import pandas as pd
import woodwork as ww
from evalml.pipelines import PipelineBase
from evalml.pipelines.pipeline_meta import PipelineBaseMeta
from evalml.utils import drop_rows_with_nans, infer_feature_types
[docs]class TimeSeriesPipelineBase(PipelineBase, metaclass=PipelineBaseMeta):
"""Pipeline base class for time series problems.
Arguments:
component_graph (list or dict): List of components in order. Accepts strings or ComponentBase subclasses in the list.
Note that when duplicate components are specified in a list, the duplicate component names will be modified with the
component's index in the list. For example, the component graph
[Imputer, One Hot Encoder, Imputer, Logistic Regression Classifier] will have names
["Imputer", "One Hot Encoder", "Imputer_2", "Logistic Regression Classifier"]
parameters (dict): Dictionary with component names as keys and dictionary of that component's parameters as values.
An empty dictionary {} implies using all default values for component parameters. Pipeline-level
parameters such as date_index, gap, and max_delay must be specified with the "pipeline" key. For example:
Pipeline(parameters={"pipeline": {"date_index": "Date", "max_delay": 4, "gap": 2}}).
random_seed (int): Seed for the random number generator. Defaults to 0.
"""
def __init__(
self,
component_graph,
parameters=None,
custom_name=None,
random_seed=0,
):
if not parameters or "pipeline" not in parameters:
raise ValueError(
"date_index, gap, and max_delay parameters cannot be omitted from the parameters dict. "
"Please specify them as a dictionary with the key 'pipeline'."
)
pipeline_params = parameters["pipeline"]
self.date_index = pipeline_params["date_index"]
self.gap = pipeline_params["gap"]
self.max_delay = pipeline_params["max_delay"]
self.forecast_horizon = pipeline_params["forecast_horizon"]
super().__init__(
component_graph,
custom_name=custom_name,
parameters=parameters,
random_seed=random_seed,
)
@staticmethod
def _convert_to_woodwork(X, y):
if X is None:
X = pd.DataFrame()
X = infer_feature_types(X)
y = infer_feature_types(y)
return X, y
[docs] def fit(self, X, y):
"""Fit a time series pipeline.
Arguments:
X (pd.DataFrame or np.ndarray): The input training data of shape [n_samples, n_features].
y (pd.Series, np.ndarray): The target training targets of length [n_samples].
Returns:
self
"""
X, y = self._convert_to_woodwork(X, y)
self._fit(X, y)
return self
@staticmethod
def _move_index_forward(index, gap):
"""Fill in the index of the gap features and values with the right values."""
if isinstance(index, (pd.DatetimeIndex, pd.PeriodIndex, pd.TimedeltaIndex)):
return index.shift(gap)
else:
return index + gap
@staticmethod
def _are_datasets_separated_by_gap(train_index, test_index, gap):
"""Determine if the train and test datasets are separated by gap number of units.
This will be true when users are predicting on unseen data but not during cross
validation since the target is known.
"""
gap_difference = gap + 1
index_difference = test_index[0] - train_index[-1]
if isinstance(
train_index, (pd.DatetimeIndex, pd.PeriodIndex, pd.TimedeltaIndex)
):
gap_difference *= test_index.freq
return index_difference == gap_difference
def _add_training_data_to_X_Y(self, X, y, X_train, y_train):
"""Append the training data to the holdout data.
Need to do this so that we have all the data we need to compute lagged features on the holdout set.
"""
last_row_of_training = self.forecast_horizon + self.max_delay + self.gap
gap_features = pd.DataFrame()
gap_target = pd.Series()
if (
self._are_datasets_separated_by_gap(X_train.index, X.index, self.gap)
and self.gap
):
# The training data does not have the gap dates so don't need to include them
last_row_of_training -= self.gap
# Instead, we'll create some dummy data to represent the missing gap dates
# These do not show up in the features used for prediction
gap_features = X_train.iloc[[-1] * self.gap]
gap_features.index = self._move_index_forward(
X_train.index[-self.gap :], self.gap
)
gap_target = y_train.iloc[[-1] * self.gap]
gap_target.index = self._move_index_forward(
y_train.index[-self.gap :], self.gap
)
features_to_concat = [
X_train.iloc[-last_row_of_training:],
gap_features,
X,
]
targets_to_concat = [
y_train.iloc[-last_row_of_training:],
gap_target,
y,
]
padded_features = pd.concat(features_to_concat, axis=0)
padded_target = pd.concat(targets_to_concat, axis=0)
padded_features.ww.init(schema=X_train.ww.schema)
padded_target = ww.init_series(
padded_target, logical_type=y_train.ww.logical_type
)
return padded_features, padded_target
[docs] def compute_estimator_features(self, X, y=None, X_train=None, y_train=None):
"""Transforms the data by applying all pre-processing components.
