Part 2 - Creating User Profiles.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating User Profiles\n",
    "\n",
    "*Moses Surumen, Ellen Peng, Kuhuk Goyal*  \n",
    "*CS 194-31  Final Project*  \n",
    "*Project Name: Music Networks*\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## File Descriptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| File                 | Description                            | \n",
    "| :--------------------|--------------------------------------: |\n",
    "| train_triplets.txt   | Provides tuples of user-song-playcount \n",
    "| taste_profiles.csv   | Sorted train_triplets by users\n",
    "| new_data_dict.txt    | Provides a mapping of users to a list of all songs they played at least 25 times\n",
    "| songs.csv            | All Million Song Dataset Songs\n",
    "| similar_songs.db     | Sqlite3 database of song communities derived from Part 1\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Whole Graph and Community Partitions from Part 1\n",
    "%store -r G\n",
    "%store -r G_main_com\n",
    "%store -r partition\n",
    "%store -r communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas\n",
    "import pandas as pd\n",
    "\n",
    "# Graph\n",
    "import community\n",
    "import networkx as nx\n",
    "\n",
    "# Plot\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Combinations\n",
    "import itertools\n",
    "\n",
    "# Reading Dictionaries\n",
    "import json\n",
    "\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Echo Nest Taste Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/train_triplets.txt', delimiter=\"\\t\", header=None, names=[\"User\", \"Song\", \"Playcount\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Song</th>\n",
       "      <th>Playcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>4</th>\n",
       "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
       "      <td>SOBFOVM12A58A7D494</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                       User                Song  Playcount\n",
       "0  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOAKIMP12A8C130995          1\n",
       "1  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOAPDEY12A81C210A9          1\n",
       "2  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBBMDR12A8C13253B          2\n",
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       "4  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBFOVM12A58A7D494          1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "48373586"
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-Processing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We drop all songs from our dataset which have been played less that 25 times, and write the updated dataset to a different file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df.loc[df['Playcount'] < 25].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "476267"
      ]
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Song</th>\n",
       "      <th>Playcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
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       "      <td>SOGKEGN12AB0185355</td>\n",
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       "      <td>SOYZLWW12AB0186148</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                          User                Song  Playcount\n",
       "503   d6589314c0a9bcbca4fee0c93b14bc402363afea  SOBONKR12A58A7A7E0         26\n",
       "787   5a905f000fc1ff3df7ca807d57edb608863db05d  SOMVTRL12A67AE0921         28\n",
       "1326  a820d2d4f16bbd53be9e41e0417dfb234bfdfba8  SOGKEGN12AB0185355         26\n",
       "2148  3f152d355d53865a2ca27ac5ceeffb7ebaea0a26  SOQGETC12AB017F1E5         26\n",
       "2209  3f152d355d53865a2ca27ac5ceeffb7ebaea0a26  SOYZLWW12AB0186148         55"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"taste_profiles.csv\", encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sort Dataframe based on User IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values('User')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Hashmap with Values as Songs and Keys as Users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = defaultdict(list)\n",
    "for k, v in zip(df.User.values,df.Song.values):\n",
