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 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.

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_fns 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_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_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.

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