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Status Future consideration
Created by Deleted user
Created on Jan 4, 2018

Add state to Discovery Service model training process after adding training samples

This was reported as an issue in the python sdk:

The issue has the following description :

I am adding training samples to Discovery Service from our training set in batches and then evaluating query performance after each batch is added to collect data on how relevancy performance improves/changes as samples from our training set are added to the collection.

After adding a batch of training samples the best way I could figure out to determine when the ranking model has updated involves using a method like this to poll the collection details api. This method relies on non-obvious logic and the fact that training_status.successfully_trained and training_status.data_updated return empty string when model has never been trained or training data has never been added (the method wrap_run_query is used to handle timeouts/connection errors and included below just for reference)

class CustomDiscovery(DiscoveryV1):

def wrap_run_query(self, run_query, max_failures=10):
"""Wrap a query with error-handling/retry logic"""
def wrapped():
num_failures = 0
timeout = 1
while True:
num_failures += 1
return run_query()
except (WatsonException, # pylint: disable=W0703
) as err:
if num_failures > max_failures:
print("Watson API failure too many times in a row. Quitting.")
raise err
error_message = str(err)
if "exceeded the rate limit" in error_message or "Query timed out" in error_message:
print("Exceeded rate limit")
elif "busy processing" in error_message:
print("Hit Update Service Limit")
elif 'Query failed' in error_message:
print("Hit Query Failed")
elif "ConnectTimeoutError" in error_message:
print("Hit Connect Timeout")
elif "Max retries exceeded" in error_message:
print("Failure when connecting")
print("HIT UNKNOWN EXCEPTION: ", error_message)
if self.options.VERBOSE:
print("Number of Failures: {0} Will retry in {1} seconds".format(num_failures, timeout))
timeout *= 1.5
return wrapped

def poll_collection(self, environment_id: str, collection_id: str):
"""poll collection details until finished processing documents/training data"""
def run_query():
while True:
details = self.get_collection(environment_id=environment_id, collection_id=collection_id)
document_counts = details["document_counts"]
training = details["training_status"]

# returns empty string if never trained
current_model_date = training["successfully_trained"]
# returns empty string if training data never added
data_update_date = training["data_updated"]
if current_model_date:
current_model_date = aniso8601.parse_datetime(current_model_date)
if data_update_date:
data_update_date = aniso8601.parse_datetime(data_update_date)

if document_counts["processing"] > 0:
print("Document updates still processing. {0} documents in processing queue".format(document_counts["processing"]))
elif training["processing"]:
print("Training updates still processing. Total number of Samples: {0}".format(training["total_examples"]))
if self.options.VERBOSE:
print("Collection Details: ", details)
elif current_model_date and data_update_date and current_model_date < data_update_date or data_update_date and not current_model_date:
"Training work is needed but training has not yet entered processing state. Total number of Samples: {0}".format(training["total_examples"]))
if self.options.VERBOSE:
print("Collection Details: ", details)
print("Number of documents available state after applying updates: {0}".format(document_counts["available"]))
print("Number of documents in processing state after applying updates: {0}".format(document_counts["processing"]))
print("Number of documents in failed state after applying updates: {0}".format(document_counts["failed"]))
if training:
print("Trained ranker available? {0}".format(training["available"]))
if training["available"]:
print("Model creation date: {0}".format(training["successfully_trained"]))

print("Number of training examples: {0}".format(training["total_examples"]))
print("Minimum Queries Added: {0}".format(training["minimum_queries_added"]))
print("Minimum Examples Added: {0}".format(training["minimum_examples_added"]))
print("Sufficient label diversity: {0}".format(training["sufficient_label_diversity"]))
print("Number of Notices: {0}".format(training["notices"]))
return details
return self.wrap_run_query(run_query)()

I'm observing that the training_status.processing field returned by the collection details api doesn't change state from value False to True until some _indeterminate_ amount of time after adding a sufficient set of training samples ... With this behavior -- the client side logic needed to evaluate the system processing state in a stateless manor is kind of ugly ... I think there should be a tri or quad-state training_status.processing_state value ["no_training_needed", "training_scheduled", "training_processing"] or maybe if an error case exists ["no_training_needed", "training_scheduled", "training_processing", "training_error"]. This would give clients a simpler way to determine when the training process has converged after training data has been added to the collection.