refactored repo a little bit

This commit is contained in:
Jonas Weinz 2019-07-26 16:43:30 +02:00
parent d3bcb10efa
commit 032e423295
10 changed files with 30260 additions and 73 deletions

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@ -15,6 +15,10 @@
"source": [
"from IPython.core.display import Markdown, HTML, display\n",
"\n",
"import sys\n",
"sys.path.insert(0, '..') # noqa\n",
"import settings # noqa\n",
"\n",
"import crf_data_generator as cdg\n",
"import pycrfsuite\n",
"\n",
@ -27,7 +31,7 @@
"metadata": {},
"outputs": [],
"source": [
"data = cdg.ConlluCRFReader(\"recipes2.conllu\")\n",
"data = cdg.ConlluCRFReader(\"../\" + settings.gzipped_conllu_data_root + \"recipes2.conllu.gz\")\n",
"\n",
"data_iterator = iter(data)"
]
@ -67,7 +71,7 @@
{
"data": {
"text/plain": [
"<contextlib.closing at 0x7eff2aa68128>"
"<contextlib.closing at 0x7f41527f5d30>"
]
},
"execution_count": 4,

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@ -0,0 +1,380 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conllu Batch Generator\n",
"\n",
"read conllu documents in batches"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"\n",
"from conllu import parse\n",
"from tagging_tools import print_visualized_tags\n",
"\n",
"from sklearn import preprocessing\n",
"import numpy as np\n",
"\n",
"sys.path.insert(0, '..')\n",
"import settings # noqa\n",
"\n",
"import gzip"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class ConlluSentenceIterator(object):\n",
" def __init__(self, conllu_reader):\n",
" self.conllu_reader = conllu_reader\n",
" self._fileobj = None\n",
" self._open()\n",
" \n",
" def _open(self):\n",
" if self.conllu_reader._path.endswith(\".gz\"):\n",
" self._fileobj = gzip.open(self.conllu_reader._path, 'r')\n",
" self._nextline = self.read_byte_line\n",
" else:\n",
" self._fileobj = open(self.conllu_reader._path, 'r')\n",
" self._nextline = self.read_str_line\n",
"\n",
" def __next__(self):\n",
" next_sent = self.next_sentence()\n",
" if next_sent is None:\n",
" raise StopIteration\n",
" return next_sent\n",
" \n",
" def read_str_line(self):\n",
" return self._fileobj.readline()\n",
" \n",
" def read_byte_line(self):\n",
" return self._fileobj.readline().decode(\"utf-8\")\n",
"\n",
" def next_sentence(self):\n",
" data = \"\"\n",
" while True:\n",
" line = self._nextline()\n",
" if line == \"\":\n",
" break\n",
" data += line\n",
" if line == \"\\n\":\n",
" break\n",
"\n",
" if data == \"\":\n",
" return None\n",
"\n",
" if data[-1] != \"\\n\":\n",
" data += \"\\n\"\n",
"\n",
" conllu_obj = parse(data)\n",
" return conllu_obj"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class ConlluDocumentIterator(object):\n",
" def __init__(self, conllu_reader):\n",
" self.conllu_reader = conllu_reader\n",
" self._fileobj = None\n",
" self._open()\n",
" \n",
" def _open(self):\n",
" if self.conllu_reader._path.endswith(\".gz\"):\n",
" self._fileobj = gzip.open(self.conllu_reader._path, 'r')\n",
" self._nextline = self.read_byte_line\n",
" else:\n",
" self._fileobj = open(self.conllu_reader._path, 'r')\n",
" self._nextline = self.read_str_line\n",
" \n",
" def read_str_line(self):\n",
" return self._fileobj.readline()\n",
" \n",
" def read_byte_line(self):\n",
" return self._fileobj.readline().decode(\"utf-8\")\n",
"\n",
" def next_document(self):\n",
" data = \"\"\n",
" last_line_empty = False\n",
" while True:\n",
" line = self._nextline()\n",
" if line == \"\":\n",
" break\n",
" data += line\n",
" if line == \"\\n\":\n",
" if last_line_empty:\n",
" break\n",
" last_line_empty = True\n",
" else:\n",
