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gm_strategy_cvr
Commit c713565e authored by 赵威's avatar 赵威
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try predict tractate

parent d9f5cd31
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Showing with 205 additions and 43 deletions
src/models/esmm/fe/tractate_fe.py
View file @ c713565e
import timeit
import pandas as pd
from utils.cache import redis_db_client
......@@ -135,4 +137,94 @@ def join_features(device_df, tractate_df, cc_df):
def device_tractate_fe(device_id, tractate_ids, device_dict, tractate_dict):
pass
time_1 = timeit.default_timer()
device_info = device_dict.get(device_id, {}).copy()
if not device_info:
device_info = {
"device_id": device_id,
"active_type": "1",
"active_days": "0",
"channel_first": "App Store",
"city_first": "beijing",
"model_first": "iPhone10",
"past_consume_ability_history": "极弱",
"potential_consume_ability_history": "极弱",
"price_sensitive_history": "不敏感无消费",
"device_click_num_1d": 0,
"device_click_num_3d": 0,
"device_click_num_7d": 0,
"device_click_num_15d": 0,
"device_click_num_30d": 0,
"device_click_num_180d": 0,
"click_tractate_id1": "-1",
"click_tractate_id2": "-1",
"click_tractate_id3": "-1",
"click_tractate_id4": "-1",
"click_tractate_id5": "-1"
}
device_fd = device_info.get("first_demands", [])
device_sd = device_info.get("second_demands", [])
device_fs = device_info.get("first_solutions", [])
device_ss = device_info.get("second_solutions", [])
device_fp = device_info.get("first_positions", [])
device_sp = device_info.get("second_positions", [])
device_p = device_info.get("projects", [])
device_info["device_fd"] = nth_element(device_fd, 0)
device_info["device_sd"] = nth_element(device_sd, 0)
device_info["device_fs"] = nth_element(device_fs, 0)
device_info["device_ss"] = nth_element(device_ss, 0)
device_info["device_fp"] = nth_element(device_fp, 0)
device_info["device_sp"] = nth_element(device_sp, 0)
device_info["device_p"] = nth_element(device_p, 0)
tractate_lst = []
tractate_ids_res = []
for id in tractate_ids:
tractate_info = tractate_dict.get(id, {}).copy()
if tractate_info:
tractate_ids_res.append(tractate_info.get("card_id", "-1"))
tractate_fd = tractate_info.get("first_demands", [])
tractate_sd = tractate_info.get("second_demands", [])
tractate_fs = tractate_info.get("first_solutions", [])
tractate_ss = tractate_info.get("second_solutions", [])
tractate_fp = tractate_info.get("first_positions", [])
tractate_sp = tractate_info.get("second_positions", [])
tractate_p = tractate_info.get("projects", [])
common_fd = common_elements(device_fd, tractate_fd)
common_sd = common_elements(device_sd, tractate_sd)
common_fs = common_elements(device_fs, tractate_fs)
common_ss = common_elements(device_ss, tractate_ss)
common_fp = common_elements(device_fp, tractate_fp)
common_sp = common_elements(device_sp, tractate_sp)
common_p = common_elements(device_p, tractate_p)
tractate_info["content_fd"] = nth_element(tractate_fd, 0)
tractate_info["content_sd"] = nth_element(tractate_sd, 0)
tractate_info["content_fs"] = nth_element(tractate_fs, 0)
tractate_info["content_ss"] = nth_element(tractate_ss, 0)
tractate_info["content_fp"] = nth_element(tractate_fp, 0)
tractate_info["content_sp"] = nth_element(tractate_sp, 0)
tractate_info["content_p"] = nth_element(tractate_p, 0)
tractate_info["fd1"] = nth_element(common_fd, 0)
tractate_info["fd2"] = nth_element(common_fd, 1)
tractate_info["fd3"] = nth_element(common_fd, 2)
tractate_info["sd1"] = nth_element(common_sd, 0)
tractate_info["sd2"] = nth_element(common_sd, 1)
tractate_info["sd3"] = nth_element(common_sd, 2)
tractate_info["fs1"] = nth_element(common_fs, 0)
tractate_info["fs2"] = nth_element(common_fs, 1)
tractate_info["fs3"] = nth_element(common_fs, 2)
tractate_info["ss1"] = nth_element(common_ss, 0)
tractate_info["ss2"] = nth_element(common_ss, 1)
tractate_info["ss3"] = nth_element(common_ss, 2)
tractate_info["fp1"] = nth_element(common_fp, 0)
tractate_info["fp2"] = nth_element(common_fp, 1)
tractate_info["fp3"] = nth_element(common_fp, 2)
