feature_selection/feature_selection.ipynb

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   "source": [
    "# Feature Selection"
   ]
  },
  {
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   "execution_count": 1,
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "> 2025-03-06 10:55:11,680 [warning] Failed resolving version info. Ignoring and using defaults\n",
      "> 2025-03-06 10:55:13,566 [warning] Server or client version is unstable. Assuming compatible: {\"client_version\":\"0.0.0+unstable\",\"server_version\":\"1.8.0\"}\n"
     ]
    }
   ],
   "source": [
    "import mlrun\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2025-03-06 10:55:14,686 [info] Loading project from path: {\"path\":\"./\",\"project_name\":\"feature-selection\",\"user_project\":false}\n",
      "> 2025-03-06 10:55:14,726 [warning] Project name mismatch, fhub-v2 != feature-selection, project is loaded from fhub-v2 project yaml. To prevent/allow this, you can take one of the following actions:\n",
      "1. Set the `allow_cross_project=True` when loading the project.\n",
      "2. Delete the existing project yaml, or ensure its name is equal to feature-selection.\n",
      "3. Use different project context dir.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Project name='feature-selection' is different than specified on the context's project yaml. This behavior is deprecated and will not be supported from version 1.9.0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2025-03-06 10:55:29,474 [info] Project loaded successfully: {\"path\":\"./\",\"project_name\":\"feature-selection\",\"stored_in_db\":true}\n"
     ]
    }
   ],
   "source": [
    "project = mlrun.get_or_create_project(\"feature-selection\",'./')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Local Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": "feature_selection = mlrun.import_function(\"function.yaml\")"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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      "> 2025-03-06 10:59:27,279 [info] Storing function: {\"db\":null,\"name\":\"feature-selection-feature-selection\",\"uid\":\"fdcbc4e3f5c44769be5e64425f10aed8\"}\n",
      "> 2025-03-06 10:59:30,808 [info] votes needed to be selected: 2\n"
     ]
    },
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     "text": [
      "/User/.pythonlibs/mlrun-extended/lib/python3.9/site-packages/mlrun/artifacts/dataset.py:387: RuntimeWarning:\n",
      "\n",
      "Converting input from bool to <class 'numpy.uint8'> for compatibility.\n",
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       "      <td>feature-selection</td>\n",
       "      <td><div title=\"fdcbc4e3f5c44769be5e64425f10aed8\"><a href=\"https://dashboard.default-tenant.app.vmdev67.lab.iguazeng.com/mlprojects/feature-selection/jobs/monitor/fdcbc4e3f5c44769be5e64425f10aed8/overview\" target=\"_blank\" >...5f10aed8</a></div></td>\n",
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       "      <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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       "      <td><div class=\"dictlist\">f_classif=store://artifacts/feature-selection/feature-selection-feature-selection_f_classif#0@fdcbc4e3f5c44769be5e64425f10aed8^eaf3bb0877b7a8502365fc3a919a6ba295febdd9</div><div class=\"dictlist\">mutual_info_classif=store://artifacts/feature-selection/feature-selection-feature-selection_mutual_info_classif#0@fdcbc4e3f5c44769be5e64425f10aed8^8d572b32e4d65007b048aa0b51899c0ebcb45219</div><div class=\"dictlist\">chi2=store://artifacts/feature-selection/feature-selection-feature-selection_chi2#0@fdcbc4e3f5c44769be5e64425f10aed8^5775ae9bbecb726fbe4d229548b82d920a43526e</div><div class=\"dictlist\">f_regression=store://artifacts/feature-selection/feature-selection-feature-selection_f_regression#0@fdcbc4e3f5c44769be5e64425f10aed8^90cd856fcf72b5daac1822b537dbef1ea504f6ef</div><div class=\"dictlist\">LinearSVC=store://artifacts/feature-selection/feature-selection-feature-selection_LinearSVC#0@fdcbc4e3f5c44769be5e64425f10aed8^9723935a28b9ea141e7aaf6f242109ce9383eae0</div><div class=\"dictlist\">LogisticRegression=store://artifacts/feature-selection/feature-selection-feature-selection_LogisticRegression#0@fdcbc4e3f5c44769be5e64425f10aed8^852483e49d4372edd493a130f7fd4aa85666af36</div><div class=\"dictlist\">ExtraTreesClassifier=store://artifacts/feature-selection/feature-selection-feature-selection_ExtraTreesClassifier#0@fdcbc4e3f5c44769be5e64425f10aed8^0f32d3f338d4a7692859d06443c5e8499773a651</div><div class=\"dictlist\">feature_scores=store://datasets/feature-selection/feature-selection-feature-selection_feature_scores#0@fdcbc4e3f5c44769be5e64425f10aed8^9abe27f14a189c6ff34456a29403fabdb54113f2</div><div class=\"dictlist\">max_scaled_scores_feature_scores=store://datasets/feature-selection/feature-selection-feature-selection_max_scaled_scores_feature_scores#0@fdcbc4e3f5c44769be5e64425f10aed8^1910b21802b1d6a0aa412fca4f93cecbc8c755b4</div><div class=\"dictlist\">selected_features_count=store://datasets/feature-selection/feature-selection-feature-selection_selected_features_count#0@fdcbc4e3f5c44769be5e64425f10aed8^e06baafaa333c72865103b3f3645da60367f9b85</div><div class=\"dictlist\">selected_features=store://datasets/feature-selection/feature-selection-feature-selection_selected_features#0@fdcbc4e3f5c44769be5e64425f10aed8^7af89e5113ae0dce4c8b90f4a52cf57500d795b5</div></td>\n",
