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  1. __pycache__/metrics.cpython-312.pyc +0 -0
  2. __pycache__/recalibration.cpython-312.pyc +0 -0
  3. __pycache__/visualization.cpython-312.pyc +0 -0
  4. app.py +26 -23
  5. new_test_saved_finetuned_model.py +5 -7
  6. plot.png +0 -0
  7. ratio_proportion_change3_2223/sch_largest_100-coded/logs/highGRschool10/log_test_finetuned.txt +0 -830
  8. ratio_proportion_change3_2223/sch_largest_100-coded/logs/highGRschool10/log_test_finetuned_info.txt +0 -0
  9. result.txt +7 -0
  10. roc_data.pkl +3 -0
  11. src/__pycache__/attention.cpython-312.pyc +0 -0
  12. src/__pycache__/bert.cpython-312.pyc +0 -0
  13. src/__pycache__/classifier_model.cpython-312.pyc +0 -0
  14. src/__pycache__/dataset.cpython-312.pyc +0 -0
  15. src/__pycache__/embedding.cpython-312.pyc +0 -0
  16. src/__pycache__/seq_model.cpython-312.pyc +0 -0
  17. src/__pycache__/transformer.cpython-312.pyc +0 -0
  18. src/__pycache__/transformer_component.cpython-312.pyc +0 -0
  19. src/__pycache__/vocab.cpython-312.pyc +0 -0
  20. train.txt +1 -0
  21. train_info.txt +1 -0
  22. train_label.txt +1 -0
__pycache__/metrics.cpython-312.pyc CHANGED
Binary files a/__pycache__/metrics.cpython-312.pyc and b/__pycache__/metrics.cpython-312.pyc differ
 
__pycache__/recalibration.cpython-312.pyc CHANGED
Binary files a/__pycache__/recalibration.cpython-312.pyc and b/__pycache__/recalibration.cpython-312.pyc differ
 
__pycache__/visualization.cpython-312.pyc CHANGED
Binary files a/__pycache__/visualization.cpython-312.pyc and b/__pycache__/visualization.cpython-312.pyc differ
 
app.py CHANGED
@@ -1,7 +1,7 @@
1
  import gradio as gr
2
  from huggingface_hub import hf_hub_download
3
  import pickle
4
- import gradio as gr
5
  import numpy as np
6
  import subprocess
7
  import shutil
@@ -9,17 +9,18 @@ import matplotlib.pyplot as plt
9
  from sklearn.metrics import roc_curve, auc
10
  # Define the function to process the input file and model selection
11
 
12
- def process_file(file,label,info, model_name):
13
-
14
- # with open(file.name, 'r') as f:
15
- # content = f.read()
16
- # saved_test_dataset = "train.txt"
17
- # saved_test_label = "train_label.txt"
18
- # saved_train_info="train_info.txt"
19
- # # Save the uploaded file content to a specified location
20
- # shutil.copyfile(file.name, saved_test_dataset)
21
- # shutil.copyfile(label.name, saved_test_label)
22
- # shutil.copyfile(info.name, saved_train_info)
 
23
  # For demonstration purposes, we'll just return the content with the selected model name
24
  # if(model_name=="highGRschool10"):
25
  # checkpoint="ratio_proportion_change3/output/FS/bert_fine_tuned.model.ep32"
@@ -29,8 +30,13 @@ def process_file(file,label,info, model_name):
29
  # checkpoint="ratio_proportion_change3/output/correctness/bert_fine_tuned.model.ep48"
30
  # else:
31
  # checkpoint=None
32
-
33
  # print(checkpoint)
 
 
 
 
 
 
34
  subprocess.run([
35
  "python", "new_test_saved_finetuned_model.py",
36
  "-workspace_name", "ratio_proportion_change3_2223/sch_largest_100-coded",
@@ -40,9 +46,9 @@ def process_file(file,label,info, model_name):
40
  "-finetuned_bert_classifier_checkpoint",
41
  "ratio_proportion_change3_2223/sch_largest_100-coded/output/highGRschool10/bert_fine_tuned.model.ep42",
42
  "-e",str(1),
43
- "-b",str(5)
44
  ])
45
-
46
  result = {}
47
  with open("result.txt", 'r') as file:
48
  for line in file:
@@ -56,8 +62,6 @@ def process_file(file,label,info, model_name):
56
  with open("roc_data.pkl", "rb") as f:
57
  fpr, tpr, _ = pickle.load(f)
58
 
