main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import math\n",
    "import pickle\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy.random\n",
    "import scipy.sparse as sp\n",
    "import sklearn.preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "import torch\n",
    "import torch.nn\n",
    "import torch.optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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      "text/plain": [
       "   user_id  female  male\n",
       "0   128355       0     1\n",
       "1   679726       0     1\n",
       "2  1035652       1     0\n",
       "3   264047       0     1\n",
       "4   561368       0     1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_gender = \n",
    "df_gender = pd.get_dummies(df_gender[['user_id', 'gender']], columns=['gender'])\n",
    "df_gender.columns = ['user_id', 'female', 'male']\n",
    "df_gender.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>article_id</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>27928</td>\n",
       "      <td>10</td>\n",
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       "      <th>1</th>\n",
       "      <td>21217</td>\n",
       "      <td>7</td>\n",
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       "      <th>2</th>\n",
       "      <td>16811</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31977</td>\n",
       "      <td>7</td>\n",
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       "      <th>4</th>\n",
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       "      <td>11</td>\n",
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      "text/plain": [
       "   article_id  category\n",
       "0       27928        10\n",
       "1       21217         7\n",
       "2       16811         7\n",
       "3       31977         7\n",
       "4       33410        11"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_article = \n",
    "df_article = df_article[['article_id', 'category']]\n",
    "df_article.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>article_id</th>\n",
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      "text/plain": [
       "   user_id  article_id\n",
       "0   666294       34258\n",
       "1   666294       34258\n",
       "2   666294        8792\n",
       "3   666294        8792\n",
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_log = \n",
    "df_log = df_log[['user_id', 'article_id']]\n",
    "df_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   user_id  views\n",
       "0        1     18\n",
       "1        2      7\n",
       "2        3     29\n",
       "3        4     99\n",
       "4        5     56"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = df_log.groupby('user_id', as_index=False).count()\n",
    "gb.columns = ['user_id', 'views']\n",
    "df_user_view = gb\n",
    "df_user_view.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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      "text/plain": [
       "   user_id  category_1  category_2  category_3  category_4  category_5  \\\n",
       "0        1         1.0         0.0         1.0         0.0         1.0   \n",
       "1        2         0.0         0.0         1.0         0.0         0.0   \n",
       "2        3         0.0         0.0         0.0         0.0         0.0   \n",
       "3        4         0.0         1.0        10.0         0.0         0.0   \n",
       "4        5         1.0         0.0         3.0         1.0         0.0   \n",
       "\n",
       "   category_6  category_7  category_8  category_9  category_10  category_11  \\\n",
       "0         7.0         2.0         0.0         0.0          5.0          1.0   \n",
       "1         1.0         1.0         1.0         0.0          3.0          0.0   \n",
       "2         2.0        19.0         3.0         0.0          3.0          2.0   \n",
       "3         0.0         5.0        16.0         0.0         37.0         30.0   \n",
       "4         1.0         4.0         9.0         0.0         18.0         19.0   \n",
       "\n",
       "   views  \n",
       "0     18  \n",
       "1      7  \n",
       "2     29  \n",
       "3     99  \n",
       "4     56  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_category = pd.get_dummies(pd.merge(df_log, df_article, how='left', on='article_id'), columns=['category']) \\\n",
