Pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "#data\n",
    "import numpy as np\n",
    "import numpy_indexed as npi\n",
    "import pandas as pd\n",
    "from scipy.spatial.distance import cdist\n",
    "from sklearn.externals import joblib\n",
    "from keras.models import load_model\n",
    "#visualization\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "#APIs\n",
    "import spotipy\n",
    "from spotipy.oauth2 import SpotifyClientCredentials\n",
    "#API Keys\n",
    "import api_keys\n",
    "#Utils\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get model, y-j transformer, std-scaler, PCA, and RNN model\n",
    "model = load_model('cloud/model.h5')\n",
    "std_scaler = joblib.load('scalers/std_scaler.pkl')\n",
    "y_j = joblib.load('scalers/yeo_johnson.pkl')\n",
    "pca = joblib.load('scalers/pca.pkl')\n",
    "\n",
    "features = [\n",
    "    'mode',\n",
    "    'acousticness',\n",
    "    'danceability',\n",
    "    'energy',\n",
    "    'instrumentalness',\n",
    "    'liveness',\n",
    "    'loudness',\n",
    "    'speechiness',\n",
    "    'valence',\n",
    "] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Authenticate\n",
    "sp = spotipy.Spotify(\n",
    "    client_credentials_manager = SpotifyClientCredentials(\n",
    "        client_id=api_keys.CLIENT_ID,\n",
    "        client_secret=api_keys.CLIENT_SECRET\n",
    "    )\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Playlist():\n",
    "    playlists = {}\n",
    "    def __init__(self):\n",
    "        self.name = input('Playlist Name:')\n",
    "        Playlist.playlists[self.name] = self\n",
    "        self.init_song_strings = []\n",
    "        self.search_results = []\n",
    "        self.recommended_track_ids = [] #list of track ids straight from spotify\n",
    "        self.trax = [] #all tracks as dict\n",
    "        self.df = None #this is where the data goes\n",
    "        self.playlist = None\n",
    "        \n",
    "        #input your songs\n",
    "        self.init_song_strings.append(input('Song 1: '))\n",
    "        self.init_song_strings.append(input('Song 2: '))\n",
    "        self.init_song_strings.append(input('Song 3: '))\n",
    "        \n",
    "        # DO EVERYTHING\n",
    "        self.get_recommendations()\n",
    "        self.get_features()\n",
    "        self.transform()\n",
    "        self.build_playlist() \n",
    "        self.show_playlist() \n",
    "        self.do_pca()\n",
    "        \n",
    "    def get_recommendations(self):\n",
    "        print('Getting Recommendations...')\n",
    "        for ss in self.init_song_strings:\n",
    "            r = sp.search(ss,limit=1)['tracks']['items'][0]\n",
    "            self.search_results.append({\n",
    "                        'id':r['id'],\n",
    "                        'artists':[i['name'] for i in r['artists']],\n",
    "                        'name':r['name']\n",
    "                })\n",
    "        for id_ in tqdm(self.search_results):\n",
    "            #append to self.recommended_track_ids\n",
    "            results = sp.recommendations(seed_tracks = [id_['id']],limit=100)\n",
    "            for r in results['tracks']:\n",
    "                if r['id'] not in [i['id'] for i in self.recommended_track_ids]:\n",
    "                    self.recommended_track_ids.append({\n",
    "                        'id':r['id'],\n",
    "                        'artists':[i['name'] for i in r['artists']],\n",
    "                        'name':r['name']\n",
    "                        }) #there may be duplicates\n",
    "        print('Getting a Few More...')\n",
    "        results_2 = sp.recommendations(\n",
    "            seed_tracks = [id_['id'] for id_ in self.search_results],\n",
    "            limit=100\n",
    "        )\n",
    "        count = 0\n",
    "        for r in results_2['tracks']:\n",
    "            if r['id'] not in [i['id'] for i in self.recommended_track_ids]:\n",
    "                count += 1\n",
    "                self.recommended_track_ids.append({\n",
