Задание 7.ipynb

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   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import KFold, GridSearchCV\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "newsgroups = datasets.fetch_20newsgroups(subset='all', categories=['alt.atheism', 'sci.space'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X, y = newsgroups.data, newsgroups.target\n",
    "tfidf = TfidfVectorizer()\n",
    "X = tfidf.fit_transform(X)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=241, shuffle=True),\n",
       "       error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=241, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'C': array([  1.00000e-05,   1.00000e-04,   1.00000e-03,   1.00000e-02,\n",
       "         1.00000e-01,   1.00000e+00,   1.00000e+01,   1.00000e+02,\n",
       "         1.00000e+03,   1.00000e+04,   1.00000e+05])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = {'C': np.power(10.0, np.arange(-5, 6))}\n",
    "kf = KFold(n_splits=5, random_state=241, shuffle=True)\n",
    "clf = SVC(kernel='linear', random_state=241)\n",
    "gs = GridSearchCV(estimator=clf, param_grid=grid, scoring='accuracy', cv=kf)\n",
    "gs.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.99328107502799556, {'C': 1.0})"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_score_, gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "best_clf = SVC(C=1.0, kernel='linear', random_state=241)\n",
    "best_clf.fit(X, y)\n",
    "weights = best_clf.coef_.toarray()[0]\n",
    "weights = list(map(lambda w: abs(w), weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "feature_mapping = tfidf.get_feature_names()\n",
    "res = []\n",
    "for idx in np.argsort(weights)[-10:]:\n",
    "    res.append(feature_mapping[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "res.sort()\n",
    "with open('answer.txt', 'w') as f:\n",
    "    f.write(' '.join(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sci', 'keith', 'bible', 'religion', 'sky', 'moon', 'atheists', 'atheism', 'god', 'space']\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  }
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