def bigml( train_csv, test_csv, result_csv ):
api = BigML(dev_mode=True)
# train model
start_training = timer()
source_train = api.create_source(train_csv)
dataset_train = api.create_dataset(source_train)
model = api.create_model(dataset_train)
end_training = timer()
print('Training model.')
print('Training took %i Seconds.' % (end_training - start_training) );
# test create_model
start_test = timer()
source_test = api.create_source(test_csv)
dataset_test = api.create_dataset(source_test)
batch_prediction = api.create_batch_prediction(
model,
dataset_test,
{
"name": "census prediction",
"all_fields": True,
"header": False,
"confidence": False
}
)
# wait until batch processing is finished
while api.get_batch_prediction(batch_prediction)['object']['status']['progress'] != 1:
print api.get_batch_prediction(batch_prediction)['object']['status']['progress']
time.sleep(1)
end_test = timer()
print('Testing took %i Seconds' % (end_test - start_test) );
api.download_batch_prediction(batch_prediction['resource'], filename=result_csv)
# cleanup
api.delete_source(source_train)
api.delete_source(source_test)
api.delete_dataset(dataset_train)
api.delete_dataset(dataset_test)
api.delete_model(model)
