{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# NLP-LAB Exercise 01 by jonas weinz\n", "----" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib ipympl\n", "import nltk\n", "import pprint\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.feature_extraction import DictVectorizer\n", "from sklearn.pipeline import Pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## implementing own classifiers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* writing an own feature funtion" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'\\n return {\\n \\'word\\': word,\\n \\'is_capitalized\\': word[0].upper() == word[0],\\n \\'prefix-1\\': word[0],\\n \\'suffix-1\\': word[-1],\\n \\'prev_word\\': \\'\\' if index == 0 else sentence[index - 1],\\n \\'next_word\\': \\'\\' if index == len(sentence) - 1 else sentence[index + 1],\\n \\'length\\': len(word),\\n \\'index\\' : index,\\n \\'rev_index\\': len(sentence) - index,\\n \\'sentence_length\\': len(sentence)#,\\n \\'relative_third\\': relative_third,\\n \\'is_punctuation_mark\\': is_punctuation_mark,\\n \\',\\': word == \",\",\\n \\'.\\': word == \".\",\\n \\'!\\': word == \"!\",\\n \\'?\\': word == \"?\"\\n }\\n'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def features(sentence, index):\n", " word = sentence[index]\n", " is_punctuation_mark = word == \"!\" or word == \".\" or word == \",\" or word == \"?\"\n", " sentence_length = len(sentence)\n", " relative_third = (index * 3) // sentence_length \n", " vowels = word.count('a') + word.count('e') + word.count('i') + word.count('o') + word.count('u')\n", " return {\n", " 'word': word,\n", " 'is_capitalized': sentence[index][0].upper() == sentence[index][0],\n", " 'prefix-1': sentence[index][0],\n", " 'suffix-1': sentence[index][-1],\n", " 'prefix-2': sentence[index][1] if len(word) > 1 else '',\n", " 'suffix-2': sentence[index][-2] if len(word) > 1 else '',\n", " 'prev_word': '' if index == 0 else sentence[index - 1],\n", " 'next_word': '' if index == len(sentence) - 1 else sentence[index + 1],\n", " 'length': len(word),\n", " 'index' : index,\n", " 'rev_index': len(sentence) - index,\n", " 'sentence_length_': len(sentence),\n", " 'relative_third': relative_third,\n", " 'numerical': word.isnumeric(),\n", " 'is_punctuation_mark': is_punctuation_mark,\n", " ',': word == \",\",\n", " '.': word == \".\",\n", " '!': word == \"!\",\n", " '?': word == \"?\",\n", " 'vowels' : vowels\n", " }\n", "'''\n", " return {\n", " 'word': word,\n", " 'is_capitalized': word[0].upper() == word[0],\n", " 'prefix-1': word[0],\n", " 'suffix-1': word[-1],\n", " 'prev_word': '' if index == 0 else sentence[index - 1],\n", " 'next_word': '' if index == len(sentence) - 1 else sentence[index + 1],\n", " 'length': len(word),\n", " 'index' : index,\n", " 'rev_index': len(sentence) - index,\n", " 'sentence_length': len(sentence)#,\n", " 'relative_third': relative_third,\n", " 'is_punctuation_mark': is_punctuation_mark,\n", " ',': word == \",\",\n", " '.': word == \".\",\n", " '!': word == \"!\",\n", " '?': word == \"?\"\n", " }\n", "'''" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "test_sentence = ['The','cake','is','a','lie','!']\n", "#for i in range(len(test_sentence)):\n", "# pprint.pprint(features(test_sentence, i))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* function for creating training sets" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def untag(tagged_sentence):\n", " return [w for w,t in tagged_sentence]\n", "\n", "def transform_to_dataset(tagged_sentences):\n", " X,y = [], []\n", " \n", " for s in tagged_sentences:\n", " for i in range(len(s)):\n", " X.append(features(untag(s),i))\n", " y.append(s[i][1])\n", " return X,y\n", "\n", "def create_training_and_test_set(annotated_sentences, relative_cutoff):\n", " cutoff = int(relative_cutoff * len(annotated_sentences))\n", " training_sentences = annotated_sentences[:cutoff]\n", " test_sentences = annotated_sentences[cutoff:]\n", " \n", " X,y = transform_to_dataset(training_sentences)\n", " tX, ty = transform_to_dataset(test_sentences)\n", " \n", " return X,y,tX,ty" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Decision Tree classifier" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def train_classifier(X,y,classifier,max_size=10000):\n", " clf = Pipeline([\n", " ('vectorizer', DictVectorizer(sparse=False)),\n", " ('classifier', classifier)\n", " ])\n", " \n", " print(\"start training…\")\n", " \n", " clf.fit(\n", " X if len(X) < max_size else X[:max_size],\n", " y if len(y) < max_size else y[:max_size]\n", " )\n", " \n", " print(\"training done\")\n", " \n", " return clf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* classifier evaluater" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def test_classifier(clf, tX, ty):\n", " accuracy = clf.score(tX, ty)\n", " print(\"Accuracy: \", accuracy)\n", " # TODO: more analytics\n", " return accuracy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 01:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performance 1\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Model 01\n", "* train and testing english custom POS tagger model:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "start training…\n", "training done\n", "Accuracy: 0.768551324916413\n" ] } ], "source": [ "def model_01(X,y,tX,ty, max_size=1000):\n", " #classifier = DecisionTreeClassifier(criterion='entropy')\n", " from sklearn.neural_network import MLPClassifier\n", " model01_clf = train_classifier(X,y,MLPClassifier(),max_size=1000)\n", " return test_classifier(clf=model01_clf, tX=tX, ty=ty)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Model 02" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8936074654423873\n" ] } ], "source": [ "def model_02(tX,ty):\n", " m2_y = nltk.pos_tag([w['word'] for w in tX])\n", " # compare results\n", " n_correct = sum((1 if m2_y[i][1] == ty[i] else 0) for i in range(len(ty)))\n", " return n_correct / len(ty)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Model 03" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "def model_03(corpus_tagged, corpus_sents, cut=0.8):\n", " \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", " s = int(len(corpus_sents) * cut)\n", " train_sents = corpus_sents[:size]\n", " test_sents = corpus_sents[size:]\n", " \n", " models = {\n", " 'def_model': nltk.DefaultTagger('NN'),\n", " 'regexp_model': nltk.RegexpTagger(patterns),\n", " 'uni_model': nltk.UnigramTagger(train_sents),\n", " 'bi_model': nltk.BigramTagger(train_sents),\n", " 'tri_model': nltk.TrigramTagger(train_sents)\n", " }\n", " \n", " performance = {}\n", " for name,model in models.items():\n", " performance[name] = model.evaluate(test_sents)\n", " \n", " return performance\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Applying models on Datasets" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "accs_p1 = [0] * 3\n", "names_p1 = [\"P1.1\", \"P1.2\", \"P1.3\"]\n", "\n", "treebank_tagged = nltk.corpus.treebank.tagged_sents()\n", "treebank_sents = nltk.corpus.treebank.sents()\n", "\n", "brown_tagged = nltk.corpus.brown.tagged_sents()\n", "brown_sents = nltk.corpus.brown.sents()\n", "\n", "X1,y1,tX1,ty1 = create_training_and_test_set(annotated_sentences=treebank_tagged, \n", " relative_cutoff=0.8)\n", "\n", "X2,y2,tX2,ty2 = create_training_and_test_set(annotated_sentences=brown_tagged, \n", " relative_cutoff=0.8)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }