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udel-cbcb · Jan 6, 2025 · 2253316 · 2253316
1 parent 10410ea
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Showing 3 changed files with 144 additions and 131 deletions.
diff --git a/Submodule_4/Exercises/Submodule_4_Exercise_3_Predicting_Wine_Quality_Solution.ipynb b/Submodule_4/Exercises/Submodule_4_Exercise_3_Predicting_Wine_Quality_Solution.ipynb
@@ -168,7 +168,6 @@
    "outputs": [],
    "source": [
     "def get_metrics(true_labels, predicted_labels):\n",
-    "\n",
     "    print(\n",
     "        \"Accuracy:\", np.round(metrics.accuracy_score(true_labels, predicted_labels), 4)\n",
     "    )\n",
@@ -194,15 +193,13 @@
     "\n",
     "\n",
     "def display_classification_report(true_labels, predicted_labels, classes=[1, 0]):\n",
-    "\n",
     "    report = metrics.classification_report(\n",
     "        y_true=true_labels, y_pred=predicted_labels, labels=classes\n",
     "    )\n",
     "    print(report)\n",
     "\n",
     "\n",
     "def display_confusion_matrix(true_labels, predicted_labels, classes=[1, 0]):\n",
-    "\n",
     "    total_classes = len(classes)\n",
     "    level_labels = [total_classes * [0], list(range(total_classes))]\n",
     "    cm = metrics.confusion_matrix(\n",
@@ -235,7 +232,6 @@
     "def plot_model_roc_curve(\n",
     "    clf, features, true_labels, label_encoder=None, class_names=None\n",
     "):\n",
-    "\n",
     "    ## Compute ROC curve and ROC area for each class\n",
     "    fpr = dict()\n",
     "    tpr = dict()\n",
@@ -309,25 +305,28 @@
     "        plt.plot(\n",
     "            fpr[\"micro\"],\n",
     "            tpr[\"micro\"],\n",
-    "            label=\"micro-average ROC curve (area = {0:0.2f})\"\n",
-    "            \"\".format(roc_auc[\"micro\"]),\n",
+    "            label=\"micro-average ROC curve (area = {0:0.2f})\" \"\".format(\n",
+    "                roc_auc[\"micro\"]\n",
+    "            ),\n",
     "            linewidth=3,\n",
     "        )\n",
     "\n",
     "        plt.plot(\n",
     "            fpr[\"macro\"],\n",
     "            tpr[\"macro\"],\n",
-    "            label=\"macro-average ROC curve (area = {0:0.2f})\"\n",
-    "            \"\".format(roc_auc[\"macro\"]),\n",
+    "            label=\"macro-average ROC curve (area = {0:0.2f})\" \"\".format(\n",
+    "                roc_auc[\"macro\"]\n",
+    "            ),\n",
     "            linewidth=3,\n",
     "        )\n",
     "\n",
     "        for i, label in enumerate(class_labels):\n",
     "            plt.plot(\n",
     "                fpr[i],\n",
     "                tpr[i],\n",
-    "                label=\"ROC curve of class {0} (area = {1:0.2f})\"\n",
-    "                \"\".format(label, roc_auc[i]),\n",
+    "                label=\"ROC curve of class {0} (area = {1:0.2f})\" \"\".format(\n",
+    "                    label, roc_auc[i]\n",
+    "                ),\n",
     "                linewidth=2,\n",
     "                linestyle=\":\",\n",
     "            )\n",
@@ -354,7 +353,6 @@
     "    alphas=None,\n",
     "    colors=None,\n",
     "):\n",
-    "\n",
     "    if train_features.shape[1] != 2:\n",
     "        raise ValueError(\"X_train should have exactly 2 columnns!\")\n",
     "\n",
@@ -1243,7 +1241,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 4
 }