Commit 4d681b0a authored by 赵威's avatar 赵威

add printer

parent ab037a28
......@@ -20,17 +20,17 @@ from models.esmm.model import esmm_model_fn, model_export, model_predict
def main():
time_begin = time.time()
# device_df, diary_df, click_df, conversion_df = read_csv_data(Path("~/data/cvr_data/"))
# # print(diary_df.sample(1))
# device_df = device_feature_engineering(device_df)
# # print(device_df.sample(1))
# diary_df = diary_feature_engineering(diary_df)
# # print(diary_df.sample(1))
# cc_df = click_feature_engineering(click_df, conversion_df)
# df = join_features(device_df, diary_df, cc_df)
# 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)
device_df, diary_df, click_df, conversion_df = read_csv_data(Path("~/data/cvr_data/"))
# print(diary_df.sample(1))
device_df = device_feature_engineering(device_df)
# print(device_df.sample(1))
diary_df = diary_feature_engineering(diary_df)
# print(diary_df.sample(1))
cc_df = click_feature_engineering(click_df, conversion_df)
df = join_features(device_df, diary_df, cc_df)
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 = build_features(df)
......@@ -49,33 +49,34 @@ def main():
# save_path = model_export(model, all_features, model_export_path)
# print("save to: " + save_path)
# predict_fn = tf.contrib.predictor.from_saved_model(save_path)
# for i in range(10):
# test_300 = test_df.sample(300)
# model_predict(test_300, predict_fn)
print("==============================")
device_id = "861601036552944"
diary_ids = [
"16195283", "16838351", "17161073", "17297878", "17307484", "17396235", "16418737", "16995481", "17312201", "12237988"
]
df = get_device_df_from_redis()
df2 = get_diary_df_from_redis()
redis_device_df = device_feature_engineering(df)
redis_diary_df = diary_feature_engineering(df2, from_redis=True)
print(list(redis_diary_df["card_id"].values)[:10])
time_1 = timeit.default_timer()
res = join_device_diary(device_id, diary_ids, redis_device_df, redis_diary_df)
print(len(res))
print(res.sample(1), "\n")
print(res.sample(1))
# model_predict(res, predict_fn)
total_1 = (timeit.default_timer() - time_1)
print("prediction total cost {:.5f}s".format(total_1))
save_path = "/home/gmuser/data/models/1595317247"
predict_fn = tf.contrib.predictor.from_saved_model(save_path)
for i in range(10):
test_300 = test_df.sample(300)
model_predict(test_300, predict_fn)
# print("==============================")
# device_id = "861601036552944"
# diary_ids = [
# "16195283", "16838351", "17161073", "17297878", "17307484", "17396235", "16418737", "16995481", "17312201", "12237988"
# ]
# df = get_device_df_from_redis()
# df2 = get_diary_df_from_redis()
# redis_device_df = device_feature_engineering(df)
# redis_diary_df = diary_feature_engineering(df2, from_redis=True)
# diary_ids = list(redis_diary_df["card_id"].values)[:300]
# time_1 = timeit.default_timer()
# res = join_device_diary(device_id, diary_ids, redis_device_df, redis_diary_df)
# print(len(res))
# print(res.sample(1), "\n")
# print(res.sample(1))
# # model_predict(res, predict_fn)
# total_1 = (timeit.default_timer() - time_1)
# print("prediction total cost {:.5f}s".format(total_1))
total_time = (time.time() - time_begin) / 60
print("cost {:.2f} mins at {}".format(total_time, datetime.now()))
......
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