moved 1 million recipes into database, starting coarse tree generation for recipe analysis
This commit is contained in:
Jonas Weinz
parent
cbccf169bb
commit
09eb58e703
223
RecipeAnalysis/Recipe Analysis.ipynb
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223
RecipeAnalysis/Recipe Analysis.ipynb
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{
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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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"# Recipe Analysis"
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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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"import sys\n",
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"sys.path.append(\"../\")\n",
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"from Recipe import Recipe"
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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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"source": [
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"import settings\n",
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"import db.db_settings as db_settings\n",
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"from db.database_connection import DatabaseConnection"
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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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"import random"
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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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{
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"data": {
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"text/plain": [
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"<db.database_connection.DatabaseConnection at 0x7f58b3f41b70>"
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]
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},
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"execution_count": 4,
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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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"DatabaseConnection(db_settings.db_host,\n",
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" db_settings.db_port,\n",
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" db_settings.db_user,\n",
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" db_settings.db_pw,\n",
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" db_settings.db_db,\n",
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" db_settings.db_charset)"
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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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"* get all recipe id's"
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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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"ids = DatabaseConnection.global_single_query(\"select id from recipes\")"
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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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"* analyse a random recipe"
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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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"test_rec = Recipe(random.choice(ids)['id'])"
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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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{
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"data": {
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"text/markdown": [
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"## Pat LaFriedas Filet Mignon Steak Sandwich\n",
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"(eaed08c862)"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/markdown": [
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"### Ingredients"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/markdown": [
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" * '4 tablespoons canola or other neutral-flavored oil , plus more as needed'\n",
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" * '2 large sweet yellow onions or Spanish onions , thinly sliced \\( about 3 cups \\)'\n",
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" * '6 ounces thinly sliced Monterey Jack cheese'\n",
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" * '1 cup beef stock'\n",
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" * '1 1/2 teaspoons balsamic glaze'\n",
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" * '12 \\( 1 1/2-inch thick \\) filet medallions \\( about 1 1/2 pounds \\)'\n",
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" * '1 tablespoon kosher salt'\n",
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" * '1/2 teaspoon turbinado sugar or light brown sugar'\n",
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" * '4 demi-baguettes \\( or 6-inch \\) segments of a long baguette'"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/markdown": [
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"### Instructions"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/markdown": [
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" * In a large skillet , heat 2 tablespoons of the oil over medium heat until it slides easily in the pan , 2 to 3 minutes .\n",
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" * Add the onions and cook , stirring occasionally so they do n't stick to the pan , until they are soft and caramelized , about 20 minutes .\n",
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" * Spread the onions out over the surface of the pan .\n",
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" * Remove from the heat and lay the cheese on top of the onions , letting it melt .\n",
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" * To make a jus , in a small saucepan , bring the stock to a simmer over medium heat .\n",
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" * Remove from the heat and stir in the balsamic glaze .\n",
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" * Cover the pan to keep the jus warm .\n",
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" * Season the meat on both sides with the salt and sugar .\n",
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" * In another large skillet , heat the remaining 2 tablespoons oil over high heat .\n",
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" * Add half the medallions , or as many as will fit in a single layer , and sear them until they are caramelized , 1 to 1 1/2 minutes per side .\n",
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" * Cook the remaining medallions in the same way , adding more oil and letting it get hot before adding the meat to the pan .\n",
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" * Meanwhile , without opening them , toast the baguettes so that the outsides , top and bottom , are hot and crispy .\n",
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" * Halve the baguettes horizontally , leaving them hinged on one side .\n",
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" * To assemble the sandwiches , lay 3 medallions on the bottom of each baguette .\n",
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" * Top with the onions and cheese , dividing them equally among the sandwiches .\n",
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" * Drizzle 1/4 cup of the jus on the inside top half of each baguette .\n",
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" * Close up the sandwiches and you 're good to go ."
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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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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"CPU times: user 1.31 ms, sys: 7.65 ms, total: 8.96 ms\n",
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"Wall time: 7.88 ms\n"
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]
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}
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],
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"source": [
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"%time test_rec.display_recipe()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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298
RecipeAnalysis/Recipe.ipynb
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298
RecipeAnalysis/Recipe.ipynb
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{
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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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"# Recipe class"
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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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"import sys\n",
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"sys.path.append(\"../\")\n",
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"\n",
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"import settings\n",
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"\n",
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"import pycrfsuite\n",
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"\n",
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"import json\n",
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"\n",
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"import db.db_settings as db_settings\n",
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"from db.database_connection import DatabaseConnection\n",
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"\n",
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"from Tagging.conllu_generator import ConlluGenerator\n",
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"from Tagging.crf_data_generator import *\n",
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"\n",
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"from IPython.display import Markdown, HTML, display"
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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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"* get vocabulary"
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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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"source": [
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"import importlib.util\n",
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"# loading ingredients:\n",
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"spec = importlib.util.spec_from_file_location(\n",
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" \"ingredients\", \"../\" + settings.ingredients_file)\n",
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"ingredients = importlib.util.module_from_spec(spec)\n",
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"spec.loader.exec_module(ingredients)\n",
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"\n",
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"# loading actions:\n",
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"spec = importlib.util.spec_from_file_location(\n",
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" \"actions\", \"../\" + settings.actions_file)\n",
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"actions = importlib.util.module_from_spec(spec)\n",
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"spec.loader.exec_module(actions)\n",
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"\n",
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"# loading containers\n",
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"spec = importlib.util.spec_from_file_location(\n",
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" \"containers\", \"../\" + settings.container_file)\n",
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"containers = importlib.util.module_from_spec(spec)\n",
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"spec.loader.exec_module(containers)\n",
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"\n",
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"# loading placeholders\n",
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"spec = importlib.util.spec_from_file_location(\n",
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" \"placeholders\", \"../\" + settings.placeholder_file)\n",
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"placeholders = importlib.util.module_from_spec(spec)\n",
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"spec.loader.exec_module(placeholders)\n"
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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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{
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"data": {
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"text/plain": [
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"<contextlib.closing at 0x7f6743611278>"
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]
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},
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"execution_count": 3,
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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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"tagger = pycrfsuite.Tagger()\n",
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"tagger.open('../Tagging/test.crfsuite')"
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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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"id_query = \"select * from recipes where id like %s\""
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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 escape_md_chars(s):\n",
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" s = s.replace(\"*\", \"\\*\")\n",
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" s = s.replace(\"(\", \"\\(\")\n",
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" s = s.replace(\")\", \"\\)\")\n",
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" s = s.replace(\"[\", \"\\[\")\n",
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" s = s.replace(\"]\", \"\\]\")\n",
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" s = s.replace(\"_\", \"\\_\")\n",
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" \n",
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" return s"
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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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"class Recipe(object):\n",
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" def __init__(self, recipe_db_id = None):\n",
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" \n",
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" self._sentences = None\n",
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" self._title = None\n",
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" self._part = None\n",
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" self._ingredients = None\n",
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" self._recipe_id = recipe_db_id\n",
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" self._get_from_db()\n",
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" \n",
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" self._extracted_ingredients = None # TODO\n",
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" \n",
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" self.annotate_ingredients()\n",
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" self.annotate_sentences()\n",
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" \n",
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" def _get_from_db(self):\n",
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" result = DatabaseConnection.global_single_query(id_query, (self._recipe_id))\n",
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" assert len(result) > 0\n",
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" result = result[0]\n",
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" self._title = result['title']\n",
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" self._part = result['part']\n",
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" \n",
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" raw_sentences = json.loads(result['instructions'])\n",
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" raw_ingredients = json.loads(result['ingredients'])\n",
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" \n",
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" # throwing the raw data through our connlu generator to annotate them right\n",
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" cg_sents = ConlluGenerator([\"\\n\".join(raw_sentences)])\n",
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" cg_ings = ConlluGenerator([\"\\n\".join(raw_ingredients)])\n",
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" \n",
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" cg_sents.tokenize()\n",
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" cg_sents.pos_tagging_and_lemmatization()\n",
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" \n",
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" cg_ings.tokenize()\n",
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" cg_ings.pos_tagging_and_lemmatization()\n",
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" \n",
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" # TODO\n",
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" self._sentences = cg_sents.get_conllu_elements()[0]\n",
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" self._ingredients = cg_ings.get_conllu_elements()[0]\n",
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" #self._sentences = json.loads(result['instructions'])\n",
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" #self._ingredients = json.loads(result['ingredients'])\n",
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" \n",
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" def avg_sentence_length(self):\n",
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" return sum([len(s) for s in self._sentences])/len(self._sentences)\n",
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" \n",
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" def n_instructions(self):\n",
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" return len(self._sentences)\n",
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" \n",
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" def max_sentence_length(self):\n",
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" return max([len(s) for s in self._sentences])\n",
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" \n",
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" def keyword_ratio(self):\n",
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" sentence_ratios = []\n",
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" for sent in self._sentences:\n",
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" # FIXME: only works if there are no other misc annotations!\n",
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" sentence_ratios.append(sum([token['misc'] is not None for token in sent]))\n",
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" return sum(sentence_ratios) / len(sentence_ratios)\n",
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" \n",
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" def predict_labels(self):\n",
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" features = [sent2features(sent) for sent in self._sentences]\n",
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" labels = [tagger.tag(feat) for feat in features]\n",
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" return labels\n",
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" \n",
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" def predict_ingredient_labels(self):\n",
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" features = [sent2features(sent) for sent in self._ingredients]\n",
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" labels = [tagger.tag(feat) for feat in features]\n",
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" return labels\n",
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" \n",
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" def _annotate_sentences(self, sent_token_list, predictions):\n",
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" # test whether we predicted an label or found it in our label list\n",
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" for i, ing in enumerate(sent_token_list):\n",
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" for j, token in enumerate(ing):\n",
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" lemma = token['lemma']\n",
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" \n",
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" # check for ingredient\n",
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" if lemma in ingredients.ingredients_stemmed:\n",
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" token.add_misc(\"food_type\", \"ingredient\")\n",
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" elif predictions[i][j] == 'ingredient':\n",
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" token.add_misc(\"food_type\", \"ingredient\")\n",
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" \n",
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" # check for action\n",
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" if lemma in actions.stemmed_cooking_verbs:\n",
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" token.add_misc(\"food_type\", \"action\")\n",
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" elif predictions[i][j] == 'action':\n",
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" token.add_misc(\"food_type\", \"action\")\n",
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" \n",
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" # check for container\n",
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" if lemma in containers.stemmed_containers:\n",
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" token.add_misc(\"food_type\", \"container\")\n",
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" elif predictions[i][j] == 'container':\n",
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" token.add_misc(\"food_type\", \"container\")\n",
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" \n",
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" # check for placeholder\n",
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" if lemma in placeholders.stemmed_placeholders:\n",
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" token.add_misc(\"food_type\", \"placeholder\")\n",
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" elif predictions[i][j] == 'placeholder':\n",
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" token.add_misc(\"food_type\", \"placeholder\")\n",
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" \n",
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" def annotate_ingredients(self):\n",
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" self._annotate_sentences(self._ingredients, self.predict_ingredient_labels())\n",
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" \n",
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" def annotate_sentences(self):\n",
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" self._annotate_sentences(self._sentences, self.predict_labels())\n",
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" \n",
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" def recipe_id(self):\n",
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" return self._recipe_id\n",
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" \n",
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" def serialize(self):\n",
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" result = \"# newdoc\\n\"\n",
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" if self._recipe_id is not None:\n",
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||||
" result += f\"# id: {self._recipe_id}\\n\"\n",
|
||||
" \n",
|
||||
" for sent in self._sentences:\n",
|
||||
" result += f\"{sent.serialize()}\"\n",
|
||||
" return result + \"\\n\"\n",
|
||||
" \n",
|
||||
" def display_recipe(self):\n",
|
||||
" display(Markdown(f\"## {self._title}\\n({self._recipe_id})\"))\n",
|
||||
" display(Markdown(f\"### Ingredients\"))\n",
|
||||
" display(Markdown(\"\\n\".join([f\" * '{escape_md_chars(self.tokenlist2str(ing))}'\" for ing in self._ingredients])))\n",
|
||||
" display(Markdown(f\"### Instructions\"))\n",
|
||||
" display(Markdown(\"\\n\".join([f\" * {escape_md_chars(self.tokenlist2str(ins))}\" for ins in self._sentences])))\n",
|
||||
" \n",
|
||||
" def tokenlist2str(self, tokenlist):\n",
|
||||
" return \" \".join([token['form'] for token in tokenlist])\n",
|
||||
" \n",
|
||||
" def tokenarray2str(self, tokenarray):\n",
|
||||
" return \"\\n\".join([self.tokenlist2str(tokenlist) for tokenlist in tokenarray])\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" def __repr__(self):\n",
|
||||
" s = \"recipe: \" + (self._recipe_id if self._recipe_id else \"\") + \"\\n\"\n",
|
||||
" s += \"instructions: \\n\"\n",
|
||||
" for sent in self._sentences:\n",
|
||||
" s += \" \".join([token['form'] for token in sent]) + \"\\n\"\n",
|
||||
" \n",
|
||||
" s += \"\\nscores:\\n\"\n",
|
||||
" s += f\"avg_sent_length: {self.avg_sentence_length()}\\n\"\n",
|
||||
" s += f\"n_instructions: {self.n_instructions()}\\n\"\n",
|
||||
" s += f\"keyword_ratio: {self.keyword_ratio()}\\n\\n\\n\"\n",
|
||||
" \n",
|
||||
" return s"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
211
RecipeAnalysis/Recipe.py
Normal file
211
RecipeAnalysis/Recipe.py
Normal file
@ -0,0 +1,211 @@
|
||||
#!/usr/bin/env python3
|
||||
# coding: utf-8
|
||||
|
||||
# # Recipe class
|
||||
|
||||
import sys
|
||||
sys.path.append("../")
|
||||
|
||||
import settings
|
||||
|
||||
import pycrfsuite
|
||||
|
||||
import json
|
||||
|
||||
import db.db_settings as db_settings
|
||||
from db.database_connection import DatabaseConnection
|
||||
|
||||
from Tagging.conllu_generator import ConlluGenerator
|
||||
from Tagging.crf_data_generator import *
|
||||
|
||||
from IPython.display import Markdown, HTML, display
|
||||
|
||||
|
||||
# * get vocabulary
|
||||
|
||||
import importlib.util
|
||||
# loading ingredients:
|
||||
spec = importlib.util.spec_from_file_location(
|
||||
"ingredients", "../" + settings.ingredients_file)
|
||||
ingredients = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(ingredients)
|
||||
|
||||
# loading actions:
|
||||
spec = importlib.util.spec_from_file_location(
|
||||
"actions", "../" + settings.actions_file)
|
||||
actions = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(actions)
|
||||
|
||||
# loading containers
|
||||
spec = importlib.util.spec_from_file_location(
|
||||
"containers", "../" + settings.container_file)
|
||||
containers = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(containers)
|
||||
|
||||
# loading placeholders
|
||||
spec = importlib.util.spec_from_file_location(
|
||||
"placeholders", "../" + settings.placeholder_file)
|
||||
placeholders = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(placeholders)
|
||||
|
||||
|
||||
tagger = pycrfsuite.Tagger()
|
||||
tagger.open('../Tagging/test.crfsuite')
|
||||
|
||||
|
||||
id_query = "select * from recipes where id like %s"
|
||||
|
||||
|
||||
def escape_md_chars(s):
|
||||
s = s.replace("*", "\*")
|
||||
s = s.replace("(", "\(")
|
||||
s = s.replace(")", "\)")
|
||||
s = s.replace("[", "\[")
|
||||
s = s.replace("]", "\]")
|
||||
s = s.replace("_", "\_")
|
||||
|
||||
return s
|
||||
|
||||
|
||||
class Recipe(object):
|
||||
def __init__(self, recipe_db_id = None):
|
||||
|
||||
self._sentences = None
|
||||
self._title = None
|
||||
self._part = None
|
||||
self._ingredients = None
|
||||
self._recipe_id = recipe_db_id
|
||||
self._get_from_db()
|
||||
self.annotate_ingredients()
|
||||
self.annotate_sentences()
|
||||
|
||||
def _get_from_db(self):
|
||||
result = DatabaseConnection.global_single_query(id_query, (self._recipe_id))
|
||||
assert len(result) > 0
|
||||
result = result[0]
|
||||
self._title = result['title']
|
||||
self._part = result['part']
|
||||
|
||||
raw_sentences = json.loads(result['instructions'])
|
||||
raw_ingredients = json.loads(result['ingredients'])
|
||||
|
||||
# throwing the raw data through our connlu generator to annotate them right
|
||||
cg_sents = ConlluGenerator(["\n".join(raw_sentences)])
|
||||
cg_ings = ConlluGenerator(["\n".join(raw_ingredients)])
|
||||
|
||||
cg_sents.tokenize()
|
||||
cg_sents.pos_tagging_and_lemmatization()
|
||||
|
||||
cg_ings.tokenize()
|
||||
cg_ings.pos_tagging_and_lemmatization()
|
||||
|
||||
# TODO
|
||||
self._sentences = cg_sents.get_conllu_elements()[0]
|
||||
