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Implementation: Current Best Learning (aimacode#593)
* Update README.md * add powerset to utils * add powerset test * Create knowledge.py * Create test_knowledge.py * add header docstring to knowledge.py * update learning docstring * minor edits in knowledge.py
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‎README.md

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@@ -108,7 +108,7 @@ Here is a table of algorithms, the figure, name of the code in the book and in t
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| 18.11 | Decision-List-Learning | `DecisionListLearner` | [`learning.py`][learning] |
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| 18.24 | Back-Prop-Learning | `BackPropagationLearner` | [`learning.py`][learning] |
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| 18.34 | AdaBoost | `AdaBoost` | [`learning.py`][learning] |
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| 19.2 | Current-Best-Learning | |
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| 19.2 | Current-Best-Learning | `current_best_learning` | [`knowledge.py`](knowledge.py) |
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| 19.3 | Version-Space-Learning | |
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| 19.8 | Minimal-Consistent-Det | |
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| 19.12 | FOIL | |
@@ -146,6 +146,7 @@ Many thanks for contributions over the years. I got bug reports, corrected code,
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[csp]:../master/csp.py
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[games]:../master/games.py
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[grid]:../master/grid.py
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[knowledge]:../master/knowledge.py
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[learning]:../master/learning.py
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[logic]:../master/logic.py
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[mdp]:../master/mdp.py

‎knowledge.py

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"""Knowledge in learning, Chapter 19"""
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from random import shuffle
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from utils import powerset
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from collections import defaultdict
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# ______________________________________________________________________________
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def current_best_learning(examples, h, examples_so_far=[]):
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""" [Figure 19.2]
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The hypothesis is a list of dictionaries, with each dictionary representing
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a disjunction."""
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if not examples:
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return h
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e = examples[0]
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if is_consistent(e, h):
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return current_best_learning(examples[1:], h, examples_so_far + [e])
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elif false_positive(e, h):
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for h2 in specializations(examples_so_far + [e], h):
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h3 = current_best_learning(examples[1:], h2, examples_so_far + [e])
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if h3 != 'FAIL':
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return h3
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elif false_negative(e, h):
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for h2 in generalizations(examples_so_far + [e], h):
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h3 = current_best_learning(examples[1:], h2, examples_so_far + [e])
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if h3 != 'FAIL':
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return h3
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return 'FAIL'
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def specializations(examples_so_far, h):
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"""Specialize the hypothesis by adding AND operations to the disjunctions"""
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hypotheses = []
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for i, disj in enumerate(h):
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for e in examples_so_far:
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for k, v in e.items():
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if k in disj or k == 'GOAL':
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continue
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h2 = h[i].copy()
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h2[k] = '!' + v
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h3 = h.copy()
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h3[i] = h2
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if check_all_consistency(examples_so_far, h3):
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hypotheses.append(h3)
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shuffle(hypotheses)
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return hypotheses
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def generalizations(examples_so_far, h):
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"""Generalize the hypothesis. First delete operations
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(including disjunctions) from the hypothesis. Then, add OR operations."""
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hypotheses = []
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# Delete disjunctions
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disj_powerset = powerset(range(len(h)))
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for disjs in disj_powerset:
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h2 = h.copy()
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for d in reversed(list(disjs)):
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del h2[d]
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if check_all_consistency(examples_so_far, h2):
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hypotheses += h2
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# Delete AND operations in disjunctions
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for i, disj in enumerate(h):
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a_powerset = powerset(disj.keys())
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for attrs in a_powerset:
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h2 = h[i].copy()
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for a in attrs:
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del h2[a]
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if check_all_consistency(examples_so_far, [h2]):
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h3 = h.copy()
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h3[i] = h2.copy()
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hypotheses += h3
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# Add OR operations
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hypotheses.extend(add_or(examples_so_far, h))
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shuffle(hypotheses)
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return hypotheses
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def add_or(examples_so_far, h):
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"""Adds an OR operation to the hypothesis. The AND operations in the disjunction
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are generated by the last example (which is the problematic one)."""
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ors = []
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e = examples_so_far[-1]
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attrs = {k: v for k, v in e.items() if k != 'GOAL'}
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a_powerset = powerset(attrs.keys())
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for c in a_powerset:
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h2 = {}
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for k in c:
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h2[k] = attrs[k]
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if check_negative_consistency(examples_so_far, h2):
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h3 = h.copy()
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h3.append(h2)
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ors.append(h3)
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return ors
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# ______________________________________________________________________________
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def check_all_consistency(examples, h):
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"""Check for the consistency of all examples under h"""
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for e in examples:
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if not is_consistent(e, h):
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return False
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return True
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def check_negative_consistency(examples, h):
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"""Check if the negative examples are consistent under h"""
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for e in examples:
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if e['GOAL']:
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continue
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if not is_consistent(e, [h]):
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return False
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return True
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def disjunction_value(e, d):
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"""The value of example e under disjunction d"""
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for k, v in d.items():
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if v[0] == '!':
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# v is a NOT expression
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# e[k], thus, should not be equal to v
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if e[k] == v[1:]:
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return False
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elif e[k] != v:
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return False
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return True
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def guess_value(e, h):
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"""Guess value of example e under hypothesis h"""
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for d in h:
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if disjunction_value(e, d):
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return True
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return False
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def is_consistent(e, h):
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return e["GOAL"] == guess_value(e, h)
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def false_positive(e, h):
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if e["GOAL"] == False:
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if guess_value(e, h):
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return True
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return False
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def false_negative(e, h):
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if e["GOAL"] == True:
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if not guess_value(e, h):
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return True
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return False

