Tweet Emotion Recognition.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sp7D0ktn5eiG"
      },
      "source": [
        "## Tweet Emotion Recognition: Natural Language Processing with TensorFlow\n",
        "\n",
        "---\n",
        "\n",
        "Dataset: [Tweet Emotion Dataset](https://github.com/dair-ai/emotion_dataset)\n",
        "\n",
        "This is a starter notebook for the guided project [Tweet Emotion Recognition with TensorFlow](https://www.coursera.org/projects/tweet-emotion-tensorflow)\n",
        "\n",
        "A complete version of this notebook is available in the course resources\n",
        "\n",
        "---\n",
        "\n",
        "## Task 1: Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cprXxkrMxIgT"
      },
      "source": [
        "## Task 2: Setup and Imports\n",
        "\n",
        "1. Installing Hugging Face's nlp package\n",
        "2. Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5agZRy-45i0g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "518b0236-71c3-4b71-cb29-6723de4f2d67"
      },
      "source": [
        "!pip install nlp"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting nlp\n",
            "  Downloading nlp-0.4.0-py3-none-any.whl (1.7 MB)\n",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (2021.5.30)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->nlp) (3.0.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->nlp) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->nlp) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->nlp) (1.15.0)\n",
            "Installing collected packages: xxhash, nlp\n",
            "Successfully installed nlp-0.4.0 xxhash-2.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKFjWz6e5eiH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "688ef81a-5bf7-4248-f09b-7b09c86538ae"
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import nlp\n",
        "import random\n",
        "\n",
        "\n",
        "def show_history(h):\n",
        "    epochs_trained = len(h.history['loss'])\n",
        "    plt.figure(figsize=(16, 6))\n",
        "\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('accuracy'), label='Training')\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('val_accuracy'), label='Validation')\n",
        "    plt.ylim([0., 1.])\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('loss'), label='Training')\n",
        "    plt.plot(range(0, epochs_trained), h.history.get('val_loss'), label='Validation')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    \n",
        "def show_confusion_matrix(y_true, y_pred, classes):\n",
        "    from sklearn.metrics import confusion_matrix\n",
        "    \n",
        "    cm = confusion_matrix(y_true, y_pred, normalize='true')\n",
        "\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    sp = plt.subplot(1, 1, 1)\n",
        "    ctx = sp.matshow(cm)\n",
        "    plt.xticks(list(range(0, 6)), labels=classes)\n",
        "    plt.yticks(list(range(0, 6)), labels=classes)\n",
        "    plt.colorbar(ctx)\n",
        "    plt.show()\n",
        "\n",
        "    \n",
        "print('Using TensorFlow version', tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using TensorFlow version 2.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7JsBpezExIga"
      },
      "source": [
        "## Task 3: Importing Data\n",
        "\n",
        "1. Importing the Tweet Emotion dataset\n",
        "2. Creating train, validation and test sets\n",
        "3. Extracting tweets and labels from the examples"
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "c7b613ab-7764-4134-9ed5-2918bf7c04c7"
      },
      "source": [
        "dataset = nlp.load_dataset('emotion')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1f70ab1f34ff41ac9c0d983764d0895f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/3.41k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6533028e95234628aa5f3354bc04bc9c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/6.01k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset emotion/default (download: 1.97 MiB, generated: 2.09 MiB, post-processed: Unknown sizetotal: 4.06 MiB) to /root/.cache/huggingface/datasets/emotion/default/0.0.0/84e07cd366f4451464584cdbd4958f512bcaddb1e921341e07298ce8a9ce42f4...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "77549bd35e8746acac2b1458a4f32fb8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.66M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bbae42902e3b42f9885079f2e2bf8888",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/204k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "690d95c2b11649ee87e8eb938cdcb95b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/207k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b70a268f056342be82894aa0f06b3dec",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "64ac64e40555441cbda991e5922c106d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6de3ed382c4f4e52930c01604cadcb1d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/default/0.0.0/84e07cd366f4451464584cdbd4958f512bcaddb1e921341e07298ce8a9ce42f4. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2s0h541FxIgc"
      },
      "source": [
        "train = dataset['train']\n",
        "val = dataset['validation']\n",
        "test = dataset['test']"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7eCnxU25eiN"
      },
      "source": [
        "def get_tweets(data):\n",
        "    tweets = [x['text'] for x in data]\n",
        "    labels = [x['label'] for x in data]\n",
        "    return tweets, labels"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDYXMfZy5eiP"
      },
      "source": [
        "tweets, labels = get_tweets(train)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeq3-vSB5eiR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "524e2244-3eee-44dd-c4ca-07a88dff5f50"
      },
      "source": [
        "tweets[0], labels[0]"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('i didnt feel humiliated', 'sadness')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHD3Tk0J5eiU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcAflLv6xIgp"
      },
      "source": [
        "## Task 4: Tokenizer\n",
        "\n",
        "1. Tokenizing the tweets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfX5-ResxIgq"
      },
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cckUvwBo5eif",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "601a2c7b-21fc-4135-acfe-11b94f0be1eb"
      },
      "source": [
        "tokenizer = Tokenizer(num_words=10000, oov_token='<UNK>')\n",
        "\n",
        "tokenizer.fit_on_texts(tweets)\n",
        "\n",
        "print(tokenizer.texts_to_sequences([tweets[0]]))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2, 139, 3, 679]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wXqvhLJv47iH",
        "outputId": "3aa03555-98dc-4c61-af04-161ee3b505d5"
      },
      "source": [
        "tokenizer.texts_to_sequences([tweets[0]])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2, 139, 3, 679]]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i3Bqm7b2xIgu"
      },
      "source": [
        "## Task 5: Padding and Truncating Sequences\n",
        "\n",
        "1. Checking length of the tweets\n",
        "2. Creating padded sequences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLvf_WFZxIgu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "2da3659a-93d0-4e76-f51a-09ae0530698b"
      },
      "source": [
        "lengths = [len(t.split(' ')) for t in tweets]\n",
        "\n",
        "plt.hist(lengths, bins=len(set(lengths)))\n",
        "plt.show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOi5lIE3xIgx"
      },
      "source": [
        "maxlen = 50\n",
        "\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9J_Iemf5eiq"
      },
      "source": [
        "def get_sequences(tokenizer, tweets):\n",
        "    sequences = tokenizer.texts_to_sequences(tweets)\n",
        "    padded_sequences = pad_sequences(sequences, truncating='post', maxlen=maxlen, padding='post')\n",
        "    return padded_sequences"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eglH77ky5ei0"
      },
      "source": [
        "padded_train_sequences = get_sequences(tokenizer, tweets)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGR473HA5ei7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7365e072-8620-471a-853f-d267de358d20"
      },
      "source": [
        "padded_train_sequences[0]"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  2, 139,   3, 679,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BURhOX_KxIg8"
      },
      "source": [
        "## Task 6: Preparing the Labels\n",
        "\n",
        "1. Creating classes to index and index to classes dictionaries\n",
        "2. Converting text labels to numeric labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SufT2bpD5ejE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3028789-0d28-45a0-922f-7630c28fe743"
      },
      "source": [
        "classes = set(labels)\n",
        "print(classes)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'sadness', 'love', 'surprise', 'joy', 'anger', 'fear'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpwzL88I7YSm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "3bf7fe23-0595-4af9-d233-85910230a7e2"
      },
      "source": [
        "plt.hist(labels, bins=11)\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNLF6rXL5ejN"
      },
      "source": [
        "classes_to_index = dict((c, i) for i, c in enumerate(classes))\n",
        "index_to_classes = dict((v, k) for k, v in classes_to_index.items())"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_08InVyM5ejc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9451675a-64b3-434e-aa36-8aeaa2e9f1c4"
      },
      "source": [
        "classes_to_index"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'anger': 4, 'fear': 5, 'joy': 3, 'love': 1, 'sadness': 0, 'surprise': 2}"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpeDoA6gxIhE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68198587-30c5-4ef7-ff97-3495c1882680"
