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Add logistic regression <-> Variational Inference #156

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Add logistic regression <-> Variational Inference #156

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5c6f1ea
Add VI for linear regression
ShouvikGhosh2048 Dec 6, 2024
3ac63ff
Add `logistic_reg_vi()` function
Nandini-Jaiswal Dec 8, 2024
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2 changes: 2 additions & 0 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@ authors = ["xKDR Forum, Sourish Das"]
version = "0.1.1"

[deps]
AdvancedVI = "b5ca4192-6429-45e5-a2d9-87aec30a685c"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
Expand All @@ -19,6 +20,7 @@ StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"

[compat]
AdvancedVI = "0.2.11"
DataFrames = "1"
Distributions = "0.25"
Documenter = "0.27, 1"
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4 changes: 2 additions & 2 deletions src/CRRao.jl
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Expand Up @@ -2,7 +2,7 @@ module CRRao

using DataFrames, GLM, Turing, StatsModels, StatsBase
using StatsBase, Distributions, LinearAlgebra
using Optim, NLSolversBase, Random, HypothesisTests
using Optim, NLSolversBase, Random, HypothesisTests, AdvancedVI
import StatsBase: coef, coeftable, r2, adjr2, loglikelihood, aic, bic, predict, residuals, cooksdistance, fit
import HypothesisTests: pvalue

Expand Down Expand Up @@ -396,7 +396,7 @@ export LinearRegression, LogisticRegression, PoissonRegression, NegBinomRegressi
export Prior_Ridge, Prior_Laplace, Prior_Cauchy, Prior_TDist, Prior_HorseShoe, Prior_Gauss
export CRRaoLink, Logit, Probit, Cloglog, Cauchit, fit
export coef, coeftable, r2, adjr2, loglikelihood, aic, bic, sigma, predict, residuals, cooksdistance, BPTest, pvalue
export FrequentistRegression, BayesianRegression
export FrequentistRegression, BayesianRegressionMCMC, BayesianRegressionVI

include("random_number_generator.jl")
include("general_stats.jl")
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26 changes: 22 additions & 4 deletions src/bayesian/getter.jl
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@@ -1,4 +1,4 @@
function predict(container::BayesianRegression{:LinearRegression}, newdata::DataFrame, prediction_chain_start::Int64 = 200)
function predict(container::BayesianRegressionMCMC{:LinearRegression}, newdata::DataFrame, prediction_chain_start::Int64 = 200)
X = modelmatrix(container.formula, newdata)

params = get_params(container.chain[prediction_chain_start:end,:,:])
Expand All @@ -11,7 +11,16 @@ function predict(container::BayesianRegression{:LinearRegression}, newdata::Data
return vec(mean(predictions, dims=2))
end

function predict(container::BayesianRegression{:LogisticRegression}, newdata::DataFrame, prediction_chain_start::Int64 = 200)
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]...), :]
predictions = X * W
return vec(mean(predictions, dims=2))
end

function predict(container::BayesianRegressionMCMC{:LogisticRegression}, newdata::DataFrame, prediction_chain_start::Int64 = 200)
X = modelmatrix(container.formula, newdata)

params = get_params(container.chain[prediction_chain_start:end,:,:])
Expand All @@ -24,7 +33,16 @@ function predict(container::BayesianRegression{:LogisticRegression}, newdata::Da
return vec(mean(container.link.link_function.(z), dims=2))
end

function predict(container::BayesianRegression{:NegativeBinomialRegression}, newdata::DataFrame, prediction_chain_start::Int64 = 200)
function predict(container::BayesianRegressionVI{:LogisticRegression}, 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]...), :]
z = X * W
return vec(mean(container.link.link_function.(z), dims=2))
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,:,:])
Expand All @@ -37,7 +55,7 @@ function predict(container::BayesianRegression{:NegativeBinomialRegression}, new
return vec(mean(exp.(z), dims=2))
end

function predict(container::BayesianRegression{:PoissonRegression}, newdata::DataFrame, prediction_chain_start::Int64 = 200)
function predict(container::BayesianRegressionMCMC{:PoissonRegression}, newdata::DataFrame, prediction_chain_start::Int64 = 200)
X = modelmatrix(container.formula, newdata)

params = get_params(container.chain[prediction_chain_start:end,:,:])
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