713 lines
46 KiB
Plaintext
713 lines
46 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# NLP-LAB Exercise 01 by jonas weinz\n",
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"----"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline \n",
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"#inline for publishing on github. For jupyter lab set ipympl here\n",
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"\n",
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"import nltk\n",
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"import pprint\n",
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"#from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"from sklearn.feature_extraction import DictVectorizer\n",
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"from sklearn.pipeline import Pipeline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* download data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[nltk_data] Downloading package treebank to /home/jonas/nltk_data...\n",
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"[nltk_data] Package treebank is already up-to-date!\n",
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"[nltk_data] Downloading package brown to /home/jonas/nltk_data...\n",
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"[nltk_data] Package brown is already up-to-date!\n",
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"[nltk_data] Downloading package punkt to /home/jonas/nltk_data...\n",
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"[nltk_data] Package punkt is already up-to-date!\n",
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"[nltk_data] Downloading package averaged_perceptron_tagger to\n",
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"[nltk_data] /home/jonas/nltk_data...\n",
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"[nltk_data] Package averaged_perceptron_tagger is already up-to-\n",
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"[nltk_data] date!\n",
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"[nltk_data] Downloading package tagsets to /home/jonas/nltk_data...\n",
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"[nltk_data] Package tagsets is already up-to-date!\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"nltk.download('treebank')\n",
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"nltk.download('brown')\n",
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"nltk.download('punkt')\n",
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"nltk.download('averaged_perceptron_tagger')\n",
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"nltk.download('tagsets')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## implementing own classifiers"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* writing an own feature funtion"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def features(sentence, index):\n",
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" word = sentence[index]\n",
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" is_punctuation_mark = word == \"!\" or word == \".\" or word == \",\" or word == \"?\"\n",
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" sentence_length = len(sentence)\n",
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" relative_third = (index * 3) // sentence_length \n",
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" vowels = word.count('a') + word.count('e') + word.count('i') + word.count('o') + word.count('u')\n",
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" return {\n",
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" 'word': word,\n",
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" 'is_capitalized': sentence[index][0].upper() == sentence[index][0],\n",
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" 'prefix-1': sentence[index][0],\n",
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" 'suffix-1': sentence[index][-1],\n",
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" 'prefix-2': sentence[index][1] if len(word) > 1 else '',\n",
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" 'suffix-2': sentence[index][-2] if len(word) > 1 else '',\n",
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" 'prev_word': '' if index == 0 else sentence[index - 1],\n",
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" 'next_word': '' if index == len(sentence) - 1 else sentence[index + 1],\n",
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" 'length': len(word),\n",
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" 'index' : index,\n",
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" 'rev_index': len(sentence) - index,\n",
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" 'sentence_length_': len(sentence),\n",
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" 'relative_third': relative_third,\n",
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" 'numerical': word.isnumeric(),\n",
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" 'is_punctuation_mark': is_punctuation_mark,\n",
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" ',': word == \",\",\n",
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" '.': word == \".\",\n",
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" '!': word == \"!\",\n",
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" '?': word == \"?\",\n",
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" 'vowels' : vowels\n",
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" }"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"#test_sentence = ['The','cake','is','a','lie','!']\n",
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"#for i in range(len(test_sentence)):\n",
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"# pprint.pprint(features(test_sentence, i))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* function for creating training sets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def untag(tagged_sentence):\n",
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" return [w for w,t in tagged_sentence]\n",
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"\n",
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"def transform_to_dataset(tagged_sentences):\n",
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" X,y = [], []\n",
