'DataFrame' 对象没有属性 '_check_fit_params'

问题描述

为了根据accuracy score去掉特征，我写了下面的代码，这里'ABCDEFGHIJKLMNO'是列（特征），一共15个特征。

features = 'ABCDEFGHIJKLMNO'

for i in range(0,len(features)):
    
    pipeline = PMMLPipeline
    ([
    ('mapper',DataFrameMapper([(X_train.columns.drop([features[i:i+1]]).values)])),('pca',PCA(n_components=3)),('classifier',DecisionTreeClassifier())
    ])
    
    pipeline.fit(training_data.drop([features[i:i+1]],axis=1),training_data['Class'])
    
    result = pipeline.predict(X_test)
    actual = np.concatenate(y_test.values)
    
    print("Dropped feature: {},Accuracy: {}".format(features[i:i+1],metrics.accuracy_score(actual,result)))

我正在使用 sklearn2pmml.pipeline 库，但在拟合数据时出现以下错误。我无法弄清楚为什么？

解决方法

似乎您的 PMMLPipeline 缩进错误，而且您很可能不需要 DataFrameMapper，因为它是（根据 help page）：

DataFrameMapper，一个用于将 Pandas 数据框列映射到的类 不同的sklearn转换

您并没有以不同的方式应用转换，因此我们不需要那样。

设置示例数据集，例如：

from sklearn2pmml.pipeline import PMMLPipeline
from sklearn_pandas import DataFrameMapper
from sklearn.decomposition import PCA
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

features = 'ABCDEFGHIJKLMNO'

X = pd.DataFrame(np.random.uniform(0,1,(50,15)),columns=[i for i in features])
y = np.random.binomial(1,0.5,50)

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)

并且运行更正后的代码可以正常工作：

for i in range(0,len(features)):
    
    pipeline = PMMLPipeline([
    ('pca',PCA(n_components=3)),('classifier',DecisionTreeClassifier())
    ])
    
    pipeline.fit(X_train.drop([features[i:i+1]],axis=1),y_train)
    
    result = pipeline.predict(X_test.drop([features[i:i+1]],axis=1))
    actual = y_test
    
    print("Dropped feature: {},Accuracy: {}".format(features[i:i+1],accuracy_score(actual,result)))


Dropped feature: A,Accuracy: 0.9333333333333333
Dropped feature: B,Accuracy: 0.6
Dropped feature: C,Accuracy: 0.7333333333333333
Dropped feature: D,Accuracy: 0.6
Dropped feature: E,Accuracy: 0.6666666666666666
Dropped feature: F,Accuracy: 0.6666666666666666
Dropped feature: G,Accuracy: 0.6
Dropped feature: H,Accuracy: 0.8
Dropped feature: I,Accuracy: 0.6666666666666666
Dropped feature: J,Accuracy: 0.6666666666666666
Dropped feature: K,Accuracy: 0.7333333333333333
Dropped feature: L,Accuracy: 0.8
Dropped feature: M,Accuracy: 0.6
Dropped feature: N,Accuracy: 0.8
Dropped feature: O,Accuracy: 0.6666666666666666