feature_selection/feature_selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlrun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%nuclio: setting kind to 'job'\n",
      "%nuclio: setting spec.image to 'mlrun/ml-models'\n"
     ]
    }
   ],
   "source": [
    "%nuclio config kind = \"job\"\n",
    "%nuclio config spec.image = \"mlrun/ml-models\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nuclio: start-code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import os\n",
    "import json\n",
    "\n",
    "# Feature selection strategies\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "# Model based feature selection\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Scale feature scores\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# SKLearn estimators list\n",
    "from sklearn.utils import all_estimators\n",
    "\n",
    "# MLRun utils\n",
    "from mlrun.mlutils.plots import gcf_clear\n",
    "from mlrun.utils.helpers import create_class\n",
    "from mlrun.artifacts import PlotArtifact\n",
    "\n",
    "# Feature Selection\n",
    "from feature_selection import feature_selection, show_values_on_bars, plot_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nuclio: end-code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlrun import code_to_function, mount_v3io, mlconf, NewTask, run_local"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlconf.artifact_path = os.path.abspath('./artifacts')\n",
    "mlconf.db_path = 'http://mlrun-api:8080'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Local Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "task = NewTask(params={'k': 2,\n",
    "                       'min_votes': 0.3,\n",
    "                       'label_column': 'is_error'},\n",
    "               inputs={'df_artifact': os.path.abspath('data/metrics.pq')})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-08-11 10:12:05,721 [info] starting run feature_selection uid=8765f9e7fde94efeb662fbe2c37a0e1a DB=http://mlrun-api:8080\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pass k=2 as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "Liblinear failed to converge, increase the number of iterations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-08-11 10:12:08,257 [info] votes needed to be selected: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Converting input from bool to <class 'numpy.uint8'> for compatibility.\n"
     ]
    },
    {
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       "<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",
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       "    </tr>\n",
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       "    <tr>\n",
       "      <td>default</td>\n",
       "      <td><div title=\"8765f9e7fde94efeb662fbe2c37a0e1a\"><a href=\"https://dashboard.default-tenant.app.az-workspace-b177.iguazio-cd1.com/mlprojects/default/jobs/monitor/8765f9e7fde94efeb662fbe2c37a0e1a/overview\" target=\"_blank\" >...c37a0e1a</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Aug 11 10:12:05</td>\n",
       "      <td>completed</td>\n",
       "      <td>feature_selection</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=admin</div><div class=\"dictlist\">kind=handler</div><div class=\"dictlist\">owner=admin</div><div class=\"dictlist\">host=jupyter-az-ffcb58655-7l9pl</div></td>\n",
       "      <td><div title=\"/User/demos/fraud-demo/data/metrics.pq\">df_artifact</div></td>\n",
       "      <td><div class=\"dictlist\">k=2</div><div class=\"dictlist\">min_votes=0.3</div><div class=\"dictlist\">label_column=is_error</div></td>\n",
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     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run 8765f9e7fde94efeb662fbe2c37a0e1a --project default , !mlrun logs 8765f9e7fde94efeb662fbe2c37a0e1a --project default\n",
      "> 2021-08-11 10:12:08,438 [info] run executed, status=completed\n"
     ]
    }
   ],
   "source": [
    "from feature_selection import feature_selection, show_values_on_bars, plot_stat\n",
    "\n",
    "runl = run_local(task=task,\n",
    "          name='feature_selection',\n",
    "          handler=feature_selection,\n",
    "          artifact_path=os.path.join(os.path.abspath('./'), 'artifacts'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Job Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-08-11 10:12:22,071 [info] function spec saved to path: function.yaml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.runtimes.kubejob.KubejobRuntime at 0x7fa98e912890>"
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     "execution_count": 13,
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     "output_type": "execute_result"
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   ],
   "source": [
    "fn = code_to_function(name='feature_selection',\n",
    "                      handler='feature_selection')\n",
    "fn.spec.default_handler = 'feature_selection'\n",
    "fn.spec.description = \"Select features through multiple Statistical and Model filters\"\n",
    "fn.metadata.categories = ['data-prep', 'ml']\n",
    "fn.metadata.labels = {\"author\": \"alexz\"}\n",
    "fn.export('function.yaml')\n",
    "fn.apply(mount_v3io())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-08-11 10:12:22,083 [info] starting run feature-selection-feature_selection uid=a702d89990924e10b093ee1571b47dc2 DB=http://mlrun-api:8080\n",
      "> 2021-08-11 10:12:22,347 [info] Job is running in the background, pod: feature-selection-feature-selection-8wkf8\n",
      "> 2021-08-11 10:14:12,748 [info] votes needed to be selected: 2\n",
      "> 2021-08-11 10:14:12,877 [info] run executed, status=completed\n",
      "Pass k=2 as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "Liblinear failed to converge, increase the number of iterations.\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
      "final state: completed\n"
     ]
    },
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       "    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>default</td>\n",
       "      <td><div title=\"a702d89990924e10b093ee1571b47dc2\"><a href=\"https://dashboard.default-tenant.app.az-workspace-b177.iguazio-cd1.com/mlprojects/default/jobs/monitor/a702d89990924e10b093ee1571b47dc2/overview\" target=\"_blank\" >...71b47dc2</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Aug 11 10:14:09</td>\n",
       "      <td>completed</td>\n",
       "      <td>feature-selection-feature_selection</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=admin</div><div class=\"dictlist\">kind=job</div><div class=\"dictlist\">owner=admin</div><div class=\"dictlist\">host=feature-selection-feature-selection-8wkf8</div></td>\n",
       "      <td><div title=\"/User/demos/fraud-demo/data/metrics.pq\">df_artifact</div></td>\n",
       "      <td><div class=\"dictlist\">k=2</div><div class=\"dictlist\">min_votes=0.3</div><div class=\"dictlist\">label_column=is_error</div></td>\n",
       "      <td></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resulte74634a1\" title=\"/files/demos/fraud-demo/artifacts/plots/feature_selection/f_classif.html\">f_classif</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resulte74634a1\" title=\"/files/demos/fraud-demo/artifacts/plots/feature_selection/mutual_info_classif.html\">mutual_info_classif</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resulte74634a1\" title=\"/files/demos/fraud-demo/artifacts/plots/feature_selection/chi2.html\">chi2</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resulte74634a1\" title=\"/files/demos/fraud-demo/artifacts/plots/feature_selection/f_regression.html\">f_regression</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resulte74634a1\" title=\"/files/demos/fraud-demo/artifacts/plots/feature_selection/LinearSVC.html\">LinearSVC</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resulte74634a1\" title=\"/files/demos/fraud-demo/artifacts/plots/feature_selection/LogisticRegression.html\">LogisticRegression</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resulte74634a1\" title=\"/files/demos/fraud-demo/artifacts/plots/feature_selection/ExtraTreesClassifier.html\">ExtraTreesClassifier</div><div title=\"/User/demos/fraud-demo/artifacts/feature_scores.parquet\">feature_scores</div><div title=\"/User/demos/fraud-demo/artifacts/max_scaled_scores_feature_scores.parquet\">max_scaled_scores_feature_scores</div><div title=\"/User/demos/fraud-demo/artifacts/selected_features_count.parquet\">selected_features_count</div><div title=\"/User/demos/fraud-demo/artifacts/selected_features.parquet\">selected_features</div></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></div>\n",
       "  <div id=\"resulte74634a1-pane\" class=\"right-pane block hidden\">\n",
       "    <div class=\"pane-header\">\n",
       "      <span id=\"resulte74634a1-title\" class=\"pane-header-title\">Title</span>\n",
       "      <span onclick=\"closePanel(this)\" paneName=\"resulte74634a1\" class=\"close clickable\">&times;</span>\n",
       "    </div>\n",
       "    <iframe class=\"fileview\" id=\"resulte74634a1-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": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run a702d89990924e10b093ee1571b47dc2 --project default , !mlrun logs a702d89990924e10b093ee1571b47dc2 --project default\n",
      "> 2021-08-11 10:14:21,908 [info] run executed, status=completed\n"
     ]
    }
   ],
   "source": [
    "fn_run = fn.run(task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "                                                                   cpu_utilization  \\\n",
       "timestamp               company     data_center     device                           \n",
       "2021-04-27 14:46:46.780 Smith_Group Denise_Crest    5124209057231        75.598891   \n",
       "                                                    2891755865712        50.090373   \n",
       "                                    Debra_Gateway   0388020295311        73.243063   \n",
       "                                                    9633813691441        60.830420   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931        72.647964   \n",
       "...                                                                            ...   \n",
       "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway   9633813691441        77.875954   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931        77.831459   \n",
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       "                                    Nicholas_Estate 8002897098167        58.265446   \n",
       "                                                    8499880735104        71.245041   \n",
       "\n",
       "                                                                     latency  \\\n",
       "timestamp               company     data_center     device                     \n",
       "2021-04-27 14:46:46.780 Smith_Group Denise_Crest    5124209057231   0.000000   \n",
       "                                                    2891755865712   3.280849   \n",
       "                                    Debra_Gateway   0388020295311   9.372341   \n",
       "                                                    9633813691441  12.241878   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931   0.535463   \n",
       "...                                                                      ...   \n",
       "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway   9633813691441   3.250584   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931   0.000000   \n",
       "                                                    6964486699383   2.977447   \n",
       "                                    Nicholas_Estate 8002897098167   4.090207   \n",
       "                                                    8499880735104   0.000000   \n",
       "\n",
       "                                                                   packet_loss  \\\n",
       "timestamp               company     data_center     device                       \n",
       "2021-04-27 14:46:46.780 Smith_Group Denise_Crest    5124209057231     0.000000   \n",
       "                                                    2891755865712     0.000000   \n",
       "                                    Debra_Gateway   0388020295311     2.170138   \n",
       "                                                    9633813691441     2.295717   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931     0.000000   \n",
