pyfathom 0.0.2

Text comprehension library for Python

pyfathom

Text comprehension library for python

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Follow the development of this project at http://jeremyorme.com

Example

Given a collection of input strings with varying syntax:

from pyfathom import *

in_strs = [
  '180g | 1 cup uncooked brown rice',
  '½ small butternut squash , cubed',
  '5½ tablespoons tahini (you can sub cashew butter)',
  'pecans 125g',
  'flat-leaf parsley a bunch, roughly chopped',
  'rocket 70g',
  'leftover marinade from the mushrooms',
  '15 oz (425 g) black beans, drained (reserve ¼ cup (60 ml) of the juice) and rinsed well',
  '1/4 teaspoon Garam Masala, for garnish',
  '2 tablespoons chopped cilantro, for garnish'
]

and a set of "knowledge" rules defining what is known about the inputs, e.g.:

knowledge = '''
/pinch/ is unit
/mls?|mL|cc|millilitres?|milliliters?/ is unit
/tsps?|t|teaspoons?/ is unit
/tbsps?|Tbsps?|T|tbl|tbs|tablespoons?/ is unit
/floz/ is unit
/fl/,/oz/ is unit
/fluid/,/ounces?/ is unit
/p|pts?|pints?/ is unit
/ls?|L|litres?|liters?/ is unit
/gals?|gallons?/ is unit
/dls?|dL|decilitre|deciliter/ is unit
/gs?|grams?|grammes?/ is unit
/oz|ounces?/ is unit
/lbs?|#|pounds?/ is unit
/kgs?|kilos?|kilograms?/ is unit
/\d+/?,/\d+\/\d+/ is number
/\d+(\.\d+)?/ is number
/\d*[½⅓⅔¼¾⅕⅖⅗⅘⅙⅚⅛⅜⅝⅞]/ is number
/a/ is number-word
number,/-|–/,number is range
/cups?/ is unit
range|number|number-word,/\-/?,unit?,/\./?,/of/? is amount
amount?,/plus/?,amount?,/[a-zA-Z\-]+/+,amount? is ,,,ingredient,
'''

PyFathom attempts to label each part of the string with a type name:

cls = classifier(knowledge)
for in_str in in_strs:
  print(cls.classify(in_str))

Output:

<amount><number>180</number><unit>g</unit></amount>|<amount><number>1</number><unit>cup</unit></amount><ingredient>uncooked brown rice</ingredient>
<number><amount>½</amount></number><ingredient>small butternut squash</ingredient>,<ingredient>cubed</ingredient>
<amount><number>5½</number><unit>tablespoons</unit></amount><ingredient>tahini</ingredient>(<ingredient>you can sub cashew butter</ingredient>)
<ingredient>pecans</ingredient><amount><number>125</number><unit>g</unit></amount>
<ingredient>flat-leaf parsley<number-word><amount>a</amount></number-word>bunch</ingredient>,<ingredient>roughly chopped</ingredient>
<ingredient>rocket</ingredient><amount><number>70</number><unit>g</unit></amount>
<ingredient>leftover marinade from the mushrooms</ingredient>
<amount><number>15</number><unit>oz</unit></amount>(<amount><number>425</number><unit>g</unit></amount>)<ingredient>black beans</ingredient>,<ingredient>drained</ingredient>(<ingredient>reserve</ingredient><amount><number>¼</number><unit>cup</unit></amount>(<amount><number>60</number><unit>ml</unit></amount>)<ingredient>of the juice</ingredient>)<ingredient>and rinsed well</ingredient>
<number><amount>1</amount></number>/<amount><number>4</number><unit>teaspoon</unit></amount><ingredient>Garam Masala</ingredient>,<ingredient>for garnish</ingredient>
<amount><number>2</number><unit>tablespoons</unit></amount><ingredient>chopped cilantro</ingredient>,<ingredient>for garnish</ingredient>

and can extract the parts of a particular type, e.g. ingredient:

for in_str in in_strs:
  print(cls.classify(in_str).extract_typed('ingredient')[0])

Output:

uncooked brown rice
small butternut squash
tahini
pecans
flat-leaf parsley a bunch
rocket
leftover marinade from the mushrooms
black beans
Garam Masala
chopped cilantro

Release notes

0.0.2

• Lazy matcher
• Bug fixes