def fit(self, X, y=None):
self._transformer = skdata.PolynomialFeatures()
X_sample = X
if isinstance(X, dd.DataFrame):
X_sample = X._meta_nonempty
if isinstance(X, da.Array):
X_sample = np.ones((1, X.shape[1]), dtype=X.dtype)
# pandas dataframe treated by sklearn and returns np.array
self._transformer.fit(X_sample)
copy_learned_attributes(self._transformer, self)
return self
def fit(
self,
X: Union[ArrayLike, DataFrameType],
y: Optional[Union[ArrayLike, SeriesType]] = None,
) -> "PolynomialFeatures":
self._transformer = sklearn.preprocessing.PolynomialFeatures(
degree=self.degree,
interaction_only=self.interaction_only,
include_bias=self.include_bias,
)
X = self._validate_data(
X,
estimator=self,
accept_dask_array=True,
accept_dask_dataframe=True,
accept_unknown_chunks=True,
preserve_pandas_dataframe=True,
)
if isinstance(self.degree, numbers.Integral):
if self.degree < 0:
raise ValueError(
f"degree must be a non-negative integer, got {self.degree}."
)
self._min_degree = 0
self._max_degree = self.degree
elif (isinstance(self.degree, collections.abc.Iterable)
and len(self.degree) == 2):
self._min_degree, self._max_degree = self.degree
if not (isinstance(self._min_degree, numbers.Integral)
and isinstance(self._max_degree, numbers.Integral)
and self._min_degree >= 0
and self._min_degree <= self._max_degree):
raise ValueError("degree=(min_degree, max_degree) must "
"be non-negative integers that fulfil "
"min_degree <= max_degree, got "
f"{self.degree}.")
else:
raise ValueError("degree must be a non-negative int or tuple "
"(min_degree, max_degree), got "
f"{self.degree}.")
X_sample = X
if isinstance(X, dd.DataFrame):
X_sample = X._meta_nonempty
if isinstance(X, da.Array):
X_sample = np.ones((1, X.shape[1]), dtype=X.dtype)
# pandas dataframe treated by sklearn and returns np.array
self._transformer.fit(X_sample)
copy_learned_attributes(self._transformer, self)
return self
def fit(self, X, y=None):
self._transformer = sklearn.preprocessing.PolynomialFeatures(
degree=self.degree,
interaction_only=self.interaction_only,
include_bias=self.include_bias,
)
X_sample = X
if isinstance(X, dd.DataFrame):
X_sample = X._meta_nonempty
if isinstance(X, da.Array):
X_sample = np.ones((1, X.shape[1]), dtype=X.dtype)
# pandas dataframe treated by sklearn and returns np.array
self._transformer.fit(X_sample)
copy_learned_attributes(self._transformer, self)
return self
