在一個熊貓數據框中保存Gridsearch中的最佳參數

Question

我需要將所有參數組合和相應的精度保存在一種熊貓數據框中。

我希望，我很清楚，請指出，如果我在做任何錯誤。

示例代碼：

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使用網格搜索簡歷，以獲得最佳的參數。但是，我擔心的是，有沒有辦法，我可以將所有不同的參數組合和相應的精度保存在一個pandas數據框中，我可以將它們保存在一個CSV文件中供以後使用。

[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，我有這些值的列表，我想它的數據幀，在CSV文件保存。

len(CV_rfc.grid_scores_) 
12 

Answer 1

我發現它在互聯網上，代碼是爲Python 2中，但我固定它的蟒蛇3

這裏是跑，我在那裏發現。

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() 

OUTPUT：

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