diff --git a/RELEASE.md b/RELEASE.md index da5bc5833b..455032a09e 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,15 +1,3 @@ - - -# Current Version (Still in Development) - -## Major Features and Improvements - -## Bug fixes and other Changes - -## Breaking Changes - -## Deprecations - # Version 0.39.0 ## Major Features and Improvements @@ -30,1487 +18,3 @@ * N/A -# Version 0.38.0 - -## Major Features and Improvements - -* Creates a VarLenTensorValue for storing batched, variable length extracts.* - Adds a load_metrics_as_dataframe util to load metrics file as dataframe. - -## Bug fixes and other Changes - -* 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. - -## Breaking Changes - -* MetricsPlotsAndValidationsWriter will now write files with an explicit - output format suffix (".tfrecord" by default). This should only affect - pipelines which directly construct `MetricsPlotsAndValidationWriter` - instances and do not set `output_file_format`. Those which use - `default_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`. - -## Deprecations - -* N/A - -# Version 0.37.0 - -## Major Features and Improvements - -* N/A - -## Bug fixes and other Changes - -* 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`. - -## Breaking Changes - -* N/A - -## Deprecations - -* N/A - -# Version 0.36.0 - -## Major Features and Improvements - -* Replaced keras metrics with TFMA implementations. To use a keras metric in a - `tfma.MetricConfig` you 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. - -## Bug fixes and other Changes - -* 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`. - -## Breaking Changes - -* 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. - -## Deprecations - -# Version 0.35.0 - -## Major Features and Improvements - -* 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 - associated `tfma.ModelSpec` has an example weight key configured, otherwise - unweighted will be used. - -## Bug fixes and other Changes - -* Added support for example_weights that are arrays. - -* Reads baseUrl in JupyterLab to support TFMA rendering: - https://github.com/tensorflow/model-analysis/issues/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`. - -## Breaking Changes - -* Confidence intervals for scalar metrics are no longer stored in the - `MetricValue.bounded_value`. Instead, the confidence interval for a metric - can be found under `MetricKeysAndValues.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 then `example_weighted` will be None. - Any metric computed outside of a `tfma.metrics.MetricConfig` setting (i.e. - metrics loaded from a saved model) will have the weighting set to None. -* `ExampleCount` is now weighted based on `tfma.MetricsSpec.example_weights` - settings. `WeightedExampleCount` has been deprecated (use `ExampleCount` - instead). To get unweighted example counts (i.e. the previous implementation - of `ExampleCount`), `ExampleCount` must now be added to a `MetricsSpec` - where `example_weights.unweighted` is true. To get a weighted example count - (i.e. what was previously `WeightedExampleCount`), `ExampleCount` must now - be added to a `MetricsSpec` where `example_weights.weighted` is true. - -## Deprecations - -* Deprecated python3.6 support. - -# Version 0.34.1 - -## Major Features and Improvements - -* N/A - -## Bug fixes and other Changes - -* 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`. - -## Breaking Changes - -* 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__.py` files): - * `tfma.CombineFnWithModels` -> `tfma.utils.CombineFnWithModels` - * `tfma.DoFnWithModels` -> `tfma.utils.DoFnWithModels` - * `tfma.get_baseline_model_spec` -> `tfma.utils.get_baseline_model_spec` - * `tfma.get_model_type` -> `tfma.utils.get_model_type` - * `tfma.get_model_spec` -> `tfma.utils.get_model_spec` - * `tfma.get_non_baseline_model_specs` -> - `tfma.utils.get_non_baseline_model_specs` - * `tfma.verify_eval_config` -> `tfma.utils.verify_eval_config` - * `tfma.update_eval_config_with_defaults` -> - `tfma.utils.update_eval_config_with_defaults` - * `tfma.verify_and_update_eval_shared_models` -> - `tfma.utils.verify_and_update_eval_shared_models` - * `tfma.create_keys_key` -> `tfma.utils.create_keys_key` - * `tfma.create_values_key` -> `tfma.utils.create_values_key` - * `tfma.compound_key` -> `tfma.utils.compound_key` - * `tfma.unique_key` -> `tfma.utils.unique_key` - -## Deprecations - -* N/A - -# Version 0.34.0 - -## Major Features and Improvements - -* Added `SparseTensorValue` and `RaggedTensorValue` types 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 either - `np.ndarray`, `SparseTensorValue`, or `RaggedTensorValue` and not - tf.compat.v1 value types. -* Add `CIDerivedMetricComputation` metric type. - -## Bug fixes and other Changes - -* 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_elementwise` could fail on - objects of types `binary_confusion_matrices.Matrices` and - `multi_class_confusion_matrix_metrics.Matrices` due to differing thresholds. - -## Breaking Changes - -* Missing baseline model when change thresholds are present is not allowed - anymore, an exception will be raised unless the rubber_stamp flag is True. - -## Deprecations - -* N/A - -# Version 0.33.0 - -## Major Features and Improvements - -* Provided functionality for `slice_keys_sql` config. It's not available under - Windows. -* The `confidence_interval` field within `metrics_for_slice_pb2.MetricValue` - has been removed and the tag number reserved. This information now lives in - `metrics_for_slice_pb2.MetricKeyAndValue.confidence_interval`. - -## Bug fixes and other Changes - -* 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`. - -## Breaking Changes - -* 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. - -## Deprecations - -* N/A - -# Version 0.32.1 - -## Major Features and Improvements - -* N/A - -## Bug fixes and other Changes - -* Depends on `google-cloud-bigquery>>=1.28.0,<2.21`. -* Depends on `tfx-bsl>=1.1.0,<1.2.0`. - -## Breaking Changes - -* N/A - -## Deprecations - -* N/A - -# Version 0.32.0 - -## Major Features and Improvements - -* N/A - -## Bug fixes and other Changes - -* 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`. - -## Breaking Changes - -* N/A - -## Deprecations - -* N/A - -# Version 0.31.0 - -## Major Features and Improvements - -* N/A - -## Bug fixes and other Changes - -* 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`. - -## Breaking Changes - -* N/A - -## Deprecations - -* N/A - -# Version 0.30.0 - -## Major Features and Improvements - -* N/A - -## Bug fixes and other Changes - -* Fix bug that `FeaturesExtractor` incorrectly 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. - -## Breaking Changes - -* N/A - -## Deprecations - -* N/A - -# Version 0.29.0 - -## Major Features and Improvements - -* Added support for output aggregation. - -## Bug fixes and other Changes - -* 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`. - -## Breaking Changes - -* N/A - -## Deprecations - -* N/A - -# Version 0.28.0 - -## Major Features and Improvements - -* 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. - -## Bug fixes and other Changes - -* 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`. - -## Breaking Changes - -* N/A - -## Deprecations - -* N/A - -# Version 0.27.0 - -## Major Features and Improvements - -* 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_options` config to ModelSpec to configure whether and - how to pad the prediction and label tensors expected by the model's metrics. - -## Bug fixes and other changes - -* 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`. - -## Breaking changes - -* N/A - -## Deprecations - -* N/A - -# Version 0.26.1 - -## Major Features and Improvements - -* N/A - -## Bug fixes and other Changes - -* 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. - -## Breaking Changes - -* N/A - -## Deprecations - -* N/A - -# Version 0.26.0 - -## Major Features and Improvements - -* 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 - the `tfma.Extracts` under the key name `tfma.ATTRIBUTIONS_KEY` must 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. - -## Bug fixes and other changes - -* 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`. - -## Breaking changes - -* Changed MultiClassConfusionMatrix threshold check to prediction > threshold - instead of prediction >= threshold. -* Changed default handling of materialize in default_extractors to False. -* Separated `tfma.extractors.BatchedInputExtractor` into - `tfma.extractors.FeaturesExtractor`, `tfma.extractors.LabelsExtractor`, and - `tfma.extractors.ExampleWeightsExtractor`. -* Change the thresholding to be inclusive, i.e. model passes when value is >= - or <= to the threshold rather than > or <. - -## Deprecations - -* N/A - -# Version 0.25.0 - -## Major Features and Improvements - -* 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-analysis - ``` - - Note: 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`. - -## Bug fixes and other changes - -* 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`. - -## Breaking changes - -* `AggregationOptions` are now independent of `BinarizeOptions`. In order to - compute `AggregationOptions.macro_average` or - `AggregationOptions.weighted_macro_average`, - `AggregationOptions.class_weights` must now be configured. If - `AggregationOptions.class_weights` are 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. - -## Deprecations - -* `tfma.extractors.FeatureExtractor`, `tfma.extractors.PredictExtractor`, - `tfma.extractors.InputExtractor`, and - `tfma.evaluators.MetricsAndPlotsEvaluator` are deprecated and may be - replaced with newer versions in upcoming releases. - -# Version 0.24.3 - -## Major Features and Improvements - -* N/A - -## Bug fixes and other changes - -* Depends on `apache-beam[gcp]>=2.24,<3`. -* Depends on `tfx-bsl>=0.24.0,<0.25.0`. - -## Breaking changes - -* N/A - -## Deprecations - -* N/A - -# Version 0.24.2 - -## Major Features and Improvements - -* N/A - -## Bug fixes and other changes - -* Added an extra requirement group `all`. As a result, barebone TFMA does not - require `tensorflowjs` , `prompt-toolkit` and `ipython` any more. -* Added an extra requirement group `all` that specifies all the extra - dependencies TFMA needs. Use `pip install tensorflow-model-analysis[all]` to - pull in those dependencies. - -## Breaking changes - -* N/A - -## Deprecations - -* N/A - -# Version 0.24.1 - -## Major Features and Improvements - -* N/A - -## Bug fixes and other changes - -* Fix Jupyter lab issue with missing data-base-url. - -## Breaking changes - -* N/A - -## Deprecations - -* N/A - -# Version 0.24.0 - -## Major Features and Improvements - -* Use TFXIO and batched extractors by default in TFMA. - -## Bug fixes and other changes - -* 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`. - -## Breaking changes - -* Query based metrics evaluations that make use of `MetricsSpecs.query_key` - are now passed `tfma.Extracts` with leaf values that are of type - `np.ndarray` containing 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 of - `tfma.Extracts` was passed instead. This allows user's to now add custom - metrics based on `tf.keras.metrics.Metric` as well as `tf.metrics.Metric` - (any previous customizations based on `tf.metrics.Metric` will need to be - updated). As part of this change the `tfma.metrics.NDCG`, - `tfma.metrics.MinValuePosition`, and `tfma.metrics.QueryStatistics` have - been updated. -* Renamed `ConfusionMatrixMetric.compute` to `ConfusionMatrixMetric.result` - for consistency with other APIs. - -## Deprecations - -* Deprecating Py3.5 support. - -# Version 0.23.0 - -## Major Features and Improvements - -* 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.MultiClassConfusionMatrixPlot` - using derived computations. -* Provide support for evaluating TFJS models. - -## Bug fixes and other changes - -* 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`. - -## Breaking changes - -* Rename EvalResult.get_slices() to EvalResult.get_slice_names(). - -## Deprecations - -* Note: We plan to remove Python 3.5 support after this release. - -# Version 0.22.2 - -## Major Features and Improvements - -* Added analyze_raw_data(), an API for evaluating TFMA metrics on Pandas - DataFrames. - -## Bug fixes and other changes - -* Previously metrics would only be computed for combinations of keys that - produced different metric values (e.g. `ExampleCount` will 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 the - `MetricSpec` definition 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`, - and `tfma.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' - -## Breaking changes - -* 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_methodology` is now at - `Options.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. - -## Deprecations - -# Version 0.22.1 - -## Major Features and Improvements - -## Bug fixes and other changes - -* Depends on `pyarrow>=0.16,<0.17`. - -## Breaking changes - -## Deprecations - -# Version 0.22.0 - -## Major Features and Improvements - -* Added support for jackknife-based confidence intervals. -* Add EvalResult.get_metrics(), which extracts slice metrics in dictionary - format from EvalResults. -* Adds TFMD `Schema` as an available argument to computations callbacks. - -## Bug fixes and other changes - -* Version is now available under `tfma.version.VERSION` or `tfma.