hyperparameter_tuning_tutorial.ipynb

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   "source": "import pandas as pd ",
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   "source": "df = pd.read_csv(\"telco_churn.csv\")",
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     },
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    "tags": [],
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    "source_hash": "dc13d7a4",
    "execution_start": 1660079592525,
    "execution_millis": 6,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 220
   },
   "source": "#convert categorical columns\ndf['gender'] = df['gender'].astype('category')\ndf['gender_cat'] = df['gender'].cat.codes\ndf['SeniorCitizen'] = df['SeniorCitizen'].astype('category')\ndf['SeniorCitizen_cat'] = df['SeniorCitizen'].cat.codes\ndf['InternetService'] = df['InternetService'].astype('category')\ndf['InternetService_cat'] = df['InternetService'].cat.codes\ndf['DeviceProtection'] = df['DeviceProtection'].astype('category')\ndf['DeviceProtection_cat'] = df['DeviceProtection'].cat.codes",
   "execution_count": 79,
   "outputs": []
  },
  {
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    "execution_start": 1660079594330,
    "execution_millis": 28,
    "deepnote_cell_type": "code",
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   },
   "source": "df[['gender_cat', 'SeniorCitizen_cat', 'InternetService_cat', 'DeviceProtection_cat']].head()",
   "execution_count": 80,
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         "OnlineSecurity": "Yes",
         "OnlineBackup": "Yes",
         "DeviceProtection": "No",
         "TechSupport": "No",
         "StreamingTV": "No",
         "StreamingMovies": "No",
         "Contract": "Month-to-month",
         "PaperlessBilling": "Yes",
         "PaymentMethod": "Mailed check",
         "MonthlyCharges": 53.85,
         "TotalCharges": "108.15",
         "Churn": "Yes",
         "gender_cat": 1,
         "_deepnote_index_column": 2
        },
        {
         "customerID": "7795-CFOCW",
         "gender": "Male",
         "SeniorCitizen": 0,
         "Partner": "No",
         "Dependents": "No",
         "tenure": 45,
         "PhoneService": "No",
         "MultipleLines": "No phone service",
         "InternetService": "DSL",
         "OnlineSecurity": "Yes",
         "OnlineBackup": "No",
         "DeviceProtection": "Yes",
         "TechSupport": "Yes",
         "StreamingTV": "No",
         "StreamingMovies": "No",
         "Contract": "One year",
         "PaperlessBilling": "No",
         "PaymentMethod": "Bank transfer (automatic)",
         "MonthlyCharges": 42.3,
         "TotalCharges": "1840.75",
         "Churn": "No",
         "gender_cat": 1,
         "_deepnote_index_column": 3
        },
        {
         "customerID": "9237-HQITU",
         "gender": "Female",
         "SeniorCitizen": 0,
         "Partner": "No",
         "Dependents": "No",
         "tenure": 2,
         "PhoneService": "Yes",
         "MultipleLines": "No",
         "InternetService": "Fiber optic",
         "OnlineSecurity": "No",
         "OnlineBackup": "No",
         "DeviceProtection": "No",
         "TechSupport": "No",
         "StreamingTV": "No",
         "StreamingMovies": "No",
         "Contract": "Month-to-month",
         "PaperlessBilling": "Yes",
         "PaymentMethod": "Electronic check",
         "MonthlyCharges": 70.7,
         "TotalCharges": "151.65",
         "Churn": "Yes",
         "gender_cat": 0,
         "_deepnote_index_column": 4
        }
       ]
      },
      "text/plain": "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n0  7590-VHVEG  Female              0     Yes         No       1           No   \n1  5575-GNVDE    Male              0      No         No      34          Yes   \n2  3668-QPYBK    Male              0      No         No       2          Yes   \n3  7795-CFOCW    Male              0      No         No      45           No   \n4  9237-HQITU  Female              0      No         No       2          Yes   \n\n      MultipleLines InternetService OnlineSecurity  ... TechSupport  \\\n0  No phone service             DSL             No  ...          No   \n1                No             DSL            Yes  ...          No   \n2                No             DSL            Yes  ...          No   \n3  No phone service             DSL            Yes  ...         Yes   \n4                No     Fiber optic             No  ...          