本文整理汇总了Python中matplotlib.gridspec.GridSpec.search_by_cv方法的典型用法代码示例。如果您正苦于以下问题：Python GridSpec.search_by_cv方法的具体用法？Python GridSpec.search_by_cv怎么用？Python GridSpec.search_by_cv使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类matplotlib.gridspec.GridSpec的用法示例。
在下文中一共展示了GridSpec.search_by_cv方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: gbdt_plus_liner_classifier_grid_search
# 需要导入模块: from matplotlib.gridspec import GridSpec [as 别名]
# 或者: from matplotlib.gridspec.GridSpec import search_by_cv [as 别名]
#.........这里部分代码省略.........
# model_test_fname)
with gzip.open(model_test_fname, "wb") as gf:
cPickle.dump([transformated_test_features, y_test],
gf,
cPickle.HIGHEST_PROTOCOL)
"""
# 2. lower model
if lower_param_keys is None:
lower_param_keys = ['model_type', 'n_neighbors', 'weights',
'algorithm', 'leaf_size', 'metric', 'p', 'n_jobs']
if lower_param_vals is None:
lower_param_vals = [[KNeighborsClassifier], [1, 2, 4, 8, 16, 24, 32, 64], ['uniform', 'distance'],
['ball_tree'], [30], ['minkowski'], [2], [4]]
lower_param_dict = dict(zip(lower_param_keys, lower_param_vals))
if lower_param_dict['model_type'] == [LogisticRegression]:
# grid search for lower model : Linear Classifier
# ExperimentL1_1 has model free. On the other hand, data is fix
model_train_fname = stack_setting_['1-Level']['gbdt_linear']['upper']['gbdt']['train']
model_test_fname = stack_setting_['1-Level']['gbdt_linear']['upper']['gbdt']['test']
exp = ExperimentL1_1(data_folder = stack_setting_['1-Level']['gbdt_linear']['upper']['gbdt']['folder'],
train_fname = model_train_fname,
test_fname = model_test_fname)
# GridSearch has a single model. model is dertermined by param
gs = GridSearch(SklearnModel, exp, lower_param_keys, lower_param_vals,
cv_folder = stack_setting_['1-Level']['gbdt_linear']['lower']['cv']['folder'],
cv_out = stack_setting_['1-Level']['gbdt_linear']['lower']['cv']['cv_out'],
cv_pred_out = stack_setting_['1-Level']['gbdt_linear']['lower']['cv']['cv_pred_out'],
refit_pred_out = stack_setting_['1-Level']['gbdt_linear']['lower']['cv']['refit_pred_out'])
lower_best_param, lower_best_score = gs.search_by_cv()
print lower_best_param
# get meta_feature
exp.write2csv_meta_feature(
model = LogisticRegression(),
meta_folder = stack_setting_['1-Level']['gbdt_linear']['lower']['meta_feature']['folder'],
meta_train_fname = stack_setting_['1-Level']['gbdt_linear']['lower']['meta_feature']['train'],
meta_test_fname = stack_setting_['1-Level']['gbdt_linear']['lower']['meta_feature']['test'],
meta_header = stack_setting_['1-Level']['gbdt_linear']['lower']['meta_feature']['header'],
best_param_ = lower_best_param
)
"""
# 2. lower model
if lower_param_keys is None:
lower_param_keys = ['model_type', 'n_neighbors', 'weights',
'algorithm', 'leaf_size', 'metric', 'p', 'n_jobs']
if lower_param_vals is None:
lower_param_vals = [[KNeighborsClassifier], [1, 2, 4, 8, 16, 24, 32, 64], ['uniform', 'distance'],
['ball_tree'], [30], ['minkowski'], [2], [4]]
lower_param_dict = dict(zip(lower_param_keys, lower_param_vals))
clf_lower_model = None
clf_lower_mname = None
# grid search for lower model : Linear Classifier
# ExperimentL1_1 has model free. On the other hand, data is fix
if lower_param_dict['model_type'] == [LogisticRegression]:
# Logistic Regression
clf_lower_model = LogisticRegression()开发者ID:Quasi-quant2010，项目名称:Stacking，代码行数:70，代码来源:run_gbdt_plus_liner_classifier_grid_search.20160414.py