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model_mvp
Commit c8ae753d authored by 王家华's avatar 王家华
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报告版

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......
feature/feature_selection_kit.py 0 → 100644
View file @ c8ae753d
from sklearn.feature_selection import VarianceThreshold
from sklearn.feature_selection import SelectFromModel
from sklearn.ensemble import GradientBoostingClassifier
# 基于gbdt的特征选择
# def selectFromGBDT(df_train, features, target):
# SelectFromModel(GradientBoostingClassifier()).fit_transform(df_train[features], df_train[target])
\ No newline at end of file
models_kit/general_methods.py
View file @ c8ae753d
......@@ -19,8 +19,10 @@ def topN_feature_importance_plot(model, clf, title="untitled", save_path='./mvp/
plt.title("Feature Importances")
path = save_path + title + "_featureImportance.png"
plt.tight_layout()
plt.savefig(path)
plt.show()
return path
......
models_kit/lightgbm.py
View file @ c8ae753d
......@@ -99,7 +99,7 @@ def lgb_params_tuning(params, features, train, val, target='target', topN=3, cv_
print('Memory Occupancy Rate: ' + (str)(psutil.virtual_memory().percent) + '%')
optimal_para = list(topn)
for deepth in np.arange(2, 4, 1):
for deepth in np.arange(2, 5, 1):
for leaves in np.arange(2, 2 ** deepth, 4):
params['max_depth'] = deepth
params['num_leaves'] = leaves
......
mvp/allocator.py
View file @ c8ae753d
'''
@allocator V1.0
作用： 程序入口，用于设置报告和refit相关的参数，设置
'''
#################################################### report settings ###################################################
from models_obj import dhb_obj
......@@ -8,15 +10,18 @@ import pandas as pd
import os
from mvp import refit
from tools import datacal
import pickle
import time
from sklearn.externals import joblib
# 渠道列表
# 渠道列表（用于报告生成）
applied_from = {'1,214,217,198': '内部', '333': '融360', '159537': '360金融'}
# 申请类型列表
applied_type = {'1,2':'首贷','1,2,3':'首付贷','1':'首申','2':'复申','3':'复贷'}
# 申请类型列表（用于报告生成）
applied_type = {'1,2':'首贷','1,2,3':'全量客群','1':'首申','2':'复申','3':'复贷'}
# workspace 路径
worksapce = 'E:\\bla\\model_mvp\\'
# workspace 路径 工作目录路径
workspace = 'E:\\bla\\model_mvp\\'
# 样本路径
sample_path = 'E:\\model\\model_mvp\\mvp\\sample.csv'
......@@ -26,26 +31,54 @@ target = 'target'
# 线上模型分字段
score = 'score'
#score = 'online_score'
# 预测模型分字段
prediction = 'predict'
# 报告生成路径
report_path = worksapce
# 报告生成路径 （这里暂时设置成workspace也就是根目录下）
report_path = workspace
# 报告名称
report_name = "lgb_report.docx"
# 切换到workspace目录下 避免相对路径不能识别问题
os.chdir(worksapce)
os.chdir(workspace)
#################################################### training settings #################################################
# 生成电话帮对象(使用默认参数)
dhb = dhb_obj.dhb(features=None, sql=None, start_time_period=None, end_time_period=None,passdue_day=15)
# 需要对特征进行调整时，在这里直接dhb.features = 赋值即可
# dhb.features = ['overview_ntdun_call_duration_above60',
# 'overview_dun_call_total_duration', 'overview_dun_call_tel_total_nums',
# 'overview_ntdun_call_tel_total_nums',
# 'last_60_and_90_days_ntdun_call_in_duration',
# 'last_two_weeks_ntdun_call_in_duration',
# 'last_two_weeks_dun_call_in_duration',
# 'last_three_weeks_ntdun_call_in_duration',
# 'last_60_and_90_days_ntdun_call_avg_duration', 'dun_first_call_time',
# 'last_30_and_60_days_ntdun_call_total_duration',
# 'overview_ntdun_call_in_times',
# 'last_30_days_ntdun_call_tel_total_nums',
# 'last_60_and_90_days_ntdun_call_total_duration',
# 'overview_ntdun_call_total_duration',
# 'last_30_days_dun_call_total_duration',
# 'overview_ntdun_call_total_times', 'overview_ntdun_call_in_duration',
# 'last_30_days_ntdun_call_total_duration',
# 'last_30_days_dun_call_duration_between15_and_30',
# 'ntdun_last_call_time', 'last_30_and_60_days_ntdun_call_in_times',
# 'last_30_and_60_days_ntdun_call_duration_between15_and_30',
# 'last_30_days_dun_call_in_times',
# 'last_30_days_ntdun_call_duration_above60',
# 'last_30_days_dun_call_in_duration',
# 'overview_ntdun_call_duration_between15_and_30',
# 'last_30_and_60_days_dun_call_tel_total_nums',
# 'last_30_and_60_days_dun_call_in_duration']
# 提取样本
#df_sample = dhb.dhb_features_extract()
# 这里直接使用csv读入样本
df_sample = pd.read_csv(sample_path,engine='python')
#df_sample['target'] = df_sample.passdue_day.map(lambda x : 1 if x > 15 else 0)
# 电话帮数据处理
# 自定义方法 / 默认数据处理方法
......@@ -59,9 +92,11 @@ df_sample = dhb.dhb_features_prepocessing(df_sample)
df_train, df_val, df_test = datacal.train_test_split_general(df_sample, val_size=0.2, test_size=0.2, stratify=target,
random_state=7, split_methods='random',
time_label='applied_at')
# del variable df_sample for saving storage space
del df_sample
# 模型refit
# 模型refit,返回对应的模型性能二维表和模型分类器lgbm
model_matrix, lgbm = refit.model_fit(df_train, df_val, df_test, dhb, target, score)
print(model_matrix)
......@@ -69,6 +104,11 @@ print(model_matrix)
status = refit.model_report(lgbm, df_train, df_val, df_test, dhb, target, model_matrix,
score, prediction, report_path, report_name, applied_from, applied_type, topN=3)
# 保存pkl
joblib.dump(lgbm,'/home/public/dhb_refit'+time.strftime('%Y-%m-%d',time.localtime(time.time()))+'.pkl')
......
