lang-model/naive-nlu/parsing.py

#!/usr/bin/env python

import knowledge_evaluation

import re
import copy
from functools import reduce
from typing import List

MAX_RECURSIONS = 10

# TODO: more flexible tokenization
def to_tokens(text):
    return re.findall(r'(\w+|[^\s])', text)


def make_template(knowledge_base, tokens, parsed):
    matcher = list(tokens)
    template = list(parsed)
    for i in range(len(matcher)):
        word = matcher[i]
        if word in template:
            template[template.index(word)] = i
            matcher[i] = {
                'groups': set(knowledge_base.knowledge[word]['groups'])
            }
    return tokens, matcher, template


def is_bottom_level(tree):
    for element in tree:
        if isinstance(element, list) or isinstance(element, tuple):
            return False
    return True


def get_lower_levels(parsed):
    lower = []
    def aux(subtree, path):
        nonlocal lower
        deeper = len(path) == 0
        for i, element in enumerate(subtree):
            if isinstance(element, list) or isinstance(element, tuple):
                aux(element, path + (i,))
                deeper = True

        if not deeper:
            lower.append((path, subtree))

    aux(parsed, path=())
    return lower


# TODO: probably optimize this, it creates lots of unnecessary tuples
def replace_position(tree, position, new_element):

    def aux(current_tree, remaining_route):
        if len(remaining_route) == 0:
            return new_element

        else:
            step = remaining_route[0]
            return (
                tree[:step]
                + (aux(tree[step], remaining_route[1:]),)
                + tree[step + 2:]
            )

    return aux(tree, position)


def integrate_language(knowledge_base, example):
    text = example["text"].lower()
    parsed = example["parsed"]

    resolved_parsed = copy.deepcopy(parsed)
    tokens = to_tokens(text)

    while True:
        print("P:", resolved_parsed)
        lower_levels = get_lower_levels(resolved_parsed)
        print("Lower:", lower_levels)
        if len(lower_levels) == 0:
            break

        for position, atom in lower_levels:
            print("\x1b[1mSelecting\x1b[0m:", atom)
            similar = get_similar_tree(knowledge_base, atom)
            print("___>", similar)
            remix, (start_bounds, end_bounds) = build_remix_matrix(knowledge_base, tokens, atom, similar)
            _, matcher, result = make_template(knowledge_base, tokens, atom)
            print("Tx:", tokens)
            print("Mx:", matcher)
            print("Rx:", result)
            print("Remix:", remix)

            after_remix = apply_remix(tokens[len(start_bounds):-len(end_bounds)], remix)
            assert(len(after_remix) + len(start_bounds) + len(end_bounds) == len(tokens))
            print( "  +->", after_remix)
            subquery_type = knowledge_evaluation.get_subquery_type(knowledge_base.knowledge, atom)
            print(r"  \-> <{}>".format(subquery_type))

            # Clean remaining tokens
            new_tokens = list(tokens)
            offset = len(start_bounds)
            for _ in range(len(remix)):
                new_tokens.pop(offset)

            # TODO: Get a specific types for... types
            new_tokens.insert(offset, (subquery_type, remix))
            tokens = new_tokens

            resolved_parsed = replace_position(resolved_parsed, position, subquery_type)
            print("#########")


    tokens, matcher, result = make_template(knowledge_base, tokens, parsed)
    print("T:", tokens)
    print("M:", matcher)
    print("R:", result)
    print()
    return tokens, matcher, result


def apply_remix(tokens, remix):
    rebuilt = []
    for i in remix:
        rebuilt.append(tokens[i])
    return rebuilt


def build_remix_matrix(knowledge_base, tokens, atom, similar):
    # print("+" * 20)

    tokens = list(tokens)
    tokens, matcher, result = make_template(knowledge_base, tokens, atom)
    similar_matcher, similar_result, similar_result_resolved, _ = similar

