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。现在我要进行预测，并希望基于新的xlsx文件为文件的每一行获取标签。

我能够存储和加载模型。运行预测后，我遇到了一个问题：

'X每个样本具有37个功能；期待44'

出了什么问题？谢谢您的提示。

...
## 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，您似乎拥有44列数据（功能）。为了使用同一模型进行预测，否则您需要使用相同数量的预测器。

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

$('body').click('#btn_status', function(event){
        //alert("button is clicked");
        var nid = $("#nid").val();

        $.post("ajax/updateStatus.php", {
                nid: nid,
                register_status: register_status
        }, function (data, status) {
                alert("button is clicked");
        });

});

如果您在尝试进行预测时未提供x2或x3，则将无法应用公式。

因此，您有两个选择，在没有七个额外功能的情况下重新训练模型（那些功能出现在训练集中而不是在预测集中），或者将这七个相同的功能添加到带有NULL值的预测模型中（不推荐）

由于功能不匹配而无法预测

1 个答案: