由于特征不匹配无法预测

Question

我使用 sklearn 创建基于 xlsx 文件的逻辑回归模型。我从数据集中删除了一些目标和冗余特征。现在我想做一个预测，并希望根据文件的每一行的新 xlsx 文件获取标签。

我能够存储和加载模型。在运行预测之后，我遇到了一个问题：

'X has 37 features per sample; expecting 44'

怎么了？谢谢你的提示。

...
## TRAIN

target = df_HR['Fluktuation'].copy()
type(target)

# remove the target feature and redundant features from the dataset
df_HR.drop(['Fluktuation', 'FTE', 'Mitarbeiternummer',
            'StandardStunden', 'Volljaehrig'], axis=1, inplace=True)
print('Size of Full dataset is: {}'.format(df_HR.shape))

X_train, X_test, y_train, y_test = train_test_split(df_HR,
                                                    target,
                                                    test_size=0.25,
                                                    random_state=7,
                                                    stratify=target) 
## CREATE MODEL AND STORE IT

kfold = model_selection.KFold(n_splits=10, random_state=7)
modelCV = LogisticRegression(solver='liblinear',
                             class_weight="balanced", 
                             random_state=7)
scoring = 'roc_auc'
results = model_selection.cross_val_score(
    modelCV, X_train, y_train, cv=kfold, scoring=scoring)
print(" Logistic Regression algorithm AUC score (STD): %.2f (%.2f)" % (results.mean(), results.std()))

param_grid = {'C': np.arange(1e-03, 2, 0.01)} # hyper-parameter list to fine-tune
log_gs = GridSearchCV(LogisticRegression(solver='liblinear', # setting GridSearchCV
                                         class_weight="balanced", 
                                         random_state=7),
                      iid=True,
                      return_train_score=True,
                      param_grid=param_grid,
                      scoring='roc_auc',
                      cv=10)

log_grid = log_gs.fit(X_train, y_train)
log_opt = log_grid.best_estimator_
results = log_gs.cv_results_

model_file_name = '%s/model.pkl' % modelFolder
joblib.dump(log_gs, model_file_name)

## LOAD MODEL AND PREDICT NEW XLSX FILE
...
df_HRE = df_sourcefileE.copy()
dfColumnsE = df_HRE.columns

leE = LabelEncoder()

le_countE = 0

for col in df_HRE.columns[1:]:
    if df_HRE[col].dtype == 'object':
        if len(list(df_HRE[col].unique())) <= 2:
            leE.fit(df_HRE[col])
            df_HRE[col] = leE.transform(df_HRE[col])
            le_countE += 1
print('{} columns label encoded.'.format(le_countE))

df_HRE = pd.get_dummies(df_HRE, drop_first=True)
#print('df_HRE',df_HRE)

# import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 5))
HRE_col = list(df_HRE.columns)
#print('datensatz HRE: ', df_HRE)
HRE_col.remove('Fluktuation')
for col in HRE_col:
    df_HRE[col] = df_HRE[col].astype(float)
    df_HRE[[col]] = scaler.fit_transform(df_HRE[[col]])
df_HRE['Fluktuation'] = pd.to_numeric(df_HRE['Fluktuation'], downcast='float')

targetE = df_HRE['Fluktuation'].copy()
type(targetE)

df_HRE.drop(['Fluktuation', 'FTE', 'Mitarbeiternummer',
    'StandardStunden', 'Volljaehrig'], axis=1, inplace=True)


# apply the whole pipeline to data
pred = loaded_model.predict(df_HRE)
print (pred)

Answer 1

当您训练初始模型时，您似乎有 44 列数据（特征）。为了使用相同的模型进行预测，否则您需要相同数量的预测变量。

例如，假设您的模型最初是 3 个变量（x1、x2、x3），其中每个变量是原始数据集中的一列，那么回归公式类似于

y=A*x1 + B*x2 + C*x3 + D

如果您在尝试预测时不提供 x2 或 x3，将无法应用该公式。

所以你有两个选择，在没有七个额外特征（那些出现在训练集中而不是预测集中的特征）的情况下重新训练模型，或者将这七个相同的特征添加到具有 NULL 值的预测模型（不是推荐）。

由于特征不匹配无法预测

Predicting not possible due to mismatch of features

python

scikit-learn

logistic-regression

joblib

sklearn-pandas