FineGrained.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /Users/ishita/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import scipy.stats\n",
    "import seaborn as sns\n",
    "import torch\n",
    "import nltk\n",
    "nltk.download(\"punkt\")\n",
    "import torch\n",
    "import pickle\n",
    "import regrFuncs as rF\n",
    "import testFuncs as tF\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "GLOVE_PATH = './Downloads/glove.840B.300d.txt'\n",
    "MODEL_PATH = './Downloads/infersent.allnli.pickle'\n",
    "REGR_MODEL_PATH = './models/'\n",
    "EMBED_STORE = None\n",
    "TEST_OUT_PATH = './regout/'\n",
    "DATA_PATH = './Downloads/SNLI/true/'\n",
    "\n",
    "outpaths = {'REGR_MODEL_PATH': REGR_MODEL_PATH, 'TEST_OUT_PATH': TEST_OUT_PATH}\n",
    "\n",
    "\n",
    "id2label = {0:'CONTRADICTION', 1:'NEUTRAL', 2:'ENTAILMENT'}\n",
    "label2id = {'CONTRADICTION': 0, 'NEUTRAL':1, 'ENTAILMENT':2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocab size : 100000\n"
     ]
    }
   ],
   "source": [
    "model = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)\n",
    "model.use_cuda = False\n",
    "model.set_glove_path(GLOVE_PATH)\n",
    "model.build_vocab_k_words(K=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names = ['InferSent', 'BOW']\n",
    "classifiers = [ 'LogReg', 'MLP']\n",
    "all_regs = {}\n",
    "for name in names:\n",
    "    for classifier in classifiers:\n",
    "        all_regs[name+classifier] = pickle.load(open('{0}{1}'.format(outpaths['REGR_MODEL_PATH'], name+classifier), 'rb'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_preds(sent_a, sent_b, verbose = True, names = names, classifiers = classifiers):\n",
    "    vals = {}\n",
    "    for name in names:\n",
    "        for classifier in classifiers:\n",
    "            A, B = rF.embed(model, sent_a, 1, name), rF.embed(model, sent_b, 1, name)\n",
    "            pred, conf = tF.predict(A, B, all_regs[name+classifier])\n",
    "            if verbose:\n",
    "                print('*'*20)\n",
    "                print(name, classifier)\n",
    "                print('*'*20, '\\n')\n",
    "            vals[name + classifier] = {}\n",
    "            vals[name + classifier]['pred'] = []\n",
    "            vals[name + classifier]['conf'] = []\n",
    "            for i in range(len(A)):\n",
    "                if verbose:\n",
    "                    print('A: ', sent_a[i], '\\t B: ', sent_b[i])\n",
    "                    print(id2label[pred[i]], conf[i][pred[i]]*100)\n",
    "                    print('\\n')\n",
    "                vals[name + classifier]['pred'].append(id2label[pred[i]])\n",
    "                vals[name + classifier]['conf'].append(conf[i][pred[i]]*100)\n",
    "            vals[name + classifier]['pred'] = np.array(vals[name + classifier]['pred'])\n",
    "            vals[name + classifier]['conf'] = np.array(vals[name + classifier]['conf'])\n",
    "            if verbose:\n",
    "                print('\\n\\n')\n",
    "    return vals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing the noun phrase lengths\n",
    "\n",
    "We expect that increasing these lengths in specific ways will cause the LSTM cell in InferSent in different ways.\n",
    "\n",
    "We start with testing \"Subj-Verb binding\" and \"Comparison\" type examples that InferSent and BOW disagree with high margin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "InferSent LogReg\n",
      "******************** \n",
      "\n",
      "A:  the boy is less cheerful than the fat man . \t B:  the boy is more cheerful than the fat man .\n",
      "CONTRADICTION 95.73730826377869\n",
      "\n",
      "\n",
      "A:  the tall boy is less cheerful than the fat man . \t B:  the tall boy is more cheerful than the fat man .\n",
      "CONTRADICTION 66.70916080474854\n",
      "\n",
      "\n",
      "A:  the tall pale boy is less cheerful than the fat man . \t B:  the tall pale boy is more cheerful than the fat man .\n",
      "CONTRADICTION 68.93191337585449\n",
      "\n",
      "\n",
      "A:  the tall pale young boy is less cheerful than the fat man . \t B:  the tall pale young boy is more cheerful than the fat man .\n",
      "ENTAILMENT 51.04919075965881\n",
      "\n",
      "\n",
      "A:  the tall pale young blonde boy is less cheerful than the fat man . \t B:  the tall pale young blonde boy is more cheerful than the fat man .\n",
      "ENTAILMENT 77.26407647132874\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "InferSent MLP\n",