    "                    'id':r['id'],\n",
    "                    'artists':[i['name'] for i in r['artists']],\n",
    "                    'name':r['name']\n",
    "                    }) #there may be duplicates\n",
    "        print('There were',count,'more!')\n",
    "    \n",
    "    def get_features(self):\n",
    "        print('Getting Initial Song Features')\n",
    "        for id_ in tqdm(self.search_results):\n",
    "            dict_ = sp.audio_features(id_['id'])[0]\n",
    "            dict_.update(id_)\n",
    "            self.trax.append(dict_)\n",
    "        print('Getting Recommended Song Features')\n",
    "        n = 100\n",
    "        results = []\n",
    "        broken_list = [self.recommended_track_ids[i * n:(i + 1) * n] for i in range(\n",
    "            (len(self.recommended_track_ids) + n - 1) // n )]\n",
    "        for list_ in broken_list:\n",
    "            results += sp.audio_features([id_['id'] for id_ in list_])\n",
    "        for i, id_ in enumerate(self.recommended_track_ids):\n",
    "            results[i].update(id_)\n",
    "            self.trax.append(results[i])\n",
    "\n",
    "    \n",
    "    def transform(self):\n",
    "        '''\n",
    "        rehape and then apply yeo-johnson and standard scaler transformations - \n",
    "        these are already trained above and are global vars\n",
    "        '''\n",
    "        print('Applying Transformations...')\n",
    "\n",
    "        columns = ['id','artists','name','tempo','time_signature','key',] + features\n",
    "        self.df = pd.DataFrame(self.trax)[columns].dropna()\n",
    "        self.df[features[1:]] = std_scaler.transform(y_j.transform(self.df[features[1:]]))\n",
    "        self.playlist = self.df.iloc[0:3].copy()\n",
    "        \n",
    "    def rnn_predict(self):\n",
    "        return model.predict(np.array(\n",
    "            [np.array(\n",
    "                self.playlist[features]\n",
    "            )]\n",
    "        ))[0,-1]\n",
    "    \n",
    "    @staticmethod\n",
    "    def tempo_similarity(t1,t2):\n",
    "        if t1 <= 0:\n",
    "             return -1\n",
    "        t2 *= (t2 > 0)\n",
    "        return np.cos(2*np.pi*np.log2(t1/t2))\n",
    "    \n",
    "    @staticmethod\n",
    "    def key_similarity(s1,s2):\n",
    "        '''between -1 and 1, 1 being most similar\n",
    "        uses the circle of fifths - optimized for broadcasting\n",
    "        perfect fifth and same octave are given values of 1.\n",
    "        s1 - a single song (row of dataframe)\n",
    "        s2 - many songs (multiple rows of dataframe)\n",
    "        '''\n",
    "        #get relative major:\n",
    "        #this is for each and is NOT OPTIMIZED >:(\n",
    "        k1 = s1['key']\n",
    "        k2 = s2['key']\n",
    "        m1 = s1['mode']\n",
    "        m2 = s2['mode']\n",
    "        #broadcast dat b00lean\n",
    "        k1 += 3*(m1==0)\n",
    "        k2 += 3*(m2==0)\n",
    "        #get remainder\n",
    "        k1,k2 = np.remainder((k1,k2),12)\n",
    "        #get the position\n",
    "        circle_of_fifths = {0:0,7:1,2:2,9:3,4:4,11:5,6:6,1:7,8:8,3:9,10:10,5:11,}\n",
    "        #return similarity\n",
    "        #https://stackoverflow.com/questions/32243062/numpy-broadcast-through-dictionary\n",
    "        diff = np.abs(\n",
    "            circle_of_fifths[k1] - npi.remap(k2, list(circle_of_fifths.keys()), list(circle_of_fifths.values()))\n",
    "        )\n",
    "        diff = np.abs((diff>6)*12-diff)\n",
    "        return 1 - ((diff == 0) + diff - 1)/2.5\n",
    "    \n",
    "    def argmin_song(self,songs):\n",
    "        '''get the next song song which minimizes objective function,\n",
    "        which itself is a function of key_similarity, tempo_similarity,\n",
    "        and euclidian distance to the vector given by rnn_predict.\n",
    "        alpha, beta, gamma, and delta are tuning parameters.\n",
    "        '''\n",
    "        song = self.playlist.iloc[-1]\n",
    "        \n",
    "        alpha = 1 #flow - how much to count distance\n",
