From 9c3add28d2746776113cdff996d6f3485a26051b Mon Sep 17 00:00:00 2001 From: Sankalp Gilda Date: Sun, 7 Jan 2024 19:59:16 -0500 Subject: [PATCH 1/6] fix: change `exog` to `y` in methods of `BaseTimeSeriesBootstrap` (#30) (#32) --- src/tsbootstrap/base_bootstrap.py | 26 +++++++++++--------------- 1 file changed, 11 insertions(+), 15 deletions(-) diff --git a/src/tsbootstrap/base_bootstrap.py b/src/tsbootstrap/base_bootstrap.py index 684a04a..c2504b4 100644 --- a/src/tsbootstrap/base_bootstrap.py +++ b/src/tsbootstrap/base_bootstrap.py @@ -42,7 +42,7 @@ def bootstrap( self, X: np.ndarray, return_indices: bool = False, - exog: np.ndarray | None = None, + y: np.ndarray | None = None, test_ratio: float = None, ) -> Iterator[np.ndarray] | Iterator[tuple[list[np.ndarray], np.ndarray]]: """Generate indices to split data into training and test set. @@ -56,7 +56,7 @@ def bootstrap( If True, a second output is retured, integer locations of index references for the bootstrap sample, in reference to original indices. Indexed values do are not necessarily identical with bootstrapped values. - exog : array-like of shape (n_timepoints, n_features_exog), default=None + y : array-like of shape (n_timepoints, n_features_exog), default=None Exogenous time series to use in bootstrapping. test_ratio : float, default=0.2 The ratio of test samples to total samples. @@ -92,14 +92,14 @@ def bootstrap( X_train, X_test = time_series_split(X, test_ratio=test_ratio) - if exog is not None: - self._check_input(exog) - exog_train, _ = time_series_split(exog, test_ratio=test_ratio) + if y is not None: + self._check_input(y) + exog_train, _ = time_series_split(y, test_ratio=test_ratio) else: exog_train = None tuple_iter = self._generate_samples( - X=X_train, return_indices=return_indices, exog=exog_train + X=X_train, return_indices=return_indices, y=exog_train ) yield from tuple_iter @@ -108,7 +108,7 @@ def _generate_samples( self, X: np.ndarray, return_indices: bool = False, - exog: np.ndarray | None = None, + y: np.ndarray | None = None, ) -> Iterator[np.ndarray] | Iterator[tuple[list[np.ndarray], np.ndarray]]: """Generates bootstrapped samples directly. @@ -124,9 +124,7 @@ def _generate_samples( """ for _ in range(self.config.n_bootstraps): - indices, data = self._generate_samples_single_bootstrap( - X=X, exog=exog - ) + indices, data = self._generate_samples_single_bootstrap(X=X, y=y) data = np.concatenate(data, axis=0) if return_indices: yield indices, data # type: ignore @@ -135,7 +133,7 @@ def _generate_samples( @abstractmethod def _generate_samples_single_bootstrap( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> tuple[list[np.ndarray], list[np.ndarray]]: """Generates list of bootstrapped indices and samples for a single bootstrap iteration. @@ -216,9 +214,7 @@ def __init__( self.X_fitted = None self.coefs = None - def _fit_model( - self, X: np.ndarray, exog: np.ndarray | None = None - ) -> None: + def _fit_model(self, X: np.ndarray, y: np.ndarray | None = None) -> None: """Fits the model to the data and stores the residuals.""" if ( self.resids is None @@ -232,7 +228,7 @@ def _fit_model( save_models=self.config.save_models, **self.config.model_params, ) - self.fit_model = fit_obj.fit(X=X, exog=exog).model + self.fit_model = fit_obj.fit(X=X, exog=y).model self.X_fitted = fit_obj.get_fitted_X() self.resids = fit_obj.get_residuals() self.order = fit_obj.get_order() From fef0359f0226cf08777763bff291a5e9f6b1a545 Mon Sep 17 00:00:00 2001 From: Sankalp Gilda Date: Mon, 8 Jan 2024 01:51:49 -0500 Subject: [PATCH 2/6] fix: change `exog` to `y` in methods of `BaseTimeSeriesBootstrap` (#30) (#33) --- .github/workflows/CI.yml | 16 ++-- src/tsbootstrap/base_bootstrap.py | 4 +- src/tsbootstrap/block_bootstrap.py | 8 +- src/tsbootstrap/bootstrap.py | 36 ++++---- src/tsbootstrap/ranklags.py | 22 ++--- src/tsbootstrap/time_series_model.py | 42 +++++----- src/tsbootstrap/time_series_simulator.py | 4 +- src/tsbootstrap/tsfit.py | 60 ++++++------- src/tsbootstrap/utils/validate.py | 20 ++--- tests/test_block_bootstrap.py | 6 +- tests/test_bootstrap.py | 4 +- tests/test_ranklags.py | 4 +- tests/test_time_series_model.py | 102 +++++++++++------------ tests/test_tsfit.py | 54 ++++++------ tests/test_validate.py | 70 ++++++++-------- 15 files changed, 225 insertions(+), 227 deletions(-) diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index d4ef2fb..fff46e5 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -2,14 +2,14 @@ name: CI on: - push: - paths: - - 'src/**' - - 'tests/**' - - '.github/workflows/CI.yml' - - 'pyproject.toml' - - 'poetry.lock' - - 'docs/**' + # push: + # paths: + # - 'src/**' + # - 'tests/**' + # - '.github/workflows/CI.yml' + # - 'pyproject.toml' + # - 'poetry.lock' + # - 'docs/**' pull_request: branches: - main diff --git a/src/tsbootstrap/base_bootstrap.py b/src/tsbootstrap/base_bootstrap.py index c2504b4..14ba746 100644 --- a/src/tsbootstrap/base_bootstrap.py +++ b/src/tsbootstrap/base_bootstrap.py @@ -228,7 +228,7 @@ def _fit_model(self, X: np.ndarray, y: np.ndarray | None = None) -> None: save_models=self.config.save_models, **self.config.model_params, ) - self.fit_model = fit_obj.fit(X=X, exog=y).model + self.fit_model = fit_obj.fit(X=X, y=y).model self.X_fitted = fit_obj.get_fitted_X() self.resids = fit_obj.get_residuals() self.order = fit_obj.get_order() @@ -530,7 +530,7 @@ def _fit_resids_model(self, X: np.ndarray) -> None: save_models=self.config.save_resids_models, **self.config.resids_model_params, ) - resids_fit_model = resids_fit_obj.fit(X, exog=None).model + resids_fit_model = resids_fit_obj.fit(X, y=None).model resids_order = resids_fit_obj.get_order() resids_coefs = resids_fit_obj.get_coefs() self.resids_fit_model = resids_fit_model diff --git a/src/tsbootstrap/block_bootstrap.py b/src/tsbootstrap/block_bootstrap.py index d717c9a..600699e 100644 --- a/src/tsbootstrap/block_bootstrap.py +++ b/src/tsbootstrap/block_bootstrap.py @@ -99,7 +99,7 @@ def _generate_blocks(self, X: np.ndarray) -> list[np.ndarray]: return blocks def _generate_samples_single_bootstrap( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> tuple[list[np.ndarray], list[np.ndarray]]: """ Generate a single bootstrap sample. @@ -174,7 +174,7 @@ def __init__(self, config: BaseBlockBootstrapConfig, **kwargs): ](config=config) def _generate_samples_single_bootstrap( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> tuple[list[np.ndarray], list[np.ndarray]]: """ Generate a single bootstrap sample using either the base BlockBootstrap method or the specified bootstrap_type. @@ -194,7 +194,7 @@ def _generate_samples_single_bootstrap( ( block_indices, block_data, - ) = super()._generate_samples_single_bootstrap(X=X, exog=exog) + ) = super()._generate_samples_single_bootstrap(X=X, y=y) else: # Generate samples using the specified bootstrap_type if hasattr( @@ -204,7 +204,7 @@ def _generate_samples_single_bootstrap( block_indices, block_data, ) = self.bootstrap_instance._generate_samples_single_bootstrap( - X=X, exog=exog + X=X, y=y ) else: raise NotImplementedError( diff --git a/src/tsbootstrap/bootstrap.py b/src/tsbootstrap/bootstrap.py index ecbf9e0..4b064b0 100644 --- a/src/tsbootstrap/bootstrap.py +++ b/src/tsbootstrap/bootstrap.py @@ -57,9 +57,9 @@ def __init__(self, config: BaseResidualBootstrapConfig): super().