‎learning.py

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"""Learn to estimate functions from examples. (Chapters 18-20)"""
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"""Learn to estimate functions from examples. (Chapters 18, 20)"""
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from utils import (
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removeall, unique, product, mode, argmax, argmax_random_tie, isclose, gaussian,

‎tests/test_knowledge.py

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from knowledge import *
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import random
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random.seed("aima-python")
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def test_current_best_learning():
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examples = restaurant
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hypothesis = [{'Alt': 'Yes'}]
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h = current_best_learning(examples, hypothesis)
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values = []
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for e in examples:
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values.append(guess_value(e, h))
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assert values == [True, False, True, True, False, True, False, True, False, False, False, True]
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examples = animals_umbrellas
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initial_h = [{'Species': 'Cat'}]
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h = current_best_learning(examples, initial_h)
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values = []
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for e in examples:
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values.append(guess_value(e, h))
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assert values == [True, True, True, False, False, False, True]
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animals_umbrellas = [
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{'Species': 'Cat', 'Rain': 'Yes', 'Coat': 'No', 'GOAL': True},
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{'Species': 'Cat', 'Rain': 'Yes', 'Coat': 'Yes', 'GOAL': True},
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{'Species': 'Dog', 'Rain': 'Yes', 'Coat': 'Yes', 'GOAL': True},
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{'Species': 'Dog', 'Rain': 'Yes', 'Coat': 'No', 'GOAL': False},
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{'Species': 'Dog', 'Rain': 'No', 'Coat': 'No', 'GOAL': False},
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{'Species': 'Cat', 'Rain': 'No', 'Coat': 'No', 'GOAL': False},
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{'Species': 'Cat', 'Rain': 'No', 'Coat': 'Yes', 'GOAL': True}
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]
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restaurant = [
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{'Alt': 'Yes', 'Bar': 'No', 'Fri': 'No', 'Hun': 'Yes', 'Pat': 'Some',
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'Price': '$$$', 'Rain': 'No', 'Res': 'Yes', 'Type': 'French', 'Est': '0-10',
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'GOAL': True},
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{'Alt': 'Yes', 'Bar': 'No', 'Fri': 'No', 'Hun': 'Yes', 'Pat': 'Full',
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'Price': '$', 'Rain': 'No', 'Res': 'No', 'Type': 'Thai', 'Est': '30-60',
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'GOAL': False},
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{'Alt': 'No', 'Bar': 'Yes', 'Fri': 'No', 'Hun': 'No', 'Pat': 'Some',
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'Price': '$', 'Rain': 'No', 'Res': 'No', 'Type': 'Burger', 'Est': '0-10',
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'GOAL': True},
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{'Alt': 'Yes', 'Bar': 'No', 'Fri': 'Yes', 'Hun': 'Yes', 'Pat': 'Full',
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'Price': '$', 'Rain': 'Yes', 'Res': 'No', 'Type': 'Thai', 'Est': '10-30',
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'GOAL': True},
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{'Alt': 'Yes', 'Bar': 'No', 'Fri': 'Yes', 'Hun': 'No', 'Pat': 'Full',
