Semantic Model (Word2Vec and GloVe).ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mDownloading emoji data ...\u001b[0m\n",
      "\u001b[92m... OK\u001b[0m (Got response in 0.31 seconds)\n",
      "\u001b[33mWriting emoji data to /Users/anweshcr7/.demoji/codes.json ...\u001b[0m\n",
      "\u001b[92m... OK\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from nltk.tokenize import word_tokenize\n",
    "import matplotlib.pyplot as plt \n",
    "import re\n",
    "import demoji\n",
    "demoji.download_codes()\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from wordcloud import WordCloud,STOPWORDS\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conda install gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# some additional imports\n",
    "# from autocorrect import speller\n",
    "stopwordsList = stopwords.words('english')\n",
    "# ended up lemmatizing instead of stemming\n",
    "from nltk.stem import PorterStemmer\n",
    "st = PorterStemmer()\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "wordnet_lemmatizer = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_dataset(fp):\n",
    "    '''\n",
    "    Loads the dataset file with label-tweet on each line and parses the dataset.\n",
    "    :param fp: filepath of dataset\n",
    "    :return:\n",
    "        corpus: list of tweet strings of each tweet.\n",
    "        y: list of labels\n",
    "    '''\n",
    "    y = []\n",
    "    corpus = []\n",
    "    with open(fp, 'rt') as data_in:\n",
    "        for line in data_in:\n",
    "            if not line.lower().startswith(\"tweet index\"): # discard first line if it contains metadata\n",
    "                line = line.rstrip() # remove trailing whitespace\n",
    "                label = int(line.split(\"\\t\")[1])\n",
    "                tweet = line.split(\"\\t\")[2]\n",
    "                y.append(label)\n",
    "                corpus.append(tweet)\n",
    "\n",
    "    return corpus, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Sweet United Nations video. Just in time for C...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>@mrdahl87 We are rumored to have talked to Erv...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Hey there! Nice to see you Minnesota/ND Winter...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3 episodes left I'm dying over here</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>\"I can't breathe!\" was chosen as the most nota...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                               tweet  label\n",
       "0  Sweet United Nations video. Just in time for C...      1\n",
       "1  @mrdahl87 We are rumored to have talked to Erv...      1\n",
       "2  Hey there! Nice to see you Minnesota/ND Winter...      1\n",
       "3                3 episodes left I'm dying over here      0\n",
       "4  \"I can't breathe!\" was chosen as the most nota...      1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training data\n",
    "train_data, train_label = parse_dataset('SemEval2018-T3-train-taskA_emoji.txt')\n",
    "df_train = pd.DataFrame(np.array(train_data).reshape(3834,1), columns = ['tweet'])\n",
    "df_train['label'] = np.array(train_label).reshape(3834,1)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>@Callisto1947 Can U Help?||More conservatives ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Just walked in to #Starbucks and asked for a \"...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>#NOT GONNA WIN http://t.co/Mc9ebqjAqj</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>@mickymantell He is exactly that sort of perso...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>So much #sarcasm at work mate 10/10 #boring 10...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                               tweet  label\n",
       "0  @Callisto1947 Can U Help?||More conservatives ...      0\n",
       "1  Just walked in to #Starbucks and asked for a \"...      1\n",
       "2              #NOT GONNA WIN http://t.co/Mc9ebqjAqj      0\n",
       "3  @mickymantell He is exactly that sort of perso...      0\n",
       "4  So much #sarcasm at work mate 10/10 #boring 10...      1"
      ]
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     "execution_count": 34,
     "metadata": {},
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    }
   ],
   "source": [
    "# testing data\n",
    "test_data, test_label = parse_dataset('SemEval2018-T3_gold_test_taskA_emoji.txt')\n",
    "df_test = pd.DataFrame(np.array(test_data).reshape(784,1), columns = ['tweet'])\n",
    "df_test['label'] = np.array(test_label).reshape(784,1)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n_eazAUaoc4e"
   },
   "source": [
    "# Project Preperation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9h9ib5NpUapx"
   },
   "source": [
    "## Setup project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OtRe6re-UiFd"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import re\n",
    "import nltk \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gKOH2YI0UPs5"
   },
   "source": [
    "## Clean and augment data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RWIq7xsJjDXC"
   },
   "outputs": [],
   "source": [
    "# Get users' texts as well as corresponding sentiments\n",
    "texts_train = df_train.iloc[:, 0].values\n",