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" \n",
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" for s in tagged_sentences:\n",
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" for i in range(len(s)):\n",
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" X.append(features(untag(s),i))\n",
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" y.append(s[i][1])\n",
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" return X,y\n",
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"\n",
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"def create_training_and_test_set(annotated_sentences, relative_cutoff):\n",
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" cutoff = int(relative_cutoff * len(annotated_sentences))\n",
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" training_sentences = annotated_sentences[:cutoff]\n",
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" test_sentences = annotated_sentences[cutoff:]\n",
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" \n",
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" X,y = transform_to_dataset(training_sentences)\n",
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" tX, ty = transform_to_dataset(test_sentences)\n",
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" \n",
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" return X,y,tX,ty"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* Train classifier"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"def train_classifier(X,y,classifier,max_size=10000):\n",
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" clf = Pipeline([\n",
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" ('vectorizer', DictVectorizer(sparse=False)),\n",
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" ('classifier', classifier)\n",
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" ])\n",
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" \n",
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" print(\"start training…\")\n",
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" \n",
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" clf.fit(\n",
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" X if len(X) < max_size else X[:max_size],\n",
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" y if len(y) < max_size else y[:max_size]\n",
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" )\n",
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" \n",
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" print(\"training done\")\n",
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" \n",
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" return clf"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* classifier evaluater"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def test_classifier(clf, tX, ty):\n",
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" accuracy = clf.score(tX, ty)\n",
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" print(\"Accuracy: \", accuracy)\n",
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" # TODO: more analytics\n",
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" return accuracy"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Task 01:"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Performance 1\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Model 01\n",
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"* train and testing english custom POS tagger model:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"def model_01(X,y,tX,ty, max_size=10000):\n",
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" #classifier = DecisionTreeClassifier(criterion='entropy')\n",
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" model01_clf = train_classifier(X,y,MLPClassifier(hidden_layer_sizes=(100,), learning_rate='adaptive'),max_size=max_size)\n",
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" return test_classifier(clf=model01_clf, tX=tX, ty=ty)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Model 02"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"def model_02(tX,ty):\n",
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" m2_y = nltk.pos_tag([w['word'] for w in tX])\n",
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" # compare results\n",
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" n_correct = sum((1 if m2_y[i][1] == ty[i] else 0) for i in range(len(ty)))\n",
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" return n_correct / len(ty)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Model 03"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"def model_03(corpus_tagged, corpus_sents, cut=0.8):\n",
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" \n",
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" patterns = [(r'.*ing$', 'VBG'), (r'.*ed$', 'VBD'), (r'.*es$', 'VBZ'), (r'.*ould$', 'MD'), (r'.*\\'s$', 'NN$'), \n",
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" (r'.*s$', 'NNS'), (r'^-?[0-9]+(.[0-9]+)?$', 'CD'), (r'.*', 'NN')]\n",
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" \n",
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" s = int(len(corpus_tagged) * cut)\n",
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" train_sents = corpus_tagged[:s]\n",
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" test_sents = corpus_tagged[s:]\n",
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" \n",
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" models = {\n",
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" 'def_model': nltk.DefaultTagger('NN'),\n",
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" 'regexp_model': nltk.RegexpTagger(patterns),\n",
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" 'uni_model': nltk.UnigramTagger(train_sents),\n",
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" 'bi_model': nltk.BigramTagger(train_sents),\n",
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" 'tri_model': nltk.TrigramTagger(train_sents)\n",
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" }\n",
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" \n",
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" performance = {}\n",
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" for name,model in models.items():\n",
