Change eval method

quadrama · Jan 25, 2024 · b8bd2e4 · b8bd2e4
1 parent 62e763e
commit b8bd2e4
Showing 1 changed file with 80 additions and 10 deletions.
diff --git a/platypus_entail.ipynb b/platypus_entail.ipynb
@@ -67,7 +67,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-12-12 16:30:36.733817: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "2024-01-25 12:24:38.657140: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
       "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
      ]
     }
@@ -282,7 +282,77 @@
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
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+       "version_minor": 0
+      },
+      "text/plain": [
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+    {
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@@ -679,18 +749,18 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.304688</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.219101</td>\n",
+       "      <td>0.267029</td>\n",
+       "      <td>0.438202</td>\n",
+       "      <td>0.192021</td>\n",
        "      <td>0.438202</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         F1  Recall  Precision  Accuracy\n",
-       "0  0.304688     0.5   0.219101  0.438202"
+       "         F1    Recall  Precision  Accuracy\n",
+       "0  0.267029  0.438202   0.192021  0.438202"
       ]
      },
      "execution_count": 25,
@@ -699,9 +769,9 @@
     }
    ],
    "source": [
-    "pd.DataFrame(list(zip([f1_score(ground_truth, predictions, average=\"macro\")],\n",
-    "                      [recall_score(ground_truth, predictions,  average=\"macro\")],\n",
-    "                      [precision_score(ground_truth, predictions, average=\"macro\")],\n",
+    "pd.DataFrame(list(zip([f1_score(ground_truth, predictions, average=\"weighted\")],\n",
+    "                      [recall_score(ground_truth, predictions,  average=\"weighted\")],\n",
+    "                      [precision_score(ground_truth, predictions, average=\"weighted\")],\n",
     "                      [accuracy_score(ground_truth, predictions,)])),\n",
     "                      columns = [\"F1\", \"Recall\", \"Precision\", \"Accuracy\"])"
    ]