diff --git a/.travis.yml b/.travis.yml index 6ead6ab0ad..86ea40d6ec 100644 --- a/.travis.yml +++ b/.travis.yml @@ -9,9 +9,10 @@ matrix: - name: "GPyTorch + PyTorch stable (Python 3.6)" python: "3.6" install: - - pip install -q cython numpy - # TODO: Remove when locked to upcoming GPyTorch release 0.3.2 - - pip install -q git+https://github.com/cornellius-gp/gpytorch.git@8f0d338d42222689f0106f5f29e62ef4777a392f + # TODO: Remove once GPyTorch 0.3.2 is released and marked a dep. of botorch + - pip install -q git+https://github.com/cornellius-gp/gpytorch.git@32911e3b51901917c0f14101972581c1295b5edb + # TODO: Remove once PyTorch 1.1 is released and marked a dep. of botorch + - pip install -q torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html - pip install -q -e .[test] script: - pytest -ra --cov=. --cov-report term-missing @@ -22,9 +23,10 @@ matrix: python: "3.7" dist: xenial install: - - pip install -q cython numpy - # TODO: Remove when locked to upcoming GPyTorch release 0.3.2 - - pip install -q git+https://github.com/cornellius-gp/gpytorch.git@8f0d338d42222689f0106f5f29e62ef4777a392f + # TODO: Remove once GPyTorch 0.3.2 is released and marked a dep. of botorch + - pip install -q git+https://github.com/cornellius-gp/gpytorch.git@32911e3b51901917c0f14101972581c1295b5edb + # TODO: Remove once PyTorch 1.1 is released and marked a dep. of botorch + - pip install -q torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html - pip install -q -e .[test] script: - pytest -ra --cov=. --cov-report term-missing @@ -32,9 +34,10 @@ matrix: - name: "GPyTorch + PyTorch latest" python: "3.6" install: - - pip install -q cython numpy - - pip install -q torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html; + # TODO: Fix gpytorch installation to work with torch nightly in pip, + # so we don't have to reinstall torch nightly after installing stable - pip install -q git+https://github.com/cornellius-gp/gpytorch.git + - pip install -q torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html - pip install -q -e .[test] script: - pytest -ra --cov=. --cov-report term-missing @@ -57,9 +60,10 @@ matrix: - name: "Docs: Sphinx (Python 3.6)" python: "3.6" install: - - pip install -q cython numpy - # TODO: Remove when locked to upcoming GPyTorch release 0.3.2 - - pip install -q git+https://github.com/cornellius-gp/gpytorch.git@8f0d338d42222689f0106f5f29e62ef4777a392f + # TODO: Remove once GPyTorch 0.3.2 is released and marked a dep. of botorch + - pip install -q git+https://github.com/cornellius-gp/gpytorch.git@32911e3b51901917c0f14101972581c1295b5edb + # TODO: Remove once PyTorch 1.1 is released and marked a dep. of botorch + - pip install -q torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html - pip install -q -e .[dev] script: # warnings treated as errors diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index d56a13e392..7d24b93e7b 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,11 +1,23 @@ -# Contributing to botorch -We want to make contributing to botorch is as easy and transparent as possible. +# Contributing to BoTorch +We want to make contributing to BoTorch is as easy and transparent as possible. + + +## Development installation + +To get the development installation with all the necessary dependencies for +linting, testing, and building the documentation, run the following: +```bash +git clone https://github.com/facebookexternal/botorch.git +cd botorch +pip install -e .[dev] +``` + ## Our Development Process #### Code Style -botorch uses the [black](https://github.com/ambv/black) code formatter to +BoTorch uses the [black](https://github.com/ambv/black) code formatter to enforce a common code style across the code base. black is installed easily via pip using `pip install black`, and run locally by calling ```bash @@ -20,7 +32,7 @@ Travis will fail on your PR if it does not adhere to the black formatting style. #### Type Hints -botorch is fully typed using python 3.6+ +BoTorch is fully typed using python 3.6+ [type hints](https://www.python.org/dev/peps/pep-0484/). We expect any contributions to also use proper type annotations. While we currently do not enforce full consistency of these in Travis (type checkers can @@ -40,7 +52,7 @@ python -m unittest #### Documentation -botorch's website is also open source, and is part of this very repository (the +BoTorch's website is also open source, and is part of this very repository (the code can be found in the [website](../website/) folder). It is built using [Docusaurus](https://docusaurus.io/), and consists of three main elements: @@ -89,5 +101,5 @@ outlined on that page and do not file a public issue. ## License -By contributing to botorch, you agree that your contributions will be licensed +By contributing to BoTorch, you agree that your contributions will be licensed under the LICENSE file in the root directory of this source tree. diff --git a/README.md b/README.md index c626bf5e96..8ce9b304b3 100644 --- a/README.md +++ b/README.md @@ -1,47 +1,91 @@ -BoTorch Logo + + BoTorch Logo + [![Build Status]( https://travis-ci.com/facebookexternal/botorch.svg?token=esFvpzSw7sLSsfe1PAr1&branch=master )](https://travis-ci.com/facebookexternal/botorch) -BoTorch is a library for Bayesian Optimization in PyTorch. +BoTorch is a library for Bayesian Optimization built on PyTorch. -It is currently an alpha version under active development - be warned! +*BoTorch is currently in alpha and under active development - be warned*! -## Installation +### Why BoTorch +BoTorch +* Provides a modular and easily extensible interface for composing Bayesian + optimization primitives, including probabilistic models, acquisition functions, + and optimizers. +* Harnesses the power of PyTorch, including auto-differentiation, native support + for highly parallelized modern hardware (e.g. GPUs) using device-agnostic code, + and a dynamic computation graph. +* Supports Monte Carlo-based acquisition functions via the + [reparameterization trick](https://arxiv.org/abs/1312.6114), which makes it + straightforward to implement new ideas without having to impose restrictive + assumptions about the underlying model. +* Enables seamless integration with deep and/or convolutional architectures in PyTorch. +* Has first-class support for state-of-the art probabilistic models in + [GPyTorch](http://www.gpytorch.ai/), including support for multi-task Gaussian + Processes (GPs) deep kernel learning, deep GPs, and approximate inference. -##### Setup Requirements (TODO: Remove once we can use torch Sobol) -The following are required to run the setup: +### Target Audience -- Python >= 3.6 -- numpy -- cython +The primary audience for hands-on use of BoTorch are researchers and +sophisticated practitioners in Bayesian Optimization and AI. + +We recommend using BoTorch as a low-level API for implementing new algorithms +for [Ax](https://github.com/facebook/Ax). Ax has been designed to be +an easy-to-use platform for end-users, which at the same time is flexible enough +for Bayesian Optimization researchers to plug into for handling of feature +transformations, (meta-)data management, storage, etc. +We recommend that end-users who are not actively doing research on Bayesian +Optimization simply use Ax. -##### Installation Requirements -- PyTorch >= 1.0.1 -- gpytorch >= 0.3.0 +## Installation + +#### Installation Requirements + +- Python >= 3.6 +- PyTorch nightly (**TODO:** peg to PyTorch 1.1 once released) +- gpytorch >= 0.3.1 (**TODO:** peg to GPyTorch 0.3.2 once released) - scipy -**Important:** -You will want to have you PyTorch build link against **MKL** (the non-optimized -version of botorch can be up to an order of magnitude slower). Setting this up -manually can be tricky - to make sure this works please use the Anaconda -installation instructions on https://pytorch.org/. +**Important note for MacOS users:** +* You will want to make sure your PyTorch build is linked against MKL (the + non-optimized version of BoTorch can be up to an order of magnitude slower in + some settings). Setting this up manually on MacOS can be tricky - to ensure + this works properly please follow the + [PyTorch installation instructions](https://pytorch.org/get-started/locally/). +* If you need CUDA on MacOS, you will need to build PyTorch from source. Please + consult the PyTorch installation instructions above. -### Install botorch +#### Installing BoTorch -To run the botorch setup, you'll need cython (**TODO:** Remove) +The latest release of BoTorch is easily installed using either pip or conda: ```bash -pip install cython +pip install botorch ``` -We recommend installing botorch using pip via ssh: +**TODO: Conda install** + + +If you'd like to try our bleeding edge features (and don't mind running into an +occasional bug here or there), you can install the latest master from GitHub +(this will also require installing the current GPyTorch master):: +```bash +pip install git+https://github.com/cornellius-gp/gpytorch.git +pip install git+https://github.com/facebookexternal/botorch.git +``` + + +#### Installing BoTorch from the private repo **TODO: REMOVE** + +BoTorch is easily installed using pip: ```bash pip install git+ssh://git@github.com/facebookexternal/botorch.git ``` @@ -63,14 +107,9 @@ To customize the installation, you can also run the following instead: -## Installation using conda - -**TODO: conda install is unsupported until the repo is public** - - ## Contributing See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out. ## License -botorch is MIT licensed, as found in the LICENSE file. +BoTorch is MIT licensed, as found in the LICENSE file. diff --git a/botorch/__init__.py b/botorch/__init__.py index 71bbf95461..e85b64ca93 100644 --- a/botorch/__init__.py +++ b/botorch/__init__.py @@ -1,5 +1,8 @@ #!