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'Price': '$$$', 'Rain': 'No', 'Res': 'Yes', 'Type': 'French', 'Est': '>60',
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'GOAL': False},
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{'Alt': 'No', 'Bar': 'Yes', 'Fri': 'No', 'Hun': 'Yes', 'Pat': 'Some',
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'Price': '$$', 'Rain': 'Yes', 'Res': 'Yes', 'Type': 'Italian', 'Est': '0-10',
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'GOAL': True},
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{'Alt': 'No', 'Bar': 'Yes', 'Fri': 'No', 'Hun': 'No', 'Pat': 'None',
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'Price': '$', 'Rain': 'Yes', 'Res': 'No', 'Type': 'Burger', 'Est': '0-10',
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'GOAL': False},
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{'Alt': 'No', 'Bar': 'No', 'Fri': 'No', 'Hun': 'Yes', 'Pat': 'Some',
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'Price': '$$', 'Rain': 'Yes', 'Res': 'Yes', 'Type': 'Thai', 'Est': '0-10',
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'GOAL': True},
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{'Alt': 'No', 'Bar': 'Yes', 'Fri': 'Yes', 'Hun': 'No', 'Pat': 'Full',
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'Price': '$', 'Rain': 'Yes', 'Res': 'No', 'Type': 'Burger', 'Est': '>60',
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'GOAL': False},
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{'Alt': 'Yes', 'Bar': 'Yes', 'Fri': 'Yes', 'Hun': 'Yes', 'Pat': 'Full',
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'Price': '$$$', 'Rain': 'No', 'Res': 'Yes', 'Type': 'Italian', 'Est': '10-30',
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'GOAL': False},
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{'Alt': 'No', 'Bar': 'No', 'Fri': 'No', 'Hun': 'No', 'Pat': 'None',
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'Price': '$', 'Rain': 'No', 'Res': 'No', 'Type': 'Thai', 'Est': '0-10',
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'GOAL': False},
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{'Alt': 'Yes', 'Bar': 'Yes', 'Fri': 'Yes', 'Hun': 'Yes', 'Pat': 'Full',
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'Price': '$', 'Rain': 'No', 'Res': 'No', 'Type': 'Burger', 'Est': '30-60',
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'GOAL': True}
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]

‎tests/test_utils.py

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@@ -51,6 +51,10 @@ def test_mode():
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assert mode("absndkwoajfkalwpdlsdlfllalsflfdslgflal") == 'l'
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def test_powerset():
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assert powerset([1, 2, 3]) == [(1,), (2,), (3,), (1, 2), (1, 3), (2, 3), (1, 2, 3)]
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def test_argminmax():
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assert argmin([-2, 1], key=abs) == 1
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assert argmax([-2, 1], key=abs) == -2

‎utils.py

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import random
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import math
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import functools
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from itertools import chain, combinations
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# ______________________________________________________________________________
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# Functions on Sequences and Iterables
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return item
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def powerset(iterable):
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"""powerset([1,2,3]) --> (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"""
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s = list(iterable)
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return list(chain.from_iterable(combinations(s, r) for r in range(len(s)+1)))[1:]
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# ______________________________________________________________________________
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# argmin and argmax
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