From 8f90c638d8534d33ffe451e9feac8e0d21a78c31 Mon Sep 17 00:00:00 2001 From: Carsten Date: Mon, 7 May 2018 18:16:01 +0200 Subject: [PATCH] weitere anpassungen, funktionierender gesamtstand --- Carsten_Solutions/Exercise 1.ipynb | 574 ++++++++++++++++++++++------- 1 file changed, 443 insertions(+), 131 deletions(-) diff --git a/Carsten_Solutions/Exercise 1.ipynb b/Carsten_Solutions/Exercise 1.ipynb index f4c77c0..c67b136 100644 --- a/Carsten_Solutions/Exercise 1.ipynb +++ b/Carsten_Solutions/Exercise 1.ipynb @@ -4,12 +4,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Exercise 1" + "# Exercise 1\n", + "Solution by:\n", + "Carsten Draschner \n", + "2719095\n", + "\n", + "Following Instructions: \n", + "https://github.com/SmartDataAnalytics/MA-INF-4222-NLP-Lab/blob/master/2018_SoSe/exercises/Task01_Instructions.ipynb" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 83, "metadata": { "collapsed": true }, @@ -25,12 +31,12 @@ "metadata": {}, "source": [ "## Classifiers\n", - "note: for model1 and model3 you can try different classifiers: Hidden Markov Model, Logistic Regression, Maximum Entropy Markov Models, Decision Trees, Naive Bayes, etc.. __choose one!__" + "**Decision Tree** import from skikit learn" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 84, "metadata": { "collapsed": true }, @@ -45,19 +51,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 1. model1 = your POS tagger model (english)" + "### 1. model1 = your POS tagger model (english)\n", + "for a words defined by its in dex with the given sentences a feature vector fot this word will be determinded" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'word': 'bims', 'length': 4, 'is_capitalized': False, 'prefix-1': 'b', 'suffix-1': 's', 'prev_word': 'i', 'next_word': 'der', 'kindOfCamelCase': False, 'includesSpace': False}\n" + "defined own feature model\n" ] } ], @@ -72,10 +79,13 @@ " 'prev_word': '' if index == 0 else sentence[index - 1],\n", " 'next_word': '' if index == len(sentence) - 1 else sentence[index + 1],\n", " 'kindOfCamelCase': sentence[index][1:].lower() != sentence[index][1:],\n", - " 'includesSpace': True if ((' ') in sentence[index]) else False #depemds on tokenizer\n", + " 'includesSpace': True if ((' ') in sentence[index]) else False, #depemds on tokenizer\n", + " 'containsNumber': sum(str(i) in (sentence[index]) for i in range(10))>0,\n", + " 'prefix-2': sentence[index][1] if len(sentence[index])>1 else \"-1\",\n", + " 'suffix-2': sentence[index][-2] if len(sentence[index])>1 else \"-1\"\n", " }\n", - "\n", - "print(features(\"halli hallo i bims der Programmierer\".strip().split(\" \"), 3))" + "print(\"defined own feature model\")\n", + "#print(features(\"halli hallo i bims der Programmierer\".strip().split(\" \"), 3))" ] }, { @@ -87,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 86, "metadata": { "collapsed": true }, @@ -112,13 +122,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"#used from description for RegexpTagger\\npatterns = [(r'.*ing$', 'VBG'), (r'.*ed$', 'VBD'), (r'.*es$', 'VBZ'), (r'.*ould$', 'MD'), (r'.*'s$', 'NN$'), \\n (r'.*s$', 'NNS'), (r'^-?[0-9]+(.[0-9]+)?$', 'CD'), (r'.*', 'NN')]\\n\\n#train taggers\\ndef_model = nltk.DefaultTagger('NN')\\nregexp_model = nltk.RegexpTagger(patterns)\\nuni_model = nltk.UnigramTagger(training_sentences_X1)\\nbi_model = nltk.BigramTagger(training_sentences_X1)\\ntri_model = nltk.TrigramTagger(training_sentences_X1)\"" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#used from description for RegexpTagger\n", + "#see Task 1.3 and 1.6\n", + "\n", + "'''#used from description for RegexpTagger\n", "patterns = [(r'.*ing$', 'VBG'), (r'.*ed$', 'VBD'), (r'.*es$', 'VBZ'), (r'.*ould$', 'MD'), (r'.*\\'s$', 'NN$'), \n", " (r'.*s$', 'NNS'), (r'^-?[0-9]+(.[0-9]+)?$', 'CD'), (r'.