lang-model/naive-nlu/tree_nlu/knowledge_base.py

import copy
import logging

from .session.org_mode import global_session as session

from .atoms import Atom
from . import layered_model
from . import knowledge_evaluation
from .modifiable_property import is_modifiable_property
import random

def diff_knowledge(before, after):
    import jsondiff
    return jsondiff.diff(before, after)



class KnowledgeBase(object):
    def __init__(self, knowledge={}, examples=[], trained=[]):
        self.knowledge = copy.copy(knowledge)
        self.originals = []
        self.examples = copy.copy(examples)
        self.trained = copy.copy(trained)
        self.layers = layered_model.BaseModel(self)

    ## Parsing
    def train(self, examples):
        knowledge_before = copy.deepcopy(self.knowledge)
        with session().log('Train'):
            # Parse everything
            for example in examples:
                # If there's parsed data, leverage it ASAP
                if 'parsed' in example and isinstance(example['parsed'], tuple):
                    with session().log('parsed information integration'):
                        result = knowledge_evaluation.integrate_information(self.knowledge, {
                            "parsed": example['parsed'],
                        })
                        self.act_upon(result)

                with session().log("language integration"):
                    for tokens, decomposition, inferred_tree in self.layers.integrate(self, example):
                        session().annotate("Tokens: {}".format(tokens))
                        session().annotate("Inferred tree: {}".format(inferred_tree))

                with session().log("full information integration"):
                    tokens = self.layers.tokenization.tokenize(example['text'], return_one=True)
                    result = knowledge_evaluation.integrate_information(self.knowledge, {
                        "elements": tokens,
                        "decomposition": decomposition,
                        "parsed": inferred_tree,
                    })

                    session().annotate("Result: {}".format(self.get_value(result)))
                    self.act_upon(result)
                    session().annotate("Set: {}".format(self.get_value(result)))
                    self.examples.append((decomposition, inferred_tree))
                    self.originals.append(example['text'])

                # Reduce values
                with session().log("reprocessing"):
                    res = self.layers.reprocess(self.examples)
                    self.trained = res

            knowledge_after = copy.deepcopy(self.knowledge)
            knowledge_diff_getter = lambda: diff_knowledge(knowledge_before,
                                                       knowledge_after)

            return knowledge_diff_getter

    def process(self, row):
        knowledge_before = copy.deepcopy(self.knowledge)
        with session().log("Process: {}".format(row)):
            fit = list(self.layers.process(self, row))
            if len(fit) == 0:
                return None

            tokens, inferred_tree = fit[0]
            result = knowledge_evaluation.integrate_information(self.knowledge,
                                                                {
                                                                    "elements": tokens,
                                                                    "parsed": inferred_tree,
                                                                })
            self.act_upon(result)
            session().annotate("Result: {}".format(result))

            knowledge_after = copy.deepcopy(self.knowledge)
            knowledge_diff_getter = lambda: diff_knowledge(knowledge_before,
                                                           knowledge_after)

            return result, inferred_tree, knowledge_diff_getter

    def get_value(self, result):
        if is_modifiable_property(result):
            return result.getter()
        else:
            return result

    def act_upon(self, result):
        if is_modifiable_property(result):
            result.setter()
        else:
            logging.warning("Cannot act upon: {}".format(result))