{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lHecaO_7JG6L"
   },
   "source": [
    "# Checking Baseline with AutoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "version: 0.1.38+40.geb12c43.dirty\n",
      "is notebook? True\n"
     ]
    }
   ],
   "source": [
    "%config InlineBackend.figure_format='retina'\n",
    "from ekorpkit import eKonf\n",
    "\n",
    "eKonf.setLogger(\"WARNING\")\n",
    "print(\"version:\", eKonf.__version__)\n",
    "print(\"is notebook?\", eKonf.is_notebook())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"../data/fomc\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load a feature set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:ekorpkit.base:Loaded .env from /workspace/projects/ekorpkit-book/config/.env\n",
      "INFO:ekorpkit.base:setting environment variable CACHED_PATH_CACHE_ROOT to /workspace/.cache/cached_path\n",
      "INFO:ekorpkit.base:setting environment variable KMP_DUPLICATE_LIB_OK to TRUE\n"
     ]
    }
   ],
   "source": [
    "fs_cfg = eKonf.compose(config_group=\"dataset=feature\")\n",
    "fs_cfg.name = \"fomc_features_small\"\n",
    "fs_cfg.data_dir = data_dir\n",
    "fs_fomc = eKonf.instantiate(fs_cfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auto ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:ekorpkit.base:No method defined to call\n"
     ]
    }
   ],
   "source": [
    "model_cfg = eKonf.compose(config_group='model/automl=classification')\n",
    "model_cfg.dataset = fs_cfg\n",
    "model_cfg.config.time_budget = 60\n",
    "model_cfg.verbose = False\n",
    "model = eKonf.instantiate(model_cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best ML leaner: lgbm\n",
      "Best hyperparmeter config: {'n_estimators': 10, 'num_leaves': 4, 'min_child_samples': 18, 'learning_rate': 0.2293009676418639, 'log_max_bin': 9, 'colsample_bytree': 0.9086551727646448, 'reg_alpha': 0.0015561782752413472, 'reg_lambda': 0.33127416269768944}\n",
      "Best accuracy on validation data: 0.672\n",
      "Training duration of best run: 0.03197 s\n"
     ]
    }
   ],
   "source": [
    "model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(colsample_bytree=0.9086551727646448,\n",
       "               learning_rate=0.2293009676418639, max_bin=511,\n",
       "               min_child_samples=18, n_estimators=10, num_leaves=4,\n",
       "               reg_alpha=0.0015561782752413472, reg_lambda=0.33127416269768944,\n",
       "               verbose=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(colsample_bytree=0.9086551727646448,\n",
       "               learning_rate=0.2293009676418639, max_bin=511,\n",
       "               min_child_samples=18, n_estimators=10, num_leaves=4,\n",
       "               reg_alpha=0.0015561782752413472, reg_lambda=0.33127416269768944,\n",
       "               verbose=-1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMClassifier(colsample_bytree=0.9086551727646448,\n",
       "               learning_rate=0.2293009676418639, max_bin=511,\n",
       "               min_child_samples=18, n_estimators=10, num_leaves=4,\n",
       "               reg_alpha=0.0015561782752413472, reg_lambda=0.33127416269768944,\n",
       "               verbose=-1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best ML leaner: lgbm\n",
      "Best hyperparmeter config: {'n_estimators': 10, 'num_leaves': 4, 'min_child_samples': 18, 'learning_rate': 0.2293009676418639, 'log_max_bin': 9, 'colsample_bytree': 0.9086551727646448, 'reg_alpha': 0.0015561782752413472, 'reg_lambda': 0.33127416269768944}\n",
      "Best accuracy on validation data: 0.672\n",
      "Training duration of best run: 0.03197 s\n"
     ]
    }
   ],
   "source": [
    "model.show_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 331,
       "width": 701
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_learning_curve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2: -0.3066542577943232\n",
      "mse: 0.7788461538461539\n",
      "mae: 0.47115384615384615\n",
      "Accuracy:  0.6826923076923077\n",
      "Precison:  0.6514423076923077\n",
      "Recall:  0.6826923076923077\n",
      "F1 Score:  0.6251761059453366\n",
      "Model Report: \n",
      "___________________________________________________\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         Cut       0.62      0.28      0.38        18\n",
      "        Hike       0.50      0.12      0.19        17\n",
      "        Hold       0.70      0.93      0.80        69\n",
      "\n",
      "    accuracy                           0.68       104\n",
      "   macro avg       0.61      0.44      0.46       104\n",
      "weighted avg       0.65      0.68      0.63       104\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 331,
       "width": 631
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 331,
       "width": 701
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_feature_importance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>columns</th>\n",
       "      <th>importances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PMI</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GDP_diff_prev</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EMP_diff_prev</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UNEMP_diff_prev</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>prev_decision</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RSALES_diff_year</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HSALES_diff_year</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Inertia_diff</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Balanced_diff</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            columns  importances\n",
       "2               PMI           28\n",
       "1     GDP_diff_prev           17\n",
       "3     EMP_diff_prev           16\n",
       "5   UNEMP_diff_prev           12\n",
       "0     prev_decision            7\n",
       "4  RSALES_diff_year            5\n",
       "6  HSALES_diff_year            5\n",
       "7      Inertia_diff            0\n",
       "8     Balanced_diff            0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_feature_importance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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