mvp/refit.py
View file @ c8ae753d
......@@ -15,6 +15,7 @@ import pandas as pd
def model_fit(df_train, df_val, df_test, model_obj, target, score):
'''
instructions : refit model with lightGBM
:param df_train: 训练集
:param df_val: 验证集
......@@ -109,10 +110,11 @@ def model_report(clf, df_train, df_val, df_test, model_obj, target,model_matrix,
document.add_paragraph('特征权重图')
# docx加入特征权重图像
document.add_picture(topnfeat_path)
document.add_picture(topnfeat_path,width=Inches(6))
# 新增 univar_chart段
document.add_paragraph('univar_chart')
# 遍历目标features画出univarchart
for i in importanct_feat:
# 训练集 univar
......@@ -133,6 +135,7 @@ def model_report(clf, df_train, df_val, df_test, model_obj, target,model_matrix,
# 新增pdp段
document.add_paragraph('PDP_chart')
# 遍历目标features 画出对应PDP
for i in range(math.ceil(len(importanct_feat)/9)):
# pdp = datacal.cal_pdp(df=df_test, score=prediction, feature=i, qcut=10)
......@@ -143,8 +146,8 @@ def model_report(clf, df_train, df_val, df_test, model_obj, target,model_matrix,
# 新增liftchart段
document.add_paragraph('lift_chart')
# 遍历给定渠道 & 客群 默认等频画出liftchart
# 遍历给定渠道 & 客群 默认等频画出liftchart
try:
lift_pred = datacal.cal_lift(df_test, score=prediction)
lift_online = datacal.cal_lift(df_test, score=score)
......@@ -157,14 +160,12 @@ def model_report(clf, df_train, df_val, df_test, model_obj, target,model_matrix,
tab_df_list=[lift_pred['count'], lift_online['count']], plot_tab=False,
saved_path='./mvp/plots/cache/')
document.add_picture(liftChart, width=Inches(7))
document.add_picture(liftChart, width=Inches(5.5))
# 遍历渠道
for channel in applied_from.keys():
print('lift ',channel)
# 数据切片
df_sliced = df_test[
df_test.applied_type.map(lambda x: True if str(x) in type.split(',') else False) & df_test.applied_from.map(
lambda x: True if str(x) in channel.split(',') else False)]
df_sliced = df_test[df_test.applied_from.map(lambda x: True if str(x) in channel.split(',') else False)]
#
lift_pred = datacal.cal_lift(df_sliced, score=prediction)
lift_online = datacal.cal_lift(df_sliced, score=score)
......@@ -182,7 +183,7 @@ def model_report(clf, df_train, df_val, df_test, model_obj, target,model_matrix,
for type in applied_type.keys():
print('lift ',type)
# 数据切片
df_sliced = df_test[df_test.applied_type.map(lambda x : True if str(x) in type.split(',') else False) & df_test.applied_from.map(lambda x : True if str(x) in channel.split(',') else False)]
df_sliced = df_test[df_test.applied_type.map(lambda x : True if str(x) in type.split(',') else False)]
#
lift_pred = datacal.cal_lift(df_sliced,score=prediction)
lift_online = datacal.cal_lift(df_sliced,score=score)
......@@ -195,7 +196,6 @@ def model_report(clf, df_train, df_val, df_test, model_obj, target,model_matrix,
except Exception as e:
print(e)
pass
# 存在某些渠道量很少的情况，加入try catch异常处理
# docx 保存
......
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