运行报错

[xycim]

请教一下大佬这个问题如何解决：
Error rate: 0.0
D:\Anaconda3\envs\py27\lib\site-packages\sklearn\metrics\ranking.py:542: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless
UndefinedMetricWarning)
auc: nan

[0012sheep1]

我也是，有哪位大神解决了？

[LaplaceZhang]

pos_label设置有问题，我用的 pos_label = 0因为有多个标签，其他类设定 1，2，3类推

  fpr, tpr, thresholds = metrics.roc_curve(y_true=y_test, y_score=pred, pos_label=1)' 

[gyxkhkl]

是直接把pos_label=1改成pos_label=0吗，还是说改成 fpr, tpr, thresholds = metrics.roc_curve(y_true=y_test, y_score=pred, pos_label=0)' ；fpr1, tpr1, thresholds = metrics.roc_curve(y_true=y_test, y_score=pred, pos_label=1)' ；

[LaplaceZhang]

这里面取决于你数据里的 pos_label 是哪个吧。具体细节记不清了，可以参考一下 metrics.roc_curve 的使用: Link

[wanzhixiao]

修改了TransferLearningGame.py, 以flag列作为标签，可以运行。但是AUC很低

# cody by chenchiwei
# -*- coding: UTF-8 -*-
import pandas as pd
from sklearn import preprocessing
from sklearn import decomposition
import trad1 as tr
import TrAdaboost as trd
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import svm
from sklearn import feature_selection
from sklearn import model_selection
from sklearn import metrics

import numpy as np


def append_feature(dataframe, istest):
    lack_num = np.asarray(dataframe.isnull().sum(axis=1))
    # lack_num = np.asarray(dataframe..sum(axis=1))
    if istest:
        X = dataframe.values
        X = X[:, 1:X.shape[1]]
    else:
        X = dataframe.values
        X = X[:, 1:X.shape[1] - 1]
    total_S = np.sum(X, axis=1)
    var_S = np.var(X, axis=1)
    X = np.c_[X, total_S]
    X = np.c_[X, var_S]
    X = np.c_[X, lack_num]

    return X


train_df = pd.DataFrame(pd.read_csv("new-data/A_train.csv"))
# train_df.fillna(value=-999999,inplace=True)
train_df1 = pd.DataFrame(pd.read_csv("new-data/B_train.csv"))
# train_df1.fillna(value=-999999,inplace=True)
test_df = pd.DataFrame(pd.read_csv("new-data/B_test.csv"))
# test_df.fillna(value=-999999,inplace=True)


train_df['label'] = train_df['flag']
train_df1['label']  = train_df1['flag']

train_df = train_df.drop('flag',axis=1)
train_df1 = train_df1.drop('flag',axis=1)

train_data_T = train_df.values
train_data_S = train_df1.values
test_data_S = test_df.values

print('data loaded.')

label_T = train_data_T[:, train_data_T.shape[1] - 1]
trans_T = train_data_T[:, 1:train_data_T.shape[1] - 1]
trans_T = append_feature(train_df, istest=False)

label_S = train_data_S[:, train_data_S.shape[1] - 1]
trans_S = train_data_S[:, 1:train_data_S.shape[1] - 1]
trans_S = append_feature(train_df1, istest=False)

test_data_no = test_data_S[:, 0]
# test_data_S = test_data_S[:, 1:test_data_S.shape[1]]
test_data_S = append_feature(test_df, istest=True)

print('data split end.', trans_S.shape, trans_T.shape, label_S.shape, label_T.shape, test_data_S.shape)

# # 加上和、方差、缺失值数量的特征，效果有所提升
# trans_T = append_feature(trans_T, train_df)
# trans_S = append_feature(trans_S, train_df1)
# test_data_S = append_feature(test_data_S, test_df)
#
# print 'append feature end.', trans_S.shape, trans_T.shape, label_S.shape, label_T.shape, test_data_S.shape

imputer_T = preprocessing.Imputer(missing_values='NaN', strategy='most_frequent', axis=0)
imputer_S = preprocessing.Imputer(missing_values='NaN', strategy='most_frequent', axis=0)
# imputer_T.fit(trans_T,label_T)
imputer_S.fit(trans_S, label_S)

trans_T = imputer_S.transform(trans_T)
trans_S = imputer_S.transform(trans_S)

test_data_S = imputer_S.transform(test_data_S)

# pca_T = decomposition.PCA(n_components=50)
# pca_S = decomposition.PCA(n_components=50)
#
# trans_T = pca_T.fit_transform(trans_T)
# trans_S = pca_S.fit_transform(trans_S)
# test_data_S = pca_S.transform(test_data_S)

print('data preprocessed.', trans_S.shape, trans_T.shape, label_S.shape, label_T.shape, test_data_S.shape)

X_train, X_test, y_train, y_test = model_selection.train_test_split(trans_S, label_S, test_size=0.33, random_state=42)

# feature scale
# scaler = preprocessing.StandardScaler()
# X_train = scaler.fit_transform(X_train, y_train)
# X_test = scaler.transform(X_test)
# print 'feature scaled end.'

pred = tr.tradaboost(X_train, trans_T, y_train, label_T, X_test, 10)
fpr, tpr, thresholds = metrics.roc_curve(y_true=y_test, y_score=pred, pos_label=1)
print(y_test)
print(pred)
print('auc:', metrics.auc(fpr, tpr))

# model = trd.TrAdaboost()
#
# model.fit(X_train, trans_T, y_train, label_T)
# predict = model.predict(X_test)