ItemBasedCF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Item Based Collaborative Filtering "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9419</td>\n",
       "      <td>63</td>\n",
       "      <td>16</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1075307025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91703</td>\n",
       "      <td>607</td>\n",
       "      <td>4963</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1113319563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10848</td>\n",
       "      <td>73</td>\n",
       "      <td>3967</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1255849638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65877</td>\n",
       "      <td>468</td>\n",
       "      <td>1198</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1296195555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75168</td>\n",
       "      <td>522</td>\n",
       "      <td>1220</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1391349962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  userId  movieId  rating   timestamp\n",
       "0        9419      63       16     5.0  1075307025\n",
       "1       91703     607     4963     4.5  1113319563\n",
       "2       10848      73     3967     3.5  1255849638\n",
       "3       65877     468     1198     3.5  1296195555\n",
       "4       75168     522     1220     5.0  1391349962"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will use the same Train and Test Splits to do Item Base CF. \n",
    "# import pandas as pd \n",
    "# X_train = pd.read_csv(\"~/movie_lens_train.csv\")\n",
    "# X_test = pd.read_csv(\"~/movie_lens_test.csv\")\n",
    "# X_train.head()\n",
    "# X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   userId  movieId  rating   timestamp\n",
      "0       1       31     2.5  1260759144\n",
      "1       1     1029     3.0  1260759179\n",
      "2       1     1061     3.0  1260759182\n",
      "3       1     1129     2.0  1260759185\n",
      "4       1     1172     4.0  1260759205\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "671"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Find Item Similarity \n",
    "#USING PYTHON - \n",
    "import math\n",
    "from collections import defaultdict\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import scipy as scipy\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# ratings = pd.read_csv(\"~/Desktop/ml-latest-small 3/ratings.csv\")\n",
    "# print(ratings.head())\n",
    "# sample_train, sample_test, sampleY_train, sampleY_test = train_test_split(ratings, ratings['rating'], test_size=0.20, random_state=42)\n",
    "# len(sample_train['userId'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Item-Item Similarity using Pearson Correlation Coefficient "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       userId  movieId  rating   timestamp\n",
      "19090     128     1028     5.0  1049690908\n",
      "99678     665     4736     1.0  1010197684\n",
      "18455     120     4002     3.0  1167420604\n",
      "35755     257     1274     4.0  1348544094\n",
      "66536     468     6440     4.0  1296191715\n"
     ]
    }
   ],
   "source": [
    "def item_similarity(sample_train1):\n",
    "    item_sim = defaultdict(lambda : defaultdict(dict))\n",
    "    outer_loop = 0\n",
    "    count = 0\n",
    "    update=0\n",
    "    for item1_array in np.nditer(unique_items):\n",
    "        item1 = item1_array.flat[0]\n",
    "        item1_df = sample_train1.loc[sample_train1['movieId'] == item1]\n",
    "        item1_users_set = set(item1_df['userId'])\n",
    "        mean_rating1 = np.mean(item1_df['rating'])\n",
    "        outer_loop+=1\n",
    "        inner_loop = 0\n",
    "        for item2_array in np.nditer(unique_items):\n",
    "            inner_loop+=1\n",
    "            count+=1\n",
    "            item2 = item2_array.flat[0]\n",
    "            if item1 == item2:\n",
    "                item_sim[item1][item2] = 1\n",
    "                item_sim[item2][item1] = 1\n",
    "                continue\n",
    "            if not item_sim[item1][item2] and item_sim[item1][item2] != 0:\n",
    "                # find similarities between items\n",
    "                # find intersection of users who rated Item 1 and Item 2\n",
    "                update+=1\n",
    "                item2_df = sample_train1.loc[sample_train1['movieId'] == item2]\n",
    "                mean_rating2 = np.mean(item2_df['rating'])\n",
    "                item2_users_set = set(item2_df['userId'])\n",
    "                common_users = item1_users_set.intersection(item2_users_set)\n",
    "                rating1 = item1_df[item1_df['userId'].isin(common_users)]['rating']\n",
    "                rating2 = item2_df[item2_df['userId'].isin(common_users)]['rating']\n",
    "\n",
    "                num = 0\n",
    "                den1 = 0\n",
    "                den2 =0\n",
    "                for r1, r2 in zip(rating1, rating2):\n",
    "                    r1_diff = r1 - mean_rating1\n",
    "                    r2_diff = r2 - mean_rating2\n",
    "                    num += r1_diff*r2_diff\n",
    "                    den1 += r1_diff**2\n",
    "                    den2 += r2_diff**2\n",
    "                den = math.sqrt(den1)*math.sqrt(den2)\n",
    "                if len(common_users) == 0 or den == 0:\n",
    "                    item_sim[item1][item2] = 0\n",
    "                else:\n",
    "                    item_sim[item1][item2] = num/den\n",
    "\n",
    "                item_sim[item2][item1] = item_sim[item1][item2]\n",
    "                if count % 10000 == 0:\n",
    "                    print('outer_loop='+str(outer_loop), 'inner_loop='+str(inner_loop), 'count='+str(count), 'update='+str(update))\n",
    "\n",
    "    persist_data(\"item_sim.json\", item_sim)\n",
    "    return item_sim\n",
    "def item_sim():\n",
    "    return random.uniform(0,1) \n",
    "def give_k_sim(list_of_movies_for_user, top_k=None):\n",
    "    if top_k:\n",
    "            return k_item_sim(top_k)\n",
    "    else:\n",
    "            return k_item_sim(len(list_of_movies_for_user))\n",
    "def k_item_sim(k):\n",
    "    return sorted([item_sim() for x in range(k)],reverse=True)[0:k]\n",
    "print(sample_test.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### N similar items in neighborhood "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# N similar items in neighborhood \n",
    "import collections\n",
    "def sorted_dict_for_item_sim(user, movieId, k):\n",
    "    # Here, use item similarity here\n",
    "    try:\n",
    "        # get item sim dict for movieId\n",
    "        movie_sim=user_similarity[str(int(movieId))]\n",
    "    except:\n",
    "        return dict()\n",
    "    list_of_movies_for_user = user_movie_dict[str(int(user))]\n",
    "    # Here, we find the similarity of the movie of interest \n",
    "    dict_you_want = { m: movie_sim[str(int(m))] for m in list_of_movies_for_user }\n",
    "    #print(len(dict_you_want),len(u_sim),len(user_of_interst), dict_you_want)\n",
    "    sorted_x = sorted(dict_you_want.items(), key=lambda kv: kv[1], reverse=True)\n",
    "    od=OrderedDict(sorted_x[0:k+1])\n",
    "    od.pop(movieId,None)\n",
    "    return od\n",
    "sorted_dict_for_item_sim(641,31,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rating Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='project_images/g.png', width = 600, height = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(128.0, 1028.0, 5.0, 3.8427455122092162),\n",
       " (665.0, 4736.0, 1.0, 3.104522697942651)]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make Predictions \n",
    "def prediction(min_sim=0, top_k=10):\n",
    "    list_tuple = []\n",
    "    \n",
    "    for row in sample_test.iterrows():\n",
    "        userId = row[1]['userId']\n",
    "        movieId = row[1]['movieId']\n",
    "        rating = row[1]['rating']\n",
    "        list_of_movies_for_user = user_movie_dict[str(int(userId))]\n",
    "        top_k_ratings=give_k_sim(list_of_movies_for_user, top_k)\n",
    "        #print(top_k_ratings)\n",
    "        num = 0\n",
    "        den = 0\n",
    "        for m,item_sim in zip(list_of_movies_for_user[0:top_k],top_k_ratings):\n",
