component_graph¶
Module Contents¶
Classes Summary¶
Component graph for a pipeline as a directed acyclic graph (DAG). |
Contents¶
-
class
evalml.pipelines.component_graph.ComponentGraph(component_dict=None, random_seed=0)[source]¶ Component graph for a pipeline as a directed acyclic graph (DAG).
- Parameters
component_dict (dict) – A dictionary which specifies the components and edges between components that should be used to create the component graph. Defaults to None.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Example
>>> component_dict = {'imputer': ['Imputer'], 'ohe': ['One Hot Encoder', 'imputer.x'], 'estimator_1': ['Random Forest Classifier', 'ohe.x'], 'estimator_2': ['Decision Tree Classifier', 'ohe.x'], 'final': ['Logistic Regression Classifier', 'estimator_1', 'estimator_2']} >>> component_graph = ComponentGraph(component_dict)
Methods
Transform all components save the final one, and gathers the data from any number of parents
The order that components will be computed or called in.
The default parameter dictionary for this pipeline.
Outputs component graph details including component parameters
Fit each component in the graph.
Fit all components save the final one, usually an estimator.
Regenerated the topologically sorted order of the graph
Retrieves a single component object from the graph.
Gets a list of all the estimator components within this graph.
Retrieves all inputs for a given component.
Retrieves the component that is computed last in the graph, usually the final estimator.
Generate an image representing the component graph
Instantiates all uninstantiated components within the graph using the given parameters. An error will be
Apply component inverse_transform methods to estimator predictions in reverse order.
Make predictions using selected features.
Transform the input using the component graph.
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compute_final_component_features(self, X, y=None)[source]¶ Transform all components save the final one, and gathers the data from any number of parents to get all the information that should be fed to the final component.
- Parameters
X (pd.DataFrame) – Data of shape [n_samples, n_features].
y (pd.Series) – The target training data of length [n_samples]. Defaults to None.
- Returns
Transformed values.
- Return type
pd.DataFrame
-
property
compute_order(self)¶ The order that components will be computed or called in.
-
property
default_parameters(self)¶ The default parameter dictionary for this pipeline.
- Returns
Dictionary of all component default parameters.
- Return type
dict
-
describe(self, return_dict=False)[source]¶ Outputs component graph details including component parameters
- Parameters
return_dict (bool) – If True, return dictionary of information about component graph. Defaults to False.
- Returns
Dictionary of all component parameters if return_dict is True, else None
- Return type
dict
-
fit(self, X, y)[source]¶ Fit each component in the graph.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series) – The target training data of length [n_samples].
-
fit_features(self, X, y)[source]¶ Fit all components save the final one, usually an estimator.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series) – The target training data of length [n_samples].
- Returns
Transformed values.
- Return type
pd.DataFrame
-
classmethod
generate_order(cls, component_dict)[source]¶ Regenerated the topologically sorted order of the graph
-
get_component(self, component_name)[source]¶ Retrieves a single component object from the graph.
- Parameters
component_name (str) – Name of the component to retrieve
- Returns
ComponentBase object
-
get_estimators(self)[source]¶ Gets a list of all the estimator components within this graph.
- Returns
All estimator objects within the graph.
- Return type
list
-
get_inputs(self, component_name)[source]¶ Retrieves all inputs for a given component.
- Parameters
component_name (str) – Name of the component to look up.
- Returns
List of inputs for the component to use.
- Return type
list[str]
-
get_last_component(self)[source]¶ Retrieves the component that is computed last in the graph, usually the final estimator.
- Returns
ComponentBase object
-
graph(self, name=None, graph_format=None)[source]¶ Generate an image representing the component graph
- Parameters
name (str) – Name of the graph. Defaults to None.
graph_format (str) – file format to save the graph in. Defaults to None.
- Returns
Graph object that can be directly displayed in Jupyter notebooks.
- Return type
graphviz.Digraph
-
instantiate(self, parameters)[source]¶ Instantiates all uninstantiated components within the graph using the given parameters. An error will be raised if a component is already instantiated but the parameters dict contains arguments for that component.
- Parameters
parameters (dict) – Dictionary with component names as keys and dictionary of that component’s parameters as values. An empty dictionary {} or None implies using all default values for component parameters. If a component in the component graph is already instantiated, it will not use any of its parameters defined in this dictionary.
-
inverse_transform(self, y)[source]¶ Apply component inverse_transform methods to estimator predictions in reverse order.
Components that implement inverse_transform are PolynomialDetrender, LabelEncoder (tbd).
- Parameters
y – (pd.Series): Final component features
-
evalml.pipelines.component_graph.logger¶