From e59893502a0c45b74c2d2999e87b1716898f0f21 Mon Sep 17 00:00:00 2001 From: Gabriel Riegner <54326829+griegner@users.noreply.github.com> Date: Tue, 23 Apr 2024 06:44:40 -0700 Subject: [PATCH] [DOC] Remove optional from docstrings (#1458) * [DOC] Remove optional from docstrings (#1451) * find `optional \(default=(.*)\)`, replace with `default=$1` * review all changes * add my contribution --- .all-contributorsrc | 9 ++ aeon/annotation/base/_base.py | 6 +- aeon/anomaly_detection/_stray.py | 10 +- aeon/benchmarking/benchmarks.py | 2 +- aeon/benchmarking/forecasting.py | 2 +- aeon/clustering/_kernel_k_means.py | 2 +- aeon/datasets/_data_loaders.py | 4 +- aeon/datasets/_single_problem_loaders.py | 32 ++--- aeon/forecasting/ardl.py | 10 +- aeon/forecasting/arima.py | 134 +++++++++--------- aeon/forecasting/base/_base.py | 80 +++++------ aeon/forecasting/base/_delegate.py | 26 ++-- aeon/forecasting/base/_fh.py | 24 ++-- aeon/forecasting/base/adapters/_fbprophet.py | 6 +- aeon/forecasting/base/adapters/_pmdarima.py | 8 +- .../base/adapters/_statsforecast.py | 10 +- .../forecasting/base/adapters/_statsmodels.py | 6 +- aeon/forecasting/base/adapters/_tbats.py | 10 +- aeon/forecasting/bats.py | 20 +-- aeon/forecasting/compose/_bagging.py | 14 +- aeon/forecasting/compose/_column_ensemble.py | 18 +-- .../compose/_hierarchy_ensemble.py | 8 +- aeon/forecasting/compose/_pipeline.py | 52 +++---- aeon/forecasting/compose/_reduce.py | 56 ++++---- aeon/forecasting/compose/_stack.py | 10 +- aeon/forecasting/conformal.py | 10 +- aeon/forecasting/croston.py | 8 +- aeon/forecasting/dynamic_factor.py | 6 +- aeon/forecasting/ets.py | 4 +- aeon/forecasting/model_selection/_split.py | 32 ++--- aeon/forecasting/model_selection/_tune.py | 34 ++--- aeon/forecasting/naive.py | 18 +-- .../online_learning/_online_ensemble.py | 10 +- aeon/forecasting/reconcile.py | 12 +- aeon/forecasting/squaring_residuals.py | 18 +-- aeon/forecasting/stream/_update.py | 20 +-- aeon/forecasting/structural.py | 6 +- aeon/forecasting/tbats.py | 20 +-- aeon/forecasting/theta.py | 14 +- aeon/forecasting/trend.py | 12 +- aeon/forecasting/var.py | 14 +- aeon/forecasting/varmax.py | 4 +- aeon/forecasting/vecm.py | 6 +- aeon/registry/_lookup.py | 18 +-- .../mock_estimators/_mock_forecasters.py | 28 ++-- aeon/transformations/collection/segment.py | 2 +- aeon/transformations/collection/truncate.py | 7 +- aeon/transformations/compose.py | 10 +- aeon/transformations/date.py | 6 +- aeon/transformations/kalman_filter.py | 34 ++--- aeon/transformations/lag.py | 4 +- aeon/transformations/outlier_detection.py | 2 +- aeon/transformations/scaledlogit.py | 2 +- aeon/transformations/summarize.py | 6 +- aeon/transformations/time_since.py | 2 +- aeon/utils/datetime.py | 2 +- aeon/utils/validation/__init__.py | 2 +- aeon/utils/validation/forecasting.py | 28 ++-- aeon/utils/validation/panel.py | 24 ++-- aeon/utils/validation/series.py | 4 +- extension_templates/annotation.py | 8 +- extension_templates/clustering.py | 4 +- extension_templates/early_classification.py | 4 +- extension_templates/forecasting.py | 30 ++-- extension_templates/forecasting_simple.py | 12 +- extension_templates/transformer.py | 6 +- extension_templates/transformer_simple.py | 4 +- 67 files changed, 533 insertions(+), 523 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index ce7d77cdb2..e24137f4f0 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -2387,6 +2387,15 @@ "contributions": [ "doc" ] + }, + { + "login": "griegner", + "name": "Gabriel Riegner", + "avatar_url": "https://avatars.githubusercontent.com/u/54326829?v=4", + "profile": "https://github.com/griegner", + "contributions": [ + "doc" + ] } ], "commitType": "docs" diff --git a/aeon/annotation/base/_base.py b/aeon/annotation/base/_base.py index 1a2a60b34a..ab15b19b9d 100644 --- a/aeon/annotation/base/_base.py +++ b/aeon/annotation/base/_base.py @@ -31,11 +31,11 @@ class BaseSeriesAnnotator(BaseEstimator): Parameters ---------- - fmt : str {"dense", "sparse"}, optional (default="dense") + fmt : str {"dense", "sparse"}, default="dense" annotation output format: * If "sparse", a sub-series of labels for only the outliers in X is returned, * If "dense", a series of labels for all values in X is returned. - labels : str {"indicator", "score"}, optional (default="indicator") + labels : str {"indicator", "score"}, default="indicator" annotation output labels: * If "indicator", returned values are boolean, indicating whether a value is an outlier, @@ -210,7 +210,7 @@ def fit_predict(self, X, Y=None): ---------- X : pd.DataFrame, pd.Series or np.ndarray Data to be transformed - Y : pd.Series or np.ndarray, optional (default=None) + Y : pd.Series or np.ndarray, default=None Target values of data to be predicted. Returns diff --git a/aeon/anomaly_detection/_stray.py b/aeon/anomaly_detection/_stray.py index 26204c548a..b0b8e8ae90 100644 --- a/aeon/anomaly_detection/_stray.py +++ b/aeon/anomaly_detection/_stray.py @@ -27,22 +27,22 @@ class STRAY(BaseTransformer): Parameters ---------- - alpha : float, optional (default=0.01) + alpha : float, default=0.01 Threshold for determining the cutoff for outliers. Observations are considered outliers if they fall in the (1 - alpha) tail of the distribution of the nearest-neighbor distances between exemplars. - k : int, optional (default=10) + k : int, default=10 Number of neighbours considered. knn_algorithm : str {"auto", "ball_tree", "kd_tree", "brute"}, optional (default="brute") Algorithm used to compute the nearest neighbors, from sklearn.neighbors.NearestNeighbors - p : float, optional (default=0.5) + p : float, default=0.5 Proportion of possible candidates for outliers. This defines the starting point for the bottom up searching algorithm. - size_threshold : int, optional (default=50) + size_threshold : int, default=50 Sample size to calculate an emperical threshold. - outlier_tail : str {"min", "max"}, optional (default="max") + outlier_tail : str {"min", "max"}, default="max" Direction of the outlier tail. Attributes diff --git a/aeon/benchmarking/benchmarks.py b/aeon/benchmarking/benchmarks.py index 977344a186..b8f1478741 100644 --- a/aeon/benchmarking/benchmarks.py +++ b/aeon/benchmarking/benchmarks.py @@ -38,7 +38,7 @@ def add_estimator( ---------- estimator : BaseEstimator object Estimator to add to the benchmark. - estimator_id : str, optional (default=None) + estimator_id : str, default=None Identifier for estimator. If none given then uses estimator's class name. """ diff --git a/aeon/benchmarking/forecasting.py b/aeon/benchmarking/forecasting.py index 283271a551..3fcc7f97dd 100644 --- a/aeon/benchmarking/forecasting.py +++ b/aeon/benchmarking/forecasting.py @@ -84,7 +84,7 @@ def add_task( Splitter used for generating validation folds. scorers : a list of Callable scoring functions Each scoring function output will be included in the results. - task_id : str, optional (default=None) + task_id : str, default=None Identifier for the benchmark task. If none given then uses dataset loader name combined with cv_splitter class name. diff --git a/aeon/clustering/_kernel_k_means.py b/aeon/clustering/_kernel_k_means.py index 747604fc1e..c4ba615a3c 100644 --- a/aeon/clustering/_kernel_k_means.py +++ b/aeon/clustering/_kernel_k_means.py @@ -44,7 +44,7 @@ class TimeSeriesKernelKMeans(BaseClusterer): convergence. verbose: bool, default=False Verbosity mode. - n_jobs : int or None, optional (default=None) + n_jobs : int or None, default=None The number of jobs to run in parallel for GAK cross-similarity matrix computations. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. diff --git a/aeon/datasets/_data_loaders.py b/aeon/datasets/_data_loaders.py index d646682ae7..745749ae89 100644 --- a/aeon/datasets/_data_loaders.py +++ b/aeon/datasets/_data_loaders.py @@ -493,10 +493,10 @@ def _load_tsc_dataset( Parameters ---------- name : string, file name to load from - split: None or one of "TRAIN", "TEST", optional (default=None) + split: None or one of "TRAIN", "TEST", default=None Whether to load the train or test instances of the problem. By default it loads both train and test instances (in a single container). - return_X_y: bool, optional (default=True) + return_X_y: bool, default=True If True, returns (features, target) separately instead of a single dataframe with columns for features and the target. return_data_type : str, optional, default = None diff --git a/aeon/datasets/_single_problem_loaders.py b/aeon/datasets/_single_problem_loaders.py index 488802e68d..cad9d0cbc9 100644 --- a/aeon/datasets/_single_problem_loaders.py +++ b/aeon/datasets/_single_problem_loaders.py @@ -51,7 +51,7 @@ def load_gunpoint(split=None, return_X_y=True, return_type="numpy3d"): return_X_y: bool, default=True If True, returns (features, target) separately instead of as single data structure. - return_type: string, optional (default="numpy3d") + return_type: string, default="numpy3d" Data structure to use for time series, should be either "numpy2d" or "numpy3d". Raises @@ -97,7 +97,7 @@ def load_osuleaf(split=None, return_X_y=True, return_type="numpy3d"): return_X_y: bool, default=True If True, returns (features, target) separately instead of as single data structure. - return_type: string, optional (default="numpy3d") + return_type: string, default="numpy3d" Data structure to use for time series, should be either "numpy2d" or "numpy3d". Raises @@ -143,7 +143,7 @@ def load_italy_power_demand(split=None, return_X_y=True, return_type="numpy3d"): return_X_y: bool, default=True If True, returns (features, target) separately instead of as single data structure. - return_type: string, optional (default="numpy3d") + return_type: string, default="numpy3d" Data structure to use for time series, should be either "numpy2d" or "numpy3d". Raises @@ -196,7 +196,7 @@ def load_unit_test(split=None, return_X_y=True, return_type="numpy3d"): return_X_y: bool, default=True If True, returns (features, target) separately instead of as single data structure. - return_type: string, optional (default="numpy3d") + return_type: string, default="numpy3d" Data structure containing series, should be either "numpy2d" or "numpy3d". Raises @@ -245,7 +245,7 @@ def load_arrow_head(split=None, return_X_y=True, return_type="numpy3d"): return_X_y: bool, default=True If True, returns (features, target) separately instead of as single data structure. - return_type: string, optional (default="numpy3d") + return_type: string, default="numpy3d" Data structure to use for time series, should be either "numpy2d" or "numpy3d". Raises @@ -293,7 +293,7 @@ def load_acsf1(split=None, return_X_y=True, return_type="numpy3d"): return_X_y: bool, default=True If True, returns (features, target) separately instead of as single data structure. - return_type: string, optional (default="numpy3d") + return_type: string, default="numpy3d" Data structure to use for time series, should be either "numpy2d" or "numpy3d". Raises @@ -342,7 +342,7 @@ def load_basic_motions(split=None, return_X_y=True, return_type="numpy3d"): return_X_y: bool, default=True If True, returns (features, target) separately instead of as single data structure. - return_type: string, optional (default="numpy3d") + return_type: string, default="numpy3d" Data structure to use for time series, should be "numpy3d" or "np-list". Raises @@ -430,10 +430,10 @@ def load_japanese_vowels(split=None, return_X_y=True, return_type="np-list"): Parameters ---------- - split: None or one of "TRAIN", "TEST", optional (default=None) + split: None or one of "TRAIN", "TEST", default=None Whether to load the train or test instances of the problem. By default it loads both train and test instances into a single array. - return_X_y: bool, optional (default=True) + return_X_y: bool, default=True If True, returns (features, target) separately instead of a single dataframe with columns for features and the target. return_type: string, default="np-list" @@ -510,7 +510,7 @@ def load_longley(y_name="TOTEMP"): Parameters ---------- - y_name: str, optional (default="TOTEMP") + y_name: str, default="TOTEMP" Name of target variable (y) Returns @@ -1029,14 +1029,14 @@ def load_covid_3month(split=None, return_X_y=True, return_type="numpy3d"): Parameters ---------- - split: None or str{"train", "test"}, optional (default=None) + split: None or str{"train", "test"}, default=None Whether to load the train or test partition of the problem. By default, it loads both. - return_X_y: bool, optional (default=True) + return_X_y: bool, default=True If True, returns (features, target) separately instead of a single dataframe with columns for features and the target. - return_type: string, optional (default="numpy3d") + return_type: string, default="numpy3d" Data structure to use for time series, should be either "numpy2d" or "numpy3d". Returns @@ -1084,14 +1084,14 @@ def load_cardano_sentiment(split=None, return_X_y=True, return_type="numpy3d"): Parameters ---------- - split: None or str{"train", "test"}, optional (default=None) + split: None or str{"train", "test"}, default=None Whether to load the train or test partition of the problem. By default, it loads both. - return_X_y: bool, optional (default=True) + return_X_y: bool, default=True If True, returns (features, target) separately instead of a single dataframe with columns for features and the target. - return_type: string, optional (default="numpy3d") + return_type: string, default="numpy3d" Data structure to use for time series, should be either "numpy2d" or "numpy3d". Returns diff --git a/aeon/forecasting/ardl.py b/aeon/forecasting/ardl.py index 2492040ebf..ad0a85c634 100644 --- a/aeon/forecasting/ardl.py +++ b/aeon/forecasting/ardl.py @@ -306,7 +306,7 @@ def _fit(self, y, X=None, fh=None): if self.get_tag("y_input_type")=="univariate": guaranteed to have a single column/variable A 1-d endogenous response variable. The dependent variable. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to fit to. Exogenous variables to include in the model. Either a DataFrame or @@ -392,10 +392,10 @@ def _predict(self, fh, X=None): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series for the forecast @@ -442,10 +442,10 @@ def _update(self, y, X=None, update_params=True): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series for the forecast - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns diff --git a/aeon/forecasting/arima.py b/aeon/forecasting/arima.py index b385533a51..228fee4943 100644 --- a/aeon/forecasting/arima.py +++ b/aeon/forecasting/arima.py @@ -44,94 +44,94 @@ class AutoARIMA(_PmdArimaAdapter): Parameters ---------- - start_p : int, optional (default=2) + start_p : int, default=2 The starting value of p, the order (or number of time lags) of the auto-regressive ("AR") model. Must be a positive integer. - d : int, optional (default=None) + d : int, default=None The order of first-differencing. If None (by default), the value will automatically be selected based on the results of the test (i.e., either the Kwiatkowski–Phillips–Schmidt–Shin, Augmented Dickey-Fuller or the Phillips–Perron test will be conducted to find the most probable value). Must be a positive integer or None. Note that if d is None, the runtime could be significantly longer. - start_q : int, optional (default=2) + start_q : int, default=2 The starting value of q, the order of the moving-average ("MA") model. Must be a positive integer. - max_p : int, optional (default=5) + max_p : int, default=5 The maximum value of p, inclusive. Must be a positive integer greater than or equal to start_p. - max_d : int, optional (default=2) + max_d : int, default=2 The maximum value of d, or the maximum number of non-seasonal differences. Must be a positive integer greater than or equal to d. - max_q : int, optional (default=5) + max_q : int, default=5 he maximum value of q, inclusive. Must be a positive integer greater than start_q. - start_P : int, optional (default=1) + start_P : int, default=1 The starting value of P, the order of the auto-regressive portion of the seasonal model. - D : int, optional (default=None) + D : int, default=None The order of the seasonal differencing. If None (by default, the value will automatically be selected based on the results of the seasonal_test. Must be a positive integer or None. - start_Q : int, optional (default=1) + start_Q : int, default=1 The starting value of Q, the order of the moving-average portion of the seasonal model. - max_P : int, optional (default=2) + max_P : int, default=2 The maximum value of P, inclusive. Must be a positive integer greater than start_P. - max_D : int, optional (default=1) + max_D : int, default=1 The maximum value of D. Must be a positive integer greater than D. - max_Q : int, optional (default=2) + max_Q : int, default=2 The maximum value of Q, inclusive. Must be a positive integer greater than start_Q. - max_order : int, optional (default=5) + max_order : int, default=5 Maximum value of p+q+P+Q if model selection is not stepwise. If the sum of p and q is >= max_order, a model will not be fit with those parameters, but will progress to the next combination. Default is 5. If max_order is None, it means there are no constraints on maximum order. - sp : int, optional (default=1) + sp : int, default=1 The period for seasonal differencing, sp refers to the number of periods in each season. For example, sp is 4 for quarterly data, 12 for monthly data, or 1 for annual (non-seasonal) data. Default is 1. Note that if sp == 1 (i.e., is non-seasonal), seasonal will be set to False. For more information on setting this parameter, see Setting sp. (link to http://alkaline-ml.com/pmdarima/tips_and_tricks.html#period) - seasonal : bool, optional (default=True) + seasonal : bool, default=True Whether to fit a seasonal ARIMA. Default is True. Note that if seasonal is True and sp == 1, seasonal will be set to False. - stationary : bool, optional (default=False) + stationary : bool, default=False Whether the time-series is stationary and d should be set to zero. - information_criterion : str, optional (default='aic') + information_criterion : str, default='aic' The information criterion used to select the best ARIMA model. One of pmdarima.arima.auto_arima.VALID_CRITERIA, ('aic', 'bic', 'hqic', 'oob'). - alpha : float, optional (default=0.05) + alpha : float, default=0.05 Level of the test for testing significance. - test : str, optional (default='kpss') + test : str, default='kpss' Type of unit root test to use in order to detect stationarity if stationary is False and d is None. - seasonal_test : str, optional (default='ocsb') + seasonal_test : str, default='ocsb' This determines which seasonal unit root test is used if seasonal is True and D is None. - stepwise : bool, optional (default=True) + stepwise : bool, default=True Whether to use the stepwise algorithm outlined in Hyndman and Khandakar (2008) to identify the optimal model parameters. The stepwise algorithm can be significantly faster than fitting all (or a random subset of) hyper-parameter combinations and is less likely to over-fit the model. - n_jobs : int, optional (default=1) + n_jobs : int, default=1 The number of models to fit in parallel in the case of a grid search (stepwise=False). Default is 1, but -1 can be used to designate "as many as possible". - start_params : array-like, optional (default=None) + start_params : array-like, default=None Starting parameters for ARMA(p,q). If None, the default is given by ARMA._fit_start_params. - trend : str, optional (default=None) + trend : str, default=None The trend parameter. If with_intercept is True, trend will be used. If with_intercept is False, the trend will be set to a no- intercept value. - method : str, optional (default='lbfgs') + method : str, default='lbfgs' The ``method`` determines which solver from ``scipy.optimize`` is used, and it can be chosen from among the following strings: @@ -148,19 +148,19 @@ class AutoARIMA(_PmdArimaAdapter): with the exception of the basin-hopping solver. Each solver has several optional arguments that are not the same across solvers. These can be passed as **fit_kwargs - maxiter : int, optional (default=50) + maxiter : int, default=50 The maximum number of function evaluations. - offset_test_args : dict, optional (default=None) + offset_test_args : dict, default=None The args to pass to the constructor of the offset (d) test. See pmdarima.arima.stationarity for more details. - seasonal_test_args : dict, optional (default=None) + seasonal_test_args : dict, default=None The args to pass to the constructor of the seasonal offset (D) test. See pmdarima.arima.seasonality for more details. - suppress_warnings : bool, optional (default=False) + suppress_warnings : bool, default=False Many warnings might be thrown inside of statsmodels. If suppress_warnings is True, all of the warnings coming from ARIMA will be squelched. - error_action : str, optional (default='warn') + error_action : str, default='warn' If unable to fit an ARIMA due to stationarity issues, whether to warn ('warn'), raise the ValueError ('raise') or ignore ('ignore'). Note that the default behavior is to warn, and fits that fail will be @@ -168,23 +168,23 @@ class AutoARIMA(_PmdArimaAdapter): ARIMA and SARIMAX models hit bugs periodically that can cause an otherwise healthy parameter combination to fail for reasons not related to pmdarima. - trace : bool, optional (default=False) + trace : bool, default=False Whether to print status on the fits. A value of False will print no debugging information. A value of True will print some. Integer values exceeding 1 will print increasing amounts of debug information at each fit. - random : bool, optional (default='False') + random : bool, default='False' Similar to grid searches, auto_arima provides the capability to perform a "random search" over a hyper-parameter space. If random is True, rather than perform an exhaustive search or stepwise search, only n_fits ARIMA models will be fit (stepwise must be False for this option to do anything). - random_state : int, long or numpy RandomState, optional (default=None) + random_state : int, long or numpy RandomState, default=None The PRNG for when random=True. Ensures replicable testing and results. - n_fits : int, optional (default=10) + n_fits : int, default=10 If random is True and a "random search" is going to be performed, n_iter is the number of ARIMA models to be fit. - out_of_sample_size : int, optional (default=0) + out_of_sample_size : int, default=0 The number of examples from the tail of the time series to hold out and use as validation examples. The model will not be fit on these samples, but the observations will be added into the model's ``endog`` @@ -195,28 +195,28 @@ class AutoARIMA(_PmdArimaAdapter): > Fit on: [0, 1, 2, 3, 4] > Score on: [5, 6] > Append [5, 6] to end of self.arima_res_.data.endog values - scoring : str, optional (default='mse') + scoring : str, default='mse' If performing validation (i.e., if out_of_sample_size > 0), the metric to use for scoring the out-of-sample data. One of ('mse', 'mae') - scoring_args : dict, optional (default=None) + scoring_args : dict, default=None A dictionary of key-word arguments to be passed to the scoring metric. - with_intercept : bool, optional (default=True) + with_intercept : bool, default=True Whether to include an intercept term. - update_pdq : bool, optional (default=True) + update_pdq : bool, default=True whether to update pdq parameters in update True: model is refit on all data seen so far, potentially updating p,d,q False: model updates only ARIMA coefficients via likelihood, as in pmdarima Further arguments to pass to the SARIMAX constructor: - - time_varying_regression : boolean, optional (default=False) + - time_varying_regression : boolean, default=False Whether or not coefficients on the exogenous regressors are allowed to vary over time. - - enforce_stationarity : boolean, optional (default=True) + - enforce_stationarity : boolean, default=True Whether or not to transform the AR parameters to enforce stationarity in the auto-regressive component of the model. - - enforce_invertibility : boolean, optional (default=True) + - enforce_invertibility : boolean, default=True Whether or not to transform the MA parameters to enforce invertibility in the moving average component of the model. - - simple_differencing : boolean, optional (default=False) + - simple_differencing : boolean, default=False Whether or not to use partially conditional maximum likelihood estimation for seasonal ARIMA models. If True, differencing is performed prior to estimation, which discards the first @@ -224,20 +224,20 @@ class AutoARIMA(_PmdArimaAdapter): state-space formulation. If False, the full SARIMAX model is put in state-space form so that all datapoints can be used in estimation. Default is False. - - measurement_error: boolean, optional (default=False) + - measurement_error: boolean, default=False Whether or not to assume the endogenous observations endog were measured with error. Default is False. - - mle_regression : boolean, optional (default=True) + - mle_regression : boolean, default=True Whether or not to use estimate the regression coefficients for the exogenous variables as part of maximum likelihood estimation or through the Kalman filter (i.e. recursive least squares). If time_varying_regression is True, this must be set to False. Default is True. - - hamilton_representation : boolean, optional (default=False) + - hamilton_representation : boolean, default=False Whether or not to use the Hamilton representation of an ARMA process (if True) or the Harvey representation (if False). Default is False. - - concentrate_scale : boolean, optional (default=False) + - concentrate_scale : boolean, default=False Whether or not to concentrate the scale (variance of the error term) out of the likelihood. This reduces the number of parameters estimated by maximum likelihood by one, but standard errors will @@ -426,7 +426,7 @@ def _update(self, y, X=None, update_params=True): ---------- y : pd.Series Target time series to which to fit the forecaster. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored Returns @@ -518,7 +518,7 @@ class ARIMA(_PmdArimaAdapter): Parameters ---------- - order : iterable or array-like, shape=(3,), optional (default=(1, 0, 0)) + order : iterable or array-like, shape=(3,), default=(1, 0, 0) The (p,d,q) order of the model for the number of AR parameters, differences, and MA parameters to use. ``p`` is the order (number of time lags) of the auto-regressive model, and is a non-negative integer. @@ -526,7 +526,7 @@ class ARIMA(_PmdArimaAdapter): had past values subtracted), and is a non-negative integer. ``q`` is the order of the moving-average model, and is a non-negative integer. Default is an AR(1) model: (1,0,0). - seasonal_order : array-like, shape=(4,), optional (default=(0, 0, 0, 0)) + seasonal_order : array-like, shape=(4,), default=(0, 0, 0, 0) The (P,D,Q,s) order of the seasonal component of the model for the AR parameters, differences, MA parameters, and periodicity. ``D`` must be an integer indicating the integration order of the process, while @@ -536,10 +536,10 @@ class ARIMA(_PmdArimaAdapter): integer giving the periodicity (number of periods in season), often it is 4 for quarterly data or 12 for monthly data. Default is no seasonal effect. - start_params : array-like, optional (default=None) + start_params : array-like, default=None Starting parameters for ``ARMA(p,q)``. If None, the default is given by ``ARMA._fit_start_params``. - method : str, optional (default='lbfgs') + method : str, default='lbfgs' The ``method`` determines which solver from ``scipy.optimize`` is used, and it can be chosen from among the following strings: @@ -556,12 +556,12 @@ class ARIMA(_PmdArimaAdapter): with the exception of the basin-hopping solver. Each solver has several optional arguments that are not the same across solvers. These can be passed as **fit_kwargs - maxiter : int, optional (default=50) + maxiter : int, default=50 The maximum number of function evaluations. Default is 50 - suppress_warnings : bool, optional (default=False) + suppress_warnings : bool, default=False Many warnings might be thrown inside of statsmodels. If ``suppress_warnings`` is True, all of these warnings will be squelched. - out_of_sample_size : int, optional (default=0) + out_of_sample_size : int, default=0 The number of examples from the tail of the time series to hold out and use as validation examples. The model will not be fit on these samples, but the observations will be added into the model's ``endog`` @@ -573,7 +573,7 @@ class ARIMA(_PmdArimaAdapter): > Fit on: [0, 1, 2, 3, 4] > Score on: [5, 6] > Append [5, 6] to end of self.arima_res_.data.endog values - scoring : str or callable, optional (default='mse') + scoring : str or callable, default='mse' If performing validation (i.e., if ``out_of_sample_size`` > 0), the metric to use for scoring the out-of-sample data: @@ -588,27 +588,27 @@ def foo_loss(y_true, y_pred) maximizing metric (such as ``sklearn.metrics.r2_score``), it is the user's responsibility to wrap the function such that it returns a negative value for minimizing. - scoring_args : dict, optional (default=None) + scoring_args : dict, default=None A dictionary of key-word arguments to be passed to the ``scoring`` metric. - trend : str or None, optional (default=None) + trend : str or None, default=None The trend parameter. If ``with_intercept`` is True, ``trend`` will be used. If ``with_intercept`` is False, the trend will be set to a no- intercept value. If None and ``with_intercept``, 'c' will be used as a default. - with_intercept : bool, optional (default=True) + with_intercept : bool, default=True Whether to include an intercept term. Default is True. Further arguments to pass to the SARIMAX constructor: - - time_varying_regression : boolean, optional (default=False) + - time_varying_regression : boolean, default=False Whether or not coefficients on the exogenous regressors are allowed to vary over time. - - enforce_stationarity : boolean, optional (default=True) + - enforce_stationarity : boolean, default=True Whether or not to transform the AR parameters to enforce stationarity in the auto-regressive component of the model. - - enforce_invertibility : boolean, optional (default=True) + - enforce_invertibility : boolean, default=True Whether or not to transform the MA parameters to enforce invertibility in the moving average component of the model. - - simple_differencing : boolean, optional (default=False) + - simple_differencing : boolean, default=False Whether or not to use partially conditional maximum likelihood estimation for seasonal ARIMA models. If True, differencing is performed prior to estimation, which discards the first @@ -616,20 +616,20 @@ def foo_loss(y_true, y_pred) state-space formulation. If False, the full SARIMAX model is put in state-space form so that all datapoints can be used in estimation. Default is False. - - measurement_error: boolean, optional (default=False) + - measurement_error: boolean, default=False Whether or not to assume the endogenous observations endog were measured with error. Default is False. - - mle_regression : boolean, optional (default=True) + - mle_regression : boolean, default=True Whether or not to use estimate the regression coefficients for the exogenous variables as part of maximum likelihood estimation or through the Kalman filter (i.e. recursive least squares). If time_varying_regression is True, this must be set to False. Default is True. - - hamilton_representation : boolean, optional (default=False) + - hamilton_representation : boolean, default=False Whether or not to use the Hamilton representation of an ARMA process (if True) or the Harvey representation (if False). Default is False. - - concentrate_scale : boolean, optional (default=False) + - concentrate_scale : boolean, default=False Whether or not to concentrate the scale (variance of the error term) out of the likelihood. This reduces the number of parameters estimated by maximum likelihood by one, but standard errors will diff --git a/aeon/forecasting/base/_base.py b/aeon/forecasting/base/_base.py index ac04f6f5bc..37769addc6 100644 --- a/aeon/forecasting/base/_base.py +++ b/aeon/forecasting/base/_base.py @@ -301,12 +301,12 @@ def fit(self, y, X=None, fh=None): For further details: on usage, see forecasting examples/forecasting on specification of formats, examples/datasets - X : time series in aeon compatible format, optional (default=None) + X : time series in aeon compatible format, default=None Exogeneous time series to fit to Should be of same abstract type (Series, Panel, or Hierarchical) as y if self.get_tag("X-y-must-have-same-index"), X.index must contain y.index there are no restrictions on number of columns (unlike for y). - fh : int, list, np.array or ForecastingHorizon, optional (default=None) + fh : int, list, np.array or ForecastingHorizon, default=None The