    "texts_test = df_test.iloc[:, 0].values\n",
    "labels_train = df_train.iloc[:, 1].values\n",
    "labels_test = df_test.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean texts\n",
    "clean_texts_train = []\n",
    "clean_texts_test = []\n",
    "for text in texts_train:\n",
    "    # remove all special characters\n",
    "    clean_text = re.sub(r'\\W', ' ', str(text))\n",
    "    # remove all single character\n",
    "    clean_text = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', clean_text)\n",
    "    # replace multiple spaces to single space\n",
    "    clean_text = re.sub(r'\\s+', ' ', clean_text, flags=re.I)\n",
    "    # to lower case\n",
    "    clean_text = clean_text.lower()\n",
    "    clean_texts_train.append(clean_text)\n",
    "    \n",
    "for text in texts_test:\n",
    "    # remove all special characters\n",
    "    clean_text = re.sub(r'\\W', ' ', str(text))\n",
    "    # remove all single character\n",
    "    clean_text = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', clean_text)\n",
    "    # replace multiple spaces to single space\n",
    "    clean_text = re.sub(r'\\s+', ' ', clean_text, flags=re.I)\n",
    "    # to lower case\n",
    "    clean_text = clean_text.lower()\n",
    "    clean_texts_test.append(clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine positive and neutral\n",
    "clean_sentiments_train = []\n",
    "clean_sentiments_test = []\n",
    "for label in labels_train:\n",
    "    if label == \"positive\" or label == \"neutral\":\n",
    "        clean_sentiment = \"non-negative\"\n",
    "    else:\n",
    "        clean_sentiment = label\n",
    "    clean_sentiments_train.append(clean_sentiment)\n",
    "    \n",
    "for label in labels_test:\n",
    "    if label == \"positive\" or label == \"neutral\":\n",
    "        clean_sentiment = \"non-negative\"\n",
    "    else:\n",
    "        clean_sentiment = label\n",
    "    clean_sentiments_test.append(clean_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.sum(clean_sentiments_train != labels_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZC57Kgj-XTIr"
   },
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sATZqo_gXhbt"
   },
   "source": [
    "## Build the base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into training and testing data\n",
    "X_train = clean_texts_train\n",
    "X_test = clean_texts_test\n",
    "y_train = labels_train\n",
    "y_test = labels_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GLbT1085X90J"
   },
   "source": [
    "## Evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L_VdlLSvY5lm"
   },
   "source": [
    "## Build the model and train on the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "words_train = [w for w in X_train if not w in stopwords.words(\"english\")]\n",
    "words_test = [w for w in X_test if not w in stopwords.words(\"english\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GloVe Model using external 1.4G Twitter embedding 100 Dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-03-11 18:25:15,881 : INFO : converting 1193517 vectors from glove.twitter.27B.100d.txt to gensim_glove_vectors.txt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1193517, 100)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gensim.scripts.glove2word2vec import glove2word2vec\n",
    "glove2word2vec(glove_input_file=\"glove.twitter.27B.100d.txt\", word2vec_output_file=\"gensim_glove_vectors.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-03-11 18:25:19,212 : INFO : loading projection weights from gensim_glove_vectors.txt\n",
      "2020-03-11 18:27:00,804 : WARNING : duplicate word '<unk>' in gensim_glove_vectors.txt, ignoring all but first\n",
      "2020-03-11 18:27:00,805 : WARNING : duplicate word '<unk>' in gensim_glove_vectors.txt, ignoring all but first\n",
      "2020-03-11 18:27:00,805 : INFO : duplicate words detected, shrinking matrix size from 1193517 to 1193515\n",
      "2020-03-11 18:27:00,806 : INFO : loaded (1193515, 100) matrix from gensim_glove_vectors.txt\n"
     ]
    }
   ],
   "source": [
    "from gensim.models.keyedvectors import KeyedVectors\n",
    "glove_model = KeyedVectors.load_word2vec_format(\"gensim_glove_vectors.txt\", binary=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word2Vec model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set values for various parameters\n",
    "num_features = 1000   # Word vector dimensionality                      \n",
    "min_word_count = 40   # Minimum word count                        \n",
    "num_workers = 4       # Number of threads to run in parallel\n",
    "context = 10          # Context window size                                                                                    \n",
    "downsampling = 1e-4   # Downsample setting for frequent words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training word2vec using tokenised training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-03-11 18:40:13,969 : INFO : collecting all words and their counts\n",
      "2020-03-11 18:40:13,969 : WARNING : Each 'sentences' item should be a list of words (usually unicode strings). First item here is instead plain <class 'str'>.\n",
      "2020-03-11 18:40:13,970 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
