01B - SciPy - TFIDF - Doc2Vec.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Toy Model (Naive Bayes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review:  {'movie': 1, 'enjoyed': 1, 'this': 1, 'i': 1} Sentiment:  good\n",
      "Review:  {'fun': 1, 'book': 1, 'was': 1, 'this': 1} Sentiment:  good\n",
      "Review:  {'horrid': 1, 'tasted': 1, 'cake': 1, 'this': 1} Sentiment:  bad\n",
      "Review:  {'ended': 1, 'i': 1, 'this': 1, 'when': 1, 'happy': 1, 'was': 1} Sentiment:  bad\n",
      "Trained Model:\n",
      "ended {'bad': 1}\n",
      "movie {'good': 1}\n",
      "happy {'bad': 1}\n",
      "this {'bad': 2, 'good': 2}\n",
      "horrid {'bad': 1}\n",
      "book {'good': 1}\n",
      "enjoyed {'good': 1}\n",
      "was {'bad': 1, 'good': 1}\n",
      "tasted {'bad': 1}\n",
      "i {'bad': 1, 'good': 1}\n",
      "fun {'good': 1}\n",
      "when {'bad': 1}\n",
      "cake {'bad': 1}\n",
      "Some predictions:\n",
      "good\n",
      "bad\n",
      "bad\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import math\n",
    "from gensim import utils\n",
    "\n",
    "class Classifier:\n",
    "    def __init__(self, filename=None):\n",
    "        # count of feature/category combinations\n",
    "        self.fc = {}\n",
    "        # count of documents in each category\n",
    "        self.cc = {}\n",
    "        # some samples\n",
    "        self.train_samples = [\n",
    "            ['i enjoyed this movie', 'good'],\n",
    "            ['this book was fun', 'good'],\n",
    "            ['this cake tasted horrid', 'bad'],\n",
    "            ['i was happy when this ended', 'bad']]\n",
    "        \n",
    "    # Increment the category count for feature\n",
    "    # e.g. (loved, good) -> 'loved': {'good': 1}\n",
    "    def inc_count_feat_categ(self, f, cat):\n",
    "        self.fc.setdefault(f,{})\n",
    "        self.fc[f].setdefault(cat, 0)\n",
    "        self.fc[f][cat] += 1\n",
    "        \n",
    "    # Return count of feature appearing in category, e.g.\n",
    "    # (loved, good) will return 1.0\n",
    "    def feat_count(self, f, cat):\n",
    "        if f in self.fc and cat in self.fc[f]:\n",
    "            return float(self.fc[f][cat])\n",
    "        return 0\n",
    "    \n",
    "    # Increment category count \n",
    "    # e.g. (good) -> {'good': 1}\n",
    "    def inc_count_category(self, cat):\n",
    "        self.cc.setdefault(cat, 0)\n",
    "        self.cc[cat] += 1\n",
    "            \n",
    "    # Return number of items in category\n",
    "    def cat_count(self, cat):\n",
    "        if cat in self.cc:\n",
    "            return float(self.cc[cat])\n",
    "        return 0\n",
    "    \n",
    "    # Total number of items supplied\n",
    "    def total_count(self):\n",
    "        return sum(self.cc.values())\n",
    "    \n",
    "    # List of all categories\n",
    "    def list_categories(self):\n",
    "        return self.cc.keys()\n",
    "    \n",
    "    # Start counting number of times word appears in a category\n",
    "    def train(self, features, cat):      \n",
    "        # Inc count for every feature in this category\n",
    "        for f in features:\n",
    "            self.inc_count_feat_categ(f, cat)\n",
    "        # Inc count for category\n",
    "        self.inc_count_category(cat)     \n",
    "\n",
    "    # Return conditional probability of observing feature, given \n",
    "    # a category\n",
    "    def feat_prob(self, f, cat):\n",
    "        if self.cat_count(cat) == 0:\n",
    "            return 0\n",
    "        # Number of times feature appears in category, \n",
    "        # Divided by total number of features in category\n",
    "        return self.feat_count(f, cat)/self.cat_count(cat)\n",
    "    \n",
    "    def weighted_prob(self, f, cat, weight=1.0, ap=0.5):\n",