Arguments:
X (pd.DataFrame): Input data to the pipeline to transform.
y (pd.Series): Targets corresponding to the pipeline targets.
X_train (pd.DataFrame): Training data used to generate generates from past observations.
y_train (pd.Series): Training targets used to generate features from past observations.
Returns:
pd.DataFrame: New transformed features.
"""
if y_train is None:
y_train = pd.Series()
X_train, y_train = self._convert_to_woodwork(X_train, y_train)
X, y = self._convert_to_woodwork(X, y)
empty_training_data = X_train.empty or y_train.empty
if empty_training_data:
features_holdout = super().compute_estimator_features(X, y)
else:
padded_features, padded_target = self._add_training_data_to_X_Y(
X, y, X_train, y_train
)
features = super().compute_estimator_features(
padded_features, padded_target
)
features_holdout = features.iloc[-len(y) :]
return features_holdout
[docs] def predict_in_sample(self, X, y, X_train, y_train, objective=None):
"""Predict on future data where the target is known, e.g. cross validation.
Arguments:
X_holdout (pd.DataFrame or np.ndarray): Future data of shape [n_samples, n_features]
y_holdout (pd.Series, np.ndarray): Future target of shape [n_samples]
X_train (pd.DataFrame, np.ndarray): Data the pipeline was trained on of shape [n_samples_train, n_feautures]
y_train (pd.Series, np.ndarray): Targets used to train the pipeline of shape [n_samples_train]
objective (ObjectiveBase, str, None): Objective used to threshold predicted probabilities, optional.
Returns:
pd.Series: Estimated labels
"""
if self.estimator is None:
raise ValueError(
"Cannot call predict_in_sample() on a component graph because the final component is not an Estimator."
)
target = infer_feature_types(y)
features = self.compute_estimator_features(X, target, X_train, y_train)
predictions = self._estimator_predict(features, target)
predictions.index = y.index
predictions = self.inverse_transform(predictions)
predictions = predictions.rename(self.input_target_name)
return infer_feature_types(predictions)
def _create_empty_series(self, y_train):
return ww.init_series(
pd.Series([y_train.iloc[0]] * self.forecast_horizon),
logical_type=y_train.ww.logical_type,
)
[docs] def predict(self, X, objective=None, X_train=None, y_train=None):
"""Predict on future data where target is not known.
Arguments:
X (pd.DataFrame, or np.ndarray): Data of shape [n_samples, n_features].
objective (str, ObjectiveBase): Used in classification problems to threshold the predictions.
objective (Object or string): The objective to use to make predictions.
X_train (pd.DataFrame or np.ndarray or None): Training data. Ignored. Only used for time series.
y_train (pd.Series or None): Training labels. Ignored. Only used for time series.
"""
X_train, y_train = self._convert_to_woodwork(X_train, y_train)
if self.estimator is None:
raise ValueError(
"Cannot call predict() on a component graph because the final component is not an Estimator."
)
y_holdout = self._create_empty_series(y_train)
X, y_holdout = self._convert_to_woodwork(X, y_holdout)
y_holdout.index = X.index
return self.predict_in_sample(
X, y_holdout, X_train, y_train, objective=objective
)
def _fit(self, X, y):
self.input_target_name = y.name
X_t = self.component_graph.fit_features(X, y)
X_t, y_shifted = drop_rows_with_nans(X_t, y)
if self.estimator is not None:
self.estimator.fit(X_t, y_shifted)
else:
self.component_graph.get_last_component().fit(X_t, y)
self.input_feature_names = self.component_graph.input_feature_names
def _estimator_predict(self, features, y):
"""Get estimator predictions.
This helper passes y as an argument if needed by the estimator.
"""
y_arg = None
if self.estimator.predict_uses_y:
y_arg = y
return self.estimator.predict(features, y=y_arg)