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Added support for additional estimators for multiseries datasets #4385

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Jan 31, 2024
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Added support for additional estimators for multiseries datasets #4385

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8ecc59b
Initial commit
christopherbunn Jan 9, 2024
3f0fb8a
Updated tests
christopherbunn Jan 26, 2024
ed7e0c9
Added addtional drop nan test case
christopherbunn Jan 26, 2024
382cec2
Updated release notes
christopherbunn Jan 26, 2024
443f7a5
Reverted series ID name
christopherbunn Jan 29, 2024
fa3dec8
Moved infer feature types
christopherbunn Jan 29, 2024
b7b6bf9
Added clarifying comments and updated test
christopherbunn Jan 30, 2024
6f765bb
Consolidated code and added additional clarifying comments
christopherbunn Jan 31, 2024
e49dfa5
Code cleanup
christopherbunn Jan 31, 2024
701eef6
Added support for ndarrays for featurizer
christopherbunn Jan 31, 2024
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1 change: 1 addition & 0 deletions docs/source/release_notes.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@ Release Notes
-------------
**Future Releases**
* Enhancements
* Added support for additional estimators for multiseries datasets :pr:`4385`
* Fixes
* Fixed bug in `_downcast_nullable_y` causing woodwork initialization issues :pr:`4369`
* Fixed multiseries prediction interval labels :pr:`4377`
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2 changes: 2 additions & 0 deletions evalml/pipelines/components/estimators/regressors/catboost_regressor.py
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Expand Up @@ -47,10 +47,12 @@ class CatBoostRegressor(Estimator):
supported_problem_types = [
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]
"""[
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]"""

def __init__(
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2 changes: 2 additions & 0 deletions evalml/pipelines/components/estimators/regressors/decision_tree_regressor.py
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Expand Up @@ -55,10 +55,12 @@ class DecisionTreeRegressor(Estimator):
supported_problem_types = [
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]
"""[
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]"""

def __init__(
Expand Down
2 changes: 2 additions & 0 deletions evalml/pipelines/components/estimators/regressors/elasticnet_regressor.py
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Expand Up @@ -33,10 +33,12 @@ class ElasticNetRegressor(Estimator):
supported_problem_types = [
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]
"""[
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]"""

def __init__(
Expand Down
2 changes: 2 additions & 0 deletions evalml/pipelines/components/estimators/regressors/et_regressor.py
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Expand Up @@ -56,10 +56,12 @@ class ExtraTreesRegressor(Estimator):
supported_problem_types = [
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]
"""[
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]"""

def __init__(
Expand Down
5 changes: 4 additions & 1 deletion evalml/pipelines/components/estimators/regressors/lightgbm_regressor.py
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Expand Up @@ -68,7 +68,10 @@ class LightGBMRegressor(Estimator):
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
]
"""[ProblemTypes.REGRESSION]"""
"""[
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
]"""

SEED_MIN = 0
SEED_MAX = SEED_BOUNDS.max_bound
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2 changes: 2 additions & 0 deletions evalml/pipelines/components/estimators/regressors/linear_regressor.py
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Expand Up @@ -28,10 +28,12 @@ class LinearRegressor(Estimator):
supported_problem_types = [
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]
"""[
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]"""

def __init__(self, fit_intercept=True, n_jobs=-1, random_seed=0, **kwargs):
Expand Down
2 changes: 2 additions & 0 deletions evalml/pipelines/components/estimators/regressors/rf_regressor.py
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Expand Up @@ -37,10 +37,12 @@ class RandomForestRegressor(Estimator):
supported_problem_types = [
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]
"""[
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]"""

def __init__(
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2 changes: 2 additions & 0 deletions evalml/pipelines/components/estimators/regressors/xgboost_regressor.py
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Expand Up @@ -40,10 +40,12 @@ class XGBoostRegressor(Estimator):
supported_problem_types = [
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]
"""[
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
]"""

# xgboost supports seeds from -2**31 to 2**31 - 1 inclusive. these limits ensure the random seed generated below
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20 changes: 17 additions & 3 deletions evalml/pipelines/components/transformers/preprocessing/drop_nan_rows_transformer.py
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@@ -1,4 +1,5 @@
"""Transformer to drop rows specified by row indices."""
import pandas as pd
from woodwork import init_series

from evalml.pipelines.components.transformers import Transformer
Expand Down Expand Up @@ -43,12 +44,25 @@ def transform(self, X, y=None):
y_t = infer_feature_types(y) if y is not None else None

