如何在python中平衡训练集?

我正在尝试将baseline模型应用到我的数据集上，但是数据集是不平衡的，只有11%的数据属于positive这一类。我没有抽样就分割了数据，阳性记录的召回率很低。我想在不平衡测试数据的情况下平衡训练数据(0.5 - 0.5阳性)。有人知道怎么做吗?

#splitting train and test data
train,test = train_test_split(coupon,test_size = 0.3,random_state = 100)

##separating dependent and independent variables
cols = [i for i in coupon.columns if i not in target_col]
train_X = train[cols]
train_Y = train[target_col]
test_X = test[cols]
test_Y = test[target_col]

#Function attributes
#dataframe     - processed dataframe
#Algorithm     - Algorithm used 
#training_x    - predictor variables dataframe(training)
#testing_x     - predictor variables dataframe(testing)
#training_y    - target variable(training)
#training_y    - target variable(testing)
#cf - ["coefficients","features"](cooefficients for logistic 
#regression,features for tree based models)

#threshold_plot - if True returns threshold plot for model
def coupon_use_prediction(algorithm,training_x,testing_x,
                         training_y,testing_y,cols,cf,threshold_plot) :

#model
algorithm.fit(training_x,training_y)
predictions   = algorithm.predict(testing_x)
probabilities = algorithm.predict_proba(testing_x)
#coeffs
if   cf == "coefficients" :
    coefficients  = pd.DataFrame(algorithm.coef_.ravel())
elif cf == "features" :
    coefficients  = pd.DataFrame(algorithm.feature_importances_)

column_df     = pd.DataFrame(cols)
coef_sumry    = (pd.merge(coefficients,column_df,left_index= True,
                          right_index= True, how = "left"))
coef_sumry.columns = ["coefficients","features"]
coef_sumry    = coef_sumry.sort_values(by = "coefficients",ascending = False)

print (algorithm)
print ("\n Classification report : \n",classification_report(testing_y,predictions))
print ("Accuracy   Score : ",accuracy_score(testing_y,predictions))

问题来源StackOverflow 地址：/questions/59465731/how-to-balance-training-set-in-python