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" performance[name] = model.evaluate(test_sents)\n",
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" \n",
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" return performance\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Applying models on Datasets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"P1.1\n",
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"start training…\n",
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"training done\n",
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"Accuracy: 0.9156644543140875\n",
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"P1.2\n",
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"P1.3\n",
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"P1.4\n",
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"start training…\n",
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"training done\n",
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"Accuracy: 0.8418517269617997\n",
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"P1.5\n",
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"P1.6\n",
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"\n",
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"All Performances:\n",
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"{'P1.1': 0.9156644543140875,\n",
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" 'P1.2': 0.8936074654423873,\n",
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" 'P1.3 -- bi_model': 0.1132791057437996,\n",
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" 'P1.3 -- def_model': 0.1447677029791906,\n",
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" 'P1.3 -- regexp_model': 0.24232746145017217,\n",
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" 'P1.3 -- tri_model': 0.06736863116922003,\n",
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" 'P1.3 -- uni_model': 0.8608213982733669,\n",
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" 'P1.4': 0.8418517269617997,\n",
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" 'P1.5': 0.615540247603449,\n",
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" 'P1.6 -- bi_model': 0.09186376993111421,\n",
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" 'P1.6 -- def_model': 0.1209595837949805,\n",
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" 'P1.6 -- regexp_model': 0.19683029047641987,\n",
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" 'P1.6 -- tri_model': 0.05867334650031312,\n",
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" 'P1.6 -- uni_model': 0.8026879907509996}\n"
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]
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}
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],
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"source": [
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"# dictionary containing all performances\n",
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"performances = {}\n",
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"\n",
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"treebank_tagged = nltk.corpus.treebank.tagged_sents()\n",
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"treebank_sents = nltk.corpus.treebank.sents()\n",
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"\n",
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"brown_tagged = nltk.corpus.brown.tagged_sents(categories='news')\n",
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"brown_sents = nltk.corpus.brown.sents(categories='news')\n",
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"\n",
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"#display(treebank_tagged[0])\n",
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"\n",
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"X1,y1,tX1,ty1 = create_training_and_test_set(annotated_sentences=treebank_tagged, \n",
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" relative_cutoff=0.8)\n",
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"\n",
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"X2,y2,tX2,ty2 = create_training_and_test_set(annotated_sentences=brown_tagged, \n",
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" relative_cutoff=0.8)\n",
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"\n",
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"\n",
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"# running performances 1.1 - 1.6\n",
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"\n",
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"print(\"P1.1\")\n",
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"performances['P1.1'] = model_01(X1,y1,tX1,ty1)\n",
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"\n",
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"print(\"P1.2\")\n",
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"performances['P1.2'] = model_02(tX1,ty1)\n",
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"\n",
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"print(\"P1.3\")\n",
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"p3 = model_03(treebank_tagged, treebank_sents)\n",
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"for k,v in p3.items():\n",
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" performances[\"P1.3 -- \" + k] = v\n",
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"\n",
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"print(\"P1.4\")\n",
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"performances['P1.4'] = model_01(X2,y2,tX2,ty2)\n",
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"\n",
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"print(\"P1.5\")\n",
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"performances['P1.5'] = model_02(tX2,ty2)\n",
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"\n",
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"print(\"P1.6\")\n",
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"p6 = model_03(brown_tagged, brown_sents)\n",
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"for k,v in p6.items():\n",
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" performances[\"P1.6 -- \" + k] = v\n",
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"\n",
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" \n",
