rov-offline/write_redis/tree_model_xgb_vov.py

import pandas as pd
from sklearn.metrics import roc_auc_score
import numpy as np
import xgboost as xgb
from tqdm import tqdm
import sys

# def func_make_data(file_path: str):
#     df = pd.read_csv(file_path)
#     for col in ['曝光占比', 'vov0', '分子', '分母', '1_vov0', '2_vov0', '3_vov0', '4_vov0', '5_vov0', '2_vov01',
#                 '3_vov01', '4_vov01', '5_vov01', '3_vov012', '4_vov012', '5_vov012', '1_vov0_分子', '1_vov0_分母',
#                 '2_vov0_分子', '2_vov0_分母', '3_vov0_分子', '3_vov0_分母', '4_vov0_分子', '4_vov0_分母', '5_vov0_分子',
#                 '5_vov0_分母', '2_vov01_分子', '2_vov01_分母', '3_vov01_分子', '3_vov01_分母', '4_vov01_分子',
#                 '4_vov01_分母', '5_vov01_分子', '5_vov01_分母', '3_vov012_分子', '3_vov012_分母', '4_vov012_分子',
#                 '4_vov012_分母', '5_vov012_分子', '5_vov012_分母']:
#         df[col] = pd.to_numeric(df[col], errors='coerce')
#
#     df.fillna(0, inplace=True)
#     df["12_change"] = df["1_vov0"] - df["2_vov0"]
#     df["23_change"] = df["2_vov0"] - df["3_vov0"]
#     df["34_change"] = df["3_vov0"] - df["4_vov0"]
#     features_name = ['1_vov0', '2_vov0', '3_vov0', '4_vov0', '5_vov0', '2_vov01', '3_vov01', '4_vov01', '5_vov01', '3_vov012', '4_vov012', '5_vov012'
#                      ,"12_change", "23_change", "34_change"
#                      ,'2_vov01', '3_vov01', '4_vov01', '5_vov01', '3_vov012', '4_vov012', '5_vov012'
#                      ,'1_vov0_分子', '1_vov0_分母',
#                 '2_vov0_分子', '2_vov0_分母', '3_vov0_分子', '3_vov0_分母', '4_vov0_分子', '4_vov0_分母', '5_vov0_分子',
#                 '5_vov0_分母', '2_vov01_分子', '2_vov01_分母', '3_vov01_分子', '3_vov01_分母', '4_vov01_分子',
#                 '4_vov01_分母', '5_vov01_分子', '5_vov01_分母', '3_vov012_分子', '3_vov012_分母', '4_vov012_分子',
#                 '4_vov012_分母', '5_vov012_分子', '5_vov012_分母'
#                      ]
#     feature_array = df[features_name].values
#     df["reg_label"] = df["vov0"].apply(lambda x: float(x))
#     label_array = df["reg_label"].values
#     return df, feature_array, label_array
#
# df, trains_array,trains_label_array = func_make_data("20240919.csv")
# header = df.columns.tolist()
# print(header) # print(df.dtypes)
# # 1 回归模型
# model = xgb.XGBRegressor(objective='reg:squarederror',
#                          learning_rate=0.01,
#                          n_estimators=100,
#                          max_depth=3)
# model.fit(trains_array, trains_label_array)
# df_test, tests_array, _ =  func_make_data("20240920.csv")
# y_pred = model.predict(tests_array)
# df_test["y_pred"] = y_pred
#
# condition_choose = ((df_test['y_pred'] <= 0.15)
#                     & ((df_test['1_vov0_分母'] > 50) | (df_test['2_vov0_分母'] > 50) | (df_test['3_vov0_分母'] > 50))
#
#                     )
# condition_choose_real = condition_choose & (df_test['vov0'] <= 0.30)
# profit_theshold = 0.34
#
# choose_bad = condition_choose.sum()
# choose_bad_realbad = condition_choose_real.sum()
# acc = choose_bad_realbad / choose_bad
# print("acc:{} 分子={} 分母={} 总视频数={}".format(acc, choose_bad_realbad, choose_bad, df_test.size))
#
# surface = df_test.loc[condition_choose, '曝光占比'].sum()
# surface_income = df_test.loc[condition_choose_real, '曝光占比'].sum()
# print("总影响面:{} 收益影响面:{}".format(surface, surface_income))
#
# df_test["profit_loss_value"] = df_test['分母'] * (df_test['vov0'] - profit_theshold)
# profit_loss_value = df_test.loc[condition_choose, 'profit_loss_value'].sum()
# profit_value = df_test.loc[condition_choose_real, 'profit_loss_value'].sum()
# print("总盈亏:{} 纯盈利:{}".format(profit_loss_value, profit_value))
# 2 分类模型
def func_make_data(file_path: str):
    df = pd.read_csv(file_path)
    df["title"] = df["title"].apply(lambda x: x.replace("\n", ""))
    for col in ['曝光占比', 'vov0', '分子', '分母', '1_vov0', '2_vov0', '3_vov0', '4_vov0', '5_vov0', '2_vov01',
                '3_vov01', '4_vov01', '5_vov01', '3_vov012', '4_vov012', '5_vov012', '1_vov0_分子', '1_vov0_分母',
                '2_vov0_分子', '2_vov0_分母', '3_vov0_分子', '3_vov0_分母', '4_vov0_分子', '4_vov0_分母', '5_vov0_分子',
                '5_vov0_分母', '2_vov01_分子', '2_vov01_分母', '3_vov01_分子', '3_vov01_分母', '4_vov01_分子',
                '4_vov01_分母', '5_vov01_分子', '5_vov01_分母', '3_vov012_分子', '3_vov012_分母', '4_vov012_分子',
                '4_vov012_分母', '5_vov012_分子', '5_vov012_分母']:
        df[col] = pd.to_numeric(df[col], errors='coerce')

