NLP.ipynb

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          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from bs4 import BeautifulSoup\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "nltk.download('wordnet')\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, accuracy_score, auc, confusion_matrix, roc_auc_score, roc_curve\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ]
    },
    {
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        "#from google.colab import drive\n",
        "#drive.mount('/content/gdrive')"
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          "text": [
            "Mounted at /content/gdrive\n"
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      "cell_type": "code",
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      "metadata": {
        "id": "VF1zwoabknic"
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      "source": [
        "data = pd.read_csv('labeledTrainData.tsv', sep='\\t')"
      ]
    },
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            "text/plain": [
              "       id  sentiment                                             review\n",
              "0  5814_8          1  With all this stuff going down at the moment w...\n",
              "1  2381_9          1  \\The Classic War of the Worlds\\\" by Timothy Hi...\n",
              "2  7759_3          0  The film starts with a manager (Nicholas Bell)...\n",
              "3  3630_4          0  It must be assumed that those who praised this...\n",
              "4  9495_8          1  Superbly trashy and wondrously unpretentious 8..."
            ],
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      "source": [
        "data.head()"
      ]
    },
    {
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      "execution_count": 5,
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          "data": {
            "text/plain": [
              "(25000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWASpAAFknie"
      },
      "source": [
        "# Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mAXig0rfknii"
      },
      "source": [
        "Use BeautifulSoup for deleting all html-staff that we don't need"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "1HL93NBHknij",
        "outputId": "07fb94fe-2df9-4d02-f8b5-b6fd73e55972"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-e0451b83039d>:1: MarkupResemblesLocatorWarning: The input looks more like a filename than markup. You may want to open this file and pass the filehandle into Beautiful Soup.\n",
            "  data.review = data.review.apply(lambda x: BeautifulSoup(x).get_text())\n"
          ]
        }
      ],
      "source": [
        "data.review = data.review.apply(lambda x: BeautifulSoup(x).get_text())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWFj2nSqknik"
      },
      "source": [
        "Remove all the punctuation marks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "RH5m0M5tknil"
      },
      "outputs": [],
      "source": [
        "def clean(text):\n",
        "    text = re.sub(\"[^a-zA-Z]\", \" \", text)  \n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "H1JxyNNEknim"
      },
      "outputs": [],
      "source": [
        "data.review = data.review.apply(clean)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwnSQ0Ghknim"
      },
      "source": [
        "Bring all words to the same register"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "LUZCLg3iknim"
      },
      "outputs": [],
      "source": [
        "data.review = data.review.apply(lambda x: x.lower())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_2zHPPuCknin"
      },
      "source": [
        "Split to separate words (tokenize)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "HJd10aaWknin"
      },
      "outputs": [],
      "source": [
        "data.review = data.review.apply(lambda x: x.split())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ulFaa77Bknin"
      },
      "source": [
        "We need to delete all stop words ('a', 'and' etc.). Actually it is about the most frequent words in corpus. We can use prepared bag of stop words from the nltk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNB0aX47knio",
        "outputId": "8c3baa28-8fe5-4d36-e9ca-935d7babab57"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
          ]
        }
      ],
      "source": [
        "print((stopwords.words(\"english\")))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "kTEKc2rHknio"
