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# YEOJOHNSON

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The YEOJOHNSON node is based on a numpy or scipy function. The description of that function is as follows: Return a dataset transformed by a Yeo-Johnson power transformation. Params: x : ndarray Input array. Should be 1-dimensional. lmbda : float If 'lmbda' is 'None', find the lambda that maximizes the log-likelihood function and return it as the second output argument. Otherwise the transformation is done for the given value. Returns: out : DataContainer type 'ordered pair', 'scalar', or 'matrix'
Python Code
from flojoy import OrderedPair, flojoy, Matrix, Scalar
import numpy as np

import scipy.stats

@flojoy
def YEOJOHNSON(
default: OrderedPair | Matrix,
lmbda: float = 0.1,
) -> OrderedPair | Matrix | Scalar:
"""The YEOJOHNSON node is based on a numpy or scipy function.

The description of that function is as follows:

Return a dataset transformed by a Yeo-Johnson power transformation.

Parameters
----------
x : ndarray
Input array.
Should be 1-dimensional.
lmbda : float, optional
If 'lmbda' is 'None', find the lambda that maximizes the
log-likelihood function and return it as the second output argument.
Otherwise the transformation is done for the given value.

Returns
-------
DataContainer
type 'ordered pair', 'scalar', or 'matrix'
"""

result = scipy.stats.yeojohnson(
x=default.y,
lmbda=lmbda,
)

if isinstance(result, np.ndarray):
result = OrderedPair(x=default.x, y=result)
else:
assert isinstance(
result, np.number | float | int
), f"Expected np.number, float or int for result, got {type(result)}"
result = Scalar(c=float(result))

return result


Find this Flojoy Block on GitHub