AutoML_AutoSklearn_Regression_Classification_Sample.ipynb

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      "provenance": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## AutoSklearn AutoML Sample Code"
      ],
      "metadata": {
        "id": "GPMn1cqTrIqH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7SurEZx_0_UP",
        "outputId": "f972ffb9-86e0-4247-b5d5-3d88c752d393"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Building wheels for collected packages: auto-sklearn, pynisher, scikit-learn, smac, liac-arff\n",
            "  Building wheel for auto-sklearn (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "Successfully built auto-sklearn pynisher scikit-learn smac liac-arff\n",
            "Installing collected packages: pyrfr, pynisher, liac-arff, emcee, distro, scikit-learn, ConfigSpace, smac, auto-sklearn\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.2.2\n",
            "    Uninstalling scikit-learn-1.2.2:\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed ConfigSpace-0.4.21 auto-sklearn-0.15.0 distro-1.8.0 emcee-3.1.4 liac-arff-2.5.0 pynisher-0.6.4 pyrfr-0.8.3 scikit-learn-0.24.2 smac-1.2\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Collecting numpy\n",
            "  Using cached numpy-1.24.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB)\n",
            "Installing collected packages: numpy\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.22.4\n",
            "    Uninstalling numpy-1.22.4:\n",
            "      Successfully uninstalled numpy-1.22.4\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "imbalanced-learn 0.10.1 requires scikit-learn>=1.0.2, but you have scikit-learn 0.24.2 which is incompatible.\n",
            "numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.24.3 which is incompatible.\n",
            "tensorflow 2.12.0 requires numpy<1.24,>=1.22, but you have numpy 1.24.3 which is incompatible.\n",
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            "\u001b[0mSuccessfully installed numpy-1.24.3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install auto-sklearn\n",
        "!pip install numpy --upgrade"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***If you get error for the following, try restating the kernel***"
      ],
      "metadata": {
        "id": "5FJ4uy2jy1oy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics"
      ],
      "metadata": {
        "id": "x7OR3HO7y8LH"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "FNCin4ckrPI7"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Regression"
      ],
      "metadata": {
        "id": "lZpHHlLfrdKo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Load Data"
      ],
      "metadata": {
        "id": "yAxV2nwbrhPq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cars = pd.read_csv('cars.csv')"
      ],
      "metadata": {
        "id": "-IPYI9dF_ytE"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cars.info()"
      ],
      "metadata": {
        "id": "pwrsEBOXB05i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55f70ba9-5880-42c5-ce39-923df28aaf05"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 38531 entries, 0 to 38530\n",
            "Data columns (total 19 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   manufacturer_name  38531 non-null  object \n",
            " 1   model_name         38531 non-null  object \n",
            " 2   transmission       38531 non-null  object \n",
            " 3   color              38531 non-null  object \n",
            " 4   odometer_value     38531 non-null  int64  \n",
            " 5   year_produced      38531 non-null  int64  \n",
            " 6   engine_fuel        38531 non-null  object \n",
            " 7   engine_has_gas     38531 non-null  bool   \n",
            " 8   engine_type        38531 non-null  object \n",
            " 9   engine_capacity    38521 non-null  float64\n",
            " 10  body_type          38531 non-null  object \n",
            " 11  has_warranty       38531 non-null  bool   \n",
            " 12  state              38531 non-null  object \n",
            " 13  drivetrain         38531 non-null  object \n",
            " 14  price_usd          38531 non-null  float64\n",
            " 15  is_exchangeable    38531 non-null  bool   \n",
            " 16  number_of_photos   38531 non-null  float64\n",
            " 17  up_counter         38531 non-null  float64\n",
            " 18  duration_listed    38531 non-null  float64\n",
            "dtypes: bool(3), float64(5), int64(2), object(9)\n",
            "memory usage: 4.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label = 'price_usd'"
      ],
      "metadata": {
        "id": "qEixYwsy30sw"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data to test and train\n",
        "from sklearn.model_selection import train_test_split\n",
        "cars_train, cars_test = train_test_split(cars, test_size=0.2, random_state=42)\n",
        "cars_test_labels = cars_test[label]\n",
        "cars_test_nolabel = cars_test.drop([label], axis=1)\n",
        "cars_train_labels = cars_train[label]\n",
        "cars_train_nolabel = cars_train.drop([label], axis=1)\n",
        "cars_train.head()"
      ],
      "metadata": {
        "id": "jk-6DgsTB8xR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "1a264c5d-30b9-43e3-93d5-11437dd20e38"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      manufacturer_name      model_name transmission   color  odometer_value  \\\n",
              "37935           Peugeot             307   mechanical  silver          320000   \n",
