Add STLDecomposer to multiseries pipelines

docs/source/release_notes.rst

@@ -5,6 +5,7 @@ Release Notes
  * Extended STLDecomposer to Support Multiseries :pr:`4253`
  * Extended TimeSeriesImputer to handle multiseries :pr:`4291`
  * Added datacheck to check for mismatched series length in multiseries :pr:`4296`
+ * Added STLDecomposer to multiseries pipelines :pr:`4299`
  * Fixes
  * Changes
  * Documentation Changes

evalml/pipelines/component_graph.py

@@ -802,14 +802,16 @@ def graph(self, name=None, graph_format=None):
  for component_name, component_class in self.component_instances.items():
  label = "%s\l" % (component_name) # noqa: W605
  if isinstance(component_class, ComponentBase):
+ # Reformat labels for nodes: cast values as strings, reformat floats to 2 decimal points and remove brackets from dictionary values so Digraph can parse it
  parameters = "\\l".join(
  [
  key + " : " + "{:0.2f}".format(val)
  if (isinstance(val, float))
- else key + " : " + str(val)
+ else key + " : " + str(val).replace("{", "").replace("}", "")
  for key, val in component_class.parameters.items()
  ],
  ) # noqa: W605
+
  label = "%s |%s\l" % (component_name, parameters) # noqa: W605
  graph.node(component_name, shape="record", label=label, nodesep="0.03")
 

evalml/pipelines/components/transformers/preprocessing/stl_decomposer.py

@@ -442,6 +442,7 @@ def inverse_transform(
  y.append(y_series)
  y_df = pd.DataFrame(y).T
  y_df.index = original_index
+ y_df.columns = y_t.columns
  return y_df
 
  def get_trend_dataframe(self, X, y):

evalml/pipelines/time_series_pipeline_base.py

@@ -265,6 +265,12 @@ def predict_in_sample(
  calculating_residuals=calculating_residuals,
  )
  predictions = self._estimator_predict(features)
+ if isinstance(predictions, pd.Series):
+ predictions = predictions.rename(self.input_target_name)
+ elif isinstance(predictions, pd.DataFrame):
+ predictions = predictions.ww.rename(
+ dict(zip(predictions.columns, y.columns)),
+ )
  if len(predictions) == len(y):
  predictions.index = y.index
  predictions = self.inverse_transform(predictions)

evalml/pipelines/utils.py

@@ -233,21 +233,27 @@
 def _get_decomposer(X, y, problem_type, estimator_class, sampler_name=None):
  components = []
  if is_time_series(problem_type) and is_regression(problem_type):
- time_index = get_time_index(X, y, None)
- # If the time index frequency is uninferrable, STL will fail
- if time_index.freq is None:
- return components
- freq = time_index.freq.name
- if STLDecomposer.is_freq_valid(freq):
- # Make sure there's a seasonal period
- order = 3 if "Q" in freq else 5
- seasonal_period = STLDecomposer.determine_periodicity(
- X,
- y,
- rel_max_order=order,
- )
- if seasonal_period is not None and seasonal_period <= DECOMPOSER_PERIOD_CAP:
- components.append(STLDecomposer)
+ if is_multiseries(problem_type):
+ components.append(STLDecomposer)
+ else:
+ time_index = get_time_index(X, y, None)
+ # If the time index frequency is uninferrable, STL will fail
+ if time_index.freq is None:
+ return components
+ freq = time_index.freq.name
+ if STLDecomposer.is_freq_valid(freq):
+ # Make sure there's a seasonal period
+ order = 3 if "Q" in freq else 5
+ seasonal_period = STLDecomposer.determine_periodicity(
+ X,
+ y,
+ rel_max_order=order,
+ )
+ if (
+ seasonal_period is not None
+ and seasonal_period <= DECOMPOSER_PERIOD_CAP
+ ):
+ components.append(STLDecomposer)
  return components
 
 
@@ -292,9 +298,12 @@
  list[Transformer]: A list of applicable preprocessing components to use with the estimator.
  """
  if is_multiseries(problem_type):
- return []
+ if include_decomposer:
+ components_functions = [_get_decomposer]
+ else:
+ return []
 
- if is_time_series(problem_type):
+ elif is_time_series(problem_type):
  components_functions = [
  _get_label_encoder,
  _get_drop_all_null,

evalml/tests/automl_tests/test_default_algorithm.py

@@ -670,7 +670,7 @@
  )
 
  first_batch = algo.next_batch()
- assert len(first_batch) == 1
+ assert len(first_batch) == 2
  pipeline = first_batch[0]
  assert pipeline.model_family == ModelFamily.VARMAX
  assert pipeline.parameters["pipeline"] == search_parameters["pipeline"]
@@ -679,7 +679,7 @@
 
  long_explore = algo.next_batch()
  long_estimators = set([pipeline.estimator.name for pipeline in long_explore])
- assert len(long_explore) == 50
+ assert len(long_explore) == 100
  assert len(long_estimators) == 1
 
 

evalml/tests/automl_tests/test_iterative_algorithm.py

@@ -18,11 +18,12 @@
  DateTimeFeaturizer,
  EmailFeaturizer,
  NaturalLanguageFeaturizer,
+ STLDecomposer,
  TimeSeriesFeaturizer,
  URLFeaturizer,
 )
 from evalml.pipelines.components.utils import get_estimators
-from evalml.pipelines.utils import make_pipeline
+from evalml.pipelines.utils import is_regression, make_pipeline
 from evalml.problem_types import ProblemTypes, is_multiseries, is_time_series
 
 
@@ -97,6 +98,7 @@
  assert algo.batch_number == 0
  assert algo.default_max_batches == 1
  estimators = get_estimators(problem_type)
+ decomposer = [STLDecomposer] if is_regression(problem_type) else []
  assert len(algo.allowed_pipelines) == len(
  [
  make_pipeline(
@@ -107,7 +109,8 @@
  parameters=search_parameters,
  )
  for estimator in estimators
- ],
+ ]
+ + decomposer,
  )
 
 

evalml/tests/pipeline_tests/test_pipeline_utils.py

@@ -170,7 +170,7 @@ def test_make_pipeline(
 
  if is_time_series(problem_type):
  if is_multiseries(problem_type):
- expected_components = dfs + [estimator_class]
+ expected_components = dfs + decomposer + [estimator_class]
  else:
  expected_components = (
  dfs