ISEAR_Embedding_Comparison.ipynb

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  "metadata": {
    "colab": {
      "name": "ISEAR Embedding Comparison",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nakamura41/emotion_detection_notebooks/blob/master/ISEAR_Embedding_Comparison.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "c95s_IW-AeyH",
        "colab_type": "code",
        "outputId": "fefd9066-5682-4ed5-9d71-74427d329433",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1099
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install gensim\n",
        "!pip install pydrive"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting gensim\n",
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            "  Downloading https://files.pythonhosted.org/packages/b7/31/05c8d001f7f87f0f07289a5fc0fc3832e9a57f2dbd4d3b0fee70e0d51365/jmespath-0.9.3-py2.py3-none-any.whl\n",
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            "\u001b[?25hBuilding wheels for collected packages: smart-open, bz2file\n",
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            "Successfully built smart-open bz2file\n",
            "Installing collected packages: boto, bz2file, jmespath, docutils, botocore, s3transfer, boto3, smart-open, gensim\n",
            "Successfully installed boto-2.49.0 boto3-1.9.42 botocore-1.12.42 bz2file-0.98 docutils-0.14 gensim-3.6.0 jmespath-0.9.3 s3transfer-0.1.13 smart-open-1.7.1\n",
            "Collecting pydrive\n",
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            "Building wheels for collected packages: pydrive\n",
            "  Running setup.py bdist_wheel for pydrive ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/fa/d2/9a/d3b6b506c2da98289e5d417215ce34b696db856643bad779f4\n",
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            "Successfully installed pydrive-1.3.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "OdaHCMv2rGKs",
        "outputId": "7f79eb98-3f4c-416d-85a5-ceb03b75d53b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import urllib.request\n",
        "import pandas as pd\n",
        "from gensim.test.utils import common_texts, get_tmpfile\n",
        "from gensim.utils import simple_preprocess\n",
        "from gensim.models import Word2Vec\n",
        "from tempfile import gettempdir\n",
        "import zipfile\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils import to_categorical\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, brier_score_loss\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.12.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "qKMmZVQloI6e",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "\n",
        "# 1. Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GiXlSKbGoNcZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# download google drive file\n",
        "def download_drive_file(drive_directory, filename):\n",
        "  list_file_query = \"title='{}' and trashed=false\".format(drive_directory)\n",
        "  file_list = drive.ListFile({'q': list_file_query}).GetList()\n",
        "\n",
        "  if len(file_list) > 0:\n",
        "    directory_id = file_list[0]['id']\n",
        "\n",
        "    list_file_query = \"'{}' in parents\".format(directory_id)\n",
        "\n",
        "    file_list = drive.ListFile({'q': list_file_query}).GetList()\n",
        "    \n",
        "    file_id = None\n",
        "    for file1 in file_list:\n",
        "      if file1['title'] == filename:\n",
        "        print(\"downloading file {}\".format(file1['title']))\n",
        "        file1.GetContentFile(file1['title'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wWC0XDwDASUM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "module_url = \"https://tfhub.dev/google/universal-sentence-encoder-large/3\" #@param [\"https://tfhub.dev/google/universal-sentence-encoder/2\", \"https://tfhub.dev/google/universal-sentence-encoder-large/3\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IElRtsQwASUP",
        "colab_type": "code",
        "outputId": "d008f4ee-cbe8-4c0f-bac7-69c653bf9abe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# Import the Universal Sentence Encoder's TF Hub module\n",
        "tf_use_embed = hub.Module(module_url)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Using /tmp/tfhub_modules to cache modules.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0RhkgBlRASUR",
        "colab_type": "code",
        "outputId": "22d4b59d-610c-4e48-b419-7d2f4463ea66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "cell_type": "code",
      "source": [
        "sentences = [\"I love you so much\", \"I hate that movie, there are no plotline or even interesting scenes\"]\n",
        "# Reduce logging output.\n",
        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "\n",
        "with tf.Session() as session:\n",
        "    session.run([tf.global_variables_initializer(), tf.tables_initializer()])\n",
        "    embedded_sentences = session.run(tf_use_embed(sentences))\n",
        "    for i, embedded_sentence in enumerate(np.array(embedded_sentences).tolist()):\n",
        "        print(\"Sentence: {}\".format(sentences[i]))\n",
        "        print(\"Embedding size: {}\".format(len(embedded_sentence)))\n",
        "        message_embedding_snippet = \", \".join(\n",
        "            (str(x) for x in embedded_sentence[:3]))\n",
        "        print(\"Embedding: [{}, ...]\\n\".format(message_embedding_snippet))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sentence: I love you so much\n",
            "Embedding size: 512\n",
            "Embedding: [-0.03638805076479912, -0.07312509417533875, 0.007526352070271969, ...]\n",
            "\n",
            "Sentence: I hate that movie, there are no plotline or even interesting scenes\n",
            "Embedding size: 512\n",
            "Embedding: [0.016185155138373375, 0.059069763869047165, -0.0056126900017261505, ...]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EzmR-eCcASUU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def embed_use(sentences):\n",
        "    with tf.Session() as session:\n",
        "        session.run([tf.global_variables_initializer(), tf.tables_initializer()])\n",
        "        embedded_sentences = session.run(tf_use_embed(sentences))\n",
        "        return embedded_sentences"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "oBynQmEcrNAy",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class ISEARDataset(object):\n",
        "  FILENAME = \"isear_databank.csv\"\n",
        "  EMOTION_CLASSES = [\"anger\", \"disgust\", \"fear\", \"guilt\", \"joy\", \"sadness\", \"shame\"]\n",
        "  EMOTION_CLASSES_DICT = {\"anger\": 0, \"disgust\": 1, \"fear\": 2, \"guilt\": 3, \"joy\": 4, \"sadness\": 5, \"shame\": 6}\n",
        "  RANDOM_STATE = 41\n",
        "  \n",
        "  def get_classes(self):\n",
        "    return self.EMOTION_CLASSES\n",
        "  \n",
        "  def get_classes_dict(self):\n",
        "    return self.EMOTION_CLASSES_DICT\n",
        "  \n",
        "  def __load_data_file(self):\n",
        "    download_drive_file(\"Datasets\", self.FILENAME)\n",
        "    data = pd.read_csv(self.FILENAME)\n",
        "    data[\"emotion\"] = data[\"Field1\"]\n",
        "    data[\"text\"] = data[\"SIT\"]\n",
        "    return data[[\"text\", \"emotion\"]]\n",
        "\n",
        "  def load_data(self):\n",
        "    train_data = None\n",
        "    test_data = None\n",
        "    \n",
        "    data = self.__load_data_file()\n",
        "    \n",
        "    train_data, test_data = train_test_split(data, test_size=0.3, random_state=self.RANDOM_STATE, stratify=data[\"emotion\"].values)\n",
        "    return train_data, test_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1n7zVExV0jJe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def print_confusion_matrix(confusion_matrix, class_names, figsize = (4,3), fontsize=15):\n",
        "    \"\"\"Prints a confusion matrix, as returned by sklearn.metrics.confusion_matrix, as a heatmap.\n",
        "    \n",
        "    Arguments\n",
        "    ---------\n",
        "    confusion_matrix: numpy.ndarray\n",
        "        The numpy.ndarray object returned from a call to sklearn.metrics.confusion_matrix. \n",
        "        Similarly constructed ndarrays can also be used.\n",
        "    class_names: list\n",
        "        An ordered list of class names, in the order they index the given confusion matrix.\n",
        "    figsize: tuple\n",
        "        A 2-long tuple, the first value determining the horizontal size of the ouputted figure,\n",
        "        the second determining the vertical size. Defaults to (10,7).\n",
        "    fontsize: int\n",
        "        Font size for axes labels. Defaults to 14.\n",
        "        \n",
        "    Returns\n",
        "    -------\n",
        "    matplotlib.figure.Figure\n",
        "        The resulting confusion matrix figure\n",
        "    \"\"\"\n",
        "    df_cm = pd.DataFrame(\n",
        "        confusion_matrix, index=class_names, columns=class_names, \n",
        "    )\n",
        "    fig = plt.figure(figsize=figsize)\n",
        "    try:\n",
        "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
        "    except ValueError:\n",
        "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
        "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
        "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    return fig"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "tevwfq4Tu6fe",
        "outputId": "88aeb151-9727-40db-f6ee-143764a8069b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "isear_dataset = ISEARDataset()\n",
        "train_data, test_data = isear_dataset.load_data()\n",
        "train_data, valid_data = train_test_split(train_data, test_size=0.1, random_state=200, stratify=train_data.emotion)\n",
        "\n",
        "print(train_data.shape)\n",
        "print(valid_data.shape)\n",
        "print(test_data.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "downloading file isear_databank.csv\n",
            "(4829, 2)\n",
            "(537, 2)\n",
            "(2300, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "J-xXkD_4175U",
        "outputId": "cf315ad3-2a69-4849-e5d7-c79115ad9db9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "emotions = train_data.emotion.unique()\n",
        "dic = dict()\n",
        "labels = []\n",
        "for i, emotion in enumerate(emotions):\n",
        "    dic[emotion]=i\n",
        "    labels.append(emotion)\n",
        "print(dic)\n",
        "print(labels)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'guilt': 0, 'sadness': 1, 'fear': 2, 'anger': 3, 'joy': 4, 'disgust': 5, 'shame': 6}\n",
            "['guilt', 'sadness', 'fear', 'anger', 'joy', 'disgust', 'shame']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MtZaeo2JASUh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_labels = np.array(train_data.emotion.apply(lambda x:dic[x]))\n",
        "test_labels = np.array(test_data.emotion.apply(lambda x:dic[x]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0wXUaCQeASUe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Embed with Universal Sentence Encoder"
      ]
    },
    {
      "metadata": {
        "id": "AvcnW45qASUg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_embedded_use = embed_use(train_data.text.tolist())\n",
        "test_embedded_use = embed_use(test_data.text.tolist())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-mgHsSbYASUk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Universal Sentence Encoder + SVM Classifier"
      ]
    },
    {
      "metadata": {
        "id": "2l8cnyxSASUk",
        "colab_type": "code",
