def test_tcn_forecaster_onnx_methods(self): train_data, val_data, test_data = create_data() forecaster = LSTMForecaster(past_seq_len=24, input_feature_num=2, output_feature_num=2, loss="mae", lr=0.01) forecaster.fit(train_data, epochs=2) try: import onnx import onnxruntime pred = forecaster.predict(test_data[0]) pred_onnx = forecaster.predict_with_onnx(test_data[0]) np.testing.assert_almost_equal(pred, pred_onnx, decimal=5) mse = forecaster.evaluate(test_data, multioutput="raw_values") mse_onnx = forecaster.evaluate_with_onnx(test_data, multioutput="raw_values") np.testing.assert_almost_equal(mse, mse_onnx, decimal=5) with pytest.raises(RuntimeError): forecaster.build_onnx(sess_options=1) sess_options = onnxruntime.SessionOptions() sess_options.intra_op_num_threads = 1 forecaster.build_onnx(sess_options=sess_options) mse = forecaster.evaluate(test_data) mse_onnx = forecaster.evaluate_with_onnx(test_data) np.testing.assert_almost_equal(mse, mse_onnx, decimal=5) except ImportError: pass
def test_tcn_forecaster_runtime_error(self): train_data, val_data, test_data = create_data() forecaster = LSTMForecaster(past_seq_len=24, input_feature_num=2, output_feature_num=2, loss="mae", lr=0.01) with pytest.raises(RuntimeError): with tempfile.TemporaryDirectory() as tmp_dir_name: ckpt_name = os.path.join(tmp_dir_name, "ckpt") forecaster.save(ckpt_name) with pytest.raises(RuntimeError): forecaster.predict(test_data[0]) with pytest.raises(RuntimeError): forecaster.evaluate(test_data)
def test_tcn_forecaster_xshard_input(self): train_data, val_data, test_data = create_data() print("original", train_data[0].dtype) init_orca_context(cores=4, memory="2g") from zoo.orca.data import XShards def transform_to_dict(data): return {'x': data[0], 'y': data[1]} def transform_to_dict_x(data): return {'x': data[0]} train_data = XShards.partition(train_data).transform_shard( transform_to_dict) val_data = XShards.partition(val_data).transform_shard( transform_to_dict) test_data = XShards.partition(test_data).transform_shard( transform_to_dict_x) for distributed in [True, False]: forecaster = LSTMForecaster(past_seq_len=24, input_feature_num=2, output_feature_num=2, loss="mae", lr=0.01, distributed=distributed) forecaster.fit(train_data, epochs=2) distributed_pred = forecaster.predict(test_data) distributed_eval = forecaster.evaluate(val_data) stop_orca_context()
def test_tcn_forecaster_distributed(self): train_data, val_data, test_data = create_data() init_orca_context(cores=4, memory="2g") forecaster = LSTMForecaster(past_seq_len=24, input_feature_num=2, output_feature_num=2, loss="mae", lr=0.01, distributed=True) forecaster.fit(train_data, epochs=2) distributed_pred = forecaster.predict(test_data[0]) distributed_eval = forecaster.evaluate(val_data) model = forecaster.get_model() assert isinstance(model, torch.nn.Module) forecaster.to_local() local_pred = forecaster.predict(test_data[0]) local_eval = forecaster.evaluate(val_data) np.testing.assert_almost_equal(distributed_pred, local_pred, decimal=5) try: import onnx import onnxruntime local_pred_onnx = forecaster.predict_with_onnx(test_data[0]) local_eval_onnx = forecaster.evaluate_with_onnx(val_data) np.testing.assert_almost_equal(distributed_pred, local_pred_onnx, decimal=5) except ImportError: pass model = forecaster.get_model() assert isinstance(model, torch.nn.Module) stop_orca_context()
def test_tcn_forecaster_fit_eva_pred(self): train_data, val_data, test_data = create_data() forecaster = LSTMForecaster(past_seq_len=24, input_feature_num=2, output_feature_num=2, hidden_dim=[32, 16], layer_num=2, dropout=[0.1, 0.2], loss="mae", lr=0.01) train_loss = forecaster.fit(train_data, epochs=2) test_pred = forecaster.predict(test_data[0]) assert test_pred.shape == test_data[1].shape test_mse = forecaster.evaluate(test_data)