word_embedding_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9dd25932",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from utils.data import load_data, get_word_tokenized_corpus, get_data_property, get_data_chunks\n",
    "from utils.embeddings import train_fasttext_embedding, get_chunk_embeddings, save_fasttext, load_fasttext\n",
    "from utils.features import get_features\n",
    "from main import setup_chunk_embeddings, setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c2ede92d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e4b703e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "755ba897",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from os.path import exists\n",
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5f63be76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SET THIS FLAG IF IMPORTING EMBS FROM MATLAB\n",
    "from_MATLAB = True\n",
    "# SET THIS FLAG FOR THE MODE (train, load, or test)\n",
    "mode = 'load'\n",
    "\n",
    "if mode == 'train':\n",
    "    # Train Model\n",
    "    class Args:\n",
    "    #     model_name = 'fasttext_model/ft_model.model'\n",
    "        model_name = 'fasttext_model/cc.en.300.bin'\n",
    "        data_file = 'data/dblp-ref-0.json'\n",
    "        chunk_embs_file = 'none'\n",
    "        proj_dir = './saved/'\n",
    "        limit = 30000\n",
    "        T = 20\n",
    "        train_model = True\n",
    "\n",
    "    args=Args()\n",
    "\n",
    "    ft_model, abstracts, citation_counts = setup(args)\n",
    "    chunk_embs = setup_chunk_embeddings(args, ft_model, abstracts)\n",
    "elif mode == 'load':\n",
    "    # Load Chunks\n",
    "    class Args:\n",
    "        model_name = 'fasttext_model/cc.en.300.bin'\n",
    "        data_file = 'data/dblp-ref-0.json'\n",
    "        chunk_embs_file = 'data/chunk_embs.txt'\n",
    "        proj_dir = './saved/'\n",
    "        limit = 30000\n",
    "        T = 20\n",
    "        train_model = False\n",
    "\n",
    "    args=Args()\n",
    "elif mode == 'test':\n",
    "    # Test sentences\n",
    "    # Load Chunks\n",
    "    class Args:\n",
    "        model_name = 'fasttext_model/wiki-news-300d-1M.vec'\n",
    "        proj_dir = './saved/'\n",
    "        T = 20\n",
    "        train_model = False\n",
    "\n",
    "    args=Args()\n",
    "    ft_model = load_fasttext(args.proj_dir + args.model_name)\n",
    "\n",
    "if from_MATLAB and mode != 'test':\n",
    "    df = pd.read_table('./saved/data/toubia_embs.txt', dtype=float, header=None, sep=',').fillna(0).values\n",
    "    chunk_embs = [np.trim_zeros(df[i], 'b').reshape(300,-1).transpose() for i in range(len(df))]\n",
    "elif mode != 'test':\n",
    "    chunk_embs = setup_chunk_embeddings(args, None, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a10d4b35",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "if mode == 'test':\n",
    "    sentences = ['The food here is ok but not worth the price.', \\\n",
    "                 'The food is mediocre and not worth the ridiculous price.', \\\n",
    "                 'The food is good but not worth the horrible customer service.', \\\n",
    "                 'The pizza and burgers are good but not worth the wait times.']\n",
    "\n",
    "    chunks = [get_data_chunks(sent, T=args.T) for sent in sentences]\n",
    "    chunk_embs = np.array([get_chunk_embeddings(ft_model, chunk) for chunk in chunks])\n",
    "    features = [get_features(chunk_emb) for chunk_emb in chunk_embs]\n",
    "    \n",
    "    for key in features[0].keys():\n",
    "        print('\\n' + key)\n",
    "        \n",
    "        if key == 'distances':\n",
    "            for i, feature in enumerate(features):\n",
    "                plt.plot(range(len(feature['distances'])), feature['distances'], label=f'Sentence{i}')\n",
    "            plt.legend()\n",
    "\n",
    "        else:\n",
    "            for feature in features:\n",
    "                print(feature[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73d7ccdf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "639142b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67daeb0f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "66467edf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " ...\n",
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      "   2.15322707e-02 -1.03015937e-02]\n",
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      " ...\n",
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      "  -8.33707647e-04 -1.17202249e-02]\n",
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      " [-3.83449616e-03 -4.81666648e-03 -2.26108526e-02 ...  1.63082946e-01\n",
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-f780ac21a70a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mget_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_emb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'circuitousness'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchunk_emb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchunk_embs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-33-f780ac21a70a>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mget_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_emb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_list\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'circuitousness'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchunk_emb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchunk_embs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/word_embedding_measures/utils/features.py\u001b[0m in \u001b[0;36mget_features\u001b[0;34m(chunk_emb, feature_list, volume_tolerance, circuitousness_tolerance)\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistances\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m                 \u001b[0mdistances\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_speed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_emb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m             \u001b[0mfeature_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_circuitousness\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_emb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcircuitousness_tolerance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistances\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdistances\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mfeature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'distances'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/word_embedding_measures/utils/features.py\u001b[0m in \u001b[0;36mget_circuitousness\u001b[0;34m(chunk_emb, tolerance, distances)\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0;31m# do travelling salesman problem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_emb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mroute\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtwo_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_emb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroute\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0mtsp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_emb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mroute\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mchunk_emb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mroute\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroute\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/word_embedding_measures/utils/algs.py\u001b[0m in \u001b[0;36mtwo_opt\u001b[0;34m(cities, improvement_threshold)\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mswap_last\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mswap_first\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroute\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# to each of the cities following,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m                 \u001b[0mnew_route\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtwo_opt_swap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroute\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mswap_first\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mswap_last\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# try reversing the order of these cities\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m                 \u001b[0mnew_distance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpath_distance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_route\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcities\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# and check the total distance with this modification.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_distance\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mbest_distance\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# If the path distance is an improvement,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m                     \u001b[0mroute\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_route\u001b[0m \u001b[0;31m# make this the accepted best route\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/word_embedding_measures/utils/algs.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(r, c)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m## Code taken from https://stackoverflow.com/questions/25585401/\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Calculate the euclidian distance in n-space of the route r traversing cities c, ending at the path start.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mpath_distance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;31m# Reverse the order of all elements from element i to element k in array r.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mtwo_opt_swap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/word_embedding_measures/utils/algs.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m## Code taken from https://stackoverflow.com/questions/25585401/\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Calculate the euclidian distance in n-space of the route r traversing cities c, ending at the path start.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mpath_distance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;31m# Reverse the order of all elements from element i to element k in array r.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mtwo_opt_swap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mnorm\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/linalg/linalg.py\u001b[0m in \u001b[0;36mnorm\u001b[0;34m(x, ord, axis, keepdims)\u001b[0m\n\u001b[1;32m   2528\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2529\u001b[0m                 \u001b[0msqnorm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2530\u001b[0;31m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqnorm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2531\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2532\u001b[0m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "features = [get_features(chunk_emb, feature_list=['circuitousness']) for chunk_emb in chunk_embs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdeb1eba",
   "metadata": {},
   "outputs": [],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cd0648e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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