Emotion_CLassification_From_Text.ipynb

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    "colab": {
      "name": "Emotion_CLassification_From_Text.ipynb",
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    "accelerator": "GPU",
    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Data"
      ],
      "metadata": {
        "id": "3_taPZzePYOn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {
        "id": "CpULMh4ZHjvo"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.upload()"
      ],
      "metadata": {
        "id": "iHwAsjhDkGgh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = pd.read_csv('tweet_emotions.csv')"
      ],
      "metadata": {
        "id": "ZWEV0gVFLOAf"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA and Preprocessing"
      ],
      "metadata": {
        "id": "y-c24myrPfpP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.head(5)"
      ],
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          "height": 206
        },
        "id": "zJ98tJ4RPShI",
        "outputId": "cfb8fc9a-dfb5-46fd-aced-6ca5535b1082"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     tweet_id   sentiment                                            content\n",
              "0  1956967341       empty  @tiffanylue i know  i was listenin to bad habi...\n",
              "1  1956967666     sadness  Layin n bed with a headache  ughhhh...waitin o...\n",
              "2  1956967696     sadness                Funeral ceremony...gloomy friday...\n",
              "3  1956967789  enthusiasm               wants to hang out with friends SOON!\n",
              "4  1956968416     neutral  @dannycastillo We want to trade with someone w..."
            ],
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              "      <td>@tiffanylue i know  i was listenin to bad habi...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1956967666</td>\n",
              "      <td>sadness</td>\n",
              "      <td>Layin n bed with a headache  ughhhh...waitin o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>sadness</td>\n",
              "      <td>Funeral ceremony...gloomy friday...</td>\n",
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              "      <th>3</th>\n",
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              "      <td>wants to hang out with friends SOON!</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
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      "source": [
        "train_data.drop('tweet_id', axis=1, inplace=True)"
      ],
      "metadata": {
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      "execution_count": 117,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    sentiment                                            content\n",
              "0       empty  @tiffanylue i know  i was listenin to bad habi...\n",
              "1     sadness  Layin n bed with a headache  ughhhh...waitin o...\n",
              "2     sadness                Funeral ceremony...gloomy friday...\n",
              "3  enthusiasm               wants to hang out with friends SOON!\n",
              "4     neutral  @dannycastillo We want to trade with someone w..."
            ],
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              "      <th>sentiment</th>\n",
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              "      <td>@tiffanylue i know  i was listenin to bad habi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sadness</td>\n",
              "      <td>Layin n bed with a headache  ughhhh...waitin o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>sadness</td>\n",
              "      <td>Funeral ceremony...gloomy friday...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>enthusiasm</td>\n",
              "      <td>wants to hang out with friends SOON!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>neutral</td>\n",
              "      <td>@dannycastillo We want to trade with someone w...</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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      "cell_type": "code",
      "source": [
        "train_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ciE5QIzWP3zH",
        "outputId": "bc783a38-3d39-4b97-ac70-ef16d6ff1245"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "train_data['sentiment'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTShwBqAWZVK",
        "outputId": "6f616f27-3c34-40ae-dbb2-3bfa3c456c13"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['empty', 'sadness', 'enthusiasm', 'neutral', 'worry', 'surprise',\n",
              "       'love', 'fun', 'hate', 'happiness', 'boredom', 'relief', 'anger'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# How many classes?\n",
        "\n",
        "len(train_data['sentiment'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DCdTlmvGWgov",
        "outputId": "c40d65ab-249a-4d87-b704-7d3184d1e58c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# How many examples of each class?\n",
