def structure_featurizer(df_input: pd.DataFrame, **kwargs) -> pd.DataFrame:
    """Return a Pandas DataFrame with all structural features"""

    # generate the "structure_oxid" column
    df_struct = StructureToOxidStructure(**kwargs).featurize_dataframe(
        df_input, col_id="structure", ignore_errors=True)
    # generate features based on EwaldEnergy
    ee_featurizer = EwaldEnergy()
    ee_featurizer.featurize_dataframe(df_struct,
                                      col_id="structure_oxid",
                                      ignore_errors=True,
                                      inplace=True)
    # generate features based on the variance in bond lengths and atomic volumes (slow to run)
    sh_featurizer = StructuralHeterogeneity()
    sh_featurizer.featurize_dataframe(df_struct,
                                      col_id="structure",
                                      ignore_errors=True,
                                      inplace=True)
    # calculate global instability index
    gii_featurizer = GlobalInstabilityIndex()
    gii_featurizer.featurize_dataframe(df_struct,
                                       col_id="structure_oxid",
                                       ignore_errors=True,
                                       inplace=True)
    # rename the column from "global instability index" to "gii"
    df_struct.rename(columns={"global instability index": "gii"}, inplace=True)
    return df_struct
def AddFeatures(df):  # Add features by Matminer
    from matminer.featurizers.conversions import StrToComposition
    df = StrToComposition().featurize_dataframe(df, "formula")

    from matminer.featurizers.composition import ElementProperty

    ep_feat = ElementProperty.from_preset(preset_name="magpie")
    df = ep_feat.featurize_dataframe(
        df, col_id="composition"
    )  # input the "composition" column to the featurizer

    from matminer.featurizers.conversions import CompositionToOxidComposition
    from matminer.featurizers.composition import OxidationStates

    df = CompositionToOxidComposition().featurize_dataframe(df, "composition")

    os_feat = OxidationStates()
    df = os_feat.featurize_dataframe(df, "composition_oxid")

    from matminer.featurizers.composition import ElectronAffinity

    ea_feat = ElectronAffinity()
    df = ea_feat.featurize_dataframe(df,
                                     "composition_oxid",
                                     ignore_errors=True)

    from matminer.featurizers.composition import BandCenter

    bc_feat = BandCenter()
    df = bc_feat.featurize_dataframe(df,
                                     "composition_oxid",
                                     ignore_errors=True)

    from matminer.featurizers.composition import CohesiveEnergy

    ce_feat = CohesiveEnergy()
    df = ce_feat.featurize_dataframe(df,
                                     "composition_oxid",
                                     ignore_errors=True)

    from matminer.featurizers.composition import Miedema

    m_feat = Miedema()
    df = m_feat.featurize_dataframe(df, "composition_oxid", ignore_errors=True)

    from matminer.featurizers.composition import TMetalFraction

    tmf_feat = TMetalFraction()
    df = tmf_feat.featurize_dataframe(df,
                                      "composition_oxid",
                                      ignore_errors=True)

    from matminer.featurizers.composition import ValenceOrbital

    vo_feat = ValenceOrbital()
    df = vo_feat.featurize_dataframe(df,
                                     "composition_oxid",
                                     ignore_errors=True)

    from matminer.featurizers.composition import YangSolidSolution

    yss_feat = YangSolidSolution()
    df = yss_feat.featurize_dataframe(df,
                                      "composition_oxid",
                                      ignore_errors=True)

    from matminer.featurizers.structure import GlobalSymmetryFeatures

    # This is the border between compositional features and structural features. Comment out the following featurizers to use only compostional features.

    gsf_feat = GlobalSymmetryFeatures()
    df = gsf_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import StructuralComplexity
    sc_feat = StructuralComplexity()
    df = sc_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import ChemicalOrdering
    co_feat = ChemicalOrdering()
    df = co_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import MaximumPackingEfficiency
    mpe_feat = MaximumPackingEfficiency()
    df = mpe_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import MinimumRelativeDistances
    mrd_feat = MinimumRelativeDistances()
    df = mrd_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import StructuralHeterogeneity
    sh_feat = StructuralHeterogeneity()
    df = sh_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import SiteStatsFingerprint

    from matminer.featurizers.site import AverageBondLength
    from pymatgen.analysis.local_env import CrystalNN
    bl_feat = SiteStatsFingerprint(
        AverageBondLength(CrystalNN(search_cutoff=20)))
    df = bl_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.site import AverageBondAngle
    ba_feat = SiteStatsFingerprint(
        AverageBondAngle(CrystalNN(search_cutoff=20)))
    df = ba_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.site import BondOrientationalParameter
    bop_feat = SiteStatsFingerprint(BondOrientationalParameter())
    df = bop_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.site import CoordinationNumber
    cn_feat = SiteStatsFingerprint(CoordinationNumber())
    df = cn_feat.featurize_dataframe(df, "structure", ignore_errors=True)

    from matminer.featurizers.structure import DensityFeatures
    df_feat = DensityFeatures()
    df = df_feat.featurize_dataframe(df, "structure", ignore_errors=True)
    return (df)