      "******************** \n",
      "\n",
      "A:  the boy is less cheerful than the fat man . \t B:  the boy is more cheerful than the fat man .\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the tall boy is less cheerful than the fat man . \t B:  the tall boy is more cheerful than the fat man .\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the tall pale boy is less cheerful than the fat man . \t B:  the tall pale boy is more cheerful than the fat man .\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the tall pale young boy is less cheerful than the fat man . \t B:  the tall pale young boy is more cheerful than the fat man .\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the tall pale young blonde boy is less cheerful than the fat man . \t B:  the tall pale young blonde boy is more cheerful than the fat man .\n",
      "CONTRADICTION 99.99717473983765\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "BOW LogReg\n",
      "******************** \n",
      "\n",
      "A:  the boy is less cheerful than the fat man . \t B:  the boy is more cheerful than the fat man .\n",
      "NEUTRAL 92.18311309814453\n",
      "\n",
      "\n",
      "A:  the tall boy is less cheerful than the fat man . \t B:  the tall boy is more cheerful than the fat man .\n",
      "NEUTRAL 87.57855892181396\n",
      "\n",
      "\n",
      "A:  the tall pale boy is less cheerful than the fat man . \t B:  the tall pale boy is more cheerful than the fat man .\n",
      "NEUTRAL 82.82160758972168\n",
      "\n",
      "\n",
      "A:  the tall pale young boy is less cheerful than the fat man . \t B:  the tall pale young boy is more cheerful than the fat man .\n",
      "NEUTRAL 86.69179081916809\n",
      "\n",
      "\n",
      "A:  the tall pale young blonde boy is less cheerful than the fat man . \t B:  the tall pale young blonde boy is more cheerful than the fat man .\n",
      "NEUTRAL 81.71389102935791\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "BOW MLP\n",
      "******************** \n",
      "\n",
      "A:  the boy is less cheerful than the fat man . \t B:  the boy is more cheerful than the fat man .\n",
      "NEUTRAL 76.29700899124146\n",
      "\n",
      "\n",
      "A:  the tall boy is less cheerful than the fat man . \t B:  the tall boy is more cheerful than the fat man .\n",
      "NEUTRAL 66.47283434867859\n",
      "\n",
      "\n",
      "A:  the tall pale boy is less cheerful than the fat man . \t B:  the tall pale boy is more cheerful than the fat man .\n",
      "NEUTRAL 55.87458610534668\n",
      "\n",
      "\n",
      "A:  the tall pale young boy is less cheerful than the fat man . \t B:  the tall pale young boy is more cheerful than the fat man .\n",
      "NEUTRAL 79.27072048187256\n",
      "\n",
      "\n",
      "A:  the tall pale young blonde boy is less cheerful than the fat man . \t B:  the tall pale young blonde boy is more cheerful than the fat man .\n",
      "NEUTRAL 74.62371587753296\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sent_a = ['the boy is less cheerful than the fat man .', \n",
    "          'the tall boy is less cheerful than the fat man .', \n",
    "          'the tall pale boy is less cheerful than the fat man .',\n",
    "         'the tall pale young boy is less cheerful than the fat man .',\n",
    "          'the tall pale young blonde boy is less cheerful than the fat man .']\n",
    "\n",
    "sent_b = ['the boy is more cheerful than the fat man .', \n",
    "          'the tall boy is more cheerful than the fat man .', \n",
    "          'the tall pale boy is more cheerful than the fat man .',\n",
    "         'the tall pale young boy is more cheerful than the fat man .',\n",
    "          'the tall pale young blonde boy is more cheerful than the fat man .']\n",
    "vals = print_preds(sent_a, sent_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "InferSent LogReg\n",
      "******************** \n",
      "\n",
      "A:  the boy is less cheerful than the fat man. \t B:  the boy is more cheerful than the fat man.\n",
      "CONTRADICTION 93.67056488990784\n",
      "\n",
      "\n",
      "A:  the boy is less cheerful than the fat man with the hat. \t B:  the boy is more cheerful than the fat man with the hat.\n",
      "CONTRADICTION 95.35843133926392\n",
      "\n",
      "\n",
      "A:  the boy is less cheerful than the fat man with the big hat. \t B:  the boy is more cheerful than the fat man with the big hat.\n",
      "CONTRADICTION 81.98245763778687\n",
      "\n",
      "\n",
