurement_grid_size=N return ServersideInputSingle(workflow_state, params) +def multiple_1D(backend, strategy, discrete_measurement_grid_size=None): + workflow_state = DialWorkflowCreationParamsService( + dataset_x=[], + dataset_y=[], + dim_x=1, # provide dim_x for empty dataset + dim_y=1, # provide dim_y for empty dataset + y_is_good=False, + kernel='rbf', + bounds=[[0, 100]], + backend=backend, + seed=42, + ) + params = DialInputMultiple( + workflow_id=DUMMY_WORKFLOW_ID, + strategy=strategy, + bounds=[[0, 100]], + discrete_measurements=bool(discrete_measurement_grid_size), + discrete_measurement_grid_size=discrete_measurement_grid_size or [20, 20], + points=20, + ) + return ServersideInputMultiple(workflow_state, params) + + def multiple_2D(backend, strategy, discrete_measurement_grid_size=None): workflow_state = DialWorkflowCreationParamsService( dataset_x=[], dataset_y=[], dim_x=2, # provide dim_x for empty dataset + dim_y=1, # provide dim_y for empty dataset y_is_good=False, kernel='rbf', bounds=[[0, 100], [-1, 1]], @@ -299,6 +325,30 @@ def prediction_1D(backend): return ServersideInputPrediction(workflow_state, params) +def prediction_1D_heteroscedastic(backend): + workflow_state = DialWorkflowCreationParamsService( + dataset_x=[[1.0], [1.5], [2.0]], + dataset_y=[[-1.0, 1e-2], [0.0, 1e2], [1.0, 1e-5]], + labels_y=['y', 'yerr'], + dim_y=2, + statistics_y=Normal(loc='y', scale='yerr'), + bounds=[[1.0, 2.0]], + kernel='rbf', + kernel_args={ + 'length_scale': 0.5, + }, + backend=backend, + preprocess_standardize=False, + y_is_good=True, + seed=42, + ) + params = DialInputPredictions( + workflow_id=DUMMY_WORKFLOW_ID, + points_to_predict=[[1], [1.25], [1.5], [1.75], [2]], + ) + return ServersideInputPrediction(workflow_state, params) + + ####### TESTS ################### @@ -306,7 +356,13 @@ def prediction_1D(backend): ('backend', 'approx'), [ ('sklearn', 1.842309), - # ('gpax', 2.0), + # pytest.param( + # 'gpax', 2.0, + # marks=pytest.mark.skipif( + # 'gpax' not in AVAILABLE_DIAL_BACKENDS, + # reason='gpax not installed', + # ), + # ), ], ) def test_EI_1D(backend, approx): @@ -323,7 +379,13 @@ def test_EI_1D(backend, approx): ('backend', 'val'), [ ('sklearn', 1.0), - # ('gpax', 2.0), + # pytest.param( + # 'gpax', 2.0, + # marks=pytest.mark.skipif( + # 'gpax' not in AVAILABLE_DIAL_BACKENDS, + # reason='gpax not installed', + # ), + # ), ], ) def test_EI_1D_discrete(backend, val): @@ -344,7 +406,13 @@ def test_EI_1D_discrete(backend, val): ('backend', 'approx'), [ ('sklearn', [1.705352, -1.682829]), - # ('gpax', [2.0, 2.0]), + # pytest.param( + # 'gpax', [2.0, 2,0], + # marks=pytest.mark.skipif( + # 'gpax' not in AVAILABLE_DIAL_BACKENDS, + # reason='gpax not installed', + # ), + # ), ], ) def test_EI_2D(backend, approx): @@ -361,7 +429,12 @@ def test_EI_2D(backend, approx): ('backend', 'approx'), [ ('sklearn', [2.000000, -1.143727, -1.859496]), - # ('gpax', [2.0, 2.0, -2.0], # WAS: [2.0, 2.0, 2.0] + # pytest.param( + # 'gpax', [2.0, 2,0, 2.0,], # WAS: [2.0,2.0,2,0] + # marks=pytest.mark.skipif( + # 'gpax' not in AVAILABLE_DIAL_BACKENDS, + # reason='gpax not installed', + # ), # ), ], ) @@ -379,7 +452,13 @@ def test_EI_3D(backend, approx): ('backend', 'approx'), [ ('sklearn', [1.5]), - # ('gpax',), + # pytest.param( + # 'gpax' [2.0], + # marks=pytest.mark.skipif( + # 'gpax' not in AVAILABLE_DIAL_BACKENDS, + # reason='gpax not installed', + # ), + # ), ], ) def test_uncertainty(backend, approx): @@ -393,7 +472,13 @@ def test_uncertainty(backend, approx): ('backend', 'approx'), [ ('sklearn', [1.790396262]), - # ('gpax', [2.0]), + # pytest.param( + # 'gpax' [2.0], + # marks=pytest.mark.skipif( + # 'gpax' not in AVAILABLE_DIAL_BACKENDS, + # reason='gpax not installed', + # ), + # ), ], ) def test_preprocessing_standardize(backend, approx): @@ -409,7 +494,13 @@ def test_preprocessing_standardize(backend, approx): ('backend', 'val'), [ ('sklearn', [1.0]), - # ('gpax', [2.0]), + # pytest.param( + # 'gpax' [2.0], + # marks=pytest.mark.skipif( + # 'gpax' not in AVAILABLE_DIAL_BACKENDS, + # reason='gpax not installed', + # ), + # ), ], ) def test_preprocessing_standardize_discrete(backend, val): @@ -433,7 +524,13 @@ def test_preprocessing_standardize_discrete(backend, val): ('backend'), [ ('sklearn'), - ('gpax'), + pytest.param( + 'gpax', + marks=pytest.mark.skipif( + 'gpax' not in AVAILABLE_DIAL_BACKENDS, + reason='gpax not installed', + ), + ), ], ) def test_random(backend): @@ -449,7 +546,13 @@ def test_random(backend): ('backend'), [ ('sklearn'), - ('gpax'), + pytest.param( + 'gpax', + marks=pytest.mark.skipif( + 'gpax' not in AVAILABLE_DIAL_BACKENDS, + reason='gpax not installed', + ), + ), ], ) def test_hypercube_single_point(backend): @@ -479,7 +582,13 @@ def test_hypercube_single_point(backend): ('backend'), [ ('sklearn'), - ('gpax'), + pytest.param( + 'gpax', + marks=pytest.mark.skipif( + 'gpax' not in AVAILABLE_DIAL_BACKENDS, + reason='gpax not installed', + ), + ), ], ) def test_random_discrete(backend): @@ -504,7 +613,13 @@ def test_random_discrete(backend): ('backend'), [ ('sklearn'), - ('gpax'), + pytest.param( + 'gpax', + marks=pytest.mark.skipif( + 'gpax' not in AVAILABLE_DIAL_BACKENDS, + reason='gpax not installed', + ), + ), ], ) def test_hypercube_single_point_discrete(backend): @@ -540,7 +655,13 @@ def test_hypercube_single_point_discrete(backend): ('backend'), [ ('sklearn'), - ('gpax'), + pytest.param( + 'gpax', + marks=pytest.mark.skipif( + 'gpax' not in AVAILABLE_DIAL_BACKENDS, + reason='gpax not installed', + ), + ), ], ) def test_hypercube_single_point_discrete_2D(backend): @@ -571,11 +692,33 @@ def test_hypercube_single_point_discrete_2D(backend): data.point_index += 1 +@pytest.mark.parametrize( + ('backend', 'strategy'), + [ + ('sklearn', 'polymer_acl_sampler'), + ], +) +def test_batch_points(backend, strategy): + data = multiple_1D(backend, strategy=strategy) + model = core.initialize_model(data) + batch_points = core.get_next_points(data, model) + assert len(batch_points) == data.points + for pt in batch_points: + print(pt) + assert 0 <= pt[0] <= 100 + + @pytest.mark.parametrize( ('backend'), [ ('sklearn'), - ('gpax'), + pytest.param( + 'gpax', + marks=pytest.mark.skipif( + 'gpax' not in AVAILABLE_DIAL_BACKENDS, + reason='gpax not installed', + ), + ), ], ) def test_random_points(backend): @@ -590,7 +733,13 @@ def test_random_points(backend): ('backend'), [ ('sklearn'), - # ('gpax'), + # pytest.param( + # 'gpax', + # marks=pytest.mark.skipif( + # 'gpax' not in AVAILABLE_DIAL_BACKENDS, + # reason='gpax not installed', + # ), + # ), ], ) def test_hypercube_multiple_points(backend): @@ -618,7 +767,7 @@ def test_hypercube_multiple_points(backend): ], [2.11126987e01, 2.96625069e01, 2.11126987e01], ), - # ( + # pytest.param( # 'gpax', # [ # 76.99987768175089, @@ -628,6 +777,7 @@ def test_hypercube_multiple_points(backend): # 82.26569221517353, # ], # [3335.7290084812175, 3327.202331393974, 3335.7290084812175], + # marks=pytest.mark.skipif('gpax' not in AVAILABLE_DIAL_BACKENDS, reason='gpax not installed') # ), ], ) @@ -639,6 +789,33 @@ def test_surrogate(backend, expected_means, expected_stddevs): assert stddevs[1:4] == pytest.approx(expected_stddevs) +@pytest.mark.parametrize( + ('backend', 'expected_means', 'expected_stddevs'), + [ + ( + 'sklearn', + [-1.0, -0.65, 0.0, 0.65, 1.0], + [1e-2, 0.42, 0.59, 0.42, 1e-5], + ), + pytest.param( + 'sable', + [-1.0, -0.56, 0.0, 0.56, 1.0], + [1e-2, 0.29, 0.34, 0.29, 1e-5], + marks=pytest.mark.skipif( + 'sable' not in AVAILABLE_DIAL_BACKENDS, + reason='sable not installed', + ), + ), + ], +) +def test_surrogate_heteroscedastic(backend, expected_means, expected_stddevs): + data = prediction_1D_heteroscedastic(backend) + model = core.train_model(data) + means, stddevs, _ = core.get_surrogate_values(data, model) + assert means == pytest.approx(expected_means, rel=0.01, abs=1e-4) + assert stddevs == pytest.approx(expected_stddevs, rel=0.01, abs=1e-4) + + @pytest.mark.parametrize( ('backend'), [ diff --git a/uv.lock b/uv.lock index 0e595b0..89d216d 100644 --- a/uv.lock +++ b/uv.lock @@ 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