07.hyperparameter_tuning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "7.hyperparameter-tuning.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "WjpiZA317zYc",
        "colab_type": "code",
        "outputId": "2cf1287a-062d-450c-ce16-c214189e6384",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        }
      },
      "source": [
        "!pip install git+https://github.com/keras-team/keras-tuner\n",
        "# !pip uninstall tensorflow -y\n",
        "# !pip install tensorflow-gpu\n",
        "!pip install gdown"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/keras-team/keras-tuner\n",
            "  Cloning https://github.com/keras-team/keras-tuner to /tmp/pip-req-build-11m2bb_7\n",
            "  Running command git clone -q https://github.com/keras-team/keras-tuner /tmp/pip-req-build-11m2bb_7\n",
            "Requirement already satisfied (use --upgrade to upgrade): keras-tuner==0.9.1 from git+https://github.com/keras-team/keras-tuner in /usr/local/lib/python3.6/dist-packages\n",
            "Requirement already satisfied: tensorflow>=2.0.0-beta1 in /usr/local/lib/python3.6/dist-packages (from keras-tuner==0.9.1) (2.0.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from keras-tuner==0.9.1) (1.16.5)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.6/dist-packages (from keras-tuner==0.9.1) (0.8.5)\n",
            "Requirement already satisfied: terminaltables in /usr/local/lib/python3.6/dist-packages (from keras-tuner==0.9.1) (3.1.0)\n",
            "Requirement already satisfied: colorama in /usr/local/lib/python3.6/dist-packages (from keras-tuner==0.9.1) (0.4.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from keras-tuner==0.9.1) (4.28.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from keras-tuner==0.9.1) (2.21.0)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.6/dist-packages (from keras-tuner==0.9.1) (5.4.8)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from keras-tuner==0.9.1) (1.3.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from keras-tuner==0.9.1) (0.21.3)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (1.11.2)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (0.33.6)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (3.1.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (3.10.0)\n",
            "Requirement already satisfied: tensorflow-estimator<2.1.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (2.0.1)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (0.8.1)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (1.1.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (1.15.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (1.0.8)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (0.1.7)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (1.12.0)\n",
            "Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (0.2.2)\n",
            "Requirement already satisfied: tensorboard<2.1.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (2.0.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (0.8.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner==0.9.1) (2019.9.11)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner==0.9.1) (1.24.3)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner==0.9.1) (2.8)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner==0.9.1) (3.0.4)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->keras-tuner==0.9.1) (0.14.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (41.2.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (2.8.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (0.16.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow>=2.0.0-beta1->keras-tuner==0.9.1) (3.1.1)\n",
            "Building wheels for collected packages: keras-tuner\n",
            "  Building wheel for keras-tuner (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-tuner: filename=keras_tuner-0.9.1-cp36-none-any.whl size=85887 sha256=cbd6effeea683e7e7bf0c1f41ebc453363e8f4f156bc04d486a398eb78098648\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-vg86sw5y/wheels/6c/55/2d/6e178386cb7a2d7da5a7059752a2b58791705c9c8718c5f07a\n",
            "Successfully built keras-tuner\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.6/dist-packages (3.6.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from gdown) (4.28.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from gdown) (2.21.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gdown) (1.12.0)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (2.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (2019.9.11)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (3.0.4)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->gdown) (1.24.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t8SXXG-t7-Uw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from sklearn.model_selection import train_test_split\n",
        "from pprint import pprint\n",
        "from kerastuner.tuners import RandomSearch, BayesianOptimization\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
        "\n",
        "rcParams['figure.figsize'] = 16, 10\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "\n",
        "np.random.seed(RANDOM_SEED)\n",
        "tf.random.set_seed(RANDOM_SEED)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9wwB5DN_VjF",
        "colab_type": "code",
        "outputId": "58ee9afc-d50a-4187-d324-c90130a1775c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "!gdown --id 1uWHjZ3y9XZKpcJ4fkSwjQJ-VDbZS-7xi --output titanic.csv"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1uWHjZ3y9XZKpcJ4fkSwjQJ-VDbZS-7xi\n",
            "To: /content/titanic.csv\n",
            "\r  0% 0.00/61.2k [00:00<?, ?B/s]\r100% 61.2k/61.2k [00:00<00:00, 21.6MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lz3WaZwx_dUZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('titanic.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V44-lOn25DRo",
        "colab_type": "code",
        "outputId": "cd66fa10-178b-4d7f-9dc4-df5a74d9715b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzanDMEC_lcv",
        "colab_type": "code",
        "outputId": "3808ea58-4b04-4f78-f890-3593934b9558",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vB3zl8caCW-9",
        "colab_type": "text"
      },
      "source": [
        "# Exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwO4z_hX_pxZ",
        "colab_type": "code",
        "outputId": "7e9490d0-c8cb-4971-e10c-d77eb635e378",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "source": [
        "sns.countplot(df.Survived);"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 972,
              "height": 608
