polynomial_detrender¶
Module Contents¶
Classes Summary¶
Removes trends from time series by fitting a polynomial to the data. |
Contents¶
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class
evalml.pipelines.components.transformers.preprocessing.polynomial_detrender.PolynomialDetrender(degree=1, random_seed=0, **kwargs)[source]¶ Removes trends from time series by fitting a polynomial to the data.
- Parameters
degree (int) – Degree for the polynomial. If 1, linear model is fit to the data. If 2, quadratic model is fit, etc. Defaults to 1.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{ “degree”: Integer(1, 3)}
model_family
ModelFamily.NONE
modifies_features
False
modifies_target
True
name
Polynomial Detrender
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 the PolynomialDetrender.
Removes fitted trend from target variable.
Adds back fitted trend to target variable.
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
Removes fitted trend from target variable.
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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
prints and returns dictionary
- Return type
None or dict
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fit(self, X, y=None)[source]¶ Fits the PolynomialDetrender.
- Parameters
X (pd.DataFrame, optional) – Ignored.
y (pd.Series) – Target variable to detrend.
- Returns
self
-
fit_transform(self, X, y=None)[source]¶ Removes fitted trend from target variable.
- Parameters
X (pd.DataFrame, optional) – Ignored.
y (pd.Series) – Target variable to detrend.
- Returns
- The first element are the input features returned without modification.
The second element is the target variable y with the fitted trend removed.
- Return type
tuple of pd.DataFrame, pd.Series
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inverse_transform(self, y)[source]¶ Adds back fitted trend to target variable.
- Parameters
X (pd.DataFrame, optional) – Ignored.
y (pd.Series) – Target variable.
- Returns
- The first element are the input features returned without modification.
The second element is the target variable y with the trend added back.
- Return type
tuple of pd.DataFrame, pd.Series
-
static
load(file_path)¶ Loads component at file path
- Parameters
file_path (str) – Location to load file
- Returns
ComponentBase object
-
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
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transform(self, X, y=None)[source]¶ Removes fitted trend from target variable.
- Parameters
X (pd.DataFrame, optional) – Ignored.
y (pd.Series) – Target variable to detrend.
- Returns
- The input features are returned without modification. The target
variable y is detrended
- Return type
tuple of pd.DataFrame, pd.Series