L1 Regularization (Lasso).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import time\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def setting_data(data=None):\n",
    "    feature_matrix = np.ones((np.ma.size(data,axis=0),np.ma.size(data,axis=1)+1))\n",
    "    feature_matrix[:,1:] = data\n",
    "    return feature_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_values(feature_matrix=None, weights=None):\n",
    "    return np.dot(a=feature_matrix,b=weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def polynomial_features_generation(data=None, degree=0):\n",
    "    return np.hstack((data,np.power(data,np.arange(start=2,stop=degree+1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardization(data=None):\n",
    "    data = data.astype(float)\n",
    "    for i in range(data.shape[1]):\n",
    "        data[:,i] = np.divide(np.subtract(data[:,i],np.mean(data[:,i])), np.std(data[:,i])).astype(float)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def min_max_scaling(data=None):\n",
    "    data = data.astype(float)\n",
    "    for i in range(data.shape[1]):\n",
    "        min_ = np.min(data[:,i])\n",
    "        max_ = np.max(data[:,i])\n",
    "        max_minus_min = max_ - min_\n",
    "        data[:,i] = np.divide(np.subtract(data[:,i],min_),max_minus_min)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(feature_matrix=None):\n",
    "    norms = np.sqrt(np.sum(np.square(feature_matrix),axis=0))\n",
    "    normalized_features = feature_matrix/norms\n",
    "    return (normalized_features,norms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def root_mean_squared_error(y=None,y_bar=None,no_of_observations=0):\n",
    "    return np.sqrt(np.divide(np.sum(np.square(np.subtract(y,y_bar))),no_of_observations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_descent(x=None,y=None,tolerance_gradient=0.01, tolerance_cost=0.0000001,\n",
    "                     alpha = 0.001, max_iter=10000,l2_regularization=0):\n",
    "   \n",
    "    feature_matrix = setting_data(data=x)\n",
    "    \n",
    "    weights = np.random.rand(x.shape[1] + 1, 1)\n",
    "    partials = np.zeros(x.shape[1]+1).reshape((-1,1))\n",
    "    \n",
    "    i = 0\n",
    "    prev_cost = 10\n",
    "    costs = []\n",
    "    gradients = []\n",
    "    \n",
    "    converged = False\n",
    "    while not converged:\n",
    "        i += 1\n",
    "        \n",
    "        predicted = predict_values(feature_matrix=feature_matrix, weights=weights)\n",
    "        errors = np.subtract(predicted, y)\n",
    "        #Updating weights\n",
    "        \n",
    "        #taking derivative\n",
    "        partials = np.dot(feature_matrix.T,errors)\n",
    "        #This is the total change :p\n",
    "#         step_size = np.multiply(alpha,partials)\n",
    "#         weights_regularized = np.vstack((weights[0],np.multiply(weights[1:],1-2*alpha*l2_regularization)))\n",
    "        \n",
    "        weights = np.subtract(np.vstack((weights[0],np.multiply(weights[1:],1-2*alpha*l2_regularization))),\n",
    "                              np.multiply(alpha,partials))\n",
    "        \n",
    "        gradient_magnitude = np.sqrt(np.sum(np.square(partials)))\n",
    "        cur_cost = np.sum(np.square(errors))\n",
    "        \n",
    "#         gradients = np.append(gradients,gradient_magnitude)\n",
    "#         costs = np.append(costs,cur_cost)\n",
    "        gradients.append(gradient_magnitude)\n",
    "        costs.append(cur_cost)\n",
    "\n",
    "#         if i%1000==0:\n",
    "#             print(cur_cost)\n",
    "#         print(abs(cur_cost - prev_cost))\n",
    "        if (gradient_magnitude <= tolerance_gradient) or (abs(cur_cost - prev_cost) < tolerance_cost):\n",
    "            if gradient_magnitude <= tolerance_gradient:\n",
    "                print('[Gradient Magnitude - Tolerance] Satisfied')\n",
    "            else:\n",
    "                print('[Cost Tolerance - Precision] Satisfied')\n",
    "            converged = True\n",
    "\n",
    "        if i >= max_iter:\n",
    "            print('Iterations Completed')\n",
    "            converged = True\n",
    "        \n",
    "        prev_cost = cur_cost\n",
    "    return (i, np.array(weights),np.array(gradients), costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def coordinate_descent(x=None,y=None, tolerance_step=0.0000001, \n",
    "                       max_iter=10000,l1_regularization=0):\n",
    "#     print('start')\n",
    "    feature_matrix = setting_data(data=x)\n",
    "    \n",
    "    weights = np.random.rand(x.shape[1] + 1, 1)\n",
    "    iteration = 0\n",
    "    max_step = 10\n",
    "    upper_limit = np.multiply(np.divide(l1_regularization,2),-1)\n",
    "    lower_limit = np.divide(l1_regularization,2)\n",
    "    \n",
    "    converged = False\n",
    "#     print('entering loop')\n",
    "    while not converged:\n",
    "        iteration += 1\n",
    "#         print('entering inner loop',iteration)\n",
    "        for j in range(0,x.shape[1]-1):\n",
    "            w_j_old = weights[j]\n",
    "            predicted = predict_values(feature_matrix=feature_matrix, weights=weights)\n",
    "            \n",
    "            predicted_no_j = predicted - np.multiply(feature_matrix[:,j],weights[j])\n",
    "            errors = np.subtract(predicted_no_j, y)\n",
