virtual_drift/virtual_drift.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Virtual Drift\n",
    "\n",
    "Drift magnitude metrics\n",
    "       Computes drift magnitude metrics between base dataset t and dataset u.  \n",
    "\n",
    "Metrics:\n",
    "- TVD (Total Variation Distance)\n",
    "- Helinger\n",
    "- KL Divergence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Steps**\n",
    "\n",
    "1. [Data exploration](#Data-exploration)\n",
    "2. [Importing the function](#Importing-the-function)\n",
    "3. [Running the function locally](#Running-the-function-locally)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Data exploration**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _wine_dataset:\n",
      "\n",
      "Wine recognition dataset\n",
      "------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 178 (50 in each of three classes)\n",
      "    :Number of Attributes: 13 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      " \t\t- Alcohol\n",
      " \t\t- Malic acid\n",
      " \t\t- Ash\n",
      "\t\t- Alcalinity of ash  \n",
      " \t\t- Magnesium\n",
      "\t\t- Total phenols\n",
      " \t\t- Flavanoids\n",
      " \t\t- Nonflavanoid phenols\n",
      " \t\t- Proanthocyanins\n",
      "\t\t- Color intensity\n",
      " \t\t- Hue\n",
      " \t\t- OD280/OD315 of diluted wines\n",
      " \t\t- Proline\n",
      "\n",
      "    - class:\n",
      "            - class_0\n",
      "            - class_1\n",
      "            - class_2\n",
      "\t\t\n",
      "    :Summary Statistics:\n",
      "    \n",
      "    ============================= ==== ===== ======= =====\n",
      "                                   Min   Max   Mean     SD\n",
      "    ============================= ==== ===== ======= =====\n",
      "    Alcohol:                      11.0  14.8    13.0   0.8\n",
      "    Malic Acid:                   0.74  5.80    2.34  1.12\n",
      "    Ash:                          1.36  3.23    2.36  0.27\n",
      "    Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
      "    Magnesium:                    70.0 162.0    99.7  14.3\n",
      "    Total Phenols:                0.98  3.88    2.29  0.63\n",
      "    Flavanoids:                   0.34  5.08    2.03  1.00\n",
      "    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
      "    Proanthocyanins:              0.41  3.58    1.59  0.57\n",
      "    Colour Intensity:              1.3  13.0     5.1   2.3\n",
      "    Hue:                          0.48  1.71    0.96  0.23\n",
      "    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
      "    Proline:                       278  1680     746   315\n",
      "    ============================= ==== ===== ======= =====\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML Wine recognition datasets.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
      "\n",
      "The data is the results of a chemical analysis of wines grown in the same\n",
      "region in Italy by three different cultivators. There are thirteen different\n",
      "measurements taken for different constituents found in the three types of\n",
      "wine.\n",
      "\n",
      "Original Owners: \n",
      "\n",
      "Forina, M. et al, PARVUS - \n",
      "An Extendible Package for Data Exploration, Classification and Correlation. \n",
      "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
      "Via Brigata Salerno, 16147 Genoa, Italy.\n",
      "\n",
      "Citation:\n",
      "\n",
      "Lichman, M. (2013). UCI Machine Learning Repository\n",
      "[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
      "School of Information and Computer Science. \n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "  (1) S. Aeberhard, D. Coomans and O. de Vel, \n",
      "  Comparison of Classifiers in High Dimensional Settings, \n",
      "  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \n",
      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
      "  (Also submitted to Technometrics). \n",
      "\n",
      "  The data was used with many others for comparing various \n",
      "  classifiers. The classes are separable, though only RDA \n",
      "  has achieved 100% correct classification. \n",
      "  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n",
      "  (All results using the leave-one-out technique) \n",
      "\n",
      "  (2) S. Aeberhard, D. Coomans and O. de Vel, \n",
      "  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \n",
      "  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n",
      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
      "  (Also submitted to Journal of Chemometrics).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Scikit-learn's wine dataset\n",
    "from sklearn.datasets import load_wine\n",
    "\n",
    "wine = load_wine()\n",
    "print(wine[\"DESCR\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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    {
     "name": "stdout",
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     "text": [
      "wine_t and wine_u are generated from the wine dataset, where wine_t is the entire dataset while wine_u is a sample (50%) of the entire dataset. \n",
      "wine_t shape is 178 and wine_u shape is 89 \n",
      "\n",
      "\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>y</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  y  prediction  \n",
       "0                          3.92   1065.0  0           0  \n",
       "1                          3.40   1050.0  0           0  \n",