X_t_schema = X_t.ww.schema
y_t_logical = None
y_t_semantic = None
if y_t is not None:
y_t_logical = y_t.ww.logical_type
y_t_semantic = y_t.ww.semantic_tags
if isinstance(y_t, pd.DataFrame):
y_t_logical = y_t.ww.logical_types
y_t_semantic = y_t.ww.semantic_tags
else:
y_t_logical = y_t.ww.logical_type
y_t_semantic = y_t.ww.semantic_tags
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X_t, y_t = drop_rows_with_nans(X_t, y_t)
X_t.ww.init_with_full_schema(X_t_schema)
if y_t is not None:
y_t = init_series(y_t, logical_type=y_t_logical, semantic_tags=y_t_semantic)
if isinstance(y_t, pd.DataFrame):
y_t.ww.init(logical_types=y_t_logical, semantic_tags=y_t_semantic)
else:
y_t = init_series(
y_t,
logical_type=y_t_logical,
semantic_tags=y_t_semantic,
)
return X_t, y_t
31 changes: 29 additions & 2 deletions evalml/pipelines/components/transformers/preprocessing/time_series_featurizer.py
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Expand Up @@ -129,7 +129,16 @@ def fit(self, X, y=None):

# For the multiseries case, where we only want the start delay lag for the baseline
if isinstance(y, pd.DataFrame):
self.statistically_significant_lags = [self.start_delay]
self.statistically_significant_lags = {}
for column in y.columns:
self.statistically_significant_lags[
column
] = self._find_significant_lags(
y[column],
conf_level=self.conf_level,
start_delay=self.start_delay,
max_delay=self.max_delay,
)
Comment on lines +141 to +150
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We can make this section more concise/easier to maintain by folding the single series case into the multiseries case by converting the single series to a dataframe and keeping this code for both cases - following the pattern in other files that already support multiseries (stl decomposer might be a good example?)

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@christopherbunn christopherbunn Jan 30, 2024 •
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Good point, I just consolidated it.

Actually I just remembered that it's structured this was so that we're still able to run self._find_significant_lags even when y is None. Is there a way you had in mind to structure it so that y can still be None?

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Hm, seems like y being None is something we'd want to have explicit behavior for, since right now the behavior is unclear. I think we should just handle it entirely separately

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We handle the y being null in self._find_significant_lags since we calculate all lags in that function (and just set significant lags to be all_lags if y is None). Should I put it off into it's own separate branch, even though the code would be identical to the case where y is a series? e.g.

        # For the multiseries case, each series ID has individualized lag values
        if isinstance(y, pd.DataFrame):
            self.statistically_significant_lags = {}
            for column in y.columns:
                self.statistically_significant_lags[
                    column
                ] = self._find_significant_lags(
                    y[column],
                    conf_level=self.conf_level,
                    start_delay=self.start_delay,
                    max_delay=self.max_delay,
                )
        elif y is None:
            self.statistically_significant_lags = self._find_significant_lags(
                y,
                conf_level=self.conf_level,
                start_delay=self.start_delay,
                max_delay=self.max_delay,
            )
        else:
            self.statistically_significant_lags = self._find_significant_lags(
                y,
                conf_level=self.conf_level,
                start_delay=self.start_delay,
                max_delay=self.max_delay,
            )

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Ok, sorry for drilling into this so much, but I think I understand now. My new potentially hot take proposal is something like:

if y is None:
    self.statistically_significant_lags = np.arange(self.start_delay, self.start_delay +self. max_delay + 1)
else:
    if isinstance(y, pd.Series): 
        y = y.to_frame()
    for column in y.columns:
        self.statistically_significant_lags = ...

And then we can remove the handling of y being None from the static function. My argument for doing this is that calling all lags the statistically significant lags is a misnomer, since we didn't actually check statistical significance. This is me getting very into the weeds though, so I very much understand if you would rather keep things closer to the way they are 😅

Regardless, even with your new proposal, we'd still be able to combine the two non y=None cases by casting the series to a dataframe

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@christopherbunn christopherbunn Jan 31, 2024 •
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Your example makes sense to me, I don't see our behavior for y is None changing anytime soon so I'm comfortable with pulling that out and changing the function. Will update!

else:
self.statistically_significant_lags = self._find_significant_lags(
y,
Expand Down Expand Up @@ -234,7 +243,25 @@ def _delay_df(
col = data[col_name]
if categorical_columns and col_name in categorical_columns:
col = X_categorical[col_name]
for t in self.statistically_significant_lags:
# Lags are stored in a dict for multiseries problems
# Returns the lags corresponding to the series ID value
if isinstance(self.statistically_significant_lags, dict):
from evalml.pipelines.utils import MULTISERIES_SEPARATOR_SYMBOL