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wp_omOmRIdpC",
        "colab_type": "code",
        "outputId": "02542836-d156-4169-e7f5-1c38645e5184",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "source": [
        "sns.distplot(df.Fare);"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 966,
              "height": 608
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Z-2vb0uCYMZ",
        "colab_type": "text"
      },
      "source": [
        "# Preprocessing\n",
        "\n",
        "## Missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxbOfWRABASj",
        "colab_type": "code",
        "outputId": "b0c7f801-5625-4ff1-f09e-275440c1e34b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "missing = df.isnull().sum()\n",
        "missing[missing > 0].sort_values(ascending=False)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin       687\n",
              "Age         177\n",
              "Embarked      2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnFZ76nvBfHP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.drop(['Cabin', 'Name', 'Ticket', 'PassengerId'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "39bHqKRbCoL4",
        "colab_type": "code",
        "outputId": "0a134bd8-a84b-4a0c-babe-3ba0349ee152",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5oZdltxGyRP",
        "colab_type": "text"
      },
      "source": [
        "### Imputation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-nD800TCau2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Age'] = df['Age'].fillna(df['Age'].mean())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlqTsCJdHj5p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Embarked'] = df['Embarked'].fillna(df['Embarked'].mode()[0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FMlJzdf9INqV",
        "colab_type": "text"
      },
      "source": [
        "## Categorical features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fiz0gDs8IWS5",
        "colab_type": "code",
        "outputId": "46f6dcda-b93f-4e7b-866b-38f6758a3b1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare',\n",
              "       'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9IhzdAwaH9I8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.get_dummies(df, columns=['Sex', 'Embarked', 'Pclass'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OG36y13gIx5m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df.drop('Survived', axis=1)\n",
        "y = df.Survived"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQr0RT4jJHLC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=RANDOM_SEED)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-68OhxIrMOKG",
        "colab_type": "text"
      },
      "source": [
        "# Hyperparameter tuning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DlblLP-0TBQF",
        "colab_type": "text"
      },
      "source": [
        "## Optimizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YbKf0WvfJO_G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tune_optimizer_model(hp):\n",
        "    model = keras.Sequential()\n",
        "    model.add(keras.layers.Dense(units=18, activation=\"relu\", input_shape=[X_train.shape[1]]))\n",
        "\n",
        "    model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "    optimizer = hp.Choice('optimizer', ['adam', 'sgd', 'rmsprop'])\n",
        "\n",
        "    model.compile(\n",
        "        optimizer=optimizer,\n",
        "        loss = 'binary_crossentropy', \n",
        "        metrics = ['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Cup93cYONBY",
        "colab_type": "code",
        "outputId": "878d7e83-45e2-4896-df3a-df0b82212439",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        }
      },
      "source": [
        "MAX_TRIALS = 20\n",
        "EXECUTIONS_PER_TRIAL = 5\n",
        "\n",
        "tuner = RandomSearch(\n",
        "    tune_optimizer_model,\n",
        "    objective='val_accuracy',\n",
        "    max_trials=MAX_TRIALS,\n",
        "    executions_per_trial=EXECUTIONS_PER_TRIAL,\n",
        "    directory='test_dir', \n",
        "    project_name='tune_optimizer',\n",
        "    seed=RANDOM_SEED\n",
        ")\n",
        "\n",
        "tuner.search_space_summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Reloading Oracle from test_dir/tune_optimizer/oracle.json\n",
            "INFO:tensorflow:Reloading Tuner from test_dir/tune_optimizer/tuner0.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Search space summary</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Default search space size: 1</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">optimizer (Choice)</h2></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-default: adam</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:blue\"> |-ordered: False</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-values: ['adam', 'sgd', 'rmsprop']</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVmHdiR3OOX5",
        "colab_type": "code",
        "outputId": "21aa77c3-b2d4-4532-8038-91a42b018878",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "TRAIN_EPOCHS = 20\n",
        "\n",
        "tuner.search(x=X_train,\n",
        "             y=y_train,\n",
        "             epochs=TRAIN_EPOCHS,\n",
        "             validation_data=(X_test, y_test))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Oracle triggered exit\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96SHxoWaOVmJ",
        "colab_type": "code",
        "outputId": "83b799ee-00e6-46f3-bcad-d241c1be686a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        }
      },
      "source": [
        "tuner.results_summary()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Results summary</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Results in test_dir/tune_optimizer</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Showing 10 best trials</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7519553303718567</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7430167198181152</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7273743152618408</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wx_mjZMbdk2Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "64362b56-d84f-4a85-89cb-9dc569df3d1c"
      },
      "source": [
        "tuner.oracle.get_best_trials(num_trials=1)[0].hyperparameters.values"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'optimizer': 'adam'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ali5UfvsfBCi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "best_model = tuner.get_best_models()[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yiL8XZn_fCo6",
