0. 比赛简介
竞赛时间：4月29日9:00-5月12日17:00
竞赛流程：4月29日9:00-5月9日24:00，赛题开放A榜数据（test_A榜），预测结果数据每天限提交3次；5月10日00:00-5月12日17:00，赛题开放B榜数据（test_B榜），预测结果数据每天限提交3次。重复提交或提交格式错误均扣除有效提交次数，请谨慎提交答案，结果提交后请务必点击“运行”按钮，方可查看当前个人排名。
排行榜依据“最终得分”计算排名，“最终得分”计算公式为：A榜最高分 * 0.3 + B榜最高分 * 0.7。“最终得分”越大，成绩排名越前。
该竞赛就是一个类似于kaggle的数据科学赛题，基于银行的脱敏金融数据，对客户是否存款进行预测（二分类预测），评价指标为AUC（Area Under Curve）。本文主要对数据分析和一些常用的机器学习trick进行分享。
1. 数据预处理
训练数据主要是由49个特征和LABEL（0和1）组成，其中特征有数值型（int or float）、字符型（str），还有 "?" 和大量的2，由于机器学习模型只能识别数值型数据，需要数据进行预处理。
1.1 缺失值转换为np.nan
import pandas as pd, numpy as np
train = pd.read_excel('fintech训练营/train.xlsx')
test = pd.read_excel('fintech训练营/test_A榜.xlsx')
datasets = [train,test]
for dataset in datasets:
for i in dataset.columns:
# 将"?"转换为空值np.nan
dataset[i] = dataset[i].apply(lambda x : np.nan if x=='?' else x)1.2 类别型特征编码
from sklearn.preprocessing import LabelEncoder
label = LabelEncoder()
for dataset in datasets:
for i in dataset.columns:
if dataset[i].dtype == 'object':
if i != 'CUST_UID':
dataset[i] = label.fit_transform(dataset[i]) # 比如"A", "B"转换成1, 22. 数据探索
2.1 判断是否数据不平衡
train['LABEL'].value_counts() LABEL
0 30000
1 10000
Name: LABEL, dtype: int64
由图可见0和1的比例为3:1，一般大于10:1时为数据不平衡，故不需要进行数据增强。
2.2 异常值
import matplotlib.pyplot as plt
import seaborn as sns
cols = 4
rows = 13
plt.figure(figsize = (4*cols, 3*rows))
i=1
for col in train.columns[2:]:
if train[col].dtype != 'object':
ax = plt.subplot(rows, cols, i)
ax = sns.boxplot(train[col], orient = 'v', width = 0.5)
ax.set_xlabel(col)
ax.set_ylabel('frequency')
ax = ax.legend(['train'])
i += 1
plt.tight_layout()
plt.show()
从箱型图中可以看到每个特征的异常值分布，有助于我们进一步分析。
2.3 核密度分布图
cols = 4
rows = 13
plt.figure(figsize = (4*cols, 3*rows))
i = 1
for col in train.columns[2:]:
ax = plt.subplot(rows,cols,i)
ax = sns.kdeplot(train[col].dropna(), color = 'red', shade = True)
ax = sns.kdeplot(test[col].dropna(), color = 'blue', shade = True)
ax.set_xlabel(col)
ax.set_ylabel('frequency')
ax = ax.legend(['train','test'])
i += 1
plt.tight_layout()
plt.show()
从核密度分布图可以看出每个特征的分布（正态、双尖峰等）情况，有助于我们进一步分析。
2.4 对抗验证
如果训练集和测试集数据分布不同，模型训练可能会因为过拟合导致泛化性较差。我们通过给训练集构造label=1，测试集label=0，选用机器学习模型，用所有特征来预测label，通过交叉验证得到AUC（Area Under Curve），判断如下:
（1）如果AUC过高，则存在训练集和测试集分布不同的特征，(该特征能准确判断训练集和测试集的差异，即数据漂移dataset shift，影响模型预测效果) , 则删除特征重要性排名较前的特征，然后再次预测
（2）重复步骤1直至AUC达到0.5~0.6左右
from sklearn.model_selection import train_test_split
train_new = train.copy()
test_new = test.copy()
train_new = train_new.drop(['CUST_UID','LABEL'], axis = 1) # 只保留特征
test_new = test_new.drop(['CUST_UID'], axis = 1) # 只保留特征
train_new['label'] = 1 # 训练集标签1
test_new['label'] = 0 # 测试集标签0
data = pd.concat([train_new,test_new], axis = 0)
test_size_pct = 0.2 # 划分训练集和测试集
X_train, X_valid, y_train, y_valid = train_test_split(data.drop(['label'], axis = 1), data['label'], test_size = test_size_pct, random_state = 42)
接下来选用LightGBM（机器学习模型，以下简称LGBM，下载命令：pip install lightgbm）进行训练：
from lightgbm import LGBMClassifier
from lightgbm import log_evaluation, early_stopping
lgb = LGBMClassifier(verbosity = -1)
lgb.fit(X_train, y_train, eval_set = [(X_valid, y_valid)], eval_metric = ['auc'],
callbacks = [log_evaluation(period = 50), early_stopping(stopping_rounds = 128)])Training until validation scores don't improve for 128 rounds
[50] valid_0's auc: 0.500989 valid_0's binary_logloss: 0.534569
[100] valid_0's auc: 0.504464 valid_0's binary_logloss: 0.536658
Did not meet early stopping. Best iteration is:
[6] valid_0's auc: 0.510417 valid_0's binary_logloss: 0.532726
接下来用LGBM进行预测分析：
from sklearn.metrics import roc_auc_score
pred_lgb = lgb.predict_proba(X_valid)[:,1]
roc_auc_score(y_valid, pred_lgb)0.510416950256567
AUC=0.51说明训练集和测试集特征分布近似，不需要删除特征。
3. 模型预测
3.1 模型初步训练
我们使用lgb对训练集的预测效果进行初步了解：
from sklearn.model_selection import train_test_split
from lightgbm import LGBMClassifier
from sklearn.metrics import roc_auc_score
ignore = ['CUST_UID','LABEL']
features = [feat for feat in train.columns if feat not in ignore]
target_feature = 'LABEL'
test_size_pct = 0.10
X_train, X_valid, y_train, y_valid = train_test_split(train[features], train[target_feature], test_size = test_size_pct, random_state = 42)
lgb = LGBMClassifier(verbosity = -1)
lgb.fit(X_train, y_train, eval_set = [(X_valid, y_valid)], eval_metric = ['auc'],
callbacks = [log_evaluation(period = 50), early_stopping(stopping_rounds = 128)])
pred_lgb = lgb.predict_proba(X_valid)[:,1]
roc_auc_score(y_valid, pred_lgb)Training until validation scores don't improve for 128 rounds
[50] valid_0's auc: 0.949571 valid_0's binary_logloss: 0.240469
[100] valid_0's auc: 0.949393 valid_0's binary_logloss: 0.240118
Did not meet early stopping. Best iteration is:
[63] valid_0's auc: 0.949871 valid_0's binary_logloss: 0.239181
0.9498706534679444
3.2 K折交叉验证
k折交叉验证（K-Fold Cross-Validation）是一种用于估计机器学习模型性能的统计方法。它是一种评估统计分析结果如何推广到独立数据集的方法，可以较大程度避免过拟合，增强模型的泛化性。详细了解可参考：https://blog.csdn.net/Rocky6688/article/details/107296546
from sklearn.model_selection import StratifiedKFold
from lightgbm import LGBMClassifier
lgb = LGBMClassifier(learning_rate = 0.05, max_depth = 20, num_leaves = 100, random_state = 1000, verbosity = -1)
strtfdKFold = StratifiedKFold(n_splits = 5, random_state = 100, shuffle = True)
# 把特征和标签传递给StratifiedKFold实例
X_train = train[features]
y_train = train[target_feature]
kfold = strtfdKFold.split(X_train, y_train)
scores = []
for k, (train1, test1) in enumerate(kfold):
lgb.fit(X_train.iloc[train1,:], y_train.iloc[train1])
pred_lgb = lgb.predict_proba(X_train.iloc[test1, :])[:,1]
score = roc_auc_score(y_train.iloc[test1], pred_lgb)
scores.append(score)
print('Fold: %2d, Training/Test Split Distribution: %s, AUC: %s' % (k+1, np.bincount(y_train.iloc[train1]), score))
print('Cross-Validation AUC: %s +/- %s' %(np.mean(scores), np.std(scores)))Fold: 1, Training/Test Split Distribution: [24000 8000], AUC: 0.9490365000000001
Fold: 2, Training/Test Split Distribution: [24000 8000], AUC: 0.9481471250000001
Fold: 3, Training/Test Split Distribution: [24000 8000], AUC: 0.9523520416666665
Fold: 4, Training/Test Split Distribution: [24000 8000], AUC: 0.9509735416666666
Fold: 5, Training/Test Split Distribution: [24000 8000], AUC: 0.9490646666666667
Cross-Validation AUC: 0.9499147749999999 +/- 0.0015283908750980848
上述模型参数只是随机设定，可以看出模型的Cross Validation（以下简称CV）分数为0.9499，可以看出模型的初始效果已经挺不错了。
3.3 多模型预测
3.3.1 单模型效果检验
我们这里对LightGBM、XGBoost、CatBoost、HistGradientBoostingClassifier四种效果较好的树模型进行预测分析：
from sklearn import model_selection, ensemble
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from catboost import CatBoostClassifier
from tqdm import tqdm
vote_est = [
#Ensemble Methods: http://scikit-learn.org/stable/modules/ensemble.html
('hgbc', ensemble.HistGradientBoostingClassifier(random_state = 42)),
#lightbgm
('lgb', LGBMClassifier(verbosity = -1, random_state = 42)),
#xgboost: http://xgboost.readthedocs.io/en/latest/model.html
('xgb', XGBClassifier(verbosity = 0, random_state = 42)),
('cbc', CatBoostClassifier(verbose = 0, random_state = 42))
]
MLA_columns = ['MLA Name','MLA Train AUC Mean', 'MLA Test AUC Mean', 'MLA Test AUC 3*STD' ,'MLA Time']
MLA_compare = pd.DataFrame(columns = MLA_columns)
row_index = 0
cv_split = model_selection.ShuffleSplit(n_splits = 5, test_size = 0.2, train_size = 0.8, random_state = 0)
for i in tqdm(vote_est):
model = i[1]
MLA_compare.loc[row_index, 'MLA Name'] = i[0]
cv_results = model_selection.cross_validate(model, train[features], train[target_feature], cv = cv_split, scoring = 'roc_auc', return_train_score = True)
MLA_compare.loc[row_index, 'MLA Time'] = cv_results['fit_time'].mean()
MLA_compare.loc[row_index, 'MLA Train AUC Mean'] = cv_results['train_score'].mean()
MLA_compare.loc[row_index, 'MLA Test AUC Mean'] = cv_results['test_score'].mean()
#if this is a non-bias random sample, then +/-3 standard deviations (std) from the mean, should statistically capture 99.7% of the subsets
MLA_compare.loc[row_index, 'MLA Test AUC 3*STD'] = cv_results['test_score'].std()*3 # let's know the worst that can happen!
row_index += 1
del model
MLA_compare.sort_values(by = ['MLA Test AUC Mean'], ascending = False, inplace = True)
MLA_compare| MLA Name | MLA Train AUC Mean | MLA Test AUC Mean | MLA Test AUC 3*STD | MLA Time |
|---|---|---|---|---|
| lgb | 0.976829 | 0.950642 | 0.007759 | 0.365629 |
| cbc | 0.979338 | 0.950619 | 0.007465 | 14.070166 |
| hgbc | 0.970688 | 0.950087 | 0.007439 | 0.70215 |
| xgb | 0.994984 | 0.947138 | 0.007695 | 0.768368 |
我们可以看出LGBM的效果最好，而CatBoost训练+预测时间最长。
3.3.2 投票法
投票法（Voting）是集成学习里面针对分类问题的一种结合策略。是一种遵循少数服从多数原则的集成学习模型，通过多个模型的集成降低噪声和方差，从而提高模型的鲁棒性。一般情况下，投票法的预测效果应当优于单一模型的预测效果。投票法主要有两种形式：
- 硬投票：预测结果是所有投票结果最多出现的类。
- 软投票：将所有模型预测样本为某一类别的概率的平均值作为标准，概率最高的对应的类型为最终的预测结果 / 预测结果是所有投票结果中概率加和最大的类。
详细了解可参考：https://blog.csdn.net/deephub/article/details/122976720
sklearn已经在ensemble库中实现了投票法，直接用，由于评价指标为AUC，所以这里只尝试软投票的方法。
grid_soft = ensemble.VotingClassifier(estimators = vote_est, voting = 'soft')
grid_soft_cv = model_selection.cross_validate(grid_soft, train[features], train[target_feature],
scoring='roc_auc', cv = cv_split, return_train_score = True)
print("Soft Voting w/Tuned Hyperparameters Training w/bin score mean: {:.2f}". format(grid_soft_cv['train_score'].mean()*100))
print("Soft Voting w/Tuned Hyperparameters Test w/bin score mean: {:.2f}". format(grid_soft_cv['test_score'].mean()*100))
print("Soft Voting w/Tuned Hyperparameters Test w/bin score 3*std: +/- {:.2f}". format(grid_soft_cv['test_score'].std()*100*3))Soft Voting w/Tuned Hyperparameters Training w/bin score mean: 98.52
Soft Voting w/Tuned Hyperparameters Test w/bin score mean: 95.20
Soft Voting w/Tuned Hyperparameters Test w/bin score 3*std: +/- 0.71
可以看出软投票的测试集分数为0.952，高于单一模型效果最好的LGBM分数0.9506。如果删除单模效果最差的XGBoost（0.947138），投票结果是不是更好呢，效果如下：
vote_est2 = [
#Ensemble Methods: http://scikit-learn.org/stable/modules/ensemble.html
('hgbc', ensemble.HistGradientBoostingClassifier(random_state = 42)),
#lightbgm
('lgb', LGBMClassifier(verbosity = -1, random_state = 42)),
('cbc', CatBoostClassifier(verbose = 0, random_state = 42))
]
grid_soft2 = ensemble.VotingClassifier(estimators = vote_est2 , voting = 'soft')
grid_soft_cv2 = model_selection.cross_validate(grid_soft2, train[features], train[target_feature],
scoring='roc_auc', cv = cv_split, return_train_score = True)
print("Soft Voting w/Tuned Hyperparameters Training w/bin score mean: {:.2f}". format(grid_soft_cv2['train_score'].mean()*100))
print("Soft Voting w/Tuned Hyperparameters Test w/bin score mean: {:.2f}". format(grid_soft_cv2['test_score'].mean()*100))
print("Soft Voting w/Tuned Hyperparameters Test w/bin score 3*std: +/- {:.2f}". format(grid_soft_cv2['test_score'].std()*100*3))Soft Voting w/Tuned Hyperparameters Training w/bin score mean: 97.77
Soft Voting w/Tuned Hyperparameters Test w/bin score mean: 95.18
Soft Voting w/Tuned Hyperparameters Test w/bin score 3*std: +/- 0.70
可以看到，删掉XGBoost后的分数反而还下降了，可见投票法在单模效果不是很差的时候，还是尽可能增加更多的模型以增强泛化性。
3.3.3 Stacking
Stacking(堆叠泛化)是指训练一个模型用于组合 (combine)其他各个模型。即首先我们先训练多个不同的模型，然后再以之前训练的各个模型的输出为输入来训练一个模型，以得到一个最终的输出。详细了解可参考：https://blog.csdn.net/ueke1/article/details/137190677
from mlxtend.classifier import StackingCVClassifier
train_new = train.copy()
test_new = test.copy()
dataset_net = [train_new,test_new]
for dataset in dataset_net:
for i in dataset.columns:
if dataset[i].dtype != 'object':
dataset[i] = dataset[i].fillna(dataset[i].mean()) # 逻辑回归需要填充空值
hgbc = ensemble.HistGradientBoostingClassifier(random_state = 42)
lgb = LGBMClassifier(verbosity = -1, random_state = 42)
xgb = XGBClassifier(verbosity = 0, random_state = 42)
cbc = CatBoostClassifier(verbose = 0, random_state = 42)
lr = linear_model.LogisticRegressionCV()
sclf = StackingCVClassifier(classifiers = [hgbc, lgb, cbc], # 第一层分类器
meta_classifier = lr, # 第二层分类器，并非表示第二次stacking，而是通过logistic regression对新的训练特征数据进行训练，得到predicted label
cv = 5)
strtfdKFold = StratifiedKFold(n_splits = 5)
#把特征和标签传递给StratifiedKFold实例
X_train = train_new[features]
y_train = train_new[target_feature]
kfold = strtfdKFold.split(X_train, y_train)
scores = []
for k, (train1, test1) in enumerate(kfold):
sclf.fit(X_train.iloc[train1,:], y_train.iloc[train1])
pred_lgb = sclf.predict_proba(X_train.iloc[test1, :])[:,1]
score = roc_auc_score(y_train.iloc[test1], pred_lgb)
scores.append(score)
print('Fold: %2d, Training/Test Split Distribution: %s, Accuracy: %.3f' % (k+1, np.bincount(y_train.iloc[train1]), score))
print('\n\nCross-Validation accuracy: %.3f +/- %.3f' %(np.mean(scores), np.std(scores)))Fold: 1, Training/Test Split Distribution: [24000 8000], Accuracy: 0.893
Fold: 2, Training/Test Split Distribution: [24000 8000], Accuracy: 0.882
Fold: 3, Training/Test Split Distribution: [24000 8000], Accuracy: 0.883
Fold: 4, Training/Test Split Distribution: [24000 8000], Accuracy: 0.880
Fold: 5, Training/Test Split Distribution: [24000 8000], Accuracy: 0.882
Cross-Validation accuracy: 0.884 +/- 0.005
简单试了几个stacking，发现效果远远不如单模，故选用软投票。
4. 模型优化技巧
4.1 伪标签
伪标签是一种半监督学习的技术，它是指利用已标注数据训练的模型对未标注数据进行预测，然后根据预测结果为未标注数据分配类别标签，目的是帮助模型学习到无标注数据中隐藏的信息。伪标签的选取一般是基于模型预测的最大概率的类，可以用于微调模型，提高模型的泛化能力。详细了解可参考：https://zhuanlan.zhihu.com/p/157325083
以下是伪标签的类：
from sklearn.utils import shuffle
from sklearn.base import BaseEstimator, RegressorMixin
class PseudoLabeler(BaseEstimator, RegressorMixin):
def __init__(self, model, test, features, target, sample_rate=0.2, seed=42):
self.sample_rate = sample_rate
self.seed = seed
self.model = model
self.model.seed = seed
self.test = test
self.features = features
self.target = target
def get_params(self, deep=True):
return {
"sample_rate": self.sample_rate,
"seed": self.seed,
"model": self.model,
"test": self.test,
"features": self.features,
"target": self.target
}
def set_params(self, **parameters):
for parameter, value in parameters.items():
setattr(self, parameter, value)
return self
def fit(self, X, y):
if self.sample_rate > 0.0:
augemented_train = self.__create_augmented_train(X, y)
self.model.fit(
augemented_train[self.features],
augemented_train[self.target]
)
else:
self.model.fit(X, y)
return self
def __create_augmented_train(self, X, y):
num_of_samples = int(len(self.test) * self.sample_rate)
# Train the model and creat the pseudo-labels
self.model.fit(X, y)
pseudo_labels = self.model.predict(self.test[self.features])
# Add the pseudo-labels to the test set
augmented_test = self.test.copy(deep=True)
augmented_test[self.target] = pseudo_labels
# Take a subset of the test set with pseudo-labels and append in onto
# the training set
sampled_test = augmented_test.sample(n=num_of_samples)
temp_train = pd.concat([X, y], axis=1)
augemented_train = pd.concat([sampled_test, temp_train])
return shuffle(augemented_train)
def predict(self, X):
return self.model.predict(X)
def predict_proba(self, X):
return self.model.predict_proba(X)
def get_model_name(self):
return self.model.__class__.__name__伪标签的调用方法如下：
strtfdKFold = StratifiedKFold(n_splits = 5)
grid_soft = ensemble.VotingClassifier(estimators = vote_est , voting = 'soft')
X_train = train[features]
y_train = train[target_feature]
X_test = test[features]
kfold = strtfdKFold.split(X_train, y_train)
pred = pd.DataFrame()
for k, (train1, test1) in enumerate(kfold):
pseudo = PseudoLabeler(grid_soft, test, features, target_feature, sample_rate = 1)
pseudo.fit(X_train.iloc[train1,:], y_train.iloc[train1])
pred_lgb = pseudo.predict_proba(X_test)[:,1]
pred[str(k)] = pred_lgb
pred['result'] = (pred['0'] + pred['1'] + pred['2'] + pred['3'] + pred['4']) / 5伪标签能使模型鲁棒性大大增强，从而增强的未知数据的预测能力，由于训练时间的问题，作者并未对伪标签做线下CV测试，但是线上分数提高了，从而证明了伪标签的有效性。
4.2 神经网络
树模型表现良好，那么nn表现如何呢，作者写了一个简单的多层感知机神经网络模型如下：
（1）数据读取以及数据预处理：
import os, gc, math, time, random, numpy as np, pandas as pd, warnings, torch
warnings.filterwarnings('ignore')
from torch import nn
from torch.utils.data import DataLoader, Dataset
from torch.nn import functional as F
from sklearn.metrics import accuracy_score, f1_score, roc_auc_score
from sklearn.model_selection import StratifiedKFold
from transformers import get_constant_schedule_with_warmup, get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup
from sklearn.preprocessing import LabelEncoder
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# ====================================================
# CFG
# ====================================================
class CFG:
seed = 42
num_hidden1 = 768
num_hidden2 = 512
num_hidden3 = 768
num_hidden4 = 768
num_output = 2
print_freq = 100
scheduler = 'cosine'
batch_size = 32
num_workers = 3
lr = 1e-5
weight_decay = 0
epochs = 5
num_warmup_steps = 0
num_cycles = 0.5
n_accumulate = 1
train = True
n_fold = 5
def seed_everything(seed=CFG.seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_everything(seed = 42)
train_net = pd.read_excel('fintech训练营/train.xlsx')
for i in train_net.columns:
train_net[i] = train_net[i].apply(lambda x : np.nan if x=='?' else x)
label = LabelEncoder()
for i in train_net.columns:
if train_net[i].dtype == 'object':
if i != 'CUST_UID':
train_net[i] = label.fit_transform(train_net[i])
else:
if i != "LABEL":
train_net[i] = train_net[i].fillna(train_net[i].mode().values[0]) # 用众数填充缺失值
train_net[i] = (train_net[i] - train_net[i].min()) / (train_net[i].max() - train_net[i].min()) # 归一化
ignore = ['CUST_UID','LABEL']
CFG.feas = [feat for feat in train_net.columns if feat not in ignore]
CFG.target_fea = 'LABEL'
skf = StratifiedKFold(n_splits = 5, random_state = CFG.seed, shuffle = True)
train_net['fold'] = -1
for i, (_, val_) in enumerate(skf.split(train_net[CFG.feas], train_net[CFG.target_fea])):
train_net.loc[val_, 'fold'] = int(i)
神经网络框架：
（1）criterion是损失函数，这里我们使用nn.CrossEntropyLoss
（2）get_score是验证集评价指标函数，评价指标为AUC
（3）FeedBackDataset是神经网络的数据类，用于数据的预处理和传入，__getitem__函数即为每个item传入数据的格式
（4）custom_collate_fn为神经网络收集整理函数，由于FeedBackDataset的数据是一个一个传入的，而一个batch可能有多个数据，所以需要对多个单独传入的数据进行整理并转化成tensor
（5）BPNetModel是神经网络的结构类，forward函数定义了数据前向传播，loss.backward()反向传播求梯度，作者在这里封装了一个loss_accumulate函数，主要是为了应对显存不足而又需要大batchsize的情况，可以在设定比如batchsize=1、accumulate=4的情况下达到和batchsize=4等同效果的结果
（6）asMinutes和timeSince为时间辅助函数，主要是为了计算神经网络运行和等待时间
（7）train_one_epoch为单个epoch的训练函数，在该函数定义训练日志、参数更新（optimizer.step）、优化器更新（scheduler.step）、梯度清零（optimizer.zero_grad）等操作
（8）valid_one_epoch为预测函数，我们都知道模型不一定是在每个epoch训练结束的时候效果最好， 该函数可以帮助我们在模型训练过程中预测验证集，通过验证集的分数来挑选最优模型
（9）train_loop为模型初始化函数，主要用于定义DataLoader、model和optimizer（Adam、SGD）等组件
def criterion(outputs, labels):
return nn.CrossEntropyLoss(reduction = 'sum')(outputs, labels)
def get_score(outputs, labels):
outputs = F.softmax(torch.tensor(outputs)).numpy()[:,1]
return roc_auc_score(labels, outputs)
class FeedBackDataset(Dataset):
def __init__(self, data):
self.data = data[CFG.feas].values
self.targets = data[CFG.target_fea].values
def __len__(self):
return len(self.targets)
def __getitem__(self, index):
return {
'feature': self.data[index],
'target': self.targets[index]
}
def custom_collate_fn(batch):
datas, targets = [], []
for batchid, data in enumerate(batch):
datas.append(data['feature'])
targets.append(data['target'])
datas = torch.tensor(datas)
targets = torch.tensor(targets)
return datas, targets
class BPNetModel(nn.Module):
def __init__(self):
super(BPNetModel, self).__init__()
self.hiddden1 = torch.nn.Linear(len(CFG.feas), CFG.num_hidden1) # 定义隐层网络
self.hiddden2 = torch.nn.Linear(CFG.num_hidden1, CFG.num_hidden2)
self.hiddden3 = torch.nn.Linear(CFG.num_hidden2, CFG.num_hidden3) # 定义隐层网络
self.hiddden4 = torch.nn.Linear(CFG.num_hidden3, CFG.num_hidden4)
self.out = torch.nn.Linear(CFG.num_hidden4, CFG.num_output) # 定义输出层网络
self.relu = torch.nn.ReLU()
def forward(self, x):
x = x.to(torch.float32)
x = self.hiddden1(x) # 隐层激活函数采用relu()函数
x = self.relu(x)
x = self.hiddden2(x)
x = self.relu(x)
x = self.hiddden3(x)
x = self.relu(x)
x = self.hiddden4(x)
x = self.relu(x)
x = self.out(x)
return x
def get_loss(self, inputs):
inputs, targets = inputs[0].to(device), inputs[1]
outs = self.forward(inputs)
loss = criterion(outs, targets.to(device))
return loss, outs
def loss_accumulate(self, data_list):
running_loss = 0
result = []
all_bs = sum(data_bts['batchsize'] for data_bts in data_list)
for data_bts in data_list:
data = data_bts['data']
loss, outs = self.get_loss(data)
loss = loss / all_bs
loss.backward()
running_loss += loss.item()
result.append(outs.detach().to('cpu').numpy())
return running_loss, all_bs, result
def asMinutes(s):
m = math.floor(s/60)
s -= m * 60
return "%dm %ds" % (m, s)
def timeSince(since, percent):
now = time.time()
s = now - since
es = s / (percent)
rs = es - s
return "%s (remain %s)" % (asMinutes(s), asMinutes(rs))
def get_scheduler(cfg, optimizer, num_train_steps):
if cfg.scheduler == 'linear':
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps = cfg.num_warmup_steps, num_training_steps = num_train_steps
)
elif cfg.scheduler == 'cosine':
scheduler = get_cosine_schedule_with_warmup(
optimizer, num_warmup_steps = cfg.num_warmup_steps, num_training_steps = num_train_steps, num_cycles = cfg.num_cycles
)
return scheduler
def train_one_epoch(model, optimizer, scheduler, dataloader, epoch, valid_data):
model.train()
dataset_size = 0
running_loss_awp = 0
epoch_loss_awp = 0
# valid_labels = valid_data.label.values
validDataset = FeedBackDataset(valid_data)
valid_loader = DataLoader(validDataset,
batch_size = CFG.batch_size,
shuffle = False,
collate_fn = custom_collate_fn,
num_workers = CFG.num_workers,
pin_memory = True)
start = end = time.time()
data_list = []
target_list = []
pred_list = []
for step, data in enumerate(dataloader):
if os.path.exists('break.txt'):
raise ValueError('break error')
batch_size = data[-1].shape[0]
data_list.append({'data': data, 'batchsize': batch_size})
target_list.append(data[1].numpy())
if (step +1) % CFG.n_accumulate == 0:
accum_loss, datalist_size, result = model.loss_accumulate(data_list)
optimizer.step()
optimizer.zero_grad()
if scheduler is not None:
scheduler.step()
pred_list += result
data_list = [] # refresh the accumulate data_list
running_loss_awp += (accum_loss * datalist_size)
dataset_size += datalist_size
# average loss
epoch_loss_awp = running_loss_awp / dataset_size
end = time.time()
if step % CFG.print_freq == 0 or step == (len(dataloader)-1):
score_train = get_score(np.concatenate(pred_list), np.concatenate(target_list))
print('Train: [{}] '
'Loss: {:.4f} '
'Train AUC: {:.4f} '
'Step: [{}/{}] '
'Elapsed {remain:s} '
.format(epoch, epoch_loss_awp, score_train, step+1, len(dataloader),
remain = timeSince(start, float(step+1)/len(dataloader))))
pred = valid_one_epoch(model, valid_loader, epoch)
gc.collect()
return epoch_loss_awp, pred
@torch.no_grad()
def valid_one_epoch(model, dataloader, epoch):
dataset_size = 0
running_loss = 0
start = end = time.time()
pred = []
for step, data in enumerate(dataloader):
if os.path.exists('break.txt'):
raise ValueError('break error')
loss, outputs = model.get_loss(data)
pred.append(outputs.to('cpu').numpy())
batch_size = data[-1].shape[0]
running_loss += (loss.item() * batch_size)
dataset_size += batch_size
epoch_loss = running_loss / dataset_size
print('EVAL: [{}] '
'Loss: {:.4f} '
'Step: [{}/{}] '
'Elapsed {remain:s} '
.format(epoch, epoch_loss, step + 1 , len(dataloader),
remain = timeSince(start, float(step+1)/len(dataloader))))
pred = np.concatenate(pred)
model.train()
return pred
def train_loop(fold):
train_data = train_net[train_net.fold != fold].reset_index(drop=True)
valid_data = train_net[train_net.fold == fold].reset_index(drop=True)
trainDataset = FeedBackDataset(train_data)
train_loader = DataLoader(trainDataset,
batch_size = CFG.batch_size,
shuffle = True,
collate_fn = custom_collate_fn,
num_workers = CFG.num_workers,
pin_memory = True)
model = BPNetModel().to(device)
optimizer = torch.optim.AdamW(model.parameters(), lr = CFG.lr)
# loop
best_score = 0
# General training
num_train_steps = int(len(train_data) / CFG.batch_size * CFG.epochs)
scheduler = get_scheduler(CFG, optimizer, num_train_steps)
for epoch in range(CFG.epochs):
print(f'-------------epoch:{epoch} training-------------')
start_time = time.time()
train_epoch_loss, pred = train_one_epoch(model, optimizer, scheduler, train_loader, epoch, valid_data)
score = get_score(pred, valid_data[CFG.target_fea].values)
elapsed = time.time() - start_time
print(f'Fold {fold} Epoch {epoch} - avg_train_loss: {train_epoch_loss:.4f} time: {elapsed:.0f}s')
if score > best_score:
best_score = score
print(f'AUC score:{score} best_score:{best_score}')
torch.cuda.empty_cache()
gc.collect()
return best_score模型结果展示：
best_scores = []
if CFG.train:
for fold in range(CFG.n_fold):
print(f'-------------fold:{fold} training-------------')
best_scores.append(train_loop(fold))
print(f"Cross Validation: {np.mean(best_scores)}")部分训练日志如下：
-------------epoch:3 training-------------
Train: [3] Loss: 0.5390 Test AUC: 0.6302 Step: [1/1000] Elapsed 0m 0s (remain 1m 7s)
Train: [3] Loss: 0.5257 Test AUC: 0.6642 Step: [101/1000] Elapsed 0m 0s (remain 0m 3s)
Train: [3] Loss: 0.5302 Test AUC: 0.6614 Step: [201/1000] Elapsed 0m 0s (remain 0m 2s)
Train: [3] Loss: 0.5326 Test AUC: 0.6601 Step: [301/1000] Elapsed 0m 0s (remain 0m 1s)
Train: [3] Loss: 0.5342 Test AUC: 0.6591 Step: [401/1000] Elapsed 0m 0s (remain 0m 1s)
Train: [3] Loss: 0.5349 Test AUC: 0.6580 Step: [501/1000] Elapsed 0m 1s (remain 0m 1s)
Train: [3] Loss: 0.5320 Test AUC: 0.6597 Step: [601/1000] Elapsed 0m 1s (remain 0m 0s)
Train: [3] Loss: 0.5323 Test AUC: 0.6583 Step: [701/1000] Elapsed 0m 1s (remain 0m 0s)
Train: [3] Loss: 0.5330 Test AUC: 0.6592 Step: [801/1000] Elapsed 0m 1s (remain 0m 0s)
Train: [3] Loss: 0.5336 Test AUC: 0.6593 Step: [901/1000] Elapsed 0m 1s (remain 0m 0s)
Train: [3] Loss: 0.5328 Test AUC: 0.6590 Step: [1000/1000] Elapsed 0m 1s (remain 0m 0s)
EVAL: [3] Loss: 17.1517 Step: [250/250] Elapsed 0m 0s (remain 0m 0s)
Fold 4 Epoch 3 - avg_train_loss: 0.5328 time: 3s
AUC score:0.6507739583333333 best_score:0.6507739583333333
-------------epoch:4 training-------------
Train: [4] Loss: 0.5875 Test AUC: 0.6727 Step: [1/1000] Elapsed 0m 0s (remain 1m 12s)
Train: [4] Loss: 0.5156 Test AUC: 0.6763 Step: [101/1000] Elapsed 0m 0s (remain 0m 2s)
Train: [4] Loss: 0.5246 Test AUC: 0.6699 Step: [201/1000] Elapsed 0m 0s (remain 0m 2s)
Train: [4] Loss: 0.5281 Test AUC: 0.6632 Step: [301/1000] Elapsed 0m 0s (remain 0m 1s)
Train: [4] Loss: 0.5335 Test AUC: 0.6615 Step: [401/1000] Elapsed 0m 0s (remain 0m 1s)
Train: [4] Loss: 0.5364 Test AUC: 0.6585 Step: [501/1000] Elapsed 0m 1s (remain 0m 1s)
Train: [4] Loss: 0.5355 Test AUC: 0.6569 Step: [601/1000] Elapsed 0m 1s (remain 0m 0s)
Train: [4] Loss: 0.5337 Test AUC: 0.6572 Step: [701/1000] Elapsed 0m 1s (remain 0m 0s)
Train: [4] Loss: 0.5336 Test AUC: 0.6570 Step: [801/1000] Elapsed 0m 1s (remain 0m 0s)
Train: [4] Loss: 0.5325 Test AUC: 0.6593 Step: [901/1000] Elapsed 0m 1s (remain 0m 0s)
Train: [4] Loss: 0.5325 Test AUC: 0.6598 Step: [1000/1000] Elapsed 0m 2s (remain 0m 0s)
EVAL: [4] Loss: 17.1514 Step: [250/250] Elapsed 0m 0s (remain 0m 0s)
Fold 4 Epoch 4 - avg_train_loss: 0.5325 time: 2s
AUC score:0.6505471666666667 best_score:0.6507739583333333
Cross Validation: 0.6568245416666667
该神经网络通过对数据归一化处理、众数填充缺失值，然后用四层线性层（768、512、768、768）构建了一个简单的多层感知机模型，可以看出该baseline的CV只有0.6568，比树差了很多（可能需要对数据和模型做进一步处理），但由于时间原因以及神经网络在二分类任务中通常弱于树模型，故放弃使用nn，继续优化树模型。
4.3 Bad Case Analysis
Bad Case，顾名思义，就是模型预测不了的数据。Bad Case分析，简单来说就是把模型预测不了的数据找出来，分析这部分数据有什么特点（特征共性、分布不同等），进而改进模型的效果，此处不再赘述，感兴趣的同学可以参考：https://zhuanlan.zhihu.com/p/104961266
4.4 模型参数调优
4.4.1 网格搜索
网格搜索，也称穷举搜索。简单来说，就是模型一个超参数有多个值（比如learning可以是1e-5、1e-4、1e-3等等），那么就遍历这所有可能的值，找到最优模型。但是随着超参数组合的增加，比如特征A有5个值、B有10个值、C有6个值，那么就有300种要遍历的方案，遍历时间会非常的长。详细可参考：https://blog.csdn.net/qq_39521554/article/details/86227582
cv_split = model_selection.ShuffleSplit(n_splits = 5, test_size = .2, train_size = .8, random_state = 0 )
vote_est = [
#Ensemble Methods: http://scikit-learn.org/stable/modules/ensemble.html
('hgbc',ensemble.HistGradientBoostingClassifier()),
#lightbgm
('lgb', LGBMClassifier()),
#xgboost: http://xgboost.readthedocs.io/en/latest/model.html
('xgb', XGBClassifier(verbosity=0)),
('cbc',CatBoostClassifier(verbose=0))
]
grid_n_estimator = [10, 50, 100, 300]
grid_ratio = [.1, .25, .5, .75, 1.0]
grid_learn = [.01, .03, .05, .1, .25]
grid_max_depth = [2, 4, 6, 8, 10, None]