self._ingredients = cg_ings.get_conllu_elements()[0]
|
||||
#self._sentences = json.loads(result['instructions'])
|
||||
#self._ingredients = json.loads(result['ingredients'])
|
||||
|
||||
def avg_sentence_length(self):
|
||||
return sum([len(s) for s in self._sentences])/len(self._sentences)
|
||||
|
||||
def n_instructions(self):
|
||||
return len(self._sentences)
|
||||
|
||||
def max_sentence_length(self):
|
||||
return max([len(s) for s in self._sentences])
|
||||
|
||||
def keyword_ratio(self):
|
||||
sentence_ratios = []
|
||||
for sent in self._sentences:
|
||||
# FIXME: only works if there are no other misc annotations!
|
||||
sentence_ratios.append(sum([token['misc'] is not None for token in sent]))
|
||||
return sum(sentence_ratios) / len(sentence_ratios)
|
||||
|
||||
def predict_labels(self):
|
||||
features = [sent2features(sent) for sent in self._sentences]
|
||||
labels = [tagger.tag(feat) for feat in features]
|
||||
return labels
|
||||
|
||||
def predict_ingredient_labels(self):
|
||||
features = [sent2features(sent) for sent in self._ingredients]
|
||||
labels = [tagger.tag(feat) for feat in features]
|
||||
return labels
|
||||
|
||||
def _annotate_sentences(self, sent_token_list, predictions):
|
||||
# test whether we predicted an label or found it in our label list
|
||||
for i, ing in enumerate(sent_token_list):
|
||||
for j, token in enumerate(ing):
|
||||
lemma = token['lemma']
|
||||
|
||||
# check for ingredient
|
||||
if lemma in ingredients.ingredients_stemmed:
|
||||
token.add_misc("food_type", "ingredient")
|
||||
elif predictions[i][j] == 'ingredient':
|
||||
token.add_misc("food_type", "ingredient")
|
||||
|
||||
# check for action
|
||||
if lemma in actions.stemmed_cooking_verbs:
|
||||
token.add_misc("food_type", "action")
|
||||
elif predictions[i][j] == 'action':
|
||||
token.add_misc("food_type", "action")
|
||||
|
||||
# check for container
|
||||
if lemma in containers.stemmed_containers:
|
||||
token.add_misc("food_type", "container")
|
||||
elif predictions[i][j] == 'container':
|
||||
token.add_misc("food_type", "container")
|
||||
|
||||
# check for placeholder
|
||||
if lemma in placeholders.stemmed_placeholders:
|
||||
token.add_misc("food_type", "placeholder")
|
||||
elif predictions[i][j] == 'placeholder':
|
||||
token.add_misc("food_type", "placeholder")
|
||||
|
||||
def annotate_ingredients(self):
|
||||
self._annotate_sentences(self._ingredients, self.predict_ingredient_labels())
|
||||
|
||||
def annotate_sentences(self):
|
||||
self._annotate_sentences(self._sentences, self.predict_labels())
|
||||
|
||||
def recipe_id(self):
|
||||
return self._recipe_id
|
||||
|
||||
def serialize(self):
|
||||
result = "# newdoc\n"
|
||||
if self._recipe_id is not None:
|
||||
result += f"# id: {self._recipe_id}\n"
|
||||
|
||||
for sent in self._sentences:
|
||||
result += f"{sent.serialize()}"
|
||||
return result + "\n"
|
||||
|
||||
def display_recipe(self):
|
||||
display(Markdown(f"## {self._title}\n({self._recipe_id})"))
|
||||
display(Markdown(f"### Ingredients"))
|
||||
display(Markdown("\n".join([f" * '{escape_md_chars(self.tokenlist2str(ing))}'" for ing in self._ingredients])))
|
||||
display(Markdown(f"### Instructions"))
|
||||
display(Markdown("\n".join([f" * {escape_md_chars(self.tokenlist2str(ins))}" for ins in self._sentences])))
|
||||
|
||||
def tokenlist2str(self, tokenlist):
|
||||
return " ".join([token['form'] for token in tokenlist])
|
||||
|
||||
def tokenarray2str(self, tokenarray):
|
||||
return "\n".join([self.tokenlist2str(tokenlist) for tokenlist in tokenarray])
|
||||
|
||||
|
||||
def __repr__(self):
|
||||
s = "recipe: " + (self._recipe_id if self._recipe_id else "") + "\n"
|
||||
s += "instructions: \n"
|
||||
for sent in self._sentences:
|
||||
s += " ".join([token['form'] for token in sent]) + "\n"
|
||||
|
||||
s += "\nscores:\n"
|
||||
s += f"avg_sent_length: {self.avg_sentence_length()}\n"
|
||||
s += f"n_instructions: {self.n_instructions()}\n"
|
||||
s += f"keyword_ratio: {self.keyword_ratio()}\n\n\n"
|
||||
|
||||
return s
|
||||
|
||||
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@ -20,7 +20,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -29,7 +29,7 @@
|
||||
"TokenList<Dissolve, Jello, in, boiling, water, .>"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -40,7 +40,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -109,7 +109,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -125,7 +125,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -135,7 +135,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -145,7 +145,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -155,7 +155,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -172,7 +172,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -181,7 +181,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -218,16 +218,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"45442"
|
||||
"47538"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -245,7 +245,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -254,7 +254,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -266,7 +266,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -282,7 +282,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -302,7 +302,7 @@
|
||||
" 'max_linesearch']"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -313,7 +313,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -322,24 +322,24 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'num': 455,\n",
|
||||
"{'num': 830,\n",
|
||||
" 'scores': {},\n",
|
||||
" 'loss': 110.581675,\n",
|
||||
" 'feature_norm': 8.594619,\n",
|
||||
" 'error_norm': 0.214403,\n",
|
||||
" 'active_features': 87,\n",
|
||||
" 'loss': 41171.669638,\n",
|
||||
" 'feature_norm': 126.341894,\n",
|
||||
" 'error_norm': 85.690855,\n",
|
||||
" 'active_features': 6055,\n",
|
||||
" 'linesearch_trials': 2,\n",
|
||||
" 'linesearch_step': 0.5,\n",
|
||||
" 'time': 0.001}"
|
||||
" 'time': 0.724}"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -357,16 +357,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<contextlib.closing at 0x7f056bc6d828>"
|
||||
"<contextlib.closing at 0x7f26d79813c8>"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -378,7 +378,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -388,7 +388,7 @@
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-22-a88100b49642>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m130\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\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[0;31m#print(' '.join(feature2tokens(X_test[i])), end='\\n\\n')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Predicted:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m' '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtagger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\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 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Correct: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m' '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\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<ipython-input-20-a88100b49642>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m130\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\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[0;31m#print(' '.join(feature2tokens(X_test[i])), end='\\n\\n')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Predicted:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m' '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtagger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\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 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Correct: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m' '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\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;31mIndexError\u001b[0m: list index out of range"
|
||||
]
|
||||
}
|
||||
|
||||
File diff suppressed because one or more lines are too long
@ -16,14 +16,15 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sys\n",
|
||||
"sys.path.append('../')\n",
|
||||
"\n",
|
||||
"from conllu import parse\n",
|
||||
"from tagging_tools import print_visualized_tags\n",
|
||||
"from Tagging.tagging_tools import print_visualized_tags\n",
|
||||
"\n",
|
||||
"from sklearn import preprocessing\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"sys.path.insert(0, '..')\n",
|
||||
"\n",
|
||||
"import settings # noqa\n",
|
||||
"\n",
|
||||
"import gzip"
|
||||
|
||||
@ -6,14 +6,15 @@
|
||||
# read conllu documents in batches
|
||||
|
||||
import sys
|
||||
sys.path.append('../')
|
||||
|
||||
from conllu import parse
|
||||
from tagging_tools import print_visualized_tags
|
||||
from Tagging.tagging_tools import print_visualized_tags
|
||||
|
||||
from sklearn import preprocessing
|
||||
import numpy as np
|
||||
|
||||
sys.path.insert(0, '..')
|
||||
|
||||
import settings # noqa
|
||||
|
||||
import gzip
|
||||
|
||||
@ -25,11 +25,14 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sys\n",
|
||||
"sys.path.append(\"../\")\n",
|
||||
"\n",
|
||||
"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\n",
|
||||
"from Tagging.stemmed_mwe_tokenizer import StemmedMWETokenizer\n",
|
||||
"from nltk.stem import WordNetLemmatizer"
|
||||
]
|
||||
},
|
||||
@ -188,7 +191,22 @@
|
||||
" result = \"\"\n",
|
||||
" for attr in CONLLU_ATTRIBUTES:\n",
|
||||
" result += str(self.__getattribute__(attr)) + \" \\t\"\n",
|
||||
" return replace_tab(result, 16)"
|
||||
" return replace_tab(result, 16)\n",
|
||||
" \n",
|
||||
" def __getitem__(self, key):\n",
|
||||
" \n",
|
||||
" # conllu module compability:\n",
|
||||
" if key == \"upostag\":\n",
|
||||
" key = \"upos\"\n",
|
||||
" if key == \"xpostag\":\n",
|
||||
" key = \"xpos\"\n",
|
||||
" \n",
|
