Add mean imputation function

sgkit/stats/preprocessing.py

@@ -1,4 +1,4 @@
-from typing import Hashable, Optional
+from typing import Hashable, Optional, Sequence, Union
 
 import dask.array as da
 import numpy as np
@@ -185,3 +185,91 @@ def filter_partial_calls(
     )
     new_ds[variables.call_genotype_complete].attrs["mixed_ploidy"] = mixed_ploidy
     return conditional_merge_datasets(ds, new_ds, merge)
+
+
+def mean_impute(
+    ds: Dataset,
+    variable: str,
+    dim: Union[Hashable, Sequence[Hashable]] = "samples",
+    merge: bool = True,
+) -> Dataset:
+    """Mean impute a masked variable
+
+    Parameters
+    ----------
+    ds
+        Dataset containing the variable to be imputed.
+    variable
+        Input variable name.
+        Variables ``{variable}`` and ``{variable}_masked`` must be present in ``ds``.
+    dim:
+        Dimension(s) along which the means are computed.
+    merge
+        If True (the default), merge the input dataset and the computed
+        output variables into a single dataset, otherwise return only
+        the imputed output variables.
+        See :ref:`dataset_merge` for more details.
+
+    Returns
+    -------
+    Dataset containing :data:`sgkit.variables.{variable}_imputed` in which masked entries are
+    replaced with the mean values of the unmasked.
+
+    Examples
+    --------
+
+    >>> import sgkit as sg, numpy as np
+    >>> from sgkit.stats.preprocessing import mean_impute
+    >>> ds = sg.simulate_genotype_call_dataset(n_variant=4, n_sample=10, seed=1, missing_pct=.1)
+    >>> sg.display_genotypes(ds) # doctest: +NORMALIZE_WHITESPACE
+    samples    S0   S1   S2   S3   S4   S5   S6   S7   S8   S9
+    variants
+    0         1/0  1/0  ./0  1/1  0/1  1/0  0/0  0/0  ./.  1/0
+    1         ./1  0/0  1/0  1/1  ./0  1/1  1/1  1/1  0/1  0/0
+    2         ./0  1/1  1/.  0/1  0/1  0/1  1/0  ./1  1/0  0/0
+    3         0/1  0/1  0/1  0/1  1/1  1/1  0/0  1/1  0/1  1/0
+
+    >>> ds["call_dosage"] = ds.call_genotype.sum(dim="ploidy").astype(float)
+    >>> ds["call_dosage_mask"] = ds.call_genotype_mask.any(dim='ploidy')
+    >>> ds["call_dosage"] = ds["call_dosage"].where(~ds["call_dosage_mask"], np.nan)
+    >>> ds["call_dosage"]  # doctest: +NORMALIZE_WHITESPACE
+    <xarray.DataArray 'call_dosage' (variants: 4, samples: 10)>
+    array([[ 1.,  1., nan,  2.,  1.,  1.,  0.,  0., nan,  1.],
+           [nan,  0.,  1.,  2., nan,  2.,  2.,  2.,  1.,  0.],
+           [nan,  2., nan,  1.,  1.,  1.,  1., nan,  1.,  0.],
+           [ 1.,  1.,  1.,  1.,  2.,  2.,  0.,  2.,  1.,  1.]])
+    Dimensions without coordinates: variants, samples
+
+    >>> ds = mean_impute(ds, variable='call_dosage', dim='samples')
+    >>> ds["call_dosage_imputed"] # doctest: +NORMALIZE_WHITESPACE
+    <xarray.DataArray 'call_dosage_imputed' (variants: 4, samples: 10)>
+    array([[1.   , 1.   , 0.875, 2.   , 1.   , 1.   , 0.   , 0.   , 0.875,
+            1.   ],
+           [1.25 , 0.   , 1.   , 2.   , 1.25 , 2.   , 2.   , 2.   , 1.   ,
+            0.   ],
+           [1.   , 2.   , 1.   , 1.   , 1.   , 1.   , 1.   , 1.   , 1.   ,
+            0.   ],
+           [1.   , 1.   , 1.   , 1.   , 2.   , 2.   , 0.   , 2.   , 1.   ,
+            1.   ]])
+    Dimensions without coordinates: variants, samples
+    Attributes:
+        comment:  Dosages imputed, encoded as floats, with NaN indicating a missi...
+
+    """
+
+    variables.validate(ds, variable)
+    variables.validate(ds, f"{variable}_mask")
+
+    unmasked = ~ds[f"{variable}_mask"]
+    new_ds = create_dataset(
+        {
+            f"{variable}_imputed": (
+                ds[variable].dims,
+                ds[variable]
+                .where(unmasked, ds[variable].where(unmasked).mean(dim=dim))
+                .data,
+            )
+        }
+    )
+
+    return conditional_merge_datasets(ds, new_ds, merge)

sgkit/variables.py

@@ -214,6 +214,15 @@ def _check_field(
     )
 )
 
+call_dosage_imputed, call_dosage_imputed_spec = SgkitVariables.register_variable(
+    ArrayLikeSpec(
+        "call_dosage_imputed",
+        kind="f",
+        ndim=2,
+        __doc__="""Dosages imputed, encoded as floats, with NaN indicating a missing value.""",
+    )
+)
+
 call_dosage_mask, call_dosage_mask_spec = SgkitVariables.register_variable(
     ArrayLikeSpec(
         "call_dosage_mask",
@@ -293,6 +302,16 @@ def _check_field(
     )
 )
 
+call_genotype_imputed, call_genotype_imputed_spec = SgkitVariables.register_variable(
+    ArrayLikeSpec(
+        "call_genotype_imputed",
+        kind="f",
+        ndim=3,
+        __doc__="""
+Call genotype imputed """,
+    )
+)
+
 (
     call_genotype_probability,
     call_genotype_probability_spec,
@@ -305,6 +324,19 @@ def _check_field(
     )
 )
 
+(
+    call_genotype_probability_imputed,
+    call_genotype_probability_imputed_spec,
+) = SgkitVariables.register_variable(
+    ArrayLikeSpec(
+        "call_genotype_probability_imputed",
+        kind="f",
+        ndim=3,
+        __doc__="""Genotype probabilities Imputed.""",
+    )
+)
+
+
 (
     call_genotype_probability_mask,
     call_genotype_probability_mask_spec,