270 lines
8.2 KiB
Python
270 lines
8.2 KiB
Python
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from typing import NamedTuple
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import numpy as np
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from . import is_scalar_nan
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def _unique(values, *, return_inverse=False):
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"""Helper function to find unique values with support for python objects.
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Uses pure python method for object dtype, and numpy method for
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all other dtypes.
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Parameters
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----------
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values : ndarray
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Values to check for unknowns.
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return_inverse : bool, default=False
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If True, also return the indices of the unique values.
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Returns
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-------
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unique : ndarray
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The sorted unique values.
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unique_inverse : ndarray
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The indices to reconstruct the original array from the unique array.
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Only provided if `return_inverse` is True.
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"""
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if values.dtype == object:
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return _unique_python(values, return_inverse=return_inverse)
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# numerical
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out = np.unique(values, return_inverse=return_inverse)
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if return_inverse:
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uniques, inverse = out
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else:
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uniques = out
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# np.unique will have duplicate missing values at the end of `uniques`
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# here we clip the nans and remove it from uniques
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if uniques.size and is_scalar_nan(uniques[-1]):
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nan_idx = np.searchsorted(uniques, np.nan)
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uniques = uniques[:nan_idx + 1]
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if return_inverse:
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inverse[inverse > nan_idx] = nan_idx
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if return_inverse:
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return uniques, inverse
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return uniques
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class MissingValues(NamedTuple):
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"""Data class for missing data information"""
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nan: bool
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none: bool
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def to_list(self):
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"""Convert tuple to a list where None is always first."""
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output = []
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if self.none:
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output.append(None)
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if self.nan:
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output.append(np.nan)
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return output
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def _extract_missing(values):
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"""Extract missing values from `values`.
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Parameters
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----------
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values: set
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Set of values to extract missing from.
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Returns
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-------
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output: set
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Set with missing values extracted.
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missing_values: MissingValues
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Object with missing value information.
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"""
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missing_values_set = {value for value in values
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if value is None or is_scalar_nan(value)}
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if not missing_values_set:
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return values, MissingValues(nan=False, none=False)
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if None in missing_values_set:
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if len(missing_values_set) == 1:
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output_missing_values = MissingValues(nan=False, none=True)
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else:
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# If there is more than one missing value, then it has to be
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# float('nan') or np.nan
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output_missing_values = MissingValues(nan=True, none=True)
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else:
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output_missing_values = MissingValues(nan=True, none=False)
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# create set without the missing values
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output = values - missing_values_set
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return output, output_missing_values
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class _nandict(dict):
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"""Dictionary with support for nans."""
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def __init__(self, mapping):
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super().__init__(mapping)
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for key, value in mapping.items():
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if is_scalar_nan(key):
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self.nan_value = value
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break
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def __missing__(self, key):
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if hasattr(self, 'nan_value') and is_scalar_nan(key):
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return self.nan_value
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raise KeyError(key)
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def _map_to_integer(values, uniques):
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"""Map values based on its position in uniques."""
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table = _nandict({val: i for i, val in enumerate(uniques)})
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return np.array([table[v] for v in values])
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def _unique_python(values, *, return_inverse):
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# Only used in `_uniques`, see docstring there for details
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try:
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uniques_set = set(values)
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uniques_set, missing_values = _extract_missing(uniques_set)
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uniques = sorted(uniques_set)
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uniques.extend(missing_values.to_list())
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uniques = np.array(uniques, dtype=values.dtype)
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except TypeError:
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types = sorted(t.__qualname__
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for t in set(type(v) for v in values))
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raise TypeError("Encoders require their input to be uniformly "
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f"strings or numbers. Got {types}")
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if return_inverse:
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return uniques, _map_to_integer(values, uniques)
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return uniques
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def _encode(values, *, uniques, check_unknown=True):
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"""Helper function to encode values into [0, n_uniques - 1].
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Uses pure python method for object dtype, and numpy method for
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all other dtypes.
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The numpy method has the limitation that the `uniques` need to
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be sorted. Importantly, this is not checked but assumed to already be
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the case. The calling method needs to ensure this for all non-object
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values.
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Parameters
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----------
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values : ndarray
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Values to encode.
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uniques : ndarray
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The unique values in `values`. If the dtype is not object, then
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`uniques` needs to be sorted.
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check_unknown : bool, default=True
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If True, check for values in `values` that are not in `unique`
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and raise an error. This is ignored for object dtype, and treated as
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True in this case. This parameter is useful for
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_BaseEncoder._transform() to avoid calling _check_unknown()
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twice.
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Returns
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-------
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encoded : ndarray
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Encoded values
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"""
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if values.dtype.kind in 'OUS':
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try:
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return _map_to_integer(values, uniques)
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except KeyError as e:
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raise ValueError(f"y contains previously unseen labels: {str(e)}")
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else:
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if check_unknown:
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diff = _check_unknown(values, uniques)
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if diff:
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raise ValueError(f"y contains previously unseen labels: "
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f"{str(diff)}")
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return np.searchsorted(uniques, values)
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def _check_unknown(values, known_values, return_mask=False):
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"""
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Helper function to check for unknowns in values to be encoded.
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Uses pure python method for object dtype, and numpy method for
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all other dtypes.
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Parameters
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----------
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values : array
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Values to check for unknowns.
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known_values : array
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Known values. Must be unique.
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return_mask : bool, default=False
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If True, return a mask of the same shape as `values` indicating
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the valid values.
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Returns
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-------
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diff : list
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The unique values present in `values` and not in `know_values`.
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valid_mask : boolean array
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Additionally returned if ``return_mask=True``.
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"""
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valid_mask = None
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if values.dtype.kind in 'OUS':
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values_set = set(values)
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values_set, missing_in_values = _extract_missing(values_set)
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uniques_set = set(known_values)
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uniques_set, missing_in_uniques = _extract_missing(uniques_set)
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diff = values_set - uniques_set
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nan_in_diff = missing_in_values.nan and not missing_in_uniques.nan
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none_in_diff = missing_in_values.none and not missing_in_uniques.none
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def is_valid(value):
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return (value in uniques_set or
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missing_in_uniques.none and value is None or
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missing_in_uniques.nan and is_scalar_nan(value))
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if return_mask:
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if diff or nan_in_diff or none_in_diff:
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valid_mask = np.array([is_valid(value) for value in values])
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else:
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valid_mask = np.ones(len(values), dtype=bool)
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diff = list(diff)
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if none_in_diff:
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diff.append(None)
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if nan_in_diff:
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diff.append(np.nan)
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else:
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unique_values = np.unique(values)
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diff = np.setdiff1d(unique_values, known_values,
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assume_unique=True)
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if return_mask:
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if diff.size:
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valid_mask = np.in1d(values, known_values)
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else:
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valid_mask = np.ones(len(values), dtype=bool)
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# check for nans in the known_values
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if np.isnan(known_values).any():
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diff_is_nan = np.isnan(diff)
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if diff_is_nan.any():
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# removes nan from valid_mask
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if diff.size and return_mask:
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is_nan = np.isnan(values)
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valid_mask[is_nan] = 1
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# remove nan from diff
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diff = diff[~diff_is_nan]
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diff = list(diff)
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if return_mask:
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return diff, valid_mask
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return diff
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