    df.fillna(0, inplace=True)
    df["12_change"] = df["1_vov0"] - df["2_vov0"]
    df["23_change"] = df["2_vov0"] - df["3_vov0"]
    df["34_change"] = df["3_vov0"] - df["4_vov0"]
    features_name = ['1_vov0', '2_vov0', '3_vov0', '4_vov0', '5_vov0', '2_vov01', '3_vov01', '4_vov01', '5_vov01',
                     '3_vov012', '4_vov012', '5_vov012'
        , "12_change", "23_change", "34_change"
        , '2_vov01', '3_vov01', '4_vov01', '5_vov01', '3_vov012', '4_vov012', '5_vov012'
        # , '1_vov0_分子', '1_vov0_分母',
        #              '2_vov0_分子', '2_vov0_分母', '3_vov0_分子', '3_vov0_分母', '4_vov0_分子', '4_vov0_分母',
        #              '5_vov0_分子',
        #              '5_vov0_分母', '2_vov01_分子', '2_vov01_分母', '3_vov01_分子', '3_vov01_分母', '4_vov01_分子',
        #              '4_vov01_分母', '5_vov01_分子', '5_vov01_分母', '3_vov012_分子', '3_vov012_分母', '4_vov012_分子',
        #              '4_vov012_分母', '5_vov012_分子', '5_vov012_分母'
                     ]
    feature_array = df[features_name].values
    df["label"] = df["vov0"].apply(lambda x: 1 if x > 0.25 else 0)
    label_array = df["label"].values
    return df, feature_array, label_array

try:
    date_train = sys.argv[1]
    date_test = sys.argv[2]
except Exception as e:
    date_train = "20240919.csv"
    date_test = "20240920.csv"


df, trains_array,trains_label_array = func_make_data(date_train)
header = df.columns.tolist()
# print(header) # print(df.dtypes)
model = xgb.XGBClassifier(
    n_estimators=100,
    learning_rate=0.01,
    max_depth=5,
    min_child_weight=1,
    gamma=0,
    subsample=0.8,
    colsample_bytree=0.8,
    objective= 'binary:logistic',
    nthread=8,
    scale_pos_weight=1,
    random_state=2024,
    seed=2024,
    # verbose=True,
)
model.fit(trains_array, trains_label_array)
df_test, tests_array, _ =  func_make_data(date_test)
y_pred = model.predict_proba(tests_array)[:, 1]
df_test["y_pred"] = y_pred