/usr/bin/env python3 +import os +import re + from . import acquisition, exceptions, models, optim, posteriors, test_functions from .cross_validation import batch_cross_validation from .fit import fit_gpytorch_model @@ -7,6 +10,11 @@ from .utils import manual_seed +# get version string from setup.py +with open(os.path.join(os.path.dirname(__file__), os.pardir, "setup.py"), "r") as f: + __version__ = re.search(r"version=['\"]([^'\"]*)['\"]", f.read(), re.M).group(1) + + __all__ = [ "acquisition", "batch_cross_validation", diff --git a/botorch/acquisition/sampler.py b/botorch/acquisition/sampler.py index 472a785532..5fb66ad201 100644 --- a/botorch/acquisition/sampler.py +++ b/botorch/acquisition/sampler.py @@ -11,10 +11,10 @@ import torch from torch import Tensor from torch.nn import Module +from torch.quasirandom import SobolEngine from ..exceptions import UnsupportedError from ..posteriors import Posterior -from ..qmc.sobol import SobolEngine from ..utils.sampling import draw_sobol_normal_samples, manual_seed diff --git a/botorch/qmc/__init__.py b/botorch/qmc/__init__.py index 298e7ec376..70eb934cae 100644 --- a/botorch/qmc/__init__.py +++ b/botorch/qmc/__init__.py @@ -1,7 +1,8 @@ #! /usr/bin/env python3 +from torch.quasirandom import SobolEngine + from .normal import MultivariateNormalQMCEngine, NormalQMCEngine -from .sobol import SobolEngine __all__ = ["MultivariateNormalQMCEngine", "NormalQMCEngine", "SobolEngine"] diff --git a/botorch/qmc/normal.py b/botorch/qmc/normal.py index 5744ec3e04..959afe6c60 100644 --- a/botorch/qmc/normal.py +++ b/botorch/qmc/normal.py @@ -11,12 +11,11 @@ """ import math -from typing import List, Optional, Union +from typing import Optional -import numpy as np -from scipy.stats import norm - -from .sobol import SobolEngine +import torch +from torch import Tensor +from torch.quasirandom import SobolEngine class NormalQMCEngine: @@ -50,32 +49,42 @@ def __init__( else: # to apply Box-Muller, we need an even number of dimensions sobol_dim = 2 * math.ceil(d / 2) - self._sobol_engine = SobolEngine(dimen=sobol_dim, scramble=True, seed=seed) + self._sobol_engine = SobolEngine(dimension=sobol_dim, scramble=True, seed=seed) - def draw(self, n: int = 1) -> np.ndarray: + def draw( + self, n: int = 1, out: Optional[Tensor] = None, dtype: torch.dtype = torch.float + ) -> Optional[Tensor]: r"""Draw n qMC samples from the standard Normal. Args: - n: The number of samples. + n: The number of samples to draw. + out: An option output tensor. If provided, draws are put into this + tensor, and the function returns None. + dtype: The desired torch data type (ignored if `out` is provided). Returns: - The samples as a numpy array. + A `n x d` tensor of samples if `out=None`, and `None` otherwise. """ # get base samples - samples = self._sobol_engine.draw(n) + samples = self._sobol_engine.draw(n, dtype=dtype) if self._inv_transform: # apply inverse transform (values to close to 0/1 result in inf values) - return norm.ppf(0.5 + (1 - 1e-10) * (samples - 0.5)) + v = 0.5 + (1 - 1e-10) * (samples - 0.5) + samples_tf = torch.erfinv(2 * v - 1) * math.sqrt(2) else: # apply Box-Muller transform (note: [1] indexes starting from 1) - even = np.arange(0, samples.shape[-1], 2) - Rs = np.sqrt(-2 * np.log(samples[:, even])) + even = torch.arange(0, samples.shape[-1], 2) + Rs = (-2 * torch.log(samples[:, even])).sqrt() thetas = 2 * math.pi * samples[:, 1 + even] - cos = np.cos(thetas) - sin = np.sin(thetas) - transf_samples = np.stack([Rs * cos, Rs * sin], -1).reshape(n, -1) + cos = torch.cos(thetas) + sin = torch.sin(thetas) + samples_tf = torch.stack([Rs * cos, Rs * sin], -1).reshape(n, -1) # make sure we only return the number of dimension requested - return transf_samples[:, : self._d] + samples_tf = samples_tf[:, : self._d] + if out is None: + return samples_tf + else: + out.copy_(samples_tf) class MultivariateNormalQMCEngine: @@ -94,8 +103,8 @@ class MultivariateNormalQMCEngine: def __init__( self, - mean: Union[float, List[float], np.ndarray], - cov: Union[float, List[List[float]], np.ndarray], + mean: Tensor, + cov: Tensor, seed: Optional[int] = None, inv_transform: bool = False, ) -> None: @@ -108,14 +117,12 @@ def __init__( underlying SobolEngine. inv_transform: If True, use inverse transform instead of Box-Muller. """ - # check for square/symmetric cov matrix and mean vector has the same d - mean = np.array(mean, copy=False, ndmin=1) - cov = np.array(cov, copy=False, ndmin=2) + # validate inputs if not cov.shape[0] == cov.shape[1]: raise ValueError("Covariance matrix is not square.") if not mean.shape[0] == cov.shape[0]: raise ValueError("Dimension mismatch between mean and covariance.") - if not np.allclose(cov, cov.transpose()): + if not torch.allclose(cov, cov.transpose(-1, -2)): raise ValueError("Covariance matrix is not symmetric.") self._mean = mean self._normal_engine = NormalQMCEngine( @@ -123,23 +130,33 @@ def __init__( ) # compute Cholesky decomp; if it fails, do the eigendecomposition try: - self._corr_matrix = np.linalg.cholesky(cov).transpose() - except np.linalg.LinAlgError: - eigval, eigvec = np.linalg.eigh(cov) - if not np.all(eigval >= -1.0e-8): + self._corr_matrix = torch.cholesky(cov).transpose(-1, -2) + except RuntimeError: + eigval, eigvec = torch.symeig(cov, eigenvectors=True) + if not torch.all(eigval >= -1e-8): raise ValueError("Covariance matrix not PSD.") - eigval = np.clip(eigval, 0.0, None) - self._corr_matrix = (eigvec * np.sqrt(eigval)).transpose() + eigval_root = eigval.clamp_min(0.0).sqrt() + self._corr_matrix = (eigvec * eigval_root).transpose(-1, -2) - def draw(self, n: int = 1) -> np.ndarray: + def draw(self, n: int = 1, out: Optional[Tensor] = None) -> Optional[Tensor]: r"""Draw n qMC samples from the multivariate Normal. Args: - n: The number of samples. + n: The number of samples to draw. + out: An option output tensor. If provided, draws are put into this + tensor, and the function returns None. + dtype: The desired torch data type (ignored if out provided). Returns: - The samples as a numpy array. + A `n x d` tensor of samples if `out=None`, and `None` otherwise. """ - base_samples = self._normal_engine.draw(n) - qmc_samples = base_samples @ self._corr_matrix + self._mean - return qmc_samples + dtype = out.dtype if out is not None else self._mean.dtype + device = out.device if out is not None else self._mean.device + base_samples = self._normal_engine.draw(n, dtype=dtype).to(device=device) + corr_mat = self._corr_matrix.to(dtype=dtype, device=device) + mean = self._mean.to(dtype=dtype, device=device) + qmc_samples = base_samples @ corr_mat + mean + if out is None: + return qmc_samples + else: + out.copy_(qmc_samples) diff --git a/botorch/qmc/sobol.pyx b/botorch/qmc/sobol.pyx deleted file mode 100644 index d0ba66665e..0000000000 --- a/botorch/qmc/sobol.pyx +++ /dev/null @@ -1,1690 +0,0 @@ -#!/usr/bin/env python3 - -cimport cython -cimport numpy as cnp -import numpy as np - -cdef int MAXDIM = 1111 # max number of dimensions -cdef int MAXDEG = 13 # max polynomial degree -cdef int MAXBIT = 30 # max number of bits -cdef int LARGEST_NUMBER = 2 ** MAXBIT # largest possible integer -cdef float RECIPD = 1.0 / LARGEST_NUMBER # normalization constant - -cdef int poly[1111] - -poly = [ - 1, 3, 7, 11, 13, 19, 25, 37, 59, 47, 61, 55, 41, 67, 97, 91, 109, - 103, 115, 131, 193, 137, 145, 143, 241, 157, 185, 167, 229, 171, - 213, 191, 253, 203, 211, 239, 247, 285, 369, 299, 301, 333, 351, - 355, 357, 361, 391, 397, 425, 451, 463, 487, 501, 529, 539, 545, - 557, 563, 601, 607, 617, 623, 631, 637, 647, 661, 675, 677, 687, - 695, 701, 719, 721, 731, 757, 761, 787, 789, 799, 803, 817, 827, - 847, 859, 865, 875, 877, 883, 895, 901, 911, 949, 953, 967, 971, - 973, 981, 985, 995, 1001, 1019, 1033, 1051, 1063, 1069, 1125, 1135, - 1153, 1163, 1221, 1239, 1255, 1267, 1279, 1293, 1305, 1315, 1329, - 1341, 1347, 1367, 1387, 1413, 1423, 1431, 1441, 1479, 1509, 1527, - 1531, 1555, 1557, 1573, 1591, 1603, 1615, 1627, 1657, 1663, 1673, - 1717, 1729, 1747, 1759, 1789, 1815, 1821, 1825, 1849, 1863, 1869, - 1877, 1881, 1891, 1917, 1933, 1939, 1969, 2011, 2035, 2041, 2053, - 2071, 2091, 2093, 2119, 2147, 2149, 2161, 2171, 2189, 2197, 2207, - 2217, 2225, 2255, 2257, 2273, 2279, 2283, 2293, 2317, 2323, 2341, - 2345, 2363, 2365, 2373, 2377, 2385, 2395, 2419, 2421, 2431, 2435, - 2447, 2475, 2477, 2489, 2503, 2521, 2533, 2551, 2561, 2567, 2579, - 2581, 2601, 2633, 2657, 2669, 2681, 2687, 2693, 2705, 2717, 2727, - 2731, 2739, 2741, 2773, 2783, 2793, 2799, 2801, 2811, 2819, 2825, - 2833, 2867, 2879, 2881, 2891, 2905, 2911, 2917, 2927, 2941, 2951, - 2955, 2963, 2965, 2991, 2999, 3005, 3017, 3035, 3037, 3047, 3053, - 3083, 3085, 3097, 3103, 3159, 3169, 3179, 3187, 3205, 3209, 3223, - 3227, 3229, 3251, 3263, 3271, 3277, 3283, 3285, 3299, 3305, 3319, - 3331, 3343, 3357, 3367, 3373, 3393, 3399, 3413, 3417, 3427, 3439, - 3441, 3475, 3487, 3497, 3515, 3517, 3529, 3543, 3547, 3553, 3559, - 3573, 3589, 3613, 3617, 3623, 3627, 3635, 3641, 3655, 3659, 3669, - 3679, 3697, 3707, 3709, 3713, 3731, 3743, 3747, 3771, 3791, 3805, - 3827, 3833, 3851, 3865, 3889, 3895, 3933, 3947, 3949, 3957, 3971, - 3985, 3991, 3995, 4007, 4013, 4021, 4045, 4051, 4069, 4073, 4179, - 4201, 4219, 4221, 4249, 4305, 4331, 4359, 4383, 4387, 4411, 4431, - 4439, 4449, 4459, 4485, 4531, 4569, 4575, 4621, 4663, 4669, 4711, - 4723, 4735, 4793, 4801, 4811, 4879, 4893, 4897, 4921, 4927, 4941, - 4977, 5017, 5027, 5033, 5127, 5169, 5175, 5199, 5213, 5223, 5237, - 5287, 5293, 5331, 5391, 5405, 5453, 5523, 5573, 5591, 5597, 5611, - 5641, 5703, 5717, 5721, 5797, 5821, 5909, 5913, 5955, 5957, 6005, - 6025, 6061, 6067, 6079, 6081, 6231, 6237, 6289, 6295, 6329, 6383, - 6427, 6453, 6465, 6501, 6523, 6539, 6577, 6589, 6601, 6607, 6631, - 6683, 6699, 6707, 6761, 6795, 6865, 6881, 6901, 6923, 6931, 6943, - 6999, 7057, 7079, 7103, 7105, 7123, 7173, 7185, 7191, 7207, 7245, - 7303, 7327, 7333, 7355, 7365, 7369, 7375, 7411, 7431, 7459, 7491, - 7505, 7515, 7541, 7557, 7561, 7701, 7705, 7727, 7749, 7761, 7783, - 7795, 7823, 7907, 7953, 7963, 7975, 8049, 8089, 8123, 8125, 8137, - 8219, 8231, 8245, 8275, 8293, 8303, 8331, 8333, 8351, 8357, 8367, - 8379, 8381, 8387, 8393, 8417, 8435, 8461, 8469, 8489, 8495, 8507, - 8515, 8551, 8555, 8569, 8585, 8599, 8605, 8639, 8641, 8647, 8653, - 8671, 8675, 8689, 8699, 8729, 8741, 8759, 8765, 8771, 8795, 8797, - 8825, 8831, 8841, 8855, 8859, 8883, 8895, 8909, 8943, 8951, 8955, - 8965, 8999, 9003, 9031, 9045, 9049, 9071, 9073, 9085, 9095, 9101, - 9109, 9123, 9129, 9137, 9143, 9147, 9185, 9197, 9209, 9227, 9235, - 9247, 9253, 9257, 9277, 9297, 9303, 9313, 9325, 9343, 9347, 9371, - 9373, 9397, 9407, 9409, 9415, 9419, 9443, 9481, 9495, 9501, 9505, - 9517, 9529, 9555, 9557, 