{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementation of Word-Embeddings\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are different options to work with Word-Embeddings:\n",
    "1. Trained Word-Embeddings can be downloaded from the web. These Word-Embeddings differ in\n",
    "    * the method, e.g. Skipgram, CBOW, GloVe, fastText \n",
    "    * in the hyperparameter applied for the selected method, e.g. context-length\n",
    "    * in the corpus, which has been applied for training\n",
    "2. By applying packages such as [gensim](https://radimrehurek.com/gensim/) word-embeddings can easily be trained from an arbitrary collection of texts. \n",
    "3. Training of a word embedding can be integrated into an end-to-end neural network for a specific application. For example, if a Deep-Nerual-Network shall be learned for document-classification, the first layer in this network can be defined, such that it learns a task-specific word-embedding from the given document-classification-training-data.\n",
    "\n",
    "In this notebook option 1 and 2 are demonstrated. Option 3 is applied in a later lecture.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply Pre-Trained Word-Embeddings\n",
    "### FastText\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [FastText project](https://fasttext.cc) provides word-embeddings for 157 different languages, trained on [Common Crawl](https://commoncrawl.org/) and [Wikipedia](https://www.wikipedia.org/). These word embeddings can easily be downloaded and imported to Python. The `KeyedVectors`-class of [gensim](https://radimrehurek.com/gensim/) can be applied for the import. This class also provides many useful tools, e.g. an index to fastly find the vector of an arbitrary word or function to calculate similarities between word-vectors. Some of these tools will be demonstrated below: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After downloading word embeddings from [FastText](https://fasttext.cc/docs/en/english-vectors.html) they can be imported into a `KeyedVectors`-object from gensim as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install numpy==1.20.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.20.1'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gensim.models import KeyedVectors\n",
    "import numpy as np\n",
    "import warnings\n",
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the model\n",
    "#en_model = KeyedVectors.load_word2vec_format('/Users/maucher/DataSets/Gensim/FastText/Gensim/FastText/wiki-news-300d-1M.vec')\n",
    "#en_model = KeyedVectors.load_word2vec_format(r'C:\\Users\\maucher\\DataSets\\Gensim\\Data\\Fasttext\\wiki-news-300d-1M.vec\\wiki-news-300d-1M.vec') #path on surface\n",
    "#en_model = KeyedVectors.load_word2vec_format('/Users/maucher/DataSets/Gensim/FastText/fasttextEnglish300.vec')\n",
    "en_model = KeyedVectors.load_word2vec_format('/Users/johannes/DataSets/Gensim/FastText/fasttextEnglish300.vec') # path on iMAC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of vectors and their length can be accessed as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Tokens: 999994\n",
      "Dimension of a word vector: 300\n"
     ]
    }
   ],
   "source": [
    "# Printing out number of tokens available\n",
    "print(\"Number of Tokens: {}\".format(en_model.vectors.shape[0]))\n",
    "\n",
    "# Printing out the dimension of a word vector \n",
    "print(\"Dimension of a word vector: {}\".format(en_model.vectors.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first 20 words in the index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[',',\n",
       " 'the',\n",
       " '.',\n",
       " 'and',\n",
       " 'of',\n",
       " 'to',\n",
       " 'in',\n",
       " 'a',\n",
       " '\"',\n",
       " ':',\n",
       " ')',\n",
       " 'that',\n",
       " '(',\n",
       " 'is',\n",
       " 'for',\n",
       " 'on',\n",
       " '*',\n",
       " 'with',\n",
       " 'as',\n",
       " 'it']"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_model.wv.index2word[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first 10 components of the word-vector for *evening*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.0219,  0.0138, -0.0924, -0.0028, -0.0823, -0.1428,  0.0269,\n",
