我正在尝试使用pymc3执行贝叶斯逻辑回归，但是在使用模型执行预测时遇到了一个问题。
数据：
我的数据集是房屋贷款违约数据的示例数据，如下所示：
BAD LOAN MORTDUE VALUE REASON JOB YOJ DEROG DELINQ CLAGE NINQ CLNO DEBTINC
1 1700 0548 40320 HomeImp Other 9 0 0 101.466002 1 8 37.113614
1 1800 28502 43034 HomeImp Other 11 0 0 88.766030 0 8 36.884894
0 2300 102370 120953 HomeImp Office 2 0 0 90.992533 0 13 31.588503
问题：
我想对测试数据集进行预测，一种方法是使用共享变量方法：
X_shared = theano.shared(X_train)
with pm.Model() as logistic_model_pred:
pm.glm.GLM(x=X_shared,
y=y_train,
labels=labels,
family=pm.glm.families.Binomial())
X_shared.set_value(X_test)
ppc = pm.sample_ppc(pred_trace,
model=logistic_model_pred,
samples=100)
但是，使用上面的代码（theano共享变量）会导致以下问题：
错误消息：
AsTensorError: ('Variable type field must be a TensorType.', <Generic>, <theano.gof.type.Generic object at 0x00000216ABB16730>)
可能的解决方案：
使用以下代码可以解决问题，但是我不知道如何对测试数据使用相同的模型。
with pm.Model() as logistic_model_pred:
pm.glm.GLM.from_formula('BAD ~ DELINQ + DEROG + DEBTINC + NINQ + CLNO + VALUE + MORTDUE + YOJ + LOAN + CLAGE + JOB',
data=pd.concat([y_train.reset_index(drop=True), X_train], axis=1),
family=pm.glm.families.Binomial())
pred_trace = pm.sample(tune=1500,
draws=1000,
chains=4,
cores=1,
init='adapt_diag')
完整代码：
%matplotlib inline
from pathlib import Path
import pickle
from collections import OrderedDict
import pandas as pd
import numpy as np
from scipy import stats
import multiprocessing
import arviz as az
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import (roc_curve, roc_auc_score, confusion_matrix, accuracy_score, f1_score,
precision_recall_curve, balanced_accuracy_score)
from mlxtend.plotting import plot_confusion_matrix
import theano
import pymc3 as pm
from pymc3.variational.callbacks import CheckParametersConvergence
import statsmodels.formula.api as smf
import arviz as az
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import seaborn as sns
from IPython.display import HTML
import sys
if not sys.warnoptions:
import warnings
warnings.simplefilter("ignore")
# intialise data of lists.
data = {'BAD':[1,1,0,1,0,0,0,1,1,0,0,1,0,0,1,0,1],
'LOAN':[1700,1800,2300,2400,2400,2900,2900,2900,2900,
3000,3200,3300,3600,3600,3700,3800,3900],
'MORTDUE':[30548,28502,102370,34863,98449,103949,104373,7750,61962,104570,
74864,130518,100693,52337,17857,51180,29896],
'VALUE':[40320,43034,120953,47471,117195,112505,120702,67996,70915,121729,
87266,164317,114743,63989,21144,63459,45960],
'REASON':['HomeImp','HomeImp','HomeImp','HomeImp','HomeImp',
'HomeImp','HomeImp','HomeImp',
'DebtCon','HomeImp','HomeImp','DebtCon','HomeImp','HomeImp',
'HomeImp','HomeImp','HomeImp'],
'JOB':['Other','Other','Office','Mgr','Office','Office','Office',
'Other','Mgr','Office','ProfExe',
'Other','Office','Office','Other','Office','Other'],
'YOJ':[9,11,2,12,4,1,2,16,2,2,7,9,6,20,5,20,11],
'DEROG':[0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0],
'DELINQ':[0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0],
'CLAGE':[101.4660019,88.76602988,90.99253347,70.49108003,
93.81177486,96.10232967,101.5402975,
122.2046628,282.8016592,85.8843719,250.6312692,
192.289149,88.47045214,204.2724988,