    "    data_dict[k].append(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "187548"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write Dictionary to File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "json.dump(data_dict, open(\"data_dict.txt\",'w'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User-Profile Construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that maps users to songs\n",
    "def map_users_to_songs(data_file):\n",
    "    hashmap = dict()\n",
    "    with open(data_file,\"r\") as f:\n",
    "        for line in f:\n",
    "            user,song,_ = line.strip().split('\\t')\n",
    "            if user in hashmap:\n",
    "                hashmap[user].add(song)\n",
    "            else:\n",
    "                hashmap[user] = set([song])\n",
    "    return hashmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that maps song communities from Part 2 to users\n",
    "def project_songs_to_users(data_file, set_users=None, ratio=1.0):\n",
    "    hashmap = dict()\n",
    "    with open(data_file,\"r\") as f:\n",
    "        for line in f:\n",
    "            if random.random()<ratio:\n",
    "                user,song,_=line.strip().split('\\t')\n",
    "                if not set_users or user in set_users:\n",
    "                    if song in hashmap:\n",
    "                        hashmap[song].add(user)\n",
    "                    else:\n",
    "                        hashmap[song]=set([user])\n",
    "    return hashmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Generates similar songs in the network \n",
    "def compute_similarity(current_songs, playlist):\n",
    "    similarity ={}\n",
    "    for s in playlist:\n",
    "        similarity[s]=0.0\n",
    "        if not (s in self.s2u_tr):\n",
    "            continue\n",
    "        for u_song in ucurrent_songs:\n",
    "            if not (u_song in self.s2u_tr):\n",
    "                continue\n",
    "            min_sim = 0.7\n",
    "            similarity[s]+=math.pow(min_sim,3)\n",
    "    return similarity\n",
    "\n",
    "def generate_similars(current_songs, G):\n",
    "    similars ={}\n",
    "    for s in G:\n",
    "        similars[s]=0.0\n",
    "        for u_song in current_songs:\n",
    "            min_sim = 0.7\n",
    "            similars[s]+=math.pow(0.7,3)\n",
    "    return similars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateRec(user, user_song_map):\n",
    "    songs_for_user_sorted =[]\n",
    "    for p in self.predictors:\n",
    "        ssongs=[]\n",
    "        if user in user_song_map:\n",
    "            ssongs = sort_dict_dec(p.Score(user_song_map[user],all_songs))\n",
    "        else:\n",
    "            ssongs=list(all_songs)\n",
    "\n",
    "        recommended_songs = []\n",
    "        for x in ssongs:\n",
    "            if len(recommended_songs)>=50:  ###### Only recommend a max of 50 songs\n",
    "                break\n",
    "            if x not in user_song_map[user]:\n",
    "                recommended_songs.append(x)   #### add song to list\n",
    "        songs_for_user_sorted += [recommended_songs]\n",
    "    return songs_for_user_sorted "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Recommender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = \"data/train_data.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_users(if_str):\n",
    "    all_users = set()\n",
    "    with open(if_str,\"r\") as f:\n",
    "        for line in f:\n",
    "            user,_,_=line.strip().split('\\t')\n",
    "            if user not in all_users:\n",
    "                all_users.add(user)\n",
    "    return all_users "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_users = get_users(train_data)\n",
    "\n",
    "\n",
    "train_users = {}\n",
    "for k,v in enumerate(all_users):\n",
    "    train_users[v] = k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# project users to micro-genres\n",
    "user_micro_genres = project_songs_to_users(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get neighbors of each node in the user's training set\n",
    "def nearestneighbors(G, node):\n",
    "    result = []\n",
    "    for k, v in first_five_train.items():\n",
    "        # set max neighborhood size to 12\n",
    "        for i in range(12):\n",