      "A:  the boy with the hat is less cheerful than the fat man. \t B:  the boy with the hat is more cheerful than the fat man.\n",
      "CONTRADICTION 65.62223434448242\n",
      "\n",
      "\n",
      "A:  the boy with the big hat is less cheerful than the fat man. \t B:  the boy with the big hat is more cheerful than the fat man.\n",
      "ENTAILMENT 65.01502394676208\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "InferSent MLP\n",
      "******************** \n",
      "\n",
      "A:  the boy is less cheerful than the fat man. \t B:  the boy is more cheerful than the fat man.\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the boy is less cheerful than the fat man with the hat. \t B:  the boy is more cheerful than the fat man with the hat.\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the boy is less cheerful than the fat man with the big hat. \t B:  the boy is more cheerful than the fat man with the big hat.\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the boy with the hat is less cheerful than the fat man. \t B:  the boy with the hat is more cheerful than the fat man.\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the boy with the big hat is less cheerful than the fat man. \t B:  the boy with the big hat is more cheerful than the fat man.\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "BOW LogReg\n",
      "******************** \n",
      "\n",
      "A:  the boy is less cheerful than the fat man. \t B:  the boy is more cheerful than the fat man.\n",
      "NEUTRAL 92.18311309814453\n",
      "\n",
      "\n",
      "A:  the boy is less cheerful than the fat man with the hat. \t B:  the boy is more cheerful than the fat man with the hat.\n",
      "NEUTRAL 82.18633532524109\n",
      "\n",
      "\n",
      "A:  the boy is less cheerful than the fat man with the big hat. \t B:  the boy is more cheerful than the fat man with the big hat.\n",
      "NEUTRAL 88.27133178710938\n",
      "\n",
      "\n",
      "A:  the boy with the hat is less cheerful than the fat man. \t B:  the boy with the hat is more cheerful than the fat man.\n",
      "NEUTRAL 82.18633532524109\n",
      "\n",
      "\n",
      "A:  the boy with the big hat is less cheerful than the fat man. \t B:  the boy with the big hat is more cheerful than the fat man.\n",
      "NEUTRAL 88.27133178710938\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "BOW MLP\n",
      "******************** \n",
      "\n",
      "A:  the boy is less cheerful than the fat man. \t B:  the boy is more cheerful than the fat man.\n",
      "NEUTRAL 76.29700899124146\n",
      "\n",
      "\n",
      "A:  the boy is less cheerful than the fat man with the hat. \t B:  the boy is more cheerful than the fat man with the hat.\n",
      "ENTAILMENT 57.7207088470459\n",
      "\n",
      "\n",
      "A:  the boy is less cheerful than the fat man with the big hat. \t B:  the boy is more cheerful than the fat man with the big hat.\n",
      "NEUTRAL 66.73509478569031\n",
      "\n",
      "\n",
      "A:  the boy with the hat is less cheerful than the fat man. \t B:  the boy with the hat is more cheerful than the fat man.\n",
      "ENTAILMENT 57.7207088470459\n",
      "\n",
      "\n",
      "A:  the boy with the big hat is less cheerful than the fat man. \t B:  the boy with the big hat is more cheerful than the fat man.\n",
      "NEUTRAL 66.73509478569031\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sent_a = ['the boy is less cheerful than the fat man.',\n",
    "          'the boy is less cheerful than the fat man with the hat.',\n",
    "          'the boy is less cheerful than the fat man with the big hat.',\n",
    "          'the boy with the hat is less cheerful than the fat man.',\n",
    "          'the boy with the big hat is less cheerful than the fat man.']\n",
    "\n",
    "sent_b = ['the boy is more cheerful than the fat man.',\n",
    "          'the boy is more cheerful than the fat man with the hat.',\n",
    "          'the boy is more cheerful than the fat man with the big hat.',\n",
    "          'the boy with the hat is more cheerful than the fat man.',\n",
    "          'the boy with the big hat is more cheerful than the fat man.']\n",
    "vals = print_preds(sent_a, sent_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "InferSent LogReg\n",
      "******************** \n",
      "\n",
      "A:  the girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "CONTRADICTION 99.96054768562317\n",
      "\n",
      "\n",
