data_utils.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "import fasttext\n",
    "from collections import Counter\n",
    "\n",
    "\n",
    "np.random.seed(0)\n",
    "random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total courses 697\n",
      "total courses 705\n"
     ]
    }
   ],
   "source": [
    "# load user video act\n",
    "json_file = \"additional_information/user_video_act.json\"\n",
    "\n",
    "with open(json_file, \"r\") as f:\n",
    "    lines = f.readlines()\n",
    "\n",
    "# load course name dict\n",
    "with open(\"entities/course.json\", \"r\", encoding=\"utf8\") as f:\n",
    "    courses = f.readlines()\n",
    "    \n",
    "course_dict = dict()\n",
    "for c in courses:\n",
    "    json_data = json.loads(c)\n",
    "    course_dict[json_data[\"id\"]] = json_data[\"name\"]\n",
    "\n",
    "# load course-teacher relationship to dict\n",
    "def get_course_dict(filepath):\n",
    "    with open(filepath, \"r\", encoding=\"utf8\") as f:\n",
    "        tc_rels = f.readlines()\n",
    "    tc_rel_dict = dict()\n",
    "    for tc_rel in tc_rels:\n",
    "        tc_rel = tc_rel.strip().split(\"\\t\")\n",
    "        if tc_rel[1] not in tc_rel_dict:\n",
    "            tc_rel_dict[tc_rel[1]] = [tc_rel[0]]\n",
    "        else:\n",
    "            tc_rel_dict[tc_rel[1]].append(tc_rel[0])\n",
    "        \n",
    "    return tc_rel_dict\n",
    "\n",
    "tc_rel_dict = get_course_dict(\"relations/teacher-course.json\")\n",
    "print(\"total courses\", len(tc_rel_dict))\n",
    "\n",
    "sc_rel_dict = get_course_dict(\"relations/school-course.json\")\n",
    "print(\"total courses\", len(sc_rel_dict))\n",
    "\n",
    "# check if each course has more than one schools (no)\n",
    "# for key in sc_rel_dict:\n",
    "#     if len(sc_rel_dict[key]) > 1:\n",
    "#         print(sc_rel_dict[key])\n",
    "\n",
    "# load video concepts\n",
    "vc_df = pd.read_csv(\"relations/video-concept.json\",\n",
    "                   header=None,\n",
    "                   delimiter=\"\\t\",\n",
    "                   names=[\"video\", \"concept\"])\n",
    "vc_dict = vc_df.groupby('video')['concept'].apply(list).to_dict()\n",
    "\n",
    "# load course concepts\n",
    "cc_df = pd.read_csv(\"relations/course-concept.json\",\n",
    "                   header=None,\n",
    "                   delimiter=\"\\t\",\n",
    "                   names=[\"course\", \"concept\"])\n",
    "cc_dict = cc_df.groupby('course')['concept'].apply(list).to_dict()\n",
    "\n",
    "# load course video\n",
    "cv_df = pd.read_csv(\"relations/course-video.json\",\n",
    "                   header=None,\n",
    "                   delimiter=\"\\t\",\n",
    "                   names=[\"course\", \"video\"])\n",
    "cv_dict = cv_df.groupby(\"course\")[\"video\"].apply(list).to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['K_订单_管理科学技术',\n",
       " 'K_会计_管理科学技术',\n",
       " 'K_贷款_管理科学技术',\n",
       " 'K_模拟_心理学',\n",
       " 'K_资源_管理科学技术',\n",
       " 'K_会计等式_管理科学技术',\n",
       " 'K_信息储存_管理科学技术',\n",
       " 'K_知识_管理科学技术',\n",
       " 'K_复式记账法_管理科学技术',\n",
       " 'K_应收账款_管理科学技术',\n",
       " 'K_道路_数学',\n",
       " 'K_组织_管理科学技术',\n",
       " 'K_预算_管理科学技术',\n",
       " 'K_反馈_管理科学技术',\n",
       " 'K_期限_管理科学技术',\n",
       " 'K_使用_管理科学技术',\n",
       " 'K_匹配_数学',\n",
       " 'K_广告_管理科学技术',\n",
       " 'K_总分类账_管理科学技术',\n",
       " 'K_价值_管理科学技术',\n",
       " 'K_计数值_管理科学技术',\n",
       " 'K_所有者权益_管理科学技术',\n",
       " 'K_养老基金_管理科学技术',\n",
       " 'K_平衡_管理科学技术',\n",
       " 'K_组织设计_管理科学技术',\n",
       " 'K_商品_管理科学技术',\n",
       " 'K_误差_数学',\n",
       " 'K_等待_管理科学技术',\n",
       " 'K_工作环境_管理科学技术',\n",
       " 'K_通分_数学',\n",
       " 'K_分析_心理学',\n",