forecasting horizon encoding the time stamps to forecast at. if self.get_tag("requires-fh-in-fit"), must be passed, not optional. @@ -365,10 +365,10 @@ def predict( Parameters ---------- - fh : int, list, np.array or ForecastingHorizon, optional (default=None) + fh : int, list, np.array or ForecastingHorizon, default=None The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional. - X : time series in aeon compatible format, optional (default=None) + X : time series in aeon compatible format, default=None Exogeneous time series to fit to Should be of same abstract type (Series, Panel, or Hierarchical) as y in fit if self.get_tag("X-y-must-have-same-index"), X.index must contain fh.index @@ -443,7 +443,7 @@ def fit_predict(self, y, X=None, fh=None): fh : int, list, np.array or ForecastingHorizon (not optional) The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional - X : time series in aeon compatible format, optional (default=None) + X : time series in aeon compatible format, default=None Exogeneous time series to fit to Should be of same abstract type (Series, Panel, or Hierarchical) as y in fit if self.get_tag("X-y-must-have-same-index"), @@ -506,11 +506,11 @@ def predict_quantiles(self, fh=None, X=None, alpha=None): fh : int, list, np.array or ForecastingHorizon (not optional) The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional - X : time series in aeon compatible format, optional (default=None) + X : time series in aeon compatible format, default=None Exogeneous time series to fit to Should be of same abstract type (Series, Panel, or Hierarchical) as y in fit if self.get_tag("X-y-must-have-same-index"), must contain fh.index - alpha : float or list of float of unique values, optional (default=[0.05, 0.95]) + alpha : float or list of float of unique values, default=[0.05, 0.95] A probability or list of, at which quantile forecasts are computed. Returns @@ -582,7 +582,7 @@ def predict_interval( fh : int, list, np.array or ForecastingHorizon (not optional) The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional. - X : time series in aeon compatible format, optional (default=None) + X : time series in aeon compatible format, default=None Exogeneous time series to fit to Should be of same abstract type (Series, Panel, or Hierarchical) as y in fit if self.get_tag("X-y-must-have-same-index"), must contain fh.index. @@ -652,12 +652,12 @@ def predict_var(self, fh=None, X=None, cov=False): fh : int, list, np.array or ForecastingHorizon (not optional) The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional - X : time series in aeon compatible format, optional (default=None) + X : time series in aeon compatible format, default=None Exogeneous time series to fit to Should be of same abstract type (Series, Panel, or Hierarchical) as y in fit if self.get_tag("X-y-must-have-same-index"), X.index must contain fh.index and y.index both - cov : bool, optional (default=False) + cov : bool, default=False if True, computes covariance matrix forecast. if False, computes marginal variance forecasts. @@ -813,12 +813,12 @@ def update(self, y, X=None, update_params=True): if self.get_tag("y_input_type")=="both": no restrictions apply For further details: See examples/forecasting, or examples/datasets, - X : time series in aeon compatible format, optional (default=None) + X : time series in aeon compatible format, default=None Exogeneous time series to fit to Should be of same type (Series, Panel, or Hierarchical) as y if self.get_tag("X-y-must-have-same-index"), X.index must contain y.index there are no restrictions on number of columns (unlike for y) - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns @@ -897,15 +897,15 @@ def update_predict( default = ExpandingWindowSplitter with `initial_window=1` and defaults = individual data points in y/X are added and forecast one-by-one, `initial_window = 1`, `step_length = 1` and `fh = 1` - X : time series in aeon compatible format, optional (default=None) + X : time series in aeon compatible format, default=None Exogeneous time series for updating and forecasting Should be of same abstract type (Series, Panel, or Hierarchical) as y if self.get_tag("X-y-must-have-same-index"), X.index must contain y.index and fh.index both there are no restrictions on number of columns (unlike for y). - update_params : bool, optional (default=True) + update_params : bool, default=True Whether model parameters should be updated in each update step. - reset_forecaster : bool, optional (default=True) + reset_forecaster : bool, default=True If True, will not change the state of the forecaster, i.e., update/predict sequence is run with a copy, and cutoff, model parameters, data memory of self do not change @@ -996,15 +996,15 @@ def update_predict_single( y must have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply For further details see examples/forecasting, or examples/datasets. - fh : int, list, np.array or ForecastingHorizon, optional (default=None) + fh : int, list, np.array or ForecastingHorizon, default=None The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional. - X : time series in aeon compatible format, optional (default=None) + X : time series in aeon compatible format, default=None Exogeneous time series for updating and forecasting Should be of same abstract type (Series, Panel, or Hierarchical) as y if self.get_tag("X-y-must-have-same-index"), X.index must contain y.index and fh.index both. - update_params : bool, optional (default=False) + update_params : bool, default=False Returns ------- @@ -1155,7 +1155,7 @@ def score(self, y, X=None, fh=None): X : pd.DataFrame, or 2D np.array, default=None Exogeneous time series to score. if self.get_tag("X-y-must-have-same-index"), X.index must contain y.index - fh : int, list, array-like or ForecastingHorizon, optional (default=None) + fh : int, list, array-like or ForecastingHorizon, default=None The forecasters horizon with the steps ahead to to predict. Returns @@ -1241,9 +1241,9 @@ def _check_X_y(self, X=None, y=None): Parameters ---------- - y : pd.Series, pd.DataFrame, or np.ndarray (1D or 2D), optional (default=None) + y : pd.Series, pd.DataFrame, or np.ndarray (1D or 2D), default=None Time series to check. - X : pd.DataFrame, or 2D np.array, optional (default=None) + X : pd.DataFrame, or 2D np.array, default=None Exogeneous time series. Returns @@ -1479,7 +1479,7 @@ def _update_y_X(self, y, X=None, enforce_index_type=None): ---------- y : pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Endogenous time series - X : pd.DataFrame or 2D np.ndarray, optional (default=None) + X : pd.DataFrame or 2D np.ndarray, default=None Exogeneous time series """ if y is not None: @@ -1762,11 +1762,11 @@ def _fit(self, y, X=None, fh=None): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True Otherwise, if not passed in _fit, guaranteed to be passed in _predict - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to fit to. @@ -1790,10 +1790,10 @@ def _predict(self, fh, X=None): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series for the forecast @@ -1829,10 +1829,10 @@ def _update(self, y, X=None, update_params=True): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series for the forecast - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns @@ -1901,10 +1901,10 @@ def _predict_interval(self, fh, X=None, coverage=0.90): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. - coverage : float or list, optional (default=0.95) + coverage : float or list, default=0.95 nominal coverage(s) of predictive interval(s) Returns @@ -1974,10 +1974,10 @@ def _predict_quantiles(self, fh, X, alpha): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. - alpha : list of float, optional (default=[0.5]) + alpha : list of float, default=[0.5] A list of probabilities at which quantile forecasts are computed. Returns @@ -2049,10 +2049,10 @@ def _predict_var(self, fh=None, X=None, cov=False): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. - cov : bool, optional (default=False) + cov : bool, default=False if True, computes covariance matrix forecast. if False, computes marginal variance forecasts. @@ -2141,10 +2141,10 @@ def _predict_proba(self, fh, X, marginal=True): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. - marginal : bool, optional (default=True) + marginal : bool, default=True whether returned distribution is marginal by time index Returns @@ -2207,16 +2207,16 @@ def _predict_moving_cutoff( y must have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply For further details see examples/forecasting, or examples/datasets - cv : temporal cross-validation generator, optional (default=None) - X : time series in aeon compatible format, optional (default=None) + cv : temporal cross-validation generator, default=None + X : time series in aeon compatible format, default=None Exogeneous time series for updating and forecasting Should be of same abstract type (Series, Panel, or Hierarchical) as y if self.get_tag("X-y-must-have-same-index"), X.index must contain y.index and fh.index both there are no restrictions on number of columns (unlike for y) - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated in each update step - reset_forecaster : bool, optional (default=True) + reset_forecaster : bool, default=True if True, will not change the state of the forecaster, i.e., update/predict sequence is run with a copy, and cutoff, model parameters, data memory of self do not change diff --git a/aeon/forecasting/base/_delegate.py b/aeon/forecasting/base/_delegate.py index 5d1abac8ff..30548fb554 100644 --- a/aeon/forecasting/base/_delegate.py +++ b/aeon/forecasting/base/_delegate.py @@ -54,11 +54,11 @@ def _fit(self, y, X=None, fh=None): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True Otherwise, if not passed in _fit, guaranteed to be passed in _predict - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to fit to. @@ -84,10 +84,10 @@ def _predict(self, fh, X=None): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series Returns @@ -123,9 +123,9 @@ def _update(self, y, X=None, update_params=True): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns @@ -165,7 +165,7 @@ def _predict_quantiles(self, fh, X=None, alpha=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. alpha : list of float (guaranteed not None and floats in [0,1] interval) @@ -200,7 +200,7 @@ def _predict_interval(self, fh, X=None, coverage=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. coverage : list of float (guaranteed not None and floats in [0,1] interval) @@ -229,12 +229,12 @@ def _predict_var(self, fh, X=None, cov=False): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series - cov : bool, optional (default=False) + cov : bool, default=False if True, computes covariance matrix forecast. if False, computes marginal variance forecasts. @@ -264,10 +264,10 @@ def _predict_proba(self, fh, X, marginal=True): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. - marginal : bool, optional (default=True) + marginal : bool, default=True whether returned distribution is marginal by time index Returns diff --git a/aeon/forecasting/base/_fh.py b/aeon/forecasting/base/_fh.py index ad2f1a04a9..ea14d0d4c1 100644 --- a/aeon/forecasting/base/_fh.py +++ b/aeon/forecasting/base/_fh.py @@ -192,7 +192,7 @@ class ForecastingHorizon: ---------- values : pd.Index, pd.TimedeltaIndex, np.array, list, pd.Timedelta, or int Values of forecasting horizon - is_relative : bool, optional (default=None) + is_relative : bool, default=None - If True, a relative ForecastingHorizon is created: values are relative to end of training series. - If False, an absolute ForecastingHorizon is created: @@ -200,7 +200,7 @@ class ForecastingHorizon: - if None, the flag is determined automatically: relative, if values are of supported relative index type absolute, if not relative and values of supported absolute index type - freq : str, pd.Index, pandas offset, or aeon forecaster, optional (default=None) + freq : str, pd.Index, pandas offset, or aeon forecaster, default=None object carrying frequency information on values ignored unless values is without inferrable freq @@ -333,7 +333,7 @@ def _new( - If None, determined automatically: same as self.is_relative - If True, values are relative to end of training series. - If False, values are absolute. - freq : str, optional (default=None) + freq : str, default=None Frequency string Returns @@ -447,7 +447,7 @@ def to_relative(self, cutoff=None): Parameters ---------- - cutoff : pd.Period, pd.Timestamp, int, or pd.Index, optional (default=None) + cutoff : pd.Period, pd.Timestamp, int, or pd.Index, default=None Cutoff value required to convert a relative forecasting horizon to an absolute one (and vice versa). If pd.Index, last/latest value is considered the cutoff @@ -485,7 +485,7 @@ def to_absolute_int(self, start, cutoff=None): ---------- start : pd.Period, pd.Timestamp, int Start value returned as zero. - cutoff : pd.Period, pd.Timestamp, int, or pd.Index, optional (default=None) + cutoff : pd.Period, pd.Timestamp, int, or pd.Index, default=None Cutoff value required to convert a relative forecasting horizon to an absolute one (and vice versa). If pd.Index, last/latest value is considered the cutoff @@ -538,7 +538,7 @@ def to_in_sample(self, cutoff=None): Parameters ---------- - cutoff : pd.Period, pd.Timestamp, int, optional (default=None) + cutoff : pd.Period, pd.Timestamp, int, default=None Cutoff value required to convert a relative forecasting horizon to an absolute one (and vice versa). @@ -556,7 +556,7 @@ def to_out_of_sample(self, cutoff=None): Parameters ---------- - cutoff : pd.Period, pd.Timestamp, int, optional (default=None) + cutoff : pd.Period, pd.Timestamp, int, default=None Cutoff value is required to convert a relative forecasting horizon to an absolute one (and vice versa). @@ -599,7 +599,7 @@ def is_all_out_of_sample(self, cutoff=None) -> bool: Parameters ---------- - cutoff : pd.Period, pd.Timestamp, int, optional (default=None) + cutoff : pd.Period, pd.Timestamp, int, default=None Cutoff value used to check if forecasting horizon is purely out-of-sample. @@ -616,10 +616,10 @@ def to_indexer(self, cutoff=None, from_cutoff=True): Parameters ---------- - cutoff : pd.Period, pd.Timestamp, int, optional (default=None) + cutoff : pd.Period, pd.Timestamp, int, default=None Cutoff value required to convert a relative forecasting horizon to an absolute one and vice versa. - from_cutoff : bool, optional (default=True) + from_cutoff : bool, default=True - If True, zero-based relative to cutoff. - If False, zero-based relative to first value in forecasting horizon. @@ -665,7 +665,7 @@ def _to_relative(fh: ForecastingHorizon, cutoff=None) -> ForecastingHorizon: Parameters ---------- fh : ForecastingHorizon - cutoff : pd.Period, pd.Timestamp, int, optional (default=None) + cutoff : pd.Period, pd.Timestamp, int, default=None Cutoff value required to convert a relative forecasting horizon to an absolute one (and vice versa). @@ -752,7 +752,7 @@ def _check_cutoff(cutoff, index): Parameters ---------- - cutoff : pd.Period, pd.Timestamp, int, optional (default=None) + cutoff : pd.Period, pd.Timestamp, int, default=None Cutoff value is required to convert a relative forecasting horizon to an absolute one and vice versa. index : pd.PeriodIndex or pd.DataTimeIndex diff --git a/aeon/forecasting/base/adapters/_fbprophet.py b/aeon/forecasting/base/adapters/_fbprophet.py index 2f62c517dc..f983f8d97b 100644 --- a/aeon/forecasting/base/adapters/_fbprophet.py +++ b/aeon/forecasting/base/adapters/_fbprophet.py @@ -56,9 +56,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables. - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. Returns @@ -220,7 +220,7 @@ def _predict_interval(self, fh, X=None, coverage=0.90): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon, default = y.index (in-sample forecast) - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series coverage : list of float (guaranteed not None and floats in [0,1] interval) nominal coverage(s) of predictive interval(s) diff --git a/aeon/forecasting/base/adapters/_pmdarima.py b/aeon/forecasting/base/adapters/_pmdarima.py index 0d8e310f09..4fb74b3bdb 100644 --- a/aeon/forecasting/base/adapters/_pmdarima.py +++ b/aeon/forecasting/base/adapters/_pmdarima.py @@ -34,9 +34,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - fh : int, list, np.array or ForecastingHorizon, optional (default=None) + fh : int, list, np.array or ForecastingHorizon, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored Returns @@ -56,7 +56,7 @@ def _update(self, y, X=None, update_params=True): ---------- y : pd.Series Target time series to which to fit the forecaster. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored Returns @@ -240,7 +240,7 @@ def _predict_interval(self, fh, X=None, coverage=0.90): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon, default = y.index (in-sample forecast) - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series coverage : list of float (guaranteed not None and floats in [0,1] interval) nominal coverage(s) of predictive interval(s) diff --git a/aeon/forecasting/base/adapters/_statsforecast.py b/aeon/forecasting/base/adapters/_statsforecast.py index dc0fb09323..68ab7b70f5 100644 --- a/aeon/forecasting/base/adapters/_statsforecast.py +++ b/aeon/forecasting/base/adapters/_statsforecast.py @@ -49,11 +49,11 @@ def _fit(self, y, X=None, fh=None): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True Otherwise, if not passed in _fit, guaranteed to be passed in _predict - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to fit to. @@ -80,10 +80,10 @@ def _predict(self, fh, X=None): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series Returns @@ -203,7 +203,7 @@ def _predict_interval(self, fh, X=None, coverage=0.90): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon, default = y.index (in-sample forecast) - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series coverage : list of float (guaranteed not None and floats in [0,1] interval) nominal coverage(s) of predictive interval(s) diff --git a/aeon/forecasting/base/adapters/_statsmodels.py b/aeon/forecasting/base/adapters/_statsmodels.py index eddc0fce86..9bef36de90 100644 --- a/aeon/forecasting/base/adapters/_statsmodels.py +++ b/aeon/forecasting/base/adapters/_statsmodels.py @@ -37,9 +37,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored Returns @@ -86,7 +86,7 @@ def _predict(self, fh, X=None): The forecasters horizon with the steps ahead to to predict. Default is one-step ahead forecast, i.e. np.array([1]) - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored. Returns diff --git a/aeon/forecasting/base/adapters/_tbats.py b/aeon/forecasting/base/adapters/_tbats.py index 56d3c8990f..c6c7bd2931 100644 --- a/aeon/forecasting/base/adapters/_tbats.py +++ b/aeon/forecasting/base/adapters/_tbats.py @@ -84,9 +84,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables (ignored) Returns @@ -110,9 +110,9 @@ def _update(self, y, X=None, update_params=True): ---------- y : pd.Series Target time series to which to fit the forecaster. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables (ignored) - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns @@ -253,7 +253,7 @@ def _predict_interval(self, fh, X, coverage): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon, default = y.index (in-sample forecast) - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Ignored, passed for interface compatibility coverage : list of float (guaranteed not None and floats in [0,1] interval) nominal coverage(s) of predictive interval(s) diff --git a/aeon/forecasting/bats.py b/aeon/forecasting/bats.py index 6d5a3e3e6c..569ac2052e 100644 --- a/aeon/forecasting/bats.py +++ b/aeon/forecasting/bats.py @@ -40,36 +40,36 @@ class BATS(_TbatsAdapter): Parameters ---------- - use_box_cox: bool or None, optional (default=None) + use_box_cox: bool or None, default=None If Box-Cox transformation of original series should be applied. When None both cases shall be considered and better is selected by AIC. - box_cox_bounds: tuple, shape=(2,), optional (default=(0, 1)) + box_cox_bounds: tuple, shape=(2,), default=(0, 1) Minimal and maximal Box-Cox parameter values. - use_trend: bool or None, optional (default=None) + use_trend: bool or None, default=None Indicates whether to include a trend or not. When None both cases shall be considered and better is selected by AIC. - use_damped_trend: bool or None, optional (default=None) + use_damped_trend: bool or None, default=None Indicates whether to include a damping parameter in the trend or not. Applies only when trend is used. When None both cases shall be considered and better is selected by AIC. - sp: Iterable or array-like of floats, optional (default=None) + sp: Iterable or array-like of floats, default=None Abbreviation of "seasonal periods". The length of each of the periods (amount of observations in each period). Accepts int and float values here. When None or empty array, non-seasonal model shall be fitted. - use_arma_errors: bool, optional (default=True) + use_arma_errors: bool, default=True When True BATS will try to improve the model by modelling residuals with ARMA. Best model will be selected by AIC. If False, ARMA residuals modeling will not be considered. - show_warnings: bool, optional (default=True) + show_warnings: bool, default=True If warnings should be shown or not. Also see Model.warnings variable that contains all model related warnings. - n_jobs: int, optional (default=None) + n_jobs: int, default=None How many jobs to run in parallel when fitting BATS model. When not provided BATS shall try to utilize all available cpu cores. - multiprocessing_start_method: str, optional (default='spawn') + multiprocessing_start_method: str, default='spawn' How threads should be started. See also: https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods - context: abstract.ContextInterface, optional (default=None) + context: abstract.ContextInterface, default=None For advanced users only. Provide this to override default behaviors See Also diff --git a/aeon/forecasting/compose/_bagging.py b/aeon/forecasting/compose/_bagging.py index 74e70f1273..4432f98fdd 100644 --- a/aeon/forecasting/compose/_bagging.py +++ b/aeon/forecasting/compose/_bagging.py @@ -143,11 +143,11 @@ def _fit(self, y, X=None, fh=None): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True Otherwise, if not passed in _fit, guaranteed to be passed in _predict - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to fit to. @@ -208,10 +208,10 @@ def _predict(self, fh, X=None): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series Returns @@ -241,7 +241,7 @@ def _predict_quantiles(self, fh, X=None, alpha=None): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series alpha : list of float (guaranteed not None and floats in [0,1] interval) A list of probabilities at which quantile forecasts are computed. @@ -267,9 +267,9 @@ def _update(self, y, X=None, update_params=True): ---------- y : pd.Series, pd.DataFrame, or np.array Target time series to which to fit the forecaster. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogeneous data - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns diff --git a/aeon/forecasting/compose/_column_ensemble.py b/aeon/forecasting/compose/_column_ensemble.py index 954d7f158e..5f08e40efc 100644 --- a/aeon/forecasting/compose/_column_ensemble.py +++ b/aeon/forecasting/compose/_column_ensemble.py @@ -170,9 +170,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.DataFrame Target time series to which to fit the forecaster. - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored. Returns @@ -259,10 +259,10 @@ def _predict(self, fh=None, X=None): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series Returns @@ -289,7 +289,7 @@ def _predict_quantiles(self, fh=None, X=None, alpha=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. alpha : list of float (guaranteed not None and floats in [0,1] interval) @@ -330,7 +330,7 @@ def _predict_interval(self, fh=None, X=None, coverage=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. coverage : list of float (guaranteed not None and floats in [0,1] interval) @@ -365,12 +365,12 @@ def _predict_var(self, fh, X=None, cov=False): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series - cov : bool, optional (default=False) + cov : bool, default=False if True, computes covariance matrix forecast. if False, computes marginal variance forecasts. diff --git a/aeon/forecasting/compose/_hierarchy_ensemble.py b/aeon/forecasting/compose/_hierarchy_ensemble.py index 854ee6f601..c41894b1f6 100644 --- a/aeon/forecasting/compose/_hierarchy_ensemble.py +++ b/aeon/forecasting/compose/_hierarchy_ensemble.py @@ -164,9 +164,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd-multiindex Target time series to which to fit the forecaster. - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored. Returns @@ -365,10 +365,10 @@ def _predict(self, fh=None, X=None): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series Returns diff --git a/aeon/forecasting/compose/_pipeline.py b/aeon/forecasting/compose/_pipeline.py index 8cdd51e1fd..fdd1d18ca0 100644 --- a/aeon/forecasting/compose/_pipeline.py +++ b/aeon/forecasting/compose/_pipeline.py @@ -448,7 +448,7 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series, pd.DataFrame Target time series to which to fit the forecaster. - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. X : pd.DataFrame, required Exogenous variables are ignored @@ -508,7 +508,7 @@ def _predict_quantiles(self, fh, X=None, alpha=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. alpha : list of float (guaranteed not None and floats in [0,1] interval) @@ -543,7 +543,7 @@ def _predict_interval(self, fh, X=None, coverage=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. coverage : list of float (guaranteed not None and floats in [0,1] interval) @@ -572,12 +572,12 @@ def _predict_var(self, fh, X=None, cov=False): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series - cov : bool, optional (default=False) + cov : bool, default=False if True, computes covariance matrix forecast. if False, computes marginal variance forecasts. @@ -607,10 +607,10 @@ def _predict_proba(self, fh, X, marginal=True): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. - marginal : bool, optional (default=True) + marginal : bool, default=True whether returned distribution is marginal by time index Returns @@ -637,7 +637,7 @@ def _update(self, y, X=None, update_params=True): ---------- y : pd.Series X : pd.DataFrame, required - update_params : bool, optional (default=True) + update_params : bool, default=True Returns ------- @@ -935,9 +935,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored Returns @@ -968,7 +968,7 @@ def _predict(self, fh=None, X=None): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series Returns @@ -992,8 +992,8 @@ def _update(self, y, X=None, update_params=True): Parameters ---------- y : pd.Series - X : pd.DataFrame, optional (default=None) - update_params : bool, optional (default=True) + X : pd.DataFrame, default=None + update_params : bool, default=True Returns ------- @@ -1071,7 +1071,7 @@ def _predict_quantiles(self, fh, X=None, alpha=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. alpha : list of float (guaranteed not None and floats in [0,1] interval) @@ -1109,7 +1109,7 @@ def _predict_interval(self, fh, X=None, coverage=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. coverage : list of float (guaranteed not None and floats in [0,1] interval) @@ -1242,7 +1242,7 @@ def _fit(self, y, X=None, fh=None): ---------- y : time series in aeon compatible format Target time series to which to fit the forecaster - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. X : time series in aeon compatible format, optional, default=None Exogenous time series to which to fit the forecaster @@ -1344,7 +1344,7 @@ def _update(self, y, X=None, update_params=True): Target time series to which to fit the forecaster X : time series in aeon compatible format, optional, default=None Exogenous time series to which to fit the forecaster - update_params : bool, optional (default=True) + update_params : bool, default=True Returns ------- @@ -1366,10 +1366,10 @@ def _predict_interval(self, fh, X=None, coverage=0.90): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. - coverage : float or list, optional (default=0.95) + coverage : float or list, default=0.95 nominal coverage(s) of predictive interval(s) Returns @@ -1401,10 +1401,10 @@ def _predict_quantiles(self, fh, X, alpha): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. - alpha : list of float, optional (default=[0.5]) + alpha : list of float, default=[0.5] A list of probabilities at which quantile forecasts are computed. Returns @@ -1430,10 +1430,10 @@ def _predict_var(self, fh=None, X=None, cov=False): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. - cov : bool, optional (default=False) + cov : bool, default=False if True, computes covariance matrix forecast. if False, computes marginal variance forecasts. @@ -1470,10 +1470,10 @@ def _predict_proba(self, fh, X, marginal=True): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. - marginal : bool, optional (default=True) + marginal : bool, default=True whether returned distribution is marginal by time index Returns diff --git a/aeon/forecasting/compose/_reduce.py b/aeon/forecasting/compose/_reduce.py index e1bd485bd5..2644f02149 100644 --- a/aeon/forecasting/compose/_reduce.py +++ b/aeon/forecasting/compose/_reduce.py @@ -74,7 +74,7 @@ def _sliding_window_transform( Window length for transformed feature variables fh : ForecastingHorizon Forecasting horizon for transformed target variable - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous series. transformers: list of transformers (default = None) A suitable list of transformers that allows for using an en-bloc approach with @@ -446,9 +446,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. Returns @@ -565,7 +565,7 @@ def _predict_last_window( ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series return_pred_int : bool alpha : float or array-like @@ -653,9 +653,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. Returns @@ -688,7 +688,7 @@ def _predict_last_window( ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series return_pred_int : bool alpha : float or array-like @@ -754,9 +754,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. Returns @@ -857,7 +857,7 @@ def _predict_last_window( ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series return_pred_int : bool alpha : float or array-like @@ -984,9 +984,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. Returns @@ -1043,7 +1043,7 @@ def _predict_last_window( ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series return_pred_int : bool alpha : float or array-like @@ -1098,7 +1098,7 @@ class DirectTabularRegressionForecaster(_DirectReducer): ---------- estimator : Estimator A tabular regression estimator as provided by scikit-learn. - window_length : int, optional (default=10) + window_length : int, default=10 The length of the sliding window used to transform the series into a tabular matrix. """ @@ -1148,7 +1148,7 @@ class MultioutputTabularRegressionForecaster(_MultioutputReducer): ---------- estimator : Estimator A tabular regression estimator as provided by scikit-learn. - window_length : int, optional (default=10) + window_length : int, default=10 The length of the sliding window used to transform the series into a tabular matrix. """ @@ -1166,7 +1166,7 @@ class RecursiveTabularRegressionForecaster(_RecursiveReducer): ---------- estimator : Estimator A tabular regression estimator as provided by scikit-learn. - window_length : int, optional (default=10) + window_length : int, default=10 The length of the sliding window used to transform the series into a tabular matrix. transformers: list of transformers (default = None) @@ -1231,7 +1231,7 @@ class DirRecTabularRegressionForecaster(_DirRecReducer): ---------- estimator : sklearn estimator object Tabular regressor. - window_length : int, optional (default=10) + window_length : int, default=10 The length of the sliding window used to transform the series into a tabular matrix """ @@ -1249,7 +1249,7 @@ class DirectTimeSeriesRegressionForecaster(_DirectReducer): ---------- estimator : Estimator A time-series regression estimator as provided by aeon. - window_length : int, optional (default=10) + window_length : int, default=10 The length of the sliding window used to transform the series into a tabular matrix. """ @@ -1267,7 +1267,7 @@ class MultioutputTimeSeriesRegressionForecaster(_MultioutputReducer): ---------- estimator : Estimator A time-series regression estimator as provided by aeon. - window_length : int, optional (default=10) + window_length : int, default=10 The length of the sliding window used to transform the series into a tabular matrix. """ @@ -1285,7 +1285,7 @@ class RecursiveTimeSeriesRegressionForecaster(_RecursiveReducer): ---------- estimator : Estimator A time-series regression estimator as provided by aeon. - window_length : int, optional (default=10) + window_length : int, default=10 The length of the sliding window used to transform the series into a tabular matrix. """ @@ -1310,7 +1310,7 @@ class DirRecTimeSeriesRegressionForecaster(_DirRecReducer): ---------- estimator : aeon estimator object Time-series regressor. - window_length : int, optional (default=10) + window_length : int, default=10 The length of the sliding window used to transform the series into a tabular matrix """ @@ -1339,12 +1339,12 @@ def make_reduction( estimator : an estimator instance Either a tabular regressor from scikit-learn or a time series regressor from aeon. - strategy : str, optional (default="recursive") + strategy : str, default="recursive" The strategy to generate forecasts. Must be one of "direct", "recursive" or "multioutput". - window_length : int, optional (default=10) + window_length : int, default=10 Window length used in sliding window transformation. - scitype : str, optional (default="infer") + scitype : str, default="infer" Legacy argument for downwards compatibility, should not be used. `make_reduction` will automatically infer the correct type of `estimator`. This internal inference can be force-overridden by the `scitype` argument. @@ -2166,11 +2166,11 @@ def _fit(self, y, X=None, fh=None): y : pd.DataFrame pd.DataFrame, pd-multiindex, or pd_multiindex_hier Time series to which to fit the forecaster. - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True Otherwise, if not passed in _fit, guaranteed to be passed in _predict - X : pd.DataFrame optional (default=None) + X : pd.DataFrame default=None pd.DataFrame, pd-multiindex, or pd_multiindex_hier Exogeneous time series to fit to. @@ -2226,10 +2226,10 @@ def _predict(self, X=None, fh=None): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None pd.DataFrame, pd-multiindex, or pd_multiindex_hier Exogeneous time series for the forecast diff --git a/aeon/forecasting/compose/_stack.py b/aeon/forecasting/compose/_stack.py index 0ca3a47207..344ebf9303 100644 --- a/aeon/forecasting/compose/_stack.py +++ b/aeon/forecasting/compose/_stack.py @@ -31,7 +31,7 @@ class StackingForecaster(_HeterogenousEnsembleForecaster): The regressor can also be a sklearn.Pipeline(). random_state : int, RandomState instance or None, default=None Used to set random_state of the default regressor. - n_jobs : int or None, optional (default=None) + n_jobs : int or None, default=None The number of jobs to run in parallel for fit. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. @@ -82,9 +82,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored Returns @@ -132,7 +132,7 @@ def _update(self, y, X=None, update_params=True): ---------- y : pd.Series X : pd.DataFrame - update_params : bool, optional (default=True) + update_params : bool, default=True Returns ------- @@ -151,7 +151,7 @@ def _predict(self, fh=None, X=None): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series Returns diff --git a/aeon/forecasting/conformal.py b/aeon/forecasting/conformal.py index 63f559ebd9..cb11915064 100644 --- a/aeon/forecasting/conformal.py +++ b/aeon/forecasting/conformal.py @@ -53,7 +53,7 @@ class ConformalIntervals(BaseForecaster): Caveat: this does not give frequentist but conformal predictive intervals "conformal": as in Stankeviciute et al, but with H=1, i.e., no Bonferroni correction under number of indices in the horizon - initial_window : float, int or None, optional (default=max(10, 0.1*len(y))) + initial_window : float, int or None, default=max(10, 0.1*len(y)) Defines the size of the initial training window If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include for the initial window for the train split. @@ -188,7 +188,7 @@ def _predict_interval(self, fh, X=None, coverage=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series for the forecast coverage : list of float (guaranteed not None and floats in [0,1] interval) @@ -271,10 +271,10 @@ def _predict_quantiles(self, fh, X, alpha): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. - alpha : list of float, optional (default=[0.5]) + alpha : list of float, default=[0.5] A list of probabilities at which quantile forecasts are computed. Returns @@ -339,7 +339,7 @@ def _compute_sliding_residuals( aeon compatible exogeneous time series to use in forecasts forecaster : aeon compatible forecaster forecaster to use in computing the sliding residuals - initial_window : float, int or None, optional (default=max(10, 0.1*len(y))) + initial_window : float, int or None, default=max(10, 0.1*len(y)) Defines the size of the initial training window If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include for the initial window for the train split. diff --git a/aeon/forecasting/croston.py b/aeon/forecasting/croston.py index e3016d313e..e1ea36de82 100644 --- a/aeon/forecasting/croston.py +++ b/aeon/forecasting/croston.py @@ -83,9 +83,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored. Returns @@ -131,9 +131,9 @@ def _predict( Parameters ---------- - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored. Returns diff --git a/aeon/forecasting/dynamic_factor.py b/aeon/forecasting/dynamic_factor.py index 9ee6e00c8d..fa03da1553 100644 --- a/aeon/forecasting/dynamic_factor.py +++ b/aeon/forecasting/dynamic_factor.py @@ -196,7 +196,7 @@ def _predict(self, fh, X=None): The forecasters horizon with the steps ahead to to predict. Default is one-step ahead forecast, i.e. np.array([1]) - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored. Returns @@ -238,7 +238,7 @@ def _predict_interval(self, fh, X=None, coverage: [float] = None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series for the forecast coverage : list of float (guaranteed not None and floats in [0,1] interval) @@ -344,7 +344,7 @@ def _fit_forecaster(self, y, X=None): ---------- y:pd.Series Target time series to which forcaster is fit. - X:pd.DataFrame , optional (default=None) + X:pd.DataFrame , default=None Exogenous variables """ from statsmodels.tsa.statespace.dynamic_factor import ( diff --git a/aeon/forecasting/ets.py b/aeon/forecasting/ets.py index 7ba505b9f1..62fa935168 100644 --- a/aeon/forecasting/ets.py +++ b/aeon/forecasting/ets.py @@ -393,7 +393,7 @@ def _predict(self, fh, X=None, **simulate_kwargs): The forecasters horizon with the steps ahead to to predict. Default is one-step ahead forecast, i.e. np.array([1]) - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored. **simulate_kwargs : see statsmodels ETSResults.get_prediction @@ -428,7 +428,7 @@ def _predict_interval(self, fh, X=None, coverage=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to predict from. coverage : list of float (guaranteed not None and floats in [0,1] interval) diff --git a/aeon/forecasting/model_selection/_split.py b/aeon/forecasting/model_selection/_split.py index a0ed79c441..b253c8b454 100644 --- a/aeon/forecasting/model_selection/_split.py +++ b/aeon/forecasting/model_selection/_split.py @@ -539,7 +539,7 @@ def get_n_splits(self, y: Optional[ACCEPTED_Y_TYPES] = None) -> int: Parameters ---------- - y : pd.Series or pd.Index, optional (default=None) + y : pd.Series or pd.Index, default=None Time series to split Returns @@ -554,7 +554,7 @@ def get_cutoffs(self, y: Optional[ACCEPTED_Y_TYPES] = None) -> np.ndarray: Parameters ---------- - y : pd.Series or pd.Index, optional (default=None) + y : pd.Series or pd.Index, default=None Time series to split Returns @@ -693,7 +693,7 @@ def get_n_splits(self, y: Optional[ACCEPTED_Y_TYPES] = None) -> int: Parameters ---------- - y : pd.Series or pd.Index, optional (default=None) + y : pd.Series or pd.Index, default=None Time series to split Returns @@ -714,7 +714,7 @@ def get_cutoffs(self, y: Optional[ACCEPTED_Y_TYPES] = None) -> np.ndarray: Parameters ---------- - y : pd.Series or pd.Index, optional (default=None) + y : pd.Series or pd.Index, default=None Time series to split Returns @@ -942,7 +942,7 @@ def get_n_splits(self, y: Optional[ACCEPTED_Y_TYPES] = None) -> int: Parameters ---------- - y : pd.Series or pd.Index, optional (default=None) + y : pd.Series or pd.Index, default=None Time series to split Returns @@ -962,7 +962,7 @@ def get_cutoffs(self, y: Optional[ACCEPTED_Y_TYPES] = None) -> np.ndarray: Parameters ---------- - y : pd.Series or pd.Index, optional (default=None) + y : pd.Series or pd.Index, default=None Time series to split Returns @@ -1032,11 +1032,11 @@ class SlidingWindowSplitter(BaseWindowSplitter): Forecasting horizon window_length : int or timedelta or pd.DateOffset Window length - step_length : int or timedelta or pd.DateOffset, optional (default=1) + step_length : int or timedelta or pd.DateOffset, default=1 Step length between windows - initial_window : int or timedelta or pd.DateOffset, optional (default=None) + initial_window : int or timedelta or pd.DateOffset, default=None Window length of first window - start_with_window : bool, optional (default=True) + start_with_window : bool, default=True - If True, starts with full window. - If False, starts with empty window. @@ -1099,11 +1099,11 @@ class ExpandingWindowSplitter(BaseWindowSplitter): Parameters ---------- - fh : int, list or np.array, optional (default=1) + fh : int, list or np.array, default=1 Forecasting horizon - initial_window : int or timedelta or pd.DateOffset, optional (default=10) + initial_window : int or timedelta or pd.DateOffset, default=10 Window length of initial training fold. If =0, initial training fold is empty. - step_length : int or timedelta or pd.DateOffset, optional (default=1) + step_length : int or timedelta or pd.DateOffset, default=1 Step length between windows Examples @@ -1216,7 +1216,7 @@ def get_n_splits(self, y: Optional[ACCEPTED_Y_TYPES] = None) -> int: Parameters ---------- - y : pd.Series or pd.Index, optional (default=None) + y : pd.Series or pd.Index, default=None Time series to split Returns @@ -1235,7 +1235,7 @@ def get_cutoffs(self, y: Optional[ACCEPTED_Y_TYPES] = None) -> np.ndarray: Parameters ---------- - y : pd.Series or pd.Index, optional (default=None) + y : pd.Series or pd.Index, default=None Time series to split Returns @@ -1293,9 +1293,9 @@ def temporal_train_test_split( ---------- y : pd.Series Target series - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous data - test_size : float, int or None, optional (default=None) + test_size : float, int or None, default=None If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the relative number of test samples. If None, the value is set to the diff --git a/aeon/forecasting/model_selection/_tune.py b/aeon/forecasting/model_selection/_tune.py index 313e6cd2bc..1a9f4c5063 100644 --- a/aeon/forecasting/model_selection/_tune.py +++ b/aeon/forecasting/model_selection/_tune.py @@ -129,9 +129,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored Returns @@ -266,10 +266,10 @@ def _update(self, y, X=None, update_params=True): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series for the forecast - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns @@ -327,27 +327,27 @@ class ForecastingGridSearchCV(BaseGridSearch): "no_update" = neither tuning parameters nor inner estimator are updated param_grid : dict or list of dictionaries Model tuning parameters of the forecaster to evaluate - scoring: function, optional (default=None) + scoring: function, default=None Function to score models for evaluation of optimal parameters. If None, then mean_absolute_percentage_error is used. - n_jobs: int, optional (default=None) + n_jobs: int, default=None Number of jobs to run in parallel if backend either "loky", "multiprocessing" or "threading". None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. - refit: bool, optional (default=True) + refit: bool, default=True True = refit the forecaster with the best parameters on the entire data in fit False = best forecaster remains fitted on the last fold in cv - verbose: int, optional (default=0) + verbose: int, default=0 return_n_best_forecasters: int, default=1 In case the n best forecaster should be returned, this value can be set and the n best forecasters will be assigned to n_best_forecasters_ - pre_dispatch: str, optional (default='2*n_jobs'). + pre_dispatch: str, default='2*n_jobs' Controls the number of jobs that get dispatched during parallel execution when using the "loky", "threading", or "multiprocessing" backend. - error_score: numeric value or the str 'raise', optional (default=np.nan) + error_score: numeric value or the str 'raise', default=np.nan The test score returned when a forecaster fails to be fitted. - return_train_score: bool, optional (default=False) + return_train_score: bool, default=False backend : {"dask", "loky", "multiprocessing", "threading"}, by default "loky". Runs parallel evaluate if specified and `strategy` is set as "refit". - "loky", "multiprocessing" and "threading": uses `joblib` Parallel loops @@ -356,7 +356,7 @@ class ForecastingGridSearchCV(BaseGridSearch): "threading" is unlikely to see speed ups due to the GIL and the serialization backend (`cloudpickle`) for "dask" and "loky" is generally more robust than the standard `pickle` library used in "multiprocessing". - pre_dispatch: str, optional (default='2*n_jobs'). + pre_dispatch: str, default='2*n_jobs' Controls the number of jobs that get dispatched during parallel execution when using the "loky", "threading", or "multiprocessing" backend. error_score : "raise" or numeric, default=np.nan @@ -589,22 +589,22 @@ class ForecastingRandomizedSearchCV(BaseGridSearch): n_iter : int, default=10 Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution. - scoring: function, optional (default=None) + scoring: function, default=None Function to score models for evaluation of optimal parameters. If None, then mean_absolute_percentage_error is used. - n_jobs: int, optional (default=None) + n_jobs: int, default=None Number of jobs to run in parallel if backend either "loky", "multiprocessing" or "threading". None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. - refit: bool, optional (default=True) + refit: bool, default=True True = refit the forecaster with the best parameters on the entire data in fit False = best forecaster remains fitted on the last fold in cv - verbose: int, optional (default=0) + verbose: int, default=0 return_n_best_forecasters: int, default=1 In case the n best forecaster should be returned, this value can be set and the n best forecasters will be assigned to n_best_forecasters_ - pre_dispatch: str, optional (default='2*n_jobs'). + pre_dispatch: str, default='2*n_jobs' Controls the number of jobs that get dispatched during parallel execution when using the "loky", "threading", or "multiprocessing" backend. random_state : int, RandomState instance or None, default=None diff --git a/aeon/forecasting/naive.py b/aeon/forecasting/naive.py index d0edd218dc..d31265afdc 100644 --- a/aeon/forecasting/naive.py +++ b/aeon/forecasting/naive.py @@ -318,7 +318,7 @@ def _predict(self, fh=None, X=None): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series """ y_pred = super()._predict(fh=fh, X=X) @@ -347,9 +347,9 @@ def _predict_quantiles(self, fh, X=None, alpha=0.5): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored. - alpha : float or list of float, optional (default=0.5) + alpha : float or list of float, default=0.5 A probability or list of, at which quantile forecasts are computed. Returns @@ -392,9 +392,9 @@ def _predict_var(self, fh, X=None, cov=False): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored. - cov : bool, optional (default=False) + cov : bool, default=False If True, return the covariance matrix. If False, return the marginal variance. @@ -636,9 +636,9 @@ def _predict_quantiles(self, fh, X=None, alpha=0.5): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series - alpha : float or list of float, optional (default=0.5) + alpha : float or list of float, default=0.5 A probability or list of, at which quantile forecasts are computed. Returns @@ -678,9 +678,9 @@ def _predict_var(self, fh, X=None, cov=False): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series - cov : bool, optional (default=False) + cov : bool, default=False If True, return the covariance matrix. If False, return the marginal variance. diff --git a/aeon/forecasting/online_learning/_online_ensemble.py b/aeon/forecasting/online_learning/_online_ensemble.py index 04889504ed..669c8099b0 100644 --- a/aeon/forecasting/online_learning/_online_ensemble.py +++ b/aeon/forecasting/online_learning/_online_ensemble.py @@ -15,7 +15,7 @@ class OnlineEnsembleForecaster(EnsembleForecaster): ---------- ensemble_algorithm : ensemble algorithm forecasters : list of (str, estimator) tuples - n_jobs : int or None, optional (default=None) + n_jobs : int or None, default=None The number of jobs to run in parallel for fit. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. @@ -42,9 +42,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored Returns @@ -67,7 +67,7 @@ def _fit_ensemble(self, y, X=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored """ fh = np.arange(len(y)) + 1 @@ -83,7 +83,7 @@ def _update(self, y, X=None, update_params=False): ---------- y : pd.Series X : pd.DataFrame - update_params : bool, optional (default=False) + update_params : bool, default=False Returns ------- diff --git a/aeon/forecasting/reconcile.py b/aeon/forecasting/reconcile.py index 51f2daadae..d62b61d5d4 100644 --- a/aeon/forecasting/reconcile.py +++ b/aeon/forecasting/reconcile.py @@ -121,7 +121,7 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.DataFrame Time series to which to fit the forecaster. - fh : ForecastingHorizon or None, optional (default=None) + fh : ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. X : pd.DataFrame, default=None Exogenous variables for the base forecaster @@ -189,9 +189,9 @@ def _predict(self, fh, X=None): Parameters ---------- - fh : ForecastingHorizon or None, optional (default=None) + fh : ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None Exogeneous time series for the forecast Returns @@ -240,7 +240,7 @@ def _update(self, y, X=None, update_params=True): Time series to which to fit the forecaster. X : pd.DataFrame, default=None Exogenous variables based to the base forecaster - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns @@ -293,10 +293,10 @@ def _get_g_matrix_mint(self, shrink=False, diag_only=False): Parameters ---------- - shrink: bool, optional (default=False) + shrink: bool, default=False Shrink the off diagonal elements of the sample covariance matrix. according to the method in [2]_ - diag_only: bool, optional (default=False) + diag_only: bool, default=False Remove the off-diagonal elements of the sample covariance matrix. Returns diff --git a/aeon/forecasting/squaring_residuals.py b/aeon/forecasting/squaring_residuals.py index 6e3cb4928e..361eb5cb6f 100644 --- a/aeon/forecasting/squaring_residuals.py +++ b/aeon/forecasting/squaring_residuals.py @@ -137,11 +137,11 @@ def _fit(self, y, X=None, fh=None): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True Otherwise, if not passed in _fit, guaranteed to be passed in _predict - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to fit to. @@ -209,10 +209,10 @@ def _predict(self, fh, X=None): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series for the forecast @@ -250,10 +250,10 @@ def _update(self, y, X=None, update_params=True): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series for the forecast - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns @@ -282,7 +282,7 @@ def _predict_quantiles(self, fh, X=None, alpha=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series for the forecast alpha : list of float (guaranteed not None and floats in [0,1] interval) @@ -327,9 +327,9 @@ def _predict_var(self, fh, X=None, cov=False): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series - cov : bool, optional (default=False) + cov : bool, default=False If True, return the covariance matrix. If False, return the marginal variance. diff --git a/aeon/forecasting/stream/_update.py b/aeon/forecasting/stream/_update.py index 4221988231..27c012a4f0 100644 --- a/aeon/forecasting/stream/_update.py +++ b/aeon/forecasting/stream/_update.py @@ -86,11 +86,11 @@ def _fit(self, y, X=None, fh=None): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True Otherwise, if not passed in _fit, guaranteed to be passed in _predict - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to fit to. @@ -129,9 +129,9 @@ def _update(self, y, X=None, update_params=True): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns @@ -267,11 +267,11 @@ def _fit(self, y, X=None, fh=None): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True Otherwise, if not passed in _fit, guaranteed to be passed in _predict - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to fit to. @@ -310,9 +310,9 @@ def _update(self, y, X=None, update_params=True): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns @@ -438,9 +438,9 @@ def _update(self, y, X=None, update_params=True): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns diff --git a/aeon/forecasting/structural.py b/aeon/forecasting/structural.py index 64041a6147..9184ed0606 100644 --- a/aeon/forecasting/structural.py +++ b/aeon/forecasting/structural.py @@ -282,7 +282,7 @@ def _fit_forecaster(self, y, X=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables. """ from statsmodels.tsa.statespace.structural import ( @@ -339,7 +339,7 @@ def _predict_interval(self, fh, X=None, coverage=None): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon, default = y.index (in-sample forecast) - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series coverage : list of float (guaranteed not None and floats in [0,1] interval) nominal coverage(s) of predictive interval(s) @@ -410,7 +410,7 @@ def simulate( time-invariant this can be any number. If the model is time-varying, then this number must be less than or equal to the number of observations. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables. measurement_shocks : array_like, optional If specified, these are the shocks to the measurement equation, diff --git a/aeon/forecasting/tbats.py b/aeon/forecasting/tbats.py index ffd823b472..6b1bd00dd7 100644 --- a/aeon/forecasting/tbats.py +++ b/aeon/forecasting/tbats.py @@ -42,36 +42,36 @@ class TBATS(_TbatsAdapter): Parameters ---------- - use_box_cox: bool or None, optional (default=None) + use_box_cox: bool or None, default=None If Box-Cox transformation of original series should be applied. When None both cases shall be considered and better is selected by AIC. - box_cox_bounds: tuple, shape=(2,), optional (default=(0, 1)) + box_cox_bounds: tuple, shape=(2,), default=(0, 1) Minimal and maximal Box-Cox parameter values. - use_trend: bool or None, optional (default=None) + use_trend: bool or None, default=None Indicates whether to include a trend or not. When None both cases shall be considered and better is selected by AIC. - use_damped_trend: bool or None, optional (default=None) + use_damped_trend: bool or None, default=None Indicates whether to include a damping parameter in the trend or not. Applies only when trend is used. When None both cases shall be considered and better is selected by AIC. - sp: Iterable or array-like of floats, optional (default=None) + sp: Iterable or array-like of floats, default=None Abbreviation of "seasonal periods". The length of each of the periods (amount of observations in each period). Accepts int and float values here. When None or empty array, non-seasonal model shall be fitted. - use_arma_errors: bool, optional (default=True) + use_arma_errors: bool, default=True When True BATS will try to improve the model by modelling residuals with ARMA. Best model will be selected by AIC. If False, ARMA residuals modeling will not be considered. - show_warnings: bool, optional (default=True) + show_warnings: bool, default=True If warnings should be shown or not. Also see Model.warnings variable that contains all model related warnings. - n_jobs: int, optional (default=None) + n_jobs: int, default=None How many jobs to run in parallel when fitting BATS model. When not provided BATS shall try to utilize all available cpu cores. - multiprocessing_start_method: str, optional (default='spawn') + multiprocessing_start_method: str, default='spawn' How threads should be started. See also: https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods - context: abstract.ContextInterface, optional (default=None) + context: abstract.ContextInterface, default=None For advanced users only. Provide this to override default behaviors See Also diff --git a/aeon/forecasting/theta.py b/aeon/forecasting/theta.py index e6b890d000..e8408a0292 100644 --- a/aeon/forecasting/theta.py +++ b/aeon/forecasting/theta.py @@ -86,9 +86,9 @@ class ThetaForecaster(ExponentialSmoothing): initial_level : float, optional The alpha value of the simple exponential smoothing, if the value is set then this will be used, otherwise it will be estimated from the data. - deseasonalize : bool, optional (default=True) + deseasonalize : bool, default=True If True, data is seasonally adjusted. - sp : int, optional (default=1) + sp : int, default=1 The number of observations that constitute a seasonal period for a multiplicative deseasonaliser, which is used if seasonality is detected in the training data. Ignored if a deseasonaliser transformer is provided. @@ -152,9 +152,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored Returns @@ -192,7 +192,7 @@ def _predict(self, fh, X=None): The forecasters horizon with the steps ahead to to predict. Default is one-step ahead forecast, i.e. np.array([1]). - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series Returns @@ -242,9 +242,9 @@ def _predict_quantiles(self, fh, X=None, alpha=None): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series - alpha : list of float, optional (default=[0.5]) + alpha : list of float, default=[0.5] A list of probabilities at which quantile forecasts are computed. Returns diff --git a/aeon/forecasting/trend.py b/aeon/forecasting/trend.py index a56ef413fa..c31e8c534d 100644 --- a/aeon/forecasting/trend.py +++ b/aeon/forecasting/trend.py @@ -78,7 +78,7 @@ def _fit(self, y, X=None, fh=None): Target time series with which to fit the forecaster. X : pd.DataFrame, default=None Exogenous variables are ignored - fh : int, list or np.array, optional (default=None) + fh : int, list or np.array, default=None The forecasters horizon with the steps ahead to to predict. Returns @@ -473,9 +473,9 @@ def _fit(self, y, X=None, fh=None): ---------- y : pd.Series Target time series to which to fit the forecaster. - fh : int, list, np.array or ForecastingHorizon, optional (default=None) + fh : int, list, np.array or ForecastingHorizon, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Returns ------- @@ -533,7 +533,7 @@ def _predict(self, fh, X=None): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series Returns @@ -554,9 +554,9 @@ def _update(self, y, X=None, update_params=True): ---------- y : pd.Series, pd.DataFrame, or np.array Target time series to which to fit the forecaster. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogeneous data - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns diff --git a/aeon/forecasting/var.py b/aeon/forecasting/var.py index f34b742697..a1696a8ee5 100644 --- a/aeon/forecasting/var.py +++ b/aeon/forecasting/var.py @@ -34,12 +34,12 @@ class VAR(_StatsModelsAdapter): "ctt" - constant, linear and quadratic trend "n" - co constant, no trend Note that these are prepended to the columns of the dataset. - missing: str, optional (default='none') + missing: str, default='none' A string specifying if data is missing - freq: str, tuple, datetime.timedelta, DateOffset or None, optional (default=None) + freq: str, tuple, datetime.timedelta, DateOffset or None, default=None A frequency specification for either `dates` or the row labels from the endog / exog data. - dates: array_like, optional (default=None) + dates: array_like, default=None An array like object containing dates. ic: One of {'aic', 'fpe', 'hqic', 'bic', None} (default=None) Information criterion to use for VAR order selection. @@ -114,9 +114,9 @@ def _fit_forecaster(self, y, X=None): ---------- y : pd.DataFrame Target time series to which to fit the forecaster. - fh : int, list, np.array or ForecastingHorizon, optional (default=None) + fh : int, list, np.array or ForecastingHorizon, default=None The forecasters horizon with the steps ahead to to predict. - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Returns ------- @@ -146,7 +146,7 @@ def _predict(self, fh, X=None): The forecasters horizon with the steps ahead to to predict. Default is one-step ahead forecast, i.e. np.array([1]) - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables are ignored. Returns @@ -207,7 +207,7 @@ def _predict_interval(self, fh, X=None, coverage: [float] = None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series for the forecast coverage : list of float (guaranteed not None and floats in [0,1] interval) diff --git a/aeon/forecasting/varmax.py b/aeon/forecasting/varmax.py index 042832844a..02eb967213 100644 --- a/aeon/forecasting/varmax.py +++ b/aeon/forecasting/varmax.py @@ -290,7 +290,7 @@ def _fit_forecaster(self, y, X=None): ---------- y : array_like The observed time-series process :math:`y`, shaped n_obs x k_endog. - X : array_like, optional (default=None) + X : array_like, default=None Array of exogenous regressors, shaped n_obs x k. Returns @@ -347,7 +347,7 @@ def _predict(self, fh, X=None): The forecasters horizon with the steps ahead to to predict. Default is one-step ahead forecast, i.e. np.array([1]) - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous variables. Returns diff --git a/aeon/forecasting/vecm.py b/aeon/forecasting/vecm.py index 896695bcc7..2c96a880b4 100644 --- a/aeon/forecasting/vecm.py +++ b/aeon/forecasting/vecm.py @@ -125,7 +125,7 @@ def _fit(self, y, fh=None, X=None): The forecasting horizon with the steps ahead to to predict. Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True Otherwise, if not passed in _fit, guaranteed to be passed in _predict - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogeneous time series to fit to. Returns @@ -162,7 +162,7 @@ def _predict(self, fh, X=None): fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series for the forecast @@ -224,7 +224,7 @@ def _predict_interval(self, fh, X=None, coverage=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series for the forecast coverage : list of float (guaranteed not None and floats in [0,1] interval) diff --git a/aeon/registry/_lookup.py b/aeon/registry/_lookup.py index 3a96d0d511..73565ccbd7 100644 --- a/aeon/registry/_lookup.py +++ b/aeon/registry/_lookup.py @@ -55,37 +55,37 @@ def all_estimators( Parameters ---------- - estimator_types: str, list of str, optional (default=None) + estimator_types: str, list of str, default=None Which kind of estimators should be returned. if None, no filter is applied and all estimators are returned. if str or list of str, string identifiers define types specified in search only estimators that are of (at least) one of the types are returned possible str values are entries of registry.BASE_CLASS_REGISTER (first col) for instance 'classifier', 'regressor', 'transformer', 'forecaster' - return_names: bool, optional (default=True) + return_names: bool, default=True if True, estimator class name is included in the all_estimators() return in the order: name, estimator class, optional tags, either as a tuple or as pandas.DataFrame columns if False, estimator class name is removed from the all_estimators() return. - filter_tags: dict of (str or list of str), optional (default=None) + filter_tags: dict of (str or list of str), default=None For a list of valid tag strings, use the registry.all_tags utility. subsets the returned estimators as follows: each key/value pair is statement in "and"/conjunction key is tag name to sub-set on value str or list of string are tag values condition is "key must be equal to value, or in set(value)" - exclude_estimators: str, list of str, optional (default=None) + exclude_estimators: str, list of str, default=None Names of estimators to exclude. - exclude_estimator_types: str, list of str, optional (default=None) + exclude_estimator_types: str, list of str, default=None Names of estimator types to exclude i.e. "collection_transformer" when you are looking for "transformer" classes - as_dataframe: bool, optional (default=False) + as_dataframe: bool, default=False if True, all_estimators will return a pandas.DataFrame with named columns for all of the attributes being returned. if False, all_estimators will return a