训练模型填充空值(fill null)的几种方法

我们在进行模型训练时，不可避免的会遇到某些特征出现空值的情况，下面整理了几种填充空值的方法

1. 用固定值填充

对于特征值缺失的一种常见的方法就是可以用固定值来填充，例如0，9999， -9999, 例如下面对灰度分这个特征缺失值全部填充为-99

data['灰度分'] = data['灰度分'].fillna('-99')

2. 用均值填充

对于数值型的特征，其缺失值也可以用未缺失数据的均值填充，下面对灰度分这个特征缺失值进行均值填充

data['灰度分'] = data['灰度分'].fillna(data['灰度分'].mean()))

3. 用众数填充

与均值类似，可以用未缺失数据的众数来填充缺失值

data['灰度分'] = data['灰度分'].fillna(data['灰度分'].mode()))

4. 用上下数据进行填充

用前一个数据进行填充

data['灰度分'] = data['灰度分'].fillna(method='pad')

用后一个数据进行填充

data['灰度分'] = data['灰度分'].fillna(method='bfill')

5. 用插值法填充

data['灰度分'] = data['灰度分'].interpolate()

6. 用KNN进行填充

from fancyimpute import BiScaler, KNN, NuclearNormMinimization, SoftImpute
dataset = KNN(k=3).complete(dataset)

7. random forest进行填充

from sklearn.ensemble import RandomForestRegressor
zero_columns_2 = ['机构查询数量', '直接联系人数量', '直接联系人在黑名单数量', '间接联系人在黑名单数量',
                '引起黑名单的直接联系人数量', '引起黑名单的直接联系人占比']
#将出现空值的除了预测的列全部取出来，不用于训练                
dataset_list2 = [x for x in dataset if x not in zero_columns_2]
dataset_2 = dataset[dataset_list2]
# 取出灰度分不为空的全部样本进行训练
know = dataset_2[dataset_2['灰度分'].notnull()]
print(know.shape) #26417, 54
# 取出灰度分为空的样本用于填充空值
unknow = dataset_2[dataset_2['灰度分'].isnull()]
print(unknow.shape) #2078, 54

y = ['灰度分']
x = [1]

know_x2 = know.copy()
know_y2 = know.copy()
print(know_y2.shape)
#
know_x2.drop(know_x2.columns[x], axis=1, inplace=True)
print(know_y2.shape)
print(know_x2.shape)
#
know_y2 = know[y]
# RandomForestRegressor
rfr = RandomForestRegressor(random_state=666, n_estimators=2000, n_jobs=-1)
rfr.fit(know_x2, know_y2)

# 填充为空的样本
unknow_x2 = unknow.copy()
unknow_x2.drop(unknow_x2.columns[x], axis=1, inplace=True)
print(unknow_x2.shape) #(2078, 53)
unknow_y2 = rfr.predict(unknow_x2)
unknow_y2 = pd.DataFrame(unknow_y2, columns=['灰度分'])                

使用fancyimpute包中的其他方法

# matrix completion using convex optimization to find low-rank solution
# that still matches observed values. Slow!
X_filled_nnm = NuclearNormMinimization().complete(X_incomplete)

# Instead of solving the nuclear norm objective directly, instead
# induce sparsity using singular value thresholding
X_filled_softimpute = SoftImpute().complete(X_incomplete_normalized)

# print mean squared error for the three imputation methods above
nnm_mse = ((X_filled_nnm[missing_mask] - X[missing_mask]) ** 2).mean()
# print mean squared error for the three imputation methods above
nnm_mse = ((X_filled_nnm[missing_mask] - X[missing_mask]) ** 2).mean()
print("Nuclear norm minimization MSE: %f" % nnm_mse)

softImpute_mse = ((X_filled_softimpute[missing_mask] - X[missing_mask]) ** 2).mean()
print("SoftImpute MSE: %f" % softImpute_mse)

knn_mse = ((X_filled_knn[missing_mask] - X[missing_mask]) ** 2).mean()
print("knnImpute MSE: %f" % knn_mse)

以上是我总结的几种填充空值的方法，具体使用哪种方法可以根据具体的业务场景选用