||||
" if key not in CONLLU_ATTRIBUTES:\n",
|
||||
" return None\n",
|
||||
" attr = self.__getattribute__(key)\n",
|
||||
" if str(attr) == \"_\":\n",
|
||||
" return None\n",
|
||||
" return attr"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -210,6 +228,9 @@
|
||||
"\n",
|
||||
" def add(self, conllu_element: ConlluElement):\n",
|
||||
" self.conllu_elements.append(conllu_element)\n",
|
||||
" \n",
|
||||
" def get_conllu_elements(self):\n",
|
||||
" return self.conllu_elements\n",
|
||||
"\n",
|
||||
" def __repr__(self):\n",
|
||||
" result = \"\"\n",
|
||||
@ -243,6 +264,9 @@
|
||||
" def add(self, conllu_sentence: ConlluSentence):\n",
|
||||
" self.conllu_sentences.append(conllu_sentence)\n",
|
||||
" \n",
|
||||
" def get_conllu_elements(self):\n",
|
||||
" return [c_sent.get_conllu_elements() for c_sent in self.conllu_sentences]\n",
|
||||
" \n",
|
||||
" def __repr__(self):\n",
|
||||
" result = \"# newdoc\\n\"\n",
|
||||
" if self.id is not None:\n",
|
||||
@ -270,15 +294,18 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class ConlluGenerator(object):\n",
|
||||
" def __init__(self, documents: list, stemmed_multi_word_tokens, stemmer=PorterStemmer(), ids=None):\n",
|
||||
" def __init__(self, documents: list, stemmed_multi_word_tokens=None, 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",
|
||||
" \n",
|
||||
" if self.stemmed_multi_word_tokens is not None:\n",
|
||||
" self.mwe_tokenizer = StemmedMWETokenizer(\n",
|
||||
" [w.split() for w in stemmed_multi_word_tokens])\n",
|
||||
" else:\n",
|
||||
" self.mwe_tokenizer = None\n",
|
||||
" \n",
|
||||
" self.stemmer = stemmer\n",
|
||||
"\n",
|
||||
" self.id_counter = 0\n",
|
||||
"\n",
|
||||
" self.conllu_documents = []\n",
|
||||
"\n",
|
||||
" self.ids = ids\n",
|
||||
@ -293,8 +320,11 @@
|
||||
" 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",
|
||||
" if self.mwe_tokenizer is None:\n",
|
||||
" tokenized_sentences.append(simple_tokenized)\n",
|
||||
" else:\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",
|
||||
@ -304,13 +334,14 @@
|
||||
" else:\n",
|
||||
" conllu_doc = ConlluDocument()\n",
|
||||
" for sent in doc:\n",
|
||||
" token_id = 0\n",
|
||||
" conllu_sent = ConlluSentence()\n",
|
||||
" for token in sent:\n",
|
||||
" token_id += 1\n",
|
||||
" conllu_sent.add(ConlluElement(\n",
|
||||
" id=self.id_counter + 1,\n",
|
||||
" id=token_id,\n",
|
||||
" form=token,\n",
|
||||
" ))\n",
|
||||
" self.id_counter += 1\n",
|
||||
" conllu_doc.add(conllu_sent)\n",
|
||||
" self.conllu_documents.append(conllu_doc)\n",
|
||||
" i += 1\n",
|
||||
@ -340,6 +371,9 @@
|
||||
" for elem in conllu_sent.conllu_elements:\n",
|
||||
" if elem.lemma in stemmed_keyword_list:\n",
|
||||
" elem.add_misc(key, value)\n",
|
||||
" \n",
|
||||
" def get_conllu_elements(self):\n",
|
||||
" return [doc.get_conllu_elements() for doc in self.conllu_documents]\n",
|
||||
"\n",
|
||||
" def __repr__(self):\n",
|
||||
" result = \"\"\n",
|
||||
|
||||
@ -10,11 +10,14 @@
|
||||
|
||||
# ## imports and settings
|
||||
|
||||
import sys
|
||||
sys.path.append("../")
|
||||
|
||||
import nltk
|
||||
from nltk.tag import pos_tag, map_tag
|
||||
from nltk.stem import PorterStemmer
|
||||
from nltk.corpus import stopwords as nltk_stopwords
|
||||
from stemmed_mwe_tokenizer import StemmedMWETokenizer
|
||||
from Tagging.stemmed_mwe_tokenizer import StemmedMWETokenizer
|
||||
from nltk.stem import WordNetLemmatizer
|
||||
|
||||
|
||||
@ -128,6 +131,21 @@ class ConlluElement(object):
|
||||
for attr in CONLLU_ATTRIBUTES:
|
||||
result += str(self.__getattribute__(attr)) + " \t"
|
||||
return replace_tab(result, 16)
|
||||
|
||||
def __getitem__(self, key):
|
||||
|
||||
# conllu module compability:
|
||||
if key == "upostag":
|
||||
key = "upos"
|
||||
if key == "xpostag":
|
||||
key = "xpos"
|
||||
|
||||
if key not in CONLLU_ATTRIBUTES:
|
||||
return None
|
||||
attr = self.__getattribute__(key)
|
||||
if str(attr) == "_":
|
||||
return None
|
||||
return attr
|
||||
|
||||
|
||||
# ## Conllu Sentence Class
|
||||
@ -138,6 +156,9 @@ class ConlluSentence(object):
|
||||
|
||||
def add(self, conllu_element: ConlluElement):
|
||||
self.conllu_elements.append(conllu_element)
|
||||
|
||||
def get_conllu_elements(self):
|
||||
return self.conllu_elements
|
||||
|
||||
def __repr__(self):
|
||||
result = ""
|
||||
@ -160,6 +181,9 @@ class ConlluDocument(object):
|
||||
def add(self, conllu_sentence: ConlluSentence):
|
||||
self.conllu_sentences.append(conllu_sentence)
|
||||
|
||||
def get_conllu_elements(self):
|
||||
return [c_sent.get_conllu_elements() for c_sent in self.conllu_sentences]
|
||||
|
||||
def __repr__(self):
|
||||
result = "# newdoc\n"
|
||||
if self.id is not None:
|
||||
@ -176,15 +200,18 @@ class ConlluDocument(object):
|
||||
# ## Conllu Generator Class
|
||||
|
||||
class ConlluGenerator(object):
|
||||
def __init__(self, documents: list, stemmed_multi_word_tokens, stemmer=PorterStemmer(), ids=None):
|
||||
def __init__(self, documents: list, stemmed_multi_word_tokens=None, stemmer=PorterStemmer(), ids=None):
|
||||
self.documents = documents
|
||||
self.stemmed_multi_word_tokens = stemmed_multi_word_tokens
|
||||
self.mwe_tokenizer = StemmedMWETokenizer(
|
||||
[w.split() for w in stemmed_multi_word_tokens])
|
||||
|
||||
if self.stemmed_multi_word_tokens is not None:
|
||||
self.mwe_tokenizer = StemmedMWETokenizer(
|
||||
[w.split() for w in stemmed_multi_word_tokens])
|
||||
else:
|
||||
self.mwe_tokenizer = None
|
||||
|
||||
self.stemmer = stemmer
|
||||
|
||||
self.id_counter = 0
|
||||
|
||||
self.conllu_documents = []
|
||||
|
||||
self.ids = ids
|
||||
@ -199,8 +226,11 @@ class ConlluGenerator(object):
|
||||
for sent in sentences:
|
||||
if (len(sent) > 0):
|
||||
simple_tokenized = nltk.tokenize.word_tokenize(sent)
|
||||
tokenized_sentences.append(
|
||||
self.mwe_tokenizer.tokenize(simple_tokenized))
|
||||
if self.mwe_tokenizer is None:
|
||||
tokenized_sentences.append(simple_tokenized)
|
||||
else:
|
||||
tokenized_sentences.append(
|
||||
self.mwe_tokenizer.tokenize(simple_tokenized))
|
||||
tokenized_documents.append(tokenized_sentences)
|
||||
|
||||
# now create initial colln-u elemnts
|
||||
@ -210,13 +240,14 @@ class ConlluGenerator(object):
|
||||
else:
|
||||
conllu_doc = ConlluDocument()
|
||||
for sent in doc:
|
||||
token_id = 0
|
||||
conllu_sent = ConlluSentence()
|
||||
for token in sent:
|
||||
token_id += 1
|
||||
conllu_sent.add(ConlluElement(
|
||||
id=self.id_counter + 1,
|
||||
id=token_id,
|
||||
form=token,
|
||||
))
|
||||
self.id_counter += 1
|
||||
conllu_doc.add(conllu_sent)
|
||||
self.conllu_documents.append(conllu_doc)
|
||||
i += 1
|
||||
@ -246,6 +277,9 @@ class ConlluGenerator(object):
|
||||
for elem in conllu_sent.conllu_elements:
|
||||
if elem.lemma in stemmed_keyword_list:
|
||||
elem.add_misc(key, value)
|
||||
|
||||
def get_conllu_elements(self):
|
||||
return [doc.get_conllu_elements() for doc in self.conllu_documents]
|
||||
|
||||
def __repr__(self):
|
||||
result = ""
|
||||
|
||||
@ -13,7 +13,17 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import conllu_batch_generator as cbg"
|
||||
"import sys\n",
|
||||
"sys.path.append(\"../\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import Tagging.conllu_batch_generator as cbg"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@ -3,7 +3,11 @@
|
||||
|
||||
# # crf data Generator
|
||||
|
||||
import conllu_batch_generator as cbg
|
||||
import sys
|
||||
sys.path.append("../")
|
||||
|
||||
|
||||
import Tagging.conllu_batch_generator as cbg
|
||||
|
||||
|
||||
def word2features(sent, i):
|
||||
|
||||
@ -46,6 +46,12 @@
|
||||
"containers = importlib.util.module_from_spec(spec)\n",
|
||||
"spec.loader.exec_module(containers)\n",
|
||||
"\n",
|
||||
"# loading placeholders\n",
|
||||
"spec = importlib.util.spec_from_file_location(\n",
|
||||
" \"placeholders\", \"../\" + settings.placeholder_file)\n",
|
||||
"placeholders = importlib.util.module_from_spec(spec)\n",
|
||||
"spec.loader.exec_module(placeholders)\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",
|
||||
@ -80,7 +86,8 @@
|
||||
" \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",
|
||||
" cg.add_misc_value_by_list(\"food_type\", \"containers\", containers.containers)\n",
|
||||
" cg.add_misc_value_by_list(\"food_type\", \"containers\", containers.stemmed_containers)\n",
|
||||
" cg.add_misc_value_by_list(\"food_type\", \"placeholders\", placeholders.stemmed_placeholders)\n",
|
||||
"\n",
|
||||
" savefile.write(str(cg))"
|
||||
]
|
||||
|
||||
@ -30,6 +30,12 @@ spec = importlib.util.spec_from_file_location(
|
||||
containers = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(containers)
|
||||
|
||||
# loading placeholders
|
||||
spec = importlib.util.spec_from_file_location(
|
||||
"placeholders", "../" + settings.placeholder_file)
|
||||
placeholders = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(placeholders)
|
||||
|
||||
# skipping recipes:
|
||||
n_skipped_recipes = int(sys.argv[1]) if len(sys.argv) > 1 else 0
|
||||
print("start reading at recipe " + str(n_skipped_recipes))
|
||||
@ -58,7 +64,8 @@ def process_instructions(instructions: list, document_ids=None):
|
||||
|
||||
cg.add_misc_value_by_list("food_type", "ingredient", [w.replace(" ","_") for w in ingredients.multi_word_ingredients_stemmed] + ingredients.ingredients_stemmed)
|
||||
cg.add_misc_value_by_list("food_type", "action", actions.stemmed_cooking_verbs)
|
||||
cg.add_misc_value_by_list("food_type", "containers", containers.containers)
|
||||
cg.add_misc_value_by_list("food_type", "containers", containers.stemmed_containers)
|
||||
cg.add_misc_value_by_list("food_type", "placeholders", placeholders.stemmed_placeholders)
|
||||
|
||||
savefile.write(str(cg))
|
||||
|
||||
|
||||
Binary file not shown.
