ALS_model.ipynb

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   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.sparse as sparse\n",
    "import random\n",
    "import implicit\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from scipy.sparse.linalg import spsolve\n",
    "from tools import *\n",
    "import matplotlib.pyplot as plt"
   ]
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   "source": [
    "# Load the sparse matrix of playlists by songs from KNeighborsModel\n",
    "raw_data = pd.read_csv('data/sparse_df.csv')\n",
    "obscured_df = pd.DataFrame.copy(raw_data)\n",
    "\n",
    "# Obscure each playlist in set by removing half the songs\n",
    "obscureds = [] #indexes of obscured lists\n",
    "n_obscured = []\n",
    "\n",
    "#We need to add in the user's seed playlist to this dataset.\n",
    "for i, playlist in raw_data.iterrows():\n",
    "    obscured_list = remove_songs(list(playlist), 0.5)\n",
    "    obscured_df.iloc[i] = obscured_list\n",
    "    obscureds.append(raw_data.shape[0] + i)\n",
    "    n_obscured.append(int(sum(obscured_list)))\n",
    "    \n",
    "# This model requires that the playlists for which it recommends new songs for are a part of the fitting matrix\n",
    "data = pd.concat([raw_data, obscured_df])"
   ]
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   "source": [
    "# The implicit library expects data as a song-play matrix so we\n",
    "# create two matricies, one for fitting the model (song_play) \n",
    "# and one for recommendations (play_song)\n",
    "sparse_song_play = sparse.csr_matrix(data)\n",
    "sparse_play_song = sparse.csr_matrix(data.transpose())"
   ]
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    "# Initialize the als model and fit it using the sparse song-playlist matrix\n",
    "model = implicit.als.AlternatingLeastSquares(factors=15, regularization=0.1, iterations=30)\n",
    "\n",
    "# regularization and alpha_val based on literature\n",
    "\n",
    "# Calculate the confidence by multiplying it by our alpha value.\n",
    "alpha_val = 40 \n",
    "data_conf = (sparse_song_play * alpha_val).astype('double')\n",
    "\n",
    "#Fit the model\n",
    "model.fit(data_conf)"
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   ],
   "source": [
    "# Vary number of factors\n",
    "mean_rprec = []\n",
    "mean_ndcgs = []\n",
    "factors = np.arange(1, 51)\n",
    "\n",
    "for fac in factors:\n",
    "    # Initialize the als model and fit it using the sparse song-playlist matrix\n",
    "    model = implicit.als.AlternatingLeastSquares(factors=fac, regularization=0.1, iterations=30)\n",
    "\n",
    "    # regularization and alpha_val based on literature\n",
    "\n",
    "    # Calculate the confidence by multiplying it by our alpha value.\n",
    "    alpha_val = 40 \n",
    "    data_conf = (sparse_song_play * alpha_val).astype('double')\n",
    "\n",
    "    #Fit the model\n",
    "    model.fit(data_conf)\n",
    "    \n",
    "    pred_songs_bool = []\n",
    "\n",
    "    for index, seed in enumerate(obscureds):\n",
    "        recommended = model.recommend(seed, sparse_play_song, N=n_obscured[index])\n",
    "        pred_songs = []\n",
    "        for item in recommended:\n",
    "            song_idx, score = item\n",
    "            pred_songs.append(data.columns[song_idx])\n",
    "        pred_songs_bool.append(get_song_bool_list(pred_songs, data))\n",
    "\n",
    "    # Score the model's performance in the test set. \n",
    "    r2 = []\n",
    "    ndcgs = []\n",
    "    for i, pred in enumerate(pred_songs_bool):\n",
    "        pred_idx = [idx for idx, p in enumerate(pred) if p == 1]\n",
    "        right = np.array(raw_data.iloc[i]) - np.array(data.iloc[i+raw_data.shape[0]])\n",
    "        right_idx = [idx for idx, r in enumerate(right) if r == 1]\n",
    "        r2.append(sum([1 for pidx in pred_idx if pidx in right_idx])/len(pred_idx))\n",
    "        r = [1 if idx in pred_idx else 0 for idx in right_idx]\n",
    "        ndcgs.append(ndcg_at_k(r, len(r)))\n",
    "        \n",
    "    mean_rprec.append(np.mean(r2))\n",
    "    mean_ndcgs.append(np.mean(ndcgs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of factors that maximizes factors: 7\n",
      "NDCGS for that model: 0.15654781307272608\n",
      "R2 precision for that model: 0.0043448114702824975\n"
     ]
    }
   ],
   "source": [
    "best_idx = np.argmax(np.array(mean_rprec) + np.array(mean_ndcgs))\n",
    "print(\"Number of factors that maximizes factors: {}\".format(factors[best_idx]))\n",
    "print(\"NDCGS for that model: {}\".format(mean_ndcgs[best_idx]))\n",
    "print(\"R2 precision for that model: {}\".format(mean_rprec[best_idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(factors, mean_rprec)\n",
    "plt.title(\"R-precision scores vs. factors for ALS model\")\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"R-precision score\")\n",
    "plt.savefig(\"data/ALS_R.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(factors, mean_ndcgs)\n",
    "plt.title(\"NDCG scores vs. factors for ALS model\")\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"NDCG score\")\n",
    "plt.savefig(\"data/ALS_NDCG.png\")"
   ]
  },
  {
   "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": 4
}