Language_classification_with_naive_bayes_Project.ipynb

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      "name": "Language classification with naive bayes Project.ipynb",
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      "metadata": {
        "id": "jaPXkIi-7dQ-",
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      "source": [
        "import matplotlib\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = \"svg\"\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "import numpy as np\n",
        "import string\n",
        "import pandas as pd\n",
        "\n",
        "from collections import defaultdict\n",
        "\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "#import joblib\n",
        "import pickle as pkl"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvXPt76MPAXE",
        "colab_type": "text"
      },
      "source": [
        "HELPER FUNCTIONS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SalBk0wm9OkG",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "# Function to print and plot the confusion matrix.\n",
        "def plot_confusion_matrix(y_true, y_pred, classes,\n",
        "                          normalize=False,\n",
        "                          title=None,\n",
        "                          cmap=plt.cm.Blues):\n",
        "    title = 'Confusion Matrix'\n",
        "\n",
        "    # Compute confusion matrix\n",
        "    cm = confusion_matrix(y_true, y_pred, labels=classes)\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(5,5))\n",
        "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    # ax.figure.colorbar(im, ax=ax)\n",
        "    ax.figure.colorbar(im, ax=ax, fraction=0.046, pad=0.04)\n",
        "    # We want to show all ticks\n",
        "    ax.set(xticks=np.arange(cm.shape[1]),\n",
        "           yticks=np.arange(cm.shape[0]),\n",
        "           # and label them with the respective list entries\n",
        "           xticklabels=classes, yticklabels=classes,\n",
        "           title=title,\n",
        "           ylabel='True label',\n",
        "           xlabel='Predicted label')\n",
        "\n",
        "    # Rotate the tick labels and set their alignment.\n",
        "    plt.setp(ax.get_xticklabels(), rotation=0)\n",
        "\n",
        "    # Loop over data dimensions and create text annotations.\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i in range(cm.shape[0]):\n",
        "        for j in range(cm.shape[1]):\n",
        "            ax.text(j, i, format(cm[i, j], fmt),\n",
        "                    ha=\"center\", va=\"center\",\n",
        "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "    fig.tight_layout()\n",
        "    \n",
        "    plt.grid(b=None)\n",
        "\n",
        "# Converts a dictionary (keys - language, values - lists of sentences) into separate pandas dataframes for each language, and plots log\n",
        "# of scales of Rank vs Frequencies, to visualize Zipfs Law.\n",
        "def do_law_of_zipf(data):\n",
        "    languages = list(data.keys())\n",
        "    words_df_dict = dict()\n",
        "    for language in languages:\n",
        "        words_df_dict[language] = pd.DataFrame()\n",
        "        words = []\n",
        "        for sentence in data[language]:\n",
        "            words.extend(sentence.split())\n",
        "        words_df_dict[language]['word'] = words\n",
        "    for language in languages:\n",
        "        freqs = words_df_dict[language]['word'].value_counts().values\n",
        "        ranks = range(1, len(freqs)+1)\n",
        "        plt.plot(freqs, ranks, label=language)\n",
        "    plt.ylabel('Frequency')\n",
        "    plt.xlabel('Rank')\n",
        "    plt.yscale('log')\n",
        "    plt.xscale('log')\n",
        "    plt.title('Zipf\\'s Law')\n",
        "    plt.legend()\n",
        "\n",
        "def split_into_subwords_function(text):\n",
        "    merges = pkl.load(open('Data/Auxiliary/merge_ordered.pkl', 'rb'))\n",
        "    subwords = []\n",
        "    for word in text.split():\n",
        "        for subword in merges:\n",
        "            if subword in word:\n",
        "                word = word.replace(subword, ' ')\n",
        "                subwords.append(subword)\n",
        "    return ' '.join(subwords)\n",
        "\n",
        "# Function to remove punctuation from a string, and convert all characters to lowercase\n",
        "def preprocess_function(text):\n",
        "    punctuation_without_hyphen = ''.join([x for x in string.punctuation if x != '-'])\n",
        "    translation_table = str.maketrans('\\n-', '  ', punctuation_without_hyphen+string.digits)\n",
        "    return text.translate(translation_table).lower()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p3NAO9sS9eyQ",
        "colab_type": "text"
      },
      "source": [
        "PROBLEM STATEMENT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XEm7TBET9W7t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# The problem at hand is to identify which language is input to the model. This can be used in detecting the language\n",
        "# in language translation systems.\n",
        "# languages used here - Slovac, Czech and English"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pkm8rS0MDQYh",
        "colab_type": "text"
      },