59
-
60
-
61
  roc_auc = auc(fpr, tpr)
62
  fig, ax = plt.subplots()
63
  ax.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
@@ -70,7 +74,7 @@ def process_file(file,label,info, model_name):
70
  plot_path = "plot.png"
71
  fig.savefig(plot_path)
72
  plt.close(fig)
73
-
74
  # Prepare text output
75
  text_output = f"Model: {model_name}\nResult:\n{result}"
76
 
@@ -80,7 +84,6 @@ def process_file(file,label,info, model_name):
80
 
81
  models = ["highGRschool10", "lowGRschoolAll", "fullTest"]
82
 
83
-
84
  # Create the Gradio interface
85
  with gr.Blocks(css="""
86
  body {
@@ -272,18 +275,18 @@ tbody.svelte-18wv37q>tr.svelte-18wv37q:nth-child(odd) {
272
 
273
  info_input = gr.File(label="Upload test info", file_types=['.txt'], elem_classes="file-box")
274
 
275
- model_dropdown = gr.Dropdown(choices=models, label="Select Finetune Task", elem_classes="dropdown-menu")
276
 
277
 
278
-
279
 
280
  with gr.Row():
281
  output_text = gr.Textbox(label="Output Text")
282
  output_image = gr.Image(label="Output Plot")
283
 
284
  btn = gr.Button("Submit")
285
-
286
- btn.click(fn=process_file, inputs=[file_input,label_input,info_input, model_dropdown], outputs=[output_text,output_image])
287
 
288
 
289
  # Launch the app
 
1
  import gradio as gr
2
  from huggingface_hub import hf_hub_download
3
  import pickle
4
+ from gradio import Progress
5
  import numpy as np
6
  import subprocess
7
  import shutil
 
9
  from sklearn.metrics import roc_curve, auc
10
  # Define the function to process the input file and model selection
11
 
12
+ def process_file(file,label,info,inc_val,progress=Progress(track_tqdm=True)):
13
+ # progress = gr.Progress(track_tqdm=True)
14
+ progress(0, desc="Starting the processing")
15
+ with open(file.name, 'r') as f:
16
+ content = f.read()
17
+ saved_test_dataset = "train.txt"
18
+ saved_test_label = "train_label.txt"
19
+ saved_train_info="train_info.txt"
20
+ # Save the uploaded file content to a specified location
21
+ shutil.copyfile(file.name, saved_test_dataset)
22
+ shutil.copyfile(label.name, saved_test_label)
23
+ shutil.copyfile(info.name, saved_train_info)
24
  # For demonstration purposes, we'll just return the content with the selected model name
25
  # if(model_name=="highGRschool10"):
26
  # checkpoint="ratio_proportion_change3/output/FS/bert_fine_tuned.model.ep32"
 
30
  # checkpoint="ratio_proportion_change3/output/correctness/bert_fine_tuned.model.ep48"
31
  # else:
32
  # checkpoint=None
 
33
  # print(checkpoint)
34
+ if (inc_val<5):
35
+ model_name="highGRschool10"
36
+ elif(inc_val>=5 & inc_val<10):
37
+ model_name="highGRschool10"
38
+ else:
39
+ model_name="highGRschool10"
40
  subprocess.run([
41
  "python", "new_test_saved_finetuned_model.py",
42
  "-workspace_name", "ratio_proportion_change3_2223/sch_largest_100-coded",
 
46
  "-finetuned_bert_classifier_checkpoint",
47
  "ratio_proportion_change3_2223/sch_largest_100-coded/output/highGRschool10/bert_fine_tuned.model.ep42",
48
  "-e",str(1),
49
+ "-b",str(1000)
50
  ])
51
+ progress(0.6,desc="Model execution completed")
52
  result = {}
53
  with open("result.txt", 'r') as file:
54
  for line in file:
 
62
  with open("roc_data.pkl", "rb") as f:
63
  fpr, tpr, _ = pickle.load(f)
64
 
 
 
65
  roc_auc = auc(fpr, tpr)
66
  fig, ax = plt.subplots()
67
  ax.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
 
74
  plot_path = "plot.png"
75
  fig.savefig(plot_path)
76
  plt.close(fig)
77
+ progress(1.0)
78
  # Prepare text output
79
  text_output = f"Model: {model_name}\nResult:\n{result}"
80
 
 
84
 
85
  models = ["highGRschool10", "lowGRschoolAll", "fullTest"]
86
 
 
87
  # Create the Gradio interface
88
  with gr.Blocks(css="""
89
  body {
 
275
 
276
  info_input = gr.File(label="Upload test info", file_types=['.txt'], elem_classes="file-box")
277
 
278
+ # model_dropdown = gr.Dropdown(choices=models, label="Select Finetune Task", elem_classes="dropdown-menu")
279
 