      },
      "outputs": [],
      "source": [
        "def clear_stop_words(text, stopwords):\n",
        "    new_text = [i for i in text if i not in stopwords]\n",
        "    return new_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "x1CHlwvAknip"
      },
      "outputs": [],
      "source": [
        "data.review = data.review.apply(lambda x: clear_stop_words(text=x,stopwords=stopwords.words('english')))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRXmeD-Rknip"
      },
      "source": [
        "We can lemmatize words to get simple forms of them. The nltk will again help us."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "g4pHlNF7knip"
      },
      "outputs": [],
      "source": [
        "lemmatizer = WordNetLemmatizer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "np-ZfAZPkniq",
        "outputId": "af442567-4fbb-4786-aa52-94b0f86319b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "messages : message\n"
          ]
        }
      ],
      "source": [
        "#an example of lemmatizing\n",
        "print(\"messages :\", lemmatizer.lemmatize(\"messages\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "kyCcEAPxkniq"
      },
      "outputs": [],
      "source": [
        "def lemm(lemmatizer, text):\n",
        "    new_text = [lemmatizer(word) for word in text]\n",
        "    return new_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "2d7PH89Gkniq"
      },
      "outputs": [],
      "source": [
        "data.review = data.review.apply(lambda x: lemm(lemmatizer.lemmatize, x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "udCAUtgpknir"
      },
      "outputs": [],
      "source": [
        "#create a raw corpus of strings  \n",
        "clean_train_reviews = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "2P35Ir19knir"
      },
      "outputs": [],
      "source": [
        "for i in range(data.review.values.shape[0]):\n",
        "    clean_train_reviews.append(\" \".join(data.review.values[i]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uhP9snyZknir"
      },
      "source": [
        "We create a bag of words using scikit-learn libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "r7dZG9Y0knir"
      },
      "outputs": [],
      "source": [
        "vectorizer = CountVectorizer(max_features = 5000) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "iLN_Md0aknis"
      },
      "outputs": [],
      "source": [
        "#get vectors of texts and convert to an array\n",
        "train_data_features = vectorizer.fit_transform(clean_train_reviews)\n",
        "train_data_features = train_data_features.toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6DZM3Eqknis",
        "outputId": "4df56a01-1cf1-4e82-adc1-8358388891b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(25000, 5000)\n"
          ]
        }
      ],
      "source": [
        "#check what we got \n",
        "print(train_data_features.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "xwEM4Xotknis"
      },
      "outputs": [],
      "source": [
        "#get a vocabulary \n",
        "vocabulary = vectorizer.get_feature_names_out()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IY4rgEqUknit",
        "outputId": "ed318f20-ff98-4ff3-f16b-7546853552c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "187 abandoned\n",
            "125 abc\n",
            "562 ability\n",
            "1259 able\n",
            "92 abraham\n",
            "73 abrupt\n",
            "118 absence\n",
            "83 absent\n",
            "352 absolute\n",
            "1485 absolutely\n",
            "306 absurd\n",
            "86 absurdity\n",
            "202 abuse\n",
            "77 abused\n",
            "91 abusive\n",
            "98 abysmal\n",
            "298 academy\n",
            "688 accent\n",
            "300 accept\n",
            "130 acceptable\n"
          ]
        }
      ],
      "source": [
        "#see how many times every word appears in texts\n",
        "dist = np.sum(train_data_features, axis=0)\n",
        "counter = 0\n",
        "for tag, count in zip(vocabulary, dist):\n",
        "    print(count, tag)\n",
        "    counter += 1\n",
        "    if counter == 20:\n",
        "        break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hU9rhsJSknit"
      },
      "source": [
        "# Machine learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aRU5kj2okniu"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d3BEVpHmkniu"
      },