              "28568               BMW             525    automatic  violet          400000   \n",
              "24311         Chevrolet         Captiva   mechanical    grey           79000   \n",
              "19393              Jeep  Grand Cherokee    automatic   other          500000   \n",
              "2540               Opel           Astra   mechanical  silver          280000   \n",
              "\n",
              "       year_produced engine_fuel  engine_has_gas engine_type  engine_capacity  \\\n",
              "37935           2005    gasoline           False    gasoline              1.6   \n",
              "28568           1997      diesel           False      diesel              2.5   \n",
              "24311           2015      diesel           False      diesel              2.2   \n",
              "19393           2004      diesel           False      diesel              2.7   \n",
              "2540            2007      diesel           False      diesel              1.7   \n",
              "\n",
              "       body_type  has_warranty  state drivetrain  price_usd  is_exchangeable  \\\n",
              "37935  universal         False  owned      front     7280.0            False   \n",
              "28568  universal         False  owned       rear     2380.0             True   \n",
              "24311        suv         False  owned        all    20300.0            False   \n",
              "19393        suv         False  owned        all     9100.0            False   \n",
              "2540   universal         False  owned      front     7000.0             True   \n",
              "\n",
              "       number_of_photos  up_counter  duration_listed  \n",
              "37935              10.0         1.0             45.5  \n",
              "28568               2.5         0.5              2.5  \n",
              "24311               4.5         6.5             30.5  \n",
              "19393               6.5        39.5            151.0  \n",
              "2540                4.0         3.0             33.0  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars_train.head()"
      ],
      "metadata": {
        "id": "fwP-jY5kCJpt",
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      "execution_count": 8,
      "outputs": [
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            "text/plain": [
              "      manufacturer_name      model_name transmission   color  odometer_value  \\\n",
              "37935           Peugeot             307   mechanical  silver          320000   \n",
              "28568               BMW             525    automatic  violet          400000   \n",
              "24311         Chevrolet         Captiva   mechanical    grey           79000   \n",
              "19393              Jeep  Grand Cherokee    automatic   other          500000   \n",
              "2540               Opel           Astra   mechanical  silver          280000   \n",
              "\n",
              "       year_produced engine_fuel  engine_has_gas engine_type  engine_capacity  \\\n",
              "37935           2005    gasoline           False    gasoline              1.6   \n",
              "28568           1997      diesel           False      diesel              2.5   \n",
              "24311           2015      diesel           False      diesel              2.2   \n",
              "19393           2004      diesel           False      diesel              2.7   \n",
              "2540            2007      diesel           False      diesel              1.7   \n",
              "\n",
              "       body_type  has_warranty  state drivetrain  price_usd  is_exchangeable  \\\n",
              "37935  universal         False  owned      front     7280.0            False   \n",
              "28568  universal         False  owned       rear     2380.0             True   \n",
              "24311        suv         False  owned        all    20300.0            False   \n",
              "19393        suv         False  owned        all     9100.0            False   \n",
              "2540   universal         False  owned      front     7000.0             True   \n",
              "\n",
              "       number_of_photos  up_counter  duration_listed  \n",
              "37935              10.0         1.0             45.5  \n",
              "28568               2.5         0.5              2.5  \n",
              "24311               4.5         6.5             30.5  \n",
              "19393               6.5        39.5            151.0  \n",
              "2540                4.0         3.0             33.0  "
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import autosklearn.regression\n",
        "\n",
        "regression = autosklearn.regression.AutoSklearnRegressor(time_left_for_this_task=5*60,\n",
        "                                                              per_run_time_limit=30, \n",
        "                                                              n_jobs=8)\n"
      ],
      "metadata": {
        "id": "XagnyJjjHBpJ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# perform the search for cars \n",
        "regression.fit(cars_train_nolabel, cars_train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5yicnZqsIHgH",