" last_line_empty = False\n",
"\n",
" if data == \"\":\n",
" return None\n",
"\n",
" if data[-1] != \"\\n\":\n",
" data += \"\\n\"\n",
"\n",
" conllu_obj = parse(data)\n",
" return conllu_obj\n",
"\n",
" def __next__(self):\n",
" next_sent = self.next_document()\n",
" if next_sent is None:\n",
" raise StopIteration\n",
" return next_sent"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class ConlluReader(object):\n",
" def __init__(self, path, iter_documents=False):\n",
" self._path = path\n",
" self.iter_documents = iter_documents\n",
"\n",
" def __iter__(self):\n",
" return ConlluDocumentIterator(self) if self.iter_documents else ConlluSentenceIterator(self)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class SlidingWindowListIterator(object):\n",
" def __init__(self, parent):\n",
" self.parent = parent\n",
" self.i = 0\n",
"\n",
" def __next__(self):\n",
" if len(self.parent) == self.i:\n",
" raise StopIteration\n",
"\n",
" self.i += 1\n",
" return self.parent[self.i - 1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class SlidingWindowList(list):\n",
" def __init__(self, sliding_window_size, input=None, border_value=None):\n",
"\n",
" self.sliding_window_size = sliding_window_size\n",
" self.border_value = border_value\n",
"\n",
" if border_value is None and input is not None:\n",
" self.border_value = type(input[0])()\n",
"\n",
" if input is not None:\n",
" super(SlidingWindowList, self).__init__(input)\n",
"\n",
" def __getitem__(self, index):\n",
"\n",
" if type(index) == slice:\n",
" start = 0 if index.start is None else index.start\n",
" stop = len(self) if index.stop is None else index.stop\n",
" step = 1 if index.step is None else index.step\n",
" return [self[i] for i in range(start, stop, step)]\n",
"\n",
" else:\n",
" n = self.sliding_window_size * 2 + 1\n",
" res = n * [self.border_value]\n",
"\n",
" j_start = index - self.sliding_window_size\n",
"\n",
" for i in range(n):\n",
" ind = j_start + i\n",
" if ind >= 0 and ind < len(self):\n",
" res[i] = super(SlidingWindowList, self).__getitem__(ind)\n",
"\n",
" return res\n",
"\n",
" def __iter__(self):\n",
" return SlidingWindowListIterator(self)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"class ConlluDataProviderIterator(object):\n",
" def __init__(self, parent):\n",
" self.parent = parent\n",
" self.conllu_reader = ConlluReader(\n",
" parent.filepath, parent.iter_documents)\n",
"\n",
" def __next__(self):\n",
" result = self.parent.getNextDataBatch(conllu_reader=self.conllu_reader)\n",
" if result is None:\n",
" raise StopIteration\n",
" return result\n",
"'''\n",
"\n",
"'''\n",
"class ConlluDataProvider(object):\n",
" def __init__(self,\n",
" filepath,\n",
" word2vec_model,\n",
" batchsize=100,\n",
" window_size=3,\n",
" iter_documents=False,\n",
" food_type=None):\n",
" self.batchsize = batchsize\n",
" self.word2vec_model = word2vec_model\n",
" self.filepath = filepath\n",
" self.conllu_reader = ConlluReader(filepath, iter_documents)\n",
" self.window_size = window_size\n",
" self.food_type = food_type\n",
" self.iter_documents = iter_documents\n",
"\n",
" # create a label binarizer for upos tags:\n",
" self.lb = preprocessing.LabelBinarizer()\n",
" self.lb.fit(['.', 'ADJ', 'ADP', 'ADV', 'CONJ', 'DET',\n",
" 'NOUN', 'NUM', 'PRON', 'PRT', 'VERB', 'X'])\n",
"\n",
" def _get_next_conllu_objects(self, n: int, conllu_reader):\n",
" i = 0\n",
" conllu_list = []\n",
"\n",
" while i < n:\n",
" try:\n",
" conllu_list.append(conllu_reader.__iter__().__next__())\n",
" i += 1\n",
"\n",
" except StopIteration:\n",
" break\n",
"\n",
" return conllu_list\n",
"\n",
" def _get_upos_X(self, conllu_list):\n",
" n_tokens = 0\n",
" l_global = []\n",
" for document in conllu_list:\n",
" l = []\n",
" for sentence in document:\n",
" for token in sentence:\n",
" upos = token['upostag']\n",
" l.append(upos)\n",
" n_tokens += 1\n",
" if len(l) > 0:\n",
" l_global.append(self.lb.transform(l))\n",
"\n",
" return l_global, n_tokens\n",
"\n",
" def _get_y(self, conllu_list, misk_key=\"food_type\", misc_val=\"ingredient\"):\n",
" n_tokens = 0\n",
" y_global = []\n",
" for document in conllu_list:\n",
" y = []\n",
" for sentence in document:\n",
" for token in sentence:\n",
" m = token['misc']\n",
" t_y = m is not None and misk_key in m and m[misk_key] == misc_val\n",
" y.append(t_y)\n",
" n_tokens += 1\n",
" if len(y) > 0:\n",
" y_global.append(y)\n",
"\n",
" return y_global, n_tokens\n",
"\n",
" def getNextDataBatch(self, y_food_type_label=None, conllu_reader=None):\n",
"\n",
" if y_food_type_label is None:\n",
" y_food_type_label = self.food_type\n",
"\n",
" if conllu_reader is None:\n",
" conllu_reader = self.conllu_reader\n",
" conllu_list = self._get_next_conllu_objects(\n",
" self.batchsize, conllu_reader)\n",
"\n",
" if len(conllu_list) == 0:\n",
" return None\n",
"\n",
" # generate features for each document/sentence\n",
" n = len(conllu_list)\n",
"\n",
" d = self.window_size * 2 + 1\n",
"\n",
" buf_X, x_tokens = self._get_upos_X(conllu_list)\n",
" buf_ingr_y, y_tokens = self._get_y(conllu_list)\n",
"\n",
" assert len(buf_X) == len(buf_ingr_y) and x_tokens == y_tokens\n",
"\n",
" X_upos = np.zeros(shape=(x_tokens, d * len(self.lb.classes_)))\n",
" y = None\n",
"\n",
" if y_food_type_label is not None:\n",
" y = np.zeros(shape=(x_tokens))\n",
"\n",
" i = 0\n",
" for xupos in buf_X:\n",
" tmp = SlidingWindowList(self.window_size,\n",
" xupos,\n",
" border_value=[0] * len(self.lb.classes_))\n",
" for upos_window in tmp:\n",
" X_upos[i, :] = np.array(upos_window).flatten()\n",
" i += 1\n",
"\n",
" i = 0\n",
" if y_food_type_label is not None:\n",
" for sentence in buf_ingr_y:\n",
" for yl in sentence:\n",
" y[i] = yl\n",
" i += 1\n",
"\n",
" return X_upos, y\n",
" \n",
" def __iter__(self):\n",
" return ConlluDataProviderIterator(self)\n",
"\n",
"'''"
]
}
],
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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@ -1,4 +1,9 @@
#!/usr/bin/env python3
# coding: utf-8
# # Conllu Batch Generator
#
# read conllu documents in batches
import sys
@ -302,3 +307,4 @@ class ConlluDataProvider(object):
return ConlluDataProviderIterator(self)
'''

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@ -0,0 +1,373 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conllu Generator\n",
"\n",
"tools for creating:\n",
"* conllu tokens\n",
"* conllu sentences\n",
"* conllu documents"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## imports and settings"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import nltk\n",
"from nltk.tag import pos_tag, map_tag\n",
"from nltk.stem import PorterStemmer\n",
"from nltk.corpus import stopwords as nltk_stopwords\n",
"from stemmed_mwe_tokenizer import StemmedMWETokenizer"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"CONLLU_ATTRIBUTES = [\n",
" \"id\",s\n",
" \"form\",\n",
" \"lemma\",\n",
" \"upos\",\n",
" \"xpos\",\n",
" \"feats\",\n",
" \"head\",\n",
" \"deprel\",\n",
" \"deps\",\n",
" \"misc\"\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# took from: https://stackoverflow.com/a/16053211\n",
"\n",
"\n",
"def replace_tab(s, tabstop=4):\n",
" result = str()\n",
" s = s.replace(\"\\t\", \" \\t\")\n",
" for c in s:\n",
" if c == '\\t':\n",
" while (len(result) % (tabstop) != 0):\n",
" result += ' '\n",
" else:\n",
" result += c\n",
" return result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conllu Dict Class"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"class ConlluDict(dict):\n",
"\n",
" def from_str(self, s: str):\n",
" entries = s.split(\"|\")\n",
" for entry in entries:\n",
" key, val = entry.split(\"=\")\n",
" self[key.strip()] = val.strip()\n",
"\n",
" def __repr__(self):\n",
" if len(self) == 0:\n",
" return \"_\"\n",
"\n",
" result = \"\"\n",
" for key, value in self.items():\n",
" result += key + \"=\" + value + \"|\"\n",
"\n",
" return result[:-1]\n",
"\n",
" def __str__(self):\n",
" return self.__repr__()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conllu Element Class"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"class ConlluElement(object):\n",
" # class uses format described here: https://universaldependencies.org/format.html\n",
" def __init__(\n",
" self,\n",
" id: int,\n",
" form: str,\n",
" lemma: str,\n",
" upos: str = \"_\",\n",
" xpos: str = \"_\",\n",
" feats: str = \"_\",\n",
" head: str = \"_\",\n",
" deprel: str = \"_\",\n",
" deps: str = \"_\",\n",
" misc: str = \"_\"):\n",
" self.id = id\n",
" self.form = form\n",
" self.lemma = lemma\n",
" self.upos = upos\n",
" self.xpos = xpos\n",
"\n",
" self.feats = ConlluDict()\n",
" if feats != \"_\":\n",
" self.feats.from_str(feats)\n",
"\n",
" self.head = head\n",
" self.deprel = deprel\n",
" self.deps = deps\n",
"\n",
" self.misc = ConlluDict()\n",
" if misc != \"_\":\n",
" self.misc.from_str(misc)\n",
"\n",
" def add_feature(self, key: str, value: str):\n",
" self.feats[key] = value\n",
"\n",
" def add_misc(self, key: str, value: str):\n",
" self.misc[key] = value\n",
"\n",
" def __repr__(self):\n",
" result = \"\"\n",
" for attr in CONLLU_ATTRIBUTES:\n",
" result += str(self.__getattribute__(attr)) + \" \\t\"\n",
" return replace_tab(result, 16)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conllu Sentence Class"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"class ConlluSentence(object):\n",
" def __init__(self):\n",
" self.conllu_elements = []\n",
"\n",
" def add(self, conllu_element: ConlluElement):\n",
" self.conllu_elements.append(conllu_element)\n",
"\n",
" def __repr__(self):\n",
" result = \"\"\n",
" for elem in self.conllu_elements:\n",
" result += elem.__repr__() + \"\\n\"\n",
"\n",
" return result\n",
"\n",
" def __str__(self):\n",
" return self.__repr__()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conllu Document Class"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"class ConlluDocument(object):\n",
" def __init__(self, id=None):\n",
" self.conllu_sentences = []\n",
" self.id = id\n",
" \n",
" def add(self, conllu_sentence: ConlluSentence):\n",
" self.conllu_sentences.append(conllu_sentence)\n",
" \n",
" def __repr__(self):\n",
" result = \"# newdoc\\n\"\n",
" if self.id is not None:\n",
" result += \"# id: \" + self.id + \"\\n\"\n",
" for elem in self.conllu_sentences:\n",
" result += elem.__repr__() + \"\\n\"\n",
"\n",
" return result\n",
"\n",
" def __str__(self):\n",
