Assignment 5

1a. Data Utilities (15 pts)

Create a util.py module that has two methods: download_data, get_data, and parse_ingredients.

The download_data method should download the food-data-sample.json datafile and store it locally. The get_data method should load the data in a module variable. Assume that the data file resides in the same directory as util.py. You can then get its absolute path via the __file__ variable of the module via:

from pathlib import Path
fname = Path(__file__).parent / 'food-data-sample.json'

Use the json module to load the data from the file. The download_data method should download the file just once, otherwise returning the local filename. Refer to the Assignment 3 starter notebook for code that can be used to download data from http://faculty.cs.niu.edu/~dakoop/cs503-2024fa/assignments/a3/food-data-sample.json. The get_data method should load and parse the file from disk once, otherwise returning the pre-loaded data. The parse_ingredients method should parse the ingredients string into a list of ingredients by splitting on a comma. Do not worry if this does not do a perfect job (e.g. you may see an ingredient labeled as “LESS THAN 2% OF: SALT”).

1b. Finding Items (15 pts)

Create an find.py module that has two functions that both take one parameter, the brand owner or description expression, respectively, and return the list of data items that match. Use the get_data method from the data module to obtain the data. The first method, get_by_brand, should return a list of the items filtered by an exact match to brand owner, ignoring case. The second method, get_by_description, should take a string that searches all item descriptions for those that match the given expression. The expressions may literal characters but also the wildcard character(*), and that character should match any number of characters. You can use Python’s regular expression library to deal with wildcards, but you will need to change any instances of * to .*. Your regular expression should ignore case. For example, consider the output when searching for "Quaker*Oat":

>>> [d['description'] for d in food_data.find.get_by_description("Quaker*Oat")]
['Quaker Oatmeal to Go Apple Cinnamon (6 - 2.1 Ounce) 12.6 Ounce 6 Pack Plastic Bag',
 'Quaker Oatmeal Squares Golden Maple 14.5 Ounce Paper Box',
 'QUAKER FRUIT & OATMEAL FRTFLLEDBR BERRY   10.4Z']

1c. Comparison (15 pts)

Create a compare.py module that calculates comparative information between two food items. Given two food items’ fdc_ids as parameters, the diff_nutrition function should return the difference between the nutrition values of the two items, and the diff_ingredients method should return the difference between the ingredients in the items. Also, diff_nutrition should additionally include the difference in serving size; it should return a list of tuples of the form (<name>, <amount>, <unit_name>). Make sure the differences are between the same measures! The ingredient list should be parsed via parse_ingredients method in the util module from Part 1a, and then create three different sets: one for shared ingredients, one for ingredients in the first item and not the second, and one for ingredients in the second it-me and not the first.

Examples:

>>> food_data.compare.diff_nutrition(622876, 1120164)
[('Serving Size', 2.0, 'g'),
 ('Saturated Fat', -3.7799999999999994, 'G'),
 ('Calories', -193.0, 'KCAL'),
 ('Total Fat', -25.91, 'G'),
 ('Sugar', 12.9, 'G'),
 ('Carbohydrates', 9.45, 'G'),
 ('Protein', -3.45, 'G'),
 ('Sodium', 448.0, 'MG'),
 ('Fiber', 0.0, 'G')]

>>> food_data.compare.diff_ingredients(622876, 1120164) 
({'CALCIUM DISODIUM EDTA ADDED TO PROTECT FLAVOR',
  'DISTILLED VINEGAR',
  'LEMON JUICE CONCENTRATE',
  'SALT',
  'SOYBEAN OIL',
  'WATER',
  'XANTHAN GUM'},
 {'ANCHOVIES',
  'CELERY SEED',
  'CHEESE CULTURE',
  'CORN SYRUP',
  'ENZYMES)',
  'GARLIC*',
  'MUSTARD SEED',
  'NATURAL FLAVOR',
  'ONION*',
  'PARMESAN CHEESE (PART-SKIM MILK',
  'POTASSIUM SORBATE (ADDED TO MAINTAIN FRESHNESS)',
  'RED WINE VINEGAR',
  'SPICE',
  'TAMARIND.'},
 {'ANNATTO COLOR.',
  'BALSAMIC VINEGAR (PRESERVED WITH SULFITES)',
  'BEET JUICE CONCENTRATE',
  'CARAMEL COLOR',
  'DEHYDRATED GARLIC',
  'HIGH FRUCTOSE CORN SYRUP',
  'SPICES',
  'SUGAR'})

