time_series_classification_pipelines

Pipeline base class for time-series classification problems.

Module Contents

Classes Summary

TimeSeriesBinaryClassificationPipeline	Pipeline base class for time series binary classification problems.
TimeSeriesClassificationPipeline	Pipeline base class for time series classification problems.
TimeSeriesMulticlassClassificationPipeline	Pipeline base class for time series multiclass classification problems.

Contents

class evalml.pipelines.time_series_classification_pipelines.TimeSeriesBinaryClassificationPipeline(component_graph, parameters=None, custom_name=None, random_seed=0)

Pipeline base class for time series binary classification problems.

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 {} implies using all default values for component parameters. Pipeline-level parameters such as time_index, gap, and max_delay must be specified with the “pipeline” key. For example: Pipeline(parameters={“pipeline”: {“time_index”: “Date”, “max_delay”: 4, “gap”: 2}}).

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

Example

>>> pipeline = TimeSeriesBinaryClassificationPipeline(component_graph=["Simple Imputer", "Logistic Regression Classifier"],
...                                                   parameters={"Logistic Regression Classifier": {"penalty": "elasticnet",
...                                                                                                  "solver": "liblinear"},
...                                                               "pipeline": {"gap": 1, "max_delay": 1, "forecast_horizon": 1, "time_index": "date"}},
...                                                   custom_name="My TimeSeriesBinary Pipeline")
...
>>> assert pipeline.custom_name == "My TimeSeriesBinary Pipeline"
>>> assert pipeline.component_graph.component_dict.keys() == {'Simple Imputer', 'Logistic Regression Classifier'}
...
>>> 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'},
...     'pipeline': {'gap': 1, 'max_delay': 1, 'forecast_horizon': 1, 'time_index': "date"}}

Attributes

problem_type

None

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	Fit a time series classification pipeline.
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.
graph_json	Generates a JSON with nodes consisting of the component names and parameters, and edges detailing component relationships.
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.
optimize_threshold	Optimize the pipeline threshold given the objective to use. Only used for binary problems with objectives whose thresholds can be tuned.
parameters	Parameter dictionary for this pipeline.
predict	Predict on future data where target is not known.
predict_in_sample	Predict on future data where the target is known, e.g. cross validation.
predict_proba	Predict on future data where the target is unknown.
predict_proba_in_sample	Predict on future data where the target is known, e.g. cross validation.
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.
threshold	Threshold used to make a prediction. Defaults to None.
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)

Fit a time series classification pipeline.

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 targets 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

Raises

• RuntimeError – If graphviz is not installed.

• ValueError – If path is not writeable.

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.

graph_json(self)

Generates a JSON with nodes consisting of the component names and parameters, and edges detailing component relationships.

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 serialized JSON representation of a DAG structure.

Return type

dag_json (str)

inverse_transform(self, y)

Apply component inverse_transform methods to estimator predictions in reverse order.

Components that implement inverse_transform are PolynomialDetrender, 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)

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.

optimize_threshold(self, X, y, y_pred_proba, objective)

Optimize the pipeline threshold given the objective to use. Only used for binary problems with objectives whose thresholds can be tuned.

Parameters

• X (pd.DataFrame) – Input features.

• y (pd.Series) – Input target values.

• y_pred_proba (pd.Series) – The predicted probabilities of the target outputted by the pipeline.

• objective (ObjectiveBase) – The objective to threshold with. Must have a tunable threshold.

Raises

ValueError – If objective is not optimizable.

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)

Predict on future data where target is not known.

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.

• X_train (pd.DataFrame or np.ndarray or None) – Training data.

• y_train (pd.Series or None) – Training labels.

Raises

ValueError – If final component is not an Estimator.

Returns

Predictions.

predict_in_sample(self, X, y, X_train, y_train, objective=None)

Predict on future data where the target is known, e.g. cross validation.

Parameters

• X (pd.DataFrame) – Future data of shape [n_samples, n_features].

• y (pd.Series) – Future target of shape [n_samples].

• X_train (pd.DataFrame) – Data the pipeline was trained on of shape [n_samples_train, n_feautures].

• y_train (pd.Series) – Targets used to train the pipeline of shape [n_samples_train].

• objective (ObjectiveBase, str) – Objective used to threshold predicted probabilities, optional. Defaults to None.

Returns

Estimated labels.

Return type

pd.Series

Raises

ValueError – If objective is not defined for time-series binary classification problems.

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

Predict on future data where the target is unknown.

Parameters

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

• X_train (pd.DataFrame, np.ndarray) – Data the pipeline was trained on of shape [n_samples_train, n_features].

• y_train (pd.Series, np.ndarray) – Targets used to train the pipeline of shape [n_samples_train].

Returns

Estimated probabilities.

Return type

pd.Series

Raises

ValueError – If final component is not an Estimator.

predict_proba_in_sample(self, X_holdout, y_holdout, X_train, y_train)

Predict on future data where the target is known, e.g. cross validation.