9571, 9585, 9591, 9607, 9611, 9621, 9625, - 9631, 9647, 9661, 9669, 9679, 9687, 9707, 9731, 9733, 9745, 9773, - 9791, 9803, 9811, 9817, 9833, 9847, 9851, 9863, 9875, 9881, 9905, - 9911, 9917, 9923, 9963, 9973, 10003, 10025, 10043, 10063, 10071, - 10077, 10091, 10099, 10105, 10115, 10129, 10145, 10169, 10183, - 10187, 10207, 10223, 10225, 10247, 10265, 10271, 10275, 10289, - 10299, 10301, 10309, 10343, 10357, 10373, 10411, 10413, 10431, - 10445, 10453, 10463, 10467, 10473, 10491, 10505, 10511, 10513, - 10523, 10539, 10549, 10559, 10561, 10571, 10581, 10615, 10621, - 10625, 10643, 10655, 10671, 10679, 10685, 10691, 10711, 10739, - 10741, 10755, 10767, 10781, 10785, 10803, 10805, 10829, 10857, - 10863, 10865, 10875, 10877, 10917, 10921, 10929, 10949, 10967, - 10971, 10987, 10995, 11009, 11029, 11043, 11045, 11055, 11063, - 11075, 11081, 11117, 11135, 11141, 11159, 11163, 11181, 11187, - 11225, 11237, 11261, 11279, 11297, 11307, 11309, 11327, 11329, - 11341, 11377, 11403, 11405, 11413, 11427, 11439, 11453, 11461, - 11473, 11479, 11489, 11495, 11499, 11533, 11545, 11561, 11567, - 11575, 11579, 11589, 11611, 11623, 11637, 11657, 11663, 11687, - 11691, 11701, 11747, 11761, 11773, 11783, 11795, 11797, 11817, - 11849, 11855, 11867, 11869, 11873, 11883, 11919, 11921, 11927, - 11933, 11947, 11955, 11961, 11999, 12027, 12029, 12037, 12041, - 12049, 12055, 12095, 12097, 12107, 12109, 12121, 12127, 12133, - 12137, 12181, 12197, 12207, 12209, 12239, 12253, 12263, 12269, - 12277, 12287, 12295, 12309, 12313, 12335, 12361, 12367, 12391, - 12409, 12415, 12433, 12449, 12469, 12479, 12481, 12499, 12505, - 12517, 12527, 12549, 12559, 12597, 12615, 12621, 12639, 12643, - 12657, 12667, 12707, 12713, 12727, 12741, 12745, 12763, 12769, - 12779, 12781, 12787, 12799, 12809, 12815, 12829, 12839, 12857, - 12875, 12883, 12889, 12901, 12929, 12947, 12953, 12959, 12969, - 12983, 12987, 12995, 13015, 13019, 13031, 13063, 13077, 13103, - 13137, 13149, 13173, 13207, 13211, 13227, 13241, 13249, 13255, - 13269, 13283, 13285, 13303, 13307, 13321, 13339, 13351, 13377, - 13389, 13407, 13417, 13431, 13435, 13447, 13459, 13465, 13477, - 13501, 13513, 13531, 13543, 13561, 13581, 13599, 13605, 13617, - 13623, 13637, 13647, 13661, 13677, 13683, 13695, 13725, 13729, - 13753, 13773, 13781, 13785, 13795, 13801, 13807, 13825, 13835, - 13855, 13861, 13871, 13883, 13897, 13905, 13915, 13939, 13941, - 13969, 13979, 13981, 13997, 14027, 14035, 14037, 14051, 14063, - 14085, 14095, 14107, 14113, 14125, 14137, 14145, 14151, 14163, - 14193, 14199, 14219, 14229, 14233, 14243, 14277, 14287, 14289, - 14295, 14301, 14305, 14323, 14339, 14341, 14359, 14365, 14375, - 14387, 14411, 14425, 14441, 14449, 14499, 14513, 14523, 14537, - 14543, 14561, 14579, 14585, 14593, 14599, 14603, 14611, 14641, - 14671, 14695, 14701, 14723, 14725, 14743, 14753, 14759, 14765, - 14795, 14797, 14803, 14831, 14839, 14845, 14855, 14889, 14895, - 14909, 14929, 14941, 14945, 14951, 14963, 14965, 14985, 15033, - 15039, 15053, 15059, 15061, 15071, 15077, 15081, 15099, 15121, - 15147, 15149, 15157, 15167, 15187, 15193, 15203, 15205, 15215, - 15217, 15223, 15243, 15257, 15269, 15273, 15287, 15291, 15313, - 15335, 15347, 15359, 15373, 15379, 15381, 15391, 15395, 15397, - 15419, 15439, 15453, 15469, 15491, 15503, 15517, 15527, 15531, - 15545, 15559, 15593, 15611, 15613, 15619, 15639, 15643, 15649, - 15661, 15667, 15669, 15681, 15693, 15717, 15721, 15741, 15745, - 15765, 15793, 15799, 15811, 15825, 15835, 15847, 15851, 15865, - 15877, 15881, 15887, 15899, 15915, 15935, 15937, 15955, 15973, - 15977, 16011, 16035, 16061, 16069, 16087, 16093, 16097, 16121, - 16141, 16153, 16159, 16165, 16183, 16189, 16195, 16197, 16201, - 16209, 16215, 16225, 16259, 16265, 16273, 16299, 16309, 16355, - 16375, 16381, -] - -cdef int vinit[1111][13] - -vinit = [ - [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 3, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 1, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 1, 3, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 3, 3, 9, 9, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 3, 7, 13, 3, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 1, 5, 11, 27, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 3, 5, 1, 15, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 1, 7, 3, 29, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 3, 7, 7, 21, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 1, 1, 9, 23, 37, 0, 0, 0, 0, 0, 0, 0], - [1, 3, 3, 5, 19, 33, 0, 0, 0, 0, 0, 0, 0], - [1, 1, 3, 13, 11, 7, 0, 0, 0, 0, 0, 0, 0], - [1, 1, 7, 13, 25, 5, 0, 0, 0, 0, 0, 0, 0], - [1, 3, 5, 11, 7, 11, 0, 0, 0, 0, 0, 0, 0], - [1, 1, 1, 3, 13, 39, 0, 0, 0, 0, 0, 0, 0], - [1, 3, 1, 15, 17, 63, 13, 0, 0, 0, 0, 0, 0], - [1, 1, 5, 5, 1, 59, 33, 0, 0, 0, 0, 0, 0], - [1, 3, 3, 3, 25, 17, 115, 0, 0, 0, 0, 0, 0], - [1, 1, 7, 15, 29, 15, 41, 0, 0, 0, 0, 0, 0], - [1, 3, 1, 7, 3, 23, 79, 0, 0, 0, 0, 0, 0], - [1, 3, 7, 9, 31, 29, 17, 0, 0, 0, 0, 0, 0], - [1, 1, 5, 13, 11, 3, 29, 0, 0, 0, 0, 0, 0], - [1, 1, 1, 9, 5, 21, 119, 0, 0, 0, 0, 0, 0], - [1, 1, 3, 1, 23, 13, 75, 0, 0, 0, 0, 0, 0], - [1, 3, 7, 11, 27, 31, 73, 0, 0, 0, 0, 0, 0], - [1, 1, 7, 7, 19, 25, 105, 0, 0, 0, 0, 0, 0], - [1, 3, 1, 5, 21, 9, 7, 0, 0, 0, 0, 0, 0], - [1, 1, 1, 15, 5, 49, 59, 0, 0, 0, 0, 0, 0], - [1, 3, 1, 1, 1, 33, 65, 0, 0, 0, 0, 0, 0], - [1, 3, 5, 15, 17, 19, 21, 0, 0, 0, 0, 0, 0], - [1, 1, 7, 11, 13, 29, 3, 0, 0, 0, 0, 0, 0], - [1, 3, 7, 5, 7, 11, 113, 0, 0, 0, 0, 0, 0], - [1, 1, 5, 11, 15, 19, 61, 0, 0, 0, 0, 0, 0], - [1, 1, 1, 1, 9, 27, 89, 7, 0, 0, 0, 0, 0], - [1, 1, 3, 7, 31, 15, 45, 23, 0, 0, 0, 0, 0], - [1, 3, 3, 9, 25, 25, 107, 39, 0, 0, 0, 0, 0], - [1, 1, 7, 7, 3, 63, 21, 217, 0, 0, 0, 0, 0], - [1, 3, 5, 7, 5, 55, 71, 141, 0, 0, 0, 0, 0], - [1, 1, 5, 1, 23, 17, 79, 27, 0, 0, 0, 0, 0], - [1, 1, 5, 15, 7, 63, 19, 53, 0, 0, 0, 0, 0], - [1, 1, 3, 15, 3, 49, 71, 181, 0, 0, 0, 0, 0], - [1, 3, 3, 15, 17, 19, 61, 169, 0, 0, 0, 0, 0], - [1, 3, 3, 13, 23, 41, 41, 35, 0, 0, 0, 0, 0], - [1, 1, 1, 3, 3, 59, 57, 15, 0, 0, 0, 0, 0], - [1, 3, 1, 3, 3, 3, 121, 207, 0, 0, 0, 0, 0], - [1, 3, 5, 15, 21, 57, 87, 45, 0, 0, 0, 0, 0], - [1, 1, 1, 5, 25, 33, 119, 247, 0, 0, 0, 0, 0], - [1, 1, 1, 9, 25, 49, 55, 185, 0, 0, 0, 0, 0], - [1, 3, 5, 7, 23, 53, 85, 117, 0, 0, 0, 0, 0], - [1, 3, 3, 13, 11, 57, 121, 41, 235, 0, 0, 0, 0], - [1, 1, 3, 3, 19, 57, 119, 81, 307, 0, 0, 0, 0], - [1, 3, 3, 7, 3, 39, 11, 223, 495, 0, 0, 0, 0], - [1, 3, 3, 5, 11, 21, 23, 151, 417, 0, 0, 0, 0], - [1, 3, 1, 11, 31, 7, 61, 81, 57, 0, 0, 0, 0], - [1, 1, 3, 9, 7, 53, 11, 189, 151, 0, 0, 0, 0], - [1, 3, 7, 1, 9, 9, 35, 61, 19, 0, 0, 0, 0], - [1, 1, 5, 9, 5, 55, 33, 95, 119, 0, 0, 0, 0], - [1, 3, 7, 1, 17, 15, 43, 185, 375, 0, 0, 0, 0], - [1, 1, 3, 5, 23, 59, 107, 23, 451, 0, 0, 0, 0], - [1, 1, 7, 7, 17, 19, 113, 73, 55, 0, 0, 0, 0], - [1, 3, 1, 13, 17, 49, 101, 113, 449, 0, 0, 0, 0], - [1, 3, 3, 9, 25, 31, 29, 239, 501, 0, 0, 0, 0], - [1, 1, 3, 9, 13, 3, 87, 85, 53, 0, 0, 0, 0], - [1, 1, 5, 1, 11, 39, 119, 9, 185, 0, 0, 0, 0], - [1, 1, 1, 7, 31, 5, 97, 201, 317, 0, 0, 0, 0], - [1, 1, 3, 3, 27, 5, 29, 83, 17, 0, 0, 0, 0], - [1, 3, 5, 5, 19, 41, 17, 53, 21, 0, 0, 0, 0], - [1, 1, 5, 1, 17, 9, 89, 183, 487, 0, 0, 0, 0], - [1, 1, 7, 11, 23, 19, 5, 203, 13, 0, 0, 0, 0], - [1, 3, 7, 11, 7, 9, 127, 91, 347, 0, 0, 0, 0], - [1, 1, 7, 13, 5, 57, 89, 149, 393, 0, 0, 0, 0], - [1, 1, 1, 7, 11, 25, 119, 101, 15, 0, 0, 0, 0], - [1, 1, 1, 7, 19, 1, 117, 13, 391, 0, 0, 0, 0], - [1, 3, 3, 9, 19, 15, 103, 111, 307, 0, 0, 0, 0], - [1, 3, 3, 9, 7, 51, 105, 239, 189, 0, 0, 0, 0], - [1, 1, 1, 1, 13, 11, 41, 3, 381, 0, 0, 0, 0], - [1, 3, 1, 1, 21, 19, 83, 205, 71, 0, 0, 0, 0], - [1, 3, 5, 3, 21, 61, 25, 253, 163, 0, 0, 0, 0], - [1, 1, 1, 9, 7, 53, 41, 247, 99, 0, 0, 0, 0], - [1, 3, 5, 15, 9, 29, 55, 121, 467, 0, 0, 0, 0], - [1, 3, 7, 1, 11, 19, 69, 189, 167, 0, 0, 0, 0], - [1, 3, 5, 5, 1, 11, 117, 169, 433, 0, 0, 0, 0], - [1, 1, 1, 13, 5, 9, 49, 179, 337, 0, 0, 0, 0], - [1, 3, 7, 1, 21, 21, 127, 197, 257, 0, 0, 0, 0], - [1, 3, 5, 9, 11, 19, 29, 175, 179, 0, 0, 0, 0], - [1, 3, 3, 9, 13, 43, 1, 217, 47, 0, 0, 0, 0], - [1, 1, 3, 9, 25, 13, 99, 249, 385, 0, 0, 0, 0], - [1, 3, 1, 9, 9, 13, 53, 195, 23, 0, 0, 0, 0], - [1, 3, 5, 9, 7, 41, 83, 95, 117, 0, 0, 0, 0], - [1, 1, 7, 13, 7, 25, 15, 63, 369, 0, 0, 0, 0], - [1, 3, 1, 11, 27, 31, 31, 19, 425, 0, 0, 0, 0], - [1, 3, 7, 3, 15, 9, 73, 7, 207, 0, 0, 0, 0], - [1, 3, 5, 5, 25, 11, 115, 5, 433, 0, 0, 0, 0], - [1, 1, 1, 11, 15, 19, 35, 75, 301, 0, 0, 0, 0], - [1, 3, 7, 11, 21, 5, 21, 217, 147, 0, 0, 0, 0], - [1, 1, 3, 13, 17, 53, 89, 245, 333, 0, 0, 0, 0], - [1, 3, 1, 5, 19, 37, 5, 111, 85, 0, 0, 0, 0], - [1, 1, 7, 3, 19, 7, 1, 189, 221, 519, 0, 0, 0], - [1, 1, 1, 15, 21, 51, 91, 165, 423, 307, 0, 0, 0], - [1, 3, 7, 1, 5, 45, 53, 169, 49, 931, 0, 0, 0], - [1, 3, 3, 11, 11, 7, 35, 141, 3, 1023, 0, 0, 0], - [1, 1, 3, 11, 3, 7, 95, 221, 43, 517, 0, 0, 0], - [1, 3, 5, 7, 5, 61, 83, 249, 229, 771, 0, 0, 0], - [1, 3, 7, 13, 29, 23, 19, 159, 227, 151, 0, 0, 0], - [1, 1, 3, 15, 31, 45, 85, 253, 201, 1023, 0, 0, 0], - [1, 1, 3, 11, 29, 7, 55, 207, 383, 539, 0, 0, 0], - [1, 1, 5, 13, 5, 59, 51, 249, 281, 725, 0, 0, 0], - [1, 3, 1, 9, 5, 41, 101, 219, 229, 45, 0, 0, 0], - [1, 3, 3, 11, 1, 1, 33, 23, 207, 927, 0, 0, 0], - [1, 1, 3, 15, 31, 29, 41, 49, 21, 707, 0, 0, 0], - [1, 3, 1, 15, 27, 61, 55, 127, 343, 29, 0, 0, 0], - [1, 3, 3, 13, 11, 37, 45, 237, 251, 125, 0, 0, 0], - [1, 1, 5, 3, 13, 27, 95, 5, 397, 371, 0, 0, 0], - [1, 3, 1, 15, 1, 47, 61, 25, 173, 275, 0, 0, 0], - [1, 1, 3, 7, 3, 15, 27, 177, 507, 279, 0, 0, 0], - [1, 1, 3, 9, 7, 31, 37, 37, 421, 817, 0, 0, 0], - [1, 3, 3, 11, 11, 35, 89, 103, 443, 389, 0, 0, 0], - [1, 3, 7, 13, 7, 31, 75, 65, 399, 453, 0, 0, 0], - [1, 3, 1, 11, 3, 17, 57, 167, 53, 989, 0, 0, 0], - [1, 1, 1, 9, 23, 51, 61, 81, 345, 1015, 0, 0, 0], - [1, 1, 7, 9, 13, 13, 15, 87, 77, 29, 0, 0, 0], - [1, 1, 3, 5, 31, 25, 117, 119, 385, 169, 0, 0, 0], - [1, 3, 1, 13, 17, 45, 15, 45, 317, 743, 0, 0, 0], - [1, 1, 3, 9, 1, 5, 21, 79, 155, 99, 0, 0, 0], - [1, 1, 7, 1, 27, 5, 27, 143, 187, 923, 0, 0, 0], - [1, 3, 5, 13, 11, 33, 25, 57, 269, 981, 0, 0, 0], - [1, 1, 5, 7, 25, 39, 27, 79, 501, 181, 0, 0, 0], - [1, 1, 7, 7, 1, 5, 123, 187, 19, 693, 0, 0, 0], - [1, 3, 5, 7, 23, 47, 39, 