- Fix the bug about batching unsized numpy arrays. ## Breaking Changes
- Depends on
attrs>=19.3.0,<24.
- Add F1, False positive rate, and Accuracy into the confusion matrix plot.
- Add support for setting top_k and class_id at the same time for confusion matrix metrics.
- Add the false positive for semantic segmentation metrics.
- Add Mean Metric (experimental) which calculates the mean of any feature. *.
Adds support of
output_keypathto ModelSignatureDoFn to explicitly set a chain of output keys in the multi-level dict (extracts). Adds output_keypath to common prediction extractors. - Add Mean Metric (experimental) which calculates the mean of any feature. *.
Adds support of
output_keypathto ModelSignatureDoFn to explicitly set a chain of output keys in the multi-level dict (extracts). Adds output_keypath to common prediction extractors. - Add ROUGE Metrics.
- Add BLEU Metric.
- Fix the bug that SetMatchRecall is always 1 when top_k is set.
- Depends on
pyarrow>=10,<11. - Depends on
apache-beam>=2.47,<3. - Depends on
numpy>=1.23.0. - Depends on
tensorflow>=2.13.0,<3. - Add 'tfma_eval' model_type in model_specs as the identifier for eval_saved_model, allowing signature='eval' to now be used with other model types.
- Add "materialized_prediction" model type to allow users bypassing model inference explicitly.
- Depend on PIL for image related metrics.
- Separate extract_key from signature names in
ModelSignaturesDoFn.
- N/A
- Add BinaryCrossEntropy and CategoricalCrossEntropy.
- Add MeanAbsolutePercentageError and MeanSquaredLogarithmicError
- Add SetMatchPrecision and SetMatchRecall
- Add SemanticSegmentationConfusionMatrix
- Fix for jupiter notebook
- Fix element dimension inconsistency when some of the extracts have missing key.
- Add public visibility to the servo beam extractor.
- Fix for bug where binary_confusion_matrices with different class_weights are considered identical and deduplicated.
- Fixes bug where positive / negative axes labels are reversed in prediction distribution plot.
- Depends on
numpy~=1.22.0. - Modify ExampleCount to not depend on labels and predictions.
- Add class_id info into sub_key in metric_key for object detection confusion matrix metrics.
- Add class_id info into sub_key in plot_key for object detection confusion matrix plot.
- Fix a bug that auto_pivot dropped nan when deciding which columns are multivalent for pivoting.
- Depends on
tensorflow>=2.12.0,<2.13. - Depends on
protobuf>=3.20.3,<5. - Depends on
tfx-bsl>=1.13.0,<1.14.0. - Depends on
tensorflow-metadata>=1.13.1,<1.14.0. - Add name for each plots.
- N/A
Deprecated python3.7 support.
- N/A
- Depends on
tensorflow>=2.11,<3 - Depends on
tfx-bsl>=1.2.0,<1.13.0. - Depends on
tensorflow-metadata>=1.12.0,<1.13.0.
- N/A
- N/A
- This is the last version that supports TensorFlow 1.15.x. TF 1.15.x support will be removed in the next version. Please check the TF2 migration guide to migrate to TF2.
- Add BooleanFlipRate metric for comparing thresholded predictions between multiple models.
- Add CounterfactualPredictionsExtractor for computing predictions on modified inputs.
- Add MeanAbsoluteError and MeanSquaredError
-
Add support for parsing the Predict API prediction log output to the experimental TFX-BSL PredictionsExtractor implementation.
-
Add support for parsing the Classification API prediction log output to the experimental TFX-BSL PredictionsExtractor implementation.
-
Update remaining predictions_extractor_test.py tests to cover PredictionsExtractorOSS. Fixes a pytype bug related to multi tensor output.
-
Depends on
tensorflow>=1.15.5,<2ortensorflow>=2.10,<3 -
Apply changes in the latest Chrome browser
-
Add InferneceInterface to experimental PredictionsExtractor implementation.
-
Stop returning empty example_ids metric from binary_confusion_matrices derived computations when example_id_key is not set but use_histrogam is true.
-
Add transformed features lookup for NDCG metrics query key and gain key.
-
Deprecate BoundedValue and TDistribution in ConfusionMatrixAtThresholds.
-
Fix a bug that dataframe auto_pivot fails if there is only Overall slice.
-
Use SavedModel PB to determine default signature instead of loading the model.
-
Reduce clutter in the multi-index columns and index in the experimental dataframe auto_pivot util.
-
Minor predictions_extractor_test.py refactor with readability improvements and improved test coverage.
- N/A
- N/A
- N/A
- Move the version to top of init.py since the original "from tensorflow_model_analysis.sdk import *" will not import private symbol.
- N/A
- N/A
- Add COCO object detection metrics, object detection related utilities, objection detection options in binary confusion matrix, Precision At Recall, and AUC. Add MaxRecall metric.
- Add support for parsing sparse tensors with explicit tensor representations via TFXIO.
- Add score_distribution_plot.
- Separate the Predictions Extractor into two extractors.
- Update PredictionsExtractor to support backwards compatibility with the Materialized Predictions Extractor.
- Depends on
apache-beam[gcp]>=2.40,<3. - Depends on
pyarrow>=6,<7. - Update merge_extracts with an option to skip squeezing one-dim arrays. Update split_extracts with an option to expand zero-dim arrays.
- Added experimental bulk inference implementation to PredictionsExtractor. Currently only supports the RegressionAPI.
- Adds multi-index columns for view.experimental.metrics_as_dataframe util.
- Changes SymmetricPredictionDifference output type from array to scalar.
- N/A
- Add object detection related utilities.
- Depends on
tensorflow>=1.15.5,<2ortensorflow>=2.9,<3 - Fix issue where labels with -1 values are one-hot encoded when they shouldn't be ## Breaking Changes
- Depends on
tfx-bsl>=1.9.0,<1.10.0. - Depends on
tensorflow-metadata>=1.9.0,<1.10.0.
- N/A
- N/A
SqlSliceKeyExtractornow supports slicing on transformed features.
- Depends on
tfx-bsl>=1.8.0,<1.9.0. - Depends on
tensorflow-metadata>=1.8.0,<1.9.0. - Depends on
apache-beam[gcp]>=2.38,<3. - Fix the incorrect keras.metrics.serialization for AUCPrecisionRecall.
- N/A
- N/A
- Creates a VarLenTensorValue for storing batched, variable length extracts.
