metric_compute_nb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pydantic import BaseModel, validator\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from enum import Enum\n",
    "from collections.abc import Iterable\n",
    "from functools import partial\n",
    "df_result = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ref = pd.read_csv('kidney_test.csv')\n",
    "\n",
    "df_ref['model_id'] = '1'\n",
    "df_ref['model_version'] = '2'\n",
    "df_ref['period'] = 'reference'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>...</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "      <th>class</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>clf_target</th>\n",
       "      <th>y_pred_proba</th>\n",
       "      <th>pred_timestamp</th>\n",
       "      <th>model_id</th>\n",
       "      <th>model_version</th>\n",
       "      <th>period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>214</td>\n",
       "      <td>55.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
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       "      <td>1.00</td>\n",
       "      <td>2022-07-10 22:30:30</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>372</td>\n",
       "      <td>28.0</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>2022-07-10 22:30:30</td>\n",
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       "      <th>2</th>\n",
       "      <td>367</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>2022-07-10 22:30:30</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>260</td>\n",
       "      <td>35.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>2022-07-15 22:30:30</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>374</td>\n",
       "      <td>61.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.91</td>\n",
       "      <td>2022-07-15 22:30:30</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>reference</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id   age    bp     sg   al  su  rbc  pc  pcc  ba  ...  pe  ane  class  \\\n",
       "0  214  55.0  80.0  1.010  3.0   1    1   0    1   1  ...   1    0      1   \n",
       "1  372  28.0  60.0  1.025  0.0   0    1   1    0   0  ...   0    0      0   \n",
       "2  367  60.0  80.0  1.025  0.0   0    1   1    0   0  ...   0    0      0   \n",
       "3  260  35.0  80.0  1.020  0.0   0    1   1    0   0  ...   0    0      0   \n",
       "4  374  61.0  70.0  1.025  0.0   0    1   1    0   0  ...   0    0      0   \n",
       "\n",
       "   y_pred  clf_target  y_pred_proba       pred_timestamp  model_id  \\\n",
       "0       1           0          1.00  2022-07-10 22:30:30         1   \n",
       "1       1           0          1.00  2022-07-10 22:30:30         1   \n",
       "2       0           0          0.99  2022-07-10 22:30:30         1   \n",
       "3       0           0          0.98  2022-07-15 22:30:30         1   \n",
       "4       1           0          0.91  2022-07-15 22:30:30         1   \n",
       "\n",
       "   model_version     period  \n",
       "0              2  reference  \n",
       "1              2  reference  \n",
       "2              2  reference  \n",
       "3              2  reference  \n",
       "4              2  reference  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ref.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from influxdb import DataFrameClient, InfluxDBClient\n",
    "\n",
    "dbhost = \"localhost\"\n",
    "dbport = 8086\n",
    "dbuser = \"admin\"\n",
    "dbpasswd = \"pass123\"\n",
    "dbname = \"testDB\"\n",
    "protocol = \"line\"\n",
    "\n",
    "client = InfluxDBClient(dbhost, dbport, dbuser, dbpasswd, dbname)\n",
    "\n",
    "query = \"select * from test5Aug\"\n",
    "\n",
    "df_new = pd.DataFrame(client.query(query, chunked=True).get_points())\n",
    "\n",
    "df_new.rename(columns = {'time': 'pred_timestamp'}, inplace = True)\n",
    "df_new[\"pred_timestamp\"]=pd.to_datetime(df_new[\"pred_timestamp\"]).dt.strftime('%Y-%m-%d %H:%M:%S')\n",
    "# df_new['pred_timestamp'] = datetime.strptime('20/8/2022 22:30:30','%d/%m/%Y %H:%M:%S')\n",
    "# df_new['eval_timestamp'] = datetime(2022,8,26)\n",
    "df_new['model_id'] = '1'\n",
    "df_new['model_version'] = '2'\n",
    "\n",
    "try:\n",
    "    dbname = \"testDB_period\"\n",
    "    client = InfluxDBClient(dbhost, dbport, dbuser, dbpasswd, dbname)\n",
    "    query = \"select * from testDB_period\"\n",
    "    df_period_end = pd.DataFrame(client.query(query, chunked=True).get_points())\n",
    "    if (len(df_period_end) >0):\n",
    "        df_new = df_new[df_new['pred_timestamp'] > max(df_period_end['time'])]\n",
    "except:\n",
    "    pass\n",
    "    # TODO: Need to add InfluxDB connection exceptions here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred_timestamp</th>\n",
       "      <th>age</th>\n",
       "      <th>al</th>\n",
       "      <th>ane</th>\n",
       "      <th>appet</th>\n",
       "      <th>ba</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bp</th>\n",
       "      <th>bu</th>\n",
       "      <th>cad</th>\n",
       "      <th>...</th>\n",
       "      <th>project_id</th>\n",
       "      <th>rbc</th>\n",
       "      <th>rbcc</th>\n",
       "      <th>sc</th>\n",
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       "      <th>sod</th>\n",
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       "      <th>wbcc</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2022-08-06 00:35:06</td>\n",
       "      <td>83.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>102.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>MPM</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2.6</td>\n",
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       "      <td>115.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12800.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-08-09 11:47:43</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>MPM</td>\n",
       "      <td>1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.025</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-08-12 12:24:23</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>MPM</td>\n",
       "      <td>1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.025</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-08-12 12:36:14</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>MPM</td>\n",
