{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.feature_selection import RFECV, RFE\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn import datasets\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from hyperopt import tpe\n",
    "import hpsklearn\n",
    "from hpsklearn import HyperoptEstimator, any_regressor, any_classifier, any_preprocessing\n",
    "!set OMP_NUM_THREADS=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_df = np.loadtxt('test_task_data_201808091645.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(13)\n",
    "X = train\n",
    "y = e\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n",
    "                                                   random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "estim = HyperoptEstimator(regressor=any_regressor('my_reg'),\n",
    "                          #classifier=any_classifier('my_clf'),\n",
    "                          #preprocessing=any_preprocessing('my_pre'),\n",
    "                          algo=tpe.suggest,\n",
    "                          max_evals=100,\n",
    "                          trial_timeout=120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Search the hyperparameter space based on the data\n",
    "estim.fit( X_train.values, y_train.values )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show the results\n",
    "print(estim.score( X_test, y_test ))\n",
    "\n",
    "print(estim.best_model())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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