preprocessing_test.ipynb

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  "language_info": {
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    "name": "ipython",
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"./data/wikipedia.csv\")\n",
    "# clean out where text is NaN\n",
    "data = data[data.text.notna()]"
   ]
  },
  {
   "source": [
    "## anaylze data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                                text  label\n",
       "0  Brot (ahd. prôt, von urgerm. *brauda-) ist ein...      0\n",
       "1  Das feste, dunkle Äußere des Brotes heißt Krus...      0\n",
       "2  Das weiche, lockere Innere des Brotes ist die ...      0\n",
       "3  Brotkrümel heißen auch Brosamen (aus dem Mitte...      0\n",
       "4  Der zu backende Teig besteht aus Mehl, Salz, W...      0"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>text</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Brot (ahd. prôt, von urgerm. *brauda-) ist ein...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Das feste, dunkle Äußere des Brotes heißt Krus...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Das weiche, lockere Innere des Brotes ist die ...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Brotkrümel heißen auch Brosamen (aus dem Mitte...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Der zu backende Teig besteht aus Mehl, Salz, W...</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(360, 2)"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Class distribution')"
      ]
     },
     "metadata": {},
     "execution_count": 34
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
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     }
    }
   ],
   "source": [
    "_x=data.label.value_counts()\n",
    "sns.barplot(_x.index,_x).set_title(\"Class distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# num of characters per entry and category\n",
    "# ERROR\n",
    "# axs = (ax1,ax2,ax3,ax4,ax5)\n",
    "fig,axs=plt.subplots(1,5,figsize=(30,5))\n",
    "\n",
    "for ax, label in zip(axs, data.label.unique()):\n",
    "    sentence_length=data.text[data.label==label].str.len()\n",
    "    ax.hist(sentence_length, color='blue')\n",
    "    ax.set_title(label)\n",
    "fig.suptitle('Characters in paragraphs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import  Counter\n",
    "\n",
    "def create_corpus(target):\n",
    "    corpus=[]\n",
    "    \n",
    "    for x in data[data['label']==target]['text'].str.split():\n",
    "        for i in x:\n",
    "            corpus.append(i)\n",
    "    return corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 57
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# find most common words\n",
    "corpus=create_corpus(0)\n",
    "\n",
    "counter = Counter(corpus)\n",
    "most=counter.most_common()\n",
    "x = [_m[1] for _m in most[:20]]\n",
    "y = [_m[0] for _m in most[:20]]\n",
    "\n",
    "sns.barplot(x=x, y=y)"
   ]
  },
  {
   "source": [
    "## word vectorization"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### GloVe (to-do)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_corpus(data):\n",
    "    corpus=[]\n",
    "    for text in tqdm(data.text):\n",
    "        words=[word.lower() for word in word_tokenize(tweet)]"
   ]
  },
  {
   "source": [
    "### Spacy"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# en_core_web_sm\n",
    "# de_core_news_sm -> efficiency\n",
    "    # can be sm, md, lg\n",
    "# de_dep_news_trf -> accuracy\n",
    "nlp = spacy.load('de_core_news_lg') # search for german"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example for word vector\n",
    "test_text = [\"Müssten wir nur noch in Lemmas sprechen\", \"hallo\"]\n",
    "\n",
    "# lemmas = [token.lemma_ for token in doc]\n",
    "def normalize(text):\n",
    "    doc = nlp(text)\n",
    "    lemma_text = ''.join([token.lemma_ + token.whitespace_ for token in doc]).strip()\n",
    "    lemma_doc = nlp(lemma_text)\n",
    "    lower_text = ''.join([token.lower_ + token.whitespace_ for token in lemma_doc]).strip()\n",
    "    return nlp(lower_text)\n",
    "\n",
    "# print(lemma_doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with nlp.disable_pipes():\n",
    "    vectors = np.array([token.vector for token in nlp(text_txt)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example for document vector\n",
    "with nlp.disable_pipes():\n",
    "    doc_vectors = np.array([nlp(text).vector for text in data.text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(359, 300)"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "doc_vectors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.9595646084885757\n"
     ]
    }
   ],
   "source": [
    "text1 = 'Die Äpfel sind rot'\n",
    "text2 = 'Die aepfel wäre rot'\n",
    "\n",
    "doc1 = normalize(text1)\n",
    "doc2 = normalize(text2)\n",
    "\n",
    "\n",
    "print(doc1.similarity(doc2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = nlp(\"Der Brauch, Brot und Salz etwa zum Bezug einer neuen Wohnung oder zur Hochzeit zu schenken, soll Wohlstand sichern.\")\n",
    "b = nlp(\"Die zunehmende Sammelleidenschaft für historische Landmaschinen in der Bevölkerung zog auch die Ausgabe diverser Fachzeitschriften nach sich, die nach langer Dominanz klassischer Vereinsnachrichten den Markt um professionelle journalistische Produkte bereicherten. Diese Publikationen sind meist im Bahnhofsbuchhandel erhältlich. Sie informieren im Zeitschriftenformat und in Farbe über Restaurierungsprojekte, Vereinsaktivitäten, Schleppertreffen und vieles mehr.\")\n",
    "c = nlp(\"„Brot wird ganz oder teilweise aus Getreide und/oder Getreideerzeugnissen, meist nach Zugabe von Flüssigkeit, sowie von anderen Lebensmitteln (z. B. Leguminosen-, Kartoffelerzeugnisse) in der Regel durch Kneten, Formen, Lockern, Backen oder Heißextrudieren des Brotteiges hergestellt. Brot enthält weniger als 10 Gewichtsteile Fett und/oder Zuckerarten auf 90 Gewichtsteile Getreide und/oder Getreideerzeugnisse.\")\n",
    "d = nlp(\"Um Unfälle mit landwirtschaftlichen Zugmaschinen abzuschwächen oder ganz zu vermeiden, empfiehlt die UDV eine bessere Schulung junger Traktorfahrer. Ferner sollten alle Verkehrsteilnehmer besser über das besondere Unfallrisiko mit Schleppern aufgeklärt werden. Das Signalbild von Traktoren mit und ohne Anhänger müsse verbessert werden, beispielsweise durch die Zulassung von Rundum-Leuchten, Reflexfolien, Begrenzungsleuchten und Konturmarkierungen. Heckleuchten und Blinker sollten sowohl größer als auch stabiler sein.\")\n",
    "# doc = nlp(\"Hallo Google\")\n",
    "# print(doc.ents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Der Brauch, Brot und Salz etwa zum Bezug einer neuen Wohnung oder zur Hochzeit zu schenken, soll Wohlstand sichern.</div></span>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "from spacy import displacy\n",
    "displacy.render(a, style=\"ent\",jupyter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{}\n"
     ]
    }
   ],
   "source": [
    "doc = nlp(\"Google ist eine Firma die von Heinz gegründet wurde\")\n",
    "print(doc.cats)\n",
    "#  for word in doc:\n",
    "#     print(word.head)\n"
   ]
  },
  {
   "source": [
    "## preprocessing ideas\n",
    "\n",
    "plan for now:\n",
    "- make everything lower-case\n",
    "\n",
    "following could be usefull\n",
    "- remove puntuation\n",
    "- transform every word to base form\n",
    "- remove stopwords\n",
    "- maybe remove links, tags, emojis etc (data cleaning)\n",
    "- spelling correction"
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}