3. word2vec and classifiers modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 在word2vec上训练情感分析模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "#from nltk.corpus import stopwords\n",
    "\n",
    "from gensim.models.word2vec import Word2Vec\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 和之前的操作一致"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def load_dataset(name, nrows=None):\n",
    "    datasets = {\n",
    "        'unlabeled_train': 'unlabeledTrainData.tsv',\n",
    "        'labeled_train': 'labeledTrainData.tsv',\n",
    "        'test': 'testData.tsv'\n",
    "    }\n",
    "    if name not in datasets:\n",
    "        raise ValueError(name)\n",
    "    data_file = os.path.join('..', 'data', datasets[name])\n",
    "    df = pd.read_csv(data_file, sep='\\t', escapechar='\\\\', nrows=nrows)\n",
    "    print('Number of reviews: {}'.format(len(df)))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "eng_stopwords = set(stopwords.words('english'))\n",
    "\n",
    "def clean_text(text, remove_stopwords=False):\n",
    "    text = BeautifulSoup(text, 'html.parser').get_text()\n",
    "    text = re.sub(r'[^a-zA-Z]', ' ', text)\n",
    "    words = text.lower().split()\n",
    "    if remove_stopwords:\n",
    "        words = [w for w in words if w not in eng_stopwords]\n",
    "    return words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读入之前训练好的Word2Vec模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_name = '300features_40minwords_10context.model'\n",
    "model = Word2Vec.load(os.path.join('..', 'models', model_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 我们可以根据word2vec的结果去对影评文本进行编码\n",
    "\n",
    "编码方式有一点粗暴，简单说来就是把这句话中的词的词向量做平均"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of reviews: 25000\n"
     ]
    },
    {
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      "text/plain": [
       "       id  sentiment                                             review\n",
       "0  5814_8          1  With all this stuff going down at the moment w...\n",
       "1  2381_9          1  \"The Classic War of the Worlds\" by Timothy Hin...\n",
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       "3  3630_4          0  It must be assumed that those who praised this...\n",
       "4  9495_8          1  Superbly trashy and wondrously unpretentious 8..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = load_dataset('labeled_train')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def to_review_vector(review):\n",
    "    words = clean_text(review, remove_stopwords=True)\n",
    "    array = np.array([model[w] for w in words if w in model])\n",
    "    return pd.Series(array.mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_features = df.review.apply(to_review_vector)\n",
    "train_data_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用随机森林构建分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "forest = RandomForestClassifier(n_estimators = 100, random_state=42)\n",
    "forest = forest.fit(train_data_features, df.sentiment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 同样在训练集上试试，确保模型能正常work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12500,     0],\n",
       "       [    0, 12500]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(df.sentiment, forest.predict(train_data_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清理占用内容的变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del df\n",
    "del train_data_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测测试集结果并上传kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of reviews: 25000\n"
     ]
    },
    {
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      "text/plain": [
       "         id                                             review\n",
       "0  12311_10  Naturally in a film who's main themes are of m...\n",
       "1    8348_2  This movie is a disaster within a disaster fil...\n",
       "2    5828_4  All in all, this is a movie for kids. We saw i...\n",
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = load_dataset('test')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
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       "      <td>-0.013085</td>\n",
       "      <td>-0.005469</td>\n",
       "      <td>0.015154</td>\n",
       "      <td>0.022737</td>\n",
       "      <td>0.009717</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005757</td>\n",
       "      <td>0.018115</td>\n",
       "      <td>-0.010495</td>\n",
       "      <td>-0.007650</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>0.018796</td>\n",
       "      <td>-0.003173</td>\n",
       "      <td>0.001657</td>\n",
       "      <td>0.014491</td>\n",
       "      <td>0.026732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.015999</td>\n",
       "      <td>-0.012097</td>\n",
       "      <td>0.022069</td>\n",
       "      <td>-0.014368</td>\n",
       "      <td>-0.020226</td>\n",
       "      <td>-0.015809</td>\n",
       "      <td>-0.000826</td>\n",
       "      <td>0.010130</td>\n",
       "      <td>0.033976</td>\n",
       "      <td>0.005700</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001799</td>\n",
       "      <td>0.012403</td>\n",
       "      <td>-0.022812</td>\n",
       "      <td>0.011651</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.009241</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>-0.002865</td>\n",
       "      <td>0.027701</td>\n",
       "      <td>0.028418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.015196</td>\n",