      "source": [
        "EXPLORATORY DATA ANALYSIS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VcLnd5st-cnb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# function to read dataset \n",
        "def open_file(filename):\n",
        "  with open(filename, 'r') as f:\n",
        "    data = f.readlines()\n",
        "  return data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qxx0R6JBFIz_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_raw = dict()\n",
        "data_raw['sk'] = open_file(\"/content/drive/My Drive/Naive bayes Data/Sentences/train_sentences.sk\")\n",
        "data_raw['cs'] = open_file(\"/content/drive/My Drive/Naive bayes Data/Sentences/train_sentences.cs\")\n",
        "data_raw['en'] = open_file(\"/content/drive/My Drive/Naive bayes Data/Sentences/train_sentences.en\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wd3Cxj_eGlvY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_statistics(data):\n",
        "    for language, sentences in data.items():\n",
        "        word_list = ' '.join(sentences).split()\n",
        "        number_of_sentences = len(sentences)\n",
        "        number_of_words = len(word_list)\n",
        "        number_of_unique_words = len(set(word_list))\n",
        "        sample_extract = ' '.join(sentences[0].split()[0:7])\n",
        "        \n",
        "        \n",
        "        print(f'Language: {language}')\n",
        "        print('-----------------------')\n",
        "        print(f'Number of sentences\\t:\\t {number_of_sentences}')\n",
        "        print(f'Number of words\\t\\t:\\t {number_of_words}')\n",
        "        print(f'Number of unique words\\t:\\t {number_of_unique_words}')\n",
        "        print(f'Sample extract\\t\\t:\\t {sample_extract}...\\n')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dv3I1yzZH7GB",
        "colab_type": "code",
        "outputId": "8d45c91a-0ced-45fc-d1a8-a961fc0732ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
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      },
      "source": [
        "show_statistics(data_raw)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Language: sk\n",
            "-----------------------\n",
            "Number of sentences\t:\t 100\n",
            "Number of words\t\t:\t 2016\n",
            "Number of unique words\t:\t 1322\n",
            "Sample extract\t\t:\t Pán de Grandes Pascual jasne vysvetlil, aká...\n",
            "\n",
            "Language: cs\n",
            "-----------------------\n",
            "Number of sentences\t:\t 10\n",
            "Number of words\t\t:\t 158\n",
            "Number of unique words\t:\t 141\n",
            "Sample extract\t\t:\t Upozorňujeme, že jejím cílem je šetřit penězi...\n",
            "\n",
            "Language: en\n",
            "-----------------------\n",
            "Number of sentences\t:\t 100\n",
            "Number of words\t\t:\t 2381\n",
            "Number of unique words\t:\t 1037\n",
            "Sample extract\t\t:\t I can understand your approach a little...\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVknf5eaICOl",
        "colab_type": "code",
        "outputId": "a9020d08-8f8b-4863-9cb9-058267879ec5",
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      "source": [
        "# Zipfs Law - It states that the frequency of a word is inversely proportional to it's rank. This means that the most frequently occuring\n",
        "# word occurs twice as the second most frequent word ans so on..There is a linear relationship between freq and rank in the log space.\n",
        "do_law_of_zipf(data_raw) # visualize this relationship"
      ],
      "execution_count": 64,
      "outputs": [
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          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xqy6YCd5Mrf5",
        "colab_type": "text"
      },
      "source": [
        "DATA CLEANING AND PREPROCESSING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SH_71EjcL-it",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def preprocess(text):\n",
        "  preprocessed_text = text\n",
        "  preprocessed_text = text.lower().replace('-',' ')\n",
        "  translation_table = str.maketrans('\\n', ' ', string.punctuation+string.digits)\n",
        "  preprocessed_text = preprocessed_text.translate(translation_table)\n",
        "  # str.translate() returns a copy of the string S in which each character has been mapped through the given translation table.\n",
        "  return preprocessed_text"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GLDB_HOkPnAJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_preprocessed = {k: [preprocess(sentence) for sentence in v] for k, v in data_raw.items()}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pw2PFyK1QIw3",
        "colab_type": "code",
        "outputId": "8f2861b3-f060-4fc9-9367-07a4541bee4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "show_statistics(data_preprocessed)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Language: sk\n",
            "-----------------------\n",
            "Number of sentences\t:\t 100\n",
            "Number of words\t\t:\t 1996\n",
            "Number of unique words\t:\t 1207\n",
            "Sample extract\t\t:\t pán de grandes pascual jasne vysvetlil aká...\n",
            "\n",
            "Language: cs\n",
            "-----------------------\n",
            "Number of sentences\t:\t 10\n",
            "Number of words\t\t:\t 155\n",
            "Number of unique words\t:\t 133\n",
            "Sample extract\t\t:\t upozorňujeme že jejím cílem je šetřit penězi...\n",
            "\n",
            "Language: en\n",
            "-----------------------\n",
            "Number of sentences\t:\t 100\n",
            "Number of words\t\t:\t 2366\n",
            "Number of unique words\t:\t 904\n",
            "Sample extract\t\t:\t i can understand your approach a little...\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "70Q3uthwQaPj",
        "colab_type": "text"
      },
      "source": [
        "NAIVE BAYES MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMjoNzFEQK6u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# bayes theorem\n",
        "# independence assumption\n",
        "# bag of words model - to represent the text data\n",
        "# laplace smoothing\n",
        "# count vectorization"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VkYgLqwHbUyH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sentences_train, y_train = [],[]\n",
        "for k, v in data_preprocessed.items():\n",
        "  for sentence in v:\n",
        "    sentences_train.append(sentence)\n",
        "    y_train.append(k)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2HjU5EeLc38a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vectorizer = CountVectorizer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_CnZQ4Gc5KQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = vectorizer.fit_transform(sentences_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ify4idiDdK9Q",
        "colab_type": "code",
        "outputId": "30e7458e-df31-416a-beaf-86449fb97b32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "X_train"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<210x2208 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 3867 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqXI_8mQIq2D",
        "colab_type": "text"
      },
      "source": [
        "###IMPLEMENTING NAIVE BAYES FROM SCRATCH"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDZf6_6QIxql",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fit(X_train, Y_train):\n",
        "    result = {}\n",
        "    class_values = set(Y_train)\n",
        "    for current_class in class_values:\n",
        "        result[current_class] = {}\n",
        "        result[\"total_data\"] = len(Y_train)\n",
        "        current_class_rows = (Y_train == current_class)\n",
        "        X_train_current = X_train[current_class_rows]\n",
        "        Y_train_current = Y_train[current_class_rows]\n",
        "        num_features = X_train.shape[1]\n",
        "        result[current_class][\"total_count\"] = len(Y_train_current)\n",
        "        for j in range(1, num_features + 1):\n",
        "            result[current_class][j] = {}\n",
        "            all_possible_values = set(X_train[:, j - 1])\n",
        "            for current_value in all_possible_values:\n",
        "                result[current_class][j][current_value] = (X_train_current[:, j - 1] == current_value).sum()\n",
        "                \n",
        "    return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJvW6VicIxu0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def probability(dictionary, x, current_class):\n",
        "    output = np.log(dictionary[current_class][\"total_count\"]) - np.log(dictionary[\"total_data\"])\n",
        "    num_features = len(dictionary[current_class].keys()) - 1;\n",
        "    for j in range(1, num_features + 1):\n",
        "        xj = x[j - 1]\n",
        "        count_current_class_with_value_xj = dictionary[current_class][j][xj] + 1\n",
        "        count_current_class = dictionary[current_class][\"total_count\"] + len(dictionary[current_class][j].keys())\n",
        "        current_xj_probablity = np.log(count_current_class_with_value_xj) - np.log(count_current_class)\n",
        "        output = output + current_xj_probablity\n",
        "    return output"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hoxn54wIx-n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predictSinglePoint(dictionary, x):\n",
        "    classes = dictionary.keys()\n",
        "    best_p = -1000\n",
        "    best_class = -1\n",
        "    first_run = True\n",
        "    for current_class in classes:\n",
        "        if (current_class == \"total_data\"):\n",
        "            continue\n",
        "        p_current_class = probability(dictionary, x, current_class)\n",
        "        if (first_run or p_current_class > best_p):\n",
        "            best_p = p_current_class\n",
        "            best_class = current_class\n",
        "        first_run = False\n",
        "    return best_class"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3x7M36AIyJY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict(dictionary, X_test):\n",
        "    y_pred = []\n",
        "    for x in X_test:\n",
        "        x_class = predictSinglePoint(dictionary, x)\n",
        "        y_pred.append(x_class)\n",
        "    return y_pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wrUrGxF0dTpt",
        "colab_type": "text"
      },
      "source": [