280
 
281
+ increment_slider = gr.Slider(minimum=1, maximum=50, step=5, label="Schools number", value=1)
282
 
283
  with gr.Row():
284
  output_text = gr.Textbox(label="Output Text")
285
  output_image = gr.Image(label="Output Plot")
286
 
287
  btn = gr.Button("Submit")
288
+
289
+ btn.click(fn=process_file, inputs=[file_input,label_input,info_input,increment_slider], outputs=[output_text,output_image])
290
 
291
 
292
  # Launch the app
new_test_saved_finetuned_model.py CHANGED
@@ -142,7 +142,7 @@ class BERTFineTuneTrainer:
142
  plabels = []
143
  tlabels = []
144
  probabs = []
145
-
146
  if phase == "train":
147
  self.model.train()
148
  else:
@@ -179,7 +179,7 @@ class BERTFineTuneTrainer:
179
  # true_labels = torch.argmax(data["label"], dim=-1)
180
  plabels.extend(predicted_labels.cpu().numpy())
181
  tlabels.extend(data['label'].cpu().numpy())
182
-
183
  # Compare predicted labels to true labels and calculate accuracy
184
  correct = (data['label'] == predicted_labels).sum().item()
185
 
@@ -204,9 +204,8 @@ class BERTFineTuneTrainer:
204
  f1_scores = f1_score(tlabels, plabels, average="weighted")
205
  cmatrix = confusion_matrix(tlabels, plabels)
206
  end_time = time.time()
207
- auc_score = roc_auc_score(tlabels, plabels)
208
  final_msg = {
209
- "epoch": f"EP{epoch}_{phase}",
210
  "avg_loss": avg_loss / len(data_iter),
211
  "total_acc": total_correct * 100.0 / total_element,
212
  "precisions": precisions,
@@ -222,7 +221,7 @@ class BERTFineTuneTrainer:
222
  for key, value in final_msg.items():
223
  file.write(f"{key}: {value}\n")
224
  print(final_msg)
225
- fpr, tpr, thresholds = roc_curve(tlabels, plabels)
226
  with open("roc_data.pkl", "wb") as f:
227
  pickle.dump((fpr, tpr, thresholds), f)
228
  print(final_msg)
@@ -427,7 +426,6 @@ class BERTFineTuneCalibratedTrainer:
427
  auc_score = roc_auc_score(tlabels, positive_class_probs)
428
  end_time = time.time()
429
  final_msg = {
430
- "epoch": f"EP{epoch}_{phase}",
431
  "avg_loss": avg_loss / len(data_iter),
432
  "total_acc": total_correct * 100.0 / total_element,
433
  "precisions": precisions,
@@ -449,7 +447,7 @@ class BERTFineTuneCalibratedTrainer:
449
  with open(self.log_folder_path+f"/log_{phase}_finetuned_info.txt", 'a') as f1:
450
  sys.stdout = f1
451
  final_msg = {
452
- "epoch": f"EP{epoch}_{phase}",
453
  "confusion_matrix": f"{cmatrix}",
454
  "true_labels": f"{tlabels if epoch == 0 else ''}",
455
  "predicted_labels": f"{plabels}",
 
142
  plabels = []
143
  tlabels = []
144
  probabs = []
145
+ positive_class_probs=[]
146
  if phase == "train":
147
  self.model.train()
148
  else:
 
179
  # true_labels = torch.argmax(data["label"], dim=-1)
180
  plabels.extend(predicted_labels.cpu().numpy())
181
  tlabels.extend(data['label'].cpu().numpy())
182
+ positive_class_probs = [prob[1] for prob in probabs]
183
  # Compare predicted labels to true labels and calculate accuracy
184
  correct = (data['label'] == predicted_labels).sum().item()
185
 
 
204
  f1_scores = f1_score(tlabels, plabels, average="weighted")
205
  cmatrix = confusion_matrix(tlabels, plabels)
206
  end_time = time.time()
207
+ auc_score = roc_auc_score(tlabels, positive_class_probs)
208
  final_msg = {
 
209
  "avg_loss": avg_loss / len(data_iter),
210
  "total_acc": total_correct * 100.0 / total_element,
211
  "precisions": precisions,
 
221
  for key, value in final_msg.items():
222
  file.write(f"{key}: {value}\n")
223
  print(final_msg)
224
+ fpr, tpr, thresholds = roc_curve(tlabels, positive_class_probs)
225
  with open("roc_data.pkl", "wb") as f:
226
  pickle.dump((fpr, tpr, thresholds), f)
227
  print(final_msg)
 
