def by_infile(self, infile):
try:
shutil.rmtree(self.OUTPUT_DIR)
except:
pass
self.db_open()
json_data = self.get_events_from_infile(infile)
# build preprocessor
ppr = Preprocessor()
# Process raw data
#X, Y, events_found = ppr.get_raw_data(DIMENSION, [RAW_FILE], bad)
X, Y, events_found = ppr.get_from_json(self.DIMENSION, json_data)
X, Y = ppr.remove_outliers(X, Y)
X, Y = ppr.normalize(X, Y)
trX, trY, teX, teY, vaX, vaY = ppr.partition_for_training(
X, Y, 0.0, 1.0)
ppr.store_training_partitions(trX, trY, teX, teY, vaX, vaY,
self.INPUT_DIR)
# build adapter
adapter = MACAdapter(self.INPUT_DIR, self.DIMENSION, self.FOLDS)
# build model
convnet = ConvNet(self.DIMENSION)
# build server
server = ConvNetServer(adapter,
self.OUTPUT_DIR,
batch_size=self.BATCH_SIZE,
verbose=True,
use=True)
x, durs, _ = server.get_testing_batch()
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
convnet.restore(sess, self.INITIAL_WEIGHTS)
predictions = sess.run((convnet.predictor),
feed_dict={
convnet.x: x,
convnet.durs: durs
})
# Get event ids
_, _, ids = adapter.get_ids()
results = [{
"eventID": int(ids[i]),
"ml": {
"aircraftProbability":
round(np.around(predictions[i][0], decimals=4), 4),
"model":
self.MODEL
}
} for i in range(0, len(ids))]
for result in results:
self.insert_result_for_event(result)
self.db_close()
# Only run if this is the main module to be run
if __name__ == '__main__':
# JSON object returned from api_call
# replace this with however you would like it to work in production
json_data = json.load(open(EXAMPLE_FILE))
# NOTE if events in json object have neither "aircraft" nor "community" fields
# in they will be labeled as community for training - probably try to avoid this
# build preprocessor
ppr = Preprocessor()
# Process raw data
#X, Y, events_found = ppr.get_raw_data(DIMENSION, [RAW_FILE], bad)
X, Y, events_found = ppr.get_from_json(DIMENSION, json_data)
X, Y = ppr.remove_outliers(X, Y)
X, Y = ppr.normalize(X, Y)
# Shove all events into the "training" subdirectory
trX, trY, teX, teY, vaX, vaY = ppr.partition_for_training(X, Y, 1.0, 0.0)
# Store events in intermediate directory (will be deleted on subsequent trainings)
ppr.store_training_partitions(trX, trY, teX, teY, vaX, vaY, INPUT_DIR)
# build adapter
adapter = MACAdapter(INPUT_DIR, DIMENSION, FOLDS)
# build model
convnet = ConvNet(DIMENSION)
# build server
server = ConvNetServer(adapter,
OUTPUT_DIR,