Releases: tensorflow/model-analysis
Releases · tensorflow/model-analysis
TensorFlow Model Analysis 0.37.0
Major Features and Improvements
- N/A
Bug fixes and other Changes
- 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
TensorFlow Model Analysis 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.
- 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
- N/A
TensorFlow Model Analysis 0.35.0
Major Features and Improvements
- Added support for specifying weighted vs unweighted metrics. The setting is
available in thetfma.MetricsSpec( example_weights=tfma.ExampleWeightOptions(weighted=True, unweighted=True))
.
If no options are provided then TFMA will default to weighted provided the
associatedtfma.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:
#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.
- no CI derived metric, deriving from private metrics such as
-
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 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_weighted
will be None.
Any metric computed outside of atfma.metrics.MetricConfig
setting (i.e.
metrics loaded from a saved model) will have the weighting set to None. ExampleCount
is now weighted based ontfma.MetricsSpec.example_weights
settings.WeightedExampleCount
has been deprecated (useExampleCount
instead). To get unweighted example counts (i.e. the previous implementation
ofExampleCount
),ExampleCount
must now be added to aMetricsSpec
whereexample_weights.unweighted
is true. To get a weighted example count
(i.e. what was previouslyWeightedExampleCount
),ExampleCount
must now
be added to aMetricsSpec
whereexample_weights.weighted
is true.
Deprecations
- Deprecated python3.6 support.
TensorFlow Model Analysis 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
TensorFlow Model Analysis 0.34.0
Major Features and Improvements
- Added
SparseTensorValue
andRaggedTensorValue
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
, orRaggedTensorValue
and not
tf.compat.v1 value types. - Add
CIDerivedMetricComputation
metric type.
Bug fixes and other Changes
- 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
.
Breaking Changes
- N/A
Deprecations
- N/A
TensorFlow Model Analysis 0.33.0
Major Features and Improvements
- Provided functionality for
slice_keys_sql
config. It's not available under
Windows.
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
TensorFlow Model Analysis 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>=1.1.1,<1.2.0
.
Breaking Changes
- N/A
Deprecations
- N/A
TensorFlow Model Analysis 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
TensorFlow Model Analysis 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
TensorFlow Model Analysis 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