1d. Package

Make sure all three analysis modules live in a single food_data package. Add an __init__.py file for completeness. It may contain documentation and the pass keyword.

2a. food_data.find (15 pts)

In this module, we will create a command-line program that accepts two flags, -b for get_by_brand searches, and -d for get_by_description searches. Both will take a single string as the final argument. That string should be passed to the respective functions, but the output should be displayed in a more succinct manner. Each data item should be listed as <fdc_id> <brand_owner> <description>. Make sure to have a usage statement that is shown when a user enters incorrect input. You should test your script via the IPython magic command %run -m food_data.find .... Some sample output:

>>> %run -m food_data.find

Usage: python -m food_data.find -b <brand> | -d <description>

>>> %run -m food_data.find -d "Quaker*Oat"

1167761 Pepsico Inc. Quaker Oatmeal to Go Apple Cinnamon (6 - 2.1 Ounce) 12.6 Ounce 6 Pack Plastic Bag
1460360 Pepsico Inc. Quaker Oatmeal Squares Golden Maple 14.5 Ounce Paper Box
1460396 Pepsico Inc. QUAKER FRUIT & OATMEAL FRTFLLEDBR BERRY   10.4Z

2b. food_data.compare (15 pts)

In this module, we will create a command-line program that accepts two flags, -n for diff_nutrition and -i for diff_ingredients. Both will take two fdc_ids as integers and pass them to their respective functions. The output for diff_nutrition should show the name of the metric, and then a +/- value for the amount (with two decimal places) followed by the unit. The output for diff_ingredients should show the common ingredients prefixed by a space, the ingredients in the first item prefixed by a - and the ingredients in the second item prefixed by a +. You can test your script via the IPython magic command %run -m food_data.compare ...

Some sample output:

>>> %run -m food_data.compare -n 622876 1120164

Serving Size: +2.00 g
Saturated Fat: -3.78 g
Calories: -193.00 kcal
Total Fat: -25.91 g
Sugar: +12.90 g
Carbohydrates: +9.45 g
Protein: -3.45 g
Sodium: +448.00 mg
Fiber: +0.00 g

>>> %run -m food_data.compare -i 622876 1120164

  WATER
  DISTILLED VINEGAR
  XANTHAN GUM
  SALT
  SOYBEAN OIL
  CALCIUM DISODIUM EDTA ADDED TO PROTECT FLAVOR
  LEMON JUICE CONCENTRATE
- DEHYDRATED GARLIC
- BALSAMIC VINEGAR (PRESERVED WITH SULFITES)
- ANNATTO COLOR.
- SPICES
- BEET JUICE CONCENTRATE
- SUGAR
- CARAMEL COLOR
- HIGH FRUCTOSE CORN SYRUP
+ CHEESE CULTURE
+ GARLIC*
+ RED WINE VINEGAR
+ CORN SYRUP
+ ONION*
+ ENZYMES)
+ NATURAL FLAVOR
+ TAMARIND.
+ CELERY SEED
+ PARMESAN CHEESE (PART-SKIM MILK
+ ANCHOVIES
+ SPICE
+ POTASSIUM SORBATE (ADDED TO MAINTAIN FRESHNESS)
+ MUSTARD SEED

Hints

• You’ll need to check particular indices of sys.argv to determine which flag is being used.