File diff suppressed because one or more lines are too long
1298
db/create_database.ipynb
Normal file
1298
db/create_database.ipynb
Normal file
File diff suppressed because it is too large
Load Diff
304
db/create_database_docker.ipynb
Normal file
304
db/create_database_docker.ipynb
Normal file
@ -0,0 +1,304 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Create Database with Docker"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"* download mariadb image"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using default tag: latest\n",
|
||||
"latest: Pulling from mariadb/server\n",
|
||||
"Digest: sha256:b5762c478d38ae54c464e3ab63e10e0c3f951633ed7619d52fa3c22bcf36218a\n",
|
||||
"Status: Image is up to date for mariadb/server:latest\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%bash \n",
|
||||
"docker pull mariadb/server"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"* create and run container"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"09342f7c540b2b6224bb96b2e3a542a5ff144e6bbc1cf243ae33b0dab9262c47\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%bash\n",
|
||||
"docker run --name recipe_db -e MYSQL_ROOT_PASSWORD=\"g00d_r3c1p3s\" -d mariadb/server"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"* checking docker logs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Initializing database\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"PLEASE REMEMBER TO SET A PASSWORD FOR THE MariaDB root USER !\n",
|
||||
"To do so, start the server, then issue the following commands:\n",
|
||||
"\n",
|
||||
"'/usr/bin/mysqladmin' -u root password 'new-password'\n",
|
||||
"'/usr/bin/mysqladmin' -u root -h password 'new-password'\n",
|
||||
"\n",
|
||||
"Alternatively you can run:\n",
|
||||
"'/usr/bin/mysql_secure_installation'\n",
|
||||
"\n",
|
||||
"which will also give you the option of removing the test\n",
|
||||
"databases and anonymous user created by default. This is\n",
|
||||
"strongly recommended for production servers.\n",
|
||||
"\n",
|
||||
"See the MariaDB Knowledgebase at http://mariadb.com/kb or the\n",
|
||||
"MySQL manual for more instructions.\n",
|
||||
"\n",
|
||||
"Please report any problems at http://mariadb.org/jira\n",
|
||||
"\n",
|
||||
"The latest information about MariaDB is available at http://mariadb.org/.\n",
|
||||
"You can find additional information about the MySQL part at:\n",
|
||||
"http://dev.mysql.com\n",
|
||||
"Consider joining MariaDB's strong and vibrant community:\n",
|
||||
"https://mariadb.org/get-involved/\n",
|
||||
"\n",
|
||||
"Database initialized\n",
|
||||
"MySQL init process in progress...\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"MySQL init process done. Ready for start up.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2019-08-08 14:46:43 0 [Note] mysqld (mysqld 10.3.13-MariaDB-1:10.3.13+maria~bionic) starting as process 104 ...\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: Using Linux native AIO\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: Mutexes and rw_locks use GCC atomic builtins\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: Uses event mutexes\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: Compressed tables use zlib 1.2.11\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: Number of pools: 1\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: Using SSE2 crc32 instructions\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: Initializing buffer pool, total size = 256M, instances = 1, chunk size = 128M\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: Completed initialization of buffer pool\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: If the mysqld execution user is authorized, page cleaner thread priority can be changed. See the man page of setpriority().\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: 128 out of 128 rollback segments are active.\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: Creating shared tablespace for temporary tables\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: Setting file './ibtmp1' size to 12 MB. Physically writing the file full; Please wait ...\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: File './ibtmp1' size is now 12 MB.\n",
|
||||
"2019-08-08 14:46:43 0 [Note] InnoDB: Waiting for purge to start\n",
|
||||
"2019-08-08 14:46:44 0 [Note] InnoDB: 10.3.13 started; log sequence number 1630815; transaction id 21\n",
|
||||
"2019-08-08 14:46:44 0 [Note] InnoDB: Loading buffer pool(s) from /var/lib/mysql/ib_buffer_pool\n",
|
||||
"2019-08-08 14:46:44 0 [Note] Plugin 'FEEDBACK' is disabled.\n",
|
||||
"2019-08-08 14:46:44 0 [Note] InnoDB: Buffer pool(s) load completed at 190808 14:46:44\n",
|
||||
"2019-08-08 14:46:44 0 [Warning] 'user' entry 'root@09342f7c540b' ignored in --skip-name-resolve mode.\n",
|
||||
"2019-08-08 14:46:44 0 [Warning] 'user' entry '@09342f7c540b' ignored in --skip-name-resolve mode.\n",
|
||||
"2019-08-08 14:46:44 0 [Warning] 'proxies_priv' entry '@% root@09342f7c540b' ignored in --skip-name-resolve mode.\n",
|
||||
"2019-08-08 14:46:44 0 [Note] Reading of all Master_info entries succeded\n",
|
||||
"2019-08-08 14:46:44 0 [Note] Added new Master_info '' to hash table\n",
|
||||
"2019-08-08 14:46:44 0 [Note] mysqld: ready for connections.\n",
|
||||
"Version: '10.3.13-MariaDB-1:10.3.13+maria~bionic' socket: '/var/run/mysqld/mysqld.sock' port: 0 mariadb.org binary distribution\n",
|
||||
"Warning: Unable to load '/usr/share/zoneinfo/leap-seconds.list' as time zone. Skipping it.\n",
|
||||
"2019-08-08 14:46:46 10 [Warning] 'proxies_priv' entry '@% root@09342f7c540b' ignored in --skip-name-resolve mode.\n",
|
||||
"2019-08-08 14:46:46 0 [Note] mysqld (initiated by: unknown): Normal shutdown\n",
|
||||
"2019-08-08 14:46:46 0 [Note] Event Scheduler: Purging the queue. 0 events\n",
|
||||
"2019-08-08 14:46:46 0 [Note] InnoDB: FTS optimize thread exiting.\n",
|
||||
"2019-08-08 14:46:46 0 [Note] InnoDB: Starting shutdown...\n",
|
||||
"2019-08-08 14:46:46 0 [Note] InnoDB: Dumping buffer pool(s) to /var/lib/mysql/ib_buffer_pool\n",
|
||||
"2019-08-08 14:46:46 0 [Note] InnoDB: Buffer pool(s) dump completed at 190808 14:46:46\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: Shutdown completed; log sequence number 1630824; transaction id 24\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: Removed temporary tablespace data file: \"ibtmp1\"\n",
|
||||
"2019-08-08 14:46:47 0 [Note] mysqld: Shutdown complete\n",
|
||||
"\n",
|
||||
