LightGBM.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.26.6) or chardet (3.0.4) doesn't match a supported version!\n",
      "  RequestsDependencyWarning)\n"
     ]
    }
   ],
   "source": [
    "import orchest\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = orchest.get_inputs()\n",
    "X_train, y_train, X_test, y_test = data[\"training_data\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score : 0.9261920435873969\n"
     ]
    }
   ],
   "source": [
    "l = LGBMClassifier(learning_rate=0.1,\n",
    "    n_estimators=1000,\n",
    "    num_leaves=120,\n",
    "    n_jobs=4,\n",
    "    min_child_samples=14,\n",
    "    min_child_weight=10,)\n",
    "l.fit(X_train, y_train)\n",
    "\n",
    "lg_pred = l.predict(X_test)\n",
    "print(\"AUC score :\", roc_auc_score(lg_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "orchest.output((l,lg_pred,X_train, y_train, X_test, y_test), name=\"lgbm\")"
   ]
  }
 ],
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