No   \n\n  StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n0          No              No  Month-to-month              Yes   \n1          No              No        One year               No   \n2          No              No  Month-to-month              Yes   \n3          No              No        One year               No   \n4          No              No  Month-to-month              Yes   \n\n               PaymentMethod MonthlyCharges TotalCharges  Churn gender_cat  \n0           Electronic check          29.85        29.85     No          0  \n1               Mailed check          56.95       1889.5     No          1  \n2               Mailed check          53.85       108.15    Yes          1  \n3  Bank transfer (automatic)          42.30      1840.75     No          1  \n4           Electronic check          70.70       151.65    Yes          0  \n\n[5 rows x 22 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>customerID</th>\n      <th>gender</th>\n      <th>SeniorCitizen</th>\n      <th>Partner</th>\n      <th>Dependents</th>\n      <th>tenure</th>\n      <th>PhoneService</th>\n      <th>MultipleLines</th>\n      <th>InternetService</th>\n      <th>OnlineSecurity</th>\n      <th>...</th>\n      <th>TechSupport</th>\n      <th>StreamingTV</th>\n      <th>StreamingMovies</th>\n      <th>Contract</th>\n      <th>PaperlessBilling</th>\n      <th>PaymentMethod</th>\n      <th>MonthlyCharges</th>\n      <th>TotalCharges</th>\n      <th>Churn</th>\n      <th>gender_cat</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>7590-VHVEG</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>1</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>No</td>\n      <td>...</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>29.85</td>\n      <td>29.85</td>\n      <td>No</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5575-GNVDE</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>34</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>One year</td>\n      <td>No</td>\n      <td>Mailed check</td>\n      <td>56.95</td>\n      <td>1889.5</td>\n      <td>No</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3668-QPYBK</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Mailed check</td>\n      <td>53.85</td>\n      <td>108.15</td>\n      <td>Yes</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>7795-CFOCW</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>45</td>\n      <td>No</td>\n      <td>No phone service</td>\n      <td>DSL</td>\n      <td>Yes</td>\n      <td>...</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>No</td>\n      <td>One year</td>\n      <td>No</td>\n      <td>Bank transfer (automatic)</td>\n      <td>42.30</td>\n      <td>1840.75</td>\n      <td>No</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9237-HQITU</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>No</td>\n      <td>No</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>No</td>\n      <td>Fiber optic</td>\n      <td>No</td>\n      <td>...</td>\n      <td>No</td>\n      <td>No</td>\n      <td>No</td>\n      <td>Month-to-month</td>\n      <td>Yes</td>\n      <td>Electronic check</td>\n      <td>70.70</td>\n      <td>151.65</td>\n      <td>Yes</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 22 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "8900859c3b794c72a5a8b39f1948bd17",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "4ec7d520",
    "execution_start": 1660079599157,
    "execution_millis": 22,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 178.6875
   },
   "source": "df['Churn'] = df['Churn'].astype('category')\ndf['Churn_cat'] = df['Churn'].cat.codes\ndf['TotalCharges'] = pd.to_numeric(df['TotalCharges'], 'coerce')\ndf['TotalCharges'].fillna(df['TotalCharges'].mean(), inplace=True)\nprint(Counter(df['Churn']))",
   "execution_count": 81,
   "outputs": [
    {
     "name": "stdout",
     "text": "Counter({'No': 5174, 'Yes': 1869})\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "2fbf9c76b0f24d1fb8dd74fde84ab6f6",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "19cc62a7",
    "execution_start": 1660079611771,
    "execution_millis": 9,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 183.6875
   },
   "source": "#define input and output\nX = df[['TotalCharges', 'MonthlyCharges', 'gender_cat', 'SeniorCitizen_cat', 'InternetService_cat', 'DeviceProtection_cat']]\ny = df['Churn_cat']\n\nprint(Counter(y))",
   "execution_count": 83,
   "outputs": [
    {
     "name": "stdout",
     "text": "Counter({0: 5174, 1: 1869})\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": "#import train test split method and split data for training and testing \nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)",
   "metadata": {
    "cell_id": "32c9f5d285054fbd9d87c068a597723c",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "6a1f442a",
