"""Pipeline base class for time series regression problems."""
from evalml.pipelines.time_series_pipeline_base import TimeSeriesPipelineBase
from evalml.problem_types import ProblemTypes
[docs]class TimeSeriesRegressionPipeline(TimeSeriesPipelineBase):
"""Pipeline base class for time series regression problems.
Args:
component_graph (ComponentGraph, list, dict): ComponentGraph instance, list of components in order, or dictionary of components.
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 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}}).
random_seed (int): Seed for the random number generator. Defaults to 0.
Example:
>>> pipeline = TimeSeriesRegressionPipeline(component_graph=["Simple Imputer", "Linear Regressor"],
... parameters={"Linear Regressor": {"normalize": True},
... "pipeline": {"gap": 1, "max_delay": 1, "forecast_horizon": 1, "time_index": "date"}},
... custom_name="My TimeSeriesRegression Pipeline")
...
>>> assert pipeline.custom_name == "My TimeSeriesRegression Pipeline"
>>> assert pipeline.component_graph.component_dict.keys() == {'Simple Imputer', 'Linear Regressor'}
The pipeline parameters will be chosen from the default parameters for every component, unless specific parameters
were passed in as they were above.
>>> assert pipeline.parameters == {
... 'Simple Imputer': {'impute_strategy': 'most_frequent', 'fill_value': None},
... 'Linear Regressor': {'fit_intercept': True, 'normalize': True, 'n_jobs': -1},
... 'pipeline': {'gap': 1, 'max_delay': 1, 'forecast_horizon': 1, 'time_index': "date"}}
"""
problem_type = ProblemTypes.TIME_SERIES_REGRESSION
"""ProblemTypes.TIME_SERIES_REGRESSION"""
[docs] def fit(self, X, y):
"""Fit a time series pipeline.
Args:
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
Raises:
ValueError: If the target is not numeric.
"""
X, y = self._convert_to_woodwork(X, y)
if "numeric" not in y.ww.semantic_tags:
raise ValueError(
"Time Series Regression pipeline can only handle numeric target data!",
)
X = self._drop_time_index(X)
self._fit(X, y)
return self
[docs] def score(self, X, y, objectives, X_train=None, y_train=None):
"""Evaluate model performance on current and additional objectives.
Args:
X (pd.DataFrame or np.ndarray): Data of shape [n_samples, n_features].
y (pd.Series): True labels of length [n_samples].
objectives (list): Non-empty list of objectives to score on.
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].
Returns:
dict: Ordered dictionary of objective scores.
"""
X, y = self._convert_to_woodwork(X, y)
X_train, y_train = self._convert_to_woodwork(X_train, y_train)
objectives = self.create_objectives(objectives)
y_predicted = self.predict_in_sample(X, y, X_train, y_train)
return self._score_all_objectives(
X,
y,
y_predicted,
y_pred_proba=None,
objectives=objectives,
)