FYS-STK4155/doc/Programs/JupyterFiles/Examples/My Own Examples/Regression With Random Linear Variables.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (37, 1)\n",
      "y_train:  (37,)\n",
      "X_test:  (13, 1)\n",
      "y_test:  (13,)\n",
      "------------------------------------\n",
      "Ordinary Least Squares\n",
      "Prediction Shape:  (13,)\n",
      "Coefficients: \n",
      " [ 0.5192848]\n",
      "Mean squared error: 1.69\n",
      "Variance score: 0.70\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a142793c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------\n",
      "Ridge Regression\n",
      "Ridge Coefficient:  [ 0.51927249]\n",
      "Ridge Intercept:  4.67629565898\n",
      "------------------------------------\n",
      "Lasso\n",
      "Lasso Coefficient:  [ 0.51840761]\n",
      "Lasso Intercept:  4.69186353707\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13e83668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "#creating data with random noise\n",
    "x=np.arange(50)\n",
    "\n",
    "delta=np.random.uniform(-2.5,2.5, size=(50))\n",
    "np.random.shuffle(delta)\n",
    "y =0.5*x+5+delta\n",
    "\n",
    "#arranging data into 2x50 matrix\n",
    "a=np.array(x) #inputs\n",
    "b=np.array(y) #outputs\n",
    "\n",
    "#Split into training and test\n",
    "X_train=a[:37, np.newaxis]\n",
    "X_test=a[37:, np.newaxis]\n",
    "y_train=b[:37]\n",
    "y_test=b[37:]\n",
    "\n",
    "print (\"X_train: \", X_train.shape)\n",
    "print (\"y_train: \", y_train.shape)\n",
    "print (\"X_test: \", X_test.shape)\n",
    "print (\"y_test: \", y_test.shape)\n",
    "\n",
    "print (\"------------------------------------\")\n",
    "\n",
    "print (\"Ordinary Least Squares\")\n",
    "#Add Ordinary Least Squares fit\n",
    "reg=LinearRegression()\n",
    "reg.fit(X_train, y_train)\n",
    "pred=reg.predict(X_test)\n",
    "print (\"Prediction Shape: \", pred.shape)\n",
    "\n",
    "print('Coefficients: \\n', reg.coef_)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(y_test, pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(y_test, pred))\n",
    "\n",
    "#plot\n",
    "plt.scatter(X_test,y_test,color='green', label=\"Training Data\")\n",
    "plt.plot(X_test, pred, color='black', label=\"Fit Line\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print (\"------------------------------------\")\n",
    "\n",
    "print (\"Ridge Regression\")\n",
    "\n",
    "ridge=linear_model.RidgeCV(alphas=[0.1,1.0,10.0])\n",
    "ridge.fit(X_train,y_train)\n",
    "print (\"Ridge Coefficient: \",ridge.coef_)\n",
    "print (\"Ridge Intercept: \", ridge.intercept_)\n",
    "#Look into graphing with Ridge fit\n",
    "\n",
    "print (\"------------------------------------\")\n",
    "\n",
    "print (\"Lasso\")\n",
    "lasso=linear_model.Lasso(alpha=0.1)\n",
    "lasso.fit(X_train,y_train)\n",
    "predl=lasso.predict(X_test)\n",
    "print(\"Lasso Coefficient: \", lasso.coef_)\n",
    "print(\"Lasso Intercept: \", lasso.intercept_)\n",
    "plt.scatter(X_test,y_test,color='green', label=\"Training Data\")\n",
    "plt.plot(X_test, predl, color='blue', label=\"Lasso\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}