Changed target name/series ID divider and added ability to return series ID column with predictions

docs/source/release_notes.rst

@@ -2,6 +2,7 @@ Release Notes
 -------------
 **Future Releases**
  * Enhancements
+ * Changed target name/series ID divider and added ability to return series ID column with predictions :pr:`4357`
  * Fixes
  * Changes
  * Pinned networkx version below 3.2 for Python version compatibility :pr:`4351`

evalml/pipelines/multiseries_regression_pipeline.py

@@ -6,6 +6,7 @@
  TimeSeriesRegressionPipeline,
 )
 from evalml.problem_types import ProblemTypes
+from evalml.utils import infer_feature_types
 
 
 class MultiseriesRegressionPipeline(TimeSeriesRegressionPipeline):
@@ -91,6 +92,7 @@
  y_train,
  objective=None,
  calculating_residuals=False,
+ include_series_id=False,
  ):
  """Predict on future data where the target is known, e.g. cross validation.
 
@@ -102,6 +104,7 @@
  objective (ObjectiveBase, str, None): Objective used to threshold predicted probabilities, optional.
  calculating_residuals (bool): Whether we're calling predict_in_sample to calculate the residuals. This means
  the X and y arguments are not future data, but actually the train data.
+ include_series_id (bool): If true, include the series ID value in the prediction results
 
  Returns:
  pd.Series: Estimated labels.
@@ -125,6 +128,33 @@
  self.time_index,
  self.input_target_name,
  )
+
+ # Order series columns to be same as expected input feature names
+ # and filter to only include features in `X_unstacked`.
+ input_features = list(self.input_feature_names.values())[0]
+ X_unstacked = X_unstacked[
+ [feature for feature in input_features if feature in X_unstacked.columns]
+ ]
+ X_train_unstacked = X_train_unstacked[
+ [
+ feature
+ for feature in input_features
+ if feature in X_train_unstacked.columns
+ ]
+ ]
+ y_overlapping_features = [
+ feature
+ for feature in y_train_unstacked.columns
+ if feature in y_unstacked.columns
+ ]
+ y_unstacked = y_unstacked[y_overlapping_features]
+ y_train_unstacked = y_train_unstacked[y_overlapping_features]
+
+ X_train_unstacked = infer_feature_types(X_train_unstacked)
+ y_train_unstacked = infer_feature_types(y_train_unstacked)
+ X_unstacked = infer_feature_types(X_unstacked)
+ y_unstacked = infer_feature_types(y_unstacked)
+
  unstacked_predictions = super().predict_in_sample(
  X_unstacked,
  y_unstacked,
@@ -133,10 +163,15 @@
  objective,
  calculating_residuals,
  )
- stacked_predictions = stack_data(unstacked_predictions)
+ stacked_predictions = stack_data(
+ unstacked_predictions,
+ include_series_id=include_series_id,
+ series_id_name=self.series_id,
+ )
 
  # Index will start at the unstacked index, so we need to reset it to the original index
  stacked_predictions.index = X.index
+ stacked_predictions = infer_feature_types(stacked_predictions)
  return stacked_predictions
 
  def get_forecast_period(self, X):

evalml/pipelines/time_series_regression_pipeline.py

@@ -225,7 +225,11 @@
  return return_intervals
 
  if self.problem_type == ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION:
- from evalml.pipelines.utils import stack_data, unstack_multiseries
+ from evalml.pipelines.utils import (
+ MULTISERIES_SEPARATOR_SYMBOL,
+ stack_data,
+ unstack_multiseries,
+ )
 
  X, y = unstack_multiseries(
  X,
@@ -268,7 +272,9 @@
  # `pred_intervals` are in {series_id: {coverage_label: bound_value}} form
  for series_id, series_intervals in pred_intervals.items():
  series_id_target_name = (
- self.input_target_name + "_" + str(series_id)
+ self.input_target_name
+ + MULTISERIES_SEPARATOR_SYMBOL
+ + str(series_id)
  )
  series_id_prediction_intervals = _get_series_intervals(
  series_intervals,

evalml/pipelines/utils.py

@@ -76,6 +76,7 @@
 from evalml.utils.gen_utils import contains_all_ts_parameters
 
 DECOMPOSER_PERIOD_CAP = 1000
+MULTISERIES_SEPARATOR_SYMBOL = "|"
 
