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ENH: add primitive type hints to BoxCoxTransformer - relates to #1454 #3368

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ENH: add primitive type hints to BoxCoxTransformer - relates to #1454 #3368

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19 changes: 12 additions & 7 deletions aeon/transformations/series/_boxcox.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@


# copy-pasted from scipy 1.7.3 since it moved in 1.8.0 and broke this estimator
def _calc_uniform_order_statistic_medians(n):
def _calc_uniform_order_statistic_medians(n: int) -> np.ndarray:
"""Approximations of uniform order statistic medians.

Parameters
Expand Down Expand Up @@ -118,7 +118,7 @@ def __init__(self, bounds=None, method="mle", sp=None):
self.sp = sp
super().__init__(axis=1)

def _fit(self, X, y=None):
def _fit(self, X: np.ndarray, y=None) -> "BoxCoxTransformer":
"""
Fit transformer to X and y.

Expand All @@ -143,7 +143,7 @@ def _fit(self, X, y=None):

return self

def _transform(self, X, y=None):
def _transform(self, X: np.ndarray, y=None) -> np.ndarray:
"""Transform X and return a transformed version.

private _transform containing the core logic, called from transform
Expand All @@ -164,7 +164,7 @@ def _transform(self, X, y=None):
Xt = boxcox(X, self.lambda_)
return Xt

def _inverse_transform(self, X, y=None):
def _inverse_transform(self, X: np.ndarray, y=None) -> np.ndarray:
"""Inverse transform X and return an inverse transformed version.

core logic
Expand All @@ -185,7 +185,7 @@ def _inverse_transform(self, X, y=None):
return Xt


def _make_boxcox_optimizer(bounds=None, brack=(-2.0, 2.0)):
def _make_boxcox_optimizer(bounds=None, brack: tuple = (-2.0, 2.0)):
# bounds is None, use simple Brent optimisation
if bounds is None:

Expand All @@ -206,7 +206,12 @@ def optimizer(func, args):
return optimizer


def _boxcox_normmax(x, bounds=None, brack=(-2.0, 2.0), method="pearsonr"):
def _boxcox_normmax(
x: np.ndarray,
bounds=None,
brack: tuple = (-2.0, 2.0),
method: str = "pearsonr",
):
optimizer = _make_boxcox_optimizer(bounds, brack)

def _pearsonr(x):
Expand Down Expand Up @@ -242,7 +247,7 @@ def _all(x):
return optimfunc(x)


def _guerrero(x, sp, bounds=None):
def _guerrero(x: np.ndarray, sp: int, bounds=None):
"""Estimate lambda using the Guerrero method as described in [1]_.

Parameters
Expand Down
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