list (either a list of estimators or a list of tuples, see Returns) - return_tags: str or list of str, optional (default=None) + return_tags: str or list of str, default=None Names of tags to fetch and return each estimator's value of. For a list of valid tag strings, use the registry.all_tags utility. if str or list of str, @@ -438,13 +438,13 @@ def all_tags( Parameters ---------- - estimator_types: string, list of string, optional (default=None) + estimator_types: string, list of string, default=None Which kind of estimators should be returned. - If None, no filter is applied and all estimators are returned. - Possible values are 'classifier', 'regressor', 'transformer' and 'forecaster' to get estimators only of these specific types, or a list of these to get the estimators that fit at least one of the types. - as_dataframe: bool, optional (default=False) + as_dataframe: bool, default=False if False, return is as described below; if True, return is converted into a pandas.DataFrame for pretty display diff --git a/aeon/testing/mock_estimators/_mock_forecasters.py b/aeon/testing/mock_estimators/_mock_forecasters.py index a2a8eabb27..96925ac5db 100644 --- a/aeon/testing/mock_estimators/_mock_forecasters.py +++ b/aeon/testing/mock_estimators/_mock_forecasters.py @@ -178,11 +178,11 @@ def _fit(self, y, X=None, fh=None): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True Otherwise, if not passed in _fit, guaranteed to be passed in _predict - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to fit to. @@ -207,10 +207,10 @@ def _predict(self, fh, X=None): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series Returns @@ -247,9 +247,9 @@ def _update(self, y, X=None, update_params=True): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns @@ -276,7 +276,7 @@ def _predict_quantiles(self, fh, X=None, alpha=None): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series alpha : list of float (guaranteed not None and floats in [0,1] interval) A list of probabilities at which quantile forecasts are computed. @@ -356,11 +356,11 @@ def _fit(self, y, X=None, fh=None): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True Otherwise, if not passed in _fit, guaranteed to be passed in _predict - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to fit to. @@ -384,10 +384,10 @@ def _predict(self, fh, X=None): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series Returns @@ -425,9 +425,9 @@ def _update(self, y, X=None, update_params=True): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns @@ -453,7 +453,7 @@ def _predict_quantiles(self, fh, X=None, alpha=None): ---------- fh : int, list, np.array or ForecastingHorizon Forecasting horizon - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series alpha : list of float (guaranteed not None and floats in [0,1] interval) A list of probabilities at which quantile forecasts are computed. diff --git a/aeon/transformations/collection/segment.py b/aeon/transformations/collection/segment.py index d2cfeb54d5..0d1906cd21 100644 --- a/aeon/transformations/collection/segment.py +++ b/aeon/transformations/collection/segment.py @@ -203,7 +203,7 @@ class RandomIntervalSegmenter(IntervalSegmenter): Default is "sqrt". - random_state : int, RandomState instance or None, optional (default=None) + random_state : int, RandomState instance or None, default=None If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used diff --git a/aeon/transformations/collection/truncate.py b/aeon/transformations/collection/truncate.py index b0b3261a0e..971b166234 100644 --- a/aeon/transformations/collection/truncate.py +++ b/aeon/transformations/collection/truncate.py @@ -17,9 +17,10 @@ class TruncationTransformer(BaseCollectionTransformer): Parameters ---------- - truncated_length : int, optional (default=None) bottom range of the values to - truncate can also be used to truncate to a specific length - if None, will find the shortest sequence and use instead. + truncated_length : int, default=None + bottom range of the values to truncate can also be used to truncate + to a specific length. + if None, will find the shortest sequence and use instead. Example ------- diff --git a/aeon/transformations/compose.py b/aeon/transformations/compose.py index 9e978f7114..5a4e35a188 100644 --- a/aeon/transformations/compose.py +++ b/aeon/transformations/compose.py @@ -329,7 +329,7 @@ def _inverse_transform(self, X, y=None): X: data structure of type X_inner_type if X_inner_type is list, _inverse_transform must support all types in it Data to be inverse transformed - y : Series or Panel of type y_inner_type, optional (default=None) + y : Series or Panel of type y_inner_type, default=None Additional data, e.g., labels for transformation Returns @@ -422,7 +422,7 @@ class FeatureUnion(_HeterogenousMetaEstimator, BaseTransformer): transformer_list : list of (string, transformer) tuples List of transformer objects to be applied to the data. The first half of each tuple is the name of the transformer. - n_jobs : int or None, optional (default=None) + n_jobs : int or None, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. @@ -430,7 +430,7 @@ class FeatureUnion(_HeterogenousMetaEstimator, BaseTransformer): transformer_weights : dict, optional Multiplicative weights for features per transformer. Keys are transformer names, values the weights. - flatten_transform_index : bool, optional (default=True) + flatten_transform_index : bool, default=True if True, columns of return DataFrame are flat, by "transformer__variablename" if False, columns are MultiIndex (transformer, variablename) has no effect if return type is one without column names @@ -729,7 +729,7 @@ def _inverse_transform(self, X, y=None): X: data structure of type X_inner_type if X_inner_type is list, _inverse_transform must support all types in it Data to be inverse transformed - y : Series or Panel of type y_inner_type, optional (default=None) + y : Series or Panel of type y_inner_type, default=None Additional data, e.g., labels for transformation Returns @@ -1729,7 +1729,7 @@ def _inverse_transform(self, X, y=None): X: data structure of type X_inner_type if X_inner_type is list, _inverse_transform must support all types in it Data to be inverse transformed - y : Series or Panel of type y_inner_type, optional (default=None) + y : Series or Panel of type y_inner_type, default=None Additional data, e.g., labels for transformation Returns diff --git a/aeon/transformations/date.py b/aeon/transformations/date.py index d44f926ce1..6dd006bcb0 100644 --- a/aeon/transformations/date.py +++ b/aeon/transformations/date.py @@ -39,7 +39,7 @@ class DateTimeFeatures(BaseTransformer): Parameters ---------- - ts_freq : str, optional (default="day") + ts_freq : str, default="day" Restricts selection of items to those with a frequency lower than the frequency of the time series given by ts_freq. E.g. if monthly data is provided and ts_freq = ("M"), it does not make @@ -61,14 +61,14 @@ class DateTimeFeatures(BaseTransformer): * T - minute * S - second * L - millisecond - feature_scope: str, optional (default="minimal") + feature_scope: str, default="minimal" Specify how many calendar features you want to be returned. E.g., rarely used features like week of quarter will only be returned with feature_scope = "comprehensive". * "minimal" * "efficient" * "comprehensive" - manual_selection: str, optional (default=None) + manual_selection: str, default=None Manual selection of dummys. Notation is child of parent for precise notation. Will ignore specified feature_scope, but will still check with warning against a specified ts_freq. diff --git a/aeon/transformations/kalman_filter.py b/aeon/transformations/kalman_filter.py index 68c763577a..998b495d1b 100644 --- a/aeon/transformations/kalman_filter.py +++ b/aeon/transformations/kalman_filter.py @@ -148,29 +148,29 @@ class BaseKalmanFilter: ---------- state_dim : int System state feature dimension - state_transition : np.ndarray, optional (default=None) + state_transition : np.ndarray, default=None of shape (state_dim, state_dim) or (time_steps, state_dim, state_dim). State transition matrix, also referred to as F, is a matrix which describes the way the underlying series moves through successive time periods. - process_noise : np.ndarray, optional (default=None) + process_noise : np.ndarray, default=None of shape (state_dim, state_dim) or (time_steps, state_dim, state_dim). Process noise matrix, also referred to as Q, the uncertainty of the dynamic model. - measurement_noise : np.ndarray, optional (default=None) + measurement_noise : np.ndarray, default=None of shape (measurement_dim, measurement_dim) or (time_steps, measurement_dim, measurement_dim). Measurement noise matrix, also referred to as R, represents the uncertainty of the measurements. - measurement_function : np.ndarray, optional (default=None) + measurement_function : np.ndarray, default=None of shape (measurement_dim, state_dim) or (time_steps, measurement_dim, state_dim). Measurement equation matrix, also referred to as H, adjusts dimensions of measurements to match dimensions of state. - initial_state : np.ndarray, optional (default=None) + initial_state : np.ndarray, default=None of shape (state_dim,). Initial estimated system state, also referred to as x0. - initial_state_covariance : np.ndarray, optional (default=None) + initial_state_covariance : np.ndarray, default=None of shape (state_dim, state_dim). Initial estimated system state covariance, also referred to as P0. @@ -294,7 +294,7 @@ def _set_attribute( The value of corresponding attribute. matrix_shape : tuple The supposed shape of a single matrix. - time_steps : int, optional (default=None) + time_steps : int, default=None """ _validate_param_shape( param_name=param_name, @@ -325,42 +325,42 @@ class KalmanFilterTransformer(BaseKalmanFilter, BaseTransformer): ---------- state_dim : int System state feature dimension. - state_transition : np.ndarray, optional (default=None) + state_transition : np.ndarray, default=None of shape (state_dim, state_dim) or (time_steps, state_dim, state_dim). State transition matrix, also referred to as `F`, is a matrix which describes the way the underlying series moves through successive time periods. - process_noise : np.ndarray, optional (default=None) + process_noise : np.ndarray, default=None of shape (state_dim, state_dim) or (time_steps, state_dim, state_dim). Process noise matrix, also referred to as `Q`, the uncertainty of the dynamic model. - measurement_noise : np.ndarray, optional (default=None) + measurement_noise : np.ndarray, default=None of shape (measurement_dim, measurement_dim) or (time_steps, measurement_dim, measurement_dim). Measurement noise matrix, also referred to as `R`, represents the uncertainty of the measurements. - measurement_function : np.ndarray, optional (default=None) + measurement_function : np.ndarray, default=None of shape (measurement_dim, state_dim) or (time_steps, measurement_dim, state_dim). Measurement equation matrix, also referred to as `H`, adjusts dimensions of measurements to match dimensions of state. - initial_state : np.ndarray, optional (default=None) + initial_state : np.ndarray, default=None of shape (state_dim,). Initial estimated system state, also referred to as `X0`. - initial_state_covariance : np.ndarray, optional (default=None) + initial_state_covariance : np.ndarray, default=None of shape (state_dim, state_dim). Initial estimated system state covariance, also referred to as `P0`. - control_transition : np.ndarray, optional (default=None) + control_transition : np.ndarray, default=None of shape (state_dim, control_variable_dim) or (time_steps, state_dim, control_variable_dim). Control transition matrix, also referred to as `G`. `control_variable_dim` is the dimension of `control variable`, also referred to as `u`. `control variable` is an optional parameter for `fit` and `transform` functions. - denoising : bool, optional (default=False). + denoising : bool, default=False This parameter affects `transform`. If False, then `transform` will be inferring hidden state. If True, uses `FilterPy` `rts_smoother` for denoising. - estimate_matrices : str or list of str, optional (default=None). + estimate_matrices : str or list of str, default=None Subset of [`state_transition`, `measurement_function`, `process_noise`, `measurement_noise`, `initial_state`, `initial_state_covariance`] @@ -568,7 +568,7 @@ def _transform(self, X, y=None): of shape (time_steps, measurement_dim). Data (measurements) to be transformed. Missing values must be represented as np.NaN or np.nan. - y : np.ndarray, optional (default=None). + y : np.ndarray, default=None of shape (control_variable_dim,) or (time_steps, control_variable_dim). `control variable`, also referred to as `u`. if 2D, must be same length as X. diff --git a/aeon/transformations/lag.py b/aeon/transformations/lag.py index 0d0346c168..137b238c68 100644 --- a/aeon/transformations/lag.py +++ b/aeon/transformations/lag.py @@ -66,11 +66,11 @@ class Lag(BaseTransformer): "original" - only original indices are retained. Will usually create NA. "extend" - both original indices and shifted indices are retained. Will usually create NA, possibly many, if shifted/original do not intersect. - flatten_transform_index : bool, optional (default=True) + flatten_transform_index : bool, default=True if True, columns of return DataFrame are flat, by "lagname__variablename" if False, columns are MultiIndex (lagname, variablename) has no effect if return type is one without column names - keep_column_names : bool, optional (default=False) + keep_column_names : bool, default=False has an effect only if `lags` contains only a single element if True, ensures that column names of `transform` output are same as in input, i.e., not `lag_x__varname` but `varname`. Overrides `flatten_transform_index`. diff --git a/aeon/transformations/outlier_detection.py b/aeon/transformations/outlier_detection.py index 6a51a4c326..a3d5a2f54e 100644 --- a/aeon/transformations/outlier_detection.py +++ b/aeon/transformations/outlier_detection.py @@ -20,7 +20,7 @@ class HampelFilter(BaseTransformer): Parameters ---------- - window_length : int, optional (default=10) + window_length : int, default=10 Lenght of the sliding window n_sigma : int, optional Defines how strong a point must outly to be an "outlier", by default 3 diff --git a/aeon/transformations/scaledlogit.py b/aeon/transformations/scaledlogit.py index 8f95944907..80b8ef76fe 100644 --- a/aeon/transformations/scaledlogit.py +++ b/aeon/transformations/scaledlogit.py @@ -159,7 +159,7 @@ def _inverse_transform(self, X, y=None): ---------- X : 2D np.ndarray Data to be inverse transformed - y : data of y_inner_type, optional (default=None) + y : data of y_inner_type, default=None Ignored argument for interface compatibility Returns diff --git a/aeon/transformations/summarize.py b/aeon/transformations/summarize.py index 0c4c83022f..1922b83269 100644 --- a/aeon/transformations/summarize.py +++ b/aeon/transformations/summarize.py @@ -21,7 +21,7 @@ class WindowSummarizer(BaseTransformer): Parameters ---------- - n_jobs : int, optional (default=-1) + n_jobs : int, default=-1 The number of jobs to run in parallel for applying the window functions. ``-1`` means using all processors. target_cols : list of str, optional (default = None) @@ -606,7 +606,7 @@ class SummaryTransformer(BaseTransformer): quantiles : str, list, tuple or None, default=(0.1, 0.25, 0.5, 0.75, 0.9) Optional list of series quantiles to calculate. If None, no quantiles are calculated, and summary_function must be non-None. - flatten_transform_index : bool, optional (default=True) + flatten_transform_index : bool, default=True if True, columns of return DataFrame are flat, by "variablename__feature" if False, columns are MultiIndex (variablename__feature) has no effect if return type is one without column names @@ -830,7 +830,7 @@ class FittedParamExtractor(BaseTransformer): An aeon estimator to extract features from. param_names : str Name of parameters to extract from the forecaster. - n_jobs : int, optional (default=None) + n_jobs : int, default=None Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. diff --git a/aeon/transformations/time_since.py b/aeon/transformations/time_since.py index f70a184570..118a3fb267 100644 --- a/aeon/transformations/time_since.py +++ b/aeon/transformations/time_since.py @@ -40,7 +40,7 @@ class TimeSince(BaseTransformer): * str: String is converted to datetime