    "        # Assumed probability (prior) is 0.5\n",
    "        # Calculate current probability (observing word, given category)\n",
    "        basic_prob = self.feat_prob(f, cat)\n",
    "        \n",
    "        # Calculate number of times feature has appeared\n",
    "        # in all categories (e.g. 4 in bad, 2 in good)\n",
    "        totals = sum([self.feat_count(f,c) for c in self.list_categories()])\n",
    "        \n",
    "        # Calculate weighted average\n",
    "        bp = ((weight*ap)+(totals*basic_prob))/(weight+totals)\n",
    "        # Return average of prior and basic probability\n",
    "        return bp\n",
    "        \n",
    "    def doc_prob(self, features, cat):\n",
    "        # Simply multiple all probabilities together\n",
    "        p = 1\n",
    "        for f in features:\n",
    "            p*= self.weighted_prob(f, cat)\n",
    "        # Probability of document given category\n",
    "        return p\n",
    "    \n",
    "    # We want the probability of category given document\n",
    "    def prob(self, features, cat):\n",
    "        # p(Cat|Doc) = p(Doc|Cat) * p(Cat) \\ p(Doc)\n",
    "        # If equal classes then cat_prob will 0.5\n",
    "        # p(Doc) is same no matter what category so will scale results by same amount\n",
    "        cat_prob = self.cat_count(cat)/self.total_count()\n",
    "        doc_prob = self.doc_prob(features, cat)\n",
    "        return doc_prob*cat_prob\n",
    "\n",
    "# Yield a review from a large .txt containing the reviews\n",
    "# Formatted as a dictionary of words\n",
    "def para_to_dict(para):\n",
    "    words = utils.to_unicode(para).split()\n",
    "    return dict([(w,1) for w in words])    \n",
    "\n",
    "def get_words(fname):\n",
    "    with utils.smart_open(fname) as f:\n",
    "        for paragraph in f:\n",
    "            yield para_to_dict(paragraph)\n",
    "\n",
    "def get_sentiment(paragraph):\n",
    "    if not isinstance(paragraph, dict):\n",
    "        raise Exception(\"Supply a dictionary of words\")\n",
    "    p_good = classy.prob(paragraph, 'good')\n",
    "    p_bad = classy.prob(paragraph, 'bad')\n",
    "    if p_good > p_bad: \n",
    "        return 'good'\n",
    "    else:\n",
    "        return 'bad'\n",
    "\n",
    "# Initialise with our feature extraction function (words)\n",
    "classy = Classifier()\n",
    "\n",
    "# Train sample\n",
    "for sample_review, sentiment in classy.train_samples:\n",
    "    print(\"Review: \", para_to_dict(sample_review), \"Sentiment: \", sentiment)\n",
    "    classy.train(para_to_dict(sample_review), sentiment)\n",
    "\n",
    "# Examine model\n",
    "print(\"Trained Model:\")\n",
    "for k in classy.fc.keys():\n",
    "    print(k, classy.fc[k])\n",
    "\n",
    "# Predict sentiment\n",
    "print(\"Some predictions:\")\n",
    "print(get_sentiment(para_to_dict('i enjoyed this book'))) # good\n",
    "print(get_sentiment(para_to_dict('my trip was horrid'))) # bad\n",
    "print(get_sentiment(para_to_dict('i am happy'))) # bad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "900000\n",
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n",
      "700000\n",
      "800000\n",
      "900000\n",
      "1000000\n",
      "0.49989456610705085\n"
     ]
    }
   ],
   "source": [
    "# Apply to movie reviews\n",
    "classy = Classifier()\n",
    "\n",
    "# Train\n",
    "i = 0\n",
    "for f in ['train_good_reviews.txt',\n",
    "          'train_bad_reviews.txt']:\n",
    "    for review in getwords(f):\n",
    "        i += 1\n",
    "        classy.train(x, f.split(\"_\")[1])\n",
    "        if i % 100000 == 0:\n",
    "            print(i)\n",
    "            \n",
    "# Test\n",
    "tot = 0\n",
    "correct = 0\n",
    "\n",
    "for f in ['test_good_reviews.txt',\n",
    "          'test_bad_reviews.txt']:\n",
