add eval as option

code/config_global_1.2.json

 {
-    "usecase_description": "Config file for usecase_1 : from a raw text, get the same text but with EDU bracket.",
+    "usecase_description": "Config file for usecase_1 : from a tokenized text, get the same text but with EDU bracket.",
     "data_raw": {
-        "name": "edgar_poe_en",
-        "exte": ".txt",
+        "name": "edgar_poe_short",
+        "exte": ".conll",
         "language": "en",
         "existing_metadata": true
     },
     "steps":{
         "main": "annotation",
         "pre-processing": {
-            "to_do": true,
+            "to_do": false,
             "syntactic_tool": "stanza",
             "sentence_split": true,
             "tokenization": true,
-            "syntactic_parsing": false,
+            "syntactic_parsing": true,
             "create_metadata": {
                 "to_do": true,
                 "line": "paragraph",
@@ -30,16 +30,17 @@
                 "validation_data_path": null
             }
         },
+        "evaluation": true,
         "gold_test_data_path": null
     },
     "output":{
         "conll_file":{
             "to_do": true,
             "metadata": true,
-            "with_gold_labels": false
+            "with_gold_labels": true
         },
         "txt_file":{
-            "to_do": false,
+            "to_do": true,
             "metadata": true
         }
     }

code/config_global_1.21.json

-{
-    "usecase_description": "Config file for usecase_1 : from a tokenized text, get the same text but with EDU bracket.",
-    "data_raw": {
-        "name": "edgar_poe_short",
-        "exte": ".conll",
-        "language": "en",
-        "existing_metadata": true
-    },
-    "steps":{
-        "main": "annotation",
-        "pre-processing": {
-            "to_do": false,
-            "syntactic_tool": "stanza",
-            "sentence_split": true,
-            "tokenization": true,
-            "syntactic_parsing": true,
-            "create_metadata": {
-                "to_do": true,
-                "line": "paragraph",
-                "sent": "sent"
-            }
-        },
-        "discourse_segmenter": {
-            "model": "/home/lriviere/andiamo/discut/Results_conllu/results_eng.rst.gum-eng_bert/model.tar.gz",
-            "training": {
-                "toolkit": null,
-                "pre_trained_lm": null,
-                "config_file": null,
-                "train_data_path": null,
-                "validation_data_path": null
-            }
-        },
-        "gold_test_data_path": null
-    },
-    "output":{
-        "conll_file":{
-            "to_do": true,
-            "metadata": true,
-            "with_gold_labels": true
-        },
-        "txt_file":{
-            "to_do": true,
-            "metadata": true
-        }
-    }
-}
-
-
-

code/config_global_2.2.json

-{
-    "usecase_description": "Config file for usecase_2 : from a gold text, make predictions with an existing model, compare gold and predictions with metrics.",
-    "data_raw": {
-        "name": "eng.pdtb.pdtb_dev",
-        "exte": ".conllu",
-        "language": "en",
-        "existing_metadata": true
-    },
-    "steps":{
-        "main": "test",
-        "pre-processing": {
-            "to_do": false,
-            "syntactic_tool": "stanza",
-            "sentence_split": true,
-            "tokenization": true,
-            "syntactic_parsing": true,
-            "create_metadata": {
-                "to_do": true,
-                "line": "paragraph",
-                "sent": "sent"
-            }
-        },
-        "discourse_segmenter": {
-            "model": "/home/lriviere/andiamo/morteza/discut/Results_conllu/results_eng.pdtb.pdtb_bert/model.tar.gz",
-            "training": {
-                "toolkit": null,
-                "pre_trained_lm": null,
-                "config_file": null,
-                "train_data_path": null,
-                "validation_data_path": null
-            }
-        },
-        "gold_test_data_path": null
-    },
-    "output":{
-        "conll_file":{
-            "to_do": true,
-            "metadata": true,
-            "with_gold_labels": true
-        },
-        "txt_file":{
-            "to_do": true,
-            "metadata": true
-        }
-    }
-}
-
-
-

code/config_global_3.json

@@ -30,6 +30,7 @@
                 "validation_data_path": "eng.rst.rstdt_dev"
             }
         },
+        "evaluation": true,
         "gold_test_data_path": "eng.rst.rstdt_dev"
     },
     "output":{

code/config_global_4.json

@@ -30,6 +30,7 @@
                 "validation_data_path": "/home/lriviere/andiamo/discut22/data/eng.sdrt.stac/eng.sdrt.stac_dev.conllu"
             }
         },
+        "evaluation": true,
         "gold_test_data_path": "eng.rst.rstdt_dev"
     },
     "output":{

code/discut22_2.py

@@ -248,6 +248,7 @@ class Process:
         self.crea_meta = infos['pre-processing']['create_metadata']['to_do']
         self.meta_line = infos['pre-processing']['create_metadata']['line']
         self.meta_sent = infos['pre-processing']['create_metadata']['sent']
+        self.eval = infos['evaluation']
 
         if self.main == "train" or "fine_tune":
             self.set_train = infos['discourse_segmenter']['training']['train_data_path']
@@ -261,11 +262,6 @@ class Process:
         self.model = infos['discourse_segmenter']['model'] # ezpz for Tony 
         self.test_data = infos['gold_test_data_path']
 
-    def get_evaluation_status(self):
-        if self.main == "test" or self.main == "train" or self.main == "fine_tune":
-            self.eval = True
-        else:
-            self.eval = False # "annotation"
 
     def get_model(self):
         self.model_path = ""
@@ -397,7 +393,6 @@ if __name__ == '__main__':
         data.make_ner_format()
         steps.get_model()
         data.make_predictions(steps) # output allennlp JSON
-        steps.get_evaluation_status()
         if steps.eval == True:
             data.evaluation(steps, prod)
         else:
@@ -410,7 +405,6 @@ if __name__ == '__main__':
         steps.update_training_config()
         steps.training(data)
         data.make_predictions(steps, js_name=steps.set_test, fi_ner=steps.test_ner)
-        steps.get_evaluation_status()
         if steps.eval == True:
             data.evaluation(steps, prod, name=steps.test_data)
     
@@ -422,7 +416,6 @@ if __name__ == '__main__':
         steps.update_training_config()
         steps.fine_tuning(data)
         data.make_predictions(steps, js_name=steps.set_test, fi_ner=steps.test_ner, model=steps.model_ft_path)
-        steps.get_evaluation_status()
         if steps.eval == True:
             data.evaluation(steps, prod, name=steps.test_data, model=steps.model_ft_path)