       "      <td>1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.025</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-08-16 11:34:56</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.025</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>ABC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-08-16 18:41:37</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.025</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>ABC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-08-16 23:14:58</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.025</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>ABC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-08-16 23:18:58</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.025</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>ABC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022-08-17 10:59:23</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.025</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>ABC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022-08-17 22:43:52</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.025</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>ABC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2022-08-17 23:12:00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.025</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>ABC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2022-08-18 01:52:13</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.025</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>ABC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         pred_timestamp   age   al  ane  appet  ba    bgr    bp    bu  cad  \\\n",
       "0   2022-08-06 00:35:06  83.0  3.0    1      1   0  102.0  70.0  60.0    0   \n",
       "1   2022-08-09 11:47:43  25.0  0.0    0      0   0  121.0  80.0  19.0    0   \n",
       "2   2022-08-12 12:24:23  25.0  0.0    0      0   0  121.0  80.0  19.0    0   \n",
       "3   2022-08-12 12:36:14  25.0  0.0    0      0   0  121.0  80.0  19.0    0   \n",
       "4   2022-08-16 11:34:56  25.0  0.0    0      0   0  121.0  80.0  19.0    0   \n",
       "5   2022-08-16 18:41:37  25.0  0.0    0      0   0  121.0  80.0  19.0    0   \n",
       "6   2022-08-16 23:14:58  25.0  0.0    0      0   0  121.0  80.0  19.0    0   \n",
       "7   2022-08-16 23:18:58  17.0  0.0    0      0   0  114.0  60.0  50.0    0   \n",
       "8   2022-08-17 10:59:23  17.0  0.0    0      0   0  114.0  60.0  50.0    0   \n",
       "9   2022-08-17 22:43:52  17.0  0.0    0      0   0  114.0  60.0  50.0    0   \n",
       "10  2022-08-17 23:12:00  25.0  0.0    0      0   0  121.0  80.0  19.0    0   \n",
       "11  2022-08-18 01:52:13  25.0  0.0    0      0   0  121.0  80.0  19.0    0   \n",
       "\n",
       "    ... project_id  rbc rbcc   sc     sg    sod su target     wbcc  y_name  \n",
       "0   ...        MPM    1  3.1  2.6  1.020  115.0  0      1  12800.0    None  \n",
       "1   ...        MPM    1  5.3  1.2  1.025  142.0  0      0   6900.0    None  \n",
       "2   ...        MPM    1  5.3  1.2  1.025  142.0  0      0   6900.0    None  \n",
       "3   ...        MPM    1  5.3  1.2  1.025  142.0  0      0   6900.0    None  \n",
       "4   ...       None    1  5.3  1.2  1.025  142.0  0      0   6900.0     ABC  \n",
       "5   ...       None    1  5.3  1.2  1.025  142.0  0      0   6900.0     ABC  \n",
       "6   ...       None    1  5.3  1.2  1.025  142.0  0      0   6900.0     ABC  \n",
       "7   ...       None    1  5.9  1.0  1.025  135.0  0      0   7200.0     ABC  \n",
       "8   ...       None    1  5.9  1.0  1.025  135.0  0      0   7200.0     ABC  \n",
       "9   ...       None    1  5.9  1.0  1.025  135.0  0      0   7200.0     ABC  \n",
       "10  ...       None    1  5.3  1.2  1.025  142.0  0      0   6900.0     ABC  \n",
       "11  ...       None    1  5.3  1.2  1.025  142.0  0      0   6900.0     ABC  \n",
       "\n",
       "[12 rows x 35 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pulsar_metrics.metrics.base import MetricResults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = MetricResults(name = \"accuracy\", type = \"performance\", model_id = \"1\", model_version = \"1.0\", period_end = datetime(2022,9,1), period_start = datetime(2022,7,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MetricResults(metric_name=None, type='performance', model_id='1', model_version='1.0', data_id=None, feature=None, value=None, status=None, threshold=None, period_start=datetime.datetime(2022, 7, 1, 0, 0), period_end=datetime.datetime(2022, 9, 1, 0, 0), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['performance', 'drift', 'custom']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pulsar_metrics.metrics.base import MetricsType\n",
    "MetricsType._member_names_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['accuracy',\n",
       " 'precision',\n",
       " 'recall',\n",
       " 'f1',\n",
       " 'log_loss',\n",
       " 'auc',\n",
       " 'aucpr',\n",
       " 'brier',\n",
       " 'mse',\n",
       " 'mae',\n",
       " 'mape',\n",
       " 'r2']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pulsar_metrics.metrics.performance import PerformanceMetricsFuncs\n",
    "PerformanceMetricsFuncs._member_names_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49579831932773105"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PerformanceMetricsFuncs['f1'].value(df_ref.y_pred, df_ref.clf_target, average = 'macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pulsar_metrics.metrics.performance import PerformanceMetric\n",
    "\n",
    "precision = PerformanceMetric(name = 'precision', data = df_ref, y_name = 'clf_target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision._result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision_result = precision.evaluate(bootstrap=False, average = 'micro', threshold = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict = vars(precision_result)\n",
    "df_compute = pd.DataFrame([dict])\n",
    "df_result = pd.concat([df_result, df_compute], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# accuracy\n",
    "precision = PerformanceMetric(name = 'accuracy', data = df_ref, y_name = 'clf_target')\n",
    "precision._result\n",
    "precision_result = precision.evaluate(bootstrap=False, threshold = 0.5)\n",
    "\n",
    "\n",
    "dict = vars(precision_result)\n",
    "df_compute = pd.DataFrame([dict])\n",
    "df_result = pd.concat([df_result, df_compute], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# recall\n",
    "precision = PerformanceMetric(name = 'recall', data = df_ref, y_name = 'clf_target')\n",
    "precision._result\n",
    "precision_result = precision.evaluate(bootstrap=False, average = 'micro', threshold = 0.5)\n",
    "\n",
    "\n",
    "dict = vars(precision_result)\n",
    "df_compute = pd.DataFrame([dict])\n",
    "df_result = pd.concat([df_result, df_compute], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# f1\n",
    "precision = PerformanceMetric(name = 'f1', data = df_ref, y_name = 'clf_target')\n",
    "precision._result\n",
    "precision_result = precision.evaluate(bootstrap=False, average = 'micro', threshold = 0.5)\n",
    "\n",
    "\n",
    "dict = vars(precision_result)\n",