tractate_info["sp1"] = nth_element(common_sp, 0)
tractate_info["sp2"] = nth_element(common_sp, 1)
tractate_info["sp3"] = nth_element(common_sp, 2)
tractate_info["p1"] = nth_element(common_p, 0)
tractate_info["p2"] = nth_element(common_p, 1)
tractate_info["p3"] = nth_element(common_p, 2)
tractate_lst.append(tractate_info)
total_1 = (timeit.default_timer() - time_1)
print("join device tractate cost {:.5f}s".format(total_1))
return device_info, tractate_lst, tractate_ids_res
src/models/esmm/tractate_model.py 0 → 100644
View file @ c713565e
import timeit
import tensorflow as tf
from .fe.tractate_fe import (CATEGORICAL_COLUMNS, FLOAT_COLUMNS, INT_COLUMNS, device_tractate_fe)
from .model import _bytes_feature, _float_feature, _int64_feature
def model_predict_tractate(device_id, tractate_ids, device_dict, tractate_dict, predict_fn):
try:
time_1 = timeit.default_timer()
device_info, tractate_lst, tractate_ids_res = device_tractate_fe(device_id, tractate_ids, device_dict, tractate_dict)
print("predict check: " + str(len(tractate_lst)) + " " + str(len(tractate_ids_res)))
int_columns = INT_COLUMNS
float_columns = FLOAT_COLUMNS
str_columns = CATEGORICAL_COLUMNS
examples = []
for tractate_info in tractate_lst:
tmp = {}
tmp.update(device_info)
tmp.update(tractate_info)
features = {}
for col in int_columns:
features[col] = _int64_feature(int(tmp[col]))
for col in float_columns:
features[col] = _float_feature(float(tmp[col]))
for col in str_columns:
features[col] = _bytes_feature(str(tmp[col]).encode(encoding="utf-8"))
example = tf.train.Example(features=tf.train.Features(feature=features))
examples.append(example.SerializeToString())
total_1 = (timeit.default_timer() - time_1)
print("make example cost {:.5f}s".format(total_1))
time_1 = timeit.default_timer()
predictions = predict_fn({"examples": examples})
print(predictions)
# res_tuple = sorted(zip(tractate_ids_res, predictions["output"].tolist()), key=lambda x: x[1], reverse=True)
# res = []
# for (id, _) in res_tuple:
# res.append(int(id))
# # print(res)
total_1 = (timeit.default_timer() - time_1)
print("prediction cost {:.5f}s".format(total_1))
# return res
except Exception as e:
print(e)
# device_info, _, _ = device_tractate_fe(device_id, tractate_ids, device_dict, tractate_dict)
# print(device_info)
return []
src/train_tractate.py
View file @ c713565e
import os
import random
import shutil
import time
import timeit
from datetime import datetime
from pathlib import Path
......@@ -10,6 +12,7 @@ from sklearn.model_selection import train_test_split
from models.esmm.fe import click_fe, device_fe, fe, tractate_fe
from models.esmm.input_fn import esmm_input_fn
from models.esmm.model import esmm_model_fn, model_export
from models.esmm.tractate_model import model_predict_tractate
def main():
......@@ -17,52 +20,69 @@ def main():
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
# data_path = Path("~/data/cvr_data").expanduser() # local
data_path = Path("/srv/apps/node2vec_git/cvr_data/") # server
tractate_df, tractate_click_df, tractate_conversion_df = tractate_fe.read_csv_data(data_path)
tractate_df = tractate_fe.tractate_feature_engineering(tractate_df)
device_df = device_fe.read_csv_data(data_path)
device_df = device_fe.device_feature_engineering(device_df, "tractate")
# print(device_df.columns)
# print(device_df.dtypes, "\n")
cc_df = click_fe.click_feature_engineering(tractate_click_df, tractate_conversion_df)
df = tractate_fe.join_features(device_df, tractate_df, cc_df)
# for i in df.columns:
# print(i)
# print(df.dtypes)
train_df, test_df = train_test_split(df, test_size=0.2)
train_df, val_df = train_test_split(train_df, test_size=0.2)
all_features = fe.build_features(df, tractate_fe.INT_COLUMNS, tractate_fe.FLOAT_COLUMNS, tractate_fe.CATEGORICAL_COLUMNS)
params = {"feature_columns": all_features, "hidden_units": [64, 32], "learning_rate": 0.1}
model_path = str(Path("~/data/model_tmp/").expanduser())
# if os.path.exists(model_path):
# shutil.rmtree(model_path)
session_config = tf.compat.v1.ConfigProto()
