Sentiment_analysis.ipynb

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      "source": [
        "'''\r\n",
        "#link to dataset below\r\n",
        "https://www.kaggle.com/rmisra/news-headlines-dataset-for-sarcasm-detection\r\n",
        "\r\n",
        "The dataset contains a JSON file with data of over 20K. It contains LINKS of news articles, their HEADLINES and a LABEL which indicates if the HEADLINE is sarcastic or not. \r\n",
        "\r\n",
        "We would try to classify if a news headline is sarcastic or not.\r\n",
        "'''\r\n",
        "import json\r\n",
        "from bs4 import BeautifulSoup\r\n",
        "import string\r\n",
        "from nltk.corpus import stopwords\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "\r\n",
        "#create dictionary to store extracted JSON\r\n",
        "datastore = {'article_link': [], 'headline': [], 'is_sarcastic': []}\r\n",
        "data = [json.loads(line) for line in open('sarcasm.json', 'r')]\r\n",
        "for item in data:\r\n",
        "    datastore['article_link'].append(item['article_link'])\r\n",
        "    datastore['headline'].append(item['headline'])\r\n",
        "    datastore['is_sarcastic'].append(item['is_sarcastic'])\r\n",
        "\r\n",
        "# data = json.dumps(datastore, indent=2) \r\n",
        "stopWords = stopwords.words('english') #store variable to remove stopwords\r\n",
        "table = str.maketrans('', '', string.punctuation) #varaible to store punctations\r\n",
        "\r\n",
        "sentences = []\r\n",
        "labels = []\r\n",
        "urls = []\r\n",
        "\r\n",
        "for item in datastore['headline']:\r\n",
        "    sentence = item.replace(\",\", \" , \").replace(\".\", \" . \").replace(\"-\", \" - \").replace(\"/\", \" / \")\r\n",
        "    soup = BeautifulSoup(sentence) #to remove all HTML and CSS tags\r\n",
        "    sentence = soup.get_text()\r\n",
        "    words = sentence.split()\r\n",
        "    filtered_sentence = ''\r\n",
        "    for word in words:\r\n",
        "        word = word.translate(table)\r\n",
        "        if word not in stopWords:\r\n",
        "            filtered_sentence = filtered_sentence + word + ' '\r\n",
        "    sentences.append(filtered_sentence)\r\n",
        "\r\n",
        "# we do not really need the URLs, just the LABELS would do\r\n",
        "labels = datastore['is_sarcastic'].copy()\r\n",
        "urls = datastore['article_link'].copy()"
      ],
      "outputs": [],
      "metadata": {
        "id": "1uuVolJA2gne"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer\r\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\r\n",
        "from tensorflow.keras.layers import Embedding, Bidirectional, Dense, LSTM\r\n",
        "from tensorflow.keras import Sequential, optimizers\r\n",
        "import numpy as np\r\n",
        "\r\n",
        "\r\n",
        "vocab_size, embedding_dim = 20000, 64\r\n",
        "training_size = 23000\r\n",
        "\r\n",
        "training_sentences = sentences[0:training_size]\r\n",
        "testing_sentences = sentences[training_size:]\r\n",
        "training_labels = labels[0:training_size]\r\n",
        "testing_labels = labels[training_size:]\r\n",
        "\r\n",
        "# the 'NUM of WORDS' parameter must be less than or equal to the VOCAB_SIZE\r\n",
        "#20K was originally used but VAL_ACC was poor\r\n",
        "tokenizer = Tokenizer(num_words=2000, oov_token='<OOV>')\r\n",
        "tokenizer.fit_on_texts(training_sentences)\r\n",
        "\r\n",
        "word_index = tokenizer.word_index #get the word index\r\n",
        "\r\n",
        "training_sentences = tokenizer.texts_to_sequences(training_sentences)\r\n",
        "testing_sentences = tokenizer.texts_to_sequences(testing_sentences)\r\n",
        "\r\n",
        "#padding is done to make each sequence of equal length\r\n",
        "training_padded = pad_sequences(training_sentences, padding='post', maxlen=10, truncating='post')\r\n",