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       "<b> > to track results use the .show() or .logs() methods  or <a href=\"https://dashboard.default-tenant.app.vmdev67.lab.iguazeng.com/mlprojects/feature-selection/jobs/monitor-jobs/feature-selection-feature-selection/fdcbc4e3f5c44769be5e64425f10aed8/overview\" target=\"_blank\">click here</a> to open in UI</b>"
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      "> 2025-03-06 10:59:31,131 [info] Run execution finished: {\"name\":\"feature-selection-feature-selection\",\"status\":\"completed\"}\n"
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   "source": [
    "fs = feature_selection.run(handler=\"feature_selection\", \n",
    "                      params={'k': 2,\n",
    "                       'min_votes': 0.3,\n",
    "                       'label_column': 'is_error'},\n",
    "                      inputs={'df_artifact': os.path.abspath('data/metrics.pq')},\n",
    "                      artifact_path=os.path.join(os.path.abspath('./'), 'artifacts'), local=True)"
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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",
       "                                                    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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlrun.get_dataitem(fs.spec.inputs['df_artifact']).as_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f_classif</th>\n",
       "      <th>mutual_info_classif</th>\n",
       "      <th>chi2</th>\n",
       "      <th>f_regression</th>\n",
       "      <th>LinearSVC</th>\n",
       "      <th>LogisticRegression</th>\n",
       "      <th>ExtraTreesClassifier</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cpu_utilization</th>\n",
       "      <td>2520.015809</td>\n",
       "      <td>0.180451</td>\n",
       "      <td>4457.429360</td>\n",
       "      <td>2520.015809</td>\n",
       "      <td>-0.037533</td>\n",
       "      <td>-0.178632</td>\n",
       "      <td>0.023102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latency</th>\n",
       "      <td>10152.151995</td>\n",
       "      <td>0.199853</td>\n",
       "      <td>272872.890194</td>\n",
       "      <td>10152.151995</td>\n",
       "      <td>0.023732</td>\n",
       "      <td>0.104103</td>\n",
       "      <td>0.023102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>packet_loss</th>\n",
       "      <td>14120.490547</td>\n",
       "      <td>0.210081</td>\n",
       "      <td>157191.427524</td>\n",
       "      <td>14120.490547</td>\n",
       "      <td>0.047873</td>\n",
       "      <td>0.214212</td>\n",
       "      <td>0.023102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>throughput</th>\n",
       "      <td>20421.721030</td>\n",
       "      <td>0.231438</td>\n",
       "      <td>109129.511665</td>\n",
       "      <td>20421.721030</td>\n",
       "      <td>-0.014421</td>\n",
       "      <td>-0.084308</td>\n",
       "      <td>0.023102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    f_classif  mutual_info_classif           chi2  \\\n",
       "cpu_utilization   2520.015809             0.180451    4457.429360   \n",
       "latency          10152.151995             0.199853  272872.890194   \n",
       "packet_loss      14120.490547             0.210081  157191.427524   \n",
       "throughput       20421.721030             0.231438  109129.511665   \n",
       "\n",
       "                 f_regression  LinearSVC  LogisticRegression  \\\n",
       "cpu_utilization   2520.015809  -0.037533           -0.178632   \n",
       "latency          10152.151995   0.023732            0.104103   \n",
       "packet_loss      14120.490547   0.047873            0.214212   \n",
       "throughput       20421.721030  -0.014421           -0.084308   \n",
       "\n",
       "                 ExtraTreesClassifier  \n",
       "cpu_utilization              0.023102  \n",
       "latency                      0.023102  \n",
       "packet_loss                  0.023102  \n",
       "throughput                   0.023102  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlrun.get_dataitem(fs.outputs['feature_scores']).as_df()"
   ]
  }
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