    "        beta = 1 #sweetness - how much to count key similarity\n",
    "        gamma = 1 #smoothness - how much to count tempo similarity\n",
    "        delta = 1.2 #spicynesss - scaler for RNN vector\n",
    "        \n",
    "        distance = cdist([self.rnn_predict()*delta],songs[features[1:]])[0]\n",
    "        key_similarity = Playlist.key_similarity(song,songs)\n",
    "        tempo_similarity = Playlist.tempo_similarity(song['tempo'],songs['tempo']).values\n",
    "        \n",
    "        \n",
    "        \n",
    "        return songs.reset_index().iloc[np.argmin(\n",
    "            alpha*distance - beta*key_similarity - gamma*tempo_similarity\n",
    "        )]\n",
    "    \n",
    "    def build_playlist(self):\n",
    "        '''use self.argmin_song() to build the whole playlist'''\n",
    "        print('Determining Best Song Sequence...')\n",
    "        for i in tqdm(range(10)):\n",
    "            songs = self.df[~self.df['id'].isin(self.playlist['id'].to_list())]\n",
    "            self.playlist = self.playlist.append(self.argmin_song(songs), ignore_index = True)\n",
    "    \n",
    "    def show_playlist(self):\n",
    "        'give the playlist showing (artist,song,id) in a dataframe'\n",
    "        display(self.playlist[['artists','name','id']])\n",
    "    \n",
    "    def do_pca(self):\n",
    "        'do the nice 3d graph based on global trained PCA'\n",
    "        print('Visualizing...')\n",
    "        x,y,z = pca.transform(self.playlist[features[1:]]).T\n",
    "        fig = go.Figure(data=[go.Scatter3d(\n",
    "            x=x,y=y,z=z,\n",
    "            mode='lines+markers',\n",
    "            text=self.playlist['artists'].apply(lambda x: ', '.join(x)) \\\n",
    "            + ' - ' + self.playlist['name'].astype(str),\n",
    "            marker=dict(\n",
    "                size=5,\n",
    "                color=z,                # set color to an array/list of desired values\n",
    "                colorscale='Viridis',   # choose a colorscale\n",
    "                opacity=0.8,\n",
    "            ),\n",
    "            line=dict(\n",
    "            color='#000000',\n",
    "            width=1\n",
    "        ) \n",
    "        )])\n",
    "        fig.update_layout(\n",
    "            margin=dict(l=0, r=0, b=0, t=0),\n",
    "            scene={\n",
    "                'xaxis_title':'PC0',\n",
    "                'yaxis_title':'PC1',\n",
    "                'zaxis_title':'PC2'\n",
    "            }\n",
    "        ),\n",
    "        fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Playlist Name:cole\n",
      "Song 1: thundercat them changes\n",
      "Song 2: acid rap chance\n",
      "Song 3: talking heads girlfriend\n",
      "Getting Recommendations...\n"
     ]
    },
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     "text": [
      "\n",
      "Getting a Few More...\n",
      "There were 64 more!\n",
      "Getting Initial Song Features\n"
     ]
    },
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     "text": [
      "\n",
      "Getting Recommended Song Features\n",
      "Applying Transformations...\n",
      "Determining Best Song Sequence...\n"
     ]
    },
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       "<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>artists</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Thundercat]</td>\n",
       "      <td>Them Changes</td>\n",
       "      <td>5dcZUsgcMW57ju0A379O7c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Chance the Rapper, Vic Mensa, Twista]</td>\n",
       "      <td>Cocoa Butter Kisses</td>\n",
       "      <td>0aMHIW1lqrulVCx0LLlr6a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Talking Heads]</td>\n",
       "      <td>Girlfriend Is Better - Live</td>\n",
       "      <td>2KCB47p9nvR5ZyT5hJKZi9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[The Psychedelic Furs]</td>\n",
       "      <td>Love My Way</td>\n",
       "      <td>1mqytV5qZcmzG6MgvJCpkQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[Todd Rundgren]</td>\n",