col_series_id = (
MULTISERIES_SEPARATOR_SYMBOL
+ col_name.split(MULTISERIES_SEPARATOR_SYMBOL)[-1]
)
for (
series_id_target_name,
lag_list,
) in self.statistically_significant_lags.items():
if series_id_target_name.endswith(col_series_id):
lags = lag_list
break
else:
lags = self.statistically_significant_lags
for t in lags:
lagged_features[self.df_colname_prefix.format(col_name, t)] = col.shift(
t,
)
Expand Down
41 changes: 35 additions & 6 deletions evalml/pipelines/multiseries_regression_pipeline.py
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Expand Up @@ -83,6 +83,7 @@ def _fit(self, X, y):

self.component_graph.fit(X_unstacked, y_unstacked)
self.input_feature_names = self.component_graph.input_feature_names
self.series_id_target_names = y_unstacked.columns

def predict_in_sample(
self,
Expand Down Expand Up @@ -144,7 +145,7 @@ def predict_in_sample(
]
y_overlapping_features = [
feature
for feature in y_train_unstacked.columns
for feature in self.series_id_target_names
if feature in y_unstacked.columns
]
y_unstacked = y_unstacked[y_overlapping_features]
Expand All @@ -154,7 +155,6 @@ def predict_in_sample(
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,
Expand All @@ -163,16 +163,45 @@ def predict_in_sample(
objective,
calculating_residuals,
)
unstacked_predictions = unstacked_predictions[
[
series_id_target
for series_id_target in y_train_unstacked.columns
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if series_id_target in unstacked_predictions.columns
]
]
unstacked_predictions.index = X_unstacked[self.time_index]
stacked_predictions = stack_data(
unstacked_predictions,
include_series_id=include_series_id,
include_series_id=True,
series_id_name=self.series_id,
)

stacked_predictions = stacked_predictions.reset_index()
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What's the reasoning behind setting the index and then immediately resetting the index? The value of the index shouldn't impact the order of stacking, right?

Either way, we can explicitly control the index in stack_data with the starting_index argument

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The goal of this snippet is to set the index as the time index column, stack the data (and thus using the dates in the time index column to generate new stacked dates) and then resetting the index so that the resulting time index column can be used when we pd.merge later on in line 193.

While it's possible to just copy over the time_index column from X after stacking, I think it's safer to just generate it from the X_unstacked index like this as we know for sure that the X_unstacked time_index aligns with the unstacked_predictions whereas it's technically possible to have an X time_index that's out of order (and thus would be incorrect if we simply copied over this column). I'm open to suggestions for a cleaner implementation!

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Ok, I think I understand now! I think a comment would be great. I also wonder if it would be useful to explicitly say reset_index(drop=False), so that even if pandas changes their defaults we don't get screwed.

My motivation here is that this is something that might be confusing to someone looking back at it in the future, since the goal isn't clear from the code itself. I hope that makes sense!

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Good call on the reset index parameter, I'll add that in. I'll add a clarifying comment or two so that it's clear what's going on here.

Your motivation makes sense! I feel like I've been so lost in the weeds of this implementation for a while now so it's good to have multiple pairs of eyes on this to highlight what's intuitive and what isn't 😅

sp_dtypes = {
self.time_index: X[self.time_index].dtype,
self.series_id: X[self.series_id].dtype,
self.input_target_name: y.dtype,
}
stacked_predictions = stacked_predictions.astype(sp_dtypes)

# Order prediction based on input (date, series_id)
output_cols = (
[self.series_id, self.input_target_name]
if include_series_id
else [self.input_target_name]
)
stacked_predictions = pd.merge(
X,
stacked_predictions,
on=[self.time_index, self.series_id],
)[output_cols]
# 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

if not include_series_id:
return infer_feature_types(stacked_predictions[self.input_target_name])
else:
return infer_feature_types(stacked_predictions)

def get_forecast_period(self, X):
"""Generates all possible forecasting time points based on latest data point in X.
Expand Down
25 changes: 13 additions & 12 deletions evalml/pipelines/utils.py
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Expand Up @@ -132,6 +132,7 @@ def _get_datetime(X, y, problem_type, estimator_class, sampler_name=None):
if add_datetime_featurizer and estimator_class.model_family not in [
ModelFamily.ARIMA,
ModelFamily.PROPHET,
ModelFamily.VARMAX,
]:
components.append(DateTimeFeaturizer)
return components
Expand Down Expand Up @@ -298,13 +299,7 @@ def _get_preprocessing_components(
Returns:
list[Transformer]: A list of applicable preprocessing components to use with the estimator.
"""
if is_multiseries(problem_type):
if include_decomposer:
components_functions = [_get_decomposer]
else:
return []

elif is_time_series(problem_type):
if is_time_series(problem_type):
components_functions = [
_get_label_encoder,
_get_drop_all_null,
Expand Down Expand Up @@ -1508,22 +1503,28 @@ def stack_X(X, series_id_name, time_index, starting_index=None, series_id_values
time_index (str): The name of the time index column.
starting_index (int): The starting index to use for the stacked DataFrame. If None, the starting index
will match that of the input data. Defaults to None.
series_id_values (set, list): The unique values of a series ID, used to generate the index. If None, values will
series_id_values (list): The unique values of a series ID, used to generate the index. If None, values will
be generated from X column values. Required if X only has time index values and no exogenous values.
Defaults to None.

