Hyperparams modifies path more explicitly and prints parameter dict after optimisation is finished

hyperparameter_search/optimize_hyperparams.py

@@ -9,7 +9,6 @@ from train_nn import train
 from datetime import datetime
 from jinja2 import Template
 from snakemake import snakemake
-sys.path.append(f'{Path(__file__).resolve()}/..')
 from helper_functions import parse_output_path
 from pathlib import Path
 
@@ -21,6 +20,7 @@ integer_hps = ['layer_size', 'num_layers', 'filter_width', 'batch_size',
 
 __location__ = os.path.dirname(Path(__file__).resolve())
 baseless_location = Path(__file__).parents[1].resolve()
+sys.path.append(str(baseless_location))
 
 def generate_header(loss):
     out_txt = f"""
@@ -64,7 +64,8 @@ def main(args):
         ranges_dict = yaml.load(fh, yaml.FullLoader)
 
     # Generate dbs
-    with open(args.kmer_list, 'r') as fh: kmer_list = [k.strip() for k in fh.readlines() if len(k.strip())]
+    with open(args.kmer_list, 'r') as fh:
+        kmer_list = [k.strip() for k in fh.readlines() if len(k.strip())]
     db_dir = parse_output_path(f'{args.out_dir}dbs/')
     logs_dir = parse_output_path(f'{args.out_dir}logs/')
 
@@ -104,9 +105,12 @@ def main(args):
                    algo=hp.tpe.suggest,
                    trials=trials,
                    max_evals=args.hyperopt_iters)
+    print(out_param_dict)
     for p in out_param_dict:
-        if ranges_dict['variable'][p]['type'] == 'int': out_param_dict[p] = int(out_param_dict[p])
-    for p in ranges_dict['nonvariable']: out_param_dict[p] = ranges_dict['nonvariable'][p]
+        if ranges_dict['variable'][p]['type'] == 'int':
+            out_param_dict[p] = int(out_param_dict[p])
+    for p in ranges_dict['nonvariable']:
+        out_param_dict[p] = ranges_dict['nonvariable'][p]
     # store hyperparams as new parameter file
     with open(f'{args.out_dir}parameterFile.yaml', 'w') as fh:
         fh.write(generate_header(trials.losses()[-1]))