        "train_data.sentiment.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ve6O4yykP6sT",
        "outputId": "8482ecd4-e5a1-4f3e-beec-314070f0fdde"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neutral       8638\n",
              "worry         8459\n",
              "happiness     5209\n",
              "sadness       5165\n",
              "love          3842\n",
              "surprise      2187\n",
              "fun           1776\n",
              "relief        1526\n",
              "hate          1323\n",
              "empty          827\n",
              "enthusiasm     759\n",
              "boredom        179\n",
              "anger          110\n",
              "Name: sentiment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = train_data.sentiment.value_counts()\n",
        "colors = ['blue', 'red', 'green', 'black', 'cyan']\n",
        "\n",
        "a.plot(x='Category', y='number_of_comments', kind='bar', legend=False, figsize=(12, 7), color=colors, grid=True)\n",
        "plt.title(\"Number of tweets per category\")\n",
        "plt.ylabel('Ammount of tweets', fontsize=12)\n",
        "plt.xlabel('category', fontsize=12)"
      ],
      "metadata": {
        "colab": {
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          "height": 528
        },
        "id": "6DSiBifiRcU9",
        "outputId": "bd1c9cb9-3e17-4c56-aac9-ccb30f3ae230"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'category')"
            ]
          },
          "metadata": {},
          "execution_count": 11
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2)  = plt.subplots(nrows=1, ncols=2, figsize=(12,8))\n",
        "explode = list((np.array(list(train_data[\"sentiment\"].dropna().value_counts()))/sum(list(train_data[\"sentiment\"].dropna().value_counts())))[::-1])[:]\n",
        "labels = list(train_data[\"sentiment\"].dropna().unique())[:]\n",
        "sizes = train_data[\"sentiment\"].value_counts()[:]\n",
        "\n",
        "ax2.pie(sizes,  explode=explode, startangle=60, labels=labels, autopct='%1.0f%%', pctdistance=0.8)\n",
        "ax2.add_artist(plt.Circle((0,0),0.4,fc='white'))\n",
        "sns.countplot(y=\"sentiment\", data=train_data, ax=ax1)\n",
        "ax1.set_title(\"Count of each emotion\")\n",
        "ax2.set_title(\"Percentage of each emotion\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "17DH1KA8f7-S",
        "outputId": "ff01bb7e-062d-4a6b-d9ff-9bede05387f5"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ObsoVyBxWt7Y",
        "outputId": "17fe83ee-7f72-4925-89fb-caef692ff954"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sentiment    object\n",
              "content      object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c = train_data['sentiment'].astype('category')\n",
        "\n",
        "d = dict(enumerate(c.cat.categories))\n",
        "print (d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ctu31PeYYZLs",
        "outputId": "53036033-e7ed-47c4-fd6c-22eb92ef214f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 'anger', 1: 'boredom', 2: 'empty', 3: 'enthusiasm', 4: 'fun', 5: 'happiness', 6: 'hate', 7: 'love', 8: 'neutral', 9: 'relief', 10: 'sadness', 11: 'surprise', 12: 'worry'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = pd.DataFrame(d, index=[0])\n",
        "d = d.transpose()\n",
        "\n",
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
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        "id": "iteDw4dqZLGc",
        "outputId": "34d48655-51a3-4a71-f65b-f1c4d5bdc234"
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      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             0\n",
              "0        anger\n",
              "1      boredom\n",
              "2        empty\n",
              "3   enthusiasm\n",
              "4          fun\n",
              "5    happiness\n",
              "6         hate\n",
              "7         love\n",
              "8      neutral\n",
              "9       relief\n",
              "10     sadness\n",
              "11    surprise\n",
              "12       worry"
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data['sentiment']= train_data['sentiment'].astype('category')\n",
        "train_data['sentiment'] = train_data['sentiment'].cat.codes\n",
        "\n",
        "train_data.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LtJcEqnoXY0X",
        "outputId": "9938a8d5-1187-4dcf-8f3d-c4ead9fc8ae8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sentiment                                            content\n",
              "0          2  @tiffanylue i know  i was listenin to bad habi...\n",
              "1         10  Layin n bed with a headache  ughhhh...waitin o...\n",
              "2         10                Funeral ceremony...gloomy friday...\n",
              "3          3               wants to hang out with friends SOON!\n",
              "4          8  @dannycastillo We want to trade with someone w..."
