rforest_seeds = [22, 37, 44, 55, 78]
split_seeds = [11, 37, 52, 71, 98]
exp = Modeling(
name=
"Pathway genes with signatures - modeling with RForest over few data splits",
param_grid=param_grid,
estimator_seeds=rforest_seeds,
split_seeds=split_seeds,
n_combinations=30,
kfolds=3,
tuning_jobs=4,
rforest_jobs=1)
# Initialize new ModelingResults object
exp_results = ModelingResults(exp)
print(exp_results.kfolds, exp_results.tuning_jobs, exp_results.scoring,
exp_results.max_iter)
# Load previously computed results
# filename = ""
# with open("../Created data/Results/" + filename, "rb") as f:
# exp_results = dill.load(f)
# ITERATE OVER DRUGS
# Get rid of warnings
import warnings
warnings.filterwarnings("ignore")
drug_counter = 0
# Create ModelingWithFeatureSelecction object
enet_seeds = [22, 37, 44, 55, 78]
split_seeds = [11, 37, 52, 71, 98]
enet_exp = Modeling(name="ElasticNet genome wide without feature selection",
param_grid=param_grid,
estimator_seeds=enet_seeds,
split_seeds=split_seeds,
n_combinations=30,
kfolds=3,
tuning_jobs=12,
max_iter=2500)
# Initialize new ModelingResults object
exp_results = ModelingResults(enet_exp)
# Load current ModelingResults object
#with open("../Created data/Results/genome_wide-enet_over_few_data_splits_without_feature_selection.pkl", "rb") as f:
#exp_results = dill.load(f)
# ITERATE OVER DRUGS
# Get rid of warnings
import warnings
warnings.filterwarnings("ignore")
drug_counter = 0
log = True # Controls verbosity during iterating over drugs
for drug_id in drugs: