AmazonReviewsGrocery.ipynb

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    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from data_preprocessing import timepoint_split, transform_indices, reindex_data, generate_ranks\n",
    "from pure_svd import build_svd_model, svd_scoring, svd_gridsearch\n",
    "from knn import uknn_scoring, uknn_gridsearch, build_uknn_model\n",
    "from evaluation import model_evaluate, topn_recommendations\n",
    "\n",
    "%config InlineBackend.figure_format = \"svg\"\n",
    "plt.rcParams[\"figure.figsize\"] = 10, 6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35f98071-4f36-4afc-961e-80a2a32f91a6",
   "metadata": {},
   "source": [
    "## Amazon-reviews"
   ]
  },
  {
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   "id": "6c4e7fc7-37a5-440d-97cf-701c863bacc5",
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   "source": [
    "# !mkdir -p data/amazon_reviews\n",
    "# !wget https://jmcauley.ucsd.edu/data/amazon_v2/categoryFilesSmall/Grocery_and_Gourmet_Food.csv --no-check-certificate\n",
    "# !mv Grocery_and_Gourmet_Food.csv data/amazon_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eaa53ec9-b7de-4a38-968f-50223d5a2037",
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = pd.read_csv(\"data/amazon_reviews/Grocery_and_Gourmet_Food.csv\", names=[\"userid\", \"itemid\", \"rating\", \"timestamp\"])"
   ]
  },
  {
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       "5074160"
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   "source": [
    "len(reviews)"
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    {
     "data": {
      "text/plain": [
       "userid        283507\n",
       "itemid       2695974\n",
       "rating             5\n",
       "timestamp       5221\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "reviews.nunique()"
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   "source": [
    "# K-core filtering\n",
    "\n",
    "k_filter = 20\n",
    "\n",
    "n_interactions = reviews.groupby(\"userid\").size()\n",
    "enough = n_interactions[n_interactions > k_filter].index\n",
    "reviews = reviews[reviews.userid.isin(enough)]\n",
    "\n",
    "n_interactions = reviews.groupby(\"itemid\").size()\n",
    "enough = n_interactions[n_interactions > k_filter].index\n",
    "reviews = reviews[reviews.itemid.isin(enough)]"
   ]
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       "0.193212"
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   ],
   "source": [
    "len(reviews) / 1e6"
   ]
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   "execution_count": 8,
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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "plt.hist(reviews.groupby(\"userid\").size(), bins=50)\n",
    "plt.yscale(\"log\")\n",
    "plt.title(\"Amount of reviews per user\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b968e822-d62f-4993-beb7-16df6f062845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(107153, 74000, 4421)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split for final evaluation\n",
    "_train, _test, _holdout = timepoint_split(reviews, time_split_q=0.95)\n",
    "\n",
    "len(_train), len(_test), len(_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1950ede2-eeb6-49af-b0ae-ac7331583ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76578, 23575, 2965)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Additional split for hyperparameter tuning\n",
    "_trainval, _testval, _holdoutval = timepoint_split(_train, time_split_q=0.95)\n",
    "\n",
    "len(_trainval), len(_testval), len(_holdoutval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c2d5493-4772-4025-ac7e-f69f7965543f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reindex_all(train, test, holdout=None, userid='userid', itemid='itemid'):\n",
    "    train_new, data_index = transform_indices(train, userid, itemid)\n",
    "    test_new = reindex_data(test, data_index, fields='items')\n",
    "    \n",
    "    if holdout is None:\n",
    "        return train_new, test_new, data_index\n",
    "    \n",
    "    holdout_new = reindex_data(holdout, data_index, fields='items')\n",
    "    return train_new, test_new, holdout_new, data_index\n",
    "\n",
    "\n",
    "trainval, testval, holdoutval, data_index_val = reindex_all(_trainval, _testval, _holdoutval)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4c71d6-5039-4b12-ad8d-1f8a2dcf5007",
   "metadata": {},
   "source": [
    "## Popular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ab988e86-8da0-449a-97a7-4604ae866731",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_description = {\n",
    "    \"n_users\": trainval.userid.nunique(),\n",
    "    \"n_items\": trainval.itemid.nunique(),\n",
    "    \"feedback\": \"rating\",\n",
    "    \"items\": \"itemid\",\n",
    "    \"users\": \"userid\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3a4f6279-294b-4758-8e05-be83839d298c",
   "metadata": {},
   "outputs": [],
   "source": [
    "topn = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "11182814-2ed0-4cb0-99c4-a1d9e6946299",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = [2 ** p for p in range(15)]\n",
    "\n",
    "popular_results = []\n",
    "\n",
    "for a in alphas:\n",
    "    def damped_mean(ratings):\n",
    "        return sum(ratings) / (len(ratings) + a)\n",
    "    \n",
    "    item2rating = trainval.groupby('itemid')[\"rating\"].apply(damped_mean)\n",
    "    best_items = item2rating.nlargest(topn).index.values\n",
    "    popular_recs = np.ones((len(holdoutval), topn)) * best_items\n",
    "    metric = model_evaluate(popular_recs, holdoutval, data_description, topn)    \n",
    "    \n",
    "    popular_results.append({\n",
    "        \"alpha\": a, \"metric\": metric\n",
    "    })\n",
    "\n",
    "popular_results = pd.DataFrame(popular_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ab75e283-6ec9-4258-a5be-d27231e179ea",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "plt.plot(popular_results[\"alpha\"], popular_results[\"metric\"])\n",
    "plt.xscale(\"log\")\n",
    "plt.xticks(popular_results[\"alpha\"], popular_results[\"alpha\"], rotation=30)\n",
    "plt.tick_params(which=\"minor\", size=0)\n",
    "plt.title(\"HitRate\")\n",
    "plt.xlabel(\"Alpha\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e365900a-e565-4fcf-b339-9da3cbc24703",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>256</td>\n",
       "      <td>0.009781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>512</td>\n",
       "      <td>0.009781</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1024</td>\n",
       "      <td>0.009781</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2048</td>\n",
       "      <td>0.009781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4096</td>\n",
       "      <td>0.009781</td>\n",
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      "text/plain": [
       "    alpha    metric\n",
       "8     256  0.009781\n",
       "9     512  0.009781\n",
       "10   1024  0.009781\n",
       "11   2048  0.009781\n",
       "12   4096  0.009781"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "popular_results = popular_results.sort_values(\"metric\", ascending=False)\n",
    "\n",
    "popular_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1320c36e-b66e-492b-a8e4-074c778a79e2",
   "metadata": {},
   "source": [
    "## Pure SVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c8d48a3a-4b29-49d4-9e18-f33146c0d656",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_description = {\n",
    "    \"n_users\": trainval.userid.nunique(),\n",
    "    \"n_items\": trainval.itemid.nunique(),\n",
    "    \"feedback\": \"rating\",\n",
    "    \"items\": \"itemid\",\n",
    "    \"users\": \"userid\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6a53bdc0-9f85-4107-80da-ecf2e49a6ed2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4, 6, 8, 12, 16, 24, 32, 48, 64, 96, 128, 192, 256, 384, 512, 768]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranks = [b * 2 ** p for p in range(0, 9) for b in [2, 3]]\n",
    "ranks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a3714f42-5b5d-4395-abf4-bcbdccc8394e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interaction matrix shape: (19038, 5548)\n",
      "Matrix density: 0.0006782151554667767\n",
      "CPU times: user 1min 4s, sys: 3.99 s, total: 1min 8s\n",
      "Wall time: 23.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pure_svd_results = svd_gridsearch(ranks, trainval, testval, holdoutval, data_description, topn=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7c0aabfe-7bc9-4ea6-84c3-27ef71919460",
   "metadata": {},
   "outputs": [],
   "source": [
    "pure_svd_results = pd.DataFrame(pure_svd_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "162af01d-d769-491c-9cf8-97e91aa1dec1",
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       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pure_svd_results[\"rank\"], pure_svd_results[\"metric\"])\n",
    "plt.xscale(\"log\")\n",
    "plt.xticks(pure_svd_results[\"rank\"], pure_svd_results[\"rank\"])\n",
    "plt.tick_params(which=\"minor\", size=0)\n",
    "plt.title(\"HitRate\")\n",
    "plt.xlabel(\"Rank\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd2bb9fd-6672-4aba-848d-1656610b40b0",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
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       "      <th>12</th>\n",
       "      <td>128</td>\n",
       "      <td>0.011467</td>\n",
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       "      <th>17</th>\n",
       "      <td>768</td>\n",
       "      <td>0.010793</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>192</td>\n",
       "      <td>0.009781</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>96</td>\n",
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       "      <th>10</th>\n",
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      "text/plain": [
       "    rank    metric\n",
       "12   128  0.011467\n",
       "17   768  0.010793\n",
       "13   192  0.009781\n",
       "11    96  0.009781\n",
       "10    64  0.009444"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pure_svd_results = pure_svd_results.sort_values(\"metric\", ascending=False)\n",
    "\n",
    "pure_svd_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "042f9329-1f9b-438a-87e9-14334f1e3235",
   "metadata": {},
   "source": [
    "## Scaled SVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ceaca7a6-6042-4f24-9bbb-c4154f9dd175",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scaled_svd import build_scaled_svd_model, scaled_svd_gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "179d63e4-4b38-4418-b97d-079431f2168d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4, 6, 8, 12, 16, 24, 32, 48, 64, 96, 128, 192, 256, 384, 512, 768]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranks = [b * 2 ** p for p in range(0, 9) for b in [2, 3]]\n",
    "ranks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6324550d-5dcb-4d80-a9bb-f9ef128aa101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.1, 0.3, 0.5, 0.7, 0.9])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scalings = np.arange(1, 10, 2) / 10\n",
    "scalings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bff6e532-6e43-4933-99d9-1d188c42c6b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaling 0.1\n",
      "Scaling 0.3\n",
      "Scaling 0.5\n",
      "Scaling 0.7\n",
      "Scaling 0.9\n",
      "CPU times: user 13min 50s, sys: 29.6 s, total: 14min 20s\n",
      "Wall time: 4min 49s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "scaled_svd_results = scaled_svd_gridsearch(ranks, scalings, trainval, testval, holdoutval, data_description, topn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c097986e-d4af-4ab3-8c1a-45b8e0e28493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>scaling</th>\n",
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       "      <th>48</th>\n",
       "      <td>128</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.011804</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>768</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.011804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>768</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.011467</td>\n",
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       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>768</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.011130</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
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      "text/plain": [
       "    rank  scaling    metric\n",
       "48   128      0.5  0.011804\n",
       "71   768      0.7  0.011804\n",
       "89   768      0.9  0.011467\n",
       "53   768      0.5  0.011130\n",
       "17   768      0.1  0.011130"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_svd_results = pd.DataFrame(scaled_svd_results)\n",
    "\n",
    "scaled_svd_results = scaled_svd_results.sort_values(\"metric\", ascending=False)\n",
    "\n",
    "scaled_svd_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29d6fd03-46f0-487f-8b1e-1159424c27f8",
   "metadata": {},
   "source": [
    "## SeqSVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c7233c15-db42-4ea9-80e3-b791a36a2928",
   "metadata": {},
   "outputs": [],
   "source": [
    "from seq_svd import full_seq_svd_gridsearch, build_seq_svd_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c0a9ef1a-fb99-4aae-a1eb-a2ed623cefaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4, 6, 8, 12, 16, 24, 32, 48, 64, 96, 128, 192]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranks = [b * 2 ** p for p in range(0, 7) for b in [2, 3]]\n",
    "ranks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "29536269-06a1-455f-9a1b-137e6543f4bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05, 0.15, 0.25, 0.35])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs = [0.25, 0.5]\n",
    "thresholds = [0.003, 0.03] \n",
    "powers = [1, 0.8]\n",
    "sim_types = [\"cosine\"]\n",
    "scalings = np.arange(0.5, 4.5) / 10\n",
    "scalings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cc23c137-fd24-48b1-938d-a9570ff7f0b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequence similarity coef: 0.25, power: 1, threshold: 0.003, similarity type: cosine, scaling: 0.05\n",
      "Sequence similarity coef: 0.25, power: 1, threshold: 0.003, similarity type: cosine, scaling: 0.15\n",
      "Sequence similarity coef: 0.25, power: 1, threshold: 0.003, similarity type: cosine, scaling: 0.25\n",
      "Sequence similarity coef: 0.25, power: 1, threshold: 0.003, similarity type: cosine, scaling: 0.35\n",
      "Sequence similarity coef: 0.25, power: 1, threshold: 0.03, similarity type: cosine, scaling: 0.05\n",
      "Sequence similarity coef: 0.25, power: 1, threshold: 0.03, similarity type: cosine, scaling: 0.15\n",
      "Sequence similarity coef: 0.25, power: 1, threshold: 0.03, similarity type: cosine, scaling: 0.25\n",
      "Sequence similarity coef: 0.25, power: 1, threshold: 0.03, similarity type: cosine, scaling: 0.35\n",
      "Sequence similarity coef: 0.25, power: 0.8, threshold: 0.003, similarity type: cosine, scaling: 0.05\n",
      "Sequence similarity coef: 0.25, power: 0.8, threshold: 0.003, similarity type: cosine, scaling: 0.15\n",
      "Sequence similarity coef: 0.25, power: 0.8, threshold: 0.003, similarity type: cosine, scaling: 0.25\n",
      "Sequence similarity coef: 0.25, power: 0.8, threshold: 0.003, similarity type: cosine, scaling: 0.35\n",
      "Sequence similarity coef: 0.25, power: 0.8, threshold: 0.03, similarity type: cosine, scaling: 0.05\n",
      "Sequence similarity coef: 0.25, power: 0.8, threshold: 0.03, similarity type: cosine, scaling: 0.15\n",
      "Sequence similarity coef: 0.25, power: 0.8, threshold: 0.03, similarity type: cosine, scaling: 0.25\n",
      "Sequence similarity coef: 0.25, power: 0.8, threshold: 0.03, similarity type: cosine, scaling: 0.35\n",
      "Sequence similarity coef: 0.5, power: 1, threshold: 0.003, similarity type: cosine, scaling: 0.05\n",
      "Sequence similarity coef: 0.5, power: 1, threshold: 0.003, similarity type: cosine, scaling: 0.15\n",
      "Sequence similarity coef: 0.5, power: 1, threshold: 0.003, similarity type: cosine, scaling: 0.25\n",
      "Sequence similarity coef: 0.5, power: 1, threshold: 0.003, similarity type: cosine, scaling: 0.35\n",
      "Sequence similarity coef: 0.5, power: 1, threshold: 0.03, similarity type: cosine, scaling: 0.05\n",
      "Sequence similarity coef: 0.5, power: 1, threshold: 0.03, similarity type: cosine, scaling: 0.15\n",
      "Sequence similarity coef: 0.5, power: 1, threshold: 0.03, similarity type: cosine, scaling: 0.25\n",
      "Sequence similarity coef: 0.5, power: 1, threshold: 0.03, similarity type: cosine, scaling: 0.35\n",
      "Sequence similarity coef: 0.5, power: 0.8, threshold: 0.003, similarity type: cosine, scaling: 0.05\n",
      "Sequence similarity coef: 0.5, power: 0.8, threshold: 0.003, similarity type: cosine, scaling: 0.15\n",
      "Sequence similarity coef: 0.5, power: 0.8, threshold: 0.003, similarity type: cosine, scaling: 0.25\n",
      "Sequence similarity coef: 0.5, power: 0.8, threshold: 0.003, similarity type: cosine, scaling: 0.35\n",
      "Sequence similarity coef: 0.5, power: 0.8, threshold: 0.03, similarity type: cosine, scaling: 0.05\n",
      "Sequence similarity coef: 0.5, power: 0.8, threshold: 0.03, similarity type: cosine, scaling: 0.15\n",
      "Sequence similarity coef: 0.5, power: 0.8, threshold: 0.03, similarity type: cosine, scaling: 0.25\n",
      "Sequence similarity coef: 0.5, power: 0.8, threshold: 0.03, similarity type: cosine, scaling: 0.35\n",
      "CPU times: user 26min 2s, sys: 2min 15s, total: 28min 18s\n",
      "Wall time: 11min 2s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "seq_svd_results = full_seq_svd_gridsearch(ranks, coefs, powers, thresholds, sim_types, scalings,\n",
    "                                          trainval, testval, holdoutval, data_description, topn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c6686051-10b6-4caa-a3a9-3a71f5ad2d84",
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_svd_results = pd.DataFrame(seq_svd_results)\n",
    "\n",
    "seq_svd_results = seq_svd_results.sort_values(\"metric\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0430f3b0-c6e8-4fcd-99dd-d9d4a5919585",
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_svd_results.to_csv(\"tuning_results/second_seq_svd_tuning_amazon_grocery.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266829fc",
   "metadata": {},
   "source": [
    "## UserKNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c0b11139",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_description = {\n",
    "    \"n_users\": trainval.userid.nunique(),\n",
    "    \"n_items\": trainval.itemid.nunique(),\n",
    "    \"feedback\": \"rating\",\n",
    "    \"weights\": \"weights\",\n",
    "    \"items\": \"itemid\",\n",
    "    \"users\": \"userid\",\n",
    "}\n",
    "\n",
    "trainval, testval = generate_ranks(trainval, data_description), generate_ranks(testval, data_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f08568e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "k_vals = [10, 25, 50, 100, 200, 250, 500, 1000, None]\n",
    "p_vals = [0] # np.linspace(0, 1, 21)\n",
    "\n",
    "config = {\n",
    "    \"similarity_A\": \"cosine\", \n",
    "    \"similarity_T\": None,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cf8781d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.13 s, sys: 229 ms, total: 1.36 s\n",
      "Wall time: 1.39 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "results = uknn_gridsearch(k_vals, p_vals, config, trainval, testval, holdoutval, data_description, topn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "33f0db98-4d76-40b8-a219-b7f589819db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_results = pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "55a15288",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>k</th>\n",
       "      <th>metric</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>0.018212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.017875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>250.0</td>\n",
       "      <td>0.017875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.017538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.017201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       k    metric\n",
       "3  100.0  0.018212\n",
       "4  200.0  0.017875\n",
       "5  250.0  0.017875\n",
       "1   25.0  0.017538\n",
       "2   50.0  0.017201"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_results.drop(\"power\", axis=1).sort_values('metric', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5f49dc32",
   "metadata": {},
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   ],
   "source": [
    "plt.plot(k_vals, knn_results.sort_values(\"k\", na_position=\"last\")[\"metric\"])\n",
    "\n",
    "plt.tick_params(which=\"minor\", size=0)\n",
    "plt.title(\"HitRate\")\n",
    "plt.xlabel(\"K\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04edade7",
   "metadata": {},
   "source": [
    "## SeqUserKNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "01cecc8f-d17b-402f-bf2c-097e4025a2ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 23 s, sys: 5.21 s, total: 28.2 s\n",
      "Wall time: 29.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "k_vals = [10, 25, 50, 100, 200, 250, 500, 1000, None]\n",
    "p_vals = np.linspace(0, 1, 21)\n",
    "\n",
    "config = {\n",
    "    \"similarity_A\": \"cosine\", \n",
    "    \"similarity_T\": \"cosine\",\n",
    "}\n",
    "\n",
    "results = uknn_gridsearch(k_vals, p_vals, config, trainval, testval, holdoutval, data_description, topn)"
   ]
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   "cell_type": "code",
   "execution_count": 41,
   "id": "edaf5f94-5554-49f8-afdf-331ff86f7296",
   "metadata": {},
   "outputs": [
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       "    power       k    metric\n",
       "62    0.3     NaN  0.021248\n",
       "61    0.3  1000.0  0.021248\n",
       "60    0.3   500.0  0.021248\n",
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    "seq_knn_results = pd.DataFrame(results)\n",
    "seq_knn_results.sort_values('metric', ascending=False).head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "179c5d8d",
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   "outputs": [
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       "#T_7507a_row13_col2, #T_7507a_row17_col0, #T_7507a_row17_col4, #T_7507a_row17_col6, #T_7507a_row17_col7, #T_7507a_row17_col8, #T_7507a_row18_col0, #T_7507a_row18_col3, #T_7507a_row18_col7, #T_7507a_row18_col8, #T_7507a_row19_col5, #T_7507a_row19_col6 {\n",
       "  background-color: #cacee5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7507a_row13_col4, #T_7507a_row14_col4, #T_7507a_row16_col6 {\n",
       "  background-color: #a2bcda;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7507a_row14_col1, #T_7507a_row15_col1, #T_7507a_row16_col1 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7507a_row14_col3, #T_7507a_row15_col4, #T_7507a_row16_col4 {\n",
       "  background-color: #c1cae2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7507a_row15_col2, #T_7507a_row16_col3, #T_7507a_row18_col4, #T_7507a_row19_col2, #T_7507a_row20_col2 {\n",
       "  background-color: #e0deed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7507a_row16_col2, #T_7507a_row17_col3, #T_7507a_row19_col3, #T_7507a_row19_col4, #T_7507a_row20_col4 {\n",
       "  background-color: #d9d8ea;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7507a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >k</th>\n",
       "      <th id=\"T_7507a_level0_col0\" class=\"col_heading level0 col0\" >nan</th>\n",
       "      <th id=\"T_7507a_level0_col1\" class=\"col_heading level0 col1\" >10.000000</th>\n",
       "      <th id=\"T_7507a_level0_col2\" class=\"col_heading level0 col2\" >25.000000</th>\n",
       "      <th id=\"T_7507a_level0_col3\" class=\"col_heading level0 col3\" >50.000000</th>\n",
       "      <th id=\"T_7507a_level0_col4\" class=\"col_heading level0 col4\" >100.000000</th>\n",
       "      <th id=\"T_7507a_level0_col5\" class=\"col_heading level0 col5\" >200.000000</th>\n",
       "      <th id=\"T_7507a_level0_col6\" class=\"col_heading level0 col6\" >250.000000</th>\n",
       "      <th id=\"T_7507a_level0_col7\" class=\"col_heading level0 col7\" >500.000000</th>\n",
       "      <th id=\"T_7507a_level0_col8\" class=\"col_heading level0 col8\" >1000.000000</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >power</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row0\" class=\"row_heading level0 row0\" >0.000000</th>\n",
       "      <td id=\"T_7507a_row0_col0\" class=\"data row0 col0\" >0.019899</td>\n",
       "      <td id=\"T_7507a_row0_col1\" class=\"data row0 col1\" >0.01315</td>\n",
       "      <td id=\"T_7507a_row0_col2\" class=\"data row0 col2\" >0.01855</td>\n",
       "      <td id=\"T_7507a_row0_col3\" class=\"data row0 col3\" >0.01889</td>\n",
       "      <td id=\"T_7507a_row0_col4\" class=\"data row0 col4\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row0_col5\" class=\"data row0 col5\" >0.02057</td>\n",
       "      <td id=\"T_7507a_row0_col6\" class=\"data row0 col6\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row0_col7\" class=\"data row0 col7\" >0.01990</td>\n",
       "      <td id=\"T_7507a_row0_col8\" class=\"data row0 col8\" >0.01990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row1\" class=\"row_heading level0 row1\" >0.050000</th>\n",
       "      <td id=\"T_7507a_row1_col0\" class=\"data row1 col0\" >0.019562</td>\n",
       "      <td id=\"T_7507a_row1_col1\" class=\"data row1 col1\" >0.01282</td>\n",
       "      <td id=\"T_7507a_row1_col2\" class=\"data row1 col2\" >0.01821</td>\n",
       "      <td id=\"T_7507a_row1_col3\" class=\"data row1 col3\" >0.01821</td>\n",
       "      <td id=\"T_7507a_row1_col4\" class=\"data row1 col4\" >0.01990</td>\n",
       "      <td id=\"T_7507a_row1_col5\" class=\"data row1 col5\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row1_col6\" class=\"data row1 col6\" >0.01990</td>\n",
       "      <td id=\"T_7507a_row1_col7\" class=\"data row1 col7\" >0.01956</td>\n",
       "      <td id=\"T_7507a_row1_col8\" class=\"data row1 col8\" >0.01956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row2\" class=\"row_heading level0 row2\" >0.100000</th>\n",
       "      <td id=\"T_7507a_row2_col0\" class=\"data row2 col0\" >0.020236</td>\n",
       "      <td id=\"T_7507a_row2_col1\" class=\"data row2 col1\" >0.01315</td>\n",
       "      <td id=\"T_7507a_row2_col2\" class=\"data row2 col2\" >0.01889</td>\n",
       "      <td id=\"T_7507a_row2_col3\" class=\"data row2 col3\" >0.01922</td>\n",
       "      <td id=\"T_7507a_row2_col4\" class=\"data row2 col4\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row2_col5\" class=\"data row2 col5\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row2_col6\" class=\"data row2 col6\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row2_col7\" class=\"data row2 col7\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row2_col8\" class=\"data row2 col8\" >0.02024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row3\" class=\"row_heading level0 row3\" >0.150000</th>\n",
       "      <td id=\"T_7507a_row3_col0\" class=\"data row3 col0\" >0.020236</td>\n",
       "      <td id=\"T_7507a_row3_col1\" class=\"data row3 col1\" >0.01349</td>\n",
       "      <td id=\"T_7507a_row3_col2\" class=\"data row3 col2\" >0.01855</td>\n",
       "      <td id=\"T_7507a_row3_col3\" class=\"data row3 col3\" >0.01821</td>\n",
       "      <td id=\"T_7507a_row3_col4\" class=\"data row3 col4\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row3_col5\" class=\"data row3 col5\" >0.01990</td>\n",
       "      <td id=\"T_7507a_row3_col6\" class=\"data row3 col6\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row3_col7\" class=\"data row3 col7\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row3_col8\" class=\"data row3 col8\" >0.02024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row4\" class=\"row_heading level0 row4\" >0.200000</th>\n",
       "      <td id=\"T_7507a_row4_col0\" class=\"data row4 col0\" >0.020573</td>\n",
       "      <td id=\"T_7507a_row4_col1\" class=\"data row4 col1\" >0.01349</td>\n",
       "      <td id=\"T_7507a_row4_col2\" class=\"data row4 col2\" >0.01889</td>\n",
       "      <td id=\"T_7507a_row4_col3\" class=\"data row4 col3\" >0.01922</td>\n",
       "      <td id=\"T_7507a_row4_col4\" class=\"data row4 col4\" >0.01990</td>\n",
       "      <td id=\"T_7507a_row4_col5\" class=\"data row4 col5\" >0.02057</td>\n",
       "      <td id=\"T_7507a_row4_col6\" class=\"data row4 col6\" >0.02057</td>\n",
       "      <td id=\"T_7507a_row4_col7\" class=\"data row4 col7\" >0.02057</td>\n",
       "      <td id=\"T_7507a_row4_col8\" class=\"data row4 col8\" >0.02057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row5\" class=\"row_heading level0 row5\" >0.250000</th>\n",