    "                .drop('article_id', axis=1) \\\n",
    "                .groupby('user_id', as_index=False).sum()\n",
    "df_user_category = pd.merge(df_user_category, df_user_view, how='left', on='user_id')\n",
    "df_user_category.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  female  male  category_1  category_2  category_3  category_4  \\\n",
       "0   128355       0     1         0.0         0.0         3.0         0.0   \n",
       "1   679726       0     1         0.0         0.0         0.0         1.0   \n",
       "2  1035652       1     0         0.0         0.0         1.0         0.0   \n",
       "3   264047       0     1         0.0         0.0        13.0         5.0   \n",
       "4   561368       0     1         0.0         1.0         2.0         1.0   \n",
       "\n",
       "   category_5  category_6  category_7  category_8  category_9  category_10  \\\n",
       "0         0.0         0.0         3.0         5.0         0.0          1.0   \n",
       "1         0.0         0.0         8.0         2.0         0.0          2.0   \n",
       "2         0.0         1.0         2.0         1.0         0.0          0.0   \n",
       "3         0.0         0.0        40.0        60.0         0.0         60.0   \n",
       "4         0.0         0.0         1.0         0.0         0.0          0.0   \n",
       "\n",
       "   category_11  views  \n",
       "0          6.0     18  \n",
       "1          3.0     16  \n",
       "2          5.0     10  \n",
       "3         37.0    215  \n",
       "4          2.0      7  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df_gender, df_user_category, how='inner', on='user_id').dropna()\n",
    "n_users = df.shape[0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = df[['user_id', 'views']].groupby('views', as_index=False).count()\n",
    "freq.columns = ['views', 'frequency']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "relu = torch.nn.ReLU()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def N(p):\n",
    "    q = relu(p) + 1e-15\n",
    "    return q / q.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categoricalLoss(p, A, q):\n",
    "    f = N(torch.mm(A, p))\n",
    "    return (f * (f/q).log()).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def oneDimWasserstein(p, A, q):\n",
    "    f = N(torch.mm(A, p))\n",
    "    return (f.cumsum(axis=0) - q.cumsum(axis=0)).abs().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = np.ones(n_users)\n",
    "indices = np.vstack((np.array(df['views']), np.array(range(n_users))))\n",
    "i = torch.LongTensor(indices)\n",
    "v = torch.FloatTensor(values)\n",
    "A_view = torch.sparse.FloatTensor(i, v)\n",
    "loss_view = lambda w: oneDimWasserstein(w, A_view, q_view)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_gender = torch.from_numpy(df[['male', 'female']].values.T).float()\n",
    "loss_gender = lambda w: categoricalLoss(w, A_gender, q_gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_category = torch.from_numpy(df[['category_%d' % i for i in range(1,12)]].values.T).float()\n",
    "loss_category = lambda w: categoricalLoss(w, A_category, q_category)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def doit(q_view, q_gender, q_category, alpha_view, alpha_gender, alpha_category, steps = 5000):\n",
    "    loss_fun = lambda w: alpha_view * loss_view(w) + alpha_gender * loss_gender(w) + alpha_category * loss_category(w)\n",
    "    \n",
    "    w = torch.ones(n_users, 1, requires_grad=True)\n",
    "    w.data = N(w.data)\n",
    "    \n",
    "    optimizer = torch.optim.SGD([w], lr=1e-7, momentum=0.9)\n",
    "#    optimizer = torch.optim.Adam([w], lr=1e-3)\n",
    "    scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lambda t: 1.0/(1.0 + t))\n",
    "    \n",
    "    for i in tqdm(range(steps)):\n",
    "        optimizer.zero_grad()\n",
    "        loss = loss_fun(w)\n",
    "        loss.backward(retain_graph=True)\n",
    "        optimizer.step()\n",
    "        w.data = N(w.data)\n",
    "        \n",
    "        #if i % 100 == 0:\n",
    "        #    print(f\"{loss}\")\n",
    "        \n",
    "        scheduler.step();\n",
    "    \n",
    "    print(f\"{loss_view(w)}, {loss_gender(w)}, {loss_category(w)}\")\n",
