recommender_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libaries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import spotipy\n",
    "from spotipy.oauth2 import SpotifyClientCredentials\n",
    "import creds\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading in song dataset\n",
    "df = pd.read_csv('drake_songs_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "track_uri           0\n",
       "track_name          0\n",
       "album_name          0\n",
       "duration_ms         0\n",
       "danceability        0\n",
       "energy              0\n",
       "key                 0\n",
       "loudness            0\n",
       "speechiness         0\n",
       "acousticness        0\n",
       "instrumentalness    0\n",
       "liveness            0\n",
       "valence             0\n",
       "tempo               0\n",
       "mode                0\n",
       "type                0\n",
       "id                  0\n",
       "uri                 0\n",
       "track_href          0\n",
       "analysis_url        0\n",
       "time_signature      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get features we want to include in the cosine similarity calculation\n",
    "features = [\n",
    "        'danceability', 'energy', 'key', 'loudness',\n",
    "        'speechiness', 'acousticness', 'instrumentalness', \n",
    "        'liveness', 'valence', 'tempo'\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.557</td>\n",
       "      <td>0.774</td>\n",
       "      <td>7</td>\n",
       "      <td>-5.275</td>\n",
       "      <td>0.3510</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3960</td>\n",
       "      <td>0.397</td>\n",
       "      <td>111.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.754</td>\n",
       "      <td>0.449</td>\n",
       "      <td>7</td>\n",
       "      <td>-9.211</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.033200</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.5520</td>\n",
       "      <td>0.357</td>\n",
       "      <td>77.169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.529</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.711</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.0930</td>\n",
       "      <td>0.366</td>\n",
       "      <td>165.921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.835</td>\n",
       "      <td>0.626</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.833</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.058900</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.3960</td>\n",
       "      <td>0.350</td>\n",
       "      <td>91.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.060300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>0.505</td>\n",
       "      <td>0.536</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.293</td>\n",
       "      <td>0.1550</td>\n",
       "      <td>0.075900</td>\n",
       "      <td>0.055600</td>\n",
       "      <td>0.0656</td>\n",
       "      <td>0.322</td>\n",
       "      <td>84.756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>0.828</td>\n",
       "      <td>0.591</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.217</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.019900</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.3610</td>\n",
       "      <td>0.202</td>\n",
       "      <td>119.251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>0.903</td>\n",
       "      <td>0.669</td>\n",
       "      <td>2</td>\n",
       "      <td>-6.851</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.068500</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.536</td>\n",
       "      <td>105.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>0.380</td>\n",
       "      <td>0.840</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.528</td>\n",
       "      <td>0.3110</td>\n",
       "      <td>0.029100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.387</td>\n",
       "      <td>159.939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>0.854</td>\n",
       "      <td>0.671</td>\n",
       "      <td>11</td>\n",
       "      <td>-6.303</td>\n",
       "      <td>0.2540</td>\n",
       "      <td>0.176000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2610</td>\n",
       "      <td>0.366</td>\n",
       "      <td>104.038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>429 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     danceability  energy  key  loudness  speechiness  acousticness  \\\n",
       "0           0.557   0.774    7    -5.275       0.3510      0.012000   \n",
       "1           0.754   0.449    7    -9.211       0.1090      0.033200   \n",
       "2           0.529   0.673    0    -4.711       0.1750      0.000307   \n",
       "3           0.835   0.626    1    -5.833       0.1250      0.058900   \n",
       "4           0.817   0.440   10    -8.482       0.0734      0.060300   \n",
       "..            ...     ...  ...       ...          ...           ...   \n",
       "424         0.505   0.536    1    -5.293       0.1550      0.075900   \n",
       "425         0.828   0.591    1    -6.217       0.1090      0.019900   \n",
       "426         0.903   0.669    2    -6.851       0.0519      0.068500   \n",
       "427         0.380   0.840    1    -4.528       0.3110      0.029100   \n",
       "428         0.854   0.671   11    -6.303       0.2540      0.176000   \n",
       "\n",
       "     instrumentalness  liveness  valence    tempo  \n",
       "0            0.000000    0.3960    0.397  111.975  \n",
       "1            0.000083    0.5520    0.357   77.169  \n",
       "2            0.000002    0.0930    0.366  165.921  \n",
       "3            0.000060    0.3960    0.350   91.030  \n",
       "4            0.000001    0.3300    0.544  142.024  \n",
       "..                ...       ...      ...      ...  \n",
       "424          0.055600    0.0656    0.322   84.756  \n",
       "425          0.000323    0.3610    0.202  119.251  \n",
       "426          0.000028    0.1140    0.536  105.081  \n",
       "427          0.000000    0.8700    0.387  159.939  \n",
       "428          0.000000    0.2610    0.366  104.038  \n",
       "\n",
       "[429 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get information about the user's track, returns track info (dictionary) or None if not a valid song\n",