129.7173231,203.7515336,146.1232422],
'NINQ':[1,0,0,1,0,0,0,2,3,0,0,0,0,0,1,0,0],
'CLNO':[8,8,13,21,13,13,13,8,37,14,12,33,14,20,9,20,14],
'DEBTINC':[37.11361356,36.88489409,31.58850318,38.26360073,
29.68182705,30.05113629,29.91585903,
36.211348,49.20639579,32.05978327,42.90999735,
35.73055919,29.39354338,20.47091551,
26.63434752,20.06704205,24.47888119]
}
# Create DataFrame
data = pd.DataFrame(data)
# datatype defining
data[['BAD', 'LOAN', 'MORTDUE', 'VALUE', 'YOJ', 'DEROG', 'DELINQ',
'CLAGE', 'NINQ', 'CLNO', 'DEBTINC']] = data[['BAD', 'LOAN', 'MORTDUE',
'VALUE', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC' ]].apply(pd.to_numeric)
data[['REASON', 'JOB']] = data[['REASON', 'JOB']].apply(lambda x: x.astype('category'))
print(data.dtypes)
data.dropna(axis=0, how='any',inplace=True)
# test train split
X = data.drop('BAD', axis=1)
y = data.BAD
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345)
labels = X_train.columns
# model training (error cause)
X_shared = theano.shared(X_train)
with pm.Model() as logistic_model_pred:
pm.glm.GLM(x=X_shared,
y=y_train,
labels=labels,
family=pm.glm.families.Binomial())
# Prediction on test data
X_shared = theano.shared(X_test)
ppc = pm.sample_ppc(pred_trace,
model=logistic_model_pred,
samples=100)
# AUC
np.mean(ppc['y'], axis=0).shape
y_score = np.mean(ppc['y'], axis=0)
roc_auc_score(y_score=np.mean(ppc['y'], axis=0),
y_true=y_test)
pred_scores = dict(y_true=y_test, y_score=y_score)
cols = ['False Positive Rate', 'True Positive Rate', 'threshold']
roc = pd.DataFrame(dict(zip(cols, roc_curve(**pred_scores))))
precision, recall, ts = precision_recall_curve(y_true=y_test, probas_pred=y_score)
pr_curve = pd.DataFrame({'Precision': precision, 'Recall': recall})
f1 = pd.Series({t: f1_score(y_true=y_test, y_pred=y_score>t) for t in ts})
best_threshold = f1.idxmax()
# Alternative solution
with pm.Model() as logistic_model_pred:
pm.glm.GLM.from_formula('BAD ~ DELINQ + DEROG + DEBTINC + NINQ +
CLNO + VALUE + MORTDUE + YOJ + LOAN + CLAGE + JOB',
data=pd.concat([y_train.reset_index(drop=True), X_train], axis=1),
family=pm.glm.families.Binomial())
pred_trace = pm.sample(tune=1500,
draws=1000,
chains=4,
cores=1,
init='adapt_diag')
答案 0 :(得分：0)
如果用以下内容替换# model training (error cause)和# AUC注释之间的代码，则应该可以运行它并开始获得一些结果：
# model training (error cause)
X_train2 = X_train[X_train.columns[0:3]].values
scaler = preprocessing.StandardScaler()
scaler.fit(X_train2)
X_train2 = scaler.transform(X_train2)
X_shared = theano.shared(X_train2) #theano.shared(X_train)
with pm.Model() as logistic_model_pred:
pm.glm.GLM(x=X_shared,
y=y_train.values,
labels=labels[0:3],
family=pm.glm.families.Binomial())
trace = pm.sample()
# Prediction on test data
X_test2 = scaler.transform(X_test[X_train.columns[0:3]].values)
#X_shared = theano.shared(X_test2)
X_shared.set_value(X_test2)
ppc = pm.sample_ppc(trace,
model=logistic_model_pred,
samples=100)
# AUC
我进行了以下更改：
theano.shared中的变量更改为一个numpy数组。X_train中的字符串列需要使用one hot encoding或类似方法进行转换。我还没做完，所以我只使用了前三列，它们恰好是数字.set_value更改了共享变量的值采样有很多差异，但我认为以上内容为您提供了设置。