interpret_community.common.constants module¶
Defines constants for interpret community.
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class
interpret_community.common.constants.
Attributes
¶ Bases:
object
Provide constants for attributes.
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EXPECTED_VALUE
= 'expected_value'¶
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class
interpret_community.common.constants.
DNNFramework
¶ Bases:
object
Provide DNN framework constants.
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PYTORCH
= 'pytorch'¶
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TENSORFLOW
= 'tensorflow'¶
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class
interpret_community.common.constants.
Defaults
¶ Bases:
object
Provide constants for default values to explain methods.
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AUTO
= 'auto'¶
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DEFAULT_BATCH_SIZE
= 100¶
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HDBSCAN
= 'hdbscan'¶
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MAX_DIM
= 50¶
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class
interpret_community.common.constants.
Dynamic
¶ Bases:
object
Provide constants for dynamically generated classes.
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GLOBAL_EXPLANATION
= 'DynamicGlobalExplanation'¶
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LOCAL_EXPLANATION
= 'DynamicLocalExplanation'¶
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class
interpret_community.common.constants.
ExplainParams
¶ Bases:
object
Provide constants for interpret community (init, explain_local and explain_global) parameters.
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BATCH_SIZE
= 'batch_size'¶
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CLASSES
= 'classes'¶
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CLASSIFICATION
= 'classification'¶
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EVAL_DATA
= 'eval_data'¶
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EVAL_Y_PRED
= 'eval_y_predicted'¶
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EVAL_Y_PRED_PROBA
= 'eval_y_predicted_proba'¶
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EXPECTED_VALUES
= 'expected_values'¶
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EXPLAIN_SUBSET
= 'explain_subset'¶
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EXPLANATION_ID
= 'explanation_id'¶
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FEATURES
= 'features'¶
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GLOBAL_IMPORTANCE_NAMES
= 'global_importance_names'¶
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GLOBAL_IMPORTANCE_RANK
= 'global_importance_rank'¶
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GLOBAL_IMPORTANCE_VALUES
= 'global_importance_values'¶
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GLOBAL_NAMES
= 'global_names'¶
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GLOBAL_RANK
= 'global_rank'¶
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GLOBAL_VALUES
= 'global_values'¶
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ID
= 'id'¶
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INCLUDE_LOCAL
= 'include_local'¶
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INIT_DATA
= 'init_data'¶
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IS_ENG
= 'is_engineered'¶
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IS_LOCAL_SPARSE
= 'is_local_sparse'¶
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IS_RAW
= 'is_raw'¶
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LOCAL_EXPLANATION
= 'local_explanation'¶
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LOCAL_IMPORTANCE_VALUES
= 'local_importance_values'¶
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METHOD
= 'method'¶
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MODEL_ID
= 'model_id'¶
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MODEL_TASK
= 'model_task'¶
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MODEL_TYPE
= 'model_type'¶
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NUM_CLASSES
= 'num_classes'¶
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NUM_EXAMPLES
= 'num_examples'¶
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NUM_FEATURES
= 'num_features'¶
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PER_CLASS_NAMES
= 'per_class_names'¶
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PER_CLASS_RANK
= 'per_class_rank'¶
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PER_CLASS_VALUES
= 'per_class_values'¶
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PROBABILITIES
= 'probabilities'¶
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SAMPLING_POLICY
= 'sampling_policy'¶
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SHAP_VALUES_OUTPUT
= 'shap_values_output'¶
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classmethod
get_private
(explain_param)¶ Return the private version of the ExplainParams property.
Parameters: - cls (ExplainParams) – ExplainParams input class.
- explain_param (str) – The ExplainParams property to get private version of.
Returns: The private version of the property.
Return type:
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classmethod
get_serializable
()¶ Return only the ExplainParams properties that have meaningful data values for serialization.
Parameters: cls (ExplainParams) – ExplainParams input class. Returns: A set of property names, e.g., ‘GLOBAL_IMPORTANCE_VALUES’, ‘MODEL_TYPE’, etc. Return type: set[str]
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class
interpret_community.common.constants.
ExplainType
¶ Bases:
object
Provide constants for model and explainer type information, useful for visualization.
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CLASSIFICATION
= 'classification'¶
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DATA
= 'data_type'¶
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EXPLAIN
= 'explain_type'¶
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EXPLAINER
= 'explainer'¶
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FUNCTION
= 'function'¶
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GLOBAL
= 'global'¶
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HAN
= 'han'¶
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IS_ENG
= 'is_engineered'¶
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IS_RAW
= 'is_raw'¶
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LIME
= 'lime'¶
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LOCAL
= 'local'¶
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METHOD
= 'method'¶
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MIMIC
= 'mimic'¶
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MODEL
= 'model_type'¶
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MODEL_CLASS
= 'model_class'¶
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MODEL_TASK
= 'model_task'¶
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PFI
= 'pfi'¶
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REGRESSION
= 'regression'¶
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SHAP
= 'shap'¶
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SHAP_DEEP
= 'shap_deep'¶
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SHAP_GPU_KERNEL
= 'shap_gpu_kernel'¶
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SHAP_KERNEL
= 'shap_kernel'¶
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SHAP_LINEAR
= 'shap_linear'¶
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SHAP_TREE
= 'shap_tree'¶
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TABULAR
= 'tabular'¶
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class
interpret_community.common.constants.
ExplainableModelType
¶ -
Provide constants for the explainable model type.
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LINEAR_EXPLAINABLE_MODEL_TYPE
= 'linear_explainable_model_type'¶
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TREE_EXPLAINABLE_MODEL_TYPE
= 'tree_explainable_model_type'¶
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class
interpret_community.common.constants.
