diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 416a2e1..914395c 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -22,7 +22,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - poetry-version: [1.3.0] + poetry-version: [1.6.1] steps: - name: Checkout repository uses: actions/checkout@v3 @@ -59,7 +59,7 @@ jobs: matrix: os: [ubuntu, macos, windows] python-version: [3.8, 3.9, "3.10"] - poetry-version: [1.3.0] + poetry-version: [1.6.1] name: build ${{ matrix.os }} - py${{ matrix.python-version }} runs-on: ${{ matrix.os }}-latest defaults: @@ -122,7 +122,7 @@ jobs: matrix: os: [ubuntu, macos, windows] python-version: [3.8, "3.10"] # oldest and newest supported versions - poetry-version: [1.3.0] + poetry-version: [1.6.1] name: Unit-test ${{ matrix.os }} - py${{ matrix.python-version }} runs-on: ${{ matrix.os }}-latest defaults: diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..f19031c --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,24 @@ +# YAML 1.2 +--- +# Metadata for citation of this software according to the CFF format (https://citation-file-format.github.io/) +cff-version: 1.2.0 +title: 'Pywhy-Stats: Statistical inference in Python.' +abstract: 'Pywhy-Stats is a Python library that leverages a simple API for performing independence and conditional independence testing.' +authors: + - given-names: Adam + family-names: Li + affiliation: 'Department of Computer Science, Columbia University, New York, NY, USA' + orcid: 'https://orcid.org/0000-0001-8421-365X' + - given-names: Patrick + family-names: Blöbaum + affiliation: 'Amazon' + email: 'bloebp@amazon.com' +type: software +repository-code: 'https://github.com/py-why/pywhy-stats' +license: MIT +keywords: + - causality + - pywhy + - statistics + - independece testing +... \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 7054dfd..27c2b54 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -141,6 +141,11 @@ When you're ready to contribute code to address an open issue, please follow the +5. Adding your name to the CITATION.cff file + + We are a community-driven open-source project and want to make sure all contributors are acknowledged. If you are a new contributor, add your name + to the ``CITATION.cff`` file and relevant metadata. + ### Writing docstrings We use [Sphinx](https://www.sphinx-doc.org/en/master/index.html) to build our API docs, which automatically parses all docstrings diff --git a/doc/api.rst b/doc/api.rst index d5af0f5..269c72a 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -57,7 +57,7 @@ contains the p-value and the test statistic and optionally additional informatio Testing for conditional independence among variables is a core part of many data analysis procedures. -.. currentmodule:: pywhy_stats +.. currentmodule:: pywhy_stats.independence .. autosummary:: :toctree: generated/ diff --git a/doc/conditional_independence.rst b/doc/conditional_independence.rst index b9f2010..cc123af 100644 --- a/doc/conditional_independence.rst +++ b/doc/conditional_independence.rst @@ -80,20 +80,21 @@ various proposals in the literature for estimating CMI, which we summarize here: estimating :math:`P(y|x)` and :math:`P(y|x,z)`, which can be used as plug-in estimates to the equation for CMI. -:mod:`pywhy_stats.fisherz` Partial (Pearson) Correlation --------------------------------------------------------- +:mod:`pywhy_stats.independence.fisherz` Partial (Pearson) Correlation +--------------------------------------------------------------------- Partial correlation based on the Pearson correlation is equivalent to CMI in the setting of normally distributed data. Computing partial correlation is fast and efficient and thus attractive to use. However, this **relies on the assumption that the variables are Gaussiany**, which may be unrealistic in certain datasets. +.. currentmodule:: pywhy_stats.independence .. autosummary:: :toctree: generated/ fisherz -:mod:`pywhy_stats.power_divergence` Discrete, Categorical and Binary Data -------------------------------------------------------------------------- +:mod:`pywhy_stats.independence.power_divergence` Discrete, Categorical and Binary Data +-------------------------------------------------------------------------------------- If one has discrete data, then the test to use is based on Chi-square tests. The :math:`G^2` class of tests will construct a contingency table based on the number of levels across each discrete variable. An exponential amount of data is needed for increasing levels @@ -104,8 +105,8 @@ for a discrete variable. power_divergence -Kernel-Approaches ------------------ +:mod:`pywhy_stats.independence.kci` Kernel-Approaches +----------------------------------------------------- Kernel independence tests are statistical methods used to determine if two random variables are independent or conditionally independent. One such test is the Hilbert-Schmidt Independence Criterion (HSIC), which examines the independence between two random variables, X and Y. HSIC employs kernel methods and, more specifically, it computes @@ -125,6 +126,12 @@ Kernel-based tests are attractive for many applications, since they are semi-par that have been shown to be robust in the machine-learning field. For more information, see :footcite:`Zhang2011`. +.. currentmodule:: pywhy_stats.independence +.. autosummary:: + :toctree: generated/ + + kci + Classifier-based Approaches --------------------------- Another suite of approaches that rely on permutation testing is the classifier-based approach. @@ -144,9 +151,9 @@ helps maintain dependence between (X, Z) and (Y, Z) (if it exists), but generate conditionally independent dataset. -======================= -Conditional Discrepancy -======================= +========================================= +Conditional Distribution 2-Sample Testing +========================================= .. currentmodule:: pywhy_stats @@ -170,23 +177,7 @@ indices of the distribution, one can convert the CD test: :math:`P_{i=j}(y|x) =? P_{i=k}(y|x)` into the CI test :math:`P(y|x,i) = P(y|x)`, which can be tested with the Chi-square CI tests. -Kernel-Approaches ------------------ -Kernel-based tests are attractive since they are semi-parametric and use kernel-based ideas -that have been shown to be robust in the machine-learning field. The Kernel CD test is a test -that computes a test statistic from kernels of the data and uses a weighted permutation testing -based on the estimated propensity scores to generate samples from the null distribution -:footcite:`Park2021conditional`, which are then used to estimate a pvalue. - - -Bregman-Divergences -------------------- -The Bregman CD test is a divergence-based test -that computes a test statistic from estimated Von-Neumann divergences of the data and uses a -weighted permutation testing based on the estimated propensity scores to generate samples from the null distribution -:footcite:`Yu2020Bregman`, which are then used to estimate a pvalue. - ========== References ========== -.. footbibliography:: +.. footbibliography:: \ No newline at end of file diff --git a/doc/conf.py b/doc/conf.py index c28203d..c983300 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -39,7 +39,7 @@ # If your documentation needs a minimal Sphinx version, state it here. # -needs_sphinx = "4.0" +needs_sphinx = "5.0" # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom @@ -146,9 +146,7 @@ "PValueResult": "pywhy_stats.pvalue_result.PValueResult", # numpy "NDArray": "numpy.ndarray", - # "ArrayLike": "numpy.typing.ArrayLike", "ArrayLike": ":term:`array_like`", - "fisherz": "pywhy_stats.fisherz", } autodoc_typehints_format = "short" diff --git a/doc/whats_new/v0.1.rst b/doc/whats_new/v0.1.rst index b8c8024..b60dbac 100644 --- a/doc/whats_new/v0.1.rst +++ b/doc/whats_new/v0.1.rst @@ -26,7 +26,7 @@ Version 0.1 Changelog --------- -- |Feature| Implement partial correlation test :func:`pywhy_stats.fisherz`, by `Adam Li`_ (:pr:`7`) +- |Feature| Implement partial correlation test :func:`pywhy_stats.independence.fisherz`, by `Adam Li`_ (:pr:`7`) - |Feature| Add (un)conditional kernel independence test by `Patrick Blöbaum`_, co-authored by `Adam Li`_ (:pr:`14`) - |Feature| Add categorical independence tests by `Adam Li`_, (:pr:`18`) diff --git a/poetry.lock b/poetry.lock index 86785fa..199cdda 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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"more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [metadata] lock-version = "2.0" python-versions = ">=3.8,<3.11" -content-hash = "e7cba9a12224a25cdca18afe2a5e45934d25d26a297fff43e672bf73d588b854" +content-hash = "0950dc21739386b21dd452e7982865d0be658be2488804c1d6a07b546f85f08f" diff --git a/pyproject.toml b/pyproject.toml index 1c32f6f..434a7d4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -95,6 +95,9 @@ dowhy = { version = "^0.8" } typing-extensions = { version = "*" } # needed in dowhy's package joblib = { version = "^1.1.0" } # needed in dowhy's package tqdm = { version = "^4.64.0" } # needed in dowhy's package +pillow = "^9.5.0" +cycler = "^0.11.0" +kiwisolver = "^1.4.4" [tool.poe.tasks] _flake8 = 'flake8' diff --git a/pywhy_stats/__init__.py b/pywhy_stats/__init__.py index a20920c..ca55e85 100644 --- a/pywhy_stats/__init__.py +++ b/pywhy_stats/__init__.py @@ -1,3 +1,4 @@ -from . import fisherz, kci from ._version import __version__ # noqa: F401 -from .independence import Methods, independence_test +from .api import Methods, independence_test +from .independence import fisherz, kci, power_divergence +from .pvalue_result import PValueResult diff --git a/pywhy_stats/independence.py b/pywhy_stats/api.py similarity index 74% rename from pywhy_stats/independence.py rename to pywhy_stats/api.py index dd99897..8c0410e 100644 --- a/pywhy_stats/independence.py +++ b/pywhy_stats/api.py @@ -3,10 +3,11 @@ from typing import Optional from warnings import warn +import numpy as np import scipy.stats from numpy.typing import ArrayLike -from pywhy_stats import fisherz, kci +from pywhy_stats.independence import fisherz, kci from .pvalue_result import PValueResult @@ -18,10 +19,10 @@ class Methods(Enum): """Choose an automatic method based on the data.""" FISHERZ = fisherz - """:py:mod:`~pywhy_stats.fisherz`: Fisher's Z test for independence""" + """:py:mod:`pywhy_stats.independence.fisherz`: Fisher's Z test for independence""" KCI = kci - """:py:mod:`~pywhy_stats.kci`: Conditional kernel independence test""" + """:py:mod:`pywhy_stats.independence.kci`: Conditional kernel independence test""" def independence_test( @@ -59,14 +60,19 @@ def independence_test( See Also -------- - fisherz : Fisher's Z test for independence - kci : Kernel Conditional Independence test + pywhy_stats.independence.fisherz : Fisher's Z test for independence + pywhy_stats.independence.kci : Kernel Conditional Independence test """ method_module: ModuleType if method == Methods.AUTO: method_module = Methods.KCI + elif not isinstance(method, Methods): + raise ValueError( + f"Invalid method type. Expected one of {Methods.