    "df_compute = pd.DataFrame([dict])\n",
    "df_result = pd.concat([df_result, df_compute], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#brier\n",
    "precision = PerformanceMetric(name = 'brier', data = df_ref, y_name = 'clf_target')\n",
    "precision._result\n",
    "precision_result = precision.evaluate(bootstrap=False,  threshold = 0.5)\n",
    "\n",
    "\n",
    "dict = vars(precision_result)\n",
    "df_compute = pd.DataFrame([dict])\n",
    "df_result = pd.concat([df_result, df_compute], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mse\n",
    "precision = PerformanceMetric(name = 'mse', data = df_ref, y_name = 'clf_target')\n",
    "precision._result\n",
    "precision_result = precision.evaluate(bootstrap=False,  threshold = 0.5)\n",
    "\n",
    "\n",
    "dict = vars(precision_result)\n",
    "df_compute = pd.DataFrame([dict])\n",
    "df_result = pd.concat([df_result, df_compute], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mae\n",
    "precision = PerformanceMetric(name = 'mae', data = df_ref, y_name = 'clf_target')\n",
    "precision._result\n",
    "precision_result = precision.evaluate(bootstrap=False,  threshold = 0.5)\n",
    "\n",
    "\n",
    "dict = vars(precision_result)\n",
    "df_compute = pd.DataFrame([dict])\n",
    "df_result = pd.concat([df_result, df_compute], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mape\n",
    "precision = PerformanceMetric(name = 'mape', data = df_ref, y_name = 'clf_target')\n",
    "precision._result\n",
    "precision_result = precision.evaluate(bootstrap=False,  threshold = 0.5)\n",
    "\n",
    "\n",
    "dict = vars(precision_result)\n",
    "df_compute = pd.DataFrame([dict])\n",
    "df_result = pd.concat([df_result, df_compute], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#r2\n",
    "precision = PerformanceMetric(name = 'r2', data = df_ref, y_name = 'clf_target')\n",
    "precision._result\n",
    "precision_result = precision.evaluate(bootstrap=False,  threshold = 0.5)\n",
    "\n",
    "\n",
    "dict = vars(precision_result)\n",
    "df_compute = pd.DataFrame([dict])\n",
    "df_result = pd.concat([df_result, df_compute], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log_loss\n",
    "# this metric is not working with kidney disease test data\n",
    "precision = PerformanceMetric(name = 'log_loss', data = df_ref, y_name = 'clf_target')\n",
    "precision._result\n",
    "precision_result = precision.evaluate(bootstrap=False, threshold = 0.5)\n",
    "\n",
    "\n",
    "dict = vars(precision_result)\n",
    "df_compute = pd.DataFrame([dict])\n",
    "df_result = pd.concat([df_result, df_compute], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#auc\n",
    "# this metric is not working with kidney disease test data\n",
    "precision = PerformanceMetric(name = 'auc', data = df_ref, y_name = 'clf_target')\n",
    "precision._result\n",
    "precision_result = precision.evaluate(bootstrap=False, threshold = 0.5)\n",
    "\n",
    "\n",
    "dict = vars(precision_result)\n",
    "df_compute = pd.DataFrame([dict])\n",
    "df_result = pd.concat([df_result, df_compute], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#aucpr\n",
    "# this metric is not working with kidney disease test data\n",
    "precision = PerformanceMetric(name = 'aucpr', data = df_ref, y_name = 'clf_target')\n",
    "precision._result\n",
    "precision_result = precision.evaluate(bootstrap=False, threshold = 0.5)\n",
    "\n",
    "\n",
    "dict = vars(precision_result)\n",
    "df_compute = pd.DataFrame([dict])\n",
    "df_result = pd.concat([df_result, df_compute], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pulsar_metrics.analyzers.base import Analyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis = Analyzer(name = 'First Analyzer', description='My first Analyzer', data = df_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drift metric 'wasserstein' for feature 'age' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'al' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'ane' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'appet' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'ba' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'bgr' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'bp' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'bu' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'cad' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'dm' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'hemo' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'htn' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'id' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'pc' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'pcc' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'pcv' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'pe' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'pot' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'rbc' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'rbcc' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'sg' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'sc' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'sod' added to the analyzer list\n",
      "Drift metric 'wasserstein' for feature 'su' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'age' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'al' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'ane' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'appet' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'ba' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'bgr' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'bp' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'bu' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'cad' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'dm' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'hemo' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'htn' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'id' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'pc' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'pcc' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'pcv' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'pe' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'pot' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'rbc' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'rbcc' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'sg' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'sc' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'sod' added to the analyzer list\n",