    "            \n",
    "            roo = np.sum(np.multiply(feature_matrix[:,j],errors))\n",
    "            \n",
    "            if j==0:\n",
    "                weights[j] = roo\n",
    "            elif roo < lower_limit:\n",
    "                weights[j] = roo + upper_limit\n",
    "            elif roo > upper_limit:\n",
    "                weights[j] = roo - upper_limit\n",
    "            else:\n",
    "                weights[j] = 0\n",
    "                \n",
    "            step = abs(w_j_old - weights[j])\n",
    "            if(step>max_step):\n",
    "                max_step = step\n",
    "            \n",
    "        \n",
    "        if (max_step < tolerance_step):\n",
    "            print('Step Tolerance - Satisfied')\n",
    "            converged = True\n",
    "\n",
    "        if iteration >= max_iter:\n",
    "            print('Iterations Completed')\n",
    "            converged = True\n",
    "\n",
    "    return (iteration, np.array(weights))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "house_data = pd.read_csv('Related Datasets/kc_house_data.csv')\n",
    "house_test_data = pd.read_csv('Related Datasets/kc_house_test_data.csv')\n",
    "house_train_data = pd.read_csv('Related Datasets/kc_house_train_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = house_train_data[['sqft_living','bedrooms','price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ = np.mean(df['sqft_living'])\n",
    "std_ = np.std(df['sqft_living'])\n",
    "m = 3\n",
    "df = df[abs(df['sqft_living'] - mean_ ) < (m*std_)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array(df[['sqft_living','bedrooms']])\n",
    "data = data[:1000,:]\n",
    "target = np.array(df[['price']])\n",
    "target = target[:1000,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_normalized = normalize(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iterations Completed\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "i,weights = coordinate_descent(x=data_normalized[0], y=target,tolerance_step=1,\n",
    "                                max_iter=10000,l1_regularization=1e7)\n",
    "end_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken:  57.44074034690857\n",
      "Iterations:  10000\n",
      "Calculated weights:\n",
      " [[-5.0769737853e+11]\n",
      " [ 2.9660045123e-01]\n",
      " [ 9.0719081209e-01]]\n"
     ]
    }
   ],
   "source": [
    "print('Time taken: ',end_time-start_time)\n",
    "print('Iterations: ',i)\n",
    "np.set_printoptions(precision=10)\n",
    "print('Calculated weights:\\n',weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = abs(weights[0]) + weights[1]*data_normalized[0][:,0] + weights[2]*data_normalized[0][:,1]\n",
    "# type(result)\n",
    "# print(result,\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f36eda07240>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=data_normalized[0][:,0],y=target.reshape(1000),color='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f36ed987b70>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ticklabel_format(useOffset=False)\n",
    "sns.scatterplot(x=data_normalized[0][:,0],y=result,color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f36ed8dc128>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ticklabel_format(useOffset=False)\n",
    "sns.scatterplot(x=data_normalized[0][:,0],y=target.reshape(1000),color='g')\n",
    "sns.scatterplot(x=data_normalized[0][:,0],y=result,color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #saving for later insertion\n",
    "#             feature_j = feature_matrix[:,j]\n",
    "#             weight_j = weights[j]\n",
    "            \n",
    "#             #deleting from matrix for performing operation on matrix\n",
    "#             feature_matrix = np.delete(feature_matrix,j,1)\n",
    "#             weights = np.delete(weights,j,0)\n",
    "            \n",
    "#             #performing operations\n",
    "#             predicted = predict_values(feature_matrix=feature_matrix, weights=weights)\n",
    "            \n",
    "#             #Inserting values back in matrices\n",
    "#             feature_matrix = np.insert(feature_matrix,j,feature_j,1)\n",
    "#             weights = np.insert(weights,j,weight_j,0)\n",
    "            \n",
    "#             #calculating errors\n",
    "#             errors = np.subtract(predicted, y)\n",
    "            \n",
    "#             roo_j = np.dot(feature_matrix.T,errors)\n",
    "#             weights[j] = roo_j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_types = dtype_dict = {'bathrooms':float, 'waterfront':int, \n",
    "#                            'sqft_above':int, 'sqft_living15':float, \n",
    "#                            'grade':int, 'yr_renovated':int, 'price':float, \n",
    "#                            'bedrooms':float, 'zipcode':str, 'long':float, \n",
    "#                            'sqft_lot15':float, 'sqft_living':float, \n",
    "#                            'floors':str, 'condition':int, 'lat':float, \n",
    "#                            'date':str, 'sqft_basement':int, 'yr_built':int, \n",
    "#                            'id':str, 'sqft_lot':int, 'view':int}\n",
    "# data= pd.read_csv('Related Datasets/kc_house_train_data.csv',dtype=data_types)"
   ]
  },
  {
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}