Merge shallow-recursive-trees into naive-nlu.

naive-nlu/knowledge_base.py

@@ -22,6 +22,7 @@ class KnowledgeBase(object):
         # Parse everything
         parsed_examples = []
         for example in examples:
+            print("\x1b[7;32m> {} \x1b[0m".format(example))
             tokens, decomposition, inferred_tree = parsing.integrate_language(self, example)
             print(tokens)
             result = knowledge_evaluation.integrate_information(self.knowledge, {
@@ -29,14 +30,14 @@ class KnowledgeBase(object):
                 "decomposition": decomposition,
                 "parsed": inferred_tree,
             })
+
+            print("\x1b[7;33m< {} \x1b[0m".format(self.get_value(result)))
             self.act_upon(result)
-            parsed_examples.append((decomposition, inferred_tree))
+            print("\x1b[7;34m< {} \x1b[0m".format(self.get_value(result)))
+            self.examples.append((decomposition, inferred_tree))
 
             # Reduce values
-        trained = parsing.reprocess_language_knowledge(self, parsed_examples)
+            self.trained = parsing.reprocess_language_knowledge(self, self.examples)
-
-        self.examples += parsed_examples
-        self.trained = trained
 
         knowledge_after = copy.deepcopy(self.knowledge)
         knowledge_diff_getter = lambda: diff_knowledge(knowledge_before,
@@ -47,6 +48,7 @@ class KnowledgeBase(object):
 
     def process(self, row):
         knowledge_before = copy.deepcopy(self.knowledge)
+        print("\x1b[7;32m> {} \x1b[0m".format(row))
         tokens, decomposition, inferred_tree = parsing.get_fit(self, row)
         result = knowledge_evaluation.integrate_information(self.knowledge,
                                                           {
@@ -60,7 +62,13 @@ class KnowledgeBase(object):
         knowledge_diff_getter = lambda: diff_knowledge(knowledge_before,
                                                        knowledge_after)
 
-        return result, knowledge_diff_getter
+        return result, inferred_tree, knowledge_diff_getter
+
+    def get_value(self, result):
+        if isinstance(result, ModifiableProperty):
+            return result.getter()
+        else:
+            return result
 
     def act_upon(self, result):
         if isinstance(result, ModifiableProperty):

naive-nlu/knowledge_evaluation.py

@@ -12,9 +12,33 @@ def resolve(knowledge_base, elements, value):
     return value
 
 
+# TODO: improve typing
+def infer_type(result):
+    if isinstance(result, bool):
+        return "bool"
+    elif isinstance(result, int):
+        return "int"
+
+    else:
+        raise Exception("Unknown type for value: {}".format(result))
+
+
+def get_subquery_type(knowledge_base, atom):
+    subquery_result = integrate_information(knowledge_base,
+                                            {
+                                                "parsed": atom,
+                                                "elements": [],
+                                            })
+    assert (subquery_result is not None)
+    result = subquery_result.getter()
+
+    result_type = infer_type(result)
+    return result_type
+
+
 def property_for_value(knowledge_base, value):
-    print(value)
+    # print(value)
-    print(knowledge_base[value])
+    # print(knowledge_base[value])
     return knowledge_base[value]['as_property']
 
 
@@ -27,6 +51,11 @@ def modifiable_property_from_property(prop, path, value):
         nonlocal prop, path, value
         prop[path] = value
 
+    return ModifiableProperty(
+        getter=getter,
+        setter=setter,
+    )
+
 
 def exists_property_with_value(knowledge_base, elements, subj, value):
     subj = resolve(knowledge_base, elements, subj)
@@ -50,6 +79,7 @@ def modifiable_element_for_existance_in_set(container, set_name, element):
     def setter():
         nonlocal container, set_name, element
         return container[set_name].add(element)
+
     return ModifiableProperty(
         getter=getter,
         setter=setter,

naive-nlu/parsing.py

@@ -1,10 +1,20 @@
 #!/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, text, parsed):
+def make_template(knowledge_base, tokens, parsed):
-    tokens = text.split()
     matcher = list(tokens)
     template = list(parsed)
     for i in range(len(matcher)):
@@ -17,16 +27,226 @@ def make_template(knowledge_base, text, parsed):
     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"]
-    tokens, matcher, result = make_template(knowledge_base, text, parsed)
+
-    print(text)
+    resolved_parsed = copy.deepcopy(parsed)
-    print(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))
@@ -34,12 +254,19 @@ def get_matching(sample, other):
         if len(other) == 0:
             return []
 
-        if not isinstance(sample[0][i], str):
+        if isinstance(sample[0][i], dict): # Dictionaries are compared by groups
-            other = list(filter(lambda x: not isinstance(x[0][i], str) and
+            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))}
@@ -56,25 +283,134 @@ def reprocess_language_knowledge(knowledge_base, examples):
     pattern_examples = []
     for i, sample in enumerate(examples):
         other = examples[:i] + examples[i + 1:]
-        print(sample)
         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 get_fit(knowledge, row):
+def fitting_return_type(knowledge,
-    row = row.lower().split()
+                        return_type, remixer,
-    for sample, ast in knowledge.trained:
+                        input_stream,
-        if len(sample) != len(row):
+                        tail_of_ouput_stream,
-            continue
+                        remaining_recursions: int):
+    indent = "  " + "  " * (MAX_RECURSIONS - remaining_recursions)
 
