python - 通过应用 RFE 选择提供最佳调整 R 平方值的特征子集

Question

我有两个目标。我想:

1. 循环遍历 1-10 的特征值，然后
2. 比较Adjusted R-Squared 值。

我知道如何针对下面代码中显示的 1 个固定功能执行此操作。我试图在 selector = RFE(regr, n_features_to_select, step=1) 中循环，但我认为我错过了这个难题的关键部分。谢谢!

from sklearn.feature_selection import RFE
regr = LinearRegression()
#parameters: estimator, n_features_to_select=None, step=1

selector = RFE(regr, 5, step=1)
selector.fit(x_train, y_train)
selector.support_

def show_best_model(support_array, columns, model):
    y_pred = model.predict(X_test.iloc[:, support_array])
    r2 = r2_score(y_test, y_pred)
    n = len(y_pred) #size of test set
    p = len(model.coef_) #number of features
    adjusted_r2 = 1-(1-r2)*(n-1)/(n-p-1)
    print('Adjusted R-squared: %.2f' % adjusted_r2)
    j = 0;
        for i in range(len(support_array)):
        if support_array[i] == True:
            print(columns[i], model.coef_[j])
            j +=1


show_best_model(selector.support_, x_train.columns, selector.estimator_)

Answer 1

您可以创建一个自定义 GridSearchCV，它会针对估算器的指定参数值执行详尽搜索。

您还可以选择任何可用的评分函数，例如 R² Score在 Scikit-learn 中。但是，您可以使用给出的简单公式从 R² 分数计算调整后的 R² here然后在自定义 GridSearchCV 中实现它。

from collections import OrderedDict
from itertools import product
from sklearn.feature_selection import RFE
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_iris
from sklearn.metrics import r2_score
from sklearn.model_selection import StratifiedKFold


def customR2Score(y_true, y_pred, n, p):
    """
    Workaround for the adjusted R^2 score
    :param y_true: Ground Truth during iterations
    :param y_pred: Y predicted during iterations
    :param n: the sample size
    :param p: the total number of explanatory variables in the model
    :return: float, adjusted R^2 score
    """
    r2 = r2_score(y_true, y_pred)
    return 1 - (1 - r2) * (n - 1) / (n - p - 1)


def CustomGridSearchCV(X, Y, param_grid, n_splits=10, n_repeats=3):
    """
    Perform GridSearchCV using adjusted R^2 as Scoring.
    Note here we are performing GridSearchCV MANUALLY because adjusted R^2
    cannot be used directly in the GridSearchCV function builtin in Scikit-learn
    :param X: array_like, shape (n_samples, n_features), Samples.
    :param Y: array_like, shape (n_samples, ), Target values.
    :param param_grid: Dictionary with parameters names (string) as keys and lists
                       of parameter settings to try as values, or a list of such
                       dictionaries, in which case the grids spanned by each dictionary
                       in the list are explored. This enables searching over any
                       sequence of parameter settings.
    :param n_splits: Number of folds. Must be at least 2. default=10
    :param n_repeats: Number of times cross-validator needs to be repeated. default=3
    :return: an Ordered Dictionary of the model object and information and best parameters
    """
    best_model = OrderedDict()
    best_model['best_params'] = {}
    best_model['best_train_AdjR2'], best_model['best_cross_AdjR2'] = 0, 0
    best_model['best_model'] = None

    allParams = OrderedDict()
    for key, value in param_grid.items():
        allParams[key] = value

    for items in product(*allParams.values()):
        params = {}
        i = 0
        for k in allParams.keys():
            params[k] = items[i]
            i += 1
        # at this point, we get different combination of parameters
        model_ = RFE(**params)
        avg_AdjR2_train = 0.
        avg_AdjR2_cross = 0.
        for rep in range(n_repeats):
            skf = StratifiedKFold(n_splits=n_splits, shuffle=True)
            AdjR2_train = 0.
            AdjR2_cross = 0.
            for train_index, cross_index in skf.split(X, Y):
                x_train, x_cross = X[train_index], X[cross_index]
                y_train, y_cross = Y[train_index], Y[cross_index]
                model_.fit(x_train, y_train)
                # find Adjusted R2 of train and cross
                y_pred_train = model_.predict(x_train)
                y_pred_cross = model_.predict(x_cross)
                AdjR2_train += customR2Score(y_train, y_pred_train, len(y_train), model_.n_features_)
                AdjR2_cross += customR2Score(y_cross, y_pred_cross, len(y_cross), model_.n_features_)
            AdjR2_train /= n_splits
            AdjR2_cross /= n_splits
            avg_AdjR2_train += AdjR2_train
            avg_AdjR2_cross += AdjR2_cross
        avg_AdjR2_train /= n_repeats
        avg_AdjR2_cross /= n_repeats
        # store the results of the first set of parameters combination
        if abs(avg_AdjR2_cross) >= abs(best_model['best_cross_AdjR2']):
            best_model['best_params'] = params
            best_model['best_train_AdjR2'] = avg_AdjR2_train
            best_model['best_cross_AdjR2'] = avg_AdjR2_cross
            best_model['best_model'] = model_

    return best_model



# Dataset for testing
iris = load_iris()
X = iris.data
Y = iris.target


regr = LinearRegression()

param_grid = {'estimator': [regr],  # you can try different estimator
              'n_features_to_select': range(1, X.shape[1] + 1)}

best_model = CustomGridSearchCV(X, Y, param_grid, n_splits=5, n_repeats=2)

print(best_model)
print(best_model['best_model'].ranking_)
print(best_model['best_model'].support_)

测试结果

OrderedDict([
('best_params', {'n_features_to_select': 3, 'estimator': 
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)}), 
('best_train_AdjR2', 0.9286382985850505), ('best_cross_AdjR2', 0.9188172567358479),
('best_model', RFE(estimator=LinearRegression(copy_X=True, fit_intercept=True, 
 n_jobs=1, normalize=False), n_features_to_select=3, step=1, verbose=0))])

[1 2 1 1]

[ True False  True  True]