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Support for many new model output types (#93)

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* General model wrappers and methods supporting multi-label classifiers
* Support for pytorch multi-label binary classifier
* New model output types + single implementation of score method that supports multiple output types. 
* Anonymization with pytorch multi-output binary model
* Support for multi-label binary models in minimizer. 
* Support for multi-label logits/probabilities
---------
Signed-off-by: abigailt <abigailt@il.ibm.com>
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abigailgold 2024-07-03 09:04:59 -04:00 • committed by GitHub
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apt/minimization/minimizer.py
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@ -16,7 +16,8 @@ from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from apt.utils.datasets import ArrayDataset, DATA_PANDAS_NUMPY_TYPE
from apt.utils.models import Model, SklearnRegressor, ModelOutputType, SklearnClassifier
from apt.utils.models import Model, SklearnRegressor, SklearnClassifier, \
CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES
@dataclass
@ -93,7 +94,10 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
if is_regression:
self.estimator = SklearnRegressor(estimator)
else:
self.estimator = SklearnClassifier(estimator, ModelOutputType.CLASSIFIER_PROBABILITIES)
# model output type is not critical as it only affects computation of nb_classes, which is in any case
# the same currently for single and multi output probabilities.
self.estimator = SklearnClassifier(estimator,
CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
self.target_accuracy = target_accuracy
self.cells = cells
self.categorical_features = []
@ -678,7 +682,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
# this is a leaf
# if it is a regression problem we do not use label
label = self._calculate_cell_label(node) if not self.is_regression else 1
hist = [int(i) for i in self._dt.tree_.value[node][0]] if not self.is_regression else []
hist = self._dt.tree_.value[node]
cell = {'label': label, 'hist': hist, 'ranges': {}, 'id': int(node)}
return [cell]
@ -709,8 +713,11 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
return cells
def _calculate_cell_label(self, node):
label_hist = self._dt.tree_.value[node][0]
return int(self._dt.classes_[np.argmax(label_hist)])
label_hist = self._dt.tree_.value[node]
if isinstance(self._dt.classes_, list):
return [self._dt.classes_[output][class_index]
for output, class_index in enumerate(np.argmax(label_hist, axis=1))]
return [self._dt.classes_[np.argmax(label_hist[0])]]
def _modify_cells(self):
cells = []
@ -807,9 +814,12 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
# else: nothing to do, stay with previous cells
def _calculate_level_cell_label(self, left_cell, right_cell, new_cell):
new_cell['hist'] = [x + y for x, y in
zip(left_cell['hist'], right_cell['hist'])] if not self.is_regression else []
new_cell['label'] = int(self._dt.classes_[np.argmax(new_cell['hist'])]) if not self.is_regression else 1
new_cell['hist'] = left_cell['hist'] + right_cell['hist']
if isinstance(self._dt.classes_, list):
new_cell['label'] = [self._dt.classes_[output][class_index]
for output, class_index in enumerate(np.argmax(new_cell['hist'], axis=1))]
else:
new_cell['label'] = [self._dt.classes_[np.argmax(new_cell['hist'][0])]]
def _get_nodes_level(self, level):
# level = distance from lowest leaf
@ -837,26 +847,28 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
# return all nodes with depth == level or leaves higher than level
return [i for i, x in enumerate(node_depth) if x == depth or (x < depth and is_leaves[i])]
def _attach_cells_representatives(self, prepared_data, originalTrainFeatures, labelFeature, level_nodes):
def _attach_cells_representatives(self, prepared_data, original_train_features, label_feature, level_nodes):
# prepared data include one hot encoded categorical data,
# if there is no categorical data prepared data is original data
nodeIds = self._find_sample_nodes(prepared_data, level_nodes)
labels_df = pd.DataFrame(labelFeature, columns=['label'])
for cell in self.cells:
cell['representative'] = {}
# get all rows in cell
indexes = [i for i, x in enumerate(nodeIds) if x == cell['id']]
original_rows = originalTrainFeatures.iloc[indexes]
original_rows = original_train_features.iloc[indexes]
sample_rows = prepared_data.iloc[indexes]
sample_labels = labels_df.iloc[indexes]['label'].values.tolist()
# get rows with matching label
if self.is_regression:
if self.is_regression or (len(label_feature.shape) > 1 and label_feature.shape[1] > 1):
match_samples = sample_rows
match_rows = original_rows
else:
indexes = [i for i, label in enumerate(sample_labels) if label == cell['label']]
labels_df = pd.DataFrame(label_feature, columns=['label'])
sample_labels = labels_df.iloc[indexes]['label'].values.tolist()
indexes = [i for i, label in enumerate(sample_labels) if label == cell['label'][0]]
match_samples = sample_rows.iloc[indexes]
match_rows = original_rows.iloc[indexes]
# find the "middle" of the cluster
array = match_samples.values
# Only works with numpy 1.9.0 and higher!!!