-        if all(map(lambda x: (not isinstance(sample[x], str)
+    for sample, ast in knowledge.trained:
-                              or sample[x] == row[x]),
+        try:
-                   range(len(sample)))):
+            parsed_input = []
-            return row, sample, ast
+            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

naive-nlu/test.py

@@ -1,32 +1,45 @@
 import json
 
 from knowledge_base import KnowledgeBase
+from modifiable_property import ModifiableProperty
 
 examples = [
     {
         "text": "icecream is cold",
         "parsed": ("exists-property-with-value", 'icecream', 'cold'),
     },
+    {
+        "text": "is icecream cold?",
+        "parsed": ("question", ("exists-property-with-value", 'icecream', 'cold'))
+    },
     {
         "text": "lava is dangerous",
         "parsed": ("exists-property-with-value", 'lava', 'dangerous')
     },
+    # {
+    #     "text": "is lava dangerous?",
+    #     "parsed": ("question", ("exists-property-with-value", 'lava', 'dangerous')),
+    # },
     {
         "text": "earth is a planet",
         "parsed": ("pertenence-to-group", 'earth', 'planet'),
     },
-    {
+    # {
-        "text": "Green is a color",
+    #     "text": "is earth a moon?",
-        "parsed": ("pertenence-to-group", 'green', 'color'),
+    #     "parsed": ("question", ("pertenence-to-group", 'earth', 'moon')),
-    },
+    # },
-    {
+    # {
-        "text": "a plane can fly",
+    #     "text": "Green is a color",
-        "parsed": ("has-capacity", 'plane', 'fly')
+    #     "parsed": ("pertenence-to-group", 'green', 'color'),
-    },
+    # },
-    {
+    # {
-        "text": "a wale can swim",
+    #     "text": "a plane can fly",
-        "parsed": ("has-capacity", 'wale', 'swim')
+    #     "parsed": ("has-capacity", 'plane', 'fly')
-    }
+    # },
+    # {
+    #     "text": "a wale can swim",
+    #     "parsed": ("has-capacity", 'wale', 'swim')
+    # },
 ]
 
 base_knowledge = {
@@ -78,11 +91,11 @@ def test_assumption(expectedResponse, knowledge, query):
     print("Query: {}".format(query['text']))
     print("Expected: {}".format(expectedResponse))
 
-    import knowledge_evaluation
+    result, abstract_tree, diff = knowledge.process(query['text'])
-    result = knowledge_evaluation.integrate_information(
+    end_result = result.getter() if isinstance(result, ModifiableProperty) else result
-        knowledge.knowledge,
+
-        query)
+    print("\x1b[0;3{}mResult: {}\x1b[0m".format("1" if end_result != expectedResponse else "2", end_result))
-    print("\x1b[0;3{}mResult: {}\x1b[0m".format("1" if result != expectedResponse else "2", result))
+    assert(end_result == expectedResponse)
 
 
 def main():
@@ -91,25 +104,27 @@ def main():
     )
 
     differences = knowledge.train(examples)
+
     print("----")
     print(differences())
     print("----")
 
-    for test in [{'text': 'a bus can run'}, {'text': 'io is a moon'}]:
+    test_assumption(True, knowledge, {'text': 'earth is a planet'})
-        row = test['text']
+    test_assumption(True, knowledge, {'text': 'is lava dangerous?'})
-        result, differences = knowledge.process(row)
+    # for test in [{'text': 'a bus can run'}, {'text': 'io is a moon'}]:
+    #     row = test['text']
+    #     result, inferred_tree, differences = knowledge.process(row)
 
-        print("result:", result)
+    #     print("result:", result)
-        print(differences())
+    #     print(differences())
-        print()
+    #     print()
-    print('-----')
+    # print('-----')
-    print(json.dumps(sorted(knowledge.knowledge.keys()), indent=4))
+    # print(json.dumps(sorted(knowledge.knowledge.keys()), indent=4))
-    print('-----')
+    # print('-----')
-    queryTrue = { "text": "is io a moon?", "parsed": ("question", ("pertenence-to-group", "io", "moon")) }
+    # queryTrue = { "text": "is io a moon?", "parsed": ("question", ("pertenence-to-group", "io", "moon")) }
-    queryFalse = { "text": "is io a planet?", "parsed": ("question", ("pertenence-to-group", "io", "planet")) }
+    # queryFalse = { "text": "is io a planet?", "parsed": ("question", ("pertenence-to-group", "io", "planet")) }
 
-    test_assumption(True, knowledge, queryTrue)
+    # test_assumption(False, knowledge, queryFalse)
-    test_assumption(False, knowledge, queryFalse)
 
 if __name__ == '__main__':
     main()