component_graph¶

Module Contents¶

Classes Summary¶

ComponentGraph

Component graph for a pipeline as a directed acyclic graph (DAG).

Attributes Summary¶

logger

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

`compute_final_component_features`	Transform all components save the final one, and gathers the data from any number of parents
`compute_order`	The order that components will be computed or called in.
`default_parameters`	The default parameter dictionary for this pipeline.
`describe`	Outputs component graph details including component parameters
`fit`	Fit each component in the graph
`fit_features`	Fit all components save the final one, usually an estimator
`generate_order`	Regenerated the topologically sorted order of the graph
`get_component`	Retrieves a single component object from the graph.
`get_estimators`	Gets a list of all the estimator components within this graph
`get_inputs`	Retrieves all inputs for a given component.
`get_last_component`	Retrieves the component that is computed last in the graph, usually the final estimator.
`graph`	Generate an image representing the component graph
`instantiate`	Instantiates all uninstantiated components within the graph using the given parameters. An error will be
`inverse_transform`	Apply component inverse_transform methods to estimator predictions in reverse order.
`predict`	Make predictions using selected features.

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.

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

predict(self, X)[source]¶

Make predictions using selected features.

Parameters: X (pd.DataFrame) – Data of shape [n_samples, n_features]
Returns: Predicted values.
Return type: pd.Series

evalml.pipelines.component_graph.logger¶

classification_pipeline multiclass_classification_pipeline