        "outputId": "810932ff-1778-41b7-8791-41e79d0040ea"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column manufacturer_name has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column model_name has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column transmission has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column color has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column engine_fuel has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column engine_type has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column body_type has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column state has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column drivetrain has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AutoSklearnRegressor(ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,\n",
              "                     n_jobs=8, per_run_time_limit=30,\n",
              "                     time_left_for_this_task=300)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn"
      ],
      "metadata": {
        "id": "nOXdD_rWO0_8"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Predict using the model¶\n",
        "predictions = regression.predict(cars_test_nolabel)\n",
        "print(\"Accuracy score:{}\".format(sklearn.metrics.mean_absolute_error(cars_test_labels, predictions)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSjOFhQbIlX0",
        "outputId": "746fc576-871d-4d7b-f0f7-089056ff2847"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score:6383.97175662547\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(regression.sprint_statistics())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TT-XVUt2I8cb",
        "outputId": "d3b42574-4a60-4606-f012-f185c6218837"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "auto-sklearn results:\n",
            "  Dataset name: a1bead31-0787-11ee-8be6-0242ac1c000c\n",
            "  Metric: r2\n",
            "  Number of target algorithm runs: 89\n",
            "  Number of successful target algorithm runs: 0\n",
            "  Number of crashed target algorithm runs: 89\n",
            "  Number of target algorithms that exceeded the time limit: 0\n",
            "  Number of target algorithms that exceeded the memory limit: 0\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "regression.leaderboard()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "timM_DOiLWtY",
        "outputId": "17639206-76e3-4b05-8211-42f3eaacfb61"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          rank  ensemble_weight  type  cost duration\n",
              "model_id                                            \n",
              "1            1              1.0  <NA>  <NA>     <NA>"
            ],
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              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>ensemble_weight</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(regression.show_models)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oKHpbSk8L_h4",
        "outputId": "537c28a8-ccfc-431e-d2c3-7ac885164d32"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<bound method AutoSklearnEstimator.show_models of AutoSklearnRegressor(ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,\n",
            "                     n_jobs=8, per_run_time_limit=30,\n",
            "                     time_left_for_this_task=300)>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classification"
      ],
      "metadata": {
        "id": "OATY2VgSzzmB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Load Data"
      ],
      "metadata": {
        "id": "TT9UAG4Pz31q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic = pd.read_csv('titanic.csv')"
      ],
      "metadata": {
        "id": "SLgm5t7BtJ5d"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "titanic.info()"
      ],
      "metadata": {
        "id": "pxLdsNzJB3xb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a87c3414-a493-44e7-dfbd-fca03265e7e5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Define the label column\n",
        "label = 'Survived'"
      ],
      "metadata": {
        "id": "q3DYfvb6BdyN"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data to test and train\n",
        "from sklearn.model_selection import train_test_split\n",
        "titanic_train, titanic_test = train_test_split(titanic, test_size=0.2, random_state=42)\n",
        "titanic_test_labels = titanic_test[label]\n",
        "titanic_test_nolabel = titanic_test.drop([label], axis=1)\n",
        "titanic_train_labels = titanic_train[label]\n",
        "titanic_train_nolabel = titanic_train.drop([label], axis=1)\n",
        "titanic_train.head()"
      ],
      "metadata": {
        "id": "WGMYR9SzCBzT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "ccd0cfd3-fe46-48a4-e3f3-b6af91001a29"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                Name  \\\n",
              "331          332         0       1                 Partner, Mr. Austen   \n",
              "733          734         0       2          Berriman, Mr. William John   \n",
              "382          383         0       3                  Tikkanen, Mr. Juho   \n",
              "704          705         0       3             Hansen, Mr. Henrik Juul   \n",
              "813          814         0       3  Andersson, Miss. Ebba Iris Alfrida   \n",
              "\n",
              "        Sex   Age  SibSp  Parch             Ticket     Fare Cabin Embarked  \n",
              "331    male  45.5      0      0             113043  28.5000  C124        S  \n",
              "733    male  23.0      0      0              28425  13.0000   NaN        S  \n",
              "382    male  32.0      0      0  STON/O 2. 3101293   7.9250   NaN        S  \n",
              "704    male  26.0      1      0             350025   7.8542   NaN        S  \n",
              "813  female   6.0      4      2             347082  31.2750   NaN        S  "
            ],
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              "      <td>23.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28425</td>\n",
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              "      <td>S</td>\n",
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              "      <th>382</th>\n",
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              "      <td>Tikkanen, Mr. Juho</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>STON/O 2. 3101293</td>\n",
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              "      <td>Hansen, Mr. Henrik Juul</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
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              "      <th>813</th>\n",
              "      <td>814</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Andersson, Miss. Ebba Iris Alfrida</td>\n",
              "      <td>female</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>347082</td>\n",
              "      <td>31.2750</td>\n",
              "      <td>NaN</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_train.head()"
      ],
      "metadata": {
        "id": "uvCCtZJPCKsm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "268e4177-6950-4501-a0ce-13a48130656f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                Name  \\\n",
              "331          332         0       1                 Partner, Mr. Austen   \n",
              "733          734         0       2          Berriman, Mr. William John   \n",
              "382          383         0       3                  Tikkanen, Mr. Juho   \n",
              "704          705         0       3             Hansen, Mr. Henrik Juul   \n",
              "813          814         0       3  Andersson, Miss. Ebba Iris Alfrida   \n",
              "\n",
              "        Sex   Age  SibSp  Parch             Ticket     Fare Cabin Embarked  \n",
              "331    male  45.5      0      0             113043  28.5000  C124        S  \n",
              "733    male  23.0      0      0              28425  13.0000   NaN        S  \n",
              "382    male  32.0      0      0  STON/O 2. 3101293   7.9250   NaN        S  \n",
              "704    male  26.0      1      0             350025   7.8542   NaN        S  \n",
              "813  female   6.0      4      2             347082  31.2750   NaN        S  "
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              "          const element = document.querySelector('#df-ca319d17-b51a-40de-98b1-b5bae01f5f87');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import autosklearn.classification\n",
        "\n",
        "classification = autosklearn.classification.AutoSklearnClassifier(time_left_for_this_task=5*60, \n",
        "                                                                 per_run_time_limit=30,\n",
        "                                                                 n_jobs=8)"
      ],
      "metadata": {
        "id": "3-0lvuFKCcPH"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#perform it \n",
        "classification.fit(titanic_train_nolabel, titanic_train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kJlAuZc2Ci9Y",
        "outputId": "0bafce80-9618-48be-a35c-e239473eec98"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column Name has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column Sex has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column Ticket has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column Cabin has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/autosklearn/data/feature_validator.py:318: UserWarning: Input Column Embarked has generic type object. Autosklearn will treat this column as string. Please ensure that this setting is suitable for your task.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AutoSklearnClassifier(ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,\n",
              "                      n_jobs=8, per_run_time_limit=30,\n",
              "                      time_left_for_this_task=300)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predict using the model¶\n",
        "predictions = classification.predict(titanic_test_nolabel)\n",
        "print(\"Accuracy score:{}\".format(sklearn.metrics.accuracy_score(titanic_test_labels, predictions)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYZXb6B9CjEt",
        "outputId": "60854aa1-071a-4765-b8ff-b34bf37304f5"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score:0.8100558659217877\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#statistics \n",
        "print(classification.sprint_statistics())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U38K5OH9GoJk",
        "outputId": "cd5d934c-cac1-44e6-fb42-e3b5ac9c08a1"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "auto-sklearn results:\n",
            "  Dataset name: 93da2151-0788-11ee-8be6-0242ac1c000c\n",
            "  Metric: accuracy\n",
            "  Best validation score: 0.829787\n",
            "  Number of target algorithm runs: 58\n",
            "  Number of successful target algorithm runs: 31\n",
            "  Number of crashed target algorithm runs: 22\n",
            "  Number of target algorithms that exceeded the time limit: 5\n",
            "  Number of target algorithms that exceeded the memory limit: 0\n",
            "\n"
          ]
        }
      ]
    }
  ]
}