"2019-08-08 14:46:47 0 [Note] mysqld (mysqld 10.3.13-MariaDB-1:10.3.13+maria~bionic) starting as process 1 ...\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: Using Linux native AIO\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: Mutexes and rw_locks use GCC atomic builtins\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: Uses event mutexes\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: Compressed tables use zlib 1.2.11\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: Number of pools: 1\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: Using SSE2 crc32 instructions\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: Initializing buffer pool, total size = 256M, instances = 1, chunk size = 128M\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: Completed initialization of buffer pool\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: If the mysqld execution user is authorized, page cleaner thread priority can be changed. See the man page of setpriority().\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: 128 out of 128 rollback segments are active.\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: Creating shared tablespace for temporary tables\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: Setting file './ibtmp1' size to 12 MB. Physically writing the file full; Please wait ...\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: File './ibtmp1' size is now 12 MB.\n",
|
||||
"2019-08-08 14:46:47 0 [Note] InnoDB: Waiting for purge to start\n",
|
||||
"2019-08-08 14:46:48 0 [Note] InnoDB: 10.3.13 started; log sequence number 1630824; transaction id 21\n",
|
||||
"2019-08-08 14:46:48 0 [Note] InnoDB: Loading buffer pool(s) from /var/lib/mysql/ib_buffer_pool\n",
|
||||
"2019-08-08 14:46:48 0 [Note] Plugin 'FEEDBACK' is disabled.\n",
|
||||
"2019-08-08 14:46:48 0 [Note] Server socket created on IP: '::'.\n",
|
||||
"2019-08-08 14:46:48 0 [Note] InnoDB: Buffer pool(s) load completed at 190808 14:46:48\n",
|
||||
"2019-08-08 14:46:48 0 [Warning] 'proxies_priv' entry '@% root@09342f7c540b' ignored in --skip-name-resolve mode.\n",
|
||||
"2019-08-08 14:46:48 0 [Note] Reading of all Master_info entries succeded\n",
|
||||
"2019-08-08 14:46:48 0 [Note] Added new Master_info '' to hash table\n",
|
||||
"2019-08-08 14:46:48 0 [Note] mysqld: ready for connections.\n",
|
||||
"Version: '10.3.13-MariaDB-1:10.3.13+maria~bionic' socket: '/var/run/mysqld/mysqld.sock' port: 3306 mariadb.org binary distribution\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%bash\n",
|
||||
"docker logs recipe_db"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"* get local db address. **NOTE**: dont forget to set this address in settings.py!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"172.17.0.2\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%bash\n",
|
||||
"docker inspect --format '{{ .NetworkSettings.IPAddress }}' recipe_db"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"* and create databse"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"mysql: [Warning] Using a password on the command line interface can be insecure.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%bash\n",
|
||||
"mysql -h 172.17.0.2 -u root --password=\"g00d_r3c1p3s\" -e \"CREATE DATABASE recipe_db\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"* check whether database is created:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Database\n",
|
||||
"information_schema\n",
|
||||
"mysql\n",
|
||||
"performance_schema\n",
|
||||
"recipe_db\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"mysql: [Warning] Using a password on the command line interface can be insecure.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%bash\n",
|
||||
"mysql -h 172.17.0.2 -u root --password=\"g00d_r3c1p3s\" -e \"show databases\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
190
db/database_connection.ipynb
Normal file
190
db/database_connection.ipynb
Normal file
@ -0,0 +1,190 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pymysql.cursors\n",
|
||||
"import sys\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_sql_time(datetime_object):\n",
|
||||
" return datetime_object.strftime('%Y-%m-%d %H:%M:%S')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class SQLInjectionError(Exception):\n",
|
||||
" def __init__(self):\n",
|
||||
"\n",
|
||||
" # Call the base class constructor with the parameters it needs\n",
|
||||
" super().__init__(\"Detected possible SQL injection attack!\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class DatabaseConnection(object):\n",
|
||||
" \"\"\"\n",
|
||||
" a singleton class for a global database connection\n",
|
||||
" \"\"\"\n",
|
||||
"\n",
|
||||
" instance = None\n",
|
||||
"\n",
|
||||
" @staticmethod\n",
|
||||
" def global_cursor():\n",
|
||||
" assert DatabaseConnection.instance is not None\n",
|
||||
" return DatabaseConnection.instance.get_cursor()\n",
|
||||
"\n",
|
||||
" @staticmethod\n",
|
||||
" def global_close():\n",
|
||||
" assert DatabaseConnection.instance is not None\n",
|
||||
" DatabaseConnection.instance.close()\n",
|
||||
"\n",
|
||||
" @staticmethod\n",
|
||||
" def global_commit():\n",
|
||||
" assert DatabaseConnection.instance is not None\n",
|
||||
" DatabaseConnection.instance.commit()\n",
|
||||
"\n",
|
||||
" @staticmethod\n",
|
||||
" def global_ping():\n",
|
||||
" assert DatabaseConnection.instance is not None\n",
|
||||
" DatabaseConnection.instance.connection.ping()\n",
|
||||
"\n",
|
||||
" @staticmethod\n",
|
||||
" def global_single_query(query, params=None):\n",
|
||||
" DatabaseConnection.global_ping()\n",
|
||||
" if ';' in query:\n",
|
||||
" # Possible injection!\n",
|
||||
" raise SQLInjectionError()\n",
|
||||
"\n",
|
||||
" with DatabaseConnection.global_cursor() as c:\n",
|
||||
" if params is None:\n",
|
||||
" c.execute(query)\n",
|
||||
" else:\n",
|
||||
" c.execute(query, params)\n",
|
||||
"\n",
|
||||
" return c.fetchall()\n",
|
||||
"\n",
|
||||
" @staticmethod\n",
|
||||
" def global_single_execution(sql_statement, params=None):\n",
|
||||
" DatabaseConnection.global_ping()\n",
|
||||
" if ';' in sql_statement:\n",
|
||||
" # Possible injection detected!\n",
|
||||
" raise SQLInjectionError()\n",
|
||||
"\n",
|
||||
" with DatabaseConnection.global_cursor() as c:\n",
|
||||
" if params is None:\n",
|
||||
" c.execute(sql_statement)\n",
|
||||
" else:\n",
|
||||
" c.execute(sql_statement, params)\n",
|
||||
" DatabaseConnection.global_commit()\n",
|
||||
"\n",
|
||||
" def __init__(self,\n",
|
||||
" host: str,\n",
