将网格搜索中的最佳参数保存在熊猫数据帧中

Question

我需要将所有参数组合和相应的精度保存在一种熊猫数据帧中。

我希望，我很清楚，如果我做错了什么，请指出。

示例代码为：

from sklearn.grid_search import GridSearchCV
import sklearn
from sklearn.ensemble import RandomForestClassifier

X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(iris.data, iris.target, test_size=0.3, random_state=0)
rfc = RandomForestClassifier(n_jobs=-1,max_features= 'sqrt' ,n_estimators=50, oob_score = True) 
param_grid = {
    'n_estimators': [200, 700],
    'max_features': ['auto', 'sqrt', 'log2'],
    'criterion' : ['gini', 'entropy']
}
CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)
CV_rfc.fit(X_train, y_train)
CV_rfc.grid_scores_

我在sklearn中使用网格搜索CV，以获得最佳参数。但是，我担心的是，有什么办法可以将所有不同的参数组合和相应的精度存储在熊猫数据帧中，我可以将其保存在 CSV 文件中以供以后使用。

[mean: 0.94286, std: 0.05344, params: {'criterion': 'gini', 'max_features': 'auto', 'n_estimators': 200},
 mean: 0.94286, std: 0.05344, params: {'criterion': 'gini', 'max_features': 'auto', 'n_estimators': 700},
 mean: 0.94286, std: 0.05344, params: {'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 200},
 mean: 0.94286, std: 0.05344, params: {'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 700},
 mean: 0.94286, std: 0.05344, params: {'criterion': 'gini', 'max_features': 'log2', 'n_estimators': 200},
 mean: 0.94286, std: 0.05344, params: {'criterion': 'gini', 'max_features': 'log2', 'n_estimators': 700},
 mean: 0.94286, std: 0.05344, params: {'criterion': 'entropy', 'max_features': 'auto', 'n_estimators': 200},
 mean: 0.94286, std: 0.05344, params: {'criterion': 'entropy', 'max_features': 'auto', 'n_estimators': 700},
 mean: 0.94286, std: 0.05344, params: {'criterion': 'entropy', 'max_features': 'sqrt', 'n_estimators': 200},
 mean: 0.94286, std: 0.05344, params: {'criterion': 'entropy', 'max_features': 'sqrt', 'n_estimators': 700},
 mean: 0.94286, std: 0.05344, params: {'criterion': 'entropy', 'max_features': 'log2', 'n_estimators': 200},
 mean: 0.94286, std: 0.05344, params: {'criterion': 'entropy', 'max_features': 'log2', 'n_estimators': 700}]

所以，我

有一个这些值的列表，我想要它的数据帧，保存在csv文件中。

len(CV_rfc.grid_scores_)
12

Answer 1

我在互联网上找到了它，代码是针对 python 2 的，但我修复了它以在 python 3 上运行。

这就是我在那里找到的。

import pandas as pd
from sklearn.grid_search import GridSearchCV
import numpy as np
class EstimatorSelectionHelper:
    def __init__(self, models, params):
        if not set(models.keys()).issubset(set(params.keys())):
            missing_params = list(set(models.keys()) - set(params.keys()))
            raise ValueError("Some estimators are missing parameters: %s" % missing_params)
        self.models = models
        self.params = params
        self.keys = models.keys()
        self.grid_searches = {}
    def fit(self, X, y, cv=3, n_jobs=1, verbose=1, scoring=None, refit=False):
        for key in self.keys:
            print("Running GridSearchCV for %s." % key)
            model = self.models[key]
            params = self.params[key]
            gs = GridSearchCV(model, params, cv=cv, n_jobs=n_jobs, 
                              verbose=verbose, scoring=scoring, refit=refit)
            gs.fit(X,y)
            self.grid_searches[key] = gs    
    def score_summary(self, sort_by='mean_score'):
        def row(key, scores, params):
            d = {
                 'estimator': key,
                 'min_score': min(scores),
                 'max_score': max(scores),
                 'mean_score': np.mean(scores),
                 'std_score': np.std(scores),
            }
            return pd.Series({**params,**d})
        rows = [row(k, gsc.cv_validation_scores, gsc.parameters) 
                     for k in self.keys
                     for gsc in self.grid_searches[k].grid_scores_]
        df = pd.concat(rows, axis=1).T.sort_values([sort_by], ascending=False)
        columns = ['estimator', 'min_score', 'mean_score', 'max_score', 'std_score']
        columns = columns + [c for c in df.columns if c not in columns]
        return df[columns]
from sklearn import datasets
iris = datasets.load_iris()
X_iris = iris.data
y_iris = iris.target
from sklearn.ensemble import (ExtraTreesClassifier, RandomForestClassifier, 
                              AdaBoostClassifier, GradientBoostingClassifier)
from sklearn.svm import SVC
models = {'RandomForestClassifier': RandomForestClassifier()}
params = {'RandomForestClassifier': { 'n_estimators': [16, 32],
                                      'max_features': ['auto', 'sqrt', 'log2'],
                                      'criterion' : ['gini', 'entropy'] }}
helper = EstimatorSelectionHelper(models, params)
helper.fit(X_iris, y_iris)
helper.score_summary()

输出：

Running GridSearchCV for RandomForestClassifier.
Fitting 3 folds for each of 12 candidates, totalling 36 fits
[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    1.7s finished
Out[31]:
estimator   min_score   mean_score  max_score   std_score   criterion   max_features    n_estimators
1   RandomForestClassifier  0.921569    0.96732 1   0.0333269   gini    auto    32
6   RandomForestClassifier  0.921569    0.96732 1   0.0333269   entropy auto    16
10  RandomForestClassifier  0.941176    0.966912    0.980392    0.0182045   entropy log2    16
2   RandomForestClassifier  0.901961    0.960784    1   0.0423578   gini    sqrt    16
4   RandomForestClassifier  0.921569    0.960376    0.980392    0.0274454   gini    log2    16
7   RandomForestClassifier  0.921569    0.960376    0.980392    0.0274454   entropy auto    32
8   RandomForestClassifier  0.921569    0.960376    0.980392    0.0274454   entropy sqrt    16
9   RandomForestClassifier  0.921569    0.960376    0.980392    0.0274454   entropy sqrt    32
3   RandomForestClassifier  0.941176    0.959967    0.980392    0.0160514   gini    sqrt    32
0   RandomForestClassifier  0.901961    0.95384 0.980392    0.0366875   gini    auto    16
11  RandomForestClassifier  0.901961    0.95384 0.980392    0.0366875   entropy log2    32
5   RandomForestClassifier  0.921569    0.953431    0.980392    0.0242635   gini    log2    32