      "Drift metric 'ttest' for feature 'su' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'age' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'al' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'ane' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'appet' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'ba' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'bgr' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'bp' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'bu' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'cad' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'dm' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'hemo' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'htn' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'id' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'pc' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'pcc' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'pcv' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'pe' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'pot' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'rbc' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'rbcc' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'sg' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'sc' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'sod' added to the analyzer list\n",
      "Drift metric 'ks_2samp' for feature 'su' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'age' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'al' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'ane' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'appet' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'ba' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'bgr' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'bp' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'bu' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'cad' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'dm' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'hemo' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'htn' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'id' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'pc' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'pcc' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'pcv' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'pe' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'pot' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'rbc' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'rbcc' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'sg' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'sc' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'sod' added to the analyzer list\n",
      "Drift metric 'kl' for feature 'su' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'age' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'al' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'ane' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'appet' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'ba' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'bgr' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'bp' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'bu' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'cad' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'dm' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'hemo' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'htn' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'id' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'pc' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'pcc' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'pcv' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'pe' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'pot' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'rbc' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'rbcc' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'sg' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'sc' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'sod' added to the analyzer list\n",
      "Drift metric 'manwu' for feature 'su' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'age' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'al' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'ane' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'appet' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'ba' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'bgr' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'bp' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'bu' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'cad' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'dm' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'hemo' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'htn' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'id' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'pc' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'pcc' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'pcv' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'pe' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'pot' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'rbc' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'rbcc' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'sg' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'sc' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'sod' added to the analyzer list\n",
      "Drift metric 'levene' for feature 'su' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'age' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'al' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'ane' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'appet' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'ba' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'bgr' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'bp' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'bu' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'cad' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'dm' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'hemo' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'htn' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'id' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'pc' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'pcc' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'pcv' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'pe' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'pot' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'rbc' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'rbcc' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'sg' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'sc' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'sod' added to the analyzer list\n",
