spotify_sparse-matrix.ipynb

{
 "cells": [
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      "10000\n"
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>session_id</th>\n",
       "      <th>track_id</th>\n",
       "      <th>skip_1</th>\n",
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       "      <th>skip_3</th>\n",
       "      <th>not_skipped</th>\n",
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       "      <th>hist_user_behavior_is_shuffle</th>\n",
       "      <th>skip_score</th>\n",
       "      <th>liking</th>\n",
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      "text/plain": [
       "   Unnamed: 0                              session_id  \\\n",
       "0           0  0_00006f66-33e5-4de7-a324-2d18e439fc1e   \n",
       "1           1  0_00079a23-1600-486a-91bd-5208be0c745a   \n",
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       "4           4  0_01a5f0dc-9938-48c9-92f1-c7e51f34d290   \n",
       "\n",
       "                                 track_id  skip_1  skip_2  skip_3  \\\n",
       "0  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.0   \n",
       "1  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.2   \n",
       "2  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.0   \n",
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       "4  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.2   \n",
       "\n",
       "   not_skipped  context_switch  hist_user_behavior_reason_start  \\\n",
       "0            1               0                              1.0   \n",
       "1            0               0                              0.3   \n",
       "2            1               0                              1.0   \n",
       "3            1               0                              0.3   \n",
       "4            0               0                              1.0   \n",
       "\n",
       "   hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  skip_score  \\\n",
       "0                            1.0                              1         1.0   \n",
       "1                            0.4                              0         0.2   \n",
       "2                            1.0                              0         1.0   \n",
       "3                            1.0                              0         1.0   \n",
       "4                            0.3                              1         0.2   \n",
       "\n",
       "   liking  rating  \n",
       "0     3.0     4.0  \n",
       "1     0.7     0.9  \n",
       "2     2.0     3.0  \n",
       "3     1.3     2.3  \n",
       "4     2.3     2.5  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy as sp\n",
    "\n",
    "df = pd.read_csv(r'F:\\AppliedDataScience\\data\\ratingdf_likescore.csv')\n",
    "print(len(df))\n",
    "df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sm_data = df[['session_id','track_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                               session_id  \\\n",
       "0  0_00006f66-33e5-4de7-a324-2d18e439fc1e   \n",
       "1  0_00079a23-1600-486a-91bd-5208be0c745a   \n",
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       "4  0_01a5f0dc-9938-48c9-92f1-c7e51f34d290   \n",
       "\n",
       "                                 track_id  \n",
       "0  t_0479f24c-27d2-46d6-a00c-7ec928f2b539  \n",
       "1  t_0479f24c-27d2-46d6-a00c-7ec928f2b539  \n",
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       "3  t_0479f24c-27d2-46d6-a00c-7ec928f2b539  \n",
       "4  t_0479f24c-27d2-46d6-a00c-7ec928f2b539  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dense_matrix2 = np.array(sm_data.as_matrix(columns = None), dtype=bool).astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dense_matrix2_df = pd.DataFrame(dense_matrix2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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       "   0  1\n",
       "0  1  1\n",
       "1  1  1\n",
       "2  1  1\n",
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       "4  1  1"
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    "dense_matrix2_df.head()"
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  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dense_matrix2_df.rename(columns = {0:'session_id', 1:'track_id'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   session_id  track_id\n",
       "0           1         1\n",
       "1           1         1\n",
       "2           1         1\n",
       "3           1         1\n",
       "4           1         1"
      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense_matrix2_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "0.0\n"
     ]
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   ],
   "source": [
    "dense_matrix = np.array(df.as_matrix(columns = {'session_id','track_id'}), dtype=bool).astype(np.int)\n",
    "sparse_matrix = sp.sparse.csr_matrix(dense_matrix)\n",
    "dense_matrix_df = pd.DataFrame(dense_matrix)\n",
    "arr = sparse_matrix.todense()\n",
    "\n",
    "from numpy import array\n",
    "from numpy import count_nonzero\n",
    "\n",
    "sparsity = 1.0 - count_nonzero(dense_matrix) / dense_matrix.size\n",
    "print(sparsity)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[1, 1],\n",
       "        [1, 1],\n",
       "        [1, 1],\n",
       "        ...,\n",
       "        [1, 1],\n",
       "        [1, 1],\n",
       "        [1, 1]], dtype=int32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_df = pd.DataFrame(arr,index = df.index,columns = ['session_id','track_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   session_id  track_id\n",
       "0           1         1\n",
       "1           1         1\n",
       "2           1         1\n",
       "3           1         1\n",
       "4           1         1"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 2)"
      ]
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     "execution_count": 39,
     "metadata": {},
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   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 14)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 2)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dfs = pd.SparseDataFrame(arr,index=df.session_id,columns=df.track_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10000.000000\n",
       "mean         1.326170\n",
       "std          1.472203\n",
       "min         -1.100000\n",
       "25%          0.300000\n",
       "50%          1.200000\n",
       "75%          2.900000\n",
       "max          4.000000\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rating.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gh = df.pivot(index = 'session_id', columns ='track_id', values = 'hist_user_behavior_is_shuffle').fillna(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.heatmap(gh)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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