example_lime-text.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.5/re.py:203: FutureWarning: split() requires a non-empty pattern match.\n",
      "  return _compile(pattern, flags).split(string, maxsplit)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Document id: 83\n",
      "Probability(christian) = 0.484\n",
      "True class: atheism\n"
     ]
    }
   ],
   "source": [
    "# run the text explainer example notebook, up to single explanation# run th \n",
    "import sklearn\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import sklearn.ensemble\n",
    "import sklearn.metrics\n",
    "import warnings\n",
    "from __future__ import print_function\n",
    "\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "categories = ['alt.atheism', 'soc.religion.christian']\n",
    "newsgroups_train = fetch_20newsgroups(subset='train', categories=categories)\n",
    "newsgroups_test = fetch_20newsgroups(subset='test', categories=categories)\n",
    "class_names = ['atheism', 'christian']\n",
    "\n",
    "vectorizer = sklearn.feature_extraction.text.TfidfVectorizer(lowercase=False)\n",
    "train_vectors = vectorizer.fit_transform(newsgroups_train.data)\n",
    "test_vectors = vectorizer.transform(newsgroups_test.data)\n",
    "\n",
    "rf = sklearn.ensemble.RandomForestClassifier(n_estimators=500)\n",
    "rf.fit(train_vectors, newsgroups_train.target)\n",
    "\n",
    "pred = rf.predict(test_vectors)\n",
    "sklearn.metrics.f1_score(newsgroups_test.target, pred, average='binary')\n",
    "\n",
    "from lime import lime_text\n",
    "from lime import submodular_pick\n",
    "from sklearn.pipeline import make_pipeline\n",
    "c = make_pipeline(vectorizer, rf)\n",
    "\n",
    "from lime.lime_text import LimeTextExplainer\n",
    "explainer = LimeTextExplainer(class_names=class_names)\n",
    "\n",
    "idx = 83\n",
    "exp = explainer.explain_instance(newsgroups_test.data[idx], c.predict_proba, num_features=6)\n",
    "print('Document id: %d' % idx)\n",
    "print('Probability(christian) =', c.predict_proba([newsgroups_test.data[idx]])[0,1])\n",
    "print('True class: %s' % class_names[newsgroups_test.target[idx]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.5/re.py:203: FutureWarning: split() requires a non-empty pattern match.\n",