       "...                                                                        ...   \n",
       "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway   9633813691441     0.000000   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931     0.000000   \n",
       "                                                    6964486699383     0.533963   \n",
       "                                    Nicholas_Estate 8002897098167     2.048268   \n",
       "                                                    8499880735104     2.929407   \n",
       "\n",
       "                                                                   throughput  \\\n",
       "timestamp               company     data_center     device                      \n",
       "2021-04-27 14:46:46.780 Smith_Group Denise_Crest    5124209057231  252.445971   \n",
       "                                                    2891755865712  229.889187   \n",
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       "                                                    9633813691441  244.238613   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931  212.944943   \n",
       "...                                                                       ...   \n",
       "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway   9633813691441  245.150281   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931  235.109321   \n",
       "                                                    6964486699383  277.622402   \n",
       "                                    Nicholas_Estate 8002897098167  272.717982   \n",
       "                                                    8499880735104  235.659211   \n",
       "\n",
       "                                                                   is_error  \n",
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       "                                                    9633813691441     False  \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931     False  \n",
       "...                                                                     ...  \n",
       "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway   9633813691441     False  \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931     False  \n",
       "                                                    6964486699383     False  \n",
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       "                                                    8499880735104     False  \n",
       "\n",
       "[5768 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlrun.get_dataitem(fn_run.spec.inputs['df_artifact']).as_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                    f_classif  mutual_info_classif           chi2  \\\n",
       "cpu_utilization   2520.015809             0.185045    4457.429360   \n",
       "latency          10152.151995             0.198947  272872.890194   \n",
       "packet_loss      14120.490547             0.210517  157191.427524   \n",
       "throughput       20421.721030             0.230557  109129.511665   \n",
       "\n",
       "                 f_regression  LinearSVC  LogisticRegression  \\\n",
       "cpu_utilization   2520.015809  -0.045040            0.232760   \n",
       "latency          10152.151995   0.034251            0.069064   \n",
       "packet_loss      14120.490547   0.048188            0.223673   \n",
       "throughput       20421.721030  -0.009259           -0.064733   \n",
       "\n",
       "                 ExtraTreesClassifier  \n",
       "cpu_utilization              0.026625  \n",
       "latency                      0.026625  \n",
       "packet_loss                  0.026625  \n",
       "throughput                   0.026625  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlrun.get_dataitem(fn_run.outputs['feature_scores']).as_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th rowspan=\"4\" valign=\"top\">Smith_Group</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Denise_Crest</th>\n",
       "      <th>5124209057231</th>\n",
       "      <td>75.598891</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>252.445971</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2891755865712</th>\n",
       "      <td>50.090373</td>\n",
       "      <td>3.280849</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>229.889187</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Debra_Gateway</th>\n",
       "      <th>0388020295311</th>\n",
       "      <td>73.243063</td>\n",
       "      <td>9.372341</td>\n",
       "      <td>2.170138</td>\n",
       "      <td>260.883807</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9633813691441</th>\n",
       "      <td>60.830420</td>\n",
       "      <td>12.241878</td>\n",
       "      <td>2.295717</td>\n",
       "      <td>244.238613</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ferrell_Ltd</th>\n",
       "      <th>Murphy_Meadow</th>\n",
       "      <th>1517129765931</th>\n",
       "      <td>72.647964</td>\n",
       "      <td>0.535463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>212.944943</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2021-04-27 15:46:46.780</th>\n",
       "      <th>Smith_Group</th>\n",
       "      <th>Debra_Gateway</th>\n",
       "      <th>9633813691441</th>\n",
       "      <td>77.875954</td>\n",
       "      <td>3.250584</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>245.150281</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Ferrell_Ltd</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Murphy_Meadow</th>\n",
       "      <th>1517129765931</th>\n",
       "      <td>77.831459</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>235.109321</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6964486699383</th>\n",
       "      <td>55.978514</td>\n",
       "      <td>2.977447</td>\n",
       "      <td>0.533963</td>\n",
       "      <td>277.622402</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Nicholas_Estate</th>\n",
       "      <th>8002897098167</th>\n",
       "      <td>58.265446</td>\n",
       "      <td>4.090207</td>\n",
       "      <td>2.048268</td>\n",
       "      <td>272.717982</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8499880735104</th>\n",