        "colab_type": "code",
        "outputId": "824c86a1-bb31-49b6-ee38-991a7a28025f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "best_model.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_2 (Dense)              (None, 18)                234       \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 19        \n",
            "=================================================================\n",
            "Total params: 253\n",
            "Trainable params: 253\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9djpCxAAV68R",
        "colab_type": "text"
      },
      "source": [
        "## Learning rate and Momentum"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qUVT39kDPpWV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tune_rl_momentum_model(hp):\n",
        "    model = keras.Sequential()\n",
        "    model.add(keras.layers.Dense(units=18, activation=\"relu\", input_shape=[X_train.shape[1]]))\n",
        "\n",
        "    model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "    lr = hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])\n",
        "    momentum = hp.Choice('momentum', [0.0, 0.2, 0.4, 0.6, 0.8, 0.9])\n",
        "\n",
        "    model.compile(\n",
        "        optimizer=keras.optimizers.SGD(lr, momentum=momentum),\n",
        "        loss = 'binary_crossentropy', \n",
        "        metrics = ['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXEoZn_w2Qam",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_random_tuner(model_builder, project_name):\n",
        "  tuner = RandomSearch(\n",
        "    model_builder,\n",
        "    objective='val_accuracy',\n",
        "    max_trials=MAX_TRIALS,\n",
        "    executions_per_trial=EXECUTIONS_PER_TRIAL,\n",
        "    directory='test_dir', \n",
        "    project_name=project_name,\n",
        "    seed=RANDOM_SEED\n",
        "  )\n",
        "\n",
        "  tuner.search_space_summary()\n",
        "  return tuner"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IL2mpWP3eyyO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def random_search_params(model_builder, project_name):\n",
        "  tuner = create_random_tuner(model_builder, project_name)\n",
        "  tuner.search(x=X_train,\n",
        "             y=y_train,\n",
        "             epochs=TRAIN_EPOCHS,\n",
        "             validation_data=(X_test, y_test))\n",
        "  \n",
        "  tuner.results_summary()\n",
        "\n",
        "  pprint(tuner.oracle.get_best_trials(num_trials=1)[0].hyperparameters.values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLfc4H3AWhSS",
        "colab_type": "code",
        "outputId": "0bb9a74d-4d28-48db-8b04-1219f5156c6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        }
      },
      "source": [
        "random_search_params(tune_rl_momentum_model, project_name=\"tune_lr_momentum\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Reloading Oracle from test_dir/tune_lr_momentum/oracle.json\n",
            "INFO:tensorflow:Reloading Tuner from test_dir/tune_lr_momentum/tuner0.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Search space summary</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Default search space size: 2</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">learning_rate (Choice)</h2></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-default: 0.01</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:blue\"> |-ordered: True</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-values: [0.01, 0.001, 0.0001]</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">momentum (Choice)</h2></span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
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          }
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-default: 0.0</span>"
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:blue\"> |-ordered: True</span>"
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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": [
              "<span style=\"color:cyan\"> |-values: [0.0, 0.2, 0.4, 0.6, 0.8, 0.9]</span>"
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Oracle triggered exit\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Results summary</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Results in test_dir/tune_lr_momentum</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Showing 10 best trials</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7743016481399536</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7720670700073242</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7709497213363647</span>"
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              "<IPython.core.display.HTML object>"
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7675977945327759</span>"
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        {
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            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7597764730453491</span>"
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7575419545173645</span>"
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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": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7575418949127197</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.756424605846405</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7541899681091309</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7441340684890747</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "{'learning_rate': 0.01, 'momentum': 0.4}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WGbaajYrcW_N",
        "colab_type": "text"
      },
      "source": [
        "# Number of parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YNiI9XKL3ot8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tune_neurons_model(hp):\n",
        "    model = keras.Sequential()\n",
        "    model.add(keras.layers.Dense(units=hp.Int('units',\n",
        "                                        min_value=8,\n",
        "                                        max_value=128,\n",
        "                                        step=16),\n",
        "                                 activation=\"relu\", \n",
        "                                 input_shape=[X_train.shape[1]]))\n",
        "\n",
        "    model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "    model.compile(\n",
        "        optimizer=\"adam\",\n",
        "        loss = 'binary_crossentropy', \n",
        "        metrics = ['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQxs8Zvx4SZf",
