def test_chain(): chain = transform.Chain(trans=[ transform.AddTimeFeatures( start_field=FieldName.START, target_field=FieldName.TARGET, output_field="time_feat", time_features=[ time_feature.DayOfWeek(), time_feature.DayOfMonth(), time_feature.MonthOfYear(), ], pred_length=10, ), transform.AddAgeFeature( target_field=FieldName.TARGET, output_field="age", pred_length=10, log_scale=True, ), transform.AddObservedValuesIndicator(target_field=FieldName.TARGET, output_field="observed_values"), ]) assert equals(chain, clone(chain)) assert not equals(chain, clone(chain, {"trans": []})) another_chain = transform.Chain(trans=[ transform.AddTimeFeatures( start_field=FieldName.START, target_field=FieldName.TARGET, output_field="time_feat", time_features=[ time_feature.DayOfWeek(), time_feature.DayOfMonth(), time_feature.MonthOfYear(), ], pred_length=10, ), transform.AddAgeFeature( target_field=FieldName.TARGET, output_field="age", pred_length=10, log_scale=False, ), transform.AddObservedValuesIndicator(target_field=FieldName.TARGET, output_field="observed_values"), ]) assert not equals(chain, another_chain)
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 test_add_time_features(): tran = transform.AddTimeFeatures( start_field=FieldName.START, target_field=FieldName.TARGET, output_field="time_feat", time_features=[ time_feature.DayOfWeek(), time_feature.DayOfMonth(), time_feature.MonthOfYear(), ], pred_length=10, ) tran2 = clone( tran, { "time_features": [ time_feature.DayOfWeek(), time_feature.DayOfMonth(), ] }, ) assert equals(tran, clone(tran)) assert not equals(tran, tran2)
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_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_forking_sequence_with_features(is_train) -> None: def make_dataset(N, train_length): # generates 2 ** N - 1 timeseries with constant increasing values n = 2 ** N - 1 targets = np.arange(n * train_length).reshape((n, train_length)) return ListDataset( [ {"start": "2012-01-01", "target": targets[i, :]} for i in range(n) ], freq="D", ) ds = make_dataset(1, 20) trans = transform.Chain( trans=[ transform.AddAgeFeature( target_field=FieldName.TARGET, output_field=FieldName.FEAT_AGE, pred_length=10, ), transform.AddTimeFeatures( start_field=FieldName.START, target_field=FieldName.TARGET, output_field=FieldName.FEAT_TIME, time_features=time_features_from_frequency_str("D"), pred_length=10, ), ForkingSequenceSplitter( train_sampler=TSplitSampler(), enc_len=5, dec_len=3, encoder_series_fields=[ FieldName.FEAT_AGE, FieldName.FEAT_TIME, ], decoder_series_fields=[FieldName.FEAT_TIME], ), ] ) out = trans(iter(ds), is_train=is_train) transformed_data = next(iter(out)) assert transformed_data["past_target"].shape == (5, 1) assert transformed_data["past_feat_dynamic_age"].shape == (5, 1) assert transformed_data["past_time_feat"].shape == (5, 3) assert transformed_data["future_time_feat"].shape == (5, 3, 3) if is_train: assert transformed_data["future_target"].shape == (5, 3)
def test_AddTimeFeatures_empty_time_features(start, target, is_train: bool): pred_length = 13 t = transform.AddTimeFeatures( start_field=FieldName.START, target_field=FieldName.TARGET, output_field="myout", pred_length=pred_length, time_features=[], ) assert_serializable(t) data = {"start": start, "target": target} res = t.map_transform(data, is_train=is_train) assert res["myout"] is None
def test_add_method(): chain = transform.AddTimeFeatures( start_field=FieldName.START, target_field=FieldName.TARGET, output_field="time_feat", time_features=[ time_feature.DayOfWeek(), time_feature.DayOfMonth(), time_feature.MonthOfYear(), ], pred_length=24, ) + transform.AddAgeFeature( target_field=FieldName.TARGET, output_field="age", pred_length=24, log_scale=True, ) assert isinstance(chain, transform.Chain)
def test_AddTimeFeatures(start, target, is_train): pred_length = 13 t = transform.AddTimeFeatures( start_field=transform.FieldName.START, target_field=transform.FieldName.TARGET, output_field="myout", pred_length=pred_length, time_features=[time_feature.DayOfWeek(), time_feature.DayOfMonth()], ) assert_serializable(t) data = {"start": start, "target": target} res = t.map_transform(data, is_train=is_train) mat = res["myout"] expected_length = len(target) + (0 if is_train else pred_length) assert mat.shape == (2, expected_length) tmp_idx = pd.date_range( start=start, freq=start.freq, periods=expected_length ) assert np.alltrue(mat[0] == time_feature.DayOfWeek()(tmp_idx)) assert np.alltrue(mat[1] == time_feature.DayOfMonth()(tmp_idx))
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"], )