__init__(config) def _generate_samples_single_bootstrap( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> tuple[list[np.ndarray], list[np.ndarray]]: - self._fit_model(X=X, exog=exog) + self._fit_model(X=X, y=y) # Resample residuals resampled_indices = generate_random_indices( @@ -105,10 +105,10 @@ def __init__( self.block_bootstrap = BaseBlockBootstrap(config=block_config) def _generate_samples_single_bootstrap( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> tuple[list[np.ndarray], list[np.ndarray]]: # Fit the model and store residuals, fitted values, etc. - BaseResidualBootstrap._fit_model(self, X=X, exog=exog) + BaseResidualBootstrap._fit_model(self, X=X, y=y) # Generate blocks of residuals ( @@ -144,10 +144,10 @@ class WholeMarkovBootstrap(BaseMarkovBootstrap): """ def _generate_samples_single_bootstrap( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> tuple[list[np.ndarray], list[np.ndarray]]: # Fit the model and store residuals, fitted values, etc. - self._fit_model(X=X, exog=exog) + self._fit_model(X=X, y=y) # Fit HMM to residuals, just once. random_seed = self.config.rng.integers(0, 1000) @@ -217,10 +217,10 @@ def __init__( self.block_bootstrap = BaseBlockBootstrap(config=block_config) def _generate_samples_single_bootstrap( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> tuple[list[np.ndarray], list[np.ndarray]]: # Fit the model and store residuals, fitted values, etc. - super()._fit_model(X=X, exog=exog) + super()._fit_model(X=X, y=y) # Generate blocks of residuals ( @@ -279,7 +279,7 @@ class WholeStatisticPreservingBootstrap(BaseStatisticPreservingBootstrap): """ def _generate_samples_single_bootstrap( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> tuple[list[np.ndarray], list[np.ndarray]]: if self.statistic_X is None: self.statistic_X = self._calculate_statistic(X=X) @@ -338,7 +338,7 @@ def __init__( self.block_bootstrap = BaseBlockBootstrap(config=block_config) def _generate_samples_single_bootstrap( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> tuple[list[np.ndarray], list[np.ndarray]]: if self.statistic_X is None: self.statistic_X = super()._calculate_statistic(X=X) @@ -385,10 +385,10 @@ class WholeDistributionBootstrap(BaseDistributionBootstrap): """ def _generate_samples_single_bootstrap( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> tuple[list[np.ndarray], list[np.ndarray]]: # Fit the model and residuals - self._fit_model(X=X, exog=exog) + self._fit_model(X=X, y=y) # Fit the specified distribution to the residuals if not self.config.refit: if self.resids_dist is None or self.resids_dist_params == (): @@ -474,10 +474,10 @@ def __init__( self.block_bootstrap = BaseBlockBootstrap(config=block_config) def _generate_samples_single_bootstrap( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> tuple[list[np.ndarray], list[np.ndarray]]: # Fit the model and residuals - super()._fit_model(X=X, exog=exog) + super()._fit_model(X=X, y=y) ( block_indices, block_data, @@ -538,9 +538,9 @@ class WholeSieveBootstrap(BaseSieveBootstrap): """ def _generate_samples_single_bootstrap( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> tuple[list[np.ndarray], list[np.ndarray]]: - self._fit_model(X=X, exog=exog) + self._fit_model(X=X, y=y) self._fit_resids_model(X=self.resids) ts_simulator = TimeSeriesSimulator( @@ -593,10 +593,10 @@ def __init__( self.block_bootstrap = BaseBlockBootstrap(config=block_config) def _generate_samples_single_bootstrap( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> tuple[list[np.ndarray], list[np.ndarray]]: # Fit the model and residuals - super()._fit_model(X=X, exog=exog) + super()._fit_model(X=X, y=y) super()._fit_resids_model(X=self.resids) ts_simulator = TimeSeriesSimulator( diff --git a/src/tsbootstrap/ranklags.py b/src/tsbootstrap/ranklags.py index 2be8dac..951a34e 100644 --- a/src/tsbootstrap/ranklags.py +++ b/src/tsbootstrap/ranklags.py @@ -43,7 +43,7 @@ def __init__( X: np.ndarray, model_type: ModelTypes, max_lag: Integral = 10, - exog: np.ndarray | None = None, + y: np.ndarray | None = None, save_models: bool = False, ) -> None: """ @@ -57,7 +57,7 @@ def __init__( The type of model to fit. One of 'ar', 'arima', 'sarima', 'var', 'arch'. max_lag : int, optional, default=10 Maximum lag to consider. - exog : np.ndarray, optional, default=None + y : np.ndarray, optional, default=None Exogenous variables to include in the model. save_models : bool, optional, default=False Whether to save the models. @@ -65,7 +65,7 @@ def __init__( self.X = X self.max_lag = max_lag self.model_type = model_type - self.exog = exog + self.y = y self.save_models = save_models self.models = [] @@ -146,7 +146,7 @@ def model_type(self, value: ModelTypes) -> None: self._model_type = value.lower() @property - def exog(self) -> np.ndarray | None: + def y(self) -> np.ndarray | None: """ Exogenous variables to include in the model. @@ -155,21 +155,21 @@ def exog(self) -> np.ndarray | None: np.ndarray Exogenous variables to include in the model. """ - return self._exog + return self._y - @exog.setter - def exog(self, value: np.ndarray | None) -> None: + @y.setter + def y(self, value: np.ndarray | None) -> None: """ Set the exogenous variables to include in the model. Parameters ---------- - exog : np.ndarray + y : np.ndarray Exogenous variables to include in the model. """ if value is not None and not isinstance(value, np.ndarray): - raise TypeError("exog must be a numpy array.") - self._exog = value + raise TypeError("y must be a numpy array.") + self._y = value def rank_lags_by_aic_bic(self) -> tuple[np.ndarray, np.ndarray]: """ @@ -188,7 +188,7 @@ def rank_lags_by_aic_bic(self) -> tuple[np.ndarray, np.ndarray]: for lag in range(1, self.max_lag + 1): try: fit_obj = TSFit(order=lag, model_type=self.model_type) - model = fit_obj.fit(X=self.X, exog=self.exog).model + model = fit_obj.fit(X=self.X, y=self.y).model except Exception as e: # raise RuntimeError(f"An error occurred during fitting: {e}") print(f"{e}") diff --git a/src/tsbootstrap/time_series_model.py b/src/tsbootstrap/time_series_model.py index b50b3f9..b0d0d86 100644 --- a/src/tsbootstrap/time_series_model.py +++ b/src/tsbootstrap/time_series_model.py @@ -15,7 +15,7 @@ from tsbootstrap.utils.validate import ( validate_integers, validate_literal_type, - validate_X_and_exog, + validate_X_and_y, ) @@ -25,7 +25,7 @@ class TimeSeriesModel: def __init__( self, X: np.ndarray, - exog: np.ndarray | None = None, + y: np.ndarray | None = None, model_type: ModelTypes = "ar", verbose: bool = True, ): @@ -35,7 +35,7 @@ def __init__( ---------- X : np.ndarray The input data. - exog : Optional[np.ndarray] + y : Optional[np.ndarray] Optional array of exogenous variables. model_type : ModelTypes, default "ar" The type of model to fit. Supported types are "ar", "arma", "arima", "sarimax", "var", "arch". @@ -49,7 +49,7 @@ def __init__( """ self.model_type = model_type self.X = X - self.exog = exog + self.y = y self.verbose = verbose @property @@ -72,7 +72,7 @@ def X(self) -> np.ndarray: @X.setter def X(self, value: np.ndarray) -> None: """Sets the input data.""" - self._X, _ = validate_X_and_exog( + self._X, _ = validate_X_and_y( value, None, model_is_var=self.model_type == "var", @@ -80,14 +80,14 @@ def X(self, value: np.ndarray) -> None: ) @property - def exog(self) -> np.ndarray | None: + def y(self) -> np.ndarray | None: """Optional