       "      <td>-0.013445</td>\n",
       "      <td>0.010499</td>\n",
       "      <td>-0.035669</td>\n",
       "      <td>-0.040131</td>\n",
       "      <td>-0.018273</td>\n",
       "      <td>-0.020452</td>\n",
       "      <td>-0.003197</td>\n",
       "      <td>0.026555</td>\n",
       "      <td>0.008284</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011720</td>\n",
       "      <td>0.010397</td>\n",
       "      <td>-0.029256</td>\n",
       "      <td>0.007422</td>\n",
       "      <td>-0.000662</td>\n",
       "      <td>0.020593</td>\n",
       "      <td>0.001274</td>\n",
       "      <td>-0.014059</td>\n",
       "      <td>0.024905</td>\n",
       "      <td>0.024326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.016140</td>\n",
       "      <td>-0.015608</td>\n",
       "      <td>0.010962</td>\n",
       "      <td>-0.008424</td>\n",
       "      <td>-0.022619</td>\n",
       "      <td>-0.022396</td>\n",
       "      <td>-0.018043</td>\n",
       "      <td>0.012519</td>\n",
       "      <td>0.032103</td>\n",
       "      <td>0.009743</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001820</td>\n",
       "      <td>0.004578</td>\n",
       "      <td>-0.008875</td>\n",
       "      <td>0.009702</td>\n",
       "      <td>-0.012013</td>\n",
       "      <td>0.010689</td>\n",
       "      <td>-0.003468</td>\n",
       "      <td>-0.003109</td>\n",
       "      <td>0.026661</td>\n",
       "      <td>0.005735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5         6    \\\n",
       "0 -0.019753 -0.005689  0.015961 -0.038633 -0.041745 -0.044680 -0.012790   \n",
       "1  0.000497 -0.004140  0.019237  0.011341 -0.020860 -0.013085 -0.005469   \n",
       "2 -0.015999 -0.012097  0.022069 -0.014368 -0.020226 -0.015809 -0.000826   \n",
       "3 -0.015196 -0.013445  0.010499 -0.035669 -0.040131 -0.018273 -0.020452   \n",
       "4 -0.016140 -0.015608  0.010962 -0.008424 -0.022619 -0.022396 -0.018043   \n",
       "\n",
       "        7         8         9      ...          290       291       292  \\\n",
       "0  0.004908  0.053838  0.008490    ...    -0.005520  0.034378 -0.027250   \n",
       "1  0.015154  0.022737  0.009717    ...     0.005757  0.018115 -0.010495   \n",
       "2  0.010130  0.033976  0.005700    ...     0.001799  0.012403 -0.022812   \n",
       "3 -0.003197  0.026555  0.008284    ...     0.011720  0.010397 -0.029256   \n",
       "4  0.012519  0.032103  0.009743    ...    -0.001820  0.004578 -0.008875   \n",
       "\n",
       "        293       294       295       296       297       298       299  \n",
       "0  0.010244 -0.008976  0.010181 -0.027196  0.010429  0.021153  0.015764  \n",
       "1 -0.007650  0.000969  0.018796 -0.003173  0.001657  0.014491  0.026732  \n",
       "2  0.011651  0.001775  0.009241  0.003241 -0.002865  0.027701  0.028418  \n",
       "3  0.007422 -0.000662  0.020593  0.001274 -0.014059  0.024905  0.024326  \n",
       "4  0.009702 -0.012013  0.010689 -0.003468 -0.003109  0.026661  0.005735  \n",
       "\n",
       "[5 rows x 300 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_features = df.review.apply(to_review_vector)\n",
    "test_data_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12311_10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8348_2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5828_4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7186_2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12128_7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  sentiment\n",
       "0  12311_10          1\n",
       "1    8348_2          0\n",
       "2    5828_4          0\n",
       "3    7186_2          0\n",
       "4   12128_7          1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = forest.predict(test_data_features)\n",
    "output = pd.DataFrame({'id':df.id, 'sentiment':result})\n",
    "output.to_csv(os.path.join('..', 'data', 'Word2Vec_model.csv'), index=False)\n",
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df\n",
    "del test_data_features\n",
    "del forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------\n",
    "### 对词向量进行聚类研究和编码\n",
    "使用Kmeans进行聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "word_vectors = model.syn0\n",
    "num_clusters = word_vectors.shape[0] // 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.03 s, sys: 377 ms, total: 2.41 s\n",
      "Wall time: 13min 19s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "kmeans_clustering = KMeans(n_clusters = num_clusters, n_jobs=4)\n",
    "idx = kmeans_clustering.fit_predict(word_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "word_centroid_map = dict(zip(model.index2word, idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "filename = 'word_centroid_map_10avg.pickle'\n",
    "with open(os.path.join('..', 'models', filename), 'bw') as f:\n",
    "    pickle.dump(word_centroid_map, f)\n",
    "    \n",
    "#with open(os.path.join('..', 'models', filename), 'br') as f:\n",
    "#    word_centroid_map = pickle.load(f)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出一些clusters看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 0\n",
      "['praised', 'appreciated', 'noted', 'avoided', 'criticized', 'admired']\n",
      "\n",
      "Cluster 1\n",
      "['misfit', 'con', 'hoodlum', 'spy', 'rogue']\n",
      "\n",
      "Cluster 2\n",
      "['contrasts', 'healthy', 'glamour', 'eroticism', 'sensual']\n",
      "\n",
      "Cluster 3\n",
