Merge pull request #207 from vertica/bugfix_calssification_lesson

Bugfix calssification lesson

docker-verticapy/enablement/Data Science Essentials/Module - Classification/Essentials_Classification_LogisticRegression_Example.ipynb

@@ -1356,63 +1356,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from verticapy.machine_learning.vertica.linear_model import LogisticRegression\n",
-    "model = LogisticRegression(\"public.LR_default\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "59bd58f8-976d-4c57-abba-b384f0dd3f13",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\n",
-       "\n",
-       "=======\n",
-       "details\n",
-       "=======\n",
-       "  predictor   |coefficient|std_err | z_value |p_value \n",
-       "--------------+-----------+--------+---------+--------\n",
-       "  Intercept   | -11.15139 | 0.57746|-19.31113| 0.00000\n",
-       "credit_balance|  0.00587  | 0.00027|21.60061 | 0.00000\n",
-       "    income    |  0.00001  | 0.00001| 0.63721 | 0.52399\n",
-       "   student    | -0.60562  | 0.27726|-2.18431 | 0.02894\n",
-       "\n",
-       "\n",
-       "==============\n",
-       "regularization\n",
-       "==============\n",
-       "type| lambda \n",
-       "----+--------\n",
-       "none| 1.00000\n",
-       "\n",
-       "\n",
-       "===========\n",
-       "call_string\n",
-       "===========\n",
-       "logistic_reg('public.LR_default', '\"public\".\"_verticapy_tmp_view_dbadmin_6064_718428356_\"', '\"default\"', '\"credit_balance\", \"income\", \"student\"'\n",
-       "USING PARAMETERS optimizer='newton', epsilon=1e-06, max_iterations=100, regularization='none', lambda=1, alpha=0.5, fit_intercept=true)\n",
-       "\n",
-       "===============\n",
-       "Additional Info\n",
-       "===============\n",
-       "       Name       |Value\n",
-       "------------------+-----\n",
-       " iteration_count  |  8  \n",
-       "rejected_row_count|  0  \n",
-       "accepted_row_count|7618 "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.fit(data, [\"credit_balance\", \"income\",\"student\"], \"default\")"
+    "file = open(\"Figures/result.PNG\", \"rb\")\n",
+    "image = file.read()\n",
+    "\n",
+    "image_0= widgets.Image(\n",
+    "                    value=image,\n",
+    "                    format='png',\n",
+    "                    width='500px'\n",
+    "                )\n",
+    "\n",
+    "display(image_0)"
    ]
   },
   {
@@ -1433,6 +1386,7 @@
     "\n",
     "```python\n",
     "model.fit(data, [\"credit_balance\", \"income\",\"student\"], \"default\")\n",
+    "model.classification_report(metrics=[\"accuracy\",\"auc\",\"precision\",\"specificity\"])\n",
     "```"
    ]
   },
@@ -1454,7 +1408,17 @@
     }
    ],
    "source": [
-    "model.classification_report(metrics=[\"accuracy\",\"auc\",\"precision\",\"specificity\"])"
+    "\n",
+    "file = open(\"Figures/report1.PNG\", \"rb\")\n",
+    "image = file.read()\n",
+    "\n",
+    "image_0= widgets.Image(\n",
+    "                    value=image,\n",
+    "                    format='png',\n",
+    "                    width='300px'\n",
+    "                )\n",
+    "\n",
+    "display(image_0)"
    ]
   },
   {
@@ -1601,80 +1565,62 @@
     "Let us train again to see if we improved."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "d73ace51",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "model.fit(balanced_data, [\"credit_balance\", \"income\",\"student\"], \"default\")\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "64efeb90-8470-445d-ba57-b7788ef47957",
+   "execution_count": null,
+   "id": "7043aad7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = open(\"Figures/result2.PNG\", \"rb\")\n",
+    "image = file.read()\n",
+    "\n",
+    "image_0= widgets.Image(\n",
+    "                    value=image,\n",
+    "                    format='png',\n",
+    "                    width='500px'\n",
+    "                )\n",
+    "\n",
+    "display(image_0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfd19aab",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\n",
-       "\n",
-       "=======\n",
-       "details\n",
-       "=======\n",
-       "  predictor   |coefficient|std_err | z_value |p_value \n",
-       "--------------+-----------+--------+---------+--------\n",
-       "  Intercept   | -8.87974  | 0.73503|-12.08072| 0.00000\n",
-       "credit_balance|  0.00589  | 0.00038|15.51593 | 0.00000\n",
-       "    income    | -0.00000  | 0.00001|-0.10420 | 0.91701\n",
-       "   student    | -0.90451  | 0.37083|-2.43915 | 0.01472\n",
-       "\n",
-       "\n",
-       "==============\n",
-       "regularization\n",
-       "==============\n",
-       "type| lambda \n",
-       "----+--------\n",
-       "none| 1.00000\n",
-       "\n",
-       "\n",
-       "===========\n",
-       "call_string\n",
-       "===========\n",
-       "logistic_reg('public.LR_default', '\"public\".\"_verticapy_tmp_view_dbadmin_6064_7892778534_\"', '\"default\"', '\"credit_balance\", \"income\", \"student\"'\n",
-       "USING PARAMETERS optimizer='newton', epsilon=1e-06, max_iterations=100, regularization='none', lambda=1, alpha=0.5, fit_intercept=true)\n",
-       "\n",
-       "===============\n",
-       "Additional Info\n",
-       "===============\n",
-       "       Name       |Value\n",
-       "------------------+-----\n",
-       " iteration_count  |  7  \n",
-       "rejected_row_count|  0  \n",
-       "accepted_row_count|1281 "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "model.fit(balanced_data, [\"credit_balance\", \"income\",\"student\"], \"default\")"
+    "```python\n",
+    "model.classification_report(metrics=[\"accuracy\",\"auc\",\"precision\",\"specificity\"])\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "82512aca-6a0e-4a0a-80f9-fda5d33f205b",
+   "execution_count": null,
+   "id": "be585235",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.9039812646370023\n",
-      "AUC: 0.951080411585369\n",
-      "Precision: 0.7854077253218884\n",
-      "Specificity: 0.9512195121951219\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "model.classification_report(metrics=[\"accuracy\",\"auc\",\"precision\",\"specificity\"])"
+    "file = open(\"Figures/report2.PNG\", \"rb\")\n",
+    "image = file.read()\n",
+    "\n",
+    "image_0= widgets.Image(\n",
+    "                    value=image,\n",
+    "                    format='png',\n",
+    "                    width='300px'\n",
+    "                )\n",
+    "\n",
+    "display(image_0)"
    ]
   },
   {
@@ -1780,7 +1726,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.12 ('web')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },