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🦉 Updates from OwlBot post-processor
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gcf-owl-bot[bot] committed Jul 13, 2023
1 parent 216a83e commit 09bc5c6
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Expand Up @@ -110,6 +110,7 @@ class AutoMlTablesInputs(proto.Message):
the prediction type. If the field is not set, a
default objective function is used.
classification (binary):
"maximize-au-roc" (default) - Maximize the
area under the receiver
operating characteristic (ROC) curve.
Expand All @@ -122,9 +123,11 @@ class AutoMlTablesInputs(proto.Message):
Maximize recall for a specified
precision value.
classification (multi-class):
"minimize-log-loss" (default) - Minimize log
loss.
regression:
"minimize-rmse" (default) - Minimize
root-mean-squared error (RMSE). "minimize-mae"
- Minimize mean-absolute error (MAE).
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Original file line number Diff line number Diff line change
Expand Up @@ -110,6 +110,7 @@ class AutoMlTablesInputs(proto.Message):
the prediction type. If the field is not set, a
default objective function is used.
classification (binary):
"maximize-au-roc" (default) - Maximize the
area under the receiver
operating characteristic (ROC) curve.
Expand All @@ -122,9 +123,11 @@ class AutoMlTablesInputs(proto.Message):
Maximize recall for a specified
precision value.
classification (multi-class):
"minimize-log-loss" (default) - Minimize log
loss.
regression:
"minimize-rmse" (default) - Minimize
root-mean-squared error (RMSE). "minimize-mae"
- Minimize mean-absolute error (MAE).
Expand Down
14 changes: 11 additions & 3 deletions google/cloud/aiplatform_v1/types/explanation.py
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Expand Up @@ -487,7 +487,9 @@ class IntegratedGradientsAttribution(proto.Message):
blurred image to the input image is created.
Using a blurred baseline instead of zero (black
image) is motivated by the BlurIG approach
explained here: https://arxiv.org/abs/2004.03383
explained here:
https://arxiv.org/abs/2004.03383
"""

step_count: int = proto.Field(
Expand All @@ -510,7 +512,9 @@ class XraiAttribution(proto.Message):
r"""An explanation method that redistributes Integrated Gradients
attributions to segmented regions, taking advantage of the
model's fully differentiable structure. Refer to this paper for
more details: https://arxiv.org/abs/1906.02825
more details:
https://arxiv.org/abs/1906.02825
Supported only by image Models.
Expand All @@ -537,7 +541,9 @@ class XraiAttribution(proto.Message):
blurred image to the input image is created.
Using a blurred baseline instead of zero (black
image) is motivated by the BlurIG approach
explained here: https://arxiv.org/abs/2004.03383
explained here:
https://arxiv.org/abs/2004.03383
"""

step_count: int = proto.Field(
Expand All @@ -562,6 +568,7 @@ class SmoothGradConfig(proto.Message):
gradients from noisy samples in the vicinity of the inputs.
Adding noise can help improve the computed gradients. Refer to
this paper for more details:
https://arxiv.org/pdf/1706.03825.pdf
This message has `oneof`_ fields (mutually exclusive fields).
Expand Down Expand Up @@ -675,6 +682,7 @@ class BlurBaselineConfig(proto.Message):
the input image is created. Using a blurred baseline instead of
zero (black image) is motivated by the BlurIG approach explained
here:
https://arxiv.org/abs/2004.03383
Attributes:
Expand Down
1 change: 1 addition & 0 deletions google/cloud/aiplatform_v1/types/index.py
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Expand Up @@ -197,6 +197,7 @@ class IndexDatapoint(proto.Message):
used to perform "restricted searches" where
boolean rule are used to filter the subset of
the database eligible for matching. See:
https://cloud.google.com/vertex-ai/docs/matching-engine/filtering
crowding_tag (google.cloud.aiplatform_v1.types.IndexDatapoint.CrowdingTag):
Optional. CrowdingTag of the datapoint, the
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Expand Up @@ -147,6 +147,7 @@ class ModelDeploymentMonitoringJob(proto.Message):
the job under customer project. Customer could
do their own query & analysis. There could be 4
log tables in maximum:
1. Training data logging predict
request/response 2. Serving data logging predict
request/response
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7 changes: 4 additions & 3 deletions google/cloud/aiplatform_v1/types/model_monitoring.py
Original file line number Diff line number Diff line change
Expand Up @@ -385,9 +385,10 @@ class ThresholdConfig(proto.Message):
value (float):
Specify a threshold value that can trigger
the alert. If this threshold config is for
feature distribution distance: 1. For
categorical feature, the distribution distance
is calculated by L-inifinity norm.
feature distribution distance:
1. For categorical feature, the distribution
distance is calculated by L-inifinity norm.
2. For numerical feature, the distribution
distance is calculated by Jensen–Shannon
divergence.
Expand Down
14 changes: 11 additions & 3 deletions google/cloud/aiplatform_v1beta1/types/explanation.py
Original file line number Diff line number Diff line change
Expand Up @@ -487,7 +487,9 @@ class IntegratedGradientsAttribution(proto.Message):
blurred image to the input image is created.
Using a blurred baseline instead of zero (black
image) is motivated by the BlurIG approach
explained here: https://arxiv.org/abs/2004.03383
explained here:
https://arxiv.org/abs/2004.03383
"""