__members__.keys()}, " + f"but got {method}." + ) else: - method_module = method + method_module = method # type: ignore if method_module == Methods.FISHERZ: if condition_on is None: @@ -74,10 +80,10 @@ def independence_test( else: data = [X, Y, condition_on] for _data in data: - _, pval = scipy.stats.normaltest(_data) + res = scipy.stats.normaltest(_data, axis=0) # XXX: we should add pinguoin as an optional dependency for doing multi-comp stuff - if pval < 0.05: + if np.atleast_1d(res.pvalue).any() < 0.05: warn( "The provided data does not seem to be Gaussian, but the Fisher-Z test " "assumes that the data follows a Gaussian distribution. The result should " @@ -85,6 +91,6 @@ def independence_test( ) if condition_on is None: - return method_module.ind(X, Y, method, **kwargs) + return method_module.value.ind(X, Y, **kwargs) else: - return method_module.condind(X, Y, condition_on, method, **kwargs) + return method_module.value.condind(X, Y, condition_on, **kwargs) diff --git a/pywhy_stats/independence/__init__.py b/pywhy_stats/independence/__init__.py new file mode 100644 index 0000000..f032043 --- /dev/null +++ b/pywhy_stats/independence/__init__.py @@ -0,0 +1 @@ +from . import fisherz, kci, power_divergence diff --git a/pywhy_stats/fisherz.py b/pywhy_stats/independence/fisherz.py similarity index 97% rename from pywhy_stats/fisherz.py rename to pywhy_stats/independence/fisherz.py index eb286e7..2e70c3d 100644 --- a/pywhy_stats/fisherz.py +++ b/pywhy_stats/independence/fisherz.py @@ -4,7 +4,7 @@ It works on Gaussian random variables. When the data is not Gaussian, this test is not valid. In this case, we recommend -using the Kernel independence test at . +using the Kernel independence test at `pywhy_stats.kci`. Examples -------- @@ -21,7 +21,7 @@ from numpy.typing import ArrayLike from scipy.stats import norm -from .pvalue_result import PValueResult +from pywhy_stats.pvalue_result import PValueResult def ind(X: ArrayLike, Y: ArrayLike, correlation_matrix: Optional[ArrayLike] = None) -> PValueResult: diff --git a/pywhy_stats/kci.py b/pywhy_stats/independence/kci.py similarity index 76% rename from pywhy_stats/kci.py rename to pywhy_stats/independence/kci.py index 866c07d..6ffde69 100644 --- a/pywhy_stats/kci.py +++ b/pywhy_stats/independence/kci.py @@ -1,15 +1,23 @@ -from functools import partial +"""Independence test using Kernel test. + +Examples +-------- +>>> import pywhy_stats as ps +>>> res = ps.kci.ind([1, 2, 3], [4, 5, 6]) +>>> print(res.pvalue) +>>> 1.0 +""" from typing import Callable, Optional, Tuple import numpy as np -from numpy._typing import ArrayLike +from numpy.typing import ArrayLike from scipy import stats -from sklearn.metrics.pairwise import rbf_kernel -from pywhy_stats.kernels import delta_kernel, estimate_squared_sigma_rbf -from pywhy_stats.pvalue_result import PValueResult +from pywhy_stats.kernel_utils import _fast_centering, _preprocess_kernel_data, compute_kernel from pywhy_stats.utils import preserve_random_state +from ..pvalue_result import PValueResult + @preserve_random_state def ind( @@ -20,7 +28,9 @@ def ind( approx: bool = True, null_sample_size: int = 1000, threshold: float = 1e-5, + centered: bool = True, normalize_data: bool = True, + n_jobs: Optional[int] = None, random_seed: Optional[int] = None, ) -> PValueResult: """ @@ -35,12 +45,14 @@ def ind( Data for variable X, which can be multidimensional. Y : ArrayLike of shape (n_samples, n_features_y) Data for variable Y, which can be multidimensional. - kernel_X : Callable[[ArrayLike], ArrayLike] + kernel_X : Callable[[ArrayLike], ArrayLike], str The kernel function for X. By default, the RBF kernel is used for numeric and the delta kernel for categorical data. Note that we currently only consider string values as categorical data. - kernel_Y : Callable[[ArrayLike], ArrayLike] + For more information on how to set the kernel, see the Notes. + kernel_Y : Callable[[ArrayLike], ArrayLike], str The kernel function for Y. By default, the RBF kernel is used for continuous and the delta kernel for categorical data. Note that we currently only consider string values as categorical data. + For more information on how to set the kernel, see the Notes. approx : bool Whether to use the Gamma distribution approximation for the pvalue, by default True. null_sample_size : int @@ -49,8 +61,12 @@ def ind( threshold : float The threshold set on the value of eigenvalues, by default 1e-5. Used to regularize the method. + centered : bool + Whether to center the kernel matrix or not, by default True. normalize_data : bool Whether the data should be standardized to unit variance, by default True. + n_jobs : Optional[int], optional + The number of jobs to run computations in parallel, by default None. random_seed : Optional[int], optional Random seed, by default None. @@ -61,12 +77,31 @@ def ind( kernel_X = func:`sklearn.metrics.pairwise.pairwise_kernels.polynomial` + If the kernel is a callable, it will have either one input for ``X``, or two inputs for ``X`` and + ``Y``. It is assumed that the kernel operates on the entire array to compute + the kernel array, that is, the callable performs a vectorized operation to compute the entire kernel matrix. + + If one has an unvectorizable kernel function, then it is advised to use the callable with the + :func:`~sklearn.metrics.pairwise.pairwise_kernels` function, which will parallelize the kernel computation + across each row of the data. + References ---------- .. footbibliography:: """ test_statistic, pvalue = _kernel_test( - X, Y, None, kernel_X, kernel_Y, None, approx, null_sample_size, threshold, normalize_data + X, + Y, + None, + kernel_X, + kernel_Y, + None, + approx, + null_sample_size, + threshold, + centered=centered, + normalize_data=normalize_data, + n_jobs=n_jobs, ) return PValueResult(pvalue=pvalue, statistic=test_statistic) @@ -82,7 +117,9 @@ def condind( approx: bool = True, null_sample_size: int = 1000, threshold: float = 1e-5, + centered: bool = True, normalize_data: bool = True, + n_jobs: Optional[int] = None, random_seed: Optional[int] = None, ) -> PValueResult: """ @@ -106,12 +143,15 @@ def condind( kernel_X : Callable[[ArrayLike], ArrayLike] The kernel function for X. By default, the RBF kernel is used for continuous and the delta kernel for categorical data. Note that we currently only consider string values as categorical data. + For more information on how to set the kernel, see the Notes. kernel_Y : Callable[[ArrayLike], ArrayLike] The kernel function for Y. By default, the RBF kernel is used for continuous and the delta kernel for categorical data. Note that we currently only consider string values as categorical data. + For more information on how to set the kernel, see the Notes. kernel_Z : Callable[[ArrayLike], ArrayLike] The kernel function for Z. By default, the RBF kernel is used for continuous and the delta kernel for categorical data. Note that we currently only consider string values as categorical data. + For more information on how to set the kernel, see the Notes. approx : bool Whether to use the Gamma distribution approximation for the pvalue, by default True. null_sample_size : int @@ -120,8 +160,12 @@ def condind( threshold : float The threshold set on the value of eigenvalues, by default 1e-5. Used to regularize the method. + centered : bool + Whether to center the kernel matrices, by default True. normalize_data : bool Whether the data should be standardized to unit variance, by default True. + n_jobs : Optional[int], optional + The number of jobs to run computations in parallel, by default None. random_seed : Optional[int], optional Random seed, by default None. @@ -132,12 +176,34 @@ def condind( kernel_X = func:`sklearn.metrics.pairwise.pairwise_kernels.polynomial` + In addition, we implement an efficient delta kernel. The delta kernel can be specified using the + 'kernel' string argument. + + If the kernel is a callable, it will have either one input for ``X``, or two inputs for ``X`` and + ``Y``. It is assumed that the kernel operates on the entire array to compute + the kernel array, that is, the callable performs a vectorized operation to compute the entire kernel matrix. + + If one has an unvectorizable kernel function, then it is advised to use the callable with the + :func:`~sklearn.metrics.pairwise.pairwise_kernels` function, which will parallelize the kernel computation + across each row of the data. + References ---------- .. footbibliography:: """ test_statistic, pvalue = _kernel_test( - X, Y, Z, kernel_X, kernel_Y, kernel_Z, approx, null_sample_size, threshold, normalize_data + X, + Y, + Z, + kernel_X, + kernel_Y, + kernel_Z, + approx, + null_sample_size, + threshold, + centered=centered, + normalize_data=normalize_data, + n_jobs=n_jobs, ) return PValueResult(pvalue=pvalue, statistic=test_statistic) @@ -153,45 +219,35 @@ def _kernel_test( null_sample_size: int, threshold: float, normalize_data: bool, + centered: bool, + n_jobs: Optional[int], ) -> Tuple[float, float]: - if X.ndim == 1: - X = X.reshape(-1, 1) - if Y.ndim == 1: - Y = Y.reshape(-1, 1) - if Z is not None and Z.ndim == 1: - Z = Z.reshape(-1, 1) - - if normalize_data: - # first normalize the data to have zero mean and unit variance - # along the columns of the data - X = _normalize_data(X) - Y = _normalize_data(Y) - if Z is not None: - Z = _normalize_data(Z) - - if kernel_X is None: - kernel_X = _get_default_kernel(X) - if kernel_Y is None: - kernel_Y = _get_default_kernel(Y) - - Kx = kernel_X(X) - Ky = kernel_Y(Y) + X, Y, Z = _preprocess_kernel_data(X, Y, Z, normalize_data) + + Ky = compute_kernel(Y, kernel=kernel_Y, centered=centered, n_jobs=n_jobs) + + # compute kernels in each data space if Z is not None: - if kernel_Z is None: - kernel_Z = _get_default_kernel(Z) - Kz = kernel_Z(Z) - # Equivalent to concatenating them beforehand. - # However, here we can then have individual kernels. - Kx = Kx * Kz - Kz = _fast_centering(Kz) - - Kx = _fast_centering(Kx) - Ky = _fast_centering(Ky) - - if Z is None: - return _ind(Kx, Ky, approx, null_sample_size, threshold) - else: + Kz = compute_kernel(Z, kernel=kernel_Z, centered=False, n_jobs=n_jobs) + + Kx = compute_kernel(X, kernel=kernel_X, centered=False, n_jobs=n_jobs) + Kx *= Kz # type: ignore + + if centered: + Kx = _fast_centering(Kx) + Kz = _fast_centering(Kz) + return _cond(Kx, Ky, Kz, approx, null_sample_size, threshold) + else: + Kx = compute_kernel(X, kernel=kernel_X, centered=centered, n_jobs=n_jobs) + + return _ind( + Kx, + Ky, + approx, + null_sample_size, + threshold, + ) def _ind(Kx, Ky, approx, null_sample_size, threshold): @@ -365,38 +421,3 @@ def _compute_null_ci(uu_prod, n_samples, threshold): # of chi-squared random variables weighted by the eigenvalue products null_dist = eig_uu.T.dot(f_rand) return null_dist - - -def _fast_centering(k: np.ndarray) -> np.ndarray: - """ - Compute centered kernel matrix in time O(n^2). - - The centered kernel matrix is defined as K_c = H @ K @ H, with - H = identity - 1/ n * ones(n,n). Computing H @ K @ H via matrix multiplication scales with n^3. - The implementation circumvents this and runs in time n^2. - - Originally authored by Jonas Kuebler - """ - n = len(k) - return ( - k - - 1 / n * np.outer(np.ones(n), np.sum(k, axis=0)) - - 1 / n * np.outer(np.sum(k, axis=1), np.ones(n)) - + 1 / n**2 * np.sum(k) * np.ones((n, n)) - ) - - -def _get_default_kernel(X: np.ndarray) -> Callable[[np.ndarray], np.ndarray]: - if X.dtype.type != np.str_: - return partial(rbf_kernel, gamma=0.5 * estimate_squared_sigma_rbf(X)) - else: - return delta_kernel - - -def _normalize_data(X: np.ndarray) -> np.ndarray: - for column in range(X.shape[1]): - if isinstance(X[0, column], int) or isinstance(X[0, column], float): - X[:, column] = stats.zscore(X[:, column]) - X[:, column] = np.nan_to_num(X[:, column]) # in case some dim of X is constant - - return X diff --git a/pywhy_stats/power_divergence.py b/pywhy_stats/independence/power_divergence.py similarity index 99% rename from pywhy_stats/power_divergence.py rename to pywhy_stats/independence/power_divergence.py index b3372e4..6dd679d 100644 --- a/pywhy_stats/power_divergence.py +++ b/pywhy_stats/independence/power_divergence.py @@ -35,7 +35,7 @@ from scipy import stats from sklearn.preprocessing import LabelEncoder -from .pvalue_result import PValueResult +from ..pvalue_result import PValueResult def ind( diff --git a/pywhy_stats/kernel_utils.py b/pywhy_stats/kernel_utils.py new file mode 100644 index 0000000..cf11424 --- /dev/null +++ b/pywhy_stats/kernel_utils.py @@ -0,0 +1,279 @@ +from typing import Callable, Dict, Optional, Tuple, Union + +import numpy as np +from numpy.typing import ArrayLike +from scipy import stats +from scipy.linalg import logm +from scipy.optimize import minimize_scalar +from sklearn.metrics import pairwise_kernels +from sklearn.metrics.pairwise import PAIRWISE_KERNEL_FUNCTIONS + +from pywhy_stats.kernels import delta_kernel, estimate_squared_sigma_rbf +from pywhy_stats.utils import TemporarilySetKey + + +def _default_regularization(K: ArrayLike) -> float: + """Compute a default regularization for Kernel Logistic Regression. + + Parameters + ---------- + K : ArrayLike of shape (n_samples, n_samples) + The kernel matrix. + + Returns + ------- + x : float + The default l2 regularization term. + """ + n_samples = K.shape[0] + svals = np.linalg.svd(K, compute_uv=False, hermitian=True) + res = minimize_scalar( + lambda reg: np.sum(svals**2 / (svals + reg) ** 2) / n_samples + reg, + bounds=(0.0001, 1000), + method="bounded", + ) + return res.x + + +def _fast_centering(k: ArrayLike) -> ArrayLike: + """ + Compute centered kernel matrix in time O(n^2). + + The centered kernel matrix is defined as K_c = H @ K @ H, with + H = identity - 1/ n * ones(n,n). Computing H @ K @ H via matrix multiplication scales with n^3. + The implementation circumvents this and runs in time n^2. + + Originally authored by Jonas Kuebler + """ + n = len(k) + return ( + k + - 1 / n * np.outer(np.ones(n), np.sum(k, axis=0)) + - 1 / n * np.outer(np.sum(k, axis=1), np.ones(n)) + + 1 / n**2 * np.sum(k) * np.ones((n, n)) + ) + + +def _get_default_kernel(X: ArrayLike) -> Tuple[str, Dict]: + """Attempt to infer the kernel function from the data type of X. + + Parameters + ---------- + X : ArrayLike + Data array. If the data type is not string, the RBF kernel is used. + If data type is string, the delta kernel is used. + + Returns + ------- + Callable[[ArrayLike], ArrayLike] + The kernel function. + """ + if X.dtype.type not in (np.str_, np.object_): + return "rbf", {"gamma": 0.5 * estimate_squared_sigma_rbf(X)} + else: + return "delta", dict() + + +def _normalize_data(X: ArrayLike) -> ArrayLike: + """Normalize data to zero mean and unit variance.""" + for column in range(X.shape[1]): + if isinstance(X[0, column], int) or isinstance(X[0, column], float): + X[:, column] = stats.zscore(X[:, column]) + X[:, column] = np.nan_to_num(X[:, column]) # in case some dim of X is constant + return X + + +def compute_kernel( + X: ArrayLike, + Y: Optional[ArrayLike] = None, + kernel: Optional[Union[Callable, str]] = None, + centered: bool = True, + n_jobs: Optional[int] = None, +) -> Tuple[ArrayLike, float]: + """Compute a kernel matrix. + + If no kernel is specified, chooses a data appropriate kernel and estimates the kernel width parameter. + + Parameters + ---------- + X : ArrayLike of shape (n_samples_X, n_features_X) + The X array. + Y : ArrayLike of shape (n_samples_Y, n_features_Y), optional + The Y array, by default None. + kernel : str, optional + Either a callable that computes a kernel matrix or a metric string as defined in + :func:`sklearn.metrics.pairwise.pairwise_kernels`. Note 'rbf' + and 'gaussian' are the same metric. If None is given, the kernel is inferred based on the data type, which is + currently either the rbf kernel for continuous data or the delta kernel for categorical (string) data. + centered : bool, optional + Whether to center the kernel matrix or not, by default True. + When centered, the kernel matrix induces a zero mean. The main purpose of + centering is to remove the bias or mean shift from the data represented + by the kernel matrix. + n_jobs : int, optional + The number of jobs to run computations in parallel, by default None. This only works for string kernel inputs. + + Returns + ------- + kernel : ArrayLike of shape (n_samples_X, n_samples_X) or (n_samples_X, n_samples_Y) + The kernel matrix. + + Notes + ----- + If the kernel is a callable, it will have either one input for ``X``, or two inputs for ``X`` and + ``Y``. It is assumed that the kernel operates on the entire array to compute + the kernel array, that is, the callable performs a vectorized operation to compute the entire kernel matrix. + + If one has an unvectorizable kernel function, then it is advised to use the callable with the + :func:`~sklearn.metrics.pairwise.pairwise_kernels` function, which will parallelize the kernel computation + across each row of the data. + """ + # Note that this is added to the list of possible kernels for :func:`~sklearn.metrics.pairwise.pairwise_kernels`. + # because it is more efficient to compute the kernel over the entire matrices at once + # since numpy has vectorized operations. + with TemporarilySetKey(PAIRWISE_KERNEL_FUNCTIONS, "delta", delta_kernel): + PAIRWISE_KERNEL_FUNCTIONS["delta"] = delta_kernel + + # if the width of the kernel is not set, then use the median trick to set the + # kernel width based on the data X + if kernel is None: + metric, kernel_params = _get_default_kernel(X) + kernel_matrix = pairwise_kernels( + X, Y=Y, metric=metric, n_jobs=n_jobs, filter_params=False, **kernel_params + ) + elif isinstance(kernel, str): + kernel_matrix = pairwise_kernels( + X, Y=Y, metric=kernel, n_jobs=n_jobs, filter_params=False + ) + else: + # kernel is a callable + if Y is None: + kernel_matrix = kernel(X) + else: + kernel_matrix = kernel(X, Y) + + if centered: + kernel_matrix = _fast_centering(kernel_matrix) + + return kernel_matrix + + +def correntropy_matrix( + data: ArrayLike, + kernel: Optional[Union[str, Callable[[ArrayLike], ArrayLike]]] = None, + centered: bool = True, + n_jobs: Optional[int] = None, +) -> ArrayLike: + r"""Compute the centered correntropy of a matrix. + + Parameters + ---------- + data : ArrayLike of shape (n_samples, n_features) + The data. + kernel : str, Callable, optional + The kernel. + centered : bool, optional + Whether to center the kernel matrix or not, by default True. + n_jobs : int, optional + The number of jobs to run computations in parallel, by default None. + + Returns + ------- + data : ArrayLike of shape (n_features, n_features) + A symmetric centered correntropy matrix of the data. + + Notes + ----- + The estimator for the correntropy array is given by the formula + :math:`1 / N \\sum_{i=1}^N k(x_i, y_i) - 1 / N**2 \\sum_{i=1}^N \\sum_{j=1}^N k(x_i, y_j)`. + The first term is the estimate, and the second term is the bias, and together they form + an unbiased estimate. + """ + n_samples, n_features = data.shape + corren_arr = np.zeros(shape=(n_features, n_features)) + + # compute kernel between each feature, which is now (n_features, n_features) array + for idx in range(n_features): + for jdx in range(idx + 1): + K = compute_kernel( + X=data[:, [idx]], + Y=data[:, [jdx]], + kernel=kernel, + centered=centered, + n_jobs=n_jobs, + ) + + # compute the bias due to finite-samples + bias = np.sum(K) / n_samples**2 + + # compute the sample centered correntropy + corren = (1.0 / n_samples) * np.trace(K) - bias + + corren_arr[idx, jdx] = corren_arr[jdx, idx] = corren + return corren_arr + + +def von_neumann_divergence(A: ArrayLike, B: ArrayLike) -> float: + """Compute Von Neumann divergence between two PSD matrices. + + Parameters + ---------- + A : ArrayLike of shape (n_features, n_features) + The first PSD matrix. + B : ArrayLike of shape (n_features, n_features) + The second PSD matrix. + + Returns + ------- + div : float + The divergence value. + + Notes + ----- + The Von Neumann divergence, or what is known as the Bregman divergence in + :footcite:`Yu2020Bregman` is computed as follows with + :math:`D(A || B) = Tr(A (log(A) - log(B)) - A + B)`. + """ + div = np.trace(A.dot(logm(A) - logm(B)) - A + B) + return div + + +def _preprocess_kernel_data( + X: ArrayLike, + Y: ArrayLike, + Z: Optional[ArrayLike] = None, + normalize_data: bool = True, +): + """Preprocess the data for kernel methods. + + Parameters + ---------- + X : ArrayLike of shape (n_samples_X, n_features_X) + The X array. + Y : ArrayLike of shape (n_samples_Y, n_features_Y) + The Y array. + Z : ArrayLike of shape (n_samples_Z, n_features_Z), optional + The Z array, by default None. + normalize_data : bool + Whether to standard-normalize the data or not. + """ + X = np.array(X) + Y = np.array(Y) + if Z is not None: + Z = np.array(Z) + + if X.ndim == 1: + X = X.reshape(-1, 1) + if Y.ndim == 1: + Y = Y.reshape(-1, 1) + if Z is not None and Z.ndim == 1: + Z = Z.reshape(-1, 1) + + if normalize_data: + # first normalize the data to have zero mean and unit variance + # along the columns of the data + X = _normalize_data(X) + Y = _normalize_data(Y) + if Z is not None: + Z = _normalize_data(Z) + return X, Y, Z diff --git a/pywhy_stats/kernels.py b/pywhy_stats/kernels.py index 1bc4763..f05d6d2 100644 --- a/pywhy_stats/kernels.py +++ b/pywhy_stats/kernels.py @@ -2,31 +2,29 @@ from numpy._typing import ArrayLike from scipy.stats import iqr from sklearn.metrics import pairwise_distances +from sklearn.metrics.pairwise import check_pairwise_arrays -def delta_kernel(X: np.ndarray) -> np.ndarray: +def delta_kernel(X: ArrayLike, Y=None) -> ArrayLike: """Delta kernel for categorical values. This is, the similarity is 1 if the values are equal and 0 otherwise. Parameters ---------- - X : ArrayLike of shape (n_samples, n_columns) + X : ArrayLike of shape (n_samples_x, n_dimensions) Input data. + Y : ArrayLike of shape (n_samples_y, n_dimensions), optional + By default None. Returns ------- result : ArrayLike of shape (n_samples, n_samples) The resulting kernel matrix after applying the delta kernel. """ - if X.ndim == 1: - X = X.reshape(-1, 1) + X, Y = check_pairwise_arrays(X, Y, dtype=str) - return ( - np.array(list(map(lambda value: value == X, X))) - .reshape(X.shape[0], X.shape[0]) - .astype(np.float32) - ) + return np.char.equal(X[:, np.newaxis], Y).all(axis=-1).astype(int) def estimate_squared_sigma_rbf( @@ -54,14 +52,12 @@ def estimate_squared_sigma_rbf( The estimated sigma**2 in K(x, x') = exp(-||x - x'|| / (2 * sigma**2)). """ if method == "silverman": - if X.ndim > 1: - if X.shape[1] > 1: - raise ValueError( - "The Silverman method to estimate the kernel bandwidth is currently only " - "supported for one dimensional data!" - ) - else: - X = X.reshape(-1) + if X.ndim > 1 and X.shape[1] > 1: + raise ValueError( + "The Silverman method to estimate the kernel bandwidth is currently only " + "supported for one dimensional data!" + ) + X = X.reshape(-1) # https://en.wikipedia.org/wiki/Kernel_density_estimation#A_rule-of-thumb_bandwidth_estimator return 1 / (0.9 * np.min([np.std(X), iqr(X) / 1.34]) * (X.shape[0] ** (-1 / 5))) ** 2 diff --git a/pywhy_stats/utils.py b/pywhy_stats/utils.py index 1301d72..8205476 100644 --- a/pywhy_stats/utils.py +++ b/pywhy_stats/utils.py @@ -5,6 +5,30 @@ import numpy as np +class TemporarilySetKey: + """A utility class for temporarily setting a key in a dictionary to a value. + + Used for context managers. + """ + + def __init__(self, dictionary, key, value): + self.dictionary = dictionary + self.key = key + self.value = value + self.was_set = False + + def __enter__(self): + if self.key in self.dictionary: + self.was_set = True + self.dictionary[self.key] = self.value + + def __exit__(self, exc_type, exc_value, traceback): + if not self.was_set: + del self.dictionary[self.key] + else: + self.dictionary[self.key] = self.value + + def preserve_random_state(func: Callable) -> Callable: """ Decorate function for setting and preserving the state of random generators before and after calling it. diff --git a/tests/test_api.py b/tests/test_api.py new file mode 100644 index 0000000..b2f84e8 --- /dev/null +++ b/tests/test_api.py @@ -0,0 +1,38 @@ +import numpy as np +import