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"print(\"\\nAll Performances:\")\n",
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"pprint.pprint(performances)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Plotting Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f9611a7b588>"
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]
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},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
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}
|
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],
|
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"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np\n",
|
|
"#weights = clf.named_steps['classifier'].feature_importances_\n",
|
|
"#labels = clf.named_steps['vectorizer'].get_feature_names()\n",
|
|
"\n",
|
|
"#sort\n",
|
|
"#weights, labels = (list(t) for t in zip(*sorted(zip(weights, labels))))\n",
|
|
"\n",
|
|
"fig_1, ax_1 = plt.subplots()\n",
|
|
"plt.bar(np.arange(len(performances)), performances.values())\n",
|
|
"plt.xticks(np.arange(len(performances)), performances.keys(), rotation=30, ha='right')\n",
|
|
"plt.ylabel(\"performance\")\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"----\n",
|
|
"## Task 2\n",
|
|
"\n",
|
|
"* the file `ru_syntagrus-ud-train-uncommented.conllu` is generated from the file `ru_syntagrus-ud-train.conllu` located here: https://github.com/UniversalDependencies/UD_Russian-SynTagRus. Changes to the original file:\n",
|
|
" * removed commentaries (lines with leading `#`)\n",
|
|
" * removed spaces in in long numbers (eg: `100 000` → `100000`)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"WORDS = 'words' #: column type for words\n",
|
|
"POS = 'pos' #: column type for part-of-speech tags\n",
|
|
"TREE = 'tree' #: column type for parse trees\n",
|
|
"CHUNK = 'chunk' #: column type for chunk structures\n",
|
|
"NE = 'ne' #: column type for named entities\n",
|
|
"SRL = 'srl' #: column type for semantic role labels\n",
|
|
"IGNORE = 'ignore' #: column type for column that should be ignored\n",
|
|
"ru_corp = nltk.corpus.ConllCorpusReader(root=\"./\", \n",
|
|
" fileids=[\"ru_syntagrus-ud-train-uncommented.conllu\"],\n",
|
|
" columntypes=[IGNORE, WORDS, IGNORE, POS],\n",
|
|
" encoding='utf-8')\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"ru_tagged = ru_corp.tagged_sents()\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"X3,y3,tX3,ty3 = create_training_and_test_set(annotated_sentences=ru_tagged, \n",
|
|
" relative_cutoff=0.8)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Model 04, Performance 2.1"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"P2.1\n",
|
|
"start training…\n",
|
|
"training done\n",
|
|
"Accuracy: 0.7951261788284127\n",
|
|
"0.7951261788284127\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(\"P2.1\")\n",
|
|
"performances2 = {}\n",
|
|
"performances2['P2.1'] = model_01(X3,y3,tX3,ty3, max_size=5000) # 10000 here consumes too much memory on my laptop\n",
|
|
"print(performances2['P2.1'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Model 05, Performance 2.2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# write russian text out to file:\n",
|
|
"f = open(\"ru_text.txt\", 'w')\n",
|
|
"for word_features in tX3:\n",
|
|
" f.write(word_features['word'] + \" \")\n",
|
|
" if word_features['word'] == '.': \n",
|
|
" f.write(\"\\n\") #new sentence → new line\n",
|
|
"f.close()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* download the python 3 fork of the rdrpos-tagger: https://github.com/jacopofar/RDRPOSTagger-python-3\n",
|
|
" ```\n",
|
|
" mkdir -p ~/src\n",
|
|
" cd ~/src\n",
|
|
" git clone https://github.com/jacopofar/RDRPOSTagger-python-3.git\n",
|
|
" ```\n",
|
|
"* adjust `RDRPOS_TAGGER_PATH` to match with the download location"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import sys, os\n",
|
|
"\n",
|
|
"# remember current working directory\n",
|
|
"cwd = os.getcwd()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"['Node', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'tabStr']\n",
|
|
"('\\nOutput file:', 'ru_text.txt.TAGGED')\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"RDRPOS_TAGGER_PATH = os.environ['HOME'] + \"/src/RDRPOSTagger-python-3/pSCRDRtagger/\"\n",
|
|
"\n",
|
|
"sys.path.insert(0, RDRPOS_TAGGER_PATH)\n",
|
|
"\n",
|
|
"# change current working directory to RDRPOSTagger repo\n",
|
|
"os.chdir(RDRPOS_TAGGER_PATH)\n",
|
|
"\n",
|
|
"import RDRPOSTagger as model05_tagger \n",
|
|
"\n",
|
|
"# train tagger (copied from RDRPOSTagger.py):\n",
|
|
"r = model05_tagger.RDRPOSTagger()\n",
|
|
"r.constructSCRDRtreeFromRDRfile(\"../Models/UniPOS/UD_Russian-SynTagRus/train.UniPOS.RDR\")\n",
|
|
"DICT = model05_tagger.readDictionary(\"../Models/UniPOS/UD_Russian-SynTagRus/train.UniPOS.DICT\")\n",
|
|
"\n",
|
|
"# change back to our cwd\n",
|
|
"os.chdir(cwd)\n",
|
|
"\n",
|
|
"# run trained tagger\n",
|
|
"r.tagRawCorpus(DICT, \"ru_text.txt\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"tagged_words = []\n",
|
|
"f = open(\"ru_text.txt.TAGGED\", 'r')\n",
|
|
"for line in f:\n",
|
|
" for splits in line.split():\n",
|
|
" w,t = splits.rsplit('/',1) # use last occurence of '/' to split.\n",
|
|
" tagged_words.append((w,t))\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"score_2_2 = sum((1 if tagged_words[i][1] == ty3[i] else 0)for i in range(len(tagged_words))) / len(tagged_words)\n",
|
|
"performances2['P2.2'] = score_2_2\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Results of performance 2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{'P2.1': 0.7951261788284127, 'P2.2': 0.8884973715119793}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"pprint.pprint(performances2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
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"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f9611a0ed68>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig_2, ax_2 = plt.subplots()\n",
|
|
"plt.bar(np.arange(len(performances2)), performances2.values())\n",
|
|
"plt.xticks(np.arange(len(performances2)), performances2.keys(), rotation=30, ha='right')\n",
|
|
"plt.ylabel(\"performance\")\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()\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
|
|
}
|