      },
      "source": [
        "index_to_classes"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'sadness', 1: 'love', 2: 'surprise', 3: 'joy', 4: 'anger', 5: 'fear'}"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jq0WJYsP5ejR"
      },
      "source": [
        "names_to_ids = lambda labels: np.array([classes_to_index.get(x) for x in labels])"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v15KnrNC5ejW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a3334fa-934d-469f-a49a-a7224ab9e63e"
      },
      "source": [
        "train_labels = names_to_ids(labels)\n",
        "print(train_labels[0])"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c-v0Mnh8xIhP"
      },
      "source": [
        "## Task 7: Creating the Model\n",
        "\n",
        "1. Creating the model\n",
        "2. Compiling the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpewXxPQ5eji",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e98e95f-1a25-470b-9af4-196da4ec8537"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Embedding(10000, 16, input_length=50),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(20, return_sequences=True)),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(20)),\n",
        "    tf.keras.layers.Dense(6, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 50, 16)            160000    \n",
            "_________________________________________________________________\n",
            "bidirectional_2 (Bidirection (None, 50, 40)            5920      \n",
            "_________________________________________________________________\n",
            "bidirectional_3 (Bidirection (None, 40)                9760      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 6)                 246       \n",
            "=================================================================\n",
            "Total params: 175,926\n",
            "Trainable params: 175,926\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0l1EEARAaiJ",
        "outputId": "62afc573-5eff-44dd-c18f-ab0efd9e8e18"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 50, 16)            160000    \n",
            "_________________________________________________________________\n",
            "bidirectional_2 (Bidirection (None, 50, 40)            5920      \n",
            "_________________________________________________________________\n",
            "bidirectional_3 (Bidirection (None, 40)                9760      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 6)                 246       \n",
            "=================================================================\n",
            "Total params: 175,926\n",
            "Trainable params: 175,926\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1HST_CHjxIhR"
      },
      "source": [
        "## Task 8: Training the Model\n",
        "\n",
        "1. Preparing a validation set\n",
        "2. Training the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ff7F3hCK5ejm"
      },
      "source": [
        "val_tweets, val_labels = get_tweets(val)\n",
        "val_sequences = get_sequences(tokenizer, val_tweets)\n",
        "val_labels = names_to_ids(val_labels)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlMKaZ3H5ejr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "751d7c29-ec2e-4241-bdf4-fdf026769982"
      },
      "source": [
        "val_tweets[0], val_labels[0]"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('im feeling quite sad and sorry for myself but ill snap out of it soon', 0)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzBqnWQ-5ejw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32a85ae2-e011-4b9a-f673-90060df017a8"
      },
      "source": [
        "h = model.fit(\n",
        "    padded_train_sequences, train_labels,\n",
        "    validation_data=(val_sequences, val_labels),\n",
        "    epochs=20,\n",
        "    callbacks=[\n",
        "        tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=2)\n",
        "    ]\n",
        ")"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "500/500 [==============================] - 33s 56ms/step - loss: 1.3338 - accuracy: 0.4519 - val_loss: 0.8027 - val_accuracy: 0.6920\n",
            "Epoch 2/20\n",
            "500/500 [==============================] - 26s 53ms/step - loss: 0.5396 - accuracy: 0.8094 - val_loss: 0.4872 - val_accuracy: 0.8410\n",
            "Epoch 3/20\n",
            "500/500 [==============================] - 26s 53ms/step - loss: 0.2874 - accuracy: 0.9104 - val_loss: 0.4215 - val_accuracy: 0.8700\n",
            "Epoch 4/20\n",
            "500/500 [==============================] - 26s 53ms/step - loss: 0.1877 - accuracy: 0.9406 - val_loss: 0.3981 - val_accuracy: 0.8760\n",
            "Epoch 5/20\n",
            "500/500 [==============================] - 26s 53ms/step - loss: 0.1423 - accuracy: 0.9529 - val_loss: 0.3832 - val_accuracy: 0.8775\n",