            ],
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              "      <th></th>\n",
              "      <th>sentiment</th>\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-c38ca9be-72fd-4853-8f1f-01c61358ca80 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c38ca9be-72fd-4853-8f1f-01c61358ca80');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's visualize some random training examples\n",
        "import random\n",
        "random_index = random.randint(0, len(train_data)-5) # create random indexes not higher than the total number of samples\n",
        "for row in train_data[[\"content\", \"sentiment\"]][random_index:random_index+5].itertuples():\n",
        "  _, tweet, sentiment = row\n",
        "  if sentiment==12:\n",
        "      a = \"(worry)\"\n",
        "  elif sentiment==11:\n",
        "      a = \"(surprise)\"\n",
        "  elif sentiment==10:\n",
        "      a = \"(sad)\"\n",
        "  elif sentiment==9:\n",
        "      a = \"(relief)\"\n",
        "  elif sentiment==8:\n",
        "      a = \"(neutral)\"\n",
        "  elif sentiment==7:\n",
        "      a = \"(love)\"\n",
        "  elif sentiment==6:\n",
        "      a = \"(hate)\"\n",
        "  elif sentiment==5:\n",
        "      a = \"(happy)\"\n",
        "  elif sentiment==4:\n",
        "      a = \"(fun)\"\n",
        "  elif sentiment==3:\n",
        "      a = \"(enthusiasm)\"\n",
        "  elif sentiment==2:\n",
        "      a = \"(empty)\"\n",
        "  elif sentiment==1:\n",
        "      a = \"(boredom)\"\n",
        "  elif sentiment==0:\n",
        "      a = \"(angry)\"\n",
        "        \n",
        "  print(f\"Sentiment: {sentiment}\", a)\n",
        "  print(f\"Text: {tweet}\\n\")\n",
        "  print(\"---\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hGs1DlwrT1yX",
        "outputId": "72c022e7-4ca9-419f-e97c-3f6d0c2c255f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentiment: 8 (neutral)\n",
            "Text: Getting used to this shoulder immobilizer thing. Even getting used to sleeping in the recliner.\n",
            "\n",
            "---\n",
            "\n",
            "Sentiment: 11 (surprise)\n",
            "Text: @elliottyamin your new song is AMAZING\n",
            "\n",
            "---\n",
            "\n",
            "Sentiment: 7 (love)\n",
            "Text: Feeling special @ looking4him first guy to give me flowers.\n",
            "\n",
            "---\n",
            "\n",
            "Sentiment: 8 (neutral)\n",
            "Text: PeeWee is napping on the couch.  http://apps.facebook.com/dogbook/profile/view/5608012\n",
            "\n",
            "---\n",
            "\n",
            "Sentiment: 5 (happy)\n",
            "Text: holy smokes! star trek was freaking awesomeeeee\n",
            "\n",
            "---\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Use train_test_split to split training data into training and validation sets\n",
        "train_sentences, val_sentences, train_labels, val_labels = train_test_split(train_data[\"content\"].to_numpy(),\n",
        "                                                                            train_data[\"sentiment\"].to_numpy(),\n",
        "                                                                            test_size=0.2, # dedicate 10% of samples to validation set\n",
        "                                                                            random_state=42) # random state for reproducibility"
      ],
      "metadata": {
        "id": "e99Yt2UIVT_y"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the lengths\n",
        "len(train_sentences), len(train_labels), len(val_sentences), len(val_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YDQ1fxt1e-zz",
        "outputId": "43c2c352-6ab1-4edd-fa27-8cbe2a79ed53"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32000, 32000, 8000, 8000)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View the first 10 training sentences and their labels\n",
        "train_sentences[:10], train_labels[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x3WckrCZfBDw",
        "outputId": "873d7fbf-ec71-4198-e40f-bda24fc60860"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array(['Thundershowers plus baseball equals awwww',\n",
              "        'The Hangover: That movie is gonna be hilarious. wish i could see it with the crew',\n",
              "        'Playing some games before I leave for work   Work til at least 11PM tonight.',\n",
              "        'About to be stick at work till freakin 2 am.  Madd suck and I worked most the day!',\n",
              "        \"@Sarah_x_ATL u mean jack barakat's?! wow so have u ever gone to his house? Hehe i mean ur ssoo lucky to have the address!\",\n",
              "        \"Ordered a new computer! Unfortunately it won't get here until the end of June.\",\n",
              "        \"@Maariiaan hahahah yeah, you're right! Plus I'm Twitteraddict so, You'll find me here all (well almost) day most likely\",\n",
              "        \"Happy Birthday Snickers!!!! ? I hope you have the best day ever! Let's go shopping!!!\",\n",
