56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
import random
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import six
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from collections import namedtuple
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from geodata.addresses.config import address_config
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from geodata.categories.config import category_config
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from geodata.encoding import safe_decode
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from geodata.math.sampling import weighted_choice, cdf
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CategoryQuery = namedtuple('CategoryQuery', 'category, prep, add_place_name')
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NULL_CATEGORY_QUERY = CategoryQuery(None, None, False)
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class Category(object):
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@classmethod
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def phrase(cls, language, key, value, is_plural=False, country=None):
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category_phrase = category_config.get_phrase(language, key, value, is_plural=is_plural)
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if not category_phrase:
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return NULL_CATEGORY_QUERY
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category_phrase = safe_decode(category_phrase)
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category_props = address_config.get_property('categories', language, country=country)
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if category_props is None:
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return CategoryQuery(category_phrase, prep=None, add_place_name=True)
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values = []
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probs = []
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for prep_phrase_type in ('near', 'nearby', 'near_me', 'in', 'null'):
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k = '{}_probability'.format(prep_phrase_type)
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prob = category_props.get(k, None)
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if prob is not None:
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values.append(prep_phrase_type)
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probs.append(prob)
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probs = cdf(probs)
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prep_phrase_type = weighted_choice(values, probs)
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if prep_phrase_type == 'null':
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return CategoryQuery(category_phrase, prep=None, add_place_name=True)
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values, probs = address_config.alternative_probabilities('categories.{}'.format(prep_phrase_type), language, country=country)
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if not values:
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return CategoryQuery(category_phrase, prep=None, add_place_name=True)
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prep_phrase, prep_phrase_props = weighted_choice(values, probs)
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prep_phrase = safe_decode(prep_phrase)
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add_place_name = prep_phrase_type not in ('nearby', 'near_me')
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return CategoryQuery(category_phrase, prep=prep_phrase, add_place_name=add_place_name)
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