PythonM透镜系综: KeyError：“[Int64Index([.dtype=‘in 64’，length=105)]都不在[列]中。”

Question

下面是我得到此错误的代码的一个小版本: KeyError：“[Int64Index(.，dtype=‘in 64’)]没有列中”

“.”是一系列似乎与我的X和y数据格式的索引相匹配的数字。

我正在使用M透镜包在一个非常大的数据集中使用SuperLearner进行建模(因此可伸缩性很重要)。我的目标是使用dataframe结构而不是Numpy数组。这将解决下游问题。

到目前为止，我已经探索过this和其他相关的文章，但是解决方案似乎不适用于这里。

数据集是在这里作为.csv：<https://datahub.io/machine-learning/iris#data/找到的Iris数据集。

注意，自定义随机森林函数运行良好。但是镜头/超级学习者的错误。

from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from mlens.ensemble.super_learner import SuperLearner
import numpy as np
import pandas as pd

df = pd.read_csv("/home/marktest/iris_csv.csv")
type(df)
N_FOLDS = 5
RF_ESTIMATORS = 100
RANDOM_STATE = 42
class RFBasedFeatureSelector(BaseEstimator):
  
    def __init__(self, n_estimators):
        self.n_estimators = n_estimators
        self.selector = None

    def fit(self, X, y):
        clf = RandomForestClassifier(n_estimators=self.n_estimators, random_state = RANDOM_STATE, class_weight = 'balanced')
        clf = clf.fit(X, y)
        self.selector = SelectFromModel(clf, prefit=True, threshold = 0.01)

    def transform(self, X):
        if self.selector is None:
            raise AttributeError('The selector attribute has not been assigned. You cannot call transform before first calling fit or fit_transform.')
        return self.selector.transform(X)

    def fit_transform(self, X, y):
        self.fit(X, y)
        return self.transform(X)
df.head()
X = df.iloc[:,0:3]                                    # split off features into new dataframe
y = df.iloc[:,4]                                     # split off outcome into new dataframe

X, X_val, y, y_val = train_test_split(X, y, test_size=.3, random_state=RANDOM_STATE, stratify=y)
from mlens.metrics import make_scorer
from sklearn.metrics import roc_auc_score, balanced_accuracy_score
accuracy_scorer = make_scorer(roc_auc_score, average='micro', greater_is_better=True)

clf = RandomForestClassifier(RF_ESTIMATORS, random_state=RANDOM_STATE,class_weight='balanced')
scaler = StandardScaler()
feature_selector = RFBasedFeatureSelector(RF_ESTIMATORS)
clf.fit(feature_selector.fit_transform(scaler.fit_transform(X), y), y)
accuracy_score(y_val, clf.predict(feature_selector.transform(scaler.transform(X_val))))

ensemble = SuperLearner(folds=N_FOLDS, shuffle=True, random_state=RANDOM_STATE, scorer=balanced_accuracy_score, backend="threading")

preprocessing = {'pipeline-1': [StandardScaler(), RFBasedFeatureSelector(RF_ESTIMATORS)]
                 
                }

estimators = {'pipeline-1': [RandomForestClassifier(RF_ESTIMATORS, random_state=RANDOM_STATE, class_weight='balanced'),                 
                                         ]
                 }

ensemble.add(estimators, preprocessing)

ensemble.add_meta(LogisticRegression(solver='liblinear', class_weight = 'balanced'))
ensemble.fit(X,y)```

Answer 1

我认为问题出在shuffle=True。我也遇到了类似的问题，当设置为‘霉运=假’时，它将不再给出错误消息。