" return self.__repr__()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conllu Generator Class"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'PorterStemmer' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-dcd3e28a755b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mclass\u001b[0m \u001b[0mConlluGenerator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdocuments\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstemmed_multi_word_tokens\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstemmer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPorterStemmer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdocuments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdocuments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstemmed_multi_word_tokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstemmed_multi_word_tokens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m self.mwe_tokenizer = StemmedMWETokenizer(\n",
"\u001b[0;32m<ipython-input-1-dcd3e28a755b>\u001b[0m in \u001b[0;36mConlluGenerator\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConlluGenerator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdocuments\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstemmed_multi_word_tokens\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstemmer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPorterStemmer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdocuments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdocuments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstemmed_multi_word_tokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstemmed_multi_word_tokens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m self.mwe_tokenizer = StemmedMWETokenizer(\n",
"\u001b[0;31mNameError\u001b[0m: name 'PorterStemmer' is not defined"
]
}
],
"source": [
"class ConlluGenerator(object):\n",
" def __init__(self, documents: list, stemmed_multi_word_tokens, stemmer=PorterStemmer(), ids=None):\n",
" self.documents = documents\n",
" self.stemmed_multi_word_tokens = stemmed_multi_word_tokens\n",
" self.mwe_tokenizer = StemmedMWETokenizer(\n",
" [w.split() for w in stemmed_multi_word_tokens])\n",
" self.stemmer = stemmer\n",
"\n",
" self.id_counter = 0\n",
"\n",
" self.conllu_documents = []\n",
"\n",
" self.ids = ids\n",
"\n",
" def tokenize_and_stem(self):\n",
" tokenized_documents = []\n",
"\n",
" i = 0\n",
" for doc in self.documents:\n",
" tokenized_sentences = []\n",
" sentences = doc.split(\"\\n\")\n",
" for sent in sentences: \n",
" if (len(sent) > 0):\n",
" simple_tokenized = nltk.tokenize.word_tokenize(sent)\n",
" tokenized_sentences.append(\n",
" self.mwe_tokenizer.tokenize(simple_tokenized))\n",
" tokenized_documents.append(tokenized_sentences)\n",
"\n",
" # now create initial colln-u elemnts\n",
" for doc in tokenized_documents:\n",
" if self.ids:\n",
" conllu_doc = ConlluDocument(self.ids[i])\n",
" else:\n",
" conllu_doc = ConlluDocument()\n",
" self.id_counter = 0\n",
" for sent in doc:\n",
" conllu_sent = ConlluSentence()\n",
" for token in sent:\n",
" stemmed_token = None\n",
" if \"_\" in token:\n",
" stemmed_token = \"_\".join(\n",
" [self.stemmer.stem(part) for part in token.split(\"_\")])\n",
" else:\n",
" stemmed_token = self.stemmer.stem(token)\n",
" conllu_sent.add(ConlluElement(\n",
" id=self.id_counter + 1,\n",
" form=token,\n",
" lemma=stemmed_token\n",
" ))\n",
" self.id_counter += 1\n",
" conllu_doc.add(conllu_sent)\n",
" self.conllu_documents.append(conllu_doc)\n",
" i += 1\n",
"\n",
" def pos_tagging(self):\n",
" for conllu_document in self.conllu_documents:\n",
" for conllu_sent in conllu_document.conllu_sentences:\n",
" tokens = [x.form for x in conllu_sent.conllu_elements]\n",
" pos_tags = pos_tag(tokens)\n",
" simplified_tags = [map_tag('en-ptb', 'universal', tag)\n",
" for word, tag in pos_tags]\n",
"\n",
" for i in range(len(tokens)):\n",
