Nearest Neighbor from eucalyptus (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>acousticness</th>\n",
       "      <th>artist_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>explicit</th>\n",
       "      <th>id</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.995</td>\n",
       "      <td>Carl Woitschach</td>\n",
       "      <td>0.708</td>\n",
       "      <td>158648</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0</td>\n",
       "      <td>6KbQ3uYMLKb5jDxLF7wYDD</td>\n",
       "      <td>0.563</td>\n",
       "      <td>10</td>\n",
       "      <td>0.151</td>\n",
       "      <td>-12.428</td>\n",
       "      <td>1</td>\n",
       "      <td>Singende Bataillone 1. Teil</td>\n",
       "      <td>0</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.0506</td>\n",
       "      <td>118.469</td>\n",
       "      <td>0.779</td>\n",
       "      <td>1928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness      artist_name  danceability  duration_ms  energy  explicit  \\\n",
       "0         0.995  Carl Woitschach         0.708       158648   0.195         0   \n",
       "\n",
       "                       id  instrumentalness  key  liveness  loudness  mode  \\\n",
       "0  6KbQ3uYMLKb5jDxLF7wYDD             0.563   10     0.151   -12.428     1   \n",
       "\n",
       "                         title  popularity release_date  speechiness    tempo  \\\n",
       "0  Singende Bataillone 1. Teil           0         1928       0.0506  118.469   \n",
       "\n",
       "   valence  year  \n",
       "0    0.779  1928  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"id\",\"title\", \"acousticness\", \"danceability\", \"duration_ms\", \"energy\", \"instrumentalness\",  \"key\", \"liveness\", \"loudness\", \"mode\", \"speechiness\", \"tempo\", \"valence\",\"popularity\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(169909, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_wnames = np.array(df['title'])\n",
    "X = np.array(df.drop(['id','popularity','title'], axis = 1))\n",
    "y = np.array(df['popularity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import decomposition\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "pca = decomposition.PCA(n_components=8)\n",
    "X_scaled = StandardScaler().fit_transform(X)\n",
    "X_pca = pca.fit_transform(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import csr_matrix, hstack\n",
    "X_final = csr_matrix(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_final, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                 metric_params=None, n_jobs=-1, n_neighbors=11, p=2,\n",
       "                 radius=1.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "knn_params = {'n_neighbors':range(1,10)}\n",
    "knn = NearestNeighbors(n_jobs=-1, n_neighbors=11)\n",
    "knn.fit(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for Things I Can't Change:\n",
      "\n",
      "1: Head Club, with distance of 0.5847750395046579\n",
      "2: More or Less, with distance of 0.6474652171441817\n",
      "3: Deadbolt, with distance of 0.6943955096867742\n",
      "4: How About You, with distance of 0.719054918477085\n",
      "5: Hyper Music, with distance of 0.7199137321348987\n",
      "6: Let Forever Be, with distance of 0.7216250975089511\n",
      "7: Trade Mistakes, with distance of 0.759473342768027\n",
      "8: Free (with Drew Love) - Party Favor Remix, with distance of 0.7693480078918741\n",
      "9: What Lies Beneath, with distance of 0.7775268404080933\n",
      "10: Hear Me Now, with distance of 0.7945041356683352\n"
     ]
    }
   ],
   "source": [
    "X_pcadf = pd.DataFrame(X_pca)\n",
    "query_index=np.random.choice(X_wnames.shape[0])\n",
    "distances, indices = knn.kneighbors(X_pcadf.iloc[query_index,:].values.reshape(1,-1), n_neighbors =11)\n",
    "for i in range(0,len(distances.flatten())):\n",
    "    if i==0:\n",
    "        print(\"Recommendations for {0}:\\n\".format(X_wnames[query_index]))\n",
    "    else:\n",
    "        print('{0}: {1}, with distance of {2}'.format(i, X_wnames[indices.flatten()[i]], distances.flatten()[i]))"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}