    "            result.append(G.neighbors(song for song in v))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_min: 1 , user_max: 100\n"
     ]
    }
   ],
   "source": [
    "user_min,user_max,osfile = 1,100,\"user_predictions.txt\"\n",
    "user_min=int(user_min)\n",
    "user_max=int(user_max)\n",
    "\n",
    "print(\"user_min: %d , user_max: %d\"%(user_min,user_max))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating predictor..\n"
     ]
    }
   ],
   "source": [
    "### s : current song entered by user\n",
    "### G : our songs network imported from Part 1\n",
    "print('Creating predictor..')\n",
    "recommendations = generateRec(s, song_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = \"data/test_data.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_users = get_users(test_data)\n",
    "\n",
    "\n",
    "test_users = {}\n",
    "for k,v in enumerate(new_users):\n",
    "    test_users[v] = k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# project users to micro-genres\n",
    "user_micro_genres = project_songs_to_users(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get neighbors of each node in the user's training set\n",
    "def nearestneighbors(G, node):\n",
    "    result = []\n",
    "    for k, v in first_five_train.items():\n",
    "        # set max neighborhood size to 50\n",
    "        for i in range(50):\n",
    "            result.append(G.neighbors(song for song in v))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## write prediction to file\n",
    "json.dumps(dict(nearestneighbors(G, node) for node in user_micro_genres))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/moses/miniconda3/envs/16b/lib/python3.6/site-packages/sklearn/cross_validation.py:41: 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": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r users\n",
    "%store -r songs_recommended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4736842105263158\n"
     ]
    }
   ],
   "source": [
    "train_users, test_users, train_songs_recommended, test_songs_recommended = train_test_split(users, songs_recommended, random_state=6)\n",
    "\n",
    "# check classification accuracy of KNN with K=50\n",
    "knn = KNeighborsClassifier(n_neighbors=50)\n",
    "knn.fit(train_users, train_songs_recommended)\n",
    "predicted_songs = knn.predict(test_users)\n",
    "metrics.accuracy_score(test_songs_recommended, predicted_songs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(\n",
    "    ax, matrix, labels, title='Confusion matrix', fontsize=9):\n",
    "\n",
    "    ax.set_xticks([x for x in range(len(labels))])\n",
    "    ax.set_yticks([y for y in range(len(labels))])\n",
    "\n",
    "    #Place labels on minor ticks\n",
    "    ax.set_xticks([x + 0.5 for x in range(len(labels))], minor=True)\n",
    "    ax.set_xticklabels(labels, rotation='90', fontsize=fontsize, minor=True)\n",
    "    ax.set_yticks([y + 0.5 for y in range(len(labels))], minor=True)\n",
    "    ax.set_yticklabels(labels[::-1], fontsize=fontsize, minor=True)\n",
    "\n",
    "    # Hide major tick labels\n",
    "    ax.tick_params(which='major', labelbottom='off', labelleft='off')\n",
    "\n",
    "    # Finally, hide minor tick marks\n",
    "    ax.tick_params(which='minor', width=0)\n",
    "\n",
    "    # Plot heat map\n",
    "    proportions = [1. * row / sum(row) for row in matrix]\n",
    "    ax.pcolor(np.array(proportions[::-1]), cmap=plt.cm.Reds)\n",
    "\n",
    "    # Plot counts as text\n",
    "    for row in range(len(matrix)):\n",
    "        for col in range(len(matrix[row])):\n",
    "            confusion = matrix[::-1][row][col]\n",
    "            if confusion != 0:\n",
    "                ax.text(col + 0.5, row + 0.5, int(confusion),\n",
    "                        fontsize=fontsize,\n",
    "                        horizontalalignment='center',\n",
    "                        verticalalignment='center')\n",
    "\n",
    "    # Add finishing touches\n",
    "    ax.grid(True, linestyle=':')\n",
    "    ax.set_title(title, fontsize=fontsize)\n",
    "    ax.set_xlabel('Test Songs', fontsize=fontsize)\n",
    "    ax.set_ylabel('Validation Songs', fontsize=fontsize)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/moses/miniconda3/envs/16b/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n",