 
 def _get_label_encoder(X, y, problem_type, estimator_class, sampler_name=None):
@@ -1418,7 +1419,7 @@
  for column_name in full_dataset.columns.drop([time_index, series_id]):
  new_column = single_series[column_name]
  new_column.index = new_time_index
- new_column.name = f"{column_name}_{s_id}"
+ new_column.name = f"{column_name}{MULTISERIES_SEPARATOR_SYMBOL}{s_id}"
 
  if column_name == target_name:
  y_unstacked_cols.append(new_column)
@@ -1435,11 +1436,15 @@
  # Reset the axes now that they've been unstacked, keep time info in X
  X_unstacked = X_unstacked.reset_index()
  y_unstacked = y_unstacked.reset_index(drop=True)
-
  return X_unstacked, y_unstacked
 
 
-def stack_data(data, include_series_id=False, series_id_name=None, starting_index=None):
+def stack_data(
+ data,
+ include_series_id=False,
+ series_id_name=None,
+ starting_index=None,
+):
  """Stacks the given DataFrame back into a single Series, or a DataFrame if include_series_id is True.
 
  Should only be used for data that is expected to be a single series. To stack multiple unstacked columns,
@@ -1464,7 +1469,9 @@
 
  # Extract the original column name
  series_id_with_name = stacked_series.index.droplevel()
- stacked_series.name = "_".join(series_id_with_name[0].split("_")[:-1])
+ stacked_series.name = MULTISERIES_SEPARATOR_SYMBOL.join(
+ series_id_with_name[0].split(MULTISERIES_SEPARATOR_SYMBOL)[:-1],
+ )
 
  # If the index is the time index, keep it
  if not data.index.is_numeric() and starting_index is None:
@@ -1481,11 +1488,14 @@
  # Pull out the series id information, if requested
  if include_series_id:
  series_id_col = pd.Series(
- series_id_with_name.map(lambda col_name: col_name.split("_")[-1]),
+ series_id_with_name.map(
+ lambda col_name: col_name.split(MULTISERIES_SEPARATOR_SYMBOL)[-1],
+ ),
  name=series_id_name or "series_id",
  index=stacked_series.index,
  )
  stacked_series = pd.concat([series_id_col, stacked_series], axis=1)
+
  return stacked_series
 
 
@@ -1511,8 +1521,8 @@
  for col in X.columns:
  if col == time_index:
  continue
- separated_name = col.split("_")
- original_columns.add("_".join(separated_name[:-1]))
+ separated_name = col.split(MULTISERIES_SEPARATOR_SYMBOL)
+ original_columns.add(MULTISERIES_SEPARATOR_SYMBOL.join(separated_name[:-1]))
  series_ids.add(separated_name[-1])
 
  if len(series_ids) == 0:

evalml/tests/component_tests/test_time_series_featurizer.py

@@ -15,6 +15,7 @@
 )
 
 from evalml.pipelines import TimeSeriesFeaturizer
+from evalml.pipelines.utils import MULTISERIES_SEPARATOR_SYMBOL
 
 ROLLING_TRANSFORM_METHOD_NAME = "_compute_rolling_transforms"
 DELAYED_FEATURES_METHOD_NAME = "_compute_delays"
@@ -991,7 +992,9 @@
 
  assert featurizer.statistically_significant_lags == [6]
 
- expected_y_cols = [f"target_{i}_delay_6" for i in range(y.shape[1])]
+ expected_y_cols = [
+ f"target{MULTISERIES_SEPARATOR_SYMBOL}{i}_delay_6" for i in range(y.shape[1])
+ ]
  X_t = featurizer.transform(X, y)
  for expected_y_col in expected_y_cols:
  assert expected_y_col in X_t.columns

evalml/tests/component_tests/test_time_series_imputer.py

@@ -11,6 +11,7 @@
 )
 
 from evalml.pipelines.components import TimeSeriesImputer
+from evalml.pipelines.utils import MULTISERIES_SEPARATOR_SYMBOL
 
 
 def test_invalid_strategy_parameters():
@@ -745,7 +746,12 @@
  _, y_imputed = imputer.transform(X, y)
  assert isinstance(y_imputed, pd.DataFrame)
 
- y_expected = pd.DataFrame({f"target_{i}": range(i, 100, 5) for i in range(5)})
+ y_expected = pd.DataFrame(
+ {
+ f"target{MULTISERIES_SEPARATOR_SYMBOL}{i}": range(i, 100, 5)
+ for i in range(5)
+ },
+ )
  assert_frame_equal(y_imputed, y_expected, check_dtype=False)
 