- Adds a load_metrics_as_dataframe util to load metrics file as dataframe.
-
Fixes issue attempting to parse metrics, plots, and attributions without a format suffix.
-
Fixes the non-deterministic key ordering caused by proto string serialization in metrics validator.
-
Update variable name to respectful terminology, rebuild JS
-
Fixes issues preventing standard preprocessors from being applied.
-
Allow merging extracts including sparse tensors with different dense shapes.
-
Allow counterfactual metrics to be calculated from predictions instead of only features.
- MetricsPlotsAndValidationsWriter will now write files with an explicit
output format suffix (".tfrecord" by default). This should only affect
pipelines which directly construct
MetricsPlotsAndValidationWriterinstances and do not setoutput_file_format. Those which usedefault_writers()should be unchanged. - Batched based extractors previously worked off of either lists of dicts of single tensor values or arrow record batches. These have been updated to be based on dicts with batched tensor values at the leaves.
- Depends on
tensorflow>=1.15.5,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,<3. - Depends on
tfx-bsl>=1.7.0,<1.8.0. - Depends on
tensorflow-metadata>=1.7.0,<1.8.0. - Depends on
apache-beam[gcp]>=2.36,<3.
- N/A
- N/A
- Fix Fairness Indicators UI bug with overlapping charts when comparing EvalResults
- Fixed issue with aggregation type not being set properly in keys associated with confusion matrix metrics.
- Enabled the sql_slice_key extractor when evaluating a model.
- Depends on
numpy>=1.16,<2. - Depends on
absl-py>=0.9,<2.0.0. - Depends on
tensorflow>=1.15.5,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,<3. - Depends on
tfx-bsl>=1.6.0,<1.7.0. - Depends on
tensorflow-metadata>=1.6.0,<1.7.0. - Depends on
apache-beam[gcp]>=2.35,<3.
- N/A
- N/A
- Replaced keras metrics with TFMA implementations. To use a keras metric in a
tfma.MetricConfigyou must now specify a module (i.e.tf.keras.metrics). - Added FixedSizeSample metric which can be used to extract a random, per-slice, fixed-sized sample of values for a user-configured feature key.
- Updated QueryStatistics to support weighted examples.
- Replace confusion matrix based metrics with numpy counterparts, shifting away from Keras metrics class.
- Depends on
apache-beam[gcp]>=2.34,<3. - Depends on
tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,<3. - Depends on
tfx-bsl>=1.5.0,<1.6.0. - Depends on
tensorflow-metadata>=1.5.0,<1.6.0.
- Removes register_metric from public API, as it is not intended to be public facing. To use a custom metric, provide the module name in which the metric is defined in the MetricConfig message, instead.
- Added support for specifying weighted vs unweighted metrics. The setting is
available in the
tfma.MetricsSpec( example_weights=tfma.ExampleWeightOptions(weighted=True, unweighted=True)). If no options are provided then TFMA will default to weighted provided the associatedtfma.ModelSpechas an example weight key configured, otherwise unweighted will be used.
-
Added support for example_weights that are arrays.
-
Reads baseUrl in JupyterLab to support TFMA rendering: tensorflow#112
-
Fixing couple of issues with CIDerivedMetricComputation:
- no CI derived metric, deriving from private metrics such as binary_confusion_matrices, was being computed
- convert_slice_metrics_to_proto method didn't have support for bounded values metrics.
-
Depends on
tfx-bsl>=1.4.0,<1.5.0. -
Depends on
tensorflow-metadata>=1.4.0,<1.5.0. -
Depends on
apache-beam[gcp]>=2.33,<3.
- Confidence intervals for scalar metrics are no longer stored in the
MetricValue.bounded_value. Instead, the confidence interval for a metric can be found underMetricKeysAndValues.confidence_interval. - MetricKeys now require specifying whether they are weighted (
tfma.metrics.MetricKey(..., example_weighted=True)) or unweighted (the default). If the weighting is unknown thenexample_weightedwill be None. Any metric computed outside of atfma.metrics.MetricConfigsetting (i.e. metrics loaded from a saved model) will have the weighting set to None. ExampleCountis now weighted based ontfma.MetricsSpec.example_weightssettings.WeightedExampleCounthas been deprecated (useExampleCountinstead). To get unweighted example counts (i.e. the previous implementation ofExampleCount),ExampleCountmust now be added to aMetricsSpecwhereexample_weights.unweightedis true. To get a weighted example count (i.e. what was previouslyWeightedExampleCount),ExampleCountmust now be added to aMetricsSpecwhereexample_weights.weightedis true.
- Deprecated python3.6 support.
- N/A
- Correctly skips non-numeric numpy array type metrics for confidence interval computations.
- Depends on
apache-beam[gcp]>=2.32,<3. - Depends on
tfx-bsl>=1.3.0,<1.4.0.
- In preparation for TFMA 1.0, the following imports have been moved (note
that other modules were also moved, but TFMA only supports types that are
explicitly declared inside of
__init__.pyfiles):tfma.CombineFnWithModels->tfma.utils.CombineFnWithModelstfma.DoFnWithModels->tfma.utils.DoFnWithModelstfma.get_baseline_model_spec->tfma.utils.get_baseline_model_spectfma.get_model_type->tfma.utils.get_model_typetfma.get_model_spec->tfma.utils.get_model_spectfma.get_non_baseline_model_specs->tfma.utils.get_non_baseline_model_specstfma.verify_eval_config->tfma.utils.verify_eval_configtfma.update_eval_config_with_defaults->tfma.utils.update_eval_config_with_defaultstfma.verify_and_update_eval_shared_models->tfma.utils.verify_and_update_eval_shared_modelstfma.create_keys_key->tfma.utils.create_keys_keytfma.create_values_key->tfma.utils.create_values_keytfma.compound_key->tfma.utils.compound_keytfma.unique_key->tfma.utils.unique_key
- N/A
- Added
SparseTensorValueandRaggedTensorValuetypes for storing in-memory versions of sparse and ragged tensor values used in extracts. Tensor values used for features, etc should now be based on eithernp.ndarray,SparseTensorValue, orRaggedTensorValueand not tf.compat.v1 value types. - Add
CIDerivedMetricComputationmetric type.
- Depends on
pyarrow>=1,<6. - Fixes bug when computing confidence intervals for
binary_confusion_metrics.ConfusionMatrixAtThresholds(or any other structured metric). - Fixed bug where example_count post_export_metric is added even if include_default_metrics is False.