        "outputId": "d581d4b6-6d56-4fed-c7c8-cc7dd3697e74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "clfSVM = svm.SVC(C=10000.0, gamma='auto', kernel='rbf')\n",
        "clfSVM.fit(train_embedded_use, train_labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=10000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
              "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
              "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "  tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "GKvNJ0g9ASUn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "test_pred_labels = clfSVM.predict(test_embedded_use)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UJw3zrZDASUp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cmSVM = confusion_matrix(test_labels, test_pred_labels)\n",
        "cmSVM"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Tt3rbFReASUr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "reportSVM = classification_report(test_labels, test_pred_labels, target_names=labels)\n",
        "print(reportSVM)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "suU_Yw-fASUw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# TfIdfVectorizer + SVM Classifier"
      ]
    },
    {
      "metadata": {
        "id": "crQ4mZBsASUx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "tfidf_vectorizer = TfidfVectorizer(max_df=70, min_df=2, ngram_range=(1, 3))\n",
        "train_tfidf_vectors = tfidf_vectorizer.fit_transform(train_data.text.tolist())\n",
        "test_tfidf_vectors = tfidf_vectorizer.transform(test_data.text.tolist())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "efLiuFqgASUz",
        "colab_type": "code",
        "outputId": "a010858d-cbbf-477f-f01a-2797a9df2382",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "clf_SVM_Tfidf = svm.SVC(C=10000.0, gamma='auto', kernel='rbf')\n",
        "clf_SVM_Tfidf.fit(train_tfidf_vectors, train_labels)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=10000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
              "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
              "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "  tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "MoS_XCtUASU3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "test_pred_labels_Tfidf = clf_SVM_Tfidf.predict(test_tfidf_vectors)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eBQ0W7MrASU5",
        "colab_type": "code",
        "outputId": "3a16f32a-7817-4d22-b198-b707e7ca24e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "cell_type": "code",
      "source": [
        "cmSVM_Tfidf = confusion_matrix(test_labels, test_pred_labels_Tfidf)\n",
        "cmSVM_Tfidf"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[144,  27,  14,  62,  12,  18,  51],\n",
              "       [ 24, 171,  15,  43,  30,  20,  26],\n",
              "       [ 23,  20, 207,  21,  18,  19,  20],\n",
              "       [ 50,  23,   9, 159,  15,  38,  35],\n",
              "       [ 17,  17,  14,  18, 233,  14,  15],\n",
              "       [ 16,  19,  18,  53,   7, 181,  35],\n",
              "       [ 64,  15,  21,  39,  18,  26, 146]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "AGQETcYH0tGS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "f462a831-38e3-4519-8bff-87430f3809a7"
      },
      "cell_type": "code",
      "source": [
        "print(print_confusion_matrix(cmSVM_Tfidf, class_names=labels))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Figure(288x216)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HdeQwNOSI3JuQmMiK9V5s3bMP311b0NXWJjEpieXrNnHZL4DYuHh6delI/949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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f2e52a31b70>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "N5YwSO_ZASU7",
        "colab_type": "code",
        "outputId": "b4126ef8-a7b3-4712-cfb6-91c5b8a7de0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "cell_type": "code",
      "source": [
        "reportSVM = classification_report(test_labels, test_pred_labels_Tfidf, target_names=labels)\n",
        "print(reportSVM)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             precision    recall  f1-score   support\n",
            "\n",
            "      guilt       0.43      0.44      0.43       328\n",
            "    sadness       0.59      0.52      0.55       329\n",
            "       fear       0.69      0.63      0.66       328\n",
            "      anger       0.40      0.48      0.44       329\n",
            "        joy       0.70      0.71      0.70       328\n",
            "    disgust       0.57      0.55      0.56       329\n",
            "      shame       0.45      0.44      0.44       329\n",
            "\n",
            "avg / total       0.55      0.54      0.54      2300\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xY_as_tfASU-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# CountVectorizer + SVM Classifier"
      ]
    },
    {
      "metadata": {
        "id": "k0LFEc-xASU-",
        "colab_type": "code",
        "outputId": "07ab30ff-ea29-4bc3-8686-025f49962706",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "# Initialize a CountVectorizer object: count_vectorizer\n",
        "count_vectorizer = CountVectorizer(stop_words=\"english\", analyzer='word', \n",
        "                                   ngram_range=(1, 3), max_df=70, min_df=2, max_features=None)\n",
        "\n",
        "train_count_vectors = count_vectorizer.fit_transform(train_data.text.tolist())\n",
        "test_count_vectors = count_vectorizer.transform(test_data.text.tolist())\n",
        "\n",
        "clf_NB_count = MultinomialNB(alpha=0.1)\n",
        "clf_SVM_count = svm.SVC(C=10000.0, gamma='auto', kernel='rbf')\n",
        "clf_NB_count.fit(train_count_vectors, train_labels)\n",
