domvwt/vaex-optuna.ipynb

## vaex-optuna.ipynb
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    "colab": {
      "name": "vaex-optuna.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP0rg+FmL1BUJYEEpNoox/b",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/domvwt/078ac4da72ada3d2229747ad827bf47f/vaex-optuna.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywOOu8YGu0Mj"
      },
      "source": [
        "# Hyperparameter Tuning with Vaex and Optuna\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mM2JrQ4VpJaY",
        "outputId": "6e80cd03-8a33-4694-ae86-daaa2719decb"
      },
      "source": [
        "%%shell\r\n",
        "echo \"Setting up environment...\"\r\n",
        "if [ ! -e \"yellow_tripdata_2019-12.csv\" ]\r\n",
        "  then \r\n",
        "    echo \"...Downloading data...\" \r\n",
        "    wget -q \\\r\n",
        "      https://nyc-tlc.s3.amazonaws.com/trip+data/yellow_tripdata_2019-12.csv\r\n",
        "fi\r\n",
        "echo \"...Installing libraries...\"\r\n",
        "pip install -Uqq vaex-core vaex-hdf5 vaex-jupyter vaex-ml catboost optuna \"ipython>=7.0.0\" &> /dev/null\r\n",
        "echo \"Setup Complete!\"\r\n",
        "echo \"Runtime restart may be required to load new packages.\""
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Setting up environment...\n",
            "...Downloading data...\n",
            "...Installing libraries...\n",
            "Setup Complete!\n",
            "Runtime restart may be required to load new packages.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tO6_1lOhugqt"
      },
      "source": [
        "import gc\n",
        "import vaex as vx\n",
        "import vaex.jupyter as vj\n",
        "import vaex.ml.catboost as cb\n",
        "import optuna\n",
        "import nest_asyncio\n",
        "\n",
        "\n",
        "from pathlib import Path\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "\n",
        "# Asyncio event loops get left running when the notebook is restarted\n",
        "# Calling nest_asyncio prevents them from interfering with new sessions\n",
        "nest_asyncio.apply()"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t13hPzuV0PfH"
      },
      "source": [
        "DATA_PATH = \"yellow_tripdata_2019-12.csv\"\r\n",
        "DATA_NEW = \"yellow_tripdata_2019-12.csv.hdf5\"\r\n",
        "\r\n",
        "if not Path(DATA_NEW).is_file():\r\n",
        "  _ = vx.from_csv(DATA_PATH, convert=True)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 885
        },
        "id": "c4mK0fm98r-v",
        "outputId": "e0dc413c-49d7-4919-cbfe-d4e6e939ca24"
      },
      "source": [
        "df00 = vx.open(DATA_NEW)\r\n",
        "df00.info()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>.vaex-description pre {\n",
              "          max-width : 450px;\n",
              "          white-space : nowrap;\n",
              "          overflow : hidden;\n",
              "          text-overflow: ellipsis;\n",
              "        }\n",
              "\n",
              "        .vex-description pre:hover {\n",
              "          max-width : initial;\n",
              "          white-space: pre;\n",
              "        }</style>"
            ],
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              "<IPython.core.display.HTML object>"
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          "metadata": {
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div><h2>yellow_tripdata_2019-12.csv</h2> <b>rows</b>: 6,896,317</div><div><b>path</b>: <i>/content/yellow_tripdata_2019-12.csv.hdf5</i></div><div><b>Description</b>: file exported by vaex, by user root, on date 2021-02-02 13:32:50.698030, from source /has/no/path/arrays-/has/no/path/arrays</div><h2>Columns:</h2><table