       "2                          3.17   1185.0  0           0  \n",
       "3                          3.45   1480.0  0           0  \n",
       "4                          2.93    735.0  0           0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_t_path = 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/virtual_drift/wine_t.pq'\n",
    "wine_u_path = 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/virtual_drift/wine_u.pq'\n",
    "wine_t=pd.read_parquet(wine_t_path)\n",
    "wine_u=pd.read_parquet(wine_u_path)\n",
    "print(f'wine_t and wine_u are generated from the wine dataset, where wine_t is the entire dataset while wine_u is a sample (50%) of the entire dataset. \\n\\\n",
    "wine_t shape is {wine_t.shape[0]} and wine_u shape is {wine_u.shape[0]} \\n\\n')\n",
    "wine_t.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Importing the function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-26 13:45:22,345 [info] created and saved project function-marketplace\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.runtimes.kubejob.KubejobRuntime at 0x7ff54a864dd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mlrun\n",
    "\n",
    "# Importing the function\n",
    "mlrun.set_environment(project='function-marketplace')\n",
    "\n",
    "fn = mlrun.import_function(\"hub://virtual_drift\")\n",
    "fn.apply(mlrun.auto_mount())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Running the function locally**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "\n",
    "container = os.path.join('/',os.environ['V3IO_HOME'].split('/')[0])\n",
    "user = os.environ[\"V3IO_USERNAME\"]\n",
    "rel_path = os.getcwd()[6:] + '/artifacts'\n",
    "tsdb_path = os.path.join(user,rel_path) + \"/output_tsdb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-26 14:00:41,020 [info] starting run virtual-drift-drift_magnitude uid=28ec7f08ce7c4c528114e2590ff49325 DB=http://mlrun-api:8080\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning - Server version '0.8.14' is different from client version '0.9.4'. Some operations may not work as expected.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-26 14:00:43,469 [info] Fitting discretizer for alcohol\n",
      "> 2021-10-26 14:00:43,471 [info] Fitting discretizer for malic_acid\n",
      "> 2021-10-26 14:00:43,471 [info] Fitting discretizer for ash\n",
      "> 2021-10-26 14:00:43,472 [info] Fitting discretizer for alcalinity_of_ash\n",
      "> 2021-10-26 14:00:43,473 [info] Fitting discretizer for magnesium\n",
      "> 2021-10-26 14:00:43,474 [info] Fitting discretizer for total_phenols\n",
      "> 2021-10-26 14:00:43,475 [info] Fitting discretizer for flavanoids\n",
      "> 2021-10-26 14:00:43,476 [info] Fitting discretizer for nonflavanoid_phenols\n",
      "> 2021-10-26 14:00:43,477 [info] Fitting discretizer for proanthocyanins\n",
      "> 2021-10-26 14:00:43,477 [info] Fitting discretizer for color_intensity\n",
      "> 2021-10-26 14:00:43,478 [info] Fitting discretizer for hue\n",
      "> 2021-10-26 14:00:43,479 [info] Fitting discretizer for od280/od315_of_diluted_wines\n",
      "> 2021-10-26 14:00:43,480 [info] Fitting discretizer for proline\n",
      "> 2021-10-26 14:00:43,531 [info] Discretizing featuers\n",
      "> 2021-10-26 14:00:43,752 [info] Compute prior metrics\n",
      "> 2021-10-26 14:00:43,889 [info] Compute class metrics\n",
      "> 2021-10-26 14:00:44,000 [info] value: inf\n",
      "> 2021-10-26 14:00:44,009 [info] Timestamp: 2021-10-26 14:00:44.008992\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "divide by zero encountered in log\n",
      "casting datetime64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n"
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       "  if (resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.remove(\"hidden\");\n",
       "  }\n",
       "}\n",
       "function closePanel(el) {\n",
       "  const panelName = \"#\" + el.getAttribute('paneName')\n",
       "  const resultPane = document.querySelector(panelName + \"-pane\");\n",
       "  if (!resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.add(\"hidden\");\n",
       "  }\n",
       "}\n",
       "\n",
       "</script>\n",
       "<div class=\"master-wrapper\">\n",
       "  <div class=\"block master-tbl\"><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>project</th>\n",
       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>function-marketplace</td>\n",
       "      <td><div title=\"28ec7f08ce7c4c528114e2590ff49325\"><a href=\"https://dashboard.default-tenant.app.dev39.lab.iguazeng.com/mlprojects/function-marketplace/jobs/monitor/28ec7f08ce7c4c528114e2590ff49325/overview\" target=\"_blank\" >...0ff49325</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Oct 26 14:00:41</td>\n",
       "      <td>completed</td>\n",
       "      <td>virtual-drift-drift_magnitude</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=dani</div><div class=\"dictlist\">kind=</div><div class=\"dictlist\">owner=dani</div><div class=\"dictlist\">host=jupyter-dani-6bfbd76d96-zxx6f</div></td>\n",
       "      <td><div title=\"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/virtual_drift/wine_t.pq\">t</div><div title=\"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/virtual_drift/wine_u.pq\">u</div></td>\n",
       "      <td><div class=\"dictlist\">label_col=y</div><div class=\"dictlist\">results_tsdb_container=users</div><div class=\"dictlist\">results_tsdb_table=dani/test/functions/virtual_drift/artifacts/output_tsdb</div></td>\n",
       "      <td><div class=\"dictlist\">prior_tvd=0.5</div><div class=\"dictlist\">prior_helinger=0.541</div><div class=\"dictlist\">prior_kld=10</div><div class=\"dictlist\">class_shift_tvd=0.017</div><div class=\"dictlist\">class_shift_helinger=0.014</div><div class=\"dictlist\">class_shift_kld=0.002</div></td>\n",
       "      <td><div title=\"/User/test/functions/virtual_drift/discritizer.pkl\">discritizers</div><div title=\"/User/test/functions/virtual_drift/t_discrete.parquet\">t_discrete</div><div title=\"/User/test/functions/virtual_drift/u_discrete.parquet\">u_discrete</div><div title=\"/User/test/functions/virtual_drift/features_t_pdf.parquet\">features_t_pdf</div><div title=\"/User/test/functions/virtual_drift/features_u_pdf.parquet\">features_u_pdf</div><div title=\"/User/test/functions/virtual_drift/class_t_pdf.parquet\">class_t_pdf</div><div title=\"/User/test/functions/virtual_drift/class_u_pdf.parquet\">class_u_pdf</div></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></div>\n",
       "  <div id=\"result3aef6bfd-pane\" class=\"right-pane block hidden\">\n",
       "    <div class=\"pane-header\">\n",
       "      <span id=\"result3aef6bfd-title\" class=\"pane-header-title\">Title</span>\n",
       "      <span onclick=\"closePanel(this)\" paneName=\"result3aef6bfd\" class=\"close clickable\">&times;</span>\n",
       "    </div>\n",
       "    <iframe class=\"fileview\" id=\"result3aef6bfd-body\"></iframe>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<b> > to track results use the .show() or .logs() methods  or <a href=\"https://dashboard.default-tenant.app.dev39.lab.iguazeng.com/mlprojects/function-marketplace/jobs/monitor/28ec7f08ce7c4c528114e2590ff49325/overview\" target=\"_blank\">click here</a> to open in UI</b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-26 14:00:44,153 [info] run executed, status=completed\n"
     ]
    }
   ],
   "source": [
    "virtual_drift_run=fn.run(params={'label_col': 'y',\n",
    "                                 'results_tsdb_container': container[1:],\n",
    "                                 'results_tsdb_table': tsdb_path},\n",
    "                         inputs={'t': wine_t_path,\n",
    "                                 'u': wine_u_path},\n",
    "                         artifact_path=os.getcwd(),\n",
    "                         local=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>u</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.348315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.382022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.269663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          u\n",
       "0  0.348315\n",
       "1  0.382022\n",
       "2  0.269663"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.331461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.398876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.269663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          t\n",
       "0  0.331461\n",
       "1  0.398876\n",
       "2  0.269663"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "virtual_drift_run.artifact('class_u_pdf').show()\n",
    "virtual_drift_run.artifact('class_t_pdf').show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning - Server version '0.8.14' is different from client version '0.9.4'. Some operations may not work as expected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>class_shift_helinger</th>\n",
       "      <th>class_shift_kld</th>\n",
       "      <th>class_shift_tvd</th>\n",
       "      <th>prior_helinger</th>\n",
       "      <th>prior_kld</th>\n",
       "      <th>prior_tvd</th>\n",
       "      <th>stream</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-26 13:58:04.445000+00:00</th>\n",
       "      <td>0.01398</td>\n",
       "      <td>0.001564</td>\n",
       "      <td>0.016854</td>\n",
       "      <td>0.541196</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>some_stream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-26 14:00:44.008000+00:00</th>\n",
       "      <td>0.01398</td>\n",
       "      <td>0.001564</td>\n",
       "      <td>0.016854</td>\n",
       "      <td>0.541196</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>some_stream</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  class_shift_helinger  class_shift_kld  \\\n",
       "time                                                                      \n",
       "2021-10-26 13:58:04.445000+00:00               0.01398         0.001564   \n",
       "2021-10-26 14:00:44.008000+00:00               0.01398         0.001564   \n",
       "\n",
       "                                  class_shift_tvd  prior_helinger  prior_kld  \\\n",
       "time                                                                           \n",
       "2021-10-26 13:58:04.445000+00:00         0.016854        0.541196       10.0   \n",
       "2021-10-26 14:00:44.008000+00:00         0.016854        0.541196       10.0   \n",
       "\n",
       "                                  prior_tvd       stream  \n",
       "time                                                      \n",
       "2021-10-26 13:58:04.445000+00:00        0.5  some_stream  \n",
       "2021-10-26 14:00:44.008000+00:00        0.5  some_stream  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import v3io_frames as v3f\n",
    "client = v3f.Client(os.environ[\"V3IO_FRAMESD\"],container=container[1:])\n",
    "client.read(backend='tsdb',table=tsdb_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to the top](#Virtual-Drift)"
   ]
  }
 ],
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