        "clf_SVM_count.fit(train_count_vectors, train_labels)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=10000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
              "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
              "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "  tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "8RGYwlr2ASVB",
        "colab_type": "code",
        "outputId": "8579835d-8af4-490f-9dd8-7ee25c9f06da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "cell_type": "code",
      "source": [
        "# get the feature names as numpy array\n",
        "feature_names = np.array(count_vectorizer.get_feature_names())\n",
        "\n",
        "# Sort the coefficients from the model\n",
        "sorted_coef_index = clf_NB_count.coef_[0].argsort()\n",
        "\n",
        "# Find the 10 smallest and 10 largest coefficients\n",
        "# The 10 largest coefficients are being indexed using [:-11:-1] \n",
        "# so the list returned is in order of largest to smallest\n",
        "print('Smallest Coefs:\\n{}\\n'.format(feature_names[sorted_coef_index[:10]]))\n",
        "print('Largest Coefs: \\n{}'.format(feature_names[sorted_coef_index[:-11:-1]]))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Smallest Coefs:\n",
            "['junior' 'moment life' 'moment didn' 'mom' 'molested' 'molest'\n",
            " 'misunderstand' 'moments' 'mistreated' 'missed train university']\n",
            "\n",
            "Largest Coefs: \n",
            "['guilt' 'caught' 'hurt' 'promised' 'help' 'feel guilty' 'realized'\n",
            " 'reason' 'forgot' 'able']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0twrTRK3ASVE",
        "colab_type": "code",
        "outputId": "adee51c8-1ad6-4f28-fd69-7e72d3200574",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "cell_type": "code",
      "source": [
        "test_pred_labels_count = clf_SVM_count.predict(test_count_vectors)\n",
        "\n",
        "cmSVM_count = confusion_matrix(test_labels, test_pred_labels_count)\n",
        "cmSVM_count"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 94,  46,  32,  57,  20,  30,  49],\n",
              "       [ 32, 153,  31,  34,  45,  12,  22],\n",
              "       [ 28,  43, 162,  26,  20,  25,  24],\n",
              "       [ 53,  47,  26, 103,  28,  34,  38],\n",
              "       [ 27,  37,  31,  14, 186,  18,  15],\n",
              "       [ 30,  35,  33,  50,  27, 121,  33],\n",
              "       [ 62,  41,  28,  36,  28,  29, 105]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "io4n_NuH04Sv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "bccf9456-55fe-4ace-f344-2642863c5d2b"
      },
      "cell_type": "code",
      "source": [
        "print(print_confusion_matrix(cmSVM_count, class_names=labels))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Figure(288x216)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/VJBbunQp0dHRtGrVitatW/Ps2TPWrFnzRRcXBAT+HxGLRDkeX5MfarhatWpVNm3a9LWr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\nC0uzhnVyLCwszRrWybGwsDRr/g9fyMVDrR6yegAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f2e52a66898>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "kjEy36O_ASVL",
        "colab_type": "code",
        "outputId": "094394f7-afa8-4a5b-d528-706ab574dc01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "cell_type": "code",
      "source": [
        "reportSVM = classification_report(test_labels, test_pred_labels_count, target_names=labels)\n",
        "print(reportSVM)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             precision    recall  f1-score   support\n",
            "\n",
            "      guilt       0.29      0.29      0.29       328\n",
            "    sadness       0.38      0.47      0.42       329\n",
            "       fear       0.47      0.49      0.48       328\n",
            "      anger       0.32      0.31      0.32       329\n",
            "        joy       0.53      0.57      0.55       328\n",
            "    disgust       0.45      0.37      0.40       329\n",
            "      shame       0.37      0.32      0.34       329\n",
            "\n",
            "avg / total       0.40      0.40      0.40      2300\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "cunC17ZVASVN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# CV 5 fold with CountVectorizer + TfIdfTransformer + SVM Classifier "
      ]
    },
    {
      "metadata": {
        "id": "PkQTjwcMASVN",
        "colab_type": "code",
        "outputId": "bc864942-c5ab-4f05-e6ff-e546c6d2d2a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "\n",
        "# Define a pipeline combining a text feature extractor with a simple\n",
        "# classifier\n",
        "pipeline = Pipeline([\n",
        "    ('vect', CountVectorizer()),\n",
        "    ('tfidf', TfidfTransformer()),\n",
        "    ('clf', svm.SVC()),\n",
        "])\n",
        "\n",
        "# uncommenting more parameters will give better exploring power but will\n",
        "# increase processing time in a combinatorial way\n",
        "parameters = {\n",
        "    'vect__max_df': (0.5, 0.75, 1.0),\n",
        "    # 'vect__max_features': (None, 5000, 10000, 50000),\n",
        "    'vect__ngram_range': ((1, 1), (1, 2), (1, 3)),  # unigrams or bigrams\n",
        "    'tfidf__use_idf': (True, False),\n",
        "    # 'tfidf__norm': ('l1', 'l2'),\n",
        "    'clf__C': (10000.0,),\n",
        "    'clf__gamma': ('auto',),\n",
        "    'clf__kernel': ('rbf',),\n",
        "}\n",
        "\n",
        "grid_search = GridSearchCV(pipeline, parameters, cv=5, n_jobs=-1, verbose=1)\n",
        "\n",
        "grid_search.fit(train_data.text.tolist(), train_labels)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 18 candidates, totalling 90 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:  6.6min\n",
