zero_svm_kernelized.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rushab/.local/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import h5py\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from ranking import *\n",
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def MSE(a,b):\n",
    "    mse =0.0\n",
    "    print(a.shape, b.shape)\n",
    "    if a.shape != b.shape:\n",
    "        print(\"Size of vector mixmatch - cannot calculate Mean Squared error\")\n",
    "    for i in range(0,len(a)):\n",
    "        mse += np.linalg.norm(b[i] - a[i])**2\n",
    "    mse = 1.0/len(a)*mse\n",
    "    return mse\n",
    "\n",
    "f = h5py.File('l2_normalized_semantic_SVM_full_data_with_val_291labels_no_zero.mat' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def F1_score(tags_pred, tags_actual,k1=3, k2=5):\n",
    "    k1_count=0.0\n",
    "    k2_count=0.0\n",
    "    for i in range(0,k1):\n",
    "        if tags_actual[tags_pred[i]] == 1:\n",
    "            k1_count += 1\n",
    "\n",
    "    for i in range(0,k2):\n",
    "        if tags_actual[tags_pred[i]] == 1:\n",
    "            k2_count += 1\n",
    "\n",
    "    num_tags = 1.0*(len(tag_word_vectors) + sum(tags_actual ))/2\n",
    "    \n",
    "    k1_recall = 1.0*k1_count/num_tags\n",
    "    k2_recall = 1.0*k2_count/num_tags \n",
    "    k1_precision = k1_count/k1\n",
    "    k2_precision = k2_count/k2\n",
    "    \n",
    "    tmp1 = 2.0*k1_precision*k1_recall/(k1_precision + k1_recall)\n",
    "    tmp2 = 2.0*k2_precision*k2_recall/(k2_precision+k2_recall)\n",
    "    if tmp1 >= 0 and tmp2 >= 0:\n",
    "        return [tmp1,tmp2]\n",
    "    elif tmp1 >= 0:\n",
    "        return [tmp1,0]\n",
    "    elif tmp2 >= 0:\n",
    "        return [0,tmp2]\n",
    "    else:\n",
    "        return [0,0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------\n",
      "Loading Data\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "print(\"-----------------------------------------\\nLoading Data\")\n",
    "n_all = 1000\n",
    "\n",
    "training_data = np.transpose(f[\"prepared_training_data\"])\n",
    "training_label = np.transpose(f[\"prepared_training_label\"])\n",
    "valid_data = np.transpose(f[\"prepared_val_data\"])\n",
    "valid_label = np.transpose(f[\"prepared_val_label\"])\n",
    "testing_data = np.transpose(f[\"prepared_testing_data\"])\n",
    "testing_label = np.transpose(f[\"prepared_testing_label\"])\n",
    "\n",
    "n_training = len(training_data)\n",
    "n_valid = len(valid_data)\n",
    "n_testing = len(testing_data)\n",
    "\n",
    "tag_word_vectors = np.transpose(h5py.File('291labels.mat')[\"semantic_mat\"])\n",
    "\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ranking SVM for Training Data\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "print(\"Ranking SVM for Training Data\")\n",
    "r=RankSVM()\n",
    "w_list=np.zeros([n_training,300])\n",
    "for i in range(0,len(training_data)):\n",
    "    r.fit(tag_word_vectors,training_label[i])\n",
    "    w_list[i] = r.coef_\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting Linear Regression model\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'training_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m-----------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-1f0e4491a57e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Fitting Linear Regression model\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mlin_reg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlin_reg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlin_reg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlin_reg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'training_data' is not defined"
     ]
    }
   ],
   "source": [
    "print(\"Fitting Linear Regression model\")\n",
    "lin_reg = LinearRegression(normalize=True)\n",
    "lin_reg.fit(training_data, w_list)\n",
    "print(lin_reg.score(training_data, w_list))\n",
    "A = lin_reg.coef_\n",
    "print(w_list.shape,\" = \", A.shape, training_data.shape)\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting Support Vector Kernelized Regression model\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "bad input shape (15015, 300)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m-----------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-276bed2778e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0msvr_poly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSVR\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'poly'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdegree\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#y_rbf = svr_rbf.fit(training_data, w_list)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0my_lin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msvr_lin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0my_poly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msvr_poly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Done\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/rushab/.local/lib/python2.7/site-packages/sklearn/svm/base.