reconnect moved files to git repo

venv/lib/python3.11/site-packages/pmdarima/preprocessing/endog/log.py

@@ -0,0 +1,130 @@
+# -*- coding: utf-8 -*-
+
+from .boxcox import BoxCoxEndogTransformer
+
+__all__ = ['LogEndogTransformer']
+
+
+class LogEndogTransformer(BoxCoxEndogTransformer):
+    """Apply a log transformation to an endogenous array
+
+    When ``y`` is your endogenous array, the log transform is
+    ``log(y + lmbda)``
+
+    Parameters
+    ----------
+
+    lmbda : float, optional (default=0.)
+        The value to add to ``y`` to make it non-negative. If, after adding
+        ``lmbda``, there are still negative values, a ValueError will be
+        raised.
+
+    neg_action : str, optional (default="raise")
+        How to respond if any values in ``y <= 0`` after adding ``lmbda``.
+        One of ('raise', 'warn', 'ignore'). If anything other than 'raise',
+        values <= 0 will be truncated to the value of ``floor``.
+
+    floor : float, optional (default=1e-16)
+        A positive value that truncate values to if there are values in ``y``
+        that are zero or negative and ``neg_action`` is not 'raise'. Note that
+        if values are truncated, invertibility will not be preserved, and the
+        transformed array may not be perfectly inverse-transformed.
+    """
+    def __init__(self, lmbda=0, neg_action="raise", floor=1e-16):
+
+        super().__init__(neg_action=neg_action, floor=floor)
+
+        # See: https://github.com/alkaline-ml/pmdarima/issues/407
+        self.lmbda = 0
+        self.lmbda2 = lmbda
+
+    def fit(self, y, X=None, **kwargs):  # TODO: kwargs go away
+        """Fit the transformer
+
+        Must be called before ``transform``.
+
+        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. Not used for
+            endogenous transformers. Default is None, and non-None values will
+            serve as pass-through arrays.
+        """
+        return super().fit(y, X, **kwargs)
+
+    def transform(self, y, X=None, **transform_kwargs):
+        """Apply the log transform to the array
+
+        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. Not used for
+            endogenous transformers. Default is None, and non-None values will
+            serve as pass-through arrays.
+
+        Returns
+        -------
+        y_transform : array-like or None
+            The log transformed y array
+
+        X : array-like or None
+            The exog array
+        """
+        return super().transform(y, X, **transform_kwargs)
+
+    def inverse_transform(self, y, X=None, **kwargs):  # TODO: kwargs go away
+        """Inverse transform a transformed array
+
+        Inverse the log transformation on the transformed array. Note that
+        if truncation happened in the ``transform`` method, invertibility will
+        not be preserved, and the transformed array may not be perfectly
+        inverse-transformed.
+
+        Parameters
+        ----------
+        y : array-like or None, shape=(n_samples,)
+            The transformed endogenous (time-series) array.
+
+        X : array-like or None, shape=(n_samples, n_features), optional
+            The exogenous array of additional covariates. Not used for
+            endogenous transformers. Default is None, and non-None values will
+            serve as pass-through arrays.
+
+        Returns
+        -------
+        y : array-like or None
+            The inverse-transformed y array
+
+        X : array-like or None
+            The inverse-transformed exogenous array
+        """
+        return super().inverse_transform(y, X, **kwargs)
+
+    def get_params(self, deep=True):
+        """Get parameters for this estimator.
+
+        Parameters
+        ----------
+        deep : bool, default=True
+            If True, will return the parameters for this estimator and
+            contained subobjects that are estimators.
+
+        Returns
+        -------
+        params : mapping of string to any
+            Parameter names mapped to their values.
+        """
+        # this method is shadowed because we shadow the the lmbda parameter
+        # name. Instead of setting lmbda, this transformer stores the parameter
+        # as lmbda2
+        # See: https://github.com/alkaline-ml/pmdarima/issues/407
+        # Thanks to @jseabold
+        params = super().get_params(deep=deep)
+        params['lmbda'] = self.lmbda2
+        return params