426
  auc_score = roc_auc_score(tlabels, positive_class_probs)
427
  end_time = time.time()
428
  final_msg = {
 
429
  "avg_loss": avg_loss / len(data_iter),
430
  "total_acc": total_correct * 100.0 / total_element,
431
  "precisions": precisions,
 
447
  with open(self.log_folder_path+f"/log_{phase}_finetuned_info.txt", 'a') as f1:
448
  sys.stdout = f1
449
  final_msg = {
450
+
451
  "confusion_matrix": f"{cmatrix}",
452
  "true_labels": f"{tlabels if epoch == 0 else ''}",
453
  "predicted_labels": f"{plabels}",
plot.png CHANGED
ratio_proportion_change3_2223/sch_largest_100-coded/logs/highGRschool10/log_test_finetuned.txt DELETED
@@ -1,830 +0,0 @@
1
- {'epoch': 0, 'iter': 0, 'avg_loss': 1.5496840476989746, 'avg_acc': 40.0, 'loss': 1.5496840476989746}
2
- {'epoch': 0, 'iter': 10, 'avg_loss': 0.8559415516528216, 'avg_acc': 52.72727272727272, 'loss': 0.10956348478794098}
3
- {'epoch': 0, 'iter': 20, 'avg_loss': 0.818257675284431, 'avg_acc': 51.42857142857142, 'loss': 0.8253201246261597}
4
- {'epoch': 0, 'iter': 30, 'avg_loss': 0.8282640369669083, 'avg_acc': 49.03225806451613, 'loss': 1.3043867349624634}
5
- {'epoch': 0, 'iter': 40, 'avg_loss': 0.7959969381733638, 'avg_acc': 51.21951219512195, 'loss': 0.5416589975357056}
6
- {'epoch': 0, 'iter': 50, 'avg_loss': 0.7974684755591785, 'avg_acc': 49.411764705882355, 'loss': 0.4128425717353821}
7
- {'epoch': 0, 'iter': 60, 'avg_loss': 0.8475138205485265, 'avg_acc': 47.21311475409836, 'loss': 0.9325758218765259}
8
- {'epoch': 0, 'iter': 70, 'avg_loss': 0.852948009967804, 'avg_acc': 47.32394366197183, 'loss': 1.046299934387207}
9
- {'epoch': 0, 'iter': 80, 'avg_loss': 0.8209105803642744, 'avg_acc': 49.382716049382715, 'loss': 0.8824451565742493}
10
- {'epoch': 0, 'iter': 90, 'avg_loss': 0.8229587893564623, 'avg_acc': 49.45054945054945, 'loss': 0.6368995904922485}
11
- {'epoch': 0, 'iter': 100, 'avg_loss': 0.8193959322306189, 'avg_acc': 50.2970297029703, 'loss': 0.9303101301193237}
12
- {'epoch': 0, 'iter': 110, 'avg_loss': 0.790709690601976, 'avg_acc': 51.891891891891895, 'loss': 0.5216326713562012}
13
- {'epoch': 0, 'iter': 120, 'avg_loss': 0.8036258492834312, 'avg_acc': 51.2396694214876, 'loss': 1.4479434490203857}
14
- {'epoch': 0, 'iter': 130, 'avg_loss': 0.791729781805104, 'avg_acc': 51.7557251908397, 'loss': 0.7250272035598755}
15
- {'epoch': 0, 'iter': 140, 'avg_loss': 0.7973896682473784, 'avg_acc': 51.06382978723404, 'loss': 0.7458365559577942}
16
- {'epoch': 0, 'iter': 150, 'avg_loss': 0.8018214718395511, 'avg_acc': 51.523178807947026, 'loss': 0.9225670695304871}
17
- {'epoch': 0, 'iter': 160, 'avg_loss': 0.7987281313594083, 'avg_acc': 51.42857142857142, 'loss': 0.9038938283920288}
18
- {'epoch': 0, 'iter': 170, 'avg_loss': 0.7995727229536626, 'avg_acc': 51.69590643274854, 'loss': 0.6161319613456726}
19
- {'epoch': 0, 'iter': 180, 'avg_loss': 0.8114044267828293, 'avg_acc': 51.049723756906076, 'loss': 1.2191474437713623}
20
- {'epoch': 0, 'iter': 190, 'avg_loss': 0.80793987175557, 'avg_acc': 51.41361256544502, 'loss': 0.48870745301246643}
21
- {'epoch': 0, 'iter': 200, 'avg_loss': 0.8185157425961092, 'avg_acc': 51.14427860696517, 'loss': 0.6329455971717834}
22
