def evaluation(self):
"""
Admin function: Runs the whole evaluation
"""
aicrowd_helpers.execution_start()
try:
with time_limit(self.inference_setup_timeout):
self.prediction_setup()
except NotImplementedError:
print("prediction_setup doesn't exist for this run, skipping...")
aicrowd_helpers.execution_running()
music_names = self.get_all_music_names()
for music_name in music_names:
with time_limit(self.inference_per_music_timeout):
self.prediction(
mixture_file_path=self.get_music_file_location(music_name),
bass_file_path=self.get_music_file_location(
music_name, "bass"),
drums_file_path=self.get_music_file_location(
music_name, "drums"),
other_file_path=self.get_music_file_location(
music_name, "other"),
vocals_file_path=self.get_music_file_location(
music_name, "vocals"),
)
if not self.verify_results(music_name):
raise Exception(
"verification failed, demixed files not found.")
aicrowd_helpers.execution_success()
def evaluation(self):
aicrowd_helpers.execution_start()
try:
with time_limit(self.predictions_setup_timeout):
self.predict_setup()
except NotImplementedError:
print("predict_setup doesn't exist for this run, skipping...")
aicrowd_helpers.execution_running()
test_df = pd.read_csv(self.test_data_path)
predictions = []
for _, row in test_df.iterrows():
with time_limit(self.predictions_timeout):
prediction_arr = self.predict(row['SMILES'])
prediction = ';'.join([','.join(sorted(set(i))) for i in prediction_arr])
predictions.append(prediction)
submission_df = pd.DataFrame({
"SMILES" : test_df.SMILES.tolist(),
"PREDICTIONS" : predictions
})
submission_df.to_csv(
self.predictions_output_path,
index=False
)
aicrowd_helpers.execution_success()