multiclass_classification_pipeline#

Pipeline subclass for all multiclass classification pipelines.

Module Contents#

Classes Summary#

MulticlassClassificationPipeline

Pipeline subclass for all multiclass classification pipelines.

Contents#

class evalml.pipelines.multiclass_classification_pipeline.MulticlassClassificationPipeline(component_graph, parameters=None, custom_name=None, random_seed=0)[source]#

Pipeline subclass for all multiclass classification pipelines.

Parameters
  • component_graph (ComponentGraph, list, dict) – ComponentGraph instance, list of components in order, or dictionary of components. 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.

Example

>>> pipeline = MulticlassClassificationPipeline(component_graph=["Simple Imputer", "Logistic Regression Classifier"],
...                                             parameters={"Logistic Regression Classifier": {"penalty": "elasticnet",
...                                                                                            "solver": "liblinear"}},
...                                             custom_name="My Multiclass Pipeline")
...
>>> assert pipeline.custom_name == "My Multiclass Pipeline"
>>> assert pipeline.component_graph.component_dict.keys() == {'Simple Imputer', 'Logistic Regression Classifier'}

The pipeline parameters will be chosen from the default parameters for every component, unless specific parameters were passed in as they were above.

>>> assert pipeline.parameters == {
...     'Simple Imputer': {'impute_strategy': 'most_frequent', 'fill_value': None},
...     'Logistic Regression Classifier': {'penalty': 'elasticnet',
...                                        'C': 1.0,
...                                        'n_jobs': -1,
...                                        'multi_class': 'auto',
...                                        'solver': 'liblinear'}}

Attributes

problem_type

ProblemTypes.MULTICLASS

Methods

can_tune_threshold_with_objective

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

classes_

Gets the class names for the pipeline. Will return None before pipeline is fit.

clone

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

create_objectives

Create objective instances from a list of strings or objective classes.

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 classification model. For string and categorical targets, classes are sorted by sorted(set(y)) and then are mapped to values between 0 and n_classes-1.

fit_transform

Fit and transform all components in the component graph, if all components are Transformers.

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_dict

Generates a dictionary with nodes consisting of the component names and parameters, and edges detailing component relationships. This dictionary is JSON serializable in most cases.

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. Not to be confused with python's __new__ method.

parameters

Parameter dictionary for this pipeline.

predict

Make predictions using selected features.

predict_proba

Make probability estimates for labels.

save

Saves pipeline at file path.

score

Evaluate model performance on objectives.

summary

A short summary of the pipeline structure, describing the list of components used.

transform

Transform the input.

transform_all_but_final

Transforms the data by applying all pre-processing components.

can_tune_threshold_with_objective(self, objective)#

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

Parameters

objective (ObjectiveBase) – Primary AutoMLSearch objective.

Returns

True if the pipeline threshold can be tuned.

Return type

bool

property classes_(self)#

Gets the class names for the pipeline. Will return None before pipeline is fit.

clone(self)#

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

Returns

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

static create_objectives(objectives)#

Create objective instances from a list of strings or objective classes.

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

Feature names and their corresponding importance

Return type

pd.DataFrame

fit(self, X, y)#

Build a classification model. For string and categorical targets, classes are sorted by sorted(set(y)) and then are mapped to values between 0 and n_classes-1.

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 labels of length [n_samples]

Returns

self

Raises
  • ValueError – If the number of unique classes in y are not appropriate for the type of pipeline.

  • TypeError – If the dtype is boolean but pd.NA exists in the series.

  • Exception – For all other exceptions.

fit_transform(self, X, y)#

Fit and transform all components in the component graph, if all components are Transformers.

Parameters
  • X (pd.DataFrame) – Input features of shape [n_samples, n_features].

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

Returns

Transformed output.

Return type

pd.DataFrame

Raises

ValueError – If final component is an Estimator.

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

Raises
  • RuntimeError – If graphviz is not installed.

  • ValueError – If path is not writeable.

graph_dict(self)#

Generates a dictionary with nodes consisting of the component names and parameters, and edges detailing component relationships. This dictionary is JSON serializable in most cases.

x_edges specifies from which component feature data is being passed. y_edges specifies from which component target data is being passed. This can be used to build graphs across a variety of visualization tools. Template: {“Nodes”: {“component_name”: {“Name”: class_name, “Parameters”: parameters_attributes}, …}}, “x_edges”: [[from_component_name, to_component_name], [from_component_name, to_component_name], …], “y_edges”: [[from_component_name, to_component_name], [from_component_name, to_component_name], …]}

Returns

A dictionary representing the DAG structure.

Return type

dag_dict (dict)

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

A bar graph showing features and their corresponding importance.

Return type

plotly.Figure

Raises

ValueError – If importance threshold is not valid.

inverse_transform(self, y)#

Apply component inverse_transform methods to estimator predictions in reverse order.

Components that implement inverse_transform are PolynomialDecomposer, LogTransformer, LabelEncoder (tbd).

Parameters

y (pd.Series) – Final component features.

Returns

The inverse transform of the target.

Return type

pd.Series

static load(file_path: Union[str, io.BytesIO])#

Loads pipeline at file path.

Parameters

file_path (str|BytesIO) – load filepath or a BytesIO object.

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, X_train=None, y_train=None)#

Make predictions using selected features.

Note: we cast y as ints first to address boolean values that may be returned from calculating predictions which we would not be able to otherwise transform if we originally had integer targets.

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

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

  • X_train (pd.DataFrame) – Training data. Ignored. Only used for time series.

  • y_train (pd.Series) – Training labels. Ignored. Only used for time series.

Returns

Estimated labels.

Return type

pd.Series

predict_proba(self, X, X_train=None, y_train=None)#

Make probability estimates for labels.

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

  • X_train (pd.DataFrame or np.ndarray or None) – Training data. Ignored. Only used for time series.

  • y_train (pd.Series or None) – Training labels. Ignored. Only used for time series.

Returns

Probability estimates

Return type

pd.DataFrame

Raises

ValueError – If final component is not an estimator.

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.

score(self, X, y, objectives, X_train=None, y_train=None)#

Evaluate model performance on objectives.

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

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

  • objectives (list) – List of objectives to score

  • X_train (pd.DataFrame) – Training data. Ignored. Only used for time series.

  • y_train (pd.Series) – Training labels. Ignored. Only used for time series.

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

Returns

A string describing the pipeline structure.

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

transform_all_but_final(self, X, y=None, X_train=None, y_train=None)#

Transforms the data by applying all pre-processing components.

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

  • y (pd.Series or None) – Targets corresponding to X. Optional.

  • X_train (pd.DataFrame or np.ndarray or None) – Training data. Only used for time series.

  • y_train (pd.Series or None) – Training labels. Only used for time series.

Returns

New transformed features.

Return type

pd.DataFrame