datetime_featurizer¶
Module Contents¶
Classes Summary¶
Transformer that can automatically extract features from datetime columns. |
Contents¶
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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
{}
model_family
ModelFamily.NONE
modifies_features
True
modifies_target
False
name
DateTime Featurization Component
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
Fits component to data
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
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.
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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
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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
prints and returns dictionary
- Return type
None or dict
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fit(self, X, y=None)[source]¶ Fits component to data
- Parameters
X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]
y (list, pd.Series, np.ndarray, optional) – The target training data of length [n_samples]
- 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
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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.
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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.
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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.
- Returns
None