delayed_feature_transformer¶

Transformer that delays input features and target variable for time series problems.

Module Contents¶

Classes Summary¶

DelayedFeatureTransformer

Transformer that delays input features and target variable for time series problems.

Contents¶

class evalml.pipelines.components.transformers.preprocessing.delayed_feature_transformer.DelayedFeatureTransformer(date_index=None, max_delay=2, gap=0, forecast_horizon=1, delay_features=True, delay_target=True, random_seed=0, **kwargs)[source]¶

Transformer that delays input features and target variable for time series problems.

Parameters

date_index (str) – Name of the column containing the datetime information used to order the data. Ignored.
max_delay (int) – Maximum number of time units to delay each feature. Defaults to 2.
forecast_horizon (int) – The number of time periods the pipeline is expected to forecast.
delay_features (bool) – Whether to delay the input features. Defaults to True.
delay_target (bool) – Whether to delay the target. Defaults to True.
gap (int) – The number of time units between when the features are collected and when the target is collected. For example, if you are predicting the next time step’s target, gap=1. This is only needed because when gap=0, we need to be sure to start the lagging of the target variable at 1. Defaults to 1.
random_seed (int) – Seed for the random number generator. This transformer performs the same regardless of the random seed provided.

Attributes

hyperparameter_ranges	{}
modifies_features	True
modifies_target	False
name	Delayed Feature Transformer
needs_fitting	False
target_colname_prefix	target_delay_{}
training_only	False

Methods

`clone`	Constructs a new component with the same parameters and random state.
`default_parameters`	Returns the default parameters for this component.
`describe`	Describe a component and its parameters.
`fit`	Fits the DelayFeatureTransformer.
`fit_transform`	Fit the component and transform the input data.
`load`	Loads component at file path.
`parameters`	Returns the parameters which were used to initialize the component.
`save`	Saves component at file path.
`transform`	Computes the delayed features for all features in X and y.

clone(self)¶

Constructs a new component with the same parameters and random state.

Returns: A new instance of this component with identical parameters and random state.

default_parameters(cls)¶

Returns the default parameters for this component.

Our convention is that Component.default_parameters == Component().parameters.

Returns: Default parameters for this component.
Return type: dict

describe(self, print_name=False, return_dict=False)¶

Describe a component and its parameters.

Parameters

print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

fit(self, X, y=None)[source]¶

Fits the DelayFeatureTransformer.

Parameters

X (pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]
y (pd.Series, optional) – The target training data of length [n_samples]

Returns

self

fit_transform(self, X, y)[source]¶

Fit the component and transform the input data.

Parameters

X (pd.DataFrame or None) – Data to transform. None is expected when only the target variable is being used.
y (pd.Series, or None) – Target.

Returns

Transformed X.

Return type

pd.DataFrame

static load(file_path)¶

Loads component at file path.

Parameters: file_path (str) – Location to load file.
Returns: ComponentBase object

property parameters(self)¶: Returns the parameters which were used to initialize the component.

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)¶

Saves component at file path.

Parameters

file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.

transform(self, X, y=None)[source]¶

Computes the delayed features for all features in X and y.

For each feature in X, it will add a column to the output dataframe for each delay in the (inclusive) range [1, max_delay]. The values of each delayed feature are simply the original feature shifted forward in time by the delay amount. For example, a delay of 3 units means that the feature value at row n will be taken from the n-3rd row of that feature

If y is not None, it will also compute the delayed values for the target variable.

Parameters

X (pd.DataFrame or None) – Data to transform. None is expected when only the target variable is being used.
y (pd.Series, or None) – Target.

Returns

Transformed X.

Return type

pd.DataFrame

datetime_featurizer

drop_null_columns