class='table-striped'><thead><tr><th>column</th><th>type</th><th>unit</th><th>description</th><th>expression</th></tr></thead><tr><td>VendorID</td><td>float64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>tpep_pickup_datetime</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>tpep_dropoff_datetime</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>passenger_count</td><td>float64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>trip_distance</td><td>float64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>RatecodeID</td><td>float64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>store_and_fwd_flag</td><td>str</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>PULocationID</td><td>int64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>DOLocationID</td><td>int64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>payment_type</td><td>float64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>fare_amount</td><td>float64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>extra</td><td>float64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>mta_tax</td><td>float64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>tip_amount</td><td>float64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>tolls_amount</td><td>float64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>improvement_surcharge</td><td>float64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>total_amount</td><td>float64</td><td></td><td ><pre></pre></td><td></td></tr><tr><td>congestion_surcharge</td><td>float64</td><td></td><td ><pre></pre></td><td></td></tr></table><h2>Data:</h2><table>\n",
              "<thead>\n",
              "<tr><th>#                                    </th><th>VendorID  </th><th>tpep_pickup_datetime  </th><th>tpep_dropoff_datetime  </th><th>passenger_count  </th><th>trip_distance  </th><th>RatecodeID  </th><th>store_and_fwd_flag  </th><th>PULocationID  </th><th>DOLocationID  </th><th>payment_type  </th><th>fare_amount  </th><th>extra  </th><th>mta_tax  </th><th>tip_amount  </th><th>tolls_amount  </th><th>improvement_surcharge  </th><th>total_amount  </th><th>congestion_surcharge  </th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td><i style='opacity: 0.6'>0</i>        </td><td>1.0       </td><td>2019-12-01 00:26:58   </td><td>2019-12-01 00:41:45    </td><td>1.0              </td><td>4.2            </td><td>1.0         </td><td>N                   </td><td>142           </td><td>116           </td><td>2.0           </td><td>14.5         </td><td>3.0    </td><td>0.5      </td><td>0.0         </td><td>0.0           </td><td>0.3                    </td><td>18.3          </td><td>2.5                   </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>1</i>        </td><td>1.0       </td><td>2019-12-01 00:12:08   </td><td>2019-12-01 00:12:14    </td><td>1.0              </td><td>0.0            </td><td>1.0         </td><td>N                   </td><td>145           </td><td>145           </td><td>2.0           </td><td>2.5          </td><td>0.5    </td><td>0.5      </td><td>0.0         </td><td>0.0           </td><td>0.3                    </td><td>3.8           </td><td>0.0                   </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>2</i>        </td><td>1.0       </td><td>2019-12-01 00:25:53   </td><td>2019-12-01 00:26:04    </td><td>1.0              </td><td>0.0            </td><td>1.0         </td><td>N                   </td><td>145           </td><td>145           </td><td>2.0           </td><td>2.5          </td><td>0.5    </td><td>0.5      </td><td>0.0         </td><td>0.0           </td><td>0.3                    </td><td>3.8           </td><td>0.0                   </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>3</i>        </td><td>1.0       </td><td>2019-12-01 00:12:03   </td><td>2019-12-01 00:33:19    </td><td>2.0              </td><td>9.4            </td><td>1.0         </td><td>N                   </td><td>138           </td><td>25            </td><td>1.0           </td><td>28.5         </td><td>0.5    </td><td>0.5      </td><td>10.0        </td><td>0.0           </td><td>0.3                    </td><td>39.8          </td><td>0.0                   </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>4</i>        </td><td>1.0       </td><td>2019-12-01 00:05:27   </td><td>2019-12-01 00:16:32    </td><td>2.0              </td><td>1.6            </td><td>1.0         </td><td>N                   </td><td>161           </td><td>237           </td><td>2.0           </td><td>9.0          </td><td>3.0    </td><td>0.5      </td><td>0.0         </td><td>0.0           </td><td>0.3                    </td><td>12.8          </td><td>2.5                   </td></tr>\n",