143, 169, 309, 0, 0, 0], - [1, 3, 5, 7, 29, 29, 109, 183, 235, 227, 0, 0, 0], - [1, 1, 3, 7, 17, 35, 93, 75, 415, 111, 0, 0, 0], - [1, 3, 1, 5, 25, 47, 51, 97, 61, 219, 0, 0, 0], - [1, 1, 3, 9, 7, 63, 21, 211, 247, 897, 0, 0, 0], - [1, 3, 3, 7, 25, 45, 91, 149, 183, 377, 0, 0, 0], - [1, 3, 3, 13, 27, 37, 109, 175, 5, 425, 0, 0, 0], - [1, 3, 1, 11, 17, 47, 107, 37, 257, 609, 0, 0, 0], - [1, 3, 3, 9, 13, 59, 45, 135, 401, 227, 0, 0, 0], - [1, 1, 3, 11, 17, 21, 15, 189, 451, 19, 0, 0, 0], - [1, 1, 7, 15, 23, 59, 93, 225, 95, 221, 0, 0, 0], - [1, 1, 3, 3, 5, 33, 127, 241, 455, 143, 0, 0, 0], - [1, 3, 3, 13, 17, 51, 3, 63, 49, 581, 0, 0, 0], - [1, 3, 1, 11, 5, 9, 53, 33, 489, 147, 0, 0, 0], - [1, 1, 7, 1, 13, 27, 81, 43, 75, 919, 0, 0, 0], - [1, 1, 5, 11, 11, 13, 79, 13, 459, 127, 0, 0, 0], - [1, 3, 1, 3, 21, 25, 107, 73, 377, 725, 0, 0, 0], - [1, 1, 7, 3, 5, 43, 79, 213, 87, 793, 0, 0, 0], - [1, 1, 7, 9, 11, 3, 87, 57, 463, 289, 0, 0, 0], - [1, 1, 5, 11, 5, 17, 35, 239, 155, 411, 0, 0, 0], - [1, 1, 7, 1, 9, 21, 109, 183, 233, 835, 0, 0, 0], - [1, 1, 5, 7, 31, 59, 73, 117, 115, 921, 0, 0, 0], - [1, 1, 1, 1, 19, 61, 35, 21, 429, 957, 0, 0, 0], - [1, 3, 3, 15, 17, 27, 83, 29, 211, 443, 0, 0, 0], - [1, 1, 1, 15, 9, 47, 107, 115, 419, 349, 0, 0, 0], - [1, 3, 7, 3, 9, 57, 1, 43, 143, 813, 0, 0, 0], - [1, 1, 3, 1, 27, 11, 51, 205, 487, 5, 0, 0, 0], - [1, 1, 7, 9, 21, 17, 7, 223, 195, 105, 0, 0, 0], - [1, 1, 3, 1, 15, 39, 59, 15, 209, 457, 0, 0, 0], - [1, 3, 5, 7, 15, 1, 33, 3, 461, 393, 7, 0, 0], - [1, 1, 7, 13, 1, 63, 115, 159, 193, 539, 2011, 0, 0], - [1, 3, 3, 11, 1, 21, 43, 51, 157, 101, 1001, 0, 0], - [1, 1, 1, 3, 29, 59, 111, 101, 193, 197, 49, 0, 0], - 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[1, 3, 5, 11, 19, 39, 103, 171, 451, 831, 895, 3073, 1947], - [1, 3, 7, 13, 17, 27, 23, 163, 311, 79, 233, 2837, 1635], - [1, 3, 7, 7, 11, 63, 125, 79, 441, 975, 759, 1567, 3963], - [1, 1, 1, 9, 25, 35, 91, 7, 47, 235, 1505, 3783, 397], - [1, 1, 5, 13, 7, 47, 31, 103, 455, 633, 677, 451, 969], - [1, 3, 7, 13, 13, 55, 91, 5, 47, 723, 1449, 2441, 4569], - [1, 3, 3, 13, 1, 17, 51, 119, 253, 297, 1573, 1181, 655], - [1, 1, 7, 15, 29, 17, 65, 155, 13, 589, 1297, 487, 6737], - [1, 1, 1, 9, 17, 17, 61, 75, 109, 317, 1821, 543, 2995], - [1, 3, 1, 5, 23, 3, 75, 11, 369, 679, 1691, 1201, 7235], - [1, 1, 3, 5, 15, 19, 69, 71, 347, 981, 791, 3735, 7713], - [1, 3, 5, 3, 7, 21, 107, 95, 11, 195, 289, 2517, 973], - [1, 3, 7, 3, 29, 13, 65, 17, 409, 399, 1187, 733, 4821], - [1, 3, 5, 3, 17, 49, 101, 13, 275, 1003, 867, 1535, 2377], - [1, 3, 3, 1, 13, 61, 59, 243, 63, 121, 1535, 2175, 1673], - [1, 3, 3, 3, 3, 39, 35, 207, 441, 501, 575, 3613, 1], - [1, 1, 3, 15, 17, 15, 15, 187, 15, 155, 183, 3019, 6541], -] - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef int bit_length(const int n): - cdef int bits = 0 - cdef int nloc = n - while nloc != 0: - nloc >>= 1 - bits += 1 - return bits - - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef int low_0_bit(const int x) nogil: - """Get the position of the right-most 0 bit for an integer. - - Examples: - >>> low_0_bit(0) - 1 - >>> low_0_bit(1) - 2 - >>> low_0_bit(2) - 1 - >>> low_0_bit(5) - 2 - >>> low_0_bit(7) - 4 - - Args: - x (int): an integer. - - Returns: - int: position of the right-most 0 bit. - - """ - cdef int z = x - cdef int i = 0 - while True: - i += 1 - if z % 2 == 0: - break - z = z // 2 - return i - - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef int ibits(const int x, const int pos, const int length) nogil: - """Extract a sequence of bits from the bit representation of an integer. - - Extract the sequence from position `pos` (inclusive) to `pos + length` - (not inclusive), leftwise. - - Examples: - >>> ibits(1, 0, 1) - 1 - >>> ibits(1, 1, 1) - 0 - >>> ibits(2, 0, 1) - 0 - >>> ibits(2, 0, 2) - 2 - >>> ibits(25, 1, 5) - 12 - - Args: - x (int): integer to convert to bit representation. - pos (int): starting position of sequence in bit representation of integer. - length (int): length of sequence (number of bits). - - Returns: - int: integer value corresponding to bit sequence. - - """ - return (x >> pos) & ((1 << length) - 1) - - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef void initialize_v(cnp.int_t[:, :] v, const int dimen): - cdef int d, i, j, k, m, p, newv, pow2 - - if dimen == 0: - return - - # first row of v is all 1s - for i in range(MAXBIT): - v[0, i] = 1 - - # Remaining rows of v (row 2 through dim, indexed by [1:dimen]) - for d in range(1, dimen): - p = poly[d] - m = bit_length(p) - 1 - - # First m elements of row d comes from vinit - for j in range(m): - v[d, j] = vinit[d][j] - - # Fill in remaining elements of V per Bratley and Fox, Section 2 - # @TODO: UPDATE - for j in range(m, MAXBIT): - newv = v[d, j - m] - pow2 = 1 - for k in range(m): - pow2 = pow2 << 1 - if (p >> (m - 1 - k)) & 1: - newv = newv ^ (pow2 * v[d, j - k - 1]) - v[d, j] = newv - - # Multiply each column of v by power of 2: - # v * [2^(maxbit-1), 2^(maxbit-2),..., 2, 1] - pow2 = 1 - for d in range(MAXBIT): - for i in range(dimen): - v[i, MAXBIT - 1 - d] *= pow2 - pow2 = pow2 << 1 - - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef void _draw( - const int n, - const int num_gen, - const int dimen, - cnp.int_t[:, :] sv, - cnp.int_t[:] quasi, - cnp.float_t[:, :] result, -) nogil: - cdef int i, j, l, qtmp - cdef int num_gen_loc = num_gen - for i in range(n): - l = low_0_bit(num_gen_loc) - for j in range(dimen): - qtmp = quasi[j] ^ sv[j, l - 1] - quasi[j] = qtmp - result[i, j] = qtmp * RECIPD - num_gen_loc += 1 - - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef void _fast_forward( - const int n, - const int num_gen, - const int dimen, - cnp.int_t[:, :] sv, - cnp.int_t[:] quasi, -) nogil: - cdef int i, j, l - cdef int num_gen_loc = num_gen - for i in range(n): - l = low_0_bit(num_gen_loc) - for j in range(dimen): - quasi[j] = quasi[j] ^ sv[j, l - 1] - num_gen_loc += 1 - - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef int cdot_pow2(cnp.int_t[:] a) nogil: - cdef int i - cdef int size = a.shape[0] - cdef int z = 0 - cdef int pow2 = 1 - for i in range(size): - z += a[size - 1 - i] * pow2 - pow2 *= 2 - return z - - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef void _cscramble( - const int dimen, - cnp.int_t[:, :, :] ltm, - cnp.int_t[:, :] sv, -) nogil: - cdef int d, i, j, k, l, lsm, lsmdp, p, t1, t2, vdj - - # Set diagonals of maxbit x maxbit arrays to 1 - for d in range(dimen): - for i in range(MAXBIT): - ltm[d, i, i] = 1 - - for d in range(dimen): - for j in range(MAXBIT): - vdj = sv[d, j] - l = 1 - t2 = 0 - for p in range(MAXBIT - 1, -1, -1): - lsmdp = cdot_pow2(ltm[d, p, :]) - t1 = 0 - for k in range(MAXBIT): - t1 += ibits(lsmdp, k, 1) * ibits(vdj, k, 1) - t1 = t1 % 2 - t2 = t2 + t1 * l - l = 2 * l - sv[d, j] = t2 - - -class SobolEngine: - """Engine for generating (scrambled) Sobol sequences. - - Sobol sequences are low-discrepancy, quasi-random numbers. Reference for 1111 - direction numbers: http://web.maths.unsw.edu.au/~fkuo/sobol/joe-kuo-old.1111. - - References: - Art B. Owen. Scrambling Sobol and Niederreiter-Xing points. Journal of - Complexity, 14(4):466-489, December 1998. - - I. M. Sobol. The distribution of points in a cube and the accurate - evaluation of integrals. Zh. Vychisl. Mat. i Mat. Phys., 7:784-802, 1967. - - Args: - dimen (int): dimensionality of the sequence. Max dimensionality is 1111. - scramble (bool, optional): if True, use Owen scrambling. - seed (int, optional): seed for reproducibility of scrambling. - - """ - - MAXDIM = 1111 - - def __init__(self, dimen, scramble=False, seed=None): - # type: (int, bool, Optional[int]) -> None - if dimen > 1111: - raise ValueError("Maximum supported dimensionality is 1111.") - self.dimen = dimen - - # v is dimen x MAXBIT matrix; - self._sv = np.zeros((dimen, MAXBIT), dtype=np.int) - initialize_v(self._sv, dimen) - - if not scramble: - self._shift = np.zeros(dimen, dtype=np.int) - else: - self._scramble(seed=seed) - - self._quasi = self._shift.copy() - self.num_generated = 0 - - def _scramble(self, seed=None): - # type: (Optional[int]) -> None - rs = np.random.RandomState(seed=seed) - # Generate shift vector - self._shift = np.dot( - rs.randint(2, size=(self.dimen, MAXBIT)), - np.array([2**j for j in range(MAXBIT)]), - ) - self._quasi = self._shift.copy() - # Generate lower triangular matrices (stacked across dimensions) - ltm = np.tril(rs.randint(2, size=(self.dimen, MAXBIT, MAXBIT))) - _cscramble(self.dimen, ltm, self._sv) - self.num_generated = 0 - - def draw(self, n=1): - # type: (int) -> np.ndarray - """Draw next point(s) in the Sobol sequence. - - Args: - n (int, optional): number of points to return. Default is 1. - - Returns: - np.array: `n x D` array, where `n` is the number of points requested - and `D` is the dimensionality of the sequence. - - """ - result = np.empty((n, self.dimen), dtype=np.float) - _draw(n, self.num_generated, self.dimen, self._sv, self._quasi, result) - self.num_generated += n - return result - - def reset(self): - # type: () -> SobolEngine - """Reset the engine to base state. - - Returns: - SobolEngine: engine, reset to its base state. - - """ - self._quasi = self._shift.copy() - self.num_generated = 0 - return self - - def fast_forward(self, n): - # type: (int) -> SobolEngine - """Fast-forward the sequence by n positions. - - Args: - n (int): number of points to skip in the sequence. - - Returns: - SobolEngine: the fast-forwarded engine. - - """ - _fast_forward(n, self.num_generated, self.dimen, self._sv, self._quasi) - self.num_generated += n - return self - - -def multinomial_qmc(n, pvals, seed=None): - # type: (int, Iterable[float], Optional[int]) -> np.ndarray - """Draw low-discreancy quasi-random samples from multinomial distribution. - - Args: - n (int): Number of experiments. - pvals (Iterable[float]): float vector of probabilities of size `p`. - Elements must be non-negative and sum to 1. - seed (int, optional): The seed for the random number generator. - - Returns: - np.ndarray: int vector of size `p` summing to `n`. - - """ - if np.min(pvals) < 0: - raise ValueError('Elements of pvals must be non-negative') - if not np.isclose(np.sum(pvals), 1): - raise ValueError('Elements of pvals must sum to 