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TensorFlow Probability GLM python package.
Classes
class Bernoulli: Bernoulli(probs=mean) where mean = sigmoid(X @ weights).
class BernoulliNormalCDF: Bernoulli(probs=mean) where mean = Normal(0, 1).cdf(X @ weights).
class Binomial: Binomial(total_count, probs=mean).
class CustomExponentialFamily: Constucts GLM from arbitrary distribution and inverse link function.
class ExponentialFamily: Specifies a mean-value parameterized exponential family.
class GammaExp: Gamma(concentration=1, rate=1 / mean) where mean = exp(X @ w)).
class GammaSoftplus: Gamma(concentration=1, rate=1 / mean) where mean = softplus(X @ w)).
class LogNormal: LogNormal(loc=log(mean) - log(2) / 2, scale=sqrt(log(2))) where mean = exp(X @ weights).
class LogNormalSoftplus: LogNormal(loc=log(mean) - log(2) / 2, scale=sqrt(log(2))) mean = softplus(X @ weights).
class NegativeBinomial: NegativeBinomial(total_count, probs=mean / (mean + total_count)).
class NegativeBinomialSoftplus: NegativeBinomial(total_count, probs=mean / (mean + total_count)).
class Normal: Normal(loc=mean, scale=1) where mean = X @ weights.
class NormalReciprocal: Normal(loc=mean, scale=1) where mean = 1 / (X @ weights).
class Poisson: Poisson(rate=mean) where mean = exp(X @ weights).
class PoissonSoftplus: Poisson(rate=mean) where mean = softplus(X @ weights).
Functions
compute_predicted_linear_response(...): Computes model_matrix @ model_coefficients + offset.
convergence_criteria_small_relative_norm_weights_change(...): Returns Python callable which indicates fitting procedure has converged.
fit(...): Runs multiple Fisher scoring steps.
fit_one_step(...): Runs one step of Fisher scoring.
fit_sparse(...): Fits a GLM using coordinate-wise FIM-informed proximal gradient descent.
fit_sparse_one_step(...): One step of (the outer loop of) the GLM fitting algorithm.