        "colab_type": "code",
        "outputId": "b508fb8a-5b3e-4da1-888c-64228bdd59d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "source": [
        "random_search_params(tune_neurons_model, project_name=\"tune_neurons\")"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Reloading Oracle from test_dir/tune_neurons/oracle.json\n",
            "INFO:tensorflow:Reloading Tuner from test_dir/tune_neurons/tuner0.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Search space summary</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Default search space size: 1</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">units (Int)</h2></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-default: None</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:blue\"> |-max_value: 128</span>"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-min_value: 8</span>"
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            "text/plain": [
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          "metadata": {
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:blue\"> |-sampling: None</span>"
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            "text/html": [
              "<span style=\"color:cyan\"> |-step: 16</span>"
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            "{'units': 72}\n"
          ],
          "name": "stdout"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gk5IcyZZcYmL",
        "colab_type": "text"
      },
      "source": [
        "# Number of hidden layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Sk5IK2YbAJM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tune_layers_model(hp):\n",
        "    model = keras.Sequential()\n",
        "\n",
        "    model.add(keras.layers.Dense(units=128,\n",
        "                                 activation=\"relu\", \n",
        "                                 input_shape=[X_train.shape[1]]))\n",
        "\n",
        "    for i in range(hp.Int('num_layers', 1, 6)):\n",
        "      model.add(keras.layers.Dense(units=hp.Int('units_' + str(i),\n",
        "                                            min_value=8,\n",
        "                                            max_value=64,\n",
        "                                            step=8),\n",
        "                               activation='relu'))\n",
        "\n",
        "    model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "    model.compile(\n",
        "        optimizer=\"adam\",\n",
        "        loss = 'binary_crossentropy', \n",
        "        metrics = ['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w_sFshyibNOd",
        "colab_type": "code",
        "outputId": "ddd1a88c-7109-46cf-a60c-ad6656aed1bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "random_search_params(tune_layers_model, project_name=\"tune_layers\")"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Reloading Oracle from test_dir/tune_layers/oracle.json\n",
            "INFO:tensorflow:Reloading Tuner from test_dir/tune_layers/tuner0.json\n"
          ],
          "name": "stdout"
        },
        {
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          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Search space summary</h1></span>"
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              "<span style=\"color:cyan\"> |-Default search space size: 7</span>"
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          "metadata": {
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">num_layers (Int)</h2></span>"
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.818994402885437</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8167597651481628</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8167597651481628</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8167597651481628</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8167597651481628</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8156424760818481</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8145251274108887</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8134077787399292</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "{'num_layers': 2,\n",
            " 'units_0': 32,\n",
            " 'units_1': 24,\n",
            " 'units_2': 64,\n",
            " 'units_3': 8,\n",
            " 'units_4': 48,\n",
            " 'units_5': 64}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n9Nm3CJEbLTy",
        "colab_type": "text"
      },
      "source": [
        "# Weight initialization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZSe5GSNc4Nx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tune_weight_init_model(hp):\n",
        "    model = keras.Sequential()\n",
        "\n",
        "    weight_init = hp.Choice('weight_init', \n",
        "                            ['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform'])\n",
        "\n",
        "    model.add(keras.layers.Dense(units=32,\n",
        "                                 activation=\"relu\",\n",
        "                                 kernel_initializer=weight_init,\n",
        "                                 input_shape=[X_train.shape[1]]))\n",
        "\n",
        "    model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "    model.compile(\n",
        "        optimizer=\"adam\",\n",
        "        loss = 'binary_crossentropy', \n",
        "        metrics = ['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sf8BJC21dnqR",
        "colab_type": "code",
        "outputId": "ccea6f39-d6a0-4c86-e73e-5260cefc8c77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        }
      },
      "source": [
        "random_search_params(tune_weight_init_model, project_name=\"tune_weight_init\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Reloading Oracle from test_dir/tune_weight_init/oracle.json\n",
            "INFO:tensorflow:Reloading Tuner from test_dir/tune_weight_init/tuner0.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Search space summary</h1></span>"
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              "<IPython.core.display.HTML object>"
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Default search space size: 1</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">weight_init (Choice)</h2></span>"
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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": [
              "<span style=\"color:cyan\"> |-default: uniform</span>"
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              "<IPython.core.display.HTML object>"