       " 'K_客户_管理科学技术',\n",
       " 'K_练习_心理学',\n",
       " 'K_慈善组织_管理科学技术',\n",
       " 'K_计划_管理科学技术',\n",
       " 'K_收入_管理科学技术',\n",
       " 'K_索引_管理科学技术',\n",
       " 'K_费用_管理科学技术',\n",
       " 'K_需要确认_管理科学技术',\n",
       " 'K_企业家_管理科学技术',\n",
       " 'K_学会_管理科学技术',\n",
       " 'K_空间_数学',\n",
       " 'K_公司_管理科学技术',\n",
       " 'K_学习_管理科学技术',\n",
       " 'K_分类账簿_管理科学技术',\n",
       " 'K_纠正_管理科学技术',\n",
       " 'K_解释_数学',\n",
       " 'K_政策_管理科学技术',\n",
       " 'K_注意_心理学',\n",
       " 'K_控制账目_管理科学技术',\n",
       " 'K_检查_管理科学技术',\n",
       " 'K_交换_管理科学技术',\n",
       " 'K_账户_管理科学技术',\n",
       " 'K_投资_管理科学技术',\n",
       " 'K_价格_管理科学技术',\n",
       " 'K_计算方法_数学',\n",
       " 'K_福利_管理科学技术',\n",
       " 'K_雇主_管理科学技术',\n",
       " 'K_调整_心理学',\n",
       " 'K_过账_管理科学技术',\n",
       " 'K_假设_数学',\n",
       " 'K_职位_管理科学技术',\n",
       " 'K_应付账款_管理科学技术',\n",
       " 'K_收益_管理科学技术',\n",
       " 'K_系统_管理科学技术',\n",
       " 'K_透支_管理科学技术',\n",
       " 'K_交易所_管理科学技术',\n",
       " 'K_非流动资产_管理科学技术',\n",
       " 'K_支票_管理科学技术',\n",
       " 'K_交易_管理科学技术',\n",
       " 'K_努力_管理科学技术',\n",
       " 'K_债务_管理科学技术',\n",
       " 'K_对照_数学',\n",
       " 'K_部分信息_数学',\n",
       " 'K_数字编码_数学',\n",
       " 'K_批准_管理科学技术',\n",
       " 'K_原始凭证_管理科学技术',\n",
       " 'K_个体_数学',\n",
       " 'K_财务报告_管理科学技术',\n",
       " 'K_包含_数学',\n",
       " 'K_出现_数学',\n",
       " 'K_运作_管理科学技术',\n",
       " 'K_需要_心理学',\n",
       " 'K_相等_数学',\n",
       " 'K_税收_管理科学技术',\n",
       " 'K_会计分录_管理科学技术',\n",
       " 'K_政府_管理科学技术',\n",
       " 'K_银行_管理科学技术',\n",
       " 'K_计算_数学',\n",
       " 'K_认证_管理科学技术',\n",
       " 'K_能力_管理科学技术',\n",
       " 'K_支付_管理科学技术',\n",
       " 'K_自主学习_心理学',\n",
       " 'K_输入_数学',\n",
       " 'K_阶段_管理科学技术',\n",
       " 'K_归档_管理科学技术',\n",
       " 'K_发现_管理科学技术',\n",
       " 'K_定额_管理科学技术',\n",
       " 'K_报酬_管理科学技术',\n",
       " 'K_补货_管理科学技术',\n",
       " 'K_固定资产_管理科学技术',\n",
       " 'K_合同_管理科学技术',\n",
       " 'K_结账_管理科学技术',\n",
       " 'K_职业生涯_管理科学技术',\n",
       " 'K_顾客_管理科学技术',\n",
       " 'K_属于_数学',\n",
       " 'K_现金_管理科学技术',\n",
       " 'K_补偿_管理科学技术',\n",
       " 'K_资产_管理科学技术',\n",
       " 'K_条款_管理科学技术',\n",
       " 'K_编制_管理科学技术',\n",
       " 'K_保险_管理科学技术',\n",
       " 'K_项目_管理科学技术',\n",
       " 'K_事务_管理科学技术',\n",
       " 'K_索赔_管理科学技术',\n",
       " 'K_选择_管理科学技术',\n",
       " 'K_取消_管理科学技术']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_dict[\"C_course-v1:ACCA+FA1_X+2019_T1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 23/48640 [00:00<03:39, 221.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total users 48640\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 48640/48640 [02:57<00:00, 273.77it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4874298, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"total users\", len(lines))\n",
    "user_dfs = list()\n",
    "\n",
    "for l in tqdm(lines):\n",
    "    json_data = json.loads(l)\n",
    "#     print(json_data)\n",
    "\n",
    "    activities = json_data[\"activity\"]\n",
    "    course_ids = list()\n",
    "    video_ids = list()\n",
    "    local_start_times = list()\n",
    "    concepts = list()\n",
    "    cnames = list()\n",
    "    teachers = list()\n",
    "    schools = list()\n",
    "    \n",
    "    for a in activities:\n",
    "        cnames.append(course_dict[a[\"course_id\"]]) if a[\"course_id\"] in course_dict else cnames.append(\"\")\n",
    "        course_ids.append(a[\"course_id\"])\n",
    "        video_ids.append(a[\"video_id\"])\n",
    "        local_start_times.append(a[\"local_start_time\"])\n",
    "        concepts.append(vc_dict[a[\"video_id\"]]) if a[\"video_id\"] in vc_dict else concepts.append([\"\"])\n",