array of exogenous variables.""" - return self._exog + return self._y - @exog.setter - def exog(self, value: np.ndarray | None) -> None: + @y.setter + def y(self, value: np.ndarray | None) -> None: """Sets the optional array of exogenous variables.""" - _, self._exog = validate_X_and_exog( + _, self._y = validate_X_and_y( self.X, value, model_is_var=self.model_type == "var", @@ -167,7 +167,7 @@ def _validate_order( ValueError If the specified order value exceeds the allowed range. """ - k = self.exog.shape[1] if self.exog is not None else 0 + k = self.y.shape[1] if self.y is not None else 0 seasonal_terms, trend_parameters = self._calculate_terms(kwargs) max_lag = (N - k - seasonal_terms - trend_parameters) // 2 @@ -261,7 +261,7 @@ def fit_ar( def fit_logic(): """Logic for fitting ARIMA model.""" - model = AutoReg(endog=self.X, lags=order, exog=self.exog, **kwargs) + model = AutoReg(endog=self.X, lags=order, exog=self.y, **kwargs) model_fit = model.fit() return model_fit @@ -305,7 +305,7 @@ def fit_arima( def fit_logic(): """Logic for fitting ARIMA model.""" - model = ARIMA(endog=self.X, order=order, exog=self.exog, **kwargs) + model = ARIMA(endog=self.X, order=order, exog=self.y, **kwargs) model_fit = model.fit() return model_fit @@ -387,7 +387,7 @@ def fit_logic(): endog=self.X, order=arima_order, seasonal_order=order, - exog=self.exog, + exog=self.y, **kwargs, ) model_fit = model.fit(disp=-1) @@ -424,7 +424,7 @@ def fit_var(self, order: int | None = None, **kwargs) -> VARResultsWrapper: def fit_logic(): """Logic for fitting ARIMA model.""" - model = VAR(endog=self.X, exog=self.exog) + model = VAR(endog=self.X, exog=self.y) model_fit = model.fit(**kwargs) return model_fit @@ -471,7 +471,7 @@ def fit_arch( if order is None: order = 1 - # Assuming a validate_X_and_exog function exists for data validation + # Assuming a validate_X_and_y function exists for data validation validate_integers(p, q, order, min_value=1) if mean_type not in ["zero", "AR"]: @@ -480,7 +480,7 @@ def fit_arch( if arch_model_type in ["GARCH", "EGARCH"]: model = arch_model( y=self.X, - x=self.exog, + x=self.y, mean=mean_type, lags=order, vol=arch_model_type, @@ -491,7 +491,7 @@ def fit_arch( elif arch_model_type == "TARCH": model = arch_model( y=self.X, - x=self.exog, + x=self.y, mean=mean_type, lags=order, vol="GARCH", @@ -504,7 +504,7 @@ def fit_arch( elif arch_model_type == "AGARCH": model = arch_model( y=self.X, - x=self.exog, + x=self.y, mean=mean_type, lags=order, vol="GARCH", @@ -568,8 +568,8 @@ def __eq__(self, other: object) -> bool: return ( np.array_equal(self.X, other.X) and ( - np.array_equal(self.exog, other.exog) - if (self.exog is not None and other.exog is not None) + np.array_equal(self.y, other.y) + if (self.y is not None and other.y is not None) else True ) and self.model_type == other.model_type diff --git a/src/tsbootstrap/time_series_simulator.py b/src/tsbootstrap/time_series_simulator.py index dd2a8af..7aa74b3 100644 --- a/src/tsbootstrap/time_series_simulator.py +++ b/src/tsbootstrap/time_series_simulator.py @@ -16,7 +16,7 @@ validate_integers, validate_literal_type, validate_rng, - validate_X_and_exog, + validate_X_and_y, ) @@ -101,7 +101,7 @@ def X_fitted(self, value: np.ndarray) -> None: """ model_is_var = isinstance(self.fitted_model, VARResultsWrapper) model_is_arch = isinstance(self.fitted_model, ARCHModelResult) - self._X_fitted, _ = validate_X_and_exog( + self._X_fitted, _ = validate_X_and_y( value, None, model_is_var=model_is_var, model_is_arch=model_is_arch ) diff --git a/src/tsbootstrap/tsfit.py b/src/tsbootstrap/tsfit.py index 23f600d..e545a8f 100644 --- a/src/tsbootstrap/tsfit.py +++ b/src/tsbootstrap/tsfit.py @@ -29,7 +29,7 @@ from tsbootstrap.utils.validate import ( validate_literal_type, validate_X, - validate_X_and_exog, + validate_X_and_y, ) @@ -46,7 +46,7 @@ class TSFit(BaseEstimator, RegressorMixin): Methods ------- - fit(X, exog=None) + fit(X, y=None) Fit the chosen model to the data. get_coefs() Return the coefficients of the fitted model. @@ -248,7 +248,7 @@ def __repr__(self): """ return f"TSFit(order={self.order}, model_type='{self.model_type}')" - def fit(self, X: np.ndarray, exog: np.ndarray | None = None) -> TSFit: + def fit(self, X: np.ndarray, y: np.ndarray | None = None) -> TSFit: """ Fit the chosen model to the data. @@ -256,7 +256,7 @@ def fit(self, X: np.ndarray, exog: np.ndarray | None = None) -> TSFit: ---------- X : np.ndarray Input data of shape (n_samples, n_features). - exog : np.ndarray, optional + y : np.ndarray, optional Exogenous variables, by default None. Returns @@ -272,15 +272,15 @@ def fit(self, X: np.ndarray, exog: np.ndarray | None = None) -> TSFit: If the maximum number of iterations is reached before the variance is within the desired range. """ # Check if the input shapes are valid - X, exog = validate_X_and_exog( + X, y = validate_X_and_y( X, - exog, + y, model_is_var=self.model_type == "var", model_is_arch=self.model_type == "arch", ) def _rescale_inputs( - X: np.ndarray, exog: np.ndarray | None = None + X: np.ndarray, y: np.ndarray | None = None ) -> tuple[ np.ndarray, np.ndarray | None, tuple[float, list[float] | None] ]: @@ -291,7 +291,7 @@ def _rescale_inputs( ---------- X : np.ndarray The input data. - exog : np.ndarray, optional + y : np.ndarray, optional The exogenous variables, by default None. Returns @@ -352,26 +352,26 @@ def rescale_array( X, x_rescale_factor = rescale_array(X) - if exog is not None: - exog_rescale_factors = [] - for i in range(exog.shape[1]): - exog[:, i], factor = rescale_array(exog[:, i]) - exog_rescale_factors.append(factor) + if y is not None: + y_rescale_factors = [] + for i in range(y.shape[1]): + y[:, i], factor = rescale_array(y[:, i]) + y_rescale_factors.append(factor) else: - exog_rescale_factors = None + y_rescale_factors = None - return X, exog, (x_rescale_factor, exog_rescale_factors) + return X, y, (x_rescale_factor, y_rescale_factors) - fit_func = TimeSeriesModel(X=X, exog=exog, model_type=self.model_type) + fit_func = TimeSeriesModel(X=X, y=y, model_type=self.model_type) self.model = fit_func.fit(order=self.order, **self.model_params) if self.model_type == "arch": ( X, - exog, - (x_rescale_factor, exog_rescale_factors), - ) = _rescale_inputs(X, exog) + y, + (x_rescale_factor, y_rescale_factors), + ) = _rescale_inputs(X, y) self.rescale_factors["x"] = x_rescale_factor - self.rescale_factors["exog"] = exog_rescale_factors + self.rescale_factors["y"] = y_rescale_factors return self @@ -574,7 +574,7 @@ def get_order(self) -> OrderTypesWithoutNone: return self._get_order_helper(self.model) def predict( - self, X: np.ndarray, exog: np.ndarray | None = None, n_steps: int = 1 + self, X: np.ndarray, y: np.ndarray | None = None, n_steps: int = 1 ) -> np.ndarray: """ Predict time series values using the fitted model. @@ -583,7 +583,7 @@ def predict( ---------- X : np.ndarray Input data of shape (n_samples, n_features). - exog : np.ndarray | None, optional + y : np.ndarray | None, optional Exogenous variables, by default None. n_steps : int, optional Number of steps to forecast, by default 1. @@ -603,17 +603,17 @@ def predict( # Check if the input shapes are valid X = validate_X(X, model_is_var=self.model_type == "var") if self.model_type == "var": - return self.model.forecast(X, n_steps, exog_future=exog) + return