具有一个热编码特征的自动 sklearn 中的功能和特征重要性

Question

我正在尝试使用Auto-Sklearn训练XGBoost模型。

https://automl.github.io/auto-sklearn/stable/

模型训练得很好，但是，我需要特征重要性来完善模型和报告目的。

autosklearn.classification.AutoSklearnClassifier没有可以为我做到这一点的功能。

我正在尝试从底层管道中获取功能和功能重要性分数。

我已经使用下面GitHub问题中给出的详细信息尝试了一些事情。

1） https://github.com/automl/auto-sklearn/issues/524

2） https://github.com/automl/auto-sklearn/issues/224

我也尝试使用"跟踪"python模块。这返回了超过 900,000 行代码。不知道从哪里开始。

我的代码正在进行中，但看起来像：

import pandas as pd
import numpy as np
import autosklearn.classification
import sklearn.model_selection
import sklearn.datasets
import sklearn.metrics
import datetime
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, roc_curve, auc
from sklearn import preprocessing
from sklearn.preprocessing import LabelEncoder
import eli5 as eli
import pdb
df = pd.read_csv('titanic_train.csv')
df_target = df['Survived']
drop_Attbr = ['PassengerId', 'Name', 'Ticket', 'Cabin','Survived','Sex','Embarked']
df_labels = df.drop(drop_Attbr,axis=1)
feature_types = ['categorical'] +['numerical']+(['categorical']* 2)+['numerical']
df_train, df_test, y_train, y_test = train_test_split(df_labels, df_target, test_size=1/3, random_state=42)
automl = autosklearn.classification.AutoSklearnClassifier(
        time_left_for_this_task=15,
        per_run_time_limit=5,
        ensemble_size=1,
        disable_evaluator_output=False,
        resampling_strategy='holdout',
        resampling_strategy_arguments={'train_size': 0.67},
        include_estimators=['xgradient_boosting']
    )
automl.fit(df_train, y_train,feat_type=feature_types)
y_hat = automl.predict(df_test)
a_score = sklearn.metrics.accuracy_score(y_test, y_hat)
print("Accuracy score "+str(a_score))

我正在寻找这样的结果：

Feature 1 : Feature Importance score 1;
Feature 2 : Feature Importance score 2;
Feature 3 : Feature Importance score 3;
Feature 4 : Feature Importance score 4;
Feature 5 : Feature Importance score 5;

Answer 1

试试这个！

for identifier in automl._automl._automl.model_:
    if identifier in automl.ensemble_.get_selected_model_identifiers():
        model = automl._automl._automl.models_[identifier].pipeline_._final_estimator()
        print(model.get_score())