DjddlmZmZddlZddlmZddlmZmZdgZ GddeeejZ Gd d Z y) ) BaseEstimatorTransformerMixinN)DTYPE) check_exog check_endogBaseTransformercpeZdZdZedZddZejdZ ejdZ y)r aA base pre-processing transformer A subclass of the scikit-learn ``TransformerMixin``, the purpose of the ``BaseTransformer`` is to learn characteristics from the training set and apply them in a transformation to the test set. For instance, a transformer aimed at normalizing features in an exogenous array would learn the means and standard deviations of the training features in the ``fit`` method, and then center and scale the features in the ``transform`` method. The ``fit`` method should only ever be applied to the *training* set to avoid any data leakage, while ``transform`` may be applied to any dataset of the same schema. cX|t|tddd}|t|ddd}||fS)zValidate inputNTF)dtypecopyforce_all_finitepreserve_series)r r r)rrr)yXs _C:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\pmdarima/preprocessing/base.py _check_y_XzBaseTransformer._check_y_XsJ =!& % A =!& A !t Nc N|j|||j||fi|S)aFit and transform the arrays Parameters ---------- y : array-like or None, shape=(n_samples,) The endogenous (time-series) array. X : array-like or None, shape=(n_samples, n_features), optional The exogenous array of additional covariates. **kwargs : keyword args Keyword arguments required by the transform function. )fit transformselfrrkwargss r fit_transformzBaseTransformer.fit_transform5s) At~~a-f--rcy)aoFit the transformer The purpose of the ``fit`` method is to learn a set of statistics or characteristics from the training set, and store them as "fit attributes" within the instance. A transformer *must* be fit before the transformation can be applied to a dataset in the ``transform`` method. Parameters ---------- y : array-like or None, shape=(n_samples,) The endogenous (time-series) array. X : array-like or None, shape=(n_samples, n_features) The exogenous array of additional covariates. Returns ------- self : BaseTransformer The scikit-learn convention is for the ``fit`` method to return the instance of the transformer, ``self``. This allows us to string ``fit(...).transform(...)`` calls together. N)rrrs rrzBaseTransformer.fitFrc y)aTransform the new array Apply the transformation to the array after learning the training set's characteristics in the ``fit`` method. Parameters ---------- y : array-like or None, shape=(n_samples,) The endogenous (time-series) array. X : array-like or None, shape=(n_samples, n_features) The exogenous array of additional covariates. **kwargs : keyword args Keyword arguments required by the transform function. Returns ------- y : array-like or None The transformed y array X : array-like or None The transformed X array Nrrs rrzBaseTransformer.transform`rrN) __name__ __module__ __qualname____doc__ staticmethodrrabcabstractmethodrrrrrr r sT *."   2   r) metaclassceZdZdZdZddZy)UpdatableMixinz6Transformers that may update their params, like ARIMAsc| tdy)Nz(endog array cannot be None when updating) ValueError)rrs r _check_endogzUpdatableMixin._check_endogs 9GH H rNc y)aUpdate the params and return the transformed arrays Parameters ---------- y : array-like or None, shape=(n_samples,) The endogenous (time-series) array. X : array-like or None, shape=(n_samples, n_features) The exogenous array of additional covariates. **kwargs : keyword args Keyword arguments required by the transform function. Nrrs rupdate_and_transformz#UpdatableMixin.update_and_transformrrr )r!r"r#r$r-r/rrrr*r*|s@I  rr*) sklearn.baserrr& compat.numpyrutilsrr__all__ABCMetar r*rrrr5sA 9 +  h m%5h V  r