      "  return _compile(pattern, flags).split(string, maxsplit)\n"
     ]
    }
   ],
   "source": [
    "sp_obj = submodular_pick.SubmodularPick(explainer, newsgroups_test.data, c.predict_proba, sample_size=2, num_features=6,num_exps_desired=2,top_labels=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SNC955CFJ6s3wkCT1ZnhIknozPCRJvRkekqTe/El2SRphff05dvAn2SVJU8jwkCT1ZnhIknozPCRJvRkekqTeDA9JUm+GhySpN8NDktSb4SFJ6s3wkCT1NqXhkWRxkgMGnh+X5KipXKckaepN9ZHHYuCAdc4lSZpT1hkeSRYluSbJCUmuS3JSkv2TfC/JD5Ls3R7nJbkkyblJnpxkY+BdwEFJLk1yUGty9yRnJbk+yZsG1vOaJBe0eT+ZZMMkr0jy4Tb9zUmub8NPSPK9KXg9JEnjMN4jj12B44GntMergecCRwFHA9cAS6vqGcDfAu+rqv9ow1+sqsVV9cXW1lOAFwF7A8cm2SjJbsBBwO9V1WJgFXAwcA6wtC23FLg9yWPa8Nkji0xyWJLlSZZzf5+XQZLUx4JxzndDVa0ASHIlcHpVVZIVwCJgG+DEJE8ECthoLW19vapWAiuT3Ao8GngBsAS4MAnAZsCtVfXzJFsm2Qp4LPA54Hl04fGVkQ1X1TJgGbSfZJckTYnxhsfKgeGHBp4/1Np4N3BmVb0sySLgrHG2taotH+DEqnr7KPOfC7weuJbuSORQ4NnAkeOsXZI0ZMO6YL4NcHMbPmRg/L3AVuNY/nTgT5I8CiDJdkl2btPOoTs9djZwCfB8YGVV3T2EuiVJEzCs8PgQ8P4kl7Dm0cyZdBfIBy+YP0xVXQUcA3wryeXAvwM7tsnn0J2yOruqVgE3Ad8dUt2SpAnwNrSSNIK3ofU2tJKkKWB4SJJ6MzwkSb0ZHpKk3gwPSVJvhockqTfDQ5LUm+EhSeptvL9tNecsWbiE5ccun+kyJGle8shDktSb4SFJ6s3wkCT1ZnhIknozPCRJvRkekqTevJ+HpPXO+ny/jnXxfh6SpCljeEiSejM8JEm9GR6SpN4MD0lSb4aHJKk3w0OS1JvhIUnqzfCQJPU26fBIcm6SfZN8bRgFSZJmv0mHR1U9Z7zzJtlwsuuTJM28YRx53NcGt07y9STXJvlEkg1WT09yfJLLgGcneUGSS5KsSPLPSTZJ8swkX2nzH5jk10k2TrJpkuvb+LOSfDDJBUmuS7J0srVLkiZmmNc89gb+Ctgd2AX44zZ+C+D8qno6sBw4ATioqp5Gdw/1PwcuARa3+ZcCVwDPBPYBzh9Yx4Kq2hs4Ajh2ZAFJDkuyPMly7h9izyRJaxhmeFxQVddX1Srg88Bz2/hVwClt+MnADVV1XXt+IvC8qvoN8KMku9GF0IeB59EFyTkD6/hK+/ciYNHIAqpqWVXtVVV7sfnwOiZJWtMww2Pkbxyvfv5AC5R1ORt4MfAg8G268Hkua4bHyvbvKrqjFknSDBjqaaskj2/XOg4CvjvKPNcCi5Ls2p6/FvhOGz6H7nTUeVX1S2B7uiOVK4ZYoyRpCIYRHquPMC4EPg5cDdwAfPVhM1Y9ALweODnJCuAh4BNt8vnAo+mOQAAuB1bUfL1blSTNYZO6k2CS7YGLq2rn4ZU0HN5JUNJYvJPg2Kb8ToJJFgLnAf9rom1IkuamCV90rqpbgCcNsRZJ0hzhb1tJknozPCRJvRkekqTeDA9JUm+GhySpN8NDktTbvP19qCULl7D82OUzXYYkzUseeUiSejM8JEm9GR6SpN4MD0lSb4aHJKk3w0OS1Nuk7ucxm3k/D0mj8V4eazfl9/OQJK2/DA9JUm+GhySpN8NDktSb4SFJ6s3wkCT1ZnhIknozPCRJvRkekqTepjU8khyRZPOB599Isu1a5j8uyVHTU50kabymLTySbAgcAfw2PKrqgKq6a7pqkCQNx9DCI8mpSS5KcmWSw9q4+5Icn+Qy4B3AQuDMJGe26Tcm2aENvy7J5UkuS/LZUdrfJclpbR3nJHnKsGqXJPUzzHuYH1pVdyTZDLgwySnAFsD5VXUkQJJDgedX1W2DCybZAzgGeE5V3ZZku1HaXwYcXlU/SLIP8H+A/Ua0cxhwGADbDLFnkqQ1DDM83pTkZW34scATgVXAKeNYdj/g5NWhUlV3DE5MsiXwHODkJKtHbzKykapaRhcy3a/qSpKmxFDCI8m+wP7As6vq/iRnAZsCD1TVqiGsYgPgrqpaPIS2JEmTNKxrHtsAd7bgeArwrDHmuxfYapTxZwCvSLI9wMjTVlV1D3BDkle06Uny9CHVLknqaVjhcRqwIMnVwAeA748x3zLgtNUXzFerqiuB9wLfaRfXPzzKsgcD/61NvxI4cEi1S5J68k6CktYr3klw7byToCRpyhgekqTeDA9JUm+GhySpN8NDktSb4SFJ6s3wkCT1ZnhIknob5g8jzipLFi5h+bHLZ7oMSZqXPPKQJPVmeEiSejM8JEm9GR6SpN4MD0lSb4aHJKk3w0OS1JvhIUnqzfCQJPU2f29Dm9wLXDvTdQzBDsBtM13EJM2HPsD86Id9mD1maz92rqpHrmumefvzJMC147kP72yXZPlc78d86APMj37Yh9ljrvfD01aSpN4MD0lSb/M5PJbNdAFDMh/6MR/6APOjH/Zh9pjT/Zi3F8wlSVNnPh95SJKmiOEhSeptToZHkj9Mcm2SHyZ52yjTN0nyxTb9/CSLBqa9vY2/NsmLprPuETVOqA9Jtk9yZpL7knx8uuseaRL9eGGSi5KsaP/uN921D9Q40T7sneTS9rgsycumu/YRdU74c9GmP669r46arppHmsS2WJTk1wPb4xPTXftAjZP5fvpPSc5LcmX7bGw6nbX3UlVz6gFsCPwIeAKwMXAZsPuIef4C+EQbfiXwxTa8e5t/E+DxrZ0N51gftgCeCxwOfHwOb4tnAAvb8FOBm+dgHzYHFrThHYFbVz+fS/0YmP5l4GTgqLnWB2ARcMVM1D3EPiwALgee3p5vPxPfT+N9zMUjj72BH1bV9VX1H8AXgANHzHMgcGIb/jLwgiRp479QVSur6gbgh6296TbhPlTVr6rqu8AD01fumCbTj0uq6pY2/kpgsySbTEvVa5pMH+6vqt+08ZsCM/nXJ5P5XJDkpcANdNtipkyqD7PEZPrwB8DlVXUZQFXdXlWrpqnu3uZieDwGuGng+U/buFHnaR/uu+lSfDzLTofJ9GE2GVY/Xg5cXFUrp6jOtZlUH5Lsk+RKYAVw+ECYTLcJ9yPJlsBfA++chjrXZrLvp8cnuSTJd5IsnepixzCZPjwJqCTfTHJxkrdOQ70TNp9/nkRzQJI9gA/S7XXNOVV1PrBHkt2AE5P8v6qaDUeFfRwHfKSq7ptdO/G9/Ax4XFXdnmQJcGqSParqnpkurIcFdKeknwncD5ye5KKqOn1myxrdXDzyuBl47MDzndq4UedJsgDYBrh9nMtOh8n0YTaZVD+S7AR8FXhdVf1oyqsd3VC2RVVdDdxHd/1mJkymH/sAH0pyI3AEcHSSv5zqgkcx4T60U9G3A1TVRXTXHZ405RU/3GS2w0+Bs6vqtqq6H/gGsOeUVzxRM33Rpe+DLp2vp7vgvfqC1B4j5nkja16Q+lIb3oM1L5hfz8xcMJ9wHwamH8LMXzCfzLbYts3/x3O4D4/ndxfMdwZuAXaYa/0YMc9xzNwF88lsi0eu/izTXay+GdhujvXhEcDFtD/EAL4N/OeZ2Bbj6utMFzDBDXQAcB3d3sU72rh3AX/Uhjel+6uRHwIXAE8YWPYdbblrgRfP0T7cCNxBt6f7U0b8Ncdc6AdwDPAr4NKBx6PmWB9eS3eB+dL2oX/pTG2Hyb6nBto4jhkKj0lui5eP2BYvmWt9aNNe0/pxBfChmXw/revhz5NIknqbi9c8JEkzzPCQJPVmeEiSejM8JEm9GR6SpN4MD0lSb4aHJKm3/w+UL07hp3XLSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "[exp.as_pyplot_figure(label=0) for exp in sp_obj.sp_explanations];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "[exp.as_pyplot_figure(label=1) for exp in sp_obj.sp_explanations];"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}