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提问者：小点点

获取“valueError:无法将字符串转换为浮点…”用于sklearn管道


我是一个试图学习SKL的初学者。我得到一个值错误ValueError:当我运行下面的代码时，无法将字符串转换为float。我不确定原因是什么，因为OneHotEncoder在将分类变量的字符串转换为浮点值时应该没有任何问题

import json
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_transformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.ensemble import RandomForestClassifier


df = pd.read_csv('https://raw.githubusercontent.com/pplonski/datasets-for-start/master/adult/data.csv', skipinitialspace=True)
x_cols = [c for c in df.columns if c!='income']
X = df[x_cols]
y = df['income']
y = LabelEncoder().fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3)

preprocessor = ColumnTransformer(
transformers=[
    ('imputer', SimpleImputer(strategy='most_frequent'),['workclass','education','native-country']),
    ('onehot', OneHotEncoder(), ['workclass', 'education', 'marital-status',
                'occupation', 'relationship', 'race', 'sex','native-country'])
]
)

clf = Pipeline([('preprocessor', preprocessor),
                ('classifier', RandomForestClassifier())])
clf.fit(X_train, y_train)

共1个答案

匿名用户

不幸的是，当Scikit学习的SimpleIm电脑试图推算字符串变量时，它存在一个问题。在他们的github页面上有一个关于它的公开问题。

为了解决这个问题，我建议将管道分为两个步骤。一个用于替换空值，另一个用于替换其他值，如下所示：

cols_with_null = ['workclass','education','native-country']
preprocessor = ColumnTransformer(
    transformers=[
        (
            'imputer', 
            SimpleImputer(missing_values=np.nan, strategy='most_frequent'),
            cols_with_null),
    ])

preprocessor.fit(X_train)
X_train_new = preprocessor.transform(X_train)

for icol, col in enumerate(cols_with_null):
    X_train.loc[:, col] = X_train_new[:, icol]

# confirm no null values in these columns:
for col in cols_with_null:
    print('{}, null values: {}'.format(col, pd.isnull(X_train[col]).sum()))

现在您有了不带空值的X\u train，其余的应该在没有SimpleComputer的情况下工作：

preprocessor = ColumnTransformer(
transformers=[
    ('onehot', OneHotEncoder(), ['workclass', 'education', 'marital-status',
                'occupation', 'relationship', 'race', 'sex','native-country'])])

clf = Pipeline([('preprocessor', preprocessor),
                ('classifier', RandomForestClassifier())])

clf.fit(X_train, y_train)

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