self.model.forecast(X, n_steps, exog_future=y) elif self.model_type == "arch": # Adjust the code according to how ARCH predictions are made in your setup return ( - self.model.forecast(horizon=n_steps, x=exog, method="analytic") + self.model.forecast(horizon=n_steps, x=y, method="analytic") .mean.values[-1] .ravel() ) elif self.model_type in ["ar", "arima", "sarima"]: # For AutoReg, ARIMA, and SARIMA, use the built-in forecast method - return self.model.forecast(steps=n_steps, exog=exog) + return self.model.forecast(steps=n_steps, exog=y) def score(self, X: np.ndarray, y_true: np.ndarray) -> float: """ @@ -837,7 +837,7 @@ class TSFitBestLag(BaseEstimator, RegressorMixin): Methods ------- - fit(X, exog=None) + fit(X, y=None) Fit the time series model to the data. get_coefs() Return the coefficients of the fitted model. @@ -873,7 +873,7 @@ def __init__( self.rank_lagger = None self.ts_fit = None self.model = None - self.rescale_factors = {"x": 1, "exog": None} + self.rescale_factors = {"x": 1, "y": None} def _compute_best_order(self, X) -> int: """ @@ -899,7 +899,7 @@ def _compute_best_order(self, X) -> int: return best_order def fit( - self, X: np.ndarray, exog: np.ndarray | None = None + self, X: np.ndarray, y: np.ndarray | None = None ) -> ( AutoRegResultsWrapper | ARIMAResultsWrapper @@ -914,7 +914,7 @@ def fit( ---------- X : np.ndarray The input data. - exog : np.ndarray, optional, default=None + y : np.ndarray, optional, default=None Exogenous variables to include in the model. Returns @@ -929,7 +929,7 @@ def fit( self.ts_fit = TSFit( order=self.order, model_type=self.model_type, **self.model_params ) - self.model = self.ts_fit.fit(X, exog=exog).model + self.model = self.ts_fit.fit(X, y=y).model self.rescale_factors = self.ts_fit.rescale_factors return self diff --git a/src/tsbootstrap/utils/validate.py b/src/tsbootstrap/utils/validate.py index 8d91d46..743d73b 100644 --- a/src/tsbootstrap/utils/validate.py +++ b/src/tsbootstrap/utils/validate.py @@ -474,9 +474,9 @@ def validate_exog(exog: np.ndarray) -> np.ndarray: return validate_X(exog, model_is_var=False, allow_multi_column=True) -def validate_X_and_exog( +def validate_X_and_y( X: np.ndarray, - exog: np.ndarray | None, + y: np.ndarray | None, model_is_var: bool = False, model_is_arch: bool = False, ) -> tuple[np.ndarray, np.ndarray | None]: @@ -489,7 +489,7 @@ def validate_X_and_exog( ---------- X : np.ndarray The input array to be validated. - exog : Optional[np.ndarray] + y : Optional[np.ndarray] The exogenous variable array to be validated. Can be None. model_is_var : bool, optional A flag to determine if the model is of VAR type. Default is False. @@ -508,19 +508,19 @@ def validate_X_and_exog( """ X = validate_X(X, model_is_var) - if exog is not None: - exog = validate_exog(exog) - if exog.shape[0] != X.shape[0]: + if y is not None: + y = validate_exog(y) + if y.shape[0] != X.shape[0]: raise ValueError( - "The number of rows in exog must be equal to the number of rows in X." + "The number of rows in y must be equal to the number of rows in X." ) # Ensure contiguous arrays for ARCH models if model_is_arch: X = np.ascontiguousarray(X) - if exog is not None: - exog = np.ascontiguousarray(exog) + if y is not None: + y = np.ascontiguousarray(y) - return X, exog + return X, y def validate_block_indices( diff --git a/tests/test_block_bootstrap.py b/tests/test_block_bootstrap.py index be9b31e..c5ca2ce 100644 --- a/tests/test_block_bootstrap.py +++ b/tests/test_block_bootstrap.py @@ -89,10 +89,10 @@ def test_block_bootstrap( @settings(max_examples=10, deadline=None) @given( X=lists(floats(), min_size=10, max_size=100), - exog=one_of(none(), lists(floats(), min_size=10, max_size=100)), + y=one_of(none(), lists(floats(), min_size=10, max_size=100)), ) def test__generate_samples_single_bootstrap( - self, X: list[float], exog: list[float] | None + self, X: list[float], y: list[float] | None ) -> None: """ Test if the BlockBootstrap's _generate_samples_single_bootstrap method runs without errors and returns the correct output. @@ -109,7 +109,7 @@ def test__generate_samples_single_bootstrap( # Check _generate_samples_single_bootstrap method indices, data = bootstrap._generate_samples_single_bootstrap( - np.array(X), exog=exog if exog is None else np.array(exog) + np.array(X), y=y if y is None else np.array(y) ) assert isinstance(indices, list) diff --git a/tests/test_bootstrap.py b/tests/test_bootstrap.py index 6359d5e..281f33c 100644 --- a/tests/test_bootstrap.py +++ b/tests/test_bootstrap.py @@ -104,7 +104,7 @@ # class TestWholeResidualBootstrap: # class TestPassingCases: - + # @settings(deadline=None, max_examples=10) # @given( # model_type=model_strategy_univariate, @@ -1335,7 +1335,7 @@ class TestPassingCases: @settings(deadline=None, max_examples=10) @given( model_type=model_strategy_univariate, - order=integers(min_value=1, max_value=5), + order=integers(min_value=1, max_value=4), save_models=booleans(), n_bootstraps=integers(min_value=1, max_value=10), rng=one_of(integers(min_value=0, max_value=2**32 - 1), none()), diff --git a/tests/test_ranklags.py b/tests/test_ranklags.py index d2ad15b..048e3cd 100644 --- a/tests/test_ranklags.py +++ b/tests/test_ranklags.py @@ -30,8 +30,8 @@ def test_exogenous_variable_initialization(self): """ X = np.random.normal(size=(100, 1)) exog = np.random.normal(size=(100, 1)) - rank_obj = RankLags(X, model_type="ar", exog=exog) - assert np.array_equal(rank_obj.exog, exog) + rank_obj = RankLags(X, model_type="ar", y=exog) + assert np.array_equal(rank_obj.y, exog) def test_save_models_flag_initialization(self): """ diff --git a/tests/test_time_series_model.py b/tests/test_time_series_model.py index df3f235..b7589ff 100644 --- a/tests/test_time_series_model.py +++ b/tests/test_time_series_model.py @@ -49,8 +49,8 @@ def test_fit_ar(input_1d, exog_1d, order): # Test with no exog, seasonal order, and set trend to 'c' (constant, default) max_lag = (input_1d.shape[0] - 1) // 2 print(f"max_lag: {max_lag}") - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="ar") - tsm_exog = TimeSeriesModel(X=input_1d, exog=exog_1d, model_type="ar") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="ar") + tsm_exog = TimeSeriesModel(X=input_1d, y=exog_1d, model_type="ar") if np.max(order) <= max_lag: model_fit = tsm.fit(order=order) assert isinstance(model_fit, AutoRegResultsWrapper) @@ -116,16 +116,16 @@ def test_fit_ar(input_1d, exog_1d, order): def test_fit_ar_errors(input_1d, input_2d): # Test order value out of bound with pytest.raises(ValueError): - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="ar") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="ar") tsm.fit(len(input_1d) + 1) # Test invalid input dimension with pytest.raises(ValueError): - tsm = TimeSeriesModel(X=input_2d, exog=None, model_type="ar") + tsm = TimeSeriesModel(X=input_2d, y=None, model_type="ar") tsm.fit(3) # Test invalid order input types - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="ar") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="ar") with pytest.raises(TypeError): tsm.fit(1.5) with pytest.raises(TypeError): @@ -134,7 +134,7 @@ def test_fit_ar_errors(input_1d, input_2d): tsm.fit([-1, 2, 3]) # Test invalid kwargs - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="ar") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="ar") with