Pou*_*eri 5 python machine-learning scikit-learn cross-validation xgboost
使用XGB模型进行嵌套交叉验证的一种方法是：
from sklearn.model_selection import GridSearchCV, cross_val_score
from xgboost import XGBClassifier
# Let's assume that we have some data for a binary classification
# problem : X (n_samples, n_features) and y (n_samples,)...
gs = GridSearchCV(estimator=XGBClassifier(),
param_grid={'max_depth': [3, 6, 9],
'learning_rate': [0.001, 0.01, 0.05]},
cv=2)
scores = cross_val_score(gs, X, y, cv=2)
但是，关于XGB参数的调整，一些教程（例如本教程）利用了Python hyperopt库。我希望能够使用hyperopt调整XGB参数来进行嵌套交叉验证（如上所述）。
为此，我编写了自己的Scikit-Learn估算器：
from hyperopt import fmin, tpe, hp, Trials, STATUS_OK
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.model_selection import train_test_split
from sklearn.exceptions import NotFittedError
from sklearn.metrics import roc_auc_score
from xgboost import XGBClassifier
def optimize_params(X, y, params_space, validation_split=0.2):
"""Estimate a set of 'best' model parameters."""
# Split X, y into train/validation
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=validation_split, stratify=y)
# Estimate XGB params
def objective(_params):
_clf = XGBClassifier(n_estimators=10000,
max_depth=int(_params['max_depth']),
learning_rate=_params['learning_rate'],
min_child_weight=_params['min_child_weight'],
subsample=_params['subsample'],
colsample_bytree=_params['colsample_bytree'],
gamma=_params['gamma'])
_clf.fit(X_train, y_train,
eval_set=[(X_train, y_train), (X_val, y_val)],
eval_metric='auc',
early_stopping_rounds=30)
y_pred_proba = _clf.predict_proba(X_val)[:, 1]
roc_auc = roc_auc_score(y_true=y_val, y_score=y_pred_proba)
return {'loss': 1. - roc_auc, 'status': STATUS_OK}
trials = Trials()
return fmin(fn=objective,
space=params_space,
algo=tpe.suggest,
max_evals=100,
trials=trials,
verbose=0)
class OptimizedXGB(BaseEstimator, ClassifierMixin):
"""XGB with optimized parameters.
Parameters
----------
custom_params_space : dict or None
If not None, dictionary whose keys are the XGB parameters to be
optimized and corresponding values are 'a priori' probability
distributions for the given parameter value. If None, a default
parameters space is used.
"""
def __init__(self, custom_params_space=None):
self.custom_params_space = custom_params_space
def fit(self, X, y, validation_split=0.3):
"""Train a XGB model.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
Data.
y : ndarray, shape (n_samples,) or (n_samples, n_labels)
Labels.
validation_split : float (default: 0.3)
Float between 0 and 1. Corresponds to the percentage of samples in X which will be used as validation data to estimate the 'best' model parameters.
"""
# If no custom parameters space is given, use a default one.
if self.custom_params_space is None:
_space = {
'learning_rate': hp.uniform('learning_rate', 0.0001, 0.05),
'max_depth': hp.quniform('max_depth', 8, 15, 1),
'min_child_weight': hp.quniform('min_child_weight', 1, 5, 1),
'subsample': hp.quniform('subsample', 0.7, 1, 0.05),
'gamma': hp.quniform('gamma', 0.9, 1, 0.05),
'colsample_bytree': hp.quniform('colsample_bytree', 0.5, 0.7, 0.05)
}
else:
_space = self.custom_params_space
# Estimate best params using X, y
opt = optimize_params(X, y, _space, validation_split)
# Instantiate `xgboost.XGBClassifier` with optimized parameters
best = XGBClassifier(n_estimators=10000,
max_depth=int(opt['max_depth']),
learning_rate=opt['learning_rate'],
min_child_weight=opt['min_child_weight'],
subsample=opt['subsample'],
gamma=opt['gamma'],
colsample_bytree=opt['colsample_bytree'])
best.fit(X, y)
self.best_estimator_ = best
return self
def predict(self, X):
"""Predict labels with trained XGB model.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
Returns
-------
output : ndarray, shape (n_samples,) or (n_samples, n_labels)
"""
if not hasattr(self, 'best_estimator_'):
raise NotFittedError('Call `fit` before `predict`.')
else:
return self.best_estimator_.predict(X)
def predict_proba(self, X):
"""Predict labels probaiblities with trained XGB model.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
Returns
-------
output : ndarray, shape (n_samples,) or (n_samples, n_labels)
"""
if not hasattr(self, 'best_estimator_'):
raise NotFittedError('Call `fit` before `predict_proba`.')
else:
return self.best_estimator_.predict_proba(X)
我的问题是：
fitmy 的方法中OptimizedXGB，best.fit(X, y)将在X，y上训练XGB模型。但是，这可能会导致过拟合，因为没有eval_set指定No 来确保尽早停止。OptimizedXGB效果要比基本的LogisticRegression分类器差。这是为什么？是否因为示例过于简单？请参阅下面的示例代码。范例：
import numpy as np
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
X, y = load_iris(return_X_y=True)
X = X[:, :2]
X = X[y < 2]
y = y[y < 2]
skf = StratifiedKFold(n_splits=2, random_state=42)
# With a LogisticRegression classifier
pipe = Pipeline([('scaler', StandardScaler()), ('lr', LogisticRegression())])
gs = GridSearchCV(estimator=pipe, param_grid={'lr__C': [1., 10.]})
lr_scores = cross_val_score(gs, X, y, cv=skf)
# With OptimizedXGB
xgb_scores = cross_val_score(OptimizedXGB(), X, y, cv=skf)
# Print results
print('Accuracy with LogisticRegression = %.4f (+/- %.4f)' % (np.mean(lr_scores), np.std(lr_scores)))
print('Accuracy with OptimizedXGB = %.4f (+/- %.4f)' % (np.mean(xgb_scores), np.std(xgb_scores)))
输出：
Accuracy with LogisticRegression = 0.9900 (+/- 0.0100)
Accuracy with OptimizedXGB = 0.9100 (+/- 0.0300)
尽管得分很接近，但我希望XGB模型的得分至少与LogisticRegression分类器一样。
编辑：