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gm_strategy_cvr
Commit 52f719f1 authored by 赵威's avatar 赵威
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train diar

parent 6550e929
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src/train_diary.py
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......@@ -19,73 +19,70 @@ from utils.cache import get_essm_model_save_path, set_essm_model_save_path
def main():
time_begin = time.time()
# tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
# # os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
# diary_train_columns = set(diary_fe.INT_COLUMNS + diary_fe.FLOAT_COLUMNS + diary_fe.CATEGORICAL_COLUMNS)
# print("features: " + str(len(diary_train_columns)))
# diary_predict_columns = set(PREDICTION_ALL_COLUMNS)
# print(diary_predict_columns.difference(diary_train_columns))
# print(diary_train_columns.difference(diary_predict_columns))
# assert diary_predict_columns == diary_train_columns
# # dataset_path = Path("~/data/cvr_data").expanduser() # local
# dataset_path = Path("/srv/apps/node2vec_git/cvr_data/") # server
# diary_df, diary_click_df, diary_conversion_df = diary_fe.read_csv_data(dataset_path)
# # print(diary_df.sample(1))
# diary_df = diary_fe.diary_feature_engineering(diary_df)
# # print(diary_df.sample(1))
# device_df = device_fe.read_csv_data(dataset_path)
# # print(diary_df.sample(1))
# device_df = device_fe.device_feature_engineering(device_df, "diary")
# # print(device_df.sample(1))
# cc_df = click_fe.click_feature_engineering(diary_click_df, diary_conversion_df)
# # print(cc_df.sample(1))
# df = diary_fe.join_features(device_df, diary_df, cc_df)
# # print(df.sample(1))
# # 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, diary_fe.INT_COLUMNS, diary_fe.FLOAT_COLUMNS, diary_fe.CATEGORICAL_COLUMNS)
# params = {"feature_columns": all_features, "hidden_units": [64, 32], "learning_rate": 0.1}
# model_path = str(Path("/data/files/model_tmp/diary/").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)
# # TODO 50000
# train_spec = tf.estimator.TrainSpec(input_fn=lambda: esmm_input_fn(train_df, shuffle=True), max_steps=15000)
# eval_spec = tf.estimator.EvalSpec(input_fn=lambda: esmm_input_fn(val_df, shuffle=False))
# res = tf.estimator.train_and_evaluate(model, train_spec, eval_spec)
# print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
# print(res[0])
# print("ctr_auc: " + str(res[0]["ctr_auc"]))
# print("ctcvr_auc: " + str(res[0]["ctcvr_auc"]))
# print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
# model_export_path = str(Path("/data/files/models/diary").expanduser())
# save_path = model_export(model, all_features, model_export_path)
# print("save to: " + save_path)
# # TODO save
# # set_essm_model_save_path("diary", save_path)
# print("============================================================")
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
diary_train_columns = set(diary_fe.INT_COLUMNS + diary_fe.FLOAT_COLUMNS + diary_fe.CATEGORICAL_COLUMNS)
print("features: " + str(len(diary_train_columns)))
diary_predict_columns = set(PREDICTION_ALL_COLUMNS)
print(diary_predict_columns.difference(diary_train_columns))
print(diary_train_columns.difference(diary_predict_columns))
assert diary_predict_columns == diary_train_columns
# dataset_path = Path("~/data/cvr_data").expanduser() # local
dataset_path = Path("/srv/apps/node2vec_git/cvr_data/") # server
diary_df, diary_click_df, diary_conversion_df = diary_fe.read_csv_data(dataset_path)
# print(diary_df.sample(1))
diary_df = diary_fe.diary_feature_engineering(diary_df)
# print(diary_df.sample(1))
device_df = device_fe.read_csv_data(dataset_path)
# print(diary_df.sample(1))
device_df = device_fe.device_feature_engineering(device_df, "diary")
# print(device_df.sample(1))
cc_df = click_fe.click_feature_engineering(diary_click_df, diary_conversion_df)
# print(cc_df.sample(1))
df = diary_fe.join_features(device_df, diary_df, cc_df)
# print(df.sample(1))
# 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, diary_fe.INT_COLUMNS, diary_fe.FLOAT_COLUMNS, diary_fe.CATEGORICAL_COLUMNS)
params = {"feature_columns": all_features, "hidden_units": [64, 32], "learning_rate": 0.1}
model_path = str(Path("/data/files/model_tmp/diary/").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))
res = tf.estimator.train_and_evaluate(model, train_spec, eval_spec)
print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
print(res[0])
print("ctr_auc: " + str(res[0]["ctr_auc"]))
print("ctcvr_auc: " + str(res[0]["ctcvr_auc"]))
print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
model_export_path = str(Path("/data/files/models/diary").expanduser())
save_path = model_export(model, all_features, model_export_path)
print("save to: " + save_path)
set_essm_model_save_path("diary", save_path)
print("============================================================")
# save_path = str(Path("~/Desktop/models/1596012827").expanduser()) # local
# save_path = "/data/files/models/diary/1597390452" # server
# tf.saved_model.load
# TODO
save_path = get_essm_model_save_path("diary")
print("load path: " + save_path)
# save_path = get_essm_model_save_path("diary")
# print("load path: " + save_path)
predict_fn = tf.contrib.predictor.from_saved_model(save_path)
device_dict = device_fe.get_device_dict_from_redis()
......
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