Assignment_1_GianlucaNotaro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assignment 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import re\n",
    "import statistics\n",
    "import nltk\n",
    "import math\n",
    "import itertools\n",
    "import scipy.stats\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daten einlesen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "people_en = pd.read_csv('people_wiki_EN.csv', sep=',')\n",
    "people_de = pd.read_csv('10k-people-raw.csv', sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hilfsfunktionen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def box_plot(dist, title = None):\n",
    "    chars = [chr(c) for c in range(ord('a'), ord('z') + 1)]\n",
    "    \n",
    "    fig1, ax1 = plt.subplots(figsize = (18, 8))\n",
    "    ax1.set_xlabel('Character')\n",
    "    ax1.set_ylabel('Frequency')\n",
    "    ax1.boxplot(dist, labels = chars)\n",
    "    ax1.set_ylim([0, 0.3])\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frequency_plot(mean_en, mean_de):\n",
    "    chars = [chr(c) for c in range(ord('a'), ord('z')+1)]\n",
    "    ind = np.arange(len(chars))\n",
    "    width = 1\n",
    "    frequency = np.log((np.array(mean_en) / np.array(mean_de)))\n",
    "    color = ['g' if f > 0 else 'b' for f in frequency]\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize = (18, 8))\n",
    "    rects1 = ax.bar(ind, frequency, width, color = color, edgecolor = 'black')\n",
    "    ax.set_xticklabels(chars)\n",
    "    ax.set_xticks(ind + (width * 0.5) - 0.5)\n",
    "    ax.set_xlabel('Character')\n",
    "    ax.set_ylim([-2, 2])\n",
    "    ax.set_ylabel('F')\n",
    "    ax.set_xlim([-0.5, len(chars) - 0.5])\n",
    "    ax.text(1, 1.5, 'More frequent in English ', bbox={'facecolor':'green', 'alpha':0.5, 'pad':10})\n",
    "    ax.text(1, 1.2, 'More frequent in German', bbox={'facecolor':'blue', 'alpha':0.5, 'pad':10})\n",
    "    ax.text(5.5, 1.35, r'$F = \\ln(\\frac{mean_e}{mean_d})$', fontsize=25)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes, title = 'Confusion matrix', cmap = plt.cm.Greens):\n",
    "    plt.title(title)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation = 45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    cm_norm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    cm_norm[np.isnan(cm_norm)] = 0\n",
    "    cm_norm = np.around(cm_norm, decimals = 3)\n",
    "        \n",
    "    plt.imshow(cm_norm, interpolation='nearest', cmap = cmap)\n",
    "    \n",
    "    thresh = cm_norm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm_norm.shape[0]), range(cm_norm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j], horizontalalignment=\"center\", color=\"white\" if cm_norm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Häufigkeitsverteilungen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def character_distribution(people):\n",
    "    # Implementieren Sie eine Funktion die die Buchstabenhäufigkeiten berechnet.\n",
    "    # Die Funktion soll folgendes zurückgeben:\n",
    "    #  mean: Durchschnittlicher relative Häufigkeit pro Buchstabe \n",
    "    #  std: Relative Standardabweichung pro Buchstabe\n",
    "    #  dist: Relative Häufigkeit pro Text und pro Buchstabe \n",
    "    mean = np.ones(shape = 26)\n",
    "    std = np.ones(shape = 26)\n",
    "    dist = np.ones(shape = (len(people), 26))\n",
    "    hist = np.ones(shape = (26,len(people)))\n",
    "    \n",
    "    N = len(people)\n",
    "    letters = 'abcdefghijklmnopqrstuvwxyz'\n",
    "    for i in range(N):\n",
    "        lenPeople = len(re.sub(r\"[^a-z]+\",'',people[i]))\n",
    "        if (lenPeople > 0):\n",
    "            fdist = nltk.FreqDist(char.lower() for char in people[i] if char in letters)\n",
    "            for idx, letter in enumerate(letters):\n",
    "                percent = np.divide(fdist[letter],lenPeople)\n",
    "                dist[i,idx] = percent\n",
    "                hist[idx,i] = percent\n",
    "    for idx, letter in enumerate(letters):\n",
    "        mean[idx] = np.mean(hist[idx])\n",
    "        std[idx] = np.std(hist[idx])          \n",
    "    \n",
    "    return mean, std, dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_de, std_de, dist_de = character_distribution(people_de['text'])\n",
    "box_plot(dist_de, 'German')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_en, std_en, dist_en = character_distribution(people_en['text'])\n",
    "box_plot(dist_en, 'English')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frequency_plot(mean_en, mean_de)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spracherkennung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def determine_language(mean_de,mean_en,texts):\n",
    "    lang = []\n",
    "    N = len(texts)\n",
    "    for i in range(N):\n",
    "        errorGerman = 0\n",
    "        errorEnglish = 0\n",
    "        errorOther = 0.25\n",
    "        mean_text = np.ones(shape = 26)\n",
    "        letters = 'abcdefghijklmnopqrstuvwxyz'\n",
    "        lenText = len(re.sub(r\"[^a-z]+\",'',texts[i]))\n",
    "        if(lenText>0):\n",
    "            fdist = nltk.FreqDist(char.lower() for char in texts[i] if char in letters)\n",
    "            for idx, letter in enumerate(letters):\n",
    "                percent = np.divide(fdist[letter],lenText)\n",
    "                mean_text[idx] = percent\n",
    "        \n",
    "        for j,letter in enumerate(mean_de):\n",
    "            errorEnglish += abs(mean_text[j]-(mean_en[j]))\n",
    "            errorGerman += abs(mean_text[j]-(mean_de[j]))\n",
    "        #decision tree\n",
    "        if errorGerman < errorEnglish and errorGerman < errorOther:\n",
    "            lang.append('G')\n",
    "        elif errorEnglish < errorGerman and errorEnglish < errorOther:\n",
    "            lang.append('E')\n",
    "        else:\n",
    "            lang.append('X')\n",
    "    return lang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(cm):\n",
    "    i = np.eye(cm.shape[0])\n",
    "    return (i * cm).sum() / cm.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "people_test = pd.read_csv('Language_test.csv', sep=';', quotechar='|')\n",
    "lang_true = np.array(people_test['language'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "lang = determine_language(mean_de,mean_en,people_test['text'])\n",
    "cm = confusion_matrix(lang_true, lang)\n",
    "print('Accuracy:', accuracy(cm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cm, ['English', 'German', 'X'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
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  "toc": {
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   "nav_menu": {},
   "number_sections": true,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
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  "varInspector": {
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     "varRefreshCmd": "print(var_dic_list())"
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     "delete_cmd_postfix": ") ",
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     "varRefreshCmd": "cat(var_dic_list()) "
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   "types_to_exclude": [
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