      "A:  the girl does shout loudly , however the tall boy does not shout loudly . \t B:  the tall boy does shout loudly .\n",
      "CONTRADICTION 98.7440824508667\n",
      "\n",
      "\n",
      "A:  the girl does shout loudly , however the tall pale boy does not shout loudly . \t B:  the tall pale boy does shout loudly .\n",
      "CONTRADICTION 82.65833854675293\n",
      "\n",
      "\n",
      "A:  the girl does shout loudly , however the tall pale young boy does not shout loudly . \t B:  the tall pale young boy does shout loudly .\n",
      "ENTAILMENT 73.01577925682068\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "InferSent MLP\n",
      "******************** \n",
      "\n",
      "A:  the girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the girl does shout loudly , however the tall boy does not shout loudly . \t B:  the tall boy does shout loudly .\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the girl does shout loudly , however the tall pale boy does not shout loudly . \t B:  the tall pale boy does shout loudly .\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the girl does shout loudly , however the tall pale young boy does not shout loudly . \t B:  the tall pale young boy does shout loudly .\n",
      "CONTRADICTION 99.99690651893616\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "BOW LogReg\n",
      "******************** \n",
      "\n",
      "A:  the girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "ENTAILMENT 99.84913468360901\n",
      "\n",
      "\n",
      "A:  the girl does shout loudly , however the tall boy does not shout loudly . \t B:  the tall boy does shout loudly .\n",
      "ENTAILMENT 99.51723217964172\n",
      "\n",
      "\n",
      "A:  the girl does shout loudly , however the tall pale boy does not shout loudly . \t B:  the tall pale boy does shout loudly .\n",
      "ENTAILMENT 98.74417185783386\n",
      "\n",
      "\n",
      "A:  the girl does shout loudly , however the tall pale young boy does not shout loudly . \t B:  the tall pale young boy does shout loudly .\n",
      "ENTAILMENT 96.87215089797974\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "BOW MLP\n",
      "******************** \n",
      "\n",
      "A:  the girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "ENTAILMENT 99.9980628490448\n",
      "\n",
      "\n",
      "A:  the girl does shout loudly , however the tall boy does not shout loudly . \t B:  the tall boy does shout loudly .\n",
      "ENTAILMENT 99.99135136604309\n",
      "\n",
      "\n",
      "A:  the girl does shout loudly , however the tall pale boy does not shout loudly . \t B:  the tall pale boy does shout loudly .\n",
      "ENTAILMENT 99.97457265853882\n",
      "\n",
      "\n",
      "A:  the girl does shout loudly , however the tall pale young boy does not shout loudly . \t B:  the tall pale young boy does shout loudly .\n",
      "ENTAILMENT 99.96269345283508\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sent_a = ['the girl does shout loudly , however the boy does not shout loudly .',\n",
    "         'the girl does shout loudly , however the tall boy does not shout loudly .',\n",
    "         'the girl does shout loudly , however the tall pale boy does not shout loudly .',\n",
    "         'the girl does shout loudly , however the tall pale young boy does not shout loudly .']\n",
    "\n",
    "sent_b = ['the boy does shout loudly .',\n",
    "         'the tall boy does shout loudly .',\n",
    "         'the tall pale boy does shout loudly .',\n",
    "         'the tall pale young boy does shout loudly .']\n",
    "\n",
    "\n",
    "vals = print_preds(sent_a, sent_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "InferSent LogReg\n",
      "******************** \n",
      "\n",
      "A:  the girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "CONTRADICTION 99.96054768562317\n",
      "\n",
      "\n",
      "A:  the tall girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "CONTRADICTION 99.45414662361145\n",
      "\n",
      "\n",
      "A:  the tall pale girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "CONTRADICTION 96.6947615146637\n",
      "\n",
      "\n",
      "A:  the tall pale young girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "CONTRADICTION 89.04898762702942\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "InferSent MLP\n",
      "******************** \n",
      "\n",