      "outputs": [],
      "source": [
        "#for the first use a Random forest. Get it from a scikit-learn library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "YVux4MK5kniu"
      },
      "outputs": [],
      "source": [
        "y = data.sentiment.to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "v98BRg69kniu"
      },
      "outputs": [],
      "source": [
        "#split train data to train and validation sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(train_data_features,\n",
        "                                                    y, test_size=0.33, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "3IHlUVFkkniu",
        "outputId": "2a5a9c33-dfd8-439d-c052-97e133e50b00"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "#we are ready to learn 'rules' with Random Forest\n",
        "clf = RandomForestClassifier(verbose = 0, random_state=0)\n",
        "clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "iohDDD_Ikniv"
      },
      "outputs": [],
      "source": [
        "#check an accuracy\n",
        "y_pred = clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6FAu8Lkkniv",
        "outputId": "90f07899-7f08-4d36-f47e-c9443e97c9f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.85      0.85      4105\n",
            "           1       0.85      0.84      0.84      4145\n",
            "\n",
            "    accuracy                           0.84      8250\n",
            "   macro avg       0.85      0.85      0.84      8250\n",
            "weighted avg       0.85      0.84      0.84      8250\n",
            "\n",
            "0.8449696969696969\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "ji4z7JqRkniv",
        "outputId": "7eaa582e-687c-4fa5-8b50-b675cdb3165b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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u4a+/EjF5ylwEzZqAqdPmIzc3Fy1aOOPGjVtYumwdAODGjVtYvXoTxo7xKd2bI9IyHR0dWFQ2f6n8zNlEJCtTsD1kKSoaGwMAZgZ+hw879MTxuL/g2rwJ9PT01K7NzcvDoT9j0euzT7gN8jtAzl9DSRoPYdy7dw/BwcHo3r07XF1d4erqiu7du2Pu3LlITS36HyUqG3K5HJ9//gmMjY1w7HicUN7ry+5QJicg/kwkZs4YD0PDf9402KKFMxLOXURKyj2hbH9EFExNFWjQ4NlPVceOxaFaNVt07PAxAMDS0gI9PvXC3n2Rb+jOiLQj6fYdtP2kNzr0/Ar+U+bgrjIFAJCbmwuZDNDX0xPqGujrQS6X4fTZxCLbivrzGNIyHqGbV/s30neisqZRBuLkyZPw9PSEkZER3N3dUafOs39QVCoVFi9ejNmzZyM8PBzNmjUrtp3s7GxkZ2erlRUWFjJq15KGDevhcMxuVKhggMzMLHzWczAuXLgMAPj5l51ISrqN5LsqODrWR9DMiahTp7Yw9GBlVQUpouzE82yFtZUlgEQcjT2Fvv1HYcvmFahQwQB6enr4I3Q/Rn0z8Y3eJ9G/4eRQFzMmfof3qtvh3v0HWL5uM/qNGIudG1fAqUE9GFaogB+Wr8O3wwagsBBYuGId8vMLcO/+gyLb+z00HC0/aApryypv+E6oNHCCoDSNAohRo0ahZ8+eWLly5Uv/2BcWFmLYsGEYNWoUYmNji20nKCgIU6dOVSuTyStCpqPQpDv0CpcuXYVzcw+YKkzQo4cX1q1diI/de+DChctYs/afMdtz5y5CeTcFEfu3olatGrh27WaJ2q9f/30smD8VM2YuwP6IaNhYW2L27EAsXzYbQ78eU1q3RaRVrVybC1/Xta8JR4e68OjRH/sO/okeXTwxf/oETJ+3FJu374ZcLkNH9zZwqGtf5A86ypRUHDlxGvOnBbzJW6BSxGWc0jQKIP766y+EhIQU+QdIJpPB19cXTZo0kWwnICAAfn5+amWVKtfTpCtUjNzcXFy9egMAcPpMApo5N8aokYMxwsf/pbrHT5wGANjXfg/Xrt2ESpWK5s3Vfw2trJ79RKVUPUvv+o8bhaOxpzD/h5UAgISEC8jKeozoqJ2YNDkYyv+ngYneJgqTiqhRrSqSbicDAFq6OGPftvV4mJYOHR0dKEwqonWXXujQzuala3eGRcBMYYI2rVq86W4TlRmNsjTW1tY4ceLEK8+fOHECVlZWku0YGBhAoVCoHRy+KD1yuRwGBvpFnmvcqAEACGO/x47FwbFhPVSpUlmo497ODenpGTh//tkwiJFRBRQUFKi1k5//7DN/Helt9fjxE9y6cxdVLNQnVVYyM4XCpCKOx8XjwcM0tP1IPUgoLCzEzj0R6NKxHfR0OS/9XSHX4vGu0uh3+5gxYzB06FDExcWhXbt2QrCgUqkQGRmJ1atXY968eRKtUGmaOWM89u07hKRbd2BiUhFfendD69au6OTVC7Vq1cCX3t2xd28k7j94CEfH+pg/dwpiYmKRkHABALA/IhrnL/yNDesXY/yEmbC2qoJpU8dhxcoNyMnJAQCEhh7AjyuD8fXQftgfEQUba0vMnz8VJ06cxt27qrK8faISm7t0Ndq0dIGttRVS7t3HsjWboKMjRyf31gCAHWH7UatGNVQyM8VfiRcxe+FK9PuiO2rWsFNr53hcPG4nK9GjS4eyuA0qJRzCkKZRAOHj4wMLCwssWLAAy5cvR35+PoBnS6GcnZ0REhKCzz//vFQ6SiVTpYoF1q9bBBsbS6SnP0JCwgV08uqFA5F/ws7OFu0+/gjfjBoMY2ND3Lp1Fzt27sHMWYuE6wsKCtC1W38sWxKEwzG7kZX1GBs3bsPkKXOFOj9t3AoTE2OMGDEAc4MnIS0tHYeijiBgwqyiukRULqlS7mHc5DlIy8iAuZkpmjg1wOYfF8C8khkA4EbSbSxcGYL0jEeoamOFof290e+L7i+183vofjR2dECtGtXe8B1QaXqXMwfaIissLCx8nQtzc3Nx796zpX4WFhbQe2G50+vQ1a/6r64nehc9Sf6zrLtAVC7pWdQq1fa/fq+n1tr68cY2rbVVnrz2gJ2enh5sbF6eTERERPS247swpHHGDxERkQjDB2kc5iEiIiKNMQNBREQkwiEMaQwgiIiIRJiel8Zn9BYbN9YHeTl3MH/es23BK1Uyw8IF05F4LgaP0q/g2pUTWPDDNCgUJsW2s3bNAuTl3FE7wv7YJJyvUcMOq36ch8uXYvEo/QouXTiCyZO+U1t5U6OGHQ5F/ob0h5dxKPI31BCtld+1YwO6d++kxbsnKp5Hj/5o2LLjS8eM+cuKrL999170Gz4GH3boiQ879MTgbwOQcP7SK9ufGrwEDVt2xMZfdwhlOTk5GD9tLlzafwov78GIPXlG7Zp1m7dj1g/LtXODRGWMGYi3VDPnRhgyuA/+OnteKLO1tYKtrRX8/afj/IW/UaO6HZYtmw1bW2t84T202Pb27TuIQUP+2V48OztH+LpeXXvI5XKM8PHHlas30KBBXfy4Yi6MjYwwbvx0AMDc4Em4k6zEkK/HYNrUcQieM0n4nj17foKCwgLs2LFHm4+AqFi/rFmktmPq5Ws3MWT0BHi0bVVk/ZOnz6JT+zZo3LA+9A30sW7TNgz