def __init__(self, documents_or_func, cache_dir):
"""
1. input is { { item_id : {feature1:count1, feature2: count2, ...} }, ... }
2. output is { { item_id : {feature1: rank1, feature2: rank2, ...} }, ... }
They're all acts like a dict, whatever persistent or not.
"""
self.documents_or_func = documents_or_func
self.cache_dir = cache_dir
# Always load idf cache, and it's really small.
d1 = DictUtils.add_default_value(self.idf_cache)
self.idf_cache = IdfResult(d1.default_factory, d1)
def entropy_cache(self):
""" 训练+测试 预料全在里面了 """
from .lib.entropy import EntropyFunc
result = EntropyFunc.process(self.documents_with_segments, self.cache_dir)
self.entropy_file = result # 采用自己的熵，给FeaturesWeight用
result = DictUtils.add_default_value(result)
"""
from etl_utils import is_regular_word, Unicode
for k1 in result.keys():
if not (is_regular_word(k1) or Unicode.is_chinese(k1)):
del result[k1]
"""
self.entropy_file = result
return result
def entropy_cache(self):
""" 训练+测试 预料全在里面了 """
from .lib.entropy import EntropyFunc
result = EntropyFunc.process(self.documents_with_segments,
self.cache_dir)
self.entropy_file = result # 采用自己的熵，给FeaturesWeight用
result = DictUtils.add_default_value(result)
"""
from etl_utils import is_regular_word, Unicode
for k1 in result.keys():
if not (is_regular_word(k1) or Unicode.is_chinese(k1)):
del result[k1]
"""
self.entropy_file = result
return result