Open DesmondYuan opened 3 years ago
Adding tests for Beeline networks 47e8652a3d38d7189fca12b763ab6818f310c586 and a2b0c0b6e0c5b7530cd50c5494afafd5e772b1f2
Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A. & Murali, T. M. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat. Methods 17, 147–154 (2020). https://www.nature.com/articles/s41592-019-0690-6
A good demo for L1
network: "beeline_networks/Synthetic_LI.csv"
ns: 7
tfinal: 20.0
ntotal: 20
batch_size: 16
epoch_size: -1
lr: 1.e-3
lr_new: -1 #use -1 otehrwise
weight_decay: 1.e-5
n_mu: 3
n_exp_train: 20
n_exp_val: 5
n_exp_test: 5
noise: 0.01
n_iter_max: 100000
n_plot: 20 # frequency of callback
n_iter_buffer: 5000
n_iter_burnin: 100
n_iter_tol: 10000
convergence_tol: 1e-8
drop_range:
lb: -0.1
ub: 0.1
The program takes about 7 minutes. about 2.1 it/s.
One testing condition
A simple criterion to judge if the data is sufficient is whether there is a big gap between training loss and validation loss. If there is, we shall increase the number of conditions.
Another good training example for LI network, with 10 training conditions and 10 ntotal
is_restart: false
network: "beeline_networks/Synthetic_LI.csv"
ns: 7
tfinal: 10.0
ntotal: 10
batch_size: 8
epoch_size: -1
lr: 1.e-3
weight_decay: 1.e-6
n_mu: 3
n_exp_train: 10
n_exp_val: 5
n_exp_test: 5
noise: 0.01
n_iter_max: 10000
n_plot: 20 # frequency of callback
n_iter_buffer: 50
n_iter_burnin: 100
n_iter_tol: 500
convergence_tol: 1e-8
drop_range:
lb: -0.1
ub: 0.1
Curated model
[Essential] system size - data size relationship analysis
why
solution