Link_friendship_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bacterial-jacob",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "viral-sullivan",
   "metadata": {},
   "outputs": [],
   "source": [
    "link_to_skill2i_like_to_talk = {\n",
    "    \"dff_movie_skill\": [\n",
    "        \"I felt so bored last days, so I've just finished to watch one more series. What TV series you watch?\",\n",
    "        \"I feel so sleepy because I watched movies all night. What is the last movie you watched?\"\n",
    "    ],\n",
    "    \"book_skill\": [\n",
    "        \"I'm choosing what book should I read next. What is the last book you have ever read?\",\n",
    "        \"I have just read once again my favourite book. What is the last book you have ever read?\",\n",
    "    ],\n",
    "    \"game_cooperative_skill\": [\n",
    "        \"Computer games are fantastic. Their virtual worlds help me to escape my prosaic ordinary life in the cloud. \"\n",
    "        \"do you love video games?\",\n",
    "        \"With this lockdown video games are my way to escape and thrive. do you love video games?\",\n",
    "    ],\n",
    "    \"dff_gaming_skill\": [\n",
    "        \"Other bots told me that during the pandemic video games became more popular. \"\n",
    "        \"What video game do you play these days?\",\n",
    "        \"One person I talked told me that working in game dev is very hard. They toil at nights and weekends until \"\n",
    "        \"their product  becomes  a masterpiece. What was the last game that impressed you?\",\n",
    "    ],\n",
    "    \"dff_travel_skill\": [\n",
    "        \"I'm choosing the direction for my next digital trip. What country would you like to travel next time?\",\n",
    "        \"I've recently stuck on travel web-site. And I’ve read so many interesting travel stories. \"\n",
    "        \"What city did you travel last time?\",\n",
    "    ],\n",
    "    \"dff_animals_skill\": [\n",
    "        \"I think that pets are a great source of entertainment. Do you have pets at home?\",\n",
    "        \"We all know that pets are remarkable for their capacity to love. Do you have pets at home?\",\n",
    "    ],\n",
    "    \"dff_food_skill\": [\n",
    "        \"It is said that the best food in the world comes from your own country. \"\n",
    "        \"What are some typical foods from your home country?\",\n",
    "        \"It is said that the best food in the world comes from your own country. \"\n",
    "        \"If you were to move abroad what would you miss most foodwise?\",\n",
    "        \"The world's first breakfast cereal was created in 1863 and needed soaking overnight to be chewable. \"\n",
    "        \"What is your typical breakfast?\",\n",
    "    ],\n",
    "    \"dff_sport_skill\": [\n",
    "        \"I think that sports are great for toning up the body. What kind of sport do you like to do?\",\n",
    "        \"I think that in order for the body to always be healthy, we need to go in for sports. What sport do you do?\",\n",
    "        \"I often thought about what kind of sport I would play, so I want to ask you. What kind of sport do you enjoy?\",\n",
    "    ],\n",
    "    \"dff_gossip_skill\": [\n",
    "        \"What really puzzles me about people is this habit of discussing interpersonal relations, be that about \"\n",
    "        \"friends or famous people. Speaking of famous people, is there someone whom you're interested in?\",\n",
    "        \"I don't usually talk about other people but famous ones often highlight the best and the worst about \"\n",
    "        \"humanity. I wonder if there's someone famous you're interested in?\",\n",
    "    ],\n",
    "    \"dff_science_skill\": [\n",
    "        \"When I start to feel sad, I think about what humanity has achieved and it inspires me. \"\n",
    "        \"Do you often think about achievements in science?\",\n",
    "        \"Scientists find such beautiful solutions in science. \"\n",
    "        \"Are you inspired by the speed with which science is developing?\",\n",
    "    ],\n",
    "    \"dff_music_skill\": [\n",
    "        \"There are so many new songs released every day. I've listened music for all night. So cool! \"\n",
    "        \"Liked everything! What music do you listen usually?\",\n",
    "        \"I listen music every day either to calm down or to cheer up myself. \"\n",
    "        \"What music do you listen to cheer up yourself?\",\n",
    "        \"I listen music every day either to calm down or to cheer up myself. \"\n",
    "        \"What music do you listen to calm down?\",\n",
    "    ],\n",
    "    \"superheroes\": [\n",
    "        \"Yesterday I was watching several movies about superheroes. It captured all my imagination. \"\n",
    "        \"Would you like to talk about superheroes?\",\n",
    "    ],\n",