*', 'NN')]\n", "\n", @@ -127,7 +148,7 @@ "regexp_model = nltk.RegexpTagger(patterns)\n", "uni_model = nltk.UnigramTagger(training_sentences_X1)\n", "bi_model = nltk.BigramTagger(training_sentences_X1)\n", - "tri_model = nltk.TrigramTagger(training_sentences_X1)" + "tri_model = nltk.TrigramTagger(training_sentences_X1)'''" ] }, { @@ -139,12 +160,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "metadata": { "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#see Task 2.1" + ] }, { "cell_type": "markdown", @@ -155,12 +178,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "metadata": { "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#see Task 2.2" + ] }, { "cell_type": "markdown", @@ -179,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -188,13 +213,15 @@ "text": [ "[nltk_data] Downloading package treebank to\n", "[nltk_data] /Users/Carsten/nltk_data...\n", - "[nltk_data] Package treebank is already up-to-date!\n" + "[nltk_data] Package treebank is already up-to-date!\n", + "downloaded treebank\n" ] } ], "source": [ "nltk.download('treebank')\n", - "x1 = nltk.corpus.treebank" + "x1 = nltk.corpus.treebank\n", + "print(\"downloaded treebank\")" ] }, { @@ -206,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -214,13 +241,15 @@ "output_type": "stream", "text": [ "[nltk_data] Downloading package brown to /Users/Carsten/nltk_data...\n", - "[nltk_data] Package brown is already up-to-date!\n" + "[nltk_data] Package brown is already up-to-date!\n", + "downloaded brown\n" ] } ], "source": [ "nltk.download('brown')\n", - "x2 = nltk.corpus.brown" + "x2 = nltk.corpus.brown\n", + "print(\"downloaded brown\")" ] }, { @@ -232,7 +261,38 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 92, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#? nltk.corpus.ConllCorpusReader" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "downloaded german tiger corpus\n" + ] + } + ], + "source": [ + "# TODO: loading german corpus \n", + "X3 = nltk.corpus.ConllCorpusReader(root='/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/german/', fileids=['tiger_release_aug07.corrected.16012013.conll09'], columntypes=['ignore', 'words', 'ignore', 'ignore', 'pos'], encoding='utf-8')\n", + "german_tagged_sents = X3.tagged_sents()\n", + "print(\"downloaded german tiger corpus\")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -241,7 +301,7 @@ "'#import pandas as pd\\n#df = pd.read_table(\"/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/croatia/set.hr.conll\")\\n#df.head()\\n\\n#x3 = other language\\n#from croatia:\\n#by ZˇeljkoAgic ́,⋆NikolaLjubesˇic ́ http://www.lrec-conf.org/proceedings/lrec2014/pdf/690_Paper.pdf\\n#licenses: https://creativecommons.org/licenses/by-sa/4.0/\\ncorp = nltk.corpus.ConllCorpusReader(root=\"/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/croatia/\", fileids=[\"set.hr.conll\"], columntypes=(\\'ignore\\',\\'ignore\\',\\'pos\\',\\'ignore\\',\\'ignore\\',\\'ignore\\',\\'ignore\\',\\'ignore\\',\\'ignore\\',\\'ignore\\'))\\nprint(corp.tagged_sents[-100])\\n#from croatia:\\n#by ZˇeljkoAgic ́,⋆NikolaLjubesˇic ́ http://www.lrec-conf.org/proceedings/lrec2014/pdf/690_Paper.pdf\\n#licenses: https://creativecommons.org/licenses/by-sa/4.0/'" ] }, - "execution_count": 64, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -262,42 +322,6 @@ "#licenses: https://creativecommons.org/licenses/by-sa/4.0/'''\n" ] }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#? nltk.corpus.ConllCorpusReader" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "50472\n", - "[('So', 'ADV'), ('kann', 'VMFIN'), ('man', 'PIS'), ('Marsilius', 'NE'), ('von', 'APPR'), ('Padua', 'NE'), ('so', 'ADV'), ('wenig', 'ADV'), ('zu', 'APPR'), ('einem', 'ART'), ('Vorläufer', 'NN'), ('moderner', 'ADJA'), ('Volkssouveränität', 'NN'), ('machen', 'VVINF'), ('wie', 'KOKOM'), ('Rousseau', 'NE'), ('zum', 'APPRART'), ('Verkünder', 'NN'), ('eines', 'ART'), ('``', '$('), ('Zurück', 'NN'), ('zur', 'APPRART'), ('Natur', 'NN'), (\"''\", '$('), ('.', '$.')]