1') - sobol = SobolEngine(1, scramble=True, seed=seed) - draws = sobol.draw(n).ravel() - p_cumulative = np.empty_like(pvals, dtype=np.float) - _fill_p_cumulative(np.array(pvals, dtype=np.float), p_cumulative) - result = np.zeros_like(pvals, dtype=np.int) - _categorize(draws, p_cumulative, result) - return result - - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef void _fill_p_cumulative( - cnp.float_t[:] p, - cnp.float_t[:] p_cumulative, -) nogil: - cdef int i - cdef int len_p = p.shape[0] - cdef float tot = 0 - cdef float t - for i in range(len_p): - t = tot + p[i] - p_cumulative[i] = t - tot = t - - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef void _categorize( - cnp.float_t[:] draws, - cnp.float_t[:] p_cumulative, - cnp.int_t[:] result, -) nogil: - cdef int i - cdef int n_p = p_cumulative.shape[0] - for i in range(draws.shape[0]): - j = _find_index(p_cumulative, n_p, draws[i]) - result[j] = result[j] + 1 - - -@cython.boundscheck(False) -@cython.wraparound(False) -cdef int _find_index( - cnp.float_t[:] p_cumulative, - const int size, - const float value, -) nogil: - cdef int l = 0 - cdef int r = size - 1 - cdef int m - while r > l: - m = (l + r) // 2 - if value > p_cumulative[m]: - l = m + 1 - else: - r = m - return r - - -def _test_find_index(p_cumulative, size, value): - # type: (np.ndarray, int, float) -> int - """Wrapper for testing in python""" - return _find_index(p_cumulative, size, value) - - -def star_L2(draw): - # type: (np.ndarray) -> float - """Star L2-discrepancy. - - Args: - draw (np.ndarray): numpy array of draws. - - Returns: - float: the star L2-discrepancy. - - References: - - T. T. Warnock. Computational investigations of low discrepancy point sets. - In S.K. Zaremba (editor), Applications of Number Theory to Numerical - Analysis, Academic Press, New York. - - """ - n, d = draw.shape - return np.sqrt( - 3**(-d) - 2**(1 - d) / n * np.sum(np.prod(1 - draw**2, axis=1)) + - np.sum([ - np.prod(1 - np.maximum(draw[k, :], draw[j, :])) - for k in range(n) for j in range(n) - ]) / n**2 - ) - - -def centered_L2(draw): - # type: (np.ndarray) -> float - """Centered L2-discrepancy. - - Args: - draw (np.ndarray): numpy array of draws. - - Returns: - float: the centered L2-discrepancy. - - References: - - Fred J. Hickernell, A generalized discrepancy and quadrature error bound, - Mathematics of Computation, v.67 n.221, p.299-322, Jan. 1998. - - """ - n, d = draw.shape - return np.sqrt( - (13 / 12)**d - 2 / n * np.sum( - [np.prod(_dev_half(draw[k, :])) for k in range(n)] - ) + - np.sum([ - np.prod(_joint_dev(draw[k, :], draw[j, :])) - for k in range(n) for j in range(n) - ]) / n**2 - ) - - -def _dev_half(points): - return 1 + 0.5 * np.abs(points - 0.5) - 0.5 * (points - 0.5)**2 - - -def _joint_dev(points1, points2): - return ( - 1 + - 0.5 * np.abs(points1 - 0.5) + - 0.5 * np.abs(points2 - 0.5) - - 0.5 * np.abs(points1 - points2) - ) diff --git a/botorch/utils/sampling.py b/botorch/utils/sampling.py index 022015bef2..fa5d16972b 100644 --- a/botorch/utils/sampling.py +++ b/botorch/utils/sampling.py @@ -10,14 +10,12 @@ import torch from torch import Tensor +from torch.quasirandom import SobolEngine from ..exceptions.warnings import SamplingWarning from ..posteriors.posterior import Posterior from ..qmc.normal import NormalQMCEngine -# TODO: Use torch Sobol engine in torch 1.1 -from ..qmc.sobol import SobolEngine - def construct_base_samples( batch_shape: torch.Size, @@ -127,10 +125,8 @@ def draw_sobol_samples( lower = bounds[0] rng = bounds[1] - bounds[0] sobol_engine = SobolEngine(d, scramble=True, seed=seed) - samples_np = sobol_engine.draw(n * q).reshape(n, q, d) - samples_raw = torch.from_numpy(samples_np).to( - device=lower.device, dtype=lower.dtype - ) + samples_raw = sobol_engine.draw(n * q, dtype=lower.dtype).view(n, q, d) + samples_raw = samples_raw.to(device=lower.device) return lower + rng * samples_raw @@ -162,11 +158,8 @@ def draw_sobol_normal_samples( >>> samples = draw_sobol_samples(2, 10, seed=1234) """ normal_qmc_engine = NormalQMCEngine(d=d, seed=seed, inv_transform=True) - samples_np = normal_qmc_engine.draw(n) - return torch.from_numpy(samples_np).to( - dtype=torch.float if dtype is None else dtype, - device=device, # None here will leave it on the cpu - ) + samples = normal_qmc_engine.draw(n, dtype=torch.float if dtype is None else dtype) + return samples.to(device=device) @contextmanager diff --git a/setup.py b/setup.py index cfdd90819b..0c1ea17f30 100755 --- a/setup.py +++ b/setup.py @@ -3,7 +3,6 @@ import sys from setuptools import find_packages, setup -from setuptools.extension import Extension REQUIRED_MAJOR = 3 @@ -35,20 +34,6 @@ def missing(package_name): ) -# check for numpy (required for building Sobol cython) -try: - import numpy -except ImportError: - missing("numpy") - - -# check for Cython itself -try: - from Cython.Build import cythonize -except ImportError: - missing("cython") - - # error out if setup dependencies not met if fatals: sys.exit( @@ -57,10 +42,6 @@ def missing(package_name): ) -# TODO: Use torch Sobol once torch 1.1 is released -EXTENSIONS = [Extension("botorch.qmc.sobol", ["botorch/qmc/sobol.pyx"])] - - TEST_REQUIRES = ["pytest>=3.6", "pytest-cov"] DEV_REQUIRES = TEST_REQUIRES + ["black", "flake8", "sphinx", "sphinx-autodoc-typehints"] @@ -83,9 +64,7 @@ def missing(package_name): python_requires=">=3.6", setup_requires=["cython", "numpy"], install_requires=["torch>=1.0.1", "gpytorch>=0.3.1", "scipy"], - include_dirs=[numpy.get_include()], packages=find_packages(), - ext_modules=cythonize(EXTENSIONS), extras_require={ "dev": DEV_REQUIRES, "test": TEST_REQUIRES, diff --git a/test/qmc/test_normal.py b/test/qmc/test_normal.py index 53398e2bb0..651d3afe7e 100644 --- a/test/qmc/test_normal.py +++ b/test/qmc/test_normal.py @@ -1,9 +1,12 @@ #!/usr/bin/env python3 +import math import unittest import numpy as np +import torch from botorch.qmc import MultivariateNormalQMCEngine, NormalQMCEngine +from botorch.utils.sampling import manual_seed from scipy.stats import shapiro @@ -12,305 +15,517 @@ def test_NormalQMCEngine(self): # d = 1 engine = NormalQMCEngine(d=1) samples = engine.draw() - self.assertEqual(samples.shape, (1, 1)) + self.assertEqual(samples.dtype, torch.float) + self.assertEqual(samples.shape, torch.Size([1, 1])) samples = engine.draw(n=5) - self.assertEqual(samples.shape, (5, 1)) + self.assertEqual(samples.shape, torch.Size([5, 1])) # d = 2 engine = NormalQMCEngine(d=2) samples = engine.draw() - self.assertEqual(samples.shape, (1, 2)) + self.assertEqual(samples.shape, torch.Size([1, 2])) samples = engine.draw(n=5) - self.assertEqual(samples.shape, (5, 2)) + self.assertEqual(samples.shape, torch.Size([5, 2])) + # test double dtype + samples = engine.draw(dtype=torch.double) + self.assertEqual(samples.dtype, torch.double) def test_NormalQMCEngineInvTransform(self): # d = 1 engine = NormalQMCEngine(d=1, inv_transform=True) samples = engine.draw() - self.assertEqual(samples.shape, (1, 1)) + self.assertEqual(samples.dtype, torch.float) + self.assertEqual(samples.shape, torch.Size([1, 1])) samples = engine.draw(n=5) - self.assertEqual(samples.shape, (5, 1)) + self.assertEqual(samples.shape, torch.Size([5, 1])) # d = 2 engine = NormalQMCEngine(d=2, inv_transform=True) samples = engine.draw() - self.assertEqual(samples.shape, (1, 2)) + self.assertEqual(samples.shape, torch.Size([1, 2])) samples = engine.draw(n=5) - self.assertEqual(samples.shape, (5, 2)) + self.assertEqual(samples.shape, torch.Size([5, 2])) + # test double dtype + samples = engine.draw(dtype=torch.double) + self.assertEqual(samples.dtype, torch.double) def test_NormalQMCEngineSeeded(self): # test even dimension engine = NormalQMCEngine(d=2, seed=12345) samples = engine.draw(n=2) - samples_expected = np.array( + self.assertEqual(samples.dtype, torch.float) + samples_expected = torch.tensor( [[-0.63099602, -1.32950772], [0.29625805, 1.86425618]] ) - self.assertTrue(np.allclose(samples, samples_expected)) + self.assertTrue(torch.allclose(samples, samples_expected)) # test odd dimension engine = NormalQMCEngine(d=3, seed=12345) samples = engine.draw(n=2) - samples_expected = np.array( + samples_expected = torch.tensor( [ [1.83169884, -1.40473647, 0.24334828], [0.36596099, 1.2987395, -1.47556275], ] ) - self.assertTrue(np.allclose(samples, samples_expected)) + self.assertTrue(torch.allclose(samples, samples_expected)) + + def test_NormalQMCEngineSeededOut(self): + # test even dimension + engine = NormalQMCEngine(d=2, seed=12345) + out = torch.empty(2, 2) + self.assertIsNone(engine.draw(n=2, out=out)) + samples_expected = torch.tensor( + [[-0.63099602, -1.32950772], [0.29625805, 1.86425618]] + ) + self.assertTrue(torch.allclose(out, samples_expected)) + # test odd dimension + engine = NormalQMCEngine(d=3, seed=12345) + out = torch.empty(2, 3) + self.assertIsNone(engine.draw(n=2, out=out)) + samples_expected = torch.tensor( + [ + [1.83169884, -1.40473647, 0.24334828], + [0.36596099, 1.2987395, -1.47556275], + ] + ) + self.assertTrue(torch.allclose(out, samples_expected)) def test_NormalQMCEngineSeededInvTransform(self): # test even dimension engine = NormalQMCEngine(d=2, seed=12345, inv_transform=True) samples = engine.draw(n=2) - samples_expected = np.array( + self.assertEqual(samples.dtype, torch.float) + samples_expected = torch.tensor( [[-0.41622922, 0.46622792], [-0.96063897, -0.75568963]] ) - self.assertTrue(np.allclose(samples, samples_expected)) + self.assertTrue(torch.allclose(samples, samples_expected)) # test odd dimension engine = NormalQMCEngine(d=3, seed=12345, inv_transform=True) samples = engine.draw(n=2) - samples_expected = np.array( + samples_expected = torch.tensor( [ [-1.40525266, 1.37652443, -0.8519666], [-0.166497, -2.3153681, -0.15975676], ] ) - self.assertTrue(np.allclose(samples, samples_expected)) + self.assertTrue(torch.allclose(samples, samples_expected)) def test_NormalQMCEngineShapiro(self): engine = NormalQMCEngine(d=2, seed=12345) samples = engine.draw(n=250) - self.assertTrue(all(np.abs(samples.mean(axis=0)) < 1e-2)) - self.assertTrue(all(np.abs(samples.std(axis=0) - 1) < 1e-2)) + self.assertEqual(samples.dtype, torch.float) + self.assertTrue(torch.all(torch.abs(samples.mean(dim=0)) < 1e-2)) + self.assertTrue(torch.all(torch.abs(samples.std(dim=0) - 