本文整理汇总了Python中mlxtend.feature_selection.SequentialFeatureSelector.get_metric_dict方法的典型用法代码示例。如果您正苦于以下问题：Python SequentialFeatureSelector.get_metric_dict方法的具体用法？Python SequentialFeatureSelector.get_metric_dict怎么用？Python SequentialFeatureSelector.get_metric_dict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mlxtend.feature_selection.SequentialFeatureSelector的用法示例。
在下文中一共展示了SequentialFeatureSelector.get_metric_dict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: get_best_logisitc
# 需要导入模块: from mlxtend.feature_selection import SequentialFeatureSelector [as 别名]
# 或者: from mlxtend.feature_selection.SequentialFeatureSelector import get_metric_dict [as 别名]
def get_best_logisitc(y):
from mlxtend.feature_selection import SequentialFeatureSelector as SFS
from sklearn.cross_validation import StratifiedKFold
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import cross_val_score
my_data = pd.read_csv('data/my_data_test.csv', encoding='utf-8')
y = my_data.target
my_data = my_data.drop('target', axis=1)
# To have better CV
skf = StratifiedKFold(y, n_folds=5, random_state=17, shuffle=False)
C_params = [0.01 , 1, 10, 50, 70, 100]
solvers = ['newton-cg', 'lbfgs', 'liblinear', 'sag']
my_result_list = []
for C_param in C_params:
for solver in solvers:
print "Looking for C : %s and solver : %s" % (C_param, solver)
model = LogisticRegression(class_weight='balanced', random_state=17,
solver=solver, C=C_param)
sfs = SFS(model,
k_features=len(my_data.columns),
forward=True,
floating=False,
scoring='roc_auc',
print_progress=False,
cv=skf,
n_jobs=-1)
sfs = sfs.fit(my_data.values, y.values)
result_sfs = pd.DataFrame.from_dict(sfs.get_metric_dict()).T
result_sfs.sort_values('avg_score', ascending=0, inplace=True)
features_sfs = result_sfs.feature_idx.head(1).tolist()
select_features_sfs = list(my_data.columns[features_sfs])
scores = cross_val_score(model, my_data[select_features_sfs], y, cv=skf, scoring='roc_auc')
my_result_list.append({'C' : C_param,
'solver' : solver,
'auc' : scores.mean(),
'std' : scores.std(),
'best_columns' : select_features_sfs,
'estimator' : model})
my_result = pd.DataFrame(my_result_list)
my_result.sort_values('auc', ascending=0, inplace=True)
best_features = my_result.best_columns.head(1).values[0]
best_model = my_result.estimator.head(1).values[0]
return best_features, best_model