        "testing_padded = pad_sequences(testing_sentences, maxlen=10, padding='post', truncating='post')\r\n",
        "#comment out below code to print out word index\r\n",
        "# print(word_index) "
      ],
      "outputs": [],
      "metadata": {
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      "cell_type": "code",
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      "source": [
        "#plot the WORD LENGTH, so we can know the lenght of each sentences\r\n",
        "x1 = []\r\n",
        "y1 = []\r\n",
        "current_item = 1\r\n",
        "for item in sentences:\r\n",
        "    x1.append(current_item)\r\n",
        "    current_item += 1\r\n",
        "    y1.append((len(item)))\r\n",
        "y2 = sorted(y1)\r\n",
        "\r\n",
        "#very few sentences have a corpus of 100+ words \r\n",
        "#this was the reason we set MAX_LEN in PAD_SEQ to 30\r\n",
        "plt.plot(x1, y2)\r\n",
        "plt.ylabel('WORD LENGTH')\r\n",
        "plt.show()"
      ],
      "outputs": [
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"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "source": [
        "from collections import OrderedDict\r\n",
        "\r\n",
        "#get the WORD COUNT in descending order\r\n",
        "wc = tokenizer.word_counts\r\n",
        "newList = (OrderedDict(sorted(wc.items(), key=lambda t: t[1], reverse=True)))\r\n",
        "# print(newList)"
      ],
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "source": [
        "#exploring Vocab Size\r\n",
        "xs, ys = [], []\r\n",
        "curr_x = 1\r\n",
        "for item in newList:\r\n",
        "    xs.append(curr_x)\r\n",
        "    curr_x += 1\r\n",
        "    ys.append(newList[item])\r\n",
        "\r\n",
        "#the below graph shows that very few words are used many times\r\n",
        "plt.plot(xs, ys)\r\n",
        "plt.xlabel('word Index in descending order')\r\n",
        "plt.ylabel('word count')\r\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
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"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "source": [
        "'''\r\n",
        "Instead of training a huge corpus of words, let's focus on the most frequent words and train on them. That is, where the plot curves\r\n",
        "'''\r\n",
        "plt.plot(xs, ys)\r\n",
        "plt.axis([300, 10000, 0, 100])\r\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "source": [
        "#convert to numpy format so we can impute into TF\r\n",
        "training_padded = np.array(training_padded)\r\n",
        "training_labels = np.array(training_labels)\r\n",
        "testing_padded = np.array(testing_padded)\r\n",
        "testing_labels = np.array(testing_labels)\r\n",
        "\r\n",
        "#you can tune the DROPOUT, Regularizer, Batch Normalization\r\n",
        "model = Sequential([\r\n",
        "    Embedding(vocab_size, embedding_dim),\r\n",
        "    Bidirectional(LSTM(embedding_dim, return_sequences=True, dropout=0.2)),\r\n",
        "    Bidirectional(LSTM(embedding_dim, dropout=0.2)),\r\n",
        "    Dense(24, activation='relu'),\r\n",
        "    Dense(1, activation='sigmoid')\r\n",
        "])\r\n",
        "\r\n",
        "adam = optimizers.Adam(learning_rate=0.000008, beta_1=0.9, beta_2=0.999, amsgrad=False)\r\n",
        "model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])"
      ],
      "outputs": [],
      "metadata": {
        "id": "vd6vlPQT2gns"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "source": [
        "history = model.fit(training_padded, training_labels, batch_size=32, epochs=40, validation_data=(testing_padded, testing_labels), verbose=2)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "719/719 - 69s - loss: 0.6903 - accuracy: 0.5567 - val_loss: 0.6859 - val_accuracy: 0.5700\n",
            "Epoch 2/40\n",
