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"""Box-Cox Transform"""
import math
from h2oaicore.transformer_utils import CustomTransformer
import datatable as dt
import numpy as np
from scipy.stats import boxcox
class BoxCoxTransformer(CustomTransformer):
_testing_can_skip_failure = False # ensure tested as if shouldn't fail
@staticmethod
def get_default_properties():
return dict(col_type="numeric", min_cols=1, max_cols=1, relative_importance=1)
def fit_transform(self, X: dt.Frame, y: np.array = None):
XX = X.to_pandas().iloc[:, 0].values
is_na = np.isnan(XX)
self._offset = -np.nanmin(XX) if np.nanmin(XX) < 0 else 0
self._offset += 1e-3
self._lmbda = None
if not any(~is_na):
return X
x = self._offset + XX[~is_na]
x = np.asarray(x)
x[x <= 0] = 1e-3
try:
self._lmbda = boxcox(x, lmbda=self._lmbda)[1] # compute lambda
except ValueError as e:
if 'Data must not be constant' in str(e):
self._lmbda = None
return X
raise
return self.transform(X)
def transform(self, X: dt.Frame):
XX = X.to_pandas().iloc[:, 0].values
is_na = np.isnan(XX) | np.array(XX <= -self._offset)
if not any(~is_na) or self._lmbda is None:
return X
x = self._offset + XX[~is_na]
x = np.asarray(x)
x[x <= 0] = 1e-3 # don't worry if not invertible, just ensure can transform and valid transforms are kept valid
try:
ret = boxcox(x, lmbda=self._lmbda) # apply transform with pre-computed lambda
except ValueError as e:
if 'Data must not be constant' in str(e):
return X
raise
XX[~is_na] = ret
XX = XX.astype(np.float32)
XX[XX > 1E30] = 1E30
XX[XX < -1E30] = -1E30
XX = dt.Frame(XX)
# Don't leave inf/-inf
for i in range(XX.ncols):
XX.replace([math.inf, -math.inf], None)
return XX