1) < 1e-2)) # perform Shapiro-Wilk test for normality for i in (0, 1): _, pval = shapiro(samples[:, i]) self.assertGreater(pval, 0.9) # make sure samples are uncorrelated - cov = np.cov(samples.transpose()) + cov = np.cov(samples.numpy().transpose()) self.assertLess(np.abs(cov[0, 1]), 1e-2) def test_NormalQMCEngineShapiroInvTransform(self): engine = NormalQMCEngine(d=2, seed=12345, inv_transform=True) samples = engine.draw(n=250) - self.assertTrue(all(np.abs(samples.mean(axis=0)) < 1e-2)) - self.assertTrue(all(np.abs(samples.std(axis=0) - 1) < 1e-2)) + self.assertEqual(samples.dtype, torch.float) + self.assertTrue(torch.all(torch.abs(samples.mean(dim=0)) < 1e-2)) + self.assertTrue(torch.all(torch.abs(samples.std(dim=0) - 1) < 1e-2)) # perform Shapiro-Wilk test for normality for i in (0, 1): _, pval = shapiro(samples[:, i]) self.assertGreater(pval, 0.9) # make sure samples are uncorrelated - cov = np.cov(samples.transpose()) + cov = np.cov(samples.numpy().transpose()) self.assertLess(np.abs(cov[0, 1]), 1e-2) class MultivariateNormalQMCTests(unittest.TestCase): - def test_MultivariateNormalQMCEngineNonPSD(self): - # try with non-psd, non-pd cov and expect an assertion error - self.assertRaises( - ValueError, MultivariateNormalQMCEngine, [0, 0], [[1, 2], [2, 1]] - ) + def test_MultivariateNormalQMCEngineNonPSD(self, cuda=False): + device = torch.device("cuda") if cuda else torch.device("cpu") + for dtype in (torch.float, torch.double): + # try with non-psd, non-pd cov and expect an assertion error + mean = torch.zeros(2, device=device, dtype=dtype) + cov = torch.tensor([[1, 2], [2, 1]], device=device, dtype=dtype) + with self.assertRaises(ValueError): + MultivariateNormalQMCEngine(mean=mean, cov=cov) - def test_MultivariateNormalQMCEngineNonPD(self): - # try with non-pd but psd cov; should work - engine = MultivariateNormalQMCEngine( - [0, 0, 0], [[1, 0, 1], [0, 1, 1], [1, 1, 2]] - ) - self.assertTrue(engine._corr_matrix is not None) + def test_MultivariateNormalQMCEngineNonPSD_cuda(self): + if torch.cuda.is_available(): + self.test_MultivariateNormalQMCEngineNonPSD(cuda=True) - def test_MultivariateNormalQMCEngineSymmetric(self): - # try with non-symmetric cov and expect an error - self.assertRaises( - ValueError, MultivariateNormalQMCEngine, [0, 0], [[1, 0], [2, 1]] - ) + def test_MultivariateNormalQMCEngineNonPD(self, cuda=False): + device = torch.device("cuda") if cuda else torch.device("cpu") + for dtype in (torch.float, torch.double): + mean = torch.zeros(3, device=device, dtype=dtype) + cov = torch.tensor( + [[1, 0, 1], [0, 1, 1], [1, 1, 2]], device=device, dtype=dtype + ) + # try with non-pd but psd cov; should work + engine = MultivariateNormalQMCEngine(mean=mean, cov=cov) + self.assertTrue(engine._corr_matrix is not None) - def test_MultivariateNormalQMCEngine(self): - # d = 1 scalar - engine = MultivariateNormalQMCEngine(mean=0, cov=5) - samples = engine.draw() - self.assertEqual(samples.shape, (1, 1)) - samples = engine.draw(n=5) - self.assertEqual(samples.shape, (5, 1)) + def test_MultivariateNormalQMCEngineNonPD_cuda(self): + if torch.cuda.is_available(): + self.test_MultivariateNormalQMCEngineNonPD(cuda=True) - # d = 2 list - engine = MultivariateNormalQMCEngine(mean=[0, 1], cov=[[1, 0], [0, 1]]) - samples = engine.draw() - self.assertEqual(samples.shape, (1, 2)) - samples = engine.draw(n=5) - self.assertEqual(samples.shape, (5, 2)) + def test_MultivariateNormalQMCEngineSymmetric(self, cuda=False): + device = torch.device("cuda") if cuda else torch.device("cpu") + for dtype in (torch.float, torch.double): + # try with non-symmetric cov and expect an error + mean = torch.zeros(2, device=device, dtype=dtype) + cov = torch.tensor([[1, 0], [2, 1]], device=device, dtype=dtype) + with self.assertRaises(ValueError): + MultivariateNormalQMCEngine(mean=mean, cov=cov) - # d = 3 np.array - mean = np.array([0, 1, 2]) - cov = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - engine = MultivariateNormalQMCEngine(mean, cov) - samples = engine.draw() - self.assertEqual(samples.shape, (1, 3)) - samples = engine.draw(n=5) - self.assertEqual(samples.shape, (5, 3)) + def test_MultivariateNormalQMCEngineSymmetric_cuda(self): + if torch.cuda.is_available(): + self.test_MultivariateNormalQMCEngineSymmetric(cuda=True) - def test_MultivariateNormalQMCEngineInvTransform(self): - # d = 1 scalar - engine = MultivariateNormalQMCEngine(mean=0, cov=5, inv_transform=True) - samples = engine.draw() - self.assertEqual(samples.shape, (1, 1)) - samples = engine.draw(n=5) - self.assertEqual(samples.shape, (5, 1)) + def test_MultivariateNormalQMCEngine(self, cuda=False): + device = torch.device("cuda") if cuda else torch.device("cpu") + for dtype in (torch.float, torch.double): - # d = 2 list - engine = MultivariateNormalQMCEngine( - mean=[0, 1], cov=[[1, 0], [0, 1]], inv_transform=True - ) - samples = engine.draw() - self.assertEqual(samples.shape, (1, 2)) - samples = engine.draw(n=5) - self.assertEqual(samples.shape, (5, 2)) + # d = 1 scalar + mean = torch.tensor([0], device=device, dtype=dtype) + cov = torch.tensor([[5]], device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine(mean=mean, cov=cov) + samples = engine.draw() + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + self.assertEqual(samples.shape, torch.Size([1, 1])) + samples = engine.draw(n=5) + self.assertEqual(samples.shape, torch.Size([5, 1])) - # d = 3 np.array - mean = np.array([0, 1, 2]) - cov = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - engine = MultivariateNormalQMCEngine(mean, cov, inv_transform=True) - samples = engine.draw() - self.assertEqual(samples.shape, (1, 3)) - samples = engine.draw(n=5) - self.assertEqual(samples.shape, (5, 3)) + # d = 2 list + mean = torch.tensor([0, 1], device=device, dtype=dtype) + cov = torch.eye(2, device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine(mean=mean, cov=cov) + samples = engine.draw() + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + self.assertEqual(samples.shape, torch.Size([1, 2])) + samples = engine.draw(n=5) + self.assertEqual(samples.shape, torch.Size([5, 2])) - def test_MultivariateNormalQMCEngineSeeded(self): - # test even dimension - np.random.seed(54321) - a = np.random.randn(2, 2) - A = a @ a.transpose() + np.diag(np.random.rand(2)) - engine = MultivariateNormalQMCEngine(np.array([0, 0]), A, seed=12345) - samples = engine.draw(n=2) - samples_expected = np.array( - [[-0.67595995, -2.27437872], [0.317369, 2.66203577]] - ) - self.assertTrue(np.allclose(samples, samples_expected)) + # d = 3 Tensor + mean = torch.tensor([0, 1, 2], device=device, dtype=dtype) + cov = torch.eye(3, device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine(mean=mean, cov=cov) + samples = engine.draw() + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + self.assertEqual(samples.shape, torch.Size([1, 3])) + samples = engine.draw(n=5) + self.assertEqual(samples.shape, torch.Size([5, 3])) - # test odd dimension - np.random.seed(54321) - a = np.random.randn(3, 3) - A = a @ a.transpose() + np.diag(np.random.rand(3)) - engine = MultivariateNormalQMCEngine(np.array([0, 0, 0]), A, seed=12345) - samples = engine.draw(n=2) - samples_expected = np.array( - [ - [2.05178452, -6.35744194, 0.67944512], - [0.40993262, 2.60517697, -1.69415825], - ] - ) - self.assertTrue(np.allclose(samples, samples_expected)) + def test_MultivariateNormalQMCEngine_cuda(self): + if torch.cuda.is_available(): + self.test_MultivariateNormalQMCEngine(cuda=True) - def test_MultivariateNormalQMCEngineSeededInvTransform(self): - # test even dimension - np.random.seed(54321) - a = np.random.randn(2, 2) - A = a @ a.transpose() + np.diag(np.random.rand(2)) - engine = MultivariateNormalQMCEngine( - np.array([0, 0]), A, seed=12345, inv_transform=True - ) - samples = engine.draw(n=2) - samples_expected = np.array( - [[-0.44588916, 0.22657776], [-1.02909281, -1.83193033]] - ) - self.assertTrue(np.allclose(samples, samples_expected)) + def test_MultivariateNormalQMCEngineInvTransform(self, cuda=False): + device = torch.device("cuda") if cuda else torch.device("cpu") + for dtype in (torch.float, torch.double): - # test odd dimension - np.random.seed(54321) - a = np.random.randn(3, 3) - A = a @ a.transpose() + np.diag(np.random.rand(3)) - engine = MultivariateNormalQMCEngine( - np.array([0, 0, 0]), A, seed=12345, inv_transform=True - ) - samples = engine.draw(n=2) - samples_expected = np.array( - [ - [-1.5740992, 5.61057598, -1.28218525], - [-0.18650226, -5.41662685, 0.023199], - ] - ) - self.assertTrue(np.allclose(samples, samples_expected)) + # d = 1 scalar + mean = torch.tensor([0], device=device, dtype=dtype) + cov = torch.tensor([[5]], device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine(mean=mean, cov=cov, inv_transform=True) + samples = engine.draw() + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + self.assertEqual(samples.shape, torch.Size([1, 1])) + samples = engine.draw(n=5) + self.assertEqual(samples.shape, torch.Size([5, 1])) - def test_MultivariateNormalQMCEngineShapiro(self): - # test the standard case - engine = MultivariateNormalQMCEngine( - mean=[0, 0], cov=[[1, 0], [0, 1]], seed=12345 - ) - samples = engine.draw(n=250) - self.assertTrue(all(np.abs(samples.mean(axis=0)) < 1e-2)) - self.assertTrue(all(np.abs(samples.std(axis=0) - 1) < 1e-2)) - # perform Shapiro-Wilk test for normality - for i in (0, 1): - _, pval = shapiro(samples[:, i]) - self.assertGreater(pval, 0.9) - # make sure samples are uncorrelated - cov = np.cov(samples.transpose()) - self.assertLess(np.abs(cov[0, 1]), 1e-2) + # d = 2 list + mean = torch.tensor([0, 1], device=device, dtype=dtype) + cov = torch.eye(2, device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine(mean=mean, cov=cov, inv_transform=True) + samples = engine.draw() + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + self.assertEqual(samples.shape, torch.Size([1, 2])) + samples = engine.draw(n=5) + self.assertEqual(samples.shape, torch.Size([5, 2])) - # test the correlated, non-zero mean case - engine = MultivariateNormalQMCEngine( - mean=[1.0, 2.0], cov=[[1.5, 0.5], [0.5, 1.5]], seed=12345 - ) - samples = engine.draw(n=250) - self.assertTrue(all(np.abs(samples.mean(axis=0) - [1, 2]) < 