Quiz3 simple.ipynb

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     "text": [
      "Length of documents: 10662\n",
      "Length of BOW: 14691\n",
      "Length of features: 1000\n",
      "Length of feature sets: 10662\n",
      "Original Naive Bayes Algo accuracy percent: 60.332632383865416\n",
      "MNB_classifier accuracy percent: 57.18791818008029\n",
      "BernoulliNB_classifier accuracy percent: 60.21793156184286\n",
      "LogisticRegression_classifier accuracy percent: 60.23704836551329\n",
      "SGDClassifier_classifier accuracy percent: 58.80328809023131\n",
      "SVC_classifier accuracy percent: 60.791435671955654\n"
     ]
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      "c:\\Users\\Janisar Hyder\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n"
     ]
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     "text": [
      "LinearSVC_classifier accuracy percent: 59.43414261135538\n",
      "NuSVC_classifier accuracy percent: 60.06499713247945\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "import pickle\n",
    "from nltk.tokenize import word_tokenize\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from nltk.classify.scikitlearn import SklearnClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB, BernoulliNB\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.svm import SVC, LinearSVC, NuSVC\n",
    "import spacy\n",
    "import random\n",
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "short_pos = open(\"D:\\\\NLP\\\\pos.txt\", \"r\").read()\n",
    "short_neg = open(\"D:\\\\NLP\\\\neg.txt\", \"r\").read()\n",
    "\n",
    "documents = []\n",
    "\n",
    "for r in short_pos.split('\\n'):\n",
    "    documents.append((r, \"pos\"))\n",
    "\n",
    "for r in short_neg.split('\\n'):\n",
    "    documents.append((r, \"neg\"))\n",
    "\n",
    "print(\"Length of documents: \" + str(len(documents)))\n",
    "\n",
    "\n",
    "all_words = []\n",
    "\n",
    "def preprocessing(text):\n",
    "    doc = nlp(text)\n",
    "    tokens = [token.text.lower() for token in doc if not (token.is_stop or not token.is_alpha)]\n",
    "    lemmas=[token.lemma_ for token in nlp(' '.join(tokens))]\n",
    "    return lemmas\n",
    "\n",
    "short_pos_words = preprocessing(short_pos)\n",
    "short_neg_words = preprocessing(short_neg)\n",
    "\n",
    "for w in short_pos_words:\n",
    "    all_words.append(w.lower())\n",
    "\n",
    "for w in short_neg_words:\n",
    "    all_words.append(w.lower())\n",
    "\n",
    "all_words = nltk.FreqDist(all_words)\n",
    "print(\"Length of BOW: \" + str(len(all_words)))\n",
    "\n",
    "lof = 1000\n",
    "\n",
    "word_features = list(all_words.keys())[:lof]\n",
    "print(\"Length of features: \" + str(len(word_features)))\n",
    "def find_features(document):\n",
    "    words = word_tokenize(document)\n",
    "    features = {}\n",
    "    for w in word_features:\n",
    "        features[w] = (w in words)\n",
    "\n",
    "    return features\n",
    "\n",
    "featuresets = [(find_features(rev), category) for (rev, category) in documents]\n",
    "print(\"Length of feature sets: \" + str(len(featuresets)))\n",
    "random.shuffle(featuresets)\n",
    "\n",
    "ratio1 = 80\n",
    "ratio2 = 20\n",
    "\n",
    "training_set = featuresets[:int((lof * ratio1) / 100)]\n",
    "testing_set = featuresets[int((lof * ratio2) / 100):]\n",
    "\n",
    "classifier = nltk.NaiveBayesClassifier.train(training_set)\n",
    "print(\"Original Naive Bayes Algo accuracy percent:\", (nltk.classify.accuracy(classifier, testing_set))*100)\n",
    "\n",
    "save_classifier = open(\"naivebayes.pickle\",\"wb\")\n",
    "pickle.dump(classifier, save_classifier)\n",
    "save_classifier.close()\n",
    "\n",
    "classifier_f = open(\"naivebayes.pickle\", \"rb\")\n",
    "classifier = pickle.load(classifier_f)\n",
    "classifier_f.close()\n",
    "\n",
    "MNB_classifier = SklearnClassifier(MultinomialNB())\n",
    "MNB_classifier.train(training_set)\n",
    "print(\"MNB_classifier accuracy percent:\", (nltk.classify.accuracy(MNB_classifier, testing_set))*100)\n",
    "\n",
    "BernoulliNB_classifier = SklearnClassifier(BernoulliNB())\n",
    "BernoulliNB_classifier.train(training_set)\n",
    "print(\"BernoulliNB_classifier accuracy percent:\", (nltk.classify.accuracy(BernoulliNB_classifier, testing_set))*100)\n",
    "\n",
    "LogisticRegression_classifier = SklearnClassifier(LogisticRegression())\n",
    "LogisticRegression_classifier.train(training_set)\n",
    "print(\"LogisticRegression_classifier accuracy percent:\", (nltk.classify.accuracy(LogisticRegression_classifier, testing_set))*100)\n",
    "\n",
    "SGDClassifier_classifier = SklearnClassifier(SGDClassifier())\n",
    "SGDClassifier_classifier.train(training_set)\n",
    "print(\"SGDClassifier_classifier accuracy percent:\", (nltk.classify.accuracy(SGDClassifier_classifier, testing_set))*100)\n",
    "\n",
    "SVC_classifier = SklearnClassifier(SVC())\n",
    "SVC_classifier.train(training_set)\n",
    "print(\"SVC_classifier accuracy percent:\", (nltk.classify.accuracy(SVC_classifier, testing_set))*100)\n",
    "\n",
    "LinearSVC_classifier = SklearnClassifier(LinearSVC())\n",
    "LinearSVC_classifier.train(training_set)\n",
    "print(\"LinearSVC_classifier accuracy percent:\", (nltk.classify.accuracy(LinearSVC_classifier, testing_set))*100)\n",
    "\n",
    "NuSVC_classifier = SklearnClassifier(NuSVC())\n",
    "NuSVC_classifier.train(training_set)\n",
    "print(\"NuSVC_classifier accuracy percent:\", (nltk.classify.accuracy(NuSVC_classifier, testing_set))*100)\n"
   ]
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