Pipeline_and_Grid_Search.ipynb

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   "source": [
    "#import required libraries\n",
    "import pandas as pd\n",
    "import string\n",
    "from pprint import pprint #pretty printer\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "#import the dataset\n",
    "df_spam_collection = pd.read_csv('C:/Users/SpamCollection',sep='\\t', names=['response','message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  response                                            message\n",
       "0      ham  Go until jurong point, crazy.. Available only ...\n",
       "1      ham                      Ok lar... Joking wif u oni...\n",
       "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3      ham  U dun say so early hor... U c already then say...\n",
       "4      ham  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view first 5 records with head method\n",
    "df_spam_collection.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import text processing libraries\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "#import SGD classifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "#import for gridsearch\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "#import for pipeline\n",
    "from sklearn.pipeline import Pipeline\n",
    "#define the pipeline\n",
    "pipeline = Pipeline([\n",
    "    ('vect', CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('clf', SGDClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#parameters for grid search\n",
    "parameters = {'tfidf__use_idf':(True,False)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "performing grid search now...\n",
      "parameters:\n",
      "{'tfidf__use_idf': (True, False)}\n",
      "Fitting 3 folds for each of 2 candidates, totalling 6 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    4.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    4.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done in 4.644s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#perform the gridsearch with pipeline and parameters\n",
    "grid_search =GridSearchCV(pipeline,parameters,n_jobs=-1,verbose=1)\n",
    "print ('performing grid search now...')\n",
    "print('parameters:')\n",
    "pprint(parameters)\n",
    "t0 = time()\n",
    "grid_search.fit(df_spam_collection['message'], df_spam_collection['response'])\n",
    "print('done in %0.3fs'%(time()-t0))\n",
    "print()"
   ]
  }
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