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msparse\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'C'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/rushab/.local/lib/python2.7/site-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    524\u001b[0m                         dtype=None)\n\u001b[1;32m    525\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 526\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    527\u001b[0m         \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    528\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_numeric\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'O'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/rushab/.local/lib/python2.7/site-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36mcolumn_or_1d\u001b[0;34m(y, warn)\u001b[0m\n\u001b[1;32m    560\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    561\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 562\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"bad input shape {0}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: bad input shape (15015, 300)"
     ]
    }
   ],
   "source": [
    "print(\"Fitting Support Vector Kernelized Regression model\")\n",
    "svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)\n",
    "svr_lin = SVR(kernel='linear', C=1e3)\n",
    "svr_poly = SVR(kernel='poly', C=1e3, degree=2)\n",
    "#y_rbf = svr_rbf.fit(training_data, w_list)\n",
    "y_lin = svr_lin.fit(training_data, w_list)\n",
    "y_poly = svr_poly.fit(training_data, w_list)\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ranking SVM for Training Data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:21: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averages : 0.506236493471 : 0.543220654681\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy for Training Data\")\n",
    "r=RankSVM()\n",
    "avg1 = 0\n",
    "avg2 = 0\n",
    "\n",
    "for j in range(0,n_training):\n",
    "    w = np.dot(training_data[j], np.transpose(A))\n",
    "    tags_pred_score = np.dot(w,np.transpose(tag_word_vectors))\n",
    "\n",
    "    tag_pred_ranked = [i[0] for i in sorted(enumerate(tags_pred_score), key=lambda x:x[1])]\n",
    "    tag_pred_ranked.reverse()\n",
    "    [tmp1, tmp2] = F1_score(tag_pred_ranked, training_label[j],3,5)\n",
    "    \n",
    "    avg1 += tmp1\n",
    "    avg2 += tmp2\n",
    "\n",
    "print(\"Averages : \" + str(avg1/n_training) + \" : \"+ str(avg2/n_training))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ranking SVM for Training Data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:21: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averages : 0.283574736374 : 0.301731812447\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy for Testing Data\")\n",
    "avg1 =0 \n",
    "avg2 = 0\n",
    "for j in range(0,n_testing):\n",
    "    w = np.dot(testing_data[j], np.transpose(A))\n",
    "    tags_pred_score = np.dot(w,np.transpose(tag_word_vectors)) \n",
    "    tag_pred_ranked = [i[0] for i in sorted(enumerate(tags_pred_score), key=lambda x:x[1])]\n",
    "    tag_pred_ranked.reverse()\n",
    "    \n",
    "    [tmp1, tmp2] = F1_score(tag_pred_ranked, testing_label[j],3,5)\n",
    "    \n",
    "    avg1 += tmp1\n",
    "    avg2 += tmp2\n",
    "\n",
    "print(\"Averages : \" + str(avg1/n_testing) + \" : \"+ str(avg2/n_testing))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ranking SVM for Training Data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:21: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Averages : 0.277139618367 : 0.29699567689\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy for Validation Data\")\n",
    "avg1 =0 \n",
    "avg2 = 0\n",
    "for j in range(0,n_valid):\n",
    "    w = np.dot(valid_data[j], np.transpose(A))\n",
    "    tags_pred_score = np.dot(w,np.transpose(tag_word_vectors)) \n",
    "    tag_pred_ranked = [i[0] for i in sorted(enumerate(tags_pred_score), key=lambda x:x[1])]\n",
    "    tag_pred_ranked.reverse()\n",
    "    \n",
    "    [tmp1, tmp2] = F1_score(tag_pred_ranked, valid_label[j],3,5)\n",
    "    \n",
    "    avg1 += tmp1\n",
    "    avg2 += tmp2\n",
    "\n",
    "print(\"Averages : \" + str(avg1/n_valid) + \" : \"+ str(avg2/n_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(\"Accuracy for Testing Data with Kernelized SVR rbf\")\n",
    "avg1 =0 \n",
    "avg2 = 0\n",
    "for j in range(0,n_testing):\n",
    "    w = svr_rbf(testing_data[j])\n",
    "    tags_pred_score = np.dot(w,np.transpose(tag_word_vectors)) \n",
    "    tag_pred_ranked = [i[0] for i in sorted(enumerate(tags_pred_score), key=lambda x:x[1])]\n",
    "    tag_pred_ranked.reverse()\n",
    "    \n",
    "    [tmp1, tmp2] = F1_score(tag_pred_ranked, testing_label[j],3,5)\n",
    "    \n",
    "    avg1 += tmp1\n",
    "    avg2 += tmp2\n",
    "\n",
    "print(\"Averages : \" + str(avg1/n_testing) + \" : \"+ str(avg2/n_testing))"
   ]
  }
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