\n" - ] - } - ], - "source": [ - "# TODO: loading german corpus \n", - "X3 = nltk.corpus.ConllCorpusReader(root='/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/german/', fileids=['tiger_release_aug07.corrected.16012013.conll09'], columntypes=['ignore', 'words', 'ignore', 'ignore', 'pos'], encoding='utf-8')\n", - "german_tagged_sents = X3.tagged_sents()\n", - "print(type(german_tagged_sents))\n", - "print(len(german_tagged_sents))\n", - "\n", - "print (german_tagged_sents[-100])" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -312,6 +336,13 @@ " * performance 1.6.x = model3.x in X2" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Generating Testdata" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -325,27 +356,26 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 95, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "#to generate trainingsdata, delete the assigned tags as a function\n", + "#to generate trainingsdata, ignore the assigned tags as a function\n", "def untag(tagged_sentence):\n", " return [w for w, t in tagged_sentence]" ] }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", "got 3131 training sentences and 783 test sentences\n" ] } @@ -365,9 +395,16 @@ "print(\"got \",len(training_sentences_X1),\" training sentences and \", len(test_sentences_X1), \" test sentences\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**transform_to_dataset** generates the input X as a list of feature dictinionaries and an output y as a list of pos tags. " + ] + }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 97, "metadata": { "collapsed": true }, @@ -385,14 +422,21 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "generated X1 (feature sets) and y1 set of teacher tags\n" + ] + } + ], "source": [ "#trainings inputset X and training teacher set y\n", - "X1, y1 = transform_to_dataset(training_sentences_X1)" + "X1, y1 = transform_to_dataset(training_sentences_X1)\n", + "print(\"generated X1 (feature sets) and y1 set of teacher tags\")" ] }, { @@ -408,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -434,14 +478,21 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "generated X2 (feature sets) and y2 set of teacher tags\n" + ] + } + ], "source": [ "#trainings inputset X and training teacher set y\n", - "X2, y2 = transform_to_dataset(training_sentences_X2)" + "X2, y2 = transform_to_dataset(training_sentences_X2)\n", + "print(\"generated X2 (feature sets) and y2 set of teacher tags\")#(X3[:3], y3[:3])" ] }, { @@ -457,14 +508,13 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 101, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", "got 40377 training sentences and 10095 test sentences\n" ] } @@ -486,12 +536,21 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 102, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "generated X3 (feature sets) and y3 set of teacher tags\n" + ] + } + ], "source": [ "#trainings inputset X and training teacher set y\n", - "X3, y3 = transform_to_dataset(training_sentences_X3)" + "X3, y3 = transform_to_dataset(training_sentences_X3)\n", + "print(\"generated X3 (feature sets) and y3 set of teacher tags\")#(X3[:3], y3[:3])" ] }, { @@ -508,15 +567,22 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "imported sktree, DictVectorizer, Pipeline\n" + ] + } + ], "source": [ "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.feature_extraction import DictVectorizer\n", - "from sklearn.pipeline import Pipeline" + "from sklearn.pipeline import Pipeline\n", + "print(\"imported sktree, DictVectorizer, Pipeline\")" ] }, { @@ -528,30 +594,44 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initialized classifier\n" + ] + } + ], "source": [ "clf = Pipeline([\n", " ('vectorizer', DictVectorizer(sparse=False)),\n", " ('classifier', DecisionTreeClassifier(criterion='entropy'))\n", - "])" + "])\n", + "print(\"Initialized classifier\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "##### Calculate performance 1.1 \n", + "#### Calculating performances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Calculate performance 1.1 - own POS tagger model with X1 = treebank\n", "* fit the decision tree for a limited amount (size) of training \n", "* test data and compare with score function on testdata" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 105, "metadata": {}, "outputs": [ { @@ -559,7 +639,7 @@ "output_type": "stream", "text": [ "training OK\n", - "Accuracy: 0.880632766106\n" + "Accuracy: 0.883077997904\n" ] } ], @@ -580,12 +660,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##### Calculate performance 1.2" + "##### Calculate performance 1.2 - pre-trained POS tagger model