使用GridSearchCV使用early_stopping_rounds或将外部测试集与Pipeline结合使用时，eval_set似乎被fit忽略
管道。
from sklearn.base import BaseEstimator
from sklearn.base import TransformerMixin
from sklearn.utils.validation import check_array, check_is_fitted
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import lightgbm as lgbm
import numpy as np
class Transformer(BaseEstimator, TransformerMixin):
def __init__(self):
pass
def get_params(self, deep=True):
return dict()
def fit(self, X, y=None):
X = check_array(X, dtype=object)
print(X.shape)
self.input_shape_ = X.shape
return self
def set_params(self, **parameters):
self.__dict__.update(parameters)
return self
def transform(self, X):
# Check is fit had been called
check_is_fitted(self, ['input_shape_'])
# Input validation
X = check_array(X, dtype=object)
Xt = np.zeros((len(X), 1), dtype=np.float32)
for i in xrange(Xt.shape[0]):
Xt[i] = np.float32(X[i][0].s)**2.0
print(Xt)
return Xt
class Foo:
def __init__(self, s):
self.s = s
if __name__ == '__main__':
x = np.array([Foo(x) for x in xrange(10)]).reshape(-1, 1)
y = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1])
x_train, x_test, y_train, y_test = train_test_split(x, y, stratify=y, test_size=0.2, random_state=42)
params = {'lgbm__learning_rate': [0.05, 0.1]}
"""
static_params = {'n_estimators': 100, # 0,
}
"""
static_params = {'n_estimators': 100, # 0,
'early_stopping_rounds': 5,
'eval_metric': 'binary_logloss',
'is_unbalance': False,
'eval_set': [[x_test, y_test]]
}
pipe = Pipeline(steps=[('transformer', Transformer()), ('lgbm', lgbm.LGBMClassifier(**static_params))])
estimator = GridSearchCV(pipe, scoring='roc_auc', param_grid=params, cv=2, n_jobs=-1)
print(x_train)
print(y_train)
estimator.fit(x_train, y_train)
函数仅应用于训练数据，eval_set数据仅传递给最终估算器，而不在其上运行变换器。
有解决这个问题的好方法吗？ 我附上了一个小例子，表明eval_set没有被管道转换。 我已经读过可以以某种方式扩展分类器，但我不确定如何从中访问管道对象。
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