__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. -* `MetricValue`s can now contain both a debug message and a value (rather than - one or the other). -* Fix issue with displaying ConfusionMatrixPlot in colab. -* `CalibrationPlot` now infers `left` and `right` values 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: - `#fairness_indicators& - p.fairness_indicators.evaluation_output_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`. - -## Breaking changes - -* 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. - -## Deprecations - -* Deprecating Py2 support. - -# Release 0.21.6 - -## Major Features and Improvements - -* 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. - -## Bug fixes and other changes - -* Populate confidence_interval field in addition to bounded_value when - confidence intervals is enabled. -* Only requires `avro-python3>=1.8.1,!=1.9.2.*,<2.0.0` on Python 3.5 + MacOS -* Fix bug in SensitivitySpecificityBase derived metrics: guarantee well - defined behaviour when the constraint lies between feasible points (see - updated docstrings). - -## Breaking changes - -## Deprecations - -# Release 0.21.5 - -## Major Features and Improvements - -* Now publish NPM under `tensorflow_model_analysis` for UI components. - -## Bug fixes and other changes - -* Depends on 'tfx-bsl>=0.21.3,<0.22', -* Depends on 'tensorflow>=1.15,<3', -* Depends on 'apache-beam[gcp]>=2.17,<3', - -## Breaking changes - -* Rollback populating TDistributionValue metric when confidence intervals is - enabled in V2. -* Drop Py2 support. - -## Deprecations - -# Release 0.21.4 - -## Major Features and Improvements - -* 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. - -## Bug fixes and other changes - -* 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', - -## Deprecations - -# Release 0.21.3 - -## Major Features and Improvements - -* 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. - -## Bug fixes and other changes - -* Fixed issue with model_name not being set in keras metrics. - -## Breaking changes - -* Populate TDistributionValue metric when confidence intervals is enabled in - V2. -* Rename the writer MetricsAndPlotsWriter to MetricsPlotsAndValidationsWriter. - -## Deprecations - -# Release 0.21.2 - -## Major Features and Improvements - -## Bug fixes and other changes - -* Adding SciPy dependency for both Python2 and Python3 -* Increased table and tooltip font in Fairness Indicators. - -## Breaking changes - -* `tfma.BinarizeOptions.class_ids`, `tfma.BinarizeOptions.k_list`, - `tfma.BinarizeOptions.top_k_list`, and `tfma.Options.disabled_outputs` are - now wrapped in an additional proto message. - -## Deprecations - -# Release 0.21.1 - -## Major Features and Improvements - -* Adding a TFLite predict extractor to enable obtaining inferences from TFLite - models. - -## Bug fixes and other changes - -* 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.CoefficientOfDiscrimination` and - `tfma.metrics.RelativeCoefficientOfDiscrimination`. - -## Breaking changes - -* Renaming k_anonymization_count field name to min_slice_size. - -## Deprecations - -# Release 0.21.0 - -## Major Features and Improvements - -* Added `tfma.metrics.MinLabelPosition` and `tfma.metrics.QueryStatistics` for - use with V2 metrics API. -* Added `tfma.metrics.CoefficientOfDiscrimination` and - `tfma.metrics.RelativeCoefficientOfDiscrimination` for 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.*` and `tfma.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 - a `tfma.EvalConfig` is used containing `metrics_specs`. -* Added support for `tfma.metrics.SquaredPearsonCorrelation` for 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`, and - `tfma.metrics.MissRate` for use with V2 metrics API. Renamed `AUCPlot` to - `ConfusionMatrixPlot`, `MultiClassConfusionMatrixAtThresholds` to - `MultiClassConfusionMatrixPlot` and `MultiLabelConfusionMatrixAtThresholds` - to `MultiLabelConfusionMatrixPlot`. -* 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. - -## Bug fixes and other changes - -* Fixed error in `tfma-multi-class-confusion-matrix-at-thresholds` with - 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. - -## Breaking changes - -* Updated proto config to remove input/output data specs in favor of passing - them directly to the run_eval. - -## Deprecations - -# Release 0.15.4 - -## Major Features and Improvements - -## Bug fixes and other changes - -* Fixed the bug that Fairness Indicator will skip metrics with NaN value. - -## Breaking changes - -## Deprecations - -# Release 0.15.3 - -## Major Features and Improvements - -## Bug fixes and other changes - -* Updated vulcanized_tfma.js with UI changes in addons/fairness_indicators. - -## Breaking