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refactor, small fixes and package updates (#575)
* refactor and small fixes to distributions * black * remove unused kwargs * update packages * update packages * update packages * update test, drop 3.13 * centralize docstrings for common methods * disable lint
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‎.github/workflows/publish-to-test-pypi.yml

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@@ -11,10 +11,10 @@ jobs:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@master
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- name: Set up Python 3.10
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- name: Set up Python 3.11
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uses: actions/setup-python@v3
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with:
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python-version: "3.10"
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python-version: "3.11"
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- name: Install pypa/build
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run: >-

‎.github/workflows/test.yml

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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: ["3.10", "3.11", "3.12"]
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python-version: ["3.11", "3.12"]
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name: Set up Python ${{ matrix.python-version }}
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steps:

‎.readthedocs.yaml

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build:
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os: ubuntu-22.04
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tools:
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python: "3.10"
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python: "3.11"
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python:
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install:

‎docs/index.rst

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@@ -52,7 +52,7 @@ The Zen of PreliZ
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Dependencies
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============
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PreliZ is tested on Python 3.10+. And depends on ArviZ, matplotlib, NumPy, and SciPy. See `pyproject.toml <https://github.com/arviz-devs/preliz/blob/main/pyproject.toml>`_ for version information.
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PreliZ is tested on Python 3.11+. And depends on ArviZ, matplotlib, NumPy, and SciPy. See `pyproject.toml <https://github.com/arviz-devs/preliz/blob/main/pyproject.toml>`_ for version information.
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‎environment-dev.yml

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@@ -3,12 +3,12 @@ channels:
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- defaults
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- conda-forge
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dependencies:
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- python >= 3.10.0
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- python >= 3.11
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- pip
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- matplotlib>=3.5
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- matplotlib>=3.7
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- numba>=0.59
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- numpy>=1.22
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- scipy>=1.9.1,<1.13
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- numpy>=1.24
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- scipy>=1.10.,<1.13
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- nbclient<0.6,>=0.2
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- ipywidgets
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- ipympl

‎environment-docs.yml

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@@ -3,11 +3,11 @@ channels:
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- defaults
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- conda-forge
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dependencies:
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- python >= 3.10
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- matplotlib>=3.5
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- python >= 3.11
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- matplotlib>=3.7
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- numba>=0.59
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- numpy>=1.22
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- scipy>=1.9.1,<1.13
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- numpy>=1.24
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- scipy>=1.10,<1.13
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- nbclient<0.6,>=0.2
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- ipywidgets
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- ipympl

‎environment.yml

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@@ -3,8 +3,8 @@ channels:
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- defaults
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- conda-forge
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dependencies:
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- python >= 3.10
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- matplotlib>=3.5
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- python >= 3.11
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- matplotlib>=3.7
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- numba>=0.59
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- numpy>=1.22
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- scipy>=1.9.1, <1.13
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- numpy>=1.24
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- scipy>=1.10, <1.13

‎preliz/distributions/asymmetric_laplace.py

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@@ -109,36 +109,21 @@ def _update(self, kappa, mu, b):
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self.is_frozen = True
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def pdf(self, x):
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"""
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Compute the probability density function (PDF) at a given point x.
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"""
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x = np.asarray(x)
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return np.exp(nb_logpdf(x, self.mu, self.b, self.kappa))
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def cdf(self, x):
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"""
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Compute the cumulative distribution function (CDF) at a given point x.
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"""
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x = np.asarray(x)
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return nb_cdf(x, self.mu, self.b, self.kappa)
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def ppf(self, q):
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"""
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Compute the percent point function (PPF) at a given probability q.
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"""
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q = np.asarray(q)
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return nb_ppf(q, self.mu, self.b, self.kappa)
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def logpdf(self, x):
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"""
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Compute the log probability density function (log PDF) at a given point x.
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"""
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return nb_logpdf(x, self.mu, self.b, self.kappa)
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def _neg_logpdf(self, x):
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"""
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Compute the neg log_pdf sum for the array x.
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"""
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return nb_neg_logpdf(x, self.mu, self.b, self.kappa)
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def entropy(self):