        "###INITIALIZING MULTINOMIAL MODEL PARAMETERS AND TRAINING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEGMfOyjdQWy",
        "colab_type": "code",
        "outputId": "d40ef529-f9dc-46fd-f2c1-c5194022adc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "naive_classifier =  MultinomialNB()\n",
        "naive_classifier.fit(X_train, y_train)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x9BuQkyPHUW5",
        "colab_type": "text"
      },
      "source": [
        "VECTORIZE VALIDATION DATA AND EVALUATE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lyGAe_ZbfgvK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_val = dict()\n",
        "data_val['sk'] = open_file(\"/content/drive/My Drive/Naive bayes Data/Sentences/val_sentences.sk\")\n",
        "data_val['cs'] = open_file(\"/content/drive/My Drive/Naive bayes Data/Sentences/val_sentences.cs\")\n",
        "data_val['en'] = open_file(\"/content/drive/My Drive/Naive bayes Data/Sentences/val_sentences.en\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y22ZnZHQIpFh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_val_preprocessed = {k : [preprocess(sentence) for sentence in v] for k, v in data_val.items()}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DAt13JmiI4zt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sentences_val, y_val = [], []\n",
        "for k, v in data_val_preprocessed.items():\n",
        "  for sentence in v:\n",
        "    sentences_val.append(sentence)\n",
        "    y_val.append(k)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3OCl4jy0JPAQ",
        "colab_type": "code",
        "outputId": "017b50d5-6179-46d8-c439-00d84c4a9c7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_val = vectorizer.transform(sentences_val) # use the previous vectorizer only\n",
        "X_val.shape"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 2208)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WCmYia5TJXNV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = naive_classifier.predict(X_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0SLmpNNJjpr",
        "colab_type": "code",
        "outputId": "dc284af4-625e-42f1-a238-53eb59bf7309",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(predictions)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5U5g_7-YJk78",
        "colab_type": "code",
        "outputId": "991e6775-e180-4199-dfca-0fa2dab0f531",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        }
      },
      "source": [
        "plot_confusion_matrix(y_val, predictions, ['sk','cs','en'])"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdsJnDj-KQep",
        "colab_type": "code",
        "outputId": "45c63815-727b-4376-eddf-be5b3d5425ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "f1_score(y_val, predictions, average='weighted') # not giving very good accuracy\n",
        "# accuracy for english is 100%, but model is confusing czech with slovak since both these languages are very similar."
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6149824401040264"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tn0L-fv1Lwhk",
        "colab_type": "text"
      },
      "source": [
        "IMPROVING MODEL ACCURACY BY MAKING SIMPLE ADJUSTMENTS TO PARAMETERS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7fkzbVdLZJv",
        "colab_type": "code",
        "outputId": "695e0564-cc7c-4e5f-d451-69f7b53da5d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "naive_classifier = MultinomialNB(alpha = 0.001, fit_prior = False)\n",
        "naive_classifier.fit(X_train, y_train)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=0.001, class_prior=None, fit_prior=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzJu3JzvMHOP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = naive_classifier.predict(X_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3zZ_JcnMQI6",
        "colab_type": "code",
        "outputId": "5d236480-d146-4075-a58c-238320786a68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        }
      },
      "source": [
        "plot_confusion_matrix(y_val, predictions, ['sk','cs','en'])"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ldal07O2MUO-",
        "colab_type": "code",
        "outputId": "1c3cb5eb-b9f2-488a-be66-e4088e879db7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "f1_score(y_val, predictions, average='weighted')"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.855734057507639"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0jUiQI9WPrjD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# alpha values and corresponding validation accuracies\n",
        "# 0.0001 = 0.8368507601649364 %\n",
        "# 0.002 = 0.8625198851740278 %\n",
        "# 0.003 = 0.8666929312716983 %\n",
        "# 0.004 = 0.8695405627489318 %\n",
        "# 0.005 = 0.8717119918794968 %\n",
        "# 0.006 = 0.8732945238022173 %\n",
        "# 0.007 = 0.873956178710146 %\n",
        "# 0.008 = 0.8747752155457776 %\n",
        "# 0.009 = 0.8754347054412756 %\n",
        "# 0.01 = 0.8760217816244569 %\n",
        "# 0.011 = 0.8765374408080376 %\n",
        "# 0.012 = 0.8774660349957215 %\n",
        "# 0.02 = 0.877665598927103 %\n",
        "# 0.025 = 0.8796088536296942 %\n",
        "# 0.028 = 0.8801065435276306 %\n",
        "# 0.03 = 0.8801131637087292 %\n",
        "# 0.035 = 0.880461855949338 %\n",
