assignment11_008.ipynb

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      "name": "assignment11_003.ipynb",
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      "authorship_tag": "ABX9TyPCW7s2Xvg5cPZH//7kooaV",
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/dlguswn3659/class-MachineLearning/blob/master/assignment11_008.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYerxiOfBiJr",
        "colab_type": "code",
        "outputId": "443a0128-6754-4304-83c4-48c012fc841d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import numpy as np\n",
        "import re\n",
        "import nltk\n",
        "from sklearn.datasets import load_files\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "import pickle\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.model_selection import train_test_split\n",
        "# from google.colab import files\n",
        "\n",
        "# uploaded = files.upload()\n",
        "\n",
        "# import zipfile\n",
        "# import io\n",
        "# zf = zipfile.ZipFile(io.BytesIO(uploaded['movie_review.zip']), \"r\")\n",
        "# zf.extractall()\n",
        "\n",
        "review_data = load_files(r\"movie_review\")\n",
        "X, y = review_data.data, review_data.target\n",
        "\n",
        "documents = []\n",
        "\n",
        "stemmer = WordNetLemmatizer()\n",
        "\n",
        "for sen in range(0, len(X)):\n",
        "    # Remove all the special characters\n",
        "    document = re.sub(r'\\W', ' ', str(X[sen]))\n",
        "    \n",
        "    # remove all single characters\n",
        "    document = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', document)\n",
        "    \n",
        "    # Remove single characters from the start\n",
        "    document = re.sub(r'\\^[a-zA-Z]\\s+', ' ', document) \n",
        "    \n",
        "    # Substituting multiple spaces with single space\n",
        "    document = re.sub(r'\\s+', ' ', document, flags=re.I)\n",
        "    \n",
        "    # Removing prefixed 'b'\n",
        "    document = re.sub(r'^b\\s+', '', document)\n",
        "    \n",
        "    # Converting to Lowercase\n",
        "    document = document.lower()\n",
        "    \n",
        "    # Lemmatization\n",
        "    document = document.split()\n",
        "    document = [stemmer.lemmatize(word) for word in document]\n",
        "    document = ' '.join(document)\n",
        "    \n",
        "    documents.append(document)\n",
        "\n",
        "vectorizer = CountVectorizer(max_features=40000, min_df=5, max_df=0.7, stop_words=stopwords.words('english'))\n",
        "print(vectorizer)\n",
        "X = vectorizer.fit_transform(documents).toarray()\n",
        "print(X)\n",
        "\n",
        "# list_x=X.tolist()\n",
        "# negative=[]\n",
        "# positive=[]\n",
        "# for i in range(0,2002):\n",
        "#   if y[i]==0:\n",
        "#     negative.append(list_x[i])\n",
        "#   else:\n",
        "#     positive.append(list_x[i])\n",
        "# negative=np.array(negative)\n",
        "# positive=np.array(positive)\n",
        "# total_sum=X.sum(axis=0)\n",
        "# neg_sum=negative.sum(axis=0)\n",
        "# pos_sum=positive.sum(axis=0)\n",
        "\n",
        "# neg_ratio=neg_sum/total_sum\n",
        "# pos_ratio=pos_sum/total_sum\n",
        "\n",
        "# good=[]\n",
        "# for i in range(0,len(pos_sum)):\n",
        "#   if neg_ratio[i]>pos_ratio[i]*3:\n",
        "#     good.append(i)\n",
        "#   elif pos_ratio[i]>neg_ratio[i]*3:\n",
        "#     good.append(i)\n",
        "# print(\"good의 길이:   \" , len(good))\n",
        "# new=np.ones((1,2002), dtype=float)\n",
        "# for i in range(0,len(good)):\n",
        "#   temp=X[:,good[i]]\n",
        "#   temp=temp[np.newaxis]\n",
        "#   new=np.vstack((new,temp))\n",
        "# new=np.transpose(new)\n",
        "# new=new[:,1:]\n",
        "\n",
        "X_train_tmp, X_test_tmp, train_labels, test_labels = train_test_split(X, y, test_size=0.3, shuffle=False)\n",
        "\n",
        "# print(X_train.shape)\n",
        "\n",
        "X_pos_sum = np.zeros(12638)\n",
        "X_neg_sum = np.zeros(12638)\n",
        "\n",
        "train_data = np.zeros((1401, 897))\n",
        "test_data = np.zeros((601, 897))\n",
        "\n",
        "for i in range(0, 2002):\n",
        "  if y[i] == 0:\n",
        "    X_neg_sum = X_neg_sum + X[i]\n",
        "  else:\n",
        "    X_pos_sum = X_pos_sum + X[i]\n",
        "\n",
        "pos_neg_voca = 0\n",
        "\n",
        "for i in range(0, 12638):\n",
        "  # if abs(X_neg_sum[i] - X_pos_sum[i]) >= 30:\n",
        "  if (X_neg_sum[i]*4 <= X_pos_sum[i] and X_pos_sum[i] > 8) or (X_pos_sum[i]*4 <= X_neg_sum[i] and X_neg_sum[i] > 8):\n",