    "    for review in getwords(f):\n",
    "        tot += 1\n",
    "        if get_sentiment(review) ==  f.split(\"_\")[1]:\n",
    "            correct += 1\n",
    "        if tot % 100000 == 0:\n",
    "            print(tot)\n",
    "            \n",
    "acc = correct/tot\n",
    "print(acc)  # 0.49989456610705085"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. TFIDF Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A. Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from gensim import utils\n",
    "\n",
    "def file_to_list(fname):\n",
    "    with utils.smart_open(fname) as f:\n",
    "        for rev in f:\n",
    "            yield rev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample review: it is and does exactly what the description said it would be and would do . couldn ' t be happier with it .\n",
      "\n",
      "Vectorising ... 999373 reviews\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# Training Data\n",
    "clean_train_reviews = []\n",
    "train_labels = []\n",
    "for f in ['train_good_reviews.txt',\n",
    "          'train_bad_reviews.txt']:\n",
    "    for review in file_to_list(f):\n",
    "        clean_train_reviews.append(utils.to_unicode(review))\n",
    "        if \"good\" in f:\n",
    "            train_labels.append(1)\n",
    "        elif \"bad\" in f:\n",
    "            train_labels.append(0)\n",
    "        else:\n",
    "            raise Exception\n",
    "        \n",
    "print(\"Sample review: %s\" % clean_train_reviews[0])\n",
    "print(\"Vectorising ... %d reviews\" % len(clean_train_reviews))\n",
    "\n",
    "## Defaults:\n",
    "#TfidfVectorizer(input='content', encoding='utf-8', decode_error='strict',\n",
    "#                strip_accents=None, lowercase=True, preprocessor=None,\n",
    "#                tokenizer=None, analyzer='word', stop_words=None,\n",
    "#                token_pattern='(?u)\\b\\w\\w+\\b', ngram_range=(1, 1),\n",
    "#                max_df=1.0, min_df=1, max_features=None, vocabulary=None,\n",
    "#                binary=False, dtype=<class 'numpy.int64'>, norm='l2',\n",
    "#                use_idf=True, smooth_idf=True, sublinear_tf=False\n",
    "\n",
    "# 40k, tri-grams, sublinear\n",
    "vectorizer = TfidfVectorizer(max_features = 40000, ngram_range = (1, 3), sublinear_tf = True)\n",
    "\n",
    "# Learn vocabulary and idf, return term-document matrix\n",
    "train_data_features = vectorizer.fit_transform(clean_train_reviews)\n",
    "\n",
    "del clean_train_reviews\n",
    "\n",
    "# Training Model\n",
    "\n",
    "## Defaults used\n",
    "#LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True,\n",
    "#                   intercept_scaling=1, class_weight=None, random_state=None,\n",
    "#                   solver='liblinear', max_iter=100, multi_class='ovr',\n",
    "#                   verbose=0, warm_start=False, n_jobs=1)\n",
    "\n",
    "#GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=100,\n",
    "#                           subsample=1.0, min_samples_split=2, min_samples_leaf=1,\n",
    "#                           min_weight_fraction_leaf=0.0, max_depth=3, init=None,\n",
    "#                           random_state=None, max_features=None, verbose=0, \n",
    "#                           max_leaf_nodes=None, warm_start=False, presort='auto')\n",
    "\n",
    "# l2, max_iter = 100\n",
    "classifier_tfidf = LogisticRegression(n_jobs=-1)\n",
    "\n",
    "classifier_tfidf.fit(train_data_features, train_labels)\n",
    "\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=-1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_tfidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "[t-SNE] Computing pairwise distances...\n",
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 1000\n",
      "[t-SNE] Mean sigma: 0.179964\n",