- {'epoch': 0, 'iter': 210, 'avg_loss': 0.8212848654572997, 'avg_acc': 51.18483412322274, 'loss': 0.8923023343086243}
23
- {'epoch': 0, 'iter': 220, 'avg_loss': 0.8161697818547892, 'avg_acc': 51.49321266968325, 'loss': 0.1399487555027008}
24
- {'epoch': 0, 'iter': 230, 'avg_loss': 0.8169654901925619, 'avg_acc': 51.25541125541125, 'loss': 0.9861614108085632}
25
- {'epoch': 0, 'iter': 240, 'avg_loss': 0.8339173226188328, 'avg_acc': 50.788381742738586, 'loss': 0.31081730127334595}
26
- {'epoch': 0, 'iter': 250, 'avg_loss': 0.8392921398835353, 'avg_acc': 50.59760956175299, 'loss': 0.6075574159622192}
27
- {'epoch': 0, 'iter': 260, 'avg_loss': 0.8401494497768723, 'avg_acc': 50.57471264367817, 'loss': 1.3042515516281128}
28
- {'epoch': 0, 'iter': 270, 'avg_loss': 0.8458465448824682, 'avg_acc': 50.258302583025824, 'loss': 1.2338275909423828}
29
- {'epoch': 0, 'iter': 280, 'avg_loss': 0.843027359225996, 'avg_acc': 50.320284697508896, 'loss': 0.4211927354335785}
30
- {'epoch': 0, 'iter': 290, 'avg_loss': 0.8444837639012288, 'avg_acc': 50.171821305841924, 'loss': 1.0499225854873657}
31
- {'epoch': 0, 'iter': 300, 'avg_loss': 0.8481888300507172, 'avg_acc': 50.033222591362126, 'loss': 0.7186862230300903}
32
- {'epoch': 0, 'iter': 310, 'avg_loss': 0.8520605850401799, 'avg_acc': 49.839228295819936, 'loss': 0.7515642046928406}
33
- {'epoch': 0, 'iter': 320, 'avg_loss': 0.8540132050155851, 'avg_acc': 49.781931464174455, 'loss': 1.2370407581329346}
34
- {'epoch': 0, 'iter': 330, 'avg_loss': 0.8492184812343733, 'avg_acc': 50.03021148036254, 'loss': 0.896265983581543}
35
- {'epoch': 0, 'iter': 340, 'avg_loss': 0.8471794229579811, 'avg_acc': 49.97067448680352, 'loss': 1.6743370294570923}
36
- {'epoch': 0, 'iter': 350, 'avg_loss': 0.8443615075487357, 'avg_acc': 50.085470085470085, 'loss': 0.5914673209190369}
37
- {'epoch': 0, 'iter': 360, 'avg_loss': 0.8454870282571731, 'avg_acc': 49.91689750692521, 'loss': 1.090012550354004}
38
- {'epoch': 0, 'iter': 370, 'avg_loss': 0.839884057339793, 'avg_acc': 50.40431266846361, 'loss': 0.1998119056224823}
39
- {'epoch': 0, 'iter': 380, 'avg_loss': 0.8448523341475196, 'avg_acc': 50.13123359580053, 'loss': 0.8307355046272278}
40
- {'epoch': 0, 'iter': 390, 'avg_loss': 0.843209407728194, 'avg_acc': 50.230179028132994, 'loss': 0.5250450372695923}
41
- {'epoch': 0, 'iter': 400, 'avg_loss': 0.8433360394062545, 'avg_acc': 50.32418952618454, 'loss': 1.0489654541015625}
42
- {'epoch': 0, 'iter': 410, 'avg_loss': 0.8395234309227507, 'avg_acc': 50.51094890510949, 'loss': 0.6405119299888611}
43
- {'epoch': 0, 'iter': 420, 'avg_loss': 0.8357288829823004, 'avg_acc': 50.641330166270784, 'loss': 0.8002420663833618}
44
- {'epoch': 0, 'iter': 430, 'avg_loss': 0.8367320633992518, 'avg_acc': 50.62645011600928, 'loss': 1.1284911632537842}
45
- {'epoch': 0, 'iter': 440, 'avg_loss': 0.8386554056588484, 'avg_acc': 50.52154195011338, 'loss': 1.08228600025177}
46
- {'epoch': 0, 'iter': 450, 'avg_loss': 0.8370322342755525, 'avg_acc': 50.46563192904656, 'loss': 1.0331103801727295}
47
- {'epoch': 0, 'iter': 460, 'avg_loss': 0.8364622235912305, 'avg_acc': 50.32537960954448, 'loss': 1.0094430446624756}
48
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