            "Epoch 6/20\n",
            "500/500 [==============================] - 26s 53ms/step - loss: 0.1084 - accuracy: 0.9667 - val_loss: 0.3937 - val_accuracy: 0.8895\n",
            "Epoch 7/20\n",
            "500/500 [==============================] - 26s 53ms/step - loss: 0.0852 - accuracy: 0.9731 - val_loss: 0.4313 - val_accuracy: 0.8825\n",
            "Epoch 8/20\n",
            "500/500 [==============================] - 26s 53ms/step - loss: 0.0800 - accuracy: 0.9743 - val_loss: 0.4900 - val_accuracy: 0.8730\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdsJyMTLxIhX"
      },
      "source": [
        "## Task 9: Evaluating the Model\n",
        "\n",
        "1. Visualizing training history\n",
        "2. Prepraring a test set\n",
        "3. A look at individual predictions on the test set\n",
        "4. A look at all predictions on the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENCfvXeLxIhX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "53fcee9c-0a24-48a5-f0ba-9f05e06b3560"
      },
      "source": [
        "show_history(h)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWuzoz8uxIha"
      },
      "source": [
        "test_tweets, test_labels = get_tweets(test)\n",
        "test_sequences = get_sequences(tokenizer, test_tweets)\n",
        "test_labels = names_to_ids(test_labels)"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vRVJ_2SxIhc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9dd34e0b-e7f4-40a7-9e11-30e338d6e17f"
      },
      "source": [
        "_ = model.evaluate(test_sequences, test_labels)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 1s 18ms/step - loss: 0.5575 - accuracy: 0.8495\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rh638vHG5ej6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "16570892-d7f5-4c8d-befa-203365d30130"
      },
      "source": [
        "i = random.randint(0, len(test_labels) - 1)\n",
        "\n",
        "print('Sentence:', test_tweets[i])\n",
        "print('Emotion:', index_to_classes[test_labels[i]])\n",
        "\n",
        "p = model.predict(np.expand_dims(test_sequences[i], axis=0))[0]\n",
        "\n",
        "print('Predicted Emotion:', index_to_classes.get(p))"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence: i feel love se inscrie intr un rafinament lejer romantic si extrem de feminin\n",
            "Emotion: love\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-70-e1d55c3cf1f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_sequences\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Predicted Emotion:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_to_classes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\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[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHl5SVCFxIhh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c0dc427-8214-4fac-f1c1-6bccc2a4176c"
      },
      "source": [
        "preds = model.predict(test_sequences)\n",
        "preds.shape, test_labels.shape"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2000, 6), (2000,))"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NC8YQ0OexIhj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "98212782-8aea-4446-f49b-01e54987fd09"
      },
      "source": [
        "show_confusion_matrix(test_labels, preds, list(classes))"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-72-064962bcc1b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mshow_confusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\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[0;32m<ipython-input-2-6d105ff8593c>\u001b[0m in \u001b[0;36mshow_confusion_matrix\u001b[0;34m(y_true, y_pred, classes)\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m     \u001b[0mcm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'true'\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     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\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/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m     \"\"\"\n\u001b[0;32m--> 268\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\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    269\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multiclass\"\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    270\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not supported\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\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[1;32m     89\u001b[0m         raise ValueError(\"Classification metrics can't handle a mix of {0} \"\n\u001b[0;32m---> 90\u001b[0;31m                          \"and {1} targets\".format(type_true, type_pred))\n\u001b[0m\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0;31m# We can't have more than one value on y_type => The set is no more needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of multiclass and continuous-multioutput targets"
          ]
        }
      ]
    }
  ]
}