              "        \"M.I.A?PJ?Beasties?Thievery?Volta?Bat4Lashes?BandOfHorses? I enjoy this line-up: http://bit.ly/16lUgX Too bad it's in San Fran\",\n",
              "        'is happy  and clean, squeaky clean'], dtype=object),\n",
              " array([ 8,  4, 12,  6, 11, 10,  8,  7,  4,  5], dtype=int8))"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Text Vectorization"
      ],
      "metadata": {
        "id": "12MVeLUFfOVW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "# Use the default TextVectorization variables\n",
        "text_vectorizer = tf.keras.layers.TextVectorization(max_tokens=None, # how many words in the vocabulary (all of the different words in your text)\n",
        "                                                    standardize=\"lower_and_strip_punctuation\", # how to process text\n",
        "                                                    split=\"whitespace\", # how to split tokens\n",
        "                                                    ngrams=None, # create groups of n-words?\n",
        "                                                    output_mode=\"int\", # how to map tokens to numbers\n",
        "                                                    output_sequence_length=None) # how long should the output sequence of tokens be?\n",
        "                                                    # pad_to_max_tokens=True) # Not valid if using max_tokens=None"
      ],
      "metadata": {
        "id": "9ZO-x0EwfDBe"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Find average number of tokens (words) in training Tweets\n",
        "round(sum([len(i.split()) for i in train_sentences])/len(train_sentences))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMyiOp-ofR4o",
        "outputId": "b3779cad-5a14-46ef-854d-0b3bba019cda"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import TextVectorization\n",
        "\n",
        "# Setup text vectorization with custom variables\n",
        "max_vocab_length = 10000 # max number of words to have in our vocabulary\n",
        "max_length = 15 # max length our sequences will be (e.g. how many words from a Tweet does our model see?)\n",
        "\n",
        "text_vectorizer = TextVectorization(max_tokens=max_vocab_length,\n",
        "                                    output_mode=\"int\",\n",
        "                                    output_sequence_length=max_length)"
      ],
      "metadata": {
        "id": "bywkRmzufTWe"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the text vectorizer to the training text\n",
        "text_vectorizer.adapt(train_sentences)"
      ],
      "metadata": {
        "id": "-dnGxldtfcox"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create sample sentence and tokenize it\n",
        "sample_sentence = \"There's a flood in my street!\"\n",
        "text_vectorizer([sample_sentence])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZAV5sw4Afd1T",
        "outputId": "7c6cf8f8-1548-4e73-8f86-40502df073b7"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 15), dtype=int64, numpy=\n",
              "array([[ 393,    5,    1,   11,    6, 1361,    0,    0,    0,    0,    0,\n",
              "           0,    0,    0,    0]])>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Choose a random sentence from the training dataset and tokenize it\n",
        "random_sentence = random.choice(train_sentences)\n",
        "print(f\"Original text:\\n{random_sentence}\\\n",
        "      \\n\\nVectorized version:\")\n",
        "text_vectorizer([random_sentence])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Sl-btq1fgIM",
        "outputId": "87277b8c-5262-47e9-b30a-6ca9733ba01f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original text:\n",
            "cleaning time      \n",
            "\n",
            "Vectorized version:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 15), dtype=int64, numpy=\n",
              "array([[657,  53,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0]])>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the unique words in the vocabulary\n",
        "words_in_vocab = text_vectorizer.get_vocabulary()\n",
        "top_5_words = words_in_vocab[:5] # most common tokens (notice the [UNK] token for \"unknown\" words)\n",
        "bottom_5_words = words_in_vocab[-5:] # least common tokens\n",
        "print(f\"Number of words in vocab: {len(words_in_vocab)}\")\n",
        "print(f\"Top 5 most common words: {top_5_words}\") \n",
        "print(f\"Bottom 5 least common words: {bottom_5_words}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bs89ioJ8fiu_",
        "outputId": "23b04171-a538-46a3-d59a-a9c901c448bc"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of words in vocab: 10000\n",
            "Top 5 most common words: ['', '[UNK]', 'i', 'to', 'the']\n",
            "Bottom 5 least common words: ['mob', 'moanasaves', 'moan', 'mnth', 'mneylon']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Embedding Layer"
      ],
      "metadata": {
        "id": "zt0U5683fp1Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "from tensorflow.keras.layers import Embedding\n",