    "execution_start": 1660079667891,
    "execution_millis": 3,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 117
   },
   "outputs": [],
   "execution_count": 84
  },
  {
   "cell_type": "code",
   "source": "#import random forest classifier and \nfrom sklearn.ensemble import RandomForestClassifier\nmodel = RandomForestClassifier()\nmodel.fit(X_train, y_train)",
   "metadata": {
    "cell_id": "2edb054855de454aa07392d2572e33d9",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "df3da36",
    "execution_start": 1660079669379,
    "execution_millis": 666,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 167.1875,
    "deepnote_output_heights": [
     21.1875
    ]
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 85,
     "data": {
      "text/plain": "RandomForestClassifier()"
     },
     "metadata": {}
    }
   ],
   "execution_count": 85
  },
  {
   "cell_type": "code",
   "source": "model.get_params()",
   "metadata": {
    "cell_id": "35bdd047b58a49658c0659efb31ed476",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "34c3e8d4",
    "execution_start": 1660079671757,
    "execution_millis": 2,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 439.515625,
    "deepnote_output_heights": [
     347.515625
    ]
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 86,
     "data": {
      "text/plain": "{'bootstrap': True,\n 'ccp_alpha': 0.0,\n 'class_weight': None,\n 'criterion': 'gini',\n 'max_depth': None,\n 'max_features': 'auto',\n 'max_leaf_nodes': None,\n 'max_samples': None,\n 'min_impurity_decrease': 0.0,\n 'min_samples_leaf': 1,\n 'min_samples_split': 2,\n 'min_weight_fraction_leaf': 0.0,\n 'n_estimators': 100,\n 'n_jobs': None,\n 'oob_score': False,\n 'random_state': None,\n 'verbose': 0,\n 'warm_start': False}"
     },
     "metadata": {}
    }
   ],
   "execution_count": 86
  },
  {
   "cell_type": "code",
   "source": "from sklearn.metrics import precision_score\ny_pred_default = model.predict(X_test)\nprecision = precision_score(y_test, y_pred_default)\nprecision",
   "metadata": {
    "cell_id": "cb9b39582b284e5c8c3b5ba196a4a97a",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ad0b8387",
    "execution_start": 1660081304070,
    "execution_millis": 85,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 167.1875,
    "deepnote_output_heights": [
     21.1875
    ]
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 132,
     "data": {
      "text/plain": "0.5631313131313131"
     },
     "metadata": {}
    }
   ],
   "execution_count": 132
  },
  {
   "cell_type": "code",
   "source": "from sklearn.model_selection import GridSearchCV",
   "metadata": {
    "cell_id": "5678d8b7e82a46449c57b38ded1e017c",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "385c9dff",
    "execution_start": 1660079675010,
    "execution_millis": 0,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 76
   },
   "outputs": [],
   "execution_count": 87
  },
  {
   "cell_type": "code",
   "source": "params = { \n    'n_estimators': [10, 100],\n    'max_features': ['sqrt'],\n    'max_depth' : [5, 20],\n    'criterion' :['gini']\n}\n",
   "metadata": {
    "cell_id": "2eed50e18778488dbcab9d19d3b9bae2",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "2cf8712f",
    "execution_start": 1660079676559,
    "execution_millis": 4,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 184
   },
   "outputs": [],
   "execution_count": 88
  },
  {
   "cell_type": "code",
   "source": "grid_search_rf = GridSearchCV(estimator=model, param_grid=params, cv= 20, scoring='precision')\ngrid_search_rf.fit(X_train, y_train)",
   "metadata": {
    "cell_id": "7cca1985e8e24b8fa6d31634616a4297",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "fd961ca2",
    "execution_start": 1660080534828,
    "execution_millis": 20575,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 193.78125,
    "deepnote_output_heights": [
     78.78125
    ]
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 113,
     "data": {
      "text/plain": "GridSearchCV(cv=20, estimator=RandomForestClassifier(),\n             param_grid={'criterion': ['gini'], 'max_depth': [5, 20],\n                         'max_features': ['sqrt'], 'n_estimators': [10, 100]},\n             scoring='precision')"
     },
     "metadata": {}
    }
   ],
   "execution_count": 113
  },
  {
   "cell_type": "code",
   "source": "gscv_params = grid_search_rf.best_params_\ngscv_params\n",
   "metadata": {
    "cell_id": "f8717f7d99fb4d989f0045ba2cd6368b",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "8b015b2",
    "execution_start": 1660080555409,
