"""Pipeline base class for time series regression problems."""
import pandas as pd
from woodwork.statistics_utils import infer_frequency
from evalml.pipelines.time_series_regression_pipeline import (
TimeSeriesRegressionPipeline,
)
from evalml.problem_types import ProblemTypes
from evalml.utils import infer_feature_types
[docs]class MultiseriesRegressionPipeline(TimeSeriesRegressionPipeline):
"""Pipeline base class for multiseries time series regression problems.
Args:
component_graph (ComponentGraph, list, dict): ComponentGraph instance, list of components in order, or dictionary of components.
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 time_index, gap, and max_delay must be specified with the "pipeline" key. For example:
Pipeline(parameters={"pipeline": {"time_index": "Date", "max_delay": 4, "gap": 2}}).
custom_name (str): Custom name for the pipeline. Defaults to None.
random_seed (int): Seed for the random number generator. Defaults to 0.
"""
problem_type = ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION
"""ProblemTypes.MULTISERIES_TIME_SERIES_REGRESSION"""
def __init__(
self,
component_graph,
parameters=None,
custom_name=None,
random_seed=0,
):
if not parameters or "pipeline" not in parameters:
raise ValueError(
"time_index, gap, max_delay, and forecast_horizon parameters cannot be omitted from the parameters dict. "
"Please specify them as a dictionary with the key 'pipeline'.",
)
if "series_id" not in parameters["pipeline"]:
raise ValueError(
"series_id must be defined for multiseries time series pipelines. Please specify it as a key in the pipeline "
"parameters dict.",
)
self.series_id = parameters["pipeline"]["series_id"]
super().__init__(
component_graph,
custom_name=custom_name,
parameters=parameters,
random_seed=random_seed,
)
[docs] def fit(self, X, y):
"""Fit a multiseries time series pipeline.
Args:
X (pd.DataFrame): The input training data of shape [n_samples, n_features].
y (pd.Series): The target training targets of length [n_samples*n_series].
Returns:
self
Raises:
ValueError: If the target is not numeric.
"""
self._fit(X, y)
return self
def _fit(self, X, y):
from evalml.pipelines.utils import unstack_multiseries
self.input_target_name = y.name
X_unstacked, y_unstacked = unstack_multiseries(
X,
y,
self.series_id,
self.time_index,
self.input_target_name,
)
self.frequency = infer_frequency(X_unstacked[self.time_index])
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
[docs] def predict_in_sample(
self,
X,
y,
X_train,
y_train,
objective=None,
calculating_residuals=False,
include_series_id=False,
):
"""Predict on future data where the target is known, e.g. cross validation.
Args:
X (pd.DataFrame or np.ndarray): Future data of shape [n_samples, n_features]
y (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.
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.
Raises:
ValueError: If final component is not an Estimator.
"""
from evalml.pipelines.utils import stack_data, unstack_multiseries
X_unstacked, y_unstacked = unstack_multiseries(
X,
y,
self.series_id,
self.time_index,
self.input_target_name,
)
X_train_unstacked, y_train_unstacked = unstack_multiseries(
X_train,
y_train,
self.series_id,
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 self.series_id_target_names
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,
X_train_unstacked,
y_train_unstacked,
objective,
calculating_residuals,
)
unstacked_predictions = unstacked_predictions[
[
series_id_target
for series_id_target in self.series_id_target_names
if series_id_target in unstacked_predictions.columns
]
]
# Add `time_index` column to index for generating stacked datetime column in `stack_data()`
unstacked_predictions.index = X_unstacked[self.time_index]
stacked_predictions = stack_data(
unstacked_predictions,
include_series_id=True,
series_id_name=self.series_id,
)
# Move datetime index into separate date column to use when merging later
stacked_predictions = stacked_predictions.reset_index(drop=False)
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
if not include_series_id:
return infer_feature_types(stacked_predictions[self.input_target_name])
else:
return infer_feature_types(stacked_predictions)
[docs] def get_forecast_period(self, X):
"""Generates all possible forecasting time points based on latest data point in X.
For the multiseries case, each time stamp is duplicated for each unique value in `X`'s `series_id` column.
Input data must be stacked in order to properly generate unique periods.
Args:
X (pd.DataFrame, np.ndarray): Stacked data the pipeline was trained on of shape [n_samples_train * n_series_ids, n_features].
Raises:
ValueError: If pipeline is not trained.
Returns:
pd.DataFrame: Dataframe containing a column with datetime periods from `gap` to `forecast_horizon + gap`
per unique `series_id` value.
"""
dates = super().get_forecast_period(X)
dates.name = self.time_index
series_id_values = X[self.series_id].unique()
new_period_df = dates.to_frame().merge(
pd.Series(series_id_values, name=self.series_id),
how="cross",
)
# Generate new numeric index
start_idx = dates.index[0] + (self.gap * len(series_id_values))
num_idx = pd.Series(range(start_idx, start_idx + len(new_period_df)))
new_period_df.index = num_idx
return new_period_df
