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Merge pull request #36 from msamsami/stats-sub-package
patch: Add a new sub-package `wnb.stats` to keep distribution-related modules
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,291 +1,11 @@ | ||
| from typing import Any, Mapping | ||
| import warnings | ||
|
|
||
| import numpy as np | ||
| from scipy.special import beta, gamma | ||
| from scipy.stats import chi2 | ||
| from wnb.stats import * | ||
|
|
||
| from .base import ContinuousDistMixin, DiscreteDistMixin | ||
| from .enums import Distribution as D | ||
|
|
||
| __all__ = [ | ||
| "NormalDist", | ||
| "LognormalDist", | ||
| "ExponentialDist", | ||
| "UniformDist", | ||
| "ParetoDist", | ||
| "GammaDist", | ||
| "BetaDist", | ||
| "ChiSquaredDist", | ||
| "TDist", | ||
| "RayleighDist", | ||
| "BernoulliDist", | ||
| "CategoricalDist", | ||
| "GeometricDist", | ||
| "PoissonDist", | ||
| ] | ||
|
|
||
|
|
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| class NormalDist(ContinuousDistMixin): | ||
| name = D.NORMAL | ||
| _support = (-np.inf, np.inf) | ||
|
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||
| def __init__(self, mu: float, sigma: float): | ||
| self.mu = mu | ||
| self.sigma = sigma | ||
| super().__init__() | ||
|
|
||
| @classmethod | ||
| def from_data(cls, data: np.ndarray, **kwargs): | ||
| return cls(mu=np.average(data), sigma=np.std(data)) | ||
|
|
||
| def pdf(self, x: float) -> float: | ||
| return (1.0 / np.sqrt(2 * np.pi * self.sigma**2)) * np.exp(-0.5 * (((x - self.mu) / self.sigma) ** 2)) | ||
|
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||
|
|
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| class LognormalDist(ContinuousDistMixin): | ||
| name = D.LOGNORMAL | ||
| _support = (0, np.inf) | ||
|
|
||
| def __init__(self, mu: float, sigma: float): | ||
| self.mu = mu | ||
| self.sigma = sigma | ||
| super().__init__() | ||
|
|
||
| @classmethod | ||
| def from_data(cls, data: np.ndarray, **kwargs): | ||
| log_data = np.log(data) | ||
| return cls(mu=np.average(log_data), sigma=np.std(log_data)) | ||
|
|
||
| def pdf(self, x: float) -> float: | ||
| return (1.0 / (x * self.sigma * np.sqrt(2 * np.pi))) * np.exp( | ||
| -0.5 * ((np.log(x) - self.mu) / self.sigma) ** 2 | ||
| ) | ||
|
|
||
|
|
||
| class ExponentialDist(ContinuousDistMixin): | ||
| name = D.EXPONENTIAL | ||
| _support = (0, np.inf) | ||
|
|
||
| def __init__(self, rate: float): | ||
| self.rate = rate | ||
| super().__init__() | ||
|
|
||
| @classmethod | ||
| def from_data(cls, data: np.ndarray, **kwargs): | ||
| return cls(rate=(len(data) - 2) / np.sum(data)) | ||
|
|
||
| def pdf(self, x: float) -> float: | ||
| return self.rate * np.exp(-self.rate * x) if x >= 0 else 0.0 | ||
|
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||
|
|
||
| class UniformDist(ContinuousDistMixin): | ||
| name = D.UNIFORM | ||
| _support = None | ||
|
|
||
| def __init__(self, a: float, b: float): | ||
| self.a = a | ||
| self.b = b | ||
| self._support = (a, b) | ||
| super().__init__() | ||
|
|
||
| @classmethod | ||
| def from_data(cls, data, **kwargs): | ||
| return cls(a=np.min(data), b=np.max(data)) | ||
|
|
||
| def pdf(self, x: float) -> float: | ||
| return 1 / (self.b - self.a) if self.a <= x <= self.b else 0.0 | ||
|
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||
|
|
||
| class ParetoDist(ContinuousDistMixin): | ||
| name = D.PARETO | ||
| _support = None | ||
|
|
||
| def __init__(self, x_m: float, alpha: float): | ||
| self.x_m = x_m | ||
| self.alpha = alpha | ||