 
@@ -777,7 +783,10 @@
  _, y_imputed = imputer.transform(X, y)
 
  y_expected = pd.DataFrame(
- {f"target_{i}": range(i, 100, 5) for i in range(num_nan_cols, 5)},
+ {
+ f"target{MULTISERIES_SEPARATOR_SYMBOL}{i}": range(i, 100, 5)
+ for i in range(num_nan_cols, 5)
+ },
  )
  assert_frame_equal(y_imputed, y_expected, check_dtype=False)
 

evalml/tests/conftest.py

@@ -1094,12 +1094,27 @@
 
 @pytest.fixture
 def multiseries_ts_data_unstacked():
- feature_a = pd.DataFrame({f"feature_a_{i}": range(i, 100, 5) for i in range(5)})
+ from evalml.pipelines.utils import MULTISERIES_SEPARATOR_SYMBOL
+
+ feature_a = pd.DataFrame(
+ {
+ f"feature_a{MULTISERIES_SEPARATOR_SYMBOL}{i}": range(i, 100, 5)
+ for i in range(5)
+ },
+ )
  feature_b = pd.DataFrame(
- {f"feature_b_{i}": range(99 - i, -1, -5) for i in range(5)},
+ {
+ f"feature_b{MULTISERIES_SEPARATOR_SYMBOL}{i}": range(99 - i, -1, -5)
+ for i in range(5)
+ },
  )
  X = pd.concat([feature_a, feature_b], axis=1)
- y = pd.DataFrame({f"target_{i}": range(i, 100, 5) for i in range(5)})
+ y = pd.DataFrame(
+ {
+ f"target{MULTISERIES_SEPARATOR_SYMBOL}{i}": range(i, 100, 5)
+ for i in range(5)
+ },
+ )
 
  X["date"] = pd.date_range(start="1/1/2018", periods=20)
  return X, y

evalml/tests/pipeline_tests/regression_pipeline_tests/test_multiseries_regression_pipeline.py

@@ -8,6 +8,7 @@
 from evalml.pipelines import MultiseriesRegressionPipeline
 from evalml.pipelines.utils import unstack_multiseries
 from evalml.preprocessing import split_multiseries_data
+from evalml.utils import infer_feature_types
 
 
 @pytest.fixture(scope="module")
@@ -90,7 +91,9 @@
  assert pipeline.frequency is not None
 
 
+@pytest.mark.parametrize("include_series_id", [True, False])
 def test_multiseries_pipeline_predict_in_sample(
+ include_series_id,
  multiseries_ts_data_stacked,
  component_graph,
  pipeline_parameters,
@@ -111,14 +114,20 @@
  y_holdout,
  X_train=X_train,
  y_train=y_train,
+ include_series_id=include_series_id,
  )
  expected = pd.Series(
  range(55, 65),
  index=range(90, 100),
  name="target",
  dtype="float64",
  )
- pd.testing.assert_series_equal(y_pred, expected)
+ if include_series_id:
+ expected = pd.concat([X_holdout["series_id"], expected], axis=1)
+ expected = infer_feature_types(expected)
+ pd.testing.assert_frame_equal(y_pred, expected)
+ else:
+ pd.testing.assert_series_equal(y_pred, expected)
 
 
 @pytest.mark.parametrize("forecast_horizon", [1, 7])

evalml/tests/pipeline_tests/test_pipeline_utils.py

@@ -43,6 +43,7 @@
  handle_component_class,
 )
 from evalml.pipelines.utils import (
+ MULTISERIES_SEPARATOR_SYMBOL,
  _get_pipeline_base_class,
  _get_preprocessing_components,
  _make_pipeline_from_multiple_graphs,
@@ -1404,7 +1405,8 @@ def test_unstack_multiseries(
  X_unstacked, y_unstacked = multiseries_ts_data_unstacked
  y.name = target_name
  y_unstacked.columns = [
- f"{target_name}_{i}" for i in range(len(y_unstacked.columns))
+ f"{target_name}{MULTISERIES_SEPARATOR_SYMBOL}{i}"
+ for i in range(len(y_unstacked.columns))
  ]
 
  X_unstacked_transformed, y_unstacked_transformed = unstack_multiseries(