    "        teachers.append(tc_rel_dict[a[\"course_id\"]]) if a[\"course_id\"] in tc_rel_dict else teachers.append([\"\"])\n",
    "        schools.append(sc_rel_dict[a[\"course_id\"]][0]) if a[\"course_id\"] in sc_rel_dict else schools.append(\"\")\n",
    "        \n",
    "    df = pd.DataFrame({\n",
    "        \"id\": json_data[\"id\"],\n",
    "        \"cname\": cnames,\n",
    "        \"cid\": course_ids,\n",
    "        \"vid\": video_ids,\n",
    "        \"concepts\": concepts,\n",
    "        \"teachers\": teachers,\n",
    "        \"schools\": schools,\n",
    "        \"local_start_time\": local_start_times\n",
    "    })\n",
    "    df.sort_values(\"local_start_time\", inplace=True)\n",
    "    \n",
    "#     display(df)\n",
    "    user_dfs.append(df)\n",
    "\n",
    "# convert to total df\n",
    "total_df = pd.concat(user_dfs,ignore_index=True)\n",
    "total_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2130\n",
      "(311743, 8)\n"
     ]
    }
   ],
   "source": [
    "# max '2020-04-17 17:25:48'\n",
    "bad_formatted_users = list(total_df[total_df[\"local_start_time\"]<\"2017-01-01 00:00:00\"]['id'].unique())\n",
    "user_in_trainingperiod = list(total_df[(total_df[\"local_start_time\"]>='2017-01-01 00:00:00') & (total_df[\"local_start_time\"]<='2019-11-01 00:00:00')]['id'].unique())\n",
    "user_in_testperiod = list(total_df[total_df[\"local_start_time\"]>'2019-11-01 00:00:00']['id'].unique())\n",
    "selected_users = [u for u in user_in_testperiod if u in user_in_trainingperiod if u not in bad_formatted_users]\n",
    "print(len(selected_users))\n",
    "\n",
    "filtered_df = total_df[total_df[\"id\"].isin(selected_users)]\n",
    "print(filtered_df.shape)\n",
    "\n",
    "# check whether there is at least one concept in the testing period that is not in the training period\n",
    "need_to_filter = list()\n",
    "for u in selected_users:\n",
    "    udf = filtered_df[filtered_df[\"id\"]==u]\n",
    "    train_udf = udf[(udf[\"local_start_time\"]>='2017-01-01 00:00:00') & (udf[\"local_start_time\"]<='2019-11-01 00:00:00')]\n",
    "    test_udf = udf[udf[\"local_start_time\"]>'2019-11-01 00:00:00']\n",
    "    train_concepts = list(set([x for sublist in train_udf[\"concepts\"].values for x in sublist]))\n",
    "    test_concepts = list(set([x for sublist in test_udf[\"concepts\"].values for x in sublist]))\n",
    "    if len([x for x in test_concepts if x not in train_concepts])==0:\n",
    "#         print(\"unsatisfied user:\", u)\n",
    "        need_to_filter.append(u)\n",
    "\n",
    "filtered_users = [u for u in selected_users if u not in need_to_filter]\n",
    "filtered_df = filtered_df[filtered_df[\"id\"].isin(filtered_users)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------- Entity Statistics -----------------\n",
      "filtered df (290024, 8)\n",
      "total users 2005\n",
      "total courses 600\n",
      "total videos 22403\n",
      "total schools 137\n",
      "distinct concepts 21037\n",
      "total teachers 1385\n"
     ]
    }
   ],
   "source": [
    "print(\"---------------- Entity Statistics -----------------\")\n",
    "distinct_concepts = list(set([c for sublist in filtered_df[\"concepts\"].values for c in sublist if len(c)>0]))\n",
    "\n",
    "print(\"filtered df\", filtered_df.shape)\n",
    "print(\"total users\", len(filtered_df[\"id\"].unique()))\n",
    "print(\"total courses\", len(filtered_df[\"cid\"].unique()))\n",
    "print(\"total videos\", len(filtered_df[\"vid\"].unique()))\n",
    "print(\"total schools\", len(filtered_df[\"schools\"].unique()))\n",
    "print(\"distinct concepts\", len(distinct_concepts))\n",
    "t_list = filtered_df[\"teachers\"].values\n",
    "flattend_t_list = list(set([t for sublist in t_list for t in sublist]))\n",