- Depends on
apache-beam[gcp]>=2.31,<2.32. - Depends on
tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,<3. - Depends on
tfx-bsl>=1.3.1,<1.4.0. - Fixes issue with jackknife confidence interval method that resulted in erroneously large intervals.
- Fixes bug where calls to
_apply_binary_op_elementwisecould fail on objects of typesbinary_confusion_matrices.Matricesandmulti_class_confusion_matrix_metrics.Matricesdue to differing thresholds.
- Missing baseline model when change thresholds are present is not allowed anymore, an exception will be raised unless the rubber_stamp flag is True.
- N/A
- Provided functionality for
slice_keys_sqlconfig. It's not available under Windows. - The
confidence_intervalfield withinmetrics_for_slice_pb2.MetricValuehas been removed and the tag number reserved. This information now lives inmetrics_for_slice_pb2.MetricKeyAndValue.confidence_interval.
- Improve rendering of HTML stubs for TFMA and Fairness Indicators UI.
- Update README for JupyterLab 3
- Provide implementation of ExactMatch metric.
- Jackknife CI method now works with cross-slice metrics.
- Depends on
apache-beam[gcp]>=2.31,<3. - Depends on
tensorflow-metadata>=1.2.0,<1.3.0. - Depends on
tfx-bsl>=1.2.0,<1.3.0.
- The binary_confusion_matrices metric formerly returned confusion matrix counts (i.e number of {true,false} {positives,negatives}) and optionally a set of representative examples in a single object. Now, this metric class generates two separate metrics values when examples are configured: one containing just the counts, and the other just examples. This should only affect users who created a custom derived metric that used binary_confusion_matrices metric as an input.
- N/A
- N/A
- Depends on
google-cloud-bigquery>>=1.28.0,<2.21. - Depends on
tfx-bsl>=1.1.0,<1.2.0.
- N/A
- N/A
- N/A
- Depends on
protobuf>=3.13,<4. - Depends on
tensorflow-metadata>=1.1.0,<1.2.0. - Depends on
tfx-bsl>=1.1.0,<1.2.0.
- N/A
- N/A
- N/A
- Depends on
apache-beam[gcp]>=2.29,<3. - Depends on
tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3. - Depends on
tensorflowjs>=3.6.0,<4. - Depends on
tensorflow-metadata>=1.0.0,<1.1.0. - Depends on
tfx-bsl>=1.0.0,<1.1.0.
- N/A
- N/A
- N/A
-
Fix bug that
FeaturesExtractorincorrectly handles RecordBatches that have only the raw input column but no other feature columns. -
Fix an issue that micro_average can get lost in MetricKey, which can cause threshold mismatch the metrics during validation.
- N/A
- N/A
- Added support for output aggregation.
- In Fairness Indicators UI, sort metrics list to show common metrics first
- For lift metrics, support negative values in the Fairness Indicator UI bar chart.
- Make legacy predict extractor also input/output batched extracts.
- Updated to use new compiled_metrics and compiled_loss APIs for keras in-graph metric computations.
- Add support for calling model.evaluate on keras models containing custom metrics.
- Add CrossSliceMetricComputation metric type.
- Add Lift metrics under addons/fairness.
- Don't add metric config from config.MetricsSpec to baseline model spec by default.
- Fix invalid calculations for metrics derived from tf.keras.losses.
- Fixes following bugs related to CrossSlicingSpec based evaluation results.
- metrics_plots_and_validations_writer was failing while writing cross slice comparison results to metrics file.
- Fairness widget view was not compatible with cross slicing key type.
- Fix support for exporting the UI from a notebook to a standalone HTML page.
- Depends on
absl-py>=0.9,<0.13. - Depends on
tensorflow-metadata>=0.29.0,<0.30.0. - Depends on
tfx-bsl>=0.29.0,<0.30.0.
- N/A
- N/A
- Add a new base computation for binary confusion matrix (other than based on calibration histogram). It also provides a sample of examples for the confusion matrix.
- Adding two new metrics - Flip Count and Flip Rate to evaluate Counterfactual Fairness.
- Fixed division by zero error for diff metrics.
- Depends on
apache-beam[gcp]>=2.28,<3. - Depends on
numpy>=1.16,<1.20. - Depends on
tensorflow-metadata>=0.28.0,<0.29.0. - Depends on
tfx-bsl>=0.28.0,<0.29.0.
- N/A
- N/A
- Created tfma.StandardExtracts with helper methods for common keys.
- Updated StandardMetricInputs to extend from the tfma.StandardExtracts.
- Created set of StandardMetricInputsPreprocessors for filtering extracts.
- Introduced a
padding_optionsconfig to ModelSpec to configure whether and how to pad the prediction and label tensors expected by the model's metrics.
- Fixed issue with metric computation deduplication logic.
- Depends on
apache-beam[gcp]>=2.27,<3. - Depends on
pyarrow>=1,<3. - Depends on
tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,<3. - Depends on
tensorflow-metadata>=0.27.0,<0.28.0. - Depends on
tfx-bsl>=0.27.0,<0.28.0.
- N/A
- N/A
- N/A
- Fix support for exporting the UI from a notebook to a standalone HTML page.
- Depends on apache-beam[gcp]>=2.25,!=2.26,<2.29.
- Depends on numpy>=1.16,<1.20.
- N/A
- N/A
- Added support for aggregating feature attributions using special metrics
that extend from
tfma.metrics.AttributionMetric(e.g.tfma.metrics.TotalAttributions,tfma.metrics.TotalAbsoluteAttributions). To use make use of these metrics a custom extractor that add attributions to thetfma.Extractsunder the key nametfma.ATTRIBUTIONS_KEYmust be manually created. - Added support for feature transformations using TFT and other preprocessing functions.
- Add support for rubber stamping (first run without a valid baseline model) when validating a model. The change threshold is ignored only when the model is rubber stamped, otherwise, an error is thrown.
- Fix the bug that Fairness Indicator UI metric list won't refresh if the input eval result changed.
- Add support for missing_thresholds failure to validations result.
- Updated to set min/max value for precision/recall plot to 0 and 1.
- Fix issue with MinLabelPosition not being sorted by predictions.
- Updated NDCG to ignore non-positive gains.
- Fix bug where an example could be aggregated more than once in a single slice if the same slice key were generated from more than one SlicingSpec.
- Add threshold support for confidence interval type metrics based on its unsampled_value.