        "\n",
        "embedding = Embedding(input_dim=max_vocab_length, # set input shape\n",
        "                      output_dim=128, # set size of embedding vector\n",
        "                      embeddings_initializer=\"uniform\", # default, intialize randomly\n",
        "                      input_length=max_length, # how long is each input\n",
        "                      name=\"embedding_1\") \n",
        "\n",
        "embedding"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylsZ-5HbfmVL",
        "outputId": "69b87e21-fcc5-4c8d-8ea9-e0fc14074ae9"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.layers.embeddings.Embedding at 0x7f131c3b1f50>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get a random sentence from training set\n",
        "random_sentence = random.choice(train_sentences)\n",
        "print(f\"Original text:\\n{random_sentence}\\\n",
        "      \\n\\nEmbedded version:\")\n",
        "\n",
        "# Embed the random sentence (turn it into numerical representation)\n",
        "sample_embed = embedding(text_vectorizer([random_sentence]))\n",
        "sample_embed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X3ytpMxyfsOo",
        "outputId": "7dbb2bc6-2847-44b1-c28c-128bd340158c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original text:\n",
            "Goodmorning twitter bugs.. happy monday to you all! Today marks my first official day at my first fulltime job.. time to get ready!      \n",
            "\n",
            "Embedded version:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 15, 128), dtype=float32, numpy=\n",
              "array([[[-0.01990565, -0.04283296,  0.03480727, ..., -0.00259724,\n",
              "         -0.03109459,  0.04915792],\n",
              "        [ 0.02534569, -0.01725786, -0.00745319, ...,  0.02127732,\n",
              "         -0.02547069, -0.03247217],\n",
              "        [ 0.0488725 , -0.00097545,  0.04267197, ...,  0.03907653,\n",
              "         -0.00334287,  0.0459348 ],\n",
              "        ...,\n",
              "        [-0.03959218,  0.01670352,  0.0441939 , ..., -0.00096967,\n",
              "          0.01916101,  0.01184867],\n",
              "        [ 0.03210879,  0.01650668,  0.01403118, ...,  0.01477012,\n",
              "          0.03275109,  0.00308426],\n",
              "        [-0.00724739, -0.04718477, -0.02565417, ..., -0.03481182,\n",
              "          0.01107268, -0.03028326]]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to evaluate: accuracy, precision, recall, f1-score\n",
        "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
        "\n",
        "def calculate_results(y_true, y_pred):\n",
        "  \"\"\"\n",
        "  Calculates model accuracy, precision, recall and f1 score of a binary classification model.\n",
        "\n",
        "  Args:\n",
        "  -----\n",
        "  y_true = true labels in the form of a 1D array\n",
        "  y_pred = predicted labels in the form of a 1D array\n",
        "\n",
        "  Returns a dictionary of accuracy, precision, recall, f1-score.\n",
        "  \"\"\"\n",
        "  # Calculate model accuracy\n",
        "  model_accuracy = accuracy_score(y_true, y_pred) * 100\n",
        "  # Calculate model precision, recall and f1 score using \"weighted\" average\n",
        "  model_precision, model_recall, model_f1, _ = precision_recall_fscore_support(y_true, y_pred, average=\"weighted\")\n",
        "  model_results = {\"accuracy\": model_accuracy,\n",
        "                  \"precision\": model_precision,\n",
        "                  \"recall\": model_recall,\n",
        "                  \"f1\": model_f1}\n",
        "  return model_results"
      ],
      "metadata": {
        "id": "i44-ri0SfwgN"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model 1: A simple dense model"
      ],
      "metadata": {
        "id": "Av1tNDzyf3ow"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = train_data['sentiment'].unique()\n",
        "\n",
        "class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfEZp2Tgf8qv",
        "outputId": "f236ee36-b1b0-4808-9678-1dc213510a19"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2, 10,  3,  8, 12, 11,  7,  4,  6,  5,  1,  9,  0], dtype=int8)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YUFIu7tRilgB",
        "outputId": "eaac90d5-b9dc-4b39-f10d-d8285916f8a1"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "W2gOdZzlRj9T",
        "outputId": "2ec47751-2cf3-48bd-9d58-b51404b61b58"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    sentiment                                            content\n",
              "0       empty  @tiffanylue i know  i was listenin to bad habi...\n",
              "1     sadness  Layin n bed with a headache  ughhhh...waitin o...\n",
              "2     sadness                Funeral ceremony...gloomy friday...\n",
              "3  enthusiasm               wants to hang out with friends SOON!\n",
              "4     neutral  @dannycastillo We want to trade with someone w..."
