尝试按列(分层)进行分层拆分时,它返回错误。

Country     ColumnA    ColumnB   ColumnC   Label
AB            0.2        0.5       0.1       14
CD            0.9        0.2       0.6       60
EF            0.4        0.3       0.8       5
FG            0.6        0.9       0.2       15

这是我的代码:
X = df.loc[:, df.columns != 'Label']
y = df['Label']

# Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0, stratify=df.Country)

from sklearn.linear_model import LinearRegression
lm = LinearRegression()
lm.fit(X_train,y_train)
lm_predictions = lm.predict(X_test)

所以我得到如下错误:
ValueError: could not convert string to float: 'AB'

最佳答案

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

df = pd.DataFrame({
        'Country': ['AB', 'CD', 'EF', 'FG']*20,
        'ColumnA' : [1]*20*4,'ColumnB' : [10]*20*4, 'Label': [1,0,1,0]*20
    })

df['Country_Code'] = df['Country'].astype('category').cat.codes

X = df.loc[:, df.columns.drop(['Label','Country'])]
y = df['Label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0, stratify=df.Country_Code)
lm = LinearRegression()
lm.fit(X_train,y_train)
lm_predictions = lm.predict(X_test)



country中的字符串值转换为数字并将其另存为新列
创建x火车数据时,放置labely)以及字符串country


方法二

如果您将根据其进行预测的测试数据稍后出现,则您将需要一种在进行预测之前将其country转换为code的机制。在这种情况下,建议的方法是使用LabelEncoder,在该方法上可以使用fit方法将字符串编码为标签,然后使用transform编码测试数据的国家/地区。

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import preprocessing

df = pd.DataFrame({
        'Country': ['AB', 'CD', 'EF', 'FG']*20,
        'ColumnA' : [1]*20*4,'ColumnB' : [10]*20*4, 'Label': [1,0,1,0]*20
    })

# Train-Validation
le = preprocessing.LabelEncoder()
df['Country_Code'] = le.fit_transform(df['Country'])
X = df.loc[:, df.columns.drop(['Label','Country'])]
y = df['Label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0, stratify=df.Country_Code)
lm = LinearRegression()
lm.fit(X_train,y_train)

# Test
test_df = pd.DataFrame({'Country': ['AB'], 'ColumnA' : [1],'ColumnB' : [10] })
test_df['Country_Code'] = le.transform(test_df['Country'])
print (lm.predict(test_df.loc[:, test_df.columns.drop(['Country'])]))

关于python - 按列(对象)分层,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/54874639/

10-12 18:25