Merge branch 'main' into add-logistic-regression-for-vi

docs/src/api/bayesian_regression.md

@@ -4,34 +4,42 @@
 BayesianRegression
 ```
 
+## Bayesian Algorithms
+
+```@docs
+BayesianAlgorithm
+MCMC
+VI
+```
+
 ## Linear Regression
 
 ### Linear Regression with User Specific Gaussian Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Gauss, alpha_prior_mean::Float64, beta_prior_mean::Vector{Float64}, sim_size::Int64 = 1000)
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Gauss, alpha_prior_mean::Float64, alpha_prior_sd::Float64, beta_prior_mean::Vector{Float64}, beta_prior_sd::Vector{Float64}, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Gauss, alpha_prior_mean::Float64, beta_prior_mean::Vector{Float64}, algorithm::BayesianAlgorithm = MCMC())
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Gauss, alpha_prior_mean::Float64, alpha_prior_sd::Float64, beta_prior_mean::Vector{Float64}, beta_prior_sd::Vector{Float64}, algorithm::BayesianAlgorithm = MCMC())
 ```
 
 ### Linear Regression with Ridge Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Ridge, h::Float64 = 0.01, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Ridge, algorithm::BayesianAlgorithm = MCMC(), h::Float64 = 0.01)
 ```
 
 ### Linear Regression with Laplace Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Laplace, h::Float64 = 0.01, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Laplace, algorithm::BayesianAlgorithm = MCMC(), h::Float64 = 0.01)
 ```
 ### Linear Regression with Cauchy Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Cauchy, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_Cauchy, algorithm::BayesianAlgorithm = MCMC())
 ```
 ### Linear Regression with T-distributed Prior
 ```@docs
-fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_TDist, h::Float64 = 2.0, sim_size::Int64 = 1000)
+fit(formula::FormulaTerm, data::DataFrame, modelClass::LinearRegression, prior::Prior_TDist, algorithm::BayesianAlgorithm = MCMC(), h::Float64 = 2.0)
 ```
 ### Linear Regression with Horse Shoe Prior
 ```@docs
-fit(formula::FormulaTerm,data::DataFrame,modelClass::LinearRegression,prior::Prior_HorseShoe,sim_size::Int64 = 1000)
+fit(formula::FormulaTerm,data::DataFrame,modelClass::LinearRegression,prior::Prior_HorseShoe,algorithm::BayesianAlgorithm = MCMC())
 ```
 
 ## Logistic Regression

src/CRRao.jl

@@ -392,11 +392,49 @@ end
 
 Cauchit() = Cauchit(Cauchit_Link)
 
+"""
+```julia
+BayesianAlgorithm
+```
+
+Abstract type representing bayesian algorithms which are used to dispatch to appropriate calls.
+"""
+abstract type BayesianAlgorithm end
+
+"""
+```julia
+MCMC <: BayesianAlgorithm
+```
+
+A type representing MCMC algorithms.
+"""
+struct MCMC <: BayesianAlgorithm
+    sim_size::Int64
+    prediction_chain_start::Int64
+end
+
+MCMC() = MCMC(1000, 200)
+
+"""
+```julia
+VI <: BayesianAlgorithm
+```
+
+A type representing variational inference algorithms.
+"""
+struct VI <: BayesianAlgorithm
+    distribution_sample_count::Int64
+    vi_max_iters::Int64
+    vi_samples_per_step::Int64
+end
+
+VI() = VI(1000, 10000, 100)
+
 export LinearRegression, LogisticRegression, PoissonRegression, NegBinomRegression, Boot_Residual
 export Prior_Ridge, Prior_Laplace, Prior_Cauchy, Prior_TDist, Prior_HorseShoe, Prior_Gauss
-export CRRaoLink, Logit, Probit, Cloglog, Cauchit, fit
+export CRRaoLink, Logit, Probit, Cloglog, Cauchit, fit, BayesianAlgorithm, MCMC, VI
 export coef, coeftable, r2, adjr2, loglikelihood, aic, bic, sigma, predict, residuals, cooksdistance, BPTest, pvalue
-export FrequentistRegression, BayesianRegressionMCMC, BayesianRegressionVI
+export FrequentistRegression, BayesianRegression
 
 include("random_number_generator.jl")
 include("general_stats.jl")

src/bayesian/getter.jl

@@ -1,35 +1,14 @@
-function predict(container::BayesianRegressionMCMC{:LinearRegression}, newdata::DataFrame, prediction_chain_start::Int64 = 200)
+function predict(container::BayesianRegression{:LinearRegression}, newdata::DataFrame)
     X = modelmatrix(container.formula, newdata)
-
-    params = get_params(container.chain[prediction_chain_start:end,:,:])
-    W = params.β
-    if isa(W, Tuple)
-        W = reduce(hcat, W)
-    end
-    #predictions = params.α' .+ X * W'
-    predictions = X * W'
-    return vec(mean(predictions, dims=2))
-end
-
-function predict(container::BayesianRegressionVI{:LinearRegression}, newdata::DataFrame, number_of_samples::Int64 = 1000)
-    X = modelmatrix(container.formula, newdata)
-
-    W = rand(CRRao_rng, container.dist, number_of_samples)
-    W = W[union(container.symbol_to_range[:β]...), :]
+    W = container.samples
     predictions = X * W
     return vec(mean(predictions, dims=2))
 end
 
-function predict(container::BayesianRegressionMCMC{:LogisticRegression}, newdata::DataFrame, prediction_chain_start::Int64 = 200)
+function predict(container::BayesianRegression{:LogisticRegression}, newdata::DataFrame, prediction_chain_start::Int64 = 200)
     X = modelmatrix(container.formula, newdata)
-
-    params = get_params(container.chain[prediction_chain_start:end,:,:])
-    W = params.β
-    if isa(W, Tuple)
-        W = reduce(hcat, W)
-    end
-    #z = params.α' .+ X * W'
-    z =  X * W'
+    W = container.samples[:, prediction_chain_start:end]
+    z =  X * W
     return vec(mean(container.link.link_function.(z), dims=2))
 end
 
@@ -44,26 +23,14 @@ end
 
 function predict(container::BayesianRegressionMCMC{:NegativeBinomialRegression}, newdata::DataFrame, prediction_chain_start::Int64 = 200)
     X = modelmatrix(container.formula, newdata)
-
-    params = get_params(container.chain[prediction_chain_start:end,:,:])
-    W = params.β
-    if isa(W, Tuple)
-        W = reduce(hcat, W)
-    end
-    #z = params.α' .+ X * W'
-    z =  X * W'
+    W = container.samples[:, prediction_chain_start:end]
+    z =  X * W
     return vec(mean(exp.(z), dims=2))
 end
 
-function predict(container::BayesianRegressionMCMC{:PoissonRegression}, newdata::DataFrame, prediction_chain_start::Int64 = 200)
+function predict(container::BayesianRegression{:PoissonRegression}, newdata::DataFrame, prediction_chain_start::Int64 = 200)
     X = modelmatrix(container.formula, newdata)
-
-    params = get_params(container.chain[prediction_chain_start:end,:,:])
-    W = params.β
-    if isa(W, Tuple)
-        W = reduce(hcat, W)
-    end
-    #z = params.α' .+ X * W'
-    z =  X * W'
+    W = container.samples[:, prediction_chain_start:end]
+    z =  X * W
     return vec(mean(exp.(z), dims=2))
 end