    "execution_millis": 0,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 206.78125,
    "deepnote_output_heights": [
     78.78125
    ]
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 114,
     "data": {
      "text/plain": "{'criterion': 'gini',\n 'max_depth': 5,\n 'max_features': 'sqrt',\n 'n_estimators': 100}"
     },
     "metadata": {}
    }
   ],
   "execution_count": 114
  },
  {
   "cell_type": "code",
   "source": "gscv_params = grid_search_rf.best_params_\nmodel_rf_gscv = RandomForestClassifier(**gscv_params)\nmodel_rf_gscv.fit(X_train, y_train)",
   "metadata": {
    "cell_id": "7f2ba63d9db84ebb8d7da142671b1ed3",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "83f34e4c",
    "execution_start": 1660080565837,
    "execution_millis": 700,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 149.1875,
    "deepnote_output_heights": [
     21.1875
    ]
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 115,
     "data": {
      "text/plain": "RandomForestClassifier(max_depth=5, max_features='sqrt')"
     },
     "metadata": {}
    }
   ],
   "execution_count": 115
  },
  {
   "cell_type": "code",
   "source": "y_pred_gscv = model_rf_gscv.predict(X_test)\nprecision_gscv = precision_score(y_test, y_pred_gscv)\nprecision_gscv\n",
   "metadata": {
    "cell_id": "569a5d0a533c4cb48a0cfb6e2f1224e5",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d7a703a5",
    "execution_start": 1660081324936,
    "execution_millis": 62,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 167.1875,
    "deepnote_output_heights": [
     21.1875
    ]
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 133,
     "data": {
      "text/plain": "0.6996197718631179"
     },
     "metadata": {}
    }
   ],
   "execution_count": 133
  },
  {
   "cell_type": "code",
   "source": "%pip install -U rbfopt",
   "metadata": {
    "cell_id": "39aa98f594b5420a8b645f963dad36c4",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c62e8ea0",
    "execution_start": 1660079701579,
    "execution_millis": 3990,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 426
   },
   "outputs": [
    {
     "name": "stdout",
     "text": "Requirement already satisfied: rbfopt in /usr/local/lib/python3.7/site-packages (4.2.4)\nRequirement already satisfied: numpy in /shared-libs/python3.7/py/lib/python3.7/site-packages (from rbfopt) (1.21.6)\nRequirement already satisfied: pyomo in /usr/local/lib/python3.7/site-packages (from rbfopt) (6.4.1)\nRequirement already satisfied: scipy in /shared-libs/python3.7/py/lib/python3.7/site-packages (from rbfopt) (1.7.3)\nRequirement already satisfied: ply in /usr/local/lib/python3.7/site-packages (from pyomo->rbfopt) (3.11)\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.2.2 is available.\nYou should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n\u001b[0mNote: you may need to restart the kernel to use updated packages.\n",
     "output_type": "stream"
    }
   ],
   "execution_count": 92
  },
  {
   "cell_type": "code",
   "source": "lbounds = [10, 5] \nubounds = [100, 20] ",
   "metadata": {
    "cell_id": "a34e60d469604a868c42ec26b750e514",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "397c0f73",
    "execution_start": 1660080668482,
    "execution_millis": 0,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 94
   },
   "outputs": [],
   "execution_count": 117
  },
  {
   "cell_type": "code",
   "source": "import rbfopt\nfrom sklearn.model_selection import cross_val_score",
   "metadata": {
    "cell_id": "f4643f6eca4c41a89bd2ec6911b6dc08",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1476ba1f",
    "execution_start": 1660080670351,
    "execution_millis": 3,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 124.6875
   },
   "outputs": [
    {
     "name": "stdout",
     "text": "Counter({0: 3892, 1: 1390})\n",
     "output_type": "stream"
    }
   ],
   "execution_count": 118
  },
  {
   "cell_type": "code",
   "source": "%pip install pyomo",
   "metadata": {
    "cell_id": "cff41ac401864ae385cd1b9b9a42acc9",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d0c66afb",
    "execution_start": 1660081894673,
    "execution_millis": 4328,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 209.671875
   },
   "outputs": [
    {
     "name": "stdout",
     "text": "Requirement already satisfied: pyomo in /usr/local/lib/python3.7/site-packages (6.4.1)\nRequirement already satisfied: ply in /usr/local/lib/python3.7/site-packages (from pyomo) (3.11)\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.2.2 is available.\nYou should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n\u001b[0mNote: you may need to restart the kernel to use updated packages.\n",