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          "data": {
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              "<span style=\"color:blue\"> |-ordered: False</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-values: ['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform']</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Oracle triggered exit\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Results summary</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Results in test_dir/tune_weight_init</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Showing 10 best trials</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8234637379646301</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8223463892936707</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8167597651481628</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8145251274108887</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8067038655281067</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8055866360664368</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8033519983291626</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.5865921974182129</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "{'weight_init': 'lecun_uniform'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3RdadQlBbDQ5",
        "colab_type": "text"
      },
      "source": [
        "# Activation function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jO7eMq4AeiO2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tune_act_model(hp):\n",
        "    model = keras.Sequential()\n",
        "\n",
        "    activation = hp.Choice('activation', \n",
        "                            ['softmax', 'softplus', 'softsign', 'relu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear'])\n",
        "\n",
        "    model.add(keras.layers.Dense(units=32,\n",
        "                                 activation=activation,\n",
        "                                 input_shape=[X_train.shape[1]]))\n",
        "\n",
        "    model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "    model.compile(\n",
        "        optimizer=\"adam\",\n",
        "        loss = 'binary_crossentropy', \n",
        "        metrics = ['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZw1nimGfNaF",
        "colab_type": "code",
        "outputId": "2727b42a-3814-4978-8fc9-702fcd44291a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        }
      },
      "source": [
        "random_search_params(tune_act_model, \"tune_activation\")"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Reloading Oracle from test_dir/tune_activation/oracle.json\n",
            "INFO:tensorflow:Reloading Tuner from test_dir/tune_activation/tuner0.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Search space summary</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Default search space size: 1</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">activation (Choice)</h2></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-default: softmax</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:blue\"> |-ordered: False</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-values: ['softmax', 'softplus', 'softsign', 'relu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear']</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Oracle triggered exit\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Results summary</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Results in test_dir/tune_activation</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Showing 10 best trials</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8134077787399292</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.811173141002655</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.8100558519363403</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7966480255126953</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7854748368263245</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7731843590736389</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7642458081245422</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.737430214881897</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "{'activation': 'linear'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rz76tYGybIrC",
        "colab_type": "text"
      },
      "source": [
        "# Dropout rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8e5E3K8hNw2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tune_dropout_model(hp):\n",
        "    model = keras.Sequential()\n",
        "\n",
        "    drop_rate = hp.Choice('drop_rate', \n",
        "                            [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])\n",
        "\n",
        "    model.add(keras.layers.Dense(units=32,\n",
        "                                 activation=\"relu\",\n",
        "                                 input_shape=[X_train.shape[1]]))\n",
        "    model.add(keras.layers.Dropout(rate=drop_rate))\n",
        "\n",
        "    model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "    model.compile(\n",
        "        optimizer=\"adam\",\n",
        "        loss = 'binary_crossentropy', \n",
        "        metrics = ['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RBylw9Qjhb0V",
        "colab_type": "code",
        "outputId": "c5bf45a1-e275-4d62-9726-a9f2a8284531",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "source": [
        "random_search_params(tune_dropout_model, \"tune_dropout\")"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Reloading Oracle from test_dir/tune_dropout/oracle.json\n",
            "INFO:tensorflow:Reloading Tuner from test_dir/tune_dropout/tuner0.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Search space summary</h1></span>"
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              "<span style=\"color:blue\"> |-ordered: True</span>"
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        {
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              "<span style=\"color:cyan\"> |-values: [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]</span>"
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        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Oracle triggered exit\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Results summary</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Results in test_dir/tune_dropout</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Showing 10 best trials</span>"
            ],
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              "<IPython.core.display.HTML object>"