" conllu_elem = conllu_sent.conllu_elements[i]\n",
" conllu_elem.upos = simplified_tags[i]\n",
" conllu_elem.xpos = pos_tags[i][1]\n",
"\n",
" def add_misc_value_by_list(self, key, value, stemmed_keyword_list):\n",
" for conllu_document in self.conllu_documents:\n",
" for conllu_sent in conllu_document.conllu_sentences:\n",
" for elem in conllu_sent.conllu_elements:\n",
" if elem.lemma in stemmed_keyword_list:\n",
" elem.add_misc(key, value)\n",
"\n",
" def __repr__(self):\n",
" result = \"\"\n",
" for document in self.conllu_documents:\n",
" result += document.__repr__() + \"\\n\"\n",
" return result\n",
"\n",
" def __str__(self):\n",
" return self.__repr__()"
]
},
{
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

View File

@ -1,4 +1,14 @@
#!/usr/bin/env python3
# coding: utf-8
# # Conllu Generator
#
# tools for creating:
# * conllu tokens
# * conllu sentences
# * conllu documents
# ## imports and settings
import nltk
from nltk.tag import pos_tag, map_tag
@ -8,7 +18,7 @@ from stemmed_mwe_tokenizer import StemmedMWETokenizer
CONLLU_ATTRIBUTES = [
"id",
"id",s
"form",
"lemma",
"upos",
@ -20,6 +30,7 @@ CONLLU_ATTRIBUTES = [
"misc"
]
# took from: https://stackoverflow.com/a/16053211
@ -35,6 +46,8 @@ def replace_tab(s, tabstop=4):
return result
# ## Conllu Dict Class
class ConlluDict(dict):
def from_str(self, s: str):
@ -57,6 +70,8 @@ class ConlluDict(dict):
return self.__repr__()
# ## Conllu Element Class
class ConlluElement(object):
# class uses format described here: https://universaldependencies.org/format.html
def __init__(
@ -102,6 +117,8 @@ class ConlluElement(object):
return replace_tab(result, 16)
# ## Conllu Sentence Class
class ConlluSentence(object):
def __init__(self):
self.conllu_elements = []
@ -120,6 +137,8 @@ class ConlluSentence(object):
return self.__repr__()
# ## Conllu Document Class
class ConlluDocument(object):
def __init__(self, id=None):
self.conllu_sentences = []
@ -141,6 +160,8 @@ class ConlluDocument(object):
return self.__repr__()
# ## Conllu Generator Class
class ConlluGenerator(object):
def __init__(self, documents: list, stemmed_multi_word_tokens, stemmer=PorterStemmer(), ids=None):
self.documents = documents
@ -223,3 +244,7 @@ class ConlluGenerator(object):
def __str__(self):
return self.__repr__()

View File

@ -0,0 +1,210 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# crf data Generator"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import conllu_batch_generator as cbg"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def word2features(sent, i):\n",
" word = sent[i]['form']\n",
" postag = sent[i]['upostag']\n",
" features = [\n",
" 'bias',\n",
" #'word.lower=' + word.lower(),\n",
" 'word[-3:]=' + word[-3:],\n",
" 'word[-2:]=' + word[-2:],\n",
" 'word.isupper=%s' % word.isupper(),\n",
" 'word.istitle=%s' % word.istitle(),\n",
" 'word.isdigit=%s' % word.isdigit(),\n",
" 'postag=' + postag,\n",
" 'postag[:2]=' + postag[:2],\n",
" ]\n",
" if i > 0:\n",
" word1 = sent[i-1]['form']\n",
" postag1 = sent[i-1]['upostag']\n",
" features.extend([\n",
" '-1:word.lower=' + word1.lower(),\n",
" '-1:word.istitle=%s' % word1.istitle(),\n",
" '-1:word.isupper=%s' % word1.isupper(),\n",
" '-1:postag=' + postag1,\n",
" '-1:postag[:2]=' + postag1[:2],\n",
" ])\n",
" if i > 1:\n",
" word1 = sent[i-2]['form']\n",
" postag1 = sent[i-2]['upostag']\n",
" features.extend([\n",
" '-2:word.lower=' + word1.lower(),\n",
" '-2:word.istitle=%s' % word1.istitle(),\n",
" '-2:word.isupper=%s' % word1.isupper(),\n",
" '-2:postag=' + postag1,\n",
" '-2:postag[:2]=' + postag1[:2],\n",
" ])\n",
" else:\n",
