如何使用 sklearn 管道和插补进行预测

Question

我正在查看 sklearn 文档页面"在构建估算器之前插补缺失值"相关代码为：

import numpy as np
from sklearn.datasets import load_boston 
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Imputer 
from sklearn.cross_validation import cross_val_score
rng = np.random.RandomState(0)
dataset = load_boston() 
X_full, y_full = dataset.data, dataset.target
n_samples = X_full.shape[0] 
n_features = X_full.shape[1]
estimator = RandomForestRegressor(random_state=0, n_estimators=100)
score = cross_val_score(estimator, X_full, y_full).mean() 
print("Score with the entire dataset = %.2f" % score)
# Add missing values in 75% of the lines
missing_rate = 0.75 
n_missing_samples = np.floor(n_samples * missing_rate) 
missing_samples = np.hstack((np.zeros(n_samples - n_missing_samples,
                                dtype=np.bool),
                       np.ones(n_missing_samples,
                               dtype=np.bool))) rng.shuffle(missing_samples) 
missing_features = rng.randint(0, n_features, n_missing_samples)
# Estimate the score without the lines containing missing values
X_filtered = X_full[~missing_samples, :] 
y_filtered = y_full[~missing_samples] estimator = RandomForestRegressor(random_state=0, n_estimators=100)          
score = cross_val_score(estimator, X_filtered, y_filtered).mean() 
print("Score without the samples containing missing values = %.2f" % score)
# Estimate the score after imputation of the missing values 
X_missing = X_full.copy()
X_missing[np.where(missing_samples)[0], missing_features] = 0 
y_missing = y_full.copy() 
estimator = Pipeline([("imputer", Imputer(missing_values=0,
                                          strategy="mean",
                                          axis=0)),
                      ("forest", RandomForestRegressor(random_state=0,
                                                       n_estimators=100))]) 
score = cross_val_score(estimator, X_missing, y_missing).mean() 
print("Score after imputation of the missing values = %.2f" % score)

现在我想预测，这应该很简单，但是

estimator.predict(X=X_filtered[1:10,:])

返回以下错误：

"AttributeError: 'Imputer' object has no attribute 'statistics_'"

这是怎么回事？

Answer 1

经过更多的研究，我已经想通了。 问题是cross_val_score已经生成了多个模型，并且没有一个模型用于预测。 预测需要单个拟合模型。 这可以通过多种方式解决，其中一个简单的方法如下：

estimator.fit_transform(X_missing, y_missing)
estimator.predict(X=X_filtered[1:10,:])

由于原始示例使用交叉验证，因此另一种可能的途径是使用 GridSearchCV，然后选择使用 best_estimator_，但这超出了本问题的范围。