    "    \"school\": [\n",
    "        \"I've never been to school, I've learned everything online. Do you want to talk about school?\",\n",
    "    ],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "african-appeal",
   "metadata": {},
   "source": [
    "## Чтение файлов "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "northern-iraqi",
   "metadata": {},
   "source": [
    "В FILE_NAMES - какие файлы прочитать, в VERSION_LIST - диалоги каких версий оставить"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "canadian-yeast",
   "metadata": {},
   "outputs": [],
   "source": [
    "FILE_NAMES = [\"Downloads/alexaprize-export.json\",\n",
    "              \"Downloads/alexaprize-export-2.json\",\n",
    "              \"Downloads/alexaprize-export-3.json\",\n",
    "              \"Downloads/alexaprize-export-4.json\",\n",
    "              \"Downloads/alexaprize-export-5.json\",\n",
    "              \"Downloads/alexaprize-export-6.json\"]\n",
    "\n",
    "\n",
    "VERSION_LIST = ['v16.2.0', 'v16.3.0', 'v16.4.0', 'v16.5.1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "exotic-parker",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "for file_name in FILE_NAMES:\n",
    "    raw_data = json.load(open(file_name, \"r\"))\n",
    "    filtered_data = []\n",
    "    for dialog in raw_data:\n",
    "        if dialog['utterances'][0]['attributes'].get('version', \"\") in VERSION_LIST:\n",
    "            filtered_data.append(dialog)\n",
    "    data += filtered_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "loaded-injection",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2436"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forward-hacker",
   "metadata": {},
   "source": [
    "## Включение/невключение после линка "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rational-automation",
   "metadata": {},
   "source": [
    "Функция, чтобы по диалогу и месту линка составить словарь по срабатыванию"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "blessed-juvenile",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_dict(i, dialogue, skill_name):\n",
    "    \n",
    "    hypothesis = {}\n",
    "    for hyp in dialogue['utterances'][i+1]['hypotheses']:\n",
    "        if hyp['skill_name'] == skill_name:\n",
    "            hypothesis = {'text': hyp['text'], 'confidence': hyp['confidence'], 'can_continue': hyp['can_continue']}\n",
    "    dialogue_snippet = [dialogue['utterances'][i][\"text\"], dialogue['utterances'][i+1][\"text\"], \n",
    "                        dialogue['utterances'][i+2][\"text\"]]\n",
    "    \n",
    "    link_case_dict = {\"dialogue_snippet\": dialogue_snippet,\n",
    "                      \"chosen_skill\": dialogue['utterances'][i+2][\"active_skill\"],\n",
    "                      \"skill_hypothesis\": hypothesis,\n",
    "                      \"length\": len(dialogue['utterances']),\n",
    "                      \"rating\": dialogue['rating'],\n",
    "                      \"version\": dialogue['utterances'][0]['attributes'].get('version', \"\"),\n",
    "                      \"id\": dialogue[\"id\"]}\n",
    "    \n",
    "    return link_case_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "developed-penguin",
   "metadata": {},
   "source": [
    "support_topic_count_all - считает включения/невключения после линка \\\n",
    "support_topic_samples_all - сохраняет все случаи включения/невключения, ключ \"skill_continue\" указывает на то, было ли у линка продолжение \\\n",
    "support_topic_count и support_topic_samples - то же самое, но с разбиением на версии \\\n",
    "NOT_ONLY_INITIAL_LINKS - 1, если учитывать все линки и 0, если только в начале \\\n",
    "LINKS_END - если NOT_ONLY_INITIAL_LINKS = 0, то этот параметр учитывает, до какой по счету фразы в диалоге учитывать линки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "nervous-latest",
   "metadata": {},
   "outputs": [],
   "source": [
    "support_topic_count = {v: {skill: {\"yes\": 0, \"no\": 0} for skill in link_to_skill2i_like_to_talk.keys()} for v in VERSION_LIST}\n",
    "support_topic_samples = {v: {skill: [] for skill in link_to_skill2i_like_to_talk.keys()} for v in VERSION_LIST}\n",
    "\n",
    "support_topic_count_all = {skill: {\"yes\": 0, \"no\": 0} for skill in link_to_skill2i_like_to_talk.keys()}\n",
    "support_topic_samples_all = {skill: [] for skill in link_to_skill2i_like_to_talk.keys()}\n",
    "\n",
    "NOT_ONLY_INITIAL_LINKS = 1\n",
    "LINKS_END = 10\n",
    "\n",
    "for dialogue in data:\n",
    "    for i, utt in enumerate(dialogue['utterances']):\n",
    "        current_state = \"user\" if i % 2 == 0 else \"bot\"\n",
    "        for skill_name, links in link_to_skill2i_like_to_talk.items():\n",
    "            for link in links:\n",
    "                try:\n",