       "      <td id=\"T_7507a_row5_col0\" class=\"data row5 col0\" >0.020573</td>\n",
       "      <td id=\"T_7507a_row5_col1\" class=\"data row5 col1\" >0.01349</td>\n",
       "      <td id=\"T_7507a_row5_col2\" class=\"data row5 col2\" >0.01889</td>\n",
       "      <td id=\"T_7507a_row5_col3\" class=\"data row5 col3\" >0.01922</td>\n",
       "      <td id=\"T_7507a_row5_col4\" class=\"data row5 col4\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row5_col5\" class=\"data row5 col5\" >0.02057</td>\n",
       "      <td id=\"T_7507a_row5_col6\" class=\"data row5 col6\" >0.02057</td>\n",
       "      <td id=\"T_7507a_row5_col7\" class=\"data row5 col7\" >0.02057</td>\n",
       "      <td id=\"T_7507a_row5_col8\" class=\"data row5 col8\" >0.02057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row6\" class=\"row_heading level0 row6\" >0.300000</th>\n",
       "      <td id=\"T_7507a_row6_col0\" class=\"data row6 col0\" >0.021248</td>\n",
       "      <td id=\"T_7507a_row6_col1\" class=\"data row6 col1\" >0.01383</td>\n",
       "      <td id=\"T_7507a_row6_col2\" class=\"data row6 col2\" >0.01922</td>\n",
       "      <td id=\"T_7507a_row6_col3\" class=\"data row6 col3\" >0.01956</td>\n",
       "      <td id=\"T_7507a_row6_col4\" class=\"data row6 col4\" >0.02091</td>\n",
       "      <td id=\"T_7507a_row6_col5\" class=\"data row6 col5\" >0.02125</td>\n",
       "      <td id=\"T_7507a_row6_col6\" class=\"data row6 col6\" >0.02125</td>\n",
       "      <td id=\"T_7507a_row6_col7\" class=\"data row6 col7\" >0.02125</td>\n",
       "      <td id=\"T_7507a_row6_col8\" class=\"data row6 col8\" >0.02125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row7\" class=\"row_heading level0 row7\" >0.350000</th>\n",
       "      <td id=\"T_7507a_row7_col0\" class=\"data row7 col0\" >0.020236</td>\n",
       "      <td id=\"T_7507a_row7_col1\" class=\"data row7 col1\" >0.01383</td>\n",
       "      <td id=\"T_7507a_row7_col2\" class=\"data row7 col2\" >0.01720</td>\n",
       "      <td id=\"T_7507a_row7_col3\" class=\"data row7 col3\" >0.01821</td>\n",
       "      <td id=\"T_7507a_row7_col4\" class=\"data row7 col4\" >0.01990</td>\n",
       "      <td id=\"T_7507a_row7_col5\" class=\"data row7 col5\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row7_col6\" class=\"data row7 col6\" >0.02057</td>\n",
       "      <td id=\"T_7507a_row7_col7\" class=\"data row7 col7\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row7_col8\" class=\"data row7 col8\" >0.02024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row8\" class=\"row_heading level0 row8\" >0.400000</th>\n",
       "      <td id=\"T_7507a_row8_col0\" class=\"data row8 col0\" >0.019562</td>\n",
       "      <td id=\"T_7507a_row8_col1\" class=\"data row8 col1\" >0.01349</td>\n",
       "      <td id=\"T_7507a_row8_col2\" class=\"data row8 col2\" >0.01720</td>\n",
       "      <td id=\"T_7507a_row8_col3\" class=\"data row8 col3\" >0.01855</td>\n",
       "      <td id=\"T_7507a_row8_col4\" class=\"data row8 col4\" >0.01990</td>\n",
       "      <td id=\"T_7507a_row8_col5\" class=\"data row8 col5\" >0.01990</td>\n",
       "      <td id=\"T_7507a_row8_col6\" class=\"data row8 col6\" >0.01990</td>\n",
       "      <td id=\"T_7507a_row8_col7\" class=\"data row8 col7\" >0.01956</td>\n",
       "      <td id=\"T_7507a_row8_col8\" class=\"data row8 col8\" >0.01956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row9\" class=\"row_heading level0 row9\" >0.450000</th>\n",
       "      <td id=\"T_7507a_row9_col0\" class=\"data row9 col0\" >0.020236</td>\n",
       "      <td id=\"T_7507a_row9_col1\" class=\"data row9 col1\" >0.01417</td>\n",
       "      <td id=\"T_7507a_row9_col2\" class=\"data row9 col2\" >0.01754</td>\n",
       "      <td id=\"T_7507a_row9_col3\" class=\"data row9 col3\" >0.01821</td>\n",
       "      <td id=\"T_7507a_row9_col4\" class=\"data row9 col4\" >0.02057</td>\n",
       "      <td id=\"T_7507a_row9_col5\" class=\"data row9 col5\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row9_col6\" class=\"data row9 col6\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row9_col7\" class=\"data row9 col7\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row9_col8\" class=\"data row9 col8\" >0.02024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row10\" class=\"row_heading level0 row10\" >0.500000</th>\n",
       "      <td id=\"T_7507a_row10_col0\" class=\"data row10 col0\" >0.020236</td>\n",
       "      <td id=\"T_7507a_row10_col1\" class=\"data row10 col1\" >0.01417</td>\n",
       "      <td id=\"T_7507a_row10_col2\" class=\"data row10 col2\" >0.01754</td>\n",
       "      <td id=\"T_7507a_row10_col3\" class=\"data row10 col3\" >0.01821</td>\n",
       "      <td id=\"T_7507a_row10_col4\" class=\"data row10 col4\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row10_col5\" class=\"data row10 col5\" >0.01990</td>\n",
       "      <td id=\"T_7507a_row10_col6\" class=\"data row10 col6\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row10_col7\" class=\"data row10 col7\" >0.02024</td>\n",
       "      <td id=\"T_7507a_row10_col8\" class=\"data row10 col8\" >0.02024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row11\" class=\"row_heading level0 row11\" >0.550000</th>\n",
       "      <td id=\"T_7507a_row11_col0\" class=\"data row11 col0\" >0.018887</td>\n",
       "      <td id=\"T_7507a_row11_col1\" class=\"data row11 col1\" >0.01383</td>\n",
       "      <td id=\"T_7507a_row11_col2\" class=\"data row11 col2\" >0.01653</td>\n",
       "      <td id=\"T_7507a_row11_col3\" class=\"data row11 col3\" >0.01720</td>\n",
       "      <td id=\"T_7507a_row11_col4\" class=\"data row11 col4\" >0.01855</td>\n",
       "      <td id=\"T_7507a_row11_col5\" class=\"data row11 col5\" >0.01889</td>\n",