- Depends on
apache-beam[gcp]>=2.25,!=2.26.*,<3. - Depends on
tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.4.*,<3. - Depends on
tensorflow-metadata>=0.26.0,<0.27.0. - Depends on
tfx-bsl>=0.26.0,<0.27.0.
- Changed MultiClassConfusionMatrix threshold check to prediction > threshold instead of prediction >= threshold.
- Changed default handling of materialize in default_extractors to False.
- Separated
tfma.extractors.BatchedInputExtractorintotfma.extractors.FeaturesExtractor,tfma.extractors.LabelsExtractor, andtfma.extractors.ExampleWeightsExtractor. - Change the thresholding to be inclusive, i.e. model passes when value is >= or <= to the threshold rather than > or <.
- N/A
-
Added support for reading and writing metrics, plots and validation results using Apache Parquet.
-
Updated the FI indicator slicing selection UI.
-
Fixed the problem that slices are refreshed when user selected a new baseline.
-
Add support for slicing on ragged and multidimensional data.
-
Load TFMA correctly in JupyterLabs even if Facets has loaded first.
-
Added support for aggregating metrics using top k values.
-
Added support for padding labels and predictions with -1 to align a batch of inputs for use in tf-ranking metrics computations.
-
Added support for fractional labels.
-
Add metric definitions as tooltips in the Fairness Inidicators metric selector UI
-
Added support for specifying label_key to use with MinLabelPosition metric.
-
From this release TFMA will also be hosting nightly packages on https://pypi-nightly.tensorflow.org. To install the nightly package use the following command:
pip install --extra-index-url https://pypi-nightly.tensorflow.org/simple tensorflow-model-analysisNote: These nightly packages are unstable and breakages are likely to happen. The fix could often take a week or more depending on the complexity involved for the wheels to be available on the PyPI cloud service. You can always use the stable version of TFMA available on PyPI by running the command
pip install tensorflow-model-analysis.
- Fix incorrect calculation with MinLabelPosition when used with weighted examples.
- Fix issue with using NDCG metric without binarization settings.
- Fix incorrect computation when example weight is set to zero.
- Depends on
apache-beam[gcp]>=2.25,<3. - Depends on
tensorflow-metadata>=0.25.0,<0.26.0. - Depends on
tfx-bsl>=0.25.0,<0.26.0.
AggregationOptionsare now independent ofBinarizeOptions. In order to computeAggregationOptions.macro_averageorAggregationOptions.weighted_macro_average,AggregationOptions.class_weightsmust now be configured. IfAggregationOptions.class_weightsare provided, any missing keys now default to 0.0 instead of 1.0.- In the UI, aggregation based metrics will now be prefixed with 'micro_', 'macro_', or 'weighted_macro_' depending on the aggregation type.
tfma.extractors.FeatureExtractor,tfma.extractors.PredictExtractor,tfma.extractors.InputExtractor, andtfma.evaluators.MetricsAndPlotsEvaluatorare deprecated and may be replaced with newer versions in upcoming releases.
- N/A
- Depends on
apache-beam[gcp]>=2.24,<3. - Depends on
tfx-bsl>=0.24.0,<0.25.0.
- N/A
- N/A
- N/A
- Added an extra requirement group
all. As a result, barebone TFMA does not requiretensorflowjs,prompt-toolkitandipythonany more. - Added an extra requirement group
allthat specifies all the extra dependencies TFMA needs. Usepip install tensorflow-model-analysis[all]to pull in those dependencies.
- N/A
- N/A
- N/A
- Fix Jupyter lab issue with missing data-base-url.
- N/A
- N/A
- Use TFXIO and batched extractors by default in TFMA.
- Updated the type hint of FilterOutSlices.
- Fix issue with precision@k and recall@k giving incorrect values when negative thresholds are used (i.e. keras defaults).
- Fix issue with MultiClassConfusionMatrixPlot being overridden by MultiClassConfusionMatrix metrics.
- Made the Fairness Indicators UI thresholds drop down list sorted.
- Fix the bug that Sort menu is not hidden when there is no model comparison.
- Depends on
absl-py>=0.9,<0.11. - Depends on
ipython>=7,<8. - Depends on
pandas>=1.0,<2. - Depends on
protobuf>=3.9.2,<4. - Depends on
tensorflow-metadata>=0.24.0,<0.25.0. - Depends on
tfx-bsl>=0.24.0,<0.25.0.
- Query based metrics evaluations that make use of
MetricsSpecs.query_keyare now passedtfma.Extractswith leaf values that are of typenp.ndarraycontaining an additional dimension representing the values matched by the query (e.g. if the labels and predictions were previously 1D arrays, they will now be 2D arrays where the first dimension's size is equal to the number of examples matching the query key). Previously a list oftfma.Extractswas passed instead. This allows user's to now add custom metrics based ontf.keras.metrics.Metricas well astf.metrics.Metric(any previous customizations based ontf.metrics.Metricwill need to be updated). As part of this change thetfma.metrics.NDCG,tfma.metrics.MinValuePosition, andtfma.metrics.QueryStatisticshave been updated. - Renamed
ConfusionMatrixMetric.computetoConfusionMatrixMetric.resultfor consistency with other APIs.
- Deprecating Py3.5 support.
- Changed default confidence interval method from POISSON_BOOTSTRAP to JACKKNIFE. This should significantly improve confidence interval evaluation performance by as much as 10x in runtime and CPU resource usage.
- Added support for additional confusion matrix metrics (FDR, FOR, PT, TS, BA, F1 score, MCC, FM, Informedness, Markedness, etc). See https://en.wikipedia.org/wiki/Confusion_matrix for full list of metrics now supported.
- Change the number of partitions used by the JACKKNIFE confidence interval methodology from 100 to 20. This will reduce the quality of the confidence intervals but support computing confidence intervals on slices with fewer examples.
- Added
tfma.metrics.MultiClassConfusionMatrixAtThresholds. - Refactoring code to compute
tfma.metrics.MultiClassConfusionMatrixPlotusing derived computations. - Provide support for evaluating TFJS models.
- Added support for labels passed as SparseTensorValues.
- Stopped requiring
avro-python3. - Fix NoneType error when passing BinarizeOptions to tfma.metrics.default_multi_class_classification_specs.
- Fix issue with custom metrics contained in modules ending in tf.keras.metric.
- Changed the BoundedValue.value to be the unsampled metric value rather than the sample average.
- Add
EvalResult.get_metric_names(). - Added errors for missing slices during metrics validation.
