def test_ExpectedNumInstanceSampler(): N = 6 train_length = 2 pred_length = 1 ds = make_dataset(N, train_length) t = transform.Chain(trans=[ transform.InstanceSplitter( target_field=FieldName.TARGET, is_pad_field=FieldName.IS_PAD, start_field=FieldName.START, forecast_start_field=FieldName.FORECAST_START, instance_sampler=transform.ExpectedNumInstanceSampler( num_instances=4, min_future=pred_length), past_length=train_length, future_length=pred_length, ) ]) assert_serializable(t) scale_hist = ScaleHistogram() repetition = 2 for i in range(repetition): for data in t(iter(ds), is_train=True): target_values = data["past_target"] # for simplicity, discard values that are zeros to avoid confusion with padding target_values = target_values[target_values > 0] scale_hist.add(target_values) expected_values = {i: 2**i * repetition for i in range(1, N)} assert expected_values == scale_hist.bin_counts
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_instance_splitter(): splitter = transform.InstanceSplitter( target_field=FieldName.TARGET, is_pad_field=FieldName.IS_PAD, start_field=FieldName.START, forecast_start_field=FieldName.FORECAST_START, instance_sampler=transform.ExpectedNumInstanceSampler(num_instances=4), past_length=100, future_length=10, time_series_fields=["dynamic_feat", "observed_values"], ) splitter2 = clone( splitter, { "instance_sampler": transform.ExpectedNumInstanceSampler(num_instances=5) }, ) assert equals(splitter, clone(splitter)) assert not equals(splitter, splitter2)
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"], )
def test_exp_num_sampler(): sampler = transform.ExpectedNumInstanceSampler(num_instances=4) assert equals(sampler, clone(sampler)) assert not equals(sampler, clone(sampler, {"num_instances": 5}))