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Init module for TensorFlow Model Analysis.
Modules
addons module: Init module for TensorFlow Model Analysis addons.
config module: Configuration types.
constants module: Constants used in TensorFlow Model Analysis.
evaluators module: Init module for TensorFlow Model Analysis evaluators.
experimental module: Init module for experimental.
export module: Library for exporting the EvalSavedModel.
exporter module: Exporter class represents different flavors of model export.
extractors module: Init module for TensorFlow Model Analysis extractors.
math_util module: Math utilities.
metrics module: Init module for TensorFlow Model Analysis metrics.
model_agnostic_eval module: Init module for TensorFlow Model Analysis model_agnostic_eval.
model_util module: Utils for working with models.
post_export_metrics module: Library containing helpers for adding post export metrics for evaluation.
size_estimator module: Size estimator.
types module: Types.
validators module: Init module for TensorFlow Model Analysis validators.
version module: Contains the version string for this release of TFMA.
view module: Initializes TFMA's view rendering api.
writers module: Init module for TensorFlow Model Analysis writers.
Classes
class AggregationOptions: A ProtocolMessage
class AttributionsForSlice: A ProtocolMessage
class BinarizationOptions: A ProtocolMessage
class CombineFnWithModels: Abstract class for CombineFns that need the shared models.
class ConfidenceIntervalOptions: A ProtocolMessage
class DoFnWithModels: Abstract class for DoFns that need the shared models.
class EvalConfig: A ProtocolMessage
class EvalResult: The result of a single model analysis run.
class EvalSharedModel: Shared model used during extraction and evaluation.
class FeaturesPredictionsLabels: FeaturesPredictionsLabels(input_ref, features, predictions, labels)
class GenericChangeThreshold: A ProtocolMessage
class GenericValueThreshold: A ProtocolMessage
class MaterializedColumn: MaterializedColumn(name, value)
class MetricConfig: A ProtocolMessage
class MetricThreshold: A ProtocolMessage
class MetricsForSlice: A ProtocolMessage
class MetricsSpec: A ProtocolMessage
class ModelLoader: Model loader is responsible for loading shared model types.
class ModelSpec: A ProtocolMessage
class Options: A ProtocolMessage
class PerSliceMetricThreshold: A ProtocolMessage
class PerSliceMetricThresholds: A ProtocolMessage
class PlotsForSlice: A ProtocolMessage
class RaggedTensorValue: RaggedTensorValue encapsulates a batch of ragged tensor values.
class SlicingSpec: A ProtocolMessage
class SparseTensorValue: SparseTensorValue encapsulates a batch of sparse tensor values.
class ValidationResult: A ProtocolMessage
Functions
BatchedInputsToExtracts(...): Converts Arrow RecordBatch inputs to Extracts.
ExtractAndEvaluate(...): Performs Extractions and Evaluations in provided order.
ExtractEvaluateAndWriteResults(...): PTransform for performing extraction, evaluation, and writing results.
InputsToExtracts(...): Converts serialized inputs (e.g. examples) to Extracts if not already.
Validate(...): Performs validation of alternative evaluations.
WriteResults(...): Writes Evaluation or Validation results using given writers.
analyze_raw_data(...): Runs TensorFlow model analysis on a pandas.DataFrame.
compound_key(...): Returns a compound key based on a list of keys.
create_keys_key(...): Creates secondary key representing the sparse keys associated with key.
create_values_key(...): Creates secondary key representing sparse values associated with key.
default_eval_shared_model(...): Returns default EvalSharedModel.
default_evaluators(...): Returns the default evaluators for use in ExtractAndEvaluate.
default_extractors(...): Returns the default extractors for use in ExtractAndEvaluate.
default_writers(...): Returns the default writers for use in WriteResults.
get_baseline_model_spec(...): Returns baseline model spec.
get_model_spec(...): Returns model spec with given model name.
get_model_type(...): Returns model type for given model spec taking into account defaults.
get_non_baseline_model_specs(...): Returns non-baseline model specs.
has_change_threshold(...): Checks whether the eval_config has any change thresholds.
is_batched_input(...): Returns true if batched input should be used.
is_legacy_estimator(...): Returns true if there is a legacy estimator.
load_attributions(...): Read and deserialize the AttributionsForSlice records.
load_eval_result(...): Loads EvalResult object for use with the visualization functions.
load_eval_results(...): Loads results for multiple models or multiple data sets.
load_metrics(...): Read and deserialize the MetricsForSlice records.
load_plots(...): Read and deserialize the PlotsForSlice records.
load_validation_result(...): Read and deserialize the ValidationResult.
make_eval_results(...): Run model analysis for a single model on multiple data sets.
model_construct_fn(...): Returns function for constructing shared models.
multiple_data_analysis(...): Run model analysis for a single model on multiple data sets.
multiple_model_analysis(...): Run model analysis for multiple models on the same data set.
run_model_analysis(...): Runs TensorFlow model analysis.
unique_key(...): Returns a unique key given a list of current keys.
update_eval_config_with_defaults(...): Returns a new config with default settings applied.
verify_and_update_eval_shared_models(...): Verifies eval shared models and normnalizes to produce a single list.
verify_eval_config(...): Verifies eval config.
Type Aliases
Extracts: The central part of internal API.
MaybeMultipleEvalSharedModels: The central part of internal API.
TensorType: The central part of internal API.
TensorTypeMaybeDict: The central part of internal API.
TensorValue: The central part of internal API.