       "      <td id=\"T_7507a_row11_col6\" class=\"data row11 col6\" >0.01922</td>\n",
       "      <td id=\"T_7507a_row11_col7\" class=\"data row11 col7\" >0.01889</td>\n",
       "      <td id=\"T_7507a_row11_col8\" class=\"data row11 col8\" >0.01889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row12\" class=\"row_heading level0 row12\" >0.600000</th>\n",
       "      <td id=\"T_7507a_row12_col0\" class=\"data row12 col0\" >0.017875</td>\n",
       "      <td id=\"T_7507a_row12_col1\" class=\"data row12 col1\" >0.01349</td>\n",
       "      <td id=\"T_7507a_row12_col2\" class=\"data row12 col2\" >0.01518</td>\n",
       "      <td id=\"T_7507a_row12_col3\" class=\"data row12 col3\" >0.01619</td>\n",
       "      <td id=\"T_7507a_row12_col4\" class=\"data row12 col4\" >0.01720</td>\n",
       "      <td id=\"T_7507a_row12_col5\" class=\"data row12 col5\" >0.01821</td>\n",
       "      <td id=\"T_7507a_row12_col6\" class=\"data row12 col6\" >0.01821</td>\n",
       "      <td id=\"T_7507a_row12_col7\" class=\"data row12 col7\" >0.01788</td>\n",
       "      <td id=\"T_7507a_row12_col8\" class=\"data row12 col8\" >0.01788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row13\" class=\"row_heading level0 row13\" >0.650000</th>\n",
       "      <td id=\"T_7507a_row13_col0\" class=\"data row13 col0\" >0.017538</td>\n",
       "      <td id=\"T_7507a_row13_col1\" class=\"data row13 col1\" >0.01383</td>\n",
       "      <td id=\"T_7507a_row13_col2\" class=\"data row13 col2\" >0.01551</td>\n",
       "      <td id=\"T_7507a_row13_col3\" class=\"data row13 col3\" >0.01619</td>\n",
       "      <td id=\"T_7507a_row13_col4\" class=\"data row13 col4\" >0.01686</td>\n",
       "      <td id=\"T_7507a_row13_col5\" class=\"data row13 col5\" >0.01720</td>\n",
       "      <td id=\"T_7507a_row13_col6\" class=\"data row13 col6\" >0.01754</td>\n",
       "      <td id=\"T_7507a_row13_col7\" class=\"data row13 col7\" >0.01754</td>\n",
       "      <td id=\"T_7507a_row13_col8\" class=\"data row13 col8\" >0.01754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row14\" class=\"row_heading level0 row14\" >0.700000</th>\n",
       "      <td id=\"T_7507a_row14_col0\" class=\"data row14 col0\" >0.016526</td>\n",
       "      <td id=\"T_7507a_row14_col1\" class=\"data row14 col1\" >0.01248</td>\n",
       "      <td id=\"T_7507a_row14_col2\" class=\"data row14 col2\" >0.01518</td>\n",
       "      <td id=\"T_7507a_row14_col3\" class=\"data row14 col3\" >0.01585</td>\n",
       "      <td id=\"T_7507a_row14_col4\" class=\"data row14 col4\" >0.01686</td>\n",
       "      <td id=\"T_7507a_row14_col5\" class=\"data row14 col5\" >0.01653</td>\n",
       "      <td id=\"T_7507a_row14_col6\" class=\"data row14 col6\" >0.01653</td>\n",
       "      <td id=\"T_7507a_row14_col7\" class=\"data row14 col7\" >0.01653</td>\n",
       "      <td id=\"T_7507a_row14_col8\" class=\"data row14 col8\" >0.01653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row15\" class=\"row_heading level0 row15\" >0.750000</th>\n",
       "      <td id=\"T_7507a_row15_col0\" class=\"data row15 col0\" >0.016526</td>\n",
       "      <td id=\"T_7507a_row15_col1\" class=\"data row15 col1\" >0.01248</td>\n",
       "      <td id=\"T_7507a_row15_col2\" class=\"data row15 col2\" >0.01450</td>\n",
       "      <td id=\"T_7507a_row15_col3\" class=\"data row15 col3\" >0.01518</td>\n",
       "      <td id=\"T_7507a_row15_col4\" class=\"data row15 col4\" >0.01585</td>\n",
       "      <td id=\"T_7507a_row15_col5\" class=\"data row15 col5\" >0.01619</td>\n",
       "      <td id=\"T_7507a_row15_col6\" class=\"data row15 col6\" >0.01653</td>\n",
       "      <td id=\"T_7507a_row15_col7\" class=\"data row15 col7\" >0.01653</td>\n",
       "      <td id=\"T_7507a_row15_col8\" class=\"data row15 col8\" >0.01653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7507a_level0_row16\" class=\"row_heading level0 row16\" >0.800000</th>\n",
       "      <td id=\"T_7507a_row16_col0\" class=\"data row16 col0\" >0.016526</td>\n",
       "      <td id=\"T_7507a_row16_col1\" class=\"data row16 col1\" >0.01248</td>\n",
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       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f47064cadf0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq_knn_results.pivot(index=\"power\", columns=\"k\", values=\"metric\").style.format(\"{:.5f}\").background_gradient(high=0.3, axis = None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d3cc6d87-6e21-4c86-bbde-aa2abc69e17f",
   "metadata": {},
   "outputs": [
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       "<Figure size 1000x600 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(k_vals, seq_knn_results[lambda x: np.isclose(x[\"power\"], 0.3)][\"metric\"], label=\"SeqUserKNN\")\n",
    "plt.plot(k_vals, knn_results[\"metric\"], label=\"UserKNN\")\n",
    "plt.tick_params(which=\"minor\", size=0)\n",
    "plt.title(\"HitRate\")\n",
    "plt.xlabel(\"K\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d9a8f1-8602-4dea-b272-5e5337b98a86",
   "metadata": {},
   "source": [
    "## Final Eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ac9167bc-0adc-481d-b40e-883d9f22a285",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test, holdout, data_index = reindex_all(_train, _test, _holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "51d398c8-0574-4402-b0c0-f0915bd49cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_description = {\n",
    "    \"n_users\": train.userid.nunique(),\n",
    "    \"n_items\": train.itemid.nunique(),\n",
    "    \"feedback\": \"rating\",\n",
    "    \"weights\": \"weights\",\n",
    "    \"items\": \"itemid\",\n",
    "    \"users\": \"userid\",\n",
    "}\n",
    "\n",
    "train = generate_ranks(train, data_description)\n",
