/
ingredientsfast copy.py
154 lines (94 loc) · 4.79 KB
/
ingredientsfast copy.py
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import pandas as pd
import random
import json
import numpy as np
def main(target_mcros):
global dfs
global dic
global values
global new_count
global train
df = pd.read_csv('/Users/shiningsunnyday/Desktop/Food/ingredients.csv')
train = pd.read_json('/Users/shiningsunnyday/Desktop/Food/train.json')
ingredients = []; count = {}
for recipe in train.values:
for ingredient in recipe[2]:
if ingredient not in ingredients:
ingredients.append(ingredient)
count[ingredient] = 0
else:
count[ingredient] += 1
dfs = df.loc[:, 'Ingredients':].dropna()
new_count = {x: count[x] for x in count.keys() if count[x] > 10}
dfs = dfs[dfs.Ingredients.isin(new_count.keys())]
dfs = dfs.reset_index().loc[:, 'Ingredients':]
df_dic = {'protein': dfs.sort_values(by = ['protein']),
'fat': dfs.sort_values(by = ['fat']),
'carbs': dfs.sort_values(by = ['carbs'])}
values = {x[0]: [x[1:], 0] for x in dfs[['Ingredients', 'calories', 'protein', 'fat', 'carbs']].values}
dic = {0: 'calories', 1: 'protein', 2: 'fat', 3: 'carbs'}
mcros, initial_list = generate([0 for x in range(len(target_mcros))], target_mcros, [])
initial_list, mcros, error = iterate(initial_list, mcros, target_mcros)
initial_list, mcros, error = iterate(initial_list, mcros, target_mcros)
initial_list, mcros, error = iterate(initial_list, mcros, target_mcros)
display(mcros, initial_list, sum([abs(mcros[i] - target_mcros[i]) for i in range(1, len(dic))]), target_mcros)
def display(mcros, list_to_display, error, target_mcros):
for i in range(len(list_to_display)):
row = dfs.loc[dfs['Ingredients'] == list_to_display[i][0]]
print("%d. " % (i+1) + row['serving_qty'].to_string(index = False) + ' ' + row['serving_unit'].to_string(index = False) + ' of ' + row['Ingredients'].to_string(index = False))
print(" ".join(["Total %s: %d %s" % (dic[i], mcros[i], '(%d)' % (mcros[i] - target_mcros[i]) if target_mcros[i] >= mcros[i] - 1 else '(+%d)' % (mcros[i] - target_mcros[i])) for i in range(len(dic))]))
print('\n')
def generate(mcros, target_mcros, ingredients):
while True:
rand = random.randint(0, len(dfs))
ing = dfs.iloc[rand]
if mcros[0] + ing[dic[0]] > target_mcros[0] * 1.1:
pass
else:
ingredients.append([ing['Ingredients'], {'calories': ing['calories'], 'protein': ing['protein'], 'fat': ing['fat'], 'carbs': ing['carbs']}])
mcros = [mcros[i] + ing[dic[i]] for i in range(len(mcros))]
if target_mcros[0] * 0.9 <= mcros[0]:
#print(ingredients)
#print((protein_, fat_, carbs_))
break
return mcros, ingredients
def iterate(ingredients, mcros, target_mcros, preferences = 4):
minimal_error = sum([abs(mcros[i] - target_mcros[i]) for i in range(1, preferences)])
net_effect = 1000
ing_to_add = "N"
boo = True
for ing in values.keys():
effect = sum([abs(values[ing][0][i] + mcros[i] - target_mcros[i]) for i in range(1, preferences)]) - minimal_error
values[ing][1] = effect
if values[ing][1] < net_effect:
net_effect = values[ing][1]
ing_to_add = ing
for ing in ingredients:
ing = ing[0]
subtract_effect = sum([abs(-values[ing][0][i] + mcros[i] - target_mcros[i]) for i in range(1, preferences)]) - minimal_error
values[ing][1] = subtract_effect
if subtract_effect < net_effect:
net_effect = subtract_effect
boo = False
ing_to_add = ing
ing_to_add = [ing_to_add, dict(zip(dic.values(), values[ing_to_add][0]))]
if boo:
ingredients.append(ing_to_add)
else:
ingredients.remove(ing_to_add)
del values[ing_to_add[0]]
return ingredients, [mcros[i] + ing_to_add[1][dic[i]] if boo else mcros[i] - ing_to_add[1][dic[i]] for i in range(len(dic))], minimal_error + net_effect
def feedback(arr, initial_list, mcros):
while True:
arr = [x - 1 for x in arr]
if not arr:
break
for i in range(len(arr)):
del_mcros = initial_list[arr[len(arr)-1-i]][1]
name = initial_list[arr[len(arr)-1-i]][0]
del initial_list[arr[len(arr)-1-i]]
del values[name]
mcros = [mcros[i] - del_mcros[dic[i]] for i in range(len(dic))]
initial_list, mcros, error = iterate(initial_list, mcros, target_mcros)
display(mcros, initial_list, error, target_mcros)
main([2000, 100, 150, 250])