def fit_x(self, x):
"""Create the list of columns to be kept for x.
Args:
:param x: a Pandas Dataframe of shape [n_samples, n_features] the dataset
Return:
:param self:
"""
self.x_cols = _check_cols(x, self.x_cols, self.logging)
self.logging('x_cols: {}'.format(self.x_cols), level=logging.DEBUG)
return self
def fit_y(self, y):
"""Create the list of columns to be kept for y.
Args:
:param y: a Pandas Dataframe of shape [n_samples] the target
Return:
:param self:
"""
self.y_cols = _check_cols(y, self.y_cols, self.logging)
self.logging('y_cols: {}'.format(self.y_cols), level=logging.DEBUG)
return self
def fit_x(self, x):
"""Create the list of columns to be converted for `x` and related mappings.
Args:
:param x: a Pandas Dataframe of shape [n_samples, n_features] the dataset
Return:
:param self:
"""
self.x_cols = _check_cols(x, self.x_cols, self.logging)
self.logging('x_cols: {}'.format(self.x_cols), level=logging.DEBUG)
self._map_category_to_number['x'], self._map_number_to_category[
'x'] = self._fit(x, self.x_cols)
return self
def fit_y(self, y):
"""Create the list of columns to be converted for `y` and related mappings.
Args:
:param y: a Pandas Dataframe of shape [n_samples] the target
Return:
:param self:
"""
self.y_cols = _check_cols(y, self.y_cols, self.logging)
self.logging('y_cols: {}'.format(self.y_cols), level=logging.DEBUG)
self._map_category_to_number['y'], self._map_number_to_category[
'y'] = self._fit(y, self.y_cols)
return self
def fit_y(self, y):
self.y_cols = _check_cols(y, self.y_cols, self.logging)
self.logging('y_cols: {}'.format(self.y_cols), level=logging.DEBUG)
if not self.y_is_categorical or len(self.y_is_categorical) != len(
self.y_cols):
self.y_is_categorical = _is_categorical_cols(y, self.y_cols)
categorical_cols = [
self.y_cols[i] for i, v in enumerate(self.y_is_categorical) if v
]
self._cat2num['y'] = Category2Number(x_cols=categorical_cols,
skipna=True) # yes, it is x_col
self._knn_models['y'] = self._fit(y, self.y_cols,
self.y_is_categorical,
self._cat2num['y'])
return self
def fit_y(self, y):
self.y_cols = _check_cols(y, self.y_cols, self.logging)
self.logging('y_cols: {}'.format(self.y_cols), level=logging.DEBUG)
self.logging('fitting y', level=logging.DEBUG)
self._fit(y, self.y_cols, self._pdfs['y'])
return self
def fit_x(self, x):
self.x_cols = _check_cols(x, self.x_cols, self.logging)
self.logging('x_cols: {}'.format(self.x_cols), level=logging.DEBUG)
self.logging('fitting x', level=logging.DEBUG)
self._fit(x, self.x_cols, self._pdfs['x'])
return self
def fit_y(self, y):
self.y_cols = _check_cols(y, self.y_cols, self.logging)
self.logging('y_cols: {}'.format(self.y_cols), level=logging.DEBUG)
return self
def fit_x(self, x):
self.x_cols = _check_cols(x, self.x_cols, self.logging)
self.logging('x_cols: {}'.format(self.x_cols), level=logging.DEBUG)
return self
def fit_y(self, y):
Combiner._check_signature(self, self.y_cols)
self.y_cols = _check_cols(y, self.y_cols, self.logging)
return self
def fit_x(self, x):
Combiner._check_signature(self, self.x_cols)
self.x_cols = _check_cols(x, self.x_cols, self.logging)
return self