Parameters

• X_holdout (pd.DataFrame or np.ndarray) – Future data of shape [n_samples, n_features].

• y_holdout (pd.Series, np.ndarray) – Future target of shape [n_samples].

• X_train (pd.DataFrame, np.ndarray) – Data the pipeline was trained on of shape [n_samples_train, n_features].

• y_train (pd.Series, np.ndarray) – Targets used to train the pipeline of shape [n_samples_train].

Returns

Estimated probabilities.

Return type

pd.Series

Raises

ValueError – If the 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 current and additional objectives.

Parameters

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

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

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

• X_train (pd.DataFrame, np.ndarray) – Data the pipeline was trained on of shape [n_samples_train, n_features].

• y_train (pd.Series, np.ndarray) – Targets used to train the pipeline of shape [n_samples_train].

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.

property threshold(self)

Threshold used to make a prediction. Defaults to None.

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) – Targets corresponding to the pipeline targets.

• X_train (pd.DataFrame) – Training data used to generate generates from past observations.

• y_train (pd.Series) – Training targets used to generate features from past observations.

Returns

New transformed features.

Return type

pd.DataFrame

class evalml.pipelines.time_series_classification_pipelines.TimeSeriesClassificationPipeline(component_graph, parameters=None, custom_name=None, random_seed=0)

Pipeline base class for time series classification problems.

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 {} implies using all default values for component parameters. Pipeline-level parameters such as time_index, gap, and max_delay must be specified with the “pipeline” key. For example: Pipeline(parameters={“pipeline”: {“time_index”: “Date”, “max_delay”: 4, “gap”: 2}}).

• 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.
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	Fit a time series classification pipeline.
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.
graph_json	Generates a JSON with nodes consisting of the component names and parameters, and edges detailing component relationships.
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	Predict on future data where target is not known.
predict_in_sample	Predict on future data where the target is known, e.g. cross validation.
predict_proba	Predict on future data where the target is unknown.
predict_proba_in_sample	Predict on future data where the target is known, e.g. cross validation.
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.
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)

Fit a time series classification pipeline.

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 targets 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

Raises

• RuntimeError – If graphviz is not installed.

• ValueError – If path is not writeable.

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.

graph_json(self)

Generates a JSON with nodes consisting of the component names and parameters, and edges detailing component relationships.

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 serialized JSON representation of a DAG structure.

Return type

dag_json (str)

inverse_transform(self, y)

Apply component inverse_transform methods to estimator predictions in reverse order.

Components that implement inverse_transform are PolynomialDetrender, 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)

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, X_train=None, y_train=None)

Predict on future data where target is not known.

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.

• X_train (pd.DataFrame or np.ndarray or None) – Training data.

• y_train (pd.Series or None) – Training labels.

Raises

ValueError – If final component is not an Estimator.

Returns

Predictions.

predict_in_sample(self, X, y, X_train, y_train, objective=None)

Predict on future data where the target is known, e.g. cross validation.

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 or np.ndarray) – Future data of shape [n_samples, n_features].

• y (pd.Series, np.ndarray) – Future target of shape [n_samples].

• X_train (pd.DataFrame, np.ndarray) – Data the pipeline was trained on of shape [n_samples_train, n_features].

• y_train (pd.Series, np.ndarray) – Targets used to train the pipeline of shape [n_samples_train].

• objective (ObjectiveBase, str, None) – Objective used to threshold predicted probabilities, optional.

Returns

Estimated labels.

Return type

pd.Series

Raises

ValueError – If final component is not an Estimator.

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

Predict on future data where the target is unknown.

Parameters

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

• X_train (pd.DataFrame, np.ndarray) – Data the pipeline was trained on of shape [n_samples_train, n_features].

• y_train (pd.Series, np.ndarray) – Targets used to train the pipeline of shape [n_samples_train].

Returns

Estimated probabilities.

Return type

pd.Series

Raises

ValueError – If final component is not an Estimator.

predict_proba_in_sample(self, X_holdout, y_holdout, X_train, y_train)

Predict on future data where the target is known, e.g. cross validation.

Parameters

• X_holdout (pd.DataFrame or np.ndarray) – Future data of shape [n_samples, n_features].

• y_holdout (pd.Series, np.ndarray) – Future target of shape [n_samples].

• X_train (pd.DataFrame, np.ndarray) – Data the pipeline was trained on of shape [n_samples_train, n_features].

• y_train (pd.Series, np.ndarray) – Targets used to train the pipeline of shape [n_samples_train].

Returns

Estimated probabilities.

Return type

pd.Series

Raises

ValueError – If the 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 current and additional objectives.

Parameters

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

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

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

• X_train (pd.DataFrame, np.ndarray) – Data the pipeline was trained on of shape [n_samples_train, n_features].

• y_train (pd.Series, np.ndarray) – Targets used to train the pipeline of shape [n_samples_train].

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) – Targets corresponding to the pipeline targets.

• X_train (pd.DataFrame) – Training data used to generate generates from past observations.

• y_train (pd.Series) – Training targets used to generate features from past observations.

