from contextlib import suppress
from collections import Counter
from typing import NamedTuple
import numpy as np
from . import is_scalar_nan
def _unique(values, *, return_inverse=False, return_counts=False):
"""Helper function to find unique values with support for python objects.
Uses pure python method for object dtype, and numpy method for
all other dtypes.
Parameters
----------
values : ndarray
Values to check for unknowns.
return_inverse : bool, default=False
If True, also return the indices of the unique values.
return_counts : bool, default=False
If True, also return the number of times each unique item appears in
values.
Returns
-------
unique : ndarray
The sorted unique values.
unique_inverse : ndarray
The indices to reconstruct the original array from the unique array.
Only provided if `return_inverse` is True.
unique_counts : ndarray
The number of times each of the unique values comes up in the original
array. Only provided if `return_counts` is True.
"""
if values.dtype == object:
return _unique_python(
values, return_inverse=return_inverse, return_counts=return_counts
)
# numerical
return _unique_np(
values, return_inverse=return_inverse, return_counts=return_counts
)
def _unique_np(values, return_inverse=False, return_counts=False):
"""Helper function to find unique values for numpy arrays that correctly
accounts for nans. See `_unique` documentation for details."""
uniques = np.unique(
values, return_inverse=return_inverse, return_counts=return_counts
)
inverse, counts = None, None
if return_counts:
*uniques, counts = uniques
if return_inverse:
*uniques, inverse = uniques
if return_counts or return_inverse:
uniques = uniques[0]
# np.unique will have duplicate missing values at the end of `uniques`
# here we clip the nans and remove it from uniques
if uniques.size and is_scalar_nan(uniques[-1]):
nan_idx = np.searchsorted(uniques, np.nan)
uniques = uniques[: nan_idx + 1]
if return_inverse:
inverse[inverse > nan_idx] = nan_idx
if return_counts:
counts[nan_idx] = np.sum(counts[nan_idx:])
counts = counts[: nan_idx + 1]
ret = (uniques,)
if return_inverse:
ret += (inverse,)
if return_counts:
ret += (counts,)
return ret[0] if len(ret) == 1 else ret
class MissingValues(NamedTuple):
"""Data class for missing data information"""
nan: bool
none: bool
def to_list(self):
"""Convert tuple to a list where None is always first."""
output = []
if self.none:
output.append(None)
if self.nan:
output.append(np.nan)
return output
def _extract_missing(values):
"""Extract missing values from `values`.
Parameters
----------
values: set
Set of values to extract missing from.
Returns
-------
output: set
Set with missing values extracted.
missing_values: MissingValues
Object with missing value information.
"""
missing_values_set = {
value for value in values if value is None or is_scalar_nan(value)
}
if not missing_values_set:
return values, MissingValues(nan=False, none=False)
if None in missing_values_set:
if len(missing_values_set) == 1:
output_missing_values = MissingValues(nan=False, none=True)
else:
# If there is more than one missing value, then it has to be
# float('nan') or np.nan
output_missing_values = MissingValues(nan=True, none=True)
else:
output_missing_values = MissingValues(nan=True, none=False)
# create set without the missing values
output = values - missing_values_set
return output, output_missing_values
class _nandict(dict):
"""Dictionary with support for nans."""
def __init__(self, mapping):
super().__init__(mapping)
for key, value in mapping.items():
if is_scalar_nan(key):
self.nan_value = value
break
def __missing__(self, key):
if hasattr(self, "nan_value") and is_scalar_nan(key):
return self.nan_value
raise KeyError(key)
def _map_to_integer(values, uniques):
"""Map values based on its position in uniques."""
table = _nandict({val: i for i, val in enumerate(uniques)})
return np.array([table[v] for v in values])
def _unique_python(values, *, return_inverse, return_counts):
# Only used in `_uniques`, see docstring there for details
try:
uniques_set = set(values)
uniques_set, missing_values = _extract_missing(uniques_set)
uniques = sorted(uniques_set)
uniques.extend(missing_values.to_list())
uniques = np.array(uniques, dtype=values.dtype)
except TypeError:
types = sorted(t.__qualname__ for t in set(type(v) for v in values))
raise TypeError(
"Encoders require their input to be uniformly "
f"strings or numbers. Got {types}"
)
ret = (uniques,)
if return_inverse:
ret += (_map_to_integer(values, uniques),)
if return_counts:
ret += (_get_counts(values, uniques),)
return ret[0] if len(ret) == 1 else ret
def _encode(values, *, uniques, check_unknown=True):
"""Helper function to encode values into [0, n_uniques - 1].
Uses pure python method for object dtype, and numpy method for
all other dtypes.
