Added class method to nonparametric. (8279cb97) · Commits · dsbowen / multiple-inference

.devcontainer/Dockerfile

0 → 100644

+22 −0

Original line number	Diff line number	Diff line
		# See here for image contents: https://github.com/microsoft/vscode-dev-containers/tree/v0.245.2/containers/python-3/.devcontainer/base.Dockerfile

		# [Choice] Python version (use -bullseye variants on local arm64/Apple Silicon): 3, 3.10, 3.9, 3.8, 3.7, 3.6, 3-bullseye, 3.10-bullseye, 3.9-bullseye, 3.8-bullseye, 3.7-bullseye, 3.6-bullseye, 3-buster, 3.10-buster, 3.9-buster, 3.8-buster, 3.7-buster, 3.6-buster
		ARG VARIANT="3.10-bullseye"
		FROM mcr.microsoft.com/vscode/devcontainers/python:0-${VARIANT}

		# [Choice] Node.js version: none, lts/*, 16, 14, 12, 10
		ARG NODE_VERSION="none"
		RUN if [ "${NODE_VERSION}" != "none" ]; then su vscode -c "umask 0002 && . /usr/local/share/nvm/nvm.sh && nvm install ${NODE_VERSION} 2>&1"; fi

		# [Optional] If your pip requirements rarely change, uncomment this section to add them to the image.
		COPY requirements.txt /tmp/pip-tmp/
		RUN pip3 --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt \
		&& rm -rf /tmp/pip-tmp
		RUN pip3 install ipykernel

		# [Optional] Uncomment this section to install additional OS packages.
		# RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
		# && apt-get -y install --no-install-recommends <your-package-list-here>

		# [Optional] Uncomment this line to install global node packages.
		# RUN su vscode -c "source /usr/local/share/nvm/nvm.sh && npm install -g <your-package-here>" 2>&1
		No newline at end of file

.devcontainer/devcontainer.json

0 → 100644

+58 −0

Original line number	Diff line number	Diff line
		// For format details, see https://aka.ms/devcontainer.json. For config options, see the README at:
		// https://github.com/microsoft/vscode-dev-containers/tree/v0.245.2/containers/python-3
		{
		"name": "multiple-inference",
		"build": {
		"dockerfile": "Dockerfile",
		"context": "..",
		"args": {
		// Update 'VARIANT' to pick a Python version: 3, 3.10, 3.9, 3.8, 3.7, 3.6
		// Append -bullseye or -buster to pin to an OS version.
		// Use -bullseye variants on local on arm64/Apple Silicon.
		"VARIANT": "3.10-bullseye",
		// Options
		"NODE_VERSION": "lts/*"
		}
		},

		// Configure tool-specific properties.
		"customizations": {
		// Configure properties specific to VS Code.
		"vscode": {
		// Set default container specific settings.json values on container create.
		"settings": {
		"python.defaultInterpreterPath": "/usr/local/bin/python",
		"python.linting.enabled": true,
		"python.linting.pylintEnabled": true,
		"python.formatting.autopep8Path": "/usr/local/py-utils/bin/autopep8",
		"python.formatting.blackPath": "/usr/local/py-utils/bin/black",
		"python.formatting.yapfPath": "/usr/local/py-utils/bin/yapf",
		"python.linting.banditPath": "/usr/local/py-utils/bin/bandit",
		"python.linting.flake8Path": "/usr/local/py-utils/bin/flake8",
		"python.linting.mypyPath": "/usr/local/py-utils/bin/mypy",
		"python.linting.pycodestylePath": "/usr/local/py-utils/bin/pycodestyle",
		"python.linting.pydocstylePath": "/usr/local/py-utils/bin/pydocstyle",
		"python.linting.pylintPath": "/usr/local/py-utils/bin/pylint"
		},

		// Add the IDs of extensions you want installed when the container is created.
		"extensions": [
		"ms-python.python",
		"ms-python.vscode-pylance",
		"ms-toolsai.jupyter",
		"njpwerner.autodocstring",
		"oderwat.indent-rainbow",
		"janisdd.vscode-edit-csv"
		]
		}
		},

		// Use 'forwardPorts' to make a list of ports inside the container available locally.
		// "forwardPorts": [],

		// Use 'postCreateCommand' to run commands after the container is created.
		"postCreateCommand": "pip3 install -e .",