            ],
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "y = pd.get_dummies(train_data.sentiment, prefix='sentiment_')"
      ],
      "metadata": {
        "id": "ZWyUxSt0Q0fL"
      },
      "execution_count": 119,
      "outputs": []
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      "outputs": [
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          "data": {
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              "(40000, 13)"
            ]
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          "metadata": {},
          "execution_count": 120
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    {
      "cell_type": "code",
      "source": [
        "y"
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      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       sentiment__anger  sentiment__boredom  sentiment__empty  \\\n",
              "0                     0                   0                 1   \n",
              "1                     0                   0                 0   \n",
              "2                     0                   0                 0   \n",
              "3                     0                   0                 0   \n",
              "4                     0                   0                 0   \n",
              "...                 ...                 ...               ...   \n",
              "39995                 0                   0                 0   \n",
              "39996                 0                   0                 0   \n",
              "39997                 0                   0                 0   \n",
              "39998                 0                   0                 0   \n",
              "39999                 0                   0                 0   \n",
              "\n",
              "       sentiment__enthusiasm  sentiment__fun  sentiment__happiness  \\\n",
              "0                          0               0                     0   \n",
              "1                          0               0                     0   \n",
              "2                          0               0                     0   \n",
              "3                          1               0                     0   \n",
              "4                          0               0                     0   \n",
              "...                      ...             ...                   ...   \n",
              "39995                      0               0                     0   \n",
              "39996                      0               0                     0   \n",
              "39997                      0               0                     0   \n",
              "39998                      0               0                     1   \n",
              "39999                      0               0                     0   \n",
              "\n",
              "       sentiment__hate  sentiment__love  sentiment__neutral  \\\n",
              "0                    0                0                   0   \n",
              "1                    0                0                   0   \n",
              "2                    0                0                   0   \n",
              "3                    0                0                   0   \n",
              "4                    0                0                   1   \n",
              "...                ...              ...                 ...   \n",
              "39995                0                0                   1   \n",
              "39996                0                1                   0   \n",
              "39997                0                1                   0   \n",
              "39998                0                0                   0   \n",
              "39999                0                1                   0   \n",
              "\n",
              "       sentiment__relief  sentiment__sadness  sentiment__surprise  \\\n",
              "0                      0                   0                    0   \n",
              "1                      0                   1                    0   \n",
              "2                      0                   1                    0   \n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45b7c2e9-1072-4e46-92dc-38d6367656e0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-45b7c2e9-1072-4e46-92dc-38d6367656e0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-45b7c2e9-1072-4e46-92dc-38d6367656e0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col_name in one_hot_data.columns:\n",
        "    print(col_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jk1JdXo8UtPg",
        "outputId": "dd087e2e-7fb3-41ec-b6a5-09d3b9274cd5"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n",
            "11\n",
            "12\n",