      "A:  the girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the tall girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the tall pale girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the tall pale young girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "BOW LogReg\n",
      "******************** \n",
      "\n",
      "A:  the girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "ENTAILMENT 99.84913468360901\n",
      "\n",
      "\n",
      "A:  the tall girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "ENTAILMENT 99.61853623390198\n",
      "\n",
      "\n",
      "A:  the tall pale girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "ENTAILMENT 98.84112477302551\n",
      "\n",
      "\n",
      "A:  the tall pale young girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "ENTAILMENT 99.75412487983704\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "BOW MLP\n",
      "******************** \n",
      "\n",
      "A:  the girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "ENTAILMENT 99.9980628490448\n",
      "\n",
      "\n",
      "A:  the tall girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "ENTAILMENT 99.99420642852783\n",
      "\n",
      "\n",
      "A:  the tall pale girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "ENTAILMENT 99.978768825531\n",
      "\n",
      "\n",
      "A:  the tall pale young girl does shout loudly , however the boy does not shout loudly . \t B:  the boy does shout loudly .\n",
      "ENTAILMENT 99.99684691429138\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sent_a = ['the girl does shout loudly , however the boy does not shout loudly .',\n",
    "         'the tall girl does shout loudly , however the boy does not shout loudly .',\n",
    "         'the tall pale girl does shout loudly , however the boy does not shout loudly .',\n",
    "         'the tall pale young girl does shout loudly , however the boy does not shout loudly .',]\n",
    "\n",
    "sent_b = ['the boy does shout loudly .',\n",
    "          'the boy does shout loudly .',\n",
    "          'the boy does shout loudly .',\n",
    "          'the boy does shout loudly .']\n",
    "\n",
    "vals = print_preds(sent_a, sent_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "InferSent LogReg\n",
      "******************** \n",
      "\n",
      "A:  the boy overtakes the old woman . \t B:  the old woman overtakes the boy .\n",
      "CONTRADICTION 92.18958020210266\n",
      "\n",
      "\n",
      "A:  the tall boy overtakes the old woman . \t B:  the old woman overtakes the tall boy .\n",
      "CONTRADICTION 90.90285301208496\n",
      "\n",
      "\n",
      "A:  the tall thin boy overtakes the old woman . \t B:  the old woman overtakes the tall thin boy .\n",
      "ENTAILMENT 57.74523615837097\n",
      "\n",
      "\n",
      "A:  the tall thin pale boy overtakes the old woman . \t B:  the old woman overtakes the tall thin pale boy .\n",
      "ENTAILMENT 82.39076137542725\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "InferSent MLP\n",
      "******************** \n",
      "\n",
      "A:  the boy overtakes the old woman . \t B:  the old woman overtakes the boy .\n",
      "CONTRADICTION 100.0\n",
      "\n",
      "\n",
      "A:  the tall boy overtakes the old woman . \t B:  the old woman overtakes the tall boy .\n",
      "CONTRADICTION 99.99986290931702\n",
      "\n",
      "\n",
      "A:  the tall thin boy overtakes the old woman . \t B:  the old woman overtakes the tall thin boy .\n",
      "CONTRADICTION 76.3245165348053\n",
      "\n",
      "\n",
      "A:  the tall thin pale boy overtakes the old woman . \t B:  the old woman overtakes the tall thin pale boy .\n",
      "ENTAILMENT 88.62413763999939\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "BOW LogReg\n",
      "******************** \n",
      "\n",
      "A:  the boy overtakes the old woman . \t B:  the old woman overtakes the boy .\n",
      "ENTAILMENT 92.98434257507324\n",
      "\n",
      "\n",
      "A:  the tall boy overtakes the old woman . \t B:  the old woman overtakes the tall boy .\n",
      "ENTAILMENT 89.67491388320923\n",
      "\n",
      "\n",
      "A:  the tall thin boy overtakes the old woman . \t B:  the old woman overtakes the tall thin boy .\n",
      "ENTAILMENT 70.89490294456482\n",
      "\n",
      "\n",
      "A:  the tall thin pale boy overtakes the old woman . \t B:  the old woman overtakes the tall thin pale boy .\n",
      "ENTAILMENT 72.68843054771423\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "********************\n",
      "BOW MLP\n",
      "******************** \n",
      "\n",