using NLTK (maxentropy english) with X1 = treebank" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 106, "metadata": {}, "outputs": [ { @@ -617,7 +697,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##### Calculate performance 1.3\n", + "##### Calculate performance 1.3 - rule-based classifiers with X1 = treebank\n", "1. DefaultTagger that simply tags everything with the same tag\n", "2. RegexpTagger that applies tags according to a set of regular expressions\n", "3. N-Gram (n-gram tagger is a generalization of a unigram tagger whose context is the current word together with the part-of-speech tags of the n-1 preceding token)\n", @@ -628,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -644,6 +724,17 @@ } ], "source": [ + "#used from description for RegexpTagger\n", + "patterns = [(r'.*ing$', 'VBG'), (r'.*ed$', 'VBD'), (r'.*es$', 'VBZ'), (r'.*ould$', 'MD'), (r'.*\\'s$', 'NN$'), \n", + " (r'.*s$', 'NNS'), (r'^-?[0-9]+(.[0-9]+)?$', 'CD'), (r'.*', 'NN')]\n", + "\n", + "#train taggers\n", + "def_model = nltk.DefaultTagger('NN')\n", + "regexp_model = nltk.RegexpTagger(patterns)\n", + "uni_model = nltk.UnigramTagger(training_sentences_X1)\n", + "bi_model = nltk.BigramTagger(training_sentences_X1)\n", + "tri_model = nltk.TrigramTagger(training_sentences_X1)\n", + "\n", "#evaluate taggers\n", "# performance of Default Tagger\n", "performance1_3_1 = def_model.evaluate(test_sentences_X1)\n", @@ -670,19 +761,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##### Calculate performance 1.4" + "##### Calculate performance 1.4 - own POS tagger model with X2 = brown" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "calculated perfomance 1.4= 0.75680543774\n" + "calculated perfomance 1.4= 0.772156918908\n" ] } ], @@ -698,12 +789,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##### Calculate performance 1.5" + "##### Calculate performance 1.5 - pre-trained POS tagger model using NLTK (maxentropy english) with X2 = brown" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -735,12 +826,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##### Calculate performance 1.6" + "##### Calculate performance 1.6 - rule-based classifiers with X2 = brown" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -793,16 +884,14 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 111, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "3.6.3\n", - "checking...\n", - "[('Hello', 'VBD-HL'), ('world', 'VBD'), (',', ','), ('lets', 'NNS'), ('do', 'DO'), ('something', 'PN'), ('awesome', 'NN'), ('today', 'NR'), ('!', 'CD')]\n" + "3.6.3\n" ] } ], @@ -816,7 +905,7 @@ "import platform\n", "print(platform.python_version())\n", "\n", - "print(list(pos_tag(word_tokenize('Hello world, lets do something awesome today!'))))" + "#print(list(pos_tag(word_tokenize('Hello world, lets do something awesome today!'))))" ] }, { @@ -835,7 +924,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 112, "metadata": { "scrolled": true }, @@ -856,7 +945,7 @@ "" ] }, - "execution_count": 49, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -890,9 +979,25 @@ " * pre-trained POS tagger model using RDRPOSTagger 1 or TreeTagger 2 (not english)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Calculate Performance 2.1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "again building a pipeline:\n", + "* first vectorizing the dictionary based on feature dict\n", + "* second, initializing and training the max entropy classifier decision tree" + ] + }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 113, "metadata": { "collapsed": true }, @@ -906,15 +1011,15 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 114, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "training OK\n", - "Accuracy: 0.836976962858\n" + "training done\n", + "Accuracy: 0.838839915374\n" ] } ], @@ -922,7 +1027,7 @@ "size=10000\n", "clf.fit(X3[:size], y3[:size])\n", " \n", - "print('training OK')\n", + "print('training done')\n", " \n", "X3_test, y3_test = transform_to_dataset(test_sentences_X3)\n", "\n", @@ -931,9 +1036,217 @@ "print(\"Accuracy:\", performance2_1)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Calculate