1nYidm1bCqoqFWt0D0UdwNvEiLC0qq5Vv27UX5y9dxuYfF+DPYyfhP2UOokN/hkwmw+1kJX77Yx9+XbsIVP5xIylpzEC8hYyNjfDTT0sxbPg4pD1ME8oTEy/h8y+GIjQsAteu3cShqCP4ftIcdPZyh46OTrFtZufkQKVKFY60tHThXPj+KAwe4oeIAzG4fj0JoaER+GHBSnTr1lGoU6/e+9i4cRuuXLmOn37ainr17AEApqYKTJsyjm/qpDfOvJIZLCqbC0f0keOoVtUGzZs4Fll/zhR/eH/aGfXq1EatGtUwdfy3KCgowLFT8Wr1VKn3ELRgBeZMHgddXfU/V9du3kLbj1rAvlYNfNmjCx6kpePh//8sTZ+3FL7Dv0JFY+NSuV/SLm5lLe1dvrd31pLFs7B3TyQiD0pvMmSqMEFGRqawa+irtHZzRfLtv5B4LgZLlwTB3LxS8e2aKvDgheDl7NnzaPdxK8hkMrR3by1sjT1ndiBWrAzB7f+/oIioLOTm5iJ0/yF09/Io8ftanj7NRl5ePkxfGAIsKChAwLR5GNDrM9jXqvHSNXXta+H02UQ8zc7GkeNxqFLZHJXMTBEafhAG+vpwb91Sa/dEVNYYQLxlPv/8EzRp0hATAoMk61auXAkTJ4xWe4V3UcL3H8KAgd/Co8MXCJgwE25uLRD2x0bI5UX/9qhd+z34jPgKq1f/M09inP901K1rj6uXj8PevibG+U9Hq49c0KhRA2zctB0/b1mJvy8exbKls//1rqVEmoqMicWjzEx069S+xNf8sGIdqliYw7XZP2+nXbtpG3R05OjTs2uR13Tv7IG69rXQtffXWLXhF8yfHoCMR5lYumYjAnyHY/GqDej4+UAM9Z0IVeq9f31fVHpkWjzeVZwD8Raxs7PFgvnT0KHTl8jOzi62rolJRfyx6ydcuPA3pk6bX2zdrVt3C1+fO3cRCQkXcPlSLNq0/hAHDx1Wq2tra42wPzZh+2+hWLtui1CenKxE1+79hc/6+vrYE7YZAweOxsSAb/HoUSYcGrphT+hmDB3SB8uWr9fk1on+ld9Dw/FRi2awrFJZujKANRu3Yu+BaKxfGiy8yTbx4mVs2rYL29YteWUWQ09XF4Hf+aiVBc78Ab17dsXFv6/iYEwsftuwHOs2b0PQgpVYOCvw390YlRou45TGDMRbpGlTR1hZVcHJ4/vw9PFNPH18E61bf4hRIwfi6eObQsagYkVj7AndjEePstCj52Dk5eVp9H2uX09Caup91K79nlq5jY0VDkRsQ+yxOAwbPq7YNgLGj8KBiBicPpMAt9au2LFjD/Ly8rBj5x60bu2qUX+I/o1kpQrHTsWX+G2Z67dsx9pNW7FqwUzUta8plJ/+6xwePExD+x790MjNC43cvJCsTMHcpWvg0aN/kW2diPsLV67fRK8eXXDyzFm0cm0OI8MK6PCxG06eOauV+6PSwTkQ0piBeIscPHgYjZp8rFa2ZvUPuHTpKubOW4aCggKYmFTE3rAtyM7ORrdPB0hmKopStaoNKleuhLvKf17NbWtrjQMR23D69FkMGuyL4t7BVq+ePby/6A7n5s/SxTo6OtDVe/ZbTU9PT3JCJ5E27QiLgHklU7i5fiBZd93mbVi14Rf8+MMMNKxfR+1clw7t0KJ5E7Wyr30D0aXDx+jWyeOltrKzczDjh2WYM3kcdHR0kF9QIPy5ycvLU1shQvQ2YgDxFsnMzEJiovq69MdZj3H//kMkJl6CiUlF7NvzMwyNKqDfgFFQKEyEPSBSU+8Lf2GdS4jGxMAg7Nq1D8bGRpgU6Iffd+yBUpWC2rXeQ1DQRFy5egP790cDeBY8REZsR1LSbYzzn44qL6SBVarUl/q5cnkwxoydgsePnwAAjh49icEDe+Py5Wvo0+cz/PrrztJ4PEQvKSgowM6wCHTt6P7SiomA6fNgaVEZvsO/AgCs3bQVS9dsRPBkf1S1scK9+w8AAEaGhjAyMoSZqQJmpgq1NnR1dWBhXgk1RfueAMDKkC1o5doc9es8W5HUxNEB85etRTcvD2z57Q80dnQojVsmLeEAhjQGEO+Qpk0c4eLSFADw98Wjaudqv++CmzdvA3i2r4Op4tlfhPn5BXB0rI++fXvCzEyB5GQVIg5EY/KUucjJebYXhHs7N7z/fk28/35NJN2IU2tX/Br2IYP7QJVyD2F7Dghl06bPx6afluHo4VCE74/C8hUhWr1voleJPXkGd1Up6O71cobgrioF8hfmMvy6Iwy5uXnwDZypVm/4wN7wGdRHo+97+doNhB/8E9tD/tm0yqPtRzh55iz6jxiD96rbIXiKv4Z3Q28S50BIkxUWl4t+g8T/EBER8CRZeqku0X+RnkWtUm0/4L1eWmsr6MYW6UpvIWYgiIiIRN7lyY/awgCCiIhIhAMY0hhkERERkcaYgSAiIhLhJEppDCCIiIhE5OVieUH5xiEMIiIi0hgzEERERCL86VoaAwgiIiIRzoCQxgCCiIhIhBkIaXxGREREpDEGEERERCJyyLR2aCIoKAjNmzeHiYkJLC0t0a1bN1y6pP4SxTZt2kAmk6kdw4YNU6uTlJQELy8vGBkZwdLSEmPHjkVeXp5anaioKDRt2hQGBgawt7dHSEiIhs+IiIiI1Mi0eGgiOjoaPj4+OHbsGCIiIpCbmwsPDw9kZWWp1RsyZAju3r0rHMHBwcK5/Px8eHl5IScnB0ePHsWGDRsQEhKCSZMmCXWuX78OLy8vtG3bFvHx8Rg9ejQGDx6M8PDwkj8jvkyLqPziy7SIilbaL9OaXUOzN7AWZ/zNTa99bWpqKiwtLREdHQ03NzcAzzIQjRs3xsKFC4u8Zu/evejcuTOSk5NhZWUFAFi5ciX8/f2RmpoKfX19+Pv7IywsDOfOnROu8/b2RlpaGvbt21eivjEDQUREJCLX4pGdnY2MjAy1Izs7u0T9SE9PBwCYm5urlW/evBkWFhZo2LAhAgIC8PjxY+FcbGwsHB0dheABADw9PZGRkYHExEShjru7u1qbnp6eiI2NLVG/AAYQREREL5EXau8ICgqCqamp2hEUFCTZh4KCAowePRotW7ZEw4YNhfJevXph06ZNOHToEAICArBx40b06fNPxkSpVKoFDwCEz0qlstg6GRkZePLkSYmeEZdxEhERlaKAgAD4+fmplRkYGEhe5+Pjg3PnzuHw4cNq5UOHDhW+dnR0hI2NDdq1a4erV6+idu3a2ul0CTADQUREJKLNSZQGBgZQKBRqh1QAMXLkSISGhuLQoUOws7Mrtq6LiwsA4MqVKwAAa2trqFQqtTrPP1tbWxdbR6FQwNDQsNjv9xwDCCIiIhFtzoHQRGFhIUaOHIkdO3bg4MGDqFmzpuQ18fHxAAAbGxsAgKurKxISEpCSkiLUiYiIgEKhgIODg1AnMjJSrZ2IiAi4urqWuK8MIIiIiMoJHx8fbNq0CVu2bIGJiQmUSiWUSqUwL+Hq1auYPn064uLicOPGDezevRv9+vWDm5sbnJycAAAeHh5wcHBA37598ddffyE8PByBgYHw8fERMh/Dhg3DtWvXMG7cOFy8eBHLly/H1q1b4evrW+K+chknUTnGZZxERSvtZZyLqmtvGee3SSVfximTFb1zxPr16zFgwADcunULffr0wblz55CVlYVq1aqhe/fuCAwMhEKhEOrfvHkTw4cPR1RUFIyNjdG/f3/Mnj0burr/TH2MioqCr68vzp8/Dzs7O3z//fcYMGBAyfvKAIKo/GIAQVS00g4gllTTXgAx6tbr7wNRnnEVBhERkQjfximNcyCIiIhIY8xAEBERifCna2kMIIiIiEQYQEjjMyIiIiKNMQNBREQkIisX6xPLNwYQREREIkzPS+MzIiIiIo0xA0FERCTCn66lMYAgIiISkXMOhCQGEERERCLciVIaszRERESkMWYgiIiIRPjTtTQGEERERCKcAyGNQRYRERFpjBkIIiIiEU6ilMYAgoiISEQOjmFI4RAGERERaYwZCCIiIhFOopTGAIKIiEiE6XlpfEZERESkMWYgiIiIRLgKQxoDCCIiIhF5ISdBSGEAQUREJMLxfWl8RkRERKQxZiCIiIhEOAdCGgMIIiIiEe5EKY1DGERERKQxZiCIiIhEuBOlNAYQREREIjIOYUjiEAYRERFpjBkIIiIiEf50LY0BBBERkQgDCGl8RkRERKQxZiCIiIhEOIlSGgMIIiIiEabnpTGAICIiEmEGQhqDLCIiItIYMxBEREQifBeGNAYQREREInK+jlMShzCIiIhIY8xAEBERiXASpTQGEERERCJMz0vjMyIiIiKNMQNBREQkIpNxCEMKAwgiIiIROQMISRzCICIiIo0xA0FERCTCbSCkMYAgIiIS4RCGtHITQDy+/EdZd4Go3Klo17qsu0BULmU/vVWq7XMSpTTOgSAiIiKNlZsMBBERUXnBIQxpzEAQERGJyGTaOzQRFBSE5s2bw8TEBJaWlujWrRsuXbqkVufp06fw8fFB5cqVUbFiRfTo0QMqlUqtTlJSEry8vGBkZARLS0uMHTsWeXl5anWioqLQtGlTGBgYwN7eHiEhIRr1lQEEERFROREdHQ0fHx8cO3YMERERyM3NhYeHB7KysoQ6vr6++OOPP7Bt2zZER0cjOTkZn376qXA+Pz8fXl5eyMnJwdGjR7FhwwaEhIRg0qRJQp3r16/Dy8sLbdu2RXx8PEaPHo3BgwcjPDy8xH2VFRYWlos8Tc7N02XdBaJyx6Ru17LuAlG5VNqTKONrfKK1thrf3P3a16ampsLS0hLR0dFwc3NDeno6qlSpgi1btuCzzz4DAFy8eBH169dHbGwsWrRogb1796Jz585ITk6GlZUVAGDlypXw9/dHamoq9PX14e/vj7CwMJw7d074Xt7e3khLS8O+fftK1DdmIIiIiETkskKtHdnZ2cjIyFA7srOzS9SP9PR0AIC5uTkAIC4uDrm5uXB3dxfq1KtXD9WrV0dsbCwAIDY2Fo6OjkLwAACenp7IyMhAYmKiUOfFNp7Xed5GiZ5RiWsSERGRxoKCgmBqaqp2BAUFSV5XUFCA0aNHo2XLlmjYsCEAQKlUQl9fH2ZmZmp1raysoFQqhTovBg/Pzz8/V1ydjIwMPHnypET3xVUYREREIppOfixOQEAA/Pz81MoMDAwkr/Px8cG5c+dw+PBh7XVGixhAEBERiWhzIykDA4MSBQwvGjlyJEJDQxETEwM7Ozuh3NraGjk5OUhLS1PLQqhUKlhbWwt1Tpw4odbe81UaL9YRr9xQqVRQKBQwNDQsUR85hEFERFROFBYWYuTIkdixYwcOHjyImjVrqp13dnaGnp4eIiMjhbJLly4hKSkJrq6uAABXV1ckJCQgJSVFqBMREQGFQgEHBwehzottPK/zvI2SYAaCiIhIpKw2kvLx8cGWLVuwa9cumJiYCHMWTE1NYWhoCFNTUwwaNAh+fn4wNzeHQqHAqFGj4OrqihYtWgAAPDw84ODggL59+yI4OBhKpRKBgYHw8fERMiHDhg3D0qVLMW7cOAwcOBAHDx7E1q1bERYWVuK+chknUTnGZZxERSvtZZwX3u+ktbbqX95T4rqyV0y+WL9+PQYMGADg2UZS3333HX7++WdkZ2fD09MTy5cvF4YnAODmzZsYPnw4oqKiYGxsjP79+2P27NnQ1f0nbxAVFQVfX1+cP38ednZ2+P7774XvUaK+MoAgKr8YQBAVrbQDiIt1tBdA1Pu75AHE24RzIIiIiEhjnANBREQkIpOXi+R8ucYAgoiISESbyzjfVRzCICIiIo0xA0FERCRSVss43yYMIIiIiERkzM9L4iMiIiIijTEDQUREJMJJlNIYQBAREYlwGac0DmEQERGRxpiBICIiEnnFKynoBQwgiIiIRDiEIY0BBBERkQgDCGmcA0FEREQaYwaCiIhIhHMgpDGAICIiEuEQhjQOYRAREZHGmIEgIiIS4bswpDGAICIiEuFW1tIYYxEREZHGmIEgIiIS4RCGNAYQREREIlyFIY0xFhEREWmMGQgiIiIRDmFIYwBBREQkxlUYkhhAEBERiTADIY2PiIiIiDTGDAQREZEIMxDSGEAQERGJMICQxkdEREREGmMGgoiISIw/XktiAEFERCTCIQxpfERERESkMWYgiIiIxPjjtSQGEERERCIcwpDGR0REREQaYwaCiIhIjD9eS2IAQUREJCKTy8q6C+UeAwgiIiIxZiAk8RERERGRxpiBICIiEuEQhjQGEERERGLMz0viIyIiIiKNMQNBREQkxiEMSQwgiIiIRDgHQhqHMIiIiEhjzEAQERGJ8cdrSQwgiIiIxDiEIYkxFhEREWmMGQgiIiIRTqKUxgCCiIhIjAGEJAYQREREYgwgJHEOBBEREWmMAQQREZGITCbT2qGJmJgYdOnSBba2tpDJZNi5c6fa+QEDBrzUfocOHdTqPHjwAL1794ZCoYCZmRkGDRqEzMxMtTpnz55Fq1atUKFCBVSrVg3BwcEaPyMGEERERGJymfYODWRlZaFRo0ZYtmzZK+t06NABd+/eFY6ff/5Z7Xzv3r2RmJiIiIgIhIaGIiYmBkOHDhXOZ2RkwMPDAzVq1EBcXBzmzp2LKVOmYNWqVRr1lXMgiIiIyomOHTuiY8eOxdYxMDCAtbV1kecuXLiAffv24eTJk2jWrBkAYMmSJejUqRPmzZsHW1tbbN68GTk5OVi3bh309fXRoEEDxMfH44cfflALNKQwA0FERCSmxQxEdnY2MjIy1I7s7OzX7lpUVBQsLS1Rt25dDB8+HPfv3xfOxcbGwszMTAgeAMDd3R1yuRzHjx8X6ri5uUFfX1+o4+npiUuXLuHhw4clf0SvfQf01po4dwUcPb586Ui6oxTOrflll9o1kUdOwtHjyzLqMVHpy356q9gjMNAXNWrYqZUl3zmLsNDNaNSoQVl3n7RNLtfaERQUBFNTU7UjKCjotbrVoUMH/PTTT4iMjMScOXMQHR2Njh07Ij8/HwCgVCphaWmpdo2uri7Mzc2hVCqFOlZWVmp1nn9+XqckOITxH9WyWSPMGDNMraySqQIAYKCvh3Vb/0BPr3YwNalYFt0jeuOq12gqfN3zsy6YNOk7ODq1EcoyM7NgYWEOAOjQ0Rvnz/+NqlVt8MP8qdi96yc4NWqL9PSMN91tegsEBATAz89PrczAwOC12vL29ha+dnR0hJOTE2rXro2oqCi0a9fuX/VTU8xA/Efp6+nBwtxM7dDRefbboUUTR1hUMsVaURaC6F2mUqUKR3rGIxQWFqqVZWU9Furev/8QKlUqTp8+i/EBM2BtbYkPmjcpw96T1mlxCMPAwAAKhULteN0AQqxWrVqwsLDAlStXAADW1tZISUlRq5OXl4cHDx4I8yasra2hUqnU6jz//Kq5FUVhAEEvkcvl+PYrb2zZFQ5l6n3pC4j+w548eQoA0NfXK+OekDbJ5DKtHaXp9u3buH//PmxsbAAArq6uSEtLQ1xcnFDn4MGDKCgogIuLi1AnJiYGubm5Qp2IiAjUrVsXlSpVKvH3ZgDxHxVz/DQ++GSAcPhNX6h2vt1HzVG39ntY/tP2sukg0VvA1FSBCQHf4tGjTJw8FV/W3aF3QGZmJuLj4xEfHw8AuH79OuLj45GUlITMzEyMHTsWx44dw40bNxAZGYmuXbvC3t4enp6eAID69eujQ4cOGDJkCE6cOIEjR45g5MiR8Pb2hq2tLQCgV69e0NfXx6BBg5CYmIhff/0VixYtemmYRQrnQPxHNW/kgO+/GSR8NqzwcjrNd9CXGDxuBgb07Pwmu0ZU7kVH7URBQQEqVjTGtWs30afPCKSk3CvrbpE2ldFW1qdOnULbtm2Fz8//Ue/fvz9WrFiBs2fPYsOGDUhLS4OtrS08PDwwffp0tSGRzZs3Y+TIkWjXrh3kcjl69OiBxYsXC+dNTU2xf/9++Pj4wNnZGRYWFpg0aZJGSzgBBhD/WYYVKqB61eLHupo51ceHzZywcO0v6Orh9oZ6RlT+9ekzAhcuXsb9+w85cfJdJSubBH2bNm1QWFj4yvPh4eGSbZibm2PLli3F1nFycsKff/6pcf9exCEMKtboQV8i+ngc/rpwuay7QlRu3LqdjGvXbjJ4eJeV0U6UbxMGEFSsOjWrw+vjj7Bl576y7goREZUjDCBIkk+/nigoJqVGRPTOYQZCkqywuMGWNyjn5umy7gJRuWNSt2tZd4GoXMp+eqtU28+a2FNrbRnP3Ka1tsoTZiCIiIhIY1yFQUREJPYODz1oCwMIIiIisTJaxvk24RMiIiIijTEDQUREJMYhDEkMIIiIiMTkTNBL4RP6j1Dde4Dxs5fiox5D0KxzP3QfOg6Jf18Vzj9+8hQzl65Hu14+aNa5H7oOHoOtoRHC+fSMTMxath5dBvqhWed+aN97JIKWheDRC684JirPhg7pi1Mn9yM15TxSU84jOmonPD3aFFl3966fkP30Fj7p4vnSub59e+LUyf1IT7uMW0lnsGjhDOFcnfdrITz8VyTdPI30tMu4eOEwpkwZC11d/qxG7x7+rv4PSH+UiX6+k9G8UQOsmOmPSqYKJN1RQlGxolAneOVGnPgrEbP9fWBrVQVH485i5pJ1qFK5Etq6NkPK/YdIvZ+G74b0Ru0adkhWpWL64rVIvf8QP0zyLcO7IyqZO3fuIjAwCFeuXIdMJkOfvj2xfftafODSERcu/C3U+2bU4Fe+i+Dbb4bg22+HImDCTJw8eQZGRoZ4r0Y14XxuXh42b/4NZ84kID09A06ODli+fA7kcjkmTZpT6vdIWsQhDEkMIP4D1m39A9ZVKmPGmGFCmZ2NpVqdv87/jU/c3dC8kQMAoKdXO2wLi0TCxato69oM79eshgUvBArVbK0w6qsvEDBnGfLy86Gro/NmboboNYXtOaD2efLkYAwd0hcuLk2EAMLJyQHffjsUH7b0QpJoczszM1NMmTIWn/b4CocOHRHKz527KHx9/XoSrl9PEj4nJd2B2y+uaNnyg9K4JSpNXIUhiU/oPyAqNg4O79eC3/SFaN3za/QcPh7b90Sq1