Delete AuxiliaryInput class

mhcflurry/auxiliary_input.py

-import numpy
-from numpy.testing import assert_equal
-import pandas
-
-AUXILIARY_FEATURES = {}
-
-
-class AuxiliaryInputEncoder(object):
-    def __init__(self, alleles=None, peptides=None):
-        if alleles is not None:
-            alleles = numpy.array(
-                alleles, copy=False).reshape((len(alleles), -1))
-            assert_equal(alleles.ndim, 2)
-        if peptides is not None:
-            peptides = numpy.array(peptides, copy=False)
-            assert_equal(peptides.ndim, 1)
-        if alleles is not None and peptides is not None:
-            assert_equal(alleles.shape[0], len(peptides))
-
-        self.alleles_shape = alleles.shape if alleles is not None else None
-        self.alleles_flat = alleles.flatten() if alleles is not None else None
-        self.peptides = numpy.repeat(
-            peptides,
-            self.alleles_shape[1] if alleles is not None else 1
-        ) if peptides is not None else None
-
-    @staticmethod
-    def fill_dataframe(result_df, features, feature_parameters={}):
-        for feature in features:
-            obj = AUXILIARY_FEATURES[feature](
-                **feature_parameters.get(feature, {}))
-            obj(result_df)
-
-    @classmethod
-    def get_columns(cls, features, feature_parameters={}):
-        result_df = pandas.DataFrame(
-            {"allele": [], "peptide": []}, dtype=str)
-        cls.fill_dataframe(result_df, features, feature_parameters)
-        del result_df["allele"]
-        del result_df["peptide"]
-        return result_df.columns.tolist()
-
-    @staticmethod
-    def split_features(how, features, feature_parameters={}):
-        predicate = None
-        if how == "peptide_independent":
-            predicate = lambda obj: not obj.requires_peptides
-        elif how == "allele_independent":
-            predicate = lambda obj: not obj.requires_alleles
-        else:
-            raise NotImplementedError("Unsupported 'how' value", how )
-
-        matching = []
-        non_matching = []
-        for feature in features:
-            obj = AUXILIARY_FEATURES[feature](
-                **feature_parameters.get(feature, {}))
-            if predicate(obj):
-                matching.append(feature)
-            else:
-                non_matching.append(feature)
-        return (matching, non_matching)
-
-    def get_array(self, features, feature_parameters={}):
-        result_df = pandas.DataFrame()
-        if self.alleles_flat is not None:
-            result_df["allele"] = self.alleles_flat
-        if self.peptides is not None:
-            result_df["peptide"] = self.peptides
-
-        for feature in features:
-            obj = AUXILIARY_FEATURES[feature](
-                **feature_parameters.get(feature, {}))
-            if obj.requires_alleles and "allele" not in result_df.columns:
-                raise ValueError("%s requires alleles" % obj.name)
-            if obj.requires_peptides and "peptide" not in result_df.columns:
-                raise ValueError("%s requires peptides" % obj.name)
-            obj(result_df)
-        if "allele" in result_df.columns:
-            del result_df["allele"]
-        if "peptide" in result_df.columns:
-            del result_df["peptide"]
-        result = numpy.reshape(
-            result_df.values, self.alleles_shape + (-1,)).astype("float32")
-        assert not numpy.isnan(result).any()
-        return result
-
-
-class AuxiliaryInputFeature(object):
-    name = None
-    requires_alleles = False
-    requires_peptides = False
-
-    def __call__(self, *args, **kwargs):
-        raise NotImplementedError()
-
-
-class Gene(AuxiliaryInputFeature):
-    name = "gene"
-    requires_alleles = True
-
-    def __call__(self, result_df):
-        result_df["gene:HLA-A"] = (
-            result_df.allele.fillna("").str.startswith("HLA-A"))
-        result_df["gene:HLA-B"] = (
-            result_df.allele.fillna("").str.startswith("HLA-B"))
-
-
-AUXILIARY_FEATURES[Gene.name] = Gene

test/test_auxiliary_input.py

-from mhcflurry import auxiliary_input
-from nose.tools import eq_
-from numpy.testing import assert_equal
-import numpy
-import pandas
-
-
-def test_gene():
-    alleles1 = [
-        "HLA-A*02:01",
-        "HLA-A*02:01",
-        "HLA-B*07:02",
-        "HLA-B*07:02",
-        "HLA-C*03:01",
-        "HLA-C*02:01",
-    ]
-    alleles2 = [
-        "HLA-A*03:01",
-        "HLA-A*20:01",
-        "HLA-B*03:01",
-        "HLA-C*03:01",
-        "HLA-C*07:01",
-        "HLA-C*02:01",
-    ]
-
-    encoder = auxiliary_input.AuxiliaryInputEncoder(
-        alleles=[alleles1, alleles2])
-    result = encoder.get_array(features=["gene"])
-    print(result)
-    assert_equal(
-        result,
-        [
-            [
-                [1, 0], [1, 0], [0, 1], [0, 1], [0, 0], [0, 0],
-            ],
-            [
-                [1, 0], [1, 0], [0, 1], [0, 0], [0, 0], [0, 0],
-            ],
-        ])
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