ClassificationWithLinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification with linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "from scipy import stats \n",
    "\n",
    "from sklearn.datasets.samples_generator import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_blobs(n_samples=100, centers= np.array([[-1],[1]]), n_features=1, shuffle=True, random_state=2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11d5b96a0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression().fit(X,y)\n",
    "xgrid = np.linspace(-3.5,3.5, 1000)\n",
    "Xgrid = xgrid.reshape(-1,1)\n",
    "Y = y.reshape(-1,1)\n",
    "yp = lr.predict(X)\n",
    "ypgrid = lr.predict(Xgrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = X.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a2d59deb8>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,y,'o', alpha=0.4, ms=5)\n",
    "plt.plot(xgrid, ypgrid)\n",
    "plt.plot(x, yp, 's', alpha=.5, ms=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeyaprob(y):\n",
    "    if y >=1.0:\n",
    "        return 1.0\n",
    "    elif y <= 0.0:\n",
    "        return 0.0\n",
    "    else:\n",
    "        return y\n",
    "vector_makeyaprob = np.vectorize(makeyaprob)\n",
    "predict_proba = lambda lr, X: vector_makeyaprob(lr.predict(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypfilt = vector_makeyaprob(ypgrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ypfilt = np.zeros(xgrid.shape[0])\n",
    "# def predicted_proba(x)\n",
    "# for i,x in enumerate(xgrid):\n",
    "#     if yp[i] >= 1.0:\n",
    "#         ypfilt[i] = 1.0\n",
    "#     elif yp[i] <= 0.0:\n",
    "#         ypfilt[i] = 0.0\n",
    "#     else:\n",
    "#         ypfilt[i] = yp[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a2e0b56a0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,y,'o')\n",
    "plt.plot(xgrid, ypfilt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = 1*(predict_proba(lr, X) >= 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y, ypred)"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}