Major refactors in order to make server the main source folder

ADA/.gitignore

-.idea
+*.idea
 *.pt
 __pycache__/
-amazon_data/
+server/agent/amazon_data/
 .DS_Store

ADA/SA/bert_analyzer.py → ADA/server/agent/SA/bert_analyzer.py

@@ -2,18 +2,16 @@ import torch
 import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader
-from tdbertnet import TDBertNet
-from bert_dataset import BertDataset, Instance, polarity_indices, generate_batch
+from agent.SA.tdbertnet import TDBertNet
+from agent.SA.bert_dataset import BertDataset, Instance, polarity_indices, generate_batch
 import time
 import numpy as np
 from sklearn import metrics
-import matplotlib.pyplot as plt
-import shap
 
-semeval_2014_train_path = 'data/SemEval-2014/Laptop_Train_v2.xml'
-semeval_2014_test_path = 'data/SemEval-2014/Laptops_Test_Gold.xml'
-amazon_test_path = 'data/Amazon/annotated_amazon_laptop_reviews.xml'
-trained_model_path = 'semeval_2014_2.pt'
+semeval_2014_train_path = 'agent/SA/data/SemEval-2014/Laptop_Train_v2.xml'
+semeval_2014_test_path = 'agent/SA/data/SemEval-2014/Laptops_Test_Gold.xml'
+amazon_test_path = 'agent/SA/data/Amazon/annotated_amazon_laptop_reviews.xml'
+trained_model_path = 'agent/SA/semeval_2014_2.pt'
 
 BATCH_SIZE = 32
 MAX_EPOCHS = 6
@@ -29,7 +27,7 @@ class BertAnalyzer:
     @staticmethod
     def default():
         sa = BertAnalyzer()
-        sa.load_saved('semeval_2014.pt')
+        sa.load_saved('agent/SA/semeval_2014.pt')
         return sa
 
     def load_saved(self, path):
@@ -133,7 +131,3 @@ class BertAnalyzer:
             # neutral or conflicted
             return 0
 
-
-sentiment_analyzer = BertAnalyzer.default()
-sentiment_analyzer.evaluate(semeval_2014_test_path)
-sentiment_analyzer.evaluate(amazon_test_path)

ADA/SA/bert_dataset.py → ADA/server/agent/SA/bert_dataset.py

@@ -2,7 +2,7 @@ import torch
 from torch.utils.data import Dataset
 import xml.etree.ElementTree as ET
 from transformers import *
-from tdbertnet import TRAINED_WEIGHTS, HIDDEN_OUTPUT_FEATURES
+from agent.SA.tdbertnet import TRAINED_WEIGHTS, HIDDEN_OUTPUT_FEATURES
 import re
 
 MAX_SEQ_LEN = 128