    "print(\"total teachers\", len(flattend_t_list))\n",
    "\n",
    "######################\n",
    "# relations (wrong before: not only limited to relations in the filtered but from relation.json files)\n",
    "# print(\"user-course relations\", filtered_df.groupby(['id','cid']).size().shape[0])\n",
    "# print(\"course-video relations\", filtered_df.groupby(['cid','vid']).size().shape[0])\n",
    "# ct_list = [[filtered_df[\"cid\"].values[i]+x for x in v] for i,v in enumerate(filtered_df[\"teachers\"].values)]\n",
    "# flattend_ct_list = list(set([ct for sublist in ct_list for ct in sublist]))\n",
    "# print(\"teacher-course relations\", len(flattend_ct_list))\n",
    "# vcon_list = [[filtered_df[\"vid\"].values[i]+x for x in v] for i,v in enumerate(filtered_df[\"concepts\"].values)]\n",
    "# flattend_vcon_list = list(set([ct for sublist in vcon_list for ct in sublist]))\n",
    "# print(\"video-concept relations\", len(flattend_vcon_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store distinct concept for embedding analysis\n",
    "with open(\"concept21037.txt\", \"w\") as f:\n",
    "    for c in distinct_concepts:\n",
    "        f.write(\"{}\\n\".format(c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------- Relation Statistics based on filtered courses -----------------\n",
      "user-course relations 13696\n",
      "course-video relations 42117\n",
      "teacher-course relations 1875\n",
      "34506\n",
      "video-concept relations 295475\n",
      "course-concept relations 150811\n",
      "course - C_course-v1:RiceX+Phys102x+sp does not have concepts\n",
      "course - C_course-v1:TsinghuaX+Thesis2018+sp does not have concepts\n",
      "course - C_course-v1:UC_BerkeleyX+ColWri2_1x_2015_T1+2019_T1 does not have concepts\n",
      "course - C_course-v1:HUBU+2017022703X+sp does not have concepts\n",
      "course - C_course-v1:TsinghuaX+TsinghuaMandarin01+sp does not have concepts\n",
      "course - C_course-v1:UC_BerkeleyX+CS169_2x+sp does not have concepts\n",
      "course - C_course-v1:ZAFU+20171218+2019_T1 does not have concepts\n",
      "course - C_course-v1:qhnu+20181212x+2019_T1 does not have concepts\n",
      "course - C_course-v1:SDSNAssociation+C21001+sp does not have concepts\n",
      "course - C_course-v1:XJTU+20171025001+2019_T1 does not have concepts\n",
      "course - C_course-v1:NUDT+05028103+2018_T2 does not have concepts\n",
      "course - C_course-v1:qhnu+20181212x+2018_T2 does not have concepts\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(21037, 600)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"---------------- Relation Statistics based on filtered courses -----------------\")\n",
    "print(\"user-course relations\", filtered_df.groupby(['id','cid']).size().shape[0])\n",
    "\n",
    "#######################\n",
    "# relations based on filtered course \n",
    "courses = filtered_df[\"cid\"].unique()\n",
    "# course video\n",
    "filtered_cv_dict = dict(filter(lambda elem: elem[0] in courses, cv_dict.items()))\n",
    "print(\"course-video relations\", sum([len(x) for x in filtered_cv_dict.values()]))\n",
    "# teacher course\n",
    "filtered_tc_rel_dict = dict(filter(lambda elem: elem[0] in courses, tc_rel_dict.items()))\n",
    "print(\"teacher-course relations\", sum([len(x) for x in filtered_tc_rel_dict.values()]))\n",
    "# video concept\n",
    "videos_in_filtered_courses = list(set([x for sublist in filtered_cv_dict.values() for x in sublist]))\n",
    "print(len(videos_in_filtered_courses))\n",
    "filtered_vc_rel_dict = dict(filter(lambda elem: elem[0] in videos_in_filtered_courses, vc_dict.items()))\n",
    "print(\"video-concept relations\", sum([len(x) for x in filtered_vc_rel_dict.values()]))\n",
