def create_transformation(self) -> transform.Transformation: return transform.Chain( trans=[ transform.AsNumpyArray( field=FieldName.TARGET, expected_ndim=1 ), transform.AddTimeFeatures( start_field=transform.FieldName.START, target_field=transform.FieldName.TARGET, output_field=transform.FieldName.FEAT_TIME, time_features=time_features_from_frequency_str(self.freq), pred_length=self.prediction_length, ), transform.VstackFeatures( output_field=FieldName.FEAT_DYNAMIC_REAL, input_fields=[FieldName.FEAT_TIME], ), transform.SetFieldIfNotPresent( field=FieldName.FEAT_STATIC_CAT, value=[0.0] ), transform.AsNumpyArray( field=FieldName.FEAT_STATIC_CAT, expected_ndim=1 ), transform.InstanceSplitter( target_field=transform.FieldName.TARGET, is_pad_field=transform.FieldName.IS_PAD, start_field=transform.FieldName.START, forecast_start_field=transform.FieldName.FORECAST_START, train_sampler=ExpectedNumInstanceSampler(num_instances=1), past_length=self.context_length, future_length=self.prediction_length, time_series_fields=[FieldName.FEAT_DYNAMIC_REAL], ), ] )
def create_transformation(self) -> transform.Transformation: return transform.Chain( trans=[ transform.AsNumpyArray( field=FieldName.TARGET, expected_ndim=1 ), transform.AddTimeFeatures( start_field=FieldName.START, target_field=FieldName.TARGET, output_field=FieldName.FEAT_TIME, time_features=time_features_from_frequency_str(self.freq), pred_length=self.prediction_length, ), transform.VstackFeatures( output_field=FieldName.FEAT_DYNAMIC_REAL, input_fields=[FieldName.FEAT_TIME], ), transform.SetFieldIfNotPresent( field=FieldName.FEAT_STATIC_CAT, value=[0.0] ), transform.AsNumpyArray( field=FieldName.FEAT_STATIC_CAT, expected_ndim=1 ), ] )
def test_map_transformation(): tran = transform.VstackFeatures( output_field="dynamic_feat", input_fields=["age", "time_feat"], drop_inputs=True, ) assert equals(tran, clone(tran)) assert not equals(tran, clone(tran, {"drop_inputs": False}))
def test_Transformation(): train_length = 100 ds = gluonts.dataset.common.ListDataset( [{"start": "2012-01-01", "target": [0.2] * train_length}], freq="1D" ) pred_length = 10 t = transform.Chain( trans=[ transform.AddTimeFeatures( start_field=transform.FieldName.START, target_field=transform.FieldName.TARGET, output_field="time_feat", time_features=[ time_feature.DayOfWeek(), time_feature.DayOfMonth(), time_feature.MonthOfYear(), ], pred_length=pred_length, ), transform.AddAgeFeature( target_field=transform.FieldName.TARGET, output_field="age", pred_length=pred_length, log_scale=True, ), transform.AddObservedValuesIndicator( target_field=transform.FieldName.TARGET, output_field="observed_values", ), transform.VstackFeatures( output_field="dynamic_feat", input_fields=["age", "time_feat"], drop_inputs=True, ), transform.InstanceSplitter( target_field=transform.FieldName.TARGET, is_pad_field=transform.FieldName.IS_PAD, start_field=transform.FieldName.START, forecast_start_field=transform.FieldName.FORECAST_START, train_sampler=transform.ExpectedNumInstanceSampler( num_instances=4 ), past_length=train_length, future_length=pred_length, time_series_fields=["dynamic_feat", "observed_values"], ), ] ) assert_serializable(t) for u in t(iter(ds), is_train=True): print(u)
def test_multi_dim_transformation(is_train): train_length = 10 first_dim = np.arange(1, 11, 1).tolist() first_dim[-1] = "NaN" second_dim = np.arange(11, 21, 1).tolist() second_dim[0] = "NaN" ds = gluonts.dataset.common.ListDataset( data_iter=[{"start": "2012-01-01", "target": [first_dim, second_dim]}], freq="1D", one_dim_target=False, ) pred_length = 2 # Looks weird - but this is necessary to assert the nan entries correctly. first_dim[-1] = np.nan second_dim[0] = np.nan t = transform.Chain( trans=[ transform.AddTimeFeatures( start_field=transform.FieldName.START, target_field=transform.FieldName.TARGET, output_field="time_feat", time_features=[ time_feature.DayOfWeek(), time_feature.DayOfMonth(), time_feature.MonthOfYear(), ], pred_length=pred_length, ), transform.AddAgeFeature( target_field=transform.FieldName.TARGET, output_field="age", pred_length=pred_length, log_scale=True, ), transform.AddObservedValuesIndicator( target_field=transform.FieldName.TARGET, output_field="observed_values", convert_nans=False, ), transform.VstackFeatures( output_field="dynamic_feat", input_fields=["age", "time_feat"], drop_inputs=True, ), transform.InstanceSplitter( target_field=transform.FieldName.TARGET, is_pad_field=transform.FieldName.IS_PAD, start_field=transform.FieldName.START, forecast_start_field=transform.FieldName.FORECAST_START, train_sampler=transform.ExpectedNumInstanceSampler( num_instances=4 ), past_length=train_length, future_length=pred_length, time_series_fields=["dynamic_feat", "observed_values"], output_NTC=False, ), ] ) assert_serializable(t) if is_train: for u in t(iter(ds), is_train=True): assert_shape(u["past_target"], (2, 10)) assert_shape(u["past_dynamic_feat"], (4, 10)) assert_shape(u["past_observed_values"], (2, 10)) assert_shape(u["future_target"], (2, 2)) assert_padded_array( u["past_observed_values"], np.array([[1.0] * 9 + [0.0], [0.0] + [1.0] * 9]), u["past_is_pad"], ) assert_padded_array( u["past_target"], np.array([first_dim, second_dim]), u["past_is_pad"], ) else: for u in t(iter(ds), is_train=False): assert_shape(u["past_target"], (2, 10)) assert_shape(u["past_dynamic_feat"], (4, 10)) assert_shape(u["past_observed_values"], (2, 10)) assert_shape(u["future_target"], (2, 0)) assert_padded_array( u["past_observed_values"], np.array([[1.0] * 9 + [0.0], [0.0] + [1.0] * 9]), u["past_is_pad"], ) assert_padded_array( u["past_target"], np.array([first_dim, second_dim]), u["past_is_pad"], )