Module: tfma

Init module for TensorFlow Model Analysis.


addons module: Init module for TensorFlow Model Analysis addons.

constants module: Constants used in TensorFlow Model Analysis.

contrib module

evaluators module: Init module for TensorFlow Model Analysis evaluators.

experimental module

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.

metrics module: Init module for TensorFlow Model Analysis metrics.

model_agnostic_eval module: Init module for TensorFlow Model Analysis model_agnostic_eval.

post_export_metrics module: Library containing helpers for adding post export metrics for evaluation.

sdk module: SDK for TensorFlow Model Analysis.

types module: Types.

utils module: Init module for TensorFlow Model Analysis utils.

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.


class AggregationOptions: A ProtocolMessage

class AttributionsForSlice: A ProtocolMessage

class BinarizationOptions: A ProtocolMessage

class ConfidenceIntervalOptions: A ProtocolMessage

class CrossSliceMetricThreshold: A ProtocolMessage

class CrossSliceMetricThresholds: A ProtocolMessage

class CrossSlicingSpec: A ProtocolMessage

class EvalConfig: A ProtocolMessage

class EvalResult: The result of a single model analysis run.

class EvalSharedModel: Shared model used during extraction and evaluation.

class ExampleWeightOptions: A ProtocolMessage

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 PaddingOptions: 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 RepeatedInt32Value: A ProtocolMessage

class RepeatedStringValue: A ProtocolMessage

class SlicingSpec: A ProtocolMessage

class SparseTensorValue: SparseTensorValue encapsulates a batch of sparse tensor values.

class ValidationResult: A ProtocolMessage

class VarLenTensorValue: VarLenTensorValue encapsulates a batch of varlen dense tensor values.


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.

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.

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.

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.

Type Aliases







ANALYSIS_KEY 'analysis'
ARROW_INPUT_COLUMN '__raw_record__'
ARROW_RECORD_BATCH_KEY 'arrow_record_batch'
ATTRIBUTIONS_KEY 'attributions'
BASELINE_KEY 'baseline'
BASELINE_SCORE_KEY 'baseline_score'
CANDIDATE_KEY 'candidate'
DATA_CENTRIC_MODE 'data_centric_mode'
EXAMPLE_SCORE_KEY 'example_score'
EXAMPLE_WEIGHTS_KEY 'example_weights'
FEATURES_KEY 'features'
INPUT_KEY 'input'
LABELS_KEY 'labels'
METRICS_KEY 'metrics'
MODEL_CENTRIC_MODE 'model_centric_mode'
MetricDirection Instance of google.protobuf.internal.enum_type_wrapper.EnumTypeWrapper
PLOTS_KEY 'plots'
PREDICTIONS_KEY 'predictions'
SLICE_KEY_TYPES_KEY '_slice_key_types'
TFMA_EVAL 'tfma_eval'
TF_ESTIMATOR 'tf_estimator'
TF_GENERIC 'tf_generic'
TF_JS 'tf_js'
TF_KERAS 'tf_keras'
TF_LITE 'tf_lite'
VALIDATIONS_KEY 'validations'