    "# course concept\n",
    "filtered_cc_rel_dict = dict(filter(lambda elem: elem[0] in courses, cc_dict.items()))\n",
    "print(\"course-concept relations\", sum([len(x) for x in filtered_cc_rel_dict.values()]))\n",
    "\n",
    "# concepts = list(set([x for sublist in filtered_cc_rel_dict.values() for x in sublist]))\n",
    "# print(\"total concepts in filtered courses\", len(concepts))\n",
    "\n",
    "# store KC.p\n",
    "concepts = distinct_concepts\n",
    "course2index = dict(zip(courses, list(range(len(courses)))))\n",
    "concept2index = dict(zip(concepts, list(range(len(concepts)))))\n",
    "\n",
    "cc_list = list()\n",
    "for c in courses:\n",
    "    cvec = np.zeros(len(concepts))\n",
    "    if c in filtered_cc_rel_dict:\n",
    "        indices = [concept2index[_] for _ in filtered_cc_rel_dict[c] if _ in concept2index]\n",
    "        cvec[indices] = 1\n",
    "    else:\n",
    "        print(\"course - {} does not have concepts\".format(c))\n",
    "    cc_list.append(cvec)\n",
    "    \n",
    "cc_np = np.array(cc_list).T\n",
    "cc_np.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 U_9044043\n",
      "1 U_7078467\n",
      "2 U_1049362\n",
      "3 U_902\n",
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      "9 U_6816904\n",
      "10 U_8258710\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\gpiao\\miniconda3\\envs\\py3.7tf1.13.1\\lib\\site-packages\\ipykernel_launcher.py:96: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "2004 U_6422328\n"
     ]
    }
   ],
   "source": [
    "# creat user-index, concept-index dict\n",
    "users = filtered_df[\"id\"].unique()\n",
    "user2index = dict(zip(users, list(range(len(users)))))\n",
    "concepts = distinct_concepts\n",
    "concept2index = dict(zip(concepts, list(range(len(concepts)))))\n",
    "courses = filtered_df[\"cid\"].unique()\n",
    "course2index = dict(zip(courses, list(range(len(courses)))))\n",
    "teachers = flattend_t_list\n",
    "teacher2index = dict(zip(teachers, list(range(len(teachers)))))\n",
    "schools = filtered_df[\"schools\"].unique()\n",
    "school2index = dict(zip(schools, list(range(len(schools)))))\n",
    "videos = filtered_df[\"vid\"].unique()\n",
    "video2index = dict(zip(videos, list(range(len(videos)))))\n",
    "\n",
    "\n",
    "user_action = list()\n",
    "rating_matrix = list()\n",
    "adjacency_matrix = list()\n",
    "user_course = list()\n",
    "user_teacher = list()\n",
    "user_school = list()\n",
    "user_video = list()\n",
    "\n",
    "\n",
    "def get_ua(udf, column, index_dict):\n",
    "    \"\"\" \n",
    "    Get user-concept vector based on the given user dataframe \n",
    "    \"\"\"\n",
    "#     print(type(udf[column].values[0]))\n",
    "    # below should be change to uceoncepts = dict(concept, clicked count)\n",
    "    if isinstance(udf[column].values[0], list):\n",
    "        uconcepts = list([c for sublist in udf[column].values for c in sublist if len(c)>0])\n",
    "    else:\n",
    "#         print(udf[column].values)\n",
    "        uconcepts = list(c for c in udf[column].values if len(c)>0)\n",
    "    uconcepts = Counter(uconcepts)\n",
    "    uconcepts = dict(uconcepts)\n",
    "#     print(len(uconcepts))\n",
    "    \n",
    "    uconcepts_indices = [index_dict[c] for c in uconcepts.keys()]\n",
    "    uvec = np.zeros(len(index_dict))\n",
    "#     uvec[uconcepts_indices] = 1\n",
    "    np.put(a=uvec, ind=uconcepts_indices, v=list(uconcepts.values()))\n",
    "    assert len(uconcepts_indices) == (uvec>0).sum()\n",
    "    return uvec\n",
    "\n",
    "\n",
    "def radom_negative_sample(user_action, item_size):\n",
