PCA and Modeling.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# To avoid warnings printed during the execution\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "import random\n",
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bigdata = pd.read_csv('bigdata(tf-idf).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
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       "   Unnamed: 0  index  genre  bookId  entity.n.01  cheerfulness.n.01  \\\n",
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     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "bigdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bigdata = bigdata.drop(bigdata.loc[bigdata['genre'] == 2].index)\n",
    "bigdata = bigdata.drop(bigdata.loc[bigdata['genre'] == 4].index)\n",
    "bigdata = bigdata.drop(bigdata.loc[bigdata['genre'] == 8].index)\n",
    "bigdata = bigdata.drop(bigdata.loc[bigdata['genre'] == 9].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
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       "   level_0  Unnamed: 0  index  genre  bookId  entity.n.01  cheerfulness.n.01  \\\n",
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       "3        3           3      3      1    2747          0.0           0.606649   \n",
       "4        4           4      4      1    2749          0.0           0.000000   \n",
       "\n",
       "   eidos.n.01  ethos.n.01  inheritance.n.04     ...      deodorant.n.01  \\\n",
       "0         0.0         0.0          0.000000     ...                 0.0   \n",
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       "4         0.0         0.0          0.000000     ...                 0.0   \n",
       "\n",
       "   hairdressing.n.01  lotion.n.01  perfume.n.02  weaponry.n.01  \\\n",
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       "3                0.0          0.0           0.0            0.0   \n",
       "4                0.0          0.0           0.0            0.0   \n",
       "\n",
       "   ammunition.n.01  armament.n.01  gunnery.n.01  hardware.n.01  totalWords  \n",
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       "3         0.392177            0.0           0.0            0.0       18298  \n",
       "4         0.000000            0.0           0.0            0.0       29215  \n",
       "\n",
       "[5 rows x 780 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigdata.reset_index().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# msk = np.random.rand(len(bigdata)) < 0.8\n",
    "# train = bigdata[msk]\n",
    "# test = bigdata[~msk]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train.shape\n",
    "# test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#temp = bigdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "genre = bigdata['genre']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "del bigdata['genre']\n",
    "del bigdata['Unnamed: 0']\n",
    "del bigdata['index']\n",
    "del bigdata['bookId']\n",
    "del bigdata['totalWords']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Words = bigdata.values\n",
    "# Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=200, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=200)\n",
    "ModelPca = pca.fit(Words)\n",
    "pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# print pca.explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pc1</th>\n",
       "      <th>pc2</th>\n",
       "      <th>pc3</th>\n",
       "      <th>pc4</th>\n",
       "      <th>pc5</th>\n",
       "      <th>pc6</th>\n",
       "      <th>pc7</th>\n",
       "      <th>pc8</th>\n",
       "      <th>pc9</th>\n",
       "      <th>pc10</th>\n",
       "      <th>...</th>\n",
       "      <th>pc191</th>\n",
       "      <th>pc192</th>\n",
       "      <th>pc193</th>\n",
       "      <th>pc194</th>\n",
       "      <th>pc195</th>\n",
       "      <th>pc196</th>\n",
       "      <th>pc197</th>\n",
       "      <th>pc198</th>\n",
       "      <th>pc199</th>\n",
       "      <th>pc200</th>\n",
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       "      <td>0.180124</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.279311</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.606649</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.372527</td>\n",
       "      <td>...</td>\n",
       "      <td>5.480163</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>7.197050</td>\n",
       "      <td>0.688816</td>\n",
       "      <td>0.155548</td>\n",
       "      <td>...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pc1       pc2  pc3  pc4       pc5       pc6       pc7       pc8       pc9  \\\n",
       "0  0.0  0.000000  0.0  0.0  0.000000  0.539379  5.596673  2.739262  0.986586   \n",
       "1  0.0  0.000000  0.0  0.0  0.171278  0.096741  7.928731  6.593808  0.694189   \n",