            "13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Use train_test_split to split training data into training and validation sets\n",
        "train_sentences, val_sentences, train_labels, val_labels = train_test_split(one_hot_data[0].to_numpy(),\n",
        "                                                                            one_hot_data.iloc[:,1:].to_numpy(),\n",
        "                                                                            test_size=0.2, # dedicate 10% of samples to validation set\n",
        "                                                                            random_state=42) # random state for reproducibility"
      ],
      "metadata": {
        "id": "G6Lk3Pb3Ue13"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build model with the Functional API\n",
        "from tensorflow.keras import layers\n",
        "inputs = layers.Input(shape=(1), dtype=\"string\") \n",
        "x = text_vectorizer(inputs) \n",
        "x = embedding(x)\n",
        "x = layers.GlobalAveragePooling1D()(x)\n",
        "outputs = layers.Dense(len(class_names), activation=\"softmax\")(x) \n",
        "model_1 = tf.keras.Model(inputs, outputs, name=\"model_1_dense\") "
      ],
      "metadata": {
        "id": "lk18nOdjS-QL"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile\n",
        "model_1.compile(loss=\"categorical_crossentropy\",\n",
        "              optimizer=tf.keras.optimizers.Adam(),\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "model_1_history = model_1.fit(train_sentences,\n",
        "                              train_labels,\n",
        "                              epochs=5,\n",
        "                              validation_data=(val_sentences, val_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OGVMhiS5UDoI",
        "outputId": "a1498508-c8ba-4e1c-9afa-14f8def35161"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1000/1000 [==============================] - 5s 4ms/step - loss: 1.7487 - accuracy: 0.4378 - val_loss: 1.9443 - val_accuracy: 0.3386\n",
            "Epoch 2/5\n",
            "1000/1000 [==============================] - 4s 4ms/step - loss: 1.3992 - accuracy: 0.5481 - val_loss: 2.0342 - val_accuracy: 0.3313\n",
            "Epoch 3/5\n",
            "1000/1000 [==============================] - 4s 4ms/step - loss: 1.2781 - accuracy: 0.5809 - val_loss: 2.1293 - val_accuracy: 0.3265\n",
            "Epoch 4/5\n",
            "1000/1000 [==============================] - 4s 4ms/step - loss: 1.2009 - accuracy: 0.6028 - val_loss: 2.2205 - val_accuracy: 0.3159\n",
            "Epoch 5/5\n",
            "1000/1000 [==============================] - 4s 4ms/step - loss: 1.1420 - accuracy: 0.6202 - val_loss: 2.3057 - val_accuracy: 0.3109\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model 6"
      ],
      "metadata": {
        "id": "RAd34YAiYBr9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example of pretrained embedding with universal sentence encoder - https://tfhub.dev/google/universal-sentence-encoder/4\n",
        "import tensorflow_hub as hub\n",
        "embed = hub.load(\"https://tfhub.dev/google/universal-sentence-encoder/4\") # load Universal Sentence Encoder\n",
        "embed_samples = embed([sample_sentence,\n",
        "                      \"When you call the universal sentence encoder on a sentence, it turns it into numbers.\"])\n",
        "\n",
        "print(embed_samples[0][:50])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8n4hH7GYBYj",
        "outputId": "7bb0d148-87d1-4c6d-d0e3-341b1973db46"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[-0.01157028  0.0248591   0.02878048 -0.012715    0.03971538  0.0882776\n",
            "  0.02680984  0.05589836 -0.0106873  -0.00597291  0.00639323 -0.01819518\n",
            "  0.00030813  0.09105888  0.05874644 -0.03180628  0.01512474 -0.05162929\n",
            "  0.00991367 -0.06865347 -0.04209306  0.02678981  0.03011006  0.00321069\n",
            " -0.00337973 -0.04787357  0.0226672  -0.00985925 -0.04063613 -0.01292092\n",
            " -0.04666384  0.05630299 -0.03949255  0.00517686  0.02495829 -0.0701444\n",
            "  0.02871508  0.04947684 -0.00633979 -0.08960192  0.02807118 -0.00808364\n",
            " -0.01360602  0.0599865  -0.10361787 -0.05195374  0.00232954 -0.02332531\n",
            " -0.03758105  0.03327728], shape=(50,), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We can use this encoding layer in place of our text_vectorizer and embedding layer\n",
        "sentence_encoder_layer = hub.KerasLayer(\"https://tfhub.dev/google/universal-sentence-encoder/4\",\n",
        "                                        input_shape=[], # shape of inputs coming to our model \n",
        "                                        dtype=tf.string, # data type of inputs coming to the USE layer\n",
        "                                        trainable=False, # keep the pretrained weights (we'll create a feature extractor)\n",