- Added support for customizing confusion matrix based metrics in keras.
- Made BatchedInputExtractor externally visible.
- Updated tfma.load_eval_results API to return empty results instead of throwing an error when evaluation results are missing for a model_name.
- Fixed an issue in Fairness Indicators UI where omitted slices error message was being displayed even if no slice was omitted.
- Fix issue with slice_spec.is_slice_applicable not working for float, int, etc types that are encoded as strings.
- Wrap long strings in table cells in Fairness Indicators UI.
- Depends on
apache-beam[gcp]>=2.23,<3. - Depends on
pyarrow>=0.17,<0.18. - Depends on
scipy>=1.4.1,<2 - Depends on
tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,<3. - Depends on
tensorflow-metadata>=0.23,<0.24. - Depends on
tfx-bsl>=0.23,<0.24.
- Rename EvalResult.get_slices() to EvalResult.get_slice_names().
- Note: We plan to remove Python 3.5 support after this release.
- Added analyze_raw_data(), an API for evaluating TFMA metrics on Pandas DataFrames.
- Previously metrics would only be computed for combinations of keys that
produced different metric values (e.g.
ExampleCountwill be the same for all models, outputs, classes, etc, so only one metric key was used). Now a metric key will be returned for each combination associated with theMetricSpecdefinition even if the values will be the same. Support for model independent metrics has also been removed. This means by default multiple ExampleCount metrics will be created when multiple models are used (one per model). - Fixed issue with label_key and prediction_key settings not working with TF based metrics.
- Fairness Indicators UI
- Thresholds are now sorted in ascending order.
- Barchart can now be sorted by either slice or eval.
- Added support for slicing on any value extracted from the inputs (e.g. raw labels).
- Added support for filtering extracts based on sub-keys.
- Added beam counters to track the feature slices being used for evaluation.
- Adding KeyError when analyze_raw_data is run without a valid label_key or prediction_key within the provided Pandas DataFrame.
- Added documentation for
tfma.analyze_raw_data,tfma.view.SlicedMetrics, andtfma.view.SlicedPlots. - Unchecked Metric thresholds now block the model validation.
- Added support for per slice threshold settings.
- Added support for sharding metrics and plots outputs.
- Updated load_eval_result to support filtering plots by model name. Added support for loading multiple models at same output path using load_eval_results.
- Fix typo in jupyter widgets breaking TimeSeriesView and PlotViewer.
- Add
tfma.slicer.stringify_slice_key(). - Deprecated external use of tfma.slicer.SingleSliceSpec (tfma.SlicingSpec should be used instead).
- Updated tfma.default_eval_shared_model and tfma.default_extractors to better support custom model types.
- Depends on 'tensorflow-metadata>=0.22.2,<0.23'
- Changed to treat CLASSIFY_OUTPUT_SCORES involving 2 values as a multi-class classification prediction instead of converting to binary classification.
- Refactored confidence interval methodology field. The old path under
Options.confidence_interval_methodologyis now atOptions.confidence_intervals.methodology. - Removed model_load_time_callback from ModelLoader construct_fn (timing is now handled by load). Removed access to shared_handle from ModelLoader.
- Depends on
pyarrow>=0.16,<0.17.
- Added support for jackknife-based confidence intervals.
- Add EvalResult.get_metrics(), which extracts slice metrics in dictionary format from EvalResults.
- Adds TFMD
Schemaas an available argument to computations callbacks.
- Version is now available under
tfma.version.VERSIONortfma.__version__. - Add auto slicing utilities for significance testing.
- Fixed error when a metric and loss with the same classname are used.
- Adding two new ratios (false discovery rate and false omission rate) in Fairness Indicators.
MetricValues can now contain both a debug message and a value (rather than one or the other).- Fix issue with displaying ConfusionMatrixPlot in colab.
CalibrationPlotnow infersleftandrightvalues from schema, when available. This makes the calibration plot useful to regression users.- Fix issue with metrics not being computed properly when mixed with specs containing micro-aggregation computations.
- Remove batched keys. Instead use the same keys for batched and unbatched extract.
- Adding support to visualize Fairness Indicators in Fairness Indicators
TensorBoard Plugin by providing remote evalution path in query parameter:
<tensorboard_url>#fairness_indicators& p.fairness_indicators.evaluation_output_path=<evaluation_path>. - Fixed invalid metrics calculations for serving models using the classification API with binary outputs.
- Moved config writing code to extend from tfma.writer.Writer and made it a member of default_writers.
- Updated tfma.ExtractEvaluateAndWriteResults to accept Extracts as input in addition to serialize bytes and Arrow RecordBatches.
- Depends on
apache-beam[gcp]>=2.20,<3. - Depends on
pyarrow>=0.16,<1. - Depends on
tensorflow>=1.15,!=2.0.*,<3. - Depends on
tensorflow-metadata>=0.22,<0.23. - Depends on
tfx-bsl>=0.22,<0.23.
- Remove desired_batch_size as an option. Large batch failures can be handled via serially processing the failed batch which also acts as a deterent from scaling up batch sizes further. Batch size can be handled via BEAM batch size tuning.
- Deprecating Py2 support.
- Integrate TFXIO in TFMA. Use batched input and predict extractor in V2. Results in ~40% reduction in CPU seconds over existing TFMA v2 (InputExtractor + PredictExtractorV2). Modify TFMA public API to take an optional tensor adapter config as input.
- Adding experimental support for pre-defined preprocessing functions that can be used as preprocessing functions for feature and label transformations.
- Populate confidence_interval field in addition to bounded_value when confidence intervals is enabled.
- Only requires
avro-python3>=1.8.1,!=1.9.2.*,<2.0.0on Python 3.5 + MacOS - Fix bug in SensitivitySpecificityBase derived metrics: guarantee well defined behaviour when the constraint lies between feasible points (see updated docstrings).
- Now publish NPM under
tensorflow_model_analysisfor UI components.
- Depends on 'tfx-bsl>=0.21.3,<0.22',
- Depends on 'tensorflow>=1.15,<3',
- Depends on 'apache-beam[gcp]>=2.17,<3',
- Rollback populating TDistributionValue metric when confidence intervals is enabled in V2.
- Drop Py2 support.
- Added support for creating metrics specs from tf.keras.losses.
- Added evaluation comparison feature to the Fairness Indicators UI in Colab.
- Added better defaults handling for eval config so that a single model spec can be used for both candidate and baseline.