grid_min_samples = [5, 10, .03, .05, .10]
grid_criterion = ['gini', 'entropy']
grid_bool = [True, False]
grid_seed = [42]
grid_param = [
[{
# hgbc
'learning_rate': grid_learn,
'max_depth': [1, 3, 5, 7, 9],
'max_iter':[50, 100, 200, 500],
'random_state': grid_seed
}],
[{
# lgb
'learning_rate': grid_learn,
'max_depth': [1, 3, 5, 7, 9],
'n_estimators': grid_n_estimator,
'colsample_bytree':[0.6, 0.7, 0.8, 0.9, 1],
'reg_alpha': [0, 0.05, 1],
'reg_lambda': [0, 0.1, 0.5, 1],
'seed': grid_seed
}],
[{
# xgb
'learning_rate': grid_learn,
'max_depth': [1, 3, 5, 7, 9],
'n_estimators': grid_n_estimator,
'gamma':[0, 0.2, 0.5],
'subsample':[0.6, 0.7, 0.8, 0.9, 1],
'seed': grid_seed ,
'verbosity':[0]
}],
[{
# cbc
'learning_rate': grid_learn,
'n_estimators': grid_n_estimator,
'depth': [4, 5, 6, 7, 8, 9],
'l2_leaf_reg': [1,3,5,7],
'seed': grid_seed ,
'verbose':[0]
}]
]
start_total = time.perf_counter() #https://docs.python.org/3/library/time.html#time.perf_counter
for clf, param in zip (vote_est, grid_param): #https://docs.python.org/3/library/functions.html#zip
start = time.perf_counter()
best_search = model_selection.GridSearchCV(estimator = clf[1], param_grid = param, cv = cv_split, scoring = 'roc_auc')
best_search.fit(train[features], train[target_feature])
run = time.perf_counter() - start
best_param = best_search.best_params_
print('The best parameter for {} is {} with a runtime of {:.2f} seconds.'.format(clf[1].__class__.__name__, best_param, run))
run_total = time.perf_counter() - start_total
print('Total optimization time was {:.2f} minutes.'.format(run_total/60))部分模型训练结果如下：
### The best parameter for HistGradientBoostingClassifier is
{'learning_rate': 0.03, 'max_depth': 7, 'max_iter': 500, 'random_state': 0}
with a runtime of 6016.12 seconds.
### The best parameter for LGBMClassifier is
{'colsample_bytree': 0.7, 'learning_rate': 0.03, 'max_depth': 7, 'n_estimators': 300, 'reg_alpha': 1, 'reg_lambda': 0.1, 'seed': 0}
with a runtime of 33976.22 seconds.
用网格搜索的参数重新训练模型，发现模型并没有什么提高，由此可见手动输入搜索的超参数很难提高模型的效果。
4.4.2 Optuna
Optuna是一个基于贝叶斯优化的超参数优化框架。它的目标是通过智能的搜索策略，尽可能少的实验次数找到最佳超参数组合Optuna是一个基于贝叶斯优化的超参数优化框架。它的目标是通过智能的搜索策略，尽可能少的实验次数找到最佳超参数组合。详细可参考Optuna官方文档：https://zh-cn.optuna.org/index.html
import numpy as np
import pandas as pd
import os
os.chdir(os.path.abspath(os.curdir))
from tqdm import tqdm
from lightgbm import LGBMClassifier
#Common Model Helpers
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import roc_auc_score
import warnings
warnings.filterwarnings('ignore')
from optuna.integration import LightGBMPruningCallback
import optuna
from lightgbm import early_stopping
import warnings
warnings.filterwarnings('ignore')
train = pd.read_excel('fintech训练营/train.xlsx')
test = pd.read_excel('fintech训练营/test_A榜.xlsx')
datasets = [train,test]
for dataset in datasets:
for i in dataset.columns:
dataset[i] = dataset[i].apply(lambda x : np.nan if x=='?' else x)
label = LabelEncoder()
for dataset in datasets:
for i in dataset.columns:
if dataset[i].dtype == 'object':
if i != 'CUST_UID':
dataset[i] = label.fit_transform(dataset[i])
ignore = ['CUST_UID','LABEL']
features = [feat for feat in train.columns if feat not in ignore]
target_feature = 'LABEL'
def objective(trial, X, y):
# 参数网格
param_grid = {
"n_estimators": trial.suggest_categorical("n_estimators", [300,500,1000]),
"learning_rate": trial.suggest_categorical("learning_rate", [0.01, 0.03, 0.05, 0.1, 0.25]),
"num_leaves": trial.suggest_int("num_leaves", 20, 3000, step=20),
"max_depth": trial.suggest_int("max_depth", 3, 12),
"subsample": trial.suggest_float("subsample", 0.001, 0.999),
"subsample_freq": trial.suggest_categorical("subsample_freq", [1]),
"colsample_bytree": trial.suggest_float("colsample_bytree", 0.001, 0.999),
"random_state": 42,
"verbosity": -1
}
# 5折交叉验证
cv = StratifiedKFold(n_splits=5,random_state=100,shuffle=True)
cv_scores = np.empty(5)
for idx, (train_idx, test_idx) in enumerate(cv.split(X, y)):
X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
y_train, y_test = y[train_idx], y[test_idx]
# LGBM建模
model = LGBMClassifier(**param_grid)
model.fit(
X_train,
y_train,
eval_set = [(X_test, y_test)],
eval_metric = ['auc'],
callbacks = [early_stopping(stopping_rounds = 100, verbose = 0)]
)
# 模型预测
preds = model.predict_proba(X_test)[:,1]
# 优化指标auc最大
cv_scores[idx] = roc_auc_score(y_test, preds)
return np.mean(cv_scores)
study = optuna.create_study(direction="maximize", study_name="LGBM Classifier")
func = lambda trial: objective(trial, train[features], train[target_feature])
study.optimize(func, n_trials=2000)
print('best params:',study.best_params)
print('best score:',study.best_value)
部分训练日志如下：
[I 2024-08-01 10:45:25,902] A new study created in memory with name: LGBM Classifier [I 2024-08-01 10:45:38,510] Trial 0 finished with value: 0.9422476416666667 and parameters: {'n_estimators': 500, 'learning_rate': 0.05, 'num_leaves': 2660, 'max_depth': 11, 'subsample': 0.0905498028535803, 'subsample_freq': 1, 'colsample_bytree': 0.2742667597572731}. Best is trial 0 with value: 0.9422476416666667.
[I 2024-08-01 10:45:43,009] Trial 1 finished with value: 0.947348975 and parameters: {'n_estimators': 300, 'learning_rate': 0.25, 'num_leaves': 2520, 'max_depth': 4, 'subsample': 0.58422461412904, 'subsample_freq': 1, 'colsample_bytree': 0.41895727668202326}. Best is trial 1 with value: 0.947348975.
[I 2024-08-01 10:45:53,076] Trial 2 finished with value: 0.94371525 and parameters: {'n_estimators': 500, 'learning_rate': 0.03, 'num_leaves': 2220, 'max_depth': 12, 'subsample': 0.027482276053391742, 'subsample_freq': 1, 'colsample_bytree': 0.5719792346676457}. Best is trial 1 with value: 0.947348975.