      "2020-03-11 18:40:14,002 : INFO : collected 66 word types from a corpus of 308795 raw words and 3834 sentences\n",
      "2020-03-11 18:40:14,003 : INFO : Loading a fresh vocabulary\n",
      "2020-03-11 18:40:14,004 : INFO : effective_min_count=40 retains 38 unique words (57% of original 66, drops 28)\n",
      "2020-03-11 18:40:14,005 : INFO : effective_min_count=40 leaves 308761 word corpus (99% of original 308795, drops 34)\n",
      "2020-03-11 18:40:14,005 : INFO : deleting the raw counts dictionary of 66 items\n",
      "2020-03-11 18:40:14,006 : INFO : sample=0.0001 downsamples 38 most-common words\n",
      "2020-03-11 18:40:14,007 : INFO : downsampling leaves estimated 16781 word corpus (5.4% of prior 308761)\n",
      "2020-03-11 18:40:14,008 : INFO : estimated required memory for 38 words and 1000 dimensions: 323000 bytes\n",
      "2020-03-11 18:40:14,008 : INFO : resetting layer weights\n",
      "2020-03-11 18:40:14,024 : INFO : training model with 4 workers on 38 vocabulary and 1000 features, using sg=0 hs=0 sample=0.0001 negative=5 window=10\n",
      "2020-03-11 18:40:14,072 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2020-03-11 18:40:14,072 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2020-03-11 18:40:14,074 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2020-03-11 18:40:14,074 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2020-03-11 18:40:14,075 : INFO : EPOCH - 1 : training on 308795 raw words (16619 effective words) took 0.0s, 351775 effective words/s\n",
      "2020-03-11 18:40:14,121 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2020-03-11 18:40:14,122 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2020-03-11 18:40:14,123 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2020-03-11 18:40:14,123 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2020-03-11 18:40:14,124 : INFO : EPOCH - 2 : training on 308795 raw words (16657 effective words) took 0.0s, 385186 effective words/s\n",
      "2020-03-11 18:40:14,172 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2020-03-11 18:40:14,172 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2020-03-11 18:40:14,173 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2020-03-11 18:40:14,173 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2020-03-11 18:40:14,174 : INFO : EPOCH - 3 : training on 308795 raw words (16736 effective words) took 0.0s, 382218 effective words/s\n",
      "2020-03-11 18:40:14,221 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2020-03-11 18:40:14,222 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2020-03-11 18:40:14,223 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2020-03-11 18:40:14,224 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2020-03-11 18:40:14,225 : INFO : EPOCH - 4 : training on 308795 raw words (16685 effective words) took 0.0s, 368845 effective words/s\n",
      "2020-03-11 18:40:14,279 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
      "2020-03-11 18:40:14,279 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
      "2020-03-11 18:40:14,280 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
      "2020-03-11 18:40:14,281 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
      "2020-03-11 18:40:14,281 : INFO : EPOCH - 5 : training on 308795 raw words (16855 effective words) took 0.0s, 339603 effective words/s\n",
      "2020-03-11 18:40:14,282 : INFO : training on a 1543975 raw words (83552 effective words) took 0.3s, 325634 effective words/s\n",
      "2020-03-11 18:40:14,282 : INFO : saving Word2Vec object under wv_tweet, separately None\n",
      "2020-03-11 18:40:14,283 : INFO : not storing attribute vectors_norm\n",
      "2020-03-11 18:40:14,283 : INFO : not storing attribute cum_table\n",
      "2020-03-11 18:40:14,289 : INFO : saved wv_tweet\n"
     ]
    }
   ],
   "source": [
    "# Apply word2vec to train the model\n",
    "from gensim.models import word2vec\n",
    "\n",
    "model = word2vec.Word2Vec(words_train, workers=num_workers, size=num_features, \n",
    "                          min_count = min_word_count, window = context, \n",
    "                          sample = downsampling)\n",
    "model_name = \"wv_tweet\"\n",
    "model.save(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to average all word vectors in a paragraph\n",
    "def featureVecMethod(words, model, num_features):\n",
    "    # Pre-initialising empty numpy array for speed\n",
    "    featureVec = np.zeros(num_features,dtype=\"float32\")\n",
    "    nwords = 0\n",
    "    \n",
    "    #Converting Index2Word which is a list to a set for better speed in the execution.\n",
    "    index2word_set = set(model.wv.index2word)\n",
    "    \n",
    "    for word in  words:\n",
    "        if word in index2word_set:\n",
    "            nwords = nwords + 1\n",
    "            featureVec = np.add(featureVec,model[word])\n",
    "    \n",
    "    # Dividing the result by number of words to get average\n",
    "    featureVec = np.divide(featureVec, nwords)\n",
    "    return featureVec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for calculating the average feature vector\n",
    "def getAvgFeatureVecs(reviews, model, num_features):\n",
    "    counter = 0\n",
    "    reviewFeatureVecs = np.zeros((len(reviews),num_features),dtype=\"float32\")\n",