            "[Parallel(n_jobs=-1)]: Done  90 out of  90 | elapsed: 12.5min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise',\n",
              "       estimator=Pipeline(memory=None,\n",
              "     steps=[('vect', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
              "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
              "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
              "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
              "        strip...,\n",
              "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "  tol=0.001, verbose=False))]),\n",
              "       fit_params=None, iid=True, n_jobs=-1,\n",
              "       param_grid={'vect__max_df': (0.5, 0.75, 1.0), 'vect__ngram_range': ((1, 1), (1, 2), (1, 3)), 'tfidf__use_idf': (True, False), 'clf__C': (10000.0,), 'clf__gamma': ('auto',), 'clf__kernel': ('rbf',)},\n",
              "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
              "       scoring=None, verbose=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "CwNXm7oTASVQ",
        "colab_type": "code",
        "outputId": "722f4a89-bb58-4ebc-a0c0-c4623ff62194",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"Best score: %0.3f\" % grid_search.best_score_)\n",
        "print(\"Best parameters set:\")\n",
        "best_parameters = grid_search.best_estimator_.get_params()\n",
        "for param_name in sorted(parameters.keys()):\n",
        "    print(\"\\t%s: %r\" % (param_name, best_parameters[param_name]))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best score: 0.535\n",
            "Best parameters set:\n",
            "\tclf__C: 10000.0\n",
            "\tclf__gamma: 'auto'\n",
            "\tclf__kernel: 'rbf'\n",
            "\ttfidf__use_idf: True\n",
            "\tvect__max_df: 0.5\n",
            "\tvect__ngram_range: (1, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ohWHM2dzASVU",
        "colab_type": "code",
        "outputId": "07cff74c-6472-4f34-c22d-a4048fd165a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "cell_type": "code",
      "source": [
        "test_pred_labels_grid = grid_search.predict(test_data.text.tolist())\n",
        "\n",
        "cmSVM_grid = confusion_matrix(test_labels, test_pred_labels_grid)\n",
        "cmSVM_grid"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[155,   7,  22,  77,  11,  28,  28],\n",
              "       [ 37, 161,  20,  51,  22,  17,  21],\n",
              "       [ 24,  12, 209,  32,  17,  21,  13],\n",
              "       [ 34,  11,  19, 209,  11,  30,  15],\n",
              "       [ 10,   9,   8,  37, 226,  24,  14],\n",
              "       [ 14,   8,  26,  51,   5, 201,  24],\n",
              "       [ 62,   5,  33,  46,  15,  42, 126]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "QThqCwgs07NQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "c7efe92a-34fd-4881-afc4-93128da544bf"
      },
      "cell_type": "code",
      "source": [
        "print(print_confusion_matrix(cmSVM_grid, class_names=labels))"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Figure(288x216)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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10ocIlpaWnDlzBoCzZ8/SvHlz6taty507d4iOjiYuLo6QkBDq16+fnWlhTk5AQEB2ZF0L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\nliYNa+RYWFiaNP8DJRh3NQe99sEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f2e52b0c630>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "IOxPmd3tASVW",
        "colab_type": "code",
        "outputId": "3a27ef96-9564-401d-b201-45e807e61aa9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "cell_type": "code",
      "source": [
        "reportSVM_grid = classification_report(test_labels, test_pred_labels_grid, target_names=labels)\n",
        "print(reportSVM_grid)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             precision    recall  f1-score   support\n",
            "\n",
            "      guilt       0.46      0.47      0.47       328\n",
            "    sadness       0.76      0.49      0.59       329\n",
            "       fear       0.62      0.64      0.63       328\n",
            "      anger       0.42      0.64      0.50       329\n",
            "        joy       0.74      0.69      0.71       328\n",
            "    disgust       0.55      0.61      0.58       329\n",
            "      shame       0.52      0.38      0.44       329\n",
            "\n",
            "avg / total       0.58      0.56      0.56      2300\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Sgdaca1cASVZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# CV 5 fold with CountVectorizer + TfIdfTransformer + SGDClassifier "
      ]
    },
    {
      "metadata": {
        "id": "ARXDnixgASVZ",
        "colab_type": "code",
        "outputId": "5c4c169d-65a5-4bb7-ebb6-699da9d03d4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "\n",
        "# Define a pipeline combining a text feature extractor with a simple\n",
        "# classifier\n",
        "pipeline = Pipeline([\n",
        "    ('vect', CountVectorizer()),\n",
        "    ('tfidf', TfidfTransformer()),\n",
        "    ('clf', SGDClassifier()),\n",
        "])\n",
        "\n",
        "# uncommenting more parameters will give better exploring power but will\n",
        "# increase processing time in a combinatorial way\n",
        "parameters = {\n",
        "    'vect__max_df': (0.5, 0.75, 1.0),\n",
        "    # 'vect__max_features': (None, 5000, 10000, 50000),\n",
        "    'vect__ngram_range': ((1, 1), (1, 2)),  # unigrams or bigrams\n",
        "    # 'tfidf__use_idf': (True, False),\n",
        "    # 'tfidf__norm': ('l1', 'l2'),\n",
        "    'clf__max_iter': (5,),\n",
        "    'clf__alpha': (0.00001, 0.000001),\n",
        "    'clf__penalty': ('l2', 'elasticnet'),\n",
        "    # 'clf__max_iter': (10, 50, 80),\n",
        "}\n",
        "\n",