            "719/719 - 53s - loss: 0.6829 - accuracy: 0.5596 - val_loss: 0.6768 - val_accuracy: 0.5700\n",
            "Epoch 3/40\n",
            "719/719 - 54s - loss: 0.6758 - accuracy: 0.5635 - val_loss: 0.6697 - val_accuracy: 0.5886\n",
            "Epoch 4/40\n",
            "719/719 - 53s - loss: 0.6682 - accuracy: 0.5961 - val_loss: 0.6610 - val_accuracy: 0.6150\n",
            "Epoch 5/40\n",
            "719/719 - 54s - loss: 0.6561 - accuracy: 0.6237 - val_loss: 0.6451 - val_accuracy: 0.6379\n",
            "Epoch 6/40\n",
            "719/719 - 54s - loss: 0.6316 - accuracy: 0.6562 - val_loss: 0.6152 - val_accuracy: 0.6759\n",
            "Epoch 7/40\n",
            "719/719 - 53s - loss: 0.5931 - accuracy: 0.6939 - val_loss: 0.5769 - val_accuracy: 0.7134\n",
            "Epoch 8/40\n",
            "719/719 - 53s - loss: 0.5505 - accuracy: 0.7269 - val_loss: 0.5403 - val_accuracy: 0.7344\n",
            "Epoch 9/40\n",
            "719/719 - 54s - loss: 0.5163 - accuracy: 0.7443 - val_loss: 0.5238 - val_accuracy: 0.7460\n",
            "Epoch 10/40\n",
            "719/719 - 54s - loss: 0.4978 - accuracy: 0.7548 - val_loss: 0.5101 - val_accuracy: 0.7517\n",
            "Epoch 11/40\n",
            "719/719 - 54s - loss: 0.4891 - accuracy: 0.7555 - val_loss: 0.5019 - val_accuracy: 0.7560\n",
            "Epoch 12/40\n",
            "719/719 - 53s - loss: 0.4803 - accuracy: 0.7631 - val_loss: 0.4954 - val_accuracy: 0.7622\n",
            "Epoch 13/40\n",
            "719/719 - 51s - loss: 0.4730 - accuracy: 0.7687 - val_loss: 0.4901 - val_accuracy: 0.7665\n",
            "Epoch 14/40\n",
            "719/719 - 52s - loss: 0.4648 - accuracy: 0.7780 - val_loss: 0.4863 - val_accuracy: 0.7668\n",
            "Epoch 15/40\n",
            "719/719 - 53s - loss: 0.4609 - accuracy: 0.7765 - val_loss: 0.4818 - val_accuracy: 0.7703\n",
            "Epoch 16/40\n",
            "719/719 - 52s - loss: 0.4544 - accuracy: 0.7781 - val_loss: 0.4777 - val_accuracy: 0.7733\n",
            "Epoch 17/40\n",
            "719/719 - 52s - loss: 0.4488 - accuracy: 0.7864 - val_loss: 0.4757 - val_accuracy: 0.7714\n",
            "Epoch 18/40\n",
            "719/719 - 51s - loss: 0.4453 - accuracy: 0.7861 - val_loss: 0.4720 - val_accuracy: 0.7751\n",
            "Epoch 19/40\n",
            "719/719 - 51s - loss: 0.4396 - accuracy: 0.7890 - val_loss: 0.4699 - val_accuracy: 0.7743\n",
            "Epoch 20/40\n",
            "719/719 - 51s - loss: 0.4368 - accuracy: 0.7907 - val_loss: 0.4674 - val_accuracy: 0.7754\n",
            "Epoch 21/40\n",
            "719/719 - 51s - loss: 0.4306 - accuracy: 0.7937 - val_loss: 0.4652 - val_accuracy: 0.7770\n",
            "Epoch 22/40\n",
            "719/719 - 52s - loss: 0.4296 - accuracy: 0.7929 - val_loss: 0.4641 - val_accuracy: 0.7754\n",
            "Epoch 23/40\n",
            "719/719 - 51s - loss: 0.4260 - accuracy: 0.7958 - val_loss: 0.4632 - val_accuracy: 0.7778\n",
            "Epoch 24/40\n",
            "719/719 - 53s - loss: 0.4223 - accuracy: 0.7983 - val_loss: 0.4620 - val_accuracy: 0.7781\n",
            "Epoch 25/40\n",
            "719/719 - 51s - loss: 0.4218 - accuracy: 0.7972 - val_loss: 0.4602 - val_accuracy: 0.7762\n",
            "Epoch 26/40\n",
            "719/719 - 50s - loss: 0.4190 - accuracy: 0.8006 - val_loss: 0.4596 - val_accuracy: 0.7789\n",
            "Epoch 27/40\n",
            "719/719 - 46s - loss: 0.4169 - accuracy: 0.8027 - val_loss: 0.4593 - val_accuracy: 0.7800\n",
            "Epoch 28/40\n",
            "719/719 - 46s - loss: 0.4141 - accuracy: 0.8030 - val_loss: 0.4586 - val_accuracy: 0.7822\n",
            "Epoch 29/40\n",
            "719/719 - 49s - loss: 0.4129 - accuracy: 0.8045 - val_loss: 0.4579 - val_accuracy: 0.7824\n",
            "Epoch 30/40\n",
            "719/719 - 47s - loss: 0.4116 - accuracy: 0.8048 - val_loss: 0.4581 - val_accuracy: 0.7776\n",
            "Epoch 31/40\n",
            "719/719 - 46s - loss: 0.4088 - accuracy: 0.8036 - val_loss: 0.4593 - val_accuracy: 0.7757\n",