      "/Users/moses/miniconda3/envs/16b/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "plot_confusion_matrix(ax, predicted_songs, labels=test_users, fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "########## PRE-PROCESSING STUFF   ##########"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove Key-Value pairs with less than 25 Songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data_dict = data_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24720"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in sorted(new_data_dict, key=lambda k: len(new_data_dict[k]), reverse=True):\n",
    "    if len(new_data_dict[k]) < 25:\n",
    "        del new_data_dict[k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "274"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "json.dump(new_data_dict, open(\"new_data_dict.txt\",'w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cat new_data_dict.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Match SongIDs to TrackIDs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Echo Nest Tastes Profiles contained a bug where there were mismatches between song-track IDs from those available in the Million Song Dataset. The mismatched songs are stored in a file **song_track_mismatches.txt** and the affected songs are removed manually from ***taste_profiles.csv**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "tastes = pd.read_csv('data/taste_profiles.csv')\n",
    "songs = pd.read_csv('data/songs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "songs = songs.drop(['song_name', 'danceability', 'duration','energy','loudness'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>song_id</th>\n",
       "      <th>track_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SOGSOUE12A58A76443</td>\n",
       "      <td>TRARRPG12903CD1DE9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SOVVDCO12AB0187AF7</td>\n",
       "      <td>TRARRER128F9328521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SOZQSGL12AF72A9145</td>\n",
       "      <td>TRARREF128F422FD96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SOBTEHX12A6D4FBF18</td>\n",
       "      <td>TRARRQO128F427B5F5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SOXGDVW12AB01864E7</td>\n",
       "      <td>TRARRMK12903CDF793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              song_id            track_id\n",
       "0  SOGSOUE12A58A76443  TRARRPG12903CD1DE9\n",
       "1  SOVVDCO12AB0187AF7  TRARRER128F9328521\n",
       "2  SOZQSGL12AF72A9145  TRARREF128F422FD96\n",
       "3  SOBTEHX12A6D4FBF18  TRARRQO128F427B5F5\n",
       "4  SOXGDVW12AB01864E7  TRARRMK12903CDF793"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "songs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>User</th>\n",
       "      <th>Song</th>\n",
       "      <th>Playcount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d6589314c0a9bcbca4fee0c93b14bc402363afea</td>\n",
       "      <td>SOBONKR12A58A7A7E0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5a905f000fc1ff3df7ca807d57edb608863db05d</td>\n",
       "      <td>SOMVTRL12A67AE0921</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a820d2d4f16bbd53be9e41e0417dfb234bfdfba8</td>\n",
       "      <td>SOGKEGN12AB0185355</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3f152d355d53865a2ca27ac5ceeffb7ebaea0a26</td>\n",
       "      <td>SOQGETC12AB017F1E5</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3f152d355d53865a2ca27ac5ceeffb7ebaea0a26</td>\n",
       "      <td>SOYZLWW12AB0186148</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       User                Song  Playcount\n",
       "0  d6589314c0a9bcbca4fee0c93b14bc402363afea  SOBONKR12A58A7A7E0         26\n",
       "1  5a905f000fc1ff3df7ca807d57edb608863db05d  SOMVTRL12A67AE0921         28\n",
       "2  a820d2d4f16bbd53be9e41e0417dfb234bfdfba8  SOGKEGN12AB0185355         26\n",
       "3  3f152d355d53865a2ca27ac5ceeffb7ebaea0a26  SOQGETC12AB017F1E5         26\n",
       "4  3f152d355d53865a2ca27ac5ceeffb7ebaea0a26  SOYZLWW12AB0186148         55"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tastes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "songs_dict = dict()\n",
    "for index, row in songs.iterrows():\n",