              "<tr><td>...                                  </td><td>...       </td><td>...                   </td><td>...                    </td><td>...              </td><td>...            </td><td>...         </td><td>...                 </td><td>...           </td><td>...           </td><td>...           </td><td>...          </td><td>...    </td><td>...      </td><td>...         </td><td>...           </td><td>...                    </td><td>...           </td><td>...                   </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>6,896,312</i></td><td>nan       </td><td>2019-12-31 00:07:00   </td><td>2019-12-31 00:46:00    </td><td>nan              </td><td>12.78          </td><td>nan         </td><td>None                </td><td>230           </td><td>72            </td><td>nan           </td><td>32.32        </td><td>2.75   </td><td>0.5      </td><td>0.0         </td><td>6.12          </td><td>0.3                    </td><td>41.99         </td><td>0.0                   </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>6,896,313</i></td><td>nan       </td><td>2019-12-31 00:20:00   </td><td>2019-12-31 00:47:00    </td><td>nan              </td><td>18.52          </td><td>nan         </td><td>None                </td><td>219           </td><td>32            </td><td>nan           </td><td>51.63        </td><td>2.75   </td><td>0.5      </td><td>0.0         </td><td>6.12          </td><td>0.3                    </td><td>61.3          </td><td>0.0                   </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>6,896,314</i></td><td>nan       </td><td>2019-12-31 00:50:00   </td><td>2019-12-31 01:21:00    </td><td>nan              </td><td>13.13          </td><td>nan         </td><td>None                </td><td>161           </td><td>76            </td><td>nan           </td><td>38.02        </td><td>2.75   </td><td>0.5      </td><td>0.0         </td><td>6.12          </td><td>0.3                    </td><td>47.69         </td><td>0.0                   </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>6,896,315</i></td><td>nan       </td><td>2019-12-31 00:38:19   </td><td>2019-12-31 01:19:37    </td><td>nan              </td><td>14.51          </td><td>nan         </td><td>None                </td><td>230           </td><td>21            </td><td>nan           </td><td>41.86        </td><td>2.75   </td><td>0.0      </td><td>0.0         </td><td>6.12          </td><td>0.3                    </td><td>51.03         </td><td>0.0                   </td></tr>\n",
              "<tr><td><i style='opacity: 0.6'>6,896,316</i></td><td>nan       </td><td>2019-12-31 00:21:00   </td><td>2019-12-31 00:56:00    </td><td>nan              </td><td>-17.16         </td><td>nan         </td><td>None                </td><td>193           </td><td>219           </td><td>nan           </td><td>44.62        </td><td>2.75   </td><td>0.5      </td><td>0.0         </td><td>0.0           </td><td>0.3                    </td><td>48.17         </td><td>0.0                   </td></tr>\n",
              "</tbody>\n",
              "</table>"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-7vln7ogxvwJ"
      },
      "source": [
        "target = \"tip_amount\"\n",
        "features = [x for x in df00.get_column_names() if x != target]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TMO2QjHewgC5"
      },
      "source": [
        "def objective(trial):\r\n",
        "\r\n",
        "  gc.collect()\r\n",
        "\r\n",
        "  # Take a random sample of 1,000,000 rows and split into train / test\r\n",
        "  df_train, df_valid = df00.sample(int(1e6)).ml.train_test_split(verbose=False)\r\n",
        "\r\n",
        "  # Catboost model parameters\r\n",
        "  cbm_params = dict(\r\n",
        "    loss_function=\"MAE\",\r\n",
        "    early_stopping_rounds=50,\r\n",
        "    verbose=False,\r\n",
        "    used_ram_limit=\"8gb\",\r\n",
        "\r\n",
        "    colsample_bylevel=trial.suggest_float(\"colsample_bylevel\", 0.01, 0.1),\r\n",