       "       -0.0193,  0.0447,  0.0336], dtype=float32)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_model[\"evening\"][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first 10 components of the word-vector for *morning*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.0025,  0.0429, -0.1727,  0.0185, -0.0414, -0.1486,  0.0326,\n",
       "       -0.0501,  0.1374, -0.1151], dtype=float32)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_model[\"morning\"][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The similarity between *evening* and *morning*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8645973"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity = en_model.similarity('morning', 'evening')\n",
    "similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 20 words, which are most similar to word *wood*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('timber', 0.7636732459068298),\n",
       " ('lumber', 0.7316348552703857),\n",
       " ('kiln-dried', 0.7024550437927246),\n",
       " ('wooden', 0.6998946666717529),\n",
       " ('oak', 0.674289345741272),\n",
       " ('plywood', 0.6731638312339783),\n",
       " ('hardwood', 0.6648495197296143),\n",
       " ('woods', 0.6632275581359863),\n",
       " ('pine', 0.654842734336853),\n",
       " ('straight-grained', 0.6503476500511169),\n",
       " ('wood-based', 0.6416549682617188),\n",
       " ('firewood', 0.6402209997177124),\n",
       " ('iroko', 0.6389516592025757),\n",
       " ('metal', 0.6362859606742859),\n",
       " ('timbers', 0.6347957849502563),\n",
       " ('quartersawn', 0.6330605149269104),\n",
       " ('Wood', 0.6307631731033325),\n",
       " ('forest', 0.6296596527099609),\n",
       " ('end-grain', 0.6279916763305664),\n",
       " ('furniture', 0.6257956624031067)]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_model.most_similar(\"wood\",topn=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GloVe\n",
    "As described [before](05representations.md) GloVe constitutes another method for calculating Word-Embbedings. Pre-trained GloVe vectors can be downloaded from\n",
    "[Glove](https://nlp.stanford.edu/projects/glove/) and imported into Python. However, gensim already provides a downloader for several word-embeddings, including GloVe embeddings of different length and different training-data. \n",
    "\n",
    "The corpora and embeddings, which are available via the gensim downloader, can be queried as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim.downloader as api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'corpora': ['semeval-2016-2017-task3-subtaskBC',\n",
       "  'semeval-2016-2017-task3-subtaskA-unannotated',\n",
       "  'patent-2017',\n",
       "  'quora-duplicate-questions',\n",
       "  'wiki-english-20171001',\n",
       "  'text8',\n",
       "  'fake-news',\n",
       "  '20-newsgroups',\n",
       "  '__testing_matrix-synopsis',\n",
       "  '__testing_multipart-matrix-synopsis'],\n",
       " 'models': ['fasttext-wiki-news-subwords-300',\n",
       "  'conceptnet-numberbatch-17-06-300',\n",
       "  'word2vec-ruscorpora-300',\n",
       "  'word2vec-google-news-300',\n",
       "  'glove-wiki-gigaword-50',\n",
       "  'glove-wiki-gigaword-100',\n",
       "  'glove-wiki-gigaword-200',\n",
       "  'glove-wiki-gigaword-300',\n",
       "  'glove-twitter-25',\n",
       "  'glove-twitter-50',\n",
       "  'glove-twitter-100',\n",
       "  'glove-twitter-200',\n",
       "  '__testing_word2vec-matrix-synopsis']}"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.info(name_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We select the GloVe word-embeddings `glove-wiki-gigaword-100` for download: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_vectors = api.load(\"glove-wiki-gigaword-100\")  # load pre-trained word-vectors from gensim-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gensim.models.keyedvectors.Word2VecKeyedVectors"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(word_vectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen in the previous output, the downloaded data is available as a `KeyedVectors`-object. Hence the same methods can now be applied as in the case of the FastText - Word Embedding in the previous section. In the sequel we will apply not only the methods used above, but also new ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Word analogy questions like *man is to king as woman is to ?* can be solved as in the code cell below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "queen: 0.7699\n"
     ]
    }
   ],
   "source": [
    "result = word_vectors.most_similar(positive=['woman', 'king'], negative=['man'])\n",
    "print(\"{}: {:.4f}\".format(*result[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outliers within sets of words can be determined as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cereal\n"
     ]
    }
   ],
   "source": [
    "print(word_vectors.doesnt_match(\"breakfast cereal dinner lunch\".split()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similiarity between a pair of words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8323494\n"
     ]
    }
   ],
   "source": [
    "similarity = word_vectors.similarity('woman', 'man')\n",
    "print(similarity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most similar words to *cat*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('dog', 0.8798074722290039),\n",
       " ('rabbit', 0.7424427270889282),\n",
       " ('cats', 0.7323004007339478),\n",
       " ('monkey', 0.7288710474967957),\n",
       " ('pet', 0.7190139293670654),\n",
       " ('dogs', 0.7163873314857483),\n",
       " ('mouse', 0.6915251016616821),\n",
       " ('puppy', 0.6800068616867065),\n",
       " ('rat', 0.6641027331352234),\n",
       " ('spider', 0.6501134634017944),\n",
       " ('elephant', 0.6372530460357666),\n",
       " ('boy', 0.6266894340515137),\n",
       " ('bird', 0.6266419887542725),\n",
       " ('baby', 0.6257247924804688),\n",
       " ('pig', 0.6254673004150391),\n",
       " ('horse', 0.6251551508903503),\n",
       " ('snake', 0.6227242350578308),\n",
       " ('animal', 0.6200780272483826),\n",
       " ('dragon', 0.6187658309936523),\n",
       " ('duck', 0.6158087253570557)]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_vectors.most_similar(\"cat\",topn=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarity between sets of words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7067\n"
     ]
    }
   ],
   "source": [
    "sim = word_vectors.n_similarity(['sushi', 'shop'], ['japanese', 'restaurant'])\n",
    "print(\"{:.4f}\".format(sim))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First 10 components of word vector for *computer*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100,)\n",
      "[-0.16298   0.30141   0.57978   0.066548  0.45835  -0.15329   0.43258\n",
      " -0.89215   0.57747   0.36375 ]\n"
     ]
    }
   ],
   "source": [
    "vector = word_vectors['computer']  # numpy vector of a word\n",
    "print(vector.shape)\n",
    "print(vector[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The magnitude of the previous word-vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.529161"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.sum(np.square(vector)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen in the previous code cell the vectors are not normalized to unique length. However, if the argument `use_norm` is enabled, the resulting vectors are normalized:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100,)\n",
      "[-0.01455544 -0.13056442  0.06381373 -0.00747831  0.10621653  0.02454428\n",
      " -0.08777763  0.1584893   0.0725054   0.08593655]\n"
     ]
    }
   ],
   "source": [
    "vector = word_vectors.word_vec('office', use_norm=True)\n",
    "print(vector.shape)\n",
    "print(vector[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(np.sum(np.square(vector)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation of Word-Vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Typical lengths of DSM word vectors are in the range between 50 and 300. In the FastText example above vectors of length 300 have been applied. The applied GloVe vectors had a length of 100. In any case they can not directly be visualised. However, methods to reduce the dimensionality of vectors in such a way, that their overall spatial distribution is maintained as much as possible can be applied to transform word vectors into 2-dimensional space. In the code cells below this is demonstrated by applying **TSNE**, the most prominent technique to transform word-vectors into 2-dimensional space: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsneModel=TSNE(n_components=2,random_state=0)\n",
    "np.set_printoptions(suppress=True)\n",
    "model2d=tsneModel.fit_transform(word_vectors[word_vectors.index2word[300:600]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib inline\n",
    "plt.figure(figsize=(19,14))\n",
    "idx=0\n",
    "for a in model2d[:300]:\n",
    "    w=word_vectors.wv.index2word[300+idx]\n",
    "    plt.plot(a[0],a[1],'r.')\n",
    "    plt.text(a[0],a[1],w)\n",
    "    idx+=1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This simple visualisation already indicates, that the vectors of similar words are closer to each other, than the vectors of unrelated words."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Word Embedding\n",
    "In this section it is demonstrated how [gensim](https://radimrehurek.com/gensim/) can be applied to train a Word2Vec (either CBOW or Skipgram) embedding from an arbitrary corpus. In this demo the applied training corpus is the complete English Wikipedia dump.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download and Extract Wikipedia Dump"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wikipedia dumps can be downloaded from [here](https://dumps.wikimedia.org/other/wikibase/wikidatawiki/). After downloading the dump the most convenient way to extract and clean the text is to apply the [WikiExtractor](https://github.com/attardi/wikiextractor). This tool generates plain text from a Wikipedia database dump, discarding any other information or annotation present in Wikipedia pages, such as images, tables, references and lists.\n",
    "The output is stored in a number of files of similar size in a given directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The class `MySentences`, as defined in the following code-cell, extracts from all directories and files under `dirnameP` the sentences in a format, which can be processed by the applied gensim model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,logging\n",
    "from gensim.models import word2vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MySentences(object):\n",
    "    def __init__(self, dirnameP):\n",
    "        self.dirnameP = dirnameP\n",
    " \n",
    "    def __iter__(self):\n",
    "        for subdir in os.listdir(self.dirnameP):\n",
    "            print(subdir)\n",
    "            if subdir==\".DS_Store\":\n",
    "                continue\n",
    "            subdirpath=os.path.join(self.dirnameP,subdir)\n",
    "            print(subdirpath)\n",
    "            for fname in os.listdir(subdirpath):\n",
    "                if fname[:4]==\"wiki\":\n",
    "                    for line in open(os.path.join(subdirpath, fname)):\n",
    "                        linelist=line.split()\n",
    "                        if len(linelist)>3 and linelist[0][0]!=\"<\":\n",
    "                            yield [w.lower().strip(\",.\"\" \\\" () :; ! ?\") for w in linelist]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The path to the directory, which contains the entire extracted Wikipedia dump is configured and the subdirectories under this path are listed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AX', 'BW', 'DN', 'DI', 'BP', 'BY', 'AV', '.DS_Store', 'DG', 'AQ', 'DU', 'BL', 'AC', 'BK', 'DR', 'AD', 'AM', 'BB', 'AJ', 'BE', 'DF', 'AP', 'BX', 'DA', 'AW', 'DH', 'BQ', 'AY', 'BV', 'DO', 'AK', 'BD', 'AL', 'DZ', 'BC', 'BJ', 'DS', 'AE', 'DT', 'BM', 'AB', 'EY', 'CG', 'EW', 'CN', 'CI', 'EP', 'EB', 'EE', 'CU', 'EL', 'FC', 'EK', 'CR', 'FD', 'CH', 'EQ', 'EV', 'CO', 'CF', 'EX', 'CA', 'EJ', 'CS', 'FE', 'CT', 'EM', 'FB', 'ED', 'CZ', 'EC', 'BH', 'DQ', 'AG', 'DV', 'BO', 'AI', 'BF', 'AN', 'DX', 'BA', 'DJ', 'BS', 'BT', 'DM', 'DD', 'AR', 'BZ', 'DC', 'AU', 'AO', 'DY', 'AH', 'BG', 'DW', 'BN', 'AA', 'BI', 'DP', 'AF', 'DB', 'AT', 'DE', 'AS', 'AZ', 'BU', 'DL', 'DK', 'BR', 'EF', 'CX', 'EA', 'EH', 'CQ', 'CV', 'EO', 'CD', 'EZ', 'CC', 'CJ', 'ES', 'ET', 'CM', 'CW', 'EN', 'FA', 'EI', 'CP', 'FF', 'CY', 'EG', 'EU', 'CL', 'CK', 'ER', 'CB', 'CE']\n"
     ]
    }
   ],
   "source": [
    "#parentDir=\"C:\\\\Users\\\\maucher\\\\DataSets\\\\Gensim\\\\Data\\\\wiki_dump_extracted\"\n",
    "parentDir=\"/Users/johannes/DataSets/wikiextractor/text\" #path on iMAC\n",
    "#parentDir=\"C:\\Users\\Johannes\\DataSets\\Gensim\\Data\\wiki_dump_extracted\"\n",
    "dirlistParent= os.listdir(parentDir)\n",
    "print(dirlistParent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training or Loading of a CBOW model\n",
