mirror of
https://github.com/osmarks/random-stuff
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stigler problem
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osmarks
+5907
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Load Diff
+177
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import scipy.optimize
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import json
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import numpy as np
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foods = []
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constraint_metrics = {
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"energy": (2000, 3000),
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"fat": (50, 90),
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"saturate": (0, 25),
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"carbohydrate": (0, 5000),
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"sugar": (0, 5000),
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"protein": (150, 500),
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"salt": (0, 6),
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"fibre": (30, 60),
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"starch": (0, 5000)
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}
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# thanks, Codex
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protein_quality = {
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"protein_supplement": 1.00,
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"egg": 1.00,
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"dairy": 0.98,
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"meat_fish": 1.00,
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"soy": 0.95,
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"legume": 0.80,
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"nuts_seeds": 0.70,
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"pseudo_meat": 0.75,
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"cereal": 0.55,
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"mixed_or_ambiguous": 0.70,
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"unknown": 0.65,
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}
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protein_keywords = {
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"protein_supplement": [
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"protein powder", "protein shake", "protein bar", "clear whey",
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"mass gainer", "meal replacement", "rtd protein",
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],
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"egg": [
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"egg", "eggs", "omelette", "omelet", "frittata", "quiche",
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"egg mayo", "egg salad",
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],
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"dairy": [
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"milk", "whole milk", "semi skimmed", "skimmed", "yoghurt", "yogurt",
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"greek yoghurt", "greek yogurt", "skyr", "kefir", "buttermilk",
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"cheese", "cheddar", "mozzarella", "parmesan", "grana padano",
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"pecorino", "edam", "gouda", "brie", "camembert", "stilton",
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"halloumi", "paneer", "cottage cheese", "cream cheese", "quark",
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"ricotta", "mascarpone", "whey", "casein", "custard",
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],
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"meat_fish": [
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"chicken", "turkey", "beef", "steak", "mince", "pork", "ham",
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"bacon", "gammon", "sausage", "salami", "pepperoni", "chorizo",
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"prosciutto", "lamb", "mutton", "duck", "venison", "veal",
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"liver", "kidney", "black pudding", "pate", "paté",
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"fish", "salmon", "tuna", "cod", "haddock", "mackerel", "sardine",
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"sardines", "anchovy", "anchovies", "trout", "pollock", "hake",
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"seabass", "sea bass", "bream", "prawn", "prawns", "shrimp",
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"crab", "lobster", "mussel", "mussels", "clam", "clams",
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"oyster", "oysters", "squid", "calamari", "octopus",
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],
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"soy": [
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"soy", "soya", "soybean", "soybeans", "tofu", "tempeh", "edamame",
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"miso", "natto", "tvp", "textured vegetable protein",
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"soy mince", "soya mince", "soy chunks", "soya chunks",
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"soy protein", "soya protein",
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],
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"legume": [
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"bean", "beans", "baked beans", "kidney bean", "kidney beans",
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"black bean", "black beans", "pinto", "haricot", "cannellini",
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"borlotti", "butter bean", "butter beans", "broad bean", "broad beans",
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"fava", "lentil", "lentils", "red lentil", "green lentil",
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"puy", "chickpea", "chickpeas", "gram", "split pea", "split peas",
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"pea", "peas", "yellow pea", "green pea", "garden peas",
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"mung", "adzuki", "azuki", "urad",
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"hummus", "houmous", "falafel",
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],
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"nuts_seeds": [
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"peanut", "peanuts", "peanut butter",
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"almond", "almonds", "cashew", "cashews", "walnut", "walnuts",
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"hazelnut", "hazelnuts", "pecan", "pecans", "pistachio", "pistachios",
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"macadamia", "brazil nut", "brazil nuts", "pine nut", "pine nuts",
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"mixed nuts",
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"seed", "seeds", "pumpkin seed", "pumpkin seeds", "sunflower seed",
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"sunflower seeds", "sesame", "tahini", "linseed", "flax", "flaxseed",
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"chia", "hemp", "poppy seed", "poppy seeds",
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],
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"pseudo_meat": [
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"seitan", "mycoprotein", "quorn", "meat free", "meat-free",
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"plant based", "plant-based", "vegan mince", "veg mince",