    "    for review in reviews:\n",
    "        # Printing a status message every 1000th review\n",
    "        if counter%1000 == 0:\n",
    "            print(\"Review %d of %d\"%(counter,len(reviews)))\n",
    "            \n",
    "        reviewFeatureVecs[counter] = featureVecMethod(review, model, num_features)\n",
    "        counter = counter+1\n",
    "        \n",
    "    return reviewFeatureVecs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtain feature representation based on the embedding model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review 0 of 3834\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anweshcr7/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review 1000 of 3834\n",
      "Review 2000 of 3834\n",
      "Review 3000 of 3834\n",
      "Review 0 of 784\n"
     ]
    }
   ],
   "source": [
    "# Pass glove_model for external corpus\n",
    "# wv_tweet for word2vec embedding trained on our english-tweets corpus (training data)\n",
    "\n",
    "trainDataVecs=getAvgFeatureVecs(words_train, glove_model, 100)\n",
    "testDataVecs=getAvgFeatureVecs(words_test, glove_model, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest (Recommended)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest = RandomForestClassifier(n_estimators=100)\n",
    "forest.fit(trainDataVecs, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SD-jt7gzgXIh",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.6667    0.5539    0.6051       473\n",
      "           1     0.4604    0.5788    0.5128       311\n",
      "\n",
      "    accuracy                         0.5638       784\n",
      "   macro avg     0.5635    0.5663    0.5590       784\n",
      "weighted avg     0.5848    0.5638    0.5685       784\n",
      "\n",
      "Accuracy: 0.5637755102040817\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "pred = forest.predict(testDataVecs)\n",
    "print(classification_report(y_test,pred, digits = 4))\n",
    "print(\"Accuracy:\", accuracy_score(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-68-dd7a7b326240>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0msvc_param_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainDataVecs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-68-dd7a7b326240>\u001b[0m in \u001b[0;36msvc_param_selection\u001b[0;34m(X, y, nfolds)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mparam_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'C'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gamma'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mgammas\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mgrid_search\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'rbf'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnfolds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    686\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m         \u001b[0;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1147\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1148\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1149\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params)\u001b[0m\n\u001b[1;32m    665\u001b[0m                                \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m                                in product(candidate_params,\n\u001b[0;32m--> 667\u001b[0;31m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    922\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 924\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    925\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    926\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    757\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 759\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    760\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    714\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 549\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    554\u001b[0m         \u001b[0mfit_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    555\u001b[0m         \u001b[0;31m# _score will return dict if is_multimetric is True\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 556\u001b[0;31m         \u001b[0mtest_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_multimetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    557\u001b[0m         \u001b[0mscore_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mfit_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    558\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_score\u001b[0;34m(estimator, X_test, y_test, scorer, is_multimetric)\u001b[0m\n\u001b[1;32m    597\u001b[0m     \"\"\"\n\u001b[1;32m    598\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_multimetric\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 599\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_multimetric_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    600\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_multimetric_score\u001b[0;34m(estimator, X_test, y_test, scorers)\u001b[0m\n\u001b[1;32m    627\u001b[0m             \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    628\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 629\u001b[0;31m             \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    630\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    