在 pandas 数据框中将最佳参数保存在Gridsearch中 [英] Save best Params in Gridsearch in a pandas dataframe

问题描述

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框中.

I needed to save all parameter combinations and corresponding accuracies in a kind of pandas dataframe.

希望我很清楚，请指出，如果我有任何错误.

I hope, I am clear, Please point out , If I m doing any mistake.

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_

我正在sklearn中使用Grid Search CV，以获取最佳参数.但是，我担心的是，有什么办法，我可以将所有不同的参数组合和相应的精度存储在一个熊猫数据框中，然后将其保存在CSV文件中，以备后用.

I am using Grid Search CV in sklearn, to get the best parameters. But, my concern is, Is there any way, I can store all the different parametric combinations and the corresponding accuracies in a pandas dataframe that I can save in a CSV file for later on purposes.

[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，我想要一个它们的数据框，保存在一个csv文件中.

So, I have a list of these values, I want a dataframe of it, to save in a csv file.

len(CV_rfc.grid_scores_)
12

推荐答案

我在互联网上找到了它，代码是针对python 2的，但我将其修复为可在python 3上运行.

I found it over the internet, the code was for python 2, but I fixed it to run on python 3.

这是什么，我在那找到了.

Here is what, I found there.

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:

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

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