pipeline_base

Module Contents

Classes Summary

PipelineBase

Machine learning pipeline made out of transformers and an Estimator.

Attributes Summary

logger

Contents

evalml.pipelines.pipeline_base.logger

class evalml.pipelines.pipeline_base.PipelineBase(component_graph, parameters=None, custom_name=None, random_seed=0)

Machine learning pipeline made out of transformers and an Estimator.

Parameters

• component_graph (list or dict) – List of components in order. Accepts strings or ComponentBase subclasses in the list. Note that when duplicate components are specified in a list, the duplicate component names will be modified with the component’s index in the list. For example, the component graph [Imputer, One Hot Encoder, Imputer, Logistic Regression Classifier] will have names [“Imputer”, “One Hot Encoder”, “Imputer_2”, “Logistic Regression Classifier”].

• 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. Defaults to None.

• custom_name (str) – Custom name for the pipeline. Defaults to None.

• random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

problem_type

None

Methods

can_tune_threshold_with_objective	Determine whether the threshold of a binary classification pipeline can be tuned.
clone	Constructs a new pipeline with the same components, parameters, and random state.
compute_estimator_features	Transforms the data by applying all pre-processing components.
create_objectives
custom_name	Custom name of the pipeline.
describe	Outputs pipeline details including component parameters
feature_importance	Importance associated with each feature. Features dropped by the feature selection are excluded.
fit	Build a model.
get_component	Returns component by name
get_hyperparameter_ranges	Returns hyperparameter ranges from all components as a dictionary.
graph	Generate an image representing the pipeline graph.
graph_feature_importance	Generate a bar graph of the pipeline’s feature importance
inverse_transform	Apply component inverse_transform methods to estimator predictions in reverse order.
load	Loads pipeline at file path
model_family	Returns model family of this pipeline.
name	Name of the pipeline.
new	Constructs a new instance of the pipeline with the same component graph but with a different set of parameters.
parameters	Parameter dictionary for this pipeline.
predict	Make predictions using selected features.
save	Saves pipeline at file path
score	Evaluate model performance on current and additional objectives.
summary	A short summary of the pipeline structure, describing the list of components used.
transform	Transform the input.

can_tune_threshold_with_objective(self, objective)

Determine whether the threshold of a binary classification pipeline can be tuned.

Parameters

• pipeline (PipelineBase) – Binary classification pipeline.

• objective – Primary AutoMLSearch objective.

clone(self)

Constructs a new pipeline with the same components, parameters, and random state.

Returns

A new instance of this pipeline with identical components, parameters, and random state.

compute_estimator_features(self, X, y=None)

Transforms the data by applying all pre-processing components.

Parameters

X (pd.DataFrame) – Input data to the pipeline to transform.

Returns

New transformed features.

Return type

pd.DataFrame

static create_objectives(objectives)

property custom_name(self)

Custom name of the pipeline.

describe(self, return_dict=False)

Outputs pipeline details including component parameters

Parameters

return_dict (bool) – If True, return dictionary of information about pipeline. Defaults to False.

Returns

Dictionary of all component parameters if return_dict is True, else None

Return type

dict

property feature_importance(self)

Importance associated with each feature. Features dropped by the feature selection are excluded.

Returns

pd.DataFrame including feature names and their corresponding importance

abstract fit(self, X, y)

Build a model.

Parameters

• X (pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features].

• y (pd.Series, np.ndarray) – The target training data of length [n_samples].

Returns

self

get_component(self, name)

Returns component by name

Parameters

name (str) – Name of component

Returns

Component to return

Return type

Component

get_hyperparameter_ranges(self, custom_hyperparameters)

Returns hyperparameter ranges from all components as a dictionary.

Parameters

custom_hyperparameters (dict) – Custom hyperparameters for the pipeline.

Returns

Dictionary of hyperparameter ranges for each component in the pipeline.

Return type

dict

graph(self, filepath=None)

Generate an image representing the pipeline graph.

Parameters

filepath (str, optional) – Path to where the graph should be saved. If set to None (as by default), the graph will not be saved.

Returns

Graph object that can be directly displayed in Jupyter notebooks.

Return type

graphviz.Digraph

graph_feature_importance(self, importance_threshold=0)

Generate a bar graph of the pipeline’s feature importance

Parameters

importance_threshold (float, optional) – If provided, graph features with a permutation importance whose absolute value is larger than importance_threshold. Defaults to zero.

Returns

plotly.Figure, a bar graph showing features and their corresponding importance

inverse_transform(self, y)

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

static load(file_path)

Loads pipeline at file path

Parameters

file_path (str) – location to load file

Returns

PipelineBase object

property model_family(self)

Returns model family of this pipeline.

property name(self)

Name of the pipeline.

new(self, parameters, random_seed=0)

Constructs a new instance of the pipeline with the same component graph but with a different set of parameters.

Not to be confused with python’s __new__ method.

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. Defaults to None.

• random_seed (int) – Seed for the random number generator. Defaults to 0.

Returns

A new instance of this pipeline with identical components.

property parameters(self)

Parameter dictionary for this pipeline.

Returns

Dictionary of all component parameters.

Return type

dict

predict(self, X, objective=None)

Make predictions using selected features.

Parameters

• X (pd.DataFrame, or np.ndarray) – Data of shape [n_samples, n_features].

• objective (Object or string) – The objective to use to make predictions.

Returns

Predicted values.

Return type

pd.Series

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)

Saves pipeline at file path

Parameters

• file_path (str) – location to save file

• pickle_protocol (int) – the pickle data stream format.

Returns

None

abstract score(self, X, y, objectives)

Evaluate model performance on current and additional objectives.

Parameters

• X (pd.DataFrame or np.ndarray) – Data of shape [n_samples, n_features].

• y (pd.Series, np.ndarray) – True labels of length [n_samples].

• objectives (list) – Non-empty list of objectives to score on.

Returns

Ordered dictionary of objective scores.

Return type

dict

property summary(self)

A short summary of the pipeline structure, describing the list of components used. Example: Logistic Regression Classifier w/ Simple Imputer + One Hot Encoder

transform(self, X, y=None)

Transform the input.

Parameters

• X (pd.DataFrame, or np.ndarray) – Data of shape [n_samples, n_features].

• y (pd.Series) – The target data of length [n_samples]. Defaults to None.

Returns

Transformed output.

Return type

pd.DataFrame

multiclass_classification_pipeline pipeline_meta