631\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'item'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/metrics/scorer.py\u001b[0m in \u001b[0;36m_passthrough_scorer\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_passthrough_scorer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;34m\"\"\"Function that wraps estimator.score\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    355\u001b[0m         \"\"\"\n\u001b[1;32m    356\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    572\u001b[0m             \u001b[0mClass\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m         \"\"\"\n\u001b[0;32m--> 574\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    575\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_for_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m         \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sparse_predict\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dense_predict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_dense_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36m_dense_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprobA_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprobB_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msvm_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msvm_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m             \u001b[0mdegree\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdegree\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoef0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgamma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gamma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 346\u001b[0;31m             cache_size=self.cache_size)\n\u001b[0m\u001b[1;32m    347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_sparse_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from sklearn import svm \n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def svc_param_selection(X, y, nfolds):\n",
    "    Cs = [0.001, 0.01, 0.1, 1, 10]\n",
    "    gammas = [0.001, 0.01, 0.1, 1]\n",
    "    param_grid = {'C': Cs, 'gamma' : gammas}\n",
    "    grid_search = GridSearchCV(svm.SVC(kernel='rbf'), param_grid, cv=nfolds)\n",
    "    grid_search.fit(X, y)\n",
    "    grid_search.best_params_\n",
    "    return grid_search.best_params_\n",
    "\n",
    "svc_param_selection(trainDataVecs, y_train, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word2Vec on embeddings trained using our dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5012755102040817\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.7847    0.2389    0.3663       473\n",
      "           1     0.4375    0.9003    0.5889       311\n",
      "\n",
      "    accuracy                         0.5013       784\n",
      "   macro avg     0.6111    0.5696    0.4776       784\n",
      "weighted avg     0.6470    0.5013    0.4546       784\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "clf = svm.SVC(C=10, gamma = 1, kernel = 'rbf')\n",
    "clf.fit(trainDataVecs, y_train)\n",
    "y_pred = clf.predict(testDataVecs)\n",
    "print(metrics.accuracy_score(y_test, y_pred))\n",
    "print(metrics.classification_report(y_test, y_pred, digits = 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotCM(cm):\n",
    "    ax = plt.subplot()\n",
    "    sns.heatmap(cm, annot=True, ax = ax, fmt=\"d\"); #annot=True to annotate cells\n",
    "\n",
    "    # labels, title and ticks\n",
    "    ax.set_xlabel('Predicted labels')\n",
    "    ax.set_ylabel('True labels')\n",
    "    ax.set_title('Confusion Matrix')\n",
    "    ax.xaxis.set_ticklabels(['Irony', 'Non-Irony']); ax.yaxis.set_ticklabels(['Irony', 'Non-Irony']);\n",
    "\n",
    "svm_cm = confusion_matrix(y_test, y_pred)\n",
    "plotCM(svm_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word2Vec on pre-trained GloVe embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5803571428571429\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.6885    0.5560    0.6152       473\n",
      "           1     0.4776    0.6174    0.5386       311\n",
      "\n",
      "    accuracy                         0.5804       784\n",
      "   macro avg     0.5830    0.5867    0.5769       784\n",
      "weighted avg     0.6048    0.5804    0.5848       784\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "clf = svm.SVC(C=10, gamma = 1, kernel = 'rbf')\n",
    "clf.fit(trainDataVecs, y_train)\n",
    "y_pred = clf.predict(testDataVecs)\n",
    "print(metrics.accuracy_score(y_test, y_pred))\n",
    "print(metrics.classification_report(y_test, y_pred, digits = 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotCM(cm):\n",
    "    ax = plt.subplot()\n",
    "    sns.heatmap(cm, annot=True, ax = ax, fmt=\"d\"); #annot=True to annotate cells\n",
    "\n",
    "    # labels, title and ticks\n",
    "    ax.set_xlabel('Predicted labels')\n",
    "    ax.set_ylabel('True labels')\n",
    "    ax.set_title('Confusion Matrix')\n",
    "    ax.xaxis.set_ticklabels(['Irony', 'Non-Irony']); ax.yaxis.set_ticklabels(['Irony', 'Non-Irony']);\n",
    "\n",
    "svm_cm = confusion_matrix(y_test, y_pred)\n",
    "plotCM(svm_cm)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "_Konigle_Challenge_NLP_Project.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}