        "    for j in range(0, 1401):\n",
        "      train_data[j][pos_neg_voca] = X_train_tmp[j][i]\n",
        "    for j in range(0, 601):\n",
        "      test_data[j][pos_neg_voca] = X_test_tmp[j][i]\n",
        "    pos_neg_voca  = pos_neg_voca + 1\n",
        "\n",
        "# tfidfconverter = TfidfTransformer()\n",
        "# X_train = tfidfconverter.fit_transform(X_train).toarray()\n",
        "# X_test = tfidfconverter.fit_transform(X_test).toarray()\n",
        "\n",
        "# train_data, test_data, train_labels, test_labels = train_test_split(new, y, test_size=0.3, shuffle=False)\n",
        "\n",
        "print(pos_neg_voca)\n",
        "\n",
        "print(train_data[0])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
            "                dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
            "                lowercase=True, max_df=0.7, max_features=40000, min_df=5,\n",
            "                ngram_range=(1, 1), preprocessor=None,\n",
            "                stop_words=['i', 'me', 'my', 'myself', 'we', 'our', 'ours',\n",
            "                            'ourselves', 'you', \"you're\", \"you've\", \"you'll\",\n",
            "                            \"you'd\", 'your', 'yours', 'yourself', 'yourselves',\n",
            "                            'he', 'him', 'his', 'himself', 'she', \"she's\",\n",
            "                            'her', 'hers', 'herself', 'it', \"it's\", 'its',\n",
            "                            'itself', ...],\n",
            "                strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
            "                tokenizer=None, vocabulary=None)\n",
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
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            " [0 0 0 ... 0 0 0]]\n",
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          ],
          "name": "stdout"
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      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "RTUbfE3nHMIf",
        "colab_type": "code",
        "outputId": "0fbc54b0-9172-4504-9d70-83a27242cebb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "max([max(sequence) for sequence in train_data])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8lORN_xHPHL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "3fdf90b3-4bca-4503-d304-9964a1beeffc"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def vectorize_sequences(sequences, dimension=pos_neg_voca):\n",
        "    # Create an all-zero matrix of shape (len(sequences), dimension)\n",
        "    results = np.zeros((len(sequences), dimension))\n",
        "    for i, sequence in enumerate(sequences):\n",
        "        results[i, sequence] = 1.  # set specific indices of results[i] to 1s\n",
        "    return results\n",
        "\n",
        "# Our vectorized training data\n",
        "x_train = vectorize_sequences(train_data)\n",
        "# Our vectorized test data\n",
        "x_test = vectorize_sequences(test_data)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-98ce1ed4b838>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# Our vectorized training data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mx_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvectorize_sequences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;31m# Our vectorized test data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mx_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvectorize_sequences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-16-98ce1ed4b838>\u001b[0m in \u001b[0;36mvectorize_sequences\u001b[0;34m(sequences, dimension)\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msequences\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdimension\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequence\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msequences\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequence\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.\u001b[0m  \u001b[0;31m# set specific indices of results[i] to 1s\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: arrays used as indices must be of integer (or boolean) type"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMmWQNt7K0x3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train[10]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0FWMx-KVLC4r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_labels[10]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dy-KJykCLHez",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Our vectorized labels\n",
        "y_train = np.asarray(train_labels).astype('float32')\n",
