272 lines
6.9 KiB
Text
272 lines
6.9 KiB
Text
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"aug_data_path = \"/Users/minjoons/data/squad/dev-v1.0-aug.json\"\n",
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"aug_data = json.load(open(aug_data_path, 'r'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(['Denver', 'Broncos'], 'Denver Broncos')\n",
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"(['Denver', 'Broncos'], 'Denver Broncos')\n",
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"(['Denver', 'Broncos'], 'Denver Broncos ')\n",
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"(['Carolina', 'Panthers'], 'Carolina Panthers')\n"
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]
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}
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],
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"source": [
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"def compare_answers():\n",
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" for article in aug_data['data']:\n",
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" for para in article['paragraphs']:\n",
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" deps = para['deps']\n",
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" nodess = []\n",
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" for dep in deps:\n",
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" nodes, edges = dep\n",
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" if dep is not None:\n",
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" nodess.append(nodes)\n",
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" else:\n",
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" nodess.append([])\n",
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" wordss = [[node[0] for node in nodes] for nodes in nodess]\n",
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" for qa in para['qas']:\n",
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" for answer in qa['answers']:\n",
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" text = answer['text']\n",
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" word_start = answer['answer_word_start']\n",
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" word_stop = answer['answer_word_stop']\n",
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" answer_words = wordss[word_start[0]][word_start[1]:word_stop[1]]\n",
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" yield answer_words, text\n",
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"\n",
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"ca = compare_answers()\n",
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"print(next(ca))\n",
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"print(next(ca))\n",
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"print(next(ca))\n",
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"print(next(ca))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"8\n"
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]
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}
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],
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"source": [
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"def counter():\n",
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" count = 0\n",
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" for article in aug_data['data']:\n",
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" for para in article['paragraphs']:\n",
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" deps = para['deps']\n",
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" nodess = []\n",
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" for dep in deps:\n",
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" if dep is None:\n",
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" count += 1\n",
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" print(count)\n",
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"counter()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0\n"
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]
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}
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],
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"source": [
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"def bad_node_counter():\n",
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" count = 0\n",
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" for article in aug_data['data']:\n",
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" for para in article['paragraphs']:\n",
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" sents = para['sents']\n",
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" deps = para['deps']\n",
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" nodess = []\n",
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" for dep in deps:\n",
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" if dep is not None:\n",
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" nodes, edges = dep\n",
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" for node in nodes:\n",
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" if len(node) != 5:\n",
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" count += 1\n",
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" print(count)\n",
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"bad_node_counter() "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"7\n"
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]
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}
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],
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"source": [
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"def noanswer_counter():\n",
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" count = 0\n",
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" for article in aug_data['data']:\n",
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" for para in article['paragraphs']:\n",
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" deps = para['deps']\n",
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" nodess = []\n",
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" for dep in deps:\n",
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" if dep is not None:\n",
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" nodes, edges = dep\n",
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" nodess.append(nodes)\n",
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" else:\n",
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" nodess.append([])\n",
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" wordss = [[node[0] for node in nodes] for nodes in nodess]\n",
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" for qa in para['qas']:\n",
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" for answer in qa['answers']:\n",
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" text = answer['text']\n",
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" word_start = answer['answer_word_start']\n",
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" word_stop = answer['answer_word_stop']\n",
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" if word_start is None:\n",
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" count += 1\n",
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" print(count)\n",
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"noanswer_counter()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"10600\n"
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]
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}
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],
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"source": [
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"print(sum(len(para['qas']) for a in aug_data['data'] for para in a['paragraphs']))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"10348\n"
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]
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}
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],
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"source": [
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"import nltk\n",
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"\n",
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"def _set_span(t, i):\n",
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" if isinstance(t[0], str):\n",
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" t.span = (i, i+len(t))\n",
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" else:\n",
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" first = True\n",
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" for c in t:\n",
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" cur_span = _set_span(c, i)\n",
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" i = cur_span[1]\n",
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" if first:\n",
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" min_ = cur_span[0]\n",
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" first = False\n",
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" max_ = cur_span[1]\n",
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" t.span = (min_, max_)\n",
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" return t.span\n",
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"\n",
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"\n",
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"def set_span(t):\n",
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" assert isinstance(t, nltk.tree.Tree)\n",
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" try:\n",
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" return _set_span(t, 0)\n",
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" except:\n",
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" print(t)\n",
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" exit()\n",
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"\n",
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"def same_span_counter():\n",
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" count = 0\n",
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" for article in aug_data['data']:\n",
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" for para in article['paragraphs']:\n",
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" consts = para['consts']\n",
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" for const in consts:\n",
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" tree = nltk.tree.Tree.fromstring(const)\n",
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" set_span(tree)\n",
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" if len(list(tree.subtrees())) > len(set(t.span for t in tree.subtrees())):\n",
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" count += 1\n",
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" print(count)\n",
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"same_span_counter()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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