    # print("NEW:")
    # print("Tokens:", tokens)
    # print("Matcher:", matcher)
    # print("Result:", result)
    # print()
    # print("Similar:")
    # print("Matcher:", similar_matcher)
    # print("Result:", similar_result)

    start_bounds, end_bounds = find_bounds(matcher, similar_matcher)
    # print()
    # print("Bounds:")
    # print("Start:", start_bounds)
    # print("End:  ", end_bounds)

    for i, element in (end_bounds + start_bounds[::-1]):
        matcher.pop(i)
        tokens.pop(i)

    possible_remixes = get_possible_remixes(matcher, similar_matcher)
    chosen_remix = possible_remixes[0]

    # print("New tokens:", tokens)
    # print("-" * 20)
    return chosen_remix, (start_bounds, end_bounds)


def get_possible_remixes(matcher, similar_matcher):
    # print("*" * 20)
    # print(matcher)
    # print(similar_matcher)

    matrix = []
    for element in matcher:
        assert(element in similar_matcher)
        indexes = all_indexes(similar_matcher, element)
        matrix.append(indexes)

    # print(matrix)
    # print([list(x) for x in list(zip(*matrix))])
    # TODO: do some scoring to find the most "interesting combination"
    return [list(x) for x in list(zip(*matrix))]


def all_indexes(collection, element):
    indexes = []
    base = 0

    for _ in range(collection.count(element)):
        i = collection.index(element, base)
        base = i + 1
        indexes.append(i)

    return indexes


def find_bounds(matcher, similar_matcher):
    start_bounds = []
    for i, element in enumerate(matcher):
        if element in similar_matcher:
            break
        else:
            start_bounds.append((i, element))

    end_bounds = []
    for i, element in enumerate(matcher[::-1]):
        if element in similar_matcher:
            break
        else:
            end_bounds.append((len(matcher) - (i + 1), element))

    return start_bounds, end_bounds


def get_similar_tree(knowledge_base, atom):
    possibilities = []

    # Find matching possibilities
    for entry, tree in knowledge_base.trained:
        if not is_bottom_level(tree):
            continue
        if tree[0] == atom[0]:
            possibilities.append((entry, tree))

    # Sort by more matching elements
    sorted_possibilities = []
    for (raw, possibility) in possibilities:
        resolved = []
        for element in atom:
            if isinstance(element, str):
                resolved.append(element)
            else:
                resolved.append(knowledge_evaluation.resolve(
                    knowledge_base.knowledge,
                    element,
                    raw))

        # TODO: Probably should take into account the categories of the elements in the "intake" ([0]) element
        score = sum([resolved[i] == atom[i]
                     for i
                     in range(min(len(resolved),
                                  len(atom)))])
        sorted_possibilities.append((raw, possibility, resolved, score))
    sorted_possibilities = sorted(sorted_possibilities, key=lambda p: p[3], reverse=True)
    if len(sorted_possibilities) < 1:
        return None

    return sorted_possibilities[0]


# TODO: unroll this mess
def get_matching(sample, other):
    l = len(sample[0])
    other = list(filter(lambda x: len(x[0]) == l, other))
    for i in range(l):
        if len(other) == 0:
            return []

        if isinstance(sample[0][i], dict): # Dictionaries are compared by groups
            other = list(filter(lambda x: isinstance(x[0][i], dict) and
                                len(x[0][i]['groups'] & sample[0][i]['groups']) > 0,
                                other))

        elif isinstance(sample[0][i], tuple): # Tuples are compared by types [0]
            other = list(filter(lambda x: isinstance(x[0][i], tuple) and
                                x[0][i][0] == sample[0][i][0],
                                other))

    return [sample[0][x] if isinstance(sample[0][x], str)
            else
            sample[0][x] if isinstance(sample[0][x], tuple)
            else {'groups': sample[0][x]['groups'] & reduce(lambda a, b: a & b,
                                                            map(lambda y: y[0][x]['groups'],
                                                                other))}
            for x
            in range(l)]


def reprocess_language_knowledge(knowledge_base, examples):
    examples = knowledge_base.examples + examples

    print('\n'.join(map(str, knowledge_base.examples)))
    print("--")

