1.导入包

from matminer.featurizers.base import MultipleFeaturizer
from matminer.featurizers import composition as cf
from matminer.featurizers.conversions import StrToComposition
import pandas as pd

2.构建dataframe格式材料数据样本

   用dataframe储存材料数据，至少要有材料的化学式，用于得到材料的Magpie特征；

  这里提供一个函数得到材料的composition；

def get_magpie_composition(formula):
    upper_letter = [\'A\', \'B\', \'C\', \'D\', \'E\', \'F\', \'G\', \'H\', \'I\', \'J\', \'K\', \'L\', \'M\', \'N\', \'O\', \'P\', \'Q\', \'R\', \'S\', \'T\',
                    \'U\', \'V\', \'W\', \'X\', \'Y\', \'Z\']
    number = [\'0\', \'1\', \'2\', \'3\', \'4\', \'5\', \'6\', \'7\', \'8\', \'9\']

    ins = []
    for i in range(len(formula)):
        if i == 0:
            continue
        if formula[i] in upper_letter:
            if formula[i - 1] not in number:
                ins.append(i)

    formula_list = list(formula)
    k = 0
    for j in ins:
        formula_list.insert(j + k, \'1\')
        k = k + 1

    formula = \'\'.join(formula_list)

    if formula[len(formula) - 1] not in number:
        formula = formula + \'1\'
    return formula

  对dataframe中的每个材料化学式执行get_magpie_composition()函数，并且将执行结果储存到dataframe中的列composition中，得到如下dataframe。

3. 计算Magpie特征

\"\"\"
需要输入材料的化学式(例如Nb1Pt1Si1)，可以得到材料的145维向量表示
\"\"\"

def magpie(df,composition):
    df = StrToComposition(target_col_id=\'composition_obj\').featurize_dataframe(df, composition)
    feature_calculators = MultipleFeaturizer([cf.Stoichiometry(), cf.ElementProperty.from_preset(\"magpie\"),
                                              cf.ValenceOrbital(props=[\'avg\']), cf.IonProperty(fast=True)])

    feature_labels = feature_calculators.feature_labels()
    print(\'feature_labels\')
    print(feature_labels)

    df = feature_calculators.featurize_dataframe(df, col_id=\'composition_obj\')

    return df

  对dataframe利用上述函数可以得到材料的145维特征，储存在原dataframe中。