Returns:
pd.DataFrame: The restacked features.
"""
original_columns = set()
series_ids = series_id_values or set()
if series_id_values is None:
if series_id_values is not None:
series_ids = series_id_values
else:
# Using list to maintain order (vs. a set)
series_ids = list()
for col in X.columns:
if col == time_index:
continue
separated_name = col.split(MULTISERIES_SEPARATOR_SYMBOL)
original_columns.add(MULTISERIES_SEPARATOR_SYMBOL.join(separated_name[:-1]))
series_ids.add(separated_name[-1])
series_ids.append(separated_name[-1])
# Remove duplicates
seen = set()
series_ids = [val for val in series_ids if not (val in seen or seen.add(val))]
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if len(series_ids) == 0:
raise ValueError(
Expand All @@ -1542,7 +1543,7 @@ def stack_X(X, series_id_name, time_index, starting_index=None, series_id_values
restacked_X = pd.DataFrame(
{
time_index: time_index_col,
series_id_name: sorted(list(series_ids)) * len(X),
series_id_name: list(series_ids) * len(X),
},
index=stacked_index,
)
Expand Down
2 changes: 1 addition & 1 deletion evalml/preprocessing/utils.py
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Expand Up @@ -79,7 +79,7 @@ def split_multiseries_data(X, y, series_id, time_index, **kwargs):

# Get unique series value from X if there is only the time_index column
# Otherwise, this information is generated in `stack_X` from the column values
series_id_values = set(X[series_id]) if len(X_unstacked.columns) == 1 else None
series_id_values = X[series_id].unique() if len(X_unstacked.columns) == 1 else None
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X_train = stack_X(
X_train_unstacked,
Expand Down
6 changes: 3 additions & 3 deletions evalml/tests/automl_tests/test_default_algorithm.py
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Expand Up @@ -670,7 +670,7 @@
)

first_batch = algo.next_batch()
assert len(first_batch) == 2
assert len(first_batch) == 8

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pipeline = first_batch[0]
assert pipeline.model_family == ModelFamily.VARMAX
assert pipeline.parameters["pipeline"] == search_parameters["pipeline"]
Expand All @@ -679,8 +679,8 @@

long_explore = algo.next_batch()
long_estimators = set([pipeline.estimator.name for pipeline in long_explore])
assert len(long_explore) == 100
assert len(long_estimators) == 1
assert len(long_explore) == 300
assert len(long_estimators) == 3

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@pytest.mark.parametrize(
Expand Down
8 changes: 4 additions & 4 deletions evalml/tests/automl_tests/test_iterative_algorithm.py
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Expand Up @@ -18,7 +18,6 @@
DateTimeFeaturizer,
EmailFeaturizer,
NaturalLanguageFeaturizer,
STLDecomposer,
TimeSeriesFeaturizer,
URLFeaturizer,
)
Expand Down Expand Up @@ -98,19 +97,20 @@
assert algo.batch_number == 0
assert algo.default_max_batches == 1
estimators = get_estimators(problem_type)
decomposer = [STLDecomposer] if is_regression(problem_type) else []
decomposer = [True, False] if is_regression(problem_type) else [True]

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Can you add a comment clarifying why you're using true false instead of the decomposer name?

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I think it would be better to just parametrize the include_decomposer argument here - this and the below section are confusing to read out of context

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For this test, we are basically only checking the number of pipelines matches up. Before, we only needed to add the decomposer one once since there was one estimator type (VARMAX).

Now, since we have multiple estimator types, each estimator type will have one pipeline with a decomposer and another without a decomposer. As such, we need to have this [True, False] and to iterate through it in order to generate the correct number of pipelines.

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I think a clarifying comment would be useful here 👍

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Added a short one to this test

assert len(algo.allowed_pipelines) == len(
[
make_pipeline(
X,
y,
estimator,
problem_type,
include_decomposer=include_decomposer,
parameters=search_parameters,
)
for estimator in estimators
]
+ decomposer,
for include_decomposer in decomposer
],
)


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1 change: 1 addition & 0 deletions evalml/tests/component_tests/test_decision_tree_regressor.py
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Expand Up @@ -14,6 +14,7 @@ def test_problem_types():
assert set(DecisionTreeRegressor.supported_problem_types) == {
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION,
}


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