- Added support to provide output file format in load_eval_result API.
- Fixed issue with keras metrics saved with the model not being calculated unless a keras metric was added to the config.
- Depends on
pandas>=0.24,<2. - Depends on
pyarrow>=0.15,<1. - Depends on 'tfx-bsl>=0.21.3,<0.23',
- Depends on 'tensorflow>=1.15,!=2.0.*,<3',
- Depends on 'apache-beam[gcp]>=2.17,<2.18',
- Added support for model validation using either value threshold or diff threshold.
- Added a writer to output model validation result (ValidationResult).
- Added support for multi-model evaluation using EvalSavedModels.
- Added support for inserting model_names by default to metrics_specs.
- Added support for selecting custom model format evals in config.
- Fixed issue with model_name not being set in keras metrics.
- Populate TDistributionValue metric when confidence intervals is enabled in V2.
- Rename the writer MetricsAndPlotsWriter to MetricsPlotsAndValidationsWriter.
- Adding SciPy dependency for both Python2 and Python3
- Increased table and tooltip font in Fairness Indicators.
tfma.BinarizeOptions.class_ids,tfma.BinarizeOptions.k_list,tfma.BinarizeOptions.top_k_list, andtfma.Options.disabled_outputsare now wrapped in an additional proto message.
- Adding a TFLite predict extractor to enable obtaining inferences from TFLite models.
- Adding support to compute deterministic confidence intervals using a seed value in tfma.run_model_analysis API for testing or experimental purposes.
- Fixed calculation of
tfma.metrics.CoefficientOfDiscriminationandtfma.metrics.RelativeCoefficientOfDiscrimination.
- Renaming k_anonymization_count field name to min_slice_size.
- Added
tfma.metrics.MinLabelPositionandtfma.metrics.QueryStatisticsfor use with V2 metrics API. - Added
tfma.metrics.CoefficientOfDiscriminationandtfma.metrics.RelativeCoefficientOfDiscriminationfor use with V2 metrics API. - Added support for using
tf.keras.metrics.*metrics with V2 metrics API. - Added support for default V2 MetricSpecs and creating specs from
tf.kera.metrics.*andtfma.metrics.*metric classes. - Added new MetricsAndPlotsEvaluator based on V2 infrastructure. Note this evaluator also supports query-based metrics.
- Add support for micro_average, macro_average, and weighted_macro_average metrics.
- Added support for running V2 extractors and evaluators. V2 extractors will
be used whenever the default_eval_saved_model is created using a non-eval
tag (e.g.
tf.saved_model.SERVING). The V2 evaluator will be used whenever atfma.EvalConfigis used containingmetrics_specs. - Added support for
tfma.metrics.SquaredPearsonCorrelationfor use with V2 metrics API. - Improved support for TPU autoscaling and handling batch_size related scaling.
- Added support for
tfma.metrics.Specificity,tfma.metrics.FallOut, andtfma.metrics.MissRatefor use with V2 metrics API. RenamedAUCPlottoConfusionMatrixPlot,MultiClassConfusionMatrixAtThresholdstoMultiClassConfusionMatrixPlotandMultiLabelConfusionMatrixAtThresholdstoMultiLabelConfusionMatrixPlot. - Added Jupyter support to Fairness Indicators. Currently does not support WIT integration.
- Added fairness indicators metrics
tfma.addons.fairness.metrics.FairnessIndicators. - Updated documentation for new metrics infrastructure and newly supported models (keras, etc).
- Added support for model diff metrics. Users need to turn on "is_baseline" in the corresponding ModelSpec.
- Fixed error in
tfma-multi-class-confusion-matrix-at-thresholdswith default classNames value. - Fairness Indicators
- Compute ratio metrics with safe division.
- Remove "post_export_metrics" from metric names.
- Move threshold dropdown selector to a metric-by-metric basis, allowing different metrics to be inspected with different thresholds. Don't show thresholds for metrics that do not support them.
- Slices are now displayed in alphabetic order.
- Adding an option to "Select all" metrics in UI.
- Added auto slice key extractor based on statistics.
- Depends on 'tensorflow-metadata>=0.21,<0.22'.
- Made InputProcessor externally visible.
- Updated proto config to remove input/output data specs in favor of passing them directly to the run_eval.
- Fixed the bug that Fairness Indicator will skip metrics with NaN value.
- Updated vulcanized_tfma.js with UI changes in addons/fairness_indicators.
- Updated to use tf.io.gfile for reading config files (fixes issue with reading from GCS/HDFS in 0.15.0 and 0.15.1 releases).
- Added support for defaulting to using class IDs when classes are not present in outputs for multi-class metrics (for use in keras model_to_estimator).
- Added example count metrics (
tfma.metrics.ExampleCountandtfma.metrics.WeightedExampleCount) for use with V2 metrics API. - Added calibration metrics (
tfma.metrics.MeanLabel,tfma.metrics.MeanPrediction, andtfma.metrics.Calibration) for use with V2 metrics API. - Added
tfma.metrics.ConfusionMatrixAtThresholdsfor use with V2 metrics API. - Added
tfma.metrics.CalibrationPlotandtfma.metrics.AUCPlotfor use with V2 metrics API. - Added multi_class / multi_label plots (
tfma.metrics.MultiClassConfusionMatrixAtThresholds,tfma.metrics.MultiLabelConfusionMatrixAtThresholds) for use with V2 metrics API. - Added
tfma.metrics.NDCGmetric for use with V2 metrics API. - Added
calibrationas a post export metric.
- Depends on
tensorflow>=1.15,<3.0.- Starting from 1.15, package
tensorflowcomes with GPU support. Users won't need to choose betweentensorflowandtensorflow-gpu. - Caveat:
tensorflow2.0.0 is an exception and does not have GPU support. Iftensorflow-gpu2.0.0 is installed before installingtensorflow_model_analysis, it will be replaced withtensorflow2.0.0. Re-installtensorflow-gpu2.0.0 if needed.
- Starting from 1.15, package
- Added V2 of PredictExtractor that uses TF 2.0 signature APIs and supports keras models (note: keras model evaluation not fully supported yet).
tfma.run_model_analysis,tfma.default_extractors,tfma.default_evaluators, andtfma.default_writersnow allow settings to be passed as anEvalConfig.tfma.run_model_analysis,tfma.default_extractors,tfma.default_evaluators, andtfma.default_writersnow allow multiple models to be passed (note: multi-model support not fully implemented yet).- Added InputExtractor for extracting labels, features, and example weights from tf.Examples.