    "def get_user_track():\n",
    "    # Get user track\n",
    "    track = input('Enter track name: ')\n",
    "    # Make API call to verify if it is a spotify song\n",
    "    # Initialize the Spotify API client with client credentials\n",
    "    sp = spotipy.Spotify(client_credentials_manager=SpotifyClientCredentials(client_id=creds.CLIENT_ID, client_secret=creds.CLIENT_SECRET))\n",
    "\n",
    "    # Make API call to search for the user's song made by Drake and extract the audio features of the song if a valid song\n",
    "    search_results = sp.search(q='track:' + track + ' artist:Drake', type='track', limit=1)\n",
    "\n",
    "    # Check if song is an actual track in Spotify\n",
    "    if search_results['tracks']['items']:\n",
    "        return search_results\n",
    "    return None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that calculates cosine similarity and makes recommendations\n",
    "def cosine_recs(drake_dataset, features, n_recs):\n",
    "    # Get the user's track\n",
    "    user_track = get_user_track()\n",
    "    # Check if valid track\n",
    "    if user_track is not None:\n",
    "        # Check if track is in the drake song dataset\n",
    "        if user_track['tracks']['items'][0]['name'] in drake_dataset['track_name'].values:\n",
    "            # Start recommendation system\n",
    "            \n",
    "            # Create an indices series to get the index of a track given its name\n",
    "            indices = pd.Series(drake_dataset.index, index = drake_dataset['track_name'])\n",
    "            user_idx = indices[user_track['tracks']['items'][0]['name']]\n",
    "\n",
    "            # Scale data\n",
    "            scaler =  StandardScaler()\n",
    "            scaled_df = scaler.fit_transform(drake_dataset[features])\n",
    "\n",
    "            # Calculate cosine similarity matrix\n",
    "            cos_matrix = cosine_similarity(scaled_df, scaled_df)\n",
    "\n",
    "            # Create a series with the similarity scores in descending order\n",
    "            scores = pd.Series(cos_matrix[user_idx]).sort_values(ascending = False)\n",
    "\n",
    "            # Get indexes of top N recommendations\n",
    "            rec_indexes = list(scores.iloc[1:(n_recs + 1)].index)\n",
    "\n",
    "            return df.iloc[rec_indexes]\n",
    "            \n",
    "    return None\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get information about the user's track, returns track info (dictionary) or None if not a valid song\n",
    "def get_user_track(user_track):\n",
    "    # Make API call to verify if it is a spotify song\n",
    "    # Initialize the Spotify API client with client credentials\n",
    "    sp = spotipy.Spotify(client_credentials_manager=SpotifyClientCredentials(client_id=creds.CLIENT_ID, client_secret=creds.CLIENT_SECRET))\n",
    "\n",
    "    # Make API call to search for the user's song made by Drake and extract the audio features of the song if a valid song\n",
    "    search_results = sp.search(q='track:' + user_track, type='track', limit=1)\n",
    "\n",
    "    # Check if song is an actual track in Spotify\n",
    "    if search_results['tracks']['items']:\n",
    "        return search_results\n",
    "    return None\n",
    "\n",
    "# Function that calculates cosine similarity and makes recommendations\n",
    "def cosine_recs(drake_dataset, features, n_recs, user_track):\n",
    "    # Get the user's track\n",
    "    user_track = get_user_track(user_track)\n",
    "    # Check if valid track\n",
    "    if user_track is not None:\n",
    "        # Check if track is in the drake song dataset\n",
    "        if user_track['tracks']['items'][0]['name'] in drake_dataset['track_name'].values:\n",
    "            # Start recommendation system\n",
    "            \n",
    "            # Create an indices series to get the index of a track given its name\n",
    "            indices = pd.Series(drake_dataset.index, index = drake_dataset['track_name'])\n",
    "            user_idx = indices[user_track['tracks']['items'][0]['name']]\n",
    "\n",
    "            # Scale data\n",
    "            scaler =  StandardScaler()\n",
    "            scaled_df = scaler.fit_transform(drake_dataset[features])\n",
    "\n",
    "            # Calculate cosine similarity matrix\n",
    "            cos_matrix = cosine_similarity(scaled_df, scaled_df)\n",
    "\n",
    "            # Create a series with the similarity scores in descending order\n",
    "            scores = pd.Series(cos_matrix[user_idx]).sort_values(ascending = False)\n",
    "\n",
    "            # Get indexes of top N recommendations\n",
    "            rec_indexes = list(scores.iloc[1:(n_recs + 1)].index)\n",
    "\n",
    "            return df.iloc[rec_indexes]\n",
    "            \n",
    "    return None\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that calculates cosine similarity and makes recommendations\n",
    "def cosine_recs(drake_dataset, features, n_recs, user_track):\n",
    "    # Check if valid track\n",
    "    if user_track is not None:\n",
    "        # Check if track is in the drake song dataset\n",
    "        if user_track in drake_dataset['track_name'].values:\n",
    "            # Start recommendation system\n",
    "            \n",
    "            # Create an indices series to get the index of a track given its name\n",
    "            indices = pd.Series(drake_dataset.index, index = drake_dataset['track_name'])\n",
    "            user_idx = indices[user_track]\n",
    "\n",
    "            # Scale data\n",
    "            scaler =  StandardScaler()\n",
    "            scaled_df = scaler.fit_transform(drake_dataset[features])\n",
    "\n",
    "            # Calculate cosine similarity matrix\n",
    "            cos_matrix = cosine_similarity(scaled_df, scaled_df)\n",
    "\n",
    "            # Create a series with the similarity scores in descending order\n",
    "            scores = pd.Series(cos_matrix[user_idx]).sort_values(ascending = False)\n",
    "\n",
    "            # Get indexes of top N recommendations\n",
    "            rec_indexes = list(scores.iloc[1:(n_recs + 1)].index)\n",
    "\n",
    "            return df.iloc[rec_indexes]\n",
    "            \n",
    "    return None"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}