实现功能：
- score自定义
- cv方法自定义
- 输出最优超参数
- 显示较为美观
- 模型中多个超参数一样好的，并列输出
分类
import numpy as np
import pandas as pd
from sklearn import linear_model, tree, naive_bayes, neighbors, svm, neural_network, ensemble
from sklearn import model_selection, datasets, metrics
X, y = datasets. \
make_classification(n_samples=1000,
n_features=10,
n_informative=2,
n_redundant=3, # 用 n_informative 线性组合出这么多个特征
n_repeated=3, # 用 n_informative+n_redundant 线性组合出这么多个特征
n_classes=2,
flip_y=0.1
)
# 定义 CV 方法
cv = model_selection.KFold(n_splits=5, shuffle=True, random_state=0)
# 定义最优的标准
scoring = 'accuracy'
all_models = [
['LogisticRegression', linear_model.LogisticRegression(), dict()],
['DecisionTreeClassifier', tree.DecisionTreeClassifier(), dict()],
['DecisionTreeClassifier_deep3', tree.DecisionTreeClassifier(), {'max_depth': [3, 4, 5]}],
['GaussianNB', naive_bayes.GaussianNB(), dict()],
# ['MultinomialNB', naive_bayes.MultinomialNB(),dict()], # 用于特征离散时使用
['BernoulliNB', naive_bayes.BernoulliNB(), dict()],
['KNeighborsClassifier', neighbors.KNeighborsClassifier(), dict()],
['LinearSVC', svm.LinearSVC(), dict()],
['SVC', svm.SVC(), dict()],
['MLPClassifier', neural_network.MLPClassifier(), dict()],
['AdaBoostClassifier', ensemble.AdaBoostClassifier(), dict()],
['GradientBoostingClassifier', ensemble.GradientBoostingClassifier(), dict()],
['RandomForestClassifier', ensemble.RandomForestClassifier(), dict()],
]
for models in all_models:
model_name = models[0]
model = models[1]
param_grid = models[2]
gscv = model_selection.GridSearchCV(estimator=model,
scoring=scoring, # 打分
param_grid=param_grid,
cv=cv, # cv 方法
return_train_score=True, # 默认不返回 train 的score
refit=False, # 默认为 True, 用最好的模型+全量数据再次训练，用 gscv.best_estimator_ 获取最好模型
n_jobs=-1)
gscv.fit(X, y)
cv_results_ = gscv.cv_results_
idx_best_test_score = (cv_results_['rank_test_score'] == 1)
models.extend([gscv.best_score_,
cv_results_['mean_test_score'][idx_best_test_score],
cv_results_['std_test_score'][idx_best_test_score],
cv_results_['mean_train_score'][idx_best_test_score],
cv_results_['std_train_score'][idx_best_test_score],
np.array(cv_results_['params'])[idx_best_test_score],
cv_results_])
print(models[0], gscv.best_score_)
# %% 输出
df = pd.DataFrame(all_models, columns=['model_name', 'model', 'param_grid', 'best_score_',
'mean_test_score', 'std_test_score', 'mean_train_score', 'std_train_score',
'best_params', 'cv_results_'])
df.sort_values('best_score_', ascending=False, inplace=True)
df_describe = df.loc[:, ['model_name', 'best_score_',
'mean_test_score', 'std_test_score', 'mean_train_score', 'std_train_score',
'best_params']]
df_describe
用最好的模型重新全量训练
best_model = df.iloc[0,:].model
best_model.fit(X, y)
回归
import numpy as np
import pandas as pd
from sklearn import linear_model, tree, naive_bayes, neighbors, svm, neural_network
from sklearn import ensemble
from sklearn import model_selection
from sklearn import datasets
from sklearn import metrics
from sklearn import datasets
X, y, coef = datasets.make_regression(n_samples=10000,
n_features=5,
n_informative=3, # 其中，3个feature是有信息的
n_targets=1, # 多少个 target
bias=1, # 就是 intercept
coef=True, # 为True时，会返回真实的coef值
noise=1,
tail_strength=1
)
# 定义 CV 方法
cv = model_selection.KFold(n_splits=5, shuffle=True, random_state=0)
# 定义最优的标准
scoring = 'r2'
all_models = [
['LinearRegression', linear_model.LinearRegression(), dict()],
['Ridge', linear_model.Ridge(), dict()],
['Lasso', linear_model.Lasso(), dict()],
['ElasticNet', linear_model.ElasticNet(), dict()],
['DecisionTreeRegressor', tree.DecisionTreeRegressor(), dict()],
['KNeighborsRegressor', neighbors.KNeighborsRegressor(), dict()],
['LinearSVR', svm.LinearSVR(), dict()],
['SVR', svm.SVR(), dict()],
['neural_network', neural_network.MLPRegressor(), dict()],
['AdaBoostRegressor', ensemble.AdaBoostRegressor(), dict()],
['GradientBoostingRegressor', ensemble.GradientBoostingRegressor(), dict()],
['RandomForestRegressor', ensemble.RandomForestRegressor(), dict()],
]
for models in all_models:
model_name = models[0]
model = models[1]
param_grid = models[2]
gscv = model_selection.GridSearchCV(estimator=model,
scoring=scoring, # 打分
param_grid=param_grid,
cv=cv, # cv 方法
return_train_score=True, # 默认不返回 train 的score
refit=False, # 默认为 True, 用最好的模型+全量数据再次训练，用 gscv.best_estimator_ 获取最好模型
n_jobs=-1)
gscv.fit(X, y)
cv_results_ = gscv.cv_results_
idx_best_test_score = (cv_results_['rank_test_score'] == 1)
models.extend([gscv.best_score_,
cv_results_['mean_test_score'][idx_best_test_score],
cv_results_['std_test_score'][idx_best_test_score],
cv_results_['mean_train_score'][idx_best_test_score],
cv_results_['std_train_score'][idx_best_test_score],
np.array(cv_results_['params'])[idx_best_test_score],
cv_results_])
print(models[0], gscv.best_score_)
#%% 输出
df = pd.DataFrame(all_models, columns=['model_name', 'model', 'param_grid', 'best_score_',
'mean_test_score', 'std_test_score', 'mean_train_score', 'std_train_score',
'best_params', 'cv_results_'])
df.sort_values('best_score_', ascending=False, inplace=True)
df_describe = df.loc[:, ['model_name', 'best_score_',
'mean_test_score', 'std_test_score', 'mean_train_score', 'std_train_score',
'best_params']]
df_describe
全量数据训练最佳模型
best_model = df.iloc[0,:].model
best_model.fit(X, y)