      "Drift metric 'bftest' for feature 'su' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'age' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'al' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'ane' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'appet' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'ba' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'bgr' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'bp' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'bu' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'cad' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'dm' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'hemo' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'htn' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'id' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'pc' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'pcc' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'pcv' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'pe' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'pot' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'rbc' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'rbcc' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'sg' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'sc' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'sod' added to the analyzer list\n",
      "Drift metric 'CvM' for feature 'su' added to the analyzer list\n"
     ]
    }
   ],
   "source": [
    "analysis.add_drift_metrics(metrics_list=['wasserstein', 'ttest', 'ks_2samp','kl','manwu','levene','bftest','CvM'], \n",
    "features_list=['age','al','ane','appet','ba','bgr','bp','bu','cad','dm','hemo','htn','id','pc','pcc','pcv','pe','pot','rbc','rbcc','sg','sc','sod','su'])\n",
    "# analysis.add_performance_metrics(metrics_list=['accuracy'], y_name = 'clf_target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/manuarya/Projects/pulsar-metrics/pulsar-metrics/.venv/lib/python3.9/site-packages/scipy/stats/morestats.py:2603: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  W = numer / denom\n"
     ]
    }
   ],
   "source": [
    "analysis.run(data_ref = df_ref, options = {'ttest': {'alpha': 0.05, 'equal_var': False}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='age', value=23.666666666666668, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='al', value=0.33333333333333315, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='ane', value=0.0, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='appet', value=0.08333333333333337, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='ba', value=0.16666666666666663, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='bgr', value=32.416666666666664, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='bp', value=1.666666666666667, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='bu', value=10.5, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='cad', value=0.08333333333333337, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='dm', value=0.16666666666666663, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='hemo', value=1.1000000000000003, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='htn', value=0.08333333333333326, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='id', value=39.08333333333333, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='pc', value=0.16666666666666666, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='pcc', value=0.16666666666666663, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='pcv', value=3.416666666666667, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='pe', value=0.16666666666666663, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='pot', value=0.7750000000000004, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='rbc', value=0.0, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='rbcc', value=0.3, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='sg', value=0.00499999999999993, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='sc', value=0.7333333333333332, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='sod', value=2.333333333333333, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='wasserstein', type='drift', model_id='1', model_version='2', data_id=None, feature='su', value=0.33333333333333337, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='age', value=0.010281831830663446, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='al', value=0.4870844297281154, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='ane', value=1.0, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='appet', value=0.3388006961962018, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='ba', value=0.16608681351829557, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='bgr', value=0.7947207081765587, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='bp', value=0.6122246277713708, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='bu', value=0.39778467682821883, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='cad', value=0.3388006961962018, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='dm', value=0.16608681351829557, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='hemo', value=0.9228977991695053, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='htn', value=0.557973688095355, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='id', value=0.4188281648388268, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='pc', value=0.16608681351829563, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='pcc', value=0.16608681351829557, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='pcv', value=0.520940986327781, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='pe', value=0.16608681351829557, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='pot', value=0.0013223765445747112, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='rbc', value=nan, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='rbcc', value=0.45301503717828395, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='sg', value=0.005408886795190589, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='sc', value=0.5300010782134345, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='sod', value=0.7329777101339188, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ttest', type='drift', model_id='1', model_version='2', data_id=None, feature='su', value=0.21988784312755902, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='age', value=0.00020413023508998745, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='al', value=1.0, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='ane', value=1.0, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='appet', value=1.0, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='ba', value=0.9984852944874484, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='bgr', value=0.2557751845677543, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='bp', value=0.9984852