98 lines
3.8 KiB
Python
98 lines
3.8 KiB
Python
import copy
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import logging
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from .session.org_mode import global_session as session
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from . import parsing
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from . import knowledge_evaluation
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from .modifiable_property import is_modifiable_property
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def diff_knowledge(before, after):
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import jsondiff
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return jsondiff.diff(before, after)
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class KnowledgeBase(object):
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def __init__(self, knowledge, examples=[], trained=[]):
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self.knowledge = copy.copy(knowledge)
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self.originals = []
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self.examples = copy.copy(examples)
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self.trained = copy.copy(trained)
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def train(self, examples):
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knowledge_before = copy.deepcopy(self.knowledge)
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with session().log('Train'):
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# Parse everything
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for example in examples:
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# If there's parsed data, leverage it ASAP
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if 'parsed' in example:
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with session().log('parsed information integration'):
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result = knowledge_evaluation.integrate_information(self.knowledge, {
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"parsed": example['parsed'],
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})
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self.act_upon(result)
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with session().log("language integration"):
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tokens, decomposition, inferred_tree = parsing.integrate_language(self, example)
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session().annotate(tokens)
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with session().log("full information integration"):
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result = knowledge_evaluation.integrate_information(self.knowledge, {
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"elements": tokens,
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"decomposition": decomposition,
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"parsed": inferred_tree,
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})
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session().annotate("Result: {}".format(self.get_value(result)))
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self.act_upon(result)
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session().annotate("Set: {}".format(self.get_value(result)))
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self.examples.append((decomposition, inferred_tree))
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self.originals.append(example['text'])
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# Reduce values
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with session().log("reprocessing"):
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self.trained = parsing.reprocess_language_knowledge(self, self.examples)
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knowledge_after = copy.deepcopy(self.knowledge)
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knowledge_diff_getter = lambda: diff_knowledge(knowledge_before,
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knowledge_after)
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return knowledge_diff_getter
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def process(self, row):
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row = row.lower()
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knowledge_before = copy.deepcopy(self.knowledge)
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with session().log("Process: {}".format(row)):
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tokens = parsing.to_tokens(row)
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fit = parsing.get_fit(self, tokens)
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if fit is None:
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return None
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tokens, inferred_tree = fit
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result = knowledge_evaluation.integrate_information(self.knowledge,
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{
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"elements": tokens,
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"parsed": inferred_tree,
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})
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self.act_upon(result)
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session().annotate("Result: {}".format(result))
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knowledge_after = copy.deepcopy(self.knowledge)
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knowledge_diff_getter = lambda: diff_knowledge(knowledge_before,
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knowledge_after)
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return result, inferred_tree, knowledge_diff_getter
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def get_value(self, result):
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if is_modifiable_property(result):
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return result.getter()
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else:
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return result
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def act_upon(self, result):
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if is_modifiable_property(result):
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result.setter()
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else:
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logging.warning("Cannot act upon: {}".format(result))
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