pytest + +from pywhy_stats import Methods, PValueResult, independence_test + +rng = np.random.default_rng(0) + + +def test_independence_test(): + # Replace these arrays with appropriate test data for your specific case + X = rng.standard_normal(size=(20, 3)) # 10 samples, 3 features for X + Y = rng.standard_normal(size=(20, 3)) # 10 samples, 3 features for Y + condition_on = rng.standard_normal(size=(20, 3)) # 10 samples, 3 features for condition_on + + # Test case for unconditional independence test + result = independence_test(X, Y, method=Methods.FISHERZ) + assert isinstance(result, PValueResult) + + # Test case for conditional independence test + result_cond = independence_test(X, Y, condition_on=condition_on, method=Methods.KCI) + assert isinstance(result_cond, PValueResult) + + # Add more test cases as needed for other scenarios and method combinations + # ... + + # Test unsupported method + with pytest.raises(ValueError, match="Invalid method type."): + independence_test(X, Y, method="unsupported_method") + + # Test invalid data is caught with testing for Gaussianity + n_samples = 1000 + X1 = rng.uniform(low=-5, high=0, size=(n_samples // 2, 3)) # 10 samples, 3 features for X + X2 = rng.normal(loc=10, scale=1, size=(n_samples // 2, 3)) # 10 samples, 3 features for X + X = np.concatenate((X1, X2), axis=0) + Y = rng.uniform(size=(n_samples, 3)) # 10 samples, 3 features for X + condition_on = rng.uniform(size=(n_samples, 3)) # 10 samples, 3 features for condition_on + with pytest.warns(UserWarning, match="The provided data does not seem to be Gaussian"): + independence_test(X, Y, condition_on, method=Methods.FISHERZ) diff --git a/tests/test_kci.py b/tests/test_kci.py index a1df2bb..563db2c 100644 --- a/tests/test_kci.py +++ b/tests/test_kci.py @@ -1,6 +1,8 @@ +from functools import partial + import numpy as np from flaky import flaky -from sklearn.metrics.pairwise import linear_kernel, polynomial_kernel, rbf_kernel +from sklearn.metrics.pairwise import pairwise_kernels, rbf_kernel from pywhy_stats import kci from pywhy_stats.kernels import delta_kernel @@ -8,12 +10,15 @@ #################################################### # Unconditional independence tests - Continuous #################################################### +seed = 12345 +rng = np.random.default_rng(seed) @flaky(max_runs=3) def test_given_continuous_independent_data_when_perform_kernel_based_test_then_not_reject(): - x = np.random.randn(1000, 1) - y = np.exp(np.random.rand(1000, 1)) + n_samples = 300 + x = rng.standard_normal((n_samples, 1)) + y = np.exp(rng.uniform(size=(n_samples, 1))) assert kci.ind(x, y, approx=True).pvalue > 0.05 assert kci.ind(x, y, approx=False).pvalue > 0.05 @@ -27,9 +32,10 @@ def test_given_continuous_dependent_data_when_perform_kernel_based_test_then_rej X <- Z -> Y """ - z = np.random.randn(1000, 1) - x = np.exp(z + np.random.rand(1000, 1)) - y = np.exp(z + np.random.rand(1000, 1)) + n_samples = 200 + z = rng.standard_normal((n_samples, 1)) + x = np.exp(z + rng.uniform(size=(n_samples, 1))) + y = np.exp(z + rng.uniform(size=(n_samples, 1))) assert kci.ind(x, y, approx=True).pvalue < 0.05 assert kci.ind(x, y, approx=False).pvalue < 0.05 @@ -42,9 +48,10 @@ def test_given_continuous_dependent_data_when_perform_kernel_based_test_then_rej @flaky(max_runs=3) def test_given_continuous_conditionally_independent_data_when_perform_kernel_based_test_then_not_reject(): - z = np.random.randn(1000, 1) - x = np.exp(z + np.random.rand(1000, 1)) - y = np.exp(z + np.random.rand(1000, 1)) + n_samples = 200 + z = rng.standard_normal((n_samples, 1)) + x = np.exp(z + rng.uniform(size=(n_samples, 1))) + y = np.exp(z + rng.uniform(size=(n_samples, 1))) assert kci.condind(x, y, z, approx=True).pvalue > 0.05 assert kci.condind(x, y, z, approx=False).pvalue > 0.05 @@ -52,13 +59,14 @@ def test_given_continuous_conditionally_independent_data_when_perform_kernel_bas @flaky(max_runs=3) def test_given_continuous_conditionally_dependent_data_when_perform_kernel_based_test_then_reject(): - z = np.random.randn(1000, 1) - w = np.random.randn(1000, 1) - x = np.exp(z + np.random.rand(1000, 1)) - y = np.exp(z + np.random.rand(1000, 1)) + n_samples = 200 + z = rng.standard_normal((n_samples, 1)) + w = rng.standard_normal((n_samples, 1)) + x = np.exp(z + rng.uniform(size=(n_samples, 1))) + y = np.exp(z + rng.uniform(size=(n_samples, 1))) - assert kci.condind(x, y, w, approx=True).pvalue < 0.05 assert kci.condind(x, y, w, approx=False).pvalue < 0.05 + assert kci.condind(x, y, w, approx=True).pvalue < 0.05 #################################################### @@ -68,8 +76,9 @@ def test_given_continuous_conditionally_dependent_data_when_perform_kernel_based @flaky(max_runs=3) def test_given_categorical_independent_data_when_perform_kernel_based_test_then_not_reject(): - x = np.random.normal(0, 1, 1000) - y = (np.random.choice(2, 1000) == 1).astype(str) + n_samples = 200 + x = rng.normal(0, 1, n_samples) + y = (rng.choice(2, n_samples) == 1).astype(str) assert kci.ind(x, y, approx=True).pvalue > 0.05 assert kci.ind(x, y, approx=False).pvalue > 0.05 @@ -77,7 +86,8 @@ def test_given_categorical_independent_data_when_perform_kernel_based_test_then_ @flaky(max_runs=3) def test_given_categorical_dependent_data_when_perform_kernel_based_test_then_reject(): - x = np.random.normal(0, 1, 1000) + n_samples = 200 + x = rng.normal(0, 1, n_samples) y = [] for v in x: @@ -93,7 +103,8 @@ def test_given_categorical_dependent_data_when_perform_kernel_based_test_then_re @flaky(max_runs=3) def test_given_dependent_mixed_data_types_when_perform_kernel_based_test_then_reject(): - x = np.random.normal(0, 1, 1000) + n_samples = 200 + x = rng.normal(0, 1, n_samples) y = [] for v in x: @@ -103,7 +114,7 @@ def test_given_dependent_mixed_data_types_when_perform_kernel_based_test_then_re y.append(1) y = np.array(y).astype(str) - w = np.random.normal(0, 