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"meatless", "veggie burger", "vegetarian burger",
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],
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"cereal": [
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"bread", "wholemeal bread", "wholewheat bread", "toastie", "toast",
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"roll", "rolls", "bagel", "bagels", "bap", "baps", "bun", "buns",
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"pitta", "pita", "wrap", "wraps", "naan", "flatbread", "crumpet",
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"muffin", "english muffin", "teacake", "scone",
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"flour", "wheat", "wholewheat", "wholemeal", "bran", "germ",
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"semolina", "bulgur", "burghul", "freekeh", "couscous",
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"rice", "brown rice", "white rice", "basmati", "jasmine rice",
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"wild rice", "risotto rice", "arborio",
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"oat", "oats", "oatmeal", "porridge", "muesli", "granola",
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"barley", "pearl barley", "rye", "spelt", "farro", "emmer",
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"einkorn", "millet", "sorghum", "maize", "corn", "polenta",
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"quinoa", "buckwheat", "amaranth", "teff",
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"cereal", "breakfast cereal", "flakes", "bran flakes", "cornflakes",
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"weetabix", "shredded wheat", "rice krispies", "special k", "cheerios",
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"pasta", "wholewheat pasta", "wholemeal pasta", "fresh pasta",
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"dried pasta", "egg pasta", "noodle", "noodles", "ramen", "udon",
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"soba", "vermicelli", "cappelletti", "tortellini", "ravioli",
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"lasagne", "lasagna", "cannelloni", "gnocchi",
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"spaghetti", "linguine", "fettuccine", "tagliatelle", "pappardelle",
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"fusilli", "penne", "rigatoni", "macaroni", "farfalle", "conchiglie",
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"shells", "orecchiette", "bucatini", "capellini", "angel hair",
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"tagliolini", "casarecce", "cavatappi", "tortiglioni", "ditalini",
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"orzo", "strozzapreti", "radiatori",
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],
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"mixed_or_ambiguous": [
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"ready meal", "meal deal", "sandwich", "burger", "pizza", "pie",
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"sausage roll", "pasty", "curry", "stew", "chilli", "chili",
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"soup", "salad", "pasta bake", "lasagne al forno", "noodle pot",
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],
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}
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def protein_quality_factor(name):
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s = name.lower()
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for cls, kws in protein_keywords.items():
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for kw in kws:
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if kw in s:
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return protein_quality[cls]
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return protein_quality["unknown"]
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mask_data_errors = {
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"tesco/293283636",
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"tesco/262750183"
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}
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with open("items.jsonl", "r") as f:
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for line in f:
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obj = json.loads(line.strip())
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if obj["slug"] not in mask_data_errors:
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if "protein" in obj["nutrition"]:
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obj["nutrition"]["protein"] *= protein_quality_factor(obj["name"])
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foods.append(obj)
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cost = np.zeros(len(foods))
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constraints_mtx = np.zeros((len(foods), len(constraint_metrics) * 2))
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for i, food in enumerate(foods):
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cost[i] = food["cost"]
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for j, metric in enumerate(constraint_metrics):
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# greater than/less than
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constraints_mtx[i, j*2] = -food["nutrition"].get(metric, 0)
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constraints_mtx[i, j*2+1] = food["nutrition"].get(metric, 0)
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constraints_vec = np.zeros(len(constraint_metrics)*2)
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for j, (lb, ub) in enumerate(constraint_metrics.values()):
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constraints_vec[j*2] = -lb
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constraints_vec[j*2+1] = ub
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print(cost, constraints_mtx, constraints_vec)
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res = scipy.optimize.linprog(cost, A_ub=constraints_mtx.T, b_ub=constraints_vec)
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print(res)
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for i, v in enumerate(res.x):
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if v > 0:
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print(foods[i], v)
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
+7744
File diff suppressed because one or more lines are too long
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from curl_cffi import AsyncSession
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from bs4 import BeautifulSoup
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import asyncio
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import re
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import base64
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from urllib.parse import unquote
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import collections
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import json
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targets = (
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"energy",
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"fat",
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"saturate",
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"carbohydrate",
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"sugar",
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"protein",
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"salt",
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"fibre",