    "            if m==str(int(movieId)):\n",
    "                      continue\n",
    "            elif min_sim > item_sim:\n",
    "                      break\n",
    "            else:\n",
    "                user_mean = user_mean_rating[str(int(userId))]\n",
    "                rating_movie = sample_train.loc[(sample_train['movieId'] == float(m)) &(sample_train['userId'] == float(userId))]['rating'].item()        \n",
    "#             print(rating_movie)\n",
    "                 \n",
    "                num += (rating_movie * item_sim)\n",
    "                den += item_sim\n",
    "#                 print(num,den) \n",
    "        if den == 0:\n",
    "#             print('sim',item_sim, top_k_ratings[0])\n",
    "            prediction = 0\n",
    "        else:\n",
    "             prediction = num/den  \n",
    "        list_tuple.append((userId, movieId, rating, prediction))\n",
    "    return list_tuple\n",
    "\n",
    "          \n",
    "opt_predictions = prediction(top_k=10)\n",
    "opt_predictions[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3:  Accuracy Measures - MAE and RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7881629893101902 1.0140846375999801\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "def mae(list_tuple):\n",
    "    truth = [t[2] for t in list_tuple]\n",
    "    pred = [t[3] for t in list_tuple]\n",
    "    truth_array = np.array(truth)\n",
    "    pred_array = np.array(pred)\n",
    "    return mean_absolute_error(truth_array, pred_array)\n",
    "\n",
    "def rmse(list_tuple):\n",
    "    truth = [t[2] for t in list_tuple]\n",
    "    pred = [t[3] for t in list_tuple]\n",
    "    truth_array = np.array(truth)\n",
    "    pred_array = np.array(pred)\n",
    "    return np.sqrt(((truth_array - pred_array) ** 2).mean())\n",
    "\n",
    "\n",
    "print(mae(opt_predictions), rmse(opt_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot \n",
    "#### Truth Vs Prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def plot_error(list_tuple):\n",
    "    truth = [t[2] for t in list_tuple]\n",
    "    pred = [t[3] for t in list_tuple]\n",
    "\n",
    "\n",
    "\n",
    "    plt.scatter(truth,pred)\n",
    "    plt.xlabel(\"Truth\")\n",
    "    plt.ylabel(\"Prediction\")\n",
    "    plt.title('Truth Vs Prediction')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_error(opt_predictions)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Violin Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/priyankaagrawal/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "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": [
    "import seaborn as sns\n",
    "\n",
    "def vodoo(list_tuple):\n",
    "    error = [abs(t[2]-t[3]) for t in list_tuple]\n",
    "    ax = sns.violinplot(data=pd.Series(error))\n",
    "#vodoo(predictions)\n",
    "vodoo(opt_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(2.472977174234535, 0.4271643471228093, 4.248292994260976)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "np_error=np.array(error)\n",
    "e=sorted(error,reverse=True)\n",
    "np.mean(np_error)\n",
    "np.std(np_error)\n",
    "#x=np.linspace(0, 4, len(error))\n",
    "plt.plot(e, norm.pdf(e, np.mean(np_error), np.std(np_error)))\n",
    "plt.show()\n",
    "np.mean(np_error),np.std(np_error), e[0]\n",
    "#sorted(error,reverse=True)[0]\n",
    "# error[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1080"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Threshold check - checks if the absolute value of rating diff is greater than or equals threshold value. \n",
    "\n",
    "def threshold_check(list_tuple, threshold=2):\n",
    "\n",
    "    too_much_error=[]\n",
    "    error=[]\n",
    "    for t in list_tuple:\n",
    "         if abs(t[2]-t[3]) >= threshold:\n",
    "            too_much_error.append(t)\n",
    "            error.append(abs(t[2]-t[3]) )\n",
    "    return len(too_much_error), len(list_tuple),too_much_error[1:10],error\n",
    "#threshold_check(predictions)\n",
    "a,b,error_list,error=threshold_check(opt_predictions)\n",
    "a"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}