def sample_raw_predict():
# Create a client
client = aiplatform_v1beta1.PredictionServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.RawPredictRequest(endpoint="endpoint_value", )
# Make the request
response = client.raw_predict(request=request)
# Handle the response
print(response)
def explain_tabular_sample(
project: str,
endpoint_id: str,
instance_dict: Dict,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
# The AI Platform services require regional API endpoints.
client_options = {"api_endpoint": api_endpoint}
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform_v1beta1.PredictionServiceClient(
client_options=client_options)
# The format of each instance should conform to the deployed model's prediction input schema.
instance = json_format.ParseDict(instance_dict, Value())
instances = [instance]
# tabular models do not have additional parameters
parameters_dict = {}
parameters = json_format.ParseDict(parameters_dict, Value())
endpoint = client.endpoint_path(project=project,
location=location,
endpoint=endpoint_id)
response = client.explain(endpoint=endpoint,
instances=instances,
parameters=parameters)
print("response")
print(" deployed_model_id:", response.deployed_model_id)
explanations = response.explanations
for explanation in explanations:
print(" explanation")
# Feature attributions.
attributions = explanation.attributions
for attribution in attributions:
print(" attribution")
print(" baseline_output_value:",
attribution.baseline_output_value)
print(" instance_output_value:",
attribution.instance_output_value)
print(" output_display_name:", attribution.output_display_name)
print(" approximation_error:", attribution.approximation_error)
print(" output_name:", attribution.output_name)
output_index = attribution.output_index
for output_index in output_index:
print(" output_index:", output_index)
predictions = response.predictions
for prediction in predictions:
print(" prediction:", dict(prediction))
def sample_explain():
# Create a client
client = aiplatform_v1beta1.PredictionServiceClient()
# Initialize request argument(s)
instances = aiplatform_v1beta1.Value()
instances.null_value = "NULL_VALUE"
request = aiplatform_v1beta1.ExplainRequest(
endpoint="endpoint_value",
instances=instances,
)
# Make the request
response = client.explain(request=request)
# Handle the response
print(response)