[I 2024-08-01 10:46:11,290] Trial 3 finished with value: 0.9489362166666666 and parameters: {'n_estimators': 1000, 'learning_rate': 0.05, 'num_leaves': 2420, 'max_depth': 3, 'subsample': 0.4658330549093411, 'subsample_freq': 1, 'colsample_bytree': 0.9729852421378948}. Best is trial 3 with value: 0.9489362166666666.
Optuna相对于网格搜索速度大大提升，而且会自动搜索超参数，我们只需要提前输入超参数的值或范围（0~1等），它就会自动搜索最优参数，实在是调参的不二之选。
4.5 特征工程
4.5.1 分箱
对于非树模型，可以对连续值或者取值较多的离散变量进行分箱操作，有助于减少异常值的影响、处理缺失值（缺失值单独为一个箱）等，详细可参考：https://blog.csdn.net/CarryLvan/article/details/108775507
grid_soft = ensemble.VotingClassifier(estimators = vote_est , voting = 'soft')
for name in train.columns[2:]:
train_cut=train.copy()
if train[name].dtype!='object':
train_cut[name] = pd.qcut(train_cut[name].rank(method='first'),5)
train_cut[name] = label.fit_transform(train_cut[name])
print(name)
strtfdKFold = StratifiedKFold(n_splits=5,random_state=100,shuffle=True)
#把特征和标签传递给StratifiedKFold实例
X_train = train_cut[features]
y_train = train_cut[target_feature]
kfold = strtfdKFold.split(X_train, y_train)
scores = []
for k, (train1, test1) in enumerate(kfold):
grid_soft.fit(X_train.iloc[train1,:], y_train.iloc[train1])
pred_lgb = grid_soft.predict_proba(X_train.iloc[test1, :])[:,1]
score=roc_auc_score(y_train.iloc[test1], pred_lgb)
scores.append(score)
print('Fold: %2d, Training/Test Split Distribution: %s, AUC: %s' % (k+1, np.bincount(y_train.iloc[train1]), score))
print('Cross-Validation AUC: %s +/- %s\n' %(np.mean(scores), np.std(scores)))
作者实现了等频分箱，主要利用pandas库的pd.cut函数，但由于使用的是树模型，分箱特征帮助不大（树模型分类本身就是分箱），最终没有使用该方法。
4.5.2 手工特征
其实特征工程算是机器学习最重要的一个模块，好的特征能够提取出更多的信息，从而提高模型的预测效果，其它的技巧只是提升上限罢了。那么该文章为什么把特征工程放在最后呢？一个很重要的原因是该数据集的特点为匿名特征、脱敏数据，你很难构造出有意义的手工强特。作者猜测特征的含义（由于涉及脱敏就不放在这里了），构造了两个强特如下：
train_ft['transfer_amount_avg'] = train_ft['MON_12_EXT_SAM_TRSF_OUT_AMT'] / train_ft['MON_12_EXT_SAM_NM_TRSF_OUT_CNT']
train_ft['dps_cur_month_peak_ratio'] = (train_ft['LAST_12_MON_COR_DPS_TM_PNT_BAL_PEAK_VAL'] - train_ft['CUR_MON_COR_DPS_MON_DAY_AVG_BAL']) / train_ft['CUR_MON_COR_DPS_MON_DAY_AVG_BAL']这两个特征在A榜作用不大（估计是训练集和测试集分布近似原因），但在B榜提升不少。
4.5.3 Featuretools
匿名手工特征很难构造，作者另辟思路，选取featuretools自动化特征工具库，它可以运用groupby、mean、max、min 等算子，快速构建丰富的数据特征，从而提高模型的效果。详细可参考：https://blog.csdn.net/ShowMeAI/article/details/123650547
import featuretools as ft
train_ft = pd.read_excel('fintech训练营/train.xlsx')
test_ft = pd.read_excel('fintech训练营/test_A榜.xlsx')
datasets = [train_ft,test_ft]
for dataset in datasets:
for i in dataset.columns:
dataset[i]=dataset[i].apply(lambda x : np.nan if x=='?' else x)
ignore = ['CUST_UID','LABEL']
categorical = ['MON_12_CUST_CNT_PTY_ID',
'AI_STAR_SCO',
'WTHR_OPN_ONL_ICO',
'SHH_BCK',
'LGP_HLD_CARD_LVL',
'NB_CTC_HLD_IDV_AIO_CARD_SITU']
features = [feat for feat in train_ft.columns if feat not in (ignore + categorical)]
target_feature = 'LABEL'
es = ft.EntitySet(id='fintech') # 用id标识实体集
es=es.add_dataframe(
dataframe_name = "fintech_train",
dataframe = train_ft[features],
index = '1',
make_index = True
)
es=es.add_dataframe(
dataframe_name = "fintech_test",
dataframe = test_ft[features],
index = '2',
make_index = True
)
feature_train, feature_defs_train = ft.dfs(entityset=es,
target_dataframe_name='fintech_train',
agg_primitives=["mean", "sum", "mode"],
trans_primitives=['add_numeric', 'subtract_numeric', 'multiply_numeric', 'divide_numeric'], # 2列相加减乘除来生成
max_depth = 1)
feature_test, feature_defs_test = ft.dfs(entityset=es,
target_dataframe_name='fintech_test',
agg_primitives=["mean", "sum", "mode"],
trans_primitives=['add_numeric', 'subtract_numeric', 'multiply_numeric', 'divide_numeric'], # 2列相加减乘除来生成
max_depth = 1)
feature_train_last = pd.concat([feature_train,train_ft[categorical+[target_feature]]],axis=1)
feature_test_last = pd.concat([feature_test,test_ft[categorical]],axis=1)
label = LabelEncoder()
datasets=[feature_train_last,feature_test_last]
for dataset in datasets:
for i in categorical:
dataset[i] = label.fit_transform(dataset[i])
for dataset in datasets:
for i in dataset.columns:
dataset[i] = dataset[i].apply(lambda x : np.nan if x==np.inf else x) # 除会产生inf, xgboost预测需要转换成nan作者运用mean、sum、mode、add_numeric、subtract_numeric、multiply_numeric、divide_numeric（2列相加减乘除）等算子，足足构造了上千个特征（可在trans_primitives参数里进一步添加cosine、sine、modulo_numeric、percentile、natural_logarithm等算子）。同时，由于除法会产生inf，这会导致XGBoost预测时报错，需要转换成nan。上千个特征导致数据占用内存特别大，不管是读取、保存、训练都会耗费很长时间，这里介绍一种减少内存的方法：
def reduce_mem_usage(df):
""" iterate through all the columns of a dataframe and modify the data type
to reduce memory usage.
