drop_nan_rows_transformer¶
Transformer to drop rows specified by row indices.
Module Contents¶
Classes Summary¶
Transformer to drop rows with NaN values. |
Contents¶
-
class
evalml.pipelines.components.transformers.preprocessing.drop_nan_rows_transformer.
DropNaNRowsTransformer
(parameters=None, component_obj=None, random_seed=0, **kwargs)[source]¶ Transformer to drop rows with NaN values.
- Parameters
random_seed (int) – Seed for the random number generator. Is not used by this component. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
True
name
Drop NaN Rows Transformer
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.
Fits component to data.
Fits on X and transforms X.
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 using fitted component.
-
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 component to data.
- Parameters
X (pd.DataFrame) – 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=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.
-
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.
- Returns
True.
-
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.