Gradient_Boosting_on_latent_space_hyperparam_search.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, f1_score, make_scorer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.load(\"data/encoded_data_train_DeepDenseVAE_mark_V_[160-256-128-15]_VDO_GNLL_400ep_lr-1e-4.npy\")\n",
    "X_test = np.load(\"data/encoded_data_test_DeepDenseVAE_mark_V_[160-256-128-15]_VDO_GNLL_400ep_lr-1e-4.npy\")\n",
    "y_train = np.load(\"data/labels_train.npy\")\n",
    "y_test = np.load(\"data/labels_test.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GradientBoostingClassifier()\n",
    "parameters = {'learning_rate':[1e-1, 1e-2],\n",
    "            'n_estimators':[100, 500, 1000, 1500],\n",
    "            'subsample': [0.7],\n",
    "            'n_iter_no_change':[20]\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric_list = {\"accuracy\": make_scorer(accuracy_score), \"F1\": make_scorer(f1_score,average=\"macro\")}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    }
   ],
   "source": [
    "GS = GridSearchCV(\n",
    "    estimator=clf,\n",
    "    param_grid=parameters,\n",
    "    scoring=metric_list,\n",
    "    cv=5,\n",
    "    refit=\"F1\",\n",
    "    n_jobs=4,\n",
    "    #return_train_score=True,\n",
    "    verbose=True\n",
    ")\n",
    "\n",
    "GS.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(GS.cv_results_)\n",
    "#df.to_csv(\"Graident_Tree_Boosting_GS_results.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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