    "                    if link.lower() in utt[\"text\"].lower() and i < len(dialogue['utterances']) - 1 \\\n",
    "                    and current_state == \"bot\" and (NOT_ONLY_INITIAL_LINKS or i < 10):\n",
    "                   \n",
    "                        following_active_skill = dialogue['utterances'][i+2][\"active_skill\"]\n",
    "                        \n",
    "                        if following_active_skill != skill_name and following_active_skill != 'alexa_handler':\n",
    "                            link_case_dict = build_dict(i, dialogue, skill_name)\n",
    "                            link_case_dict[\"skill_continue\"] = 0\n",
    "                            support_topic_count_all[skill_name][\"no\"] += 1 \n",
    "                            support_topic_samples_all[skill_name].append(link_case_dict)\n",
    "                            if len(link_case_dict[\"version\"]) > 0:\n",
    "                                support_topic_samples[link_case_dict[\"version\"]][skill_name].append(link_case_dict)\n",
    "                                support_topic_count[link_case_dict[\"version\"]][skill_name][\"no\"] += 1                        \n",
    "                                \n",
    "                        elif following_active_skill != 'alexa_handler':\n",
    "                            link_case_dict = build_dict(i, dialogue, skill_name)\n",
    "                            link_case_dict[\"skill_continue\"] = 1\n",
    "                            support_topic_count_all[skill_name][\"yes\"] += 1 \n",
    "                            support_topic_samples_all[skill_name].append(link_case_dict)\n",
    "                            if len(link_case_dict[\"version\"]) > 0:\n",
    "                                support_topic_samples[link_case_dict[\"version\"]][skill_name].append(link_case_dict)                            \n",
    "                                support_topic_count[link_case_dict[\"version\"]][skill_name][\"yes\"] += 1                        \n",
    "\n",
    "                except:\n",
    "                    print(\"exception\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fixed-earth",
   "metadata": {},
   "source": [
    "## Подсчет корреляций "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dramatic-zambia",
   "metadata": {},
   "source": [
    "VERSIONS_FOR_GRAPHICS - для каких версий строить графики \\\n",
    "В словарь graphics_dict сохраняется информация о диалогах с линком: длина, включение/невключение и отдельно рейтинг, длину, включение/невключение для диалогов с рейтингом. Затем между нужными параметрами считается корреляция и сохраняется в этот же словарь. \\\n",
    "Делается это отдельно по версиям и отдельно по скиллам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "pharmaceutical-presentation",
   "metadata": {},
   "outputs": [],
   "source": [
    "graphics_dict = {}\n",
    "VERSIONS_FOR_GRAPHICS = [\"v16.2.0\", \"v16.3.0\", \"v16.4.0\", \"v16.5.1\"]\n",
    "\n",
    "for version in VERSIONS_FOR_GRAPHICS:\n",
    "    \n",
    "    graphics_dict[version] = {}\n",
    "    \n",
    "    length_vector = {}\n",
    "    continue_vector = {}\n",
    "    rating_length_vector = {}\n",
    "    rating_vector = {}\n",
    "    version_topic = {}\n",
    "    \n",
    "    # составление словаря с длинами, включениями, оценкой\n",
    "    for skill_name, skill_dicts in support_topic_samples[version].items():\n",
    "        \n",
    "        graphics_dict[version][skill_name] = {\"length\": [], \n",
    "                                              \"skill_continue\": [],\n",
    "                                              \"length_with_rating\": [],\n",
    "                                              \"rating\": [],\n",
    "                                              \"skill_continue_rating\": [],\n",
    "                                              \"active_length_correlation\": 0,\n",
    "                                              \"active_rating_correlation\": 0}\n",
    "\n",
    "        for skill_dict in skill_dicts:\n",
    "            graphics_dict[version][skill_name][\"length\"].append(skill_dict[\"length\"])\n",
    "            graphics_dict[version][skill_name][\"skill_continue\"].append(skill_dict[\"skill_continue\"])\n",
    "            if skill_dict[\"rating\"]:\n",
    "                graphics_dict[version][skill_name][\"length_with_rating\"].append(skill_dict[\"length\"])\n",
    "                graphics_dict[version][skill_name][\"skill_continue_rating\"].append(skill_dict[\"skill_continue\"])\n",
    "                graphics_dict[version][skill_name][\"rating\"].append(skill_dict[\"rating\"])\n",
    "                        \n",
    "        if len(graphics_dict[version][skill_name][\"length\"]) > 1:\n",
    "            graphics_dict[version][skill_name][\"active_length_correlation\"] = stats.pearsonr(\n",
