"""Pipeline base class for time series regression problems."""
import pandas as pd
from woodwork.statistics_utils import infer_frequency
from evalml.pipelines.time_series_pipeline_base import TimeSeriesPipelineBase
from evalml.problem_types import ProblemTypes
from evalml.utils.woodwork_utils import infer_feature_types
[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)
self.frequency = infer_frequency(X[self.time_index])
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, y)
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,
)
[docs] def get_forecast_period(self, X):
"""Generates all possible forecasting time points based on latest data point in X.
Args:
X (pd.DataFrame, np.ndarray): Data the pipeline was trained on of shape [n_samples_train, n_feautures].
Raises:
ValueError: If pipeline is not trained.
Returns:
pd.Series: Datetime periods out to `forecast_horizon + gap`.
Example:
>>> X = pd.DataFrame({'date': pd.date_range(start='1-1-2022', periods=10, freq='D'), 'feature': range(10, 20)})
>>> y = pd.Series(range(0, 10), name='target')
>>> gap = 1
>>> forecast_horizon = 2
>>> pipeline = TimeSeriesRegressionPipeline(component_graph=["Linear Regressor"],
... parameters={"Linear Regressor": {"normalize": True},
... "pipeline": {"gap": gap, "max_delay": 1, "forecast_horizon": forecast_horizon, "time_index": "date"}},
... )
>>> pipeline.fit(X, y)
pipeline = TimeSeriesRegressionPipeline(component_graph={'Linear Regressor': ['Linear Regressor', 'X', 'y']}, parameters={'Linear Regressor':{'fit_intercept': True, 'normalize': True, 'n_jobs': -1}, 'pipeline':{'gap': 1, 'max_delay': 1, 'forecast_horizon': 2, 'time_index': 'date'}}, random_seed=0)
>>> dates = pipeline.get_forecast_period(X)
>>> expected = pd.Series(pd.date_range(start='2022-01-11', periods=(gap + forecast_horizon), freq='D'), name='date', index=[10, 11, 12])
>>> assert dates.equals(expected)
"""
if not self._is_fitted:
raise ValueError("Pipeline must be fitted before getting forecast.")
X = infer_feature_types(X)
# Generate prediction periods
first_date = X.iloc[-1][self.time_index]
predicted_date_range = pd.Series(
pd.date_range(
start=first_date,
periods=self.forecast_horizon
+ self.gap
+ 1, # Add additional period to account for dropping first date row
freq=self.frequency,
),
)
# Generate numerical index
first_idx = len(X) - 1 if not isinstance(X.index.dtype, int) else X.index[-1]
num_idx = pd.Series(range(first_idx, first_idx + predicted_date_range.size))
predicted_date_range.index = num_idx
predicted_date_range = predicted_date_range.drop(predicted_date_range.index[0])
predicted_date_range.name = self.time_index
return predicted_date_range
[docs] def get_forecast_predictions(self, X, y):
"""Generates all possible forecasting predictions based on last period of X.
Args:
X (pd.DataFrame, np.ndarray): Data the pipeline was trained on of shape [n_samples_train, n_feautures].
y (pd.Series, np.ndarray): Targets used to train the pipeline of shape [n_samples_train].
Returns:
Predictions out to `forecast_horizon + gap` periods.
"""
X, y = self._convert_to_woodwork(X, y)
pred_dates = pd.DataFrame(self.get_forecast_period(X))
preds = self.predict(pred_dates, objective=None, X_train=X, y_train=y)
return preds