|
||||
" port: int,\n",
|
||||
" user: str,\n",
|
||||
" password: str,\n",
|
||||
" db: str,\n",
|
||||
" charset: str):\n",
|
||||
"\n",
|
||||
" assert DatabaseConnection.instance is None\n",
|
||||
" try:\n",
|
||||
" self.connection = pymysql.connect(\n",
|
||||
" host=host,\n",
|
||||
" port=port,\n",
|
||||
" user=user,\n",
|
||||
" password=password,\n",
|
||||
" db=db,\n",
|
||||
" charset=charset,\n",
|
||||
" cursorclass=pymysql.cursors.DictCursor)\n",
|
||||
" DatabaseConnection.instance = self\n",
|
||||
" except Exception as e:\n",
|
||||
" sys.stderr.write(\"could not connect to database '\" +\n",
|
||||
" str(db) +\n",
|
||||
" \"' at \" +\n",
|
||||
" user +\n",
|
||||
" \"@\" +\n",
|
||||
" host +\n",
|
||||
" \":\" +\n",
|
||||
" str(port) +\n",
|
||||
" \"\\nCheck the configuration in settings.py!\\n\")\n",
|
||||
" raise Exception('could not connect to database')\n",
|
||||
"\n",
|
||||
" def get_cursor(self):\n",
|
||||
" return self.connection.cursor()\n",
|
||||
"\n",
|
||||
" def close(self):\n",
|
||||
" self.connection.close()\n",
|
||||
" DatabaseConnection.instance = None\n",
|
||||
"\n",
|
||||
" def commit(self):\n",
|
||||
" self.connection.commit()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def test_connection():\n",
|
||||
" import db_settings as settings\n",
|
||||
" DatabaseConnection(settings.db_host,\n",
|
||||
" settings.db_port,\n",
|
||||
" settings.db_user,\n",
|
||||
" settings.db_pw,\n",
|
||||
" settings.db_db,\n",
|
||||
" settings.db_charset)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_connection()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"()"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"DatabaseConnection.global_single_query(\"show tables\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
125
db/database_connection.py
Normal file
125
db/database_connection.py
Normal file
@ -0,0 +1,125 @@
|
||||
#!/usr/bin/env python3
|
||||
# coding: utf-8
|
||||
|
||||
import pymysql.cursors
|
||||
import sys
|
||||
|
||||
|
||||
def get_sql_time(datetime_object):
|
||||
return datetime_object.strftime('%Y-%m-%d %H:%M:%S')
|
||||
|
||||
|
||||
class SQLInjectionError(Exception):
|
||||
def __init__(self):
|
||||
|
||||
# Call the base class constructor with the parameters it needs
|
||||
super().__init__("Detected possible SQL injection attack!")
|
||||
|
||||
|
||||
class DatabaseConnection(object):
|
||||
"""
|
||||
a singleton class for a global database connection
|
||||
"""
|
||||
|
||||
instance = None
|
||||
|
||||
@staticmethod
|
||||
def global_cursor():
|
||||
assert DatabaseConnection.instance is not None
|
||||
return DatabaseConnection.instance.get_cursor()
|
||||
|
||||
@staticmethod
|
||||
def global_close():
|
||||
assert DatabaseConnection.instance is not None
|
||||
DatabaseConnection.instance.close()
|
||||
|
||||
@staticmethod
|
||||
def global_commit():
|
||||
assert DatabaseConnection.instance is not None
|
||||
DatabaseConnection.instance.commit()
|
||||
|
||||
@staticmethod
|
||||
def global_ping():
|
||||
assert DatabaseConnection.instance is not None
|
||||
DatabaseConnection.instance.connection.ping()
|
||||
|
||||
@staticmethod
|
||||
def global_single_query(query, params=None):
|
||||
DatabaseConnection.global_ping()
|
||||
if ';' in query:
|
||||
# Possible injection!
|
||||
raise SQLInjectionError()
|
||||
|
||||
with DatabaseConnection.global_cursor() as c:
|
||||
if params is None:
|
||||
c.execute(query)
|
||||
else:
|
||||
c.execute(query, params)
|
||||
|
||||
return c.fetchall()
|
||||
|
||||
@staticmethod
|
||||
def global_single_execution(sql_statement, params=None):
|
||||
DatabaseConnection.global_ping()
|
||||
if ';' in sql_statement:
|
||||
# Possible injection detected!
|
||||
raise SQLInjectionError()
|
||||
|
||||
with DatabaseConnection.global_cursor() as c:
|
||||
if params is None:
|
||||
c.execute(sql_statement)
|
||||
else:
|
||||
c.execute(sql_statement, params)
|
||||
DatabaseConnection.global_commit()
|
||||
|
||||
def __init__(self,
|
||||
host: str,
|
||||
port: int,
|
||||
user: str,
|
||||
password: str,
|
||||
db: str,
|
||||
charset: str):
|
||||
|
||||
assert DatabaseConnection.instance is None
|
||||
try:
|
||||
self.connection = pymysql.connect(
|
||||
host=host,
|
||||
port=port,
|
||||
user=user,
|
||||
password=password,
|
||||
db=db,
|
||||
charset=charset,
|
||||
cursorclass=pymysql.cursors.DictCursor)
|
||||
DatabaseConnection.instance = self
|
||||
except Exception as e:
|
||||
sys.stderr.write("could not connect to database '" +
|
||||
str(db) +
|
||||
"' at " +
|
||||
user +
|
||||
"@" +
|
||||
host +
|
||||
":" +
|
||||
str(port) +
|
||||
"\nCheck the configuration in settings.py!\n")
|
||||
raise Exception('could not connect to database')
|
||||
|
||||
def get_cursor(self):
|
||||
return self.connection.cursor()
|
||||
|
||||
def close(self):
|
||||
self.connection.close()
|
||||
DatabaseConnection.instance = None
|
||||
|
||||
def commit(self):
|
||||
self.connection.commit()
|
||||
|
||||
|
||||
def test_connection():
|
||||
import settings
|
||||
DatabaseConnection(settings.db_host,
|
||||
settings.db_port,
|
||||
settings.db_user,
|
||||
settings.db_pw,
|
||||
settings.db_db,
|
||||
settings.db_charset)
|
||||
|
||||
48
db/db_settings.ipynb
Normal file
48
db/db_settings.ipynb
Normal file
@ -0,0 +1,48 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"db_host = \"172.17.0.2\"\n",
|
||||
"db_port = 3306\n",
|
||||
"\n",
|
||||
"db_user = \"root\"\n",
|
||||
"db_pw = \"g00d_r3c1p3s\"\n",
|
||||
"db_db = \"recipe_db\"\n",
|
||||
"\n",
|
||||
"db_charset = 'utf8mb4'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
13
db/db_settings.py
Normal file
13
db/db_settings.py
Normal file
@ -0,0 +1,13 @@
|
||||
#!/usr/bin/env python3
|
||||
# coding: utf-8
|
||||
|
||||
db_host = "172.17.0.2"
|
||||
db_port = 3306
|
||||
|
||||
db_user = "root"
|
||||
db_pw = "g00d_r3c1p3s"
|
||||
db_db = "recipe_db"
|
||||
|
||||
db_charset = 'utf8mb4'
|
||||
|
||||
|
||||
2247
reports/demos.ipynb
Normal file
2247
reports/demos.ipynb
Normal file
File diff suppressed because one or more lines are too long
@ -9,5 +9,6 @@ fooddb_folder = data_root + "foodb_2017_06_29_csv/"
|
||||
ingredients_file = data_root + "ingredients.py"
|
||||
actions_file = data_root + "actions.py"
|
||||
container_file = data_root + "containers.py"
|
||||
placeholder_file = data_root + "placeholders.py"
|
||||
|
||||
gzipped_conllu_data_root = data_root + "1M_recipes_conllu/"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user