    "    songs_dict[row[0]] = row[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SOGSOUE12A58A76443': 'TRARRPG12903CD1DE9',\n",
       " 'SOVVDCO12AB0187AF7': 'TRARRER128F9328521'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import islice\n",
    "dict(islice(songs_dict.items(), 0, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of major micro-genres 85\n",
      "\n",
      "Main communities and their center node: {4: 'SODSOBX12AC4686B4D', 9: 'SOTIDVB12A58A7E5C7', 11: 'SOVBQWR12A8C137843', 16: 'SOYWZTU12AB0180E26', 19: 'SOJLDJL12A81C238A0', 25: 'SOWPAXV12A67ADA046', 32: 'SOSXQPZ12AB01800CB', 33: 'SOXYNYD12A8C13DF7B', 36: 'SOTAMUT12A8C13757E', 42: 'SOFMHGN12A8C136BCB', 52: 'SOCIJOM12A58A7CBF8', 55: 'SOKJEMV12A8C1374AD', 57: 'SOWRILA12A8C1360D0', 59: 'SOQTFZH12AB0188264', 62: 'SOCUDML12A5829113A', 63: 'SOPOSOZ12AB018C568', 64: 'SOPSDCQ12A8C135CB6', 65: 'SODOOMJ12AB0185178', 68: 'SOGXGUK12A6D4FC1B4', 72: 'SOUTQDI12A67AE11FD', 75: 'SOACLMC12A8C13FA4C', 79: 'SOLBPPU12AB0187DD7', 86: 'SODRQRZ12A58A7BBC4', 88: 'SOYNXWG12AC468F51A', 97: 'SOEEEIL12A8C138EEE', 99: 'SOUNCKW12AB0188044', 109: 'SONOVXO12AB01877FE', 110: 'SOYBNLW12A6D4FC930', 124: 'SOGRGML12A8C136D75', 140: 'SOUTLPX12A8C133854', 142: 'SOTUHSG12A8AE474EA', 157: 'SOVNIWE12A8C144C9E', 159: 'SOEDGSG12AB0184A57', 161: 'SOVQQYE12AB0183BD8', 166: 'SOXMSDY12A8C142C7E', 176: 'SOLXPTW12AB017D6EB', 178: 'SOZLTVI12A6D4F77B8', 198: 'SOFVBVJ12A8C1404EB', 201: 'SOIUGDX12A6D4FDECA', 229: 'SOTESSB12AB017F56D', 234: 'SOVVYPN12A8C1435D7', 242: 'SOWIAKP12AC3DFA050', 243: 'SOAOFMX12A8C1421AD', 256: 'SOTODXV12AB017E98D', 281: 'SORSZKZ12A8C13FEF1', 289: 'SOKLQWF12A8C1423F2', 290: 'SOVONGN12A58A791F9', 295: 'SOJYYHN12AB0182966', 301: 'SOAWDUW12AB0183A26', 322: 'SOESVCK12AB0181CCE', 326: 'SOJNBYN12AB0184232', 328: 'SOXRWWL12A8C1345F8', 343: 'SOAOFAT12AB018975D', 344: 'SOOCYBE12A58A7928D', 348: 'SORYYCT12A8C13FFAE', 374: 'SOWCTGJ12AB0184820', 383: 'SOBSVDV12AB0186089', 389: 'SOHJJTY12A8C139876', 405: 'SOWIOVE12AB0183FF9', 454: 'SOIQQYQ12A6D4F4AB7', 464: 'SOORFMU12A8C13C17C', 466: 'SONHEUO12AF72A4792', 540: 'SOMOYIF12AB01838BF', 592: 'SOTIBYV12A8C135CF7', 593: 'SOJMXSD12A6D4F81F8', 613: 'SOVOHEI12AB017C590', 614: 'SOOJJQO12A6D4FB913', 619: 'SOFGDDZ12AB0186E67', 626: 'SOFHZHN12AB0180EA6', 631: 'SONYFKQ12A58A7B29F', 658: 'SOANXLR12A6D4F82BF', 796: 'SOACCCG12AC468C7BC', 886: 'SOJMWVB12A8C14216A', 889: 'SODGYDA12A8C140E96', 901: 'SOCWHIM12A6D4FAAA7', 905: 'SOSYPFY12A58A7BF1D', 1005: 'SOFQGCQ12AB0183609', 1065: 'SOMORCS12A58A7E211', 1282: 'SOOONHL12AB01824B1', 1297: 'SOKIXDM12A8C136E04', 1386: 'SOSGKSM12A6D4F5626', 1508: 'SOGDDAA12A8C1391A9', 1592: 'SOFCVXH12AB0184394'}\n"
     ]
    }
   ],
   "source": [
    "print('Number of major micro-genres', len(ind.nodes()))\n",
    "print('\\nMain communities and their center node:', communities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set training data to be first half of a user's playlist\n",
    "# set testing data to be second half of a user's playlist\n",
    "train_data, test_data = train_test_split(X, y, test_size=(len(all_users[k])//2)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training data to be first half of a user's playlist\n",
    "train_data = {k: all_users[k][0:(len(all_users[k])//2)] for k in all_users.keys()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'c0ff0f1c93f67c1fb372b36b1b08bb4c76bead7d': ['SOAKPQJ12A8C13D812',\n",
       "  'SOBGXEU12A8AE45903',\n",
       "  'SOBQWQX12A58A80CF8',\n",
       "  'SOBWWUF12A8C13AC82',\n",
       "  'SOCSBXQ12AB01806AC',\n",
       "  'SOEYEQN12A58A75F3F',\n",
       "  'SOFNSLY12A8C13B1C2',\n",
       "  'SOFTXKI12A6D4F71DA',\n",
       "  'SOFUHZF12A6D4F5A3F',\n",
       "  'SOHZPVD12AB01839E8',\n",
       "  'SOIOZPA12A8C137498',\n",
       "  'SOJDIWD12AB0186CE9',\n",
       "  'SOLAPGI12AF72A3955',\n",
       "  'SONMSZZ12A6701F352'],\n",
       " '283882c3d18ff2ad0e17124002ec02b847d06e9a': ['SOAKMDU12A8C1346A9',\n",
       "  'SOAXGDH12A8C13F8A1',\n",
       "  'SOAYCLH12A81C22D59',\n",
       "  'SOCCYYG12AB0184DE8',\n",
       "  'SOCQOZB12AB0185685',\n",
       "  'SOFEJPJ12A8C145455',\n",
       "  'SOGBFOO12A6D4FC933',\n",
       "  'SOHFJAQ12AB017E4AF',\n",
       "  'SOHFNKO12AB017C772',\n",
       "  'SOHGWFC12AB017F2E7',\n",
       "  'SOKUECJ12A6D4F6129',\n",
       "  'SOMCAFM12A58A7B024',\n",
       "  'SOMKDZU12AB0185690',\n",