    "test = generate_ranks(test, data_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b6794125-b0a6-42c0-9753-861822da0861",
   "metadata": {},
   "outputs": [],
   "source": [
    "grand_total = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1042dca9-ea7b-4f87-932b-423d553d59b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "256.0\n"
     ]
    }
   ],
   "source": [
    "idx = popular_results.idxmax()[\"metric\"]\n",
    "best_alpha = popular_results.loc[idx][\"alpha\"]\n",
    "print(best_alpha)\n",
    "\n",
    "def damped_mean(ratings):\n",
    "    return sum(ratings) / (len(ratings) + best_alpha)\n",
    "\n",
    "item2rating = train.groupby('itemid')[\"rating\"].apply(damped_mean)\n",
    "best_items = item2rating.nlargest(topn).index.values\n",
    "popular_recs = np.ones((len(holdout), topn)) * best_items\n",
    "metric = model_evaluate(popular_recs, holdout, data_description, topn)    \n",
    "\n",
    "grand_total.append(\n",
    "    {\"method\": \"popularity\", \"metric\": metric}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ed1b60f1-ccf3-4c6b-9719-e18ed9ed622d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best rank: 128.0\n",
      "Interaction matrix shape: (22920, 5604)\n",
      "Matrix density: 0.000777585942726026\n"
     ]
    }
   ],
   "source": [
    "idx = pure_svd_results.idxmax()[\"metric\"]\n",
    "best_rank = pure_svd_results.loc[idx][\"rank\"]\n",
    "print(\"Best rank:\", best_rank)\n",
    "\n",
    "model = build_svd_model({\"rank\": best_rank}, train, data_description)\n",
    "scores = svd_scoring(model, test, data_description)\n",
    "svd_recs = topn_recommendations(scores, topn)\n",
    "\n",
    "metric = model_evaluate(svd_recs, holdout, data_description, topn)\n",
    "\n",
    "grand_total.append(\n",
    "    {\"method\": \"PureSVD\", \"metric\": metric}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3f5d271b-1f82-4e30-bc3b-888876e95f2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'rank': 128.0, 'scaling': 0.5}\n"
     ]
    }
   ],
   "source": [
    "idx = scaled_svd_results.idxmax()[\"metric\"]\n",
    "\n",
    "best_config = {\n",
    "    \"rank\": scaled_svd_results.loc[idx][\"rank\"], \n",
    "    \"scaling\": scaled_svd_results.loc[idx][\"scaling\"],\n",
    "}\n",
    "print(best_config)\n",
    "\n",
    "model = build_scaled_svd_model(best_config, train, data_description)\n",
    "scores = svd_scoring(model, test, data_description)\n",
    "svd_recs = topn_recommendations(scores, topn)\n",
    "\n",
    "metric = model_evaluate(svd_recs, holdout, data_description, topn)\n",
    "\n",
    "grand_total.append(\n",
    "    {\"method\": \"ScaledSVD\", \"metric\": metric}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2f2cb2b9-f2ad-4982-b94e-5e89d598e803",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'rank': 32, 'sequence_similarity_coef': 0.5, 'similarity_type': 'cosine', 'incomplete_cholesky_threshold': 0.03, 'scaling': 0.05, 'power': 0.8}\n"
     ]
    }
   ],
   "source": [
    "idx = seq_svd_results[\"metric\"].idxmax()\n",
    "\n",
    "best_config = {\n",
    "    \"rank\": seq_svd_results[\"rank\"].loc[idx], \n",
    "    \"sequence_similarity_coef\": seq_svd_results[\"sequence_similarity_coef\"].loc[idx],\n",
    "    \"similarity_type\": seq_svd_results[\"similarity_type\"].loc[idx],\n",
    "    \"incomplete_cholesky_threshold\": seq_svd_results[\"incomplete_cholesky_threshold\"].loc[idx],\n",
    "    \"scaling\": seq_svd_results[\"scaling\"].loc[idx],\n",
    "    \"power\": seq_svd_results[\"power\"].loc[idx],\n",
    "}\n",
    "print(best_config)\n",
    "\n",
    "model = build_seq_svd_model(best_config, train, data_description)\n",
    "scores = svd_scoring(model, test, data_description)\n",
    "svd_recs = topn_recommendations(scores, topn)\n",
    "\n",
    "metric = model_evaluate(svd_recs, holdout, data_description, topn)\n",
    "\n",
    "grand_total.append(\n",
    "    {\"method\": \"SeqSVD\", \"metric\": metric}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "133c2183",
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = knn_results[\"metric\"].idxmax()\n",
    "\n",
    "best_config = {\n",
    "    \"power\": knn_results[\"power\"].loc[idx],\n",
    "    \"k\": knn_results[\"k\"].loc[idx],\n",
    "    \"similarity_A\": \"cosine\",\n",
    "    \"similarity_T\": None\n",
    "}\n",
    "\n",
    "model = build_uknn_model(best_config, train, data_description)\n",
    "scores = uknn_scoring(model, test, data_description)\n",
    "uknn_recs = topn_recommendations(scores, topn)\n",
    "\n",
    "metric = model_evaluate(uknn_recs, holdout, data_description, topn)\n",
    "\n",
    "grand_total.append(\n",
    "    {\"method\": \"UserKNN\", \"metric\": metric}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d8129483",
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = seq_knn_results[\"metric\"].idxmax()\n",
    "\n",
    "best_config = {\n",
    "    \"power\": seq_knn_results[\"power\"].loc[idx],\n",
    "    \"k\": seq_knn_results[\"k\"].loc[idx],\n",
    "    \"similarity_A\": \"cosine\",\n",
    "    \"similarity_T\": \"cosine\"\n",
    "}\n",
    "\n",
    "model = build_uknn_model(best_config, train, data_description)\n",
    "scores = uknn_scoring(model, test, data_description)\n",
    "seq_uknn_recs = topn_recommendations(scores, topn)\n",
    "\n",
    "metric = model_evaluate(seq_uknn_recs, holdout, data_description, topn)\n",
    "\n",
    "grand_total.append(\n",
    "    {\"method\": \"SeqUserKNN\", \"metric\": metric}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d5a89dfb-0706-4aae-bb82-9b6f61546401",
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   "outputs": [
    {
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       "       method    metric\n",
       "5  SeqUserKNN  0.022619\n",
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