WnkUAdRx+KguvcAhYWFOBGfiJt37uJDZ6dXtpuZ9RgVjQwZPNBbRy6Xo2fPT2BsbIhjx54FCoaGFfDThiUYPToQKlXqS9e0a9cKcrkMtrbW+Cv+IK5eOYHNm5bDzs7mld+ndq334OHRGn/+eazU7oVKCbeylqT1AOLWrVsYOHBgsXWys7ORkZGhdmRn52i7K/R/t++mYGvoAdSoao2VQePxeef2mL18A3btjxbqTPAZgNrVq8K9lw+aduqLYRNnY+LIr9DMqX6RbT5Mz8CPm3fgs07t3tRtEP1rDRrUw/17F/Eo4yqWLpmFzz8fgosXn71pdt7cyYg9Foc/QvcXeW3NmtUhl8vhP24kxoyZii97fQ1zczPsCdsCPT09tbpRh3YgPe0yzp//E0eOnMDUqfNK/d6I3jStBxAPHjzAhg0biq0TFBQEU1NTtSN4+Xptd4X+r6CwAPXffw/fDvRGffua6OnVDj06foytYf9kIbbsCsfZi1ewZOoY/LJsJsYM7YOZS9cj9nTCS+1lZj2GT2AwalWviuF9e7zJWyH6V/7++yo++KADPmr1CVat3og1axagXr330dmrPdq0aYkxY6a88lq5XA59fX34fTcZEQeiceLEGfTtNxL29jXRpvWHanX79B0Blxad0LffSHTo0A5+vl+X8p2Rtsnkcq0d7yqN50Ds3r272PPXrl2TbCMgIAB+fn5qZTLleU27QiVUxbwSale3UyurVb0qDhw+AQB4mp2DRet/waLJfnBzaQoAqFurBi5dvYkN20Ph2tRRuC7r8RMMmzgbRkaGWDTFD3qcXU5vkdzcXFy9dgMAcOZMApo5N8KokQPx5MlT1KpVAymqRLX6v/zyIw4fOQEPj8+hVKYAgNqEy3v3HuDevQeoVt1W7brbt+8CAC5evAwdHTmWL5uDBQtXoaCgoBTvjrTqHR560BaN//bv1q0bZDLZK2cpA4BMVvyDNzAwgIGBgVpZzkN9TbtCJdS4QR3cuJ2sVnbj9l3YWFkAAPLy8pCXlw+ZaNKQXC5HQcE/v86ZWY/x9YTZ0NfTxZKpY2Cgz18zervJ5HLoGxhg2vQfsG79L2rnzpw+gLFjpwqTL2OPngQA1KlTG3fuKAEAlSqZwcLCHElJd175PeRyOfT0dP//54kBBL07NA4gbGxssHz5cnTtWvRrhuPj4+Hs7PyvO0ba0+/TTug7ejJW/7wTnm4tkHDpKn7bcxCTRg8GAFQ0NkIzp/r4YfVmVDDQh42lBU4lXMAfB2Iw9uu+AP4fPAQE4Ul2Nmb7f4esx0+Q9fgJAKCSqQI6Ou9umo7eDdOn+yM8PAq3bt1BxYoV4e3dFa3dXNG5Sx+oVKlFTpy8dSsZN248e2305SvXsXt3OObPm4oRPv7IyMjEjOn+uHTpCqKijgIAvL27ITc3D4nnLiI7JwdNmzph+rTx2Lb9D+Tl5b3R+6V/iaswJGkcQDg7OyMuLu6VAYRUdoLevIZ1a2PhZD8sXPcLVm76HVWtq2Dc8L7o3O4joc7cCd9g4bpfMH72UqQ/yoSNZRWMGvAFPu/sDgC4cOUGzl68AgDoNGC0Wvv7flqMqtZV3tj9EL2OKlUssHbtAthYWyI9/RHOnbuAzl36IDLyzxK3MXDQaMydOxk7d4SgoKAQf/55DF0+6SsEB3l5+Rjz3XC8/34tyGQyJCXdxoqVIVi8eE1p3RaVFg5hSJIVaviv/Z9//omsrCx06NChyPNZWVk4deoUWrdurVFHckRrrokIMKlbdKBO9F+X/fRWqbb/+IchWmvLyG+11toqTzTOQLRq1arY88bGxhoHD0REROXKO7x6Qls4hZ6IiEhMYjEAcSdKIiIieg3MQBAREYlxCEMSAwgiIiIxBhCS+ITecvn5BVgSshUd+n6DZp37oWP/b7Fy0+/FLqU9cPgEhvjPhFvPoWjRbSB6fzsJR079pVbHs+8oOHp8+dIxY8k6oU7wyo1o2WMw3Hv5IDTysNr14THHMPL7udq9WaISGjNmBLKf3sK8uZOFskGDemH//q1ITTmP7Ke3YGqqkGynYkVjzJs7GX//HYu0h5cRdWgHnJ0bqdUxNjbCwgXTcfXKCaQ9vIz4M5EYMriPWp3gOZNwNzkBV64ch7d3N7Vzn37qhd9/WwcqZ/gyLUnMQLzl1m3dja2hEZg5djhq16iGxL+v4fv5K2FibITe3YteahuXcAGuzo74dqA3TCoaYWd4NEZOmosti6ejvn1NAMDPS2aq7Zp3+cYtDB0/C55uLQA8e8PnnkNHsCooADfvKDFp/o9o2cwJlUwVeJT1GEvW/4rVcyaW/gMgEnF2boQhg3vj7Fn17fGNDA2xf38U9u+PwswZASVqa+WKuWjQoA4GDhyNu8kqfNmrO/bu2YLGTdohOfnZbpRzgyehTZuW+GrgN7h58zbc3d2weNFM3L2rQmhYBLw6ueOLL7rCq3Nv2NvXxKof5yEiIhr37z+EQmGCaVPHoWOnL7X+HIhKGzMQb7n483+jrWszuLk0RVXrKvBwc8GHzk5IuHTlldf4D++PgZ9/goZ1a6NGVRt8O9AbNapaI+rYP3txmJspYGFuJhwxx0+jmq2V8HbOa7fuoLmTAxrUqY1ObVvC2MgQd5TPdvL7YfUWfN65PWwsLUr35olEjI2NsCFkMYaP8MfDtHS1c0uWrsW8ectx4kTJ9pypUKECunfviAkTZuHw4eO4eu0GZsxYgKtXb2Do0L5CvRYtmmHjpu2IiTmGmzdvY+3aLTh79jyaNW8MAKhXzx4xMcdw+vRZbN26CxkZj/Dee9UBAEGzJmDVqo24dSu5qC5QWZLJtXe8o97dO/uPaOxQB8fjz+HG/1/ec+nqTZw+dxEf/f8vr5IoKChA1uOnMDWpWOT53Nw8hEYeRnfPNsJ7TurWqoHEy9eQ/igTiX9fQ3ZOLqrZWuH0uYu4cOU6encrOvtBVJoWLZqBvXsP4uDBw9KVJejq6kBXVxdPs7PVyp88fYoPP2wufD527BQ6e7WHra01AKB1a1e8/34tHDgQAwA4e/YCnJ2dYGZmiiZNHGFoWAFXr97Ahx82R+PGjli6jMMX5ZJcrr3jHcUhjLfcoC8+QebjJ/hk0HfQkcuRX1CAbwZ8rrZNtZSQ7aF4/PSpMDwhFnn0JB5lPkZXDzehrGWzRuj88Uf4clQgDPT1MXPMcBhVqIDpi9dhxphh+DU0Aj/vCoeZwgSTRw+G/XvV/vW9EhWnZ89P0KSxIz5s2Vkr7WVmZiE29hQCAr7FxYtXoFKl4osvuqKFizOuXr0h1BvtOwnLl8/G9WsnkZubi4KCAgwf4Y/Dh48DACIORGPLz7/j6JFQPHnyFIMG+yEr6zGWLJ6FwUP88PXQvhgx4ivcu/8AI0aMV3vbJ1F5xgDiLRcefQxhkYcxZ/xI1H7PDpeu3sScFT+hSuVK6OohvSNo2MEjWLnxdyya+h0qVzItss6OfVH4qHljWFY2Vysf0e8zjOj3mfB5xcbtaNGkIXR1dbBqyw78/mMwoo+fxoTgFdi6fNa/u1GiYtjZ2WD+vCno5NUL2aKMwb8xcNBo/PjjPNy4fgp5eXk4c+Ycft26C02b/POKe58RX8Hlg6b49NOvcDPpNlp95IJFC2fg7l2VkAmZMWMBZsxYIFwzceJoHDx4GLm5uRg//hs4N2uPTp3csW7tArh+6KW1/tPrk73Dkx+1hQHEW27+6s0Y5N0VHdt+CACoU7M6klWpWPPLbskAYu+ho5iyYBXmB34L16aORdZJVqXi2JkELJjkV2xb15LuIDTyCLatCMKOfVFwdqwHczMFPN1aYNL8H5H1+AmMjQxf7yaJJDRt4gQrqyo4fmyvUKarq4tWH7lg+PABMFHUfq1XaV+7dhPt2/eEkZEhFAoTKJUp2LRxOa5fTwLwbJ7EtGnj8PnnQ7B330EAwLlzF+HUqAF8R39d5FBK3Tq10evLT/GBSwcM6P8FDh8+jnv3HmD79j+wetV8VKxojMzMrNd8EqQ17/DcBW1hAPGWe5qdA7loy1UduRyFhcX/Zbnn0BFMmv8jgid8AzeXpq+stzM8GuZmpnBzafLKOoWFhZi2aA3Gft0HRoYVkF9QgLy8fABAXv6z/+e/xl/eRCV18NBhNGnqrla2etV8XPr7CubNW/FawcOLHj9+gsePn8DMzBTt27thwsRnGTU9PV3o6+u/1H5Bfj7krxj7XrpsNsaNm4asrMfQ0dGBnp7e/9t69n8dHZ1/1VeiN4UBxFuudYumWPXzTthYVkbtGtVw8coN/PT7HnTzbCPUWbj2Z6Tcf4hZ40YAeDZsETh3BfyH94NTPXvce5AGADAw0IeJsZFwXUFBAXbuj8Yn7d2gW8xfar/tPQhzUwXauDoDAJo0qIMVG7fjrwuXcfhkPGrXqApFRWPt3zzR/2VmZuH8+UtqZVmPH+PB/YdCuZVVFVhZVUHt2u8BABo2rIdHjzJx61YyHj5MAwDs2/szdu3ahxUrNwAA2ru3hkwmw9+Xr6J27fcQNGsiLl26ig0btgIAHj3KRHRMLIKCAvHk6VMkJd1Bq1Yt0Lv3Zxg3btpL/Rw48EvcS72PsD0HAABHY08hMNAXH3zQBJ6ebXH+/CWkp2eUxiMiTb3Dkx+1hQHEW26CzwAs3bAVM5asx4O0dFSpXAmfdWqH4X16CHVSH6Thbso94fP2PZHIy8/HzKXrMXPpeqH8k/ZumDl2uPD52OlzuJtyD91fCEbE7j1Mw+qfd2LjwqlCmWM9e/T7zAs+gcEwN1OotUlUVoYM6YPvA/8ZijsY+RsAYPAQP2zcuA0AULNWDVS2+Geuj8LUBDOmj0fVqtZ48CANO3fuxaTJwcjLyxPq9O3rg+nTxyNk/RKYm5shKek2Jk8OxqrVG9W+v6WlBfz9R6FNm+5C2alT8Vi4aBV27tiA1NR7GDS4+KFCeoMYQEiSFRa3ZeEblHOzZGuzif5LTOp2LesuEJVL2U9vlWr7TzaM11pbhv1na62t8oQZCCIiIjG+zlsSAwgiIiIxDmFIYgBBREQkxmWckviEiIiISGPMQBAREYlxCEMSAwgiIiIxBhCS+ISIiIhIY8xAEBERiXEZpyQGEERERGIcwpDEJ0REREQaYwaCiIhIjBkISQwgiIiIxLiRlCQ+ISIiItIYMxBERERiHMKQxACCiIhIjEMYkviEiIiIxORy7R0aiImJQZcuXWBrawuZTIadO3eqnS8sLMSkSZNgY2MDQ0NDuLu74/Lly2p1Hjx4gN69e0OhUMDMzAyDBg1CZmamWp2zZ8+iVatWqFChAqpVq4bg4GDNH5HGVxAREVGpyMrKQqNGjbBs2bIizwcHB2Px4sVYuXIljh8/DmNjY3h6euLp06dCnd69eyMxMREREREIDQ1FTEwMhg4dKpzPyMiAh4cHatSogbi4OMydOxdTpkzBqlWrNOqrrLCwsPD1blO7cm6eLusuEJU7JnW7lnUXiMql7Ke3SrX9J2ELtdaWodfo17pOJpNhx44d6NatG4Bn2QdbW1t89913GDNmDAAgPT0dVlZWCAkJgbe3Ny5cuAAHBwecPHkSzZo1AwDs27cPnTp1wu3bt2Fra4sVK1Zg4sSJUCqV0NfXBwCMHz8eO3fuxMWLF0vcP2YgiIiIxLQ4hJGdnY2MjAy1Izs7W+MuXb9+HUqlEu7u7kKZqakpXFxcEBsbCwCIjY2FmZmZEDwAgLu7O+RyOY4fPy7UcXNzE4IHAPD09MSlS5fw8OHDkj8ije+AiIiISiwoKAimpqZqR1BQkMbtKJVKAICVlZVauZWVlXBOqVTC0tJS7byuri7Mzc3V6hTVxovfoyS4CoOIiEhMi8s4AwIC4Ofnp1ZmYGCgtfbLCgMIIiIiMS0u4zQwMNBKwGBtbQ0AUKlUsLGxEcpVKhUaN24s1ElJSVG7Li8vDw8ePBCut7a2hkqlUqvz/PPzOiXBIQwiIqK3QM2aNWFtbY3IyEihLCMjA8ePH4erqysAwNXVFWlpaYiLixPqHDx4EAUFBXBxcRHqxMTEIDc3V6gTERGBunXrolKlSiXuDwMIIiIisTLaByIzMxPx8fGIj48H8GziZHx8PJKSkiCTyTB69GjMmDEDu3fvRkJCAvr16wdbW1thpUb9+vXRoUMHDBkyBCdOnMCRI0cwcuRIeHt7w9bWFgDQq1cv6OvrY9CgQUhMTMSvv/6KRYsWvTTMIoVDGERERGJltBPlqVOn0LZtW+Hz83/U+/fvj5CQEIwbNw5ZWVkYOnQo0tLS8NFHH2Hfvn2oUKGCcM3mzZsxcuRItGvXDnK5HD169MDixYuF86ampti/fz98fHzg7OwMCwsLTJo0SW2viJLgPhBE5Rj3gSAqWqnvAxGp2aZKxTFsp9k/zG8LZiCIiIjE+DItSQwgiIiIxPgyLUkMIIiIiMSYgZDEJ0REREQaYwaCiIhIRCbTKesulHsMIIiIiMQ4hCGJT4iIiIg0xgwEERGRGDMQkhhAEBERiXEZpyQ+ISIiItIYMxBERERiHMKQxACCiIhIjEMYkviEiIiISGPMQBAREYnJuZGUFAYQREREYhzCkMQAgoiISIyTKCXxCREREZHGmIEgIiIS4xCGJAYQREREInwbpzSGWERERKQxZiCIiIjEOIlSEgMIIiIiMc6BkMQnRERERBpjBoKIiEiMQxiSGEAQERGJcQhDEp8QERERaYwZCCIiIjG+TEsSAwgiIiIxDmFIYgBBREQkxkmUkviEiIiISGPMQBAREYnIOIQhiQEEERGRGIcwJPEJERERkcaYgSAiIhLjEIYkBhBERERi3AdCEkMsIiIi0hgzEERERGIcwpDEAIKIiEiMqzAk8QkRERGRxpiBICIiEuFGUtIYQBAREYlxCEMSAwgiIiIxZiAk8QkRERGRxpiBICIiEuNGUpJkhYWFhWXdCSo/srOzERQUhICAABgYGJR1d4jKBf65IHoZAwhSk5GRAVNTU6Snp0OhUJR1d4jKBf65IHoZ50AQERGRxhhAEBERkcYYQBAREZHGGECQGgMDA0yePJkTxYhewD8XRC/jJEoiIiLSGDMQREREpDEGEERERKQxBhBERESkMQYQREREpDEGEERERKQxBhAkWLZsGd577z1UqFABLi4uOHHiRFl3iahMxcTEoEuXLrC1tYVMJsPOnTvLuktE5QYDCAIA/Prrr/Dz88PkyZNx+vRpNGrUCJ6enkhJSSnrrhGVmaysLDRq1AjLli0r664QlTvcB4IAAC4uLmjevDmWLl0KACgoKEC1atUwatQojB8/vox7R1T2ZDIZduzYgW7dupV1V4jKBWYgCDk5OYiLi4O7u7tQJpfL4e7ujtjY2DLsGRERlVcMIAj37t1Dfn4+rKys1MqtrKygVCrLqFdERFSeMYAgIiIijTGAIFhYWEBHRwcqlUqtXKVSwdrauox6RURE5RkDCIK+vj6cnZ0RGRkplBUUFCAyMhKurq5l2DMiIiqvdMu6A1Q++Pn5oX///mjWrBk++OADLFy4EFlZWfjqq6/KumtEZSYzMxNXrlwRPl+/fh3x8fEwNzdH9erVy7BnRGWPyzhJsHTpUsydOxdKpRKNGzfG4sWL4eLiUtbdIiozUVFRaNu27Uvl/fv3R0hIyJvvEFE5wgCCiIiINMY5EERERKQxBhBERESkMQYQREREpDEGEERERKQxBhBERESkMQYQREREpDEGEERERKQxBhBERESkMQYQREREpDEGEERERKQxBhBERESksf8B+elaZkHen5MAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cf_matrix = confusion_matrix(y_test, y_pred)\n",
        "group_names = ['TN','FP','FN','TP']\n",
        "group_counts = [\"{0:0.0f}\".format(value) for value in\n",
        "                cf_matrix.flatten()]\n",
        "group_percentages = [\"{0:.2%}\".format(value) for value in\n",
        "                     cf_matrix.flatten()/np.sum(cf_matrix)]\n",
        "labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in\n",
        "          zip(group_names,group_counts,group_percentages)]\n",
        "labels = np.asarray(labels).reshape(2,2)\n",
        "sns.heatmap(cf_matrix, annot=labels, fmt=\"\", cmap='rocket_r')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o3-R-VcCkniw"
      },
      "source": [
        "With this simple method we got a good score. Also we checked if lemmatizing is really helpful. It turned out that preprocessing without lemmatizing gave the same result."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekA7rEizknix"
      },
      "source": [
        "### Neural Network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pk1dEPI2knix"
      },
      "source": [
        "Let's try to use a neural network"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UGXSvwh7mquJ",