pytest.raises(ValueError): tsm.fit(order=1, seasonal="True") with pytest.raises(ValueError): @@ -153,7 +153,7 @@ def test_fit_arima(input_1d, exog_1d, exog_2d, arima_order): Testing ARIMA model fitting with different orders and with or without exogenous variables. """ # Test with no exog - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="arima") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="arima") try: model_fit = tsm.fit(arima_order) assert isinstance(model_fit, ARIMAResultsWrapper) @@ -161,7 +161,7 @@ def test_fit_arima(input_1d, exog_1d, exog_2d, arima_order): pass # Test with 1D exog - tsm = TimeSeriesModel(X=input_1d, exog=exog_1d, model_type="arima") + tsm = TimeSeriesModel(X=input_1d, y=exog_1d, model_type="arima") try: model_fit_exog_1d = tsm.fit(arima_order) assert isinstance(model_fit_exog_1d, ARIMAResultsWrapper) @@ -169,7 +169,7 @@ def test_fit_arima(input_1d, exog_1d, exog_2d, arima_order): pass # Test with 2D exog - tsm = TimeSeriesModel(X=input_1d, exog=exog_2d, model_type="arima") + tsm = TimeSeriesModel(X=input_1d, y=exog_2d, model_type="arima") try: model_fit_exog_2d = tsm.fit(arima_order) assert isinstance(model_fit_exog_2d, ARIMAResultsWrapper) @@ -183,22 +183,22 @@ def test_fit_arima_errors(input_1d, exog_1d, exog_2d): """ # Test invalid arima_order input types with pytest.raises(ValueError): - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="arima") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="arima") tsm.fit((1, 0)) # less than 3 elements with pytest.raises(ValueError): - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="arima") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="arima") tsm.fit((1, 0, 0, 1)) # more than 3 elements # Test invalid exog dimensions with pytest.raises(ValueError): tsm = TimeSeriesModel( - X=input_1d, exog=np.random.rand(100, 2, 2), model_type="arima" + X=input_1d, y=np.random.rand(100, 2, 2), model_type="arima" ) # Test with incompatible exog size with pytest.raises(ValueError): tsm = TimeSeriesModel( - X=input_1d, exog=np.random.rand(101, 1), model_type="arima" + X=input_1d, y=np.random.rand(101, 1), model_type="arima" ) @@ -243,7 +243,7 @@ def test_fit_sarima(input_1d, exog_1d, exog_2d, orders): arima_order, sarima_order = orders # Test with no exog and arima_order - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="sarima") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="sarima") try: model_fit = tsm.fit(order=sarima_order, arima_order=None) assert isinstance(model_fit, SARIMAXResultsWrapper) @@ -251,7 +251,7 @@ def test_fit_sarima(input_1d, exog_1d, exog_2d, orders): pass # Test with arima_order and 1D exog - tsm = TimeSeriesModel(X=input_1d, exog=exog_1d, model_type="sarima") + tsm = TimeSeriesModel(X=input_1d, y=exog_1d, model_type="sarima") try: model_fit_exog_1d = tsm.fit( order=sarima_order, arima_order=arima_order @@ -261,7 +261,7 @@ def test_fit_sarima(input_1d, exog_1d, exog_2d, orders): pass # Test with arima_order and 2D exog - tsm = TimeSeriesModel(X=input_1d, exog=exog_2d, model_type="sarima") + tsm = TimeSeriesModel(X=input_1d, y=exog_2d, model_type="sarima") try: model_fit_exog_2d = tsm.fit( order=sarima_order, arima_order=arima_order @@ -276,7 +276,7 @@ def test_fit_sarima_errors(input_1d): Testing SARIMA model fitting with invalid orders and exogenous variables. """ # Test invalid arima_order input types - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="sarima") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="sarima") with pytest.raises(ValueError): # sarima_order has less than 4 elements tsm.fit(order=(1, 0, 0), arima_order=(1, 0, 0)) @@ -296,17 +296,17 @@ def test_fit_sarima_errors(input_1d): # Test invalid exog dimensions with pytest.raises(ValueError): tsm = TimeSeriesModel( - X=input_1d, exog=np.random.rand(100, 2, 2), model_type="sarima" + X=input_1d, y=np.random.rand(100, 2, 2), model_type="sarima" ) # Test with incompatible exog size with pytest.raises(ValueError): tsm = TimeSeriesModel( - X=input_1d, exog=np.random.rand(101, 1), model_type="sarima" + X=input_1d, y=np.random.rand(101, 1), model_type="sarima" ) # Test duplication of order - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="sarima") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="sarima") with pytest.raises(ValueError): # 'p' >= 's' and 'P' != 0 tsm.fit(order=(1, 0, 0, 2), arima_order=(3, 0, 0)) @@ -318,7 +318,7 @@ def test_fit_sarima_errors(input_1d): # Tests for fit_var def test_fit_var(input_2d, input_2d_short, exog_1d, exog_2d, exog_2d_short): # Test with no exog - tsm = TimeSeriesModel(X=input_2d, exog=None, model_type="var") + tsm = TimeSeriesModel(X=input_2d, y=None, model_type="var") try: model_fit = tsm.fit() assert isinstance(model_fit, VARResultsWrapper) @@ -326,7 +326,7 @@ def test_fit_var(input_2d, input_2d_short, exog_1d, exog_2d, exog_2d_short): pass # Test with exog - tsm = TimeSeriesModel(X=input_2d, exog=exog_1d, model_type="var") + tsm = TimeSeriesModel(X=input_2d, y=exog_1d, model_type="var") try: model_fit_exog = tsm.fit() assert isinstance(model_fit_exog, VARResultsWrapper) @@ -334,7 +334,7 @@ def test_fit_var(input_2d, input_2d_short, exog_1d, exog_2d, exog_2d_short): pass # Test with different kwargs - tsm = TimeSeriesModel(X=input_2d, exog=exog_2d, model_type="var") + tsm = TimeSeriesModel(X=input_2d, y=exog_2d, model_type="var") try: model_fit_no_trend = tsm.fit(trend="n") assert isinstance(model_fit_no_trend, VARResultsWrapper) @@ -342,7 +342,7 @@ def test_fit_var(input_2d, input_2d_short, exog_1d, exog_2d, exog_2d_short): except LinAlgError: pass - tsm = TimeSeriesModel(X=input_2d, exog=exog_2d, model_type="var") + tsm = TimeSeriesModel(X=input_2d, y=exog_2d, model_type="var") try: model_fit_trend = tsm.fit(trend="c") assert isinstance(model_fit_trend, VARResultsWrapper) @@ -350,7 +350,7 @@ def test_fit_var(input_2d, input_2d_short, exog_1d, exog_2d, exog_2d_short): except LinAlgError: pass - tsm = TimeSeriesModel(X=input_2d, exog=exog_2d, model_type="var") + tsm = TimeSeriesModel(X=input_2d, y=exog_2d, model_type="var") try: model_fit_trend = tsm.fit(trend="ct") assert isinstance(model_fit_trend, VARResultsWrapper) @@ -358,7 +358,7 @@ def test_fit_var(input_2d, input_2d_short, exog_1d, exog_2d, exog_2d_short): except LinAlgError: pass - tsm = TimeSeriesModel(X=input_2d, exog=exog_2d, model_type="var") + tsm = TimeSeriesModel(X=input_2d, y=exog_2d, model_type="var") try: model_fit_trend = tsm.fit(trend="ctt") assert isinstance(model_fit_trend, VARResultsWrapper) @@ -367,13 +367,13 @@ def test_fit_var(input_2d, input_2d_short, exog_1d, exog_2d, exog_2d_short): pass # Test with 1D exog - tsm = TimeSeriesModel(X=input_2d, exog=exog_1d, model_type="var") + tsm = TimeSeriesModel(X=input_2d, y=exog_1d, model_type="var") model_fit_exog_1d = tsm.fit() assert isinstance(model_fit_exog_1d, VARResultsWrapper) # Test with 2D exog of different width exog_2d_wide = np.random.rand(input_2d.shape[0], input_2d.shape[1] + 1) - tsm = TimeSeriesModel(X=input_2d, exog=exog_2d_wide, model_type="var") + tsm = TimeSeriesModel(X=input_2d, y=exog_2d_wide, model_type="var") try: model_fit_exog_2d_wide = tsm.fit() assert isinstance(model_fit_exog_2d_wide, VARResultsWrapper) @@ -381,9 +381,7 @@ def test_fit_var(input_2d, input_2d_short, exog_1d, exog_2d, exog_2d_short): pass # Test with short input arrays - tsm = TimeSeriesModel( - X=input_2d_short, exog=exog_2d_short, model_type="var" - ) + tsm = TimeSeriesModel(X=input_2d_short, y=exog_2d_short, model_type="var") try: model_fit_short = tsm.fit() assert isinstance(model_fit_short, VARResultsWrapper) @@ -392,7 +390,7 @@ def test_fit_var(input_2d, input_2d_short, exog_1d, exog_2d, exog_2d_short): # Test deterministic input deterministic_2d = np.ones_like(input_2d) - tsm = TimeSeriesModel(X=deterministic_2d, exog=exog_2d, model_type="var") + tsm = TimeSeriesModel(X=deterministic_2d, y=exog_2d, model_type="var") try: model_fit_deterministic = tsm.fit(trend="n") assert isinstance(model_fit_deterministic, VARResultsWrapper) @@ -404,13 +402,13 @@ def test_fit_var(input_2d, input_2d_short, exog_1d, exog_2d, exog_2d_short): def test_fit_var_errors(input_1d, input_2d, exog_2d): # Test invalid input dimension with pytest.raises(ValueError): - TimeSeriesModel(X=input_1d, exog=None, model_type="var") + TimeSeriesModel(X=input_1d, y=None, model_type="var") # Test exog of different length with pytest.raises(ValueError): TimeSeriesModel( X=input_2d, - exog=np.random.rand(input_2d.shape[0] + 1), + y=np.random.rand(input_2d.shape[0] + 1), model_type="var", ) @@ -418,12 +416,12 @@ def test_fit_var_errors(input_1d, input_2d, exog_2d): with pytest.raises(ValueError): TimeSeriesModel( X=input_2d, - exog=np.random.rand(input_2d.shape[0], input_2d.shape[1], 2), + y=np.random.rand(input_2d.shape[0], input_2d.shape[1], 2), model_type="var", ) # Test invalid trend option - tsm = TimeSeriesModel(X=input_2d, exog=exog_2d, model_type="var") + tsm = TimeSeriesModel(X=input_2d, y=exog_2d, model_type="var") with pytest.raises(ValueError): tsm.fit(trend="invalid") @@ -431,21 +429,21 @@ def test_fit_var_errors(input_1d, input_2d, exog_2d): with pytest.raises(ValueError): TimeSeriesModel( X=np.random.rand(input_2d.shape[0], input_2d.shape[1], 2), - exog=None, + y=None, model_type="var", ) # Test invalid dtype with pytest.raises(TypeError): - TimeSeriesModel(X=input_2d.astype(str), exog=None, model_type="var") + TimeSeriesModel(X=input_2d.astype(str), y=None, model_type="var") # Test with empty arrays with pytest.raises(ValueError): - TimeSeriesModel(X=np.empty(shape=(0, 0)), exog=None, model_type="var") + TimeSeriesModel(X=np.empty(shape=(0, 0)), y=None, model_type="var") with pytest.raises(ValueError): TimeSeriesModel( X=np.empty(shape=(0, 0)), - exog=np.empty(shape=(0, 0)), + y=np.empty(shape=(0, 0)), model_type="var", ) @@ -463,7 +461,7 @@ def test_fit_arch(input_1d, exog_1d, p, q, arch_model_type, order, mean_type): if np.max(order) <= max_lag: # Test with no exog - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="arch") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="arch") model_fit = tsm.fit( p=p, q=q, @@ -474,7 +472,7 @@ def test_fit_arch(input_1d, exog_1d, p, q, arch_model_type, order, mean_type): assert isinstance(model_fit, ARCHModelResult) # Test with exog - tsm = TimeSeriesModel(X=input_1d, exog=exog_1d, model_type="arch") + tsm = TimeSeriesModel(X=input_1d, y=exog_1d, model_type="arch") model_fit_exog = tsm.fit( p=p, q=q, @@ -486,7 +484,7 @@ def test_fit_arch(input_1d, exog_1d, p, q, arch_model_type, order, mean_type): else: with pytest.raises(ValueError): - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="arch") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="arch") tsm.fit( p=p, q=q, @@ -495,7 +493,7 @@ def test_fit_arch(input_1d, exog_1d, p, q, arch_model_type, order, mean_type): mean_type=mean_type, ) with pytest.raises(ValueError): - tsm = TimeSeriesModel(X=input_1d, exog=exog_1d, model_type="arch") + tsm = TimeSeriesModel(X=input_1d, y=exog_1d, model_type="arch") tsm.fit( p=p, q=q, @@ -508,10 +506,10 @@ def test_fit_arch(input_1d, exog_1d, p, q, arch_model_type, order, mean_type): def test_fit_arch_errors(input_1d, input_2d): # Test invalid input dimension with pytest.raises(ValueError): - TimeSeriesModel(X=input_2d, exog=None, model_type="arch") + TimeSeriesModel(X=input_2d, y=None, model_type="arch") # Test invalid order input types - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="arch") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="arch") with pytest.raises(TypeError): tsm.fit(p=1, q=1, arch_model_type="GARCH", order=1.5) with pytest.raises(TypeError): @@ -520,31 +518,31 @@ def test_fit_arch_errors(input_1d, input_2d): tsm.fit(p=1, q=1, arch_model_type="GARCH", order=[-1, 2, 3]) # Test invalid model_type - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="arch") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="arch") with pytest.raises(ValueError): tsm.fit(p=1, q=1, arch_model_type="INVALID", order=1) # Test model_type set to 'ARCH' - tsm = TimeSeriesModel(X=input_1d, exog=None, model_type="arch") + tsm = TimeSeriesModel(X=input_1d, y=None, model_type="arch") with pytest.raises(ValueError): tsm.fit(p=1, q=1, order=1, arch_model_type=None) # Test input with NaN values with pytest.raises(ValueError, match="Input contains NaN."): TimeSeriesModel( - X=np.array([1.0, 2.0, np.nan]), exog=None, model_type="arch" + X=np.array([1.0, 2.0, np.nan]), y=None, model_type="arch" ) # Test exog with NaN values with pytest.raises(ValueError, match="Input contains NaN."): TimeSeriesModel( - X=input_1d, exog=np.array([1.0, 2.0, np.nan]), model_type="arch" + X=input_1d, y=np.array([1.0, 2.0, np.nan]), model_type="arch" ) # Test with zero-length input with pytest.raises(ValueError): - TimeSeriesModel(X=np.array([]), exog=None, model_type="arch") + TimeSeriesModel(X=np.array([]), y=None, model_type="arch") # Test with single value input with pytest.raises(ValueError): - TimeSeriesModel(X=np.array([1.0]), exog=None, model_type="arch") + TimeSeriesModel(X=np.array([1.0]), y=None, model_type="arch") diff --git a/tests/test_tsfit.py b/tests/test_tsfit.py index b980082..79355e8 100644 --- a/tests/test_tsfit.py +++ b/tests/test_tsfit.py @@ -217,7 +217,7 @@ def test_fit_valid_ar_with_exog(self, data, order, model_type, exog): data = np.array(data).reshape(-1, 1) exog = np.array(exog) tsfit = TSFit(order, model_type) - fitted_model = tsfit.fit(data, exog=exog).model + fitted_model = tsfit.fit(data, y=exog).model assert isinstance(fitted_model, AutoRegResultsWrapper) @settings(deadline=None) @@ -236,7 +236,7 @@ def test_fit_valid_arima_with_exog( tsfit = TSFit(order, model_type) var = np.var(data) if not math.isclose(var, 0, abs_tol=0.01): - fitted_model = tsfit.fit(data, exog=exog).model + fitted_model = tsfit.fit(data, y=exog).model assert isinstance(fitted_model, ARIMAResultsWrapper) @settings(deadline=None) @@ -256,7 +256,7 @@ def test_fit_valid_sarima_with_exog( var = np.var(data) if not math.isclose(var, 0, abs_tol=0.01): try: - fitted_model = tsfit.fit(data, exog=exog).model + fitted_model = tsfit.fit(data, y=exog).model assert isinstance(fitted_model, SARIMAXResultsWrapper) except LinAlgError: pass @@ -287,7 +287,7 @@ def test_fit_valid_var_arch_with_exog( if model_type == "var": if not is_data_var_zero and not is_exog_var_zero: try: - fitted_model = tsfit.fit(data, exog=exog).model + fitted_model = tsfit.fit(data, y=exog).model assert isinstance(fitted_model, VARResultsWrapper) except ValueError as e: if "x contains one or more constant columns" in str(e): @@ -296,7 +296,7 @@ def test_fit_valid_var_arch_with_exog( raise else: if not is_data_var_zero