datetime_featurizer¶
Transformer that can automatically extract features from datetime columns.
Module Contents¶
Classes Summary¶
Transformer that can automatically extract features from datetime columns. |
Contents¶
-
class
evalml.pipelines.components.transformers.preprocessing.datetime_featurizer.DateTimeFeaturizer(features_to_extract=None, encode_as_categories=False, date_index=None, random_seed=0, **kwargs)[source]¶ Transformer that can automatically extract features from datetime columns.
- Parameters
features_to_extract (list) – List of features to extract. Valid options include “year”, “month”, “day_of_week”, “hour”. Defaults to None.
encode_as_categories (bool) – Whether day-of-week and month features should be encoded as pandas “category” dtype. This allows OneHotEncoders to encode these features. Defaults to False.
date_index (str) – Name of the column containing the datetime information used to order the data. Ignored.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
DateTime Featurization Component
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fit the datetime featurizer component.
Fits on X and transforms X.
Gets the categories of each datetime feature.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data X by creating new features using existing DateTime columns, and then dropping those DateTime columns.
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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
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fit(self, X, y=None)[source]¶ Fit the datetime featurizer component.
- Parameters
X (pd.DataFrame) – Input features.
y (pd.Series, optional) – Target data. Ignored.
- Returns
self
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fit_transform(self, X, y=None)¶ Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
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get_feature_names(self)[source]¶ Gets the categories of each datetime feature.
- Returns
- Dictionary, where each key-value pair is a column name and a dictionary
mapping the unique feature values to their integer encoding.
- Return type
dict
-
static
load(file_path)¶ Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
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needs_fitting(self)¶ Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
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property
parameters(self)¶ Returns the parameters which were used to initialize the component.
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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.