944874484, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='bu', value=0.5360977695073805, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='cad', value=1.0, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='dm', value=0.9984852944874484, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='hemo', value=0.09954677170991616, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='htn', value=1.0, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='id', value=0.5360977695073805, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='pc', value=0.9984852944874484, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='pcc', value=0.9984852944874484, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='pcv', value=0.09954677170991616, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='pe', value=0.9984852944874484, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='pot', value=0.0014969550573265746, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='rbc', value=1.0, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='rbcc', value=0.5360977695073805, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='sg', value=0.007859014050964517, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='sc', value=0.2557751845677543, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='sod', value=0.09954677170991616, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='ks_2samp', type='drift', model_id='1', model_version='2', data_id=None, feature='su', value=0.9984852944874484, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='age', value=inf, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='al', value=0.6931471805599453, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='ane', value=0.0, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='appet', value=nan, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='ba', value=nan, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='bgr', value=0.662768815835448, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='bp', value=0.07860266338791666, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='bu', value=inf, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='cad', value=nan, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='dm', value=nan, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='hemo', value=0.42883219825925545, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='htn', value=0.0296053997739689, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='id', value=nan, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='pc', value=nan, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='pcc', value=nan, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='pcv', value=0.512659670364559, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='pe', value=nan, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='pot', value=inf, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='rbc', value=0.0, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='rbcc', value=0.2947065102006001, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='sg', value=0.8465286662375084, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='sc', value=inf, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='sod', value=0.8003417428163397, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='kl', type='drift', model_id='1', model_version='2', data_id=None, feature='su', value=nan, status=None, threshold=None, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='age', value=0.004255990771376145, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='al', value=0.5464935954065819, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='ane', value=1.0, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='appet', value=0.3593173383295707, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='ba', value=0.16582395411666384, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='bgr', value=0.701836307516398, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='bp', value=0.8085658989757079, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='bu', value=0.9529901245365892, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='cad', value=0.3593173383295707, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='dm', value=0.16582395411666384, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='hemo', value=0.4580047600616356, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='htn', value=0.579670226896996, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='id', value=0.836749573825345, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='pc', value=0.16582395411666384, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='pcc', value=0.16582395411666384, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='pcv', value=0.4793010097806445, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='pe', value=0.16582395411666384, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='pot', value=0.006565728517156525, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='rbc', value=1.0, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='rbcc', value=0.48992608267642024, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='sg', value=0.001369409486193363, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='sc', value=0.26544411452337924, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='sod', value=0.2558841280303308, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='manwu', type='drift', model_id='1', model_version='2', data_id=None, feature='su', value=0.16622459112835397, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='age', value=0.27172889028822, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='al', value=0.14546098326089785, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='ane', value=1.0, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='appet', value=0.03860664521157211, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='ba', value=0.0012258189595018203, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='bgr', value=0.0019565923917759537, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='bp', value=0.14604334898542848, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='bu', value=0.4767128543483763, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='cad', value=0.03860664521157211, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='dm', value=0.0012258189595018203, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='hemo', value=0.15905009130258882, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='htn', value=0.23400468310504421, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='id', value=0.7175657323096454, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='pc', value=0.0012258189595018229, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='pcc', value=0.0012258189595018203, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