121 lines
2.2 KiB
Plaintext
121 lines
2.2 KiB
Plaintext
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{
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"cells": [
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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 numpy as np\n",
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"import json \n",
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"from pprint import pprint\n",
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"from gensim.test.utils import common_texts, get_tmpfile\n",
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"from gensim.models import Word2Vec\n",
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"\n",
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"from json_buffered_reader import JSON_buffered_reader as JSON_br\n",
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"\n",
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"import settings"
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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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"* reading in all ingredients with json stream:"
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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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"buffered_reader = JSON_br(settings.yummly_train)"
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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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"ingredient_sets = []"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"for recipe in buffered_reader:\n",
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" ingredient_sets.append(recipe['ingredients'])"
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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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"* training a word2vec approach on the ingredient set"
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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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"model = Word2Vec(ingredient_sets, size=100, window=5, min_count=1, workers=4)"
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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": 21,
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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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"0.46477914"
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]
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},
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"execution_count": 21,
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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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"model.wv.similarity('eggs', 'pepper')"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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": 2
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}
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