        "    depth=trial.suggest_int(\"depth\", 1, 12),\r\n",
        "    bootstrap_type=trial.suggest_categorical(\r\n",
        "        \"boostrap_type\", [\"Bayesian\", \"Bernoulli\", \"MVS\"]\r\n",
        "      ),\r\n",
        "  )\r\n",
        "\r\n",
        "  if cbm_params[\"bootstrap_type\"] == \"Bayesian\":\r\n",
        "      cbm_params[\"bagging_temperature\"] = trial.suggest_float(\"bagging_temperature\", 0, 10)\r\n",
        "  elif cbm_params[\"bootstrap_type\"] == \"Bernoulli\":\r\n",
        "      cbm_params[\"subsample\"] = trial.suggest_float(\"subsample\", 0.1, 1)\r\n",
        "\r\n",
        "  # Vaex wrapper parameters\r\n",
        "  # - Use prediction_type to determine between classification / regression\r\n",
        "  # - Use pool_params to pass cat_features index\r\n",
        "  vcb_params = dict(\r\n",
        "      features=features,\r\n",
        "      target=target,\r\n",
        "      \r\n",
        "      prediction_type=\"RawFormulaVal\",\r\n",
        "      params=cbm_params,\r\n",
        "      num_boost_round=200,\r\n",
        "      # pool_params=pool_params\r\n",
        "  )    \r\n",
        "\r\n",
        "  cbm = cb.CatBoostModel(**vcb_params)\r\n",
        "  cbm.fit(df_train, evals=[df_valid])\r\n",
        "\r\n",
        "  preds = cbm.predict(df_valid)\r\n",
        "  mae = mean_absolute_error(preds, df_valid[target].values)\r\n",
        "\r\n",
        "  return mae"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SM32jp47E6AF",
        "outputId": "989f8c9d-a241-446a-c5a1-68c5c10cc05b"
      },
      "source": [
        "study = optuna.create_study(\r\n",
        "    storage=\"sqlite:///optuna.db\", \r\n",
        "    study_name=\"catboost-tips\",\r\n",
        "    direction=\"minimize\",\r\n",
        "    load_if_exists=True\r\n",
        ")\r\n",
        "\r\n",
        "study_df = study.trials_dataframe()\r\n",
        "\r\n",
        "if not study_df.empty:\r\n",
        "  trial_runs = study_df[study_df.state != \"FAIL\"].shape[0]\r\n",
        "else:\r\n",
        "  trial_runs = 0\r\n",
        "\r\n",
        "n_trials = 200 - trial_runs\r\n",
        "\r\n",
        "print(f\"Remaining trials: {n_trials}\")\r\n",
        "print(\"Starting hyperparameter search...\")\r\n",
        "print(\"...Collecting garbage...\")\r\n",
        "gc.collect()\r\n",
        "print(\"...Running study...\")\r\n",
        "study.optimize(objective, n_trials=n_trials, n_jobs=4)\r\n",
        "\r\n",
        "print(\"Number of finished trials: {}\".format(len(study.trials)))\r\n",
        "\r\n",
        "print(\"Best trial:\")\r\n",
        "trial = study.best_trial\r\n",
        "\r\n",
        "print(\"  Value: {}\".format(trial.value))\r\n",
        "\r\n",
        "print(\"  Params: \")\r\n",
        "for key, value in trial.params.items():\r\n",
        "    print(\"    {}: {}\".format(key, value))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-02-02 17:43:17,549]\u001b[0m Using an existing study with name 'catboost-tips' instead of creating a new one.\u001b[0m\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Remaining trials: 0\n",
            "Starting hyperparameter search...\n",
            "...Collecting garbage...\n",
            "...Running study...\n",
            "Number of finished trials: 200\n",
            "Best trial:\n",
            "  Value: 0.7167503577102808\n",
            "  Params: \n",
            "    boostrap_type: Bernoulli\n",
            "    colsample_bylevel: 0.09915981893666519\n",
            "    depth: 12\n",
            "    subsample: 0.6726329766751142\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "uy9392xeIgaT",
        "outputId": "888f31cd-af61-48fb-a091-be1275763935"
      },
      "source": [
        "optuna.visualization.matplotlib.plot_slice(study);"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: ExperimentalWarning:\n",
            "\n",
            "plot_slice is experimental (supported from v2.2.0). The interface can change in the future.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMXSp8qFKzck"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}