='pcv', value=0.32056585540371413, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='pe', value=0.0012258189595018203, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='pot', value=1.6582121661949187e-05, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='rbc', value=nan, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='rbcc', value=0.45658034123139357, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='sg', value=0.041511256896041134, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='sc', value=0.018518821409863478, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='sod', value=0.48412074997597465, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='levene', type='drift', model_id='1', model_version='2', data_id=None, feature='su', value=0.008996190253439826, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='age', value=0.31611696368484393, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='al', value=0.4856951219894099, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='ane', value=1.0, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='appet', value=0.32818326188066926, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='ba', value=0.1521961586404443, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='bgr', value=0.0034720612519651734, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='bp', value=0.6117989411929938, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='bu', value=0.4041378329155698, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='cad', value=0.32818326188066926, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='dm', value=0.1521961586404443, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='hemo', value=0.181606131342808, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='htn', value=0.557465884886903, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='id', value=0.3860874465122969, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='pc', value=0.1521961586404443, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='pcc', value=0.1521961586404443, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='pcv', value=0.23006829927790393, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='pe', value=0.1521961586404443, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='pot', value=1.593961142994423e-05, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='rbc', value=nan, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='rbcc', value=0.619178454900682, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='sg', value=0.0522051514149966, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='sc', value=0.12640773769298602, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='sod', value=0.9505180063860165, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='bftest', type='drift', model_id='1', model_version='2', data_id=None, feature='su', value=0.2067583803731632, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='age', value=0.0003841302917635403, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='al', value=0.9999489740106431, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='ane', value=1.0, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='appet', value=0.999991368054848, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='ba', value=0.7255782095658768, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='bgr', value=0.030344298131796288, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='bp', value=0.9988042870979117, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='bu', value=0.12481173178647886, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='cad', value=0.999991368054848, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='dm', value=0.7255782095658768, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='hemo', value=0.03926525652931945, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='htn', value=0.999991368054848, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='id', value=0.10654007115626907, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='pc', value=0.7255782095658768, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='pcc', value=0.7255782095658768, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='pcv', value=0.060415520271724255, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='pe', value=0.7255782095658768, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='pot', value=0.0004723133339273877, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='rbc', value=1.0, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='rbcc', value=0.1750612847050882, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='sg', value=0.0005647836573249299, status=True, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='sc', value=0.13216650181162903, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='sod', value=0.06441498348555341, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None),\n",
       " MetricResults(metric_name='CvM', type='drift', model_id='1', model_version='2', data_id=None, feature='su', value=0.7362948401852459, status=False, threshold=0.05, period_start=Timestamp('2022-08-06 00:35:06'), period_end=Timestamp('2022-08-18 01:52:13'), eval_timestamp=datetime.datetime(2022, 9, 15, 1, 12, 56, 119578), conf_int=None)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis._results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_result_drift = analysis.results_to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20333/2230772446.py:2: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_result = df_result.append(df_result_drift, ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "# df_result = pd.concat((df_result, df_result_drift), axis=1, ignore_index=True)\n",
    "df_result = df_result.append(df_result_drift, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric_name</th>\n",
       "      <th>type</th>\n",
       "      <th>model_id</th>\n",
       "      <th>model_version</th>\n",
       "      <th>data_id</th>\n",
       "      <th>feature</th>\n",
       "      <th>value</th>\n",
       "      <th>status</th>\n",
       "      <th>threshold</th>\n",
       "      <th>period_start</th>\n",
       "      <th>period_end</th>\n",
       "      <th>eval_timestamp</th>\n",
       "      <th>conf_int</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>precision</td>\n",
       "      <td>performance</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>prediction</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>False</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2022-07-10 22:30:30</td>\n",
       "      <td>2022-08-21 15:30:30</td>\n",
       "      <td>2022-09-15 01:12:56.119578</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>accuracy</td>\n",
