Merge pull request #48 from BackofenLab/input

fixed eval call

README.md

@@ -216,10 +216,33 @@ 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 [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
+In the following is a example call to validate a theoretical model build from DataA with data form a different source e.g. DataB
 ```
-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
+cherri eval -i1 /path/to/Model_folder/Datab/feature_files/feature_filtered_<DataB>_context_<150>_pos_occ -g human -l human -o /path/to/Model_folder -n <eval_modelA_using_DataB> -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
 ```
+
+In the following is a example call to cross validate a theoretical model build from DataA. The biofilm-cv will split the data into 5 parts used 4 to retrain a model and the left out to evaluate. 
+
+
+```
+python -m biofilm.biofilm-cv --infile /path/to/Model_folder/DataA/feature_files/training_data_<DataA>_context_<150>.npz --foldselect 0 --model /path/to/Model_folder/DataA/model/optimized/full_<DataA>_context_<150>.model --out /path/to/Model_folder
+```
+
+```
+python -m biofilm.biofilm-cv --infile /path/to/Model_folder/DataA/feature_files/training_data_<DataA>_context_<150>.npz --foldselect 1 --model /path/to/Model_folder/DataA/model/optimized/full_<DataA>_context_<150>.model --out /path/to/Model_folder
+```
+```
+python -m biofilm.biofilm-cv --infile /path/to/Model_folder/DataA/feature_files/training_data_<DataA>_context_<150>.npz --foldselect 2 --model /path/to/Model_folder/DataA/model/optimized/full_<DataA>_context_<150>.model --out /path/to/Model_folder
+```
+```
+python -m biofilm.biofilm-cv --infile /path/to/Model_folder/DataA/feature_files/training_data_<DataA>_context_<150>.npz --foldselect 3 --model /path/to/Model_folder/DataA/model/optimized/full_<DataA>_context_<150>.model --out /path/to/Model_folder
+```
+```
+python -m biofilm.biofilm-cv --infile /path/to/Model_folder/DataA/feature_files/training_data_<DataA>_context_<150>.npz --foldselect 4 --model /path/to/Model_folder/DataA/model/optimized/full_<DataA>_context_<150>.model --out /path/to/Model_folder
+```
+
+
+
 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

@@ -14,7 +14,7 @@ from ubergauss.tools import loadfile, dumpfile
 
 #logging.basicConfing(filenema='cherri.log'level=logging.DEBUG)
 
-__version__ = "0.6"
+__version__ = "0.7"
 
 """
 

bin/find_occupied_regions.py

@@ -209,7 +209,7 @@ def main():
 
     if len(replicates) == 1:
         print('Info: only one experiment is used to build occupied regions')
-        in_file = replicates[0]
+        in_file = input_path_RRIs + replicates[0]
         print(in_file)
         # df_replicat = rl.read_chira_data(in_file)
         sep = rl.check_file_type(in_file)

setup.py

@@ -11,7 +11,7 @@
 
 setup(
     name='cherri',
-    version='0.6',
+    version='0.7',
     description='Cherri is a tool for classification of RNA-RNA interactions.',
     long_description=readme,
     long_description_content_type='text/markdown',