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Formatting
Signed-off-by: abigailt <abigailt@il.ibm.com>
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abigailt
parent
0f5a1bcaa0
commit
a4816878f9
6 changed files with 68 additions and 88 deletions
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@ -4,7 +4,7 @@ from enum import Enum, auto
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import numpy as np
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from scipy.special import expit
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from apt.utils.datasets import Dataset, Data, DatasetWithPredictions, array2numpy, OUTPUT_DATA_ARRAY_TYPE
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from apt.utils.datasets import Dataset, Data, array2numpy, OUTPUT_DATA_ARRAY_TYPE
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from art.estimators.classification import BlackBoxClassifier
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from art.utils import check_and_transform_label_format
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@ -43,40 +43,40 @@ def is_one_hot(y: OUTPUT_DATA_ARRAY_TYPE) -> bool:
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def is_multi_label(output_type: ModelOutputType) -> bool:
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return (output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CATEGORICAL or
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output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_PROBABILITIES or
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output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CLASS_PROBABILITIES or
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output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS or
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output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CLASS_LOGITS)
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return (output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CATEGORICAL
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or output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_PROBABILITIES
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or output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CLASS_PROBABILITIES
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or output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS
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or output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CLASS_LOGITS)
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def is_multi_label_binary(output_type: ModelOutputType) -> bool:
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return (output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_PROBABILITIES or
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output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS)
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return (output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_PROBABILITIES
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or output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS)
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def is_binary(output_type: ModelOutputType) -> bool:
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return (output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_PROBABILITIES or
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output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS or
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output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_BINARY_PROBABILITIES or
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output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_BINARY_LOGITS)
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return (output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_PROBABILITIES
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or output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS
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or output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_BINARY_PROBABILITIES
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or output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_BINARY_LOGITS)
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def is_categorical(output_type: ModelOutputType) -> bool:
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return (output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL or
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output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CATEGORICAL)
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return (output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL
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or output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CATEGORICAL)
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def is_probabilities(output_type: ModelOutputType) -> bool:
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return (output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES or
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output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CLASS_PROBABILITIES)
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return (output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES
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or output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CLASS_PROBABILITIES)
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def is_logits(output_type: ModelOutputType) -> bool:
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return (output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS or
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output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CLASS_LOGITS or
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output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_BINARY_LOGITS or
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output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS)
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return (output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS
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or output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CLASS_LOGITS
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or output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_BINARY_LOGITS
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or output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS)
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def get_nb_classes(y: OUTPUT_DATA_ARRAY_TYPE, output_type: ModelOutputType) -> int:
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@ -114,10 +114,10 @@ def check_correct_model_output(y: OUTPUT_DATA_ARRAY_TYPE, output_type: ModelOutp
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:type output_type: ModelOutputType
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:raises: ValueError (in case of mismatch)
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"""
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if not is_one_hot(y) and (output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES or
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output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS):
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raise ValueError("Incompatible model output types. Model outputs 1D array of categorical scalars while "
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"output type is set to ", output_type)
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if not is_one_hot(y) and (output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES
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or output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS):
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raise ValueError("Incompatible model output types. Model outputs 1D array of categorical scalars while "
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"output type is set to ", output_type)
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class Model(metaclass=ABCMeta):
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@ -209,13 +209,13 @@ class Model(metaclass=ABCMeta):
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if scoring_method == ScoringMethod.ACCURACY:
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if not is_multi_label(self.output_type) and not is_binary(self.output_type) and nb_classes is not None:
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y = check_and_transform_label_format(y, nb_classes=nb_classes)
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if (self.output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES or
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self.output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS or
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self.output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL):
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if (self.output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES
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or self.output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS
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or self.output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL):
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# categorical has been 1-hot encoded by check_and_transform_label_format
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return np.count_nonzero(np.argmax(y, axis=1) == np.argmax(predicted, axis=1)) / predicted.shape[0]
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elif (self.output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CLASS_LOGITS or
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self.output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES):
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elif (self.output_type == ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CLASS_LOGITS
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or self.output_type == ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES):
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if predicted.shape != y.shape:
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raise ValueError('Do not know how to compare arrays with different shapes')
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elif len(predicted.shape) < 3:
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@ -372,7 +372,7 @@ class BlackboxClassifier(Model):
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"""
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Score the model using test data.
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"""
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kwargs ['nb_classes'] = self.nb_classes
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kwargs['nb_classes'] = self.nb_classes
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return super().score(test_data, **kwargs)
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def fit(self, train_data: Dataset, **kwargs) -> None:
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