Returns

New transformed features.

Return type

pd.DataFrame

class evalml.pipelines.time_series_classification_pipelines.TimeSeriesMulticlassClassificationPipeline(component_graph, parameters=None, custom_name=None, random_seed=0)

Pipeline base class for time series multiclass classification problems.

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 {} implies using all default values for component parameters. Pipeline-level parameters such as time_index, gap, and max_delay must be specified with the “pipeline” key. For example: Pipeline(parameters={“pipeline”: {“time_index”: “Date”, “max_delay”: 4, “gap”: 2}}).

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

Example

>>> pipeline = TimeSeriesMulticlassClassificationPipeline(component_graph=["Simple Imputer", "Logistic Regression Classifier"],
...                                                       parameters={"Logistic Regression Classifier": {"penalty": "elasticnet",
...                                                                                                      "solver": "liblinear"},
...                                                                   "pipeline": {"gap": 1, "max_delay": 1, "forecast_horizon": 1, "time_index": "date"}},
...                                                       custom_name="My TimeSeriesMulticlass Pipeline")
>>> assert pipeline.custom_name == "My TimeSeriesMulticlass Pipeline"
>>> assert pipeline.component_graph.component_dict.keys() == {'Simple Imputer', 'Logistic Regression Classifier'}
>>> 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'},
...     'pipeline': {'gap': 1, 'max_delay': 1, 'forecast_horizon': 1, 'time_index': "date"}}

Attributes

problem_type

ProblemTypes.TIME_SERIES_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	Fit a time series classification pipeline.
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.
graph_json	Generates a JSON with nodes consisting of the component names and parameters, and edges detailing component relationships.
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	Predict on future data where target is not known.
predict_in_sample	Predict on future data where the target is known, e.g. cross validation.
predict_proba	Predict on future data where the target is unknown.
predict_proba_in_sample	Predict on future data where the target is known, e.g. cross validation.
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.
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)

Fit a time series classification pipeline.

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 targets 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

Raises

• RuntimeError – If graphviz is not installed.

• ValueError – If path is not writeable.

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.

graph_json(self)

Generates a JSON with nodes consisting of the component names and parameters, and edges detailing component relationships.

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 serialized JSON representation of a DAG structure.

Return type

dag_json (str)

inverse_transform(self, y)

Apply component inverse_transform methods to estimator predictions in reverse order.

Components that implement inverse_transform are PolynomialDetrender, 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)

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, X_train=None, y_train=None)

Predict on future data where target is not known.

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.

• X_train (pd.DataFrame or np.ndarray or None) – Training data.

• y_train (pd.Series or None) – Training labels.

Raises

ValueError – If final component is not an Estimator.

Returns

Predictions.

predict_in_sample(self, X, y, X_train, y_train, objective=None)

Predict on future data where the target is known, e.g. cross validation.

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 or np.ndarray) – Future data of shape [n_samples, n_features].

• y (pd.Series, np.ndarray) – Future target of shape [n_samples].

• X_train (pd.DataFrame, np.ndarray) – Data the pipeline was trained on of shape [n_samples_train, n_features].

• y_train (pd.Series, np.ndarray) – Targets used to train the pipeline of shape [n_samples_train].

• objective (ObjectiveBase, str, None) – Objective used to threshold predicted probabilities, optional.

Returns

Estimated labels.

Return type

pd.Series

Raises

ValueError – If final component is not an Estimator.

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

Predict on future data where the target is unknown.

Parameters

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

• X_train (pd.DataFrame, np.ndarray) – Data the pipeline was trained on of shape [n_samples_train, n_features].

• y_train (pd.Series, np.ndarray) – Targets used to train the pipeline of shape [n_samples_train].

Returns

Estimated probabilities.

Return type

pd.Series

Raises

ValueError – If final component is not an Estimator.

predict_proba_in_sample(self, X_holdout, y_holdout, X_train, y_train)

Predict on future data where the target is known, e.g. cross validation.

Parameters

• X_holdout (pd.DataFrame or np.ndarray) – Future data of shape [n_samples, n_features].

• y_holdout (pd.Series, np.ndarray) – Future target of shape [n_samples].

• X_train (pd.DataFrame, np.ndarray) – Data the pipeline was trained on of shape [n_samples_train, n_features].

• y_train (pd.Series, np.ndarray) – Targets used to train the pipeline of shape [n_samples_train].

Returns

Estimated probabilities.

Return type

pd.Series

Raises

ValueError – If the 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 current and additional objectives.

Parameters

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

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

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

• X_train (pd.DataFrame, np.ndarray) – Data the pipeline was trained on of shape [n_samples_train, n_features].

• y_train (pd.Series, np.ndarray) – Targets used to train the pipeline of shape [n_samples_train].

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) – Targets corresponding to the pipeline targets.

• X_train (pd.DataFrame) – Training data used to generate generates from past observations.

• y_train (pd.Series) – Training targets used to generate features from past observations.

Returns

New transformed features.

Return type

pd.DataFrame

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