Mercurial > repos > bimib > marea_2_0
changeset 113:8b8131f13692 draft
Uploaded
| author | luca_milaz |
|---|---|
| date | Tue, 02 Jul 2024 19:51:40 +0000 |
| parents | dff08700bbb8 |
| children | e32ee8b88c2e |
| files | marea_2_0/flux_sampling.py |
| diffstat | 1 files changed, 31 insertions(+), 8 deletions(-) [+] |
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--- a/marea_2_0/flux_sampling.py Tue Jul 02 19:51:23 2024 +0000 +++ b/marea_2_0/flux_sampling.py Tue Jul 02 19:51:40 2024 +0000 @@ -88,15 +88,22 @@ def OPTGP_sampler(model:cobra.Model, model_name:str, n_samples:int=1000, thinning:int=100, n_batches:int=1, seed:int=0)-> None: - if not os.path.exists(ARGS.output_folder + "OPTGP/" + ARGS.model_name): - os.makedirs(ARGS.output_folder + "OPTGP/" + ARGS.model_name) + if not os.path.exists(ARGS.output_folder + "OPTGP/"): + os.makedirs(ARGS.output_folder + "OPTGP/") for i in range(0, n_batches): optgp = OptGPSampler(model, thinning, seed) samples = optgp.sample(n_samples) - write_to_file(samples, ARGS.output_folder + "OPTGP/" + + ARGS.model_name + '/OPTGP_thinning' + str(thinning) + '_samples_' + str(n_samples) + '_batch_' + str(i) + '.tsv') + samples.to_csv(ARGS.output_folder + "OPTGP/" + ARGS.model_name + '_'+ str(i)+'.csv') i+=1 seed+=1 + samplesTotal = pd.DataFrame() + for i in range(0, n_batches): + samples_batch = pd.read_csv(ARGS.output_folder + "OPTGP/" + ARGS.model_name + '_'+ str(i)+'.csv') + samplesTotal = pd.concat([samplesTotal, samples_batch], ignore_index = True) + write_to_file(samplesTotal, ARGS.output_folder + "OPTGP/" + ARGS.model_name) + for i in range(0, n_batches): + os.remove(ARGS.output_folder + "OPTGP/" + ARGS.model_name + '_'+ str(i)+'.csv') pass @@ -120,8 +127,14 @@ ARGS.out_log) CBS_backend.randomObjectiveFunctionSampling_cobrapy(model, n_samples, df_coefficients.iloc[:,i*n_samples:(i+1)*n_samples], samples) + samples.to_csv(ARGS.output_folder + "CBS/" + ARGS.model_name + '_'+ str(i)+'.csv') - write_to_file(samples, ARGS.output_folder + "CBS/" + ARGS.model_name + '/CBS_samples_' + str(n_samples) + '_batch_' + str(i) + '.tsv') + for i in range(0, n_batches): + samples_batch = pd.read_csv(ARGS.output_folder + "CBS/" + ARGS.model_name + '_'+ str(i)+'.csv') + samplesTotal = pd.concat([samplesTotal, samples_batch], ignore_index = True) + write_to_file(samplesTotal, ARGS.output_folder + "CBS/" + ARGS.model_name) + for i in range(0, n_batches): + os.remove(ARGS.output_folder + "CBS/" + ARGS.model_name + '_'+ str(i)+'.csv') pass @@ -167,12 +180,22 @@ ARGS = process_args(sys.argv) ARGS.output_folder = 'flux_sampling/' + + # load custom model - model = load_custom_model( - utils.FilePath.fromStrPath(ARGS.input_model), utils.FilePath.fromStrPath(ARGS.model_name).ext) + model1 = load_custom_model( + utils.FilePath.fromStrPath(ARGS.input_model[0]), utils.FilePath.fromStrPath(ARGS.model_name[0]).ext) + model2 = load_custom_model( + utils.FilePath.fromStrPath(ARGS.input_model[1]), utils.FilePath.fromStrPath(ARGS.model_name[1]).ext) - ARGS.model_name = os.path.splitext(os.path.basename(ARGS.input_model))[0] # extract model filename only + #ARGS.model_name = os.path.splitext(os.path.basename(ARGS.input_model))[0] # extract model filename only + + + with open(ARGS.output_folder, 'w') as f: + f.write([model1, model2]) + + ''' if ARGS.sampling_algorithm == 'OPTGP': OPTGP_sampler(model, ARGS.model_name, ARGS.n_samples, ARGS.thinning, ARGS.n_batches, ARGS.seed, ARGS.out_dir) @@ -181,7 +204,7 @@ CBS_sampler(model, ARGS.model_name, ARGS.n_samples, ARGS.n_batches, ARGS.seed, ARGS.out_dir) else: raise utils.ValueErr(ARGS.sampling_algorithm, - f"Algorithm \"{ARGS.sampling_algorithm}\" is not recognized, only OPTGP and CBS are supported.") + f"Algorithm \"{ARGS.sampling_algorithm}\" is not recognized, only OPTGP and CBS are supported.")''' ############################################################################## if __name__ == "__main__":