       "  'SOOALOT12A8C13ABD9']}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sanity check\n",
    "dict(islice(train_data.items(), 0, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# testing data to be second half of a user's playlist\n",
    "test_data = {k: all_users[k][(len(all_users[k])//2):] for k in all_users.keys()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'c0ff0f1c93f67c1fb372b36b1b08bb4c76bead7d': ['SOOOLOP12AB0189B72',\n",
       "  'SOOPHIF12A6D4F71DC',\n",
       "  'SOPAJOR12A58A81CC0',\n",
       "  'SOTSJHY12AF729C1A9',\n",
       "  'SOUNQLL12A6D4F5A3B',\n",
       "  'SOUYDLS12A6D4F6C0B',\n",
       "  'SOVGANS12A81C2268D',\n",
       "  'SOVNFJP12AF72A6545',\n",
       "  'SOVNHMU12A6D4F5A38',\n",
       "  'SOWKLHD12A67020290',\n",
       "  'SOXALRW12A8159E8D5',\n",
       "  'SOXASRE12A6D4F6C0C',\n",
       "  'SOXKBTV12AF72A3A89',\n",
       "  'SOXXZRM12A6D4F7F22'],\n",
       " '283882c3d18ff2ad0e17124002ec02b847d06e9a': ['SOODSPH12AB01819C3',\n",
       "  'SOOEEPE12A8AE459A4',\n",
       "  'SOOJQUT12A8C13EDA8',\n",
       "  'SOPUCYA12A8C13A694',\n",
       "  'SOQYRUF12AC3DF7136',\n",
       "  'SOSLQGG12A6D4F5045',\n",
       "  'SOSNGDI12A6D4FB4A1',\n",
       "  'SOTEFFR12A8C144765',\n",
       "  'SOTLAOJ12A6D4FB4B2',\n",
       "  'SOURSIU12A8C140B9A',\n",
       "  'SOWJIBM12A81C2242B',\n",
       "  'SOWPRYI12A58A7B51A',\n",
       "  'SOXIEDM12AB018A952',\n",
       "  'SOYDETO12A6D4F6F7E',\n",
       "  'SOZVCRW12A67ADA0B7']}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sanity check\n",
    "dict(islice(test_data.items(), 0, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the nearest 12 neighbors in the database based on similarity\n",
    "join_statement = 'sid_tag JOIN tids ON (sim_measure >= 0.7) JOIN \\\n",
    "tags ON (tags.ROWID=sim_measure) JOIN track_id ON (sim_measure >= 0.7)'\n",
    "\n",
    "query = \"SELECT tids.tid, song_name, artist_name FROM {0} LIMIT 12;\".format(join_statement)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Juelz Santana': '45', 'Beenie Man': '35'}\n"
     ]
    }
   ],
   "source": [
    "pd.read_sql_query(query[0], conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "______"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "conn = sqlite3.connect(\"data/similar_songs.db\")\n",
    "cursor = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlite3.Cursor at 0x1a272a8810>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See tables in database\n",
    "cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_sql_query(\"SELECT * FROM songs\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# User 1\n",
    "# 7 out of 14 similarities : \n",
    "# Juelz Santana - Why ; \n",
    "# Juelz Santana - Squalie; \n",
    "# Juelz Santana - This Is For My Homies; \n",
    "# Juelz Santana - Shottas;\n",
    "# Juelz Santana - Lil' Boy Fresh; \n",
    "# Juelz Santana - Intro (What The Game's Been Missing);\n",
    "# Juelz Santana - Let's Go\n",
    "\n",
    "\n",
    "#   50% accuracy for querried user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# User 2\n",
    "# 6 out of 15 similarities : \n",
    "# Los Campesinos - You! Me! Dancing!\n",
    "# Angus & Julia Stone - Mango Tree\n",
    "# The Killers - Somebody Told Me\n",
    "# Angus & Julia Stone - Just A Boy\n",
    "# Modest Mouse - Float On\n",
    "# Angus & Julia Stone - Black Crow\n",
    "\n",
    "#   ~40% accuracy for querried user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################################################################################################\n",
    "#                       AUTOMATING SONG COMMUNITY DATABASE SEARCH\n",
    "##############################################################################################################\n",
    "\n",
    "\n",
    "\n",
    "\n",
    " \n",
    "#     Pushed database to Heroku postgresql dynos ................. DONE\n",
    "#     Database server: https://surumen-mixtape.herokuapp.com  ........ SERVER LIVE\n",
    "\n",
    "\n",
    "#     Front-end query site hosted on Github page:   https://surumen.github.io/mixtape/ ........... DONE\n",
    "#     Code for query site:     https://github.com/surumen/mixtape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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