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Compute generalizations with test data when possible (for computing better representatives).

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Signed-off-by: abigailt <abigailt@il.ibm.com>
This commit is contained in:
abigailt 2023-08-07 15:59:22 +03:00
parent b48b829a01
commit c2e0fced03
2 changed files with 50 additions and 24 deletions
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57
apt/minimization/minimizer.py
View file

@ -325,7 +325,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
self._attach_cells_representatives(x_prepared, used_X_train, y_train, nodes)
# self._cells currently holds the generalization created from the tree leaves
self._calculate_generalizations()
self._calculate_generalizations(X_test)
if generalize_using_transform:
generalized = self._generalize_from_tree(X_test, x_prepared_test, nodes, self.cells, self._cells_by_id)
else:
@ -355,7 +355,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
self._attach_cells_representatives(x_prepared, used_X_train, y_train, nodes)
self._calculate_generalizations()
self._calculate_generalizations(X_test)
if generalize_using_transform:
generalized = self._generalize_from_tree(X_test, x_prepared_test, nodes, self.cells,
self._cells_by_id)
@ -385,7 +385,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
if removed_feature is None:
break
self._calculate_generalizations()
self._calculate_generalizations(X_test)
if generalize_using_transform:
generalized = self._generalize_from_tree(X_test, x_prepared_test, nodes, self.cells,
self._cells_by_id)
@ -1084,6 +1084,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
self._generalizations['ranges'],
self._generalizations['categories'])
# categorical - use most common value
old_category_representatives = category_representatives
category_representatives = {}
for feature in self._generalizations['categories']:
category_representatives[feature] = []
@ -1092,34 +1093,42 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
# for c_index in range(len(group)):
# indexes = [i for i, s in enumerate(sample_indexes) if s[feature][g_index] == c_index]
indexes = [i for i, s in enumerate(sample_indexes) if s[feature] == g_index]
rows = samples[indexes]
values = rows[:, feature]
category = Counter(values).most_common(1)[0][0]
category_representatives[feature].append(group[category])
# c_count = len([s for s in sample_indexes if s[feature][g_index] == c_index])
# if c_count > max_count:
# max_count = c_count
# category = c_index
# category_representatives[feature].append(group[category])
if indexes:
rows = samples.iloc[indexes]
values = rows[feature]
category = Counter(values).most_common(1)[0][0]
category_representatives[feature].append(category)
# c_count = len([s for s in sample_indexes if s[feature][g_index] == c_index])
# if c_count > max_count:
# max_count = c_count
# category = c_index
# category_representatives[feature].append(group[category])
else:
category_representatives[feature].append(old_category_representatives[feature][g_index])
# numerical - use actual value closest to mean
old_range_representatives = range_representatives
range_representatives = {}
for feature in self._generalizations['ranges']:
range_representatives[feature] = []
# find the mean value (per feature)
for index in range(len(self._generalizations['ranges'][feature])):
indexes = [i for i, s in enumerate(sample_indexes) if s[feature] == index]
rows = samples[indexes]
values = rows[:, feature]
median = np.median(values)
min_value = max(values)
min_dist = float("inf")
for value in values:
# euclidean distance between two floating point values
dist = abs(value - median)
if dist < min_dist:
min_dist = dist
min_value = value
range_representatives[feature].append(min_value)
if indexes:
rows = samples.iloc[indexes]
values = rows[feature]
median = np.median(values)
min_value = max(values)
min_dist = float("inf")
for value in values:
# euclidean distance between two floating point values
dist = abs(value - median)
if dist < min_dist:
min_dist = dist
min_value = value
range_representatives[feature].append(min_value)
else:
range_representatives[feature].append(old_range_representatives[feature][index])
self._generalizations['category_representatives'] = category_representatives
self._generalizations['range_representatives'] = range_representatives

17
tests/test_minimizer.py
View file

@ -49,7 +49,24 @@ def test_minimizer_params():
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_PROBABILITIES)
model.fit(ArrayDataset(X, y))
expected_generalizations = {'categories': {}, 'category_representatives': {},
'range_representatives': {'age': [38, 0.5, 40], 'height': [170, 0.5, 172]},
'ranges': {'age': [38, 39], 'height': [170, 171]}, 'untouched': []}
gen = GeneralizeToRepresentative(model, cells=cells)
gener = gen.generalizations
for key in expected_generalizations['ranges']:
assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
for key in expected_generalizations['categories']:
assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
== set([frozenset(sl) for sl in gener['categories'][key]]))
assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
for key in expected_generalizations['range_representatives']:
assert (set(expected_generalizations['range_representatives'][key]) == set(gener['range_representatives'][key]))
for key in expected_generalizations['category_representatives']:
assert (set([frozenset(sl) for sl in expected_generalizations['category_representatives'][key]])
== set([frozenset(sl) for sl in gener['category_representatives'][key]]))
gen.fit()
gen.transform(dataset=ArrayDataset(X, features_names=features))