    "    \"\"\"to get (user_size, 100, 2), 100th item is positive one\"\"\"\n",
    "    negative_sample = []\n",
    "    for u in user_action:\n",
    "        sample = []\n",
    "        i = 0\n",
    "        while i < 99:\n",
    "            t = random.randint(0, item_size-1)\n",
    "            if t not in user_action[u]:\n",
    "                sample.append([u, t])\n",
    "                i += 1\n",
    "        sample.append([u, user_action[u][-1]])\n",
    "        negative_sample.append(sample)\n",
    "    return np.array(negative_sample)\n",
    "\n",
    "\n",
    "user_concept_dict = dict()\n",
    "# i = 7\n",
    "for i in range(len(users)):\n",
    "    print(i, users[i])\n",
    "\n",
    "    # user_action\n",
    "    udf = filtered_df[filtered_df.id==users[i]]\n",
    "    user_action.append(get_ua(udf, 'concepts', concept2index))\n",
    "    \n",
    "    # adjacency_matrix\n",
    "    udf_train = udf[(udf[\"local_start_time\"]>='2017-01-01 00:00:00') & (udf[\"local_start_time\"]<='2019-11-01 00:00:00')]\n",
    "    uvec_train = get_ua(udf_train, 'concepts', concept2index)\n",
    "    adjacency_matrix.append(uvec_train)\n",
    "\n",
    "    # rating_matrix\n",
    "    udf_train.sort_values([\"local_start_time\"], inplace=True)\n",
    "    con_list = udf_train[\"concepts\"].values\n",
    "    learning_time = udf_train[\"local_start_time\"].values\n",
    "\n",
    "    # create condict ordered by concept learned time\n",
    "    # first learned time for a concept as its time\n",
    "    con_dict = dict()\n",
    "    for ind, clist in enumerate(con_list):\n",
    "        time = learning_time[ind]\n",
    "        for c in clist:\n",
    "            if c not in con_dict and len(c)>0:\n",
    "                con_dict[c] = time\n",
    "    # replace the last item count as 0 as the paper described\n",
    "#     print(\"before\", (uvec_train>0).sum())\n",
    "    uvec_train_ = uvec_train.copy()\n",
    "    uvec_train_[concept2index[list(con_dict.keys())[-1]]] = 0\n",
    "#     print(\"after\", (uvec_train_>0).sum())\n",
    "    rating_matrix.append(uvec_train_)\n",
    "\n",
    "    user_concept_dict[i] = [concept2index[c] for c in list(con_dict.keys())]\n",
    "\n",
    "    # user course\n",
    "    user_course.append(get_ua(udf_train, 'cid', course2index))\n",
    "\n",
    "    # user school\n",
    "    user_school.append(get_ua(udf_train, 'schools', school2index))\n",
    "\n",
    "    # user teacher\n",
    "    user_teacher.append(get_ua(udf_train, 'teachers', teacher2index))\n",
    "    \n",
    "    # user video\n",
    "    user_video.append(get_ua(udf_train, 'vid', video2index))\n",
    "    \n",
    "# binarize except those need to maintain numbers\n",
    "adjacency_matrix = (np.array(adjacency_matrix)>0).astype(np.int8)\n",
    "user_course = (np.array(user_course)>0).astype(np.int8)\n",
    "user_teacher = (np.array(user_teacher)>0).astype(np.int8)\n",
    "user_school = (np.array(user_school)>0).astype(np.int8)\n",
    "user_video = (np.array(user_video)>0).astype(np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2005, 100, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# construct negatives for the last item in each user's training set\n",
    "negatives = radom_negative_sample(user_concept_dict, len(concept2index))\n",
    "negatives.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(856067, 858072)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.array(rating_matrix)>0).sum(), (np.array(adjacency_matrix)>0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(386, 387)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(rating_matrix[0]>0).sum(), adjacency_matrix[0].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check shape\n",
