docs/zh_CN/source/examples/62.RNSGAII_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RNSGA-II Searcher example\n",
    "\n",
    "This is an example about how using RNSGAIISearcher for multi-objectives optimization.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import modules and prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hypernets.core.random_state import set_random_state\n",
    "set_random_state(1234)\n",
    "\n",
    "\n",
    "\n",
    "from hypernets.utils import logging as hyn_logging\n",
    "from hypernets.examples.plain_model import PlainModel, PlainSearchSpace\n",
    "from hypergbm import make_experiment\n",
    "\n",
    "from hypernets.tabular import get_tool_box\n",
    "from hypernets.tabular.datasets import dsutils\n",
    "from hypernets.tabular.sklearn_ex import MultiLabelEncoder\n",
    "\n",
    "#hyn_logging.set_level(hyn_logging.WARN)\n",
    "df = dsutils.load_bank().head(1000)\n",
    "tb = get_tool_box(df)\n",
    "df_train, df_test = tb.train_test_split(df, test_size=0.2, random_state=9527)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Run an experiment within NSGAIISearcher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RNSGAIISearcher(objectives=[PredictionObjective(name=logloss, scorer=make_scorer(log_loss, needs_proba=True), direction=min), NumOfFeatures(name=nf, sample_size=1000, direction=min)], recombination=SinglePointCrossOver(random_state=RandomState(MT19937))), mutation=SinglePointMutation(random_state=RandomState(MT19937), proba=0.7)), survival=_RDominanceSurvival(ref_point=[0.1 2. ], weights=[0.1 2. ], threshold=0.3, random_state=RandomState(MT19937))), random_state=RandomState(MT19937)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "experiment = make_experiment(df_train,\n",
    "                             eval_data=df_test.copy(),\n",
    "                             callbacks=[],\n",
    "                             random_state=1234,\n",
    "                             search_callbacks=[],\n",
    "                             target='y',\n",
    "                             searcher='rnsga2',  # available MOO searchers: moead, nsga2, rnsga2\n",
    "                             searcher_options=dict(ref_point=np.array([0.1, 2 ]), weights=np.array([0.1, 2]), population_size=10),\n",
    "                             reward_metric='logloss',\n",
    "                             objectives=['nf'],\n",
    "                             early_stopping_rounds=30,\n",
    "                             drift_detection=False)\n",
    "\n",
    "estimators = experiment.run(max_trials=30)\n",
    "hyper_model = experiment.hyper_model_\n",
    "hyper_model.searcher"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Summary trails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>trial_no</th>\n",
       "      <th>succeeded</th>\n",
       "      <th>elapsed</th>\n",
       "      <th>non_dominated</th>\n",
       "      <th>model_index</th>\n",
       "      <th>reward_logloss</th>\n",
       "      <th>reward_nf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.386380</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.217409</td>\n",
       "      <td>0.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>0.798336</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.218916</td>\n",
       "      <td>0.4375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>True</td>\n",
       "      <td>0.682108</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.287458</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "      <td>0.455569</td>\n",
       "      <td>True</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.221304</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>28</td>\n",
       "      <td>True</td>\n",
       "      <td>0.568367</td>\n",
       "      <td>True</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.045741</td>\n",
       "      <td>0.9375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "      <td>0.407018</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.141022</td>\n",
       "      <td>0.6875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    trial_no  succeeded   elapsed  non_dominated  model_index reward_logloss  \\\n",
       "4          5       True  0.386380           True          0.0       0.217409   \n",
       "9         10       True  0.798336           True          1.0       0.218916   \n",
       "11        12       True  0.682108           True          2.0       0.287458   \n",
       "14        15       True  0.455569           True          3.0       0.221304   \n",
       "26        28       True  0.568367           True          4.0       0.045741   \n",
       "28        30       True  0.407018           True          5.0       0.141022   \n",
       "\n",
       "   reward_nf  \n",
       "4      0.625  \n",
       "9     0.4375  \n",
       "11       0.0  \n",
       "14     0.375  \n",
       "26    0.9375  \n",
       "28    0.6875  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trials = hyper_model.history.to_df().copy().drop(['scores', 'reward'], axis=1)\n",
    "df_trials[df_trials['non_dominated'] == True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Plot pareto font\n",
    "\n",
    "We can pick model accord to Decision Maker's preferences from the pareto plot, the number in the figure indicates the index of pipeline models.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax  = hyper_model.history.plot_best_trials()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Plot population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax  = hyper_model.searcher.plot_population()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Evaluate the selected model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of pipeline: 6 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'auc': 0.8417038690476191,\n",
       " 'accuracy': 0.855,\n",
       " 'f1': 0.17142857142857143,\n",
       " 'recall': 0.09375,\n",
       " 'precision': 1.0}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Number of pipeline: {len(estimators)} \")\n",
    "\n",
    "pipeline_model = estimators[0]  # selection the first pipeline model\n",
    "X_test = df_test.copy()\n",
    "y_test = X_test.pop('y')\n",
    "\n",
    "preds = pipeline_model.predict(X_test)\n",
    "proba = pipeline_model.predict_proba(X_test)\n",
    "\n",
    "tb.metrics.calc_score(y_test, preds, proba, metrics=['auc', 'accuracy', 'f1', 'recall', 'precision'], pos_label=\"yes\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}