'DataFrame' 对象没有属性 '_check_fit_params'答案

【问题标题】：'DataFrame' object has no attribute '_check_fit_params''DataFrame' 对象没有属性 '_check_fit_params'
【发布时间】：2021-07-14 16:24:03
【问题描述】：

为了根据准确率分下降特征，我写了如下代码，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？

【问题讨论】：

标签： python python-3.x dataframe scikit-learn pmml

【解决方案1】：

似乎您的PMMLPipeline 缩进错误，很可能您不需要DataFrameMapper，因为它是（根据help page）：

DataFrameMapper，用于将 pandas 数据框列映射到的类不同的 sklearn 转换

你没有以不同的方式应用转换，所以我们不需要。

设置一个示例数据集，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)

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

【讨论】：