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7977653741836548</span>"
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              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.792178750038147</span>"
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              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7899441123008728</span>"
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7731842994689941</span>"
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              "<IPython.core.display.HTML object>"
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              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7709497213363647</span>"
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              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7586592435836792</span>"
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              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7586592435836792</span>"
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7564245462417603</span>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.7262569665908813</span>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.6469273567199707</span>"
            ],
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "{'drop_rate': 0.0}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VhGzO9ludRmE",
        "colab_type": "text"
      },
      "source": [
        "# Complete example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3afYrIljlLOm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tune_nn_model(hp):\n",
        "    model = keras.Sequential()\n",
        "\n",
        "    model.add(keras.layers.Dense(units=128,\n",
        "                                 activation=\"relu\", \n",
        "                                 input_shape=[X_train.shape[1]]))\n",
        "\n",
        "    for i in range(hp.Int('num_layers', 1, 6)):\n",
        "      units = hp.Int(\n",
        "          'units_' + str(i), \n",
        "          min_value=8,\n",
        "          max_value=64,\n",
        "          step=8\n",
        "      )\n",
        "      model.add(keras.layers.Dense(units=units, activation='relu'))\n",
        "      drop_rate = hp.Choice('drop_rate_' + str(i), \n",
        "                            [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])\n",
        "      model.add(keras.layers.Dropout(rate=drop_rate))\n",
        "\n",
        "    model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "    model.compile(\n",
        "        optimizer=\"adam\",\n",
        "        loss = 'binary_crossentropy', \n",
        "        metrics = ['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8L080nqZltMp",
        "colab_type": "code",
        "outputId": "3438b4e3-468d-430a-b262-9d26fe621826",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "random_search_params(tune_nn_model, \"tune_nn\")"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Reloading Oracle from test_dir/tune_nn/oracle.json\n",
            "INFO:tensorflow:Reloading Tuner from test_dir/tune_nn/tuner0.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Search space summary</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Default search space size: 13</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">num_layers (Int)</h2></span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-default: None</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "data": {
            "text/html": [
              "<span style=\"color:blue\"> |-max_value: 6</span>"
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              "<span style=\"color:cyan\"> |-min_value: 1</span>"
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              "<span style=\"color:blue\"> |-sampling: None</span>"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">units_0 (Int)</h2></span>"
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
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          }
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        {
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          "data": {
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-min_value: 8</span>"
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        {
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">drop_rate_0 (Choice)</h2></span>"
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              "<span style=\"color:blue\"> |-ordered: True</span>"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-values: [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]</span>"
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          "output_type": "display_data",
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            "text/html": [
              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">units_1 (Int)</h2></span>"
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          "data": {
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          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Results summary</h1></span>"
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            "{'drop_rate_0': 0.1,\n",
            " 'drop_rate_1': 0.6,\n",
            " 'drop_rate_2': 0.8,\n",
            " 'drop_rate_3': 0.8,\n",
            " 'drop_rate_4': 0.9,\n",
            " 'drop_rate_5': 0.2,\n",
            " 'num_layers': 1,\n",
            " 'units_0': 56,\n",
            " 'units_1': 56,\n",
            " 'units_2': 8,\n",
            " 'units_3': 40,\n",
            " 'units_4': 56,\n",
            " 'units_5': 56}\n"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vCHPDzk1dTZk",
        "colab_type": "text"
      },
      "source": [
        "# Bayesian Tuner"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWeq1HoGbHC9",
        "colab_type": "code",
        "outputId": "6b4e82c0-a839-4222-ed67-256460aedd66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "b_tuner = BayesianOptimization(\n",
        "    tune_nn_model,\n",
        "    objective='val_accuracy',\n",
        "    max_trials=MAX_TRIALS,\n",
        "    executions_per_trial=EXECUTIONS_PER_TRIAL,\n",
        "    directory='test_dir', \n",
        "    project_name='b_tune_nn',\n",
        "    seed=RANDOM_SEED\n",
        ")\n",
        "\n",
        "b_tuner.search_space_summary()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Reloading Oracle from test_dir/b_tune_nn/oracle.json\n",
            "INFO:tensorflow:Reloading Tuner from test_dir/b_tune_nn/tuner0.json\n"
          ],
          "name": "stdout"
        },
