如何在管道中找到不同 "steps" 的最佳参数？

Question

我有以下结合了预处理、特征选择和估计器的管道：

## Selecting categorical and numeric features
numerical_ix = X.select_dtypes(include=np.number).columns
categorical_ix = X.select_dtypes(exclude=np.number).columns

## Create preprocessing pipelines for each datatype 
numerical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())])

categorical_transformer = Pipeline(steps=[
    ('encoder', OrdinalEncoder()),
    ('scaler', StandardScaler())])

## Putting the preprocessing steps together
preprocessor = ColumnTransformer([
        ('numerical', numerical_transformer, numerical_ix),
        ('categorical', categorical_transformer, categorical_ix)],
         remainder='passthrough')

## Create example pipeline with kNN
example_pipe = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('selector', SelectKBest(k=len(X.columns))), # keep the same amount of columns for now
    ('classifier', KNeighborsClassifier())
])

cross_val_score(example_pipe, X, y, cv=5, scoring='accuracy').mean()

我编写了以下代码，为 SelectKBest“尝试”不同的 ks 并绘制它。

但是我怎样才能同时在 kNN 分类器中寻找 k 的最优值？ 我不知道不一定要绘制它，只是找到最佳值。我的猜测是 GridSearchCV，但我不知道如何将其应用于管道中的不同步骤。

k_range = list(range(1, len(X.columns))) # 1 until 18
k_scores = []

for k in k_range:
  example_pipe = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('selector', SelectKBest(k=k)), # keep the same amount of columns for now
    ('classifier', KNeighborsClassifier())])
  score = cross_val_score(example_pipe, X, y, cv=5, scoring='accuracy').mean()
  k_scores.append(score)

plt.plot(k_range, k_scores)
plt.xlabel('Value of k in SelectKBEST')
plt.xticks(k_range, rotation=20) 
plt.ylabel('Cross-Validated Accuracy')

对于那些感兴趣的人，输出是：

Answer 1

您正在寻找 KNeighborsClassifier 的最佳 n_neighbors 值。

您对使用 GridSearchCV 的猜测是正确的。如果您想了解它与管道的结合使用，请查看 Pipeline:

的文档

The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a ‘__’

你的情况：

example_pipe = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('selector', SelectKBest()),
    ('classifier', KNeighborsClassifier())
])

param_grid = {
    "selector__k": [5, 10, 15],
    "classifier__n_neighbors": [3, 5, 10]
}

gs = GridSearchCV(example_pipe, param_grid=param_grid)
gs.fit(X, y)

然后用best_params_检索最佳参数：

best_k = gs.best_params_['selector__k']
best_n_neighbors = gs.best_params_['classifier__n_neighbors']

如何在管道中找到不同 "steps" 的最佳参数？

How to find the best parameters for different "steps" in a pipeline?

python

scikit-learn

classification

pipeline

hyperparameters