    "    return N(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws['uniform'] = N(torch.ones(n_users, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [06:51<00:00, 12.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.936881065368652, 0.00018542353063821793, 0.4388040602207184\n"
     ]
    }
   ],
   "source": [
    "s = 1.0\n",
    "q_view = N(torch.FloatTensor([[1.0/(k+1)**s] for k in range(A_view.shape[0])]))\n",
    "q_gender = N(torch.ones(2, 1))\n",
    "q_category = N(torch.ones(11, 1))\n",
    "\n",
    "alpha_view = 1.0\n",
    "alpha_gender = 3.0\n",
    "alpha_category = 10.0\n",
    "\n",
    "ws['KPI1-gender-category'] = doit(q_view, q_gender, q_category, alpha_view, alpha_gender, alpha_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [06:50<00:00, 12.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6886212825775146, 0.0005119442939758301, 0.02490229904651642\n"
     ]
    }
   ],
   "source": [
    "s = 2.0\n",
    "q_view = N(torch.FloatTensor([[1.0/(k+1)**s] for k in range(A_view.shape[0])]))\n",
    "q_gender = N(torch.ones(2, 1))\n",
    "q_category = N(torch.ones(11, 1))\n",
    "\n",
    "alpha_view = 1.0\n",
    "alpha_gender = 3.0\n",
    "alpha_category = 10.0\n",
    "\n",
    "ws['KPI2-gender-category'] = doit(q_view, q_gender, q_category, alpha_view, alpha_gender, alpha_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [06:50<00:00, 12.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.555795669555664, 1.6148551367223263e-06, 0.5564760565757751\n"
     ]
    }
   ],
   "source": [
    "s = 1.0\n",
    "q_view = N(torch.FloatTensor([[1.0/(k+1)**s] for k in range(A_view.shape[0])]))\n",
    "q_gender = N(torch.ones(2, 1))\n",
    "q_category = N(torch.ones(11, 1))\n",
    "\n",
    "alpha_view = 1.0\n",
    "alpha_gender = 3.0\n",
    "alpha_category = 0.0\n",
    "\n",
    "ws['KPI1-gender'] = doit(q_view, q_gender, q_category, alpha_view, alpha_gender, alpha_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [07:02<00:00, 11.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9000703692436218, 1.0898103937506676e-05, 0.51181960105896\n"
     ]
    }
   ],
   "source": [
    "s = 2.0\n",
    "q_view = N(torch.FloatTensor([[1.0/(k+1)**s] for k in range(A_view.shape[0])]))\n",
    "q_gender = N(torch.ones(2, 1))\n",
    "q_category = N(torch.ones(11, 1))\n",
    "\n",
    "alpha_view = 1.0\n",
    "alpha_gender = 3.0\n",
    "alpha_category = 0.0\n",
    "\n",
    "ws['KPI2-gender'] = doit(q_view, q_gender, q_category, alpha_view, alpha_gender, alpha_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [06:51<00:00, 12.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.62843132019043, 0.053909361362457275, 0.43652158975601196\n"
     ]
    }
   ],
   "source": [
    "s = 1.0\n",
    "q_view = N(torch.FloatTensor([[1.0/(k+1)**s] for k in range(A_view.shape[0])]))\n",
    "q_gender = N(torch.ones(2, 1))\n",
    "q_category = N(torch.ones(11, 1))\n",
    "\n",
    "alpha_view = 1.0\n",
    "alpha_gender = 0.0\n",
    "alpha_category = 10.0\n",
    "\n",
    "ws['KPI1-category'] = doit(q_view, q_gender, q_category, alpha_view, alpha_gender, alpha_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [06:51<00:00, 12.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6881354451179504, 0.04794919490814209, 0.02455867826938629\n"
     ]
    }
   ],
   "source": [
    "s = 2.0\n",
    "q_view = N(torch.FloatTensor([[1.0/(k+1)**s] for k in range(A_view.shape[0])]))\n",
    "q_gender = N(torch.ones(2, 1))\n",
    "q_category = N(torch.ones(11, 1))\n",
    "\n",
    "alpha_view = 1.0\n",
    "alpha_gender = 0.0\n",
    "alpha_category = 10.0\n",
    "\n",
    "ws['KPI2-category'] = doit(q_view, q_gender, q_category, alpha_view, alpha_gender, alpha_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [07:16<00:00, 11.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.680130004882812, 0.009119294583797455, 0.5565736293792725\n"
     ]
    }
   ],
   "source": [
    "s = 1.0\n",
    "q_view = N(torch.FloatTensor([[1.0/(k+1)**s] for k in range(A_view.shape[0])]))\n",
    "q_gender = N(torch.ones(2, 1))\n",
    "q_category = N(torch.ones(11, 1))\n",
    "\n",
    "alpha_view = 1.0\n",
    "alpha_gender = 0.0\n",
    "alpha_category = 0.0\n",
    "\n",
    "ws['KPI1'] = doit(q_view, q_gender, q_category, alpha_view, alpha_gender, alpha_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [06:50<00:00, 12.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8381972312927246, 0.031105175614356995, 0.49590933322906494\n"