and not is_exog_var_zero: - fitted_model = tsfit.fit(data, exog=exog).model + fitted_model = tsfit.fit(data, y=exog).model assert isinstance(fitted_model, ARCHModelResult) @settings(deadline=None) @@ -312,8 +312,8 @@ def test_predict_valid_ar(self, data, order, model_type, exog): data = np.array(data).reshape(-1, 1) exog = np.array(exog) tsfit = TSFit(order, model_type) - fitted_model = tsfit.fit(data, exog=exog) - predicted = fitted_model.predict(data, n_steps=5, exog=exog[:5, :]) + fitted_model = tsfit.fit(data, y=exog) + predicted = fitted_model.predict(data, n_steps=5, y=exog[:5, :]) assert isinstance(predicted, np.ndarray) assert predicted.shape == (5,) @@ -329,8 +329,8 @@ def test_predict_valid_arima(self, data, order, model_type, exog): data = np.array(data).reshape(-1, 1) exog = np.array(exog) tsfit = TSFit(order, model_type) - fitted_model = tsfit.fit(data, exog=exog) - predicted = fitted_model.predict(data, n_steps=5, exog=exog[:5, :]) + fitted_model = tsfit.fit(data, y=exog) + predicted = fitted_model.predict(data, n_steps=5, y=exog[:5, :]) assert isinstance(predicted, np.ndarray) assert predicted.shape == (5,) @@ -347,9 +347,9 @@ def test_predict_valid_sarima(self, data, order, model_type, exog): exog = np.array(exog) tsfit = TSFit(order, model_type) try: - fitted_model = tsfit.fit(data, exog=exog) + fitted_model = tsfit.fit(data, y=exog) predicted = fitted_model.predict( - data, n_steps=5, exog=exog[:5, :] + data, n_steps=5, y=exog[:5, :] ) assert isinstance(predicted, np.ndarray) assert predicted.shape == (5,) @@ -380,9 +380,9 @@ def test_predict_valid_var_arch(self, data, order, model_type, exog): data = np.hstack((data, data)) if not is_data_var_zero and not is_exog_var_zero: try: - fitted_model = tsfit.fit(data, exog=exog) + fitted_model = tsfit.fit(data, y=exog) predicted = fitted_model.predict( - data, n_steps=5, exog=exog[:5, :] + data, n_steps=5, y=exog[:5, :] ) assert isinstance(predicted, np.ndarray) assert predicted.shape == (5, 2) @@ -393,7 +393,7 @@ def test_predict_valid_var_arch(self, data, order, model_type, exog): raise else: if not is_data_var_zero and not is_exog_var_zero: - fitted_model = tsfit.fit(data, exog=exog) + fitted_model = tsfit.fit(data, y=exog) predicted = fitted_model.predict(data, n_steps=5) print(f"predicted.type: {type(predicted)}") print(f"predicted: {predicted}") @@ -413,7 +413,7 @@ def test_get_residuals_valid_ar(self, data, order, model_type, exog): data = np.array(data).reshape(-1, 1) exog = np.array(exog) tsfit = TSFit(order, model_type) - fitted_model = tsfit.fit(data, exog=exog) + fitted_model = tsfit.fit(data, y=exog) residuals = fitted_model.get_residuals() assert isinstance(residuals, np.ndarray) assert residuals.shape == (len(data), 1) @@ -432,7 +432,7 @@ def test_get_residuals_valid_arima( data = np.array(data).reshape(-1, 1) exog = np.array(exog) tsfit = TSFit(order, model_type) - fitted_model = tsfit.fit(data, exog=exog) + fitted_model = tsfit.fit(data, y=exog) residuals = fitted_model.get_residuals() assert isinstance(residuals, np.ndarray) assert residuals.shape == (len(data), 1) @@ -451,7 +451,7 @@ def test_get_residuals_valid_sarima( data = np.array(data).reshape(-1, 1) exog = np.array(exog) tsfit = TSFit(order, model_type) - fitted_model = tsfit.fit(data, exog=exog) + fitted_model = tsfit.fit(data, y=exog) residuals = fitted_model.get_residuals() assert isinstance(residuals, np.ndarray) assert residuals.shape == (len(data), 1) @@ -482,7 +482,7 @@ def test_get_residuals_valid_var_arch( data = np.hstack((data, data)) if not is_data_var_zero and not is_exog_var_zero: try: - fitted_model = tsfit.fit(data, exog=exog) + fitted_model = tsfit.fit(data, y=exog) residuals = fitted_model.get_residuals() assert isinstance(residuals, np.ndarray) assert residuals.shape == (len(data), 2) @@ -493,7 +493,7 @@ def test_get_residuals_valid_var_arch( raise else: if not is_data_var_zero and not is_exog_var_zero: - fitted_model = tsfit.fit(data, exog=exog) + fitted_model = tsfit.fit(data, y=exog) residuals = fitted_model.get_residuals() assert isinstance(residuals, np.ndarray) assert residuals.shape == (len(data), 1) @@ -511,7 +511,7 @@ def test_get_fitted_X_valid_ar(self, data, order, model_type, exog): data = np.array(data).reshape(-1, 1) exog = np.array(exog) tsfit = TSFit(order, model_type) - fitted_model = tsfit.fit(data, exog=exog) + fitted_model = tsfit.fit(data, y=exog) fitted_X = fitted_model.get_fitted_X() assert isinstance(fitted_X, np.ndarray) assert fitted_X.shape == (len(data), 1) @@ -528,7 +528,7 @@ def test_get_fitted_X_valid_arima(self, data, order, model_type, exog): data = np.array(data).reshape(-1, 1) exog = np.array(exog) tsfit = TSFit(order, model_type) - fitted_model = tsfit.fit(data, exog=exog) + fitted_model = tsfit.fit(data, y=exog) fitted_X = fitted_model.get_fitted_X() assert isinstance(fitted_X, np.ndarray) assert fitted_X.shape == (len(data), 1) @@ -547,7 +547,7 @@ def test_get_fitted_X_valid_sarima( data = np.array(data).reshape(-1, 1) exog = np.array(exog) tsfit = TSFit(order, model_type) - fitted_model = tsfit.fit(data, exog=exog) + fitted_model = tsfit.fit(data, y=exog) fitted_X = fitted_model.get_fitted_X() assert isinstance(fitted_X, np.ndarray) assert fitted_X.shape == (len(data), 1) @@ -578,7 +578,7 @@ def test_get_fitted_X_valid_var_arch( if model_type == "var": if not is_data_var_zero and not is_exog_var_zero: try: - fitted_model = tsfit.fit(data, exog=exog) + fitted_model = tsfit.fit(data, y=exog) fitted_X = fitted_model.get_fitted_X() assert isinstance(fitted_X, np.ndarray) assert fitted_X.shape == (len(data), 2) @@ -589,7 +589,7 @@ def test_get_fitted_X_valid_var_arch( raise else: if not is_data_var_zero and not is_exog_var_zero: - fitted_model = tsfit.fit(data, exog=exog) + fitted_model = tsfit.fit(data, y=exog) fitted_X = fitted_model.get_fitted_X() assert isinstance(fitted_X, np.ndarray) assert fitted_X.shape == (len(data),) @@ -637,13 +637,13 @@ def test_tsfit_predict_invalid_exog(self): model = TSFit(model_type="ar", order=1) model.fit(np.arange(10)) with pytest.raises(ValueError): - model.predict(np.array([1, 2, 3]), exog=[]) + model.predict(np.array([1, 2, 3]), y=[]) def test_tsfit_fit_invalid_exog(self): """Test TSFit fit method with invalid exog.""" model = TSFit(model_type="ar", order=1) with pytest.raises(TypeError): - model.fit(np.array([1, 2, 3]), exog=[]) + model.fit(np.array([1, 2, 3]), y=[]) def test_tsfit_fit_invalid_data_type(self): """Test TSFit fit method with invalid data type.""" @@ -663,5 +663,5 @@ def test_tsfit_fit_invalid_data_shape_with_exog(self): with pytest.raises(ValueError): model.fit( np.array([1, 2, 3]).reshape(-1, 1), - exog=np.array([1, 2, 3]).reshape(-1, 1), + y=np.array([1, 2, 3]).reshape(-1, 1), ) diff --git a/tests/test_validate.py b/tests/test_validate.py index a57b54f..34dfb0f 100644 --- a/tests/test_validate.py +++ b/tests/test_validate.py @@ -7,7 +7,7 @@ validate_blocks, validate_integers, validate_weights, - validate_X_and_exog, + validate_X_and_y, ) MIN_INT_VALUE = np.iinfo(np.int64).min @@ -222,83 +222,83 @@ def test_numpy_array_with_invalid_element_type(self, arr: np.ndarray): ) -class TestValidateXAndExog: +class TestValidateXAndY: """ - Test the validate_X_and_exog function. + Test the validate_X_and_y function. """ class TestPassingCases: """ - Test cases where