TPOT:对多类数据分类失败

Question

我不能让TPot (v0.9.2，Python2.7)处理多类数据(尽管我在TPot的文档中找不到任何东西，说它只做二进制分类)。

下面提供了一个示例。它运行到9%，然后死掉，并出现错误：

RuntimeError: There was an error in the TPOT optimization process. 
This could be because the data was not formatted properly, or because
data for a regression problem was provided to the TPOTClassifier 
object. Please make sure you passed the data to TPOT correctly.

但是将n_classes更改为2，它就可以正常运行了。

from sklearn.metrics import f1_score, make_scorer
from sklearn.datasets import make_classification
from tpot import TPOTClassifier

scorer = make_scorer(f1_score)
X, y = make_classification(n_samples=200, n_features=100,
                           n_informative=20, n_redundant=10,
                           n_classes=3, random_state=42)
tpot = TPOTClassifier(generations=10, population_size=20, verbosity=20, scoring=scorer)
tpot.fit(X, y)

Answer 1

实际上，TPOT也应该处理多类数据-- example in the docs处理的是MNIST数据集(10个类)。

该错误与f1_score有关；将您的代码保存在n_classes=3中，并要求

tpot = TPOTClassifier(generations=10, population_size=20, verbosity=2)

(即使用默认scoring='accuracy')工作正常：

Warning: xgboost.XGBClassifier is not available and will not be used by TPOT.

Generation 1 - Current best internal CV score: 0.7447422496202984                                                                                
Generation 2 - Current best internal CV score: 0.7447422496202984                                                                                  
Generation 3 - Current best internal CV score: 0.7454927186634503                                                                                   
Generation 4 - Current best internal CV score: 0.7454927186634503             
Generation 5 - Current best internal CV score: 0.7706334316090413
Generation 6 - Current best internal CV score: 0.7706334316090413
Generation 7 - Current best internal CV score: 0.7706334316090413
Generation 8 - Current best internal CV score: 0.7706334316090413
Generation 9 - Current best internal CV score: 0.7757616367372464
Generation 10 - Current best internal CV score: 0.7808898418654516

Best pipeline: 

LogisticRegression(KNeighborsClassifier(DecisionTreeClassifier(input_matrix, criterion=entropy, max_depth=3, min_samples_leaf=15, min_samples_split=12), n_neighbors=6, p=2, weights=uniform), C=0.01, dual=False, penalty=l2)

TPOTClassifier(config_dict={'sklearn.linear_model.LogisticRegression': {'penalty': ['l1', 'l2'], 'C': [0.0001, 0.001, 0.01, 0.1, 0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0], 'dual': [True, False]}, 'sklearn.decomposition.PCA': {'iterated_power': range(1, 11), 'svd_solver': ['randomized']}, 'sklearn.feature_selection.Se...ocessing.PolynomialFeatures': {'degree': [2], 'interaction_only': [False], 'include_bias': [False]}},
        crossover_rate=0.1, cv=5, disable_update_check=False,
        early_stop=None, generations=10, max_eval_time_mins=5,
        max_time_mins=None, memory=None, mutation_rate=0.9, n_jobs=1,
        offspring_size=20, periodic_checkpoint_folder=None,
        population_size=20, random_state=None, scoring=None, subsample=1.0,
        verbosity=2, warm_start=False)

使用usage suggested in the docs查询F1得分，即：

tpot = TPOTClassifier(generations=10, population_size=20, verbosity=2, scoring='f1')

再次生成您报告的错误，可能是因为f1_score中的default argument是average='binary'，这对于多类问题确实没有意义，而简单的f1只适用于二进制问题(docs)。

在scoring中显式使用F1分数的其他变体，例如f1_macro、f1_micro或f1_weighted，工作正常(未显示)。