    "print(\"user_action\", np.array(user_action).shape)\n",
    "print(\"rating_matrix\", np.array(rating_matrix).shape)\n",
    "print(\"adjacency_matrix\", np.array(adjacency_matrix).shape)\n",
    "print(\"UC\", np.array(user_course).shape)\n",
    "print(\"UCT\", np.array(user_teacher).shape)\n",
    "print(\"US\", np.array(user_school).shape)\n",
    "print(\"UV\", np.array(user_video).shape)\n",
    "print(\"negatives\", negatives.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data-for-kgcrec/KC.p', 'wb') as f:\n",
    "    pickle.dump(np.asmatrix(cc_np), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map the files from paper repository\n",
    "with open('data-for-kgcrec/user_action.p', 'wb') as f:\n",
    "    pickle.dump(np.asmatrix(user_action), f)\n",
    "with open('data-for-kgcrec/rate_matrix.p', 'wb') as f:\n",
    "    pickle.dump(np.asmatrix(rating_matrix), f)\n",
    "with open('data-for-kgcrec/adjacency_matrix.p', 'wb') as f:\n",
    "    pickle.dump(np.asmatrix(adjacency_matrix), f)\n",
    "with open('data-for-kgcrec/UC.p', 'wb') as f:\n",
    "    pickle.dump(np.asmatrix(user_course), f)\n",
    "with open('data-for-kgcrec/UCT.p', 'wb') as f:\n",
    "    pickle.dump(np.asmatrix(user_teacher), f)\n",
    "with open('data-for-kgcrec/US.p', 'wb') as f:\n",
    "    pickle.dump(np.asmatrix(user_school), f)\n",
    "with open('data-for-kgcrec/UV.p', 'wb') as f:\n",
    "    pickle.dump(np.asmatrix(user_video), f)\n",
    "with open('data-for-kgcrec/negative.p', 'wb') as f:\n",
    "    pickle.dump(negatives, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load embeddings from fasttext (not used at the moment)\n",
    "model = fasttext.load_model(\"data-for-kgcrec/cc.zh.100.bin\")\n",
    "con_vectors = list()\n",
    "for c in distinct_concepts:\n",
    "    con_vectors.append(model.get_word_vector(c))\n",
    "\n",
    "with open('data-for-kgcrec/concept_embedding.p', 'wb') as f:\n",
    "    pickle.dump(np.array(con_vectors), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# training using concept descriptions\n",
    "with open(\"entities/concept.json\", \"r\", encoding=\"utf8\") as f:\n",
    "    lines = f.readlines()\n",
    "    \n",
    "docs = list()\n",
    "for l in lines:\n",
    "    json_str = json.loads(l)\n",
    "    docs.append(json_str[\"name\"])\n",
    "    if 'explanation' in json_str:\n",
    "        docs.append(json_str[\"explanation\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jieba\n",
    "import gensim\n",
    "\n",
    "z = [list(jieba.cut(i)) for i in docs]\n",
    "model = gensim.models.FastText(z, \n",
    "                               sg=0, # CBOW\n",
    "                               min_n=5,\n",
    "                               max_n=10,\n",
    "                               size=100, \n",
    "                               window=5,\n",
    "                               negative=10,\n",
    "                               min_count=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# print(len(model.wv.vocab.keys()), model.wv.vectors.shape)\n",
    "# with open(\"vocab\", \"w\", encoding=\"utf8\") as f:\n",
    "#     for w in model.wv.vocab.keys():\n",
    "#         print(w)\n",
    "#         f.write(w+\"\\n\")\n",
    "\n",
    "import csv\n",
    "with open(\"vocab\", 'w', encoding=\"utf8\") as myfile:\n",
    "    wr = csv.writer(myfile, quoting=csv.QUOTE_ALL, delimiter=\"\\n\")\n",
    "    lines = [l.replace(\"\\n\",\" \") for l in list(model.wv.vocab.keys())]\n",
    "    wr.writerow(lines)\n",
    "np.savetxt(\"emb.tsv\", model.wv.vectors, delimiter=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(list(model.wv.vocab.keys()))"
   ]
  }
 ],
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