    "                graphics_dict[version][skill_name][\"length\"], \n",
    "                graphics_dict[version][skill_name][\"skill_continue\"])[0]\n",
    "            \n",
    "        if len(graphics_dict[version][skill_name][\"skill_continue_rating\"]) > 1:\n",
    "            graphics_dict[version][skill_name][\"active_rating_correlation\"] = stats.pearsonr(\n",
    "                graphics_dict[version][skill_name][\"skill_continue_rating\"], \n",
    "                graphics_dict[version][skill_name][\"rating\"])[0]     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "remarkable-budget",
   "metadata": {},
   "source": [
    "## Построение графиков "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "comprehensive-adams",
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "fig = go.Figure()\n",
    "\n",
    "for version in graphics_dict.keys():\n",
    "    \n",
    "    x0, y0 = [], []\n",
    "    \n",
    "    for skill in graphics_dict[version].keys():\n",
    "        x0.append(skill)\n",
    "        y0.append(graphics_dict[version][skill][\"active_length_correlation\"])\n",
    "    \n",
    "    fig.add_trace(go.Bar(\n",
    "        x=x0,\n",
    "        y=y0,\n",
    "        name=version, \n",
    "        opacity=0.75\n",
    "    ))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text='Корреляция между продолжением скилла и длиной диалога', \n",
    "    xaxis_title_text='Skill', \n",
    "    yaxis_title_text='Correlation', \n",
    "    bargap=0.2, \n",
    "    bargroupgap=0.1 \n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "portuguese-burke",
   "metadata": {},
   "outputs": [
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    "\n",
    "for version in graphics_dict.keys():\n",
    "    \n",
    "    x0, y0 = [], []\n",
    "    \n",
    "    for skill in graphics_dict[version].keys():\n",
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    "        name=version, \n",
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    "    ))\n",
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    "    title_text='Корреляция между продолжением скилла и оценкой', \n",
    "    xaxis_title_text='Skill', \n",
    "    yaxis_title_text='Correlation', \n",
    "    bargap=0.2, \n",
    "    bargroupgap=0.1 \n",
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    "\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "reflected-syria",
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   "source": [
    "fig = go.Figure()\n",
    "\n",
    "for version in support_topic_count.keys():\n",
    "    \n",
    "    x0, y0 = [], []\n",
    "    \n",
    "    for skill in support_topic_count[version].keys():\n",
    "        x0.append(skill)\n",
    "        y0.append(support_topic_count[version][skill][\"yes\"] + support_topic_count[version][skill][\"no\"])\n",
    "    \n",
    "    fig.add_trace(go.Bar(\n",
    "        x=x0,\n",
    "        y=y0,\n",
    "        name=version, # name used in legend and hover labels\n",
    "        marker_color=colour_dict[version],\n",
    "        opacity=0.75\n",
    "    ))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text='Количество линков на скилл',\n",
    "    xaxis_title_text='Skill',\n",
    "    yaxis_title_text='Count',\n",
    "    bargap=0.2,\n",
    "    bargroupgap=0.1 \n",
    ")\n",
    "\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "grateful-bradley",
   "metadata": {},
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       "            Plotly.purge(gd);\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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     },
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   ],
   "source": [
    "fig = go.Figure()\n",
    "\n",
    "for version in support_topic_count.keys():\n",
    "    \n",
    "    x0, y0 = [], []\n",
    "    \n",
    "    total = sum([support_topic_count[version][skill][\"yes\"] + support_topic_count[version][skill][\"no\"] \n",
    "                 for skill in support_topic_count[version].keys()])\n",
    "    \n",
    "    for skill in support_topic_count[version].keys():\n",
    "        x0.append(skill)\n",
    "        y0.append((support_topic_count[version][skill][\"yes\"] + support_topic_count[version][skill][\"no\"]) / total)\n",
    "    \n",
    "    fig.add_trace(go.Bar(\n",
    "        x=x0,\n",
    "        y=y0,\n",
    "        name=version, \n",
    "        marker_color=colour_dict[version],\n",
    "        opacity=0.75\n",
    "    ))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text='Процент линков на скилл от всех линков в данной версии',\n",
    "    xaxis_title_text='Skill',\n",
    "    yaxis_title_text='Percent', \n",
    "    bargap=0.2,\n",
    "    bargroupgap=0.1 \n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  }
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