     "output_type": "stream"
    }
   ],
   "execution_count": 135
  },
  {
   "cell_type": "code",
   "source": "def precision_objective(X):\n   n_estimators, max_depth = X\n   n_estimators = int(n_estimators)\n   max_depth = int(max_depth)\n   params = {'n_estimators':n_estimators, 'max_depth': max_depth}\n   model_rbfopt = RandomForestClassifier(criterion='gini', max_features='sqrt', **params)\n   model_rbfopt.fit(X_train, y_train)\n   precision = cross_val_score(model_rbfopt, X_train, y_train, cv=20, scoring='precision')\n\n   return -np.mean(precision)\n\n# specify number of runs for optimizer\nnum_runs = 1\n# specify number of function calls\nmax_fun_calls = 8\n# specify diminsionality of numerical input\nndim = 2\n\nobj_fun = precision_objective\n\nbb = rbfopt.RbfoptUserBlackBox(\n    dimension=ndim,\n    var_lower=np.array(lbounds, dtype=np.float),\n    var_upper=np.array(ubounds, dtype=np.float),\n    var_type=['R'] * ndim,\n    obj_funct=obj_fun)\nsettings = rbfopt.RbfoptSettings(max_evaluations=max_fun_calls)\nalg = rbfopt.RbfoptAlgorithm(settings, bb)\n\nfval, sol, iter_count, eval_count, fast_eval_count = alg.optimize()\n\nobj_vals  = fval\n",
   "metadata": {
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    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a8096c4",
    "execution_start": 1660082156990,
    "execution_millis": 48095,
    "deepnote_cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
     "text": "  Iter  Cycle  Action             Objective value      Time      Gap\n  ----  -----  ------             ---------------      ----      ---\n/shared-libs/python3.7/py-core/lib/python3.7/site-packages/ipykernel_launcher.py:23: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n/shared-libs/python3.7/py-core/lib/python3.7/site-packages/ipykernel_launcher.py:24: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\nDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n     0      0  Initialization           -0.592415     18.72   100.00  \n     0      0  Initialization           -0.666765     18.72   100.00 *\n     0      0  Initialization           -0.621460     18.72   100.00  \n     0      0  GlobalStep               -0.676269     25.98   100.00 *\n     1      0  GlobalStep               -0.685104     30.75   100.00 *\n     2      0  GlobalStep               -0.688581     36.70   100.00 *\n     3      0  GlobalStep               -0.686750     42.17   100.00  \n     4      0  GlobalStep               -0.681975     48.14   100.00  \nSummary: iters   5 evals   8 noisy_evals   0 cycles   0 opt_time   48.14 tot_time   48.14 obj        -0.688581 gap 100.00\n",
     "output_type": "stream"
    }
   ],
   "execution_count": 139
  },
  {
   "cell_type": "code",
   "source": "sol_int = [int(x) for x in sol]\nparams_rbfopt = {'n_estimators': sol_int[0], 'max_depth': sol_int[1]}\nparams_rbfopt",
   "metadata": {
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    "execution_start": 1660081213475,
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    {
     "output_type": "execute_result",
     "execution_count": 128,
     "data": {
      "text/plain": "{'n_estimators': 81, 'max_depth': 5}"
     },
     "metadata": {}
    }
   ],
   "execution_count": 128
  },
  {
   "cell_type": "code",
   "source": "model_rbfopt = RandomForestClassifier(criterion='gini', max_features='sqrt', **params_rbfopt)\nmodel_rbfopt.fit(X_train, y_train)\n",
   "metadata": {
    "cell_id": "d40a02042c024fdd8443fbb31550ccb7",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "15d09231",
    "execution_start": 1660081216033,
    "execution_millis": 264,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 154.1875,
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     21.1875
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   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 129,
     "data": {
      "text/plain": "RandomForestClassifier(max_depth=5, max_features='sqrt', n_estimators=81)"
     },
     "metadata": {}
    }
   ],
   "execution_count": 129
  },
  {
   "cell_type": "code",
   "source": "y_pred_rbfopt = model_rbfopt.predict(X_test)\nprecision_rbfopt = precision_score(y_test, y_pred_rbfopt)\nprecision_rbfopt",
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    "execution_start": 1660081287230,
    "execution_millis": 2,
    "deepnote_cell_type": "code",
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   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 131,
     "data": {
      "text/plain": "0.7015503875968992"
     },
     "metadata": {}
    }
   ],
   "execution_count": 131
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