" features.append('BOS')\n",
"\n",
" if i < len(sent)-1:\n",
" word1 = sent[i+1]['form']\n",
" postag1 = sent[i+1]['upostag']\n",
" features.extend([\n",
" '+1:word.lower=' + word1.lower(),\n",
" '+1:word.istitle=%s' % word1.istitle(),\n",
" '+1:word.isupper=%s' % word1.isupper(),\n",
" '+1:postag=' + postag1,\n",
" '+1:postag[:2]=' + postag1[:2],\n",
" ])\n",
" if i < len(sent)-2:\n",
" word1 = sent[i+1]['form']\n",
" postag1 = sent[i+1]['upostag']\n",
" features.extend([\n",
" '+2:word.lower=' + word1.lower(),\n",
" '+2:word.istitle=%s' % word1.istitle(),\n",
" '+2:word.isupper=%s' % word1.isupper(),\n",
" '+2:postag=' + postag1,\n",
" '+2:postag[:2]=' + postag1[:2],\n",
" ])\n",
" else:\n",
" features.append('EOS')\n",
"\n",
" return features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def sent2labels(sent):\n",
" labels = []\n",
" for token in sent:\n",
" if token['misc'] is not None and 'food_type' in token['misc']:\n",
" labels.append(token['misc']['food_type'])\n",
" else:\n",
" labels.append(\"0\")\n",
" return labels"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def sent2features(sent):\n",
" return [word2features(sent, i) for i in range(len(sent))]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def sent2tokens(sent):\n",
" return [token['form'] for token in sent]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def feature2tokens(sent):\n",
" return [t[1].split(\"=\")[1] for t in sent]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"class ConlluCRFReaderIterator(object):\n",
" def __init__(self, parent):\n",
" self._parent = parent\n",
" self._iter = self._parent._conllu_reader.__iter__()\n",
"\n",
" def __next__(self):\n",
" features = None\n",
" labels = None\n",
" tokens = None\n",
"\n",
" if not self._parent._iter_documents:\n",
" next_sent = self._iter.__next__()[0]\n",
" features = sent2features(next_sent)\n",
" labels = sent2labels(next_sent)\n",
" tokens = sent2tokens(next_sent)\n",
" else:\n",
" next_doc = self._iter.__next__()\n",
" features = [sent2features(sentence) for sentence in next_doc]\n",
" labels = [sent2labels(sentence) for sentence in next_doc]\n",
" tokens = [sent2tokens(sentence) for sentence in next_doc]\n",
"\n",
" return features, labels, tokens"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"class ConlluCRFReader(object):\n",
" def __init__(self, path, iter_documents=False):\n",
" self._path = path\n",
" self._iter_documents = iter_documents\n",
"\n",
" self._conllu_reader = cbg.ConlluReader(path, iter_documents)\n",
"\n",
" def __iter__(self):\n",
" return ConlluCRFReaderIterator(self)"
]
},
{
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

View File

@ -1,4 +1,7 @@
#!/usr/bin/env python3
# coding: utf-8
# # crf data Generator
import conllu_batch_generator as cbg
@ -121,3 +124,7 @@ class ConlluCRFReader(object):
def __iter__(self):
return ConlluCRFReaderIterator(self)

View File

@ -0,0 +1,162 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Recipe Conllu Generator"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, '..')\n",
"\n",
"from conllu_generator import ConlluDict, ConlluElement, ConlluDocument, ConlluGenerator\n",
"import settings\n",
"import importlib.util\n",
"from json_buffered_reader import JSON_buffered_reader as JSON_br"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# loading ingredients:\n",
"spec = importlib.util.spec_from_file_location(\n",
" \"ingredients\", \"../\" + settings.ingredients_file)\n",
"ingredients = importlib.util.module_from_spec(spec)\n",
"spec.loader.exec_module(ingredients)\n",
