020_build_models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from sklearn.cluster import * "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pickle.load(open(\"music_backup.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘models’: File exists\n"
     ]
    }
   ],
   "source": [
    "%mkdir models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=8, max_iter=10000, algorithm = 'auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=10000,\n",
       "       n_clusters=8, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(df.iloc[:,3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 4, 7, ..., 6, 6, 7], dtype=int32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.predict(df.iloc[:,3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = df.iloc[:,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "names.insert(2, \"kmean_prediction\", kmeans.predict(df.iloc[:,3:]), True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(kmeans, open(\"models/kmeans.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean Shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_shift = MeanShift(bandwidth=1000).fit(df.iloc[:,3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 9, 1, ..., 0, 0, 9])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_shift.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "names.insert(2, \"mean_shift_prediction\", mean_shift.labels_, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(mean_shift, open(\"models/mean_shift.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save Modelled Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>index</th>\n",
       "      <th>source</th>\n",
       "      <th>mean_shift_prediction</th>\n",
       "      <th>kmean_prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>/home/echo/Music/The Killers/The Killers/02 Mr...</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>/home/echo/Music/The Killers/Hot Fuss/02 Mr. B...</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>/home/echo/Music/The Killers/Hot Fuss/04 Someb...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>/home/echo/Music/The Killers/Day &amp; Age/08 Neon...</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>/home/echo/Music/The Killers/Day &amp; Age/09 The ...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1671</td>\n",
       "      <td>0</td>\n",
       "      <td>/home/echo/Music/Alter Bridge/Blackbird/06 Bef...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1672</td>\n",
       "      <td>0</td>\n",
       "      <td>/home/echo/Music/Alter Bridge/Blackbird/10 Wat...</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1673</td>\n",
       "      <td>0</td>\n",
       "      <td>/home/echo/Music/Alter Bridge/One Day Remains/...</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1674</td>\n",
       "      <td>0</td>\n",
       "      <td>/home/echo/Music/Alter Bridge/One Day Remains/...</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1675</td>\n",
       "      <td>0</td>\n",
       "      <td>/home/echo/Music/Alter Bridge/One Day Remains/...</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1676 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index                                             source  \\\n",
       "0        0  /home/echo/Music/The Killers/The Killers/02 Mr...   \n",
       "1        0  /home/echo/Music/The Killers/Hot Fuss/02 Mr. B...   \n",
       "2        0  /home/echo/Music/The Killers/Hot Fuss/04 Someb...   \n",
       "3        0  /home/echo/Music/The Killers/Day & Age/08 Neon...   \n",
       "4        0  /home/echo/Music/The Killers/Day & Age/09 The ...   \n",
       "...    ...                                                ...   \n",
       "1671     0  /home/echo/Music/Alter Bridge/Blackbird/06 Bef...   \n",
       "1672     0  /home/echo/Music/Alter Bridge/Blackbird/10 Wat...   \n",
       "1673     0  /home/echo/Music/Alter Bridge/One Day Remains/...   \n",
       "1674     0  /home/echo/Music/Alter Bridge/One Day Remains/...   \n",
       "1675     0  /home/echo/Music/Alter Bridge/One Day Remains/...   \n",
       "\n",
       "      mean_shift_prediction  kmean_prediction  \n",
       "0                         9                 7  \n",
       "1                         9                 4  \n",
       "2                         1                 7  \n",
       "3                         0                 6  \n",
       "4                         1                 7  \n",
       "...                     ...               ...  \n",
       "1671                      1                 7  \n",
       "1672                      0                 6  \n",
       "1673                      0                 6  \n",
       "1674                      0                 6  \n",
       "1675                      9                 7  \n",
       "\n",
       "[1676 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = names.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "names.to_csv(\"modelling_results.csv\")"
   ]
  }
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