The numpy method has the limitation that the `uniques` need to
be sorted. Importantly, this is not checked but assumed to already be
the case. The calling method needs to ensure this for all non-object
values.
Parameters
----------
values : ndarray
Values to encode.
uniques : ndarray
The unique values in `values`. If the dtype is not object, then
`uniques` needs to be sorted.
check_unknown : bool, default=True
If True, check for values in `values` that are not in `unique`
and raise an error. This is ignored for object dtype, and treated as
True in this case. This parameter is useful for
_BaseEncoder._transform() to avoid calling _check_unknown()
twice.
Returns
-------
encoded : ndarray
Encoded values
"""
if values.dtype.kind in "OUS":
try:
return _map_to_integer(values, uniques)
except KeyError as e:
raise ValueError(f"y contains previously unseen labels: {str(e)}")
else:
if check_unknown:
diff = _check_unknown(values, uniques)
if diff:
raise ValueError(f"y contains previously unseen labels: {str(diff)}")
return np.searchsorted(uniques, values)
def _check_unknown(values, known_values, return_mask=False):
"""
Helper function to check for unknowns in values to be encoded.
Uses pure python method for object dtype, and numpy method for
all other dtypes.
Parameters
----------
values : array
Values to check for unknowns.
known_values : array
Known values. Must be unique.
return_mask : bool, default=False
If True, return a mask of the same shape as `values` indicating
the valid values.
Returns
-------
diff : list
The unique values present in `values` and not in `know_values`.
valid_mask : boolean array
Additionally returned if ``return_mask=True``.
"""
valid_mask = None
if values.dtype.kind in "OUS":
values_set = set(values)
values_set, missing_in_values = _extract_missing(values_set)
uniques_set = set(known_values)
uniques_set, missing_in_uniques = _extract_missing(uniques_set)
diff = values_set - uniques_set
nan_in_diff = missing_in_values.nan and not missing_in_uniques.nan
none_in_diff = missing_in_values.none and not missing_in_uniques.none
def is_valid(value):
return (
value in uniques_set
or missing_in_uniques.none
and value is None
or missing_in_uniques.nan
and is_scalar_nan(value)
)
if return_mask:
if diff or nan_in_diff or none_in_diff:
valid_mask = np.array([is_valid(value) for value in values])
else:
valid_mask = np.ones(len(values), dtype=bool)
diff = list(diff)
if none_in_diff:
diff.append(None)
if nan_in_diff:
diff.append(np.nan)
else:
unique_values = np.unique(values)
diff = np.setdiff1d(unique_values, known_values, assume_unique=True)
if return_mask:
if diff.size:
valid_mask = np.in1d(values, known_values)
else:
valid_mask = np.ones(len(values), dtype=bool)
# check for nans in the known_values
if np.isnan(known_values).any():
diff_is_nan = np.isnan(diff)
if diff_is_nan.any():
# removes nan from valid_mask
if diff.size and return_mask:
is_nan = np.isnan(values)
valid_mask[is_nan] = 1
# remove nan from diff
diff = diff[~diff_is_nan]
diff = list(diff)
if return_mask:
return diff, valid_mask
return diff
class _NaNCounter(Counter):
"""Counter with support for nan values."""
def __init__(self, items):
super().__init__(self._generate_items(items))
def _generate_items(self, items):
"""Generate items without nans. Stores the nan counts separately."""
for item in items:
if not is_scalar_nan(item):
yield item
continue
if not hasattr(self, "nan_count"):
self.nan_count = 0
self.nan_count += 1
def __missing__(self, key):
if hasattr(self, "nan_count") and is_scalar_nan(key):
return self.nan_count
raise KeyError(key)
def _get_counts(values, uniques):
"""Get the count of each of the `uniques` in `values`.
The counts will use the order passed in by `uniques`. For non-object dtypes,
`uniques` is assumed to be sorted and `np.nan` is at the end.
"""
if values.dtype.kind in "OU":
counter = _NaNCounter(values)
output = np.zeros(len(uniques), dtype=np.int64)
for i, item in enumerate(uniques):
with suppress(KeyError):
output[i] = counter[item]
return output
unique_values, counts = _unique_np(values, return_counts=True)
# Recorder unique_values based on input: `uniques`
uniques_in_values = np.isin(uniques, unique_values, assume_unique=True)
if np.isnan(unique_values[-1]) and np.isnan(uniques[-1]):
uniques_in_values[-1] = True
unique_valid_indices = np.searchsorted(unique_values, uniques[uniques_in_values])
output = np.zeros_like(uniques, dtype=np.int64)
output[uniques_in_values] = counts[unique_valid_indices]
return output