       "      <td>I Saw the Light</td>\n",
       "      <td>0B1zVsLqmV9ibIFdNS5tGs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[Pavement]</td>\n",
       "      <td>Cut Your Hair</td>\n",
       "      <td>3QuzslMgjJGsw6wC1E9eRi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[David Bowie]</td>\n",
       "      <td>Let's Dance (Single Version) [2002 Remastered ...</td>\n",
       "      <td>4cVHMHgmWgudD399ZdhQ3L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[LCD Soundsystem]</td>\n",
       "      <td>I Can Change</td>\n",
       "      <td>2073QOEC8rBtSyTsRyaWiP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[TV Girl]</td>\n",
       "      <td>Lovers Rock</td>\n",
       "      <td>1H7zdcRD0gLGQY0w5ejGgX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[WILLOW]</td>\n",
       "      <td>Wait a Minute!</td>\n",
       "      <td>0y60itmpH0aPKsFiGxmtnh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[TV Girl]</td>\n",
       "      <td>Birds Dont Sing</td>\n",
       "      <td>0V34LU5aBA3hYgq178hFFY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[The Sugarcubes]</td>\n",
       "      <td>Hit</td>\n",
       "      <td>6YkpmWq5D3PL8wediqF7S1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[Kurt Vile]</td>\n",
       "      <td>Loading Zones</td>\n",
       "      <td>69o00fGvsei250jH7bF781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   artists  \\\n",
       "0                             [Thundercat]   \n",
       "1   [Chance the Rapper, Vic Mensa, Twista]   \n",
       "2                          [Talking Heads]   \n",
       "3                   [The Psychedelic Furs]   \n",
       "4                          [Todd Rundgren]   \n",
       "5                               [Pavement]   \n",
       "6                            [David Bowie]   \n",
       "7                        [LCD Soundsystem]   \n",
       "8                                [TV Girl]   \n",
       "9                                 [WILLOW]   \n",
       "10                               [TV Girl]   \n",
       "11                        [The Sugarcubes]   \n",
       "12                             [Kurt Vile]   \n",
       "\n",
       "                                                 name                      id  \n",
       "0                                        Them Changes  5dcZUsgcMW57ju0A379O7c  \n",
       "1                                 Cocoa Butter Kisses  0aMHIW1lqrulVCx0LLlr6a  \n",
       "2                         Girlfriend Is Better - Live  2KCB47p9nvR5ZyT5hJKZi9  \n",
       "3                                         Love My Way  1mqytV5qZcmzG6MgvJCpkQ  \n",
       "4                                     I Saw the Light  0B1zVsLqmV9ibIFdNS5tGs  \n",
       "5                                       Cut Your Hair  3QuzslMgjJGsw6wC1E9eRi  \n",
       "6   Let's Dance (Single Version) [2002 Remastered ...  4cVHMHgmWgudD399ZdhQ3L  \n",
       "7                                        I Can Change  2073QOEC8rBtSyTsRyaWiP  \n",
       "8                                         Lovers Rock  1H7zdcRD0gLGQY0w5ejGgX  \n",
       "9                                      Wait a Minute!  0y60itmpH0aPKsFiGxmtnh  \n",
       "10                                    Birds Dont Sing  0V34LU5aBA3hYgq178hFFY  \n",
       "11                                                Hit  6YkpmWq5D3PL8wediqF7S1  \n",
       "12                                      Loading Zones  69o00fGvsei250jH7bF781  "
      ]
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     "text": [
      "Visualizing...\n"
     ]
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      "text/plain": [
       "<__main__.Playlist at 0x13805acf8>"
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     },
     "execution_count": 22,
     "metadata": {},
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   "source": [
    "Playlist() "
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   "execution_count": null,
   "metadata": {},
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   "source": []
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