Mlxtend.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install mlxtend --upgrade\n",
    "# http://rasbt.github.io/mlxtend/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import california_housing\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import train_test_split,cross_val_score,KFold\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.decomposition import TruncatedSVD,PCA\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor,ExtraTreesRegressor\n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error\n",
    "from matplotlib.pylab import plt\n",
    "from utils import *\n",
    "%matplotlib inline\n",
    "from mlxtend.regressor import StackingCVRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=california_housing.fetch_california_housing()\n",
    "x=data.data\n",
    "y=data.target\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=2121)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf1_0 = Ridge(alpha=0.01)\n",
    "clf1_1 = RandomForestRegressor(n_estimators=100,random_state=2121,n_jobs=-1)\n",
    "clf1_2 = ExtraTreesRegressor(n_estimators=400,random_state=2121,n_jobs=-1)\n",
    "clf1_3 = make_pipeline(MinMaxScaler(),MLPRegressor(hidden_layer_sizes=(20,20,20)))\n",
    "clf1_4 = GradientBoostingRegressor(n_estimators=580,max_depth=4)\n",
    "\n",
    "clf2_0 = Ridge(random_state=2112,alpha=0.01)\n",
    "\n",
    "np.random.seed(2121)\n",
    "clf = StackingCVRegressor(regressors=(clf1_0,clf1_1,clf1_2,clf1_3,clf1_4),\n",
    "                            meta_regressor=clf2_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/i350114/Documents/python_env/pyth3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/Users/i350114/Documents/python_env/pyth3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/Users/i350114/Documents/python_env/pyth3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/Users/i350114/Documents/python_env/pyth3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "/Users/i350114/Documents/python_env/pyth3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE -  0.20492649924771367\n",
      "MAE -  0.30150740595266223\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf.fit(x_train,y_train)\n",
    "preds = clf.predict(x_test)\n",
    "plt_sct(y_test,preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}