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Merge pull request #92 from reworkhow/BayesNN
add partial-connected Bayesian Neural Networks
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| #Below function is to check parameters for NNBayes and print information | ||
| function nnbayes_check_print_parameter(num_latent_traits,nonlinear_function,activation_function) | ||
| printstyled("Bayesian Neural Network is used with follwing information: \n",bold=false,color=:green) | ||
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| #part1: fully/partial-connected NN | ||
| if typeof(num_latent_traits) == Int64 #fully-connected. e.g, num_latent_traits=5 | ||
| printstyled(" - Neural network: fully connected neural network \n",bold=false,color=:green) | ||
| printstyled(" - Number of hidden nodes: $num_latent_traits \n",bold=false,color=:green) | ||
| nnbayes_partial=false | ||
| elseif num_latent_traits == false #partial-connected. | ||
| printstyled(" - Neural network: partially connected neural network \n",bold=false,color=:green) | ||
| nnbayes_partial=true | ||
| else | ||
| error("Please check you number of latent traits") | ||
| end | ||
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| #part2: activation function/user-defined non-linear function | ||
| if nonlinear_function == "Neural Network" #NN | ||
| if activation_function in ["tanh","sigmoid","relu","leakyrelu","linear"] | ||
| printstyled(" - Activation function: $activation_function.\n",bold=false,color=:green) | ||
| printstyled(" - Sampler: Hamiltonian Monte Carlo. \n",bold=false,color=:green) | ||
| else | ||
| error("Please select the activation function from tanh/sigmoid/relu/leakyrelu/linear") | ||
| end | ||
| elseif isa(nonlinear_function, Function) #user-defined nonlinear function. e.g, CropGrowthModel() | ||
| if activation_function == false | ||
| printstyled(" - Nonlinear function: user-defined nonlinear_function for the relationship between hidden nodes and observed trait is used.\n",bold=false,color=:green) | ||
| printstyled(" - Sampler: Matropolis-Hastings.\n",bold=false,color=:green) | ||
| else | ||
| error("activation function is not allowed for user-defined nonlinear function") | ||
| end | ||
| else | ||
| error("nonlinear_function can only be Neural Network or a user-defined nonlinear function") | ||
| end | ||
| return nnbayes_partial | ||
| end | ||
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| #Below function is to re-phase modelm for NNBayes | ||
| function nnbayes_model_equation(model_equations,num_latent_traits) | ||
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| lhs, rhs = strip.(split(model_equations,"=")) | ||
| model_equations = "" | ||
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| if typeof(num_latent_traits) == Int64 #fully-connected | ||
| # old: y=intercept+geno | ||
| # new: y1=intercept+geno;y2=intercept+geno | ||
| for i = 1:num_latent_traits | ||
| model_equations = model_equations*lhs*string(i)*"="*rhs*";" | ||
| end | ||
| elseif num_latent_traits == false #partially-connected | ||
| # old: y=intercept+geno1+geno2 | ||
| # new: y1= intercept+geno1;y2=intercept+geno2 | ||
| rhs_split=strip.(split(rhs,"+")) | ||
| geno_term=[] | ||
| for i in rhs_split | ||
| if isdefined(Main,Symbol(i)) && typeof(getfield(Main,Symbol(i))) == Genotypes | ||
| push!(geno_term,i) | ||
| end | ||
| end | ||
| non_gene_term = filter(x->x ∉ geno_term,rhs_split) | ||
| non_gene_term = join(non_gene_term,"+") | ||
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| for i = 1:length(geno_term) | ||
| model_equations = model_equations*lhs*string(i)*"="*non_gene_term*"+"*geno_term[i]*";" | ||
| end | ||
| end | ||
| model_equations = model_equations[1:(end-1)] | ||
| end | ||
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| # below function is to check whether the loaded genotype matches the model equation | ||
| function nnbayes_check_nhiddennode(num_latent_traits,mme) | ||
| if typeof(num_latent_traits) == Int64 #fully-connected. e.g, num_latent_traits=5 | ||
| if length(mme.M)>1 | ||
| error("fully-connected NN only allow one genotype; num_latent_traits is not allowed in partial-connected NN ") | ||
| end | ||
| elseif num_latent_traits == false #partial-connected. | ||
| if length(mme.M)==1 | ||
| error("partial-connected NN requirs >1 genotype group") | ||
| else | ||
| num_latent_traits = length(mme.M) | ||
| printstyled(" - Number of hidden nodes: $num_latent_traits \n",bold=false,color=:green) | ||
| end #Note, if only geno1 & geno2 are loaded by get_genotypes, but there is "geno3" in equation, then geno3 will be treated like age. | ||
| end | ||
| end | ||
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| # below function is to define the activation function for neural network | ||
| function nnbayes_activation(activation_function) | ||
| if activation_function == "tanh" | ||
| mytanh(x) = tanh(x) | ||
| return mytanh | ||
| elseif activation_function == "sigmoid" | ||
| mysigmoid(x) = 1/(1+exp(-x)) | ||
| return mysigmoid | ||
| elseif activation_function == "relu" | ||
| myrelu(x) = max(0, x) | ||
| return myrelu | ||
| elseif activation_function == "leakyrelu" | ||
| myleakyrelu(x) = max(0.01x, x) | ||
| return myleakyrelu | ||
| elseif activation_function == "linear" | ||
| mylinear(x) = x | ||
| return mylinear | ||
| else | ||
| error("invalid actication function") | ||
| end | ||
| end | ||
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| # below function is to modify mme from multi-trait model to multiple single trait models | ||
| # coded by Hao | ||
| function nnbayes_mega_trait(mme) | ||
| #mega_trait | ||
| if mme.nModels == 1 | ||
| error("more than 1 trait is required for MegaLMM analysis.") | ||
| end | ||
| mme.MCMCinfo.constraint = true | ||
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| ##sample from scale-inv-χ2, not InverseWishart | ||
| mme.df.residual = mme.df.residual - mme.nModels | ||
| mme.scaleR = diag(mme.scaleR/(mme.df.residual - 1))*(mme.df.residual-2)/mme.df.residual #diag(R_prior_mean)*(ν-2)/ν | ||
| if mme.M != 0 | ||
| for Mi in mme.M | ||
| Mi.df = Mi.df - mme.nModels | ||
| Mi.scale = diag(Mi.scale/(Mi.df - 1))*(Mi.df-2)/Mi.df | ||
| end | ||
| end | ||
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| end | ||
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| # below function is to modify essential parameters for partial connected NN | ||
| function nnbayes_partial_para_modify2(mme) | ||
| for Mi in mme.M | ||
| Mi.scale = Mi.scale[1] | ||
| Mi.G = Mi.G[1,1] | ||
| Mi.genetic_variance=Mi.genetic_variance[1,1] | ||
| end | ||
| end | ||
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| # below function is to modify essential parameters for partial connected NN | ||
| function nnbayes_partial_para_modify3(mme) | ||
| for Mi in mme.M | ||
| Mi.meanVara = Mi.meanVara[1] | ||
| Mi.meanVara2 = Mi.meanVara2[1] | ||
| Mi.meanScaleVara = Mi.meanScaleVara[1] | ||
| Mi.meanScaleVara2 = Mi.meanScaleVara2[1] | ||
| Mi.π = Mi.π[1] | ||
| Mi.mean_pi = Mi.mean_pi[1] | ||
| Mi.mean_pi2 = Mi.mean_pi2[1] | ||
| end | ||
| end |
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