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"starch"
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)
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def fix_commas(x):
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return re.sub(r"(\d+),(\d{1,2})", r"\1.\2", re.sub(r"(\d+),(\d{3})", r"\1\2", x))
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def process():
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with open("tesco.jsonl", "r") as f:
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with open("items.jsonl", "a") as h:
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for line in f:
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obj = json.loads(line.strip())
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cost = obj["price"]
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values = {}
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#print(obj["title"], obj["details"]["nutrition"])
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nutrition = obj["details"]["nutrition"]
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try:
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for target in targets:
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# TODO check perComp first line is reasonable
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for row in nutrition:
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label = row["name"]
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value = row["perComp"]
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if label and target.lower() in label.lower():
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value = fix_commas(value.lower().removeprefix("(").removeprefix("nutritioninformation/").split("/")[0].split("(")[0].strip().split("kcal")[0])
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if value.lower() == "trace" or value == "-" or value == "nil": value = "0"
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is_kj = "kj" in value or "kj" in label.split("/")[0].strip()
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if target not in values:
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if is_kj:
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value = value.split("kj")[0].removeprefix("<").strip().replace(" ", "")
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value = float(value) / 4.2 # kcal
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elif value.endswith("%"):
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value = value.removeprefix("less than").strip().removeprefix("<").removesuffix("%")
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value = float(value) / 100 * 1000
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else:
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value = value.removeprefix("less than").replace(" ", "").removeprefix("<").removesuffix(")").removesuffix("*").removesuffix("g").removesuffix("calories").removeprefix("=")
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value = float(value)
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values[target] = value
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break
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except:
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import traceback
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traceback.print_exc()
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if cost["unitOfMeasure"] not in {"ltr", "kg"}:
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continue
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cost = cost["unitPrice"]
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if values:
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json.dump({
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"slug": "tesco/" + obj["id"],
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"nutrition": values,
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"cost": cost,
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"name": obj["title"]
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}, h)
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h.write("\n")
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process()
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@@ -0,0 +1,383 @@
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import argparse
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import asyncio
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import json
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import sys
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from curl_cffi import AsyncSession
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XAPI_URL = "https://xapi.tesco.com/"
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# This key is shipped to browsers in Tesco's own groceries page config.
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XAPI_KEY = "TvOSZJHlEk0pjniDGQFAc9Q59WGAR4dA"
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HEADERS = {
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"accept": "application/json",
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"content-type": "application/json",
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"origin": "https://www.tesco.com",
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"referer": "https://www.tesco.com/groceries/en-GB/search?consumer=ghsapp-uk",
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"x-apikey": XAPI_KEY,
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}
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DEFAULT_TERMS = [
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"bread",
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"butter",
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"cheese",
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"egg",
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"flour",
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"milk",
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"oats",
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"pasta",
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"potato",
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"rice",
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"tomato",
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"vegetable",
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]
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TAXONOMY_QUERY = """
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query Taxonomy($includeInspirationEvents: Boolean = false, $configs: [ConfigArgType]) {
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taxonomy(includeInspirationEvents: $includeInspirationEvents, configs: $configs) {
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catId: id
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name
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label
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parent
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children {
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catId: id
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name
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label
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parent
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children {
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catId: id
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name
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label
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parent
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children {
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catId: id