     ]
    }
   ],
   "source": [
    "s = 2.0\n",
    "q_view = N(torch.FloatTensor([[1.0/(k+1)**s] for k in range(A_view.shape[0])]))\n",
    "q_gender = N(torch.ones(2, 1))\n",
    "q_category = N(torch.ones(11, 1))\n",
    "\n",
    "alpha_view = 1.0\n",
    "alpha_gender = 0.0\n",
    "alpha_category = 0.0\n",
    "\n",
    "ws['KPI2'] = doit(q_view, q_gender, q_category, alpha_view, alpha_gender, alpha_category)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name,Male,Female\n",
      "uniform,0.4039668142795563,0.5960332751274109\n",
      "KPI1-gender-category,0.4903731942176819,0.5096268057823181\n",
      "KPI2-gender-category,0.4840018153190613,0.5159981846809387\n",
      "KPI1-gender,0.5008817911148071,0.49911820888519287\n",
      "KPI2-gender,0.4976617395877838,0.5023382902145386\n",
      "KPI1-category,0.33731406927108765,0.6626859307289124\n",
      "KPI2-category,0.34641462564468384,0.6535853147506714\n",
      "KPI1,0.43257805705070496,0.5674219727516174\n",
      "KPI2,0.37594109773635864,0.6240589618682861\n"
     ]
    }
   ],
   "source": [
    "print('name,Male,Female')\n",
    "for key in ws.keys():\n",
    "    print(key, end=\"\")\n",
    "    Aw = N(torch.mm(A_gender, ws[key])).data\n",
    "    for i in range(2):\n",
    "        print(f\",{Aw[i][0]}\", end=\"\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name,Category 0,Category 1,Category 2,Category 3,Category 4,Category 5,Category 6,Category 7,Category 8,Category 9,Category 10\n",
      "uniform,0.0021951068192720413,0.02584131993353367,0.08289823681116104,0.019662978127598763,0.010672674514353275,0.04314826428890228,0.21515880525112152,0.12677933275699615,0.008058397099375725,0.2817538380622864,0.1838310807943344\n",
      "KPI1-gender-category,0.003812469309195876,0.0318237841129303,0.08712201565504074,0.023365972563624382,0.017546966671943665,0.058185067027807236,0.16637341678142548,0.167876735329628,0.012960309162735939,0.28732630610466003,0.14360688626766205\n",
      "KPI2-gender-category,0.06774763017892838,0.08470705896615982,0.10121854394674301,0.0882943794131279,0.05357291176915169,0.09196330606937408,0.11242089420557022,0.1001308262348175,0.0702110230922699,0.12339913845062256,0.10633419454097748\n",
      "KPI1-gender,0.0011058844393119216,0.02585228718817234,0.07447943091392517,0.017263639718294144,0.015295562334358692,0.026210544630885124,0.18226347863674164,0.18886391818523407,0.009079703129827976,0.3079339265823364,0.15165162086486816\n",
      "KPI2-gender,0.0019105228129774332,0.024168461561203003,0.10783443599939346,0.01994428038597107,0.008394204080104828,0.03429530933499336,0.24866129457950592,0.144370898604393,0.008400038816034794,0.21111729741096497,0.1909032166004181\n",
      "KPI1-category,0.003929628059267998,0.03210380673408508,0.08684226125478745,0.023513631895184517,0.017680449411273003,0.059553515166044235,0.16614475846290588,0.1671639233827591,0.012848409824073315,0.28741398453712463,0.14280569553375244\n",
      "KPI2-category,0.06799446046352386,0.08504285663366318,0.10027023404836655,0.08864887803792953,0.05394204333424568,0.09216967225074768,0.11278551071882248,0.09930003434419632,0.0701860710978508,0.12375273555517197,0.10590752959251404\n",
      "KPI1,0.0011242225300520658,0.025906724855303764,0.07418713718652725,0.017246128991246223,0.015277075581252575,0.026614228263497353,0.18257911503314972,0.18811337649822235,0.008999213576316833,0.3087484538555145,0.15120428800582886\n",
      "KPI2,0.002662717830389738,0.02686348743736744,0.10258669406175613,0.020459095016121864,0.009546770714223385,0.046469930559396744,0.25362446904182434,0.12936466932296753,0.006773514207452536,0.22217957675457,0.17946910858154297\n"
     ]
    }
   ],
   "source": [
    "print(\"name\", end=\"\")\n",
    "for i in range(11):\n",
    "      print(f\",Category {i}\", end=\"\")\n",
    "print(\"\")\n",
    "for key in ws.keys():\n",
    "    print(key, end=\"\")\n",
    "    Aw = N(torch.mm(A_category, ws[key])).data\n",
    "    for i in range(11):\n",
    "        print(f\",{Aw[i][0]}\", end=\"\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "views,uniform,KPI1-gender-category,KPI2-gender-category,KPI1-gender,KPI2-gender,KPI1-category,KPI2-category,KPI1,KPI2\n",
