满堂花醉三千客，一剑霜寒十四洲。这篇文章主要讲述Python编程学习：深度剖析shap.datasets.adult()源码中的X,y和X_display,y_display输出数区别相关的知识，希望能为你提供帮助。
python编程学习：深度剖析shap.datasets.adult()源码中的X,y和X_display,y_display输出数区别
目录
??深度剖析shap.datasets.adult()源码中的X,y和X_display,y_display??
??读取源码??
??理解源代码??
??data与raw_data对比结果??
??X.shape
??
??X_display.shape
??
深度剖析shap.datasets.adult()源码中的X,y和X_display,y_display
X,y = shap.datasets.adult()
X_display,y_display = shap.datasets.adult(display=True)
读取源码
def adult(display=False):
""" Return the Adult census data in a nice package. """
dtypes = [
("Age", "float32"), ("Workclass", "category"), ("fnlwgt", "float32"),
("Education", "category"), ("Education-Num", "float32"), ("Marital Status", "category"),
("Occupation", "category"), ("Relationship", "category"), ("Race", "category"),
("Sex", "category"), ("Capital Gain", "float32"), ("Capital Loss", "float32"),
("Hours per week", "float32"), ("Country", "category"), ("Target", "category")
]
raw_data = https://www.songbingjia.com/android/pd.read_csv(
cache(github_data_url + "adult.data"),
names=[d[0] for d in dtypes],
na_values="?",
dtype=dict(dtypes)
)
data = https://www.songbingjia.com/android/raw_data.drop(["Education"], axis=1)# redundant with Education-Num
filt_dtypes = list(filter(lambda x: not (x[0] in ["Target", "Education"]), dtypes))
data["Target"] = data["Target"] == " > 50K"
rcode =
"Not-in-family": 0,
"Unmarried": 1,
"Other-relative": 2,
"Own-child": 3,
"Husband": 4,
"Wife": 5
for k, dtype in filt_dtypes:
if dtype == "category":
if k == "Relationship":
data[k] = np.array([rcode[v.strip()] for v in data[k]])
else:
data[k] = data[k].cat.codes
if display:
return raw_data.drop(["Education", "Target", "fnlwgt"], axis=1), data["Target"].values
return data.drop(["Target", "fnlwgt"], axis=1), data["Target"].values
理解源代码
data与raw_data对比结果结论：
data：是基于raw_data读入的csv文件数据，为新定义的新数据，共计drop了3列(第1个红色矩形框)，又进行了目标特征的二分类(第2个红色矩形框)，最后进行了类别特征进行了数值化/编码化(第3个红色矩形框)；经过处理后的数据均为数字列且目标特征为二分类的dataframe。
raw_data：为原始数据，从csv读入，仅经过drop了3列，其余原封不同输出数据。
X.shape
(32561, 12) X.shape
ageworkclass...hours-per-week native-country
039State-gov...40United-States
150Self-emp-not-inc...13United-States
238Private...40United-States
353Private...40United-States
428Private...40Cuba
..................
3255627Private...38United-States
3255740Private...40United-States
3255858Private...40United-States
3255922Private...20United-States
3256052Self-emp-inc...40United-States
[32561 rows x 12 columns]
| age | workclass | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | |
| 0 | 39 | State-gov | 13 | Never-married | Adm-clerical | Not-in-family | White | Male | 2174 | 0 | 40 | United-States |
| 1 | 50 | Self-emp-not-inc | 13 | Married-civ-spouse | Exec-managerial | Husband | White | Male | 0 | 0 | 13 | United-States |
| 2 | 38 | Private | 9 | Divorced | Handlers-cleaners | Not-in-family | White | Male | 0 | 0 | 40 | United-States |
| 3 | 53 | Private | 7 | Married-civ-spouse | Handlers-cleaners | Husband | Black | Male | 0 | 0 | 40 | United-States |
| 4 | 28 | Private | 13 | Married-civ-spouse | Prof-specialty | Wife | Black | Female | 0 | 0 | 40 | Cuba |
| 5 | 37 | Private | 14 | Married-civ-spouse | Exec-managerial | Wife | White | Female | 0 | 0 | 40 | United-States |
| 6 | 49 | Private | 5 | Married-spouse-absent | Other-service | Not-in-family | Black | Female | 0 | 0 | 16 | Jamaica |
| 7 | 52 | Self-emp-not-inc | 9 | Married-civ-spouse | Exec-managerial | Husband | White | Male | 0 | 0 | 45 | United-States |
| 8 | 31 | Private | 14 | Never-married | Prof-specialty | Not-in-family | White | Female | 14084 | 0 | 50 | United-States |
| 9 | 42 | Private | 13 | Married-civ-spouse | Exec-managerial | Husband | White | Male | 5178 | 0 | 40 | United-States |
(32561, 12) X_display.shape
ageworkclass...hours-per-week native-country
039State-gov...40United-States
150Self-emp-not-inc...13United-States
238Private...40United-States
353Private...40United-States
428Private...40Cuba
..................
3255627Private...38United-States
3255740Private...40United-States
3255858Private...40United-States
3255922Private...20United-States
3256052Self-emp-inc...40United-States
[32561 rows x 12 columns]
| age | workclass | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | |
| 0 | 39 | State-gov | 13 | Never-married | Adm-clerical | Not-in-family | White | Male | 2174 | 0 | 40 | United-States |
| 1 | 50 | Self-emp-not-inc | 13 | Married-civ-spouse | Exec-managerial | Husband | White | Male | 0 | 0 | 13 | 【Python编程学习（深度剖析shap.datasets.adult()源码中的X,y和X_display,y_display输出数区别）】
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