‎preliz/distributions/bernoulli.py

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@@ -95,37 +95,22 @@ def _fit_mle(self, sample):
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optimize_ml(self, sample)
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def pdf(self, x):
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"""
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Compute the probability density function (PDF) at a given point x.
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"""
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x = np.asarray(x)
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return nb_pdf(x, self.p)
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def cdf(self, x):
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"""
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Compute the cumulative distribution function (CDF) at a given point x.
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"""
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x = np.asarray(x)
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return nb_cdf(x, self.p)
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def ppf(self, q):
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"""
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Compute the percent point function (PPF) at a given probability q.
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"""
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q = np.asarray(q)
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return nb_ppf(q, self.p)
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def logpdf(self, x):
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"""
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Compute the log probability density function (log PDF) at a given point x.
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"""
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x = np.asarray(x)
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return nb_logpdf(x, self.p)
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def _neg_logpdf(self, x):
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"""
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Compute the neg log_pdf sum for the array x.
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"""
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return nb_neg_logpdf(x, self.p)
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def entropy(self):

‎preliz/distributions/beta.py

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@@ -144,36 +144,21 @@ def _update(self, alpha, beta):
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self.is_frozen = True
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def pdf(self, x):
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"""
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Compute the probability density function (PDF) at a given point x.
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"""
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x = np.asarray(x)
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return np.exp(nb_logpdf(x, self.alpha, self.beta))
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def cdf(self, x):
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"""
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Compute the cumulative distribution function (CDF) at a given point x.
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"""
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x = np.asarray(x)
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return nb_cdf(x, self.alpha, self.beta, self.support[0], self.support[1])
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def ppf(self, q):
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"""
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Compute the percent point function (PPF) at a given probability q.
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"""
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q = np.asarray(q)
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return nb_ppf(q, self.alpha, self.beta, self.support[0], self.support[1])
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def logpdf(self, x):
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"""
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Compute the log probability density function (log PDF) at a given point x.
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"""
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return nb_logpdf(x, self.alpha, self.beta)
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def _neg_logpdf(self, x):
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"""
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Compute the neg log_pdf sum for the array x.
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"""
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return nb_neg_logpdf(x, self.alpha, self.beta)
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def entropy(self):

‎preliz/distributions/betabinomial.py

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self.is_frozen = True
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def pdf(self, x):
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"""
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Compute the probability density function (PDF) at a given point x.
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"""
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x = np.asarray(x)
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return np.exp(self.logpdf(x))
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def cdf(self, x):
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"""
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Compute the cumulative distribution function (CDF) at a given point x.
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"""
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if isinstance(x, (np.ndarray, list, tuple)):
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cdf_values = np.zeros_like(x, dtype=float)
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for i, val in enumerate(x):
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return cdf_bounds(np.sum(self.pdf(x_vals)), x, *self.support)
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def ppf(self, q):
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"""
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Compute the percent point function (PPF) at a given probability q.
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"""
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q = np.asarray(q)
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return find_ppf(self, q)
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def logpdf(self, x):
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"""
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Compute the log probability density function (log PDF) at a given point x.
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"""
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return nb_logpdf(x, self.alpha, self.beta, self.n, *self.support)
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def _neg_logpdf(self, x):
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"""
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Compute the neg log_pdf sum for the array x.
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"""
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return nb_neg_logpdf(x, self.alpha, self.beta, self.n, *self.support)
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def entropy(self):

