Pytorch Breeding
Project description
ChewC
In short, this will be a GPU-enabled stochastic simulation for breeding programs with an emphasis on cost-benefit-analysis for novel breeding tools and creating a suitable interface for RL agents.
We will also incorporate an emphasis on budget and costs associated with each action to manage long-term breeding budgets. As well as model theoretical tools in the plant breeder’s toolbox. e.g.
a treatment which increases crossover rates
a treatment which reduces flowering time
a treatment which enables gene drive at select loci
Each treatment will cost $$ ultimately helping guide the implementation in real-world breeding programs.
Install
pip install chewc
How to use
First, define the genome of your crop
# import random
# ploidy = 2
# number_chromosomes = 10
# loci_per_chromosome = 100
# genetic_map = create_random_genetic_map(number_chromosomes,loci_per_chromosome)
# crop_genome = Genome(ploidy, number_chromosomes, loci_per_chromosome, genetic_map)
# n_founders = 500
# founder_pop = create_random_founder_pop(crop_genome , n_founders)
# sim_param = SimParam
# sim_param.founder_pop = founder_pop
# sim_param.genome = crop_genome
# #add a single additive trait
# qtl_loci = 20
# qtl_map = select_qtl_loci(qtl_loci,sim_param.genome)
# ta = TraitA(qtl_map,sim_param,0, 1)
# ta.sample_initial_effects()
# ta.scale_genetic_effects()
# ta.calculate_intercept()
# # Ensure sim_param.device is defined and correct
# device = sim_param.device
# years = 20
# current_pop = founder_pop.to(device)
# pmean = []
# pvar = []
# for _ in range(years):
# # phenotype current pop
# TOPK = 10
# new_pop = []
# pheno = ta.phenotype(current_pop, h2=0.14).to(device)
# topk = torch.topk(pheno, TOPK).indices.to(device)
# for _ in range(200):
# sampled_indices = torch.multinomial(torch.ones(topk.size(0), device=device), 2, replacement=False)
# sampled_parents = topk[sampled_indices]
# m, f = current_pop[sampled_parents[0]], current_pop[sampled_parents[1]]
# new_pop.append(make_cross(sim_param, m, f).to(device))
# current_pop = torch.stack(new_pop).to(device)
# pmean.append(ta.calculate_genetic_values(current_pop).mean().item())
# pvar.append(ta.calculate_genetic_values(current_pop).var().item())
# pmean_normalized = torch.tensor(pmean, device=device) / max(pmean)
# pvar_normalized = torch.tensor(pvar, device=device) / max(pvar)
# plt.scatter(range(len(pmean_normalized)), pmean_normalized.cpu())
# plt.scatter(range(len(pvar_normalized)), pvar_normalized.cpu())
# plt.show()
Project details
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