Performance 2.2\n", + "* using RDRPOS Taggger in a python 3 port rom https://github.com/jacopofar/RDRPOSTagger-python-3" + ] + }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "#RDRPOSTagger port python 3 from https://github.com/jacopofar/RDRPOSTagger-python-3" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wrote file de_text.tx in cwd with each word of the sentence seperated by a space\n" + ] + } + ], + "source": [ + "#generate a german txt text file:\n", + "f = open(\"de_text.txt\", 'w')\n", + "for sentence in test_sentences_X3:\n", + " for word, tag in sentence:\n", + " f.write(word + \" \")\n", + " f.write(\"\\n\")\n", + "f.close()\n", + "\n", + "print(\"wrote file de_text.tx in cwd with each word of the sentence seperated by a space\")" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stored: /Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions\n" + ] + } + ], + "source": [ + "#to use RDRPOSTagger we have to store the path where we are working currently and where the donwnloaded RDRPOSTagger is stored\n", + "import sys, os\n", + "\n", + "#current working directory to restore it later\n", + "dir_path = os.getcwd()\n", + "print(\"stored: \", dir_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "switched to path: /Users/Carsten/Downloads/RDRPOSTagger-python-3-master/pSCRDRtagger\n" + ] + } + ], + "source": [ + "#set the rdrpos as path to work in lownloaded api\n", + "RDRPOS_TAGGER_PATH = \"/Users/Carsten/Downloads/RDRPOSTagger-python-3-master/pSCRDRtagger\"\n", + "sys.path.insert(0, RDRPOS_TAGGER_PATH)\n", + "os.chdir(RDRPOS_TAGGER_PATH)\n", + "print(\"switched to path:\", RDRPOS_TAGGER_PATH)" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Node', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'tabStr']\n" + ] + } + ], + "source": [ + "# import and rename for easier use\n", + "import RDRPOSTagger\n", + "r = RDRPOSTagger.RDRPOSTagger()\n", + "\n", + "#load files\n", + "r.constructSCRDRtreeFromRDRfile(\"../Models/POS/German.RDR\")\n", + "DICT = RDRPOSTagger.readDictionary(\"../Models/POS/German.DICT\")" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('\\nOutput file:', 'de_text.txt.TAGGED')\n" + ] + } + ], + "source": [ + "#switch back to dir in which we worked at the start\n", + "os.chdir(dir_path)\n", + "\n", + "# generate file with tags after each word with the delimiter /\n", + "r.tagRawCorpus(DICT, \"de_text.txt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#from generated textfile above, seperate the word and tags\n", + "tagged_words = []\n", + "f = open(\"de_text.txt.TAGGED\", 'r')\n", + "for line in f:\n", + " for splits in line.split():\n", + " cmp = splits.rsplit('/',1)\n", + " if len(cmp) != 2:\n", + " print(\"error parsing: \", cmp)\n", + " else:\n", + " w,t = cmp\n", + " tagged_words.append((w,t))" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('CUPERTINO', 'NE'), ('(', '$('), ('rtr', 'NE'), ('/', '$('), ('whp', 'XY'), (')', '$('), ('.', '$.'), ('Der', 'ART'), ('Chef', 'NN'), ('des', 'ART')]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[[('CUPERTINO', 'NE')], [('(', '$('), ('rtr', 'NE'), ('/', '$('), ('whp', 'XY'), (')', '$('), ('.', '$.')], ...]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(tagged_words[:10])\n", + "display(test_sentences_X3[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy 2.2 = 0.9754407616361072\n" + ] + } + ], + "source": [ + "performance2_2 = 0 # for test \n", + "\n", + "#counter for the words\n", + "i = 0\n", + "\n", + "#evaluate accuracy\n", + "for sent in test_sentences_X3:\n", + " for tagged_w in sent:\n", + " if tagged_w[1] == tagged_words[i][1]:\n", + " performance2_2 += 1\n", + " i += 1\n", + "performance2_2 = performance2_2 / len(tagged_words)\n", + "print(\"Accuracy 2.2 = \",performance2_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 145, "metadata": {}, "outputs": [ { @@ -952,14 +1265,13 @@ "" ] }, - "execution_count": 77, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "performance2_2 = 0\n", - "\n", + "#visualize results with plotly\n", "data = [go.Bar(\n", " x=['performance 2.1', 'performance 2.2'],\n", " y=[performance2_1, performance2_2]\n",