validate_X_and_exog should work correctly. + Test cases where validate_X_and_y should work correctly. """ @given(array_1d) - def test_1d_X_no_exog(self, X: np.ndarray): - """Test that the function accepts a 1D X array and no exog array.""" - validate_X_and_exog(X, None) + def test_1d_X_no_y(self, X: np.ndarray): + """Test that the function accepts a 1D X array and no y array.""" + validate_X_and_y(X, None) @given(array_1d) - def test_1d_X_1d_exog(self, X: np.ndarray): - """Test that the function accepts a 1D X array and a 1D exog array.""" - validate_X_and_exog(X, X) + def test_1d_X_1d_y(self, X: np.ndarray): + """Test that the function accepts a 1D X array and a 1D y array.""" + validate_X_and_y(X, X) @given(array_1d) - def test_1d_X_2d_exog(self, X: np.ndarray): - """Test that the function accepts a 1D X array and a 2D exog array.""" - validate_X_and_exog(X, X[:, np.newaxis]) + def test_1d_X_2d_y(self, X: np.ndarray): + """Test that the function accepts a 1D X array and a 2D y array.""" + validate_X_and_y(X, X[:, np.newaxis]) @given(array_2d) - def test_2d_X_no_exog_var_model(self, X: np.ndarray): - """Test that the function accepts a 2D X array and no exog array when model_is_var=True.""" - validate_X_and_exog(X, None, model_is_var=True) + def test_2d_X_no_y_var_model(self, X: np.ndarray): + """Test that the function accepts a 2D X array and no y array when model_is_var=True.""" + validate_X_and_y(X, None, model_is_var=True) @given(array_2d) - def test_2d_X_1d_exog_var_model(self, exog: np.ndarray): - """Test that the function accepts a 2D X array and a 1D exog array when model_is_var=True.""" - validate_X_and_exog(exog, exog[:, 0], model_is_var=True) + def test_2d_X_1d_y_var_model(self, y: np.ndarray): + """Test that the function accepts a 2D X array and a 1D y array when model_is_var=True.""" + validate_X_and_y(y, y[:, 0], model_is_var=True) @given(array_2d) - def test_2d_X_2d_exog_var_model(self, exog: np.ndarray): - """Test that the function accepts a 2D X array and a 2D exog array when model_is_var=True.""" - validate_X_and_exog(exog, exog, model_is_var=True) + def test_2d_X_2d_y_var_model(self, y: np.ndarray): + """Test that the function accepts a 2D X array and a 2D y array when model_is_var=True.""" + validate_X_and_y(y, y, model_is_var=True) @given(array_1d) - def test_1d_X_no_exog_arch_model(self, X: np.ndarray): - """Test that the function accepts a 1D X array and no exog array when model_is_arch=True.""" - validate_X_and_exog(X, None, model_is_arch=True) + def test_1d_X_no_y_arch_model(self, X: np.ndarray): + """Test that the function accepts a 1D X array and no y array when model_is_arch=True.""" + validate_X_and_y(X, None, model_is_arch=True) @given(array_1d) - def test_1d_X_1d_exog_arch_model(self, exog: np.ndarray): - """Test that the function accepts a 1D X array and a 1D exog array when model_is_arch=True.""" - validate_X_and_exog(exog, exog, model_is_arch=True) + def test_1d_X_1d_y_arch_model(self, y: np.ndarray): + """Test that the function accepts a 1D X array and a 1D y array when model_is_arch=True.""" + validate_X_and_y(y, y, model_is_arch=True) @given(array_1d) - def test_1d_X_2d_exog_arch_model(self, X: np.ndarray): - """Test that the function accepts a 1D X array and a 2D exog array when model_is_arch=True.""" - validate_X_and_exog(X, X[:, np.newaxis], model_is_arch=True) + def test_1d_X_2d_y_arch_model(self, X: np.ndarray): + """Test that the function accepts a 1D X array and a 2D y array when model_is_arch=True.""" + validate_X_and_y(X, X[:, np.newaxis], model_is_arch=True) class TestFailingCases: """ - Test cases where validate_X_and_exog should fail. + Test cases where validate_X_and_y should fail. """ @given(array_2d) def test_error_X_not_1d(self, X: np.ndarray): """Test that a ValueError is raised if X is not 1D when model_is_var=False.""" with pytest.raises(ValueError): - validate_X_and_exog(X, None, model_is_var=False) + validate_X_and_y(X, None, model_is_var=False) @given(array_1d) def test_error_X_not_2d_or_less_than_2_columns(self, X: np.ndarray): """Test that a ValueError is raised if X is not 2D or has less than 2 columns when model_is_var=True.""" with pytest.raises(ValueError): - validate_X_and_exog(X, None, model_is_var=True) + validate_X_and_y(X, None, model_is_var=True) @given(array_2d) def test_error_X_2d_with_only_1_column(self, X: np.ndarray): """Test that a ValueError is raised if X is 2D with only 1 column when model_is_var=True.""" with pytest.raises(ValueError): - validate_X_and_exog(X[:, 0], None, model_is_var=True) + validate_X_and_y(X[:, 0], None, model_is_var=True) # Hypothesis strategy for generating valid block indices and corresponding input length From c5c841004a5ae41eb025e8dae199b53a59ef181d Mon Sep 17 00:00:00 2001 From: Sankalp Gilda Date: Tue, 16 Jan 2024 17:00:14 -0500 Subject: [PATCH 3/6] build: removed commitlint from CI workflow (#34) commented out the relevant snippet in .github/workflows/CI.yml --- .github/workflows/CI.yml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index fff46e5..fa4110a 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -178,13 +178,13 @@ jobs: # fail_ci_if_error: true # verbose: true - commitlint: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - with: - fetch-depth: 0 - - uses: wagoid/commitlint-github-action@v4 + # commitlint: + # runs-on: ubuntu-latest + # steps: + # - uses: actions/checkout@v3 + # with: + # fetch-depth: 0 + # - uses: wagoid/commitlint-github-action@v4 docs: From e6fab39ec5d25a34abd80c324a1b11634cb5b0d3 Mon Sep 17 00:00:00 2001 From: Sankalp Gilda Date: Tue, 16 Jan 2024 17:45:02 -0500 Subject: [PATCH 4/6] build: added poetry.lock to .gitignore (#36) --- .gitignore | 3 +++ README.md | 1 - 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index acccf76..dad1295 100644 --- a/.gitignore +++ b/.gitignore @@ -133,3 +133,6 @@ README_template.md # scratch file scratch* + +# poetry.lock +poetry.lock diff --git a/README.md b/README.md index 299e2f1..2d6918b 100644 --- a/README.md +++ b/README.md @@ -140,7 +140,6 @@ The `tsbootstrap` package contains various modules that handle tasks such as boo | [.codeclimate.yml](https://github.com/astrogilda/tsbootstrap/blob/main/.codeclimate.yml) | Configuration for Code Climate quality checks. | | [.gitignore](https://github.com/astrogilda/tsbootstrap/blob/main/.gitignore) | Specifies files and folders to be ignored by Git. | | [.pre-commit-config.yaml](https://github.com/astrogilda/tsbootstrap/blob/main/.pre-commit-config.yaml) | Configuration for pre-commit hooks. | -| [poetry.lock](https://github.com/astrogilda/tsbootstrap/blob/main/poetry.lock) | Lock file generated by Poetry, pinning exact package versions. | | [poetry.toml](https://github.com/astrogilda/tsbootstrap/blob/main/poetry.toml) | Configuration file for Poetry package management. | | [tsbootstrap_logo.png](https://github.com/astrogilda/tsbootstrap/blob/main/tsbootstrap_logo.png) | Project logo image. | From 0c99b8416ca08e86406b09d5b50d7b33a1555626 Mon Sep 17 00:00:00 2001 From: Sankalp Gilda Date: Thu, 18 Jan 2024 16:45:43 -0500 Subject: [PATCH 5/6] readme: replaced pypi with pepy.tech (#37) --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 2d6918b..8c8af4d 100644 --- a/README.md +++ b/README.md @@ -17,8 +17,8 @@ pypi-python-version - 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