       "2  0.0  0.000000  0.0  0.0  0.188879  0.000000  6.053220  4.448405  0.180124   \n",
       "3  0.0  0.606649  0.0  0.0  0.000000  0.000000  8.026438  4.083569  1.399720   \n",
       "4  0.0  0.000000  0.0  0.0  0.000000  0.148352  6.663877  7.197050  0.688816   \n",
       "\n",
       "       pc10  ...        pc191  pc192  pc193     pc194     pc195  pc196  pc197  \\\n",
       "0  0.094257  ...    16.709685    0.0    0.0  0.535027  0.000000    0.0    0.0   \n",
       "1  0.101434  ...    18.095715    0.0    0.0  0.000000  0.233771    0.0    0.0   \n",
       "2  0.000000  ...     7.279311    0.0    0.0  0.000000  0.000000    0.0    0.0   \n",
       "3  0.372527  ...     5.480163    0.0    0.0  0.000000  0.572367    0.0    0.0   \n",
       "4  0.155548  ...     4.421328    0.0    0.0  0.000000  0.716972    0.0    0.0   \n",
       "\n",
       "   pc198  pc199  pc200  \n",
       "0    0.0    0.0    0.0  \n",
       "1    0.0    0.0    0.0  \n",
       "2    0.0    0.0    0.0  \n",
       "3    0.0    0.0    0.0  \n",
       "4    0.0    0.0    0.0  \n",
       "\n",
       "[5 rows x 200 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame()\n",
    "for i in range(pca.explained_variance_ratio_.shape[0]):\n",
    "    df[\"pc%i\" % (i+1)] = Words[:,i]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df, genre, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pc1</th>\n",
       "      <th>pc2</th>\n",
       "      <th>pc3</th>\n",
       "      <th>pc4</th>\n",
       "      <th>pc5</th>\n",
       "      <th>pc6</th>\n",
       "      <th>pc7</th>\n",
       "      <th>pc8</th>\n",
       "      <th>pc9</th>\n",
       "      <th>pc10</th>\n",
       "      <th>...</th>\n",
       "      <th>pc191</th>\n",
       "      <th>pc192</th>\n",
       "      <th>pc193</th>\n",
       "      <th>pc194</th>\n",
       "      <th>pc195</th>\n",
       "      <th>pc196</th>\n",
       "      <th>pc197</th>\n",
       "      <th>pc198</th>\n",
       "      <th>pc199</th>\n",
       "      <th>pc200</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1431</th>\n",
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       "      <td>3.535526</td>\n",
       "      <td>...</td>\n",
       "      <td>1.763064</td>\n",
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       "    <tr>\n",
       "      <th>1607</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1858</th>\n",
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       "<p>5 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pc1       pc2  pc3  pc4       pc5  pc6        pc7       pc8       pc9  \\\n",
       "1431  0.0  0.000000  0.0  0.0  0.000000  0.0  10.671306  1.447783  0.000000   \n",
       "662   0.0  0.000000  0.0  0.0  0.000000  0.0   4.290866  0.000000  0.000000   \n",
       "1602  0.0  0.000000  0.0  0.0  0.000000  0.0   8.414816  3.605189  1.897753   \n",
       "1607  0.0  0.000000  0.0  0.0  0.000000  0.0   8.424738  1.785921  0.376040   \n",
       "1858  0.0  0.250559  0.0  0.0  0.051961  0.0   5.936324  5.114715  0.396421   \n",
       "\n",
       "          pc10  ...       pc191  pc192  pc193  pc194  pc195     pc196  pc197  \\\n",
       "1431  1.419807  ...    0.708016    0.0    0.0    0.0    0.0  0.000000    0.0   \n",
       "662   0.000000  ...    7.117226    0.0    0.0    0.0    0.0  0.000000    0.0   \n",
       "1602  3.535526  ...    1.763064    0.0    0.0    0.0    0.0  0.000000    0.0   \n",
       "1607  0.000000  ...    3.493512    0.0    0.0    0.0    0.0  0.000000    0.0   \n",
       "1858  0.030772  ...    9.445000    0.0    0.0    0.0    0.0  0.072004    0.0   \n",
       "\n",
       "      pc198  pc199  pc200  \n",
       "1431    0.0    0.0    0.0  \n",
       "662     0.0    0.0    0.0  \n",
       "1602    0.0    0.0    0.0  \n",
       "1607    0.0    0.0    0.0  \n",
       "1858    0.0    0.0    0.0  \n",
       "\n",
       "[5 rows x 200 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME',\n",
       "          base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=1,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best'),\n",
       "          learning_rate=1.0, n_estimators=20, random_state=None)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf =AdaBoostClassifier(DecisionTreeClassifier(max_depth=1),\n",
    "                         algorithm=\"SAMME\",\n",
    "                         n_estimators=20)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83211678832116787"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre=rf.predict(X_test)\n",
    "accuracy_score(y_test, pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80913242009132424"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre=rf.predict(X_train)\n",
    "accuracy_score(y_train, pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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