"\n",
"# loading actions:\n",
"spec = importlib.util.spec_from_file_location(\n",
" \"ingredients\", \"../\" + settings.actions_file)\n",
"actions = importlib.util.module_from_spec(spec)\n",
"spec.loader.exec_module(actions)\n",
"\n",
"# skipping recipes:\n",
"n_skipped_recipes = int(sys.argv[1]) if len(sys.argv) > 1 else 0\n",
"print(\"start reading at recipe \" + str(n_skipped_recipes))\n",
"\n",
"# settings:\n",
"recipe_buffer_size = 1000\n",
"recipe_buffers_per_file = 5\n",
"\n",
"\n",
"# create reader\n",
"buffered_reader_1M = JSON_br(\"../\" + settings.one_million_recipes_file)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def process_instructions(instructions: list, document_ids=None):\n",
"\n",
" if len(instructions) == 0:\n",
" return\n",
"\n",
" conllu_input_docs = instructions\n",
"\n",
" cg = ConlluGenerator(\n",
" conllu_input_docs, ingredients.multi_word_ingredients_stemmed, ids=document_ids)\n",
" cg.tokenize_and_stem()\n",
" cg.pos_tagging()\n",
" cg.add_misc_value_by_list(\"food_type\", \"ingredient\", [w.replace(\" \",\"_\") for w in ingredients.multi_word_ingredients_stemmed] + ingredients.ingredients_stemmed)\n",
" cg.add_misc_value_by_list(\"food_type\", \"action\", actions.stemmed_cooking_verbs)\n",
"\n",
" savefile.write(str(cg))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"i = 0\n",
"buffer_count = n_skipped_recipes % recipe_buffer_size\n",
"file_count = n_skipped_recipes // (recipe_buffer_size * recipe_buffers_per_file)\n",
"\n",
"savefile = open(f\"recipes{file_count}.conllu\", 'w')\n",
"instructions = []\n",
"ids = []"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for raw_recipe in buffered_reader_1M:\n",
"\n",
" i += 1\n",
"\n",
" if i > n_skipped_recipes:\n",
"\n",
" instruction = \"\"\n",
" for item in raw_recipe['instructions']:\n",
" instruction += item['text'] + '\\n'\n",
" ids.append(raw_recipe['id'])\n",
"\n",
" instructions.append(instruction)\n",
"\n",
" if i % recipe_buffer_size == 0:\n",
" process_instructions(instructions, ids)\n",
" print(f\"processed {i} recipes\")\n",
" instructions = []\n",
" ids = []\n",
" buffer_count += 1\n",
" if buffer_count % recipe_buffers_per_file == 0:\n",
" savefile.close()\n",
" file_count += 1\n",
" savefile = open(f\"recipes{file_count}.conllu\", 'w')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" \n",
"\n",
"process_instructions(instructions)\n",
"print(f\"processed {i} recipes\")\n",
"\n",
"savefile.close()"
]
}
],
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

View File

@ -1,4 +1,7 @@
#!/usr/bin/env python3
# coding: utf-8
# # Recipe Conllu Generator
import sys
sys.path.insert(0, '..')
@ -9,7 +12,6 @@ import importlib.util
from json_buffered_reader import JSON_buffered_reader as JSON_br
# loading ingredients:
spec = importlib.util.spec_from_file_location(
"ingredients", "../" + settings.ingredients_file)
@ -34,7 +36,6 @@ recipe_buffers_per_file = 5
# create reader
buffered_reader_1M = JSON_br("../" + settings.one_million_recipes_file)
# open savefile:
def process_instructions(instructions: list, document_ids=None):
@ -52,6 +53,7 @@ def process_instructions(instructions: list, document_ids=None):
savefile.write(str(cg))
i = 0
buffer_count = n_skipped_recipes % recipe_buffer_size
file_count = n_skipped_recipes // (recipe_buffer_size * recipe_buffers_per_file)
@ -60,6 +62,7 @@ savefile = open(f"recipes{file_count}.conllu", 'w')
instructions = []
ids = []
for raw_recipe in buffered_reader_1M:
i += 1
@ -92,3 +95,4 @@ process_instructions(instructions)
print(f"processed {i} recipes")
savefile.close()