prophet_regressor

Module Contents

Classes Summary

ProphetRegressor

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.

Contents

class evalml.pipelines.components.estimators.regressors.prophet_regressor.ProphetRegressor(date_index=None, changepoint_prior_scale=0.05, seasonality_prior_scale=10, holidays_prior_scale=10, seasonality_mode='additive', random_seed=0, stan_backend='CMDSTANPY', **kwargs)[source]

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

More information here: https://facebook.github.io/prophet/

Attributes

hyperparameter_ranges

{ “changepoint_prior_scale”: Real(0.001, 0.5), “seasonality_prior_scale”: Real(0.01, 10), “holidays_prior_scale”: Real(0.01, 10), “seasonality_mode”: [“additive”, “multiplicative”],}

model_family

ModelFamily.PROPHET

modifies_features

True

modifies_target

False

name

Prophet Regressor

predict_uses_y

False

supported_problem_types

[ProblemTypes.TIME_SERIES_REGRESSION]

Methods

build_prophet_df

clone

Constructs a new component with the same parameters and random state.

default_parameters

Returns the default parameters for this component.

describe

Describe a component and its parameters

feature_importance

Returns array of 0’s with len(1) as feature_importance is not defined for Prophet regressor.

fit

Fits component to data

get_params

load

Loads component at file path

needs_fitting

Returns boolean determining if component needs fitting before

parameters

Returns the parameters which were used to initialize the component

predict

Make predictions using selected features.

predict_proba

Make probability estimates for labels.

save

Saves component at file path

static build_prophet_df(X, y=None, date_column='ds')[source]
clone(self)

Constructs a new component with the same parameters and random state.

Returns

A new instance of this component with identical parameters and random state.

default_parameters(cls)

Returns the default parameters for this component.

Our convention is that Component.default_parameters == Component().parameters.

Returns

default parameters for this component.

Return type

dict

describe(self, print_name=False, return_dict=False)

Describe a component and its parameters

Parameters
  • print_name (bool, optional) – whether to print name of component

  • return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

prints and returns dictionary

Return type

None or dict

property feature_importance(self)

Returns array of 0’s with len(1) as feature_importance is not defined for Prophet regressor.

fit(self, X, y=None)[source]

Fits component to data

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

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

Returns

self

get_params(self)[source]
static load(file_path)

Loads component at file path

Parameters

file_path (str) – Location to load file

Returns

ComponentBase object

needs_fitting(self)

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances. This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.

property parameters(self)

Returns the parameters which were used to initialize the component

predict(self, X, y=None)[source]

Make predictions using selected features.

Parameters

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

Returns

Predicted values

Return type

pd.Series

predict_proba(self, X)

Make probability estimates for labels.

Parameters

X (pd.DataFrame, or np.ndarray) – Features

Returns

Probability estimates

Return type

pd.Series

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

Saves component at file path

Parameters
  • file_path (str) – Location to save file

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

Returns

None