    "In the following code cell a name for the word2vec-model is specified. If the specified directory already contains a model with the specified name, it is loaded. Otherwise, it is generated and saved under the specified name. A **skipgram-model** can be generated in the same way. In this case `model = word2vec.Word2Vec(sentences,size=200,sorted_vocab=1)` has to be replaced by `model = word2vec.Word2Vec(sentences,size=200,sorted_vocab=1,sg=1)`. \n",
    "See [gensim model.Word2Vec documentation](https://radimrehurek.com/gensim/models/word2vec.html) for the configuration of more parameters. \n",
    "\n",
    "> Note that the training of this model takes several hours. If you like to generate a much smaller model from a smaller corpus (English!) you can download the text8 corpus from [http://mattmahoney.net/dc/text8.zip](http://mattmahoney.net/dc/text8.zip), extract it and replace the code in the following code-cell by this:\n",
    "```\n",
    "sentences = word2vec.Text8Corpus('C:\\\\Users\\\\maucher\\\\DataSets\\\\Gensim\\\\Data\\\\text8')\n",
    "model = word2vec.Word2Vec(sentences,size=200)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Already existing model is loaded\n"
     ]
    }
   ],
   "source": [
    "modelName=\"/Users/johannes/DataSets/wikiextractor/models/wikiEng20201007.model\"\n",
    "try:\n",
    "    model=word2vec.Word2Vec.load(modelName)\n",
    "    print(\"Already existing model is loaded\")\n",
    "except:\n",
    "    print(\"Model doesn't exist. Training of word2vec model started.\")\n",
    "    sentences = MySentences(parentDir) # a memory-friendly iterator\n",
    "    model = word2vec.Word2Vec(sentences,size=200,sorted_vocab=1)\n",
    "model.init_sims(replace=True)\n",
    "model.save(modelName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gensim.models.word2vec.Word2Vec"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code cell above the Word2Vec model has either been created or loaded. For the returned object of type `Word2Vec` basically the same functions are available as for the pretrained FastText and GloVe word embeddings in the sections above. \n",
    "\n",
    "For example the most similar words for *cat* are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('dog', 0.8456131219863892),\n",
       " ('rabbit', 0.796753466129303),\n",
       " ('monkey', 0.742573082447052),\n",
       " ('kitten', 0.7295641899108887),\n",
       " ('pug', 0.7040250301361084),\n",
       " ('dachshund', 0.7017481327056885),\n",
       " ('poodle', 0.7012854814529419),\n",
       " ('cats', 0.6982499361038208),\n",
       " ('rat', 0.6904729604721069),\n",
       " ('mouse', 0.6898518800735474),\n",
       " ('rottweiler', 0.6748534440994263),\n",
       " ('pet', 0.6683299541473389),\n",
       " ('puppy', 0.666095495223999),\n",
       " ('goat', 0.6655623912811279),\n",
       " ('doll', 0.6638575792312622),\n",
       " ('pig', 0.6573764085769653),\n",
       " ('scaredy', 0.6482968330383301),\n",
       " ('parrot', 0.6454174518585205),\n",
       " (\"cat's\", 0.6422038078308105),\n",
       " ('feline', 0.6345723271369934)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar(\"cat\",topn=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the trained Word2Vec-model also parameters, which describe training, corpus and the model itself, can be accessed, as demonstrated below: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of words in the corpus used for training the model:  38539221\n",
      "Number of words in the model:  2366981\n",
      "Time [s], required for training the model:  10237.432892589999\n",
      "Count of trainings performed to generate this model:  1\n",
      "Length of the word2vec vectors:  200\n",
      "Applied context length for generating the model:  5\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of words in the corpus used for training the model: \",model.corpus_count)\n",
    "print(\"Number of words in the model: \",len(model.wv.index2word))\n",
    "print(\"Time [s], required for training the model: \",model.total_train_time)\n",
    "print(\"Count of trainings performed to generate this model: \",model.train_count)\n",
    "print(\"Length of the word2vec vectors: \",model.vector_size)\n",
    "print(\"Applied context length for generating the model: \",model.window)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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