            "Epoch 32/40\n",
            "719/719 - 47s - loss: 0.4075 - accuracy: 0.8070 - val_loss: 0.4576 - val_accuracy: 0.7781\n",
            "Epoch 33/40\n",
            "719/719 - 48s - loss: 0.4057 - accuracy: 0.8061 - val_loss: 0.4620 - val_accuracy: 0.7733\n",
            "Epoch 34/40\n",
            "719/719 - 50s - loss: 0.4042 - accuracy: 0.8082 - val_loss: 0.4575 - val_accuracy: 0.7784\n",
            "Epoch 35/40\n",
            "719/719 - 51s - loss: 0.4046 - accuracy: 0.8080 - val_loss: 0.4612 - val_accuracy: 0.7749\n",
            "Epoch 36/40\n",
            "719/719 - 51s - loss: 0.4027 - accuracy: 0.8104 - val_loss: 0.4567 - val_accuracy: 0.7800\n",
            "Epoch 37/40\n",
            "719/719 - 47s - loss: 0.4016 - accuracy: 0.8075 - val_loss: 0.4573 - val_accuracy: 0.7867\n",
            "Epoch 38/40\n",
            "719/719 - 46s - loss: 0.3982 - accuracy: 0.8117 - val_loss: 0.4571 - val_accuracy: 0.7838\n",
            "Epoch 39/40\n",
            "719/719 - 48s - loss: 0.3978 - accuracy: 0.8126 - val_loss: 0.4569 - val_accuracy: 0.7840\n",
            "Epoch 40/40\n",
            "719/719 - 49s - loss: 0.3974 - accuracy: 0.8116 - val_loss: 0.4577 - val_accuracy: 0.7784\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ox_WeghM2gnu",
        "outputId": "004bb422-6a2d-41c6-fc68-31253b6851b5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "source": [
        "#model did not perform well enough\r\n",
        "plt.plot(history.history['loss'])\r\n",
        "plt.plot(history.history['val_loss'])\r\n",
        "plt.title('model loss')\r\n",
        "plt.ylabel('loss')\r\n",
        "plt.xlabel('epoch')\r\n",
        "plt.legend(['train', 'val'], loc='upper left')\r\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "JLbj48zH2gny",
        "outputId": "be20d98b-e343-4a83-f4fd-194f343ed50c"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "source": [
        "#the training and Validation ACC are far apart which shows that the model is over-fitting\r\n",
        "plt.plot(history.history['accuracy'])\r\n",
        "plt.plot(history.history['val_accuracy'])\r\n",
        "plt.title('model ACC')\r\n",
        "plt.ylabel('accuracy')\r\n",
        "plt.xlabel('epoch')\r\n",
        "plt.legend(['train', 'val'], loc='upper left')\r\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
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"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at the 2 graphs above, we can see that around the 40th epoch, things go brhhhhhhhh"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "source": [
        "#classify some sentences\r\n",
        "\r\n",
        "sentences_x = [\"granny starting to fear spiders in the garden might be real\", \"game of thrones season finale showing this sunday night\", \"TensorFlow book will be a best seller\"]\r\n",
        "\r\n",
        "\r\n",
        "#you can create a function to call for preprocessing\r\n",
        "sentences_x = tokenizer.texts_to_sequences(sentences_x)\r\n",
        "padded_x = pad_sequences(sentences_x, maxlen=10, padding='post', truncating='post')\r\n",
        "\r\n",
        "#values from '0.5' to '1' indicate SARCASTIC headline, while values below indicate REAL headlines\r\n",
        "#looking at the examples, we can say the model performed quite well.\r\n",
        "predicted = model.predict(padded_x)\r\n",
        "print(predicted)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.7774762 ]\n",
            " [0.34712964]\n",
            " [0.33548635]]\n"
          ]
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "source": [
        "#function to output classes\r\n",
        "def sarc_check(x):\r\n",
        "    for i in x:\r\n",
        "        if i >= 0.5:\r\n",
        "            print('SARCASTIC')\r\n",
        "        else:\r\n",
        "            print('Real headline')\r\n",
        "\r\n",