        "# 0.038 = 0.8806749715451472 %  - BEST\n",
        "# 0.04 = 0.88060393899013 %\n",
        "# 0.045 = 0.8803908054539813 %\n",
        "# 0.05 = 0.880461855949338 %\n",
        "# 0.06 = 0.8796046465044701 %"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aTc_L73gPfHb",
        "colab_type": "code",
        "outputId": "2cd39f3a-54bc-4e41-8507-d83c09db2ea8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        }
      },
      "source": [
        "naive_classifier = MultinomialNB(alpha = 0.038, fit_prior = False)\n",
        "naive_classifier.fit(X_train, y_train)\n",
        "predictions = naive_classifier.predict(X_val)\n",
        "plot_confusion_matrix(y_val, predictions, ['sk','cs','en'])\n",
        "f1_score(y_val, predictions, average='weighted')"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8806749715451472"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDLwpMq6M7F8",
        "colab_type": "text"
      },
      "source": [
        "USING SUBWORDS TO SHIFT PERSPECTIVE  - Creating new subwords from the dataset\n",
        "\n",
        "(concept taken from the paper - https://arxiv.org/abs/1508.07909)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPZj_ZpYMW8g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# step 1 - break each word into characters\n",
        "# step 2 - find common character sequences\n",
        "# step 3 - use the sequences obtained in step 2 as new words for the vocabulary\n",
        "\n",
        "# example :-\n",
        "# playing ; eating ; play ; reads ; tea\n",
        "# playing > p l a y i n g\n",
        "# ea, ing, play\n",
        "# play ing ; ea t ing ; play ; r ea d s ; t ea"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhvhru3xJhLK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re, collections\n",
        "def get_stats(vocab):\n",
        "    pairs = collections.defaultdict(int) \n",
        "    for word, freq in vocab.items():\n",
        "        symbols = word.split()\n",
        "        for i in range(len(symbols)-1):\n",
        "            pairs[symbols[i],symbols[i+1]] += freq \n",
        "    return pairs\n",
        "\n",
        "def merge_vocab(pair, v_in):\n",
        "    v_out = {}\n",
        "    bigram = re.escape(' '.join(pair))\n",
        "    p = re.compile(r'(?<!\\S)' + bigram + r'(?!\\S)')\n",
        "    for word in v_in:\n",
        "        w_out = p.sub(''.join(pair), word)\n",
        "        v_out[w_out] = v_in[word] \n",
        "    return v_out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loKz0y7LJiJS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_vocab(data):\n",
        "  words = []\n",
        "  for sentence in data:\n",
        "    words.extend(sentence.split())\n",
        "  vocab = defaultdict(int)\n",
        "  for word in words:\n",
        "    vocab[' '.join(word)] += 1\n",
        "  return vocab"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDEvaMDlKxh8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vocab = get_vocab(sentences_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m8nOPiBQLAAL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(100):\n",
        "    pairs = get_stats(vocab)\n",
        "    best = max(pairs, key=pairs.get) \n",
        "    vocab = merge_vocab(best, vocab)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LdM1jmTzLIAC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "merges = defaultdict(int)\n",
        "for k, v in vocab.items():\n",
        "    for subword in k.split():\n",
        "        if len(subword) >= 2:\n",
        "            merges[subword] += v"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MDruRiCzLmBU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "merge_ordered = sorted(merges, key=merges.get, reverse=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3JvH-pZALoGy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pkl.dump(merge_ordered, open('/content/drive/My Drive/Naive bayes Data/Auxillary/merge_ordered.pkl', 'wb'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWYs3k3ELyIC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def split_into_subwords(text):\n",
        "    merges = pkl.load(open('/content/drive/My Drive/Naive bayes Data/Auxillary/merge_ordered.pkl', 'rb'))\n",
        "    subwords = []\n",
        "    for word in text.split():\n",
        "        for subword in merges:\n",
        "            subword_count = word.count(subword)\n",
        "            if subword_count > 0:\n",
        "                word = word.replace(subword, ' ')\n",
        "                subwords.extend([subword]*subword_count)\n",
        "    return ' '.join(subwords)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBRcm0YSL0lv",
        "colab_type": "code",
        "outputId": "89273a58-cbe8-42e1-94cc-2ccf5fbdab8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "split_into_subwords('this is Chetna here')"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'is th is na re'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q2ztiSbWL3-G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_preprocessed_subwords = {k: [split_into_subwords(sentence) for sentence in v] for k, v in data_preprocessed.items()}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3BYqsF6NMBIz",
        "colab_type": "code",