changes - -## Deprecations - -# Release 0.15.2 - -## Major Features and Improvements - -## Bug fixes and other changes - -* 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). - -## Breaking changes - -## Deprecations - -# Release 0.15.1 - -## Major Features and Improvements - -* 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.ExampleCount` and - `tfma.metrics.WeightedExampleCount`) for use with V2 metrics API. -* Added calibration metrics (`tfma.metrics.MeanLabel`, - `tfma.metrics.MeanPrediction`, and `tfma.metrics.Calibration`) for use with - V2 metrics API. -* Added `tfma.metrics.ConfusionMatrixAtThresholds` for use with V2 metrics - API. -* Added `tfma.metrics.CalibrationPlot` and `tfma.metrics.AUCPlot` for 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.NDCG` metric for use with V2 metrics API. -* Added `calibration` as a post export metric. - -## Bug fixes and other changes - -* Depends on `tensorflow>=1.15,<3.0`. - * Starting from 1.15, package `tensorflow` comes with GPU support. Users - won't need to choose between `tensorflow` and `tensorflow-gpu`. - * Caveat: `tensorflow` 2.0.0 is an exception and does not have GPU - support. If `tensorflow-gpu` 2.0.0 is installed before installing - `tensorflow_model_analysis`, it will be replaced with `tensorflow` - 2.0.0. Re-install `tensorflow-gpu` 2.0.0 if needed. - -## Breaking changes - -## Deprecations - -# Release 0.15.0 - -## Major Features and Improvements - -* 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`, and `tfma.default_writers` now allow settings to - be passed as an `EvalConfig`. -* `tfma.run_model_analysis`, `tfma.default_extractors`, - `tfma.default_evaluators`, and `tfma.default_writers` now 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. - -## Bug fixes and other changes - -* 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-transform` with `tfx-bsl`. If running - with latest master, `tfx-bsl` must 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`. - -## Breaking changes - -* 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. - -## Deprecations - -# Release 0.14.0 - -## Major Features and Improvements - -* Added documentation on architecture. -* Added an `adapt_to_remove_metrics` function to `tfma.exporter` which can be - used to remove metrics incompatible with TFMA (e.g. `py_func` or 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. - -## Bug fixes and other changes - -* 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. - -## Breaking changes - -* Removed uses of deprecated tf.contrib packages (where possible). -* `tfma.default_writers` now requires the `eval_saved_model` to be passed as - an argument. -* Requires pre-installed TensorFlow >=1.14,<2. - -## Deprecations - -# Release 0.13.1 - -## Major Features and Improvements - -* 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. - -## Bug fixes and other changes - -* 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. - -## Breaking changes - -* Expose Python 3 types in the code (this will break Python 2 compatibility) - -## Deprecations - -# Release 0.13.0 - -## Major Features and Improvements - -* Python 3.5 is supported. - -## Bug fixes and other changes - -* 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. - -## Breaking changes - -* 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. - -## Deprecations - -# Release 0.12.0 - -## Major Features and Improvements - -* 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](g3doc/architecture.md) 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](http://www.unofficialgoogledatascience.com/2015/08/an-introduction-to-poisson-bootstrap26.html). - To use, set the num_bootstrap_samples to a number greater than 1--20 is - recommended for confidence intervals. - -## Bug fixes and other changes - -* 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 label `l` in `[0, - 1]` as two examples, a positive example with weight `l` and a negative - example with weight `1 - 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) - -## Breaking changes - -* Moved tfma.SingleSliceSpec to tfma.slicer.SingleSliceSpec. - -## Deprecations - -# Release 0.11.0 - -## Major Features and Improvements - -* We now support unsupervised models which have `model_fn`s that do not take a - `labels` argument. -* Improved performance by using `make_callable` instead of repeated - `session.run` calls. -* 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_ref` parameter - to your `EvalInputReceiver`. 