- Added Fairness Indicator as an addon.
- Enabled TF 2.0 support using compat.v1.
- Added support for slicing on native dicts of features in addition to FPL types.
- For multi-output and / or multi-class models, please provide output_name and / or class_id to tfma.view.render_plot.
- Replaced dependency on
tensorflow-transformwithtfx-bsl. If running with latest master,tfx-bslmust also be latest master. - Depends on
tfx-bsl>=0.15,<0.16. - Slicing now supports conversion between int/floats and strings.
- Depends on
apache-beam[gcp]>=2.16,<3. - Depends on
six==1.12.
- tfma.EvalResult.slicing_metrics now contains nested dictionaries of output, class id and then metric names.
- Update config serialization to use JSON instead of pickling and reformat config to include input_data_specs, model_specs, output_data_specs, and metrics_specs.
- Requires pre-installed TensorFlow >=1.15,<3.
- Added documentation on architecture.
- Added an
adapt_to_remove_metricsfunction totfma.exporterwhich can be used to remove metrics incompatible with TFMA (e.g.py_funcor streaming metrics) before exporting the TFMA EvalSavedModel. - Added support for passing sparse int64 tensors to precision/recall@k.
- Added support for binarization of multiclass metrics that use labels of the from (N) in addition to (N, 1).
- Added support for using iterators with EvalInputReceiver.
- Improved performance of confidence interval computations by modifying the pipeline shape.
- Added QueryBasedMetricsEvaluator which supports computing query-based metrics (e.g. normalized discounted cumulative gain).
- Added support for merging metrics produced by different evaluators.
- Added support for blacklisting specified features from fetches.
- Added functionality to the FeatureExtractor to specify the features dict as a possible destination.
- Added support for label vocabularies for binary and multi-class estimators that support the new ALL_CLASSES prediction output.
- Move example parsing in aggregation into the graph for performance improvements in both standard and model_agnostic evaluation modes.
- Created separate ModelLoader type for loading the EvalSavedModel.
- Upgraded codebase for TF 2.0 compatibility.
- Make metrics-related operations thread-safe by wrapping them with locks. This eliminates race conditions that were previously possible in multi-threaded runners which could result in incorrect metric values.
- More flexible
FanoutSlices. - Limit the number of sampling buckets to 20.
- Improved performance in Confidence Interval computation.
- Refactored poisson bootstrap code to be re-usable in other evaluators.
- Refactored k-anonymity code to be re-usable in other evaluators.
- Fixed slicer feature string value handling in Python3.
- Added support for example weight keys for multi-output models.
- Added option to set the desired batch size when calling run_model_analysis.
- Changed TFRecord compression type from UNCOMPRESSED to AUTO.
- Depends on
apache-beam[gcp]>=2.14,<3. - Depends on
numpy>=1.16,<2. - Depends on
protobuf>=3.7,<4. - Depends on
scipy==1.1.0. - Added support to change k_anonymization_count value via EvalConfig.
- Removed uses of deprecated tf.contrib packages (where possible).
tfma.default_writersnow requires theeval_saved_modelto be passed as an argument.- Requires pre-installed TensorFlow >=1.14,<2.
- Added support for squared pearson correlation (R squared) post export metric.
- Added support for mean absolute error post export metric.
- Added support for mean squared error and root mean squared error post export metric.
- Added support for not computing metrics for slices with less than a given number of examples.
- Cast / convert labels for precision / recall at K so that they work even if the label and the classes Tensors have different types, as long as the types are compatible.
- Post export metrics will now also search for prediction keys prefixed by metric_tag if it is specified.
- Added support for precision/recall @ k using canned estimators provided label vocab not used.
- Preserve unicode type of slice keys when serialising to and deserialising from disk, instead of always converting them to bytes.
- Use
__slots__in accumulators.
- Expose Python 3 types in the code (this will break Python 2 compatibility)
- Python 3.5 is supported.
- Added support for fetching additional tensors at prediction time besides features, predictions, and labels (predict now returns FetchedTensorValues type).
- Removed internal usages of encoding.NODE_SUFFIX indirection within dicts in the eval_saved_model module (encoding.NODE_SUFFIX is still used in FeaturesPredictionLabels)
- Predictions are now returned as tensors (vs dicts) when "predictions" is the only output (this is consistent with how features and labels work).
- Depends on
apache-beam[gcp]>=2.11,<3. - Depends on
protobuf>=3.7,<4. - Depends on
scipy==1.1.0. - Add support for multiple plots in a single evaluation.
- Add support for changeable confidence levels.
- Post export metrics for precision_recall_at_k were split into separate fuctions: precision_at_k and recall_at_k.
- Requires pre-installed TensorFlow >=1.13,<2.
- Python 3.5 readiness complete (all tests pass). Full Python 3.5 compatibility is expected to be available with the next version of Model Analysis (after Apache Beam 2.11 is released).
- Added support for customizing the pipeline (via extractors, evaluators, and writers). See architecture for more details.
- Added support for excluding the default metrics from the saved model graph during evaluation.
- Added a mechanism for performing evaluations via post_export_metrics without access to a Tensorflow EvalSavedModel.
- Added support for computing metrics with confidence intervals using the Poisson bootstrap technique. To use, set the num_bootstrap_samples to a number greater than 1--20 is recommended for confidence intervals.
- Fixed bugs where TFMA was incorrectly modifying elements in DoFns, which violates the Beam API.
- Fixed correctness issue stemming from TFMA incorrectly relying on evaluation ordering that TF doesn't guarantee.
- We now store feature and label Tensor information in SignatureDef inputs instead of Collections in anticipation of Collections being deprecated in TF 2.0.
- Add support for fractional labels in AUC, AUPRC and confusion matrix at
thresholds. Previously the labels were being passed directly to TensorFlow,
which would cast them to
bool, which meant that all non-zero labels were treated as positive examples. Now we treat a fractional labellin[0, 1]as two examples, a positive example with weightland a negative example with weight1 - l. - Depends on
numpy>=1.14.5,<2. - Depends on
scipy==0.19.1. - Depends on
protobuf==3.7.0rc2. - Chicago Taxi example is moved to tfx repo (https://github.com/tensorflow/tfx/tree/master/tfx/examples/chicago_taxi)
- Moved tfma.SingleSliceSpec to tfma.slicer.SingleSliceSpec.