‎preliz/distributions/betascaled.py

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return mu, sigma
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def pdf(self, x):
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"""
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Compute the probability density function (PDF) at a given point x.
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"""
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x = np.asarray(x)
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return np.exp(self.logpdf(x))
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def cdf(self, x):
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"""
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Compute the cumulative distribution function (CDF) at a given point x.
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"""
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x = np.asarray(x)
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return nb_cdf(x, self.alpha, self.beta, self.lower, self.upper)
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def ppf(self, q):
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"""
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Compute the percent point function (PPF) at a given probability q.
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"""
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q = np.asarray(q)
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return nb_ppf(q, self.alpha, self.beta, self.support[0], self.support[1])
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def logpdf(self, x):
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"""
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Compute the log probability density function (log PDF) at a given point x.
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"""
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return nb_logpdf(x, self.alpha, self.beta, self.lower, self.upper)
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def _neg_logpdf(self, x):
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"""
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Compute the neg log_pdf sum for the array x.
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"""
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return nb_neg_logpdf(x, self.alpha, self.beta, self.lower, self.upper)
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def entropy(self):

‎preliz/distributions/binomial.py

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self._update(*nb_fit_mle(sample))
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def pdf(self, x):
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"""
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x = np.asarray(x)
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return np.exp(self.logpdf(x))
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def cdf(self, x):
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"""
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Compute the cumulative distribution function (CDF) at a given point x.
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"""
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return nb_cdf(x, self.n, self.p, self.support[0], self.support[1])
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def ppf(self, q):
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"""
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Compute the percent point function (PPF) at a given probability q.
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"""
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return nb_ppf(q, self.n, self.p, self.support[0], self.support[1])
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def logpdf(self, x):
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"""
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Compute the log probability density function (log PDF) at a given point x.
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"""
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return nb_logpdf(x, self.n, self.p)
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def _neg_logpdf(self, x):
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"""
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"""
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return nb_neg_logpdf(x, self.n, self.p)
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def entropy(self):

‎preliz/distributions/categorical.py

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def pdf(self, x):
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"""
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x = np.atleast_1d(x)
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return nb_pdf(x, self.p)
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9491
def cdf(self, x):
95-
"""
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Compute the cumulative distribution function (CDF) at a given point x.
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"""
9892
x = np.atleast_1d(x)
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return nb_cdf(x, self.p)
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def ppf(self, q):
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"""
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Compute the percent point function (PPF) at a given probability q.
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"""
10596
q = np.atleast_1d(q)
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return nb_ppf(q, self.p)
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10899
def logpdf(self, x):
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"""
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Compute the log probability density function (log PDF) at a given point x.
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"""
112100
x = np.atleast_1d(x)
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return nb_logpdf(x, self.p)
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def _neg_logpdf(self, x):
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"""
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Compute the neg log_pdf sum for the array x.
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"""
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def entropy(self):

‎preliz/distributions/cauchy.py

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def pdf(self, x):
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"""
7269
x = np.asarray(x)
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return np.exp(nb_logpdf(x, self.alpha, self.beta))
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7572
def cdf(self, x):
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"""
77-
Compute the cumulative distribution function (CDF) at a given point x.
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"""
7973
x = np.asarray(x)
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return nb_cdf(x, self.alpha, self.beta)
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8276
def ppf(self, q):
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"""
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Compute the percent point function (PPF) at a given probability q.
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"""
8677
q = np.asarray(q)
8778
return nb_ppf(q, self.alpha, self.beta, -np.inf, np.inf)
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8980
def logpdf(self, x):
90-
"""
91-
Compute the log probability density function (log PDF) at a given point x.
92-
"""
9381
return nb_logpdf(x, self.alpha, self.beta)
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9583
def _neg_logpdf(self, x):
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"""
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Compute the neg log_pdf sum for the array x.
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"""
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return nb_neg_logpdf(x, self.alpha, self.beta)
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def entropy(self):
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127112
def _fit_moments(self, mean, sigma):
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self._update(mean, sigma)
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130-
def _fit_mle(self, sample, **kwargs):
115+
def _fit_mle(self, sample):
131116
optimize_ml(self, sample)
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