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name
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label
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parent
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}
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}
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}
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}
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}
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"""
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SEARCH_QUERY = """
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query Search($query: String!, $page: Int, $count: Int, $sortBy: String) {
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search(query: $query, page: $page, count: $count, sortBy: $sortBy) {
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results {
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node {
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... on ProductInterface {
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id
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tpnb
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tpnc
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gtin
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title
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brandName
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superDepartmentName
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departmentName
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aisleName
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shelfName
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price {
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actual
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unitPrice
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unitOfMeasure
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}
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}
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}
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}
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}
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}
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"""
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CATEGORY_QUERY = """
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query Category($facet: ID, $page: Int, $count: Int, $sortBy: String) {
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category(facet: $facet, page: $page, count: $count, sortBy: $sortBy) {
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info {
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total
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page
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count
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pageSize
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offset
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}
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results {
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node {
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... on ProductInterface {
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id
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tpnb
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tpnc
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gtin
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title
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brandName
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superDepartmentName
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departmentName
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aisleName
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shelfName
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price {
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actual
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unitPrice
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unitOfMeasure
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}
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||||
}
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}
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}
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}
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}
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"""
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PRODUCT_QUERY = """
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query GetProduct($tpnc: String) {
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product(tpnc: $tpnc) {
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id
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tpnb
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tpnc
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gtin
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title
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||||
brandName
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description
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||||
foodIcons
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||||
superDepartmentName
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||||
departmentName
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||||
aisleName
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||||
shelfName
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price {
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||||
actual
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unitPrice
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||||
unitOfMeasure
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||||
}
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||||
details {
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ingredients
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netContents
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packSize {
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value
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||||
units
|
||||
}
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||||
nutrition {
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||||
name
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perComp: value1
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perServing: value2
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referenceIntake: value3
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referencePercentage: value4
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||||
}
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||||
guidelineDailyAmount {
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||||
title
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||||
dailyAmounts {
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name
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||||
value
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||||
percent
|
||||
rating
|
||||
}
|
||||
}
|
||||
productMarketing
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||||
preparationAndUsage
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storage
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features
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||||
healthClaims
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||||
}
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||||
}
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||||
}
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||||
"""
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||||
|
||||
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||||