      "A:  the boy overtakes the old woman . \t B:  the old woman overtakes the boy .\n",
      "ENTAILMENT 99.96013045310974\n",
      "\n",
      "\n",
      "A:  the tall boy overtakes the old woman . \t B:  the old woman overtakes the tall boy .\n",
      "ENTAILMENT 99.8973548412323\n",
      "\n",
      "\n",
      "A:  the tall thin boy overtakes the old woman . \t B:  the old woman overtakes the tall thin boy .\n",
      "ENTAILMENT 99.31206703186035\n",
      "\n",
      "\n",
      "A:  the tall thin pale boy overtakes the old woman . \t B:  the old woman overtakes the tall thin pale boy .\n",
      "ENTAILMENT 99.42812323570251\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sent_a = ['the boy overtakes the old woman .',\n",
    "           'the tall boy overtakes the old woman .',\n",
    "           'the tall thin boy overtakes the old woman .',\n",
    "           'the tall thin pale boy overtakes the old woman .']\n",
    "\n",
    "sent_b = ['the old woman overtakes the boy .',\n",
    "           'the old woman overtakes the tall boy .',\n",
    "           'the old woman overtakes the tall thin boy .',\n",
    "           'the old woman overtakes the tall thin pale boy .']\n",
    "\n",
    "vals = print_preds(sent_a, sent_b)         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we see that the confidence in the contradiction judgement falls as the lengths increase and it defaults to entailment when it's too long. \n",
    "\n",
    "## Negating adjectives: Does InferSent understand bigrams?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['adorable', 'adventurous', 'aggressive', 'alert', 'attractive']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"./testData/adjs\") as f:\n",
    "    adjs = f.readlines()\n",
    "    adjs = [x.strip() for x in adjs]\n",
    "\n",
    "sent_a0 = ['', 'not ', 'more ', 'less ']\n",
    "\n",
    "sent_b0 = ['not ', '', 'less ', 'more ']\n",
    "\n",
    "maptype = {}\n",
    "labels = np.array([xa+'* + '+xb+'*' for xa, xb in zip(sent_a0, sent_b0)])\n",
    "for i, label in enumerate(labels):\n",
    "    maptype[i] = label\n",
    "adjs[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sent_a = np.array([])\n",
    "sent_b = np.array([])\n",
    "\n",
    "for adj in adjs:\n",
    "#     sent_a.append([x + adj for x in sent_a0])\n",
    "#     sent_b.append([x + adj for x in sent_b0])\n",
    "    sent_a = np.concatenate((sent_a, np.array([x + adj for x in sent_a0])))\n",
    "    sent_b = np.concatenate((sent_b, np.array([x + adj for x in sent_b0])))\n",
    "\n",
    "vals = print_preds(sent_a, sent_b, verbose = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for name in names:\n",
    "    for classifier in classifiers:\n",
    "        vals[name + classifier]['type'] = np.tile([maptype[x] for x in np.arange(len(sent_a0))] , len(adjs))\n",
    "#         for key in vals[name + classifier]:\n",
    "#             vals[name + classifier][key] = np.reshape(vals[name + classifier][key], (-1,4))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_hist(data):\n",
    "    # Create the bar plot\n",
    "    ax = sns.countplot(\n",
    "        x=\"type\",\n",
    "        hue=\"pred\",\n",
    "        order=labels,\n",
    "        hue_order=[\"CONTRADICTION\", \"NEUTRAL\", \"ENTAILMENT\"],\n",
    "        data=data)\n",
    "    return plt.gcf(), ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAewAAAHsCAYAAAAQKp5gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cbbed30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAewAAAHsCAYAAAAQKp5gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14cfdeb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAewAAAHsCAYAAAAQKp5gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105aa1780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAewAAAHsCAYAAAAQKp5gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c799d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create figure for histogram split across task and label\n",
    "for m in vals:\n",
    "    plt.figure(figsize=(7,7))\n",
    "    a, b = plot_hist(vals[m])\n",
    "    legend = plt.legend(loc='upper right', fontsize = 14, title = \"Predicted label\")\n",
    "    legend.get_title().set_fontsize('12') #legend 'Title' fontsize\n",
    "    lim = max(np.bincount([label2id[x.strip()] for x in vals[m]['pred']]))*0.9\n",
    "    b.set_ylim([0,lim])\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('./figures/adj' + m)\n",
    "    plt.show()\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}