def mqtt_predict(db_uri, mqtt_hostname, topic, dataset, sample_interval,
build_name, debug, model_dir):
event_queue = queue.Queue()
if debug:
print('Starting MQTT listener')
listen_thread = listener.MQTTSubscribe(event_queue, mqtt_hostname, topic)
listen_thread.start()
if debug:
tf.logging.set_verbosity(tf.logging.DEBUG)
print('Entering main loop')
while True:
event = event_queue.get()
if debug:
print('Received event with build uuid %s' % event['build_uuid'])
results = gather_results.get_subunit_results(
event['build_uuid'], dataset, sample_interval, db_uri, build_name,
data_path=model_dir, use_cache=False)
if results:
print('Obtained dstat file for %s' % event['build_uuid'])
else:
print('Build uuid: %s is not of proper build_name, skipping'
% event['build_uuid'])
for res in results:
vector, status, labels = trainer.normalize_example(res)
model = svm_trainer.SVMTrainer(
vector, [event['build_uuid']] * len(results), labels, [status],
dataset_name=dataset, model_path=model_dir)
model.predict()
def db_predict(db_uri, dataset, sample_interval, build_name, debug,
build_uuid):
if debug:
tf.logging.set_verbosity(tf.logging.DEBUG)
results = gather_results.get_subunit_results(
build_uuid, dataset, sample_interval, db_uri, build_name)
if results:
print('Obtained dstat file for %s' % build_uuid)
else:
print('Build uuid: %s is not of proper build_uuid, skipping'
% build_uuid)
for res in results:
vector, status, labels = trainer.normalize_example(res)
model = svm_trainer.SVMTrainer(
vector, [build_uuid] * len(results), labels, [status],
dataset_name=dataset)
model.predict()
def db_batch_predict(db_uri, dataset, slice, gpu, debug):
"""Run predict on all DB items on included in the dataset yet
Takes a dataset and a build name. It builds the list of runs in the DB
that fit the specified build name, and that are not yet used for training
in the specified dataset. It runs prediction on all of them.
"""
if debug:
tf.logging.set_verbosity(tf.logging.DEBUG)
# Get the configuration for the model
model_config = gather_results.load_model_config(dataset)
# Get the list of runs from the dataset
run_uuids = gather_results.load_run_uuids(dataset)
# Get the list of runs from the DB
runs = gather_results.get_runs_by_name(
db_uri=db_uri, build_name=model_config['build_name'])
# Run a predict loop, include all runs not in the train dataset
predict_runs = [r for r in runs if r.uuid not in run_uuids]
predict_runs = predict_runs[]
if len(predict_runs) == 0:
print("Empty prediction set, nothing to do.")
sys.exit(0)
# Initialize the array
examples = np.ndarray(
shape=(len(predict_runs), model_config['num_features']))
idx = 0
classes = []
labels = []
print("All runs: %d, dataset size: %d, predict size: %d" % (
len(runs), len(run_uuids), len(predict_runs)))
for run in predict_runs:
# This will also store new runs in cache. In future we may want to
# train on those as well, but nor now let's try to predict only
results = gather_results.get_subunit_results_for_run(
run, model_config['sample_interval'], db_uri=db_uri)
for result in results:
# Skip runs with no data
if result is None:
continue
if model_config['features_regex']:
df = result['dstat']
col_regex = re.compile(model_config['features_regex'])
result['dstat'] = df[list(filter(
col_regex.search, df.columns))]
# Normalize examples
vector, status, labels = trainer.normalize_example(
result, model_config['normalized_length'],
model_config['labels'])
examples[idx] = vector.values
classes.append(status)
idx += 1
# Normalize dataset
n_examples, _ = trainer.normalize_dataset(
examples, labels, params=model_config['normalization_params'])
# Prepare other arrays
classes = np.array(classes)
run_uuids = [r.uuid for r in predict_runs]
# Configure TF
config = tf.ConfigProto(log_device_placement=True,)
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
# Now do the prediction
model = svm_trainer.SVMTrainer(n_examples, run_uuids, labels,
classes, dataset_name=dataset,
force_gpu=gpu)
predictions = model.predict()
errors = []
for prediction, actual in zip(predictions, classes):
if prediction['classes'] != actual:
errors.append((prediction, actual))
print("Prediction of %d inputs completed." % len(classes))
print("Input set composition: %d PASS, %s FAIL" % (
len([x for x in classes if x == 0]),
len([x for x in classes if x == 1])))
if len(errors) > 0:
print("There were some prediction errors: %s" % errors)
else:
print("All predicted correctly.")