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            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Search space summary</h1></span>"
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              "<IPython.core.display.HTML object>"
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          },
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Default search space size: 13</span>"
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          },
          "metadata": {
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          }
        },
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              "<span style=\"color:#7E57C2\"><h2 style=\"font-size:16px\">num_layers (Int)</h2></span>"
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        "\n",
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.694972038269043</span>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
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        {
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='val_accuracy', direction='max') Score: 0.6636871695518494</span>"
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "{'drop_rate_0': 0.1,\n",
            " 'drop_rate_1': 0.3,\n",
            " 'drop_rate_2': 0.8,\n",
            " 'drop_rate_3': 0.2,\n",
            " 'drop_rate_4': 0.2,\n",
            " 'drop_rate_5': 0.2,\n",
            " 'num_layers': 2,\n",
            " 'units_0': 24,\n",
            " 'units_1': 56,\n",
            " 'units_2': 16,\n",
            " 'units_3': 64,\n",
            " 'units_4': 64,\n",
            " 'units_5': 32}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmulnJwTvNY3",
        "colab_type": "text"
      },
      "source": [
        "# Scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDVWT-7QuMJh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import kerastuner as kt\n",
        "from sklearn import ensemble\n",
        "from sklearn import metrics\n",
        "from sklearn import datasets\n",
        "from sklearn import model_selection"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rOeIl7qovSog",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "fbb96cff-2d23-418c-88cc-0ae57d4f97ba"
      },
      "source": [
        "def build_tree_model(hp):      \n",
        "  return ensemble.RandomForestClassifier(\n",
        "      n_estimators=hp.Int('n_estimators', 10, 80, step=5),\n",
        "      max_depth=hp.Int('max_depth', 3, 10, step=1),\n",
        "      max_features=hp.Choice('max_features', ['auto', 'sqrt', 'log2'])\n",
        "  )\n",
        "\n",
        "sk_tuner = kt.tuners.Sklearn(\n",
        "  oracle=kt.oracles.BayesianOptimization(\n",
        "      objective=kt.Objective('score', 'max'),\n",
        "      max_trials=MAX_TRIALS,\n",
        "      seed=RANDOM_SEED\n",
        "  ),\n",
        "  hypermodel=build_tree_model,\n",
        "  scoring=metrics.make_scorer(metrics.accuracy_score),\n",
        "  cv=model_selection.StratifiedKFold(5),\n",
        "  directory='test_dir',\n",
        "  project_name='tune_rf'\n",
        ")"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Reloading Oracle from test_dir/tune_rf/oracle.json\n",
            "INFO:tensorflow:Reloading Tuner from test_dir/tune_rf/tuner0.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MDUAFESvvqE7",
        "colab_type": "code",
        "outputId": "15cd4042-2e1c-443f-8b07-dfd9bc9911b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sk_tuner.search(X_train.values, y_train.values)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Oracle triggered exit\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJTs0SUV4d8q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "outputId": "ad3af44e-7b0a-4fc0-b252-c7c280157597"
      },
      "source": [
        "sk_tuner.results_summary()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:#4527A0\"><h1 style=\"font-size:18px\">Results summary</h1></span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Results in test_dir/tune_rf</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Showing 10 best trials</span>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='score', direction='max') Score: 0.8258713317448709</span>"
            ],
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            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='score', direction='max') Score: 0.8244628810406456</span>"
            ],
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            ]
          },
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        {
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          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='score', direction='max') Score: 0.8216359906201081</span>"
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            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='score', direction='max') Score: 0.8216358509136453</span>"
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              "<IPython.core.display.HTML object>"
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        {
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            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='score', direction='max') Score: 0.8202275399158827</span>"
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='score', direction='max') Score: 0.8201878632804614</span>"
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            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='score', direction='max') Score: 0.8188883137639505</span>"
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              "<span style=\"color:cyan\"> |-Objective: Objective(name='score', direction='max') Score: 0.8188487768349921</span>"
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            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='score', direction='max') Score: 0.8188287988108186</span>"
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              "<IPython.core.display.HTML object>"
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          },
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<span style=\"color:cyan\"> |-Objective: Objective(name='score', direction='max') Score: 0.8188287988108186</span>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2bfgrNawVbW",
        "colab_type": "code",
        "outputId": "71a09c0b-538a-4ea3-fd1e-936f30d7eec9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "pprint(sk_tuner.oracle.get_best_trials(num_trials=1)[0].hyperparameters.values)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'max_depth': 4, 'max_features': 'sqrt', 'n_estimators': 60}\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}