async def graphql(session, query, variables):
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while True:
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||||
response = await session.post(
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||||
XAPI_URL,
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||||
json={"query": query, "variables": variables},
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)
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if response.status_code != 504:
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response.raise_for_status()
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||||
payload = response.json()
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||||
if payload.get("errors"):
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print(json.dumps(payload["errors"], indent=2))
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else:
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return payload["data"]
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||||
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||||
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||||
async def search_page(session, term, page, count):
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data = await graphql(
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session,
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SEARCH_QUERY,
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{"query": term, "page": page, "count": count, "sortBy": "price-ascending"},
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||||
)
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||||
nodes = []
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||||
for result in data["search"]["results"]:
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||||
node = result.get("node")
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||||
if node and node.get("tpnc"):
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||||
nodes.append(node)
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||||
return nodes
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||||
|
||||
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||||
async def category_page(session, cat_id, page, count):
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||||
data = await graphql(
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||||
session,
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||||
CATEGORY_QUERY,
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||||
{"facet": cat_id, "page": page, "count": count, "sortBy": "price-ascending"},
|
||||
)
|
||||
category = data["category"]
|
||||
nodes = []
|
||||
for result in category["results"]:
|
||||
node = result.get("node")
|
||||
if node and node.get("tpnc"):
|
||||
nodes.append(node)
|
||||
return category["info"], nodes
|
||||
|
||||
|
||||
async def get_taxonomy(session):
|
||||
data = await graphql(
|
||||
session,
|
||||
TAXONOMY_QUERY,
|
||||
{"includeInspirationEvents": False, "configs": []},
|
||||
)
|
||||
return data["taxonomy"]
|
||||
|
||||
|
||||
def flatten_taxonomy(nodes, path=()):
|
||||
for node in nodes:
|
||||
current_path = (*path, node["name"])
|
||||
yield {
|
||||
"catId": node["catId"],
|
||||
"name": node["name"],
|
||||
"label": node["label"],
|
||||
"path": " > ".join(current_path),
|
||||
}
|
||||
yield from flatten_taxonomy(node.get("children") or [], current_path)
|
||||
|
||||
|
||||
def category_filter(categories, args):
|
||||
selected = categories
|
||||
if args.category_label:
|
||||
selected = [c for c in selected if c["label"] in args.category_label]
|
||||
if args.category_contains:
|
||||
needles = [needle.lower() for needle in args.category_contains]
|
||||
selected = [
|
||||
c for c in selected
|
||||
if any(needle in c["path"].lower() for needle in needles)
|
||||
]
|
||||
return selected
|
||||
|
||||
|
||||
async def get_product(session, tpnc):
|
||||
data = await graphql(session, PRODUCT_QUERY, {"tpnc": tpnc})
|
||||
return data["product"]
|
||||
|
||||
|
||||
async def bounded_gather(limit, items, fn):
|
||||
semaphore = asyncio.Semaphore(limit)
|
||||
|
||||
async def run(item):
|
||||
async with semaphore:
|
||||
return await fn(item)
|
||||
|
||||
return await asyncio.gather(*(run(item) for item in items))
|
||||
|
||||
|
||||
async def scrape(args):
|
||||
seen = set()
|
||||
|
||||
async with AsyncSession(headers=HEADERS, impersonate="chrome120", timeout=30) as session:
|
||||
with open(args.output, "w") as output:
|
||||
if args.categories:
|
||||
taxonomy = await get_taxonomy(session)
|
||||
categories = category_filter(list(flatten_taxonomy(taxonomy)), args)
|
||||
print(f"{len(categories)} categories selected", file=sys.stderr)
|
||||
|
||||
for category in categories:
|
||||
print(f"category {category['path']!r}", file=sys.stderr)
|
||||
for page in range(1, args.pages + 1):
|
||||
info, rows = await category_page(
|
||||
session, category["catId"], page, args.count
|
||||
)
|
||||
if not rows:
|
||||
break
|
||||
|
||||
rows = [row for row in rows if row["tpnc"] not in seen]
|
||||
for row in rows:
|
||||
seen.add(row["tpnc"])
|
||||
|
||||
products = await bounded_gather(
|
||||
args.concurrency,
|
||||
rows,
|
||||
lambda row: get_product(session, row["tpnc"]),
|
||||
)
|
||||
|
||||
for product in products:
|
||||
product["matchedCategory"] = category
|
||||
json.dump(product, output, ensure_ascii=False)
|
||||
output.write("\n")
|
||||
|
||||
output.flush()
|
||||
print(
|
||||
f" page {page}: {len(products)} new products "
|
||||
f"({info['total']} listed)",
|
||||
file=sys.stderr,
|
||||
)
|
||||
|
||||
if info["offset"] + info["count"] >= info["total"]:
|
||||
break
|
||||
return
|
||||
|
||||
terms = args.term or DEFAULT_TERMS
|
||||
for term in terms:
|
||||
print(f"search {term!r}", file=sys.stderr)
|
||||
for page in range(1, args.pages + 1):
|
||||
rows = await search_page(session, term, page, args.count)
|
||||
if not rows:
|
||||
break
|
||||
|
||||
rows = [row for row in rows if row["tpnc"] not in seen]
|
||||
for row in rows:
|
||||
seen.add(row["tpnc"])
|
||||
|
||||
products = await bounded_gather(
|
||||
args.concurrency,
|
||||
rows,
|
||||
lambda row: get_product(session, row["tpnc"]),
|
||||
)
|
||||
|
||||
for product in products:
|
||||
product["matchedSearchTerm"] = term
|
||||
json.dump(product, output, ensure_ascii=False)
|
||||
output.write("\n")
|
||||
|
||||
output.flush()
|
||||
print(
|
||||
f" page {page}: {len(products)} new products",
|
||||
file=sys.stderr,
|
||||
)
|
||||
|
||||
if len(rows) < args.count:
|
||||
break
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Scrape Tesco-owned price and nutrition data through Tesco XAPI."
|
||||
)
|
||||
parser.add_argument("term", nargs="*", help="Search terms to scrape")
|
||||
parser.add_argument("-o", "--output", default="tesco.jsonl")
|
||||
parser.add_argument(
|
||||
"--categories",
|
||||
action="store_true",
|
||||
help="Scrape Tesco taxonomy categories instead of keyword search.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--category-label",
|
||||
action="append",
|
||||
choices=["superDepartment", "department", "aisle", "shelf"],
|
||||
help="Restrict --categories to a taxonomy level. Repeatable.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--category-contains",
|
||||
action="append",
|
||||
help="Restrict --categories to paths containing this text. Repeatable.",
|
||||
)
|
||||
parser.add_argument("--pages", type=int, default=2)
|
||||
parser.add_argument("--count", type=int, default=48)
|
||||
parser.add_argument("--concurrency", type=int, default=8)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(scrape(parse_args()))
|
||||
Reference in New Issue
Block a user