       "      <td>71.245041</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.929407</td>\n",
       "      <td>235.659211</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5768 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   cpu_utilization  \\\n",
       "timestamp               company     data_center     device                           \n",
       "2021-04-27 14:46:46.780 Smith_Group Denise_Crest    5124209057231        75.598891   \n",
       "                                                    2891755865712        50.090373   \n",
       "                                    Debra_Gateway   0388020295311        73.243063   \n",
       "                                                    9633813691441        60.830420   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931        72.647964   \n",
       "...                                                                            ...   \n",
       "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway   9633813691441        77.875954   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931        77.831459   \n",
       "                                                    6964486699383        55.978514   \n",
       "                                    Nicholas_Estate 8002897098167        58.265446   \n",
       "                                                    8499880735104        71.245041   \n",
       "\n",
       "                                                                     latency  \\\n",
       "timestamp               company     data_center     device                     \n",
       "2021-04-27 14:46:46.780 Smith_Group Denise_Crest    5124209057231   0.000000   \n",
       "                                                    2891755865712   3.280849   \n",
       "                                    Debra_Gateway   0388020295311   9.372341   \n",
       "                                                    9633813691441  12.241878   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931   0.535463   \n",
       "...                                                                      ...   \n",
       "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway   9633813691441   3.250584   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931   0.000000   \n",
       "                                                    6964486699383   2.977447   \n",
       "                                    Nicholas_Estate 8002897098167   4.090207   \n",
       "                                                    8499880735104   0.000000   \n",
       "\n",
       "                                                                   packet_loss  \\\n",
       "timestamp               company     data_center     device                       \n",
       "2021-04-27 14:46:46.780 Smith_Group Denise_Crest    5124209057231     0.000000   \n",
       "                                                    2891755865712     0.000000   \n",
       "                                    Debra_Gateway   0388020295311     2.170138   \n",
       "                                                    9633813691441     2.295717   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931     0.000000   \n",
       "...                                                                        ...   \n",
       "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway   9633813691441     0.000000   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931     0.000000   \n",
       "                                                    6964486699383     0.533963   \n",
       "                                    Nicholas_Estate 8002897098167     2.048268   \n",
       "                                                    8499880735104     2.929407   \n",
       "\n",
       "                                                                   throughput  \\\n",
       "timestamp               company     data_center     device                      \n",
       "2021-04-27 14:46:46.780 Smith_Group Denise_Crest    5124209057231  252.445971   \n",
       "                                                    2891755865712  229.889187   \n",
       "                                    Debra_Gateway   0388020295311  260.883807   \n",
       "                                                    9633813691441  244.238613   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931  212.944943   \n",
       "...                                                                       ...   \n",
       "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway   9633813691441  245.150281   \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931  235.109321   \n",
       "                                                    6964486699383  277.622402   \n",
       "                                    Nicholas_Estate 8002897098167  272.717982   \n",
       "                                                    8499880735104  235.659211   \n",
       "\n",
       "                                                                   is_error  \n",
       "timestamp               company     data_center     device                   \n",
       "2021-04-27 14:46:46.780 Smith_Group Denise_Crest    5124209057231     False  \n",
       "                                                    2891755865712     False  \n",
       "                                    Debra_Gateway   0388020295311     False  \n",
       "                                                    9633813691441     False  \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931     False  \n",
       "...                                                                     ...  \n",
       "2021-04-27 15:46:46.780 Smith_Group Debra_Gateway   9633813691441     False  \n",
       "                        Ferrell_Ltd Murphy_Meadow   1517129765931     False  \n",
       "                                                    6964486699383     False  \n",
       "                                    Nicholas_Estate 8002897098167     False  \n",
       "                                                    8499880735104     False  \n",
       "\n",
       "[5768 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlrun.get_dataitem(fn_run.outputs['selected_features']).as_df()"
   ]
  }
 ],
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