mertkarabacak commited on
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218d1bb
1 Parent(s): 8d7edae

Upload app.py

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  1. app.py +5 -5
app.py CHANGED
@@ -119,7 +119,7 @@ tabpfn = TabPFNClassifier(device='cuda', N_ensemble_configurations=1)
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  y3_model = tabpfn
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  y3_model = y3_model.fit(x3, y3, overwrite_warning=True)
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- y3_calib_model = CalibratedClassifierCV(y3_model, method='sigmoid', cv='prefit')
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  y3_calib_model = y3_calib_model.fit(x3, y3)
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  y3_explainer = shap.Explainer(y3_model.predict, x3)
@@ -131,7 +131,7 @@ tabpfn = TabPFNClassifier(device='cuda', N_ensemble_configurations=1)
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  y4_model = tabpfn
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  y4_model = y4_model.fit(x4, y4, overwrite_warning=True)
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- y4_calib_model = CalibratedClassifierCV(y4_model, method='sigmoid', cv='prefit')
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  y4_calib_model = y4_calib_model.fit(x4, y4)
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  y4_explainer = shap.Explainer(y4_model.predict, x4)
@@ -326,7 +326,7 @@ with gr.Blocks(title = "NCDB-Meningioma") as demo:
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  </tr>
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  <tr>
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  <td>6-Month Mortality</td>
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- <td>TabPFN</td>
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  <td>0.755 (0.733 - 0.777)</td>
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  <td>0.767 (0.745 - 0.789)</td>
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  <td>0.764 (0.742 - 0.786)</td>
@@ -336,7 +336,7 @@ with gr.Blocks(title = "NCDB-Meningioma") as demo:
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  </tr>
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  <tr>
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  <td>12-Month Mortality</td>
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- <td>TabPFN</td>
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  <td>0.685 (0.661 - 0.709)</td>
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  <td>0.728 (0.705 - 0.751)</td>
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  <td>0.707 (0.683 - 0.731)</td>
@@ -393,7 +393,7 @@ with gr.Blocks(title = "NCDB-Meningioma") as demo:
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  Tumor_Size = gr.Slider(label = "Tumor Size (mm)", minimum = 1, maximum = 300, step = 1, value = 30)
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- Extent_of_Resection = gr.Dropdown(label = 'Extent of Resection', choices = ['No resective surgery was performed', 'Gross total resection'], type = 'index', value = 'Gross total resection')
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  Radiotherapy = gr.Dropdown(label = 'Radiotherapy', choices = ['No', 'Yes'], type = 'index', value = 'Yes')
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  y3_model = tabpfn
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  y3_model = y3_model.fit(x3, y3, overwrite_warning=True)
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+ y3_calib_model = CalibratedClassifierCV(y3_model, method='isotonic', cv='prefit')
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  y3_calib_model = y3_calib_model.fit(x3, y3)
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  y3_explainer = shap.Explainer(y3_model.predict, x3)
 
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  y4_model = tabpfn
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  y4_model = y4_model.fit(x4, y4, overwrite_warning=True)
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+ y4_calib_model = CalibratedClassifierCV(y4_model, method='isotonic', cv='prefit')
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  y4_calib_model = y4_calib_model.fit(x4, y4)
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  y4_explainer = shap.Explainer(y4_model.predict, x4)
 
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  </tr>
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  <tr>
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  <td>6-Month Mortality</td>
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+ <td>Random Forest</td>
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  <td>0.755 (0.733 - 0.777)</td>
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  <td>0.767 (0.745 - 0.789)</td>
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  <td>0.764 (0.742 - 0.786)</td>
 
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  </tr>
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  <tr>
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  <td>12-Month Mortality</td>
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+ <td>Random Forest</td>
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  <td>0.685 (0.661 - 0.709)</td>
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  <td>0.728 (0.705 - 0.751)</td>
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  <td>0.707 (0.683 - 0.731)</td>
 
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  Tumor_Size = gr.Slider(label = "Tumor Size (mm)", minimum = 1, maximum = 300, step = 1, value = 30)
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+ Extent_of_Resection = gr.Dropdown(label = 'Extent of Resection', choices = ['No resective surgery was performed', 'Gross total resection', 'Subtotal resection'], type = 'index', value = 'Gross total resection')
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  Radiotherapy = gr.Dropdown(label = 'Radiotherapy', choices = ['No', 'Yes'], type = 'index', value = 'Yes')
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