        "y_test = np.asarray(test_labels).astype('float32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3LOut8q3LJiN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train[10]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S66MrXU9LMLr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(layers.Dense(16, activation='relu', input_shape=(pos_neg_voca,)))\n",
        "model.add(layers.Dense(16, activation='relu'))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CW5JC7_VLSzC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='rmsprop',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3ra7-VFLe58",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import optimizers\n",
        "\n",
        "model.compile(optimizer=optimizers.RMSprop(lr=0.0001),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zi1iyxuJLgKj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import losses\n",
        "from keras import metrics\n",
        "\n",
        "model.compile(optimizer=optimizers.RMSprop(lr=0.0001),\n",
        "              loss=losses.binary_crossentropy,\n",
        "              metrics=[metrics.binary_accuracy])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGM5qupoLhfV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_val = x_train[:pos_neg_voca]\n",
        "partial_x_train = x_train[pos_neg_voca:]\n",
        "\n",
        "y_val = y_train[:pos_neg_voca]\n",
        "partial_y_train = y_train[pos_neg_voca:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3qgc7ctLlTN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history = model.fit(partial_x_train,\n",
        "                    partial_y_train,\n",
        "                    epochs=2000\n",
        "                    batch_size=512,\n",
        "                    validation_data=(x_val, y_val))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EGSizO_RLqUg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history_dict = history.history\n",
        "history_dict.keys()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHDc0hwrLtNl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "# \"bo\" is for \"blue dot\"\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "# b is for \"solid blue line\"\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUpmNTT8Luej",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plt.clf()   # clear figure\n",
        "acc = history.history['binary_accuracy']\n",
        "val_acc = history.history['val_binary_accuracy']\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vi8jHzG5Lv4Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = models.Sequential()\n",
        "model.add(layers.Dense(16, activation='relu', input_shape=(pos_neg_voca,)))\n",
        "model.add(layers.Dense(16, activation='relu'))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.compile(optimizer='rmsprop',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(x_train, y_train, \n",
        "          epochs=4, \n",
        "          batch_size=512,\n",
        "          validation_data=(x_val, y_val))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AS_bomBBL1Z6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 3 datasets: _test, _val, _train\n",
        "# this last evaluation is on realy never seen data\n",
        "results = model.evaluate(x_test, y_test)\n",
        "results\n",
        "# first is loss and second is metric as defined above"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQZQrnLwL86Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predicted = model.predict(x_test)\n",
        "predicted"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAYbKvXDMAEd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "predicted_classes = [0 if float(i) < 0.5 else 1 for i in predicted]\n",
        "\n",
        "target_names=[\"neg. review\", \"pos. review\"]\n",
        "print(classification_report(y_test, predicted_classes, target_names=target_names))\n",
        "\n",
        "cnf_matrix=confusion_matrix(y_test, predicted_classes)\n",
        "cnf_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9dDNd5tNMGpw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import itertools\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "\n",
        "# Compute confusion matrix\n",
        "cnf_matrix=confusion_matrix(y_test, predicted_classes)\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "# Plot non-normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=target_names,\n",
        "                      title='Confusion matrix, without normalization')\n",
        "plt.show()\n",
        "\n",
        "# Plot normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=target_names, normalize=True,\n",
        "                      title='Normalized confusion matrix')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}