probas

tulane-cmps6730 · Apr 30, 2024 · 39c7fe2 · 39c7fe2
1 parent e366dba
commit 39c7fe2
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Showing 2 changed files with 36 additions and 15 deletions.
diff --git a/nlp/app/routes.py b/nlp/app/routes.py
@@ -60,14 +60,14 @@ def index():
             model = load_model(model_path, compile=False)
             text = basic_process(input_field)
             text = cnn_process(text)
-            preds = model.predict(text)
-            probas = (preds > 0.5).astype(int) 
+            probas = model.predict(text)
+            preds = (probas > 0.5).astype(int) 
             if preds == 1:
                 prediction = "WIN"
-                proba = probas[1]
+                proba = probas
             else:
                 prediction = "LOSS"
-                proba = proba[0]
+                proba = probas
         elif model_choice == 'bert':
             tokenizer = BertTokenizerFast.from_pretrained('prajjwal1/bert-mini')
             model = AutoModelForSequenceClassification.from_pretrained('/Users/jackiecollopy/Downloads/project-reddit/notebooks/bert.pth')

diff --git a/notebooks/Experiment-CNN-1.ipynb b/notebooks/Experiment-CNN-1.ipynb
@@ -369,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 27,
    "id": "8cfa7100-4fed-4538-b8ce-213a5efb4d1f",
    "metadata": {},
    "outputs": [
@@ -386,15 +386,15 @@
    ],
    "source": [
     "predictions = best_model_cnn.predict(X_val)\n",
-    "predictions = (predictions > 0.5).astype(int) \n",
+    "preds = (predictions > 0.5).astype(int) \n",
     "\n",
-    "f1 = f1_score(y_val, predictions)\n",
+    "f1 = f1_score(y_val, preds)\n",
     "print(\"F1 Score:\", round(f1,3))\n",
     "# Calculate Precision\n",
-    "precision = precision_score(y_val, predictions)\n",
+    "precision = precision_score(y_val, preds)\n",
     "print(\"Precision:\", round(precision, 3))\n",
     "# Calculate recall\n",
-    "recall = recall_score(y_val, predictions)\n",
+    "recall = recall_score(y_val, preds)\n",
     "print(\"Recall:\", round(recall, 3))"
    ]
   },
@@ -459,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "id": "9612ac53-cc11-4ee6-acff-d33cabf98e73",
    "metadata": {},
    "outputs": [
@@ -490,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "ff0623cb-4337-4e8f-8f38-6f2a3a215edf",
    "metadata": {},
    "outputs": [
@@ -506,7 +506,7 @@
        "       [0]])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -561,11 +561,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "e96b0c61-e235-4fb2-88af-6149c17184a6",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'cnn_model' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[28], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m results_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mActual_Label\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m y_test\n\u001b[1;32m      5\u001b[0m results_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPredicted_Label\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m preds\n\u001b[0;32m----> 6\u001b[0m probs \u001b[38;5;241m=\u001b[39m \u001b[43mcnn_model\u001b[49m\u001b[38;5;241m.\u001b[39mpredict(X_test)\n\u001b[1;32m      7\u001b[0m results_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpredict_proba\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m probs\n\u001b[1;32m      8\u001b[0m results_df\u001b[38;5;241m.\u001b[39mhead()\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'cnn_model' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "## Getting a dataframe\n",
+    "\n",
+    "results_df = test_df.copy()\n",
+    "results_df[\"Actual_Label\"] = y_test\n",
+    "results_df[\"Predicted_Label\"] = preds\n",
+    "probs = cnn_model.predict(X_test)\n",
+    "results_df['predict_proba'] = probs\n",
+    "results_df.head()"
+   ]
   },
   {
    "cell_type": "code",