updated biofilm call + temp_dir

README.md

@@ -214,13 +214,13 @@ Throughout the program, several output files are generated and stored in the fol
 
 
 #### Validate your model using the **eval** mode
-You can also use CheRRIs **eval** mode to create a validation result table and than use the [coupute_f1](./scripts/plots/compute_f1.py) to get the F1 score.
+You can also use CheRRIs **eval** mode to create a validation result table and than use the [compute_f1](./scripts/plots/compute_f1.py) to get the F1 score.
 
 In the following is a example call to validate a theoretical model build from DataA
 ```
 cherri eval -i1 /path/to/Model_folder/DataA/feature_files/feature_filtered_<DataA>_context_<150>_pos_occ -g human -l human -o /path/to/Model_folder -n <val_modelA> -c 150 -st on -m  /path/to/Model_folder/DataA/model/optimized/full_<DataA>_context_<150>.model -mp  /path/to/Model_folder/DataA/feature_files/training_data_<DataA>_context_<150>.npz -j 10 -on evaluation -hf on
 ```
-Than use the result file to compute the F1 score using [coupute_f1](./scripts/plots/compute_f1.py).
+Than use the result file to compute the F1 score using [compute_f1](./scripts/plots/compute_f1.py).
 
 ### Build a new CheRRI model in training mode
 

bin/cherri

@@ -8,11 +8,13 @@ import sys
 import rrieval.lib as rl
 from scipy.sparse import csr_matrix, vstack, hstack, load_npz, save_npz
 from ubergauss.tools import loadfile, dumpfile
+#import logging
+## DeBug, INFO
 # import subprocess
 
+#logging.basicConfing(filenema='cherri.log'level=logging.DEBUG)
 
-
-__version__ = "0.5"
+__version__ = "0.6"
 
 """
 
@@ -201,7 +203,9 @@ def setup_argument_parser():
     p_mrg.add_argument("-on", "--out_name",
                         default="non",
                         help= "Name for the output directory, default 'date_Cherri_evaluating_RRIs' ")
-
+    p_mrg.add_argument("-tp", "--temp_dir",
+                        default="off",
+                        help= "Set a temporary directory for autosklearn. Either provede a path or 'out' to set it to the output directory Defalut:off")
 
 
     return p
@@ -542,6 +546,7 @@ def main_train(args):
     mixed = args.mixed
     filter_hybrid = args.filter_hybrid
     out_name = args.out_name
+    temp_dir = args.temp_dir
 
 
     methods = (f'extra_trees passive_aggressive random_forest sgd '
@@ -728,12 +733,18 @@ def main_train(args):
 
     if use_structure == 'ON' or use_structure == 'on':
         loaddata = f' --infile {feat_output} '
-        opt_call = (f'python -W ignore -m biofilm.biofilm-optimize6 {loaddata} '
+        opt_call = (f'python -W ignore -m biofilm.optimize6 {loaddata} '
                     f'--memoryMBthread {memoryPerThread} --folds 0 '
                     f'--out {opt_path}{midel_name} --preprocess True '
                     f'--n_jobs {n_jobs} --time {run_time} --methods {methods}'
                     # f' --tmp_folder {opt_path}\autosklearn_temp'
                     )
+        if temp_dir != 'off':
+            if temp_dir == 'out' or temp_dir == 'OUT':
+                opt_call = opt_call + f' --tmp_folder {opt_path}/autosklearn_temp'
+            else:
+                opt_call = opt_call + f' --tmp_folder {temp_dir}/autosklearn_temp'
+
 
         print('4a. Optimize model\n')
         #print(opt_call)
@@ -750,10 +761,17 @@ def main_train(args):
         rl.call_script(call_refit)
     elif use_structure == 'OFF' or use_structure == 'off':
         loaddata = f' --infile {feat_output}  --featurefile {out_feat}'
-        opt_call = (f'python -W ignore -m biofilm.biofilm-optimize6 {loaddata} '
+        opt_call = (f'python -W ignore -m biofilm.optimize6 {loaddata} '
                     f'--memoryMBthread {memoryPerThread} --folds 0 '
                     f'--out {opt_path}{midel_name} --preprocess True '
                     f'--n_jobs {n_jobs} --time {run_time} --methods {methods}')
+        if temp_dir != 'off':
+            if temp_dir == 'out' or temp_dir == 'OUT':
+                opt_call= f'{opt_call} --tmp_folder {opt_path}\autosklearn_temp'
+            else:
+                opt_call= f'{opt_call} --tmp_folder {temp_dir}\autosklearn_temp'
+
+
         print('4b. Optimize model\n')
         #print(opt_call)
         out = rl.call_script(opt_call, reprot_stdout=True,asset_err=False)

setup.py

@@ -11,7 +11,7 @@
 
 setup(
     name='cherri',
-    version='0.5',
+    version='0.6',
     description='Cherri is a tool for classification of RNA-RNA interactions.',
     long_description=readme,
     long_description_content_type='text/markdown',