        "outputId": "2dc96264-d09f-4a01-ccf4-861df5df5232",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "show_statistics(data_preprocessed_subwords)"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Language: sk\n",
            "-----------------------\n",
            "Number of sentences\t:\t 100\n",
            "Number of words\t\t:\t 3431\n",
            "Number of unique words\t:\t 75\n",
            "Sample extract\t\t:\t de an de al as ne as...\n",
            "\n",
            "Language: cs\n",
            "-----------------------\n",
            "Number of sentences\t:\t 10\n",
            "Number of words\t\t:\t 239\n",
            "Number of unique words\t:\t 59\n",
            "Sample extract\t\t:\t po je me or že je le...\n",
            "\n",
            "Language: en\n",
            "-----------------------\n",
            "Number of sentences\t:\t 100\n",
            "Number of words\t\t:\t 3863\n",
            "Number of unique words\t:\t 75\n",
            "Sample extract\t\t:\t an st an er ou ro ch...\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvissjnhMDrE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_train_subwords = []\n",
        "for sentence in sentences_train:\n",
        "    data_train_subwords.append(split_into_subwords(sentence))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1JykwAlMF3-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_val_subwords = []\n",
        "for sentence in sentences_val:\n",
        "    data_val_subwords.append(split_into_subwords(sentence))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7h3oZGzLMH7I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vectorizer = CountVectorizer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEeGnI3vMJx9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = vectorizer.fit_transform(data_train_subwords)\n",
        "X_val = vectorizer.transform(data_val_subwords)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xiQIOJn6ML9G",
        "colab_type": "code",
        "outputId": "0d269386-1adb-4e24-cd98-3159f56fd4ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "naive_classifier_2 = MultinomialNB(fit_prior=False)\n",
        "naive_classifier_2.fit(X_train, y_train)"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4EFFrjgMRln",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = naive_classifier_2.predict(X_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PTPL7twMWJz",
        "colab_type": "code",
        "outputId": "4f1dab78-f00d-4320-aafa-c42014ba0876",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        }
      },
      "source": [
        "plot_confusion_matrix(y_val, predictions, ['sk', 'cs', 'en'])\n",
        "f1_score(y_val, predictions, average='weighted')"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8456381060126386"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3zIl91kOuWd",
        "colab_type": "text"
      },
      "source": [
        "TESTING THE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsjKSOJUOwox",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1cdd5fb7-06af-4681-c7a0-1a18d2389eb5"
      },
      "source": [
        "text = 'okrem iného ako durič na brlohárenie'\n",
        "text = preprocess_function(text)\n",
        "text = [split_into_subwords(text)]\n",
        "text_vectorized = vectorizer.transform(text)\n",
        "naive_classifier_2.predict(text_vectorized)"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['sk'], dtype='<U2')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOlbmh_AOws5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict_language(text):\n",
        "  text = preprocess_function(text)\n",
        "  text = [split_into_subwords(text)]\n",
        "  text_vectorized = vectorizer.transform(text)\n",
        "  pred = naive_classifier_2.predict(text_vectorized)\n",
        "  if pred[0] == 'cs':\n",
        "    print(\"Czech\")\n",
        "  if pred[0] == 'sk':\n",
        "    print(\"Slovak\")\n",
        "  if pred[0] == 'en':\n",
        "    print(\"English\")\n",
        "  return pred[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-0VSZk27OOh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "d8696472-3eb3-4a4a-8c37-a35f470d876b"
      },
      "source": [
        "predict_language(\"Grapes explode when you put them in the microwave.\")"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "English\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'en'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGZ6SVIY-0j6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "6e0a1882-3e0e-4f16-9101-4f140c7cf882"
      },
      "source": [
        "predict_language(\"Slovensko bolo prvou krajinou, ktorá vynalezla padák.\") # Slovak - correctly classified"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Slovak\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'sk'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ps_x3k9D_Onf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "9bcc210b-956f-4038-da49-99576509a51d"
      },
      "source": [
        "predict_language(\"Země je kulatá.\") # Czech - correctly classified"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Czech\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cs'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCIb2Tm89wc5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# end"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}