GrideSearchCV 优化算法参数

很多机器学习算法有参数，比如 linear_model.LogisticRegression()中有参数C.
sklearn中的GrideSearchCV可方便调参过程．如下：

import numpy as np
from sklearn import linear_model
from skearn.grid_search import GridSearchCV

# read my data
X_train, X_test, y_train, y_test=load_data(file='total_data.csv', X_start=2, X_end=37, y_position=64, classification=False)

# 参数 C 的搜索空间
Cs = np.logspace(-1, 1, num = 100)

model = linear_model.LogisticRegression()
grid = GridSearchCV(estimator=model, param_grid=dict(C=Cs))
grid.fit(X_train, y_train)

print grid
print grid.best_score_
print grid.best_estimator_

输出：

GridSearchCV(cv=None, error_score='raise',
       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False),
       fit_params={}, iid=True, n_jobs=1,
       param_grid={'C': array([  0.1    ,   0.10476, ...,   9.54548,  10.     ])},
       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)
0.694444444444
LogisticRegression(C=0.23101297000831597, class_weight=None, dual=False,
          fit_intercept=True, intercept_scaling=1, max_iter=100,
          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,
          solver='liblinear', tol=0.0001, verbose=0, warm_start=False)

要注意grid = GridSearchCV(estimator=model, param_grid=dict(C=Cs))的C, C必须是LogisticRegression的一个参数名字，否则报错．