or period, depending on the index type, \ to give the start time. - to_numeric : string, optional (default=True) + to_numeric : string, default=True Return the integer number of periods elapsed since `start`; the period is defined by the frequency of the data. Converts datetime types to pd.Period before calculating time differences. diff --git a/aeon/utils/datetime.py b/aeon/utils/datetime.py index 4e7c9887c2..d84a9a4f28 100644 --- a/aeon/utils/datetime.py +++ b/aeon/utils/datetime.py @@ -250,7 +250,7 @@ def _get_duration(x, y=None, coerce_to_int=False, unit=None): Parameters ---------- x : pd.Index, pd.Timestamp, pd.Period, int - y : pd.Timestamp, pd.Period, int, optional (default=None) + y : pd.Timestamp, pd.Period, int, default=None coerce_to_int : bool If True, duration is returned as integer value for given unit unit : str diff --git a/aeon/utils/validation/__init__.py b/aeon/utils/validation/__init__.py index 5353321118..3f34bc611c 100644 --- a/aeon/utils/validation/__init__.py +++ b/aeon/utils/validation/__init__.py @@ -208,7 +208,7 @@ def check_window_length( - If float, the fraction of time points relative to `n_timepoints`. - If timedelta, length in corresponding time units - If pd.DateOffset, length in corresponding time units following calendar rules - n_timepoints: positive int, optional (default=None) + n_timepoints: positive int, default=None The number of time points to which to apply `window_length` when passed as a float (fraction). Will be ignored if `window_length` is an integer. diff --git a/aeon/utils/validation/forecasting.py b/aeon/utils/validation/forecasting.py index 8519f621bd..918fdc2978 100644 --- a/aeon/utils/validation/forecasting.py +++ b/aeon/utils/validation/forecasting.py @@ -49,12 +49,12 @@ def check_y_X( Parameters ---------- y : pd.Series - X : pd.DataFrame, optional (default=None) - allow_empty : bool, optional (default=False) + X : pd.DataFrame, default=None + allow_empty : bool, default=False If True, empty `y` does not raise an error. - allow_constant : bool, optional (default=True) + allow_constant : bool, default=True If True, constant `y` does not raise an error. - enforce_index_type : type, optional (default=None) + enforce_index_type : type, default=None type of time index Raises @@ -89,11 +89,11 @@ def check_X( Parameters ---------- X : pd.Series, pd.DataFrame, np.ndarray - allow_empty : bool, optional (default=False) + allow_empty : bool, default=False If False, empty `X` raises an error. - enforce_index_type : type, optional (default=None) + enforce_index_type : type, default=None type of time index - enforce_univariate : bool, optional (default=False) + enforce_univariate : bool, default=False If True, multivariate X will raise an error. Returns @@ -130,13 +130,13 @@ def check_y( Parameters ---------- y : pd.Series - allow_empty : bool, optional (default=False) + allow_empty : bool, default=False If False, empty `y` raises an error. - allow_constant : bool, optional (default=True) + allow_constant : bool, default=True If True, constant `y` does not raise an error. - enforce_index_type : type, optional (default=None) + enforce_index_type : type, default=None type of time index - allow_index_names : bool, optional (default=None) + allow_index_names : bool, default=None If False, names of y.index will be set to None Returns @@ -249,7 +249,7 @@ def check_sp(sp, enforce_list=False): ---------- sp : int or [int/float] Seasonal periodicity - enforce_list : bool, optional (default=False) + enforce_list : bool, default=False If true, convert sp to list if not list. Returns @@ -277,9 +277,9 @@ def check_fh(fh, enforce_relative: bool = False, freq=None): ---------- fh : int, list, np.array, pd.Index or ForecastingHorizon Forecasting horizon specifying the time points to predict. - enforce_relative : bool, optional (default=False) + enforce_relative : bool, default=False If True, checks if fh is relative. - freq : str, or pd.Index, optional (default=None) + freq : str, or pd.Index, default=None object carrying frequency information on values ignored unless values is without inferrable freq Frequency string or pd.Index diff --git a/aeon/utils/validation/panel.py b/aeon/utils/validation/panel.py index c331bd4927..bd27cc16bf 100644 --- a/aeon/utils/validation/panel.py +++ b/aeon/utils/validation/panel.py @@ -40,15 +40,15 @@ def check_X( ---------- X : pd.DataFrame or np.array Input data - enforce_univariate : bool, optional (default=False) + enforce_univariate : bool, default=False Enforce that X is univariate. - enforce_min_cases : int, optional (default=1) + enforce_min_cases : int, default=1 Enforce minimum number of instances. - enforce_min_columns : int, optional (default=1) + enforce_min_columns : int, default=1 Enforce minimum number of columns (or time-series variables). - coerce_to_numpy : bool, optional (default=False) + coerce_to_numpy : bool, default=False If True, X will be coerced to a 3-dimensional numpy array. - coerce_to_pandas : bool, optional (default=False) + coerce_to_pandas : bool, default=False If True, X will be coerced to a nested pandas DataFrame. Returns @@ -132,9 +132,9 @@ def check_y(y, enforce_min_cases=1, coerce_to_numpy=False): Parameters ---------- y : pd.Series or np.array - enforce_min_cases : int, optional (default=1) + enforce_min_cases : int, default=1 Enforce minimum number of instances. - coerce_to_numpy : bool, optional (default=False) + coerce_to_numpy : bool, default=False If True, y will be coerced to a numpy array. Returns @@ -182,15 +182,15 @@ def check_X_y( ---------- X : pd.DataFrame y : pd.Series or np.array - enforce_univariate : bool, optional (default=False) + enforce_univariate : bool, default=False Enforce that X is univariate. - enforce_min_cases : int, optional (default=1) + enforce_min_cases : int, default=1 Enforce minimum number of instances. - enforce_min_columns : int, optional (default=1) + enforce_min_columns : int, default=1 Enforce minimum number of columns (or time-series variables). - coerce_to_numpy : bool, optional (default=False) + coerce_to_numpy : bool, default=False If True, X will be coerced to a 3-dimensional numpy array. - coerce_to_pandas : bool, optional (default=False) + coerce_to_pandas : bool, default=False If True, X will be coerced to a nested pandas DataFrame. Returns diff --git a/aeon/utils/validation/series.py b/aeon/utils/validation/series.py index 45e7d293de..4b2a4129c8 100644 --- a/aeon/utils/validation/series.py +++ b/aeon/utils/validation/series.py @@ -315,9 +315,9 @@ def check_time_index( ---------- index : pd.Index or np.array Time index - allow_empty : bool, optional (default=False) + allow_empty : bool, default=False If False, empty `index` raises an error. - enforce_index_type : type, optional (default=None) + enforce_index_type : type, default=None type of time index var_name : str, default = "input" - variable name printed in error messages diff --git a/extension_templates/annotation.py b/extension_templates/annotation.py index f57f37e7dd..9cefd842a9 100644 --- a/extension_templates/annotation.py +++ b/extension_templates/annotation.py @@ -41,11 +41,11 @@ class MySeriesAnnotator(BaseSeriesAnnotator): Parameters ---------- - fmt : str {"dense", "sparse"}, optional (default="dense") + fmt : str {"dense", "sparse"}, default="dense" annotation output format: * If "sparse", a sub-series of labels for only the outliers in X is returned, * If "dense", a series of labels for all values in X is returned. - labels : str {"indicator", "score"}, optional (default="indicator") + labels : str {"indicator", "score"}, default="indicator" annotation output labels: * If "indicator", returned values are boolean, indicating whether a value is an outlier, @@ -53,9 +53,9 @@ class MySeriesAnnotator(BaseSeriesAnnotator): parama : int descriptive explanation of parama - paramb : string, optional (default='default') + paramb : string, default='default' descriptive explanation of paramb - paramc : boolean, optional (default= whether paramb is not the default) + paramc : boolean, default= whether paramb is not the default descriptive explanation of paramc and so on diff --git a/extension_templates/clustering.py b/extension_templates/clustering.py index 17fdb7e395..d6197ef0d2 100644 --- a/extension_templates/clustering.py +++ b/extension_templates/clustering.py @@ -47,9 +47,9 @@ class MyClusterer(BaseClusterer): ---------------- parama : int descriptive explanation of parama - paramb : string, optional (default='default') + paramb : string, default='default' descriptive explanation of paramb - paramc : boolean, optional (default= whether paramb is not the default) + paramc : boolean, default= whether paramb is not the default descriptive explanation of paramc and so on diff --git a/extension_templates/early_classification.py b/extension_templates/early_classification.py index 3a05ed048d..107dec302f 100644 --- a/extension_templates/early_classification.py +++ b/extension_templates/early_classification.py @@ -52,9 +52,9 @@ class MyEarlyTimeSeriesClassifier(BaseEarlyClassifier): ---------------- parama : int descriptive explanation of parama - paramb : string, optional (default='default') + paramb : string, default='default' descriptive explanation of paramb - paramc : boolean, optional (default= whether paramb is not the default) + paramc : boolean, default= whether paramb is not the default descriptive explanation of paramc and so on diff --git a/extension_templates/forecasting.py b/extension_templates/forecasting.py index 5b01f509cc..72898e2b74 100644 --- a/extension_templates/forecasting.py +++ b/extension_templates/forecasting.py @@ -56,9 +56,9 @@ class MyForecaster(BaseForecaster): ---------- parama : int descriptive explanation of parama - paramb : string, optional (default='default') + paramb : string, default='default' descriptive explanation of paramb - paramc : boolean, optional (default= whether paramb is not the default) + paramc : boolean, default= whether paramb is not the default descriptive explanation of paramc and so on est : aeon.estimator, BaseEstimator descendant @@ -209,11 +209,11 @@ def _fit(self, y, X=None, fh=None): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True Otherwise, if not passed in _fit, guaranteed to be passed in _predict - X : aeon time series object, optional (default=None) + X : aeon time series object, default=None guaranteed to be of an mtype in self.get_tag("X_inner_type") Exogeneous time series to fit to. @@ -251,10 +251,10 @@ def _predict(self, fh, X=None): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : aeon time series object, optional (default=None) + X : aeon time series object, default=None guaranteed to be of an mtype in self.get_tag("X_inner_type") Exogeneous time series for the forecast @@ -296,10 +296,10 @@ def _update(self, y, X=None, update_params=True): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - X : aeon time series object, optional (default=None) + X : aeon time series object, default=None guaranteed to be of an mtype in self.get_tag("X_inner_type") Exogeneous time series for the forecast - update_params : bool, optional (default=True) + update_params : bool, default=True whether model parameters should be updated Returns @@ -351,7 +351,7 @@ def _predict_quantiles(self, fh, X=None, alpha=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : aeon time series object, optional (default=None) + X : aeon time series object, default=None guaranteed to be of an mtype in self.get_tag("X_inner_type") Exogeneous time series for the forecast alpha : list of float (guaranteed not None and floats in [0,1] interval) @@ -394,7 +394,7 @@ def _predict_interval(self, fh, X=None, coverage=None): ---------- fh : guaranteed to be ForecastingHorizon The forecasting horizon with the steps ahead to to predict. - X : aeon time series object, optional (default=None) + X : aeon time series object, default=None guaranteed to be of an mtype in self.get_tag("X_inner_type") Exogeneous time series for the forecast coverage : list of float (guaranteed not None and floats in [0,1] interval) @@ -434,13 +434,13 @@ def _predict_var(self, fh, X=None, cov=False): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : aeon time series object, optional (default=None) + X : aeon time series object, default=None guaranteed to be of an mtype in self.get_tag("X_inner_type") Exogeneous time series for the forecast - cov : bool, optional (default=False) + cov : bool, default=False if True, computes covariance matrix forecast. if False, computes marginal variance forecasts. @@ -483,12 +483,12 @@ def _predict_proba(self, fh, X, marginal=True): fh : int, list, np.array or ForecastingHorizon (not optional) The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional - X : aeon time series object, optional (default=None) + X : aeon time series object, default=None Exogeneous time series for the forecast Should be of same scitype (Series, Panel, or Hierarchical) as y in fit if self.get_tag("X-y-must-have-same-index"), X.index must contain fh.index and y.index both - marginal : bool, optional (default=True) + marginal : bool, default=True whether returned distribution is marginal by time index Returns diff --git a/extension_templates/forecasting_simple.py b/extension_templates/forecasting_simple.py index e95a877c98..c4246163b8 100644 --- a/extension_templates/forecasting_simple.py +++ b/extension_templates/forecasting_simple.py @@ -53,9 +53,9 @@ class MyForecaster(BaseForecaster): ---------- parama : int descriptive explanation of parama - paramb : string, optional (default='default') + paramb : string, default='default' descriptive explanation of paramb - paramc : boolean, optional (default= whether paramb is not the default) + paramc : boolean, default= whether paramb is not the default descriptive explanation of paramc and so on """ @@ -139,11 +139,11 @@ def _fit(self, y, X=None, fh=None): if self.get_tag("y_input_type")=="multivariate": guaranteed to have 2 or more columns if self.get_tag("y_input_type")=="both": no restrictions apply - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. Required (non-optional) here if self.get_tag("requires-fh-in-fit")==True Otherwise, if not passed in _fit, guaranteed to be passed in _predict - X : optional (default=None) + X : default=None guaranteed to be of a type in self.get_tag("X_inner_type") Exogeneous time series to fit to. @@ -179,10 +179,10 @@ def _predict(self, fh, X=None): Parameters ---------- - fh : guaranteed to be ForecastingHorizon or None, optional (default=None) + fh : guaranteed to be ForecastingHorizon or None, default=None The forecasting horizon with the steps ahead to to predict. If not passed in _fit, guaranteed to be passed here - X : pd.DataFrame, optional (default=None) + X : pd.DataFrame, default=None Exogenous time series Returns diff --git a/extension_templates/transformer.py b/extension_templates/transformer.py index b6fb373b3a..a62d342883 100644 --- a/extension_templates/transformer.py +++ b/extension_templates/transformer.py @@ -56,9 +56,9 @@ class MyTransformer(BaseTransformer): ---------- parama : int descriptive explanation of parama - paramb : string, optional (default='default') + paramb : string, default='default' descriptive explanation of paramb - paramc : boolean, optional (default= whether paramb is not the default) + paramc : boolean, default= whether paramb is not the default descriptive explanation of paramc and so on est : aeon.estimator, BaseEstimator descendant @@ -365,7 +365,7 @@ def _inverse_transform(self, X, y=None): X : Series or Panel of mtype X_inner_type if X_inner_type is list, _inverse_transform must support all types in it Data to be inverse transformed - y : Series or Panel of mtype y_inner_type, optional (default=None) + y : Series or Panel of mtype y_inner_type, default=None Additional data, e.g., labels for transformation Returns diff --git a/extension_templates/transformer_simple.py b/extension_templates/transformer_simple.py index 81f6478325..493ad1d6d9 100644 --- a/extension_templates/transformer_simple.py +++ b/extension_templates/transformer_simple.py @@ -56,9 +56,9 @@ class MyTransformer(BaseTransformer): ---------- parama : int descriptive explanation of parama - paramb : string, optional (default='default') + paramb : string, default='default' descriptive explanation of paramb - paramc : boolean, optional (default= whether paramb is not the default) + paramc : boolean, default= whether paramb is not the default descriptive explanation of paramc and so on """