      "['matthew', 'kingsley', 'klein', 'hackman', 'meyers', 'perry', 'simpson', 'pullman', 'dana', 'olsen', 'ryan', 'barrie', 'caan', 'tho', 'farina', 'stiller', 'hutton', 'sparks', 'lillard', 'broderick', 'kline', 'reprise', 'mcconaughey', 'carvey', 'harrelson']\n",
      "\n",
      "Cluster 4\n",
      "['wolves', 'papillon', 'continent']\n",
      "\n",
      "Cluster 5\n",
      "['tick', 'drain', 'nailed', 'puke', 'boil', 'stalk']\n",
      "\n",
      "Cluster 6\n",
      "['cotton', 'denver', 'windsor', 'marsh', 'bell']\n",
      "\n",
      "Cluster 7\n",
      "['lighting', 'costumes', 'sfx', 'props', 'design', 'costuming', 'designs', 'makeup']\n",
      "\n",
      "Cluster 8\n",
      "['decline', 'swashbuckling', 'swashbuckler', 'prestige', 'potboiler', 'latter', 'glory', 'untouchables', 'fame']\n",
      "\n",
      "Cluster 9\n",
      "['slashed', 'butchered', 'mutilated', 'eaten', 'slaughtered', 'continually']\n"
     ]
    }
   ],
   "source": [
    "for cluster in range(0,10):\n",
    "    print(\"\\nCluster %d\" % cluster)\n",
    "    print([w for w,c in word_centroid_map.items() if c == cluster])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 把评论数据转成cluster bag vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wordset = set(word_centroid_map.keys())\n",
    "\n",
    "def make_cluster_bag(review):\n",
    "    words = clean_text(review, remove_stopwords=True)\n",
    "    return (pd.Series([word_centroid_map[w] for w in words if w in wordset])\n",
    "              .value_counts()\n",
    "              .reindex(range(num_clusters+1), fill_value=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of reviews: 25000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5814_8</td>\n",
       "      <td>1</td>\n",
       "      <td>With all this stuff going down at the moment w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2381_9</td>\n",
       "      <td>1</td>\n",
       "      <td>\"The Classic War of the Worlds\" by Timothy Hin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7759_3</td>\n",
       "      <td>0</td>\n",
       "      <td>The film starts with a manager (Nicholas Bell)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3630_4</td>\n",
       "      <td>0</td>\n",
       "      <td>It must be assumed that those who praised this...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9495_8</td>\n",
       "      <td>1</td>\n",
       "      <td>Superbly trashy and wondrously unpretentious 8...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  sentiment                                             review\n",
       "0  5814_8          1  With all this stuff going down at the moment w...\n",
       "1  2381_9          1  \"The Classic War of the Worlds\" by Timothy Hin...\n",
       "2  7759_3          0  The film starts with a manager (Nicholas Bell)...\n",
       "3  3630_4          0  It must be assumed that those who praised this...\n",
       "4  9495_8          1  Superbly trashy and wondrously unpretentious 8..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = load_dataset('labeled_train')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
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       "</table>\n",
       "<p>5 rows × 1306 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0     1     2     3     4     5     6     7     8     9     ...   1296  \\\n",
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       "\n",
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       "\n",
       "[5 rows x 1306 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_features = df.review.apply(make_cluster_bag)\n",
    "train_data_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 再用随机森林算法建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forest = RandomForestClassifier(n_estimators = 100, random_state=42)\n",
    "forest = forest.fit(train_data_features, df.sentiment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 在训练集上试一试效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12500,     0],\n",
       "       [    0, 12500]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(df.sentiment, forest.predict(train_data_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 去掉无用的占内存的量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df\n",
    "del train_data_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 载入测试数据做预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of reviews: 25000\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "         id                                             review\n",
       "0  12311_10  Naturally in a film who's main themes are of m...\n",
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   "source": [
    "df = load_dataset('test')\n",
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
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      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_features = df.review.apply(make_cluster_bag)\n",
    "test_data_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "         id  sentiment\n",
       "0  12311_10          1\n",
       "1    8348_2          0\n",
       "2    5828_4          1\n",
       "3    7186_2          0\n",
       "4   12128_7          1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = forest.predict(test_data_features)\n",
    "output = pd.DataFrame({'id':df.id, 'sentiment':result})\n",
    "output.to_csv(os.path.join('..', 'data', 'Word2Vec_BagOfClusters.csv'), index=False)\n",
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df\n",
    "del test_data_features\n",
    "del forest"
   ]
  }
 ],
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