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Updated README, generate_acc_sen_spe_table.py and added resent results csv #4

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Updated README, generate_acc_sen_spe_table.py and added resent results csv #4

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76f7d47
Update README
Paras1242 Sep 17, 2024
b5cdc24
Merge branch 'pcdslab:main' into main
Paras1242 Sep 17, 2024
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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -89,6 +89,7 @@ System with Ubuntu 20.04 or later with at least 4 CPU cores, atleast 1 GPU, 64GB
9. **Install Required Packages**:
- Install the necessary Python packages by running:
```sh
pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu118
python3 -m pip install -r requirements.txt
```

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50 changes: 34 additions & 16 deletions generate_acc_sen_spe_table.py
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Expand Up @@ -73,29 +73,44 @@ def create_excel(swin_m,mit_m,longfor_m,resnet_m):
for i in range(1, 13):
sheet[f'A{i+2}'] = i
sheet[f'A{i+2}'].alignment = center_alignment
sheet[f'A{i+2}'].border = thin_border
# sheet[f'A{i+2}'].border = thin_border

sheet[f'B{i+2}'] = swin_m[i-1][0]
sheet[f'B{i+2}'].number_format = '0.00%'
sheet[f'C{i+2}'] = swin_m[i-1][1]
sheet[f'C{i+2}'].number_format = '0.00%'
sheet[f'D{i+2}'] = swin_m[i-1][2]

sheet[f'E{i}'] = mit_m[i-1][0]
sheet[f'F{i}'] = mit_m[i-1][1]
sheet[f'G{i}'] = mit_m[i-1][2]

sheet[f'H{i}'] = longfor_m[i-1][0]
sheet[f'I{i}'] = longfor_m[i-1][1]
sheet[f'J{i}'] = longfor_m[i-1][2]

sheet[f'K{i}'] = resnet_m[i-1][0]
sheet[f'L{i}'] = resnet_m[i-1][1]
sheet[f'M{i}'] = resnet_m[i-1][2]
sheet[f'D{i+2}'].number_format = '0.00%'

sheet[f'E{i+2}'] = mit_m[i-1][0]
sheet[f'E{i+2}'].number_format = '0.00%'
sheet[f'F{i+2}'] = mit_m[i-1][1]
sheet[f'F{i+2}'].number_format = '0.00%'
sheet[f'G{i+2}'] = mit_m[i-1][2]
sheet[f'G{i+2}'].number_format = '0.00%'

sheet[f'H{i+2}'] = longfor_m[i-1][0]
sheet[f'H{i+2}'].number_format = '0.00%'
sheet[f'I{i+2}'] = longfor_m[i-1][1]
sheet[f'I{i+2}'].number_format = '0.00%'
sheet[f'J{i+2}'] = longfor_m[i-1][2]
sheet[f'J{i+2}'].number_format = '0.00%'

sheet[f'K{i+2}'] = resnet_m[i-1][0]
sheet[f'K{i+2}'].number_format = '0.00%'
sheet[f'L{i+2}'] = resnet_m[i-1][1]
sheet[f'L{i+2}'].number_format = '0.00%'
sheet[f'M{i+2}'] = resnet_m[i-1][2]
sheet[f'M{i+2}'].number_format = '0.00%'

sheet[f'A15'] = 'Avg'
sheet[f'A15'].font = bold_font
sheet[f'A15'].alignment = center_alignment

lis = ["B","C","D","E","F","G","H","I","J","K","L","M"]
for ch in lis:
sheet[f'{ch}15'] = f"=ROUND(AVERAGE({ch}3:{ch}14),4)"
sheet[f'{ch}15'].number_format = '0.00%'


# Save the workbook
workbook.save('model_performance.xlsx')
Expand Down Expand Up @@ -137,7 +152,7 @@ def get_majority_vote_pred(pred_lab):
new_pred = []
chunk_size=20
for i in range(0,len(pred_lab), chunk_size):
chunk = pred_labels[i:i + chunk_size]
chunk = pred_lab[i:i + chunk_size]
result = check_ones(chunk)
new_pred.append(result)
return new_pred
Expand All @@ -148,9 +163,11 @@ def get_acc_sen_spe_llms(prob_lab_paths,bm_num):
else:
pred_probs = pd.read_csv(prob_lab_paths[0],header=None).values.flatten()
true_labels = pd.read_csv(prob_lab_paths[1],header=None).values.flatten()
print(f"len of true lables {len(true_labels)} len of pred_probs {len(pred_probs)}")

pred_labels = (pred_probs >= 0.5).astype(int)
majority_pred = get_majority_vote_pred(pred_labels)
print(f"len of true lables {len(true_labels)} len of pred_probs {len(majority_pred)}")

cm = confusion_matrix(true_labels, majority_pred)
TN, FP, FN, TP = cm.ravel()
Expand Down Expand Up @@ -205,4 +222,5 @@ def get_acc_sen_spe_llms(prob_lab_paths,bm_num):
Longfor_metrics.append(get_acc_sen_spe_llms((load_csv_file(file1_prob,prob_file_name,base_path_longformer_custom,base_path_longformer),
load_csv_file(file1_label_LLM,labels_file_name_LLM,base_path_longformer_custom,base_path_longformer)),i))

#resnet remaining and putting them into excel is remaning
#resnet remaining and putting them into excel is remaning
create_excel(Swinv2_metrics,MIT_metrics,Longfor_metrics,resnet_metrics)
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