column_selectors¶
Module Contents¶
Classes Summary¶
Initalizes an transformer that drops specified columns in input data. |
|
Drops specified columns in input data. |
|
Selects columns by specified Woodwork logical type or semantic tag in input data. |
|
Selects specified columns in input data. |
Contents¶
-
class
evalml.pipelines.components.transformers.column_selectors.ColumnSelector(columns=None, random_seed=0, **kwargs)[source]¶ Initalizes an transformer that drops specified columns in input data.
- Parameters
columns (list(string)) – List of column names, used to determine which columns to select.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
model_family
ModelFamily.NONE
modifies_features
True
modifies_target
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 the transformer by checking if column names are present in the dataset.
Fits on X and transforms X
Loads component at file path
Returns string name of this component
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.
-
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
-
fit(self, X, y=None)[source]¶ Fits the transformer by checking if column names are present in the dataset.
- Parameters
X (pd.DataFrame) – Data to check.
y (pd.Series, optional) – Targets.
- 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
-
static
load(file_path)¶ Loads component at file path
- Parameters
file_path (str) – Location to load file
- Returns
ComponentBase object
-
property
name(cls)¶ Returns string name of this component
-
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.
-
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.
- Returns
None
-
class
evalml.pipelines.components.transformers.column_selectors.DropColumns(columns=None, random_seed=0, **kwargs)[source]¶ Drops specified columns in input data.
- Parameters
columns (list(string)) – List of column names, used to determine which columns to drop.
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
Drop Columns Transformer
needs_fitting
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 the transformer by checking if column names are present in the dataset.
Fits on X and transforms X
Loads component at file path
Returns the parameters which were used to initialize the component
Saves component at file path
Transforms data X by dropping columns.
-
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
-
fit(self, X, y=None)¶ Fits the transformer by checking if column names are present in the dataset.
- Parameters
X (pd.DataFrame) – Data to check.
y (pd.Series, optional) – Targets.
- 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
-
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.
- Returns
None
-
class
evalml.pipelines.components.transformers.column_selectors.SelectByType(column_types=None, random_seed=0, **kwargs)[source]¶ Selects columns by specified Woodwork logical type or semantic tag in input data.
- Parameters
column_types (string, ww.LogicalType, list(string), list(ww.LogicalType)) – List of Woodwork types or tags, used to determine which columns to select.
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
Select Columns By Type Transformer
needs_fitting
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 the transformer by checking if column names are present in the dataset.
Fits on X and transforms X
Loads component at file path
Returns the parameters which were used to initialize the component
Saves component at file path
Transforms data X.
-
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
-
fit(self, X, y=None)¶ Fits the transformer by checking if column names are present in the dataset.
- Parameters
X (pd.DataFrame) – Data to check.
y (pd.Series, optional) – Targets.
- 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
-
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.
- Returns
None
-
class
evalml.pipelines.components.transformers.column_selectors.SelectColumns(columns=None, random_seed=0, **kwargs)[source]¶ Selects specified columns in input data.
- Parameters
columns (list(string)) – List of column names, used to determine which columns to select.
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
Select Columns Transformer
needs_fitting
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 the transformer by checking if column names are present in the dataset.
Fits on X and transforms X
Loads component at file path
Returns the parameters which were used to initialize the component
Saves component at file path
Transforms data X by selecting columns.
-
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
-
fit(self, X, y=None)¶ Fits the transformer by checking if column names are present in the dataset.
- Parameters
X (pd.DataFrame) – Data to check.
y (pd.Series, optional) – Targets.
- 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
-
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.
- Returns
None