condition_choose = ((df_test['y_pred'] <= 0.2)
                     # & ((df_test['1_vov0_分母'] > 50) | (df_test['2_vov0_分母'] > 50) | (df_test['3_vov0_分母'] > 50))
                    & (df_test.index <= 10000)
                    )
profit_theshold = 0.3
condition_choose_real = condition_choose & (df_test['vov0'] <= profit_theshold)
df_test["condition_choose"] = condition_choose
df_test[["vid","title","曝光占比","vov0", "condition_choose"]].to_csv("new_" + date_test, sep="\t", index=False)

choose_bad = condition_choose.sum()
choose_bad_realbad = condition_choose_real.sum()
acc = choose_bad_realbad / choose_bad
print("acc:{} 分子={} 分母={} 总视频数={} 盈利计算标注vov0大于:{}".format(acc, choose_bad_realbad, choose_bad, df_test.size, profit_theshold))

surface = df_test.loc[condition_choose, '曝光占比'].sum()
surface_income = df_test.loc[condition_choose_real, '曝光占比'].sum()
print("总影响面:{} 盈利影响面:{} 亏损影响面:{}".format(round(surface, 6), round(surface_income, 6), round(surface-surface_income, 6)))

df_test["profit_loss_value"] = df_test['分母'] * (df_test['vov0'] - profit_theshold)
profit_loss_value = df_test.loc[condition_choose, 'profit_loss_value'].sum()
profit_value = df_test.loc[condition_choose_real, 'profit_loss_value'].sum()
print("总盈亏:{} 纯盈利:{} 纯亏损:{} 盈利效率:{}".format(round(profit_loss_value, 1), round(profit_value, 1), round(profit_loss_value-profit_value, 1), round(profit_loss_value/profit_value, 6)))

# 3 多分类模型 概率




# names = []
# with open("a_features_recsys.txt", "r") as file:
#     for line in file.readlines():
#         line = line.strip()
#         names.append(line)
#
# trains = []
# trains_label = []
# # trains_id = []
# with open("20240728.txt", "r") as file:
#     for line in tqdm(file.readlines()):
#         lines = line.strip().split("\t")
#         label = lines[0]
#         trains_label.append(int(label))
#         m = dict([(l.split(":")[0], l.split(":")[1]) for l in lines[1:]])
#         row = [float(m.get(name, "0.0")) for name in names]
#         trains.append(row)
#         # for key in m.keys():
#         #     if key.startswith("cid_"):
#         #         trains_id.append(key.replace("cid_", ""))
#         #         break
#
# trains_array = np.array(trains)
# trains_label_array = np.array(trains_label)
# print("train samples={} positive={} rate={}".format(
#     len(trains_label),
#     sum(trains_label),
#     format(1.0 * sum(trains_label)/len(trains_label), ".6f"))
# )
#
#
# model = xgb.XGBClassifier(
#     n_estimators=2000,
#     learning_rate=0.01,
#     max_depth=5,
#     min_child_weight=1,
#     gamma=0,
#     subsample=0.8,
#     colsample_bytree=0.8,
#     objective= 'binary:logistic',
#     nthread=8,
#     scale_pos_weight=1,
#     random_state=2024,
#     seed=2024,
#     verbose=True,
# )
# model.fit(trains_array, trains_label_array)
#
# tests = []
# tests_label = []
# # tests_id = []
# with open("20240729.txt", "r") as file:
#     for line in tqdm(file.readlines()):
#         lines = line.strip().split("\t")
#         label = lines[0]
#         tests_label.append(int(label))
#         m = dict([(l.split(":")[0], l.split(":")[1]) for l in lines[1:]])
#         row = [float(m.get(name, "0.0")) for name in names]
#         tests.append(row)
#         # for key in m.keys():
#         #     if key.startswith("cid_"):
#         #         tests_id.append(key.replace("cid_", ""))
#         #         break
# tests_array = np.array(tests)
# tests_label_array = np.array(tests_label)
# print("test samples={} positive={} rate={}".format(
#     len(tests_label),
#     sum(tests_label),
#     format(1.0 * sum(tests_label)/len(tests_label), ".6f"))
# )
#
# # 进行预测
# y_pred = model.predict(tests_array)
# probabilities = model.predict_proba(tests_array)
#
#
#
# auc = roc_auc_score(tests_label_array, probabilities[:, 1])
# print("auc:{}".format(auc))


"""
https://zhuanlan.zhihu.com/p/688993572
"""