        "                                        name=\"USE\")"
      ],
      "metadata": {
        "id": "416-gk9tUZsg"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create model using the Sequential API\n",
        "model_6 = tf.keras.Sequential([\n",
        "  sentence_encoder_layer, # take in sentences and then encode them into an embedding\n",
        "  layers.Dense(64, activation=\"relu\"),\n",
        "  layers.Dense(len(class_names), activation=\"softmax\")\n",
        "], name=\"model_6_USE\")\n",
        "\n",
        "# Compile model\n",
        "model_6.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "model_6.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WasFl7a5YHRW",
        "outputId": "b35a7b43-a99b-4771-a206-75057663021a"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_6_USE\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " USE (KerasLayer)            (None, 512)               256797824 \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 64)                32832     \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 13)                845       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 256,831,501\n",
            "Trainable params: 33,677\n",
            "Non-trainable params: 256,797,824\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a classifier on top of pretrained embeddings\n",
        "model_6_history = model_6.fit(train_sentences,\n",
        "                              train_labels,\n",
        "                              epochs=10,\n",
        "                              validation_data=(val_sentences, val_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oKfPz4-cYdU4",
        "outputId": "bfe623a9-1a06-4b0d-82ee-c7c90553acd2"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1000/1000 [==============================] - 13s 12ms/step - loss: 1.9468 - accuracy: 0.3457 - val_loss: 1.8560 - val_accuracy: 0.3650\n",
            "Epoch 2/10\n",
            "1000/1000 [==============================] - 12s 12ms/step - loss: 1.8241 - accuracy: 0.3741 - val_loss: 1.8406 - val_accuracy: 0.3638\n",
            "Epoch 3/10\n",
            "1000/1000 [==============================] - 12s 12ms/step - loss: 1.8004 - accuracy: 0.3799 - val_loss: 1.8373 - val_accuracy: 0.3705\n",
            "Epoch 4/10\n",
            "1000/1000 [==============================] - 12s 12ms/step - loss: 1.7860 - accuracy: 0.3835 - val_loss: 1.8352 - val_accuracy: 0.3735\n",
            "Epoch 5/10\n",
            "1000/1000 [==============================] - 12s 12ms/step - loss: 1.7736 - accuracy: 0.3877 - val_loss: 1.8359 - val_accuracy: 0.3731\n",
            "Epoch 6/10\n",
            "1000/1000 [==============================] - 12s 12ms/step - loss: 1.7625 - accuracy: 0.3907 - val_loss: 1.8453 - val_accuracy: 0.3715\n",
            "Epoch 7/10\n",
            "1000/1000 [==============================] - 12s 12ms/step - loss: 1.7518 - accuracy: 0.3944 - val_loss: 1.8416 - val_accuracy: 0.3740\n",
            "Epoch 8/10\n",
            "1000/1000 [==============================] - 12s 12ms/step - loss: 1.7403 - accuracy: 0.3987 - val_loss: 1.8518 - val_accuracy: 0.3670\n",
            "Epoch 9/10\n",
            "1000/1000 [==============================] - 12s 12ms/step - loss: 1.7301 - accuracy: 0.4029 - val_loss: 1.8482 - val_accuracy: 0.3720\n",
            "Epoch 10/10\n",
            "1000/1000 [==============================] - 12s 12ms/step - loss: 1.7197 - accuracy: 0.4055 - val_loss: 1.8515 - val_accuracy: 0.3701\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import svm\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix"
      ],
      "metadata": {
        "id": "ZtJT5INXh9ZR"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ex1 = \"I am sad and angry\""
      ],
      "metadata": {
        "id": "0pRbQ5_CiN15"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe_dt = Pipeline([('vect', CountVectorizer()),\n",
        "                 ('tfidf', TfidfTransformer()),\n",
        "                 ('model', DecisionTreeClassifier(random_state=100))])"
      ],
      "metadata": {
        "id": "ufu_kp8riQSd"
      },
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe_dt.fit(train_sentences, train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X4fQK4dRiRqq",
        "outputId": "95616de7-9a7d-4c94-9009-c2f459a175cb"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('vect', CountVectorizer()), ('tfidf', TfidfTransformer()),\n",
              "                ('model', DecisionTreeClassifier(random_state=100))])"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe_dt.score(val_sentences, val_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-GpY5P0difzU",
        "outputId": "cc328de0-f8a4-4a8c-888c-f8a9ea5738af"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.24375"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ryBjgMRiiyzR"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}