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. See - `eval_saved_model/example_trainers/fake_multi_examples_per_input_estimator.py` - for an example. -* Added support for metrics with string `value_op`s. -* Added support for metrics whose `value_op`s return multidimensional arrays. -* We now support including your serving graph in the EvalSavedModel. You can - do this by passing a `serving_input_receiver_fn` to `export_eval_savedmodel` - or any of the TFMA Exporters. -* We now support customizing prediction and label keys for - post_export_metrics. - -## Bug fixes and other changes - -* 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__.py` for usage. -* Update `EvalInputReceiver` so the TFMA collections written to the graph only - contain the results of the last call if multiple calls to - `EvalInputReceiver` are 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 - `features` and `labels` in `tf.identity`, which could cause TFMA to - encounter TensorFlow issue #17568 if there were control dependencies on - these `features` or `labels`. -* We now correctly preserve `dtypes` when 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. - -## Breaking changes - -* Requires pre-installed TensorFlow >=1.11,<2. -* We now require that `EvalInputReceiver`, `export_eval_savedmodel`, - `make_export_strategy`, `make_final_exporter`, `FinalExporter` and - `LatestExporter` be called with keyword arguments only. -* Removed `check_metric_compatibility` from `EvalSavedModel`. -* We now enforce that the `receiver_tensors` dictionary for - `EvalInputReceiver` contains exactly one key named `examples`. -* Post-export metrics have now been moved up one level to - `tfma.post_export_metrics`. They should now be accessed via - `tfma.post_export_metrics.auc` instead of - `tfma.post_export_metrics.post_export_metrics.auc` as they were before. -* Separated extraction from evaluation. `EvaluteAndWriteResults` is now called - `ExtractEvaluateAndWriteResults`. -* Added `EvalSharedModel` type to encapsulate `model_path` and - `add_metrics_callbacks` along with a handle to a shared model instance. - -## Deprecations - -# Release 0.9.2 - -## Major Features and Improvements - -* Improved performance especially when slicing across many features and/or - feature values. - -## Bug fixes and other changes - -* Depends on `tensorflow-transform>=0.9,<1`. -* Requires pre-installed TensorFlow >=1.9,<2. - -## Breaking changes - -## Deprecations - -# Release 0.9.1 - -## Major Features and Improvements - -## Bug fixes and other changes - -* 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. - -## Breaking changes - -* Requires pre-installed TensorFlow >=1.10,<2. - -## Deprecations - -# Release 0.9.0 - -## Major Features and Improvements - -* Add a TFMA unit test library for unit testing your the exported model and - associated metrics computations. -* Add `tfma.export.make_export_strategy` which is analogous to - `tf.contrib.learn.make_export_strategy`. -* Add `tfma.exporter.FinalExporter` and `tfma.exporter.LatestExporter` which - are analogous to `tf.estimator.FinalExporter` and - `tf.estimator.LastExporter`. -* Add `tfma.export.build_parsing_eval_input_receiver_fn` which is analogous to - `tf.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`) - -## Bug fixes and other changes - -* 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_taxi` example. -* Added dependency on `protobuf>=3.6.0<4` for 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. - -## Breaking changes - -* 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. - -## Deprecations - -# Release 0.6.0 - -* Initial release of TensorFlow Model Analysis. diff --git a/tensorflow_model_analysis/notebook/jupyter/js/package.json b/tensorflow_model_analysis/notebook/jupyter/js/package.json index 64fbaf4b1d..ca1ddcdc2e 100644 --- a/tensorflow_model_analysis/notebook/jupyter/js/package.json +++ b/tensorflow_model_analysis/notebook/jupyter/js/package.json @@ -1,6 +1,6 @@ { "name": "tensorflow_model_analysis", - "version": "0.40.0.dev", + "version": "0.39.0", "homepage": "https://github.com/tensorflow/model-analysis", "bugs": "https://github.com/tensorflow/model-analysis/issues", "license": "Apache-2.0", diff --git a/tensorflow_model_analysis/version.py b/tensorflow_model_analysis/version.py index 893bc5959e..1a45357ba5 100644 --- a/tensorflow_model_analysis/version.py +++ b/tensorflow_model_analysis/version.py @@ -15,4 +15,4 @@ # Version string for this release of TFMA. # Note that setup.py reads and uses this version. -VERSION = '0.40.0.dev' +VERSION = '0.39.0'