| self._support = (self.x_m, np.inf) | ||
| super().__init__() | ||
|
|
||
| @classmethod | ||
| def from_data(cls, data, **kwargs): | ||
| x_m = np.min(data) | ||
| return cls(x_m=x_m, alpha=len(data) / np.sum(np.log(data / x_m))) | ||
|
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| def pdf(self, x: float) -> float: | ||
| return (self.alpha * self.x_m**self.alpha) / x ** (self.alpha + 1) if x >= self.x_m else 0.0 | ||
|
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||
|
|
||
| class GammaDist(ContinuousDistMixin): | ||
| name = D.GAMMA | ||
| _support = (0, np.inf) | ||
|
|
||
| def __init__(self, k: float, theta: float): | ||
| self.k = k | ||
| self.theta = theta | ||
| super().__init__() | ||
|
|
||
| @classmethod | ||
| def from_data(cls, data, **kwargs): | ||
| n = len(data) | ||
| return cls( | ||
| k=n * np.sum(data) / (n * np.sum(data * np.log(data)) - np.sum(data * np.sum(np.log(data)))), | ||
| theta=(n * np.sum(data * np.log(data)) - np.sum(data * np.sum(np.log(data)))) / n**2, | ||
| ) | ||
|
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||
| def pdf(self, x: float) -> float: | ||
| return (x ** (self.k - 1) * np.exp(-x / self.theta)) / (gamma(self.k) * self.theta**self.k) | ||
|
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||
|
|
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| class BetaDist(ContinuousDistMixin): | ||
| name = D.BETA | ||
| _support = (0, 1) | ||
|
|
||
| def __init__(self, alpha: float, beta: float): | ||
| self.alpha = alpha | ||
| self.beta = beta | ||
| super().__init__() | ||
|
|
||
| @classmethod | ||
| def from_data(cls, data, **kwargs): | ||
| mu_hat = np.average(data) | ||
| var_hat = np.var(data, ddof=1) | ||
| multiplied_term = (mu_hat * (1 - mu_hat) / var_hat) - 1 | ||
| return cls( | ||
| alpha=mu_hat * multiplied_term, | ||
| beta=(1 - mu_hat) * multiplied_term, | ||
| ) | ||
|
|
||
| def pdf(self, x: float) -> float: | ||
| return ((x ** (self.alpha - 1)) * (1 - x) ** (self.beta - 1)) / beta(self.alpha, self.beta) | ||
|
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|
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| class ChiSquaredDist(ContinuousDistMixin): | ||
| name = D.CHI_SQUARED | ||
| _support = (0, np.inf) | ||
|
|
||
| def __init__(self, k: int): | ||
| self.k = k | ||
| super().__init__() | ||
|
|
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| @classmethod | ||
| def from_data(cls, data, **kwargs): | ||
| return cls(k=round(np.average(data))) | ||
|
|
||
| def pdf(self, x: float) -> float: | ||
| return chi2.pdf(x, self.k) | ||
|
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||
|
|
||
| class TDist(ContinuousDistMixin): | ||
| name = D.T | ||
| _support = (-np.inf, np.inf) | ||
|
|
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| def __init__(self, df: float): | ||
| self.df = df | ||
| super().__init__() | ||
|
|
||
| @classmethod | ||
| def from_data(cls, data, **kwargs): | ||
| return cls(df=len(data) - 1) | ||
|
|
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| def pdf(self, x: float) -> float: | ||
| return (gamma((self.df + 1) / 2) / (np.sqrt(self.df * np.pi) * gamma(self.df / 2))) * ( | ||
| 1 + (x**2 / self.df) | ||
| ) ** (-(self.df + 1) / 2) | ||
|
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|
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||
| class RayleighDist(ContinuousDistMixin): | ||
| name = D.RAYLEIGH | ||
| _support = (0, np.inf) | ||
|
|
||
| def __init__(self, sigma: float): | ||
| self.sigma = sigma | ||