        "grid_search = GridSearchCV(pipeline, parameters, cv=5, n_jobs=-1, verbose=1)\n",
        "\n",
        "grid_search.fit(train_data.text.tolist(), train_labels)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   14.4s\n",
            "[Parallel(n_jobs=-1)]: Done 120 out of 120 | elapsed:   38.4s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise',\n",
              "       estimator=Pipeline(memory=None,\n",
              "     steps=[('vect', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
              "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
              "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
              "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
              "        strip...='l2', power_t=0.5, random_state=None,\n",
              "       shuffle=True, tol=None, verbose=0, warm_start=False))]),\n",
              "       fit_params=None, iid=True, n_jobs=-1,\n",
              "       param_grid={'vect__max_df': (0.5, 0.75, 1.0), 'vect__ngram_range': ((1, 1), (1, 2)), 'clf__max_iter': (5,), 'clf__alpha': (1e-05, 1e-06), 'clf__penalty': ('l2', 'elasticnet')},\n",
              "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
              "       scoring=None, verbose=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "r5_eE1yDASVb",
        "colab_type": "code",
        "outputId": "2c247c96-3d78-4e2e-b25e-cf87954506c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"Best score: %0.3f\" % grid_search.best_score_)\n",
        "print(\"Best parameters set:\")\n",
        "best_parameters = grid_search.best_estimator_.get_params()\n",
        "for param_name in sorted(parameters.keys()):\n",
        "    print(\"\\t%s: %r\" % (param_name, best_parameters[param_name]))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best score: 0.547\n",
            "Best parameters set:\n",
            "\tclf__alpha: 1e-05\n",
            "\tclf__max_iter: 5\n",
            "\tclf__penalty: 'l2'\n",
            "\tvect__max_df: 0.75\n",
            "\tvect__ngram_range: (1, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tyT61VJzASVd",
        "colab_type": "code",
        "outputId": "af186953-4724-40f7-8965-c460a42b082d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "cell_type": "code",
      "source": [
        "train_pred_labels_grid = grid_search.predict(train_data.text.tolist())\n",
        "\n",
        "cmSVM_grid = confusion_matrix(train_labels, train_pred_labels_grid)\n",
        "cmSVM_grid"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[676,  13,   0,   0,   0,   0,   0],\n",
              "       [  0, 690,   0,   0,   0,   0,   0],\n",
              "       [  0,   3, 687,   0,   0,   0,   0],\n",
              "       [  1,   5,   0, 683,   0,   0,   1],\n",
              "       [  0,   2,   0,   0, 688,   0,   0],\n",
              "       [  0,   7,   2,   0,   0, 681,   0],\n",
              "       [  4,  15,   0,   0,   0,   0, 671]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "88fdb04a-e3a8-494e-ebc0-348febe34286",
        "id": "1fWbdbUjeYf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "cell_type": "code",
      "source": [
        "test_pred_labels_grid = grid_search.predict(test_data.text.tolist())\n",
        "\n",
        "cmSVM_grid = confusion_matrix(test_labels, test_pred_labels_grid)\n",
        "cmSVM_grid"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[154,  22,  14,  68,  13,  18,  39],\n",
              "       [ 26, 189,  13,  47,  24,  12,  18],\n",
              "       [ 23,  23, 213,  21,  17,  13,  18],\n",
              "       [ 44,  33,  13, 164,  13,  34,  28],\n",
              "       [ 10,  21,  14,  24, 232,  10,  17],\n",
              "       [ 21,  23,  25,  50,  11, 169,  30],\n",
              "       [ 63,  19,  25,  38,  22,  16, 146]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "kvPI72W51B6-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "0d9ce2a7-ef22-4062-87ca-68f9075d74ef"
      },
      "cell_type": "code",
      "source": [
        "print(print_confusion_matrix(cmSVM_grid, class_names=labels))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Figure(288x216)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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10ocIlpaWnDlzBoCzZ8/SvHlz6taty507d4iOjiYuLo6QkBDq16+fnWlhTk5AQEB2ZF0L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\nliYNa+RYWFiaNP8DJRh3NQe99sEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f2e65fbc198>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ZBccwVRWASVh",
        "colab_type": "code",
        "outputId": "2bd85980-94ae-4b91-8b5a-1df5dcad8901",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "cell_type": "code",
      "source": [
        "reportSVM_grid = classification_report(test_labels, test_pred_labels_grid, target_names=labels)\n",
        "print(reportSVM_grid)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             precision    recall  f1-score   support\n",
            "\n",
            "      guilt       0.45      0.47      0.46       328\n",
            "    sadness       0.57      0.57      0.57       329\n",
            "       fear       0.67      0.65      0.66       328\n",
            "      anger       0.40      0.50      0.44       329\n",
            "        joy       0.70      0.71      0.70       328\n",
            "    disgust       0.62      0.51      0.56       329\n",
            "      shame       0.49      0.44      0.47       329\n",
            "\n",
            "avg / total       0.56      0.55      0.55      2300\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "oDLbHXHUASVk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# spaCy vectorization + SVM Classifier"
      ]
    },
    {
      "metadata": {
        "id": "VjGysCs9CjJC",
        "colab_type": "code",
        "outputId": "5bc46976-d650-4ce9-a4fa-a849c2d59181",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        }
      },
      "cell_type": "code",
      "source": [
        "!python -m spacy download en_core_web_lg"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting en_core_web_lg==2.0.0 from https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.0.0/en_core_web_lg-2.0.0.tar.gz#egg=en_core_web_lg==2.0.0\n",
            "\u001b[?25l  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.0.0/en_core_web_lg-2.0.0.tar.gz (852.3MB)\n",
            "\u001b[K    100% |████████████████████████████████| 852.3MB 32.1MB/s \n",
            "\u001b[?25hInstalling collected packages: en-core-web-lg\n",
            "  Running setup.py install for en-core-web-lg ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \bdone\n",
            "\u001b[?25hSuccessfully installed en-core-web-lg-2.0.0\n",
            "\n",
            "\u001b[93m    Linking successful\u001b[0m\n",
            "    /usr/local/lib/python3.6/dist-packages/en_core_web_lg -->\n",
            "    /usr/local/lib/python3.6/dist-packages/spacy/data/en_core_web_lg\n",
            "\n",
            "    You can now load the model via spacy.load('en_core_web_lg')\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "C5Tw3LHsASVl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "\n",
        "nlp = spacy.load('en_core_web_lg')\n",
        "train_spacy_vectors = train_data.text.apply(lambda x: nlp(x).vector).tolist()\n",
        "test_spacy_vectors = test_data.text.apply(lambda x: nlp(x).vector).tolist()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cVMwEmhsASVo",
        "colab_type": "code",
        "outputId": "f70093a8-2993-4b69-de4c-e9be43b781f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "clf_SVM_spacy = svm.SVC(C=10000.0, gamma='auto', kernel='rbf')\n",
        "clf_SVM_spacy.fit(train_spacy_vectors, train_labels)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=10000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
              "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
              "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "  tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "HPESeGFEASVs",
        "colab_type": "code",
        "outputId": "4405b353-36dd-46a4-bdb9-f1eb09a7c116",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "cell_type": "code",
      "source": [
        "test_pred_labels_spacy = clf_SVM_spacy.predict(test_spacy_vectors)\n",
        "\n",
        "cmSVM_spacy = confusion_matrix(test_labels, test_pred_labels_spacy)\n",
        "cmSVM_spacy"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[159,  26,  17,  54,  10,  15,  47],\n",
              "       [ 33, 177,  21,  29,  36,  14,  19],\n",
              "       [ 29,  20, 204,  17,  21,  19,  18],\n",
              "       [ 56,  27,  17, 155,  10,  31,  33],\n",
              "       [ 18,  37,  14,  19, 212,  12,  16],\n",
              "       [ 19,  24,  28,  50,  11, 164,  33],\n",
              "       [ 76,  20,  25,  36,  21,  29, 122]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "j90TW-aoASVv",
        "colab_type": "code",
        "outputId": "66a5564f-6f3d-4428-f250-21b6507ca3f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "cell_type": "code",
      "source": [
        "reportSVM_spacy = classification_report(test_labels, test_pred_labels_spacy, target_names=labels)\n",
        "print(reportSVM_spacy)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             precision    recall  f1-score   support\n",
            "\n",
            "      guilt       0.41      0.48      0.44       328\n",
            "    sadness       0.53      0.54      0.54       329\n",
            "       fear       0.63      0.62      0.62       328\n",
            "      anger       0.43      0.47      0.45       329\n",
            "        joy       0.66      0.65      0.65       328\n",
            "    disgust       0.58      0.50      0.54       329\n",
            "      shame       0.42      0.37      0.40       329\n",
            "\n",
            "avg / total       0.52      0.52      0.52      2300\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dYIUwbr5ASVz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Using spaCy TextCategorizer"
      ]
    },
    {
      "metadata": {
        "id": "zbrLaQf_ASVz",
        "colab_type": "code",
        "outputId": "3fbd5b25-dae8-43af-cba2-99a398d0965f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "labels"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['guilt', 'sadness', 'fear', 'anger', 'joy', 'disgust', 'shame']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "qYJWx21QASV2",
        "colab_type": "code",
        "outputId": "ee7ebb6b-a60d-4aa1-e721-70585def34ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from spacy.pipeline import TextCategorizer\n",
        "\n",
        "model = 'en_core_web_lg'\n",
        "nlp = spacy.load(model)  # load existing spaCy model\n",
        "print(\"Loaded model '%s'\" % model)\n",
        "\n",
        "textcat = nlp.create_pipe('textcat')\n",
        "nlp.add_pipe(textcat, last=True)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded model 'en_core_web_lg'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8Iq1guxgASV4",
        "colab_type": "code",
        "outputId": "e9e9331a-d70a-41b2-c039-ddc76c84f00b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# assign label\n",
        "for label in labels:\n",
        "    textcat.add_label(label)\n",
        "    \n",
        "textcat.labels"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['guilt', 'sadness', 'fear', 'anger', 'joy', 'disgust', 'shame']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "_6YDUTDuASV6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_texts = train_data.text.tolist()\n",
        "valid_texts = valid_data.text.tolist()\n",
        "test_texts = test_data.text.tolist()\n",
