Explainable AI SDK

This is a Python SDK for
Google Cloud Explainable AI, an
explanation service that provides insight into machine learning models deployed
on AI Platform. The Explainable AI SDK
helps to visualize explanation results, and to define explanation metadata for
the explanation service.

Explanation metadata tells the explanation service which of your model's inputs
and outputs to use for your explanation request. The SDK has metadata builders
that help you to build and save an explanation metadata file before you deploy
your model to AI Platform.

The Explainable AI SDK also helps you to visualize feature attribution results
on models deployed to AI Platform.

Installation

The Explainable AI SDK supports models built with:

Python 3.7 and later
TensorFlow 1.15 or TensorFlow 2.x.

The Explainable AI SDK is preinstalled on
Google Cloud AI Platform Notebooks
images.

For other platforms:

Make sure that you have
installed Cloud SDK. In
order to communicate with Cloud AI Platform, the Explainable AI SDK requires
a shell environment with Cloud SDK installed.
Install the Explainable AI SDK:
```
pip install explainable-ai-sdk
```

Metadata Builders

After you build your model, you use a metadata builder to create your
explanation metadata. This produces a JSON file that is used for model
deployment on AI Platform.

There are different metadata builders for TensorFlow 1.x and 2.x in
their respective folders.

TensorFlow 2.x

For TensorFlow 2.x, there is one metadata builder that takes a
SavedModel, and uploads both your model and metadata file to Cloud Storage.

For example:

from explainable_ai_sdk.metadata.tf.v2 import SavedModelMetadataBuilder
builder = SavedModelMetadataBuilder(
    model_path)
builder.save_model_with_metadata('gs://my_bucket/model')  # Save the model and the metadata.

TensorFlow 1.x

For TensorFlow 1.x, the Explainable AI SDK supports models built with Keras,
Estimator and the low-level TensorFlow API. There is a different metadata
builder for each of these three TensorFlow APIs. An example usage for a Keras
model would be as follows:

from explainable_ai_sdk.metadata.tf.v1 import KerasGraphMetadataBuilder
my_model = keras.models.Sequential()
my_model.add(keras.layers.Dense(32, activation='relu', input_dim=10))
my_model.add(keras.layers.Dense(32, activation='relu'))
my_model.add(keras.layers.Dense(1, activation='sigmoid'))
builder = KerasGraphMetadataBuilder(my_model)
builder.save_model_with_metadata('gs://my_bucket/model')  # Save the model and the metadata.

For examples using the Estimator and TensorFlow Core builders, refer to the
v1 README file.

Making Predict and Explain Calls

The Explainable AI SDK includes a model interface to help you communicate with
the deployed model more easily. With this interface, you can call predict()
and explain() functions to get predictions and explanations for the provided
data points, respectively.

Here is an example snippet for using the model interface:

project_id = "example_project"
model_name = "example_model"
version_name = "v1"

m = explainable_ai_sdk.load_model_from_ai_platform(project_id, model_name, version_name)
instances = []

# ... steps for preparing instances

predictions = m.predict(instances)
explanations = m.explain(instances)

Explanation, Attribution, and Visualization

The explain() function returns a list of Explanation objects --
one Explanation per input instance. This object makes it easier to interact
with returned attributions. You can use the Explanation object to get
feature importance and raw attributions, and to visualize attributions.

Note: Currently, the feature_importance() and as_tensors() functions
only work on tabular models, due to the limited payload size. We are working on
making both functions available for image models.**

Get feature importance

The feature_importance() function returns the imporance of each feature
based on feature attributions. Note that if a feature has more than one
dimension, the importance is calculated based on the aggregation.

explanations[0].feature_importance()

Get raw attributions

To get feature attributions over each dimension, use the as_tensors()
function to return the raw attributions as tensors.

explanations[0].as_tensors()

Visualize attributions

The Explanation class allows you to visualize feature attributions directly.
For both image and tabular models, you can call visualize_attributions()
to see feature attributions.

explantions[0].visualize_attributions()

Here is an example visualization:

An attribution visualization for a tabular model

Caveats

This library works with (and depends) on either major version of TensorFlow.
Do not import the metadata/tf/v1 and metadata/tf/v2 folders in the
same Python runtime. If you do, there may be unintended side effects of mixing
TensorFlow 1.x and 2.x behavior.

Explainable AI documentation

For more information about Explainable AI, refer to the
Explainable AI documentation.

License

All files in this repository are under the
Apache License, Version 2.0
unless noted otherwise.

Note: We are not accepting contributions at this time.

Dependency	Optional
google.auth	false
ipython	false
matplotlib	false
numpy	false
requests	false
tensorflow	false

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