        "outputId": "8f530159-10f3-4024-bba5-fdcdc67ea911"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16750, 5000)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "fhvUkzXCknix"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Dense(128, activation='relu', input_shape=[5000]),\n",
        "    layers.Dense(128, activation='relu'),    \n",
        "    layers.Dense(1, activation='sigmoid'),\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "wmoM7XMSknix"
      },
      "outputs": [],
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='binary_crossentropy',\n",
        "    metrics=['binary_accuracy'],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "early_stopping = keras.callbacks.EarlyStopping(\n",
        "    patience=10,\n",
        "    min_delta=0.001,\n",
        "    restore_best_weights=True,\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    X_train, y_train,\n",
        "    validation_data=(X_test, y_test),\n",
        "    batch_size=512,\n",
        "    epochs=1000,\n",
        "    callbacks=[early_stopping],\n",
        "    verbose=0, \n",
        ")"
      ],
      "metadata": {
        "id": "1HuE7v7Wm3E_"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_nn = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2MuJBtofn37n",
        "outputId": "70971370-47dd-4a6f-bcf4-43ad8f91a376"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "258/258 [==============================] - 1s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "auc_test = roc_auc_score(y_test, y_pred_nn)\n",
        "fpr, tpr, _ = roc_curve(y_test, y_pred_nn)"
      ],
      "metadata": {
        "id": "yBVWp95qn6nM"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_auc_curve(fpr, tpr, auc):\n",
        "    plt.figure(figsize = (16,6))\n",
        "    plt.plot(fpr,tpr,'black',linestyle = '-')\n",
        "    plt.fill_between(fpr, tpr, alpha = 1, color='#D49E26')\n",
        "    plt.ylabel('True Postive Rate') \n",
        "    plt.xlabel('False Postive Rate')\n",
        "    plt.title(f'ROC Curve Having AUC = {auc}')"
      ],
      "metadata": {
        "id": "tFKn6fd3pXiI"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_auc_curve(fpr, tpr, auc_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "ANteehAuoDDS",
        "outputId": "48c4c114-e4ca-4059-cb28-c38871741d6d"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#function to convert probabilities into classes\n",
        "def transform_to_class(y_pred, threshold=0.5):\n",
        "    y_class = [1. if y_cont > threshold else 0. for y_cont in y_pred] \n",
        "    return y_class"
      ],
      "metadata": {
        "id": "oRS72axAoSkb"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_nn_classes = transform_to_class(y_pred_nn)"
      ],
      "metadata": {
        "id": "36tfHkRVom4M"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred_nn_classes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dxl-ezQNozXF",
        "outputId": "cd9346d4-a256-4ade-bfaf-64d252d688d4"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.90      0.89      4105\n",
            "           1       0.90      0.87      0.88      4145\n",
            "\n",
            "    accuracy                           0.88      8250\n",
            "   macro avg       0.89      0.88      0.88      8250\n",
            "weighted avg       0.89      0.88      0.88      8250\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(y_test, y_pred_nn_classes)\n",
        "group_names = ['TN','FP','FN','TP']\n",
        "group_counts = [\"{0:0.0f}\".format(value) for value in\n",
        "                cf_matrix.flatten()]\n",
        "group_percentages = [\"{0:.2%}\".format(value) for value in\n",
        "                     cf_matrix.flatten()/np.sum(cf_matrix)]\n",
        "labels = [f\"{v1}\\n{v2}\\n{v3}\" for v1, v2, v3 in\n",
        "          zip(group_names,group_counts,group_percentages)]\n",
        "labels = np.asarray(labels).reshape(2,2)\n",
        "sns.heatmap(cf_matrix, annot=labels, fmt=\"\", cmap='rocket_r')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "L2a-mSZoo5O-",
        "outputId": "42ae5a43-80b4-4c2c-8aa1-7f1893c6c89c"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Score was improved"
      ],
      "metadata": {
        "id": "MWLcoEwzqAOm"
      }
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "TWASpAAFknie",
        "hU9rhsJSknit",
        "aRU5kj2okniu",
        "ekA7rEizknix"
      ]
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}