otto_click2click_regular.ipynb

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    "# \"Regular\" click2click co-visitation matrix\n",
    "This notebook prepares one of click-to-click co-visitation matrixes. It is used to generate candidates and calculate features for the clicks model.\n",
    "There are two different outputs, both for the cross-validation and for test datasets. One of those outputs is a full co-visitation matrix, and it is used to generate features for the clicks model. Another one is the same matrix reduced to top_70 results, and it is used for candidate generation.\n",
    "## Imports and definitions"
   ]
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    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "        \n",
    "import gc\n",
    "from humanize import naturalsize\n",
    "\n",
    "# functions and classes common for several notebooks of current project\n",
    "import otto_common"
   ]
  },
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   "source": [
    "class CalculateCovisitationMatrixRegular(otto_common.CalculateCovisitationMatrix):\n",
    "    '''\n",
    "    Child class with logic specific to this type of co-visitation matrix.\n",
    "    It counts all aids before or after each aid if time between those events was less than 5 minutes.\n",
    "    Weight value is calculated in a way that makes later events more important.\n",
    "    '''\n",
    "\n",
    "    def __init__(self, *args, timebase, **kwargs):\n",
    "        self.timebase = timebase\n",
    "        super().__init__(*args, **kwargs)\n",
    "\n",
    "    def calculate_weights(self, df_i):\n",
    "        df_i = df_i.sort_values(['session','ts'],ascending=[True,False])\n",
    "        df_i = df_i.reset_index(drop=True)\n",
    "        df_i = df_i.merge(df_i,on='session')\n",
    "        df_i = df_i.loc[ ((df_i.ts_x - df_i.ts_y).abs()< 5 * 60) & (df_i.aid_x != df_i.aid_y) ]\n",
    "        df_i = df_i[['session', 'aid_x', 'aid_y','ts_x']].drop_duplicates(['session', 'aid_x', 'aid_y']) \n",
    "        df_i['wgt'] = (1 + 3*(df_i.ts_x - self.timebase)/3024000)   \n",
    "        df_i = df_i[['aid_x','aid_y','wgt']]\n",
    "        df_i = df_i.loc[df_i['aid_x'].isin(self.aid_list)]\n",
    "        df_i.wgt = df_i.wgt.astype('float32')\n",
    "        df_i = self.groupby_reset_and_reduce(df_i)\n",
    "        return df_i"
   ]
  },
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   "source": [
    "## Co-visitation matrix for cross-validation dataset"
   ]
  },
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    "# Make a list of AIDs, that appear in cross-validation sets.\n",
    "# aid_x is filtered to only include aids from the list, while aid_y are not filtered\n",
    "# This reduces size of resulting matrix.\n",
    "trunked_sessions_path = '/kaggle/input/otto-prepare-cv/cv_inputs.parquet'\n",
    "trunked_sessions_path2 = '/kaggle/input/otto-prepare-cv/cv_inputs2.parquet'\n",
    "cv_list = otto_common.build_aid_list(trunked_sessions_path, trunked_sessions_path2)"
   ]
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   "source": [
    "# Input parameters and matrix calculation itself.\n",
    "click_data_cv = '/kaggle/input/otto-prepare-cv/cv_train.parquet'\n",
    "n_splits = 200\n",
    "n_splits_concat = 25\n",
    "timebase_cv = 1658700000\n",
    "\n",
    "covisitation_cv = CalculateCovisitationMatrixRegular(n_splits, n_splits_concat, cv_list, timebase=timebase_cv)\n",
    "df_matrix_cv = covisitation_cv.generate_covisitation_matrix(click_data_cv)\n",
    "df_matrix_cv.to_parquet('regular_click2click_matrix_cv.parquet')"
   ]
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     "text": [
      "981.1 MB\n"
     ]
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   "source": [
    "# Reduce the matrix to top_n aid_y for each aid_x\n",
    "top_n = 70 # This parameter is both for cv and test\n",
    "df_matrix_cv = covisitation_cv.get_top_n(df_matrix_cv, top_n)\n",
    "# Check dataframe size\n",
    "size = df_matrix_cv.memory_usage(deep='True').sum()\n",
    "print(naturalsize(size))\n",
    "# Save output.\n",
    "df_matrix_cv.to_parquet('regular_click2click_matrix_cv_top_n.parquet')"
   ]
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   "source": [
    "del df_matrix_cv, cv_list, covisitation_cv\n",
    "gc.collect()"
   ]
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   "source": [
    "## Co-visitation matrix for test dataset\n",
    "Exactly the same steps to prepare co-visitation matrix for the test dataset."
   ]
  },
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    "trunked_sessions_path = '/kaggle/input/otto-prepare-cv/test.parquet'\n",
    "full_aid_list = otto_common.build_aid_list(trunked_sessions_path)"
   ]
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     ]
    }
   ],
   "source": [
    "click_data_full = '/kaggle/input/otto-prepare-cv/train_full.parquet'\n",
    "n_splits = 350\n",
    "n_splits_concat = 40\n",
    "timebase_test = 1659304800\n",
    "\n",
    "covisitation_test = CalculateCovisitationMatrixRegular(n_splits, n_splits_concat, full_aid_list, timebase=timebase_test)\n",
    "df_matrix_full = covisitation_test.generate_covisitation_matrix(click_data_full)\n",
    "df_matrix_full.to_parquet('regular_click2click_matrix_test.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fbed62b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-24T15:52:31.402997Z",
     "iopub.status.busy": "2023-03-24T15:52:31.401661Z",
     "iopub.status.idle": "2023-03-24T15:57:12.187662Z",
     "shell.execute_reply": "2023-03-24T15:57:12.186291Z"
    },
    "papermill": {
     "duration": 280.82844,
     "end_time": "2023-03-24T15:57:12.190660",
     "exception": false,
     "start_time": "2023-03-24T15:52:31.362220",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "916.9 MB\n"
     ]
    }
   ],
   "source": [
    "df_matrix_full = covisitation_test.get_top_n(df_matrix_full, top_n)\n",
    "\n",
    "# Check dataframe size\n",
    "size = df_matrix_full.memory_usage(deep='True').sum()\n",
    "print(naturalsize(size))\n",
    "\n",
    "df_matrix_full.to_parquet('regular_click2click_matrix_test_top_n.parquet')"
   ]
  }
 ],
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