ExplanationParams
¶ Bases:
object
Provide constants for explanation parameters.
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CLASSES
= 'classes'¶
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EXPECTED_VALUES
= 'expected_values'¶
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class
interpret_community.common.constants.
Extension
¶ Bases:
object
Provide constants for extensions to interpret package.
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BLACKBOX
= 'blackbox'¶
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GLASSBOX
= 'model'¶
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GLOBAL
= 'global'¶
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GREYBOX
= 'specific'¶
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LOCAL
= 'local'¶
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class
interpret_community.common.constants.
InterpretData
¶ Bases:
object
Provide Data and Visualize constants for interpret core.
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BASE_VALUE
= 'Base Value'¶
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EXPLANATION_CLASS_DIMENSION
= 'explanation_class_dimension'¶
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EXPLANATION_TYPE
= 'explanation_type'¶
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EXTRA
= 'extra'¶
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FEATURE_LIST
= 'feature_list'¶
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GLOBAL_FEATURE_IMPORTANCE
= 'global_feature_importance'¶
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INTERCEPT
= 'intercept'¶
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LOCAL_FEATURE_IMPORTANCE
= 'local_feature_importance'¶
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MLI
= 'mli'¶
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MULTICLASS
= 'multiclass'¶
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NAMES
= 'names'¶
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OVERALL
= 'overall'¶
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PERF
= 'perf'¶
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SCORES
= 'scores'¶
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SINGLE
= 'single'¶
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SPECIFIC
= 'specific'¶
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TYPE
= 'type'¶
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UNIVARIATE
= 'univariate'¶
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VALUE
= 'value'¶
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VALUES
= 'values'¶
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class
interpret_community.common.constants.
LightGBMParams
¶ Bases:
object
Provide constants for LightGBM.
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CATEGORICAL_FEATURE
= 'categorical_feature'¶
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class
interpret_community.common.constants.
LightGBMSerializationConstants
¶ Bases:
object
Provide internal class that defines fields used for MimicExplainer serialization.
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IDENTITY
= '_identity'¶
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LOGGER
= '_logger'¶
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MODEL_STR
= 'model_str'¶
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MULTICLASS
= 'multiclass'¶
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OBJECTIVE
= 'objective'¶
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REGRESSION
= 'regression'¶
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TREE_EXPLAINER
= '_tree_explainer'¶
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enum_properties
= ['_shap_values_output']¶
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nonify_properties
= ['_logger', '_tree_explainer']¶
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save_properties
= ['_lgbm']¶
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class
interpret_community.common.constants.
MimicSerializationConstants
¶ Bases:
object
Provide internal class that defines fields used for MimicExplainer serialization.
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ALLOW_ALL_TRANSFORMATIONS
= '_allow_all_transformations'¶
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FUNCTION
= 'function'¶
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IDENTITY
= '_identity'¶
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INITIALIZATION_EXAMPLES
= 'initialization_examples'¶
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LOGGER
= '_logger'¶
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MODEL
= 'model'¶
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ORIGINAL_EVAL_EXAMPLES
= '_original_eval_examples'¶
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PREDICT_PROBA_FLAG
= 'predict_proba_flag'¶
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RESET_INDEX
= 'reset_index'¶
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TIMESTAMP_FEATURIZER
= '_timestamp_featurizer'¶
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enum_properties
= ['_shap_values_output']¶
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nonify_properties
= ['_logger', 'model', 'function', 'initialization_examples', '_original_eval_examples', '_timestamp_featurizer']¶
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save_properties
= ['surrogate_model']¶
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class
interpret_community.common.constants.
ModelTask
¶ -
Provide model task constants. Can be ‘classification’, ‘regression’, or ‘unknown’.
By default the model domain is inferred if ‘unknown’, but this can be overridden if you specify ‘classification’ or ‘regression’.
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Classification
= 'classification'¶
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Regression
= 'regression'¶
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Unknown
= 'unknown'¶
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class
interpret_community.common.constants.
ResetIndex
¶ -
Provide index column handling constants. Can be ‘ignore’, ‘reset’ or ‘reset_teacher’.
By default the index column is ignored, but you can override to reset it and make it a feature column that is then featurized to numeric, or reset it and ignore it during featurization but set it as the index when calling predict on the original model.
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Ignore
= 'ignore'¶
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Reset
= 'reset'¶
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ResetTeacher
= 'reset_teacher'¶
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class
interpret_community.common.constants.
SHAPDefaults
¶ Bases:
object
Provide constants for default values to SHAP.
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INDEPENDENT
= 'independent'¶
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class
interpret_community.common.constants.
SKLearn
¶ Bases:
object
Provide scikit-learn related constants.
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EXAMPLES
= 'examples'¶
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LABELS
= 'labels'¶
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PREDICTIONS
= 'predictions'¶
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PREDICT_PROBA
= 'predict_proba'¶
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class
interpret_community.common.constants.
Scipy
¶ Bases:
object
Provide scipy related constants.
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CSR_FORMAT
= 'csr'¶
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class
interpret_community.common.constants.
ShapValuesOutput
¶ -
Provide constants for the SHAP values output from the explainer.
Can be ‘default’, ‘probability’ or ‘teacher_probability’. If ‘teacher_probability’ is specified, we use the probabilities from the teacher model.
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DEFAULT
= 'default'¶
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PROBABILITY
= 'probability'¶
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TEACHER_PROBABILITY
= 'teacher_probability'¶
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