| super().__init__() | ||
|
|
||
| @classmethod | ||
| def from_data(cls, data, **kwargs): | ||
| sigma = np.sqrt(np.mean(data**2) / 2) | ||
| return cls(sigma=sigma) | ||
|
|
||
| def pdf(self, x: float) -> float: | ||
| return (x / self.sigma**2) * np.exp(-(x**2) / (2 * self.sigma**2)) if x >= 0 else 0.0 | ||
|
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||
|
|
||
| class BernoulliDist(DiscreteDistMixin): | ||
| name = D.BERNOULLI | ||
| _support = [0, 1] | ||
|
|
||
| def __init__(self, p: float): | ||
| self.p = p | ||
| super().__init__() | ||
|
|
||
| @classmethod | ||
| def from_data(cls, data, **kwargs): | ||
| alpha = kwargs.get("alpha", 1e-10) | ||
| return cls(p=((np.array(data) == 1).sum() + alpha) / len(data)) | ||
|
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||
| def pmf(self, x: int) -> float: | ||
| if x not in self._support: | ||
| return 0.0 | ||
| else: | ||
| return self.p if x == 1 else 1 - self.p | ||
|
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||
|
|
||
| class CategoricalDist(DiscreteDistMixin): | ||
| name = D.CATEGORICAL | ||
| _support = None | ||
|
|
||
| def __init__(self, prob: Mapping[Any, float]): | ||
| self.prob = prob | ||
| self._support = list(self.prob.keys()) | ||
| super().__init__() | ||
|
|
||
| @classmethod | ||
| def from_data(cls, data, **kwargs): | ||
| alpha = kwargs.get("alpha", 1e-10) | ||
| values, counts = np.unique(data, return_counts=True) | ||
| return cls(prob={v: (c + alpha) / len(data) for v, c in zip(values, counts)}) | ||
|
|
||
| def pmf(self, x: Any) -> float: | ||
| return self.prob.get(x, 0.0) | ||
|
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||
|
|
||
| class GeometricDist(DiscreteDistMixin): | ||
| name = D.GEOMETRIC | ||
| _support = (1, np.inf) | ||
|
|
||
| def __init__(self, p: float): | ||
| self.p = p | ||
| super().__init__() | ||
|
|
||
| @classmethod | ||
| def from_data(cls, data, **kwargs): | ||
| return cls(p=len(data) / np.sum(data)) | ||
|
|
||
| def pmf(self, x: int) -> float: | ||
| return self.p * (1 - self.p) ** (x - 1) if x >= self._support[0] and x - int(x) == 0 else 0.0 | ||
|
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|
|
||
| class PoissonDist(DiscreteDistMixin): | ||
| name = D.POISSON | ||
| _support = (0, np.inf) | ||
|
|
||
| def __init__(self, rate: float): | ||
| self.rate = rate | ||
| super().__init__() | ||
|
|
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| @classmethod | ||
| def from_data(cls, data, **kwargs): | ||
| return cls(rate=np.sum(data) / len(data)) | ||
|
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| def pmf(self, x: int) -> float: | ||
| return ( | ||
| (np.exp(-self.rate) * self.rate**x) / np.math.factorial(x) | ||
| if x >= self._support[0] and x - int(x) == 0 | ||
| else 0.0 | ||
| ) | ||
|
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|
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| AllDistributions = {cls.name: cls for cls in (globals()[name] for name in __all__)} | ||
|
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|
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| NonNumericDistributions = [D.CATEGORICAL] | ||
| warnings.warn( | ||
| "The `wnb.dist` module is deprecated and will be removed in a future release. " | ||
| "Please update your imports to use `wnb` or `wnb.stats` directly. " | ||
| "Using `wnb.dist` will continue to work in this version, but it may be removed in future versions.", | ||
| DeprecationWarning, | ||
| stacklevel=2, | ||
| ) |
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