数据处理-5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8c11acb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2369d725",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.89030365e-25, 1.22061887e-16, 1.89983599e-21, 1.74652069e-53,\n",
       "       7.64580047e-32, 7.36790967e-28, 1.99043489e-37, 1.47726716e-26,\n",
       "       7.58449633e-22, 1.36715798e-58, 6.11244319e-13, 4.09395731e-46,\n",
       "       2.60113807e-71, 0.00000000e+00, 8.22013121e-26, 1.00000000e+00,\n",
       "       2.13310268e-10, 7.13593409e-32, 1.30186146e-18, 1.28598067e-95,\n",
       "       7.60510386e-28, 2.44400280e-16, 3.94526625e-16])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nptest = np.load('data/clustering_features.npy')\n",
    "nptest[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5adc6f37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nptest = np.load('data/node_info.npy')\n",
    "nptest[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "35fb19a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 3.,  0.,  0., ...,  0.,  0.,  9.],\n",
       "       [ 8.,  0.,  1., ...,  0.,  0., 11.],\n",
       "       ...,\n",
       "       [27.,  0.,  0., ...,  0.,  0., 13.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  0.,  4.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nptest[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48e018e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.00408163, 0.01632653, 0.01428571, 0.0755102 ,\n",
       "       0.00816326, 0.00204082, 0.01020408, 0.00816326, 0.00204082,\n",
       "       0.00204082, 0.01428571, 0.14285715, 0.0122449 , 0.0122449 ,\n",
       "       0.00204082, 0.        , 0.00408163, 0.        , 0.00204082,\n",
       "       0.00204082, 0.0122449 , 0.        , 0.6530612 ], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nptest = np.load('data/walk_label_features.npy')\n",
    "nptest[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "407ca562",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = './data'\n",
    "publish_path = ''\n",
    "\n",
    "link_p1_path = os.path.join(base_path, publish_path, 'link_phase1.csv')\n",
    "nodes_path = os.path.join(base_path, publish_path, 'IDandLabels.csv')\n",
    "edges_df = pd.read_csv(link_p1_path)\n",
    "nodes_df = pd.read_csv(nodes_path, dtype={'Label':str})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb98fd01",
   "metadata": {},
   "outputs": [],
   "source": [
    "cite1_df = edges_df.groupby('paper_id').count()['reference_paper_id']\n",
    "cite2_df = edges_df.groupby('reference_paper_id').count()['paper_id']\n",
    "count_df = pd.merge(cite1_df, cite2_df, how='outer', left_index=True, right_index=True)\n",
    "count_df.fillna(0, inplace=True)\n",
    "count_df.columns=['cite1', 'cite2']\n",
    "# 对引用数做归一化\n",
    "count_df = (count_df-count_df.mean())/(count_df.std()+0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "791383e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_arr = nodes_df.merge(count_df, how='left', left_on='paper_id', right_index=True)[['cite1','cite2']].values\n",
    "feat_arr[np.isnan(feat_arr)] = -1\n",
    "# 使用Numpy保存特征为.npy格式，以节省存储空间和提高读写速度\n",
    "with open(os.path.join(base_path, publish_path, './count_features.npy'), 'wb') as f:\n",
    "    np.save(f, feat_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b150c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "cite1_df = edges_df.groupby('paper_id').count()['reference_paper_id']\n",
    "cite2_df = edges_df.groupby('reference_paper_id').count()['paper_id']\n",
    "count_df = pd.merge(cite1_df, cite2_df, how='outer', left_index=True, right_index=True)\n",
    "count_df.fillna(0, inplace=True)\n",
    "count_df.columns=['cite1', 'cite2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a974380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "count_df['cite1'].hist(bins=100)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c377d2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9102.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(count_df['cite1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2b71b88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "count_df['cite2'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "90298720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15981.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(count_df['cite2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "35605d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cite1</th>\n",
       "      <th>cite2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.031367e+06</td>\n",
       "      <td>3.031367e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.622276e+00</td>\n",
       "      <td>9.622276e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.152019e+01</td>\n",
       "      <td>4.937654e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.102000e+03</td>\n",
       "      <td>1.598100e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              cite1         cite2\n",
       "count  3.031367e+06  3.031367e+06\n",
       "mean   9.622276e+00  9.622276e+00\n",
       "std    1.152019e+01  4.937654e+01\n",
       "min    0.000000e+00  0.000000e+00\n",
       "25%    3.000000e+00  0.000000e+00\n",
       "50%    7.000000e+00  2.000000e+00\n",
       "75%    1.300000e+01  7.000000e+00\n",
       "max    9.102000e+03  1.598100e+04"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "25eb2b25",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-58c69d6a50e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "np.zeros((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62260c05",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}