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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.
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.
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 PlotsForSlice
: A ProtocolMessage
class SlicingSpec
: A ProtocolMessage
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_model_spec(...)
: Returns model spec with given model name.
get_model_type(...)
: Returns model type for given model spec taking into account defaults.
has_change_threshold(...)
: Checks whether the eval_config has any change thresholds.
is_batched_input(...)
: Returns true if batched input should be used.
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.