- We now support unsupervised models which have
model_fns that do not take alabelsargument. - Improved performance by using
make_callableinstead of repeatedsession.runcalls. - Improved performance by better choice of default "combine" batch size.
- We now support passing in custom extractors in the model_eval_lib API.
- Added support for models which have multiple examples per raw input (e.g.
input is a compressed example which expands to multiple examples when parsed
by the model). For such models, you must specify an
example_refparameter to yourEvalInputReceiver. This 1-D integer Tensor should be batch aligned with features, predictions and labels and each element in it is an index in the raw input tensor to identify which input each feature / prediction / label came from. Seeeval_saved_model/example_trainers/fake_multi_examples_per_input_estimator.pyfor an example. - Added support for metrics with string
value_ops. - Added support for metrics whose
value_ops return multidimensional arrays. - We now support including your serving graph in the EvalSavedModel. You can
do this by passing a
serving_input_receiver_fntoexport_eval_savedmodelor any of the TFMA Exporters. - We now support customizing prediction and label keys for post_export_metrics.
- Depends on
apache-beam[gcp]>=2.8,<3. - Depends on
tensorflow-transform>=0.11,<1. - Requires pre-installed TensorFlow >=1.11,<2.
- Factor our utility functions for adding sliceable "meta-features" to FPL.
- Added public API docs
- Add an extractor to add sliceable "meta-features" to FPL.
- Potentially improved performance by fanning out large slices.
- Add support for assets_extra in
tfma.exporter.FinalExporter. - Add a light-weight library that includes only the export-related modules for
TFMA for use in your Trainer. See docstring in
tensorflow_model_analysis/export_only/__init__.pyfor usage. - Update
EvalInputReceiverso the TFMA collections written to the graph only contain the results of the last call if multiple calls toEvalInputReceiverare made. - We now finalize the graph after it's loaded and post-export metrics are added, potentially improving performance.
- Fix a bug in post-export PrecisionRecallAtK where labels with only 1 dimension were not correctly handled.
- Fix an issue where we were not correctly wrapping SparseTensors for
featuresandlabelsintf.identity, which could cause TFMA to encounter TensorFlow issue #17568 if there were control dependencies on thesefeaturesorlabels. - We now correctly preserve
dtypeswhen splitting and concatenating SparseTensors internally. The failure to do so previously could result in unexpectedly large memory usage if string values were involved due to the inefficient pickling of NumPy string arrays with a large number of elements.
- Requires pre-installed TensorFlow >=1.11,<2.
- We now require that
EvalInputReceiver,export_eval_savedmodel,make_export_strategy,make_final_exporter,FinalExporterandLatestExporterbe called with keyword arguments only. - Removed
check_metric_compatibilityfromEvalSavedModel. - We now enforce that the
receiver_tensorsdictionary forEvalInputReceivercontains exactly one key namedexamples. - Post-export metrics have now been moved up one level to
tfma.post_export_metrics. They should now be accessed viatfma.post_export_metrics.aucinstead oftfma.post_export_metrics.post_export_metrics.aucas they were before. - Separated extraction from evaluation.
EvaluateAndWriteResultsis now calledExtractEvaluateAndWriteResults. - Added
EvalSharedModeltype to encapsulatemodel_pathandadd_metrics_callbacksalong with a handle to a shared model instance.
- Improved performance especially when slicing across many features and/or feature values.
- Depends on
tensorflow-transform>=0.9,<1. - Requires pre-installed TensorFlow >=1.9,<2.
- Depends on
apache-beam[gcp]>=2.6,<3. - Updated ExampleCount to use the batch dimension as the example count. It also now tries a few fallbacks if none of the standard keys are found in the predictions dictionary: the first key in sorted order in the predictions dictionary, or failing that, the first key in sorted order in the labels dictionary, or failing that, it defaults to zero.
- Fix bug where we were mutating an element in a DoFn - this is prohibited in the Beam model and can cause subtle bugs.
- Fix bug where we were creating a separate Shared handle for each stage in Evaluate, resulting in no sharing of the model across stages.
- Requires pre-installed TensorFlow >=1.10,<2.
- Add a TFMA unit test library for unit testing your the exported model and associated metrics computations.
- Add
tfma.export.make_export_strategywhich is analogous totf.contrib.learn.make_export_strategy. - Add
tfma.exporter.FinalExporterandtfma.exporter.LatestExporterwhich are analogous totf.estimator.FinalExporterandtf.estimator.LastExporter. - Add
tfma.export.build_parsing_eval_input_receiver_fnwhich is analogous totf.estimator.export.build_parsing_serving_input_receiver_fn. - Add integration testing for DNN-based estimators.
- Add new post export metrics:
- AUC (
tfma.post_export_metrics.post_export_metrics.auc) - Precision/Recall at K
(
tfma.post_export_metrics.post_export_metrics.precision_recall_at_k) - Confusion matrix at thresholds
(
tfma.post_export_metrics.post_export_metrics.confusion_matrix_at_thresholds)
- AUC (
- Peak memory usage for large DataFlow jobs should be lower with a fix in when we compact batches of metrics during the combine phase of metrics computation.
- Remove batch size override in
chicago_taxiexample. - Added dependency on
protobuf>=3.6.0<4for protocol buffers. - Updated SparseTensor code to work with SparseTensors of any dimension. Previously on SparseTensors with dimension 2 (batch_size x values) were supported in the features dictionary.
- Updated code to work with SparseTensors and dense Tensors of variable lengths across batches.
- EvalSavedModels produced by TFMA 0.6.0 will not be compatible with later
versions due to the following changes:
- EvalSavedModels are now written out with a custom "eval_saved_model" tag, as opposed to the "serving" tag before.
- EvalSavedModels now include version metadata about the TFMA version that they were exported with.
- Metrics and plot outputs now are converted into proto and serialized. Metrics and plots produced by TFMA 0.6.0 will not be compatible with later versions.
- Requires pre-installed TensorFlow >=1.9,<2.
- TFMA now uses the TensorFlow Estimator functionality for exporting models of different modes behind the scenes. There are no user-facing changes API-wise, but EvalSavedModels produced by earlier versions of TFMA will not be compatible with this version of TFMA.
- tf.contrib.learn Estimators are no longer supported by TFMA. Only tf.estimator Estimators are supported.
- Metrics and plot outputs now include version metadata about the TFMA version that they were exported with. Metrics and plots produced by earlier versions of TFMA will not be compatible with this version of TFMA.
- Initial release of TensorFlow Model Analysis.