Added Predictor code

run.py

@@ -0,0 +1,4 @@
+import classifier
+import testing.intent_predictor as pred
+
+pred.load_model_and_run_predictions("/mount/arbeitsdaten/studenten1/team-lab-nlp/mandavsi_rileyic/saved_models/experiment_4")

testing/__init__.py

@@ -0,0 +1,2 @@
+from utils.reader import *
+from classifier.nn import *

testing/intent_predictor.py

@@ -1,17 +1,62 @@
+from typing import Dict, List, Tuple
+
 from allennlp.common import JsonDict
 from allennlp.data import Instance
 from allennlp.predictors import Predictor
 from overrides import overrides
+from allennlp.models import Model
+from allennlp.data.dataset_readers import DatasetReader
+from allennlp.models.archival import load_archive
+from utils.reader import DataReaderJsonLines, CitationDataSetReader
+
+import os
 
 
 @Predictor.register('citation_intent_predictor')
 class IntentClassificationPredictor(Predictor):
     """"Predictor for Citation Intent Classifier"""
 
+    def predict(self, text: str, intent: str):
+        return self.predict_json({"citation_text": text, "intent": intent})
+
     @overrides
     def _json_to_instance(self, json_dict: JsonDict) -> Instance:
-        pass
+        return self._dataset_reader.text_to_instance(json_dict["citation_text"], json_dict["intent"])
 
-    @overrides
+
-    def predict_json(self, inputs: JsonDict) -> JsonDict:
+def make_predictions(model: Model, dataset_reader: DatasetReader, file_path: str) -> Tuple[
-        pass
+    List[Dict[str, float]], list]:
+    """Make predictions using the given model and dataset reader"""
+
+    predictor = IntentClassificationPredictor(model, dataset_reader)
+
+    prediction_list = []
+    true_list = []
+
+    vocab = model.vocab
+
+    jsonl_reader = DataReaderJsonLines(file_path)
+    i = 0
+    for citation in jsonl_reader.read():
+        i += 1
+        true_list.append(citation.intent)
+        output = predictor.predict(citation.text, citation.intent)
+        prediction_list.append({vocab.get_token_from_index(label_id, 'labels'): prob
+                                for label_id, prob in enumerate(output['probs'])})
+        if i == 10:
+            break
+
+    return prediction_list, true_list
+
+
+def load_model_and_run_predictions(saved_model_dir: str):
+    project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    train_file_path = project_root + '/data/tsv/train.tsv'
+    test_file_path = project_root + '/data/tsv/test.tsv'
+
+    model_archive = load_archive(os.path.join(saved_model_dir, 'model.tar.gz'))
+    citation_dataset_reader = CitationDataSetReader()
+
+    y_pred, y_true = make_predictions(model_archive.model, citation_dataset_reader, test_file_path)
+
+    print(y_pred)