      "1,9.998699840700902e-16,9.999822159356014e-16,1.0002271143364763e-15,9.998732663227608e-16,9.999141356624658e-16,9.9998496879268e-16,1.000437072628277e-15,1.0000193795061622e-15,9.996441439105285e-16\n",
      "2,0.09127980470657349,0.18430906534194946,0.7601456642150879,0.1839902102947235,0.6658598780632019,0.18412040174007416,0.7601284980773926,0.18437977135181427,0.6500738263130188\n",
      "3,0.1524934023618698,0.22517836093902588,0.8277188539505005,0.22501687705516815,0.8275295495986938,0.225079745054245,0.827704131603241,0.22522211074829102,0.8276591897010803\n",
      "4,0.20021061599254608,0.25584664940834045,0.8657225370407104,0.25587978959083557,0.8643217086791992,0.25586608052253723,0.8657134771347046,0.25580015778541565,0.8653619289398193\n",
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    {
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    {
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     ]
    },
    {
     "name": "stdout",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "1640,0.9999983310699463,0.9886881113052368,0.9999533891677856,0.9883233904838562,0.99994957447052,0.9885107278823853,0.9999524354934692,0.988801121711731,0.9999493956565857\n",
      "1641,0.9999983310699463,0.9886881113052368,0.9999533891677856,0.9883233904838562,0.99994957447052,0.9885107278823853,0.9999524354934692,0.988801121711731,0.9999493956565857\n",
      "1642,0.9999983310699463,0.9886881113052368,0.9999533891677856,0.9883233904838562,0.99994957447052,0.9885107278823853,0.9999524354934692,0.988801121711731,0.9999493956565857\n",
      "1643,0.9999983310699463,0.9886881113052368,0.9999533891677856,0.9883233904838562,0.99994957447052,0.9885107278823853,0.9999524354934692,0.988801121711731,0.9999493956565857\n",
      "1644,0.9999983310699463,0.9886881113052368,0.9999533891677856,0.9883233904838562,0.99994957447052,0.9885107278823853,0.9999524354934692,0.988801121711731,0.9999493956565857\n",
      "1645,0.9999983310699463,0.9886881113052368,0.9999533891677856,0.9883233904838562,0.99994957447052,0.9885107278823853,0.9999524354934692,0.988801121711731,0.9999493956565857\n",
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      "1894,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1895,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1896,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1897,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1898,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1899,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1900,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1901,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1902,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1903,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1904,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1905,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1906,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1907,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1908,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1909,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1910,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1911,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1912,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1913,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1914,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1915,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1916,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1917,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1918,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1919,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1920,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1921,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1922,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1923,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1924,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1925,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1926,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1927,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1928,0.9999991059303284,0.9939360618591309,0.9999819397926331,0.9937662482261658,0.9999828338623047,0.9938390851020813,0.9999822378158569,0.9940195679664612,0.9999826550483704\n",
      "1929,0.9999999403953552,1.0,1.0,0.9999998807907104,1.0,0.9999999403953552,1.0,1.0,0.9999998807907104\n"
     ]
    }
   ],