        "#our model worked well as \r\n",
        "print(sarc_check(predicted))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SARCASTIC\n",
            "Real headline\n",
            "Real headline\n",
            "None\n"
          ]
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "#USing pretrained EMbeddings with RNNs"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "'''\r\n",
        "Link below is to th 'GloVe' pre-trained embeddings\r\n",
        "https://github.com/stanfordnlp/GloVe\r\n",
        "\r\n",
        "The link contains pretrained word vectors for a variety of datasets. The one used for this practice is the 'Twitter Data' with 25-dimension version\r\n",
        "\r\n",
        "Unfortunately, I didn't download it but the below code should work on it\r\n",
        "'''"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "'''\r\n",
        "Link below is to th 'GloVe' pre-trained embeddings\r\n",
        "https://github.com/stanfordnlp/GloVe\r\n",
        "\r\n",
        "The link contains pretrained word vectors for a variety of datasets. The one used for this practice is the 'Twitter Data' with 25-dimension version\r\n",
        "\r\n",
        "Unfortunately, I didn't download it but the below code should work on it.\r\n",
        "Easiest way to use the dataset is to read it into a Dictionary as each entry in the dataset is a word, and the values are for the embeddings of the word.\r\n",
        "'''\r\n",
        "\r\n",
        "glove_embeddings = dict()\r\n",
        "f = open('input file path link')\r\n",
        "for line in f:\r\n",
        "    values = line.split()\r\n",
        "    word = values[0] #get the word\r\n",
        "    coefs = np.asarray(values[1:], dtype='float32') #get the embeddings\r\n",
        "    glove_embeddings[word] = coefs #store them here\r\n",
        "\r\n",
        "f.close()\r\n",
        "\r\n",
        "#you can look up coefficients of any word like this\r\n",
        "glove_embeddings['word'] #outputs the coefs\r\n",
        "\r\n",
        "#get the word index, and create the Embedding matrix \r\n",
        "embedding_matrix = np.zeros((vocab_size, embedding_dim))\r\n",
        "for word, index in tokenizer.word_index.items():\r\n",
        "    if index > vocab_size -1 :\r\n",
        "        break\r\n",
        "    else:\r\n",
        "        embedding_vector = glove_embeddings.get(word)\r\n",
        "        if embedding_vector is not None:\r\n",
        "            embedding_matrix[index] = embedding_vector\r\n",
        "\r\n",
        "'''\r\n",
        "This simply creates a matrix with the dimensions of your desired vocab size and the embedding dimension. Then, for every item in the tokenizer’s word index, you look up the coefficients from GloVe in glove_embeddings, and add those values to the matrix.\r\n",
        "\r\n",
        "You then amend the embedding layer to use the pretrained embeddings by setting the weights parameter, and specify that you don’t want the layer to be trained by setting trainable=False:\r\n",
        "'''\r\n",
        "#set the weights to the Embedding Matrix we created above\r\n",
        "\r\n",
        "model = Sequential([\r\n",
        "    Embedding(vocab_size, embedding_dim, weights=[embedding_matrix], trainable=False),\r\n",
        "    Bidirectional(LSTM(embedding_dim, return_sequences=True)),\r\n",
        "    Bidirectional(LSTM(embedding_dim)),\r\n",
        "    Dense(24, activation='relu'),\r\n",
        "    Dense(1, activation='sigmoid')\r\n",
        "])\r\n",
        "adam = optimizers.Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, amsgrad=False)\r\n",
        "model.compile(loss='binary_crossentropy',optimizer=adam, metrics=['accuracy'])\r\n",
        "hist = model.fit(training_padded, training_labels, epochs=30, batch_size=32, validation_data=(testing_padded, testing_labels))\r\n",
        "\r\n",