step_count: int = proto.Field(
Expand All @@ -510,7 +512,9 @@ class XraiAttribution(proto.Message):
r"""An explanation method that redistributes Integrated Gradients
attributions to segmented regions, taking advantage of the
model's fully differentiable structure. Refer to this paper for
more details: https://arxiv.org/abs/1906.02825
more details:
https://arxiv.org/abs/1906.02825
Supported only by image Models.
Expand All @@ -537,7 +541,9 @@ class XraiAttribution(proto.Message):
blurred image to the input image is created.
Using a blurred baseline instead of zero (black
image) is motivated by the BlurIG approach
explained here: https://arxiv.org/abs/2004.03383
explained here:
https://arxiv.org/abs/2004.03383
"""

step_count: int = proto.Field(
Expand All @@ -562,6 +568,7 @@ class SmoothGradConfig(proto.Message):
gradients from noisy samples in the vicinity of the inputs.
Adding noise can help improve the computed gradients. Refer to
this paper for more details:
https://arxiv.org/pdf/1706.03825.pdf
This message has `oneof`_ fields (mutually exclusive fields).
Expand Down Expand Up @@ -675,6 +682,7 @@ class BlurBaselineConfig(proto.Message):
the input image is created. Using a blurred baseline instead of
zero (black image) is motivated by the BlurIG approach explained
here:
https://arxiv.org/abs/2004.03383
Attributes:
Expand Down
1 change: 1 addition & 0 deletions google/cloud/aiplatform_v1beta1/types/index.py
Original file line number Diff line number Diff line change
Expand Up @@ -197,6 +197,7 @@ class IndexDatapoint(proto.Message):
used to perform "restricted searches" where
boolean rule are used to filter the subset of
the database eligible for matching. See:
https://cloud.google.com/vertex-ai/docs/matching-engine/filtering
crowding_tag (google.cloud.aiplatform_v1beta1.types.IndexDatapoint.CrowdingTag):
Optional. CrowdingTag of the datapoint, the
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -147,6 +147,7 @@ class ModelDeploymentMonitoringJob(proto.Message):
the job under customer project. Customer could
do their own query & analysis. There could be 4
log tables in maximum:
1. Training data logging predict
request/response 2. Serving data logging predict
request/response
Expand Down
7 changes: 4 additions & 3 deletions google/cloud/aiplatform_v1beta1/types/model_monitoring.py
Original file line number Diff line number Diff line change
Expand Up @@ -445,9 +445,10 @@ class ThresholdConfig(proto.Message):
value (float):
Specify a threshold value that can trigger
the alert. If this threshold config is for
feature distribution distance: 1. For
categorical feature, the distribution distance
is calculated by L-inifinity norm.
feature distribution distance:
1. For categorical feature, the distribution
distance is calculated by L-inifinity norm.
2. For numerical feature, the distribution
distance is calculated by Jensen–Shannon
divergence.
Expand Down
13 changes: 0 additions & 13 deletions owl-bot-staging/v1/.coveragerc

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33 changes: 0 additions & 33 deletions owl-bot-staging/v1/.flake8

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2 changes: 0 additions & 2 deletions owl-bot-staging/v1/MANIFEST.in

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49 changes: 0 additions & 49 deletions owl-bot-staging/v1/README.rst

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10 changes: 0 additions & 10 deletions owl-bot-staging/v1/docs/aiplatform_v1/dataset_service.rst

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10 changes: 0 additions & 10 deletions owl-bot-staging/v1/docs/aiplatform_v1/endpoint_service.rst

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10 changes: 0 additions & 10 deletions owl-bot-staging/v1/docs/aiplatform_v1/featurestore_service.rst

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10 changes: 0 additions & 10 deletions owl-bot-staging/v1/docs/aiplatform_v1/index_endpoint_service.rst

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10 changes: 0 additions & 10 deletions owl-bot-staging/v1/docs/aiplatform_v1/index_service.rst

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10 changes: 0 additions & 10 deletions owl-bot-staging/v1/docs/aiplatform_v1/job_service.rst

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6 changes: 0 additions & 6 deletions owl-bot-staging/v1/docs/aiplatform_v1/match_service.rst

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10 changes: 0 additions & 10 deletions owl-bot-staging/v1/docs/aiplatform_v1/metadata_service.rst

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