"""
start_mem = df.memory_usage().sum() / 1024**2
print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
for col in df.columns:
col_type = df[col].dtype
if col_type != object:
c_min = df[col].min()
c_max = df[col].max()
if str(col_type)[:3] == 'int':
if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
df[col] = df[col].astype(np.int32)
elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
df[col] = df[col].astype(np.int32)
elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
df[col] = df[col].astype(np.int32)
elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
df[col] = df[col].astype(np.int64)
else:
if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
df[col] = df[col].astype(np.float32)
elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
df[col] = df[col].astype(np.float32)
else:
df[col] = df[col].astype(np.float64)
else:
df[col] = df[col].astype('category')
end_mem = df.memory_usage().sum() / 1024**2
print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))
print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))
return dfMemory usage of dataframe is 1393.43 MB
Memory usage after optimization is: 703.28 MB
Decreased by 49.5%
Memory usage of dataframe is 417.94 MB
Memory usage after optimization is: 209.01 MB
Decreased by 50.0%
该函数通过对数值型变量的转换，可以大大缩减特征的内存占用大小。由上部分运行记录可见，可以把一个dataframe的内存占用缩减一半，同时不会对数据的精度造成太大影响。
4.5.4 特征筛选
对featuretools构造的特征进行训练预测，结果如下（代码同上）：
Fold: 1, Training/Test Split Distribution: [24000 8000], AUC: 0.9479184166666665
Fold: 2, Training/Test Split Distribution: [24000 8000], AUC: 0.9481912083333333
Fold: 3, Training/Test Split Distribution: [24000 8000], AUC: 0.9511504583333334
Fold: 4, Training/Test Split Distribution: [24000 8000], AUC: 0.9495554166666667
Fold: 5, Training/Test Split Distribution: [24000 8000], AUC: 0.9486693333333335
Cross-Validation AUC: 0.9490969666666667 +/- 0.0011678401394241225
大家可以看到，效果反而比不构造特征的时候更差了，这是因为这是直接特征之间交互（加减乘除）构造的特征，包含很多噪声，导致模型效果较差。这里介绍一种表现优异的特征筛选方法--逐步特征筛选法（以n个特征为例）：
（1）用n个特征进行模型训练预测，用模型（比如LGBM）的特征重要性进行排名
（2）剔除重要性排名最后的n/2（每次剔除的数量可灵活调整）个特征
（3）用n/2个特征重新训练预测，重复步骤（1）、（2）
为什么用这个方法呢？这其实是贪心算法的一个变种，因为特征是耦合的，只保留一次训练中重要性排名靠前的特征可能不是最优的，所以每次只删一部分。最好的情况是每次只删除一个特征，但是特征数量一多会导致训练时间大大增加，所以用微小数据精度提升的可能性来换取时间。
import numpy as np
import pandas as pd
import os
os.chdir(os.path.abspath(os.curdir))
from tqdm import tqdm
from sklearn import svm, tree, linear_model, neighbors, naive_bayes, ensemble, discriminant_analysis, gaussian_process
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from catboost import CatBoostClassifier
#Common Model Helpers
from sklearn.preprocessing import LabelEncoder
from sklearn import feature_selection
from sklearn import model_selection
from sklearn import metrics
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import time
def get_importance(importance, names, model_type):
#Create arrays from feature importance and feature names
feature_importance = np.array(importance)
feature_names = np.array(names)
#Create a DataFrame using a Dictionary
data={'feature_names':feature_names,'feature_importance':feature_importance}
fi_df = pd.DataFrame(data)
#Sort the DataFrame in order decreasing feature importance
fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)
return fi_df
def score(X_train,y_train):
start = time.time()
print(len(X_train.columns))
params_hgbc = {'max_iter': 300,
'learning_rate': 0.03,
'max_leaf_nodes': 1980,
'max_depth': 6,
'random_state': 22934
}
params_lgb = {'n_estimators': 300,
'learning_rate': 0.03,
'num_leaves': 1240,
'max_depth': 8,
'subsample': 0.614339420520959,
'subsample_freq': 1,
'colsample_bytree': 0.9711563047222685,
'random_state' : 2022
}
params_xgb = {'n_estimators': 300,
'learning_rate': 0.03,
'num_leaves': 1880,
'max_depth': 8,
'subsample': 0.7574143599011826,
'subsample_freq': 1,
'colsample_bytree': 0.682578966844618,
'verbosity':0,
'random_state': 2022
}
vote_est_new = [
#Ensemble Methods: http://scikit-learn.org/stable/modules/ensemble.html
('hgbc',ensemble.HistGradientBoostingClassifier(**params_hgbc)),
#lightbgm
('lgb', LGBMClassifier(**params_lgb)),
#xgboost: http://xgboost.readthedocs.io/en/latest/model.html
('xgb', XGBClassifier(**params_xgb)),
]
grid_soft2 = ensemble.VotingClassifier(estimators = vote_est_new , voting = 'soft' ,weights = [0.2,0.4,0.4])
strtfdKFold = StratifiedKFold(n_splits=5,random_state=100,shuffle=True)
#把特征和标签传递给StratifiedKFold实例
kfold = strtfdKFold.split(X_train, y_train)
scores = []
for k, (train1, test1) in enumerate(kfold):
grid_soft2.fit(X_train.iloc[train1,:], y_train.iloc[train1])
pred_lgb = grid_soft2.predict_proba(X_train.iloc[test1, :])[:,1]
score=roc_auc_score(y_train.iloc[test1], pred_lgb)
scores.append(score)
print('Fold: %2d, Training/Test Split Distribution: %s, AUC: %s' % (k+1, np.bincount(y_train.iloc[train1]), score))
print('\nCross-Validation AUC: %s +/- %s\n' %(np.mean(scores), np.std(scores)))
print('time:',time.time()-start)
return np.mean(scores)
train = pd.read_parquet('data/train_feature_last.parquet')
test = pd.read_parquet('data/test_feature_last.parquet')
test_original = pd.read_excel('fintech训练营/test_A榜.xlsx')
ignore = ['CUST_UID','LABEL']
original_feature = list(test_original.columns)
target_feature = 'LABEL'
important=pd.read_csv('important/step_10/important_790.csv')
result_1 = pd.DataFrame(columns=['num','score'])
row = 0
for num in range(780,100,-10):
features = [feat for feat in important['feature_names'][:num] if feat not in ignore]
score_new = score(train[features],train[target_feature])
lgb=LGBMClassifier()
lgb.fit(train[features], train[target_feature])
important = get_importance(lgb.feature_importances_,features,'LGBM ')
important.to_csv('important/no_add_original_800/important'+str(num)+'.csv',index=False)
result_1.loc[row,'num'] = num
result_1.loc[row,'score'] = score_new
result_1.to_csv('important/no_add_original_800/result.csv',index=False)
row += 1通过上述代码，筛选出分数最高的特征组合，再用optuna调参，模型的表现如下：
| MLA Name |
MLA Train AUC Mean |
MLA Test AUC Mea |
MLA Test AUC 3*STD |
MLA Time |
|---|---|---|---|---|
| xgb | 0.997148 | 0.952981 | 0.006871 | 121.238519 |
| lgb | 0.996984 | 0.952962 |
0.007715 |
23.109799 |
| lghc | 0.979313 | 0.952644 |
0.0074 |
26.32936 |
| cbc | 0.986432 | 0.951637 |
0.008834 |
47.699232 |
可见每个模型的CV都有较大提高，同时线上分数也同样提升，可见策略的有效性。
5. 赛题总结
5.1 A榜
本次竞赛A榜的训练集和测试集分布一致，导致大家评分非常接近（用LGBM跑baseline都有0.93，作者CV: 0.9539、LB: 0.9551第53名，0.956估计第1），剩下的就是卷过拟合罢了。
5.2 B榜
B榜的训练集和测试集分布不一致，数据漂移非常严重，有“毒特征”（CV很好，LeaderBoard很差）。
（1）作者通过对抗验证筛掉了漂移严重的特征（对抗验证AUC 0.99->0.71）
（2）构造手工强特，用这些基础特征通过featuretools构造特征（5000多个特征），并用逐步特征筛选法筛选（180个特征）
（3）训练集加入了A榜（用A榜模型打标签）、B测试集的伪标签
（4）选用LGBM、XGBoost、CatBoost、HistGradientBoostingClassifier四个集成树模型，并用optuna进行树模型超参数的调优，最后软投票融合，融合权重为LGBM：0.35，XGBoost：0.35，CatBoost：0.15，HistGradientBoostingClassifier：0.15）
运用这些技巧，作者最终B榜的分数为CV: 0.863，LB: 0.872，与A榜分数按比例计分后排名第4。
文章涉及的数据集和源代码可从Github下载：https://github.com/CNLCNL/2022-fintech，希望这些方法和技巧能对大家有所帮助，谢谢！