1e-2)) - self.assertTrue(all(np.abs(samples.std(axis=0) - np.sqrt(1.5)) < 1e-2)) - # perform Shapiro-Wilk test for normality - for i in (0, 1): - _, pval = shapiro(samples[:, i]) - self.assertGreater(pval, 0.9) - # check covariance - cov = np.cov(samples.transpose()) - self.assertLess(np.abs(cov[0, 1] - 0.5), 1e-2) - - def test_MultivariateNormalQMCEngineShapiroInvTransform(self): - # test the standard case - engine = MultivariateNormalQMCEngine( - mean=[0, 0], cov=[[1, 0], [0, 1]], seed=12345, inv_transform=True - ) - samples = engine.draw(n=250) - self.assertTrue(all(np.abs(samples.mean(axis=0)) < 1e-2)) - self.assertTrue(all(np.abs(samples.std(axis=0) - 1) < 1e-2)) - # perform Shapiro-Wilk test for normality - for i in (0, 1): - _, pval = shapiro(samples[:, i]) - self.assertGreater(pval, 0.9) - # make sure samples are uncorrelated - cov = np.cov(samples.transpose()) - self.assertLess(np.abs(cov[0, 1]), 1e-2) + # d = 3 Tensor + mean = torch.tensor([0, 1, 2], device=device, dtype=dtype) + cov = torch.eye(3, device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine(mean=mean, cov=cov, inv_transform=True) + samples = engine.draw() + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + self.assertEqual(samples.shape, torch.Size([1, 3])) + samples = engine.draw(n=5) + self.assertEqual(samples.shape, torch.Size([5, 3])) - # test the correlated, non-zero mean case - engine = MultivariateNormalQMCEngine( - mean=[1.0, 2.0], - cov=[[1.5, 0.5], [0.5, 1.5]], - seed=12345, - inv_transform=True, - ) - samples = engine.draw(n=250) - self.assertTrue(all(np.abs(samples.mean(axis=0) - [1, 2]) < 1e-2)) - self.assertTrue(all(np.abs(samples.std(axis=0) - np.sqrt(1.5)) < 1e-2)) - # perform Shapiro-Wilk test for normality - for i in (0, 1): - _, pval = shapiro(samples[:, i]) - self.assertGreater(pval, 0.9) - # check covariance - cov = np.cov(samples.transpose()) - self.assertLess(np.abs(cov[0, 1] - 0.5), 1e-2) - - def test_MultivariateNormalQMCEngineDegenerate(self): - # X, Y iid standard Normal and Z = X + Y, random vector (X, Y, Z) - engine = MultivariateNormalQMCEngine( - mean=[0.0, 0.0, 0.0], - cov=[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [1.0, 1.0, 2.0]], - seed=12345, - ) - samples = engine.draw(n=2000) - self.assertTrue(all(np.abs(samples.mean(axis=0)) < 1e-2)) - self.assertTrue(np.abs(np.std(samples[:, 0]) - 1) < 1e-2) - self.assertTrue(np.abs(np.std(samples[:, 1]) - 1) < 1e-2) - self.assertTrue(np.abs(np.std(samples[:, 2]) - np.sqrt(2)) < 1e-2) - for i in (0, 1, 2): - _, pval = shapiro(samples[:, i]) - self.assertGreater(pval, 0.9) - cov = np.cov(samples.transpose()) - self.assertLess(np.abs(cov[0, 1]), 1e-2) - self.assertLess(np.abs(cov[0, 2] - 1), 1e-2) - # check to see if X + Y = Z almost exactly - self.assertTrue( - all(np.abs(samples[:, 0] + samples[:, 1] - samples[:, 2]) < 1e-5) - ) + def test_MultivariateNormalQMCEngineInvTransform_cuda(self): + if torch.cuda.is_available(): + self.test_MultivariateNormalQMCEngineInvTransform(cuda=True) + + def test_MultivariateNormalQMCEngineSeeded(self, cuda=False): + device = torch.device("cuda") if cuda else torch.device("cpu") + for dtype in (torch.float, torch.double): + + # test even dimension + with manual_seed(54321): + a = torch.randn(2, 2) + cov = a @ a.transpose(-1, -2) + torch.rand(2).diag() + + mean = torch.zeros(2, device=device, dtype=dtype) + cov = cov.to(device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine(mean=mean, cov=cov, seed=12345) + samples = engine.draw(n=2) + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + samples_expected = torch.tensor( + [[-0.849047422, -0.713852942], [0.398635030, 1.350660801]], + device=device, + dtype=dtype, + ) + self.assertTrue(torch.allclose(samples, samples_expected)) + + # test odd dimension + with manual_seed(54321): + a = torch.randn(3, 3) + cov = a @ a.transpose(-1, -2) + torch.rand(3).diag() + + mean = torch.zeros(3, device=device, dtype=dtype) + cov = cov.to(device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine(mean, cov, seed=12345) + samples = engine.draw(n=2) + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + samples_expected = torch.tensor( + [ + [3.113158941, -3.262257099, -0.819938779], + [0.621987879, 2.352285624, -1.992680788], + ], + device=device, + dtype=dtype, + ) + self.assertTrue(torch.allclose(samples, samples_expected)) + + def test_MultivariateNormalQMCEngineSeeded_cuda(self): + if torch.cuda.is_available(): + self.test_MultivariateNormalQMCEngineSeeded(cuda=True) + + def test_MultivariateNormalQMCEngineSeededOut(self, cuda=False): + device = torch.device("cuda") if cuda else torch.device("cpu") + for dtype in (torch.float, torch.double): + + # test even dimension + with manual_seed(54321): + a = torch.randn(2, 2) + cov = a @ a.transpose(-1, -2) + torch.rand(2).diag() + + mean = torch.zeros(2, device=device, dtype=dtype) + cov = cov.to(device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine(mean=mean, cov=cov, seed=12345) + out = torch.empty(2, 2, device=device, dtype=dtype) + self.assertIsNone(engine.draw(n=2, out=out)) + samples_expected = torch.tensor( + [[-0.849047422, -0.713852942], [0.398635030, 1.350660801]], + device=device, + dtype=dtype, + ) + self.assertTrue(torch.allclose(out, samples_expected)) + + # test odd dimension + with manual_seed(54321): + a = torch.randn(3, 3) + cov = a @ a.transpose(-1, -2) + torch.rand(3).diag() + + mean = torch.zeros(3, device=device, dtype=dtype) + cov = cov.to(device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine(mean, cov, seed=12345) + out = torch.empty(2, 3, device=device, dtype=dtype) + self.assertIsNone(engine.draw(n=2, out=out)) + samples_expected = torch.tensor( + [ + [3.113158941, -3.262257099, -0.819938779], + [0.621987879, 2.352285624, -1.992680788], + ], + device=device, + dtype=dtype, + ) + self.assertTrue(torch.allclose(out, samples_expected)) + + def test_MultivariateNormalQMCEngineSeededOut_cuda(self): + if torch.cuda.is_available(): + self.test_MultivariateNormalQMCEngineSeededOut(cuda=True) + + def test_MultivariateNormalQMCEngineSeededInvTransform(self, cuda=False): + device = torch.device("cuda") if cuda else torch.device("cpu") + for dtype in (torch.float, torch.double): + # test even dimension + with manual_seed(54321): + a = torch.randn(2, 2) + cov = a @ a.transpose(-1, -2) + torch.rand(2).diag() + + mean = torch.zeros(2, device=device, dtype=dtype) + cov = cov.to(device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine( + mean=mean, cov=cov, seed=12345, inv_transform=True + ) + samples = engine.draw(n=2) + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + samples_expected = torch.tensor( + [[-0.560064316, 0.629113674], [-1.292604208, -0.048077226]], + device=device, + dtype=dtype, + ) + self.assertTrue(torch.allclose(samples, samples_expected)) + + # test odd dimension + with manual_seed(54321): + a = torch.randn(3, 3) + cov = a @ a.transpose(-1, -2) + torch.rand(3).diag() + + mean = torch.zeros(3, device=device, dtype=dtype) + cov = cov.to(device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine( + mean=mean, cov=cov, seed=12345, inv_transform=True + ) + samples = engine.draw(n=2) + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + samples_expected = torch.tensor( + [ + [-2.388370037, 3.071142435, -0.319439292], + [-0.282978594, -4.350236893, -1.085214734], + ], + device=device, + dtype=dtype, + ) + self.assertTrue(torch.allclose(samples, samples_expected)) + + def test_MultivariateNormalQMCEngineSeededInvTransform_cuda(self): + if torch.cuda.is_available(): + self.test_MultivariateNormalQMCEngineSeededInvTransform(cuda=True) + + def test_MultivariateNormalQMCEngineShapiro(self, cuda=False): + device = torch.device("cuda") if cuda else torch.device("cpu") + for dtype in (torch.float, torch.double): + # test the standard case + mean = torch.zeros(2, device=device, dtype=dtype) + cov = torch.eye(2, device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine(mean=mean, cov=cov, seed=12345) + samples = engine.draw(n=250) + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + self.assertTrue(torch.all(torch.abs(samples.mean(dim=0)) < 1e-2)) + self.assertTrue(torch.all(torch.abs(samples.std(dim=0) - 1) < 1e-2)) + # perform Shapiro-Wilk test for normality + samples = samples.cpu().numpy() + for i in (0, 1): + _, pval = shapiro(samples[:, i]) + self.assertGreater(pval, 0.9) + # make sure samples are uncorrelated + cov = np.cov(samples.transpose()) + self.assertLess(np.abs(cov[0, 1]), 1e-2) + + # test the correlated, non-zero mean case + mean = torch.tensor([1.0, 2.0], device=device, dtype=dtype) + cov = torch.tensor([[1.5, 0.5], [0.5, 1.5]], device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine(mean=mean, cov=cov, seed=12345) + samples = engine.draw(n=250) + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + self.assertTrue(torch.all(torch.abs(samples.mean(dim=0) - mean) < 1e-2)) + self.assertTrue( + torch.all(torch.abs(samples.std(dim=0) - math.sqrt(1.5)) < 1e-2) + ) + # perform Shapiro-Wilk test for normality + samples = samples.cpu().numpy() + for i in (0, 1): + _, pval = shapiro(samples[:, i]) + self.assertGreater(pval, 0.9) + # check covariance + cov = np.cov(samples.transpose()) + self.assertLess(np.abs(cov[0, 1] - 0.5), 1e-2) + + def test_MultivariateNormalQMCEngineShapiro_cuda(self): + if torch.cuda.is_available(): + self.test_MultivariateNormalQMCEngineShapiro(cuda=True) + + def test_MultivariateNormalQMCEngineShapiroInvTransform(self, cuda=False): + device = torch.device("cuda") if cuda else torch.device("cpu") + for dtype in (torch.float, torch.double): + # test the standard case + mean = torch.zeros(2, device=device, dtype=dtype) + cov = torch.eye(2, device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine( + mean=mean, cov=cov, seed=12345, inv_transform=True + ) + samples = engine.draw(n=250) + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + self.assertTrue(torch.all(torch.abs(samples.mean(dim=0)) < 1e-2)) + self.assertTrue(torch.all(torch.abs(samples.std(dim=0) - 1) < 1e-2)) + # perform Shapiro-Wilk test for normality + samples = samples.cpu().numpy() + for i in (0, 1): + _, pval = shapiro(samples[:, i]) + self.assertGreater(pval, 0.9) + # make sure samples are uncorrelated + cov = np.cov(samples.transpose()) + self.assertLess(np.abs(cov[0, 1]), 1e-2) + + # test the correlated, non-zero mean case + mean = torch.tensor([1.0, 2.0], device=device, dtype=dtype) + cov = torch.tensor([[1.5, 0.5], [0.5, 1.5]], device=device, dtype=dtype) + engine = MultivariateNormalQMCEngine( + mean=mean, cov=cov, seed=12345, inv_transform=True + ) + samples = engine.draw(n=250) + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + self.assertTrue(torch.all(torch.abs(samples.mean(dim=0) - mean) < 1e-2)) + self.assertTrue( + torch.all(torch.abs(samples.std(dim=0) - math.sqrt(1.5)) < 1e-2) + ) + # perform Shapiro-Wilk test for normality + samples = samples.cpu().numpy() + for i in (0, 1): + _, pval = shapiro(samples[:, i]) + self.assertGreater(pval, 0.9) + # check covariance + cov = np.cov(samples.transpose()) + self.assertLess(np.abs(cov[0, 1] - 0.5), 1e-2) + + def test_MultivariateNormalQMCEngineShapiroInvTransform_cuda(self): + if torch.cuda.is_available(): + self.test_MultivariateNormalQMCEngineShapiroInvTransform(cuda=True) + + def test_MultivariateNormalQMCEngineDegenerate(self, cuda=False): + device = torch.device("cuda") if cuda else torch.device("cpu") + for dtype in (torch.float, torch.double): + # X, Y iid standard Normal and Z = X + Y, random vector (X, Y, Z) + mean = torch.zeros(3, device=device, dtype=dtype) + cov = torch.tensor( + [[1, 0, 1], [0, 1, 1], [1, 1, 2]], device=device, dtype=dtype + ) + engine = MultivariateNormalQMCEngine(mean=mean, cov=cov, seed=12345) + samples = engine.draw(n=2000) + self.assertEqual(samples.dtype, dtype) + self.assertEqual(samples.device.type, device.type) + self.assertTrue(torch.all(torch.abs(samples.mean(dim=0)) < 1e-2)) + self.assertTrue(torch.abs(torch.std(samples[:, 0]) - 1) < 1e-2) + self.assertTrue(torch.abs(torch.std(samples[:, 1]) - 1) < 1e-2) + self.assertTrue(torch.abs(torch.std(samples[:, 2]) - math.sqrt(2)) < 1e-2) + for i in (0, 1, 2): + _, pval = shapiro(samples[:, i].cpu().numpy()) + self.assertGreater(pval, 0.9) + cov = np.cov(samples.cpu().numpy().transpose()) + self.assertLess(np.abs(cov[0, 1]), 1e-2) + self.assertLess(np.abs(cov[0, 2] - 1), 1e-2) + # check to see if X + Y = Z almost exactly + self.assertTrue( + torch.all( + torch.abs(samples[:, 0] + samples[:, 1] - samples[:, 2]) < 1e-5 + ) + ) + + def test_MultivariateNormalQMCEngineDegenerate_cuda(self): + if torch.cuda.is_available(): + self.test_MultivariateNormalQMCEngineDegenerate(cuda=True) diff --git a/test/qmc/test_sobol.py b/test/qmc/test_sobol.py deleted file mode 100644 index 4c0822a2c6..0000000000 --- a/test/qmc/test_sobol.py +++ /dev/null @@ -1,232 +0,0 @@ -#!/usr/bin/env python3 - -import unittest -from collections import Counter - -import numpy as np -from botorch.qmc.sobol import SobolEngine, _test_find_index, multinomial_qmc - - -class SobolTests(unittest.TestCase): - # set maxDiff to None to show all differences when tests fail - maxDiff = None - - def setUp(self): - engine_unscrambled_1d = SobolEngine(1) - self.draws_unscrambled_1d = engine_unscrambled_1d.draw(10) - engine_unscrambled_3d = SobolEngine(3) - self.draws_unscrambled_3d = engine_unscrambled_3d.draw(10) - engine_scrambled_1d = SobolEngine(1, scramble=True, seed=12345) - self.draws_scrambled_1d = engine_scrambled_1d.draw(10) - engine_scrambled_3d = SobolEngine(3, scramble=True, seed=12345) - self.draws_scrambled_3d = engine_scrambled_3d.draw(10) - - def test_Unscrambled1DSobol(self): - expected = [0.5, 0.75, 0.25, 0.375, 0.875, 0.625, 0.125, 0.1875, 0.6875, 0.9375] - self.assertEqual(self.draws_unscrambled_1d.shape[0], 10) - self.assertEqual(self.draws_unscrambled_1d.shape[1], 1) - self.assertTrue( - np.array_equal(self.draws_unscrambled_1d.flatten(), np.array(expected)) - ) - - def test_Unscrambled3DSobol(self): - expected_dim3 = [ - 0.5, - 0.75, - 0.25, - 0.625, - 0.125, - 0.375, - 0.875, - 0.3125, - 0.8125, - 0.5625, - ] - self.assertEqual(self.draws_unscrambled_3d.shape[0], 10) - self.assertEqual(self.draws_unscrambled_3d.shape[1], 3) - self.assertTrue( - np.array_equal(self.draws_unscrambled_3d[:, 2], np.array(expected_dim3)) - ) - self.assertTrue( - np.array_equal( - self.draws_unscrambled_3d[:, 0], self.draws_unscrambled_1d.flatten() - ) - ) - - def test_Unscrambled3DAsyncSobol(self): - engine_unscrambled_3d = SobolEngine(3) - draws = np.vstack([engine_unscrambled_3d.draw() for i in range(10)]) - self.assertTrue(np.array_equal(self.draws_unscrambled_3d, draws)) - - def test_UnscrambledFastForwardAndResetSobol(self): - engine_unscrambled_3d = SobolEngine(3).fast_forward(5) - draws = engine_unscrambled_3d.draw(5) - self.assertTrue(np.array_equal(self.draws_unscrambled_3d[5:10, :], draws)) - - engine_unscrambled_3d.reset() - even_draws = [] - for i in range(10): - if i % 2 == 0: - even_draws.append(engine_unscrambled_3d.draw()) - else: - engine_unscrambled_3d.fast_forward(1) - self.assertTrue( - np.array_equal( - self.draws_unscrambled_3d[[i for i in range(10) if i % 2 == 0],], - np.vstack(even_draws), - ) - ) - - def test_UnscrambledHighDimSobol(self): - engine = SobolEngine(1111) - count1 = Counter(engine.draw().flatten().tolist()) - count2 = Counter(engine.draw().flatten().tolist()) - count3 = Counter(engine.draw().flatten().tolist()) - self.assertEqual(count1, Counter({0.5: 1111})) - self.assertEqual(count2, Counter({0.25: 580, 0.75: 531})) - self.assertEqual(count3, Counter({0.25: 531, 0.75: 580})) - - def test_UnscrambledSobolBounds(self): - engine = SobolEngine(1111) - draws = engine.draw(1000) - self.assertTrue(np.all(draws >= 0)) - self.assertTrue(np.all(draws <= 1)) - - def test_UnscrambledDistributionSobol(self): - engine = SobolEngine(1111) - draws = engine.draw(1000) - self.assertTrue( - np.allclose(np.mean(draws, axis=0), np.repeat(0.5, 1111), atol=0.01) - ) - self.assertTrue( - np.allclose( - np.percentile(draws, 25, axis=0), np.repeat(0.25, 1111), atol=0.01 - ) - ) - self.assertTrue( - np.allclose( - np.percentile(draws, 75, axis=0), np.repeat(0.75, 1111), atol=0.01 - ) - ) - - def test_Scrambled1DSobol(self): - expected = [ - 0.46784395, - 0.03562005, - 0.91319746, - 0.86014303, - 0.23796839, - 0.25856809, - 0.63636296, - 0.69455189, - 0.316758, - 0.18673652, - ] - print(self.draws_scrambled_1d.flatten()) - self.assertEqual(self.draws_scrambled_1d.shape[0], 10) - self.assertEqual(self.draws_scrambled_1d.shape[1], 1) - self.assertTrue( - np.allclose(self.draws_scrambled_1d.flatten(), np.array(expected)) - ) - - def test_Scrambled3DSobol(self): - expected_dim3 = [ - 0.19711632, - 0.43653634, - 0.79965184, - 0.08670237, - 0.70811484, - 0.90994149, - 0.29499525, - 0.83833538, - 0.46057166, - 0.15769824, - ] - self.assertEqual(self.draws_scrambled_3d.shape[0], 10) - self.assertEqual(self.draws_scrambled_3d.shape[1], 3) - self.assertTrue( - np.allclose( - self.draws_scrambled_3d[:, 2], np.array(expected_dim3), atol=1e-5 - ) - ) - - def test_Scrambled3DAsyncSobol(self): - engine_unscrambled_3d = SobolEngine(3) - draws = np.vstack([engine_unscrambled_3d.draw() for i in range(10)]) - self.assertTrue(np.array_equal(self.draws_unscrambled_3d, draws)) - - def test_ScrambledSobolBounds(self): - engine = SobolEngine(100, scramble=True) - draws = engine.draw(1000) - self.assertTrue(np.all(draws >= 0)) - self.assertTrue(np.all(draws <= 1)) - - def test_ScrambledFastForwardAndResetSobol(self): - engine_scrambled_3d = SobolEngine(3, scramble=True, seed=12345).fast_forward(5) - draws = engine_scrambled_3d.draw(5) - self.assertTrue(np.array_equal(self.draws_scrambled_3d[5:10,], draws)) - - engine_scrambled_3d.reset() - even_draws = [] - for i in range(10): - if i % 2 == 0: - even_draws.append(engine_scrambled_3d.draw()) - else: - engine_scrambled_3d.fast_forward(1) - self.assertTrue( - np.array_equal( - self.draws_scrambled_3d[[i for i in range(10) if i % 2 == 0],], - np.vstack(even_draws), - ) - ) - - def test_ScrambledDistributionSobol(self): - engine = SobolEngine(10, scramble=True, seed=12345) - draws = engine.draw(1000) - self.assertTrue( - np.allclose(np.mean(draws, axis=0), np.repeat(0.5, 10), atol=0.01) - ) - self.assertTrue( - np.allclose( - np.percentile(draws, 25, axis=0), np.repeat(0.25, 10), atol=0.01 - ) - ) - self.assertTrue( - np.allclose( - np.percentile(draws, 75, axis=0), np.repeat(0.75, 10), atol=0.01 - ) - ) - - def test_0Dim(self): - engine = SobolEngine(0) - draws = engine.draw(5) - self.assertTrue(np.array_equal(np.empty((5, 0)), draws)) - - -class MultinomialQMCTests(unittest.TestCase): - def test_MultinomialNegativePs(self): - p = np.array([0.12, 0.26, -0.05, 0.35, 0.22]) - self.assertRaises(ValueError, multinomial_qmc, 10, p) - - def test_MultinomialSumOfPTooLarge(self): - p = np.array([0.12, 0.26, 0.1, 0.35, 0.22]) - self.assertRaises(ValueError, multinomial_qmc, 10, p) - - def test_MultinomialBasicDraw(self): - p = np.array([0.12, 0.26, 0.05, 0.35, 0.22]) - expected = np.array([12, 25, 6, 34, 23]) - self.assertTrue(np.array_equal(multinomial_qmc(100, p, seed=12345), expected)) - - def test_MultinomialDistribution(self): - p = np.array([0.12, 0.26, 0.05, 0.35, 0.22]) - draws = multinomial_qmc(10000, p, seed=12345) - np.testing.assert_almost_equal(draws / np.sum(draws), p, decimal=4) - - def test_FindIndex(self): - p_cumulative = np.array([0.1, 0.4, 0.45, 0.6, 0.75, 0.9, 0.99, 1.0]) - size = len(p_cumulative) - self.assertEqual(_test_find_index(p_cumulative, size, 0.0), 0) - self.assertEqual(_test_find_index(p_cumulative, size, 0.4), 2) - self.assertEqual(_test_find_index(p_cumulative, size, 0.44999), 2) - self.assertEqual(_test_find_index(p_cumulative, size, 0.45001), 3) - self.assertEqual(_test_find_index(p_cumulative, size, 1.0), size - 1) diff --git a/test/utils/test_sampling.py b/test/utils/test_sampling.py index 0ab808968d..8279ccd5db 100644 --- a/test/utils/test_sampling.py +++ b/test/utils/test_sampling.py @@ -6,13 +6,13 @@ import torch from botorch.exceptions.warnings import SamplingWarning from botorch.posteriors.gpytorch import GPyTorchPosterior -from botorch.qmc.sobol import SobolEngine from botorch.utils.sampling import ( construct_base_samples, construct_base_samples_from_posterior, manual_seed, ) from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal +from torch.quasirandom import SobolEngine class TestConstructBaseSamples(unittest.TestCase):