2017-2-23(1)跨语言回归模型(2).ipynb

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     "name": "stderr",
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     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "run baseline/baseline_regression.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.502002002002\n",
      "0.49\n"
     ]
    }
   ],
   "source": [
    "print(logreg.score(X_train, y_train))\n",
    "print(logreg.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[ 0.  1.  0.  0.  1.  1.  1.  0.  0.  0.  1.  1.  0.  0.  1.  1.  1.  1.\n",
      "  1.]\n"
     ]
    }
   ],
   "source": [
    "print(logreg.predict(X_train)[1:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.91038948, -0.72159664, -0.58937845, ..., -1.01986415,\n",
       "        -0.93512196, -1.08217682],\n",
       "       [-0.91038948, -0.623465  , -0.51083135, ...,  0.80549526,\n",
       "        -0.22467513, -0.69292472],\n",
       "       [-0.68037454, -0.32907007,  0.43173387, ...,  0.05017412,\n",
       "         1.24696474, -0.7813911 ],\n",
       "       ..., \n",
       "       [-0.45035959, -0.03467514, -0.19664294, ..., -0.32748644,\n",
       "         0.13054829,  1.43026859],\n",
       "       [ 2.30981974,  1.63356277,  2.00267591, ..., -1.06182643,\n",
       "        -1.23959918, -1.08217682],\n",
       "       [ 0.0096703 , -0.32907007, -0.74647266, ..., -0.4533733 ,\n",
       "        -1.03661437, -0.44521883]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "X_train = np.array([i[0] for i in (train_right + train_wrong)])\n",
    "scaler = preprocessing.StandardScaler().fit(X_train)\n",
    "X_train_std = scaler.transform(X_train)\n",
    "X_test_std = scaler.transform(X_test) \n",
    "logreg = linear_model.LogisticRegression(C=1e5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48396111339010794"
      ]
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     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_test = np.array([i[0] for i in (test_right + test_wrong)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "X_test1 = StandardScaler().fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "X_test2 = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=100000.0, class_weight=None, dual=False,\n",
       "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
       "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
       "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50200200200200196"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48999999999999999"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.score(X_test1, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45600000000000002"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.score(X_test2[:999:2], y_test[:999:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50200200200200196"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.score(X_train[:],y_train[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l2 100 0.5\n",
      "l2 1 0.5\n",
      "l2 0.01 0.5\n"
     ]
    }
   ],
   "source": [
    "for i, C in enumerate((100, 1, 0.01)):\n",
    "    # turn down tolerance for short training time\n",
    "    clf_l1_LR = LogisticRegression(C=C, penalty='l1', tol=0.01)\n",
    "    clf_l2_LR = LogisticRegression(C=C, penalty='l2', tol=0.01)\n",
    "    clf_l1_LR.fit(X_train_std, y_train)\n",
    "    clf_l2_LR.fit(X_train_std, y_train)\n",
    "#     print \"l1\",C, clf_l1_LR.score(X_train_std, y_train)\n",
    "    print \"l2\",C, clf_l2_LR.score(X_test_std, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_l1_LR = LogisticRegression(C=1, penalty='l2', tol=0.01)\n",
    "clf_l1_LR.fit(X_train_std, y_train)\n",
    "clf_l1_LR.score(X_train_std, y_train)\n",
    "clf_l1_LR.predict(X_test_std).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   2,   6, ...,   2,   6,   0],\n",
       "       [  0,   3,   7, ...,  89,  20,  22],\n",
       "       [  1,   6,  19, ...,  53,  49,  17],\n",
       "       ..., \n",
       "       [  2,   9,  11, ...,  35,  27, 142],\n",
       "       [ 14,  26,  39, ...,   0,   0,   0],\n",
       "       [  4,   6,   4, ...,  29,   4,  36]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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