        "#the code below is used to checkout how many of the tokens in our original dataset is present in the Glove Dataset so that we can adjust the parameters of the vocab_size & embedding_dim\r\n",
        "xs = []\r\n",
        "ys = []\r\n",
        "cumulative_x = []\r\n",
        "cumulative_y = []\r\n",
        "total_y = 0\r\n",
        "for word, index in tokenizer.word_index.items():\r\n",
        "    xs.append(index)\r\n",
        "    cumulative_x.append(index)\r\n",
        "    if glove_embeddings.get(word) is not None:\r\n",
        "        total_y += 1\r\n",
        "        ys.append(1)\r\n",
        "    else:\r\n",
        "        ys.append(0)\r\n",
        "    cumulative_y.append(total_y / index)\r\n",
        "\r\n",
        "#plot XS and YS\r\n",
        "fig, ax = plt.subplots(figsize=(12, 2))\r\n",
        "ax.spines['top'].set_visible(False)\r\n",
        "\r\n",
        "plt.margins(x=0, y=None, tight=True)\r\n"
      ],
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "source": [
        "#playing around with SPINES in matplotlib, this ain't part of the tutorial\r\n",
        "x = np.linspace(0, 2*np.pi, 100)\r\n",
        "y = np.sin(x)\r\n",
        "\r\n",
        "ax = plt.subplot(111)\r\n",
        "ax.plot(x, y)\r\n",
        "\r\n",
        "# Hide the right and top spines\r\n",
        "ax.spines['right'].set_visible(False)\r\n",
        "ax.spines['top'].set_visible(False)\r\n",
        "\r\n",
        "# Only show ticks on the left and bottom spines\r\n",
        "ax.yaxis.set_ticks_position('left')\r\n",
        "ax.xaxis.set_ticks_position('bottom')"
      ],
      "outputs": [
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"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "# <OOV>': 1, 'new': 2,\r\n",
        "#get the reversed word index and the weights for each word\r\n",
        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\r\n",
        "weights = model.layers[0].get_weights()[0]\r\n",
        "\r\n",
        "#embeddings for the WORD at a certain index\r\n",
        "print(reverse_word_index[5])\r\n",
        "print(weights[5])"
      ],
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "import io\r\n",
        "\r\n",
        "#create variables to store the EMBEDDing vectors and words, so we can visualize them\r\n",
        "out_v = io.open('vecs.tsv', 'w', encoding='utf-8')\r\n",
        "out_m = io.open('meta.tsv', 'w', encoding='utf-8')\r\n",
        "\r\n",
        "# vocab_size = 2000, you should declare this ahead so you can call it whenever\r\n",
        "for word_num in range(1, 2000):\r\n",
        "    word = reverse_word_index[word_num]\r\n",
        "    embeddings = weights[word_num]\r\n",
        "    out_m.write(word + '\\n')\r\n",
        "    out_v.write('\\t'.join([str(x) for x in embeddings]) + '\\n')\r\n",
        "out_v.close()\r\n",
        "out_m.close()\r\n",
        "#you can visualize the embeddings above using Tensor Board"
      ],
      "outputs": [],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Example of using pre-trained Embeddings"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "import tensorflow_hub as hub\r\n",
        "\r\n",
        "#download the pre-trained embedding which comes with preprocessing\r\n",
        "hub_layer = hub.keras_layer(\"https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1\", output_shape=[20], input_shape=[], dtype=tf.string, trainable=False)\r\n",
        "\r\n",
        "#pass directly to the model\r\n",
        "model = tf.keras.Sequential([hub_layer,\r\n",
        "                            Dense(16, activation='relu',\r\n",
        "                            Dense(1, activation='sigmoid'))]\r\n",
        ")\r\n",
        "\r\n",
        "adam = optimizers.Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999, amsgrad=False)\r\n",
        "\r\n",
        "model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])"
      ],
      "outputs": [],
      "metadata": {}
    }
  ]
}