    pattern_examples = []
    for i, sample in enumerate(examples):
        other = examples[:i] + examples[i + 1:]
        match = get_matching(sample, other)
        print("->", match)
        if len(match) > 0:
            sample = (match, sample[1],)
        pattern_examples.append(sample)
        print()
    print("\x1b[7m--\x1b[0m")
    return pattern_examples


def fitting_return_type(knowledge,
                        return_type, remixer,
                        input_stream,
                        tail_of_ouput_stream,
                        remaining_recursions: int):
    indent = "  " + "  " * (MAX_RECURSIONS - remaining_recursions)

    for sample, ast in knowledge.trained:
        try:
            parsed_input = []
            parsed_output = []

            remaining_input = reverse_remix(input_stream, remixer)
            print(indent + "RMXin:", remaining_input)
            remaining_output = copy.deepcopy(sample)

            print(indent + "S:", sample)
            print(indent + "A:", ast)
            print()

            while len(remaining_output) > 0:
                ((input, output),
                 (remaining_input, remaining_output)) = match_token(knowledge,
                                                                    remaining_input,
                                                                    remaining_output,
                                                                    remaining_recursions - 1)
                parsed_input += input
                parsed_output += output
                print(indent + "INP:", input)
                print(indent + "OUT:", output)

            print(indent + "Pi:", parsed_input)
            print(indent + "Po:", parsed_output)
            print("\x1b[7m", end='')
            print(indent + "Ri:", remaining_input)
            print(indent + "Ro:", remaining_output)
            print("\x1b[0m")
            return ((parsed_input, parsed_output),
                    (remaining_input, remaining_output + tail_of_ouput_stream))
        except TypeError as e:
            print(indent + "X    " + str(e))
            pass
        except IndexError as e:
            print(indent + "X    " + str(e))
            pass
    raise TypeError("No matching type found")


def reverse_remix(tree_section, remix):
    result_section = []
    for origin in remix:
        result_section.append(copy.deepcopy(tree_section[origin]))
    return result_section + tree_section[len(remix):]


def match_token(knowledge,
                input: List[str],
                trained: List[str],
                remaining_recursions: int):
    if remaining_recursions < 1:
        return None

    # print("#" * (MAX_RECURSIONS - remaining_recursions))
    # print("Input:", input)
    # print("Output:", trained)
    indent = " " + "  " * (MAX_RECURSIONS - remaining_recursions)
    first_input = input[0]
    expected_first = trained[0]
    print(indent + "Ex?", expected_first)
    print(indent + "Fo!", first_input)

    if isinstance(expected_first, dict):
        # TODO: check if the dictionary matches the values
        return (([first_input], [expected_first]), (input[1:], trained[1:]))

    elif isinstance(expected_first, tuple):
        return_type, remixer = expected_first
        return fitting_return_type(knowledge,
                                   return_type, remixer,
                                   input, trained[1:],
                                   remaining_recursions)

    elif expected_first == first_input:
        return (([first_input], [expected_first]), (input[1:], trained[1:]))

    return None


def get_fit(knowledge, row, remaining_recursions=MAX_RECURSIONS):
    tokens = to_tokens(row)
    indent = "  " * (MAX_RECURSIONS - remaining_recursions)
    for sample, ast in knowledge.trained:
        print("-----")
        print("TOK:", tokens)
        try:
            remaining_input = copy.deepcopy(tokens)
            remaining_output = copy.deepcopy(sample)
            print(indent + "AST:", ast)
            print(indent + "S:", sample)

            # TODO: merge with get_return type, as uses the same mechanism
            while len(remaining_output) > 0:
                ((_, _), (remaining_input, remaining_output)) = match_token(knowledge,
                                                                            remaining_input,
                                                                            remaining_output,
                                                                            remaining_recursions)
                print(indent + "Ri:", remaining_input)
                print(indent + "Ro:", remaining_output)

            if len(remaining_input) == 0 and len(remaining_input) == 0:
                print("!!!", tokens, sample, ast)
                return tokens, sample, ast
        except TypeError as e:
            print(indent + "X    " + str(e))
            pass
        except IndexError as e:
            print(indent + "X    " + str(e))
            pass
        print()
    else:
        return None