python - 我试图使我的数据平衡，因为我的目标变量具有多类，并且我想对其进行过采样以使我的数据平衡

Question

让x包含变量:print(x)

    Restaurant  Cuisines    Average_Cost    Rating  Votes   Reviews Area
    0   3.526361    0.693147    5.303305    1.504077    2.564949    1.609438    7.214504
    1   1.386294    4.127134    4.615121    1.504077    2.484907    1.609438    5.905362
    2   2.772589    1.386294    5.017280    1.526056    4.605170    3.433987    6.131226
    3   3.912023    2.833213    5.525453    1.547563    5.176150    4.564348    7.643483
    4   3.526361    2.708050    5.303305    1.435085    5.948035    5.046646    6.126869
    ... ... ... ... ... ... ... ...
    11089   3.912023    0.693147    5.525453    1.648659    5.789960    5.046646    3.135494
    11090   1.386294    6.028279    4.615121    1.526056    3.610918    2.833213    7.643483
    11091   1.386294    2.397895    4.615121    1.504077    3.828641    2.944439    5.814131
    11092   1.386294    6.028279    4.615121    1.410987    3.218876    2.302585    5.905362
    11093   1.386294    6.028279    4.615121    1.029619    0.000000    0.000000    5.564520
    11094 rows × 7 columns

并让 y 为多类目标变量。 打印(y.value_counts())

    30 minutes     7406
    45 minutes     2665
    65 minutes      923
    120 minutes      62
    20 minutes       20
    80 minutes       14
    10 minutes        4
    Name: Delivery_Time, dtype: int64

探索 y 变量后，我们可以看到 30 分钟 类别与其他类别相比具有更高的计数。

为了平衡这些，我尝试 SMOTETomek 对数据进行过采样。但我得到了一个错误:

from imblearn.combine import SMOTETomek
smk = SMOTEtomek(ratio = 1)
x_res, y_res = smk.fit_sample(x,y)

错误:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-54-426e8b86623d> in <module>()
        1 from imblearn.combine import SMOTETomek
        2 smk = SMOTETomek(ratio = 1)
----> 3 x_res, y_res = smk.fit_sample(x,y)

2 frames
/usr/local/lib/python3.6/dist-packages/imblearn/utils/_validation.py in _sampling_strategy_float(sampling_strategy, y, sampling_type)
    311     if type_y != 'binary':
    312         raise ValueError(
--> 313             '"sampling_strategy" can be a float only when the type '
    314             'of target is binary. For multi-class, use a dict.')
    315     target_stats = _count_class_sample(y)

ValueError: "sampling_strategy" can be a float only when the type of target is binary. For multi-class, use a dict.

Answer 1

您可以看到Smote的实际实现: https://github.com/scikit-learn-contrib/imbalanced-learn/blob/master/imblearn/utils/_validation.py#L355

您只需传递错误中提到的字典即可。但SMOTE算法内部负责多类设置。

做:

from imblearn.oversampling import SMOTE
smote=SMOTE("minority")
X,Y=smote.fit_sample(x_train,y_train)

When dict, the keys correspond to the targeted classes. The
values correspond to the desired number of samples for each targeted
class.