1, 1000) + w = rng.normal(0, 1, n_samples) y = np.vstack([y, w]).T def my_custom_kernel(X): @@ -122,14 +133,15 @@ def my_custom_kernel(X): @flaky(max_runs=3) def test_given_categorical_conditionally_independent_data_when_perform_kernel_based_test_then_not_reject(): - x = np.random.normal(0, 1, 1000) + n_samples = 300 + x = rng.normal(0, 1, n_samples) z = [] for v in x: if v > 0: z.append(0) else: z.append(1) - y = z + np.random.randn(len(z)) + y = z + rng.standard_normal(len(z)) z = np.array(z).astype(str) z[z == "0"] = "Class 1" z[z == "1"] = "Class 2" @@ -140,14 +152,15 @@ def test_given_categorical_conditionally_independent_data_when_perform_kernel_ba @flaky(max_runs=3) def test_given_categorical_conditionally_dependent_data_when_perform_kernel_based_test_then_reject(): - x = np.random.normal(0, 1, 1000) + n_samples = 200 + x = rng.normal(0, 1, n_samples) z = [] for v in x: if v > 0: z.append(0) else: z.append(1) - y = z + np.random.randn(len(z)) + y = z + rng.standard_normal(len(z)) z = np.array(z).astype(str) z[z == "0"] = "Class 1" z[z == "1"] = "Class 2" @@ -156,20 +169,26 @@ def test_given_categorical_conditionally_dependent_data_when_perform_kernel_base assert kci.condind(x, z, y, approx=False).pvalue < 0.05 +#################################################### +# Conditional independence tests - Mixed Data Types +#################################################### + + @flaky(max_runs=3) def test_given_conditionally_dependent_mixed_data_types_with_custom_kernel_when_perform_kernel_based_test_then_reject(): - x = np.random.normal(0, 1, 1000) + n_samples = 200 + x = rng.normal(0, 1, n_samples) z = [] for v in x: if v > 0: z.append(0) else: z.append(1) - y = z + np.random.randn(len(z)) + y = z + rng.standard_normal(len(z)) z = np.array(z).astype(str) z[z == "0"] = "Class 1" z[z == "1"] = "Class 2" - w = np.random.normal(0, 1, 1000) + w = rng.normal(0, 1, n_samples) z = np.vstack([z, w]).T def my_custom_kernel(X): @@ -188,38 +207,43 @@ def my_custom_kernel(X): @flaky(max_runs=3) def test_given_gaussian_data_and_linear_kernel_when_perform_kernel_based_test_then_returns_expected_result(): - X = np.random.randn(300, 1) - X1 = np.random.randn(300, 1) - Y = X + X1 + 0.5 * np.random.randn(300, 1) - Z = Y + 0.5 * np.random.randn(300, 1) - - assert kci.ind(X, X1, kernel_X=linear_kernel, kernel_Y=linear_kernel).pvalue > 0.05 - assert kci.ind(X, Z, kernel_X=linear_kernel, kernel_Y=linear_kernel).pvalue < 0.05 + n_samples = 300 + X = rng.standard_normal((n_samples, 1)) + X1 = rng.standard_normal((n_samples, 1)) + Y = X + X1 + 0.5 * rng.standard_normal((n_samples, 1)) + Z = Y + 0.5 * rng.standard_normal((n_samples, 1)) + + assert kci.ind(X, X1, kernel_X="linear", kernel_Y="linear").pvalue > 0.05 + assert kci.ind(X, Z, kernel_X="linear", kernel_Y="linear").pvalue < 0.05 assert ( - kci.condind( - X, Z, Y, kernel_X=linear_kernel, kernel_Y=linear_kernel, kernel_Z=linear_kernel - ).pvalue - > 0.05 + kci.condind(X, Z, Y, kernel_X="linear", kernel_Y="linear", kernel_Z="linear").pvalue > 0.05 ) @flaky(max_runs=3) def test_given_gaussian_data_and_polynomial_kernel_when_perform_kernel_based_test_then_returns_expected_result(): - X = np.random.randn(300, 1) - X1 = np.random.randn(300, 1) - Y = X + X1 + 0.5 * np.random.randn(300, 1) - Z = Y + 0.5 * np.random.randn(300, 1) + X = rng.standard_normal((300, 1)) + X1 = rng.standard_normal((300, 1)) + Y = X + X1 + 0.5 * rng.standard_normal((300, 1)) + Z = Y + 0.5 * rng.standard_normal((300, 1)) - assert kci.ind(X, X1, kernel_X=polynomial_kernel, kernel_Y=polynomial_kernel).pvalue > 0.05 - assert kci.ind(X, Z, kernel_X=polynomial_kernel, kernel_Y=polynomial_kernel).pvalue < 0.05 + assert kci.ind(X, X1, kernel_X="polynomial", kernel_Y="polynomial").pvalue > 0.05 + assert kci.ind(X, Z, kernel_X="polynomial", kernel_Y="polynomial").pvalue < 0.05 assert ( kci.condind( X, Z, Y, - kernel_X=polynomial_kernel, - kernel_Y=polynomial_kernel, - kernel_Z=polynomial_kernel, + kernel_X="polynomial", + kernel_Y="polynomial", + kernel_Z="polynomial", ).pvalue > 0.05 ) + + assert ( + kci.ind( + X, Z, kernel_X=partial(pairwise_kernels, metric="polynomial"), kernel_Y="polynomial" + ).pvalue + < 0.05 + ) diff --git a/tests/test_kernels.py b/tests/test_kernels.py index 94ea37e..34b96f6 100644 --- a/tests/test_kernels.py +++ b/tests/test_kernels.py @@ -1,7 +1,10 @@ import numpy as np import pytest +from numpy.testing import assert_array_equal +from sklearn.metrics.pairwise import pairwise_kernels -from pywhy_stats.kernels import estimate_squared_sigma_rbf +from pywhy_stats.kernel_utils import compute_kernel +from pywhy_stats.kernels import delta_kernel, estimate_squared_sigma_rbf def test_given_simple_data_when_estimate_squared_sigma_rbf_then_returns_correct_results(): @@ -13,3 +16,18 @@ def test_given_simple_data_when_estimate_squared_sigma_rbf_then_returns_correct_ # 1 / median_distance_between_points**2 assert estimate_squared_sigma_rbf(X, method="median") == pytest.approx(0.1111111111111111) + + +def test_delta_kernel_notworks_with_sklearn(): + X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + Y = np.array([[1, 2, 3], [7, 8, 9], [10, 11, 12]]) + + kernel = delta_kernel(X, Y) + assert_array_equal(kernel, np.array([[1, 0, 0], [0, 0, 0], [0, 1, 0]])) + + # this should work + kernel = compute_kernel(X, Y, kernel="delta") + + with pytest.raises(ValueError, match="Unknown kernel"): + kernel = pairwise_kernels(X, Y, metric="delta") + # assert_array_equal(kernel, np.array([[1, 0], [0, 0], [0, 1]])) diff --git a/tests/test_power_divergence.py b/tests/test_power_divergence.py index 11accc2..b4b247a 100644 --- a/tests/test_power_divergence.py +++ b/tests/test_power_divergence.py @@ -5,7 +5,7 @@ import pytest from numpy.testing import assert_almost_equal -from pywhy_stats import power_divergence +from pywhy_stats.independence import power_divergence from .testdata import testdata