      "[t-SNE] Error after 100 iterations with early exaggeration: 1.799137\n",
      "[t-SNE] Error after 175 iterations: 1.697676\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x208dc6273c8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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1552nge/s08uuTKjz7Aq1vro1MlcGsK0AAPxGyAwA8EUUw4ClqCYs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\nvpN2JQBK99GtdhE3L/r9usGNBSKDvsgkfZMBwEws/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x208c9b277b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise the documents\n",
    "%pylab inline\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Select 1000 examples to visualise\n",
    "idx = np.random.choice(np.arange(len(train_labels)), 1000, replace=False)\n",
    "\n",
    "X = train_data_features[idx].toarray()\n",
    "y = np.array(train_labels)[idx]\n",
    "\n",
    "#pca = PCA(n_components=50)\n",
    "#pca_50_r = pca.fit(X).transform(X)\n",
    "\n",
    "tsne = TSNE(n_components=2, verbose=1)\n",
    "X_r = tsne.fit_transform(X)\n",
    "\n",
    "plt.style.context('seaborn-whitegrid')\n",
    "plt.figure(figsize=(20,10))\n",
    "for c, i, target_name in zip(\"rg\", [0, 1], ['Negative', 'Positive']):\n",
    "    plt.scatter(X_r[y == i, 0], X_r[y == i, 1], c=c, alpha=0.5, label=target_name)\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "plt.title('PCA of 100D feature vectors for reviews')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B. Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Tree based estimators can be used to compute feature importances\n",
    "import numpy as np\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "# Build a forest and compute the feature importances\n",
    "# n_jobs = -1 can make this fly!\n",
    "forest = ExtraTreesClassifier(n_estimators=250, n_jobs=-1)\n",
    "\n",
    "X, y = train_data_features, train_labels\n",
    "forest.fit(X, y)\n",
    "\n",
    "importances = forest.feature_importances_\n",
    "# Include standard deviation\n",
    "std = np.std([tree.feature_importances_ for tree in forest.estimators_],\n",
    "             axis=0)\n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "vocab = vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature \"not\" (0.011544)\n",
      "2. feature \"great\" (0.008364)\n",
      "3. feature \"love\" (0.005857)\n",
      "4. feature \"waste\" (0.003796)\n",
      "5. feature \"and\" (0.003290)\n",
      "6. feature \"easy to\" (0.003021)\n",
      "7. feature \"disappointed\" (0.003010)\n",
      "8. feature \"return\" (0.002764)\n",
      "9. feature \"would not\" (0.002692)\n",
      "10. feature \"easy\" (0.002570)\n",
      "11. feature \"perfect\" (0.002526)\n",
      "12. feature \"money\" (0.002447)\n",
      "13. feature \"but\" (0.002403)\n",
      "14. feature \"at all\" (0.002236)\n",
      "15. feature \"do not\" (0.002144)\n",
      "16. feature \"very disappointed\" (0.002111)\n",
      "17. feature \"waste of\" (0.002111)\n",
      "18. feature \"well\" (0.002063)\n",
      "19. feature \"did not\" (0.002042)\n",
      "20. feature \"highly\" (0.002030)\n"
     ]
    }
   ],
   "source": [
    "# Print the feature ranking\n",
    "feat, feat_imp, feat_std = [], [], []\n",
    "\n",
    "print(\"Feature ranking:\")\n",
    "for f in range(20):\n",
    "    print(\"%d. feature \\\"%s\\\" (%f)\" % (f + 1, vocab[int(indices[f])], importances[indices[f]]))\n",
    "    feat.append(vocab[int(indices[f])])\n",
    "    feat_imp.append(importances[indices[f]])\n",
    "    feat_std.append(std[indices[f]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['indices', 'std', 'random', 'f']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    },
    {
     "data": {