   "source": [
    "Aw = {}\n",
    "print('views', end=\"\")\n",
    "for (i, key) in enumerate(ws.keys()):\n",
    "    print(f\",{key}\", end=\"\")\n",
    "    Aw[key] = N(torch.mm(A_view, ws[key])).cumsum(axis=0).data\n",
    "print(\"\")\n",
    "for i in range(Aw['uniform'].shape[0]):\n",
    "    print(f\"{i+1}\", end=\"\")\n",
    "    for key in ws.keys():\n",
    "        print(f\",{Aw[key][i][0]}\", end=\"\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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"
    }
   ],
   "source": [
    "for key in [\"KPI1\", \"KPI1-category\", \"KPI1-gender\"]:\n",
    "    m = Aw[key].shape[0]\n",
    "    plt.plot(list(range(m)), [Aw[key][i][0] for i in range(m)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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"
    }
   ],
   "source": [
    "for key in [\"KPI2\", \"KPI2-category\", \"KPI2-gender\"]:\n",
    "    m = Aw[key].shape[0]\n",
    "    plt.plot(list(range(m)), [Aw[key][i][0] for i in range(m)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explicit Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.108209e-07, 8.108209e-07, 8.108209e-07, ..., 8.108209e-07,\n",
       "       8.108209e-07, 8.108209e-07], dtype=float32)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(ws['uniform']).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of users: 1233318\n",
      "sample size: 60000\n"
     ]
    },
    {
     "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>uniform</th>\n",
       "      <th>KPI1-gender-category</th>\n",
       "      <th>KPI2-gender-category</th>\n",
       "      <th>KPI1-gender</th>\n",
       "      <th>KPI2-gender</th>\n",
       "      <th>KPI1-category</th>\n",
       "      <th>KPI2-category</th>\n",
       "      <th>KPI1</th>\n",
       "      <th>KPI2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>56</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>57</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51</td>\n",
       "      <td>44</td>\n",
       "      <td>58</td>\n",
       "      <td>75</td>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>52</td>\n",
       "      <td>79</td>\n",
       "      <td>76</td>\n",
       "      <td>45</td>\n",
       "      <td>55</td>\n",
       "      <td>58</td>\n",
       "      <td>95</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>129</td>\n",
       "      <td>111</td>\n",
       "      <td>85</td>\n",
       "      <td>97</td>\n",
       "      <td>49</td>\n",
       "      <td>110</td>\n",
       "      <td>75</td>\n",
       "      <td>111</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>152</td>\n",
       "      <td>177</td>\n",
       "      <td>129</td>\n",
       "      <td>103</td>\n",
       "      <td>50</td>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>124</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   uniform  KPI1-gender-category  KPI2-gender-category  KPI1-gender  \\\n",
       "0       28                     7                     9           56   \n",
       "1       51                    44                    58           75   \n",
       "2       70                    52                    79           76   \n",
       "3      129                   111                    85           97   \n",
       "4      152                   177                   129          103   \n",
       "\n",
       "   KPI2-gender  KPI1-category  KPI2-category  KPI1  KPI2  \n",
       "0           11              7             11    57     9  \n",
       "1           32             19             32    76    21  \n",
       "2           45             55             58    95    31  \n",
       "3           49            110             75   111   107  \n",
       "4           50            114            114   124   111  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"total number of users: {n_users}\")\n",
    "sample_size = 60000\n",
    "print(f\"sample size: {sample_size}\")\n",
    "df_out = pd.DataFrame()\n",
    "for key in ws.keys():\n",
    "    S = sorted(np.random.choice(df['user_id'], p=np.array(ws[key].data).flatten(), size=sample_size, replace=False))\n",
    "    df_out[key] = S\n",
    "\n",
    "df_out.to_csv('samples.csv')\n",
    "df_out.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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