session_config.gpu_options.allow_growth = True
session_config.gpu_options.per_process_gpu_memory_fraction = 0.9
estimator_config = tf.estimator.RunConfig(session_config=session_config)
model = tf.estimator.Estimator(model_fn=esmm_model_fn, params=params, model_dir=model_path, config=estimator_config)
train_spec = tf.estimator.TrainSpec(input_fn=lambda: esmm_input_fn(train_df, shuffle=True), max_steps=50000)
eval_spec = tf.estimator.EvalSpec(input_fn=lambda: esmm_input_fn(val_df, shuffle=False))
tf.estimator.train_and_evaluate(model, train_spec, eval_spec)
model_export_path = str(Path("~/data/models/tractate/").expanduser())
save_path = model_export(model, all_features, model_export_path)
print("save to: " + save_path)
# # data_path = Path("~/data/cvr_data").expanduser() # local
# data_path = Path("/srv/apps/node2vec_git/cvr_data/") # server
# tractate_df, tractate_click_df, tractate_conversion_df = tractate_fe.read_csv_data(data_path)
# tractate_df = tractate_fe.tractate_feature_engineering(tractate_df)
# device_df = device_fe.read_csv_data(data_path)
# device_df = device_fe.device_feature_engineering(device_df, "tractate")
# # print(device_df.columns)
# # print(device_df.dtypes, "\n")
# cc_df = click_fe.click_feature_engineering(tractate_click_df, tractate_conversion_df)
# df = tractate_fe.join_features(device_df, tractate_df, cc_df)
# # for i in df.columns:
# # print(i)
# # print(df.dtypes)
# train_df, test_df = train_test_split(df, test_size=0.2)
# train_df, val_df = train_test_split(train_df, test_size=0.2)
# all_features = fe.build_features(df, tractate_fe.INT_COLUMNS, tractate_fe.FLOAT_COLUMNS, tractate_fe.CATEGORICAL_COLUMNS)
# params = {"feature_columns": all_features, "hidden_units": [64, 32], "learning_rate": 0.1}
# model_path = str(Path("~/data/model_tmp/").expanduser())
# # if os.path.exists(model_path):
# # shutil.rmtree(model_path)
# session_config = tf.compat.v1.ConfigProto()
# session_config.gpu_options.allow_growth = True
# session_config.gpu_options.per_process_gpu_memory_fraction = 0.9
# estimator_config = tf.estimator.RunConfig(session_config=session_config)
# model = tf.estimator.Estimator(model_fn=esmm_model_fn, params=params, model_dir=model_path, config=estimator_config)
# train_spec = tf.estimator.TrainSpec(input_fn=lambda: esmm_input_fn(train_df, shuffle=True), max_steps=50000)
# eval_spec = tf.estimator.EvalSpec(input_fn=lambda: esmm_input_fn(val_df, shuffle=False))
# tf.estimator.train_and_evaluate(model, train_spec, eval_spec)
# model_export_path = str(Path("~/data/models/tractate/").expanduser())
# save_path = model_export(model, all_features, model_export_path)
# print("save to: " + save_path)
total_time = (time.time() - time_begin) / 60
print("total cost {:.2f} mins at {}".format(total_time, datetime.now()))
print("============================================================")
# save_path = str(Path("~/data/models/tractate/1596089465").expanduser()) # local
# save_path = "/home/gmuser/data/models/tractate/" # server
# # save_path = str(Path("~/data/models/tractate/1596089465").expanduser()) # local
save_path = "/home/gmuser/data/models/tractate/1596092061" # server
predict_fn = tf.contrib.predictor.from_saved_model(save_path)
print("============================================================")
device_dict = device_fe.get_device_dict_from_redis()
diary_dict = tractate_fe.get_diary_dict_from_redis()
device_ids = list(device_dict.keys())[:20]
diary_ids = list(diary_dict.keys())
print(device_dict(device_ids[0]), "\n")
print(diary_dict(diary_ids[0]), "\n")
for i in range(5):
time_1 = timeit.default_timer()
res = model_predict_tractate(
random.sample(device_ids, 1)[0], random.sample(diary_ids, 200), device_dict, diary_dict, predict_fn)
print(res[:10])
total_1 = (timeit.default_timer() - time_1)
print("total prediction cost {:.5f}s".format(total_1), "\n")
total_time = (time.time() - time_begin) / 60
print("total cost {:.2f} mins at {}".format(total_time, datetime.now()))
if __name__ == "__main__":
......
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