		// Comment out to connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.
		"remoteUser": "vscode"
		}

src/multiple_inference/confidence_set.py

+8 −2

Original line number	Diff line number	Diff line
		@@ -79,7 +79,9 @@ def _test_hypotheses(
		# stepwise rejection will take a long time with more than 10000 parameters
		rejected, newly_rejected = np.full(z_stat_rvs.shape[1], False), None
		while newly_rejected is None or (newly_rejected.any() and not rejected.all()):
		critical_value = np.quantile(z_stat_rvs[:, ~rejected].max(axis=1), 1 - alpha)
		critical_value = np.quantile(
		z_stat_rvs[:, ~rejected].max(axis=1), 1 - alpha
		)
		newly_rejected = (z_stats > critical_value) & ~rejected
		rejected = rejected \| newly_rejected
		else:
		@@ -116,7 +118,11 @@ class ConfidenceSetResults(ResultsBase):
		)
		/ self._std_diagonal
		)
		self._max_z_stats = self._z_stat_rvs.copy() if self.model.n_params == 1 else abs(self._z_stat_rvs).max(axis=1)
		self._max_z_stats = (
		self._z_stat_rvs.copy()
		if self.model.n_params == 1
		else abs(self._z_stat_rvs).max(axis=1)
		)
		self._set_pvalues()
		self._set_qvalues()

src/multiple_inference/stats.py

+38 −1

Original line number	Diff line number	Diff line
		@@ -149,6 +149,28 @@ class nonparametric(rv_continuous):
		self._scale = 1
		self._scale = 1 / quad(self._pdf, self.xk[0], self.xk[-1])[0]

		@classmethod
		def from_cdf(cls, values, args, *kwargs):
		"""Create a nonparametric distribution using the CDF instead of the PMF.

		Args:
		values (tuple[np.array, np.array]): (n,) array of x values, (n,) array of the
		cumulative distribution function evaluated at x.
		*args (Any): Passed to :class:`nonparametric` constructor.
		**kwargs (Any): Passed to :class:`nonparametric` constructor.

		Returns:
		nonparametric: Nonparametric distribution.
		"""
		xk, cdf_values = np.array(values[0], float), np.array(values[1], float)
		argsort = xk.argsort()
		diff = np.diff(cdf_values[argsort]) / np.diff(xk[argsort])
		pk = np.nanmean([np.insert(diff, 0, np.nan), np.append(diff, np.nan)], axis=0)
		pk = pk[argsort.argsort()]
		new = cls((xk, pk), args, *kwargs)
		new._cdf_values = cdf_values
		return new

		def _pdf(self, x: np.ndarray) -> np.ndarray:
		x = np.atleast_1d(x)
		pdf = np.zeros(len(x))
		@@ -164,7 +186,22 @@ class nonparametric(rv_continuous):
		cdf[in_range] = interp1d(self.xk, self._cdf_values, kind=self._kind)(
		x[in_range]
		)
		return cdf
		# make sure the CDF is monotonically increasing
		cdf = cdf[x.argsort()]
		min_correction = cdf.copy()
		while (np.diff(min_correction) < 0).any():
		min_correction = np.clip(
		min_correction, a_min=np.insert(min_correction[:-1], 0, 0), a_max=None
		)

		max_correction = cdf.copy()
		while (np.diff(max_correction) < 0).any():
		max_correction = np.clip(
		max_correction, a_min=None, a_max=np.append(max_correction[1:], 1)
		)

		cdf = 0.5 * (min_correction + max_correction)
		return cdf[x.argsort().argsort()]

		def _ppf(self, q: np.ndarray) -> np.ndarray:
		return weighted_quantile(self.xk, q, self.pk)

tests/test_stats.py

+6 −0

Original line number	Diff line number	Diff line
		@@ -71,6 +71,12 @@ class TestNonparametric:
		x = np.linspace(-3, 3)
		dist = nonparametric((x, norm.pdf(x)))

		def test_from_cdf(self):
		dist_from_cdf = nonparametric.from_cdf((self.x, norm.cdf(self.x)))
		np.testing.assert_array_almost_equal(
		self.dist.pdf(self.x), dist_from_cdf.pdf(self.x), decimal=2
		)

		def test_pdf(self):
		np.testing.assert_array_almost_equal(
		self.dist.pdf(self.x), norm.pdf(self.x), decimal=2

src/multiple_inference/bayes/normal.py

+14 −14

File changed.

Contains only whitespace changes.