      "image/png": 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keUVVnZPkU2vT7gY1PNaf84NJnpfkaUles2kF33wU1QMAAADscisrK1lZWblz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\nSZIkqRX/H6/8wz3pap0AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae86a9ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "ind = np.arange(len(feat))\n",
    "width = .75\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.xlabel('n-gram')\n",
    "plt.ylabel('Feature Importance')\n",
    "plt.title('TFIDF Sentiment - Top 20 Features')\n",
    "\n",
    "plt.xticks(ind + width/2., (feat))\n",
    "plt.bar(ind, feat_imp, width, color=\"g\", yerr=feat_std)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del train_data_features\n",
    "del train_labels\n",
    "del importances\n",
    "del vocab\n",
    "del indices\n",
    "#del forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## C. Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample review: easy to use a mobile . if you ' re taller than ft , be ready to tuck your legs behind you as you hang and pull .\n",
      "\n",
      "Vectorising ... 1000627 reviews\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "# Testing Data\n",
    "clean_test_reviews = []\n",
    "test_labels = []\n",
    "for f in ['test_good_reviews.txt',\n",
    "          'test_bad_reviews.txt']:\n",
    "    for review in file_to_list(f):\n",
    "        clean_test_reviews.append(utils.to_unicode(review))\n",
    "        if \"good\" in f:\n",
    "            test_labels.append(1)\n",
    "        elif \"bad\" in f:\n",
    "            test_labels.append(0)\n",
    "        else:\n",
    "            raise Exception\n",
    "                   \n",
    "print(\"Sample review: %s\" % clean_test_reviews[0])\n",
    "print(\"Vectorising ... %d reviews\" % len(clean_test_reviews))  \n",
    "\n",
    "# Transform documents to document-term matrix\n",
    "test_data_features = vectorizer.transform(clean_test_reviews)\n",
    "\n",
    "del clean_test_reviews\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93074642199341018"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run classifier\n",
    "classifier_tfidf.score(test_data_features, test_labels) \n",
    "\n",
    "# 50k gives 0.915\n",
    "# 500k gives 0.928\n",
    "# 1mill gives 0.931"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89869451853687743"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By the way, the forest we trained doesn't do so well:\n",
    "forest.score(test_data_features, test_labels) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## D. Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tricky_sentences = [\n",
    "    \"Most movies are rubbish, however this one was good\",\n",
    "    \"This is a product you would love to hate\",\n",
    "    \"Rubbish from start to finish\",\n",
    "    \"I was so happy when this ended\",\n",
    "    \"Very good\",\n",
    "    \"Horrible\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "most movies are rubbish , however this one was good\n",
      " [0]\n",
      "this is a product you would love to hate\n",
      " [0]\n",
      "rubbish from start to finish\n",
      " [0]\n",
      "i was so happy when this ended\n",
      " [1]\n",
      "very good\n",
      " [1]\n",
      "horrible\n",
      " [0]\n"
     ]
    }
   ],
   "source": [
    "# Some tests ...\n",
    "def tfidf_sample_sentiment(mystr):\n",
    "    assert isinstance(mystr, str)\n",
    "    test_data_feat = vectorizer.transform([mystr])\n",
    "    pred = classifier_tfidf.predict(test_data_feat)\n",
    "    return pred\n",
    "\n",
    "for x in tricky_sentences:\n",
    "    clean = clean_review(x)\n",
    "    print(clean, tfidf_sample_sentiment(clean))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del vectorizer\n",
    "del classifier_tfidf\n",
    "del test_data_features\n",
    "del test_labels"
   ]
  }
 ],
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