        "\n",
        "train_labels = np.array(train_data.emotion.apply(lambda x:dic[x]))\n",
        "valid_labels = np.array(valid_data.emotion.apply(lambda x:dic[x]))\n",
        "test_labels = np.array(test_data.emotion.apply(lambda x:dic[x]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ni-mxu8FASV-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def to_spacy_categorical(label_data, labels):\n",
        "    new_data = dict()\n",
        "    for label in labels:\n",
        "        new_data[label] = (label_data == label)\n",
        "    return new_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "q3i7mn0zASWA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_encoded_labels = np.array(train_data.emotion.apply(lambda x:to_spacy_categorical(x, labels)))\n",
        "valid_encoded_labels = np.array(valid_data.emotion.apply(lambda x:to_spacy_categorical(x, labels)))\n",
        "test_encoded_labels = np.array(test_data.emotion.apply(lambda x:to_spacy_categorical(x, labels)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "D4b4BSk6ASWD",
        "colab_type": "code",
        "outputId": "5a2bfdd2-6ad4-4b14-ac2d-e6e816f4e5bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "cell_type": "code",
      "source": [
        "train_encoded_labels"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([{'guilt': True, 'sadness': False, 'fear': False, 'anger': False, 'joy': False, 'disgust': False, 'shame': False},\n",
              "       {'guilt': False, 'sadness': True, 'fear': False, 'anger': False, 'joy': False, 'disgust': False, 'shame': False},\n",
              "       {'guilt': False, 'sadness': True, 'fear': False, 'anger': False, 'joy': False, 'disgust': False, 'shame': False},\n",
              "       ...,\n",
              "       {'guilt': False, 'sadness': False, 'fear': False, 'anger': False, 'joy': False, 'disgust': True, 'shame': False},\n",
              "       {'guilt': False, 'sadness': True, 'fear': False, 'anger': False, 'joy': False, 'disgust': False, 'shame': False},\n",
              "       {'guilt': False, 'sadness': False, 'fear': False, 'anger': True, 'joy': False, 'disgust': False, 'shame': False}],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "metadata": {
        "id": "5MNtZ2hFASWF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_spacy_data = list(zip(train_texts, [{'cats': cats} for cats in train_encoded_labels]))\n",
        "valid_spacy_data = list(zip(valid_texts, [{'cats': cats} for cats in valid_encoded_labels]))\n",
        "test_spacy_data = list(zip(test_texts, [{'cats': cats} for cats in test_encoded_labels]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "k5XLQpIoASWG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from spacy.util import minibatch\n",
        "\n",
        "def evaluate(tokenizer, textcat, texts, cats):\n",
        "    docs = (tokenizer(text) for text in texts)\n",
        "    tp = 1e-8  # True positives\n",
        "    fp = 1e-8  # False positives\n",
        "    fn = 1e-8  # False negatives\n",
        "    tn = 1e-8  # True negatives\n",
        "    for i, doc in enumerate(textcat.pipe(docs)):\n",
        "        gold = cats[i]\n",
        "        for label, score in doc.cats.items():\n",
        "            if label not in gold:\n",
        "                continue\n",
        "            if score >= 0.5 and gold[label] >= 0.5:\n",
        "                tp += 1.\n",
        "            elif score >= 0.5 and gold[label] < 0.5:\n",
        "                fp += 1.\n",
        "            elif score < 0.5 and gold[label] < 0.5:\n",
        "                tn += 1\n",
        "            elif score < 0.5 and gold[label] >= 0.5:\n",
        "                fn += 1\n",
        "    precision = tp / (tp + fp)\n",
        "    recall = tp / (tp + fn)\n",
        "    f_score = 2 * (precision * recall) / (precision + recall)\n",
        "    return {'textcat_p': precision, 'textcat_r': recall, 'textcat_f': f_score}\n",
        "\n",
        "\n",
        "def training(spacy_nlp, train_data, valid_data, epoch=20):\n",
        "    valid_texts, valid_labels = zip(*valid_data)\n",
        "    \n",
        "    other_pipes = [pipe for pipe in spacy_nlp.pipe_names if pipe != 'textcat']\n",
        "    with spacy_nlp.disable_pipes(*other_pipes):  # only train textcat\n",
        "        optimizer = spacy_nlp.begin_training()\n",
        "        print(\"Training the model...\")\n",
        "        print('{:^5}\\t{:^5}\\t{:^5}\\t{:^5}\\t{:^5}'.format('EPOCH', 'LOSS', 'P', 'R', 'F'))\n",
        "        for i in range(epoch):\n",
        "            losses = {}\n",
        "            # batch up the examples using spaCy's minibatch\n",
        "            batches = minibatch(train_data, size=4)\n",
        "\n",
        "            for batch in batches:\n",
        "                texts, annotations = zip(*batch)\n",
        "                spacy_nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)\n",
        "\n",
        "            with textcat.model.use_params(optimizer.averages):\n",
        "                # evaluate on the dev data split off in load_data()\n",
        "                scores = evaluate(spacy_nlp.tokenizer, textcat, valid_texts, valid_labels)\n",
        "\n",
        "            print('{0:.0f}\\t{1:.3f}\\t{2:.3f}\\t{3:.3f}\\t{4:.3f}'  # print a simple table\n",
        "                  .format(i + 1, losses['textcat'], scores['textcat_p'],\n",
        "                          scores['textcat_r'], scores['textcat_f']))\n",
        "\n",
        "    return spacy_nlp"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xWxmVAB6ASWJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "nlp = training(nlp, train_spacy_data, valid_spacy_data, epoch=20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LDcKudmEASWK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}