f5.ipynb

{
 "metadata": {
  "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
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitness_func(chromosome):\n",
    "    columns = []\n",
    "    for i in range(len(x_train.columns)):\n",
    "        if i in chromosome:\n",
    "            columns.append(x_train.columns[i])\n",
    "    dist.append(columns)\n",
    "    training_set = x_train[columns]\n",
    "    print(training_set)\n",
    "    test_set = x_test[columns]\n",
    "    lg = LinearRegression().fit(training_set.values, y_train.values)\n",
    "    preds = lg.predict(test_set.values)\n",
    "    return 100 / np.sqrt(mean_squared_error(y_test.values, preds))\n",
    "    "
   ]
  }
 ]
}