       "      <td>performance</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>prediction</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>False</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2022-07-10 22:30:30</td>\n",
       "      <td>2022-08-21 15:30:30</td>\n",
       "      <td>2022-09-15 01:12:56.119578</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>recall</td>\n",
       "      <td>performance</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>prediction</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>False</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2022-07-10 22:30:30</td>\n",
       "      <td>2022-08-21 15:30:30</td>\n",
       "      <td>2022-09-15 01:12:56.119578</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f1</td>\n",
       "      <td>performance</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>prediction</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>False</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2022-07-10 22:30:30</td>\n",
       "      <td>2022-08-21 15:30:30</td>\n",
       "      <td>2022-09-15 01:12:56.119578</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>brier</td>\n",
       "      <td>performance</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>prediction</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>True</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2022-07-10 22:30:30</td>\n",
       "      <td>2022-08-21 15:30:30</td>\n",
       "      <td>2022-09-15 01:12:56.119578</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>CvM</td>\n",
       "      <td>drift</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>rbcc</td>\n",
       "      <td>0.175061</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2022-08-06 00:35:06</td>\n",
       "      <td>2022-08-18 01:52:13</td>\n",
       "      <td>2022-09-15 01:12:56.119578</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>CvM</td>\n",
       "      <td>drift</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>sg</td>\n",
       "      <td>0.000565</td>\n",
       "      <td>True</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2022-08-06 00:35:06</td>\n",
       "      <td>2022-08-18 01:52:13</td>\n",
       "      <td>2022-09-15 01:12:56.119578</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>CvM</td>\n",
       "      <td>drift</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>sc</td>\n",
       "      <td>0.132167</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2022-08-06 00:35:06</td>\n",
       "      <td>2022-08-18 01:52:13</td>\n",
       "      <td>2022-09-15 01:12:56.119578</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>CvM</td>\n",
       "      <td>drift</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>sod</td>\n",
       "      <td>0.064415</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2022-08-06 00:35:06</td>\n",
       "      <td>2022-08-18 01:52:13</td>\n",
       "      <td>2022-09-15 01:12:56.119578</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>CvM</td>\n",
       "      <td>drift</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>su</td>\n",
       "      <td>0.736295</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2022-08-06 00:35:06</td>\n",
       "      <td>2022-08-18 01:52:13</td>\n",
       "      <td>2022-09-15 01:12:56.119578</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>204 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    metric_name         type model_id model_version data_id     feature  \\\n",
       "0     precision  performance        1             2    None  prediction   \n",
       "1      accuracy  performance        1             2    None  prediction   \n",
       "2        recall  performance        1             2    None  prediction   \n",
       "3            f1  performance        1             2    None  prediction   \n",
       "4         brier  performance        1             2    None  prediction   \n",
       "..          ...          ...      ...           ...     ...         ...   \n",
       "199         CvM        drift        1             2    None        rbcc   \n",
       "200         CvM        drift        1             2    None          sg   \n",
       "201         CvM        drift        1             2    None          sc   \n",
       "202         CvM        drift        1             2    None         sod   \n",
       "203         CvM        drift        1             2    None          su   \n",
       "\n",
       "        value status  threshold        period_start          period_end  \\\n",
       "0    0.583333  False       0.50 2022-07-10 22:30:30 2022-08-21 15:30:30   \n",
       "1    0.583333  False       0.50 2022-07-10 22:30:30 2022-08-21 15:30:30   \n",
       "2    0.583333  False       0.50 2022-07-10 22:30:30 2022-08-21 15:30:30   \n",
       "3    0.583333  False       0.50 2022-07-10 22:30:30 2022-08-21 15:30:30   \n",
       "4    0.416667   True       0.50 2022-07-10 22:30:30 2022-08-21 15:30:30   \n",
       "..        ...    ...        ...                 ...                 ...   \n",
       "199  0.175061  False       0.05 2022-08-06 00:35:06 2022-08-18 01:52:13   \n",
       "200  0.000565   True       0.05 2022-08-06 00:35:06 2022-08-18 01:52:13   \n",
       "201  0.132167  False       0.05 2022-08-06 00:35:06 2022-08-18 01:52:13   \n",
       "202  0.064415  False       0.05 2022-08-06 00:35:06 2022-08-18 01:52:13   \n",
       "203  0.736295  False       0.05 2022-08-06 00:35:06 2022-08-18 01:52:13   \n",
       "\n",
       "                eval_timestamp conf_int  \n",
       "0   2022-09-15 01:12:56.119578     None  \n",
       "1   2022-09-15 01:12:56.119578     None  \n",
       "2   2022-09-15 01:12:56.119578     None  \n",
       "3   2022-09-15 01:12:56.119578     None  \n",
       "4   2022-09-15 01:12:56.119578     None  \n",
       "..                         ...      ...  \n",
       "199 2022-09-15 01:12:56.119578     None  \n",
       "200 2022-09-15 01:12:56.119578     None  \n",
       "201 2022-09-15 01:12:56.119578     None  \n",
       "202 2022-09-15 01:12:56.119578     None  \n",
       "203 2022-09-15 01:12:56.119578     None  \n",
       "\n",
       "[204 rows x 13 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_period = df_result.filter(['type','period_end'], axis=1)\n",
    "df_period.set_index(\"period_end\", inplace=True)\n",
    "# df_period.loc[df_period[\"period_end\"].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_period.to_csv('df_period.csv', index=False)\n",
    "db_name = \"testDB_period\"\n",
    "client = DataFrameClient(dbhost, dbport, dbuser, dbpasswd, db_name)\n",
    "client.create_database(db_name)\n",
    "client.get_list_database()\n",
    "client.switch_database(db_name)\n",
    "\n",
    "client.write_points(df_period, db_name, protocol='line', time_precision=None)"
   ]
  }
 ],
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